Tensor:\\n res = x * torch.log(y)\\n res[x == 0] = 0.0\\n return res\",\n \"def forward(self, x: torch.Tensor) -> torch.Tensor:\\r\\n a = x[:,0,:,:].unsqueeze(dim=1)\\r\\n b = x[:,2,:,:].unsqueeze(dim=1)\\r\\n c = x[:,2,:,:].unsqueeze(dim=1)\\r\\n d = x[:,1,:,:].unsqueeze(dim=1)\\r\\n\\r\\n det = (torch.mul(a,d) - torch.mul(b,c))\\r\\n trace = a + d\\r\\n \\r\\n output = det - self.alpha * (torch.mul(trace,trace))\\r\\n\\r\\n return output\",\n \"def div_proxy(x, y):\\r\\n f = eval('%s_div' % int_or_true_div(as_scalar(x).type in discrete_types,\\r\\n as_scalar(y).type in discrete_types))\\r\\n return f(x, y)\",\n \"def forward(self, x, y):\\n x1 = x.clone()\\n x1[range(x1.size(0)), y] = -float(\\\"Inf\\\")\\n x_gt = x[range(x.size(0)), y].unsqueeze(1)\\n x_topk = torch.topk(x1, 15, dim=1)[0] # 15 Negative classes to focus on, its a hyperparameter\\n x_new = torch.cat([x_gt, x_topk], dim=1)\\n\\n return self.ce(x_new, torch.zeros(x_new.size(0)).cuda().long())\",\n \"def forward_minee(self, X=None, Y=None):\\n XY = None\\n if X is None or Y is None:\\n XY, X, Y = self.XY, self.X, self.Y\\n else:\\n XY = torch.cat((X, Y), dim=1)\\n X_ref_minee = _uniform_sample(X, batch_size=int(\\n self.ref_batch_factor * X.shape[0]))\\n Y_ref_minee = _uniform_sample(Y, batch_size=int(\\n self.ref_batch_factor * Y.shape[0]))\\n XY_ref_minee = torch.cat((X_ref_minee, Y_ref_minee), dim=1)\\n dXY = _div(self.XY_net, XY, XY_ref_minee).cpu().item()\\n dX = _div(self.X_net, X, X_ref_minee).cpu().item()\\n dY = _div(self.Y_net, Y, Y_ref_minee).cpu().item()\\n return dXY, dX, dY\",\n \"def forward(cls, y, y_hat):\\n raise Exception(\\\"Unimplemented\\\")\",\n \"def logits_or(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:\\n f = -(x + y) / 2\\n t = logaddexp(logaddexp((x - y) / 2, (y - x) / 2), -f)\\n return t - f\",\n \"def _process(self, x: np.ndarray, y: np.ndarray) -> None:\\n\\n if (((self.support_vectors - x) ** 2).sum(axis=1) ** 0.5 < self.ERR).any():\\n return\\n\\n self._add_support_vectors(x, y)\\n\\n if y[0] == 1:\\n i = self.support_vectors.shape[0] - 1\\n j = self._find_min_gradient_id()\\n\\n else:\\n j = self.support_vectors.shape[0] - 1\\n i = self._find_max_gradient_id()\\n\\n if not self._is_violating_pair(i, j):\\n return\\n\\n self._update_parameters(i=i, j=j)\",\n \"def fwd_pass( self, X, ls_emb, sp_emb, y, optimizer, loss_function, train=False, report=True):\\n if train:\\n self.zero_grad()\\n outputs = self(X, ls_emb, sp_emb)\\n loss = loss_function(outputs, y)\\n\\n if train:\\n loss.backward()\\n optimizer.step()\\n\\n if report:\\n tp = 0\\n tn = 0\\n fn = 0\\n fp = 0\\n for i,j in zip(outputs, y):\\n if torch.argmax(i) == torch.argmax(j):\\n if j.data.cpu().numpy()[0] == 1: #positive instance\\n tp += 1\\n else: \\n tn += 1\\n else:\\n if j.data.cpu().numpy()[0] == 1:\\n fn += 1\\n else:\\n fp += 1\\n\\n #matches = [torch.argmax(i) == torch.argmax(j) for i,j in zip(outputs, y)]\\n acc = (tp+tn)/(tp+tn+fp+fn)\\n conf = [tp, fp, fn, tn]\\n return acc, loss, conf\\n else:\\n return None,None\",\n \"def div_proxy(x, y):\\r\\n f = eval('%s_div' % scal.int_or_true_div(\\r\\n as_tensor_variable(x).dtype in discrete_dtypes,\\r\\n as_tensor_variable(y).dtype in discrete_dtypes))\\r\\n return f(x, y)\",\n \"def forward(self, x):\\r\\n # Is it possible to calculate the acc and gyro convolution in parallel??? TO DO\\r\\n # split x\\r\\n x_split = torch.split(x, 8, dim=3)\\r\\n # acc\\r\\n acc_out = F.relu(self.acc_bn1(self.acc_conv1(x_split[0])))\\r\\n acc_out = F.relu(self.acc_bn2(self.acc_conv2(acc_out)))\\r\\n # gyro\\r\\n gyro_out = F.relu(self.gyro_bn1(self.gyro_conv1(x_split[1])))\\r\\n gyro_out = F.relu(self.gyro_bn2(self.gyro_conv2(gyro_out)))\\r\\n\\r\\n sensor_data = torch.cat([acc_out, gyro_out], 3)\\r\\n out = F.relu(self.bn3(self.conv3(sensor_data)))\\r\\n return out\",\n \"def user_function(x, y):\\r\\n return x ** 2 + 2 * y ** 2\",\n \"def forward(self, x):\\n pass\",\n \"def forward(self, x):\\n raise NotImplementedError\",\n \"def forward(self, x):\\n raise NotImplementedError\",\n \"def forward(self, x):\\n\\n def run0(x, dummy):\\n lout1 = self.lconv1(x)\\n out1 = self.conv1(lout1)\\n lout2 = self.lconv2(out1 + lout1)\\n out2 = self.conv2(lout2)\\n lout3 = self.lconv3(out2 + lout2)\\n out3 = self.conv3(lout3)\\n lout4 = self.lconv4(out3 + lout3)\\n out4 = self.conv4(lout4)\\n lout5 = self.lconv5(out4 + lout4)\\n out5 = self.conv5(lout5)\\n lout6 = self.lconv6(out5 + lout5)\\n out6 = self.conv6(lout6)\\n lout7 = self.lconv7(out6 + lout6)\\n out7 = self.conv7(lout7)\\n mat = out7[:, :, :, None] + out7[:, :, None, :]\\n cur = mat\\n if self.num_1d:\\n output1d = self.final_1d(out7)\\n return cur, output1d\\n else:\\n return cur\\n\\n dummy = torch.Tensor(1)\\n dummy.requires_grad = True\\n if self.num_1d:\\n cur, output1d = checkpoint(run0, x, dummy)\\n else:\\n cur = checkpoint(run0, x, dummy)\\n\\n def run1(cur):\\n first = True\\n for lm, m in zip(self.lconvtwos[:7], self.convtwos[:7]):\\n if first:\\n cur = lm(cur)\\n\\n first = False\\n else:\\n cur = lm(cur) + cur\\n cur = m(cur) + cur\\n return cur\\n\\n def run2(cur):\\n for lm, m in zip(self.lconvtwos[7:13], self.convtwos[7:13]):\\n cur = lm(cur) + cur\\n cur = m(cur) + cur\\n return cur\\n\\n def run3(cur):\\n for lm, m in zip(self.lconvtwos[13:], self.convtwos[13:]):\\n cur = lm(cur) + cur\\n cur = m(cur) + cur\\n\\n cur = self.final(cur)\\n cur = 0.5 * cur + 0.5 * cur.transpose(2, 3)\\n return cur\\n\\n cur = checkpoint(run1, cur)\\n cur = checkpoint(run2, cur)\\n cur = checkpoint(run3, cur)\\n\\n if self.num_1d:\\n return cur, output1d\\n else:\\n return cur\",\n \"def forward(self, x, y, x_out):\\r\\n # Determine the grid on which to evaluate functional representation.\\r\\n x_min = min(torch.min(x[:,:,0]).cpu().numpy(),\\r\\n torch.min(x_out[:,:,0]).cpu().numpy(), 0.) - 0.1\\r\\n x_max = max(torch.max(x[:,:,0]).cpu().numpy(),\\r\\n torch.max(x_out[:,:,0]).cpu().numpy(), 1.) + 0.1\\r\\n num_points = int(to_multiple(self.points_per_unit * (x_max - x_min),\\r\\n self.multiplier))\\r\\n \\r\\n \\\"\\\"\\\"x_min_1 = min(torch.min(x[:,:,1]).cpu().numpy(),\\r\\n torch.min(x_out[:,:,1]).cpu().numpy(), 0.) - 0.1\\r\\n x_max_1 = max(torch.max(x[:,:,1]).cpu().numpy(),\\r\\n torch.max(x_out[:,:,1]).cpu().numpy(), 1.) + 0.1\\r\\n num_points_1 = int(to_multiple(self.points_per_unit * (x_max_1 - x_min_1),\\r\\n self.multiplier))\\\"\\\"\\\"\\r\\n\\r\\n x_grid = torch.linspace(x_min, x_max, num_points).to(device)\\r\\n x_grid = x_grid[None, :, None].repeat(x.shape[0], 1, 1)\\r\\n\\r\\n # Apply first layer and conv net. Take care to put the axis ranging\\r\\n # over the data last.\\r\\n h = self.activation(self.encoder(x, y, x_grid))\\r\\n h = h.permute(0, 2, 1)\\r\\n h = h.reshape(h.shape[0], h.shape[1], num_points)\\r\\n h = self.rho(h)\\r\\n h = h.reshape(h.shape[0], h.shape[1], -1).permute(0, 2, 1)\\r\\n\\r\\n # Check that shape is still fine!\\r\\n if h.shape[1] != x_grid.shape[1]:\\r\\n raise RuntimeError('Shape changed.')\\r\\n\\r\\n # Produce means and standard deviations.\\r\\n mean = self.mean_layer(x_grid, h, x_out)\\r\\n sigma = self.sigma_fn(self.sigma_layer(x_grid, h, x_out))\\r\\n\\r\\n return mean, sigma\",\n \"def forward(self, x):\\n out = self.conv(x)\\n out = out.view(out.size(0), -1)\\n\\n # critic\\n out1 = self.fc1(out)\\n\\n # auxiliary classifier\\n out2 = self.fc10(out)\\n\\n return out1, out2\",\n \"def f(self,y,psi):\\r\\n raise NotImplementedError\",\n \"def forward(self, a: torch.FloatTensor, b: torch.FloatTensor) -> torch.FloatTensor:\",\n \"def test_exp_2ndord_2vars():\\n x, y = fwd.Variable(), fwd.Variable()\\n f = fwd.exp(x/y)\\n df_dxdy = lambda x, y: -(x*np.exp(x/y) + y*np.exp(x/y)) / y**3\\n assert equals(f.derivative_at((x, x), {x: 1.5, y:2.5}, order=2),\\n f.derivative_at( x, {x: 1.5, y:2.5}, order=2)) \\n assert equals(f.derivative_at((x, y), {x: 1.5, y:2.5}, order=2), \\n f.derivative_at((y, x), {x: 1.5, y:2.5}, order=2))\\n assert equals(f.derivative_at((x, y), {x: 1.5, y:2.5}, order=2), \\n df_dxdy(1.5, 2.5))\",\n \"def forward(self, x1, x2, diag=False):\\n cov = self.kernels.forward(x1, x2, diag, embed=False).prod(0)\\n\\n return cov\",\n \"def forward(self, input: torch.Tensor, target: torch.Tensor, **ig_kwargs):\\n\\n # assume logits as input\\n probs, labels = torch.max(torch.softmax(input, dim=1), dim=1)\\n probs = torch.clamp(probs, min=self.epsilon, max=1. - self.epsilon)\\n\\n matched = torch.where(labels == target, torch.ones_like(labels), torch.zeros_like(labels))\\n n_samples = len(matched)\\n n_correct = torch.sum(matched)\\n\\n # divide all probabilities by matched/not matched\\n probs_false = probs[matched == 0]\\n probs_correct = probs[matched == 1]\\n\\n # compute kernels between different combinations\\n kernel_false = self.kernel(probs_false, probs_false)\\n kernel_correct = self.kernel(probs_correct, probs_correct)\\n kernel_mixed = self.kernel(probs_correct, probs_false)\\n\\n probs_false = torch.unsqueeze(probs_false, dim=1)\\n inv_probs_correct = torch.unsqueeze(1. - probs_correct, dim=1)\\n\\n diff_false = torch.matmul(probs_false, probs_false.transpose(1, 0))\\n diff_correct = torch.matmul(inv_probs_correct, inv_probs_correct.transpose(1, 0))\\n diff_mixed = torch.matmul(inv_probs_correct, probs_false.transpose(1, 0))\\n\\n # MMCE calculation scheme (see paper for mathematical details)\\n part_false = torch.sum(diff_false * kernel_false) / float((n_samples - n_correct) ** 2) if n_samples - n_correct > 0 else 0.\\n part_correct = torch.sum(diff_correct * kernel_correct) / float(n_correct ** 2) if n_correct > 0 else 0.\\n part_mixed = 2 * torch.sum(diff_mixed * kernel_mixed) / float((n_samples - n_correct) * n_correct) if (n_samples - n_correct) * n_correct > 0 else 0.\\n\\n mmce = self.weight * torch.sqrt(part_false + part_correct - part_mixed)\\n\\n return mmce\",\n \"def forward(self, x):\\n output1, output2 = self.model(x)\\n return output1, output2\",\n \"def _forward_log_det_jacobian(self, x):\\n d = self._compute_shared(x=x)\\n relx = (x - d.x_k) / d.w_k\\n relx = relx # tf.where(d.out_of_bounds, 0.5*tf.ones_like(x), relx)\\n grad = (\\n 2 * tf.math.log(d.s_k) +\\n tf.math.log(d.d_kp1 * relx**2 + 2 * d.s_k * relx * (1 - relx) + # newln\\n d.d_k * (1 - relx)**2) -\\n 2 * tf.math.log((d.d_kp1 + d.d_k - 2 * d.s_k) * relx *\\n (1 - relx) + d.s_k))\\n return grad # tf.where(d.out_of_bounds, tf.zeros_like(grad), grad)\",\n \"def forward(self, x, y):\\n # calculate softmax probabilities\\n x = x - np.max(x, axis=1, keepdims=True)\\n prob = np.exp(x)\\n prob = prob / np.sum(prob, axis=1, keepdims=True)\\n\\n loss = -np.sum(y * np.log(prob + 1e-8))\\n if self.reduction == \\\"mean\\\":\\n m, _ = x.shape\\n loss /= m\\n\\n # save cache for back-propagation\\n self.cache = prob, y\\n\\n return loss\",\n \"def forward(self, X, primary_y, dual_y, mode):\\r\\n primary_proj = self.label_primary(primary_y)\\r\\n dual_proj = self.label_dual(dual_y)\\r\\n\\r\\n X = self.basemodel.conv1(X)\\r\\n X = self.basemodel.bn1(X)\\r\\n X = self.basemodel.relu(X)\\r\\n X = self.basemodel.maxpool(X)\\r\\n X = self.basemodel.layer1(X)\\r\\n X = self.basemodel.layer2(X)\\r\\n X = self.basemodel.layer3(X)\\r\\n X = self.basemodel.layer4(X)\\r\\n X = self.basemodel.avgpool(X)\\r\\n X = X.squeeze(2).squeeze(2)\\r\\n #X = torch.mean(torch.mean(X4,2),2)\\r\\n if mode == 'train':\\r\\n primary_pred = self.fc_primary(torch.cat((X, dual_proj), 1))\\r\\n dual_pred = self.fc_dual(torch.cat((X, primary_proj), 1))\\r\\n return primary_pred, dual_pred\\r\\n else:\\r\\n fc_t = self.fc_primary\\r\\n fc_t.weight.data = fc_t.weight.data[:512]\\r\\n fc_t.bias.data = fc_t.bias.data[:512]\\r\\n primary_pred = fc_t(X) #only take image features as input\\r\\n return primary_pred\",\n \"def logistic_loss_step(x_, rho, X, y, tensor_name, calculate_by_hand=True):\\n\\n if calculate_by_hand:\\n # prob = torch.exp( -y * X.mm(x_.data))\\n prob = torch.exp( -y * torch.matmul(X, x_.data))\\n alpha = prob / (1+prob)\\n x_.grad = rho * x_.data - torch.mean(alpha*y*X, dim = 0).reshape(-1,1)\\n return\\n\\n else:\\n if os.getenv('BLUEFOG_TIMELINE'):\\n with bf.timeline_context(tensor_name=tensor_name,\\n activity_name=\\\"gradient computation\\\"):\\n loss_ = torch.mean(torch.log(1 + torch.exp(-y*X.mm(x_)))) + \\\\\\n 0.5*rho*torch.norm(x_, p=2)\\n loss_.backward()\\n else:\\n loss_ = torch.mean(torch.log(1 + torch.exp(-y*X.mm(x_)))) + \\\\\\n 0.5*rho*torch.norm(x_, p=2)\\n loss_.backward()\\n return\",\n \"def log_forward_computations(self, x: list): \\n n_x = len(x)\\n \\n # log_f_x initialized to -Inf because log(0) = -Inf\\n log_f_x = np.zeros((self.n_states, n_x)) + logzero()\\n x_emission_scores = np.array([self.scores['emission'][:, self.word_to_pos[w] if w in list(self.word_to_pos.keys()) else self.word_to_pos['UnknownWord']] for w in x]).T\\n \\n log_f_x[:,0] = x_emission_scores[:, 0] + self.scores['initial']\\n \\n for i in range(1,n_x):\\n for s in range(self.n_states):\\n log_f_x[s,i] = logsumexp(self.scores['transition'][s,:] + \\n log_f_x[:,i-1]) + x_emission_scores[s, i]\\n\\n \\n log_likelihood = logsumexp(self.scores['final'] + log_f_x[:,-1])\\n \\n return log_f_x, log_likelihood\",\n \"def forward(self, x_context, y_context, x_target, y_target=None, y0=None):\\n # Infer quantities from tensor dimensions\\n _, num_target, _ = x_target.size()\\n _, _, y_dim = y_context.size()\\n\\n if self.training:\\n # Encode target and context (context needs to be encoded to\\n # calculate kl term)\\n mu_target, sigma_target = self.xy_to_mu_sigma(x_target, y_target, y0)\\n mu_context, sigma_context = self.xy_to_mu_sigma(x_context, y_context, y0)\\n # Sample from encoded distribution using reparameterization trick\\n q_target = Normal(mu_target, sigma_target)\\n q_context = Normal(mu_context, sigma_context)\\n z_sample = q_target.rsample()\\n # Get parameters of output distribution\\n p_y_pred = self.xz_to_y(x_target, z_sample)\\n return p_y_pred, q_target, q_context\\n else:\\n # At testing time, encode only context\\n mu_context, sigma_context = self.xy_to_mu_sigma(x_context, y_context, y0)\\n # Sample from distribution based on context\\n q_context = Normal(mu_context, sigma_context)\\n z_sample = q_context.rsample()\\n # Predict target points based on context\\n p_y_pred = self.xz_to_y(x_target, z_sample)\\n return p_y_pred\",\n \"def _derX(self, x, y):\\n raise NotImplementedError()\",\n \"def forward(self, x, y):\\r\\n x = torch.div(x, x.norm())\\r\\n y = torch.div(y, y.norm())\\r\\n if not self.is_cat:\\r\\n #x = x.mean(dim=0).unsqueeze(0) # addition over channels, x.shape = (1,256,7,7)\\r\\n x = torch.sum(x, dim=0).unsqueeze(0)\\r\\n else:\\r\\n x = x.reshape(-1) # under construction\\r\\n x = self.conv1(x)\\r\\n x = x.view(x.size(0), -1) # Size (1,256*7*7)\\r\\n\\r\\n y = self.conv2(y)\\r\\n y = torch.nn.functional.interpolate(y, size=(7,7), mode=\\\"bilinear\\\") #Size(1, 256, 7, 7)\\r\\n y = y.view(y.size(0), -1) # Size(1, 256*7*7)\\r\\n x = self.fc1(x) # Size (1,256)\\r\\n # x = self.bn1(x)\\r\\n # x = self.bn1(self.fc1(x))\\r\\n x = self.relu(x) # Size (1, 256)\\r\\n y = self.fc2(y)\\r\\n y = self.relu(y)\\r\\n\\r\\n # concat 2 features\\r\\n x = torch.cat((x, y), 1) # Size(1,512)\\r\\n\\r\\n x = self.fc3(x) # Size (1, 64)\\r\\n x = self.relu2(x)\\r\\n x = self.fc4(x) # Size (1, 4)\\r\\n return x\",\n \"def test_div_2ndord_2vars():\\n x, y = fwd.Variable(), fwd.Variable()\\n f = x**2 / y**2\\n assert equals(f.derivative_at((x, x), {x: 1.5, y:2.5}, order=2), \\n f.derivative_at( x, {x: 1.5, y:2.5}, order=2)) \\n assert equals(f.derivative_at((x, y), {x: 1.5, y:2.5}, order=2), \\n f.derivative_at((y, x), {x: 1.5, y:2.5}, order=2))\\n assert equals(f.derivative_at((x, y), {x: 1.5, y:2.5}, order=2), \\n -4.0*1.5/2.5**3)\",\n \"def forward_differences(f, h, x):\\n\\treturn (f(x + h) - f(x)) / h\",\n \"def forward(self, input, logdet=None, reverse=False):\\n\\n if self.min_singular is not None:\\n # clip singular values\\n with torch.no_grad():\\n self.weight.copy_(clip_singular_values(self.weight.double(), min=self.min_singular).float())\\n\\n weight, dlogdet = self.get_weight(input, reverse)\\n if not reverse:\\n z = F.conv2d(input, weight)\\n if logdet is not None:\\n logdet = logdet + dlogdet\\n return z, logdet\\n else:\\n z = F.conv2d(input, weight)\\n if logdet is not None:\\n logdet = logdet - dlogdet\\n return z, logdet\",\n \"def forward(self, obs):\\n res = self.hidden_layers(obs)\\n logits = self.logits(res)\\n value = self.value_branch(res)\\n return logits, value\",\n \"def intern_F(self):\\n if self.A is None:\\n def Fx(x,y):\\n if self.hx is None:\\n fx = self.gradf(x)\\n self.Fz = fx, None, None\\n return fx, None, None\\n else:\\n vec_prod = np.zeros(len(x))\\n fy = np.zeros(len(y))\\n for i in range(len(y)):\\n gh = self.gradh[i+1](x,i+1)\\n vec_prod += y[i] * gh\\n if self.optimized:\\n fy[i] = -self.hx[i+1](x, i+1, gh)\\n else:\\n fy[i] = -self.hx[i+1](x, i+1)\\n fx = self.gradf(x)+ vec_prod\\n self.Fz = fx, fy, None\\n return fx, fy, None\\n else:\\n def Fx(x,y,u):\\n if self.hx is None:\\n fx = self.gradf(x)\\n fu = self.b-self.A@x\\n self.Fz = fx, None, fu\\n return fx, None, fu\\n else:\\n vec_prod = np.zeros(len(x))\\n fy = np.zeros(len(y))\\n for i in range(len(y)):\\n gh = self.gradh[i+1](x,i+1)\\n vec_prod += y[i] * gh\\n if self.optimized:\\n fy[i] = -self.hx[i+1](x, i+1, gh)\\n else:\\n fy[i] = -self.hx[i+1](x, i+1)\\n fx = self.gradf(x)+ vec_prod\\n fu = self.b-self.A@x\\n self.Fz = fx, fy, fu\\n return fx, fy, fu\\n return Fx\",\n \"def forward(self, x1, x2, diag=False):\\n cov = self.kernels.forward(x1, x2, diag, embed=False).sum(0)\\n\\n return cov\",\n \"def forward(self, x, y, t):\\r\\n # Compute shapes.\\r\\n batch_size = x.shape[0]\\r\\n n_in = x.shape[1]\\r\\n n_out = t.lshape[1]\\r\\n \\r\\n #pdb.set_trace()\\r\\n\\r\\n # Compute the pairwise distances.\\r\\n # Shape: (batch, n_in, n_out).\\r\\n \\r\\n #pdb.set_tracdb.set_trace()\\r\\n dists = compute_dists(x, t)\\r\\n\\r\\n # Compute the weights.\\r\\n # Shape: (batch, n_in, n_out, in_channels).\\r\\n wt = self.rbf(dists)\\r\\n\\r\\n # Compute the extra density channel.\\r\\n # Shape: (batch, n_in, 1).\\r\\n density = torch.ones(batch_size, n_in, 1).to(device)\\r\\n\\r\\n # Concatenate the channel.\\r\\n # Shape: (batch, n_in, in_channels + 1).\\r\\n y_out = torch.cat([density, y], dim=2)\\r\\n\\r\\n # Perform the weighting.\\r\\n # Shape: (batch, n_in, n_out, in_channels + 1).\\r\\n y_out = y_out.view(batch_size, n_in, -1, self.in_channels) * wt\\r\\n\\r\\n # Sum over the inputs.\\r\\n # Shape: (batch, n_out, in_channels + 1).\\r\\n y_out = y_out.sum(1)\\r\\n\\r\\n # Use density channel to normalize convolution.\\r\\n density, conv = y_out[..., :1], y_out[..., 1:]\\r\\n normalized_conv = conv / (density + 1e-8)\\r\\n y_out = torch.cat((density, normalized_conv), dim=-1)\\r\\n\\r\\n # Apply the point-wise function.\\r\\n # Shape: (batch, n_out, out_channels).\\r\\n y_out = y_out.view(batch_size * n_out, self.in_channels)\\r\\n y_out = self.g(y_out)\\r\\n y_out = y_out.view(batch_size, n_out, self.out_channels)\\r\\n\\r\\n return y_out\",\n \"def _forward(self, *args, calc_score=False):\\n with chainer.using_config('train', False), chainer.no_backprop_mode():\\n if calc_score:\\n self(*args)\\n return self.y\\n else:\\n if self.predictor is None:\\n print(\\\"[ERROR] predictor is not set or not build yet.\\\")\\n return\\n # TODO: it passes all the args, sometimes (x, y) which is too many arguments.\\n # Consider how to deal with the number of input\\n if hasattr(self.predictor, '_forward'):\\n fn = self.predictor._forward\\n else:\\n fn = self.predictor\\n return fn(*filter_args(fn, args))\",\n \"def forward(self, x_sag, x_cor):\\n sag_out_convM1 = self.convM1_sag(x_sag) # output of convM1\\n cor_out_convM1 = self.convM1_cor(x_cor) # output of convM2\\n out_xM1_sag, out_xM1_cor = self.xModule1(sag_out_convM1, cor_out_convM1) # output of x-Module 1\\n out_xM2_sag, out_xM2_cor = self.xModule2(out_xM1_sag, out_xM1_cor) # output of x-Module 2\\n out_xM3_sag, out_xM3_cor = self.xModule3(out_xM2_sag, out_xM2_cor) # output of x-Module 3\\n out1_SLE_sag, out2_SLE_sag = self.SLE_sag(out_xM3_sag) # output of spinal landmark estimator\\n out1_SLE_cor, out2_SLE_cor = self.SLE_cor(out_xM3_cor) # output of spinal landmark estimator\\n out_CAE_sag = self.CAE_sag(out1_SLE_sag, out2_SLE_sag) # output of cobb angle estimator\\n out_CAE_cor = self.CAE_cor(out1_SLE_cor, out2_SLE_cor) # output of cobb angle estimator\\n return out2_SLE_sag, out2_SLE_cor, out_CAE_sag, out_CAE_cor\",\n \"def logistic_loss(x, y):\\n N = x.shape[0]\\n x = np.squeeze(x)\\n y_prime = (y + 1)/2\\n h = 1 /(1 + np.exp(-x))\\n loss = np.sum(-np.log( (h**y_prime) * ((1-h)**(1-y_prime)) ))/N\\n dx = np.exp(-y*x)*(-y)/(1+np.exp(-y*x))/N\\n return loss, dx\",\n \"def forward(self, x):\\n # action\\n act = self.act_fc1(x)\\n act = torch.tanh(act)\\n act = self.act_fc2(act)\\n act = torch.tanh(act)\\n mean = self.mu(act) # N, num_actions\\n logstd = self.logstd.expand_as(mean)\\n std = torch.exp(logstd)\\n action = torch.normal(mean, std)\\n\\n # value\\n v = self.value_fc1(x)\\n v = torch.tanh(v)\\n v = self.value_fc2(v)\\n v = torch.tanh(v)\\n v = self.value_fc3(v)\\n\\n # action prob on log scale\\n logprob = log_normal_density(action, mean, std=std, log_std=logstd)\\n return v, action, logprob, mean\",\n \"def _evaluate(self, x, y):\\n raise NotImplementedError()\",\n \"def _derX(self, x, y):\\n m = len(x)\\n temp = np.zeros((m, self.funcCount))\\n for j in range(self.funcCount):\\n temp[:, j] = self.functions[j](x, y)\\n i = self.argcompare(temp, axis=1)\\n dfdx = np.zeros_like(x)\\n for j in range(self.funcCount):\\n c = i == j\\n dfdx[c] = self.functions[j].derivativeX(x[c], y[c])\\n return dfdx\",\n \"def forward(\\n self,\\n m_q: torch.Tensor,\\n logs_q: torch.Tensor,\\n m_p: torch.Tensor,\\n logs_p: torch.Tensor,\\n ) -> torch.Tensor:\\n posterior_norm = D.Normal(m_q, torch.exp(logs_q))\\n prior_norm = D.Normal(m_p, torch.exp(logs_p))\\n loss = D.kl_divergence(posterior_norm, prior_norm).mean()\\n return loss\",\n \"def forward(self, x, y=None):\\n # propagate networks\\n self.prior_latent_distribution = self.prior(x)\\n self.unet_features = self.unet.forward(x)\\n if y is not None:\\n y_onehot = F.one_hot(\\n y[:, 0], num_classes=self.num_classes).permute(0, -1, 1, 2)\\n xy = torch.cat([x, y_onehot], dim=1)\\n self.posterior_latent_distribution = self.posterior(xy)\\n\\n # sample latent\\n if y is not None:\\n self.z = self.posterior_latent_distribution.rsample()\\n else:\\n self.z = self.prior_latent_distribution.sample()\\n\\n # reconstruct image\\n self.y_hat_raw = self.fcomb(self.unet_features, self.z)\\n\\n return self.y_hat_raw\",\n \"def forward(self, x):\\n x1, x2 = x\\n y1 = self.conv_net.forward(x1)\\n y2 = self.sparse_net.forward(x2)\\n return y1, y2\",\n \"def forward_pass_on_convolutions(self, x):\\n conv_output = None\\n for module_name, module in self.model._modules.items():\\n print(module_name)\\n if module_name == 'fc':\\n return conv_output, x\\n x = module(x) # Forward\\n # print(module_name, module)\\n if module_name == self.target_layer:\\n print('True')\\n x.register_hook(self.save_gradient)\\n conv_output = x # Save the convolution output on that layer\\n return conv_output, x\",\n \"def forward(self, x, c):\\n batch_size, _, _, _ = x.size()\\n mu, logvar = self.recognition_model(x, c)\\n std = torch.exp(0.5 * logvar)\\n eps = torch.randn([batch_size, self.latent_size]).to('cuda' if torch.cuda.is_available() else 'cpu')\\n z_hat = eps * std + mu\\n x_hat = self.generation_model(z_hat, c)\\n return x_hat, mu, logvar\",\n \"def forward(self, x, c):\\n batch_size, _, _, _ = x.size()\\n mu, logvar = self.recognition_model(x, c)\\n std = torch.exp(0.5 * logvar)\\n eps = torch.randn([batch_size, self.latent_size]).to('cuda' if torch.cuda.is_available() else 'cpu')\\n z_hat = eps * std + mu\\n x_hat = self.generation_model(z_hat, c)\\n return x_hat, mu, logvar\",\n \"def forward(self, x, **kwargs):\\n\\n\\t\\toutput = self.lin1(x)\\n\\t\\toutput = self.lin2(output)\\n\\t\\toutput = self.lin3(output)\\n\\t\\toutput = self.lin_out(output)\\n\\n\\t\\treturn output\",\n \"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\",\n \"def __replicated_secret_sharing_protocol(op, x, y, *args, **kwargs):\\n assert op in {\\n \\\"mul\\\",\\n \\\"matmul\\\",\\n \\\"conv1d\\\",\\n \\\"conv2d\\\",\\n \\\"conv_transpose1d\\\",\\n \\\"conv_transpose2d\\\",\\n }\\n x_shares, y_shares = replicate_shares([x.share, y.share])\\n x1, x2 = x_shares\\n y1, y2 = y_shares\\n\\n z = x.shallow_copy()\\n z.share = getattr(torch, op)(x1, y1, *args, **kwargs)\\n z.share += getattr(torch, op)(x1, y2, *args, **kwargs)\\n z.share += getattr(torch, op)(x2, y1, *args, **kwargs)\\n\\n return z\",\n \"def _aux_forward(self, F, pred1, pred2, label, **kwargs):\\n loss1 = super(MixSoftmaxCrossEntropyOHEMLoss, self). \\\\\\n hybrid_forward(F, pred1, label, **kwargs)\\n loss2 = super(MixSoftmaxCrossEntropyOHEMLoss, self). \\\\\\n hybrid_forward(F, pred2, label, **kwargs)\\n return loss1 + self.aux_weight * loss2\",\n \"def forward(self, input_):\\n feature = self.seq(input_)\\n mu = self.fc1(feature)\\n logvar = self.fc2(feature)\\n std = torch.exp(0.5 * logvar)\\n return mu, std, logvar\",\n \"def forward(self, x):\\n\\n embeds = self.dvector(x)\\n logits = self.linear(embeds)\\n\\n return logits\",\n \"def _forward(self, X, **kwargs):\\n raise NotImplementedError()\",\n \"def compute(self, x, y, u=None, grad=False):\\n assert_histograms(x, y)\\n if u is not None:\\n self.__u = u\\n else:\\n self.__u = rescale(self.__u)\\n self.__assert_correct_size(x, y)\\n self.__compute(x, y)\\n\\n obj = self.__ground_metric.gamma * (x.T.dot(log_with_zeros(self.__u)) + y.T.dot(log_with_zeros(self.__v)) - 1\\n + self.__u.T.dot(self.__ground_metric.dot(self.__v)))\\n\\n if grad:\\n return obj, self.get_gradient(self.__v)\\n return obj\",\n \"def forward(self, input, condition):\\n scale, bias = self.mlp(condition).chunk(2, dim=-1)\\n scale = (F.tanh(scale) * self.rescale)\\n output = (input + bias) * scale.exp()\\n log_det = scale\\n return output, log_det\",\n \"def log_hchg(x, a1, a2, mu1, mu2):\\n assert np.alltrue(mu1 > 0) and np.alltrue(mu2 > 0)\\n assert np.alltrue(a1 > 0) and np.alltrue(a2 > 0)\\n \\n out_shp = np.broadcast(x, a1, a2, mu1, mu2).shape\\n if out_shp == ():\\n out_shp = (1,)\\n \\n x = np.broadcast_to(x, out_shp).ravel()[:, np.newaxis]\\n a1 = np.broadcast_to(a1, out_shp).ravel()[:, np.newaxis]\\n a2 = np.broadcast_to(a2, out_shp).ravel()[:, np.newaxis]\\n mu1 = np.broadcast_to(mu1, out_shp).ravel()[:, np.newaxis]\\n mu2 = np.broadcast_to(mu2, out_shp).ravel()[:, np.newaxis]\\n \\n j = np.arange(250)\\n \\n out = j * np.log(mu1 * x) - sp.gammaln(j+1)\\n out += log_poch(a1+a2, j) - log_poch(a1, j)\\n out += np.log(sp.hyp1f1(a1+a2+j, a2, mu2*(1-x)))\\n out = sp.logsumexp(out, axis=1)\\n return out.reshape(out_shp) if out.size > 1 else float(out)\",\n \"def forward(self, x: \\\"tf.Tensor\\\", y: Optional[\\\"tf.Tensor\\\"] = None) -> Tuple[\\\"tf.Tensor\\\", Optional[\\\"tf.Tensor\\\"]]:\\n import tensorflow as tf # lgtm [py/repeated-import]\\n import tensorflow_addons as tfa\\n\\n x_ndim = x.ndim\\n\\n if x_ndim == 4:\\n x_nhwc = x\\n elif x_ndim == 5:\\n # NFHWC --> NHWC\\n nb_clips, clip_size, height, width, channels = x.shape\\n x_nhwc = tf.reshape(x, (nb_clips * clip_size, height, width, channels))\\n else:\\n raise ValueError(\\n \\\"Unrecognized input dimension. Spatial smoothing can only be applied to image (NHWC) and video (NFHWC) \\\"\\n \\\"data.\\\"\\n )\\n\\n x_nhwc = tfa.image.median_filter2d(\\n x_nhwc, filter_shape=[self.window_size, self.window_size], padding=\\\"REFLECT\\\", constant_values=0, name=None\\n )\\n\\n if x_ndim == 4:\\n x = x_nhwc\\n elif x_ndim == 5: # lgtm [py/redundant-comparison]\\n # NFHWC <-- NHWC\\n x = tf.reshape(x_nhwc, (nb_clips, clip_size, height, width, channels))\\n\\n if self.clip_values is not None:\\n x = x.clip_by_value(min=self.clip_values[0], max=self.clip_values[1])\\n\\n return x, y\",\n \"def stream_function(self, X, Y):\\n self.psi = self.Vinf * Y\",\n \"def forward(self, x):\\n shared_out = self.shared_block(x)\\n res_out = self.res_conv(shared_out)\\n return res_out\",\n \"def test_forward_backward(self):\\n f = forward(self.obs, self.S, self.A, self.E)\\n b = backward(self.obs, self.S, self.A, self.E)\\n fp = logsumexp(f[:, -1])\\n emission = precompute_emission(np.log(self.E))[tuple(self.obs[0])]\\n bp = logsumexp(np.log(self.S) + emission + b[:, 0])\\n assert_allclose(fp, bp)\",\n \"def forward_propagate(self, x):\\n self.z_h = np.dot( x, self.w_ih ) + self.b_h\\n #Activations of hidden layer\\n self.a_h = self.sigmoid( self.z_h )\\n self.z_o = np.dot( self.a_h, self.w_ho ) + self.b_o\\n #yEst = activations of output layer\\n yEst = self.sigmoid( self.z_o )\\n return yEst\",\n \"def forward(self, z: torch.Tensor):\\n batch_size, group_size, n_of_groups = z.size()\\n W = self.conv.weight.squeeze()\\n\\n # forward computation\\n log_det_W = batch_size * n_of_groups * torch.logdet(W)\\n z = self.conv(z)\\n return z, log_det_W\",\n \"def hvp(func, x, backend='autograd'):\\n if backend == 'autograd':\\n return ag.differential_operators.make_hvp(func, x)\\n elif backend == 'pytorch':\\n raise NotImplementedError('VJP for Pytorch backend is not implemented yet.')\",\n \"def forward(self, input1, input2):\\n output1 = self.forward_once(input1)\\n output2 = self.forward_once(input2)\\n return output1, output2\",\n \"def fc_forward(x, w, b):\\n out = None\\n ###########################################################################\\n # TODO: Implement the forward pass. Store the result in out. #\\n ###########################################################################\\n out = np.dot(x, w) + b\\n ###########################################################################\\n # END OF YOUR CODE #\\n ###########################################################################\\n cache = (x, w, b)\\n return out, cache\",\n \"def test_local_sigm_times_exp(self):\\r\\n def match(func, ops):\\r\\n #print [node.op.scalar_op for node in func.maker.fgraph.toposort()]\\r\\n assert [node.op for node in func.maker.fgraph.toposort()] == ops\\r\\n m = self.get_mode(excluding=['local_elemwise_fusion', 'inplace'])\\r\\n x, y = tensor.vectors('x', 'y')\\r\\n\\r\\n f = theano.function([x], sigmoid(-x) * tensor.exp(x), mode=m)\\r\\n match(f, [sigmoid])\\r\\n\\r\\n f = theano.function([x], sigmoid(x) * tensor.exp(-x), mode=m)\\r\\n match(f, [tensor.neg, sigmoid])\\r\\n\\r\\n f = theano.function([x], -(-(-(sigmoid(x)))) * tensor.exp(-x), mode=m)\\r\\n match(f, [tensor.neg, sigmoid, tensor.neg])\\r\\n\\r\\n f = theano.function(\\r\\n [x, y],\\r\\n (sigmoid(x) * sigmoid(-y) * -tensor.exp(-x) *\\r\\n tensor.exp(x * y) * tensor.exp(y)),\\r\\n mode=m)\\r\\n match(f, [sigmoid, tensor.mul, tensor.neg, tensor.exp, sigmoid,\\r\\n tensor.mul])\",\n \"def forward(self, *args):\\n matrix_args = None\\n inv_quad_rhs = None\\n if self.inv_quad:\\n matrix_args = args[1:]\\n inv_quad_rhs = args[0]\\n else:\\n matrix_args = args\\n\\n # Get closure for matmul\\n lazy_tsr = self.representation_tree(*matrix_args)\\n matmul_closure = lazy_tsr._matmul\\n\\n # Collect terms for LinearCG\\n # We use LinearCG for both matrix solves and for stochastically estimating the log det\\n rhs_list = []\\n num_random_probes = 0\\n num_inv_quad_solves = 0\\n\\n # Probe vector for lanczos quadrature (log_det estimation)\\n probe_vectors = None\\n probe_vector_norms = None\\n if self.log_det:\\n num_random_probes = settings.num_trace_samples.value()\\n probe_vectors = torch.empty(self.matrix_shape[-1], num_random_probes, dtype=self.dtype, device=self.device)\\n probe_vectors.bernoulli_().mul_(2).add_(-1)\\n probe_vector_norms = torch.norm(probe_vectors, 2, dim=-2, keepdim=True)\\n if self.batch_shape is not None:\\n probe_vectors = probe_vectors.expand(*self.batch_shape, self.matrix_shape[-1], num_random_probes)\\n probe_vector_norms = probe_vector_norms.expand(*self.batch_shape, 1, num_random_probes)\\n probe_vectors = probe_vectors.div(probe_vector_norms)\\n rhs_list.append(probe_vectors)\\n\\n # RHS for inv_quad\\n self.is_vector = False\\n if self.inv_quad:\\n if inv_quad_rhs.ndimension() == 1:\\n inv_quad_rhs = inv_quad_rhs.unsqueeze(-1)\\n self.is_vector = True\\n rhs_list.append(inv_quad_rhs)\\n num_inv_quad_solves = inv_quad_rhs.size(-1)\\n\\n # Perform solves (for inv_quad) and tridiagonalization (for estimating log_det)\\n rhs = torch.cat(rhs_list, -1)\\n t_mat = None\\n if self.log_det:\\n solves, t_mat = linear_cg(\\n matmul_closure,\\n rhs,\\n n_tridiag=num_random_probes,\\n max_iter=settings.max_cg_iterations.value(),\\n max_tridiag_iter=settings.max_lanczos_quadrature_iterations.value(),\\n preconditioner=self.preconditioner,\\n )\\n\\n else:\\n solves = linear_cg(\\n matmul_closure,\\n rhs,\\n n_tridiag=num_random_probes,\\n max_iter=settings.max_cg_iterations.value(),\\n preconditioner=self.preconditioner,\\n )\\n\\n # Final values to return\\n log_det_term = torch.empty(0, dtype=self.dtype, device=self.device)\\n inv_quad_term = torch.empty(0, dtype=self.dtype, device=self.device)\\n\\n # Compute log_det from tridiagonalization\\n if self.log_det:\\n if self.batch_shape is None:\\n t_mat = t_mat.unsqueeze(1)\\n eigenvalues, eigenvectors = lanczos_tridiag_to_diag(t_mat)\\n slq = StochasticLQ()\\n log_det_term, = slq.evaluate(self.matrix_shape, eigenvalues, eigenvectors, [lambda x: x.log()])\\n\\n # Add correction\\n if self.log_det_correction is not None:\\n log_det_term = log_det_term + self.log_det_correction\\n\\n # Extract inv_quad solves from all the solves\\n if self.inv_quad:\\n inv_quad_solves = solves.narrow(-1, num_random_probes, num_inv_quad_solves)\\n inv_quad_term = (inv_quad_solves * inv_quad_rhs).sum(-2)\\n\\n self.num_random_probes = num_random_probes\\n self.num_inv_quad_solves = num_inv_quad_solves\\n\\n to_save = list(matrix_args) + [solves, probe_vectors, probe_vector_norms]\\n self.save_for_backward(*to_save)\\n\\n if not settings.memory_efficient.on():\\n self._lazy_tsr = lazy_tsr\\n\\n return inv_quad_term, log_det_term\",\n \"def log_pacc(xy, x, y, h):\\n logp = log_q(xy, y, x, h)\\n logp += xy.log_density(y)\\n logp -= log_q(xy, x, y, h)\\n logp -= xy.log_density(x)\\n return logp\",\n \"def __feature_change(train_x, train_y, val_x, test_x, processors):\\n if processors:\\n for processor in processors:\\n train_x, val_x, test_x = processor(train_x, train_y, val_x, test_x)\\n return train_x, val_x, test_x\",\n \"def logpdf(self, f, y, Y_metadata=None):\\n if isinstance(self.gp_link, link_functions.Identity):\\n return self.logpdf_link(f, y, Y_metadata=Y_metadata)\\n else:\\n inv_link_f = self.gp_link.transf(f)\\n return self.logpdf_link(inv_link_f, y, Y_metadata=Y_metadata)\",\n \"def forward_propagation(self, x):\\n # np.array -> [float, float]\\n feed_dict = {self.x_in: x}\\n return self.session.run([self.y_obtained], feed_dict=feed_dict)\",\n \"def forward(self, mu: torch.Tensor, logvar: torch.Tensor) -> torch.Tensor:\\n loss = (torch.square(mu) + torch.exp(logvar) - 1 - logvar).div(2)\\n\\n if self.reduction == \\\"none\\\":\\n return loss\\n elif self.reduction == \\\"sum\\\":\\n loss = torch.mean(loss, dim=-1).sum()\\n elif self.reduction == \\\"mean\\\":\\n loss = torch.mean(loss, dim=-1).mean()\\n else:\\n raise NotImplementedError\\n return loss\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6799488","0.5780545","0.561331","0.54602194","0.5458519","0.5442325","0.5433614","0.5368498","0.5366357","0.53153783","0.53071904","0.5299364","0.52870744","0.5253826","0.5222706","0.5199964","0.5198029","0.5174347","0.5129113","0.5123612","0.51117826","0.5101053","0.5094771","0.50697976","0.50590366","0.50410074","0.5038904","0.5036365","0.5033819","0.5028807","0.49954313","0.49638206","0.4957459","0.49477848","0.49452397","0.49439532","0.49359065","0.4935079","0.4935079","0.4931732","0.49240994","0.4920107","0.49197733","0.49187756","0.4915264","0.4915079","0.49095526","0.490682","0.49003422","0.48839718","0.48771045","0.48690388","0.48686305","0.48653096","0.48640764","0.48562768","0.48551357","0.4851994","0.48508936","0.48497805","0.48492172","0.48320502","0.48296538","0.48279935","0.48112315","0.48093098","0.4795634","0.47934455","0.4793035","0.47901127","0.47855452","0.47821185","0.47779307","0.47776634","0.47776634","0.47771913","0.4776895","0.47755054","0.47663596","0.47656754","0.47642896","0.47627103","0.4745172","0.47414932","0.47395527","0.47305456","0.47211897","0.47191164","0.47174904","0.47174618","0.4713949","0.4712756","0.47111857","0.47094613","0.4706331","0.4706159","0.46966338","0.4696205","0.46938515","0.46903723","0.46893886"],"string":"[\n \"0.6799488\",\n \"0.5780545\",\n \"0.561331\",\n \"0.54602194\",\n \"0.5458519\",\n \"0.5442325\",\n \"0.5433614\",\n \"0.5368498\",\n \"0.5366357\",\n \"0.53153783\",\n \"0.53071904\",\n \"0.5299364\",\n \"0.52870744\",\n \"0.5253826\",\n \"0.5222706\",\n \"0.5199964\",\n \"0.5198029\",\n \"0.5174347\",\n \"0.5129113\",\n \"0.5123612\",\n \"0.51117826\",\n \"0.5101053\",\n \"0.5094771\",\n \"0.50697976\",\n \"0.50590366\",\n \"0.50410074\",\n \"0.5038904\",\n \"0.5036365\",\n \"0.5033819\",\n \"0.5028807\",\n \"0.49954313\",\n \"0.49638206\",\n \"0.4957459\",\n \"0.49477848\",\n \"0.49452397\",\n \"0.49439532\",\n \"0.49359065\",\n \"0.4935079\",\n \"0.4935079\",\n \"0.4931732\",\n \"0.49240994\",\n \"0.4920107\",\n \"0.49197733\",\n \"0.49187756\",\n \"0.4915264\",\n \"0.4915079\",\n \"0.49095526\",\n \"0.490682\",\n \"0.49003422\",\n \"0.48839718\",\n \"0.48771045\",\n \"0.48690388\",\n \"0.48686305\",\n \"0.48653096\",\n \"0.48640764\",\n \"0.48562768\",\n \"0.48551357\",\n \"0.4851994\",\n \"0.48508936\",\n \"0.48497805\",\n \"0.48492172\",\n \"0.48320502\",\n \"0.48296538\",\n \"0.48279935\",\n \"0.48112315\",\n \"0.48093098\",\n \"0.4795634\",\n \"0.47934455\",\n \"0.4793035\",\n \"0.47901127\",\n \"0.47855452\",\n \"0.47821185\",\n \"0.47779307\",\n \"0.47776634\",\n \"0.47776634\",\n \"0.47771913\",\n \"0.4776895\",\n \"0.47755054\",\n \"0.47663596\",\n \"0.47656754\",\n \"0.47642896\",\n \"0.47627103\",\n \"0.4745172\",\n \"0.47414932\",\n \"0.47395527\",\n \"0.47305456\",\n \"0.47211897\",\n \"0.47191164\",\n \"0.47174904\",\n \"0.47174618\",\n \"0.4713949\",\n \"0.4712756\",\n \"0.47111857\",\n \"0.47094613\",\n \"0.4706331\",\n \"0.4706159\",\n \"0.46966338\",\n \"0.4696205\",\n \"0.46938515\",\n \"0.46903723\",\n \"0.46893886\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":92578,"cells":{"query":{"kind":"string","value":"Compute the forward transformation (Appendix A.1)."},"document":{"kind":"string","value":"def _forward(self, x):\n d = self._compute_shared(x=x)\n relx = (x - d.x_k) / d.w_k\n spline_val = (\n d.y_k + ((d.h_k * (d.s_k * relx**2 + d.d_k * relx * (1 - relx))) /\n (d.s_k + (d.d_kp1 + d.d_k - 2 * d.s_k) * relx * (1 - relx))))\n y_val = spline_val #tf.where(d.out_of_bounds, x, spline_val)\n return y_val"},"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 forward_transform(self):\n\n if self._pipeline:\n #return functools.reduce(lambda x, y: x | y, [step[1] for step in self._pipeline[: -1]])\n return functools.reduce(lambda x, y: x | y, [step.transform for step in self._pipeline[:-1]])\n else:\n return None","def transform_fn(self):\n if hasattr(self._transform_fn, 'forward'):\n return self._transform_fn.forward\n return self._transform_fn","def forward(self, x):\n x = self.conv(x)\n x = self.bn(x)\n x = self.act(x)\n return x","def forward(self, x):\n x = self.conv(x)\n x = self.bn(x)\n x = self.act(x)\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: 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 affine_forward(x, W, b):\r\n x2d = np.reshape(x, (x.shape[0], -1)) # convert 4D input matrix to 2D \r\n out = np.dot(x2d, W) + b # linear transformation\r\n cache = (x, W, b) # keep for backward step (stay with us)\r\n return out, cache","def forward(self, x):\n x = self.lin(x)\n\n return x","def forward(self, x):\n\n def run0(x, dummy):\n lout1 = self.lconv1(x)\n out1 = self.conv1(lout1)\n lout2 = self.lconv2(out1 + lout1)\n out2 = self.conv2(lout2)\n lout3 = self.lconv3(out2 + lout2)\n out3 = self.conv3(lout3)\n lout4 = self.lconv4(out3 + lout3)\n out4 = self.conv4(lout4)\n lout5 = self.lconv5(out4 + lout4)\n out5 = self.conv5(lout5)\n lout6 = self.lconv6(out5 + lout5)\n out6 = self.conv6(lout6)\n lout7 = self.lconv7(out6 + lout6)\n out7 = self.conv7(lout7)\n mat = out7[:, :, :, None] + out7[:, :, None, :]\n cur = mat\n if self.num_1d:\n output1d = self.final_1d(out7)\n return cur, output1d\n else:\n return cur\n\n dummy = torch.Tensor(1)\n dummy.requires_grad = True\n if self.num_1d:\n cur, output1d = checkpoint(run0, x, dummy)\n else:\n cur = checkpoint(run0, x, dummy)\n\n def run1(cur):\n first = True\n for lm, m in zip(self.lconvtwos[:7], self.convtwos[:7]):\n if first:\n cur = lm(cur)\n\n first = False\n else:\n cur = lm(cur) + cur\n cur = m(cur) + cur\n return cur\n\n def run2(cur):\n for lm, m in zip(self.lconvtwos[7:13], self.convtwos[7:13]):\n cur = lm(cur) + cur\n cur = m(cur) + cur\n return cur\n\n def run3(cur):\n for lm, m in zip(self.lconvtwos[13:], self.convtwos[13:]):\n cur = lm(cur) + cur\n cur = m(cur) + cur\n\n cur = self.final(cur)\n cur = 0.5 * cur + 0.5 * cur.transpose(2, 3)\n return cur\n\n cur = checkpoint(run1, cur)\n cur = checkpoint(run2, cur)\n cur = checkpoint(run3, cur)\n\n if self.num_1d:\n return cur, output1d\n else:\n return cur","def forward(self, x):\n x = self.conv(x)\n return x","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 affine_forward(x, w, b):\n #raise NotImplementedError\n #######################################################################\n # #\n # #\n # TODO: YOUR CODE HERE #\n # #\n # #\n #######################################################################\n out=np.dot(x,w)+b\n cache=(x,w,b)\n return(out, cache)","def forward(self, x_in):\r\n # x_out = torch.zeros_like(x_in)\r\n\r\n for layer in self.layers: #Call forward function of each layer in order\r\n x_out = layer.forward(x_in)\r\n # print(\"Forward pass Seq: \", layer, x_in, x_out)\r\n x_in = x_out # output of the layer is passed as input to the next layer\r\n self.temp = x_in\r\n return x_out","def forward(self, X):\n self._X = X # For backprop later on.\n self._z = np.dot(X, self._W) + self._b\n a = self._act.a(self._z)\n return a","def forward(self, inp):\n return inp.dot(self.W) + self.b","def forward(self, x):\n return self.l1(x)","def forward(self, x):\n l0 = self.conv0(x)\n l1 = self.conv1(l0)\n l2 = self.conv2(l1)\n l3 = self.conv3(l2)\n l4 = self.conv4(l3)\n y = self.linear(l4.flatten(1))\n return y, (l1, l2, l3, l4)","def forward(self, x): # used to be __call__\n\n g = torch.matmul(x, self.W) # shape = (?, self.size)\n self.output = []\n\n in_i = 0 # input index\n out_i = 0 # output index\n # Apply functions with only a single input\n while out_i < self.n_single:\n self.output.append(self.funcs[out_i](g[:, in_i]))\n in_i += 1\n out_i += 1\n # Apply functions that take 2 inputs and produce 1 output\n while out_i < self.n_funcs:\n self.output.append(self.funcs[out_i](g[:, in_i], g[:, in_i+1]))\n in_i += 2\n out_i += 1\n\n self.output = torch.stack(self.output, dim=1)\n\n return self.output","def forward(self, x):\n x = self.arch(x)\n x = self.swish(x)\n x_out = self.arch._fcOut(x)\n return x_out","def forward(self, x):\r\n x = self.conv1(x)\r\n x = self.conv1_BN(x)\r\n x = F.relu(x)\r\n x = self.conv1_dp(x)\r\n x = self.Block2_1(x)\r\n x = self.Block2_2(x)\r\n x = self.Block3_1(x)\r\n x = self.Block3_2(x)\r\n x = self.Block3_3(x)\r\n x = self.Block3_4(x)\r\n x = self.Block4_1(x)\r\n x = self.Block4_2(x)\r\n x = self.Block4_3(x)\r\n x = self.Block4_4(x)\r\n x = self.Block5_1(x)\r\n x = self.Block5_2(x)\r\n x = self.MP(x)\r\n x = x.view(x.size(0),-1)\r\n x = self.fc(x)\r\n \r\n return x","def forward(self, inputs):\n return inputs * self.fc(inputs.mean(-1).mean(-1).mean(-1)).view(\n inputs.shape[0], inputs.shape[1], 1, 1, 1)","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 x = F.relu(self.fc1(x))\n x = F.relu(self.fc2(x))\n x = self.fc3(x)\n return x","def forward(self, inputs):\r\n #print (len(inputs))\r\n out = self.fc1(inputs)\r\n out = self.fc2(out)\r\n self.out = out\r\n return out\r\n #raise NotImplementedError('Implement the forward method of the model')\r","def forward(self, x):\n self.a[0] = x/255 - 0.5 #normalize and Center the input values between [-0.5,0.5]\n # TODO\n for i in range(1,len(self.z)):\n self.z[i]= np.dot(self.w[i],self.a[i-1])+self.b[i]\n if i!=self.L-1:\n self.a[i]=self.phi(self.z[i])\n else:\n self.a[i]=self.softmax(self.z[i])\n \n return(self.a[self.L-1])","def forward(self, x):\n #delete all cts\n #self.cts = [self.cts[-1]]\n \n #forward\n length, batch, dim = x.shape\n res = []\n res.append(self.one_step(x[0], torch.zeros((batch, self.latent_size), dtype=torch.float)))\n\n for i in range(1,length):\n res.append(self.one_step(x[i], res[i-1]))\n\n return torch.stack(res)","def forward(self, state):\n x = F.relu(self.fc1(state))\n x = F.relu(self.fc2(x))\n x = F.relu(self.fc3(x))\n return self.fc4(x)","def forward(self, a: torch.FloatTensor, b: torch.FloatTensor) -> torch.FloatTensor:","def forward(self, x):\n #print(\"full frwd x shape:\",x.shape)\n y=np.zeros((x.shape[0],self.W.shape[0]))\n y=np.dot(x,np.transpose(self.W))+self.b\n self.x=np.copy(x)\n return y","def forward(self, x):\n pass","def forward(self, x):\n x = self.input(x)\n x = self.in0(x)\n x = self.block0(x) + x\n x = self.block1(x) + x\n x = self.block2(x) + x\n x = self.block3(x) + x\n x = self.block4(x) + x\n x = self.in0(x)\n\n out = self.out(x)\n\n return out","def forward(self, state):\n x = self.fc(state)\n return x","def forward(self, x):\n\n\t\t## Conv layers\n\t\tx = self.avgpool(F.tanh(self.conv1(x)))\n\t\tx = self.avgpool(F.tanh(self.conv2(x)))\n\t\tx = F.tanh(self.conv3(x))\n\n\t\t## Flatten\n\t\tx = x.view(x.size(0), -1)\n\n\t\t## Fully connected layers\n\t\tx = F.tanh(self.fc1(x))\n\t\tx = self.fc2(x)\n\n\t\tx = F.softmax(x, dim=1)\n\n\t\treturn x","def forward_features(self, x, flows_backward, flows_forward):\n x1 = self.stage1(x, flows_backward[0::4], flows_forward[0::4])\n x2 = self.stage2(x1, flows_backward[1::4], flows_forward[1::4])\n x3 = self.stage3(x2, flows_backward[2::4], flows_forward[2::4])\n x4 = self.stage4(x3, flows_backward[3::4], flows_forward[3::4])\n x = self.stage5(x4, flows_backward[2::4], flows_forward[2::4])\n x = self.stage6(x + x3, flows_backward[1::4], flows_forward[1::4])\n x = self.stage7(x + x2, flows_backward[0::4], flows_forward[0::4])\n x = x + x1\n for layer in self.stage8:\n x = layer(x)\n x = rearrange(x, 'n c d h w -> n d h w c')\n x = self.norm(x)\n x = rearrange(x, 'n d h w c -> n c d h w')\n return x","def forward(self, x):\n # pylint: disable=arguments-differ\n x = x.permute([0, 3, 1, 2, ])\n x = self.power(x * 0.00392156885937 + 0.0, 1.0)\n cat = self.conv1(x)\n x = self.conv2(cat)\n x = self.concat8((cat, x), 1)\n conv_final = self.conv3(x)\n conv_final_permute = conv_final.permute([0, 2, 3, 1])\n loc_pred, obj_pred, cls_pred = torch.split(conv_final_permute, [64, 16, 64], 3)\n # obj_perm = self.obj_pred(obj_perm) # sigmoid might lead to gradient losing.\n #cls_reshape = torch.reshape(cls_perm, (-1, 4))\n #cls_pred_prob = self.cls_pred_prob(cls_reshape)\n #cls_pred = torch.reshape(cls_pred_prob, cls_perm.shape)\n return obj_pred, cls_pred, loc_pred","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, state):\n x = self._format(state)\n x = self.activation_fc(self.input_layer(x))\n for hidden_layer in self.hidden_layers:\n x = self.activation_fc(hidden_layer(x))\n x = self.output_layer(x)\n x = self.out_activation_fc(x)\n return self.rescale_fn(x)","def forward(self, x):\n\n identity = x\n\n out = self.conv1(x)\n out = self.bn1(out)\n out = self.activation(out)\n\n out = self.conv2(out)\n out = self.bn2(out)\n\n if self.upsample is not None:\n identity = self.upsample(x)\n\n out += identity\n out = self.activation(out)\n\n return out","def forward(self, state):\n\t\tx = F.relu(self.fc1(state))\n\t\tx = F.relu(self.fc2(x))\n\t\tx = F.tanh(self.fc3(x)) # outputs are in the range [-1, 1]\n\n\t\treturn x","def feed_forward(self):\n pre = self.pre_layer.o\n self.post_layer.i = torch.matmul(pre, self.weight)","def forward(self, state):#forward pass\n x = F.relu(self.fc1(state))\n x = F.relu(self.fc2(x))\n return torch.tanh(self.fc3(x))","def forward(self, X):\n # Propaga a entrada pela rede\n self.z = np.dot(X, self.W1) # Produto escalar da entrada com a primeira matrix de pesos\n self.z2 = self.sigmoid(self.z) # Função de ativação\n self.z3 = np.dot(self.z2, self.W2) # Produto escalar da hidden layer com a segunda matrix de pesos\n return self.sigmoid(self.z3) # Função de ativação na saída ","def forward(self, state):\n output = self.conv_layers(state)\n output = output.view(-1, 7*7*64)\n output = self.fc(output)\n return output","def forward(self, x):\n n, c, t, v = x.size()\n x1 = x.view(n, c * t, v)\n y = None\n for i in range(self.num_subset):\n A1 = self.PA[i]\n z = self.conv_d[i](torch.matmul(x1, A1).view(n, c, t, v))\n y = z + y if y is not None else z\n A2 = self.cen(x)\n z2 = torch.matmul(x1, A2).view(n, c, t, v)\n z2 = self.conv_cen(z2)\n y += self.lamb * z2\n y = self.bn(y)\n y += self.down(x)\n y = self.relu(y)\n y = self.attention(y)\n return y","def forward(self, x):\n out = self.conv1(x)\n out = self.bn1(out)\n out = self.relu(out)\n out = self.maxpool(out)\n out = self.avgpool(out)\n out = self.layer1(out)\n out = self.layer2(out)\n out = self.layer3(out)\n out = self.layer4(out)\n out = self.avgpool(out)\n out = torch.flatten(out, 1)\n out = self.fc(out)\n return out","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, state):\n x = F.relu(self.input(state))\n for layer in self.layers:\n x = F.relu(layer(x))\n if self.duel:\n # Value function estimator\n val = F.relu(self.val_fc_input(x))\n val = self.val_fc_output(val)\n # Advantage function estimator\n adv = F.relu(self.adv_fc_input(x))\n adv = self.adv_fc_output(adv)\n # Subtract mean so that V and A are uniquely identifiable for a given Q\n return val + adv - adv.mean(1).unsqueeze(1).expand(state.size(0), self.action_size)\n else:\n return self.output(x)","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):\n theta = self.L(x[:, :6])\n theta = torch.cat((theta, self.U(torch.cat((x[:, :9], theta), 1))), 1)\n theta = torch.cat((theta, self.R(torch.cat((x[:, 3:12], theta), 1))), 1)\n theta = torch.cat((theta, self.B(torch.cat((x[:, 6:15], theta), 1))), 1)\n # Prepends T to the angle vector theta to maintain order S,L,U,R,B\n theta = torch.cat((self.S(torch.cat((x[:, 12:], x[:, :4], theta[:, :3]), 1)), theta), 1)\n return self.FC(theta)","def affine_forward(x, w, b):\n out = None\n ###########################################################################\n # TODO: Implement the affine forward pass. Store the result in out. You #\n # will need to reshape the input into rows. #\n ###########################################################################\n # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n t = np.reshape(x,(x.shape[0],np.prod(np.shape(x)[1:])))\n \n\n out = np.dot(t,w) + b\n \n #print(np.shape(out))\n\n pass\n\n # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n ###########################################################################\n # END OF YOUR CODE #\n ###########################################################################\n cache = (x, w, b)\n return out, cache","def forward(self, state):\n '''\n state = F.relu(self.conv1(state))\n state = F.relu(self.conv2(state))\n state = F.relu(self.conv3(state))\n state = F.relu(self.fc1(state))\n \n action = F.relu(self.fc2(state))\n \n return action\n '''\n \n x = F.relu(self.fc1(state))\n x = F.relu(self.fc2(x))\n x = self.fc3(x)\n \n return x","def propagate_forward(self, X):\n\n A = X\n L = self.L - 1\n\n for i in range(0, L - 1):\n Z = np.matmul(self.W[i], A) + self.b[i]\n cache = ((A, self.W[i], self.b[i]), Z)\n A = relu(Z)\n self.caches[i] = cache\n Z = np.matmul(self.W[L - 1], A) + self.b[L - 1]\n cache = ((A, self.W[L - 1], self.b[L - 1]), Z)\n AL = sigmoid(Z)\n self.caches[L - 1] = cache\n return AL","def forward(self, input_tensor):\n rv = torch.randn(input_tensor.size(), device=self.device) * 0.02\n augmented_input = input_tensor + rv\n intermediate = augmented_input\n intermediates = [augmented_input]\n for module in self.down:\n intermediate = module(intermediate)\n intermediates.append(intermediate)\n intermediates = intermediates[:-1][::-1]\n\n down = intermediate.view(-1, 6*6*512)\n\n intermediate = self.reducer(intermediate)\n\n for index, module in enumerate(self.up):\n intermediate = torch.cat((intermediate, intermediates[index]), 1)\n intermediate = module(intermediate)\n\n intermediate = torch.cat((intermediate, input_tensor), 1)\n\n for module in self.down_again:\n intermediate = module(intermediate)\n\n intermediate = intermediate.view(-1, 6*6*64)\n intermediate = torch.cat((down, intermediate), -1)\n\n projected = self.projection(intermediate)\n\n return projected","def forward(self, x, **kwargs):\n\n\t\toutput = self.lin1(x)\n\t\toutput = self.lin2(output)\n\t\toutput = self.lin3(output)\n\t\toutput = self.lin_out(output)\n\n\t\treturn output","def affine_forward(x, w, b):\n out = None\n x_shape = x.shape\n x_reshaped = x.reshape(x_shape[0], np.prod(x_shape[1:]))\n out = np.dot(x_reshaped, w) + b\n cache = (x, w, b)\n return out, cache","def forward(self, x):\n if x.dim() not in [2, 3]:\n raise RuntimeError(\"Expect 2D/3D tensor, but got {:d}D\".format(\n x.dim()))\n # if N x S, reshape N x 1 x S\n if x.dim() == 2:\n x = th.unsqueeze(x, 1)\n # N x 2F x T\n c = F.conv1d(x, self.K, stride=self.stride, padding=0)\n # N x F x T\n r, i = th.chunk(c, 2, dim=1)\n m = (r**2 + i**2)**0.5\n p = th.atan2(i, r)\n return m, p","def forward(self, input):\n\n x = self.conv(input)\n x = self.bn(x)\n out = self.act(x)\n return out","def forward(self, x, contextState):\r\n \r\n #concatenate input and context state\r\n #x = x.t()\r\n xAndContext = torch.cat((x, contextState), 1)\r\n\r\n #calculate next context state (hidden output for current t) with tanh(xAndContext * W1)\r\n contextState = torch.tanh(xAndContext.mm(self.W1))\r\n \r\n # Calculates final output\r\n output = contextState.mm(self.V)\r\n\r\n return (output, contextState)","def forward(self):\n self.iteration_number += 1\n x = self.x\n self.x = self.alpha * self.x + self.betta\n t = x - self.x\n\n return (t * t).sum()","def forward_pass(self, x):\r\n self.a = (self.w.T @ x.T).T + self.b # Weighted sum of x with weight matrix(augmented with bias)\r\n self.x = x\r\n return self.a","def forward(self, x):\n if self.training:\n x = self.input_pert(x)\n x = self.encoder(x)\n x = self.decoder(x)\n return x","def affine_forward(X, W, b):\n return np.dot(X, W) + b","def forward(self, x):\n x=T.div(x,255.0)\n \n #print(state[20:,20:,0])\n #print(state[:,0,:,:])\n conv1 = F.relu(self.conv1(x))\n conv2 = F.relu(self.conv2(conv1))\n conv3 = F.relu(self.conv3(conv2))\n ###\n conv_state = conv3.view(conv3.size()[0], -1)\n flat1 = F.relu(self.fc1(conv_state))\n flat2 = F.relu(self.fc2(flat1))\n\n V = self.V(flat2)\n A = self.A(flat2)\n\n return V, A\n return x","def fc_forward(x, w, b):\n out = None\n ###########################################################################\n # TODO: Implement the forward pass. Store the result in out. #\n ###########################################################################\n out = np.dot(x, w) + b\n ###########################################################################\n # END OF YOUR CODE #\n ###########################################################################\n cache = (x, w, b)\n return out, cache","def affine_forward(x,w,b):\n out=None\n N=x.shape[0]\n x_row=x.reshape(N,-1)\n out=np.dot(x_row,w)+b\n cache=(x,w,b)\n return out,cache","def affine_forward(x, w, b):\n out = None\n ###########################################################################\n # TODO: Implement the affine forward pass. Store the result in out. You #\n # will need to reshape the input into rows. #\n ###########################################################################\n dim_size = x[0].shape\n X = x.reshape(x.shape[0], np.prod(dim_size))\n out = X.dot(w) + b\n ###########################################################################\n # END OF YOUR CODE #\n ###########################################################################\n cache = (x, w, b)\n return out, cache","def forward(self, x: torch.Tensor) -> torch.Tensor:\n identity = x\n\n out = self.conv_reduce(x)\n out = self.relu(self.bn_reduce(out))\n\n out = self.conv_conv(out)\n out = self.relu(self.bn(out))\n\n out = self.conv_expand(out)\n out = self.bn_expand(out)\n\n res = self.shortcut(identity)\n return self.relu(res + out)","def forward(self, x):\n x = self.sparseModel(x)\n x = self.linear(x)\n return x","def forwardPropagate (self, x):\n\t\tif type(x) is not list:\n\t\t\tx = [x]\n\t\tx = np.concatenate((np.array([1]),np.array(x)),axis=0) # absorb w_0\n\n\t\t# No transformation, but needed later\n\t\tfor i in range(self.inn):\n\t\t\tself.z_in[i] = x[i]\n\t\t\n\t\t# For every hidden neuron (1 hidden layer only!)\n\t\tfor j in range(self.hidden):\n\t\t\tsumIn = 0\n\t\t\tfor i in range(self.inn):\n\t\t\t\tsumIn += self.w_hd[j][i]*self.z_in[i]\n\t\t\tself.a_hd[j] = sumIn # Needed for backprop (5.56)\n\t\t\tself.z_hd[j] = self.act(sumIn)\n\n\t\t# For every output neuron\n\t\tfor k in range(self.out):\n\t\t\tsumHdn = 0\n\t\t\tfor j in range(self.hidden):\n\t\t\t\tsumHdn += self.w_out[k][j]*self.z_hd[j]\n\t\t\tself.z_out[k] = sumHdn\n\t\treturn self.z_out","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, x):\n raise NotImplementedError","def forward(self, x):\n raise NotImplementedError","def affine_forward(x, w, b):\n out = None\n ########################################################################\n # TODO: Implement the affine forward pass. Store the result in out. #\n # You will need to reshape the input into rows. #\n ########################################################################\n\n x_reshaped = x.reshape(x.shape[:1] + (-1,))\n out = x_reshaped.dot(w) + b\n\n ########################################################################\n # END OF YOUR CODE #\n ########################################################################\n cache = (x, w, b)\n return out, cache","def forward(self, x):\n out_conv1 = self.conv1(x)\n out_conv2 = self.conv2(out_conv1)\n out_conv3 = self.conv3(out_conv2)\n out_conv4 = self.conv4(out_conv3)\n out_conv5 = self.conv5(out_conv4)\n out_conv6 = self.conv6(out_conv5)\n out_conv7 = self.conv7(out_conv6)\n\n out_upconv7 = self.crop_top_left(self.upconv7(out_conv7), out_conv6)\n concat7 = torch.cat((out_upconv7, out_conv6), 1)\n out_iconv7 = self.iconv7(concat7)\n\n out_upconv6 = self.crop_top_left(self.upconv6(out_iconv7), out_conv5)\n concat6 = torch.cat((out_upconv6, out_conv5), 1)\n out_iconv6 = self.iconv6(concat6)\n\n out_upconv5 = self.crop_top_left(self.upconv5(out_iconv6), out_conv4)\n concat5 = torch.cat((out_upconv5, out_conv4), 1)\n out_iconv5 = self.iconv5(concat5)\n\n out_upconv4 = self.crop_top_left(self.upconv4(out_iconv5), out_conv3)\n concat4 = torch.cat((out_upconv4, out_conv3), 1)\n out_iconv4 = self.iconv4(concat4)\n disp4 = self.alpha * self.predict_disp4(out_iconv4) + self.beta\n\n out_upconv3 = self.crop_top_left(self.upconv3(out_iconv4), out_conv2)\n disp4_up = self.crop_top_left(torch.nn.functional.interpolate(disp4,\n scale_factor=2,\n mode='bilinear',\n align_corners=False), out_conv2)\n concat3 = torch.cat((out_upconv3, out_conv2, disp4_up), 1)\n out_iconv3 = self.iconv3(concat3)\n disp3 = self.alpha * self.predict_disp3(out_iconv3) + self.beta\n\n out_upconv2 = self.crop_top_left(self.upconv2(out_iconv3), out_conv1)\n disp3_up = self.crop_top_left(torch.nn.functional.interpolate(disp3,\n scale_factor=2,\n mode='bilinear',\n align_corners=False), out_conv1)\n concat2 = torch.cat((out_upconv2, out_conv1, disp3_up), 1)\n out_iconv2 = self.iconv2(concat2)\n disp2 = self.alpha * self.predict_disp2(out_iconv2) + self.beta\n\n out_upconv1 = self.crop_top_left(self.upconv1(out_iconv2), x)\n disp2_up = self.crop_top_left(torch.nn.functional.interpolate(disp2,\n scale_factor=2,\n mode='bilinear',\n align_corners=False), x)\n concat1 = torch.cat((out_upconv1, disp2_up), 1)\n out_iconv1 = self.iconv1(concat1)\n disp1 = self.alpha * self.predict_disp1(out_iconv1) + self.beta\n\n if self.training:\n return disp1, disp2\n else:\n return disp1","def forward(self, x):\n t1 = self.stn1(x)\n t1 = t1.view(-1, 3, 4)\n p1 = affine_transform(x, t1)\n\n s1 = self.sampler1(p1)\n t2 = self.stn2(s1)\n t2 = t2.view(-1, 3, 4)\n p2 = affine_transform(s1, t2)\n\n s2 = self.sampler2(p2)\n t3 = self.stn3(s2)\n\n predicted_theta = t3.view(-1, 3, 4)\n\n return predicted_theta","def forward(self, x):\r\n # 1. step 0\r\n x_0 = self.mg0(x)\r\n a_0 = self.mhsa0(x_0)\r\n a_0 = a_0.unsqueeze(dim = 3) # [m, h, w, 1, c]\r\n a_0 = t.matmul(a_0, self.W_p0).squeeze().permute(0, -1, 1, 2) + x # transformation # [m, c, h, w]\r\n a_0_ = a_0\r\n a_0 = a_0.permute(0, 2, 3, 1)\r\n a_0 = self.mlp0(a_0)\r\n a_0 = a_0.permute(0, -1, 1, 2) + a_0_\r\n x_0 = self.max_pool0(a_0) + self.avg_pool0(a_0)\r\n\r\n # 2. step 1\r\n x_1 = self.mg1(x_0)\r\n a_1 = self.mhsa1(x_1)\r\n a_1 = a_1.unsqueeze(dim = 3) # [m, h, w, 1, c]\r\n a_1 = t.matmul(a_1, self.W_p1).squeeze().permute(0, -1, 1, 2) + x_0 # transformation # [m, c, h, w]\r\n a_1_ = a_1\r\n a_1 = a_1.permute(0, 2, 3, 1)\r\n a_1 = self.mlp1(a_1)\r\n a_1 = a_1.permute(0, -1, 1, 2) + a_1_\r\n x_1 = self.max_pool1(a_1) + self.avg_pool1(a_1)\r\n\r\n # 3. step 2\r\n x_2 = self.mg2(x_1)\r\n a_2 = self.mhsa2(x_2)\r\n a_2 = a_2.unsqueeze(dim = 3) # [m, h, w, 1, c]\r\n a_2 = t.matmul(a_2, self.W_p2).squeeze().permute(0, -1, 1, 2) + x_1 # transformation # [m, c, h, w]\r\n a_2_ = a_2\r\n a_2 = a_2.permute(0, 2, 3, 1)\r\n a_2 = self.mlp0(a_2)\r\n a_2 = a_2.permute(0, -1, 1, 2) + a_2_\r\n\r\n # 4. Upsample\r\n a_1 = self.upsample1(a_1)\r\n a_2 = self.upsample2(a_2)\r\n output = a_0 + a_1 + a_2\r\n\r\n return output","def forward(self, inputs):\n raise NotImplementedError","def affine_forward(x, w, b):\n out = None\n ###########################################################################\n # TODO: Implement the affine forward pass. Store the result in out. You #\n # will need to reshape the input into rows. #\n ###########################################################################\n reshaped_inp = np.reshape(x,(int(x.shape[0]),int(np.prod(x.shape) / x.shape[0])))\n out = reshaped_inp.dot(w) + b\n ###########################################################################\n # END OF YOUR CODE #\n ###########################################################################\n cache = (x, w, b)\n return out, cache","def forward(self, x, dt, do_normalization=True):\n return x","def forward_substitution(self):\r\n for col in range(0, self.SIZE):\r\n self.check_solvability(self.matrix[col][col], self.result[col])\r\n self.result[col] = self.divide(self.result[col], self.matrix[col][col])\r\n for row in range(col + 1, self.SIZE):\r\n self.result[row] -= (self.result[col] * self.matrix[row][col])\r\n return self.result","def linear_forward(A_prev, W, b):\n Z = W.dot(A_prev) + b \n \n assert(Z.shape == (W.shape[0], A_prev.shape[1]))\n cache = (A_prev, W, b) \n \n return Z, cache","def forward(self, feats):\n return multi_apply(self.forward_single, feats, self.scales)","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(self, input_):\n out = self.fc(input_)\n out = self.bn(out)\n out = self.relu(out)\n return torch.cat([out, input_], dim=1)","def makeFastFeedForwardFunction(self):\n\n\t\toutWeightMatrix = []\n\t\tfor unit in self.outputLayer:\n\n\t\t\trow = []\n\t\t\tfor b in unit.branchesIn:\n\t\t\t\tprint b.weight\n\t\t\t\trow.append(b.weight)\n\t\t\t\n\t\t\toutWeightMatrix.append(row)\n\t\toutWeightMatrix = np.array(outWeightMatrix).squeeze()\n\n\t\thiddenMatrices = []\n\t\tfor layer in self.hiddenLayers:\n\t\t\tmatrix = []\n\t\t\t#ignore the bias unit, since it has no branches in\n\t\t\tfor unit in layer[1:]:\n\t\t\t\trow = []\n\t\t\t\tfor b in unit.branchesIn:\n\t\t\t\t\trow.append(b.weight)\n\n\t\t\t\tmatrix.append(row)\n\t\t\tmatrix = np.array(matrix)\n\n\t\t\thiddenMatrices.append(matrix)\n\n\t\thidActFunc = (self.hiddenLayers[0])[1].activationFunction\n\t\toutActFunc = self.outputLayer[0].activationFunction\n\n\t\tdef ffFunc(inp):\n\t\n\t\t\tforward = np.insert(inp.T,0,1.0,axis=0)\n\t\t\tfor matrix in hiddenMatrices:\n\t\t\t\tnext = np.dot(matrix,forward)\n\t\t\t\tnext = hidActFunc(next)\n\t\t\t\tforward = np.insert(next,0,1.0,axis=0)\n\n\t\t\tout = np.dot(outWeightMatrix,forward)\n\n\t\t\treturn outActFunc(out)\n\n\t\treturn ffFunc","def forward(self, x):\n x = F.relu(self.enc(x))\n x = F.relu(self.dec(x))\n return x","def forward(self, x):\n\n # Encoding path\n x1 = self.ins(x)\n\n # Apply Downsampling and encoding modules\n x2 = self.ds_0(x1)\n x2 = self.en_1(x2)\n\n # Apply Downsampling and encoding modules\n x3 = self.ds_1(x2)\n x3 = self.en_2(x3)\n\n # Apply Downsampling and encoding modules\n x4 = self.ds_2(x3)\n x4 = self.en_3(x4)\n\n # Apply Downsampling and encoding modules\n x5 = self.ds_3(x4)\n x5 = self.en_4(x5)\n\n # Decoding path\n x = self.us_3(x5)\n x = self.de_3(x, x4)\n x = self.us_2(x)\n x = self.de_2(x, x3)\n x = self.us_1(x)\n x = self.de_1(x, x2)\n x = self.us_0(x)\n x = self.de_0(x, x1)\n x = self.out(x)\n\n # Return output tensors\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 self.save_net()\n self.perturb_tensors()\n out = self.net.forward(x)\n return out","def fc_forward(x, w, b):\n out = None\n ###########################################################################\n # TODO: Implement the forward pass. Store the result in out. #\n ###########################################################################\n Xshape = x.shape\n x_flat = np.reshape(x,(x.shape[0],-1))\n out = x_flat.dot(w) + b \n ###########################################################################\n # END OF YOUR CODE #\n ###########################################################################\n cache = (x, w, b)\n # out = np.reshape(out,Xshape[1:])\n return out, cache","def forward(self, state):\n x = F.relu(self.fc1(state))\n x = F.relu(self.fc2(x))\n return self.fc3(x)","def forward(self, state):\n x = F.relu(self.fc1(state))\n x = F.relu(self.fc2(x))\n return self.fc3(x)","def forward(self, state):\n x = F.relu(self.fc1(state))\n x = F.relu(self.fc2(x))\n return self.fc3(x)","def forward(self, state):\n \n x = F.relu(self.fc1(state)) \n x = F.relu(self.fc2(x)) \n x = F.tanh(self.fc3(x)) \n \n \n ####x = F.relu(self.bn1(self.fc1(state)))\n ####x = F.relu(self.bn2(self.fc2(x)))\n ####x = torch.tanh(self.bn3(self.fc3(x)))\n ##x = torch.tanh(self.fc3(x))\n\n return x","def forward(self, inputs):\n\n down0 = self.layer_0(inputs=inputs)\n down1 = self.layer_1(inputs=down0)\n down2 = self.layer_2(inputs=down1)\n down3 = self.layer_3(inputs=down2)\n down4 = self.layer_4(inputs=down3)\n\n up1 = self.layer_7(down4, down3)\n\n up2 = self.layer_8(up1, down2)\n\n up3 = self.layer_9(up2, down1)\n\n up4 = self.layer_10(up3, down0)\n\n up5 = self.layer_11(up4)\n return up5","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):\n return x","def forward(self, input):\n raise NotImplementedError","def forward(self, input):\n raise NotImplementedError","def Forward(self, X):\n Y = np.dot(X, self.w) + self.w0\n Y[Y>0] = 1\n Y[Y<=0] = -1\n return Y","def forward(self, state):\n x = F.relu(self.linear1(state))\n x = F.relu(self.linear2(x))\n x = self.linear3(x)\n return x"],"string":"[\n \"def forward_transform(self):\\n\\n if self._pipeline:\\n #return functools.reduce(lambda x, y: x | y, [step[1] for step in self._pipeline[: -1]])\\n return functools.reduce(lambda x, y: x | y, [step.transform for step in self._pipeline[:-1]])\\n else:\\n return None\",\n \"def transform_fn(self):\\n if hasattr(self._transform_fn, 'forward'):\\n return self._transform_fn.forward\\n return self._transform_fn\",\n \"def forward(self, x):\\n x = self.conv(x)\\n x = self.bn(x)\\n x = self.act(x)\\n return x\",\n \"def forward(self, x):\\n x = self.conv(x)\\n x = self.bn(x)\\n x = self.act(x)\\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: 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 affine_forward(x, W, b):\\r\\n x2d = np.reshape(x, (x.shape[0], -1)) # convert 4D input matrix to 2D \\r\\n out = np.dot(x2d, W) + b # linear transformation\\r\\n cache = (x, W, b) # keep for backward step (stay with us)\\r\\n return out, cache\",\n \"def forward(self, x):\\n x = self.lin(x)\\n\\n return x\",\n \"def forward(self, x):\\n\\n def run0(x, dummy):\\n lout1 = self.lconv1(x)\\n out1 = self.conv1(lout1)\\n lout2 = self.lconv2(out1 + lout1)\\n out2 = self.conv2(lout2)\\n lout3 = self.lconv3(out2 + lout2)\\n out3 = self.conv3(lout3)\\n lout4 = self.lconv4(out3 + lout3)\\n out4 = self.conv4(lout4)\\n lout5 = self.lconv5(out4 + lout4)\\n out5 = self.conv5(lout5)\\n lout6 = self.lconv6(out5 + lout5)\\n out6 = self.conv6(lout6)\\n lout7 = self.lconv7(out6 + lout6)\\n out7 = self.conv7(lout7)\\n mat = out7[:, :, :, None] + out7[:, :, None, :]\\n cur = mat\\n if self.num_1d:\\n output1d = self.final_1d(out7)\\n return cur, output1d\\n else:\\n return cur\\n\\n dummy = torch.Tensor(1)\\n dummy.requires_grad = True\\n if self.num_1d:\\n cur, output1d = checkpoint(run0, x, dummy)\\n else:\\n cur = checkpoint(run0, x, dummy)\\n\\n def run1(cur):\\n first = True\\n for lm, m in zip(self.lconvtwos[:7], self.convtwos[:7]):\\n if first:\\n cur = lm(cur)\\n\\n first = False\\n else:\\n cur = lm(cur) + cur\\n cur = m(cur) + cur\\n return cur\\n\\n def run2(cur):\\n for lm, m in zip(self.lconvtwos[7:13], self.convtwos[7:13]):\\n cur = lm(cur) + cur\\n cur = m(cur) + cur\\n return cur\\n\\n def run3(cur):\\n for lm, m in zip(self.lconvtwos[13:], self.convtwos[13:]):\\n cur = lm(cur) + cur\\n cur = m(cur) + cur\\n\\n cur = self.final(cur)\\n cur = 0.5 * cur + 0.5 * cur.transpose(2, 3)\\n return cur\\n\\n cur = checkpoint(run1, cur)\\n cur = checkpoint(run2, cur)\\n cur = checkpoint(run3, cur)\\n\\n if self.num_1d:\\n return cur, output1d\\n else:\\n return cur\",\n \"def forward(self, x):\\n x = self.conv(x)\\n return x\",\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 affine_forward(x, w, b):\\n #raise NotImplementedError\\n #######################################################################\\n # #\\n # #\\n # TODO: YOUR CODE HERE #\\n # #\\n # #\\n #######################################################################\\n out=np.dot(x,w)+b\\n cache=(x,w,b)\\n return(out, cache)\",\n \"def forward(self, x_in):\\r\\n # x_out = torch.zeros_like(x_in)\\r\\n\\r\\n for layer in self.layers: #Call forward function of each layer in order\\r\\n x_out = layer.forward(x_in)\\r\\n # print(\\\"Forward pass Seq: \\\", layer, x_in, x_out)\\r\\n x_in = x_out # output of the layer is passed as input to the next layer\\r\\n self.temp = x_in\\r\\n return x_out\",\n \"def forward(self, X):\\n self._X = X # For backprop later on.\\n self._z = np.dot(X, self._W) + self._b\\n a = self._act.a(self._z)\\n return a\",\n \"def forward(self, inp):\\n return inp.dot(self.W) + self.b\",\n \"def forward(self, x):\\n return self.l1(x)\",\n \"def forward(self, x):\\n l0 = self.conv0(x)\\n l1 = self.conv1(l0)\\n l2 = self.conv2(l1)\\n l3 = self.conv3(l2)\\n l4 = self.conv4(l3)\\n y = self.linear(l4.flatten(1))\\n return y, (l1, l2, l3, l4)\",\n \"def forward(self, x): # used to be __call__\\n\\n g = torch.matmul(x, self.W) # shape = (?, self.size)\\n self.output = []\\n\\n in_i = 0 # input index\\n out_i = 0 # output index\\n # Apply functions with only a single input\\n while out_i < self.n_single:\\n self.output.append(self.funcs[out_i](g[:, in_i]))\\n in_i += 1\\n out_i += 1\\n # Apply functions that take 2 inputs and produce 1 output\\n while out_i < self.n_funcs:\\n self.output.append(self.funcs[out_i](g[:, in_i], g[:, in_i+1]))\\n in_i += 2\\n out_i += 1\\n\\n self.output = torch.stack(self.output, dim=1)\\n\\n return self.output\",\n \"def forward(self, x):\\n x = self.arch(x)\\n x = self.swish(x)\\n x_out = self.arch._fcOut(x)\\n return x_out\",\n \"def forward(self, x):\\r\\n x = self.conv1(x)\\r\\n x = self.conv1_BN(x)\\r\\n x = F.relu(x)\\r\\n x = self.conv1_dp(x)\\r\\n x = self.Block2_1(x)\\r\\n x = self.Block2_2(x)\\r\\n x = self.Block3_1(x)\\r\\n x = self.Block3_2(x)\\r\\n x = self.Block3_3(x)\\r\\n x = self.Block3_4(x)\\r\\n x = self.Block4_1(x)\\r\\n x = self.Block4_2(x)\\r\\n x = self.Block4_3(x)\\r\\n x = self.Block4_4(x)\\r\\n x = self.Block5_1(x)\\r\\n x = self.Block5_2(x)\\r\\n x = self.MP(x)\\r\\n x = x.view(x.size(0),-1)\\r\\n x = self.fc(x)\\r\\n \\r\\n return x\",\n \"def forward(self, inputs):\\n return inputs * self.fc(inputs.mean(-1).mean(-1).mean(-1)).view(\\n inputs.shape[0], inputs.shape[1], 1, 1, 1)\",\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 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, inputs):\\r\\n #print (len(inputs))\\r\\n out = self.fc1(inputs)\\r\\n out = self.fc2(out)\\r\\n self.out = out\\r\\n return out\\r\\n #raise NotImplementedError('Implement the forward method of the model')\\r\",\n \"def forward(self, x):\\n self.a[0] = x/255 - 0.5 #normalize and Center the input values between [-0.5,0.5]\\n # TODO\\n for i in range(1,len(self.z)):\\n self.z[i]= np.dot(self.w[i],self.a[i-1])+self.b[i]\\n if i!=self.L-1:\\n self.a[i]=self.phi(self.z[i])\\n else:\\n self.a[i]=self.softmax(self.z[i])\\n \\n return(self.a[self.L-1])\",\n \"def forward(self, x):\\n #delete all cts\\n #self.cts = [self.cts[-1]]\\n \\n #forward\\n length, batch, dim = x.shape\\n res = []\\n res.append(self.one_step(x[0], torch.zeros((batch, self.latent_size), dtype=torch.float)))\\n\\n for i in range(1,length):\\n res.append(self.one_step(x[i], res[i-1]))\\n\\n return torch.stack(res)\",\n \"def forward(self, state):\\n x = F.relu(self.fc1(state))\\n x = F.relu(self.fc2(x))\\n x = F.relu(self.fc3(x))\\n return self.fc4(x)\",\n \"def forward(self, a: torch.FloatTensor, b: torch.FloatTensor) -> torch.FloatTensor:\",\n \"def forward(self, x):\\n #print(\\\"full frwd x shape:\\\",x.shape)\\n y=np.zeros((x.shape[0],self.W.shape[0]))\\n y=np.dot(x,np.transpose(self.W))+self.b\\n self.x=np.copy(x)\\n return y\",\n \"def forward(self, x):\\n pass\",\n \"def forward(self, x):\\n x = self.input(x)\\n x = self.in0(x)\\n x = self.block0(x) + x\\n x = self.block1(x) + x\\n x = self.block2(x) + x\\n x = self.block3(x) + x\\n x = self.block4(x) + x\\n x = self.in0(x)\\n\\n out = self.out(x)\\n\\n return out\",\n \"def forward(self, state):\\n x = self.fc(state)\\n return x\",\n \"def forward(self, x):\\n\\n\\t\\t## Conv layers\\n\\t\\tx = self.avgpool(F.tanh(self.conv1(x)))\\n\\t\\tx = self.avgpool(F.tanh(self.conv2(x)))\\n\\t\\tx = F.tanh(self.conv3(x))\\n\\n\\t\\t## Flatten\\n\\t\\tx = x.view(x.size(0), -1)\\n\\n\\t\\t## Fully connected layers\\n\\t\\tx = F.tanh(self.fc1(x))\\n\\t\\tx = self.fc2(x)\\n\\n\\t\\tx = F.softmax(x, dim=1)\\n\\n\\t\\treturn x\",\n \"def forward_features(self, x, flows_backward, flows_forward):\\n x1 = self.stage1(x, flows_backward[0::4], flows_forward[0::4])\\n x2 = self.stage2(x1, flows_backward[1::4], flows_forward[1::4])\\n x3 = self.stage3(x2, flows_backward[2::4], flows_forward[2::4])\\n x4 = self.stage4(x3, flows_backward[3::4], flows_forward[3::4])\\n x = self.stage5(x4, flows_backward[2::4], flows_forward[2::4])\\n x = self.stage6(x + x3, flows_backward[1::4], flows_forward[1::4])\\n x = self.stage7(x + x2, flows_backward[0::4], flows_forward[0::4])\\n x = x + x1\\n for layer in self.stage8:\\n x = layer(x)\\n x = rearrange(x, 'n c d h w -> n d h w c')\\n x = self.norm(x)\\n x = rearrange(x, 'n d h w c -> n c d h w')\\n return x\",\n \"def forward(self, x):\\n # pylint: disable=arguments-differ\\n x = x.permute([0, 3, 1, 2, ])\\n x = self.power(x * 0.00392156885937 + 0.0, 1.0)\\n cat = self.conv1(x)\\n x = self.conv2(cat)\\n x = self.concat8((cat, x), 1)\\n conv_final = self.conv3(x)\\n conv_final_permute = conv_final.permute([0, 2, 3, 1])\\n loc_pred, obj_pred, cls_pred = torch.split(conv_final_permute, [64, 16, 64], 3)\\n # obj_perm = self.obj_pred(obj_perm) # sigmoid might lead to gradient losing.\\n #cls_reshape = torch.reshape(cls_perm, (-1, 4))\\n #cls_pred_prob = self.cls_pred_prob(cls_reshape)\\n #cls_pred = torch.reshape(cls_pred_prob, cls_perm.shape)\\n return obj_pred, cls_pred, loc_pred\",\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, state):\\n x = self._format(state)\\n x = self.activation_fc(self.input_layer(x))\\n for hidden_layer in self.hidden_layers:\\n x = self.activation_fc(hidden_layer(x))\\n x = self.output_layer(x)\\n x = self.out_activation_fc(x)\\n return self.rescale_fn(x)\",\n \"def forward(self, x):\\n\\n identity = x\\n\\n out = self.conv1(x)\\n out = self.bn1(out)\\n out = self.activation(out)\\n\\n out = self.conv2(out)\\n out = self.bn2(out)\\n\\n if self.upsample is not None:\\n identity = self.upsample(x)\\n\\n out += identity\\n out = self.activation(out)\\n\\n return out\",\n \"def forward(self, state):\\n\\t\\tx = F.relu(self.fc1(state))\\n\\t\\tx = F.relu(self.fc2(x))\\n\\t\\tx = F.tanh(self.fc3(x)) # outputs are in the range [-1, 1]\\n\\n\\t\\treturn x\",\n \"def feed_forward(self):\\n pre = self.pre_layer.o\\n self.post_layer.i = torch.matmul(pre, self.weight)\",\n \"def forward(self, state):#forward pass\\n x = F.relu(self.fc1(state))\\n x = F.relu(self.fc2(x))\\n return torch.tanh(self.fc3(x))\",\n \"def forward(self, X):\\n # Propaga a entrada pela rede\\n self.z = np.dot(X, self.W1) # Produto escalar da entrada com a primeira matrix de pesos\\n self.z2 = self.sigmoid(self.z) # Função de ativação\\n self.z3 = np.dot(self.z2, self.W2) # Produto escalar da hidden layer com a segunda matrix de pesos\\n return self.sigmoid(self.z3) # Função de ativação na saída \",\n \"def forward(self, state):\\n output = self.conv_layers(state)\\n output = output.view(-1, 7*7*64)\\n output = self.fc(output)\\n return output\",\n \"def forward(self, x):\\n n, c, t, v = x.size()\\n x1 = x.view(n, c * t, v)\\n y = None\\n for i in range(self.num_subset):\\n A1 = self.PA[i]\\n z = self.conv_d[i](torch.matmul(x1, A1).view(n, c, t, v))\\n y = z + y if y is not None else z\\n A2 = self.cen(x)\\n z2 = torch.matmul(x1, A2).view(n, c, t, v)\\n z2 = self.conv_cen(z2)\\n y += self.lamb * z2\\n y = self.bn(y)\\n y += self.down(x)\\n y = self.relu(y)\\n y = self.attention(y)\\n return y\",\n \"def forward(self, x):\\n out = self.conv1(x)\\n out = self.bn1(out)\\n out = self.relu(out)\\n out = self.maxpool(out)\\n out = self.avgpool(out)\\n out = self.layer1(out)\\n out = self.layer2(out)\\n out = self.layer3(out)\\n out = self.layer4(out)\\n out = self.avgpool(out)\\n out = torch.flatten(out, 1)\\n out = self.fc(out)\\n return out\",\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, state):\\n x = F.relu(self.input(state))\\n for layer in self.layers:\\n x = F.relu(layer(x))\\n if self.duel:\\n # Value function estimator\\n val = F.relu(self.val_fc_input(x))\\n val = self.val_fc_output(val)\\n # Advantage function estimator\\n adv = F.relu(self.adv_fc_input(x))\\n adv = self.adv_fc_output(adv)\\n # Subtract mean so that V and A are uniquely identifiable for a given Q\\n return val + adv - adv.mean(1).unsqueeze(1).expand(state.size(0), self.action_size)\\n else:\\n return self.output(x)\",\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):\\n theta = self.L(x[:, :6])\\n theta = torch.cat((theta, self.U(torch.cat((x[:, :9], theta), 1))), 1)\\n theta = torch.cat((theta, self.R(torch.cat((x[:, 3:12], theta), 1))), 1)\\n theta = torch.cat((theta, self.B(torch.cat((x[:, 6:15], theta), 1))), 1)\\n # Prepends T to the angle vector theta to maintain order S,L,U,R,B\\n theta = torch.cat((self.S(torch.cat((x[:, 12:], x[:, :4], theta[:, :3]), 1)), theta), 1)\\n return self.FC(theta)\",\n \"def affine_forward(x, w, b):\\n out = None\\n ###########################################################################\\n # TODO: Implement the affine forward pass. Store the result in out. You #\\n # will need to reshape the input into rows. #\\n ###########################################################################\\n # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\\n t = np.reshape(x,(x.shape[0],np.prod(np.shape(x)[1:])))\\n \\n\\n out = np.dot(t,w) + b\\n \\n #print(np.shape(out))\\n\\n pass\\n\\n # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\\n ###########################################################################\\n # END OF YOUR CODE #\\n ###########################################################################\\n cache = (x, w, b)\\n return out, cache\",\n \"def forward(self, state):\\n '''\\n state = F.relu(self.conv1(state))\\n state = F.relu(self.conv2(state))\\n state = F.relu(self.conv3(state))\\n state = F.relu(self.fc1(state))\\n \\n action = F.relu(self.fc2(state))\\n \\n return action\\n '''\\n \\n x = F.relu(self.fc1(state))\\n x = F.relu(self.fc2(x))\\n x = self.fc3(x)\\n \\n return x\",\n \"def propagate_forward(self, X):\\n\\n A = X\\n L = self.L - 1\\n\\n for i in range(0, L - 1):\\n Z = np.matmul(self.W[i], A) + self.b[i]\\n cache = ((A, self.W[i], self.b[i]), Z)\\n A = relu(Z)\\n self.caches[i] = cache\\n Z = np.matmul(self.W[L - 1], A) + self.b[L - 1]\\n cache = ((A, self.W[L - 1], self.b[L - 1]), Z)\\n AL = sigmoid(Z)\\n self.caches[L - 1] = cache\\n return AL\",\n \"def forward(self, input_tensor):\\n rv = torch.randn(input_tensor.size(), device=self.device) * 0.02\\n augmented_input = input_tensor + rv\\n intermediate = augmented_input\\n intermediates = [augmented_input]\\n for module in self.down:\\n intermediate = module(intermediate)\\n intermediates.append(intermediate)\\n intermediates = intermediates[:-1][::-1]\\n\\n down = intermediate.view(-1, 6*6*512)\\n\\n intermediate = self.reducer(intermediate)\\n\\n for index, module in enumerate(self.up):\\n intermediate = torch.cat((intermediate, intermediates[index]), 1)\\n intermediate = module(intermediate)\\n\\n intermediate = torch.cat((intermediate, input_tensor), 1)\\n\\n for module in self.down_again:\\n intermediate = module(intermediate)\\n\\n intermediate = intermediate.view(-1, 6*6*64)\\n intermediate = torch.cat((down, intermediate), -1)\\n\\n projected = self.projection(intermediate)\\n\\n return projected\",\n \"def forward(self, x, **kwargs):\\n\\n\\t\\toutput = self.lin1(x)\\n\\t\\toutput = self.lin2(output)\\n\\t\\toutput = self.lin3(output)\\n\\t\\toutput = self.lin_out(output)\\n\\n\\t\\treturn output\",\n \"def affine_forward(x, w, b):\\n out = None\\n x_shape = x.shape\\n x_reshaped = x.reshape(x_shape[0], np.prod(x_shape[1:]))\\n out = np.dot(x_reshaped, w) + b\\n cache = (x, w, b)\\n return out, cache\",\n \"def forward(self, x):\\n if x.dim() not in [2, 3]:\\n raise RuntimeError(\\\"Expect 2D/3D tensor, but got {:d}D\\\".format(\\n x.dim()))\\n # if N x S, reshape N x 1 x S\\n if x.dim() == 2:\\n x = th.unsqueeze(x, 1)\\n # N x 2F x T\\n c = F.conv1d(x, self.K, stride=self.stride, padding=0)\\n # N x F x T\\n r, i = th.chunk(c, 2, dim=1)\\n m = (r**2 + i**2)**0.5\\n p = th.atan2(i, r)\\n return m, p\",\n \"def forward(self, input):\\n\\n x = self.conv(input)\\n x = self.bn(x)\\n out = self.act(x)\\n return out\",\n \"def forward(self, x, contextState):\\r\\n \\r\\n #concatenate input and context state\\r\\n #x = x.t()\\r\\n xAndContext = torch.cat((x, contextState), 1)\\r\\n\\r\\n #calculate next context state (hidden output for current t) with tanh(xAndContext * W1)\\r\\n contextState = torch.tanh(xAndContext.mm(self.W1))\\r\\n \\r\\n # Calculates final output\\r\\n output = contextState.mm(self.V)\\r\\n\\r\\n return (output, contextState)\",\n \"def forward(self):\\n self.iteration_number += 1\\n x = self.x\\n self.x = self.alpha * self.x + self.betta\\n t = x - self.x\\n\\n return (t * t).sum()\",\n \"def forward_pass(self, x):\\r\\n self.a = (self.w.T @ x.T).T + self.b # Weighted sum of x with weight matrix(augmented with bias)\\r\\n self.x = x\\r\\n return self.a\",\n \"def forward(self, x):\\n if self.training:\\n x = self.input_pert(x)\\n x = self.encoder(x)\\n x = self.decoder(x)\\n return x\",\n \"def affine_forward(X, W, b):\\n return np.dot(X, W) + b\",\n \"def forward(self, x):\\n x=T.div(x,255.0)\\n \\n #print(state[20:,20:,0])\\n #print(state[:,0,:,:])\\n conv1 = F.relu(self.conv1(x))\\n conv2 = F.relu(self.conv2(conv1))\\n conv3 = F.relu(self.conv3(conv2))\\n ###\\n conv_state = conv3.view(conv3.size()[0], -1)\\n flat1 = F.relu(self.fc1(conv_state))\\n flat2 = F.relu(self.fc2(flat1))\\n\\n V = self.V(flat2)\\n A = self.A(flat2)\\n\\n return V, A\\n return x\",\n \"def fc_forward(x, w, b):\\n out = None\\n ###########################################################################\\n # TODO: Implement the forward pass. Store the result in out. #\\n ###########################################################################\\n out = np.dot(x, w) + b\\n ###########################################################################\\n # END OF YOUR CODE #\\n ###########################################################################\\n cache = (x, w, b)\\n return out, cache\",\n \"def affine_forward(x,w,b):\\n out=None\\n N=x.shape[0]\\n x_row=x.reshape(N,-1)\\n out=np.dot(x_row,w)+b\\n cache=(x,w,b)\\n return out,cache\",\n \"def affine_forward(x, w, b):\\n out = None\\n ###########################################################################\\n # TODO: Implement the affine forward pass. Store the result in out. You #\\n # will need to reshape the input into rows. #\\n ###########################################################################\\n dim_size = x[0].shape\\n X = x.reshape(x.shape[0], np.prod(dim_size))\\n out = X.dot(w) + b\\n ###########################################################################\\n # END OF YOUR CODE #\\n ###########################################################################\\n cache = (x, w, b)\\n return out, cache\",\n \"def forward(self, x: torch.Tensor) -> torch.Tensor:\\n identity = x\\n\\n out = self.conv_reduce(x)\\n out = self.relu(self.bn_reduce(out))\\n\\n out = self.conv_conv(out)\\n out = self.relu(self.bn(out))\\n\\n out = self.conv_expand(out)\\n out = self.bn_expand(out)\\n\\n res = self.shortcut(identity)\\n return self.relu(res + out)\",\n \"def forward(self, x):\\n x = self.sparseModel(x)\\n x = self.linear(x)\\n return x\",\n \"def forwardPropagate (self, x):\\n\\t\\tif type(x) is not list:\\n\\t\\t\\tx = [x]\\n\\t\\tx = np.concatenate((np.array([1]),np.array(x)),axis=0) # absorb w_0\\n\\n\\t\\t# No transformation, but needed later\\n\\t\\tfor i in range(self.inn):\\n\\t\\t\\tself.z_in[i] = x[i]\\n\\t\\t\\n\\t\\t# For every hidden neuron (1 hidden layer only!)\\n\\t\\tfor j in range(self.hidden):\\n\\t\\t\\tsumIn = 0\\n\\t\\t\\tfor i in range(self.inn):\\n\\t\\t\\t\\tsumIn += self.w_hd[j][i]*self.z_in[i]\\n\\t\\t\\tself.a_hd[j] = sumIn # Needed for backprop (5.56)\\n\\t\\t\\tself.z_hd[j] = self.act(sumIn)\\n\\n\\t\\t# For every output neuron\\n\\t\\tfor k in range(self.out):\\n\\t\\t\\tsumHdn = 0\\n\\t\\t\\tfor j in range(self.hidden):\\n\\t\\t\\t\\tsumHdn += self.w_out[k][j]*self.z_hd[j]\\n\\t\\t\\tself.z_out[k] = sumHdn\\n\\t\\treturn self.z_out\",\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, x):\\n raise NotImplementedError\",\n \"def forward(self, x):\\n raise NotImplementedError\",\n \"def affine_forward(x, w, b):\\n out = None\\n ########################################################################\\n # TODO: Implement the affine forward pass. Store the result in out. #\\n # You will need to reshape the input into rows. #\\n ########################################################################\\n\\n x_reshaped = x.reshape(x.shape[:1] + (-1,))\\n out = x_reshaped.dot(w) + b\\n\\n ########################################################################\\n # END OF YOUR CODE #\\n ########################################################################\\n cache = (x, w, b)\\n return out, cache\",\n \"def forward(self, x):\\n out_conv1 = self.conv1(x)\\n out_conv2 = self.conv2(out_conv1)\\n out_conv3 = self.conv3(out_conv2)\\n out_conv4 = self.conv4(out_conv3)\\n out_conv5 = self.conv5(out_conv4)\\n out_conv6 = self.conv6(out_conv5)\\n out_conv7 = self.conv7(out_conv6)\\n\\n out_upconv7 = self.crop_top_left(self.upconv7(out_conv7), out_conv6)\\n concat7 = torch.cat((out_upconv7, out_conv6), 1)\\n out_iconv7 = self.iconv7(concat7)\\n\\n out_upconv6 = self.crop_top_left(self.upconv6(out_iconv7), out_conv5)\\n concat6 = torch.cat((out_upconv6, out_conv5), 1)\\n out_iconv6 = self.iconv6(concat6)\\n\\n out_upconv5 = self.crop_top_left(self.upconv5(out_iconv6), out_conv4)\\n concat5 = torch.cat((out_upconv5, out_conv4), 1)\\n out_iconv5 = self.iconv5(concat5)\\n\\n out_upconv4 = self.crop_top_left(self.upconv4(out_iconv5), out_conv3)\\n concat4 = torch.cat((out_upconv4, out_conv3), 1)\\n out_iconv4 = self.iconv4(concat4)\\n disp4 = self.alpha * self.predict_disp4(out_iconv4) + self.beta\\n\\n out_upconv3 = self.crop_top_left(self.upconv3(out_iconv4), out_conv2)\\n disp4_up = self.crop_top_left(torch.nn.functional.interpolate(disp4,\\n scale_factor=2,\\n mode='bilinear',\\n align_corners=False), out_conv2)\\n concat3 = torch.cat((out_upconv3, out_conv2, disp4_up), 1)\\n out_iconv3 = self.iconv3(concat3)\\n disp3 = self.alpha * self.predict_disp3(out_iconv3) + self.beta\\n\\n out_upconv2 = self.crop_top_left(self.upconv2(out_iconv3), out_conv1)\\n disp3_up = self.crop_top_left(torch.nn.functional.interpolate(disp3,\\n scale_factor=2,\\n mode='bilinear',\\n align_corners=False), out_conv1)\\n concat2 = torch.cat((out_upconv2, out_conv1, disp3_up), 1)\\n out_iconv2 = self.iconv2(concat2)\\n disp2 = self.alpha * self.predict_disp2(out_iconv2) + self.beta\\n\\n out_upconv1 = self.crop_top_left(self.upconv1(out_iconv2), x)\\n disp2_up = self.crop_top_left(torch.nn.functional.interpolate(disp2,\\n scale_factor=2,\\n mode='bilinear',\\n align_corners=False), x)\\n concat1 = torch.cat((out_upconv1, disp2_up), 1)\\n out_iconv1 = self.iconv1(concat1)\\n disp1 = self.alpha * self.predict_disp1(out_iconv1) + self.beta\\n\\n if self.training:\\n return disp1, disp2\\n else:\\n return disp1\",\n \"def forward(self, x):\\n t1 = self.stn1(x)\\n t1 = t1.view(-1, 3, 4)\\n p1 = affine_transform(x, t1)\\n\\n s1 = self.sampler1(p1)\\n t2 = self.stn2(s1)\\n t2 = t2.view(-1, 3, 4)\\n p2 = affine_transform(s1, t2)\\n\\n s2 = self.sampler2(p2)\\n t3 = self.stn3(s2)\\n\\n predicted_theta = t3.view(-1, 3, 4)\\n\\n return predicted_theta\",\n \"def forward(self, x):\\r\\n # 1. step 0\\r\\n x_0 = self.mg0(x)\\r\\n a_0 = self.mhsa0(x_0)\\r\\n a_0 = a_0.unsqueeze(dim = 3) # [m, h, w, 1, c]\\r\\n a_0 = t.matmul(a_0, self.W_p0).squeeze().permute(0, -1, 1, 2) + x # transformation # [m, c, h, w]\\r\\n a_0_ = a_0\\r\\n a_0 = a_0.permute(0, 2, 3, 1)\\r\\n a_0 = self.mlp0(a_0)\\r\\n a_0 = a_0.permute(0, -1, 1, 2) + a_0_\\r\\n x_0 = self.max_pool0(a_0) + self.avg_pool0(a_0)\\r\\n\\r\\n # 2. step 1\\r\\n x_1 = self.mg1(x_0)\\r\\n a_1 = self.mhsa1(x_1)\\r\\n a_1 = a_1.unsqueeze(dim = 3) # [m, h, w, 1, c]\\r\\n a_1 = t.matmul(a_1, self.W_p1).squeeze().permute(0, -1, 1, 2) + x_0 # transformation # [m, c, h, w]\\r\\n a_1_ = a_1\\r\\n a_1 = a_1.permute(0, 2, 3, 1)\\r\\n a_1 = self.mlp1(a_1)\\r\\n a_1 = a_1.permute(0, -1, 1, 2) + a_1_\\r\\n x_1 = self.max_pool1(a_1) + self.avg_pool1(a_1)\\r\\n\\r\\n # 3. step 2\\r\\n x_2 = self.mg2(x_1)\\r\\n a_2 = self.mhsa2(x_2)\\r\\n a_2 = a_2.unsqueeze(dim = 3) # [m, h, w, 1, c]\\r\\n a_2 = t.matmul(a_2, self.W_p2).squeeze().permute(0, -1, 1, 2) + x_1 # transformation # [m, c, h, w]\\r\\n a_2_ = a_2\\r\\n a_2 = a_2.permute(0, 2, 3, 1)\\r\\n a_2 = self.mlp0(a_2)\\r\\n a_2 = a_2.permute(0, -1, 1, 2) + a_2_\\r\\n\\r\\n # 4. Upsample\\r\\n a_1 = self.upsample1(a_1)\\r\\n a_2 = self.upsample2(a_2)\\r\\n output = a_0 + a_1 + a_2\\r\\n\\r\\n return output\",\n \"def forward(self, inputs):\\n raise NotImplementedError\",\n \"def affine_forward(x, w, b):\\n out = None\\n ###########################################################################\\n # TODO: Implement the affine forward pass. Store the result in out. You #\\n # will need to reshape the input into rows. #\\n ###########################################################################\\n reshaped_inp = np.reshape(x,(int(x.shape[0]),int(np.prod(x.shape) / x.shape[0])))\\n out = reshaped_inp.dot(w) + b\\n ###########################################################################\\n # END OF YOUR CODE #\\n ###########################################################################\\n cache = (x, w, b)\\n return out, cache\",\n \"def forward(self, x, dt, do_normalization=True):\\n return x\",\n \"def forward_substitution(self):\\r\\n for col in range(0, self.SIZE):\\r\\n self.check_solvability(self.matrix[col][col], self.result[col])\\r\\n self.result[col] = self.divide(self.result[col], self.matrix[col][col])\\r\\n for row in range(col + 1, self.SIZE):\\r\\n self.result[row] -= (self.result[col] * self.matrix[row][col])\\r\\n return self.result\",\n \"def linear_forward(A_prev, W, b):\\n Z = W.dot(A_prev) + b \\n \\n assert(Z.shape == (W.shape[0], A_prev.shape[1]))\\n cache = (A_prev, W, b) \\n \\n return Z, cache\",\n \"def forward(self, feats):\\n return multi_apply(self.forward_single, feats, self.scales)\",\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(self, input_):\\n out = self.fc(input_)\\n out = self.bn(out)\\n out = self.relu(out)\\n return torch.cat([out, input_], dim=1)\",\n \"def makeFastFeedForwardFunction(self):\\n\\n\\t\\toutWeightMatrix = []\\n\\t\\tfor unit in self.outputLayer:\\n\\n\\t\\t\\trow = []\\n\\t\\t\\tfor b in unit.branchesIn:\\n\\t\\t\\t\\tprint b.weight\\n\\t\\t\\t\\trow.append(b.weight)\\n\\t\\t\\t\\n\\t\\t\\toutWeightMatrix.append(row)\\n\\t\\toutWeightMatrix = np.array(outWeightMatrix).squeeze()\\n\\n\\t\\thiddenMatrices = []\\n\\t\\tfor layer in self.hiddenLayers:\\n\\t\\t\\tmatrix = []\\n\\t\\t\\t#ignore the bias unit, since it has no branches in\\n\\t\\t\\tfor unit in layer[1:]:\\n\\t\\t\\t\\trow = []\\n\\t\\t\\t\\tfor b in unit.branchesIn:\\n\\t\\t\\t\\t\\trow.append(b.weight)\\n\\n\\t\\t\\t\\tmatrix.append(row)\\n\\t\\t\\tmatrix = np.array(matrix)\\n\\n\\t\\t\\thiddenMatrices.append(matrix)\\n\\n\\t\\thidActFunc = (self.hiddenLayers[0])[1].activationFunction\\n\\t\\toutActFunc = self.outputLayer[0].activationFunction\\n\\n\\t\\tdef ffFunc(inp):\\n\\t\\n\\t\\t\\tforward = np.insert(inp.T,0,1.0,axis=0)\\n\\t\\t\\tfor matrix in hiddenMatrices:\\n\\t\\t\\t\\tnext = np.dot(matrix,forward)\\n\\t\\t\\t\\tnext = hidActFunc(next)\\n\\t\\t\\t\\tforward = np.insert(next,0,1.0,axis=0)\\n\\n\\t\\t\\tout = np.dot(outWeightMatrix,forward)\\n\\n\\t\\t\\treturn outActFunc(out)\\n\\n\\t\\treturn ffFunc\",\n \"def forward(self, x):\\n x = F.relu(self.enc(x))\\n x = F.relu(self.dec(x))\\n return x\",\n \"def forward(self, x):\\n\\n # Encoding path\\n x1 = self.ins(x)\\n\\n # Apply Downsampling and encoding modules\\n x2 = self.ds_0(x1)\\n x2 = self.en_1(x2)\\n\\n # Apply Downsampling and encoding modules\\n x3 = self.ds_1(x2)\\n x3 = self.en_2(x3)\\n\\n # Apply Downsampling and encoding modules\\n x4 = self.ds_2(x3)\\n x4 = self.en_3(x4)\\n\\n # Apply Downsampling and encoding modules\\n x5 = self.ds_3(x4)\\n x5 = self.en_4(x5)\\n\\n # Decoding path\\n x = self.us_3(x5)\\n x = self.de_3(x, x4)\\n x = self.us_2(x)\\n x = self.de_2(x, x3)\\n x = self.us_1(x)\\n x = self.de_1(x, x2)\\n x = self.us_0(x)\\n x = self.de_0(x, x1)\\n x = self.out(x)\\n\\n # Return output tensors\\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 self.save_net()\\n self.perturb_tensors()\\n out = self.net.forward(x)\\n return out\",\n \"def fc_forward(x, w, b):\\n out = None\\n ###########################################################################\\n # TODO: Implement the forward pass. Store the result in out. #\\n ###########################################################################\\n Xshape = x.shape\\n x_flat = np.reshape(x,(x.shape[0],-1))\\n out = x_flat.dot(w) + b \\n ###########################################################################\\n # END OF YOUR CODE #\\n ###########################################################################\\n cache = (x, w, b)\\n # out = np.reshape(out,Xshape[1:])\\n return out, cache\",\n \"def forward(self, state):\\n x = F.relu(self.fc1(state))\\n x = F.relu(self.fc2(x))\\n return self.fc3(x)\",\n \"def forward(self, state):\\n x = F.relu(self.fc1(state))\\n x = F.relu(self.fc2(x))\\n return self.fc3(x)\",\n \"def forward(self, state):\\n x = F.relu(self.fc1(state))\\n x = F.relu(self.fc2(x))\\n return self.fc3(x)\",\n \"def forward(self, state):\\n \\n x = F.relu(self.fc1(state)) \\n x = F.relu(self.fc2(x)) \\n x = F.tanh(self.fc3(x)) \\n \\n \\n ####x = F.relu(self.bn1(self.fc1(state)))\\n ####x = F.relu(self.bn2(self.fc2(x)))\\n ####x = torch.tanh(self.bn3(self.fc3(x)))\\n ##x = torch.tanh(self.fc3(x))\\n\\n return x\",\n \"def forward(self, inputs):\\n\\n down0 = self.layer_0(inputs=inputs)\\n down1 = self.layer_1(inputs=down0)\\n down2 = self.layer_2(inputs=down1)\\n down3 = self.layer_3(inputs=down2)\\n down4 = self.layer_4(inputs=down3)\\n\\n up1 = self.layer_7(down4, down3)\\n\\n up2 = self.layer_8(up1, down2)\\n\\n up3 = self.layer_9(up2, down1)\\n\\n up4 = self.layer_10(up3, down0)\\n\\n up5 = self.layer_11(up4)\\n return up5\",\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):\\n return x\",\n \"def forward(self, input):\\n raise NotImplementedError\",\n \"def forward(self, input):\\n raise NotImplementedError\",\n \"def Forward(self, X):\\n Y = np.dot(X, self.w) + self.w0\\n Y[Y>0] = 1\\n Y[Y<=0] = -1\\n return Y\",\n \"def forward(self, state):\\n x = F.relu(self.linear1(state))\\n x = F.relu(self.linear2(x))\\n x = self.linear3(x)\\n return x\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7412555","0.6997475","0.6997431","0.6997431","0.68757826","0.6822799","0.6821863","0.67669487","0.67491704","0.6735819","0.67256963","0.67136383","0.67097676","0.6668364","0.6662344","0.6639448","0.6637169","0.6623411","0.661096","0.660531","0.66014105","0.6587117","0.65834284","0.6560788","0.65489167","0.65459406","0.654096","0.65370536","0.65331936","0.6532924","0.6522619","0.652212","0.6521943","0.650588","0.64996254","0.6498788","0.64923704","0.64908814","0.64884615","0.6475106","0.6469716","0.64613765","0.6454817","0.6449853","0.644924","0.6446683","0.6446073","0.64451313","0.6445114","0.6443222","0.64408475","0.644062","0.643762","0.64332527","0.6416402","0.6410828","0.6410431","0.6408313","0.6406485","0.6403339","0.64028054","0.64017147","0.6400724","0.6399381","0.6398679","0.63958836","0.63899994","0.6389385","0.63819146","0.63817346","0.6381429","0.6381429","0.6378753","0.637685","0.6373798","0.6373177","0.6373018","0.6357435","0.6351547","0.6350675","0.6348908","0.63443524","0.6340619","0.6335074","0.6332577","0.63290554","0.63286775","0.63244575","0.6322087","0.63209707","0.63192797","0.63192797","0.63192797","0.6319078","0.6314124","0.63112587","0.63105667","0.6307165","0.6307165","0.6304323","0.63031787"],"string":"[\n \"0.7412555\",\n \"0.6997475\",\n \"0.6997431\",\n \"0.6997431\",\n \"0.68757826\",\n \"0.6822799\",\n \"0.6821863\",\n \"0.67669487\",\n \"0.67491704\",\n \"0.6735819\",\n \"0.67256963\",\n \"0.67136383\",\n \"0.67097676\",\n \"0.6668364\",\n \"0.6662344\",\n \"0.6639448\",\n \"0.6637169\",\n \"0.6623411\",\n \"0.661096\",\n \"0.660531\",\n \"0.66014105\",\n \"0.6587117\",\n \"0.65834284\",\n \"0.6560788\",\n \"0.65489167\",\n \"0.65459406\",\n \"0.654096\",\n \"0.65370536\",\n \"0.65331936\",\n \"0.6532924\",\n \"0.6522619\",\n \"0.652212\",\n \"0.6521943\",\n \"0.650588\",\n \"0.64996254\",\n \"0.6498788\",\n \"0.64923704\",\n \"0.64908814\",\n \"0.64884615\",\n \"0.6475106\",\n \"0.6469716\",\n \"0.64613765\",\n \"0.6454817\",\n \"0.6449853\",\n \"0.644924\",\n \"0.6446683\",\n \"0.6446073\",\n \"0.64451313\",\n \"0.6445114\",\n \"0.6443222\",\n \"0.64408475\",\n \"0.644062\",\n \"0.643762\",\n \"0.64332527\",\n \"0.6416402\",\n \"0.6410828\",\n \"0.6410431\",\n \"0.6408313\",\n \"0.6406485\",\n \"0.6403339\",\n \"0.64028054\",\n \"0.64017147\",\n \"0.6400724\",\n \"0.6399381\",\n \"0.6398679\",\n \"0.63958836\",\n \"0.63899994\",\n \"0.6389385\",\n \"0.63819146\",\n \"0.63817346\",\n \"0.6381429\",\n \"0.6381429\",\n \"0.6378753\",\n \"0.637685\",\n \"0.6373798\",\n \"0.6373177\",\n \"0.6373018\",\n \"0.6357435\",\n \"0.6351547\",\n \"0.6350675\",\n \"0.6348908\",\n \"0.63443524\",\n \"0.6340619\",\n \"0.6335074\",\n \"0.6332577\",\n \"0.63290554\",\n \"0.63286775\",\n \"0.63244575\",\n \"0.6322087\",\n \"0.63209707\",\n \"0.63192797\",\n \"0.63192797\",\n \"0.63192797\",\n \"0.6319078\",\n \"0.6314124\",\n \"0.63112587\",\n \"0.63105667\",\n \"0.6307165\",\n \"0.6307165\",\n \"0.6304323\",\n \"0.63031787\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":92579,"cells":{"query":{"kind":"string","value":"Compute the inverse transformation (Appendix A.3)."},"document":{"kind":"string","value":"def _inverse(self, y):\n d = self._compute_shared(y=y)\n rely = y - d.y_k # tf.where(d.out_of_bounds, tf.zeros_like(y), y - d.y_k)\n term2 = rely * (d.d_kp1 + d.d_k - 2 * d.s_k)\n # These terms are the a, b, c terms of the quadratic formula.\n a = d.h_k * (d.s_k - d.d_k) + term2\n b = d.h_k * d.d_k - term2\n c = -d.s_k * rely\n # The expression used here has better numerical behavior for small 4*a*c.\n relx = tf.where(\n tf.equal(rely, 0), tf.zeros_like(a),\n (2 * c) / (-b - tf.sqrt(b**2 - 4 * a * c)))\n return relx * d.w_k + d.x_k #tf.where(d.out_of_bounds, y, relx * d.w_k + d.x_k)"},"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 inverse_transform(self, X):\n ...","def inverse_transform(self, X):\n ...","def inverse_transform(self, X):\n ...","def inverse_transform(self, X):\n ...","def inverse_transform(self, X):\n ...","def inverse_transform(self, X, copy=...):\n ...","def inverse_transform(self, X):\n\n pass # pragma: no cover","def inverse_transform(self, x):\n raise NotImplementedError()","def inverse_transform(self, X):\n return self._apply_method(X, \"inverse_transform\")","def __invert__(self):\n return self.inverse()","def inverse(self):\n rotation_matrix = self.pose_mat[:3, :3]\n translation_vector = self.pose_mat[:3, 3]\n\n rot = np.transpose(rotation_matrix)\n trans = - np.matmul(np.transpose(rotation_matrix), translation_vector)\n return Transformation(rot, trans)","def inverse_cayley_transform(z: torch.Tensor) -> torch.Tensor:\n identity = identity_like(z)\n i_identity = multiply_by_i(identity)\n\n z_minus_id = z - i_identity\n inv_z_plus_id = inverse(z + i_identity)\n return z_minus_id @ inv_z_plus_id","def inverse_transform(self, X):\n raise NotImplementedError()","def inverse(self, x, y):","def inverse(self):\n invr = np.linalg.inv(self.affine_matrix)\n return SymmOp(invr)","def inverse_transform(self, Xt):\n return self.transformer.inverse_transform(Xt)","def inverse(self):\n return self.solve(Matrix.I(self.nrows))","def _inv(self) -> None:\n\n self.inv(inplace=True)","def inverse_transform(self, y: Array2D) -> Array2D:","def invert(x):\n return linalg.inv(x)","def affine_transform_inverse(np_transform):\n rotation = np_transform[:3, :3]\n translation = np_transform[:3, 3]\n rotation_inv = numpy.linalg.inv(rotation)\n translation_inv = -1 * numpy.dot(rotation_inv, translation)\n result = numpy.identity(4)\n result[:3, :3] = rotation_inv\n result[:3, 3] = translation_inv.flatten()\n return result","def inverse(self):\r\n \r\n Mi=mat4()\r\n d=self.determinant()\r\n for i in range(4):\r\n for j in range(4):\r\n sign=1-((i+j)%2)*2\r\n m3=self._submat(i,j)\r\n Mi[j,i]=sign*m3.determinant()/d\r\n return Mi","def inverse(self):\n data = np.linalg.inv(self._data)\n return self.create(self.rows, self.cols, data)","def inverse(self):\n return self.invert()","def _inverse_transform(self, X, y=None):\n return clone(self.transformer).fit(X=X, y=y).inverse_transform(X=X, y=y)","def inverse_rigid_trans(Tr): \n inv_Tr = np.zeros_like(Tr) # 3x4\n inv_Tr[0:3,0:3] = np.transpose(Tr[0:3,0:3])\n inv_Tr[0:3,3] = np.dot(-np.transpose(Tr[0:3,0:3]), Tr[0:3,3])\n return inv_Tr","def inv_inplace(a):","def inverse(self):\n return self._inverse","def inverse_transform(v):\n v, k = divmod(v - 1, N)\n v, j = divmod(v, N)\n v, i = divmod(v, N)\n return i, j, k","def invert(self, transformed_input):\n return transformed_input * self.__diff + self.low","def invert(self, transformed_input):\n raise NotImplementedError()","def inverse(self):\n n = self.norm()\n c = self.conj()\n d = 1.0 / (n * n)\n c.scale(d)\n return c","def inv(transform_matrix):\n\n r = transform_matrix[0:3, 0:3]\n t = transform_matrix[0:3, 3]\n t_inv = -1 * r.T.dot(t)\n transform_inv = np.eye(4)\n transform_inv[0:3, 0:3] = r.T\n transform_inv[0:3, 3] = t_inv\n\n return transform_inv","def inverse(self):\n cdef StdVectorFst result = self.copy()\n result.invert()\n return result","def test_inverse_transform(self):","def invert(self, img):\n return self.inverse()(img)","def inverse(self):\n def inv(v):\n v[0], v[1] = v[1] , v[0]\n for v in [self.point1 , self.pointN , self.unitv, self.normalv]:\n inv(v)\n\n self.points = numpy.roll(self.points,1,axis=1)\n self.a, self.b = self.b, self.a\n self.angle = numpy.arccos( self.unitv[0] )*numpy.sign(self.unitv[1] )\n return","def _inverse_transform(self, x):\n if x.atleast_2d().shape[1] != self.w.size:\n raise ValueError(\"array to revert must have {:} \"\n \"features (columns).\".format(self.w.size))\n\n v = (x - self.b).atleast_2d()\n\n v[:, self.w != 0] /= self.w[self.w != 0] # avoids division by zero\n\n return v.ravel() if x.ndim <= 1 else v","def inverse(self) -> 'Invertible':\n raise NotImplementedError","def GetInverse(self, *args):\n return _itkTranslationTransformPython.itkTranslationTransformD2_GetInverse(self, *args)","def inverse(self):\n self.check_square()\n\n\n N = self.rows\n\n inverse = make_matrix(N, N)\n\n # Solve on a per-column basis using Ax = b formalism\n for j in range(N):\n b = make_matrix(N, 1)\n b[j, 0] = 1\n\n x = self.solve_linear_system(b)\n\n for i in range(N):\n inverse[i, j] = x[i, 0]\n\n return inverse","def inverse(self, x):\n return self.mul(self.weights, x.unsqueeze(-1)).squeeze(-1) + self.shift\n #return self.mul(torch.inverse(self.weights), (x - self.shift).unsqueeze(-1)).squeeze(-1)","def inverse(self):\n rotation = self.rotation.inverse()\n translation = rotation * (-self.translation)\n\n return Transform(rotation, translation)","def inverse_affine_transformation_matrix(A):\n n, n = A.shape\n\n # extract components R, an n-1xn-1 linear transformation matrix, and T, an nx1 translation matrix\n R = A[:n-1, :n-1]\n T = A[:n-1, n-1]\n\n # find R^-1\n R_inv = np.linalg.inv(R)\n\n # Find A^-1/A_inv\n A_inv = np.copy(A).astype(float) # copy A for base of A^-1 matrix and ensure it is of data type float\n A_inv[:n-1, :n-1] = R_inv # set top left nxn sub matrix equal to R^-1\n A_inv[:n-1, n-1] = np.negative(R_inv.dot(T)) # place -R^-1*T in top right corner\n\n return A_inv","def inverse_transform(self, X):\n X, _ = validate_is_pd(X, None)\n Xi = self.get_decomposition().inverse_transform(X)\n return Xi","def GetInverse(self, *args):\n return _itkTranslationTransformPython.itkTranslationTransformD3_GetInverse(self, *args)","def inverse_transform(self, Xt):\n self._check_is_fitted('inverse_transform')\n return self.best_estimator_.inverse_transform(Xt)","def inverse_transform(self, Y: numpy.ndarray) -> numpy.ndarray:\n\n Y_filled = self.__zerofill_reduced_repr(Y, self.gate_)\n intermediate_reduced_repr = self.gate_.inverse_transform(Y_filled)\n\n Y_intr = self.__decompose_intermediate_repr(intermediate_reduced_repr)\n\n return self.intermediate_inverse_transform(Y_intr)","def inverse_transform(self, data: np.ndarray) -> np.ndarray:\n for i in range(self.n_layers):\n j = self.n_layers-1-i\n new_data = np.nan_to_num(data)\n new_data = self.ica_list[j].inverse_transform(X=new_data)\n if i != self.n_layers - 1:\n new_data = self.power_list[j-1].transform(new_data)\n data = new_data\n return data","def ssc.inverse (x_ij):\n\n Hij = xyzrph2matrix (x_ij)\n Rji = Hij[0:3, 0:3]\n tij = Hij[0:3,3]\n Rij = Rji.transpose ()\n tji = -Rij.dot (tij)\n Hji = zeros ((4,4))\n Hji[0:3,0:3] = Rij\n Hji[0:3,3] = tji\n Hji[3,3] = 1\n return matrix2xyzrph (Hji)","def inverse(self,mat):\n result = np.linalg.inv(mat)\n self.out = result\n return self.out","def inv(self):\n\n self.x, self.y = self.y, self.x\n self._x_, self._y_ = self._y_, self._x_\n self.xfac, self.yfac = 1 / self.yfac, 1 / self.xfac\n self._xfac_, self._yfac_ = 1 / self._yfac_, 1 / self._xfac_\n self._u = 1 / self._u.conj()","def inverse (x_ij):\n\n Hij = xyzrph2matrix (x_ij)\n Rji = Hij[0:3, 0:3]\n tij = Hij[0:3,3]\n Rij = Rji.transpose ()\n tji = -Rij.dot (tij)\n Hji = numpy.zeros ((4,4))\n Hji[0:3,0:3] = Rij\n Hji[0:3,3] = tji\n Hji[3,3] = 1\n return matrix2xyzrph (Hji)","def _get_inv(self):\n m,d = self.B.shape\n Im = np.eye(m)\n Id = np.eye(d)\n BBt = self.B@self.B.T\n I_BBt_inv = np.linalg.pinv(Im + BBt)\n \n return (1/self.alpha)*(Id - self.B.T@( I_BBt_inv@self.B/self.alpha))","def __invert__(self):\n return self.fam.c_unop('invert', self)","def inverse(self: Float[LinearOperator, \"*batch N N\"]) -> Float[LinearOperator, \"*batch N N\"]:\n return self.__class__(self._diag.reciprocal())","def inverse(self):\n # find the determinant of the matrix\n determinant = self.determinant()\n # find the matrix of minors of the matrix\n matrix_of_minors = self.matrix_of_minors()\n # find the cofactor of the matrix of minors\n cofactor_matrix = self.cofactor_matrix(matrix_of_minors)\n # find the transpose of the cofactor matrix\n transpose_cofactor_matrix = self.transpose(cofactor_matrix)\n # find the adjugate (inverse) matrix\n inverse_matrix = self.adjugate_matrix(determinant, transpose_cofactor_matrix)\n\n return inverse_matrix","def invert(self):\n\n if self.rows != self.columns:\n raise ValueError(\"Matrix must be square to invert\")\n\n A, operations = self.to_reduced_row_echelon()\n if not A.is_identity():\n return 0\n\n # If A was reduced to the identity matrix, then the same set of operations will take I to the inverse of A.\n # [A I] -> [I A^(-1)]\n\n I = IdentityMatrix(size = self.rows)\n for operation in operations:\n func = I.__getattribute__(operation[0])\n args = operation[1:]\n func(*args)\n\n return I","def inverse_transform2(alpha, tx = 0.0, ty = 0.0, mirror = 0):\n\n\tt = Transform({\"type\":\"2D\",\"alpha\":alpha,\"tx\":tx,\"ty\":ty,\"mirror\":mirror,\"scale\":1.0})\n\tt = t.inverse()\n\tt = t.get_params(\"2D\")\n\treturn t[ \"alpha\" ], t[ \"tx\" ], t[ \"ty\" ], t[ \"mirror\" ]","def test__inverse_transform_continuous(self):","def inv(self):\n\t\tdeterminant = self.det()\n\t\tif determinant:\n\t\t\treturn self.adj() / determinant\n\t\telse:\n\t\t\traise ValueError(\"Not Invertible\")","def inverse(self, y):\n device = y.device\n return t.einsum('ij,k,kj->ik', y, 1. / t.sqrt(self.eig).to(device), self.rot.to(device))","def __invert__(self):\n from sage.matrix.constructor import matrix\n from .comp import Components\n if self._is_identity:\n return self\n if self._inverse is None:\n if self._name is None:\n inv_name = None\n else:\n inv_name = self._name + '^(-1)'\n if self._latex_name is None:\n inv_latex_name = None\n else:\n inv_latex_name = self._latex_name + r'^{-1}'\n fmodule = self._fmodule\n si = fmodule._sindex\n nsi = fmodule._rank + si\n self._inverse = self.__class__(fmodule, inv_name, inv_latex_name)\n for basis in self._components:\n try:\n mat = self.matrix(basis)\n except (KeyError, ValueError):\n continue\n mat_inv = mat.inverse()\n cinv = Components(fmodule._ring, basis, 2, start_index=si,\n output_formatter=fmodule._output_formatter)\n for i in range(si, nsi):\n for j in range(si, nsi):\n cinv[i, j] = mat_inv[i-si,j-si]\n self._inverse._components[basis] = cinv\n self._inverse._inverse = self\n return self._inverse","def inv(self):\n return self.conjugate()","def inverse(self: T) -> T:","def inverse( m, context = FloatContext, copy_m=True ):\n n,n_ = shape_mat(m)\n assert (n==n_) #matris should be square\n\n return solve( m, eye(n), context=context, copy_b=False, copy_a=copy_m )","def inverseTransformationMatrix(self,index=None):\n if self.method == 'pca':\n if index is not None:\n coordinateIndex = distribution1D.vectori_cxx(len(index))\n for i in range(len(index)):\n coordinateIndex[i] = index[i]\n matrixDim = self._distribution.getInverseTransformationMatrixDimensions(coordinateIndex)\n inverseTransformation = self._distribution.getInverseTransformationMatrix(coordinateIndex)\n else:\n matrixDim = self._distribution.getInverseTransformationMatrixDimensions()\n inverseTransformation = self._distribution.getInverseTransformationMatrix()\n row = matrixDim[0]\n column = matrixDim[1]\n # convert 1D vector to 2D array\n L = np.atleast_1d(inverseTransformation).reshape(row,column)\n else:\n self.raiseAnError(NotImplementedError,' inverse transformationMatrix is not yet implemented for ' + self.method + ' method')\n return L","def inverse_transform(self, X):\n X = np.asarray(X, dtype=np.float64)\n \n if self.with_scaling:\n X *= self.scale_\n if self.with_centering:\n X += self.center_\n \n return X","def inverse_transform(self, matrix):\n\n x = matrix.shape[0]\n y = matrix.shape[1]\n N = x\n\n # Inverse Fourier Transform matrix:\n ift = np.zeros([x, y], complex)\n\n for i in range(0, x):\n for j in range(0, y):\n sum_ift = 0\n for u in range(0, x):\n for v in range(0, y):\n sum_ift = sum_ift + matrix[u, v] * (np.cos(((2 * np.pi) / N) * (u * i + v * j)) + 1j * np.sin(((2 * np.pi) / N) * (u * i + v * j)))\n\n ift[i, j] = sum_ift\n\n\n return ift/(x*x)","def inv(X):\n R, t = Rt(X)\n Ri = R.T\n return np.concatenate((\n np.concatenate((Ri, -Ri.dot(t)[:,np.newaxis]), axis=1),\n np.array([[0, 0, 1]])))","def inverse_transform(self, Xt):\n Xt = _validate_X(Xt)\n return self.best_estimator_.inverse_transform(Xt)","def getInverseMatrix(self) -> CMatrix4:\n ...","def inverse(self, x):\n x = np.asarray(x)\n def r(vec):\n return utils.recycled(vec, as_=x)\n if self.zero is not None and self.multiplier is not None:\n x = x / r(self.multiplier) + r(self.zero)\n elif self.zero is not None:\n x = x + r(self.zero)\n elif self.multiplier is not None:\n x = x / r(self.multiplier)\n return x","def inverse(self):\n return Rotation(self.matrix.transposed())","def inverse(self):\n if not self.is_square():\n raise(ValueError, \"Non-square Matrix does not have an inverse.\")\n if self.h > 2:\n raise(NotImplementedError, \"inversion not implemented for matrices larger than 2x2.\")\n\n # TODO - your code here\n if self.h == 1:\n inverse = [[1/self.g[0][0]]];\n else:\n a = self.g[0][0];\n b = self.g[0][1];\n c = self.g[1][0];\n d = self.g[1][1];\n if(a*d==b*c):\n raise ValueError('matrix does not have a inverse!');\n else:\n weigh = 1/(a*d-b*c);\n inverse = [[weigh*d,weigh*-1*b],[weigh*-1*c,weigh*a]];\n return Matrix(inverse);","def _r_inv(self):\n raise NotImplementedError","def inverse_transform(self, y_enc):\n return self.le_.inverse_transform(\n self.bsidx_lexsorted_[np.require(y_enc, dtype=int)])","def _inverse(self, x):\n alpha, beta = self._get_alpha_beta()\n diff = x - self.x0\n r = tf.linalg.norm(diff, axis=-1, keepdims=True)\n h = 1. / (alpha + r)\n beta_h = beta * h\n return x + beta_h * diff","def inverse(self):\n if not self.is_square():\n raise(ValueError, \"Non-square Matrix does not have an inverse.\")\n if self.h > 2:\n raise(NotImplementedError, \"inversion not implemented for matrices larger than 2x2.\")\n # TODO - your code here\n inverse = []\n if self.h == 1:\n temp = []\n temp.append(1/self.g[0][0])\n inverse.append(temp)\n else:\n identity_matrix = identity(self.h)\n det_term = 1/self.determinant()\n trace_term = self.trace()\n # implement intermediate scaling step locally\n # trace_x_I = trace_term * identity_matrix\n trace_x_I = []\n for i in range(len(self.g)):\n temp_row = []\n for j in range(len(self.g[i])):\n temp_row.append(trace_term * identity_matrix[i][j])\n trace_x_I.append(temp_row)\n # implement sub-traction locally\n # sub_term = trace_x_I - self.g\n sub_term = []\n for i in range(len(trace_x_I)):\n temp_row = []\n for j in range(len(trace_x_I[i])):\n temp_row.append(trace_x_I[i][j] - self.g[i][j])\n sub_term.append(temp_row)\n # implement final scaling step locally\n # inverse = det_term * sub_term\n inverse = []\n for i in range(len(sub_term)):\n temp_row = []\n for j in range(len(sub_term[i])):\n temp_row.append(det_term * sub_term[i][j])\n inverse.append(temp_row)\n return Matrix(inverse)\n # TODO - your code here","def Inverse(self, freedofs: pyngcore.BitArray = None, inverse: str = '') -> BaseMatrix:","def Inverse(self, freedofs: pyngcore.BitArray = None, inverse: str = '') -> BaseMatrix:","def inverse_transform(data, flip, rotate):\n if flip == 'FLIP_LEFT_RIGHT':\n data = np.fliplr(data)\n\n if rotate == 'ROTATE_90':\n data = np.rot90(data, 3)\n\n if rotate == 'ROTATE_180':\n data = np.rot90(data, 2)\n\n if rotate == 'ROTATE_270':\n data = np.rot90(data, 1)\n\n return data","def __invert__(self) -> Operators:\n return self.operate(inv)","def inv(self):\n return MoebGen(self._d / self._det, - self._b / self._det, - self._c / self._det, self._a / self._det)","def __invert__(self):\n return Factorization([(p,-e) for p,e in reversed(self)],\n cr=self._cr(), unit=self.unit()**(-1))","def inverse_transform ( x ):\n x_out = x*1.\n # Cab, posn 1\n x_out[1] = -100.*np.log ( x[1] )\n # Cab, posn 2\n x_out[2] = -100.*np.log ( x[2] )\n # Cw, posn 4\n x_out[4] = (-1./50.)*np.log ( x[4] )\n #Cm, posn 5\n x_out[5] = (-1./100.)*np.log ( x[5] )\n # LAI, posn 6\n x_out[6] = -2.*np.log ( x[6] )\n # ALA, posn 7\n x_out[7] = 90.*x[7]\n return x_out","def inverse_transform(self, data: np.ndarray) -> np.ndarray:\n for i in range(self.n_layers):\n j = self.n_layers-1-i\n new_data = np.nan_to_num(data)\n new_data = self.pca_list[j].inverse_transform(X=new_data)\n if i != self.n_layers - 1:\n new_data = self.power_list[j-1].transform(new_data)\n data = new_data\n return data","def inverse_transform(self):\n res = np.sum(self.support * self.transform, 0)\n self.filter += res * (res > 0)\n self.model = self.filter\n return res","def modular_inverse(self):\n i = gmpy2.invert(self.c2, self.n)\n mx = pow(self.c1, self.a, self.n)\n my = pow(i, int(-self.b), self.n)\n self.m= mx * my % self.n","def compute_inverse(in1, in2):\n aL = [in1]\n bL = [in2]\n tL = [0]\n t = 1\n sL = [1]\n s = 0\n q = math.floor((aL[0] / bL[0]))\n r = (aL[0] - (q * bL[0]))\n\n while r > 0:\n temp = (tL[0] - (q * bL[0]))\n tL[0] = t\n t = temp\n temp = (sL[0] - (q * s))\n sL[0] = s\n s = temp\n aL[0] = bL[0]\n bL[0] = r\n q = math.floor(aL[0] / bL[0])\n r = (aL[0] - (q * bL[0]))\n\n inverse = s % in2\n return inverse","def inverse(self, u: Tensor, covariates: Tensor) -> Tensor:\n return self.real_nvp.inverse(u, covariates)","def inverse(self):\n if self.determinant() != 0:\n ops = reduce_to_red_echelon(self.data.copy(), True)[1]\n matrix = identity_matrix(self.n_rows).data\n \n if ops:\n if isinstance(ops[0], str):\n ops = [ops]\n \n for op in ops:\n if op[0] == 'swap':\n matrix = row_swap(matrix, op[1], op[2])\n elif op[0] == 'multiplication':\n matrix = row_multiply(matrix, op[1], op[2])\n elif op[0] == 'subtract':\n matrix = row_subtract(matrix, op[1], op[2], op[3])\n else:\n raise ValueError('Row operation not recognized')\n else:\n raise ValueError('Matrix has a determinant of 0 and is not invertible')\n return Matrix(matrix)","def backward_transform(self):\n try:\n backward = self.forward_transform.inverse\n except NotImplementedError as err:\n raise NotImplementedError(\"Could not construct backward transform. \\n{0}\".format(err))\n return backward","def inverseDataTransform(self):\n # transforms are updated in setOpts call.\n return self._inverseDataTransform","def inverse_transform(self, X):\n X = super(PowerTransformer, self).inverse_transform(X)\n\n if self.pre_center:\n X = self.pre_centerer_.inverse_transform(X)\n\n if self.rescale:\n X = self.rescaler_.inverse_transform(X)\n\n return X","def inverse(\n self, input: Tensor, params: Optional[List[ParamItem]] = None, extra_args: Dict[str, Any] = {}\n ) -> Tensor:\n if params is None:\n if self._params is not None:\n params = self._params\n else:\n raise RuntimeError(\"No valid params to inverse the transformation.\")\n\n return self.inverse_inputs(input, params, extra_args=extra_args)","def inv(self,output,*args,**kwargs):\n raise ModelTypeError('Model is not invertible')","def inverse(self):\n if self.inv is None:\n if self.size > 0:\n self.inv = inverseIndex(self)\n else:\n self.inv = Connectivity()\n return self.inv","def __invert__(self):\n try:\n B = ~(self.matrix())\n except ZeroDivisionError:\n raise ZeroDivisionError(\"matrix morphism not invertible\")\n try:\n return self.parent().reversed()(B)\n except TypeError:\n raise ZeroDivisionError(\"matrix morphism not invertible\")","def inverse(self, ys):\n with torch.no_grad():\n xs = torch.matmul(ys, torch.diag(torch.reciprocal(torch.exp(self.scaling_diag))))\n xs = self.layer4.inverse(xs)\n xs = self.layer3.inverse(xs)\n xs = self.layer2.inverse(xs)\n xs = self.layer1.inverse(xs)\n return xs"],"string":"[\n \"def inverse_transform(self, X):\\n ...\",\n \"def inverse_transform(self, X):\\n ...\",\n \"def inverse_transform(self, X):\\n ...\",\n \"def inverse_transform(self, X):\\n ...\",\n \"def inverse_transform(self, X):\\n ...\",\n \"def inverse_transform(self, X, copy=...):\\n ...\",\n \"def inverse_transform(self, X):\\n\\n pass # pragma: no cover\",\n \"def inverse_transform(self, x):\\n raise NotImplementedError()\",\n \"def inverse_transform(self, X):\\n return self._apply_method(X, \\\"inverse_transform\\\")\",\n \"def __invert__(self):\\n return self.inverse()\",\n \"def inverse(self):\\n rotation_matrix = self.pose_mat[:3, :3]\\n translation_vector = self.pose_mat[:3, 3]\\n\\n rot = np.transpose(rotation_matrix)\\n trans = - np.matmul(np.transpose(rotation_matrix), translation_vector)\\n return Transformation(rot, trans)\",\n \"def inverse_cayley_transform(z: torch.Tensor) -> torch.Tensor:\\n identity = identity_like(z)\\n i_identity = multiply_by_i(identity)\\n\\n z_minus_id = z - i_identity\\n inv_z_plus_id = inverse(z + i_identity)\\n return z_minus_id @ inv_z_plus_id\",\n \"def inverse_transform(self, X):\\n raise NotImplementedError()\",\n \"def inverse(self, x, y):\",\n \"def inverse(self):\\n invr = np.linalg.inv(self.affine_matrix)\\n return SymmOp(invr)\",\n \"def inverse_transform(self, Xt):\\n return self.transformer.inverse_transform(Xt)\",\n \"def inverse(self):\\n return self.solve(Matrix.I(self.nrows))\",\n \"def _inv(self) -> None:\\n\\n self.inv(inplace=True)\",\n \"def inverse_transform(self, y: Array2D) -> Array2D:\",\n \"def invert(x):\\n return linalg.inv(x)\",\n \"def affine_transform_inverse(np_transform):\\n rotation = np_transform[:3, :3]\\n translation = np_transform[:3, 3]\\n rotation_inv = numpy.linalg.inv(rotation)\\n translation_inv = -1 * numpy.dot(rotation_inv, translation)\\n result = numpy.identity(4)\\n result[:3, :3] = rotation_inv\\n result[:3, 3] = translation_inv.flatten()\\n return result\",\n \"def inverse(self):\\r\\n \\r\\n Mi=mat4()\\r\\n d=self.determinant()\\r\\n for i in range(4):\\r\\n for j in range(4):\\r\\n sign=1-((i+j)%2)*2\\r\\n m3=self._submat(i,j)\\r\\n Mi[j,i]=sign*m3.determinant()/d\\r\\n return Mi\",\n \"def inverse(self):\\n data = np.linalg.inv(self._data)\\n return self.create(self.rows, self.cols, data)\",\n \"def inverse(self):\\n return self.invert()\",\n \"def _inverse_transform(self, X, y=None):\\n return clone(self.transformer).fit(X=X, y=y).inverse_transform(X=X, y=y)\",\n \"def inverse_rigid_trans(Tr): \\n inv_Tr = np.zeros_like(Tr) # 3x4\\n inv_Tr[0:3,0:3] = np.transpose(Tr[0:3,0:3])\\n inv_Tr[0:3,3] = np.dot(-np.transpose(Tr[0:3,0:3]), Tr[0:3,3])\\n return inv_Tr\",\n \"def inv_inplace(a):\",\n \"def inverse(self):\\n return self._inverse\",\n \"def inverse_transform(v):\\n v, k = divmod(v - 1, N)\\n v, j = divmod(v, N)\\n v, i = divmod(v, N)\\n return i, j, k\",\n \"def invert(self, transformed_input):\\n return transformed_input * self.__diff + self.low\",\n \"def invert(self, transformed_input):\\n raise NotImplementedError()\",\n \"def inverse(self):\\n n = self.norm()\\n c = self.conj()\\n d = 1.0 / (n * n)\\n c.scale(d)\\n return c\",\n \"def inv(transform_matrix):\\n\\n r = transform_matrix[0:3, 0:3]\\n t = transform_matrix[0:3, 3]\\n t_inv = -1 * r.T.dot(t)\\n transform_inv = np.eye(4)\\n transform_inv[0:3, 0:3] = r.T\\n transform_inv[0:3, 3] = t_inv\\n\\n return transform_inv\",\n \"def inverse(self):\\n cdef StdVectorFst result = self.copy()\\n result.invert()\\n return result\",\n \"def test_inverse_transform(self):\",\n \"def invert(self, img):\\n return self.inverse()(img)\",\n \"def inverse(self):\\n def inv(v):\\n v[0], v[1] = v[1] , v[0]\\n for v in [self.point1 , self.pointN , self.unitv, self.normalv]:\\n inv(v)\\n\\n self.points = numpy.roll(self.points,1,axis=1)\\n self.a, self.b = self.b, self.a\\n self.angle = numpy.arccos( self.unitv[0] )*numpy.sign(self.unitv[1] )\\n return\",\n \"def _inverse_transform(self, x):\\n if x.atleast_2d().shape[1] != self.w.size:\\n raise ValueError(\\\"array to revert must have {:} \\\"\\n \\\"features (columns).\\\".format(self.w.size))\\n\\n v = (x - self.b).atleast_2d()\\n\\n v[:, self.w != 0] /= self.w[self.w != 0] # avoids division by zero\\n\\n return v.ravel() if x.ndim <= 1 else v\",\n \"def inverse(self) -> 'Invertible':\\n raise NotImplementedError\",\n \"def GetInverse(self, *args):\\n return _itkTranslationTransformPython.itkTranslationTransformD2_GetInverse(self, *args)\",\n \"def inverse(self):\\n self.check_square()\\n\\n\\n N = self.rows\\n\\n inverse = make_matrix(N, N)\\n\\n # Solve on a per-column basis using Ax = b formalism\\n for j in range(N):\\n b = make_matrix(N, 1)\\n b[j, 0] = 1\\n\\n x = self.solve_linear_system(b)\\n\\n for i in range(N):\\n inverse[i, j] = x[i, 0]\\n\\n return inverse\",\n \"def inverse(self, x):\\n return self.mul(self.weights, x.unsqueeze(-1)).squeeze(-1) + self.shift\\n #return self.mul(torch.inverse(self.weights), (x - self.shift).unsqueeze(-1)).squeeze(-1)\",\n \"def inverse(self):\\n rotation = self.rotation.inverse()\\n translation = rotation * (-self.translation)\\n\\n return Transform(rotation, translation)\",\n \"def inverse_affine_transformation_matrix(A):\\n n, n = A.shape\\n\\n # extract components R, an n-1xn-1 linear transformation matrix, and T, an nx1 translation matrix\\n R = A[:n-1, :n-1]\\n T = A[:n-1, n-1]\\n\\n # find R^-1\\n R_inv = np.linalg.inv(R)\\n\\n # Find A^-1/A_inv\\n A_inv = np.copy(A).astype(float) # copy A for base of A^-1 matrix and ensure it is of data type float\\n A_inv[:n-1, :n-1] = R_inv # set top left nxn sub matrix equal to R^-1\\n A_inv[:n-1, n-1] = np.negative(R_inv.dot(T)) # place -R^-1*T in top right corner\\n\\n return A_inv\",\n \"def inverse_transform(self, X):\\n X, _ = validate_is_pd(X, None)\\n Xi = self.get_decomposition().inverse_transform(X)\\n return Xi\",\n \"def GetInverse(self, *args):\\n return _itkTranslationTransformPython.itkTranslationTransformD3_GetInverse(self, *args)\",\n \"def inverse_transform(self, Xt):\\n self._check_is_fitted('inverse_transform')\\n return self.best_estimator_.inverse_transform(Xt)\",\n \"def inverse_transform(self, Y: numpy.ndarray) -> numpy.ndarray:\\n\\n Y_filled = self.__zerofill_reduced_repr(Y, self.gate_)\\n intermediate_reduced_repr = self.gate_.inverse_transform(Y_filled)\\n\\n Y_intr = self.__decompose_intermediate_repr(intermediate_reduced_repr)\\n\\n return self.intermediate_inverse_transform(Y_intr)\",\n \"def inverse_transform(self, data: np.ndarray) -> np.ndarray:\\n for i in range(self.n_layers):\\n j = self.n_layers-1-i\\n new_data = np.nan_to_num(data)\\n new_data = self.ica_list[j].inverse_transform(X=new_data)\\n if i != self.n_layers - 1:\\n new_data = self.power_list[j-1].transform(new_data)\\n data = new_data\\n return data\",\n \"def ssc.inverse (x_ij):\\n\\n Hij = xyzrph2matrix (x_ij)\\n Rji = Hij[0:3, 0:3]\\n tij = Hij[0:3,3]\\n Rij = Rji.transpose ()\\n tji = -Rij.dot (tij)\\n Hji = zeros ((4,4))\\n Hji[0:3,0:3] = Rij\\n Hji[0:3,3] = tji\\n Hji[3,3] = 1\\n return matrix2xyzrph (Hji)\",\n \"def inverse(self,mat):\\n result = np.linalg.inv(mat)\\n self.out = result\\n return self.out\",\n \"def inv(self):\\n\\n self.x, self.y = self.y, self.x\\n self._x_, self._y_ = self._y_, self._x_\\n self.xfac, self.yfac = 1 / self.yfac, 1 / self.xfac\\n self._xfac_, self._yfac_ = 1 / self._yfac_, 1 / self._xfac_\\n self._u = 1 / self._u.conj()\",\n \"def inverse (x_ij):\\n\\n Hij = xyzrph2matrix (x_ij)\\n Rji = Hij[0:3, 0:3]\\n tij = Hij[0:3,3]\\n Rij = Rji.transpose ()\\n tji = -Rij.dot (tij)\\n Hji = numpy.zeros ((4,4))\\n Hji[0:3,0:3] = Rij\\n Hji[0:3,3] = tji\\n Hji[3,3] = 1\\n return matrix2xyzrph (Hji)\",\n \"def _get_inv(self):\\n m,d = self.B.shape\\n Im = np.eye(m)\\n Id = np.eye(d)\\n BBt = self.B@self.B.T\\n I_BBt_inv = np.linalg.pinv(Im + BBt)\\n \\n return (1/self.alpha)*(Id - self.B.T@( I_BBt_inv@self.B/self.alpha))\",\n \"def __invert__(self):\\n return self.fam.c_unop('invert', self)\",\n \"def inverse(self: Float[LinearOperator, \\\"*batch N N\\\"]) -> Float[LinearOperator, \\\"*batch N N\\\"]:\\n return self.__class__(self._diag.reciprocal())\",\n \"def inverse(self):\\n # find the determinant of the matrix\\n determinant = self.determinant()\\n # find the matrix of minors of the matrix\\n matrix_of_minors = self.matrix_of_minors()\\n # find the cofactor of the matrix of minors\\n cofactor_matrix = self.cofactor_matrix(matrix_of_minors)\\n # find the transpose of the cofactor matrix\\n transpose_cofactor_matrix = self.transpose(cofactor_matrix)\\n # find the adjugate (inverse) matrix\\n inverse_matrix = self.adjugate_matrix(determinant, transpose_cofactor_matrix)\\n\\n return inverse_matrix\",\n \"def invert(self):\\n\\n if self.rows != self.columns:\\n raise ValueError(\\\"Matrix must be square to invert\\\")\\n\\n A, operations = self.to_reduced_row_echelon()\\n if not A.is_identity():\\n return 0\\n\\n # If A was reduced to the identity matrix, then the same set of operations will take I to the inverse of A.\\n # [A I] -> [I A^(-1)]\\n\\n I = IdentityMatrix(size = self.rows)\\n for operation in operations:\\n func = I.__getattribute__(operation[0])\\n args = operation[1:]\\n func(*args)\\n\\n return I\",\n \"def inverse_transform2(alpha, tx = 0.0, ty = 0.0, mirror = 0):\\n\\n\\tt = Transform({\\\"type\\\":\\\"2D\\\",\\\"alpha\\\":alpha,\\\"tx\\\":tx,\\\"ty\\\":ty,\\\"mirror\\\":mirror,\\\"scale\\\":1.0})\\n\\tt = t.inverse()\\n\\tt = t.get_params(\\\"2D\\\")\\n\\treturn t[ \\\"alpha\\\" ], t[ \\\"tx\\\" ], t[ \\\"ty\\\" ], t[ \\\"mirror\\\" ]\",\n \"def test__inverse_transform_continuous(self):\",\n \"def inv(self):\\n\\t\\tdeterminant = self.det()\\n\\t\\tif determinant:\\n\\t\\t\\treturn self.adj() / determinant\\n\\t\\telse:\\n\\t\\t\\traise ValueError(\\\"Not Invertible\\\")\",\n \"def inverse(self, y):\\n device = y.device\\n return t.einsum('ij,k,kj->ik', y, 1. / t.sqrt(self.eig).to(device), self.rot.to(device))\",\n \"def __invert__(self):\\n from sage.matrix.constructor import matrix\\n from .comp import Components\\n if self._is_identity:\\n return self\\n if self._inverse is None:\\n if self._name is None:\\n inv_name = None\\n else:\\n inv_name = self._name + '^(-1)'\\n if self._latex_name is None:\\n inv_latex_name = None\\n else:\\n inv_latex_name = self._latex_name + r'^{-1}'\\n fmodule = self._fmodule\\n si = fmodule._sindex\\n nsi = fmodule._rank + si\\n self._inverse = self.__class__(fmodule, inv_name, inv_latex_name)\\n for basis in self._components:\\n try:\\n mat = self.matrix(basis)\\n except (KeyError, ValueError):\\n continue\\n mat_inv = mat.inverse()\\n cinv = Components(fmodule._ring, basis, 2, start_index=si,\\n output_formatter=fmodule._output_formatter)\\n for i in range(si, nsi):\\n for j in range(si, nsi):\\n cinv[i, j] = mat_inv[i-si,j-si]\\n self._inverse._components[basis] = cinv\\n self._inverse._inverse = self\\n return self._inverse\",\n \"def inv(self):\\n return self.conjugate()\",\n \"def inverse(self: T) -> T:\",\n \"def inverse( m, context = FloatContext, copy_m=True ):\\n n,n_ = shape_mat(m)\\n assert (n==n_) #matris should be square\\n\\n return solve( m, eye(n), context=context, copy_b=False, copy_a=copy_m )\",\n \"def inverseTransformationMatrix(self,index=None):\\n if self.method == 'pca':\\n if index is not None:\\n coordinateIndex = distribution1D.vectori_cxx(len(index))\\n for i in range(len(index)):\\n coordinateIndex[i] = index[i]\\n matrixDim = self._distribution.getInverseTransformationMatrixDimensions(coordinateIndex)\\n inverseTransformation = self._distribution.getInverseTransformationMatrix(coordinateIndex)\\n else:\\n matrixDim = self._distribution.getInverseTransformationMatrixDimensions()\\n inverseTransformation = self._distribution.getInverseTransformationMatrix()\\n row = matrixDim[0]\\n column = matrixDim[1]\\n # convert 1D vector to 2D array\\n L = np.atleast_1d(inverseTransformation).reshape(row,column)\\n else:\\n self.raiseAnError(NotImplementedError,' inverse transformationMatrix is not yet implemented for ' + self.method + ' method')\\n return L\",\n \"def inverse_transform(self, X):\\n X = np.asarray(X, dtype=np.float64)\\n \\n if self.with_scaling:\\n X *= self.scale_\\n if self.with_centering:\\n X += self.center_\\n \\n return X\",\n \"def inverse_transform(self, matrix):\\n\\n x = matrix.shape[0]\\n y = matrix.shape[1]\\n N = x\\n\\n # Inverse Fourier Transform matrix:\\n ift = np.zeros([x, y], complex)\\n\\n for i in range(0, x):\\n for j in range(0, y):\\n sum_ift = 0\\n for u in range(0, x):\\n for v in range(0, y):\\n sum_ift = sum_ift + matrix[u, v] * (np.cos(((2 * np.pi) / N) * (u * i + v * j)) + 1j * np.sin(((2 * np.pi) / N) * (u * i + v * j)))\\n\\n ift[i, j] = sum_ift\\n\\n\\n return ift/(x*x)\",\n \"def inv(X):\\n R, t = Rt(X)\\n Ri = R.T\\n return np.concatenate((\\n np.concatenate((Ri, -Ri.dot(t)[:,np.newaxis]), axis=1),\\n np.array([[0, 0, 1]])))\",\n \"def inverse_transform(self, Xt):\\n Xt = _validate_X(Xt)\\n return self.best_estimator_.inverse_transform(Xt)\",\n \"def getInverseMatrix(self) -> CMatrix4:\\n ...\",\n \"def inverse(self, x):\\n x = np.asarray(x)\\n def r(vec):\\n return utils.recycled(vec, as_=x)\\n if self.zero is not None and self.multiplier is not None:\\n x = x / r(self.multiplier) + r(self.zero)\\n elif self.zero is not None:\\n x = x + r(self.zero)\\n elif self.multiplier is not None:\\n x = x / r(self.multiplier)\\n return x\",\n \"def inverse(self):\\n return Rotation(self.matrix.transposed())\",\n \"def inverse(self):\\n if not self.is_square():\\n raise(ValueError, \\\"Non-square Matrix does not have an inverse.\\\")\\n if self.h > 2:\\n raise(NotImplementedError, \\\"inversion not implemented for matrices larger than 2x2.\\\")\\n\\n # TODO - your code here\\n if self.h == 1:\\n inverse = [[1/self.g[0][0]]];\\n else:\\n a = self.g[0][0];\\n b = self.g[0][1];\\n c = self.g[1][0];\\n d = self.g[1][1];\\n if(a*d==b*c):\\n raise ValueError('matrix does not have a inverse!');\\n else:\\n weigh = 1/(a*d-b*c);\\n inverse = [[weigh*d,weigh*-1*b],[weigh*-1*c,weigh*a]];\\n return Matrix(inverse);\",\n \"def _r_inv(self):\\n raise NotImplementedError\",\n \"def inverse_transform(self, y_enc):\\n return self.le_.inverse_transform(\\n self.bsidx_lexsorted_[np.require(y_enc, dtype=int)])\",\n \"def _inverse(self, x):\\n alpha, beta = self._get_alpha_beta()\\n diff = x - self.x0\\n r = tf.linalg.norm(diff, axis=-1, keepdims=True)\\n h = 1. / (alpha + r)\\n beta_h = beta * h\\n return x + beta_h * diff\",\n \"def inverse(self):\\n if not self.is_square():\\n raise(ValueError, \\\"Non-square Matrix does not have an inverse.\\\")\\n if self.h > 2:\\n raise(NotImplementedError, \\\"inversion not implemented for matrices larger than 2x2.\\\")\\n # TODO - your code here\\n inverse = []\\n if self.h == 1:\\n temp = []\\n temp.append(1/self.g[0][0])\\n inverse.append(temp)\\n else:\\n identity_matrix = identity(self.h)\\n det_term = 1/self.determinant()\\n trace_term = self.trace()\\n # implement intermediate scaling step locally\\n # trace_x_I = trace_term * identity_matrix\\n trace_x_I = []\\n for i in range(len(self.g)):\\n temp_row = []\\n for j in range(len(self.g[i])):\\n temp_row.append(trace_term * identity_matrix[i][j])\\n trace_x_I.append(temp_row)\\n # implement sub-traction locally\\n # sub_term = trace_x_I - self.g\\n sub_term = []\\n for i in range(len(trace_x_I)):\\n temp_row = []\\n for j in range(len(trace_x_I[i])):\\n temp_row.append(trace_x_I[i][j] - self.g[i][j])\\n sub_term.append(temp_row)\\n # implement final scaling step locally\\n # inverse = det_term * sub_term\\n inverse = []\\n for i in range(len(sub_term)):\\n temp_row = []\\n for j in range(len(sub_term[i])):\\n temp_row.append(det_term * sub_term[i][j])\\n inverse.append(temp_row)\\n return Matrix(inverse)\\n # TODO - your code here\",\n \"def Inverse(self, freedofs: pyngcore.BitArray = None, inverse: str = '') -> BaseMatrix:\",\n \"def Inverse(self, freedofs: pyngcore.BitArray = None, inverse: str = '') -> BaseMatrix:\",\n \"def inverse_transform(data, flip, rotate):\\n if flip == 'FLIP_LEFT_RIGHT':\\n data = np.fliplr(data)\\n\\n if rotate == 'ROTATE_90':\\n data = np.rot90(data, 3)\\n\\n if rotate == 'ROTATE_180':\\n data = np.rot90(data, 2)\\n\\n if rotate == 'ROTATE_270':\\n data = np.rot90(data, 1)\\n\\n return data\",\n \"def __invert__(self) -> Operators:\\n return self.operate(inv)\",\n \"def inv(self):\\n return MoebGen(self._d / self._det, - self._b / self._det, - self._c / self._det, self._a / self._det)\",\n \"def __invert__(self):\\n return Factorization([(p,-e) for p,e in reversed(self)],\\n cr=self._cr(), unit=self.unit()**(-1))\",\n \"def inverse_transform ( x ):\\n x_out = x*1.\\n # Cab, posn 1\\n x_out[1] = -100.*np.log ( x[1] )\\n # Cab, posn 2\\n x_out[2] = -100.*np.log ( x[2] )\\n # Cw, posn 4\\n x_out[4] = (-1./50.)*np.log ( x[4] )\\n #Cm, posn 5\\n x_out[5] = (-1./100.)*np.log ( x[5] )\\n # LAI, posn 6\\n x_out[6] = -2.*np.log ( x[6] )\\n # ALA, posn 7\\n x_out[7] = 90.*x[7]\\n return x_out\",\n \"def inverse_transform(self, data: np.ndarray) -> np.ndarray:\\n for i in range(self.n_layers):\\n j = self.n_layers-1-i\\n new_data = np.nan_to_num(data)\\n new_data = self.pca_list[j].inverse_transform(X=new_data)\\n if i != self.n_layers - 1:\\n new_data = self.power_list[j-1].transform(new_data)\\n data = new_data\\n return data\",\n \"def inverse_transform(self):\\n res = np.sum(self.support * self.transform, 0)\\n self.filter += res * (res > 0)\\n self.model = self.filter\\n return res\",\n \"def modular_inverse(self):\\n i = gmpy2.invert(self.c2, self.n)\\n mx = pow(self.c1, self.a, self.n)\\n my = pow(i, int(-self.b), self.n)\\n self.m= mx * my % self.n\",\n \"def compute_inverse(in1, in2):\\n aL = [in1]\\n bL = [in2]\\n tL = [0]\\n t = 1\\n sL = [1]\\n s = 0\\n q = math.floor((aL[0] / bL[0]))\\n r = (aL[0] - (q * bL[0]))\\n\\n while r > 0:\\n temp = (tL[0] - (q * bL[0]))\\n tL[0] = t\\n t = temp\\n temp = (sL[0] - (q * s))\\n sL[0] = s\\n s = temp\\n aL[0] = bL[0]\\n bL[0] = r\\n q = math.floor(aL[0] / bL[0])\\n r = (aL[0] - (q * bL[0]))\\n\\n inverse = s % in2\\n return inverse\",\n \"def inverse(self, u: Tensor, covariates: Tensor) -> Tensor:\\n return self.real_nvp.inverse(u, covariates)\",\n \"def inverse(self):\\n if self.determinant() != 0:\\n ops = reduce_to_red_echelon(self.data.copy(), True)[1]\\n matrix = identity_matrix(self.n_rows).data\\n \\n if ops:\\n if isinstance(ops[0], str):\\n ops = [ops]\\n \\n for op in ops:\\n if op[0] == 'swap':\\n matrix = row_swap(matrix, op[1], op[2])\\n elif op[0] == 'multiplication':\\n matrix = row_multiply(matrix, op[1], op[2])\\n elif op[0] == 'subtract':\\n matrix = row_subtract(matrix, op[1], op[2], op[3])\\n else:\\n raise ValueError('Row operation not recognized')\\n else:\\n raise ValueError('Matrix has a determinant of 0 and is not invertible')\\n return Matrix(matrix)\",\n \"def backward_transform(self):\\n try:\\n backward = self.forward_transform.inverse\\n except NotImplementedError as err:\\n raise NotImplementedError(\\\"Could not construct backward transform. \\\\n{0}\\\".format(err))\\n return backward\",\n \"def inverseDataTransform(self):\\n # transforms are updated in setOpts call.\\n return self._inverseDataTransform\",\n \"def inverse_transform(self, X):\\n X = super(PowerTransformer, self).inverse_transform(X)\\n\\n if self.pre_center:\\n X = self.pre_centerer_.inverse_transform(X)\\n\\n if self.rescale:\\n X = self.rescaler_.inverse_transform(X)\\n\\n return X\",\n \"def inverse(\\n self, input: Tensor, params: Optional[List[ParamItem]] = None, extra_args: Dict[str, Any] = {}\\n ) -> Tensor:\\n if params is None:\\n if self._params is not None:\\n params = self._params\\n else:\\n raise RuntimeError(\\\"No valid params to inverse the transformation.\\\")\\n\\n return self.inverse_inputs(input, params, extra_args=extra_args)\",\n \"def inv(self,output,*args,**kwargs):\\n raise ModelTypeError('Model is not invertible')\",\n \"def inverse(self):\\n if self.inv is None:\\n if self.size > 0:\\n self.inv = inverseIndex(self)\\n else:\\n self.inv = Connectivity()\\n return self.inv\",\n \"def __invert__(self):\\n try:\\n B = ~(self.matrix())\\n except ZeroDivisionError:\\n raise ZeroDivisionError(\\\"matrix morphism not invertible\\\")\\n try:\\n return self.parent().reversed()(B)\\n except TypeError:\\n raise ZeroDivisionError(\\\"matrix morphism not invertible\\\")\",\n \"def inverse(self, ys):\\n with torch.no_grad():\\n xs = torch.matmul(ys, torch.diag(torch.reciprocal(torch.exp(self.scaling_diag))))\\n xs = self.layer4.inverse(xs)\\n xs = self.layer3.inverse(xs)\\n xs = self.layer2.inverse(xs)\\n xs = self.layer1.inverse(xs)\\n return xs\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7994033","0.7994033","0.7994033","0.7994033","0.7994033","0.7689036","0.76750916","0.76586753","0.7580127","0.75664145","0.7531505","0.74678403","0.7457205","0.7444826","0.7433826","0.74283147","0.7378608","0.73695505","0.7344377","0.733746","0.7327305","0.7319486","0.7280055","0.7279066","0.7266716","0.7262725","0.72535115","0.7231953","0.7226411","0.72200125","0.7217018","0.7178336","0.7161664","0.71549493","0.7144121","0.7136875","0.7128353","0.71253484","0.710304","0.7099799","0.7097162","0.70864815","0.70837677","0.7082963","0.70784384","0.70769596","0.70536554","0.70385575","0.70253664","0.70115286","0.7005709","0.7005679","0.7000594","0.69978094","0.69964844","0.69956696","0.6992042","0.6990824","0.69721454","0.697211","0.69663215","0.6964427","0.6958103","0.69520247","0.6946496","0.6938432","0.69322395","0.69251275","0.69248194","0.69212973","0.6917245","0.69157547","0.6913844","0.6910233","0.691006","0.6905907","0.69043165","0.6894161","0.6856274","0.68547046","0.68547046","0.6854502","0.68543804","0.68498105","0.68462044","0.68406075","0.6836346","0.68322116","0.683168","0.68249166","0.68244517","0.68227834","0.68023187","0.6789422","0.67836976","0.6781122","0.67809564","0.6780236","0.6771797","0.6769513"],"string":"[\n \"0.7994033\",\n \"0.7994033\",\n \"0.7994033\",\n \"0.7994033\",\n \"0.7994033\",\n \"0.7689036\",\n \"0.76750916\",\n \"0.76586753\",\n \"0.7580127\",\n \"0.75664145\",\n \"0.7531505\",\n \"0.74678403\",\n \"0.7457205\",\n \"0.7444826\",\n \"0.7433826\",\n \"0.74283147\",\n \"0.7378608\",\n \"0.73695505\",\n \"0.7344377\",\n \"0.733746\",\n \"0.7327305\",\n \"0.7319486\",\n \"0.7280055\",\n \"0.7279066\",\n \"0.7266716\",\n \"0.7262725\",\n \"0.72535115\",\n \"0.7231953\",\n \"0.7226411\",\n \"0.72200125\",\n \"0.7217018\",\n \"0.7178336\",\n \"0.7161664\",\n \"0.71549493\",\n \"0.7144121\",\n \"0.7136875\",\n \"0.7128353\",\n \"0.71253484\",\n \"0.710304\",\n \"0.7099799\",\n \"0.7097162\",\n \"0.70864815\",\n \"0.70837677\",\n \"0.7082963\",\n \"0.70784384\",\n \"0.70769596\",\n \"0.70536554\",\n \"0.70385575\",\n \"0.70253664\",\n \"0.70115286\",\n \"0.7005709\",\n \"0.7005679\",\n \"0.7000594\",\n \"0.69978094\",\n \"0.69964844\",\n \"0.69956696\",\n \"0.6992042\",\n \"0.6990824\",\n \"0.69721454\",\n \"0.697211\",\n \"0.69663215\",\n \"0.6964427\",\n \"0.6958103\",\n \"0.69520247\",\n \"0.6946496\",\n \"0.6938432\",\n \"0.69322395\",\n \"0.69251275\",\n \"0.69248194\",\n \"0.69212973\",\n \"0.6917245\",\n \"0.69157547\",\n \"0.6913844\",\n \"0.6910233\",\n \"0.691006\",\n \"0.6905907\",\n \"0.69043165\",\n \"0.6894161\",\n \"0.6856274\",\n \"0.68547046\",\n \"0.68547046\",\n \"0.6854502\",\n \"0.68543804\",\n \"0.68498105\",\n \"0.68462044\",\n \"0.68406075\",\n \"0.6836346\",\n \"0.68322116\",\n \"0.683168\",\n \"0.68249166\",\n \"0.68244517\",\n \"0.68227834\",\n \"0.68023187\",\n \"0.6789422\",\n \"0.67836976\",\n \"0.6781122\",\n \"0.67809564\",\n \"0.6780236\",\n \"0.6771797\",\n \"0.6769513\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6896039"},"document_rank":{"kind":"string","value":"77"}}},{"rowIdx":92580,"cells":{"query":{"kind":"string","value":"Compute the forward derivative (Appendix A.2)."},"document":{"kind":"string","value":"def _forward_log_det_jacobian(self, x):\n d = self._compute_shared(x=x)\n relx = (x - d.x_k) / d.w_k\n relx = relx # tf.where(d.out_of_bounds, 0.5*tf.ones_like(x), relx)\n grad = (\n 2 * tf.math.log(d.s_k) +\n tf.math.log(d.d_kp1 * relx**2 + 2 * d.s_k * relx * (1 - relx) + # newln\n d.d_k * (1 - relx)**2) -\n 2 * tf.math.log((d.d_kp1 + d.d_k - 2 * d.s_k) * relx *\n (1 - relx) + d.s_k))\n return grad # tf.where(d.out_of_bounds, tf.zeros_like(grad), grad)"},"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 derivative(func: Callable, x: float, delta: float) -> float:\n return (func(x + delta) - func(x - delta)) / (2.0 * delta)","def derivative ( self ):\n return self.__derivative","def deriv(func: Callable[[ndarrray], ndarray],\n input_: ndarray,\n delta: float = 0.001) -> ndarray:\n return (func(input_ + delta) - func(input_ - delta)) / (2*delta)","def Derivative(self, *args):\n return _Adaptor3d.Adaptor3d_InterFunc_Derivative(self, *args)","def forward(self, input, derivative=0):\n\n # get the distance matrices\n input_ = input.view(-1, self.nelec, self.ndim)\n dist = self._get_distance_quadratic(input_)\n\n # eosilon on the diag needed for back prop\n eps_ = self.eps * \\\n torch.diag(dist.new_ones(dist.shape[-1])).expand_as(dist)\n\n # extact the diagonal as diag can be negative someties\n # due to numerical noise\n diag = torch.diag_embed(\n torch.diagonal(\n dist, dim1=-1, dim2=-2))\n\n # remove diagonal and add eps for backprop\n dist = torch.sqrt(dist - diag + eps_)\n\n if derivative == 0:\n return dist\n\n elif derivative == 1:\n\n eps_ = self.eps * \\\n torch.diag(dist.new_ones(\n dist.shape[-1])).expand_as(dist)\n\n invr = (1. / (dist + eps_)).unsqueeze(1)\n diff_axis = input_.transpose(1, 2).unsqueeze(3)\n diff_axis = diff_axis - diff_axis.transpose(2, 3)\n return diff_axis * invr\n\n elif derivative == 2:\n\n eps_ = self.eps * \\\n torch.diag(dist.new_ones(\n dist.shape[-1])).expand_as(dist)\n invr3 = (1. / (dist**3 + eps_)).unsqueeze(1)\n diff_axis = input_.transpose(1, 2).unsqueeze(3)\n diff_axis = (diff_axis - diff_axis.transpose(2, 3))**2\n\n diff_axis = diff_axis[:, [\n [1, 2], [2, 0], [0, 1]], ...].sum(2)\n return (diff_axis * invr3)","def derivative(self, cache):\n if self.__activation == 'sig':\n derivative = cache * (1 - cache)\n else:\n derivative = 1 - cache ** 2\n return derivative","def derivative(self,x,dx=None):\n if dx is None:\n x = np.array(x,copy=False)\n if len(x.shape)>0:\n dx = np.convolve(x,[1,-1],mode='valid')\n dx = np.insert(dx,0,np.mean(dx))/2\n else:\n dx = 1\n return (self(x+dx)-self(x))/dx","def derivative(self, *args):\n if self.i_dim == 0:\n return np.ones_like(*args[0])\n else:\n return np.zeros_like(*args[0])","def dalf(x):\n return derivative(alf, x, dx=1e-6)","def __call__ ( self , func , x , h , der = False ) :\n\n ## calculate differences \n imax = self.__order + 2 if der else self.__order + 1\n i = 0\n while i < imax : \n j = i + 1\n self.__df[i] = func ( x + j * h ) - func ( x - j * h )\n i += 1\n \n ## 1) calculate 1st derivative \n result = dot_fma ( self.__order + 1 , self.__df , self.__d1 ) / ( self.__sf1 * h ) \n if not der : return result \n \n ## 2) calculate Nth derivative \n dd = dot_fma ( self.__order + 2 , self.__df , self.__d2 ) / ( self.__sf2 * h**(self.__order*2+3) ) \n \n return result, dd","def ddalf(x):\n return derivative(dalf, x, dx=1e-6)","def ddalf(x):\n return derivative(dalf, x, dx=1e-6)","def calculate_derivative_value(self, x: np.array) -> np.array:\n pass","def derivative(self, x):\n z = np.asarray(x)\n return (self._der(z.flatten())).reshape(z.shape)","def derivative(a, y, z):\n return a - y + (z * 0)","def backward_pass(self, delta):\r\n self.d_x = np.dot(delta, self.w.T)\r\n self.d_b = np.matmul(np.ones((1, delta.shape[0])), delta)\r\n self.d_w = np.dot(self.x.T, delta)\r\n return self.d_x","def func_deriv(x,remain):\n #df_x0 = -1*remain[0]/x[0]**2\n #df_x1 = -1*remain[2]/x[1]**2\n #df_x2 = -1*remain[1]/x[2]**2\n return np.array(-1*remain/x**2)","def derivative(a, y, z):\n return (a - y) * Sigmoid.derivative(z)","def _evalAndDer(self, x):\n m = len(x)\n fx = np.zeros((m, self.funcCount))\n for j in range(self.funcCount):\n fx[:, j] = self.functions[j](x)\n i = self.argcompare(fx, axis=1)\n y = fx[np.arange(m), i]\n dydx = np.zeros_like(y)\n for j in range(self.funcCount):\n c = i == j\n dydx[c] = self.functions[j].derivative(x[c])\n return y, dydx","def _evalAndDer(self, x):\n m = len(x)\n fx = np.zeros((m, self.funcCount))\n for j in range(self.funcCount):\n fx[:, j] = self.functions[j](x)\n i = self.argcompare(fx, axis=1)\n y = fx[np.arange(m), i]\n dydx = np.zeros_like(y)\n for j in range(self.funcCount):\n c = i == j\n dydx[c] = self.functions[j].derivative(x[c])\n return y, dydx","def directional_deriv(f, x, v, s=_DEFAULT_STEP):\n v = np.asarray(v)\n v = v / la.norm(v)\n return (f(x + s*v) - f(x)) / s","def _derivative_(self, x, diff_param=None):\n return 2*exp(-x**2)/sqrt(pi)","def forward_differences(f, h, x):\n\treturn (f(x + h) - f(x)) / h","def derivative_activation(z):\n return activation(z) * (1 - activation(z))","def derivative ( fun , x , h = 0 , I = 2 , err = False ) :\n\n func = lambda x : float ( fun ( x ) )\n \n ## get the function value at the given point \n f0 = func(x)\n\n ## adjust the rule \n I = min ( max ( I , 1 ) , 8 )\n J = 2 * I + 1\n \n _dfun_ = _funcs_[I]\n delta = _delta_ ( x )\n \n ## if the intial step is too small, choose another one \n if abs ( h ) < _numbers_[I][3] or abs ( h ) < delta : \n if iszero( x ) : h = _numbers_[0][I]\n else : h = abs ( x ) * _numbers_[I][3] \n\n h = max ( h , 2 * delta )\n \n ## 1) find the estimate for first and \"J\"th the derivative with the given step \n d1 , dJ = _dfun_( func , x , h , True )\n \n ## find the optimal step \n if iszero ( dJ ) or ( iszero ( f0 ) and iszero ( x * d1 ) ) :\n if iszero ( x ) : hopt = _numbers_[0][I] \n else : hopt = abs ( x ) * _numbers_[I][3]\n else : \n hopt = _numbers_[I][2] * ( ( abs ( f0 ) + abs ( x * d1 ) ) / abs ( dJ ) )**( 1.0 / J )\n\n ## finally get the derivative \n if not err : return _dfun_ ( func , x , hopt , False )\n\n ## estimate the uncertainty, if needed \n d1,dJ = _dfun_ ( func , x , hopt , True )\n \n e = _numbers_[I][1] / _numbers_[I][2] * J / ( J - 1 ) \n e2 = e * e * ( J * _eps_ + abs ( f0 ) + abs( x * d1 ) )**( 2 - 2./J ) * abs( dJ )**(2./J) \n return VE ( d1 , 4 * e2 )","def forward(h, n, u, v, f, dt, dx, dy, du, dv, dn, beta=0, eps=0, gamma=0, mu=0.3, nu=0, dudt_x=dudt, dvdt_x=dvdt, dndt_x=dndt, grav=True, cori=True, advx=True, advy=True, attn=True): # forward euler and forward/backward timestep\n beta = np.float32(beta)\n mu = np.float32(mu)\n \n du1, du0 = du[:2]\n dv1, dv0 = dv[:2]\n dn0 = dn[0]\n \n dndt_x(h, n, u, v, dx, dy, dn0) # calculate dndt and put it into dn0\n \n n1 = n + ( dn0 )*dt\n \n dudt_x(h, n, f, u, v, dx, dy, du0, grav=grav, cori=cori, advx=advx, advy=advy, attn=attn,nu=nu,mu=mu)\n dvdt_x(h, n, f, u, v, dx, dy, dv0, grav=grav, cori=cori, advx=advx, advy=advy, attn=attn,nu=nu,mu=mu)\n dudt_x(h, n1, f, u, v, dx, dy, du1, grav=grav, cori=cori, advx=advx, advy=advy, attn=attn,nu=nu,mu=mu)\n dvdt_x(h, n1, f, u, v, dx, dy, dv1, grav=grav, cori=cori, advx=advx, advy=advy, attn=attn,nu=nu,mu=mu)\n \n u1 = u + ( beta*du1 + (one-beta)*du0 )*dt\n v1 = v + ( beta*dv1 + (one-beta)*dv0 )*dt\n \n n, u, v = n1, u1, v1\n \n du = [du1, du0, du0, du0]\n dv = [dv1, dv0, dv0, dv0]\n dn = [dn0, dn0, dn0]\n return n1, u1, v1, du, dv, dn","def f(self, x , u , t = 0 ):\n y = x[0]\n dy = x[1]\n \n \n ddy = - y + self.mu * dy * ( 1 - y**2 )\n \n dx = np.zeros(self.n) # State derivative vector\n \n dx[0] = dy\n dx[1] = ddy\n \n return dx","def __call__ ( self , x ) :\n return derivative ( self.__func ,\n x ,\n self.__step ,\n self.__order ,\n self.__err )","def _derX(self, x, y):\n m = len(x)\n temp = np.zeros((m, self.funcCount))\n for j in range(self.funcCount):\n temp[:, j] = self.functions[j](x, y)\n i = self.argcompare(temp, axis=1)\n dfdx = np.zeros_like(x)\n for j in range(self.funcCount):\n c = i == j\n dfdx[c] = self.functions[j].derivativeX(x[c], y[c])\n return dfdx","def calc_derivative(self, array_in, direction1, direction2 = False):\r\n A = array_in.copy()\r\n if direction1 != direction2:\r\n #Remove Nyquist frequency for even sample size and odd order of differentiation\r\n if direction1 == 'x' or direction2 == 'x':\r\n A[0,:] = 0.0\r\n if direction1 == 'y' or direction2 == 'y':\r\n A[:,0] = 0.0\r\n\r\n # Note that 'x' corresponds to the x1 direction, and 'y' to the\r\n # x2 direction\r\n # Perform first derivative in desired direction\r\n if direction1 == 'x':\r\n out = self.deriv_mat_x1*A\r\n elif direction1 == 'y':\r\n out = self.deriv_mat_x2*A\r\n\r\n # Perform second derivative in desired direction\r\n if direction2 == 'x':\r\n out = self.deriv_mat_x1*out\r\n elif direction2 == 'y':\r\n out = self.deriv_mat_x2*out\r\n\r\n return out","def calculate_derivative(current_data):\n\n # Declare array with first value equals zero to build gradient\n derivative_data = [0]\n derivative_data = np.gradient(current_data[\"4. close\"])\n # Add gradient values as column to current dataframe\n current_data[\"gradient\"] = derivative_data\n\n return current_data","def derivatives(self, state):\n raise NotImplementedError()","def _computeDerivative(self,angles, distances):\n slope=[]\n slope.append(0)\n for i in xrange(1,len(angles)):\n der = (distances[i]-distances[i-1])/(angles[i]-angles[i-1])\n slope.append(der)\n #slope.append(0)\n return slope","def first_derivative(x, f):\n deriv = np.diff(f, n=1)\n deriv = np.insert(deriv, -1, 2 * deriv[-1] - deriv[-2])\n dx = x[1] - x[2]\n return deriv / dx","def fc(self, x = np.zeros(2) , u = np.array([0,1]) , t = 0 ):\n \n dx = np.zeros(self.n) # State derivative vector\n \n q = x[0]\n dq = x[1]\n \n ddq = self.ddq_a( q , dq , u[0] , u[1] ) # Include hybrid input\n \n dx[0] = dq\n dx[1] = ddq\n \n return dx","def derivatives(self):\n self.rdot = self.v\n self.vdot[:,:] = 0.0\n \n for nl in self.nlists: \n nl.separations()\n \n for force in self.forces:\n force.apply()\n\n # Controllers is the new implementation of forces\n for controller in self.controllers:\n controller.apply()","def ddx(n, dx, f):\n fx = np.zeros(n)\n for j in range(n):\n fx[j] = (f[get_index(j+1, n)]-f[get_index(j-1, n)])/(2*dx)\n return fx","def derivative(f, x, epsilon = 1e-10):\n\n x_ = x + epsilon\n value = (f(x_) - f(x)) / epsilon\n\n return value","def __backward(self, dA, cache, derivative_activate_fn):\n A_prev, W, b, Z, D = cache\n\n m = A_prev.shape[1]\n\n # Mask\n dA = np.multiply(dA, D) / self.keep_prob\n\n dZ = dA * derivative_activate_fn(Z)\n dW = (1.0 / m) * np.dot(dZ, A_prev.T)\n db = (1.0 / m) * np.sum(dZ, axis=1, keepdims=True)\n dA_prev = np.dot(W.T, dZ)\n\n\n assert (dW.shape == W.shape)\n assert (db.shape == b.shape)\n assert (dA_prev.shape == A_prev.shape)\n\n return dA_prev, dW, db","def fc_backward(dout, cache):\n x, w, b = cache\n dx, dw, db = None, None, None\n ###########################################################################\n # TODO: Implement the affine backward pass. #\n ###########################################################################\n dx = np.dot(dout, w.T)\n dw = np.dot(x.T, dout)\n db = np.average(dout, axis = 0)\n ###########################################################################\n # END OF YOUR CODE #\n ###########################################################################\n return dx, dw, db","def derivatives(x_p, y_p):\r\n # set up the matrix equation\r\n n = x_p.shape[0]\r\n M = np.zeros( [n,n] )\r\n d = np.zeros( [n,1] )\r\n \r\n # fill in the constants where they can be\r\n for i in np.arange(1,n-1 ): # for all but the first and last row\r\n M[i,i-1 ] = ( x_p[i] - x_p[i-1] ) / 6.\r\n M[i,i] = ( x_p[i+1] - x_p[i-1] ) / 3.\r\n M[i,i+1] = ( x_p[i+1] - x_p[i] ) /6.\r\n d[i,0 ] = ( y_p[i+1] - y_p[i] ) / ( x_p[i+1] - x_p[i] ) - ( y_p[i] - y_p[i-1] ) / ( x_p[i] - x_p[i-1] )\r\n \r\n M[0,0],M[-1,-1] = 1.,1. # compactly sets the BCs\r\n \r\n LU = lu.LU_decomp(M) # solves the matrix equations\r\n return lu.FB_sub(LU.Low, LU.Upp, d) # find and return 2nd derivatives\r","def linear_backward(dZ, cache):\n pass","def _core_calc_degrad(self,bd,Ld) :\n\t\tdegrad = np.dot(Ld,bd) # Do matrix multiplication \n\t\tdegrad = np.exp(degrad) # Exponentiate to convert log to real\n\t\treturn degrad","def derivative_func(t, x, Approx_func):\n return x.dot(Approx_func)","def derivatives(self):\n self.rdot = self.v\n self.vdot[:,:] = 0.0\n self.udot[:] = 0.0\n\n t = time()\n for nl in self.nlists: \n nl.separations()\n #nl.apply_minimum_image()\n self.timing['pairsep time'] = (time() - t)\n\n t = time()\n if SPROPS:\n properties.spam_properties(self,self.nl_default \\\n ,self.h[0:self.n],self.hlr[0:self.n])\n self.timing['SPAM time'] = time() - t\n \n t = time()\n for force in self.forces:\n force.apply()\n self.timing['force time'] = time() - t\n \n if ADVECTIVE:\n self.rdot[:,:] = 0.0","def fc_backward(dout, cache):\n x, w, b = cache\n dx, dw, db = None, None, None\n ###########################################################################\n # TODO: Implement the affine backward pass. #\n ########################################################################### \n N = x.shape[0]\n x2d = x.reshape(N, -1)\n \n dx = dout.dot(w.T)\n dx = dx.reshape(x.shape)\n dw = x2d.T.dot(dout)\n db = dout.sum(axis=0) #add from top to down\n ###########################################################################\n # END OF YOUR CODE #\n ###########################################################################\n return dx, dw, db","def get_derivative(self,var,g=None):\n if (g==None):g=self.g\n A=np.zeros([self.n+1,self.n])\n B=np.zeros([self.n+1])\n for i in range(self.n):\n B[i]=self.gamma*2.*g*self.N*(self.n-self.N)+np.sum([self.XXZ.Z(k,i)*(var[k]-var[i]) for k in range(self.n) if k!=i])\n A[self.n][i]=1\n for j in range(self.n):\n if(i==j): A[i][j]=2.*var[i]+2.+g*np.sum([self.XXZ.Z(k,i) for k in range(self.n) if k!=i])\n else: A[i][j]=-g*self.XXZ.Z(j,i)\n Ainv=np.linalg.pinv(A)\n der=np.dot(Ainv,B)\n return der","def derivative(requestContext, seriesList):\n results = []\n for series in seriesList:\n newValues = []\n prev = None\n for val in series:\n if None in (prev,val):\n newValues.append(None)\n prev = val\n continue\n newValues.append(val - prev)\n prev = val\n newName = \"derivative(%s)\" % series.name\n newSeries = TimeSeries(newName, series.start, series.end, series.step, newValues)\n newSeries.pathExpression = newName\n results.append(newSeries)\n return results","def ddalf(x):\n return dalf_spl.derivatives(x)[1]","def GetDerivative(self, *args):\n return _ITKCostFunctionsPython.itkSingleValuedCostFunction_GetDerivative(self, *args)","def backwards(delta,params,name='',activation_deriv=sigmoid_deriv):\n # everything you may need for this layer\n W = params['W' + name]\n b = params['b' + name]\n X, pre_act, post_act = params['cache_' + name]\n # your code here\n # do the derivative through activation first\n # then compute the derivative W,b, and X\n \n delta_pre = activation_deriv(post_act) * delta\n # (in_dim, out_dim) = (in_dim, examples) @ (examples, out_dim)\n grad_W = X.transpose() @ delta_pre\n grad_b = np.sum(delta_pre, axis=0, keepdims=True) # (1, out_dim)\n # (examples, in_dim) = (examples, out_dim) @ (out_dim, in_dim)\n grad_X = delta_pre @ W.transpose()\n\n # store the gradients\n params['grad_W' + name] = grad_W\n params['grad_b' + name] = grad_b\n return grad_X","def rnn_step_backward(self, grad_next, cache):\n\n th, h_prev, x = cache\n dz = grad_next * (1 - th**2)\n dh_prev = np.dot(dz, self.W.T)\n self.dW += np.dot(h_prev.T, dz) / grad_next.shape[0]\n self.dU += np.dot(x.T, dz) / grad_next.shape[0]\n self.db += np.sum(dz, axis=0) / grad_next.shape[0]\n\n return dh_prev","def _numerical_derivative_two_sided (func:typing.Callable[[float],float], x_0:float) -> typing.Tuple[float,float]:\n epsilon = 1.0e-9\n func_x_0 = func(x_0)\n deriv_neg = (func_x_0 - func(x_0-epsilon)) / epsilon\n deriv_pos = (func(x_0+epsilon) - func_x_0) / epsilon\n return deriv_neg, deriv_pos","def eval_numerical_gradient(f,x):\n\n\tgrad = np.zeros(x.shape)\n\th = 0.0001\n\n\t# iterate over all indexes in x\n\tit = np.nditer(x, flag = ['multi_index'], op_flags = ['readwrite'])\n\n\twhile not it.finished:\n\t\tix = it.multi_index\n\t\told_value = x[ix]\n\n\t\tx[ix] = old_value + h\n\t\tfxh_left = f(x)\n\n\t\tx[ix] = old_value - h\n\t\tfxh_right = f(x)\n\n\t\tx[ix] = old_value\n\n\t\t# compute the partial derivative\n\t\tgrad[ix] = (fxh_left - fxh_right) / (2 * h)\n\t\tit.iterate()\n\n\treturn grad","def _derX(self, x, y, z):\n m = len(x)\n temp = np.zeros((m, self.funcCount))\n for j in range(self.funcCount):\n temp[:, j] = self.functions[j](x, y, z)\n i = self.argcompare(temp, axis=1)\n dfdx = np.zeros_like(x)\n for j in range(self.funcCount):\n c = i == j\n dfdx[c] = self.functions[j].derivativeX(x[c], y[c], z[c])\n return dfdx","def derivativeX(self, x, y, z):\n xShift = self.lowerBound(y)\n dfdx_out = self.func.derivativeX(x - xShift, y, z)\n return dfdx_out","def derivativeX(self, x, y):\n xShift = self.lowerBound(y)\n dfdx_out = self.func.derivativeX(x - xShift, y)\n return dfdx_out","def feature_derivative(self, errors, weights, l2, is_feature_constant = False):\n # Compute the dot product of errors and feature\n if is_feature_constant: \n derivative = np.dot(self.train_feature_x.T, errors) - 2*l2*weights\n else:\n weight0 = np.zeros(1)\n if weights.shape[0] == 2:\n weight1 = np.array(weights[1])\n else: weight1 = weights[1:]\n derivative = np.dot(self.train_feature_x.T, errors) - np.concatenate((weight0, 2*l2*weight1), axis = 0)\n return derivative","def fundemental_derivative(tab, spec, *XYf):\n if tab._backend != 'vdw':\n raise ValueError('This derived variable is only compatible with the vdw backend!')\n XYf_DT = XYf[:]\n rho = XYf_DT[0]\n units = EosUnits(tab.Pt_DT._requested_units, 'cgs')\n Pt = tab.get_table('P{s}_DT', spec)(*XYf_DT)*units.o2r('P')\n delta = tab.Pt_DT['delta']\n a = tab.Pt_DT['a']\n b = tab.Pt_DT['b']\n P_frac_1 = (Pt + a*rho**2)/(1./rho - b)**2\n P_frac_2 = rho*(Pt + a*rho**2)/(1./rho - b)\n num = (delta+1)*(delta+2) * P_frac_1 - 6*a*rho**4 \n denum = 2*(delta+1)*P_frac_2 - 4*a*rho**4\n return num/denum","def backward_differences(f, h, x):\n\treturn (f(x) - f(x - h)) / h","def f( self , x , u , t ):\n \n dx = np.zeros(self.n) # State derivative vector\n \n ################################################\n # Place holder: put the equations of motion here\n raise NotImplementedError\n ################################################\n \n return dx","def deriv(y, x):\n answer = np.zeros(len(y))\n answer[1:-1] = (y[2:] - y[:-2])/(x[2:] - x[:-2])\n return answer","def backward(self, delta):\n self.d_x = np.dot(delta, np.transpose(self.w))\n self.d_w = np.dot(np.transpose(self.x), delta)/self.x.shape[0]\n self.d_b = np.mean(delta, axis=0).reshape((1, delta.shape[1]))\n return self.d_x\n\n raise NotImplementedError(\"Backprop for Layer not implemented.\")","def func_deriv(x, sign=1.0):\n dfdx0 = sign*(-2*x[0] + 2*x[1] + 2)\n dfdx1 = sign*(2*x[0] - 4*x[1])\n return np.array([ dfdx0, dfdx1 ])","def _finite_diff_gradient(self, parameters):\n assert isinstance(parameters,np.ndarray)\n # get the free indicies\n free_inds = np.nonzero(np.logical_not(self._fixed_indicies))[0]\n\n # first take a forward step in each direction\n step = 1e-6 # finite difference step\n log_like_fs = np.zeros(free_inds.size)\n for i,param_idx in enumerate(free_inds):\n p_fs = parameters.copy()\n p_fs[param_idx] += step # take a step forward\n log_like_fs[i] = self._compute_log_likelihood(p_fs)\n\n # compute the log likelihood at current point\n log_like = self._compute_log_likelihood(parameters)\n\n # compute the gradient\n gradient = np.zeros(parameters.shape) # default gradient is zero\n gradient[free_inds] = (log_like_fs-log_like)\n gradient[free_inds] = gradient[free_inds]/step # divide by step length\n return log_like, gradient","def relu_backward(dout, cache):\n dx, x = None, cache\n dx = dout\n dout[x <= 0] = 0.0\n return dx","def rnn_step_backward(dnext_h, cache):\n dx, dprev_h, dWx, dWh, db = None, None, None, None, None\n ##############################################################################\n # TODO: Implement the backward pass for a single step of a vanilla RNN. #\n # #\n # HINT: For the tanh function, you can compute the local derivative in terms #\n # of the output value from tanh. #\n ##############################################################################\n\n x, next_h, prev_h, Wx, Wh, b = cache\n # this is because in vanilla RNN h = tanh(z) and derivative of next_h = tanh(z) = 1-z*z;\n dz = (1-next_h*next_h)*dnext_h\n # THIS ERROR IS SPREAD AMONG THE\n # np.dot(x, Wx) + np.dot(prev_h, Wh) + b)\n dx = np.dot(dz,Wx.T)\n dprev_h = np.dot(dz,Wh.T)\n db = np.sum(dz,axis=0)\n dWx = np.dot(x.T,dz)\n dWh = np.dot(prev_h.T,dz)\n #d(tanh) = 1- tanh*tanh\n\n\n ##############################################################################\n # END OF YOUR CODE #\n ##############################################################################\n return dx, dprev_h, dWx, dWh, db","def dgdx(self, X):\n \n return 2*(X[0]) - 2","def fc(self, x = np.zeros(4) , u = np.zeros(3) , t = 0 ):\n \n dx = np.zeros(self.n) # State derivative vector\n \n [ q , dq ] = self.x2q( x ) # from state vector (x) to angle and speeds (q,dq)\n \n ddq = self.ddq_a( q , dq , u[0:self.dof] , u[self.dof] , t )\n \n dx = self.q2x( dq , ddq ) # from angle and speeds diff (dq,ddq) to state vector diff (dx)\n \n return dx","def fc(self, x = np.zeros(4) , u = np.zeros(3) , t = 0 ):\n \n dx = np.zeros(self.n) # State derivative vector\n \n [ q , dq ] = self.x2q( x ) # from state vector (x) to angle and speeds (q,dq)\n \n ddq = self.ddq_a( q , dq , u[0:self.dof] , u[self.dof] , t )\n \n dx = self.q2x( dq , ddq ) # from angle and speeds diff (dq,ddq) to state vector diff (dx)\n \n return dx","def relu_backward(dout, cache):\n #raise NotImplementedError\n #######################################################################\n # #\n # #\n # TODO: YOUR CODE HERE #\n # #\n # #\n #######################################################################\n x=cache\n dout[x<=0]=0\n dx=dout\n return dx","def softmax_derivative(Z):\n\treturn None","def dalf(x):\n return grad(alf)(x)","def eval_numerical_gradient(f, x, verbose = True, h = 0.00001):\n fx = f(x) # evaluate function value at original point\n grad = np.zeros_like(x) # iterate over all indexese in x\n it = np.nditer(x, flags = ['multi_index'], op_flags = ['readwrite'])\n while not it.finished:\n # evaluate function at x+h\n ix = it.multi_index\n oldval = x[ix]\n x[ix] = oldval + h # increment by h\n fxph = f(x) # evaluate f(x+h)\n x[ix] = oldval - h\n fxmh = f(x) # evaluate f(x-h)\n x[ix] = oldval # restore\n \n #compute the partial derivative with centered fromula.\n grad[ix] = (fxph - fxmh) / (2 * h)\n if verbose:\n print(ix, grad[ix])\n it.iternext()\n return grad","def softmax_derivative(x):\n der = derivative(softmax,x,dx=1e-9)\n return der","def fc_backward(dout, cache):\n x, w, b = cache\n dx, dw, db = None, None, None\n ###########################################################################\n # TODO: Implement the affine backward pass. #\n ###########################################################################\n Xshape = x.shape\n x_flat = np.reshape(x,(x.shape[0],-1))\n dx = np.dot(dout,w.T)\n dw = x_flat.T.dot(dout)#np.matmul(x[...,None],dout[:,None,:]).sum(axis=0)\n db = np.sum(dout,axis=0)\n dx = np.reshape(dx,Xshape)\n ###########################################################################\n # END OF YOUR CODE #\n ###########################################################################\n return dx, dw, db","def derivitive(x):\n return x * 1","def fd_derivative(f, t):\n dfdt = np.empty_like(f)\n if (f.ndim == 1):\n _derivative(f, t, dfdt)\n elif (f.ndim == 2):\n _derivative_2d(f, t, dfdt)\n elif (f.ndim == 3):\n _derivative_3d(f, t, dfdt)\n else:\n raise NotImplementedError(\"Taking derivatives of {0}-dimensional arrays is not yet implemented\".format(f.ndim))\n return dfdt","def _derivativeTerm(self):\n\n\t\treturn self._Kd * (self._getErrorFunction() - self._previousError) / self._dt","def eval_with_derivative(self, x):\n z = np.asarray(x)\n y, dydx = self._evalAndDer(z.flatten())\n return y.reshape(z.shape), dydx.reshape(z.shape)","def make_derivate_fn(self):\n\n def derivative_fn(y, t):\n self.time = t\n self.compartments = self.convert_list_to_compartments(y)\n self.prepare_vars_and_flows()\n flow_vector = self.convert_compartments_to_list(self.flows)\n self.checks()\n return flow_vector\n\n return derivative_fn","def backward_D(self):\n self.loss_D.backward()","def _dddx(t, x, ivp):\n evl = ivp.rhs(t, x)\n try:\n jac = ivp.jacobian(t, x)\n except NotImplementedError:\n jac = np.zeros((len(x), len(x)))\n try:\n hess = ivp.hessian(t, x)\n except NotImplementedError:\n hess = np.zeros((len(x), len(x), len(x)))\n if np.isscalar(evl):\n evl = np.array([evl])\n jac = np.array([jac])\n hess = np.array([hess])\n return (hess @ evl) @ evl + jac @ (jac @ evl)","def linear_backward(dZ, cache):\n A_prev, W, b = cache\n m = A_prev.shape[1]\n\n dW = np.dot(dZ,A_prev.T) / m\n db = np.sum(dZ,axis = 1,keepdims = True) / m\n dA_prev = np.dot(W.T,dZ)\n\n return dA_prev,dW,db","def get_derivative(self, model, params, n):\n params1 = np.array(params)\n params2 = np.array(params)\n\n params1[n] += self.eps\n params2[n] -= self.eps\n\n res1 = model.run(params1)\n res2 = model.run(params2)\n\n d = (res1 - res2) / (2 * self.eps)\n\n return d.ravel()","def eeg_derivative(array):\n\tif len(array.shape) == 1:\n\t\tderivative = []\t\n\t\tfor i in xrange(1,len(array)):\n\t\t\tderivative.append(array[i]-array[i-1])\n\telse:\n\t\tprint \"Not yet implemented\"\n\t\tderivative = []\n\tderivative = np.array(derivative)\n\treturn derivative","def ddalf(x):\n return dalf_spl.derivatives(x)[1]","def GetDerivative(self, *args):\n return _ITKCostFunctionsPython.itkMultipleValuedCostFunction_GetDerivative(self, *args)","def eval_numerical_gradient(f, x, verbose=True, h=0.00001):\n\n fx = f(x) # evaluate function value at original point\n grad = np.zeros_like(x)\n # iterate over all indexes in x\n it = np.nditer(x, flags=['multi_index'], op_flags=['readwrite'])\n while not it.finished:\n\n # evaluate function at x+h\n ix = it.multi_index\n oldval = x[ix]\n x[ix] = oldval + h # increment by h\n fxph = f(x) # evalute f(x + h)\n x[ix] = oldval - h\n fxmh = f(x) # evaluate f(x - h)\n x[ix] = oldval # restore\n\n # compute the partial derivative with centered formula\n grad[ix] = (fxph - fxmh) / (2 * h) # the slope\n if verbose:\n print(ix, grad[ix])\n it.iternext() # step to next dimension\n\n return grad","def backward(self):\n assert self.cache is not None, \"Cannot backprop without forward first.\"\n prob, y = self.cache\n\n dX = prob - y\n if self.reduction == \"mean\":\n m, _ = prob.shape\n dX /= m\n\n # clear cache\n self.cache = None\n\n return dX","def gradient(f, *varargs):\n N = len(f.shape) # number of dimensions\n n = len(varargs)\n if n == 0:\n dx = [1.0]*N\n elif n == 1:\n dx = [varargs[0]]*N\n elif n == N:\n dx = list(varargs)\n else:\n raise SyntaxError, \"invalid number of arguments\"\n\n # use central differences on interior and first differences on endpoints\n\n outvals = []\n\n # create slice objects --- initially all are [:, :, ..., :]\n slice1 = [slice(None)]*N\n slice2 = [slice(None)]*N\n slice3 = [slice(None)]*N\n\n otype = f.dtype.char\n if otype not in ['f', 'd', 'F', 'D']:\n otype = 'd'\n\n for axis in range(N):\n # select out appropriate parts for this dimension\n out = zeros(f.shape, f.dtype.char)\n slice1[axis] = slice(1, -1)\n slice2[axis] = slice(2, None)\n slice3[axis] = slice(None, -2)\n # 1D equivalent -- out[1:-1] = (f[2:] - f[:-2])/2.0\n out[slice1] = (f[slice2] - f[slice3])/2.0\n slice1[axis] = 0\n slice2[axis] = 1\n slice3[axis] = 0\n # 1D equivalent -- out[0] = (f[1] - f[0])\n out[slice1] = (f[slice2] - f[slice3])\n slice1[axis] = -1\n slice2[axis] = -1\n slice3[axis] = -2\n # 1D equivalent -- out[-1] = (f[-1] - f[-2])\n out[slice1] = (f[slice2] - f[slice3])\n\n # divide by step size\n outvals.append(out / dx[axis])\n\n # reset the slice object in this dimension to \":\"\n slice1[axis] = slice(None)\n slice2[axis] = slice(None)\n slice3[axis] = slice(None)\n\n if N == 1:\n return outvals[0]\n else:\n return outvals","def linear_backward_calculation(dZ, internal_params):\n\n A_prev, W, b = internal_params\n nb = A_prev.shape[1]\n\n ### START CODE HERE ### (≈ 3 lines of code)\n dW =np.multiply((np.dot(dZ, A_prev.T)),1/nb)\n db = np.multiply ((np.sum(dZ, axis=1, keepdims=True),1/nb))\n dA_prev = np.dot(W.T, dZ)\n # raise NotImplementedError\n return dA_prev,dW,db","def compute_cost_derivative(AL, Y):\n pass","def second_derivative(self,show=False):\n\n df = [None]\n d2f = [None]\n\n # Take first difference: f1 = (f(x+dx)-f(x))/dx\n for i in range(0,len(self.ans)-1,1):\n df += [int((self.ans[i+1][1] - self.ans[i][1]) / (self.ans[i+1][0] - self.ans[i][0]))]\n\n for i in range(1,len(self.ans)-1,1):\n d2f += [int((df[i+1] - df[i]) / (self.ans[i+1][0] - self.ans[i][0]))]\n d2f += [None]\n\n if show == True:\n print('First derivative:', df)\n print('Second derivative:', d2f)\n\n return d2f","def _der(self, x):\n y, dydx = self._evalAndDer(x)\n return dydx # Sadly, this is the fastest / most convenient way...","def _der(self, x):\n y, dydx = self._evalAndDer(x)\n return dydx # Sadly, this is the fastest / most convenient way...","def deriv(self, x, d=None):\n if self.weights is None:\n self.compute_weights()\n\n val = 0.0\n for i in range(self.M):\n val += self.weights[i]*self.model_list[i].deriv(x, d)\n return val","def derivative(self, variable):\n cached_derivative = self._first_derivatives.get(variable, None)\n if cached_derivative: \n return cached_derivative\n else:\n return self._first_derivatives.setdefault(variable,\n em.diff_expr(self.math, \n variable))","def backward(self):\n gradient = blah\n return gradient","def backward(self):\n gradient = blah\n return gradient","def newton1d(f, df, ddf, x, niter=10):\n for i in xrange(niter):\n x_new = x - df(x)/ddf(x)\n x = x_new\n return x"],"string":"[\n \"def derivative(func: Callable, x: float, delta: float) -> float:\\n return (func(x + delta) - func(x - delta)) / (2.0 * delta)\",\n \"def derivative ( self ):\\n return self.__derivative\",\n \"def deriv(func: Callable[[ndarrray], ndarray],\\n input_: ndarray,\\n delta: float = 0.001) -> ndarray:\\n return (func(input_ + delta) - func(input_ - delta)) / (2*delta)\",\n \"def Derivative(self, *args):\\n return _Adaptor3d.Adaptor3d_InterFunc_Derivative(self, *args)\",\n \"def forward(self, input, derivative=0):\\n\\n # get the distance matrices\\n input_ = input.view(-1, self.nelec, self.ndim)\\n dist = self._get_distance_quadratic(input_)\\n\\n # eosilon on the diag needed for back prop\\n eps_ = self.eps * \\\\\\n torch.diag(dist.new_ones(dist.shape[-1])).expand_as(dist)\\n\\n # extact the diagonal as diag can be negative someties\\n # due to numerical noise\\n diag = torch.diag_embed(\\n torch.diagonal(\\n dist, dim1=-1, dim2=-2))\\n\\n # remove diagonal and add eps for backprop\\n dist = torch.sqrt(dist - diag + eps_)\\n\\n if derivative == 0:\\n return dist\\n\\n elif derivative == 1:\\n\\n eps_ = self.eps * \\\\\\n torch.diag(dist.new_ones(\\n dist.shape[-1])).expand_as(dist)\\n\\n invr = (1. / (dist + eps_)).unsqueeze(1)\\n diff_axis = input_.transpose(1, 2).unsqueeze(3)\\n diff_axis = diff_axis - diff_axis.transpose(2, 3)\\n return diff_axis * invr\\n\\n elif derivative == 2:\\n\\n eps_ = self.eps * \\\\\\n torch.diag(dist.new_ones(\\n dist.shape[-1])).expand_as(dist)\\n invr3 = (1. / (dist**3 + eps_)).unsqueeze(1)\\n diff_axis = input_.transpose(1, 2).unsqueeze(3)\\n diff_axis = (diff_axis - diff_axis.transpose(2, 3))**2\\n\\n diff_axis = diff_axis[:, [\\n [1, 2], [2, 0], [0, 1]], ...].sum(2)\\n return (diff_axis * invr3)\",\n \"def derivative(self, cache):\\n if self.__activation == 'sig':\\n derivative = cache * (1 - cache)\\n else:\\n derivative = 1 - cache ** 2\\n return derivative\",\n \"def derivative(self,x,dx=None):\\n if dx is None:\\n x = np.array(x,copy=False)\\n if len(x.shape)>0:\\n dx = np.convolve(x,[1,-1],mode='valid')\\n dx = np.insert(dx,0,np.mean(dx))/2\\n else:\\n dx = 1\\n return (self(x+dx)-self(x))/dx\",\n \"def derivative(self, *args):\\n if self.i_dim == 0:\\n return np.ones_like(*args[0])\\n else:\\n return np.zeros_like(*args[0])\",\n \"def dalf(x):\\n return derivative(alf, x, dx=1e-6)\",\n \"def __call__ ( self , func , x , h , der = False ) :\\n\\n ## calculate differences \\n imax = self.__order + 2 if der else self.__order + 1\\n i = 0\\n while i < imax : \\n j = i + 1\\n self.__df[i] = func ( x + j * h ) - func ( x - j * h )\\n i += 1\\n \\n ## 1) calculate 1st derivative \\n result = dot_fma ( self.__order + 1 , self.__df , self.__d1 ) / ( self.__sf1 * h ) \\n if not der : return result \\n \\n ## 2) calculate Nth derivative \\n dd = dot_fma ( self.__order + 2 , self.__df , self.__d2 ) / ( self.__sf2 * h**(self.__order*2+3) ) \\n \\n return result, dd\",\n \"def ddalf(x):\\n return derivative(dalf, x, dx=1e-6)\",\n \"def ddalf(x):\\n return derivative(dalf, x, dx=1e-6)\",\n \"def calculate_derivative_value(self, x: np.array) -> np.array:\\n pass\",\n \"def derivative(self, x):\\n z = np.asarray(x)\\n return (self._der(z.flatten())).reshape(z.shape)\",\n \"def derivative(a, y, z):\\n return a - y + (z * 0)\",\n \"def backward_pass(self, delta):\\r\\n self.d_x = np.dot(delta, self.w.T)\\r\\n self.d_b = np.matmul(np.ones((1, delta.shape[0])), delta)\\r\\n self.d_w = np.dot(self.x.T, delta)\\r\\n return self.d_x\",\n \"def func_deriv(x,remain):\\n #df_x0 = -1*remain[0]/x[0]**2\\n #df_x1 = -1*remain[2]/x[1]**2\\n #df_x2 = -1*remain[1]/x[2]**2\\n return np.array(-1*remain/x**2)\",\n \"def derivative(a, y, z):\\n return (a - y) * Sigmoid.derivative(z)\",\n \"def _evalAndDer(self, x):\\n m = len(x)\\n fx = np.zeros((m, self.funcCount))\\n for j in range(self.funcCount):\\n fx[:, j] = self.functions[j](x)\\n i = self.argcompare(fx, axis=1)\\n y = fx[np.arange(m), i]\\n dydx = np.zeros_like(y)\\n for j in range(self.funcCount):\\n c = i == j\\n dydx[c] = self.functions[j].derivative(x[c])\\n return y, dydx\",\n \"def _evalAndDer(self, x):\\n m = len(x)\\n fx = np.zeros((m, self.funcCount))\\n for j in range(self.funcCount):\\n fx[:, j] = self.functions[j](x)\\n i = self.argcompare(fx, axis=1)\\n y = fx[np.arange(m), i]\\n dydx = np.zeros_like(y)\\n for j in range(self.funcCount):\\n c = i == j\\n dydx[c] = self.functions[j].derivative(x[c])\\n return y, dydx\",\n \"def directional_deriv(f, x, v, s=_DEFAULT_STEP):\\n v = np.asarray(v)\\n v = v / la.norm(v)\\n return (f(x + s*v) - f(x)) / s\",\n \"def _derivative_(self, x, diff_param=None):\\n return 2*exp(-x**2)/sqrt(pi)\",\n \"def forward_differences(f, h, x):\\n\\treturn (f(x + h) - f(x)) / h\",\n \"def derivative_activation(z):\\n return activation(z) * (1 - activation(z))\",\n \"def derivative ( fun , x , h = 0 , I = 2 , err = False ) :\\n\\n func = lambda x : float ( fun ( x ) )\\n \\n ## get the function value at the given point \\n f0 = func(x)\\n\\n ## adjust the rule \\n I = min ( max ( I , 1 ) , 8 )\\n J = 2 * I + 1\\n \\n _dfun_ = _funcs_[I]\\n delta = _delta_ ( x )\\n \\n ## if the intial step is too small, choose another one \\n if abs ( h ) < _numbers_[I][3] or abs ( h ) < delta : \\n if iszero( x ) : h = _numbers_[0][I]\\n else : h = abs ( x ) * _numbers_[I][3] \\n\\n h = max ( h , 2 * delta )\\n \\n ## 1) find the estimate for first and \\\"J\\\"th the derivative with the given step \\n d1 , dJ = _dfun_( func , x , h , True )\\n \\n ## find the optimal step \\n if iszero ( dJ ) or ( iszero ( f0 ) and iszero ( x * d1 ) ) :\\n if iszero ( x ) : hopt = _numbers_[0][I] \\n else : hopt = abs ( x ) * _numbers_[I][3]\\n else : \\n hopt = _numbers_[I][2] * ( ( abs ( f0 ) + abs ( x * d1 ) ) / abs ( dJ ) )**( 1.0 / J )\\n\\n ## finally get the derivative \\n if not err : return _dfun_ ( func , x , hopt , False )\\n\\n ## estimate the uncertainty, if needed \\n d1,dJ = _dfun_ ( func , x , hopt , True )\\n \\n e = _numbers_[I][1] / _numbers_[I][2] * J / ( J - 1 ) \\n e2 = e * e * ( J * _eps_ + abs ( f0 ) + abs( x * d1 ) )**( 2 - 2./J ) * abs( dJ )**(2./J) \\n return VE ( d1 , 4 * e2 )\",\n \"def forward(h, n, u, v, f, dt, dx, dy, du, dv, dn, beta=0, eps=0, gamma=0, mu=0.3, nu=0, dudt_x=dudt, dvdt_x=dvdt, dndt_x=dndt, grav=True, cori=True, advx=True, advy=True, attn=True): # forward euler and forward/backward timestep\\n beta = np.float32(beta)\\n mu = np.float32(mu)\\n \\n du1, du0 = du[:2]\\n dv1, dv0 = dv[:2]\\n dn0 = dn[0]\\n \\n dndt_x(h, n, u, v, dx, dy, dn0) # calculate dndt and put it into dn0\\n \\n n1 = n + ( dn0 )*dt\\n \\n dudt_x(h, n, f, u, v, dx, dy, du0, grav=grav, cori=cori, advx=advx, advy=advy, attn=attn,nu=nu,mu=mu)\\n dvdt_x(h, n, f, u, v, dx, dy, dv0, grav=grav, cori=cori, advx=advx, advy=advy, attn=attn,nu=nu,mu=mu)\\n dudt_x(h, n1, f, u, v, dx, dy, du1, grav=grav, cori=cori, advx=advx, advy=advy, attn=attn,nu=nu,mu=mu)\\n dvdt_x(h, n1, f, u, v, dx, dy, dv1, grav=grav, cori=cori, advx=advx, advy=advy, attn=attn,nu=nu,mu=mu)\\n \\n u1 = u + ( beta*du1 + (one-beta)*du0 )*dt\\n v1 = v + ( beta*dv1 + (one-beta)*dv0 )*dt\\n \\n n, u, v = n1, u1, v1\\n \\n du = [du1, du0, du0, du0]\\n dv = [dv1, dv0, dv0, dv0]\\n dn = [dn0, dn0, dn0]\\n return n1, u1, v1, du, dv, dn\",\n \"def f(self, x , u , t = 0 ):\\n y = x[0]\\n dy = x[1]\\n \\n \\n ddy = - y + self.mu * dy * ( 1 - y**2 )\\n \\n dx = np.zeros(self.n) # State derivative vector\\n \\n dx[0] = dy\\n dx[1] = ddy\\n \\n return dx\",\n \"def __call__ ( self , x ) :\\n return derivative ( self.__func ,\\n x ,\\n self.__step ,\\n self.__order ,\\n self.__err )\",\n \"def _derX(self, x, y):\\n m = len(x)\\n temp = np.zeros((m, self.funcCount))\\n for j in range(self.funcCount):\\n temp[:, j] = self.functions[j](x, y)\\n i = self.argcompare(temp, axis=1)\\n dfdx = np.zeros_like(x)\\n for j in range(self.funcCount):\\n c = i == j\\n dfdx[c] = self.functions[j].derivativeX(x[c], y[c])\\n return dfdx\",\n \"def calc_derivative(self, array_in, direction1, direction2 = False):\\r\\n A = array_in.copy()\\r\\n if direction1 != direction2:\\r\\n #Remove Nyquist frequency for even sample size and odd order of differentiation\\r\\n if direction1 == 'x' or direction2 == 'x':\\r\\n A[0,:] = 0.0\\r\\n if direction1 == 'y' or direction2 == 'y':\\r\\n A[:,0] = 0.0\\r\\n\\r\\n # Note that 'x' corresponds to the x1 direction, and 'y' to the\\r\\n # x2 direction\\r\\n # Perform first derivative in desired direction\\r\\n if direction1 == 'x':\\r\\n out = self.deriv_mat_x1*A\\r\\n elif direction1 == 'y':\\r\\n out = self.deriv_mat_x2*A\\r\\n\\r\\n # Perform second derivative in desired direction\\r\\n if direction2 == 'x':\\r\\n out = self.deriv_mat_x1*out\\r\\n elif direction2 == 'y':\\r\\n out = self.deriv_mat_x2*out\\r\\n\\r\\n return out\",\n \"def calculate_derivative(current_data):\\n\\n # Declare array with first value equals zero to build gradient\\n derivative_data = [0]\\n derivative_data = np.gradient(current_data[\\\"4. close\\\"])\\n # Add gradient values as column to current dataframe\\n current_data[\\\"gradient\\\"] = derivative_data\\n\\n return current_data\",\n \"def derivatives(self, state):\\n raise NotImplementedError()\",\n \"def _computeDerivative(self,angles, distances):\\n slope=[]\\n slope.append(0)\\n for i in xrange(1,len(angles)):\\n der = (distances[i]-distances[i-1])/(angles[i]-angles[i-1])\\n slope.append(der)\\n #slope.append(0)\\n return slope\",\n \"def first_derivative(x, f):\\n deriv = np.diff(f, n=1)\\n deriv = np.insert(deriv, -1, 2 * deriv[-1] - deriv[-2])\\n dx = x[1] - x[2]\\n return deriv / dx\",\n \"def fc(self, x = np.zeros(2) , u = np.array([0,1]) , t = 0 ):\\n \\n dx = np.zeros(self.n) # State derivative vector\\n \\n q = x[0]\\n dq = x[1]\\n \\n ddq = self.ddq_a( q , dq , u[0] , u[1] ) # Include hybrid input\\n \\n dx[0] = dq\\n dx[1] = ddq\\n \\n return dx\",\n \"def derivatives(self):\\n self.rdot = self.v\\n self.vdot[:,:] = 0.0\\n \\n for nl in self.nlists: \\n nl.separations()\\n \\n for force in self.forces:\\n force.apply()\\n\\n # Controllers is the new implementation of forces\\n for controller in self.controllers:\\n controller.apply()\",\n \"def ddx(n, dx, f):\\n fx = np.zeros(n)\\n for j in range(n):\\n fx[j] = (f[get_index(j+1, n)]-f[get_index(j-1, n)])/(2*dx)\\n return fx\",\n \"def derivative(f, x, epsilon = 1e-10):\\n\\n x_ = x + epsilon\\n value = (f(x_) - f(x)) / epsilon\\n\\n return value\",\n \"def __backward(self, dA, cache, derivative_activate_fn):\\n A_prev, W, b, Z, D = cache\\n\\n m = A_prev.shape[1]\\n\\n # Mask\\n dA = np.multiply(dA, D) / self.keep_prob\\n\\n dZ = dA * derivative_activate_fn(Z)\\n dW = (1.0 / m) * np.dot(dZ, A_prev.T)\\n db = (1.0 / m) * np.sum(dZ, axis=1, keepdims=True)\\n dA_prev = np.dot(W.T, dZ)\\n\\n\\n assert (dW.shape == W.shape)\\n assert (db.shape == b.shape)\\n assert (dA_prev.shape == A_prev.shape)\\n\\n return dA_prev, dW, db\",\n \"def fc_backward(dout, cache):\\n x, w, b = cache\\n dx, dw, db = None, None, None\\n ###########################################################################\\n # TODO: Implement the affine backward pass. #\\n ###########################################################################\\n dx = np.dot(dout, w.T)\\n dw = np.dot(x.T, dout)\\n db = np.average(dout, axis = 0)\\n ###########################################################################\\n # END OF YOUR CODE #\\n ###########################################################################\\n return dx, dw, db\",\n \"def derivatives(x_p, y_p):\\r\\n # set up the matrix equation\\r\\n n = x_p.shape[0]\\r\\n M = np.zeros( [n,n] )\\r\\n d = np.zeros( [n,1] )\\r\\n \\r\\n # fill in the constants where they can be\\r\\n for i in np.arange(1,n-1 ): # for all but the first and last row\\r\\n M[i,i-1 ] = ( x_p[i] - x_p[i-1] ) / 6.\\r\\n M[i,i] = ( x_p[i+1] - x_p[i-1] ) / 3.\\r\\n M[i,i+1] = ( x_p[i+1] - x_p[i] ) /6.\\r\\n d[i,0 ] = ( y_p[i+1] - y_p[i] ) / ( x_p[i+1] - x_p[i] ) - ( y_p[i] - y_p[i-1] ) / ( x_p[i] - x_p[i-1] )\\r\\n \\r\\n M[0,0],M[-1,-1] = 1.,1. # compactly sets the BCs\\r\\n \\r\\n LU = lu.LU_decomp(M) # solves the matrix equations\\r\\n return lu.FB_sub(LU.Low, LU.Upp, d) # find and return 2nd derivatives\\r\",\n \"def linear_backward(dZ, cache):\\n pass\",\n \"def _core_calc_degrad(self,bd,Ld) :\\n\\t\\tdegrad = np.dot(Ld,bd) # Do matrix multiplication \\n\\t\\tdegrad = np.exp(degrad) # Exponentiate to convert log to real\\n\\t\\treturn degrad\",\n \"def derivative_func(t, x, Approx_func):\\n return x.dot(Approx_func)\",\n \"def derivatives(self):\\n self.rdot = self.v\\n self.vdot[:,:] = 0.0\\n self.udot[:] = 0.0\\n\\n t = time()\\n for nl in self.nlists: \\n nl.separations()\\n #nl.apply_minimum_image()\\n self.timing['pairsep time'] = (time() - t)\\n\\n t = time()\\n if SPROPS:\\n properties.spam_properties(self,self.nl_default \\\\\\n ,self.h[0:self.n],self.hlr[0:self.n])\\n self.timing['SPAM time'] = time() - t\\n \\n t = time()\\n for force in self.forces:\\n force.apply()\\n self.timing['force time'] = time() - t\\n \\n if ADVECTIVE:\\n self.rdot[:,:] = 0.0\",\n \"def fc_backward(dout, cache):\\n x, w, b = cache\\n dx, dw, db = None, None, None\\n ###########################################################################\\n # TODO: Implement the affine backward pass. #\\n ########################################################################### \\n N = x.shape[0]\\n x2d = x.reshape(N, -1)\\n \\n dx = dout.dot(w.T)\\n dx = dx.reshape(x.shape)\\n dw = x2d.T.dot(dout)\\n db = dout.sum(axis=0) #add from top to down\\n ###########################################################################\\n # END OF YOUR CODE #\\n ###########################################################################\\n return dx, dw, db\",\n \"def get_derivative(self,var,g=None):\\n if (g==None):g=self.g\\n A=np.zeros([self.n+1,self.n])\\n B=np.zeros([self.n+1])\\n for i in range(self.n):\\n B[i]=self.gamma*2.*g*self.N*(self.n-self.N)+np.sum([self.XXZ.Z(k,i)*(var[k]-var[i]) for k in range(self.n) if k!=i])\\n A[self.n][i]=1\\n for j in range(self.n):\\n if(i==j): A[i][j]=2.*var[i]+2.+g*np.sum([self.XXZ.Z(k,i) for k in range(self.n) if k!=i])\\n else: A[i][j]=-g*self.XXZ.Z(j,i)\\n Ainv=np.linalg.pinv(A)\\n der=np.dot(Ainv,B)\\n return der\",\n \"def derivative(requestContext, seriesList):\\n results = []\\n for series in seriesList:\\n newValues = []\\n prev = None\\n for val in series:\\n if None in (prev,val):\\n newValues.append(None)\\n prev = val\\n continue\\n newValues.append(val - prev)\\n prev = val\\n newName = \\\"derivative(%s)\\\" % series.name\\n newSeries = TimeSeries(newName, series.start, series.end, series.step, newValues)\\n newSeries.pathExpression = newName\\n results.append(newSeries)\\n return results\",\n \"def ddalf(x):\\n return dalf_spl.derivatives(x)[1]\",\n \"def GetDerivative(self, *args):\\n return _ITKCostFunctionsPython.itkSingleValuedCostFunction_GetDerivative(self, *args)\",\n \"def backwards(delta,params,name='',activation_deriv=sigmoid_deriv):\\n # everything you may need for this layer\\n W = params['W' + name]\\n b = params['b' + name]\\n X, pre_act, post_act = params['cache_' + name]\\n # your code here\\n # do the derivative through activation first\\n # then compute the derivative W,b, and X\\n \\n delta_pre = activation_deriv(post_act) * delta\\n # (in_dim, out_dim) = (in_dim, examples) @ (examples, out_dim)\\n grad_W = X.transpose() @ delta_pre\\n grad_b = np.sum(delta_pre, axis=0, keepdims=True) # (1, out_dim)\\n # (examples, in_dim) = (examples, out_dim) @ (out_dim, in_dim)\\n grad_X = delta_pre @ W.transpose()\\n\\n # store the gradients\\n params['grad_W' + name] = grad_W\\n params['grad_b' + name] = grad_b\\n return grad_X\",\n \"def rnn_step_backward(self, grad_next, cache):\\n\\n th, h_prev, x = cache\\n dz = grad_next * (1 - th**2)\\n dh_prev = np.dot(dz, self.W.T)\\n self.dW += np.dot(h_prev.T, dz) / grad_next.shape[0]\\n self.dU += np.dot(x.T, dz) / grad_next.shape[0]\\n self.db += np.sum(dz, axis=0) / grad_next.shape[0]\\n\\n return dh_prev\",\n \"def _numerical_derivative_two_sided (func:typing.Callable[[float],float], x_0:float) -> typing.Tuple[float,float]:\\n epsilon = 1.0e-9\\n func_x_0 = func(x_0)\\n deriv_neg = (func_x_0 - func(x_0-epsilon)) / epsilon\\n deriv_pos = (func(x_0+epsilon) - func_x_0) / epsilon\\n return deriv_neg, deriv_pos\",\n \"def eval_numerical_gradient(f,x):\\n\\n\\tgrad = np.zeros(x.shape)\\n\\th = 0.0001\\n\\n\\t# iterate over all indexes in x\\n\\tit = np.nditer(x, flag = ['multi_index'], op_flags = ['readwrite'])\\n\\n\\twhile not it.finished:\\n\\t\\tix = it.multi_index\\n\\t\\told_value = x[ix]\\n\\n\\t\\tx[ix] = old_value + h\\n\\t\\tfxh_left = f(x)\\n\\n\\t\\tx[ix] = old_value - h\\n\\t\\tfxh_right = f(x)\\n\\n\\t\\tx[ix] = old_value\\n\\n\\t\\t# compute the partial derivative\\n\\t\\tgrad[ix] = (fxh_left - fxh_right) / (2 * h)\\n\\t\\tit.iterate()\\n\\n\\treturn grad\",\n \"def _derX(self, x, y, z):\\n m = len(x)\\n temp = np.zeros((m, self.funcCount))\\n for j in range(self.funcCount):\\n temp[:, j] = self.functions[j](x, y, z)\\n i = self.argcompare(temp, axis=1)\\n dfdx = np.zeros_like(x)\\n for j in range(self.funcCount):\\n c = i == j\\n dfdx[c] = self.functions[j].derivativeX(x[c], y[c], z[c])\\n return dfdx\",\n \"def derivativeX(self, x, y, z):\\n xShift = self.lowerBound(y)\\n dfdx_out = self.func.derivativeX(x - xShift, y, z)\\n return dfdx_out\",\n \"def derivativeX(self, x, y):\\n xShift = self.lowerBound(y)\\n dfdx_out = self.func.derivativeX(x - xShift, y)\\n return dfdx_out\",\n \"def feature_derivative(self, errors, weights, l2, is_feature_constant = False):\\n # Compute the dot product of errors and feature\\n if is_feature_constant: \\n derivative = np.dot(self.train_feature_x.T, errors) - 2*l2*weights\\n else:\\n weight0 = np.zeros(1)\\n if weights.shape[0] == 2:\\n weight1 = np.array(weights[1])\\n else: weight1 = weights[1:]\\n derivative = np.dot(self.train_feature_x.T, errors) - np.concatenate((weight0, 2*l2*weight1), axis = 0)\\n return derivative\",\n \"def fundemental_derivative(tab, spec, *XYf):\\n if tab._backend != 'vdw':\\n raise ValueError('This derived variable is only compatible with the vdw backend!')\\n XYf_DT = XYf[:]\\n rho = XYf_DT[0]\\n units = EosUnits(tab.Pt_DT._requested_units, 'cgs')\\n Pt = tab.get_table('P{s}_DT', spec)(*XYf_DT)*units.o2r('P')\\n delta = tab.Pt_DT['delta']\\n a = tab.Pt_DT['a']\\n b = tab.Pt_DT['b']\\n P_frac_1 = (Pt + a*rho**2)/(1./rho - b)**2\\n P_frac_2 = rho*(Pt + a*rho**2)/(1./rho - b)\\n num = (delta+1)*(delta+2) * P_frac_1 - 6*a*rho**4 \\n denum = 2*(delta+1)*P_frac_2 - 4*a*rho**4\\n return num/denum\",\n \"def backward_differences(f, h, x):\\n\\treturn (f(x) - f(x - h)) / h\",\n \"def f( self , x , u , t ):\\n \\n dx = np.zeros(self.n) # State derivative vector\\n \\n ################################################\\n # Place holder: put the equations of motion here\\n raise NotImplementedError\\n ################################################\\n \\n return dx\",\n \"def deriv(y, x):\\n answer = np.zeros(len(y))\\n answer[1:-1] = (y[2:] - y[:-2])/(x[2:] - x[:-2])\\n return answer\",\n \"def backward(self, delta):\\n self.d_x = np.dot(delta, np.transpose(self.w))\\n self.d_w = np.dot(np.transpose(self.x), delta)/self.x.shape[0]\\n self.d_b = np.mean(delta, axis=0).reshape((1, delta.shape[1]))\\n return self.d_x\\n\\n raise NotImplementedError(\\\"Backprop for Layer not implemented.\\\")\",\n \"def func_deriv(x, sign=1.0):\\n dfdx0 = sign*(-2*x[0] + 2*x[1] + 2)\\n dfdx1 = sign*(2*x[0] - 4*x[1])\\n return np.array([ dfdx0, dfdx1 ])\",\n \"def _finite_diff_gradient(self, parameters):\\n assert isinstance(parameters,np.ndarray)\\n # get the free indicies\\n free_inds = np.nonzero(np.logical_not(self._fixed_indicies))[0]\\n\\n # first take a forward step in each direction\\n step = 1e-6 # finite difference step\\n log_like_fs = np.zeros(free_inds.size)\\n for i,param_idx in enumerate(free_inds):\\n p_fs = parameters.copy()\\n p_fs[param_idx] += step # take a step forward\\n log_like_fs[i] = self._compute_log_likelihood(p_fs)\\n\\n # compute the log likelihood at current point\\n log_like = self._compute_log_likelihood(parameters)\\n\\n # compute the gradient\\n gradient = np.zeros(parameters.shape) # default gradient is zero\\n gradient[free_inds] = (log_like_fs-log_like)\\n gradient[free_inds] = gradient[free_inds]/step # divide by step length\\n return log_like, gradient\",\n \"def relu_backward(dout, cache):\\n dx, x = None, cache\\n dx = dout\\n dout[x <= 0] = 0.0\\n return dx\",\n \"def rnn_step_backward(dnext_h, cache):\\n dx, dprev_h, dWx, dWh, db = None, None, None, None, None\\n ##############################################################################\\n # TODO: Implement the backward pass for a single step of a vanilla RNN. #\\n # #\\n # HINT: For the tanh function, you can compute the local derivative in terms #\\n # of the output value from tanh. #\\n ##############################################################################\\n\\n x, next_h, prev_h, Wx, Wh, b = cache\\n # this is because in vanilla RNN h = tanh(z) and derivative of next_h = tanh(z) = 1-z*z;\\n dz = (1-next_h*next_h)*dnext_h\\n # THIS ERROR IS SPREAD AMONG THE\\n # np.dot(x, Wx) + np.dot(prev_h, Wh) + b)\\n dx = np.dot(dz,Wx.T)\\n dprev_h = np.dot(dz,Wh.T)\\n db = np.sum(dz,axis=0)\\n dWx = np.dot(x.T,dz)\\n dWh = np.dot(prev_h.T,dz)\\n #d(tanh) = 1- tanh*tanh\\n\\n\\n ##############################################################################\\n # END OF YOUR CODE #\\n ##############################################################################\\n return dx, dprev_h, dWx, dWh, db\",\n \"def dgdx(self, X):\\n \\n return 2*(X[0]) - 2\",\n \"def fc(self, x = np.zeros(4) , u = np.zeros(3) , t = 0 ):\\n \\n dx = np.zeros(self.n) # State derivative vector\\n \\n [ q , dq ] = self.x2q( x ) # from state vector (x) to angle and speeds (q,dq)\\n \\n ddq = self.ddq_a( q , dq , u[0:self.dof] , u[self.dof] , t )\\n \\n dx = self.q2x( dq , ddq ) # from angle and speeds diff (dq,ddq) to state vector diff (dx)\\n \\n return dx\",\n \"def fc(self, x = np.zeros(4) , u = np.zeros(3) , t = 0 ):\\n \\n dx = np.zeros(self.n) # State derivative vector\\n \\n [ q , dq ] = self.x2q( x ) # from state vector (x) to angle and speeds (q,dq)\\n \\n ddq = self.ddq_a( q , dq , u[0:self.dof] , u[self.dof] , t )\\n \\n dx = self.q2x( dq , ddq ) # from angle and speeds diff (dq,ddq) to state vector diff (dx)\\n \\n return dx\",\n \"def relu_backward(dout, cache):\\n #raise NotImplementedError\\n #######################################################################\\n # #\\n # #\\n # TODO: YOUR CODE HERE #\\n # #\\n # #\\n #######################################################################\\n x=cache\\n dout[x<=0]=0\\n dx=dout\\n return dx\",\n \"def softmax_derivative(Z):\\n\\treturn None\",\n \"def dalf(x):\\n return grad(alf)(x)\",\n \"def eval_numerical_gradient(f, x, verbose = True, h = 0.00001):\\n fx = f(x) # evaluate function value at original point\\n grad = np.zeros_like(x) # iterate over all indexese in x\\n it = np.nditer(x, flags = ['multi_index'], op_flags = ['readwrite'])\\n while not it.finished:\\n # evaluate function at x+h\\n ix = it.multi_index\\n oldval = x[ix]\\n x[ix] = oldval + h # increment by h\\n fxph = f(x) # evaluate f(x+h)\\n x[ix] = oldval - h\\n fxmh = f(x) # evaluate f(x-h)\\n x[ix] = oldval # restore\\n \\n #compute the partial derivative with centered fromula.\\n grad[ix] = (fxph - fxmh) / (2 * h)\\n if verbose:\\n print(ix, grad[ix])\\n it.iternext()\\n return grad\",\n \"def softmax_derivative(x):\\n der = derivative(softmax,x,dx=1e-9)\\n return der\",\n \"def fc_backward(dout, cache):\\n x, w, b = cache\\n dx, dw, db = None, None, None\\n ###########################################################################\\n # TODO: Implement the affine backward pass. #\\n ###########################################################################\\n Xshape = x.shape\\n x_flat = np.reshape(x,(x.shape[0],-1))\\n dx = np.dot(dout,w.T)\\n dw = x_flat.T.dot(dout)#np.matmul(x[...,None],dout[:,None,:]).sum(axis=0)\\n db = np.sum(dout,axis=0)\\n dx = np.reshape(dx,Xshape)\\n ###########################################################################\\n # END OF YOUR CODE #\\n ###########################################################################\\n return dx, dw, db\",\n \"def derivitive(x):\\n return x * 1\",\n \"def fd_derivative(f, t):\\n dfdt = np.empty_like(f)\\n if (f.ndim == 1):\\n _derivative(f, t, dfdt)\\n elif (f.ndim == 2):\\n _derivative_2d(f, t, dfdt)\\n elif (f.ndim == 3):\\n _derivative_3d(f, t, dfdt)\\n else:\\n raise NotImplementedError(\\\"Taking derivatives of {0}-dimensional arrays is not yet implemented\\\".format(f.ndim))\\n return dfdt\",\n \"def _derivativeTerm(self):\\n\\n\\t\\treturn self._Kd * (self._getErrorFunction() - self._previousError) / self._dt\",\n \"def eval_with_derivative(self, x):\\n z = np.asarray(x)\\n y, dydx = self._evalAndDer(z.flatten())\\n return y.reshape(z.shape), dydx.reshape(z.shape)\",\n \"def make_derivate_fn(self):\\n\\n def derivative_fn(y, t):\\n self.time = t\\n self.compartments = self.convert_list_to_compartments(y)\\n self.prepare_vars_and_flows()\\n flow_vector = self.convert_compartments_to_list(self.flows)\\n self.checks()\\n return flow_vector\\n\\n return derivative_fn\",\n \"def backward_D(self):\\n self.loss_D.backward()\",\n \"def _dddx(t, x, ivp):\\n evl = ivp.rhs(t, x)\\n try:\\n jac = ivp.jacobian(t, x)\\n except NotImplementedError:\\n jac = np.zeros((len(x), len(x)))\\n try:\\n hess = ivp.hessian(t, x)\\n except NotImplementedError:\\n hess = np.zeros((len(x), len(x), len(x)))\\n if np.isscalar(evl):\\n evl = np.array([evl])\\n jac = np.array([jac])\\n hess = np.array([hess])\\n return (hess @ evl) @ evl + jac @ (jac @ evl)\",\n \"def linear_backward(dZ, cache):\\n A_prev, W, b = cache\\n m = A_prev.shape[1]\\n\\n dW = np.dot(dZ,A_prev.T) / m\\n db = np.sum(dZ,axis = 1,keepdims = True) / m\\n dA_prev = np.dot(W.T,dZ)\\n\\n return dA_prev,dW,db\",\n \"def get_derivative(self, model, params, n):\\n params1 = np.array(params)\\n params2 = np.array(params)\\n\\n params1[n] += self.eps\\n params2[n] -= self.eps\\n\\n res1 = model.run(params1)\\n res2 = model.run(params2)\\n\\n d = (res1 - res2) / (2 * self.eps)\\n\\n return d.ravel()\",\n \"def eeg_derivative(array):\\n\\tif len(array.shape) == 1:\\n\\t\\tderivative = []\\t\\n\\t\\tfor i in xrange(1,len(array)):\\n\\t\\t\\tderivative.append(array[i]-array[i-1])\\n\\telse:\\n\\t\\tprint \\\"Not yet implemented\\\"\\n\\t\\tderivative = []\\n\\tderivative = np.array(derivative)\\n\\treturn derivative\",\n \"def ddalf(x):\\n return dalf_spl.derivatives(x)[1]\",\n \"def GetDerivative(self, *args):\\n return _ITKCostFunctionsPython.itkMultipleValuedCostFunction_GetDerivative(self, *args)\",\n \"def eval_numerical_gradient(f, x, verbose=True, h=0.00001):\\n\\n fx = f(x) # evaluate function value at original point\\n grad = np.zeros_like(x)\\n # iterate over all indexes in x\\n it = np.nditer(x, flags=['multi_index'], op_flags=['readwrite'])\\n while not it.finished:\\n\\n # evaluate function at x+h\\n ix = it.multi_index\\n oldval = x[ix]\\n x[ix] = oldval + h # increment by h\\n fxph = f(x) # evalute f(x + h)\\n x[ix] = oldval - h\\n fxmh = f(x) # evaluate f(x - h)\\n x[ix] = oldval # restore\\n\\n # compute the partial derivative with centered formula\\n grad[ix] = (fxph - fxmh) / (2 * h) # the slope\\n if verbose:\\n print(ix, grad[ix])\\n it.iternext() # step to next dimension\\n\\n return grad\",\n \"def backward(self):\\n assert self.cache is not None, \\\"Cannot backprop without forward first.\\\"\\n prob, y = self.cache\\n\\n dX = prob - y\\n if self.reduction == \\\"mean\\\":\\n m, _ = prob.shape\\n dX /= m\\n\\n # clear cache\\n self.cache = None\\n\\n return dX\",\n \"def gradient(f, *varargs):\\n N = len(f.shape) # number of dimensions\\n n = len(varargs)\\n if n == 0:\\n dx = [1.0]*N\\n elif n == 1:\\n dx = [varargs[0]]*N\\n elif n == N:\\n dx = list(varargs)\\n else:\\n raise SyntaxError, \\\"invalid number of arguments\\\"\\n\\n # use central differences on interior and first differences on endpoints\\n\\n outvals = []\\n\\n # create slice objects --- initially all are [:, :, ..., :]\\n slice1 = [slice(None)]*N\\n slice2 = [slice(None)]*N\\n slice3 = [slice(None)]*N\\n\\n otype = f.dtype.char\\n if otype not in ['f', 'd', 'F', 'D']:\\n otype = 'd'\\n\\n for axis in range(N):\\n # select out appropriate parts for this dimension\\n out = zeros(f.shape, f.dtype.char)\\n slice1[axis] = slice(1, -1)\\n slice2[axis] = slice(2, None)\\n slice3[axis] = slice(None, -2)\\n # 1D equivalent -- out[1:-1] = (f[2:] - f[:-2])/2.0\\n out[slice1] = (f[slice2] - f[slice3])/2.0\\n slice1[axis] = 0\\n slice2[axis] = 1\\n slice3[axis] = 0\\n # 1D equivalent -- out[0] = (f[1] - f[0])\\n out[slice1] = (f[slice2] - f[slice3])\\n slice1[axis] = -1\\n slice2[axis] = -1\\n slice3[axis] = -2\\n # 1D equivalent -- out[-1] = (f[-1] - f[-2])\\n out[slice1] = (f[slice2] - f[slice3])\\n\\n # divide by step size\\n outvals.append(out / dx[axis])\\n\\n # reset the slice object in this dimension to \\\":\\\"\\n slice1[axis] = slice(None)\\n slice2[axis] = slice(None)\\n slice3[axis] = slice(None)\\n\\n if N == 1:\\n return outvals[0]\\n else:\\n return outvals\",\n \"def linear_backward_calculation(dZ, internal_params):\\n\\n A_prev, W, b = internal_params\\n nb = A_prev.shape[1]\\n\\n ### START CODE HERE ### (≈ 3 lines of code)\\n dW =np.multiply((np.dot(dZ, A_prev.T)),1/nb)\\n db = np.multiply ((np.sum(dZ, axis=1, keepdims=True),1/nb))\\n dA_prev = np.dot(W.T, dZ)\\n # raise NotImplementedError\\n return dA_prev,dW,db\",\n \"def compute_cost_derivative(AL, Y):\\n pass\",\n \"def second_derivative(self,show=False):\\n\\n df = [None]\\n d2f = [None]\\n\\n # Take first difference: f1 = (f(x+dx)-f(x))/dx\\n for i in range(0,len(self.ans)-1,1):\\n df += [int((self.ans[i+1][1] - self.ans[i][1]) / (self.ans[i+1][0] - self.ans[i][0]))]\\n\\n for i in range(1,len(self.ans)-1,1):\\n d2f += [int((df[i+1] - df[i]) / (self.ans[i+1][0] - self.ans[i][0]))]\\n d2f += [None]\\n\\n if show == True:\\n print('First derivative:', df)\\n print('Second derivative:', d2f)\\n\\n return d2f\",\n \"def _der(self, x):\\n y, dydx = self._evalAndDer(x)\\n return dydx # Sadly, this is the fastest / most convenient way...\",\n \"def _der(self, x):\\n y, dydx = self._evalAndDer(x)\\n return dydx # Sadly, this is the fastest / most convenient way...\",\n \"def deriv(self, x, d=None):\\n if self.weights is None:\\n self.compute_weights()\\n\\n val = 0.0\\n for i in range(self.M):\\n val += self.weights[i]*self.model_list[i].deriv(x, d)\\n return val\",\n \"def derivative(self, variable):\\n cached_derivative = self._first_derivatives.get(variable, None)\\n if cached_derivative: \\n return cached_derivative\\n else:\\n return self._first_derivatives.setdefault(variable,\\n em.diff_expr(self.math, \\n variable))\",\n \"def backward(self):\\n gradient = blah\\n return gradient\",\n \"def backward(self):\\n gradient = blah\\n return gradient\",\n \"def newton1d(f, df, ddf, x, niter=10):\\n for i in xrange(niter):\\n x_new = x - df(x)/ddf(x)\\n x = x_new\\n return x\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7290176","0.7042805","0.69695157","0.6969174","0.68731177","0.6854815","0.6830507","0.6792242","0.67888516","0.67416143","0.6734721","0.6734721","0.6696811","0.6691667","0.6688757","0.666151","0.6658981","0.6657034","0.6640104","0.6640104","0.66360956","0.66311634","0.66167605","0.65925837","0.65819323","0.6546534","0.6540859","0.65340006","0.6516515","0.6504227","0.64886934","0.6476517","0.6476329","0.6469499","0.6467663","0.6451606","0.6430625","0.64260596","0.6413446","0.64088506","0.64025015","0.6401071","0.6398325","0.6396595","0.638823","0.6381719","0.63815147","0.63683563","0.63643694","0.6363737","0.6362601","0.63578373","0.6354802","0.6340064","0.63376737","0.6333867","0.63246644","0.632219","0.6316938","0.63091797","0.63072914","0.63064855","0.62989485","0.6293416","0.6291972","0.62902945","0.6280656","0.62704664","0.62639374","0.62639374","0.6253409","0.6250444","0.62392414","0.6232113","0.62289166","0.6226992","0.6225678","0.6217459","0.6215034","0.6206413","0.6206264","0.6204288","0.62042636","0.62035257","0.61974615","0.61823577","0.61757356","0.61752194","0.6173897","0.6169168","0.61663276","0.6163282","0.6158162","0.6157743","0.6151498","0.6151498","0.6150564","0.614647","0.6139774","0.6139774","0.61389214"],"string":"[\n \"0.7290176\",\n \"0.7042805\",\n \"0.69695157\",\n \"0.6969174\",\n \"0.68731177\",\n \"0.6854815\",\n \"0.6830507\",\n \"0.6792242\",\n \"0.67888516\",\n \"0.67416143\",\n \"0.6734721\",\n \"0.6734721\",\n \"0.6696811\",\n \"0.6691667\",\n \"0.6688757\",\n \"0.666151\",\n \"0.6658981\",\n \"0.6657034\",\n \"0.6640104\",\n \"0.6640104\",\n \"0.66360956\",\n \"0.66311634\",\n \"0.66167605\",\n \"0.65925837\",\n \"0.65819323\",\n \"0.6546534\",\n \"0.6540859\",\n \"0.65340006\",\n \"0.6516515\",\n \"0.6504227\",\n \"0.64886934\",\n \"0.6476517\",\n \"0.6476329\",\n \"0.6469499\",\n \"0.6467663\",\n \"0.6451606\",\n \"0.6430625\",\n \"0.64260596\",\n \"0.6413446\",\n \"0.64088506\",\n \"0.64025015\",\n \"0.6401071\",\n \"0.6398325\",\n \"0.6396595\",\n \"0.638823\",\n \"0.6381719\",\n \"0.63815147\",\n \"0.63683563\",\n \"0.63643694\",\n \"0.6363737\",\n \"0.6362601\",\n \"0.63578373\",\n \"0.6354802\",\n \"0.6340064\",\n \"0.63376737\",\n \"0.6333867\",\n \"0.63246644\",\n \"0.632219\",\n \"0.6316938\",\n \"0.63091797\",\n \"0.63072914\",\n \"0.63064855\",\n \"0.62989485\",\n \"0.6293416\",\n \"0.6291972\",\n \"0.62902945\",\n \"0.6280656\",\n \"0.62704664\",\n \"0.62639374\",\n \"0.62639374\",\n \"0.6253409\",\n \"0.6250444\",\n \"0.62392414\",\n \"0.6232113\",\n \"0.62289166\",\n \"0.6226992\",\n \"0.6225678\",\n \"0.6217459\",\n \"0.6215034\",\n \"0.6206413\",\n \"0.6206264\",\n \"0.6204288\",\n \"0.62042636\",\n \"0.62035257\",\n \"0.61974615\",\n \"0.61823577\",\n \"0.61757356\",\n \"0.61752194\",\n \"0.6173897\",\n \"0.6169168\",\n \"0.61663276\",\n \"0.6163282\",\n \"0.6158162\",\n \"0.6157743\",\n \"0.6151498\",\n \"0.6151498\",\n \"0.6150564\",\n \"0.614647\",\n \"0.6139774\",\n \"0.6139774\",\n \"0.61389214\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":92581,"cells":{"query":{"kind":"string","value":"Flattens and normalizes the plastic strain time derivative tensor. Normalize plastic strain time derivative by dividing by the strain time derivative magnitude"},"document":{"kind":"string","value":"def calc_output(self, plastic_strain_dot, strain_dot):\n\n # Calculate Frobenius norm of elastic strain time derivative tensor\n strain_dot_mag = np.sqrt(np.mean(np.mean(np.square(strain_dot), axis=2), axis=1))\n\n # To avoid singularities, if strain rate magnitude is zero, set it equal to one\n strain_dot_mag[strain_dot_mag == 0] = 1\n\n # Non-dimensionalize by the elastic strain time derivative tensor\n for i in range(3):\n for j in range(3):\n plastic_strain_dot[:, i, j] /= strain_dot_mag\n\n # Flatten\n num_points = plastic_strain_dot.shape[0]\n plastic_strain_dot_flat = np.zeros((num_points, 9))\n for i in range(3):\n for j in range(3):\n plastic_strain_dot_flat[:, 3*i+j] = plastic_strain_dot[:, i, j]\n return plastic_strain_dot_flat"},"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 normalize_transition(self):\n self._t /= self._t.sum(1)[:, np.newaxis]","def _partial_flatten_and_normalize(x):\n x = np.reshape(x, (x.shape[0], -1))\n return (x - np.mean(x)) / np.std(x)","def unnormalize(tensor, mean, std):\n for t, m, s in zip(tensor, mean, std):\n t.mul_(s).add_(m)\n return tensor","def partial_flatten_and_normalize(x):\n x = np.reshape(x, (x.shape[0], -1))\n return (x - np.mean(x)) / np.std(x)","def _normalize_tensor(input_tensor):\n\n rms_tensor = K.sqrt(K.mean(K.square(input_tensor)))\n return input_tensor / (rms_tensor + K.epsilon())","def z_normalize(ts):\n\n ts -= np.mean(ts)\n std = np.std(ts)\n\n if std == 0:\n raise ValueError(\"The Standard Deviation cannot be zero\")\n\n #ts /= std\n return ts / std","def snorm(x):\n return np.dot(x.flatten().T, x.flatten())","def un_normalize(tensor, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]):\n mean = torch.FloatTensor(mean).view(1,3,1,1)\n std = torch.FloatTensor(std).view(1,3,1,1)\n \n image = tensor.cpu().detach()\n image = image*std+mean\n image = image.numpy()\n \n image = np.transpose(image, (0,2,3,1))\n \n #print(np.max(image))\n #print(np.min(image))\n return image","def _normalizeTime(self, t : float) -> float:\n return (t - self.t0)/self.tau","def _normalize(a: np.ndarray, u: float=0, s: float=1) -> np.ndarray:\n a_norm = (a - np.mean(a)) / (np.std(a) + STABILITY)\n a_rescaled = a_norm * s + u\n\n return a_rescaled","def normalize(tensor: np.ndarray):\n if len(tensor.shape) < 4:\n tensor = np.expand_dims(tensor, axis=2)\n mean = np.array([tensor[..., chn, :].mean() for chn in range(tensor.shape[2])])\n std = np.array([tensor[..., chn, :].std() for chn in range(tensor.shape[2])])\n return (tensor - mean[:, np.newaxis]) / std[:, np.newaxis]","def denormalize(tensors):\r\n for c in range(3):\r\n tensors[:, c].mul_(std[c]).add_(mean[c])\r\n return torch.clamp(tensors, 0, 255)","def normalise(self):\n s = self._sum()\n if s != 0:\n for element, value in self.focals.items():\n self.focals[element] /= s","def denormalize(tensors):\n if len(tensors.shape) < 4:\n for c in range(3):\n tensors[c, ...].mul_(std[c]).add_(mean[c])\n else:\n for c in range(3):\n tensors[:, c].mul_(std[c]).add_(mean[c])\n return torch.clamp(tensors, 0, 255)","def normalise(self):\n return self / self.mean(axis=1).reshape(self.shape[0], 1)","def _normalize(self, x, axis, eps=1e-5):\n return x / (\n tf.sqrt(tf.reduce_sum(tf.square(x), axis=axis, keep_dims=True)) + 1e-5)","def normalise(x, dim=1):\n norm = torch.sqrt( torch.pow(x,2.).sum(dim) )\n if dim>0:\n x /= norm.unsqueeze(dim)\n return x","def make_norm_power(X, T_ent):\n Y = F.unfold(X, kernel_size=[T_ent, 1], stride=T_ent)\n Y = torch.transpose(Y, 1, 2)\n Yf = torch.fft.rfft(Y, dim=1)\n Yp = torch.mean(abs(Yf)**2, dim=1)\n return Yp / torch.sum(Yp, dim=1, keepdim=True)","def unnormalize_deltas(deltas, mean, std):\n return deltas * std + mean","def simplify_am(self, tensor: Tensor):\n\n tensor = tensor.map2amps(_rewrite_cg)\n\n # Initial simplification of some summations.\n tensor = tensor.simplify_sums(\n simplifiers=self._am_sum_simplifiers.bcast\n )\n\n # Deltas could come from some simplification rules.\n tensor = tensor.simplify_deltas()\n\n # Some summations could become simplifiable after the delta resolution.\n tensor = tensor.simplify_sums()\n\n return tensor","def _denormalizeState(self, Z : vector) -> vector:\n return Z / self.D","def Normalise(SNR,sig1,freq_bin,delta_t,PSD):\n Normalise_Factor = InnerProduct(sig1,sig1,freq_bin,delta_t,PSD)\n return (SNR / np.sqrt(Normalise_Factor)) * sig1","def __normalizeData__(self,tensors = None):\n if(tensors is None):\n tensors = self.__tensors__\n new_tensors=[]\n for tensor in tensors:\n new_tensors.append( [ [1/(1+x) for x in tensor[0] ] , tensor[1],tensor[2],tensor[3] ])\n return new_tensors","def schlichtkrull_normal_(tensor, shape, gain=1.):\n std = schlichtkrull_std(shape, gain)\n with torch.no_grad():\n return tensor.normal_(0.0, std)","def _normalize(x):\n tol = 1e-10\n dims = x.shape\n\n x = x.flatten()\n inverse = (np.sum(x**2) + tol) ** -.5\n x = x * inverse\n x = np.reshape(x, dims)\n\n return x","def temperature_scaling(x,t):\n n,d = x.shape\n res = np.copy(x)\n\n res *= (1./t)\n for i in range(n):\n res[i] = softmax(res[i])\n return(res)","def rescale(self):\n # Get the L1 norm of data and scale correction for each fiber\n data_dims = self.data_dims\n if data_dims is ():\n tens_scale = self.data.abs()\n else:\n tens_scale = torch.sum(self.data.abs(), dim=data_dims, keepdim=True)\n log_shift = torch.floor(TARGET_SCALE(self.shape, data_dims) - \n torch.log2(tens_scale))\n\n # Keep the scale for zero fibers unchanged\n if torch.any(torch.isinf(log_shift)):\n log_shift = torch.where(torch.isfinite(log_shift), log_shift,\n torch.zeros_like(log_shift))\n\n return STensor(self.data*(2**log_shift), \n self.scale-log_shift)","def normaliseTracesMagnitude(traces, unfolded_level): \n return traces / np.abs(unfolded_level)","def _normalizeDeltaTime(self, dt : float) -> float:\n return dt / self.tau","def unnormalize(tensor, mean, std, inplace: bool = False) :\n if not isinstance(tensor, torch.Tensor):\n raise TypeError('Input tensor should be a torch tensor. Got {}.'.format(type(tensor)))\n\n if tensor.ndim < 3:\n raise ValueError('Expected tensor to be a tensor image of size (..., C, H, W). Got tensor.size() = '\n '{}.'.format(tensor.size()))\n\n if not inplace:\n tensor = tensor.clone()\n\n dtype = tensor.dtype\n mean = torch.as_tensor(mean, dtype=dtype, device=tensor.device)\n std = torch.as_tensor(std, dtype=dtype, device=tensor.device)\n if (std == 0).any():\n raise ValueError('std evaluated to zero after conversion to {}, leading to division by zero.'.format(dtype))\n if mean.ndim == 1:\n mean = mean.view(-1, 1, 1)\n if std.ndim == 1:\n std = std.view(-1, 1, 1)\n tensor.mul_(std).add_(mean)\n return tensor","def compute_normalization_fixed_point(activations, t, num_iters=5):\n\n mu = tf.math.reduce_max(activations, -1, keepdims=True)\n normalized_activations_step_0 = activations - mu\n shape_normalized_activations = tf.shape(normalized_activations_step_0)\n\n def iter_body(i, normalized_activations):\n logt_partition = tf.math.reduce_sum(\n exp_t(normalized_activations, t), -1, keepdims=True)\n normalized_activations_t = tf.reshape(\n normalized_activations_step_0 * tf.math.pow(logt_partition, 1.0 - t),\n shape_normalized_activations)\n return [i + 1, normalized_activations_t]\n\n _, normalized_activations_t = for_loop(num_iters, iter_body,\n [0, normalized_activations_step_0])\n\n logt_partition = tf.math.reduce_sum(\n exp_t(normalized_activations_t, t), -1, keepdims=True)\n return -log_t(1.0 / logt_partition, t) + mu","def _normalizeState(self, Z : vector) -> vector:\n return Z * self.D","def df_normalizer(df):\n df = tf.keras.utils.normalize(df, axis=1)\n\n return df","def normalize(self):\n return Vector(self.args + []) / self.magnitude()","def morletft(s, w, w0, dt):\n \n p = 0.75112554446494251 # pi**(-1.0/4.0)\n wavelet = np.zeros((s.shape[0], w.shape[0]))\n pos = w > 0\n\n for i in range(s.shape[0]):\n n = normalization(s[i], dt)\n wavelet[i][pos] = n * p * np.exp(-(s[i] * w[pos] - w0)**2 / 2.0)\n \n return wavelet","def _normalize(image):\n return tf.multiply(tf.subtract(image, 0.5), 2.0)","def call(self, state):\n x = tf.cast(state, tf.float32)\n x = self.flatten(x)\n x -= self.min_vals\n x /= self.max_vals - self.min_vals\n x = 2.0 * x - 1.0 # Rescale in range [-1, 1].\n x = self.dense1(x)\n x = self.dense2(x)\n x = self.last_layer(x)\n return x","def batch_norm_tensor(t):\n return (t - t.mean()) / torch.sqrt(t.var())","def normalize(v):\n\tdim = v.shape \n\tfor i in range(0, dim[0]-1):\n\t\tv[i,:,:] = (v[i,:,:].T/np.sum(v[i,:,:],1)).T\n\n\treturn v","def normalize(x):\n MEAN_VALUES = np.array([104, 117, 123])\n means = theano.shared(MEAN_VALUES.astype(\"float32\"))\n return x[:, ::-1, :, :] - means[np.newaxis, :, np.newaxis, np.newaxis]","def normalize(tensor, mean, std):\n if not _is_tensor_image(tensor):\n raise TypeError('tensor is not a torch image.')\n # TODO: make efficient\n for t, m, s in zip(tensor, mean, std):\n t.sub_(m).div_(s)\n return tensor","def normalize(tensor, mean, std):\n if not _is_tensor_image(tensor):\n raise TypeError('tensor is not a torch image.')\n # TODO: make efficient\n for t, m, s in zip(tensor, mean, std):\n t.sub_(m).div_(s)\n return tensor","def normalise(x):\n return (x - jnp.min(x)) / (jnp.max(x) - jnp.min(x))","def normalize(w: torch.Tensor):\n\n if w.dim() > 1:\n return _matrix(w)\n\n return _vector(w)","def normalized(first):\n if isinstance(first,FreeCAD.Vector):\n l=length(first)\n return FreeCAD.Vector(first.x/l, first.y/l, first.z/l)","def normalize(self, tensor: torch.Tensor, mean: List[float], std: List[float]):\n\n # standard operation defined in ToTensor\n tensor = tensor.div(255)\n dtype = tensor.dtype\n mean = torch.as_tensor(mean, dtype=dtype, device=tensor.device)\n std = torch.as_tensor(std, dtype=dtype, device=tensor.device)\n\n if (std == 0).any():\n raise ValueError(\n f\"std evaluated to zero after conversion to {dtype}, leading to division by zero.\"\n )\n if mean.ndim == 1:\n mean = mean.view(-1, 1, 1)\n if std.ndim == 1:\n std = std.view(-1, 1, 1)\n\n tensor = tensor.sub(mean)\n tensor = tensor.div(std)\n\n return tensor","def normalize_emission(self):\n self._e /= self._e.sum(0)","def normalize(self):\n total = self.total()\n for x in self.d:\n self.d[x] /= total\n return total","def demean_normalize(one_d_array):\n\n temp_arr = one_d_array - np.nanmean(one_d_array)\n\n return temp_arr/np.nanstd(temp_arr)","def _l2normalize(v, eps=1e-12):\n return v / (tf.reduce_sum(v ** 2) ** 0.5 + eps)","def normalize_input(inputs: [float]) -> [float]:","def vis_normalize(a, s=0.1):\n return s * (a - a.mean()) / (max(a.std(), 1e-4)) + 0.5","def normalize(x):\n\n x_norm = np.linalg.norm(x, axis=1, keepdims=True)\n print(x_norm)\n x = x / x_norm\n ### END\n\n return x","def standardize(tX):\n features = tX.T\n features_len = len(features)\n means = np.reshape(np.mean(features, axis=1), [features_len, 1])\n stds = np.reshape(np.std(features, axis=1), [features_len, 1])\n features_std = (features - means) / stds\n new_tX = features_std.T\n return new_tX","def __normalize_after_fft(arr):\n\n n1, n2 = arr.shape[0], arr.shape[1]\n for i in range(n1):\n for j in range(n2):\n arr[i, j] *= n1 * n2\n\n return arr","def normalize(X, m, s):\n return (X - m)/s","def dS_dt(self, species_conc, t):\n ret = self.model.species_volume_prefactor * numpy.dot(self.model.N, self.flux(species_conc, t))\n # handle rate rules\n for var in self.model.rate_rules:\n f = self.model.rate_rules[var].replace(Model.TIME_VARIABLE, str(t))\n f = self.model.rate_rules[var].replace(Model.TIME_VARIABLE, str(t))\n species2conc = dict(zip(self.model.ode_variables, species_conc))\n species2conc['math'] = globals()['math']\n # rate = eval( f, species2conc, self._external_species_conc )\n rate = eval(f, self.model.external_species_concentrations, species2conc)\n if self.model.species_2_position.has_key(var):\n ret[self.model.species_2_position[var]] = rate\n else:\n l = ret.tolist()\n l.append(rate)\n ret = numpy.array(l)\n return ret","def dt_normalize(self):\n return DateTimeDefault.register(pandas.Series.dt.normalize)(self)","def renorm(self):\n self.U /= (np.sum(np.abs(self.U)**2)*self.dx)**0.5","def unnormalize(self, image, transpose=False):\n return unnormalize(image, self.mean, self.std, transpose)","def test_normalization(self):\n u = np.array([np.array([0.7, 1.2]), np.array([0.5, 1.6])])\n with tf.Session() as sess:\n n = sess.run(AbstractModel.l2_normalization_layer(u, axis=1))\n magnitude = np.linalg.norm(n, axis=1)\n np.testing.assert_allclose(magnitude, np.array([1.0, 1.0]))","def unstandardize(\n x: torch.Tensor,\n stats: Dict[str, torch.Tensor]) -> torch.Tensor:\n x_scaled = x * stats['std'] + stats['mean']\n return x_scaled","def forward(self, x, dt, do_normalization=True):\n return x","def dynstall_mhh_dxdt_simple(t, x, U, U_dot, omega, alpha_34, p):\n # States\n x1=x[0] # Downwash memory term 1\n x2=x[1] # Downwash memory term 2\n x3=x[2] # Clp', Lift coefficient with a time lag to the attached lift coeff\n x4=x[3] # f'' , Final separation point function\n # Parameters\n alpha0 = p['alpha0']\n Cla = p['Cla']\n c = p['chord']\n A1 = p['A1']\n A2 = p['A2']\n b1 = p['b1']\n b2 = p['b2']\n F_st = p['F_st']\n # Variables derived from inputs\n U = max(U, 0.01)\n Tu = max(c/(2*U), 1e-4) # Eq. 23\n Tf = p['Tf0']*Tu # OLD was twice: Tf = p['Tf0']*c/U\n Tp = p['Tp0']*Tu # OLD was twice: Tp = p['Tp0']*c/U\n # Variables derived from states\n if p['alpha0_in_x1x2']:\n alphaE = alpha_34*(1-A1-A2)+ x1 + x2 # Eq. 12\n else:\n alphaE = (alpha_34-alpha0)*(1-A1-A2)+ x1 + x2 + alpha0 # Eq. 12\n\n# alphaE = u['alphaE'](t) # <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< HACK HACK TODO TODO TODO TODO TODO\n\n Clp = Cla * (alphaE-alpha0) + np.pi * Tu * omega # Eq. 13\n alphaF = x3/Cla+alpha0 # p. 13\n fs_aF = F_st(alphaF) # p. 13\n if(fs_aF<0):\n print('Problematic fs:',fs_aF)\n x4 = np.clip(x4, 1e-16, 1.0) # Constraining x4 between 0 and 1 increases numerical stability\n # State equation\n xdot = [0]*4\n if p['alpha0_in_x1x2']:\n xdot[0] = -1/Tu * (b1 + c * U_dot/(2*U**2)) * x1 + b1 * A1 / Tu * alpha_34\n xdot[1] = -1/Tu * (b2 + c * U_dot/(2*U**2)) * x2 + b2 * A2 / Tu * alpha_34\n else:\n xdot[0] = -1/Tu * (b1 + c * U_dot/(2*U**2)) * x1 + b1 * A1 / Tu * (alpha_34-alpha0)\n xdot[1] = -1/Tu * (b2 + c * U_dot/(2*U**2)) * x2 + b2 * A2 / Tu * (alpha_34-alpha0)\n xdot[2] = -1/Tp * x3 + 1/Tp * Clp\n xdot[3] = -1/Tf * x4 + 1/Tf * fs_aF\n return xdot","def normalize(w):\n s = sum(w)\n for i in range(len(w)):\n w[i] /= s\n return w","def normalize(x):\n return (x + 1e-10) / (K.sqrt(K.mean(K.square(x))) + 1e-10)","def normalize(self, lam):\n return (lam.T / np.sum(lam, axis=1)).T","def _normalize(self, x):\n # TODO: imagenet normalization\n\n return x","def normalize(self):\n det = self._mat[0][0]*self._mat[1][1] - self._mat[0][1]*self._mat[1][0]\n for i in range(2):\n for j in range(2):\n self._mat[i][j] = (self._mat[i][j])/(np.sqrt(det))","def normalize_temp(temp):\n\n\tnorme = np.empty(temp.shape[2])\n\tfor i in range(temp.shape[2]):\n\t\tnorme[i] = np.linalg.norm(temp[:, :, i])\n\t\ttemp[:, :, i] = temp[:, :, i] / norme[i]\n\treturn (norme)","def ftcs_mixed(T, nt, dt, dx, alpha):\n for n in range(nt):\n Tn = T.copy()\n T[1:-1] = Tn[1:-1] + alpha*dt/dx**2*(Tn[2:] -2*Tn[1:-1] + Tn[0:-2])\n T[-1] = T[-2]\n return T","def autoscales(N, dt, dj, wf, w0):\n \n if wf == 'morlet':\n s0 = (dt * (w0 + np.sqrt(2 + w0**2))) / (PI2)\n else:\n raise ValueError('wavelet function not available')\n\n J = np.floor(dj**-1 * np.log2((N * dt) / s0))\n s = np.empty(int(J + 1))\n\n for i in range(s.shape[0]):\n s[i] = s0 * 2**(i * dj)\n\n return s","def normalize(X, m, s):\n return (X - m) / s","def _unsqueeze_ft(tensor):\n return tensor.unsqueeze(0).unsqueeze(-1)","def norm01(t):\n return (t - t.min()) / (t.max() - t.min())","def denormalize(y, close):\n mean = close[:,-1].reshape(y.shape[0],1).repeat(1,y.shape[1])\n std = torch.std(close, dim=1).reshape(y.shape[0],1).repeat(1,y.shape[1])\n return std*y + mean","def normalize(X, m, s):\n return ((X - m) / s)","def dynstall_oye_dxdt(t,fs,u,p):\n alpha = u['alpha'](t)\n f_st = p['F_st'](alpha)\n return 1/p['tau'] * (f_st - fs)","def normalise_tensor(tensor, upper, lower):\n\n normalised_tensor = (tensor-lower)/(upper-lower)\n return normalised_tensor","def normaliza(self):\n return self * (1 / self.norma())","def _temporal_expand(self, x):\n\n if self._temporal_meth == 'depth_to_time':\n check_shape = x.shape[-1] % self._temporal_mult\n if check_shape != 0:\n msg = ('Temporal expansion of factor {} is being attempted on '\n 'input tensor of shape {}, but the last dimension of '\n 'the input tensor ({}) must be divisible by the '\n 'temporal factor ({}).'\n .format(self._temporal_mult, x.shape, x.shape[-1],\n self._temporal_mult))\n logger.error(msg)\n raise RuntimeError(msg)\n\n shape = (x.shape[0], x.shape[1], x.shape[2],\n x.shape[3] * self._temporal_mult,\n x.shape[4] // self._temporal_mult)\n out = tf.reshape(x, shape)\n out = tf.roll(out, self._t_roll, axis=3)\n\n else:\n temp_expand_shape = tf.stack([x.shape[2],\n x.shape[3] * self._temporal_mult])\n out = []\n for x_unstack in tf.unstack(x, axis=1):\n out.append(tf.image.resize(x_unstack, temp_expand_shape,\n method=self._temporal_meth))\n out = tf.stack(out, axis=1)\n\n return out","def normal(self, t=0):\n p = self.lerp(t)\n if self.da < 0:\n return Line(p, self.c - p)\n else:\n return Line(p, p - self.c)","def normalise_tensor(inp, scope, *, axis=-1, epsilon=1e-5):\n with tf.variable_scope(scope):\n e_dim = inp.get_shape().as_list()[-1]\n g = tf.get_variable('g', [e_dim], initializer=tf.constant_initializer(1))\n b = tf.get_variable('b', [e_dim], initializer=tf.constant_initializer(0))\n\n u = tf.reduce_mean(inp, axis=axis, keepdims=True)\n s = tf.reduce_mean(tf.square(inp - u), axis=axis, keepdims=True)\n out = (inp - u) * tf.rsqrt(s + epsilon)\n out = out * g + b\n\n return out","def _untransform(self, X: Tensor) -> Tensor:\n if len(self.batch_shape) > 0:\n if self.batch_shape != X.shape[-2 - len(self.batch_shape) : -2]:\n raise BotorchTensorDimensionError(\n \"The right most batch dims of X must match self.batch_shape: \"\n f\"({self.batch_shape}).\"\n )\n X_tf = X.clone()\n k = Kumaraswamy(\n concentration1=self.concentration1, concentration0=self.concentration0\n )\n # unnormalize from [eps, 1-eps] to [0,1]\n X_tf[..., self.indices] = (\n (k.icdf(X_tf[..., self.indices]) - self._X_min) / self._X_range\n ).clamp(0.0, 1.0)\n return X_tf","def triax2squeeze(t,e):\n a = 1.\n c = e\n b = np.sqrt(c**2 - t*(c**2-a**2))\n return b","def apply_spectral_norm(x, is_training=False):\n weights_shape = shape_list(x)\n other, num_filters = tf.reduce_prod(weights_shape[:-1]), weights_shape[-1]\n\n # Reshape into a 2-D matrix with outer size num_filters.\n weights_2d = tf.reshape(x, (other, num_filters))\n\n # v = Wu / ||W u||\n with tf.variable_scope(\"u\", reuse=tf.AUTO_REUSE):\n u = tf.get_variable(\"u\", [num_filters, 1], initializer=tf.truncated_normal_initializer(), trainable=False)\n v = tf.nn.l2_normalize(tf.matmul(weights_2d, u))\n\n # u_new = vW / ||v W||\n u_new = tf.nn.l2_normalize(tf.matmul(tf.transpose(v), weights_2d))\n\n # s = v*W*u\n spectral_norm = tf.squeeze(tf.matmul(tf.transpose(v), tf.matmul(weights_2d, tf.transpose(u_new))))\n\n # set u equal to u_new in the next iteration.\n assign_op = tf.assign(u, tf.transpose(u_new))\n if is_training:\n with tf.control_dependencies([assign_op]):\n result = tf.divide(x, spectral_norm)\n else:\n result = tf.divide(x, spectral_norm)\n return result","def normalise_layer(tensor, scope=None, epsilon=1e-5):\n\n # Check if tensor is 2D\n assert len(tensor.get_shape()) == 2\n\n # Calculate mean and variance of tensor\n mean, variance = tf.nn.moments(tensor, axes=[1], keep_dims=True)\n\n # Ensure scope is a string\n if not isinstance(scope, str):\n scope = ''\n\n with tf.variable_scope(scope + 'layer_norm'):\n scale = tf.get_variable('scale',\n shape=[tensor.get_shape()[1]],\n initializer=tf.constant_initializer(1))\n shift = tf.get_variable('shift',\n shape=[tensor.get_shape()[1]],\n initializer=tf.constant_initializer(0))\n\n layer_norm_initial = (tensor - mean) / tf.sqrt(variance + epsilon)\n\n return layer_norm_initial * scale + shift","def normalize_data(mjd, flux, ignore):\n\n t = mjd - np.min(mjd)\n y_err = np.sqrt(flux) \n y = flux[t > ignore]\n y_err = y_err[t > ignore]\n t = t[t > ignore]\n y_err /= np.max(y)\n y /= np.max(y)\n\n sorted_tups = sorted(list(zip(t, y, y_err)), key=lambda x: x[0])\n t = np.array([tup[0] for tup in sorted_tups])\n y = np.array([tup[1] for tup in sorted_tups])\n y_err = np.array([tup[2] for tup in sorted_tups])\n\n return t, y, y_err","def normalize(array, inplace=False):\n if inplace:\n array -= ds_mean\n array /= ds_std\n else:\n array = (array - ds_mean) / ds_std\n return array","def release_slopes_wt_normalized(self):\n if np.any(np.isnan(self.wt_r_slopes)):\n raise ValueError(\"Can't normalize to WT because there are \"\n \"missing entries for initial WT release \"\n \"rates (found NaNs in the matrix).\")\n (wt_r_avgs, wt_r_stds) = self.wt_release_avg_std()\n r_slopes_norm = np.zeros(self.r_slopes.shape)\n for act_ix in range(r_slopes_norm.shape[0]):\n r_slopes_norm[act_ix] = self.r_slopes[act_ix] / wt_r_avgs[act_ix]\n return r_slopes_norm","def _denormalize_joints(x, mean, std):\n assert x.ndim == 3\n assert x.shape == mean.shape == std.shape\n return x * std + mean","def _normalize(X: np.ndarray) -> np.ndarray:\n # return X * np.sqrt(1 / np.sum(X ** 2, axis=1))[:, None]\n return X * np.sqrt(X.shape[1] / np.sum(X ** 2, axis=1))[:, None]","def normalize(self):\n\n if not self.magnitude():\n return Vector(0, 0)\n\n l = 1 / self.magnitude()\n return self.scale(l)","def normalise_tensor(inp, scope, *, axis=-1, epsilon=1e-5):\n with tf.variable_scope(scope):\n e_dim = inp.get_shape().as_list()[-1]\n g = tf.get_variable(\n 'g', [e_dim], initializer=tf.constant_initializer(1))\n b = tf.get_variable(\n 'b', [e_dim], initializer=tf.constant_initializer(0))\n\n u = tf.reduce_mean(inp, axis=axis, keepdims=True)\n s = tf.reduce_mean(tf.square(inp - u), axis=axis, keepdims=True)\n out = (inp - u) * tf.math.rsqrt(s + epsilon)\n out = out * g + b\n\n return out","def _normalize(self, inp):\n \n return inp/inp.sum()","def norm(t):\n return (t - t.min()) / (t.max()-t.min())","def normalize_weights(w, dims=(0,), bias=1e-5):\n with tf.name_scope('normalization'):\n return w / (tf.sqrt(tf.reduce_sum(tf.square(w), dims, keep_dims=True) + bias))","def t_to_repr(self, wave):\n samples_n = tf.shape(wave)[1]\n tf.assert_equal(tf.truncatemod(samples_n, self.freq_n), 0,\n f'Number of samples ({samples_n}) needs to be a multiple of {self.freq_n}')\n\n return self.mdctransformer.transform(wave)","def _normalize(self):\n\n n = len(self.e2[0])\n E = []\n\n for e2 in self.e2:\n if len(e2) != n:\n print 'WARNING: non consistent length in error statistics!!!'\n E.append(np.nansum(np.sqrt(e2))) # temporal aggregation\n\n E = np.asarray(E)\n EM = E.mean() # take square root, as e2 is still the squared error!\n self.e_norm = (E - EM) / EM # see Glecker et al, eq.2","def renormalize(self):\n update_set, coeff_tots = self.coeff_totals()\n coeffs = [c for c in self.coeff]\n for i,u in enumerate(self.updates):\n for j,u2 in enumerate(update_set):\n if u is u2:\n coeffs[i] /= coeff_tots[j]\n return ExplicitIntegrator(coeffs, self.updates)"],"string":"[\n \"def normalize_transition(self):\\n self._t /= self._t.sum(1)[:, np.newaxis]\",\n \"def _partial_flatten_and_normalize(x):\\n x = np.reshape(x, (x.shape[0], -1))\\n return (x - np.mean(x)) / np.std(x)\",\n \"def unnormalize(tensor, mean, std):\\n for t, m, s in zip(tensor, mean, std):\\n t.mul_(s).add_(m)\\n return tensor\",\n \"def partial_flatten_and_normalize(x):\\n x = np.reshape(x, (x.shape[0], -1))\\n return (x - np.mean(x)) / np.std(x)\",\n \"def _normalize_tensor(input_tensor):\\n\\n rms_tensor = K.sqrt(K.mean(K.square(input_tensor)))\\n return input_tensor / (rms_tensor + K.epsilon())\",\n \"def z_normalize(ts):\\n\\n ts -= np.mean(ts)\\n std = np.std(ts)\\n\\n if std == 0:\\n raise ValueError(\\\"The Standard Deviation cannot be zero\\\")\\n\\n #ts /= std\\n return ts / std\",\n \"def snorm(x):\\n return np.dot(x.flatten().T, x.flatten())\",\n \"def un_normalize(tensor, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]):\\n mean = torch.FloatTensor(mean).view(1,3,1,1)\\n std = torch.FloatTensor(std).view(1,3,1,1)\\n \\n image = tensor.cpu().detach()\\n image = image*std+mean\\n image = image.numpy()\\n \\n image = np.transpose(image, (0,2,3,1))\\n \\n #print(np.max(image))\\n #print(np.min(image))\\n return image\",\n \"def _normalizeTime(self, t : float) -> float:\\n return (t - self.t0)/self.tau\",\n \"def _normalize(a: np.ndarray, u: float=0, s: float=1) -> np.ndarray:\\n a_norm = (a - np.mean(a)) / (np.std(a) + STABILITY)\\n a_rescaled = a_norm * s + u\\n\\n return a_rescaled\",\n \"def normalize(tensor: np.ndarray):\\n if len(tensor.shape) < 4:\\n tensor = np.expand_dims(tensor, axis=2)\\n mean = np.array([tensor[..., chn, :].mean() for chn in range(tensor.shape[2])])\\n std = np.array([tensor[..., chn, :].std() for chn in range(tensor.shape[2])])\\n return (tensor - mean[:, np.newaxis]) / std[:, np.newaxis]\",\n \"def denormalize(tensors):\\r\\n for c in range(3):\\r\\n tensors[:, c].mul_(std[c]).add_(mean[c])\\r\\n return torch.clamp(tensors, 0, 255)\",\n \"def normalise(self):\\n s = self._sum()\\n if s != 0:\\n for element, value in self.focals.items():\\n self.focals[element] /= s\",\n \"def denormalize(tensors):\\n if len(tensors.shape) < 4:\\n for c in range(3):\\n tensors[c, ...].mul_(std[c]).add_(mean[c])\\n else:\\n for c in range(3):\\n tensors[:, c].mul_(std[c]).add_(mean[c])\\n return torch.clamp(tensors, 0, 255)\",\n \"def normalise(self):\\n return self / self.mean(axis=1).reshape(self.shape[0], 1)\",\n \"def _normalize(self, x, axis, eps=1e-5):\\n return x / (\\n tf.sqrt(tf.reduce_sum(tf.square(x), axis=axis, keep_dims=True)) + 1e-5)\",\n \"def normalise(x, dim=1):\\n norm = torch.sqrt( torch.pow(x,2.).sum(dim) )\\n if dim>0:\\n x /= norm.unsqueeze(dim)\\n return x\",\n \"def make_norm_power(X, T_ent):\\n Y = F.unfold(X, kernel_size=[T_ent, 1], stride=T_ent)\\n Y = torch.transpose(Y, 1, 2)\\n Yf = torch.fft.rfft(Y, dim=1)\\n Yp = torch.mean(abs(Yf)**2, dim=1)\\n return Yp / torch.sum(Yp, dim=1, keepdim=True)\",\n \"def unnormalize_deltas(deltas, mean, std):\\n return deltas * std + mean\",\n \"def simplify_am(self, tensor: Tensor):\\n\\n tensor = tensor.map2amps(_rewrite_cg)\\n\\n # Initial simplification of some summations.\\n tensor = tensor.simplify_sums(\\n simplifiers=self._am_sum_simplifiers.bcast\\n )\\n\\n # Deltas could come from some simplification rules.\\n tensor = tensor.simplify_deltas()\\n\\n # Some summations could become simplifiable after the delta resolution.\\n tensor = tensor.simplify_sums()\\n\\n return tensor\",\n \"def _denormalizeState(self, Z : vector) -> vector:\\n return Z / self.D\",\n \"def Normalise(SNR,sig1,freq_bin,delta_t,PSD):\\n Normalise_Factor = InnerProduct(sig1,sig1,freq_bin,delta_t,PSD)\\n return (SNR / np.sqrt(Normalise_Factor)) * sig1\",\n \"def __normalizeData__(self,tensors = None):\\n if(tensors is None):\\n tensors = self.__tensors__\\n new_tensors=[]\\n for tensor in tensors:\\n new_tensors.append( [ [1/(1+x) for x in tensor[0] ] , tensor[1],tensor[2],tensor[3] ])\\n return new_tensors\",\n \"def schlichtkrull_normal_(tensor, shape, gain=1.):\\n std = schlichtkrull_std(shape, gain)\\n with torch.no_grad():\\n return tensor.normal_(0.0, std)\",\n \"def _normalize(x):\\n tol = 1e-10\\n dims = x.shape\\n\\n x = x.flatten()\\n inverse = (np.sum(x**2) + tol) ** -.5\\n x = x * inverse\\n x = np.reshape(x, dims)\\n\\n return x\",\n \"def temperature_scaling(x,t):\\n n,d = x.shape\\n res = np.copy(x)\\n\\n res *= (1./t)\\n for i in range(n):\\n res[i] = softmax(res[i])\\n return(res)\",\n \"def rescale(self):\\n # Get the L1 norm of data and scale correction for each fiber\\n data_dims = self.data_dims\\n if data_dims is ():\\n tens_scale = self.data.abs()\\n else:\\n tens_scale = torch.sum(self.data.abs(), dim=data_dims, keepdim=True)\\n log_shift = torch.floor(TARGET_SCALE(self.shape, data_dims) - \\n torch.log2(tens_scale))\\n\\n # Keep the scale for zero fibers unchanged\\n if torch.any(torch.isinf(log_shift)):\\n log_shift = torch.where(torch.isfinite(log_shift), log_shift,\\n torch.zeros_like(log_shift))\\n\\n return STensor(self.data*(2**log_shift), \\n self.scale-log_shift)\",\n \"def normaliseTracesMagnitude(traces, unfolded_level): \\n return traces / np.abs(unfolded_level)\",\n \"def _normalizeDeltaTime(self, dt : float) -> float:\\n return dt / self.tau\",\n \"def unnormalize(tensor, mean, std, inplace: bool = False) :\\n if not isinstance(tensor, torch.Tensor):\\n raise TypeError('Input tensor should be a torch tensor. Got {}.'.format(type(tensor)))\\n\\n if tensor.ndim < 3:\\n raise ValueError('Expected tensor to be a tensor image of size (..., C, H, W). Got tensor.size() = '\\n '{}.'.format(tensor.size()))\\n\\n if not inplace:\\n tensor = tensor.clone()\\n\\n dtype = tensor.dtype\\n mean = torch.as_tensor(mean, dtype=dtype, device=tensor.device)\\n std = torch.as_tensor(std, dtype=dtype, device=tensor.device)\\n if (std == 0).any():\\n raise ValueError('std evaluated to zero after conversion to {}, leading to division by zero.'.format(dtype))\\n if mean.ndim == 1:\\n mean = mean.view(-1, 1, 1)\\n if std.ndim == 1:\\n std = std.view(-1, 1, 1)\\n tensor.mul_(std).add_(mean)\\n return tensor\",\n \"def compute_normalization_fixed_point(activations, t, num_iters=5):\\n\\n mu = tf.math.reduce_max(activations, -1, keepdims=True)\\n normalized_activations_step_0 = activations - mu\\n shape_normalized_activations = tf.shape(normalized_activations_step_0)\\n\\n def iter_body(i, normalized_activations):\\n logt_partition = tf.math.reduce_sum(\\n exp_t(normalized_activations, t), -1, keepdims=True)\\n normalized_activations_t = tf.reshape(\\n normalized_activations_step_0 * tf.math.pow(logt_partition, 1.0 - t),\\n shape_normalized_activations)\\n return [i + 1, normalized_activations_t]\\n\\n _, normalized_activations_t = for_loop(num_iters, iter_body,\\n [0, normalized_activations_step_0])\\n\\n logt_partition = tf.math.reduce_sum(\\n exp_t(normalized_activations_t, t), -1, keepdims=True)\\n return -log_t(1.0 / logt_partition, t) + mu\",\n \"def _normalizeState(self, Z : vector) -> vector:\\n return Z * self.D\",\n \"def df_normalizer(df):\\n df = tf.keras.utils.normalize(df, axis=1)\\n\\n return df\",\n \"def normalize(self):\\n return Vector(self.args + []) / self.magnitude()\",\n \"def morletft(s, w, w0, dt):\\n \\n p = 0.75112554446494251 # pi**(-1.0/4.0)\\n wavelet = np.zeros((s.shape[0], w.shape[0]))\\n pos = w > 0\\n\\n for i in range(s.shape[0]):\\n n = normalization(s[i], dt)\\n wavelet[i][pos] = n * p * np.exp(-(s[i] * w[pos] - w0)**2 / 2.0)\\n \\n return wavelet\",\n \"def _normalize(image):\\n return tf.multiply(tf.subtract(image, 0.5), 2.0)\",\n \"def call(self, state):\\n x = tf.cast(state, tf.float32)\\n x = self.flatten(x)\\n x -= self.min_vals\\n x /= self.max_vals - self.min_vals\\n x = 2.0 * x - 1.0 # Rescale in range [-1, 1].\\n x = self.dense1(x)\\n x = self.dense2(x)\\n x = self.last_layer(x)\\n return x\",\n \"def batch_norm_tensor(t):\\n return (t - t.mean()) / torch.sqrt(t.var())\",\n \"def normalize(v):\\n\\tdim = v.shape \\n\\tfor i in range(0, dim[0]-1):\\n\\t\\tv[i,:,:] = (v[i,:,:].T/np.sum(v[i,:,:],1)).T\\n\\n\\treturn v\",\n \"def normalize(x):\\n MEAN_VALUES = np.array([104, 117, 123])\\n means = theano.shared(MEAN_VALUES.astype(\\\"float32\\\"))\\n return x[:, ::-1, :, :] - means[np.newaxis, :, np.newaxis, np.newaxis]\",\n \"def normalize(tensor, mean, std):\\n if not _is_tensor_image(tensor):\\n raise TypeError('tensor is not a torch image.')\\n # TODO: make efficient\\n for t, m, s in zip(tensor, mean, std):\\n t.sub_(m).div_(s)\\n return tensor\",\n \"def normalize(tensor, mean, std):\\n if not _is_tensor_image(tensor):\\n raise TypeError('tensor is not a torch image.')\\n # TODO: make efficient\\n for t, m, s in zip(tensor, mean, std):\\n t.sub_(m).div_(s)\\n return tensor\",\n \"def normalise(x):\\n return (x - jnp.min(x)) / (jnp.max(x) - jnp.min(x))\",\n \"def normalize(w: torch.Tensor):\\n\\n if w.dim() > 1:\\n return _matrix(w)\\n\\n return _vector(w)\",\n \"def normalized(first):\\n if isinstance(first,FreeCAD.Vector):\\n l=length(first)\\n return FreeCAD.Vector(first.x/l, first.y/l, first.z/l)\",\n \"def normalize(self, tensor: torch.Tensor, mean: List[float], std: List[float]):\\n\\n # standard operation defined in ToTensor\\n tensor = tensor.div(255)\\n dtype = tensor.dtype\\n mean = torch.as_tensor(mean, dtype=dtype, device=tensor.device)\\n std = torch.as_tensor(std, dtype=dtype, device=tensor.device)\\n\\n if (std == 0).any():\\n raise ValueError(\\n f\\\"std evaluated to zero after conversion to {dtype}, leading to division by zero.\\\"\\n )\\n if mean.ndim == 1:\\n mean = mean.view(-1, 1, 1)\\n if std.ndim == 1:\\n std = std.view(-1, 1, 1)\\n\\n tensor = tensor.sub(mean)\\n tensor = tensor.div(std)\\n\\n return tensor\",\n \"def normalize_emission(self):\\n self._e /= self._e.sum(0)\",\n \"def normalize(self):\\n total = self.total()\\n for x in self.d:\\n self.d[x] /= total\\n return total\",\n \"def demean_normalize(one_d_array):\\n\\n temp_arr = one_d_array - np.nanmean(one_d_array)\\n\\n return temp_arr/np.nanstd(temp_arr)\",\n \"def _l2normalize(v, eps=1e-12):\\n return v / (tf.reduce_sum(v ** 2) ** 0.5 + eps)\",\n \"def normalize_input(inputs: [float]) -> [float]:\",\n \"def vis_normalize(a, s=0.1):\\n return s * (a - a.mean()) / (max(a.std(), 1e-4)) + 0.5\",\n \"def normalize(x):\\n\\n x_norm = np.linalg.norm(x, axis=1, keepdims=True)\\n print(x_norm)\\n x = x / x_norm\\n ### END\\n\\n return x\",\n \"def standardize(tX):\\n features = tX.T\\n features_len = len(features)\\n means = np.reshape(np.mean(features, axis=1), [features_len, 1])\\n stds = np.reshape(np.std(features, axis=1), [features_len, 1])\\n features_std = (features - means) / stds\\n new_tX = features_std.T\\n return new_tX\",\n \"def __normalize_after_fft(arr):\\n\\n n1, n2 = arr.shape[0], arr.shape[1]\\n for i in range(n1):\\n for j in range(n2):\\n arr[i, j] *= n1 * n2\\n\\n return arr\",\n \"def normalize(X, m, s):\\n return (X - m)/s\",\n \"def dS_dt(self, species_conc, t):\\n ret = self.model.species_volume_prefactor * numpy.dot(self.model.N, self.flux(species_conc, t))\\n # handle rate rules\\n for var in self.model.rate_rules:\\n f = self.model.rate_rules[var].replace(Model.TIME_VARIABLE, str(t))\\n f = self.model.rate_rules[var].replace(Model.TIME_VARIABLE, str(t))\\n species2conc = dict(zip(self.model.ode_variables, species_conc))\\n species2conc['math'] = globals()['math']\\n # rate = eval( f, species2conc, self._external_species_conc )\\n rate = eval(f, self.model.external_species_concentrations, species2conc)\\n if self.model.species_2_position.has_key(var):\\n ret[self.model.species_2_position[var]] = rate\\n else:\\n l = ret.tolist()\\n l.append(rate)\\n ret = numpy.array(l)\\n return ret\",\n \"def dt_normalize(self):\\n return DateTimeDefault.register(pandas.Series.dt.normalize)(self)\",\n \"def renorm(self):\\n self.U /= (np.sum(np.abs(self.U)**2)*self.dx)**0.5\",\n \"def unnormalize(self, image, transpose=False):\\n return unnormalize(image, self.mean, self.std, transpose)\",\n \"def test_normalization(self):\\n u = np.array([np.array([0.7, 1.2]), np.array([0.5, 1.6])])\\n with tf.Session() as sess:\\n n = sess.run(AbstractModel.l2_normalization_layer(u, axis=1))\\n magnitude = np.linalg.norm(n, axis=1)\\n np.testing.assert_allclose(magnitude, np.array([1.0, 1.0]))\",\n \"def unstandardize(\\n x: torch.Tensor,\\n stats: Dict[str, torch.Tensor]) -> torch.Tensor:\\n x_scaled = x * stats['std'] + stats['mean']\\n return x_scaled\",\n \"def forward(self, x, dt, do_normalization=True):\\n return x\",\n \"def dynstall_mhh_dxdt_simple(t, x, U, U_dot, omega, alpha_34, p):\\n # States\\n x1=x[0] # Downwash memory term 1\\n x2=x[1] # Downwash memory term 2\\n x3=x[2] # Clp', Lift coefficient with a time lag to the attached lift coeff\\n x4=x[3] # f'' , Final separation point function\\n # Parameters\\n alpha0 = p['alpha0']\\n Cla = p['Cla']\\n c = p['chord']\\n A1 = p['A1']\\n A2 = p['A2']\\n b1 = p['b1']\\n b2 = p['b2']\\n F_st = p['F_st']\\n # Variables derived from inputs\\n U = max(U, 0.01)\\n Tu = max(c/(2*U), 1e-4) # Eq. 23\\n Tf = p['Tf0']*Tu # OLD was twice: Tf = p['Tf0']*c/U\\n Tp = p['Tp0']*Tu # OLD was twice: Tp = p['Tp0']*c/U\\n # Variables derived from states\\n if p['alpha0_in_x1x2']:\\n alphaE = alpha_34*(1-A1-A2)+ x1 + x2 # Eq. 12\\n else:\\n alphaE = (alpha_34-alpha0)*(1-A1-A2)+ x1 + x2 + alpha0 # Eq. 12\\n\\n# alphaE = u['alphaE'](t) # <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< HACK HACK TODO TODO TODO TODO TODO\\n\\n Clp = Cla * (alphaE-alpha0) + np.pi * Tu * omega # Eq. 13\\n alphaF = x3/Cla+alpha0 # p. 13\\n fs_aF = F_st(alphaF) # p. 13\\n if(fs_aF<0):\\n print('Problematic fs:',fs_aF)\\n x4 = np.clip(x4, 1e-16, 1.0) # Constraining x4 between 0 and 1 increases numerical stability\\n # State equation\\n xdot = [0]*4\\n if p['alpha0_in_x1x2']:\\n xdot[0] = -1/Tu * (b1 + c * U_dot/(2*U**2)) * x1 + b1 * A1 / Tu * alpha_34\\n xdot[1] = -1/Tu * (b2 + c * U_dot/(2*U**2)) * x2 + b2 * A2 / Tu * alpha_34\\n else:\\n xdot[0] = -1/Tu * (b1 + c * U_dot/(2*U**2)) * x1 + b1 * A1 / Tu * (alpha_34-alpha0)\\n xdot[1] = -1/Tu * (b2 + c * U_dot/(2*U**2)) * x2 + b2 * A2 / Tu * (alpha_34-alpha0)\\n xdot[2] = -1/Tp * x3 + 1/Tp * Clp\\n xdot[3] = -1/Tf * x4 + 1/Tf * fs_aF\\n return xdot\",\n \"def normalize(w):\\n s = sum(w)\\n for i in range(len(w)):\\n w[i] /= s\\n return w\",\n \"def normalize(x):\\n return (x + 1e-10) / (K.sqrt(K.mean(K.square(x))) + 1e-10)\",\n \"def normalize(self, lam):\\n return (lam.T / np.sum(lam, axis=1)).T\",\n \"def _normalize(self, x):\\n # TODO: imagenet normalization\\n\\n return x\",\n \"def normalize(self):\\n det = self._mat[0][0]*self._mat[1][1] - self._mat[0][1]*self._mat[1][0]\\n for i in range(2):\\n for j in range(2):\\n self._mat[i][j] = (self._mat[i][j])/(np.sqrt(det))\",\n \"def normalize_temp(temp):\\n\\n\\tnorme = np.empty(temp.shape[2])\\n\\tfor i in range(temp.shape[2]):\\n\\t\\tnorme[i] = np.linalg.norm(temp[:, :, i])\\n\\t\\ttemp[:, :, i] = temp[:, :, i] / norme[i]\\n\\treturn (norme)\",\n \"def ftcs_mixed(T, nt, dt, dx, alpha):\\n for n in range(nt):\\n Tn = T.copy()\\n T[1:-1] = Tn[1:-1] + alpha*dt/dx**2*(Tn[2:] -2*Tn[1:-1] + Tn[0:-2])\\n T[-1] = T[-2]\\n return T\",\n \"def autoscales(N, dt, dj, wf, w0):\\n \\n if wf == 'morlet':\\n s0 = (dt * (w0 + np.sqrt(2 + w0**2))) / (PI2)\\n else:\\n raise ValueError('wavelet function not available')\\n\\n J = np.floor(dj**-1 * np.log2((N * dt) / s0))\\n s = np.empty(int(J + 1))\\n\\n for i in range(s.shape[0]):\\n s[i] = s0 * 2**(i * dj)\\n\\n return s\",\n \"def normalize(X, m, s):\\n return (X - m) / s\",\n \"def _unsqueeze_ft(tensor):\\n return tensor.unsqueeze(0).unsqueeze(-1)\",\n \"def norm01(t):\\n return (t - t.min()) / (t.max() - t.min())\",\n \"def denormalize(y, close):\\n mean = close[:,-1].reshape(y.shape[0],1).repeat(1,y.shape[1])\\n std = torch.std(close, dim=1).reshape(y.shape[0],1).repeat(1,y.shape[1])\\n return std*y + mean\",\n \"def normalize(X, m, s):\\n return ((X - m) / s)\",\n \"def dynstall_oye_dxdt(t,fs,u,p):\\n alpha = u['alpha'](t)\\n f_st = p['F_st'](alpha)\\n return 1/p['tau'] * (f_st - fs)\",\n \"def normalise_tensor(tensor, upper, lower):\\n\\n normalised_tensor = (tensor-lower)/(upper-lower)\\n return normalised_tensor\",\n \"def normaliza(self):\\n return self * (1 / self.norma())\",\n \"def _temporal_expand(self, x):\\n\\n if self._temporal_meth == 'depth_to_time':\\n check_shape = x.shape[-1] % self._temporal_mult\\n if check_shape != 0:\\n msg = ('Temporal expansion of factor {} is being attempted on '\\n 'input tensor of shape {}, but the last dimension of '\\n 'the input tensor ({}) must be divisible by the '\\n 'temporal factor ({}).'\\n .format(self._temporal_mult, x.shape, x.shape[-1],\\n self._temporal_mult))\\n logger.error(msg)\\n raise RuntimeError(msg)\\n\\n shape = (x.shape[0], x.shape[1], x.shape[2],\\n x.shape[3] * self._temporal_mult,\\n x.shape[4] // self._temporal_mult)\\n out = tf.reshape(x, shape)\\n out = tf.roll(out, self._t_roll, axis=3)\\n\\n else:\\n temp_expand_shape = tf.stack([x.shape[2],\\n x.shape[3] * self._temporal_mult])\\n out = []\\n for x_unstack in tf.unstack(x, axis=1):\\n out.append(tf.image.resize(x_unstack, temp_expand_shape,\\n method=self._temporal_meth))\\n out = tf.stack(out, axis=1)\\n\\n return out\",\n \"def normal(self, t=0):\\n p = self.lerp(t)\\n if self.da < 0:\\n return Line(p, self.c - p)\\n else:\\n return Line(p, p - self.c)\",\n \"def normalise_tensor(inp, scope, *, axis=-1, epsilon=1e-5):\\n with tf.variable_scope(scope):\\n e_dim = inp.get_shape().as_list()[-1]\\n g = tf.get_variable('g', [e_dim], initializer=tf.constant_initializer(1))\\n b = tf.get_variable('b', [e_dim], initializer=tf.constant_initializer(0))\\n\\n u = tf.reduce_mean(inp, axis=axis, keepdims=True)\\n s = tf.reduce_mean(tf.square(inp - u), axis=axis, keepdims=True)\\n out = (inp - u) * tf.rsqrt(s + epsilon)\\n out = out * g + b\\n\\n return out\",\n \"def _untransform(self, X: Tensor) -> Tensor:\\n if len(self.batch_shape) > 0:\\n if self.batch_shape != X.shape[-2 - len(self.batch_shape) : -2]:\\n raise BotorchTensorDimensionError(\\n \\\"The right most batch dims of X must match self.batch_shape: \\\"\\n f\\\"({self.batch_shape}).\\\"\\n )\\n X_tf = X.clone()\\n k = Kumaraswamy(\\n concentration1=self.concentration1, concentration0=self.concentration0\\n )\\n # unnormalize from [eps, 1-eps] to [0,1]\\n X_tf[..., self.indices] = (\\n (k.icdf(X_tf[..., self.indices]) - self._X_min) / self._X_range\\n ).clamp(0.0, 1.0)\\n return X_tf\",\n \"def triax2squeeze(t,e):\\n a = 1.\\n c = e\\n b = np.sqrt(c**2 - t*(c**2-a**2))\\n return b\",\n \"def apply_spectral_norm(x, is_training=False):\\n weights_shape = shape_list(x)\\n other, num_filters = tf.reduce_prod(weights_shape[:-1]), weights_shape[-1]\\n\\n # Reshape into a 2-D matrix with outer size num_filters.\\n weights_2d = tf.reshape(x, (other, num_filters))\\n\\n # v = Wu / ||W u||\\n with tf.variable_scope(\\\"u\\\", reuse=tf.AUTO_REUSE):\\n u = tf.get_variable(\\\"u\\\", [num_filters, 1], initializer=tf.truncated_normal_initializer(), trainable=False)\\n v = tf.nn.l2_normalize(tf.matmul(weights_2d, u))\\n\\n # u_new = vW / ||v W||\\n u_new = tf.nn.l2_normalize(tf.matmul(tf.transpose(v), weights_2d))\\n\\n # s = v*W*u\\n spectral_norm = tf.squeeze(tf.matmul(tf.transpose(v), tf.matmul(weights_2d, tf.transpose(u_new))))\\n\\n # set u equal to u_new in the next iteration.\\n assign_op = tf.assign(u, tf.transpose(u_new))\\n if is_training:\\n with tf.control_dependencies([assign_op]):\\n result = tf.divide(x, spectral_norm)\\n else:\\n result = tf.divide(x, spectral_norm)\\n return result\",\n \"def normalise_layer(tensor, scope=None, epsilon=1e-5):\\n\\n # Check if tensor is 2D\\n assert len(tensor.get_shape()) == 2\\n\\n # Calculate mean and variance of tensor\\n mean, variance = tf.nn.moments(tensor, axes=[1], keep_dims=True)\\n\\n # Ensure scope is a string\\n if not isinstance(scope, str):\\n scope = ''\\n\\n with tf.variable_scope(scope + 'layer_norm'):\\n scale = tf.get_variable('scale',\\n shape=[tensor.get_shape()[1]],\\n initializer=tf.constant_initializer(1))\\n shift = tf.get_variable('shift',\\n shape=[tensor.get_shape()[1]],\\n initializer=tf.constant_initializer(0))\\n\\n layer_norm_initial = (tensor - mean) / tf.sqrt(variance + epsilon)\\n\\n return layer_norm_initial * scale + shift\",\n \"def normalize_data(mjd, flux, ignore):\\n\\n t = mjd - np.min(mjd)\\n y_err = np.sqrt(flux) \\n y = flux[t > ignore]\\n y_err = y_err[t > ignore]\\n t = t[t > ignore]\\n y_err /= np.max(y)\\n y /= np.max(y)\\n\\n sorted_tups = sorted(list(zip(t, y, y_err)), key=lambda x: x[0])\\n t = np.array([tup[0] for tup in sorted_tups])\\n y = np.array([tup[1] for tup in sorted_tups])\\n y_err = np.array([tup[2] for tup in sorted_tups])\\n\\n return t, y, y_err\",\n \"def normalize(array, inplace=False):\\n if inplace:\\n array -= ds_mean\\n array /= ds_std\\n else:\\n array = (array - ds_mean) / ds_std\\n return array\",\n \"def release_slopes_wt_normalized(self):\\n if np.any(np.isnan(self.wt_r_slopes)):\\n raise ValueError(\\\"Can't normalize to WT because there are \\\"\\n \\\"missing entries for initial WT release \\\"\\n \\\"rates (found NaNs in the matrix).\\\")\\n (wt_r_avgs, wt_r_stds) = self.wt_release_avg_std()\\n r_slopes_norm = np.zeros(self.r_slopes.shape)\\n for act_ix in range(r_slopes_norm.shape[0]):\\n r_slopes_norm[act_ix] = self.r_slopes[act_ix] / wt_r_avgs[act_ix]\\n return r_slopes_norm\",\n \"def _denormalize_joints(x, mean, std):\\n assert x.ndim == 3\\n assert x.shape == mean.shape == std.shape\\n return x * std + mean\",\n \"def _normalize(X: np.ndarray) -> np.ndarray:\\n # return X * np.sqrt(1 / np.sum(X ** 2, axis=1))[:, None]\\n return X * np.sqrt(X.shape[1] / np.sum(X ** 2, axis=1))[:, None]\",\n \"def normalize(self):\\n\\n if not self.magnitude():\\n return Vector(0, 0)\\n\\n l = 1 / self.magnitude()\\n return self.scale(l)\",\n \"def normalise_tensor(inp, scope, *, axis=-1, epsilon=1e-5):\\n with tf.variable_scope(scope):\\n e_dim = inp.get_shape().as_list()[-1]\\n g = tf.get_variable(\\n 'g', [e_dim], initializer=tf.constant_initializer(1))\\n b = tf.get_variable(\\n 'b', [e_dim], initializer=tf.constant_initializer(0))\\n\\n u = tf.reduce_mean(inp, axis=axis, keepdims=True)\\n s = tf.reduce_mean(tf.square(inp - u), axis=axis, keepdims=True)\\n out = (inp - u) * tf.math.rsqrt(s + epsilon)\\n out = out * g + b\\n\\n return out\",\n \"def _normalize(self, inp):\\n \\n return inp/inp.sum()\",\n \"def norm(t):\\n return (t - t.min()) / (t.max()-t.min())\",\n \"def normalize_weights(w, dims=(0,), bias=1e-5):\\n with tf.name_scope('normalization'):\\n return w / (tf.sqrt(tf.reduce_sum(tf.square(w), dims, keep_dims=True) + bias))\",\n \"def t_to_repr(self, wave):\\n samples_n = tf.shape(wave)[1]\\n tf.assert_equal(tf.truncatemod(samples_n, self.freq_n), 0,\\n f'Number of samples ({samples_n}) needs to be a multiple of {self.freq_n}')\\n\\n return self.mdctransformer.transform(wave)\",\n \"def _normalize(self):\\n\\n n = len(self.e2[0])\\n E = []\\n\\n for e2 in self.e2:\\n if len(e2) != n:\\n print 'WARNING: non consistent length in error statistics!!!'\\n E.append(np.nansum(np.sqrt(e2))) # temporal aggregation\\n\\n E = np.asarray(E)\\n EM = E.mean() # take square root, as e2 is still the squared error!\\n self.e_norm = (E - EM) / EM # see Glecker et al, eq.2\",\n \"def renormalize(self):\\n update_set, coeff_tots = self.coeff_totals()\\n coeffs = [c for c in self.coeff]\\n for i,u in enumerate(self.updates):\\n for j,u2 in enumerate(update_set):\\n if u is u2:\\n coeffs[i] /= coeff_tots[j]\\n return ExplicitIntegrator(coeffs, self.updates)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.62458545","0.5984606","0.59279716","0.5894949","0.58369017","0.57565486","0.5707113","0.5672379","0.5637169","0.56276435","0.5559953","0.55526054","0.5547735","0.55388963","0.55312216","0.5528915","0.5524366","0.551839","0.5514702","0.54963565","0.54954207","0.54805493","0.5448263","0.5420744","0.54082334","0.54031914","0.53980225","0.5385055","0.53569645","0.5346754","0.53392357","0.5306293","0.5293489","0.5289978","0.5283621","0.5279813","0.52775574","0.5269147","0.52563447","0.5237199","0.5230318","0.5230318","0.52202755","0.5196988","0.5193961","0.5181316","0.5169053","0.5168255","0.51673955","0.5163731","0.51603806","0.51567936","0.5146705","0.5146102","0.5142262","0.51402175","0.5137592","0.51341856","0.5128173","0.51157296","0.51156473","0.51148945","0.51147026","0.5112054","0.51009214","0.5096889","0.5094434","0.50867575","0.50847167","0.50814223","0.507639","0.5075917","0.5074613","0.50743276","0.5071935","0.5060003","0.5059561","0.50566137","0.5026154","0.50257874","0.5025073","0.501934","0.50185364","0.5017306","0.50078005","0.5002168","0.5001852","0.5000293","0.4999081","0.499908","0.49968073","0.49965024","0.49962053","0.49921012","0.4990967","0.4985491","0.49801537","0.49781093","0.4972959","0.49685958"],"string":"[\n \"0.62458545\",\n \"0.5984606\",\n \"0.59279716\",\n \"0.5894949\",\n \"0.58369017\",\n \"0.57565486\",\n \"0.5707113\",\n \"0.5672379\",\n \"0.5637169\",\n \"0.56276435\",\n \"0.5559953\",\n \"0.55526054\",\n \"0.5547735\",\n \"0.55388963\",\n \"0.55312216\",\n \"0.5528915\",\n \"0.5524366\",\n \"0.551839\",\n \"0.5514702\",\n \"0.54963565\",\n \"0.54954207\",\n \"0.54805493\",\n \"0.5448263\",\n \"0.5420744\",\n \"0.54082334\",\n \"0.54031914\",\n \"0.53980225\",\n \"0.5385055\",\n \"0.53569645\",\n \"0.5346754\",\n \"0.53392357\",\n \"0.5306293\",\n \"0.5293489\",\n \"0.5289978\",\n \"0.5283621\",\n \"0.5279813\",\n \"0.52775574\",\n \"0.5269147\",\n \"0.52563447\",\n \"0.5237199\",\n \"0.5230318\",\n \"0.5230318\",\n \"0.52202755\",\n \"0.5196988\",\n \"0.5193961\",\n \"0.5181316\",\n \"0.5169053\",\n \"0.5168255\",\n \"0.51673955\",\n \"0.5163731\",\n \"0.51603806\",\n \"0.51567936\",\n \"0.5146705\",\n \"0.5146102\",\n \"0.5142262\",\n \"0.51402175\",\n \"0.5137592\",\n \"0.51341856\",\n \"0.5128173\",\n \"0.51157296\",\n \"0.51156473\",\n \"0.51148945\",\n \"0.51147026\",\n \"0.5112054\",\n \"0.51009214\",\n \"0.5096889\",\n \"0.5094434\",\n \"0.50867575\",\n \"0.50847167\",\n \"0.50814223\",\n \"0.507639\",\n \"0.5075917\",\n \"0.5074613\",\n \"0.50743276\",\n \"0.5071935\",\n \"0.5060003\",\n \"0.5059561\",\n \"0.50566137\",\n \"0.5026154\",\n \"0.50257874\",\n \"0.5025073\",\n \"0.501934\",\n \"0.50185364\",\n \"0.5017306\",\n \"0.50078005\",\n \"0.5002168\",\n \"0.5001852\",\n \"0.5000293\",\n \"0.4999081\",\n \"0.499908\",\n \"0.49968073\",\n \"0.49965024\",\n \"0.49962053\",\n \"0.49921012\",\n \"0.4990967\",\n \"0.4985491\",\n \"0.49801537\",\n \"0.49781093\",\n \"0.4972959\",\n \"0.49685958\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.630154"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":92582,"cells":{"query":{"kind":"string","value":"Perform beta forecast for a set of scenarios on a regression."},"document":{"kind":"string","value":"def forecast(run_metadata,\n specification,\n output_root, mark_best, production_tag,\n preprocess_only,\n verbose, with_debugger,\n **input_versions):\n cli_tools.configure_logging_to_terminal(verbose)\n\n do_forecast(\n run_metadata=run_metadata,\n specification=specification,\n output_root=output_root,\n mark_best=mark_best,\n production_tag=production_tag,\n preprocess_only=preprocess_only,\n with_debugger=with_debugger,\n input_versions=input_versions,\n )\n\n logger.info('**Done**')"},"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 linear_regression_forecasting(x_train,y_train,x_valid,y_valid,x_test,y_test):\n y_train = y_train.reshape(TRAINING_BATCH_SIZE,N_PREDICTIONS*N_OUTPUT_FEATURES)\n y_valid = y_valid.reshape(VALIDATION_BATCH_SIZE,N_PREDICTIONS*N_OUTPUT_FEATURES)\n layer1 = keras.layers.Flatten(input_shape=[N_INPUT_STEPS,N_INPUT_FEATURES]) #input layer flattens each batch instance from [n_steps,n_input_features] to [n_steps*n_input_features]\n layer2 = keras.layers.Dense(N_PREDICTIONS*N_OUTPUT_FEATURES) #fully connected layer solves combination of linear equations\n model = keras.models.Sequential([layer1,layer2])\n model.compile(loss=\"mse\",optimizer=\"adam\")\n training_history = model.fit(x_train,y_train,epochs=N_EPOCHS,validation_data=(x_valid,y_valid),verbose=0)\n y_pred = model.predict(x_test, TESTING_BATCH_SIZE)\n y_pred = y_pred.reshape(TESTING_BATCH_SIZE,N_PREDICTIONS,N_OUTPUT_FEATURES)\n return training_history.history, y_pred, model","def _regression_loop(endog, exog, model, lasso_positive, alpha=50):\n mod_result = _build_regression(exog, endog, model, lasso_positive, alpha=alpha)\n beta = np.hstack([mod_result.intercept_, mod_result.coef_])\n \n return beta","def Empirical_Bayes_Estimator(df_train, df_test):\n\n # number of individual forecasts and number of periods\n K = df_test.shape[1]\n T = df_train.shape[0]\n\n # define the prior weights (simple average, with intercept equal zero)\n beta_0 = np.append(np.array(0), np.full(K, fill_value=1/K, dtype=float))\n\n # design matrix (intercept + individual forecasts)\n F = df_train.iloc[:, 1:].values\n F = np.insert(F, 0, 1, axis=1)\n\n # define y (observed values)\n y = df_train.iloc[:, 0].values\n\n # OLS weights\n beta_hat = np.dot(np.linalg.inv(np.dot(np.transpose(F), F)),\n np.dot(np.transpose(F), y))\n\n # sigma\n sigma_hat_sq = np.dot(\n np.transpose(y - np.dot(F, beta_hat)),\n y - np.dot(F, beta_hat)\n ) / T\n\n # tau\n num = np.dot(np.transpose(beta_hat - beta_0), beta_hat - beta_0)\n denum = np.trace(np.linalg.inv(np.dot(np.transpose(F), F)))\n tau_hat_sq = num / denum - sigma_hat_sq\n\n # combining weights\n shrinkage = 1 - sigma_hat_sq / (sigma_hat_sq + tau_hat_sq)\n beta_1_hat = beta_0 + shrinkage*(beta_hat - beta_0)\n\n # predictions\n df_pred = pd.DataFrame(\n {\"Empirical Bayes Estimator\":\n beta_1_hat[0] + np.dot(df_test, beta_1_hat[1:])},\n index=df_test.index)\n\n return df_pred","def run_forecast(self, people, accounts, debts):\n # We don't want to mutate the inputs, so create copies:\n memo = {}\n people = deepcopy(people, memo=memo)\n accounts = deepcopy(accounts, memo=memo)\n debts = deepcopy(debts, memo=memo)\n\n # Build Scenario first so that we have access to initial_year:\n memo = {}\n scenario = self.get_param(Parameter.SCENARIO, memo=memo)\n initial_year = scenario.initial_year # extract for convenience\n\n # Retrieve the necessary strategies for building SubForecasts:\n living_expenses_strategy = self.get_param(\n Parameter.LIVING_EXPENSES_STRATEGY, memo=memo)\n saving_strategy = self.get_param(\n Parameter.SAVING_STRATEGY, memo=memo)\n withdrawal_strategy = self.get_param(\n Parameter.WITHDRAWAL_STRATEGY, memo=memo)\n tax_treatment = self.get_param(\n Parameter.TAX_TREATMENT, memo=memo)\n\n # Now build each of the SubForecast objects required by Forecast\n income_forecast = IncomeForecast(\n initial_year=initial_year,\n people=people)\n living_expenses_forecast = LivingExpensesForecast(\n initial_year=initial_year,\n people=people,\n living_expenses_strategy=living_expenses_strategy)\n saving_forecast = SavingForecast(\n initial_year=initial_year,\n retirement_accounts=accounts,\n debt_accounts=debts,\n transaction_strategy=saving_strategy)\n withdrawal_forecast = WithdrawalForecast(\n initial_year=initial_year,\n people=people,\n accounts=accounts,\n transaction_strategy=withdrawal_strategy)\n tax_forecast = TaxForecast(\n initial_year=initial_year,\n people=people,\n tax_treatment=tax_treatment)\n\n # With these SubForecasts defined, building Forecast is trivial:\n forecast = Forecast(\n income_forecast=income_forecast,\n living_expenses_forecast=living_expenses_forecast,\n saving_forecast=saving_forecast,\n withdrawal_forecast=withdrawal_forecast,\n tax_forecast=tax_forecast,\n scenario=scenario)\n\n # Forecasts run automatically on init, so we're done!\n return forecast","def run():\n\n df = read_input() # the parameters\n df = add_time_period(df) # a feature\n df = is_holiday(df) # a feature\n df = scale_continous(df) # continous feature transformation\n df = encode_dummy(df) # categorical feature transformation\n df = order_columns(df) # ordering model inputs\n model = load_model() # the multiple linear regression model\n prediction = int(model.predict(df)) # form a prediction\n return prediction # return the prediction","def test_run_regressions(test_data, model):\n answer = model._Run_Regressions(\n test_data,\n 100,\n forecasts={\"arima\", \"holtwinters\", \"prophet\", \"arima_r\", \"sarima_r\"}\n )","def AdaBoost(df_train, df_test, phi):\n\n # number of periods\n T = df_train.shape[0]\n T_test = df_test.shape[0]\n\n # initialize observation weights\n w = np.full(T, 1, dtype=float)\n w_sum = np.sum(w)\n\n # matrix of individual forecasts\n F = df_train.iloc[:, 1:]\n\n # dependent variable\n y = df_train.iloc[:, 0]\n\n # prepare testing set\n test_F = df_test.values\n test_y_pred = np.full((T_test, 50), np.nan, dtype=float)\n\n # prepare MLP object\n MLP = MLPRegressor(hidden_layer_sizes=2, activation=\"tanh\", solver=\"lbfgs\")\n\n # AdaBoost iteration steps\n for i in range(50):\n\n # compute observation probabilities from weights\n prob = w / w_sum\n\n # generate the training sample\n train_index = np.random.choice(T, size=T, p=prob)\n train_F = df_train.iloc[train_index, 1:]\n # train_F_t = np.swapaxes(train_F, 0, 1)\n train_y = df_train.iloc[train_index, 0]\n\n # training the model by OLS\n # theta_hat = np.linalg.multi_dot(\n # [np.linalg.inv(np.dot(train_F_t, train_F)), train_F_t,train_y]\n # )\n\n # fit MLP\n MLP_fit = MLP.fit(train_F, train_y)\n\n # predictions (on the original dataset)\n # y_pred = np.dot(F, theta_hat)\n # test_y_pred[:, i] = np.dot(test_F, theta_hat)\n y_pred = MLP_fit.predict(F)\n test_y_pred[:, i] = MLP_fit.predict(test_F)\n\n # absolute relative errors\n ARE = np.abs((y - y_pred)/y)\n\n # threshold losses\n L = (ARE > phi)*1\n\n # weighted average loss\n L_bar = np.dot(prob, L)\n\n # I believe this is a necesarry condition for the AdaBoost algorithm\n # to work (even though it is not discussed by the author), because,\n # else if the error is too low, beta becomes > 1 and the model could\n # get super-overfit on just few observations\n # L_bar > 1/np.exp(1) ensures, that some weight updating always happens\n # L_bar = max(L_bar, 1/np.exp(1))\n L_bar = max(L_bar, 0.5)\n # model confidence\n beta = np.log(1/L_bar)\n\n # observation weights updating, absolute value for computational rsns.\n w = np.abs(np.multiply(w, beta**(1-L)))\n w_sum = np.sum(w)\n\n # forecast combination\n pred = np.mean(test_y_pred, axis=1)\n\n # output\n df_pred = pd.DataFrame(\n {\"AdaBoost\": pred},\n index=df_test.index\n )\n\n return df_pred","def predict():\n model = LinearRegression().fit(input_data[['x']], input_data['y'])\n future_vals = [[20], [21], [22]]\n return None","def rtest_predictoutcome():\n\n #define cohort size\n npatients = 2\n\n #init healthy patients\n simulator = AbbcEnvironment(patients=npatients)\n\n #simulate healthy patients for long term in short term increments\n nstep = int(long_term/short_term)\n\n #define action taken : -1 means patients will be simulated as healthy\n action = np.repeat(-1, npatients)\n\n #init episode list\n episode = [simulator.state]\n\n #main simulation loop to generate episodes\n for step in range(nstep):\n episode += simulator.take_action(action=action, simtime=short_term)\n\n #episode length is 1+2*nstep consisting of intit state (5xnpat) followed by\n # next state and reward (1xnpat) repeating each time step.\n #print(episode)\n #print(len(episode))\n\n #---semi gradient temporal difference (0) algorithm ---\n #init hyperparameters\n alpha = .1 #learning rate\n #init Value function model\n agent = AbbcAgent(discount=1.0)\n #loop over episodes\n for patient in range(npatients):\n #state = [nstep]\n #state += episode[0][:,patient] #get inital state\n state = np.append(episode[0][:,patient],nstep).reshape((6,1)) #get inital state\n\n print(state)\n #loop over time steps in episode\n for k in range(1,nstep+1):\n #get next state and reward\n #nextstate = [nstep-k]\n #nextstate = episode[k*2-1][:,patient]\n nextstate = np.append(episode[k*2-1][:,patient],nstep-k).reshape((6,1))\n\n reward = episode[k*2][patient]\n\n #get magnitude for forces\n magnitude = alpha * (reward + agent.discount * agent.get_value(nextstate)\n - agent.get_value(state))\n #compute forces\n forces = computeforces(agent.prednet, state, 0, \"iden\")\n\n #update model\n for layer in forces:\n index = layer[\"layer\"]\n agent.prednet[index][\"weight\"] += magnitude * layer[\"fweight\"]\n agent.prednet[index][\"bias\"] += magnitude * layer[\"fbias\"]\n\n state = np.copy(nextstate)\n\n\n #make predictions\n state = np.append(episode[0][:,patient],nstep).reshape((6,1)) #get inital state\n print(agent.get_value(state))\n\n #Value function approximates outcome return at time horizon.\n assert(False)\n\n ##define action taken\n #action = np.repeat(2, npatients)\n ##main simulation loop\n #for step in range(nstep):\n # _, drugy_reward[step,:] = simulator.take_action(action=action, simtime=short_term)","def forecast(self, requests):\n responses = []\n SOC = self.SOC \n\n for number in range(self.numHVAC): #loop through all HVACs\n TinRB = self.TinInitialB[number]\n TwallRB = self.TwallInitialB[number]\n TmassRB = self.TmassInitialB[number]\n TatticRB = self.TatticInitialB[number]\n elementOnRB = self.elementOnB[number]\n\n TinR = self.TinInitial[number]\n TwallR = self.TwallInitial[number]\n TmassR = self.TmassInitial[number]\n TatticR = self.TatticInitial[number]\n\n SOC_bR = self.SOCb[number]\n SOCR = self.SOC[number]\n\n elementOnR = self.elementOn[number]\n lockonR = self.lockon[number]\n lockoffR = self.lockoff[number]\n cycle_offR = self.cycle_off_base[number]\n cycle_on_baseR = self.cycle_on_base[number]\n cycle_off_gridR = self.cycle_off_grid[number]\n cycle_on_gridR = self.cycle_on_grid[number]\n\n # Iterate and process each request in fleet_requests\n for req in requests:\n ts = req.ts_req\n dt = int(req.sim_step.total_seconds())\n p_req = req.P_req\n q_req = req.Q_req\n res = self.run(p_req, q_req, self.SOC, self.time, dt, ts)\n\n return res\n \n # reset the model\n for number in range(self.numHVAC): #loop through all HVACs\n self.SOC = SOCR \n self.SOCb = SOC_bR \n\n self.TinInitialB[number] = TinRB\n self.TwallInitialB[number] = TwallRB\n self.TmassInitialB[number] = TmassRB\n self.TatticInitialB[number] = TatticRB\n\n self.TinInitial[number] = TinR\n self.TwallInitial[number] = TwallR\n self.TmassInitial[number] = TmassR\n self.TatticInitial[number] = TatticR\n\n self.elementOn[number] = elementOnR\n self.elementOnB[number] = elementOnRB\n\n self.lockon[number] = lockonR\n self.lockoff[number] = lockoffR\n self.cycle_off_base[number] = cycle_offR\n self.cycle_on_base[number] = cycle_on_baseR\n self.cycle_off_grid[number] = cycle_off_gridR\n self.cycle_on_grid[number] = cycle_on_gridR\n\n return responses","def alpha_beta(returns, factor_returns):\n\n ret_index = returns.index\n beta, alpha = sp.sp.stats.linregress(factor_returns.loc[ret_index].values,\n returns.values)[:2]\n\n return alpha * APPROX_BDAYS_PER_YEAR, beta","def fbeta(true_label, prediction):\n true_label = np.asarray(true_label)\n prediction = np.asarray(prediction)\n return fbeta_score(true_label, prediction, beta=2, average='samples')","def calc_beta(self) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:\n snr_limit = 5\n noisy_data = NoisyData(*self._get_args(), snr_limit)\n beta = noisy_data.screen_data(self.processed_data['backscatter'])\n beta_smooth = self._calc_beta_smooth(beta)\n beta_smooth = noisy_data.screen_data(beta_smooth, is_smoothed=True)\n return self.processed_data['backscatter'], beta, beta_smooth","def predict(self, days=None, name=\"Main\"):\n # Arguments\n if name not in self._reghandler_dict:\n raise UnExecutedError(f\"Scenario.fit(name={name})\")\n # Prediction with regression model\n handler = self._reghandler_dict[name]\n df = handler.predict()\n # -> end_date/parameter values\n df.index = [date.strftime(self.DATE_FORMAT) for date in df.index]\n df.index.name = \"end_date\"\n # Days to predict\n days = days or [len(df) - 1]\n self._ensure_list(days, candidates=list(range(len(df))), name=\"days\")\n phase_df = df.reset_index().loc[days, :]\n # Set new future phases\n for phase_dict in phase_df.to_dict(orient=\"records\"):\n self.add(name=name, **phase_dict)\n return self","def predict(self, **kwargs):\n\n logging.debug(\n \"Call predict() with parameters. \"\n \"Forecast 1 step only, kwargs:{kwargs}\".format(kwargs=kwargs)\n )\n\n X_test = self.df[-self.step :][self.feature_names]\n X_test = self.scaler.transform(X_test)\n y_predict = self.model.predict(X_test)\n poly_now = self.y_train_season_obj[-1]\n first_occ = np.where(self.y_train_season_obj == poly_now)\n polynext = self.y_train_season_obj[first_occ[0][0] + self.step]\n now = self.df[\"y\"][-self.step :]\n return (now - poly_now) - y_predict + polynext","def bias_var(x, y, z, first_poly = 4, complexity = 10, N = 100, method = 'OLS', seed = 42, lam = 0, train = 0.7, folds = 5):\n\n bias = np.zeros(complexity + 1)\n variance = np.zeros(complexity + 1)\n z_real = FrankeFunction(x, y)\n\n complx = np.arange(first_poly, first_poly + complexity + 1, 1)\n\n for i in range(complexity + 1):\n print(i)\n model = regression(x, y, z, k = first_poly + i, split = True, train = train, seed = seed)\n\n _, _, _, z_real_test = model.train_test(X = model.X_full, z = np.ravel(z_real), train = train, seed = seed)\n\n counter = 0\n z_tildes = np.zeros((np.size(z_real_test), N))\n for j in range(N):\n\n z_new = FrankeFunction(x, y) + np.random.normal(0, 1, size = x.shape)\n _, _, z_train, _ = model.train_test(X = model.X_full, z = np.ravel(z_new), train = train)\n if method == 'OLS':\n beta = model.OLS(z = z_train)\n elif method == 'Ridge':\n beta = model.Ridge(lam = lam, z = z_train)\n elif method == 'Lasso':\n beta = model.Lasso(lam = lam, z = z_train)\n\n z_tilde = model.z_tilde(beta, X = model.X_test)\n z_tildes[:, j] = np.ravel(z_tilde)\n\n\n bias[i] = np.mean((np.ravel(z_real_test).reshape(-1, 1) - np.mean(z_tildes, axis = 1, keepdims = True))**2)\n variance[i] = np.mean(np.var(z_tildes, axis = 1, keepdims = True))\n\n plt.title(method + ' with N = ' + str(N) + ' times pr complexity')\n plt.plot(complx, bias, 'go--', label = 'Bias', color = 'blue')\n plt.plot(complx, variance, 'go--', label = 'Variance', color = 'red')\n #plt.ylim([np.min(errors_R2[2]*1.2), np.max(errors_R2[0]*1.2)])\n plt.legend()\n plt.xlabel('Polynomial maximum order', fontsize = 14)\n plt.ylabel('Bias/variance', fontsize = 14)\n plt.tight_layout()\n plt.savefig(results_dir + 'bias_variance' + method + '.png')\n\n plt.show()","def estimate_sample_beta(sample):\n x_s, y_s = zip(*sample)\n reg.fit(x_s, y_s)\n betas = reg.weights_\n return betas","def eval_step_b(self, sess, task_b_data):\r\n fdict = self._prerun(sess, None, task_b_data)\r\n prediction_b, y_b = sess.run([self.prediction_b_all, self.labels_b_v_all],\r\n feed_dict=fdict)\r\n\r\n return prediction_b, y_b","def RegressionMain(full_raw, target_col, feature_cols, test_size, model='Ridge', max_train_size=200, embargo_size=1, logpx=True, \n resample_per='B', ewm_span=50, verbose=False, alpha_override=None, lasso_positive=False):\n \n # pre-process\n cols = np.hstack((target_col, feature_cols))\n data = full_raw[cols].copy(deep=True)\n raw_clean = data.asfreq(resample_per).dropna(how='any')\n if ewm_span is not None:\n data = data.ewm(span=ewm_span).mean()\n data = data.asfreq(resample_per).dropna(how='any')\n if logpx:\n data = data.apply(np.log)\n raw_clean = raw_clean.apply(np.log)\n \n dates, betas = [],[]\n \n # get alpha to use in model fits\n ## we only use first quarter of data to not cheat so hard\n x_full = data[feature_cols]\n y_full = data[target_col]\n x_raw_clean = raw_clean[feature_cols]\n \n x_find_alpha = x_full.iloc[:int(data.shape[0]/4)]\n y_find_alpha = y_full.iloc[:int(data.shape[0]/4)]\n tss = TimeSeriesSplit(n_splits=20)\n alpha_space = np.logspace(-6,2,25)\n if model == 'Ridge':\n cv = RidgeCV(alphas=alpha_space, cv=tss)\n cv.fit(x_find_alpha, y_find_alpha)\n alpha = cv.alpha_\n \n elif model == 'Lasso':\n cv = LassoCV(alphas=alpha_space, cv=tss)\n cv.fit(x_find_alpha, y_find_alpha)\n alpha = cv.alpha_\n \n else:\n alpha = 0.0001\n if alpha_override is not None: alpha = alpha_override\n if verbose: print(alpha)\n \n pred_full = pd.Series(name='Pred')\n for train_idx, embargo_idx, test_idx in _embargo_ts_splitter(data, test_size, max_train_size=max_train_size,\n embargo=embargo_size):\n x_train = x_full.iloc[train_idx]\n y_train = y_full.iloc[train_idx]\n beta = _regression_loop(y_train, x_train, model, lasso_positive, alpha=alpha)\n \n x_test = x_raw_clean.iloc[test_idx]\n pred = sm.add_constant(x_test).dot(beta).rename('Pred')\n pred_full = pred_full.append(pred)\n \n # save to return params\n # date associated with beta (for rebalancing) should be day after computation\n dates.append(data.index[test_idx[0]])\n betas.append(beta)\n \n #rescale if necessary\n if logpx:\n pred_full = pred_full.apply(np.exp)\n \n return pred_full, dates, betas","def Componentwise_Boosting(df_train, df_test, nu):\n\n # number of individual forecasts and number of periods\n K = df_test.shape[1]\n T = df_train.shape[0]\n T_test = df_test.shape[0]\n\n # variable of interest\n y = df_train.iloc[:, 0].values[:, np.newaxis]\n y_bar = np.mean(y)\n\n # individual forecasts\n F = np.swapaxes(df_train.iloc[:, 1:].values, 0, 1)[:, :, np.newaxis]\n F_t = np.swapaxes(F, 1, 2)\n F_test = np.swapaxes(df_test.values, 0, 1)[:, :, np.newaxis]\n\n # 5-fold CV to determine optimal M\n # length of training and testing sets\n T_cv_test = int(T/5)\n T_cv_train = T - T_cv_test\n\n # initialize vector to store the precision of fit\n SSR_vec = np.full(1000, 0, dtype=float)\n\n # find the optimal number of boosting iterationts using 5-fold CV\n # CV folds\n for k in range(5):\n\n # definition of the test and training sample for the given CV round\n cv_index = np.full(T, True, dtype=bool)\n cv_index[(k*T_cv_test):((k+1)*T_cv_test)] = False\n\n y_cv = y[cv_index]\n y_cv_bar = np.mean(y_cv)\n y_cv_test = y[~cv_index]\n\n F_cv = F[:, cv_index, :]\n F_cv_t = F_t[:, :, cv_index]\n F_cv_test = F[:, ~cv_index, :]\n\n # initialization step\n psi = np.tile(y_cv_bar, (T_cv_train, 1))\n psi_test = np.tile(y_cv_bar, (T_cv_test, 1))\n\n # main steps\n for m in range(1000):\n\n # compute the negative gradient vector\n u = y_cv - psi\n\n # regress the negative gradient vector on the ind. forecasts\n beta_hat = np.matmul(\n 1/np.matmul(F_cv_t, F_cv),\n np.matmul(F_cv_t, np.tile(u, (K, 1, 1)))\n )\n\n # save the sums of the squared residuals\n SSR = np.dot(np.ones(T_cv_train), (u - (beta_hat * F_cv))**2)\n\n # find the minimum SSR and its corresponding ind.forecast\n k_star = np.argmin(SSR)\n\n # update\n psi += nu * beta_hat[k_star, :, :] * F_cv[k_star, :, :]\n psi_test += nu * beta_hat[k_star, :, :] * F_cv_test[\n k_star, :, :]\n\n # save the precision of the fit\n SSR_vec[m] += np.sum((y_cv_test - psi_test)**2)\n\n # find number of iterations for which the MSE is the lowest\n M = np.argmin(SSR_vec) + 1\n\n # final Gradient Boosting with pre-determined number of iterations M\n # initialization step\n psi = np.tile(y_bar, (T, 1))\n psi_test = np.tile(y_bar, (T_test, 1))\n\n # main steps\n for m in range(M):\n\n # compute the negative gradient vector\n u = y - psi\n\n # regress the negative gradient vector on the individual forecasts\n beta_hat = np.matmul(\n 1/np.matmul(F_t, F),\n np.matmul(F_t, np.tile(u, (K, 1, 1)))\n )\n\n # save the sums of the squared residuals\n SSR = np.dot(np.ones(T), (u - (beta_hat * F))**2)\n\n # find the minimum SSR and its corresponding individual forecast\n k_star = np.argmin(SSR)\n\n # update\n psi += nu * beta_hat[k_star, :, :] * F[k_star, :, :]\n psi_test += nu * beta_hat[k_star, :, :] * F_test[k_star, :, :]\n\n # predictions\n df_pred = pd.DataFrame(\n {\"Componentwise Boosting\": psi_test.flatten()},\n index=df_test.index\n )\n\n return df_pred","def train_predict(self, xTrain, yTrain, xTest, yTest):\n trainStats = {}\n # TODO: DO SGD\n #beta = np.linalg.inv(xTrain.transpose().dot(xTrain)).dot(xTrain.transpose()).dot(yTrain)\n LinearRegression.beta = [[0],[0]]\n intercept = 0\n n = len(xTrain)\n startall = time.time()\n list = [x for x in range(0,len(xTrain))]\n for i in range(self.mEpoch):\n random.shuffle(list)\n Dbetalist = []\n Dinterceptlist = []\n start = time.time()\n for j in range(self.bs):\n # Dbetalist = []\n # Dinterceptlist = []\n indexstart = len(list)/self.bs * j\n indexend = len(list)/self.bs * j + len(list)/self.bs\n subset = xTrain[int(indexstart):int(indexend)]\n #print(subset)\n ypredict = np.dot(subset,LinearRegression.beta) + intercept\n subyTrain = yTrain[int(indexstart):int(indexend)]\n Dbeta = (-2/n)*sum(np.dot(subset.transpose(),(subyTrain-ypredict)))\n Dbetalist.append(Dbeta)\n Dintercept = (-2/n)*sum(subyTrain-ypredict)\n Dinterceptlist.append(Dintercept)\n\n end = time.time()\n timeElapse = end - start\n\n avgDbeta = sum(Dbetalist) / len(Dbetalist)\n avgDintercept = sum(Dinterceptlist) / len(Dinterceptlist)\n #print(avgDbeta)\n # print(avgDintercept)\n LinearRegression.beta = LinearRegression.beta - self.lr * avgDbeta\n #print(LinearRegression.beta)\n # print(beta)\n intercept = intercept - self.lr * avgDintercept\n\n value = {}\n value['time'] = timeElapse\n value['train-mse'] = LinearRegression.mse(LinearRegression, xTrain, yTrain)\n value['test-mse'] = LinearRegression.mse(LinearRegression, xTest, yTest)\n trainStats[i*self.bs] = value\n\n endall = time.time()\n totaltime = endall - startall\n\n\n return trainStats","def cAPM_Constant(df_train, df_test, MaxRPT_r1, MaxRPT, no_rounds):\n\n # number of periods in samples\n T = df_train.shape[0]\n T_test = df_test.shape[0]\n\n # number of agents\n K = df_test.shape[1]\n\n # individual forecasts\n F = df_train.iloc[:, 1:].values\n F_test = df_test.iloc[:, :].values\n\n # outcomes\n outcomes = df_train.iloc[:, 0].values\n\n # initialize matrices containing all predictions and bets\n pred_mat = np.full((no_rounds, K), np.nan, dtype=float)\n bet_mat = np.full((no_rounds, K), np.nan, dtype=float)\n\n # budget initialization\n budgets = np.full(K, 1, dtype=float)\n\n # training the market\n for i in range(T):\n\n # first round betting\n pred_mat[0, :] = F[i, :]\n bet_mat[0, :] = budgets * MaxRPT_r1\n budgets = budgets * (1-MaxRPT_r1)\n\n # rest of rounds betting\n for j in range(1, no_rounds):\n\n pred_mat[j, :] = F[i, :]\n bet_mat[j, :] = budgets * MaxRPT\n budgets = budgets * (1-MaxRPT)\n\n # use IQR to determine outlier error threshold\n abs_errors = np.abs(outcomes[i] - pred_mat)\n Q3, Q1 = np.percentile(abs_errors, [75, 25])\n IQR = Q3 - Q1\n oet = Q3 + (IQR*1.5)\n\n # reward the agents based on accuracy of their predictions\n accuracy = np.maximum(100 * (1 - abs_errors/oet), 1)\n score = np.log(accuracy)\n revenue = np.multiply(score, bet_mat)\n budgets = budgets + np.sum(revenue, axis=0)\n\n # rescaling the budget (only for computational reasons)\n budgets = budgets/np.sum(budgets)\n\n # using the market for the out-of-sample predictions\n pred = np.full(T_test, np.nan, dtype=float)\n for i in range(T_test):\n\n # reset test budget\n budgets_test = np.copy(budgets)\n\n # first round betting\n pred_mat[0, :] = F_test[i, :]\n bet_mat[0, :] = budgets_test * MaxRPT_r1\n budgets_test = budgets_test * (1-MaxRPT_r1)\n\n # rest of rounds betting\n for j in range(1, no_rounds):\n\n pred_mat[j, :] = F_test[i, :]\n bet_mat[j, :] = budgets_test * MaxRPT\n budgets_test = budgets_test * (1-MaxRPT)\n\n # final round market prediction is the c-APM prediction\n pred[i] = np.dot(\n pred_mat[no_rounds-1, :],\n bet_mat[no_rounds-1, :])/np.sum(bet_mat[no_rounds-1, :])\n\n # output\n df_pred = pd.DataFrame(\n {\"c-APM (Constant)\": pred},\n index=df_test.index\n )\n\n return df_pred","def a_test2_bbvi():\n model = ARIMAX(formula=\"y ~ x1 + x2\", data=data, ar=1, ma=1, family=Exponential())\n x = model.fit('BBVI',iterations=100)\n assert(len(model.latent_variables.z_list) == 5)\n lvs = np.array([i.value for i in model.latent_variables.z_list])\n assert(len(lvs[np.isnan(lvs)]) == 0)","def a_test2_bbvi():\n model = ARIMAX(formula=\"y ~ x1 + x2\", data=data, ar=1, ma=1, family=Exponential())\n x = model.fit('BBVI',iterations=100)\n assert(len(model.latent_variables.z_list) == 5)\n lvs = np.array([i.value for i in model.latent_variables.z_list])\n assert(len(lvs[np.isnan(lvs)]) == 0)","def _simulation_forecast(\n self,\n parameters: NDArray,\n resids: NDArray,\n backcast: Union[float, NDArray],\n var_bounds: NDArray,\n start: int,\n horizon: int,\n simulations: int,\n rng: RNGType,\n ) -> VarianceForecast:","def evaluate(self):\n # define start index test set\n start_test_set = int(len(self.X) * 2 / 3)\n\n # Different methods for cummulativa vs day-ahead forecasting\n if self.forecast_horizon == 1:\n # In sample\n lin_residuals_in_sample = self.y - (self.betas[0] + np.dot(self.X, self.betas[1]))\n self.rmse_in_sample = np.mean(lin_residuals_in_sample ** 2) ** 0.5\n self.var_in_sample = np.var(self.y)\n\n # Out of sample\n # Calculate MSE of wls-ev prediction\n self.mse_wlsev = np.mean((self.y[start_test_set:] - self.ols_predict()) ** 2)\n # Calculate MSE of benchmark prediction\n self.mse_benchmark = np.mean((self.y[start_test_set:] - self.benchmark_predict()) ** 2)\n else:\n # In Sample with betas estimated on full time series\n lin_residuals_in_sample = rolling_sum(self.y, self.forecast_horizon) - (\n self.betas[0] + np.dot(self.X[:-(self.forecast_horizon-1)], self.betas[1]))\n self.rmse_in_sample = np.mean(lin_residuals_in_sample ** 2) ** 0.5\n self.var_in_sample = np.var(rolling_sum(self.y, self.forecast_horizon))\n\n # Out of sample\n # calculate realized cummulative returns over forecast horizon sequences\n cum_rets_realized = rolling_sum(self.y[start_test_set:], self.forecast_horizon)\n # Calculate MSE of wls-ev prediction, only where realized values are available\n self.mse_wlsev = np.mean((cum_rets_realized - self.ols_predict()[:-(self.forecast_horizon-1)]) ** 2)\n # Calculate MSE of benchmark prediction, only where realized values are available\n self.mse_benchmark = np.mean(\n (cum_rets_realized - self.benchmark_predict()[:-(self.forecast_horizon-1)]) ** 2)\n\n # Calculate out of sample r-squared\n self.oos_r_squared = 1 - (self.mse_wlsev / self.mse_benchmark)\n # Calculate in sample r-squared\n self.in_sample_r_squared = 1.0 - (self.rmse_in_sample ** 2) / self.var_in_sample","def run_regression(auto_input,\n variables: list = ['magasin', 'tilsig'],\n regions: list = ['NO1', 'NO2', 'NO3', 'NO4', 'NO5', 'SE1', 'SE2', 'SE3', 'SE4'],\n jupyter: bool = False,\n backup: bool = False,\n loop: bool = False,\n write: bool = True,\n week_nb: int = False,\n year: int = False) -> None:\n\n start_tuning = utctime_now()\n\n for region in regions:\n\n if not region in ['NO1', 'NO2', 'NO3', 'NO4', 'NO5', 'SE1', 'SE2', 'SE3', 'SE4']:\n sys.exit(\"Region must be one out of: 'NO1', 'NO2', 'NO3', 'NO4', 'NO5', 'SE1', 'SE2', 'SE3', 'SE4'\")\n\n for variable in variables:\n\n if not variable in ['magasin', 'tilsig']:\n sys.exit(\"Variable must be either 'tilsig' or 'magasin'\")\n\n print('---------------------------------------------------------------')\n print(' {}, {} '.format(region, variable))\n print('---------------------------------------------------------------')\n\n \n \n df_week, MagKap = auto_input[variable]\n \n period, forecast_time, read_start = rs.get_timeperiods(variable, week_nb, year)\n \n reg_end = (pd.to_datetime(time.strftime(forecast_time), format=\"%Y.%m.%d\") - Timedelta(days=7)).strftime(\n '%Y.%m.%d')\n\n if (0 <= today.weekday() <= 1) or (today.weekday() == 2 and today.hour < 14): # True for tipping\n last_forecast = forecast_time\n else:\n last_forecast = forecast_time\n\n df_cleaned = deletingNaNs(df_week.loc[:last_forecast])\n\n if loop:\n if variable == 'tilsig':\n max_kandidater = 196\n min_kandidater = 2\n\n else:\n max_kandidater = 171\n min_kandidater = 2\n\n max_weeks = 238 #288\n min_weeks = 16\n print('max ant. kandidater: {}, min ant. kandidater: {}'.format(max_kandidater, min_kandidater))\n print('max ant. uker: {}, min ant. uker: {}'.format(max_weeks, min_weeks))\n\n start_time_loop = utctime_now()\n fasit, fasit_key = rs.make_fasit(variable, region, reg_end, period)\n print('Fasit er lest inn.\\n')\n\n if fasit[fasit_key][:reg_end].isnull().any():\n print('OBS: Det mangler verdier på fasiten! Går videre til neste region i loopen..')\n print(fasit[fasit_key][:reg_end].isnull())\n continue\n\n sorted_r2 = get_R2_sorted(variable, df_cleaned, fasit, fasit_key)\n\n if loop:\n max_p = 0.001 # Beste testresultat SvF\n\n # First loop: Tuning number of candidates for best possible R2 combined\n df_ant_kandidater = pd.DataFrame(columns=columns)\n for antall in range(min_kandidater, max_kandidater + 1, 1):\n if antall > len(sorted_r2):\n chosen_r2 = sorted_r2\n else:\n chosen_r2 = sorted_r2[:antall]\n output = make_estimate(df_cleaned, fasit, fasit_key, last_forecast, first_period, max_p, chosen_r2,\n loop=True)\n df_ant_kandidater = df_ant_kandidater.append(\n {columns[0]: output[0], columns[1]: output[1], columns[2]: output[2], columns[3]: output[3],\n columns[4]: output[4], columns[5]: output[5], columns[6]: output[6]}, ignore_index=True)\n if antall > len(sorted_r2):\n print('Feilmelding: Ønsket antall kandidater overskrider maks (%i).\\n' % len(sorted_r2))\n break\n idx_max = df_ant_kandidater.r2_samlet.idxmax(skipna=True)\n ant_kandidater_beste = int(df_ant_kandidater.ant_kandidater.values[idx_max])\n print('Beste ant_kandidater loop 1: ', ant_kandidater_beste)\n\n # Second loop: tuning length of the short regression for best possible R2 combined, using the best number of\n # candidates found in the First loop.\n final_chosen_r2 = sorted_r2[:ant_kandidater_beste]\n df_short_period = pd.DataFrame(columns=columns)\n for short_period in range(min_weeks, max_weeks + 1, 1):\n short_period = int(short_period)\n output = make_estimate(df_cleaned, fasit, fasit_key, last_forecast, short_period, max_p,\n final_chosen_r2, loop=True)\n df_short_period = df_short_period.append(\n {columns[0]: output[0], columns[1]: output[1], columns[2]: output[2], columns[3]: output[3],\n columns[4]: output[4], columns[5]: output[5], columns[6]: output[6]}, ignore_index=True)\n idx_max = df_short_period.r2_samlet.idxmax(skipna=True)\n short_period_beste = int(df_short_period.short_period.values[idx_max])\n print('Beste short_period loop 2: ', short_period_beste)\n\n # Getting the best input variables from loop and write to input_variables_from_tuning.txt\n df_all_methods = pd.concat([df_ant_kandidater, df_short_period], ignore_index=True, sort=False)\n idx_max = df_all_methods.r2_samlet.idxmax(skipna=True)\n ant_kandidater_beste = int(df_all_methods.ant_kandidater.values[idx_max])\n chosen_r2_beste = sorted_r2[:ant_kandidater_beste]\n short_period_beste = df_all_methods.short_period.values[idx_max]\n ws.write_input_variables_to_file(region, variable, max_p, ant_kandidater_beste, short_period_beste)\n\n print('\\nTuning for regionen tok %.0f minutter. \\n' % ((utctime_now() - start_time_loop) / 60))\n\n else:\n # getting the best variables from input_variables_from_tuning.txt or input_variables_backup.txr\n short_period_beste, max_p, ant_kandidater_beste, input_file = rs.get_input_variables_from_file(variable,region,backup)\n chosen_r2_beste = sorted_r2[:ant_kandidater_beste]\n print(\"Input variables was read from: \", input_file)\n\n # MAKE ESTIMATE AND SHOW RESULTS\n input1 = make_estimate(df_cleaned, fasit, fasit_key, last_forecast, short_period_beste, max_p,\n chosen_r2_beste, loop=False)\n input2 = fasit_key, ant_kandidater_beste, max_p, reg_end, read_start\n\n\n #WRITE RESULTS\n if write:\n # Write results from the regression to SMG.\n fasit, long_results, short_results, df_tot, chosen_p, chosen_r2, r2_modelled, r2_modelled_long, prediction, tipping_ps, short_period, nb_weeks_tipping = input1\n\n # write to SMG:\n ws.write_SMG_regresjon(variable, region, tipping_ps[-1:])\n\n # write to SMG, virtual:\n ws.write_V_SMG_Regresjon(short_results, chosen_p, fasit_key, r2_modelled, r2_modelled_long, short_period_beste, MagKap)\n\n if jupyter:\n ws.show_result_jupyter(input1, input2)\n else:\n ws.show_result(input1, input2, input_file)\n\n print('---------------------------------------------------------------')\n print(' SLUTT ')\n print('---------------------------------------------------------------')\n print('\\nRegresjon for alle regioner og variabler brukte totalt %.0f minutter. \\n' % (\n (utctime_now() - start_tuning) / 60))","def _fit(self, y, X, fh):\n _, forecasters = self._check_forecasters()\n\n # get training data for meta-model\n if X is not None:\n y_train, y_test, X_train, X_test = temporal_train_test_split(\n y, X, test_size=self.test_size\n )\n else:\n y_train, y_test = temporal_train_test_split(y, test_size=self.test_size)\n X_train, X_test = None, None\n\n # fit ensemble models\n fh_test = ForecastingHorizon(y_test.index, is_relative=False)\n self._fit_forecasters(forecasters, y_train, X_train, fh_test)\n\n if self.method == \"feature-importance\":\n self.regressor_ = check_regressor(\n regressor=self.regressor, random_state=self.random_state\n )\n X_meta = pd.concat(self._predict_forecasters(fh_test, X_test), axis=1)\n X_meta.columns = pd.RangeIndex(len(X_meta.columns))\n\n # fit meta-model (regressor) on predictions of ensemble models\n # with y_test as endog/target\n self.regressor_.fit(X=X_meta, y=y_test)\n\n # check if regressor is a sklearn.Pipeline\n if isinstance(self.regressor_, Pipeline):\n # extract regressor from pipeline to access its attributes\n self.weights_ = _get_weights(self.regressor_.steps[-1][1])\n else:\n self.weights_ = _get_weights(self.regressor_)\n\n elif self.method == \"inverse-variance\":\n # get in-sample forecasts\n if self.regressor is not None:\n Warning(f\"regressor will not be used because ${self.method} is set.\")\n inv_var = np.array(\n [\n 1 / np.var(y_test - y_pred_test)\n for y_pred_test in self._predict_forecasters(fh_test, X_test)\n ]\n )\n # standardize the inverse variance\n self.weights_ = list(inv_var / np.sum(inv_var))\n else:\n raise NotImplementedError(\n f\"Given method {self.method} does not exist, \"\n f\"please provide valid method parameter.\"\n )\n\n self._fit_forecasters(forecasters, y, X, fh)\n return self","def compute_beta_vae_sklearn(ground_truth_data,\n representation_function,\n random_state,\n batch_size=gin.REQUIRED,\n num_train=gin.REQUIRED,\n num_eval=gin.REQUIRED):\n logging.info(\"Generating training set.\")\n train_points, train_labels = _generate_training_batch(\n ground_truth_data, representation_function, batch_size, num_train,\n random_state)\n\n logging.info(\"Training sklearn model.\")\n model = linear_model.LogisticRegression(random_state=random_state)\n model.fit(train_points, train_labels)\n\n logging.info(\"Evaluate training set accuracy.\")\n train_accuracy = model.score(train_points, train_labels)\n train_accuracy = np.mean(model.predict(train_points) == train_labels)\n logging.info(\"Training set accuracy: %.2g\", train_accuracy)\n\n logging.info(\"Generating evaluation set.\")\n eval_points, eval_labels = _generate_training_batch(\n ground_truth_data, representation_function, batch_size, num_eval,\n random_state)\n\n logging.info(\"Evaluate evaluation set accuracy.\")\n eval_accuracy = model.score(eval_points, eval_labels)\n logging.info(\"Evaluation set accuracy: %.2g\", eval_accuracy)\n scores_dict = {}\n scores_dict[\"train_accuracy\"] = train_accuracy\n scores_dict[\"eval_accuracy\"] = eval_accuracy\n return scores_dict","def beta_and_alpha(self):\n # make scatter plot\n sp_temp = self.daily_returns(self.sp.rename(columns={'Adj Close': '^GSPC'}))\n symbol_temp = self.daily_returns(self.daily.rename(columns={'Adj Close': self.symbol}))\n joined = sp_temp.merge(symbol_temp, on='Date')\n\n # beta and alpha\n beta, alpha = np.polyfit(joined[\"^GSPC\"], joined[self.symbol], 1)\n beta = round(beta, 3)\n alpha = round(alpha, 5)\n if alpha > 0:\n self.buys += 1\n self.debug += '\\nAlpha > 0: buys + {}'.format(alpha)\n else:\n self.debug += '\\nAlpha < 0: {}'.format(alpha)\n\n # assuming favorable market conditions. else, it would be sells + 1.\n if beta > 1:\n self.buys += 1\n self.debug += '\\nBeta > 1: buys + {}'.format(beta)\n else:\n self.debug += '\\nBeta < 1: {}'.format(beta)\n\n # finish plotting scatter\n if self.will_plot:\n ax = joined.plot(title=self.symbol + ' vs The Market', kind = 'scatter', x='^GSPC', y=self.symbol)\n ax.set_xlabel(\"S&P 500\")\n plt.plot(joined[\"^GSPC\"], beta * joined['^GSPC'] + alpha, '-', color='r', label='Correlation')\n\n # plot expected beta (slope) of 1 and alpha (y- int.) of zero\n plt.plot(joined[\"^GSPC\"], 1 * joined['^GSPC'] + 0, '-', color='gray', label='Beta of 1')\n plt.plot(joined[\"^GSPC\"], 0 * joined['^GSPC'] + 0, '-', color='gray', label='Alpha of 0')\n plt.legend(loc='best')","def main():\n np.set_printoptions(threshold=25)\n ts_length = 1000\n window_size = 50\n\n print('\\nSimple single timeseries vector prediction')\n timeseries = np.arange(ts_length) # The timeseries f(t) = t\n evaluate_timeseries(timeseries, window_size)\n\n print('\\nMultiple-input, multiple-output prediction')\n timeseries = np.array([np.arange(ts_length), -np.arange(ts_length)]).T # The timeseries f(t) = [t, -t]\n evaluate_timeseries(timeseries, window_size)","def run_model(Xp_train=None,y_train=None,Xp_test=None,y_test=None,mt='bl', params={}):\n models = { \n \"bl\": lambda : None,\n \"lr\": LinearRegression,\n \"dtr\": DecisionTreeRegressor,\n \"gbr\": GradientBoostingRegressor\n }\n\n # Select Model, returns None for baseline\n model = models[mt](**params)\n\n # Fit Model and make predictions\n if mt != 'bl': # If model not baseline\n # Dummy encoding , (each category of n levels or attributes is converted into n-1 dichotomous variables)\n X_train = pd.get_dummies(Xp_train, columns=['type', 'duration','location'],drop_first=True)\n X_test = pd.get_dummies(Xp_test, columns=['type', 'duration','location'],drop_first=True)\n model.fit(X_train,y_train)\n # Make Predictions\n y_pred = model.predict(X_test)\n #print X_test.shape\n y_predtr = model.predict(X_train) \n else: # Compute baseline\n y_pred = np.median(y_train).repeat(len(y_test))\n y_predtr = np.median(y_train).repeat(len(y_train))\n \n \n\n # Report metrics\n print(\"Model name: %s\" % (model.__class__.__name__ if model else \"Price Median Baseline\"))\n if mt != 'bl':\n print(\"hyper-parameters: \" + \", \".join( \"{0}: {1}\".format(k,v) for (k,v) in params.items() ) )\n print(\"Mean absolute error training set: %.2f\" % mean_absolute_error(y_train, y_predtr)) \n print(\"Mean absolute error testing set: %.2f \\n\" % mean_absolute_error(y_test, y_pred))","def delta_learning(self, inputs, targets, eta, val_inputs=None, val_targets=None,\n early_stop_threshold=10e-5, early_stop_tolerance=100, epochs=10000):\n pocket_mae = float('inf')\n pocket_epoch = 0\n pocket_weights = None\n no_improvement = 0\n\n for epoch in tqdm(range(1, epochs + 1)):\n self._rbf_forward(inputs)\n\n indices = np.arange(self.rbf_outputs.shape[0])\n np.random.shuffle(indices)\n\n # one epoch\n for i in indices:\n output = self.rbf_outputs[i:i + 1] @ self.slp_weights\n self.slp_weights += eta * (targets[i:i + 1] - output) * self.rbf_outputs[i:i + 1].T\n\n # use validation for early stopping if it is available\n if val_inputs is not None and val_targets is not None:\n error = self.evaluate_mae(val_inputs, val_targets)\n else:\n error = self.evaluate_mae(inputs, targets)\n\n if error + early_stop_threshold < pocket_mae:\n no_improvement = 0\n pocket_mae = error\n pocket_epoch = epoch\n pocket_weights = self.slp_weights.copy()\n else:\n no_improvement += 1\n if no_improvement > early_stop_tolerance:\n break\n\n self.slp_weights = pocket_weights\n return pocket_mae, pocket_epoch","def predict_sales() -> str:\r\n # HTML tables and list that are filled depending on the user input.\r\n html_gr_month_table = app.config[\"table_monthly_growth\"]\r\n html_gr_week_table = app.config[\"table_weekly_growth\"]\r\n html_csv_list = \"\"\r\n # Empty filename and path to sales forecast graph; also dep. on user input.\r\n graph_filename = \"\"\r\n graph_filepath = \"'File has not been created yet!'\"\r\n # Contains HTML form data inputted by the user submitted using POST.\r\n response = request.form\r\n num_months_input = response.get(\"num_months_input\")\r\n csv_filename_input = response.get(\"csv_filename_input\")\r\n # True if user inputted num of past months for prediction and uploaded CSV.\r\n if num_months_input is not None and csv_filename_input is not None:\r\n # Loads data from CSV into dataframe & removes rows with missing values\r\n file_name = csv_filename_input\r\n dataframe = load_csv_clean_to_dataframe(file_name)\r\n try:\r\n # Replaces index numbers of df with datetimes to get a time series.\r\n dataframe = convert_to_time_series(dataframe)\r\n except KeyError as error:\r\n error_message = (\"The following column name is missing \"\r\n \"in the data: \" + str(error))\r\n app.config[\"logger\"].error(error_message)\r\n return error_message\r\n # Calculates Quantity ordered averages for every past week.\r\n column_to_avg = \"Quantity ordered\"\r\n average_by = \"W\" # \"W\" = week; \"MS\" = month\r\n past_avgs_per_week = average_data(dataframe, column_to_avg, average_by)\r\n # Calculates Quantity ordered averages for every past month.\r\n average_by = \"MS\" # \"W\" = week; \"MS\" = month\r\n past_avgs_per_month = average_data(dataframe, column_to_avg,\r\n average_by)\r\n # Calculates past Quantity ordered average growth rates.\r\n growth_rates_per_week = past_avgs_per_week.pct_change()\r\n growth_rates_per_month = past_avgs_per_month.pct_change()\r\n # Creates HTML table containing past monthly sales growth rates.\r\n html_gr_month_table = update_growth_rate_table(growth_rates_per_month)\r\n app.config[\"table_monthly_growth\"] = html_gr_month_table\r\n # Creates HTML table containing past weekly sales growth rates.\r\n html_gr_week_table = update_growth_rate_table(growth_rates_per_week)\r\n app.config[\"table_weekly_growth\"] = html_gr_week_table\r\n # Filters dataframe for beer types.\r\n dunkel = dataframe.loc[dataframe[\"Recipe\"] == \"Organic Dunkel\"]\r\n pilsner = dataframe.loc[dataframe[\"Recipe\"] == \"Organic Pilsner\"]\r\n red_helles = dataframe.loc[dataframe[\"Recipe\"] == \"Organic Red Helles\"]\r\n # Monthly basis;\r\n # calculates dunkel's Quantity ordered sums for each month.\r\n column_to_sum = \"Quantity ordered\"\r\n sum_by = \"MS\" # \"W\" = week; \"MS\" = month\r\n dunk_order_sum_per_month = sum_data(dunkel, column_to_sum, sum_by)\r\n # Calculates pilsner's Quantity ordered sums for each month.\r\n pils_order_sum_per_month = sum_data(pilsner, column_to_sum, sum_by)\r\n # Calculates red_helles' Quantity ordered sums for each month.\r\n redh_order_sum_per_month = sum_data(red_helles, column_to_sum, sum_by)\r\n all_order_sum_month = {\"dunkers\": dunk_order_sum_per_month,\r\n \"pilsner\": pils_order_sum_per_month,\r\n \"red_helles\": redh_order_sum_per_month}\r\n # Clears monthly sales forecasts from the dictionary.\r\n app.config[\"monthly_sales_forecasts\"] = {}\r\n # Hyperparameters (hp) for seasonal ARIMA model.\r\n order = app.config[\"order\"]\r\n # Sets num_months_in. as num of past periods to be considered by model.\r\n app.config[\"seasonal_order\"][3] = int(num_months_input)\r\n seasonal_order = app.config[\"seasonal_order\"]\r\n # Fits ARIMA model and forecasts monthly sales for each beer type.\r\n try:\r\n for beer_type in all_order_sum_month:\r\n model = define_and_fit_model(all_order_sum_month[beer_type],\r\n False, order, seasonal_order)\r\n # Forecasts 12 months into the future with the fitted model.\r\n forecast = model.get_forecast(steps=12)\r\n # Stores monthly sales forecasts in dictionary by beer_type.\r\n app.config[\"monthly_sales_forecasts\"][beer_type] = forecast\r\n # Plots actual and predicted num of beers sold and saves plot.\r\n plot_size = (15, 10)\r\n plot_sales_forecast(forecast, all_order_sum_month[beer_type],\r\n plot_size, beer_type)\r\n except ValueError as error:\r\n error_message = (\"Wrong hyperparameters are set, the model \"\r\n + \"can not be fitted: \" + str(error))\r\n app.config[\"logger\"].error(error_message)\r\n return error_message\r\n # Gets filepath of this module and thus filepath to graph (png file).\r\n graph_filepath = os.path.dirname(os.path.abspath(__file__))\r\n graph_filename = \"monthly_forecast_and_past.png\"\r\n try:\r\n # Opens plotted graph.\r\n img = Image.open(graph_filename)\r\n img.show()\r\n except (FileNotFoundError, PermissionError) as error:\r\n app.config[\"logger\"].error(error)\r\n # Finds names of all uploaded CSV files in folder where this module is.\r\n all_uploaded_csv = []\r\n for csv_file in glob.glob(\"*.csv\"):\r\n all_uploaded_csv.append(csv_file)\r\n # Creates HTML drop-down list containing all uploaded CSV filenames.\r\n html_csv_list = update_csv_list(all_uploaded_csv)\r\n return (\"\"\"<style>\r\n h1, h2, h3 {\r\n font-family: arial, sans-serif;\r\n }\r\n table {\r\n font-family: arial, sans-serif;\r\n border-collapse: collapse;\r\n width: 100%;\r\n }\r\n td, th {\r\n border: 1px solid #dddddd;\r\n text-align: left;\r\n padding: 8px;\r\n }\r\n tr:nth-child(even) {\r\n background-color: #dddddd;\r\n }\r\n </style>\r\n <h2>Predict sales</h2>\r\n <form action=\"/predict_sales\" method=\"POST\">\r\n Please select an uploaded CSV file that should be used for the \r\n sales forecast (if the list is empty, no CSV file has been \r\n uploaded yet; to upload a file, please go back to the \r\n tracking screen).<br>\r\n <select name=\"csv_filename_input\">\"\"\"\r\n + html_csv_list\r\n + \"\"\"</select><br><br>\r\n Please enter how many <b>months</b> of past data should be\r\n used for the sales forecast:<br>\r\n (Note: If the number of past periods is higher than the number\r\n of periods in the past sales data, the nonexistent periods are\r\n recorded as 0 sales, which, if too many periods are 0,\r\n makes the prediction zero.\r\n <br>\r\n <input type=\"number\" name=\"num_months_input\" min=\"2\"\r\n required=\"required\">\r\n <br><br>\r\n <input type=\"submit\" value=\"Predict sales\">\r\n </form>\r\n In case the graph is not displayed, please open the file \"\"\"\r\n + graph_filename + \" manually using the path: \"\r\n + graph_filepath\r\n + \"\"\"<form action=\"/\" method=\"POST\">\r\n <input type=\"hidden\">\r\n <br>\r\n <input type=\"submit\" value=\"Go back to tracking screen\">\r\n </form> \r\n <h3>Average sales growth rates of the past months</h3>\r\n <table>\r\n <tr>\r\n <th>Date</th>\r\n <th>Monthly average</th>\r\n </tr>\"\"\" + html_gr_month_table + \"</table>\"\r\n + \"\"\"<h3>Average sales growth rates of the past weeks</h3>\r\n <table>\r\n <tr>\r\n <th>Date</th>\r\n <th>Weekly average</th>\r\n </tr>\"\"\" + html_gr_week_table + \"</table>\")","def fbeta1(y_test, predictions, beta=1):\n pr = precision1(y_test, predictions)\n re = recall1(y_test, predictions)\n\n num = float((1+pow(beta,2))*pr*re)\n den = float(pow(beta,2)*pr + re)\n\n if den != 0:\n fbeta = num/den\n else:\n fbeta = 0.0\n return fbeta","def beta(G, responses = [], covariates = [], conditionants = []):\n variables = covariates + conditionants \n \n means = mean(G, responses, variables)\n \n def collect(index):\n return sp.collect(expr = sp.expand(means[index]), \n syms = variables)\n \n collections = [collect(index) for index in range(len(means))]\n \n betas = sp.Matrix([collection.coeff(variables) \n for collection, variables \n in product(collections, variables)])\n \n betas.reshape(len(collections), len(variables)).T\n \n indices = variable_indices(G, values = covariates, \n restrictions = variables, \n sort = True)\n \n return betas[indices, :]","def estim_F(Xn, Ret, continuous, h, Std, Std2, minH, minR, IS, lagBM): \n # Copy to avoid modifying original df\n Ret = Ret.copy(deep=True)\n Xn = Xn.copy(deep=True)\n \n if lagBM != 0:\n Xn.index = Xn.index + MonthEnd(lagBM)\n \n # Main standardization choice (not mentioned in KP paper)\n if Std:\n Xn = Xn / Xn.std()\n\n # Transform to \"annual\" *realized* returns if needed, \n # and only if function is run in-sample\n if h != 1 and IS:\n Ret = changeFreq(Ret, new_frequency=h, continuous=continuous,\n percent=False)\n # else: Ret frequency already fine\n\n # Merge to enforce correct dates:\n data = pd.merge(Xn, Ret.shift(-h), how='left', \n left_index=True, right_index=True)\n idxR = Xn.shape[1] # Index of 'RM' column\n # => data.iloc[:,:idxR] = Xn\n # data.iloc[:,idxR] = Ret.shift(-h)\n \n # First pass: Regress BMs on future returns (one time-series reg per BM)\n ########################################################################\n # Apply on columns of BMs:\n phi = np.array(data.iloc[:, :idxR].apply(get_phi,\n args=(data.iloc[:, idxR], minH,\n minR),\n axis=0))\n \n # Here is the second normalization made by KP \n # (only for OOS, also not mentioned in their paper)\n # But this one does not impact the results, in fact.\n if Std2:\n phi = phi / np.nanstd(phi)\n\n # Second pass: Regress BMs on phi (one cross-sec regression per t)\n ###################################################################\n data['F'] = data.iloc[:, :idxR].apply(get_ft,\n args=(phi,),\n axis=1)\n return data['F']","def run(self, function, beta):\n if self.info_requested(Info.ok):\n self.info_set(Info.ok, False)\n\n# step = function.step(beta)\n\n z = betanew = betaold = beta\n\n if self.info_requested(Info.time):\n t = []\n if self.info_requested(Info.fvalue):\n f = []\n if self.info_requested(Info.converged):\n self.info_set(Info.converged, False)\n\n for i in xrange(1, max(self.min_iter, self.max_iter) + 1):\n\n if self.info_requested(Info.time):\n tm = utils.time_cpu()\n\n z = betanew + ((i - 2.0) / (i + 1.0)) * (betanew - betaold)\n\n step = function.step(z)\n\n betaold = betanew\n betanew = function.prox(z - step * function.grad(z),\n step)\n\n if self.info_requested(Info.time):\n t.append(utils.time_cpu() - tm)\n if self.info_requested(Info.fvalue):\n f.append(function.f(betanew))\n\n if self.conesta_stop is not None:\n mu_min = self.conesta_stop[0]\n# print \"mu_min:\", mu_min\n mu_old = function.set_mu(mu_min)\n# print \"mu_old:\", mu_old\n stop_step = function.step(betanew)\n# print \"step :\", step\n # Take one ISTA step for use in the stopping criterion.\n stop_z = function.prox(betanew - stop_step \\\n * function.grad(betanew),\n stop_step)\n function.set_mu(mu_old)\n# print \"err :\", maths.norm(betanew - z)\n# print \"sc err:\", (1.0 / step) * maths.norm(betanew - z)\n# print \"eps :\", self.eps\n\n if (1. / stop_step) * maths.norm(betanew - stop_z) < self.eps \\\n and i >= self.min_iter:\n\n if self.info_requested(Info.converged):\n self.info_set(Info.converged, True)\n\n break\n\n else:\n if step > 0.0:\n if (1.0 / step) * maths.norm(betanew - z) < self.eps \\\n and i >= self.min_iter:\n\n if self.info_requested(Info.converged):\n self.info_set(Info.converged, True)\n\n break\n\n else: # TODO: Fix this!\n if maths.norm(betanew - z) < self.eps \\\n and i >= self.min_iter:\n\n if self.info_requested(Info.converged):\n self.info_set(Info.converged, True)\n\n break\n\n self.num_iter = i\n\n if self.info_requested(Info.num_iter):\n self.info_set(Info.num_iter, i)\n if self.info_requested(Info.time):\n self.info_set(Info.time, t)\n if self.info_requested(Info.fvalue):\n self.info_set(Info.fvalue, f)\n if self.info_requested(Info.ok):\n self.info_set(Info.ok, True)\n\n return betanew","def test_models_regression(model):\n atom = ATOMRegressor(X_reg, y_reg, test_size=0.24, random_state=1)\n atom.run(\n models=model,\n metric=\"neg_mean_absolute_error\",\n n_calls=2,\n n_initial_points=1,\n bo_params={\"base_estimator\": \"gbrt\", \"cv\": 1},\n )\n assert not atom.errors\n assert hasattr(atom, model)","def MK(df_train, df_test):\n\n # number of periods\n T = df_train.shape[0]\n\n # matrix of individual forecasts\n F = df_train.iloc[:, 1:].values\n F_test = df_test.iloc[:, :].values\n\n # number of agents\n K = df_test.shape[1]\n\n # true outcomes\n y = df_train.iloc[:, 0].values\n\n # initialize agent wealth (sum equal to 1 -> results in convex combination)\n w = np.full(K, 1/K, dtype=float)\n\n # initialize standard deviations\n sigma = np.full(K, 1, dtype=float)\n\n # run market for all observations (training the market)\n for i in range(T):\n\n # individual predictions in the current market\n ind_pred = F[i, :]\n\n # size of bet at the true outcome (kernel evaluated at true outcome)\n kernel_val = sps.norm.pdf(y[i], loc=ind_pred, scale=sigma)\n\n # scale by the size of budget of each agent\n w_kernel_val = np.multiply(w, kernel_val)\n\n # reward agents\n w = w_kernel_val / np.sum(w_kernel_val)\n\n # update sigma\n abs_error = np.abs(y[i] - ind_pred)\n sigma = ((i+1)*sigma + abs_error) / (i + 2)\n\n # using the trained market for out of sample forecasts\n # obtain the aggregated market prediction in each test market\n market_pred = np.dot(F_test, w)\n\n # output\n df_pred = pd.DataFrame(\n {\"Market for Kernels\": market_pred},\n index=df_test.index\n )\n\n return df_pred","def compute_backward_variables(self, normalized_logits, target):\n\n target_length = target.shape[0]\n num_time_steps = normalized_logits.shape[0]\n\n\t\t######################\n\t\t### YOUR CODE HERE ###\n\t\t######################\n\n blank_label = normalized_logits.shape[1] - 1\n l = add_blanks(target, blank_label)\n target_length = l.shape[0]\n\n beta = np.zeros((target_length, num_time_steps))\n\n # init\n beta[target_length - 1, num_time_steps - 1] = normalized_logits[num_time_steps - 1, l[-1]]\n beta[target_length - 2, num_time_steps - 1] = normalized_logits[num_time_steps - 1, l[-2]]\n\n for s in xrange(target_length - 2):\n beta[s, num_time_steps - 2] = 0.0\n\n # recursive case\n for t in xrange(num_time_steps - 2, -1, -1):\n for s in xrange(target_length - 3, -1, -1):\n b_bar = beta[s, t+1] + beta[s+1, t+1]\n \n if l[s] == blank_label or l[s+2] == l[s]:\n beta[s, t] = b_bar * normalized_logits[t, l[s]]\n else:\n beta[s, t] = (b_bar + beta[s+2, t+1]) * normalized_logits[t, l[2]]\n\n return beta","def predict():\n if (not request.json):\n abort(400)\n \n product = {\n 'brand': request.json['brand'],\n 'category-1': request.json['category-1'],\n 'category-2': request.json['category-2'],\n 'category-3': request.json['category-3'],\n 'colour': request.json['colour'],\n 'fabric_type': request.json['fabric_type'],\n 'ftp_acrylic': request.json['ftp_acrylic'],\n 'ftp_cotton': request.json['ftp_cotton'],\n 'ftp_elastane': request.json['ftp_elastane'],\n 'ftp_linen': request.json['ftp_linen'],\n 'ftp_other': request.json['ftp_other'],\n 'ftp_polyamide': request.json['ftp_polyamide'],\n 'ftp_polyester': request.json['ftp_polyester'],\n 'ftp_polypropylene': request.json['ftp_polypropylene'],\n 'ftp_silk': request.json['ftp_silk'],\n 'ftp_viscose': request.json['ftp_viscose'],\n 'ftp_wool': request.json['ftp_wool'],\n 'gender': request.json['gender'],\n 'label': request.json['label'],\n 'made_in': request.json['made_in'],\n 'season': request.json['season'],\n 'size': request.json['size'],\n 'unspsc_code': request.json['unspsc_code'],\n 'weight': request.json['weight'],\n 'ML-model': request.json['ML-model']\n }\n\n product['co2_total'] = None\n ml_model = product.pop('ML-model', None)\n if (ml_model == None or ml_model == ''):\n print('Loading default model: LGBM')\n ml_model = 'lgbm_default'\n else:\n print(f'Loading model: {ml_model}')\n model = load_model(ml_model)\n print('Model loaded')\n \n pred_with_intervals = do_prediction_with_params(model, product, intervals=True)\n \n prediction = pred_with_intervals[0][0]\n percentile_5 = pred_with_intervals[0][1] if len(pred_with_intervals[0]) == 3 and pred_with_intervals[0][1] is not None else None\n percentile_95 = pred_with_intervals[0][2] if len(pred_with_intervals[0]) == 3 and pred_with_intervals[0][2] is not None else None\n result = {\n \"prediction\": prediction,\n \"5-percentile\": percentile_5,\n \"95-percentile\": percentile_95\n }\n \n print('CO2e prediction complete, returning result')\n print(result)\n \n resp = jsonify(result)\n resp.status_code = 201\n return resp","def ANN(df_train, df_test):\n\n # number of individual forecasts and number of periods\n K = df_test.shape[1]\n T = df_train.shape[0]\n T_all = df_train.shape[0] + df_test.shape[0]\n\n # matrix of individual forecasts (including train and test sets)\n F = np.concatenate((df_train.iloc[:, 1:].values, df_test.values), axis=0)\n\n # matrix of standardized forecasts and the intercept\n y = df_train.iloc[:, 0]\n y_bar = np.mean(y)\n y_std = np.std(y, ddof=1)\n\n Z = np.concatenate(\n (np.full((T_all, 1), 1, dtype=float),\n (F - y_bar) / y_std),\n axis=1)\n\n # randomly draw 10 Gamma sets (elements from uniform (-1, 1))\n len_Gamma = 10\n Gamma = []\n\n for i in range(len_Gamma):\n Gamma += [\n (np.random.rand(K+1, 3) * 2) - 1\n ]\n\n # prepare design matrices for the estimation\n X_list = []\n\n # add intercepts\n for i in range(len_Gamma * 2 * 3 + 1):\n X_list += [np.full((T_all, 1), 1, dtype=float)]\n\n # add linear nodes to half of the ANNs + the first fully linear model\n for i in range(len_Gamma * 3 + 1):\n\n X_list[i] = np.concatenate((X_list[i], F), axis=1)\n\n # add logistic nodes to all models (2 halves) except the first one\n for h in range(2):\n\n for i in range(len_Gamma):\n\n for p in range(1, 4):\n\n logistic_nodes = 1 / (1 + np.exp(-np.dot(Z, Gamma[i][:, :p])))\n\n X_list[(h * len_Gamma + i) * 3 + p] = np.concatenate(\n (X_list[(h * len_Gamma + i) * 3 + p], logistic_nodes),\n axis=1)\n\n # search for the best model specification using the 5-fold CV\n cv_sq_errors = np.full(len(X_list), 0, dtype=float)\n\n for i in range(5):\n\n # train and test indices\n cv_y_train_index = np.full(T, True, dtype=bool)\n cv_y_train_index[int(i * T/5):int((i+1) * T/5)] = False\n\n cv_X_train_index = np.append(cv_y_train_index,\n np.full(T_all-T, False, dtype=bool))\n cv_X_test_index = np.append(~cv_y_train_index,\n np.full(T_all-T, False, dtype=bool))\n\n # variable to be forecast\n cv_y_train = y[cv_y_train_index]\n cv_y_test = y[~cv_y_train_index]\n\n # design matrix for each model separately\n for j in range(len(X_list)):\n\n # set for training\n cv_X_train = X_list[j][cv_X_train_index, :]\n\n # set for testing\n cv_X_test = X_list[j][cv_X_test_index, :]\n\n # estimate the model\n # first, check if the X'X matrix is regular\n in_mat = np.dot(np.transpose(cv_X_train), cv_X_train)\n if np.linalg.det(in_mat) == 0:\n est_par = np.full(cv_X_train.shape[1], 0, dtype=float)\n else:\n est_par = np.dot(\n np.linalg.inv(in_mat),\n np.dot(np.transpose(cv_X_train), cv_y_train)\n )\n\n # predict\n cv_y_pred = np.dot(cv_X_test, est_par)\n\n # save the squared errors\n cv_sq_errors[j] += np.sum((cv_y_test - cv_y_pred)**2)\n\n # turn the sum of squared errors to mean squared errors\n cv_MSE = cv_sq_errors/T\n\n # find the ANN specification with the minimal CV MSE\n best_spec_index = np.argmin(cv_MSE)\n\n # train the best ANN\n X_train = X_list[best_spec_index][:T, :]\n\n est_par = np.dot(\n np.linalg.inv(np.dot(np.transpose(X_train), X_train)),\n np.dot(np.transpose(X_train), y)\n )\n\n # final predictions\n X_test = X_list[best_spec_index][T:, :]\n\n pred = np.dot(X_test, est_par)\n\n df_pred = pd.DataFrame(\n {\"ANN\": pred},\n index=df_test.index\n )\n\n return df_pred","def a_test_bbvi():\n model = ARIMAX(formula=\"y ~ x1\", data=data, ar=1, ma=1, family=Exponential())\n x = model.fit('BBVI',iterations=100)\n assert(len(model.latent_variables.z_list) == 4)\n lvs = np.array([i.value for i in model.latent_variables.z_list])\n assert(len(lvs[np.isnan(lvs)]) == 0)","def forecast_ARIMA(train, test, p, d, q, model_selection, prod_num):\n model = ARIMA(train['STU'].dropna(), order=(p, d, q))\n model_fit = model.fit(disp=0)\n if model_selection:\n forecast, st_errs, conf_int = model_fit.forecast(len(test), alpha=0.05)\n else:\n predictions_range = pd.date_range('2019-04-01', '2019-09-29', freq=\"W-MON\").to_series()\n\n df_predictions = pd.DataFrame(predictions_range, columns=[\"Week\"])\n\n forecast, st_errs, conf_int = model_fit.forecast(26, alpha=0.05)\n df_predictions['Sales_Prediction'] = forecast\n df_predictions['Week'] = pd.to_datetime(df_predictions[\"Week\"])\n df_predictions.set_index(\"Week\", inplace=True)\n\n plt.figure(figsize=(12, 5))\n plt.plot(train['STU'], label='training')\n plt.plot(df_predictions['Sales_Prediction'], label='forecast')\n plt.title('Forecast vs Actuals')\n plt.legend(loc='upper left', fontsize=8)\n plt.savefig(\"Product\" + f\"_{prod_num}\" + \".pdf\")\n if model_selection:\n MSE = mean_squared_error(test, forecast)\n return MSE\n return df_predictions","def run(self, function, beta):\n if self.info_requested(Info.ok):\n self.info_set(Info.ok, False)\n\n step = function.step(beta)\n\n betanew = betaold = beta\n\n if self.info_requested(Info.time):\n t = []\n if self.info_requested(Info.fvalue):\n f = []\n if self.info_requested(Info.converged):\n self.info_set(Info.converged, False)\n\n for i in xrange(1, self.max_iter + 1):\n\n if self.info_requested(Info.time):\n tm = utils.time_cpu()\n\n step = function.step(betanew)\n\n betaold = betanew\n betanew = function.prox(betaold - step * function.grad(betaold),\n step)\n\n if self.info_requested(Info.time):\n t.append(utils.time_cpu() - tm)\n if self.info_requested(Info.fvalue):\n f.append(function.f(betanew))\n\n if (1.0 / step) * maths.norm(betanew - betaold) < self.eps \\\n and i >= self.min_iter:\n\n if self.info_requested(Info.converged):\n self.info_set(Info.converged, True)\n\n break\n\n self.num_iter = i\n\n if self.info_requested(Info.num_iter):\n self.info_set(Info.num_iter, i)\n if self.info_requested(Info.time):\n self.info_set(Info.time, t)\n if self.info_requested(Info.fvalue):\n self.info_set(Info.fvalue, f)\n if self.info_requested(Info.ok):\n self.info_set(Info.ok, True)\n\n return betanew","def SGD_beta(X, y, eta=1e-4, gamma=0.01):\n\n\t# Stochastic Gradient Descent, shuffle?\n\tbeta = np.random.randn(len(X[0]), 1)\n\tn = len(X)\n\tM = 10 #0.05*n \t # Size of each minibatch, should be smaller than n\n\tm = int(n/M) \t # Number of minibatches\n\tn_epochs = 500 \t\t # Nmber of epochs\n\n\tacc = np.zeros(n_epochs+1)\n\tepoch_list = np.zeros(n_epochs+1)\n\n\t#z_i = np.zeros(m)\n\t#model_i = np.zeros(m)\n\t#y_i = np.zeros(m)\n\n\tfor epoch in range(1,n_epochs+1):\n\t\tfor i in range(m):\n\n\t\t\trandom_index = np.random.randint(m) #Pick the k-th minibatch at random\n\t\t\txi = X[random_index:random_index+1]\n\t\t\tyi = y[random_index:random_index+1]\n\n\t\t\t#Compute the gradient using the data in minibatch Bk\n\t\t\tgrad_beta_C = beta_gradients(xi, yi, beta)\n\t\t\tbeta -= eta - gamma * grad_beta_C\n\n\t\t\t#y_i[i] = yi\n\t\t\t#z_i[i] = xi@beta\n\t\t\t#model_i[i] = logistic_function(z_i[i])\n\n\t\t#acc[epoch] = accuracy(model_i, y_i)\n\t\t#epoch_list[epoch] = epoch\n\n\treturn beta","def train(self, df, feature, max_range, extra=False, defender=False):\n\n df2 = self._train_preprocess(df, feature, extra)\n\n # No need for names anymore\n if defender:\n df2 = df2.drop([\"Player Id\"], axis=1)\n\n # Instantiate the models\n self.rfrg = RandomForestRegressor(n_estimators=1000, n_jobs=-1, random_state=69420)\n\n if not defender:\n self.gbrg = LGBMRegressor(n_estimators=1000, learning_rate=0.01)\n\n # Then, perform regression -> This is to see how it performs over weeks\n mean_error1 = []\n mean_error2 = []\n\n for week in range(max_range - 5, max_range):\n train = df2[df2['week'] < week]\n val = df2[df2['week'] == week]\n\n x_train, x_test = train.drop([feature], axis=1), val.drop([feature], axis=1)\n y_train, y_test = train[feature].values, val[feature].values\n\n self.rfrg.fit(x_train, y_train)\n preds1 = self.rfrg.predict(x_test)\n error1 = rmsle(y_test, preds1)\n print('Week %d - Error for Random Forest %.5f' % (week, error1))\n\n mean_error1.append(error1)\n if not defender:\n self.gbrg.fit(x_train, np.log1p(y_train))\n preds2 = np.expm1(self.gbrg.predict(x_test))\n error2 = rmsle(y_test, preds2)\n print('Week %d - Error for Gradient Boosting %.5f' % (week, error2))\n mean_error2.append(error2)\n\n print()\n print()\n print(\"Feature statistics:\")\n print(f\"Min value for feature {feature}: {df[feature].min()}\")\n print(f\"Max value for feature {feature}: {df[feature].max()}\")\n print(f\"Mean value for feature {feature}: {df[feature].mean()}\")\n print(f\"Standard deviation for feature {feature}: {df[feature].std()}\")\n print()\n print(\"Results\")\n print('Mean Error for Random Forest = %.5f' % np.mean(mean_error1))\n\n # Note: the final model is trained on every week and stored in self.model!\n final_xtrain = df2.drop([feature], axis=1)\n final_ytrain = df2[feature].values\n self.rfrg.fit(final_xtrain, final_ytrain)\n\n if not defender:\n print('Mean Error for Gradient Boosting = %.5f' % np.mean(mean_error2))\n self.gbrg.fit(final_xtrain, np.log1p(final_ytrain))","def test_timescale_beta(self):\n param_pairs = [(0, 1, self.rule.tau2)]\n\n nsteps = 10\n self.conductor.out_fct = lambda i: 10*np.ones(self.Nc) if i == 0 \\\n else np.zeros(self.Nc)\n\n sim = simulation.Simulation(self.conductor, self.student, self.tutor,\n self.syns, self.rule, dt=self.dt)\n W0 = np.copy(self.syns.W)\n\n for params in param_pairs:\n self.rule.alpha = params[0]\n self.rule.beta = params[1]\n tau = params[2]\n\n self.tutor.out_fct = lambda i: (self.rule.theta + (10 if i == 0 else 0))*\\\n np.ones(self.Ns)\n\n self.syns.W = np.copy(W0)\n sim.run(self.dt)\n\n change0 = self.syns.W - W0\n\n self.assertGreater(np.linalg.norm(change0), 1e-10)\n \n self.tutor.out_fct = lambda i: (self.rule.theta + (10\n if i == nsteps-1 else 0))*np.ones(self.Ns)\n\n self.syns.W = np.copy(W0)\n sim.run(nsteps*self.dt)\n\n change1 = self.syns.W - W0\n\n change1_exp = change0*(1 - float(self.dt)/tau)**(nsteps-1)\n\n self.assertTrue(np.allclose(change1, change1_exp),\n msg=\"Timescale not verified, alpha={}, beta={}.\".format(*params[:2]))","def var_predict(temperature_data, n_forwards=(1, 3), n_lags=4, test_ratio=0.2):\n data = temperature_data.transpose(1, 0, 2)[:, :, 0]\n num_samples, num_output = data.shape\n n_test = int(round(num_samples * test_ratio))\n n_train = num_samples - n_test\n train_data, test_data = data[:n_train, ...], data[n_train:, ...]\n\n scaler = StandardScaler(mean=train_data.mean(), std=train_data.std())\n _data = scaler.transform(train_data)\n var_model = VAR(_data)\n var_result = var_model.fit(n_lags)\n max_n_forwards = np.max(n_forwards)\n # Do forecasting.\n result = np.zeros(shape=(len(n_forwards), n_test, num_output))\n start = n_train - n_lags - max_n_forwards + 1\n for input_ind in range(start, num_samples - n_lags):\n prediction = var_result.forecast(scaler.transform(data[input_ind: input_ind + n_lags]), max_n_forwards)\n for i, n_forward in enumerate(n_forwards):\n result_ind = input_ind - n_train + n_lags + n_forward - 1\n if 0 <= result_ind < n_test:\n result[i, result_ind, :] = prediction[n_forward - 1, :]\n\n predicts = []\n for i, n_forward in enumerate(n_forwards):\n predict = scaler.inverse_transform(result[i])\n predicts.append(predict)\n return predicts, test_data","def predict(self, forecastyear, scenario=None):\n\n start_time = time()\n\n results = []\n for m in self.model:\n\n # \"TARGET\" -> ISO-3 country code of destination country\n R = {}\n R['target'] = m['target']\n\n # Forecast year in the input query\n R['year'] = forecastyear\n\n # Scenario in the input query\n R['scenario'] = scenario if scenario else {}\n\n Xv = self.__adjust_features(m['feature'], scenario)\n # logger.info(\"Return adjust features in {:3.2f} sec.\".format(time() - start_time))\n\n R['forecast'] = m['clf'].predict(Xv)[0]\n # logger.info(\"Return forecast predict in {:3.2f} sec.\".format(time() - start_time))\n\n # Explanation of point forecast using SHAP values\n # R['explanation'] = self.__get_explanations(m['explainer'], Xv, m['Xt'].columns)\n\n # Confidence intervals are now one-sided\n # R['CI'] = [0.0, m['CI'].predict(Xv)[0]]\n # logger.info(\"Return CI predict in {:3.2f} sec.\".format(time() - start_time))\n\n results.append(R)\n\n logger.debug(\"Return predict in {:3.2f} sec.\".format(time() - start_time))\n\n return results","def get_alphas_and_betas(context, data):\r\n all_assets = context.portfolio.positions.keys()\r\n if context.index not in all_assets:\r\n all_assets.append(context.index)\r\n prices = data.history(all_assets, 'price', context.lookback, '1d')\r\n returns = prices.pct_change()[1:]\r\n # index_returns = returns[context.index]\r\n factors = {}\r\n for asset in context.portfolio.positions:\r\n try:\r\n y = returns[asset]\r\n factors[asset] = linreg(returns[context.index], y)\r\n except:\r\n log.warn(\"[Failed Beta Calculation] asset = %s\" % asset.symbol)\r\n return pd.DataFrame(factors, index=['alpha', 'beta'])","def test_multiple_gradient_descent_with_backprop():\n layer_list = [7, 7, 7, 2]\n print(\"test\", \"layer_list\", layer_list)\n\n X, W, B = initialize_parameters_for_layers(layer_list, 5)\n\n alpha = 0.01\n num_iterations = 2000\n num_layers = len(layer_list) - 1\n\n Y = np.arange(10).reshape(2, 5)\n W, B, yhat = gradient_descent_for_n_layers(num_layers, W, B, X, Y, alpha, num_iterations)\n\n print(\"test done.\") # final W and B are\", W, \"and\", B)\n print(\"final yhat\", yhat)","def backtesting_engine(symbol, strategy, fromdate, todate, args=None):\n\n # Backtesting Engine\n cerebro = bt.Cerebro()\n\n # Add a Strategy if no Data Required for the model\n if args is None:\n cerebro.addstrategy(strategy)\n # If the Strategy requires a Model and therefore data\n elif args is not None:\n cerebro.addstrategy(strategy, args)\n\n # Retrieve Data from Alpaca\n data = bt.feeds.YahooFinanceData(\n dataname=symbol,\n fromdate=fromdate, # datetime.date(2015, 1, 1)\n todate=todate, # datetime.datetime(2016, 1, 1)\n reverse=False\n )\n\n # Add Data to Backtesting Engine\n cerebro.adddata(data)\n\n # Set Initial Portfolio Value\n cerebro.broker.setcash(100000.0)\n\n # Add Analysis Tools\n cerebro.addanalyzer(bt.analyzers.SharpeRatio, _name='sharpe')\n cerebro.addanalyzer(bt.analyzers.Returns, _name='returns')\n cerebro.addanalyzer(bt.analyzers.SQN, _name='sqn')\n cerebro.addanalyzer(bt.analyzers.DrawDown, _name='drawdown')\n cerebro.addanalyzer(bt.analyzers.PositionsValue, _name='posval')\n cerebro.addanalyzer(bt.analyzers.PyFolio, _name='pyfolio')\n\n # Starting Portfolio Value\n print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue())\n\n # Run the Backtesting Engine\n backtest = cerebro.run()\n\n # Print Analysis and Final Portfolio Value\n print(\n 'Final Portfolio Value: %.2f' % cerebro.broker.getvalue()\n )\n print(\n 'Return: ', backtest[0].analyzers.returns.get_analysis()\n )\n print(\n 'Sharpe Ratio: ', backtest[0].analyzers.sharpe.get_analysis()\n )\n print(\n 'System Quality Number: ', backtest[0].analyzers.sqn.get_analysis()\n )\n print(\n 'Drawdown: ', backtest[0].analyzers.drawdown.get_analysis()\n )\n print(\n 'Active Position Value: ', backtest[0].analyzers.posval.get_analysis()\n )\n print(\n 'Pyfolio: ', backtest[0].analyzers.pyfolio.get_analysis()\n )\n\n # Print Analysis and Final Portfolio Value\n pyfoliozer = backtest[0].analyzers.getbyname('pyfolio')\n returns, positions, transactions, gross_lev = pyfoliozer.get_pf_items()\n\n # See if we can add regular FB data to compare against returns of algo\n pf.create_full_tear_sheet(\n returns, positions=positions, transactions=transactions\n )","def arimax_by_month_forecast_fit_predict (y_train,y_test,months_ahead, months_list):\n predictions_df= pd.DataFrame(np.random.randn(1, 2),\\\n columns=['month_year', 'predicted_evictions'])\n\n now = datetime.datetime.now()\n\n model = pf.ARIMAX(data=y_train,formula='Eviction_Notice~1+CASANF0URN',ar=2, ma=2)\n model.fit()\n y_hat = model.predict(h=months_ahead,oos_data=y_test)\n\n predicted = y_hat.Eviction_Notice.values.tolist()\n temp_df = pd.DataFrame({'predicted_evictions':predicted,'months_ahead':range(1,months_ahead+1)})\n temp_df['month_year']=months_list\n\n predictions_df = predictions_df.append(temp_df, ignore_index=True)\n predictions_df = predictions_df[1:]\n predictions_df['month_year']=pd.to_datetime(predictions_df['month_year'])\n\n return predictions_df, y_hat","def run_hbvedu(temp, prec, month, PE_m, T_m, snow_init, soil_init, s1_init, \n s2_init, params):\n # Number of simulation timesteps\n num_timesteps = len(prec)\n \n # Unpack the model parameters\n T_t = params['T_t']\n DD = params['DD']\n FC = params['FC']\n Beta = params['Beta']\n C = params['C']\n PWP = params['PWP']\n K_0 = params['K_0']\n K_1 = params['K_1']\n K_2 = params['K_2']\n K_p = params['K_p']\n L = params['L']\n \n # initialize empty arrays for all reservoirs and outflow\n snow = np.zeros(num_timesteps, np.float64)\n soil = np.zeros(num_timesteps, np.float64)\n s1 = np.zeros(num_timesteps, np.float64)\n s2 = np.zeros(num_timesteps, np.float64)\n qsim = np.zeros(num_timesteps, np.float64)\n\n # set initial values\n snow[0] = snow_init\n soil[0] = soil_init\n s1[0] = s1_init\n s2[0] = s2_init\n\n # Start the model simulation as loop over all timesteps\n for t in range(1, num_timesteps):\n\n # Check if temperature is below threshold\n if temp[t] < T_t:\n # accumulate snow\n snow[t] = snow[t-1] + prec[t]\n # no liquid water\n liquid_water = 0\n else:\n # melt snow\n snow[t] = max(0, snow[t-1] - DD * (temp[t] - T_t))\n # add melted snow to available liquid water\n liquid_water = prec[t] + min(snow[t-1], DD * (temp[t] - T_t))\n\n # calculate the effective precipitation\n prec_eff = liquid_water * (soil[t-1] / FC) ** Beta\n\n # Calculate the potential evapotranspiration\n pe = (1 + C * (temp[t] - T_m[month[t]])) * PE_m[month[t]]\n\n # Calculate the actual evapotranspiration\n if soil[t-1] > PWP:\n ea = pe\n else:\n ea = pe * (soil[t-1] / PWP)\n\n # calculate the actual level of the soil reservoir\n soil[t] = soil[t-1] + liquid_water - prec_eff - ea\n\n # calculate the actual level of the near surface flow reservoir\n s1[t] = (s1[t-1]\n + prec_eff\n - max(0, s1[t-1] - L) * K_0\n - s1[t-1] * K_1\n - s1[t-1] * K_p)\n\n # calculate the actual level of the base flow reservoir\n s2[t] = (s2[t-1]\n + s1[t-1] * K_p\n - s2[t-1] * K_2)\n\n qsim[t] = ((max(0, s1[t-1] - L)) * K_0\n + s1[t] * K_1\n + s2[t] * K_2)\n \n return qsim, snow, soil, s1, s2","def naive_forecasting(x_test, y_test):\n y_pred = np.concatenate([x_test[:,-1].reshape((TESTING_BATCH_SIZE,1,N_INPUT_FEATURES)) for x in range(N_PREDICTIONS)],axis=1)\n y_pred = y_pred[:,:,:N_OUTPUT_FEATURES]\n return np.mean(keras.losses.mean_squared_error(y_test,y_pred)), y_pred","def fbeta_score(self, y_true=None, y_pred=None, beta=1.0, labels=None, average=\"macro\", decimal=None, **kwargs):\n y_true, y_pred, binary, representor, decimal = self.get_processed_data(y_true, y_pred, decimal)\n matrix, imap, imap_count = cu.calculate_confusion_matrix(y_true, y_pred, labels, normalize=None)\n metrics = cu.calculate_single_label_metric(matrix, imap, imap_count, beta=beta)\n\n list_fbeta = np.array([item[\"fbeta\"] for item in metrics.values()])\n list_weights = np.array([item[\"n_true\"] for item in metrics.values()])\n\n if average == \"micro\":\n tp_global = np.sum(np.diag(matrix))\n fp_global = fn_global = np.sum(matrix) - tp_global\n precision = tp_global / (tp_global + fp_global)\n recall = tp_global / (tp_global + fn_global)\n fbeta = ((1 + beta ** 2) * precision * recall) / (beta ** 2 * precision + recall)\n elif average == \"macro\":\n fbeta = np.mean(list_fbeta)\n elif average == \"weighted\":\n fbeta = np.dot(list_weights, list_fbeta) / np.sum(list_weights)\n else:\n fbeta = dict([(label, np.round(item[\"fbeta\"], decimal)) for label, item in metrics.items()])\n return fbeta if type(fbeta) == dict else np.round(fbeta, decimal)","def _process( self, raw_data, regression_var ):\n\n reg_idx = int( regression_var )\n\n Y = raw_data.ix[:,reg_idx]\n X = raw_data.ix[:, raw_data.columns != raw_data.columns[reg_idx] ]\n\n col_names = list( raw_data.columns.values )\n\n Y = Y.as_matrix().astype(np.float64)\n X = X.as_matrix().astype(np.float64)\n\n C = 0.75\n\n lamdbaMax = 2 * max( abs( np.dot( np.transpose( X ), Y ) ) ) / Y.size\n\n lambdaGrid = np.arange( 0, 100 )\n lambdaGrid = np.apply_along_axis( lambda x: lamdbaMax / 1.3**x, 0, lambdaGrid )\n\n betas = glmnet_py.glmnetCoef( glmnet_py.glmnet( x = X.copy(), y = Y.copy(), lambdau = lambdaGrid ) )\n\n j = 0\n t = 1\n\n while( t > 0 and j < 99 ):\n j += 1\n beta_j = betas[:,j]\n lambda_j = lambdaGrid[j]\n\n for k in np.arange( 1, ( j - 1 ) ) :\n beta_k = betas[:,k]\n lambda_k = lambdaGrid[k]\n t = t * (max(abs(beta_j - beta_k)) / (lambda_j + lambda_k) <= C)\n\n beta = betas[:,j]\n\n # fit['beta'] = fit['beta'] * (abs(fit['beta']) >= 3 * C * fit['lambdau'])\n # beta = beta * (abs( beta ) >= 3 * C * lambdaGrid[j] )\n\n coefficients = beta\n\n nz_indices = coefficients.nonzero()[0]\n support_coefs = coefficients[ nz_indices ]\n\n # cvglmnetCoef places the intercept term in the first position of the\n # coefficients vector -- need to manually add Intercept name to vector of column names\n col_names.insert( 0, \"Intercept\" )\n col_names = [ col_names[idx] for idx in nz_indices ]\n\n return( pd.DataFrame( data = support_coefs , index = col_names ).to_json(orient='columns') )","def perform_backtests(self):\r\n \r\n for test_name in self.testing_dates:\r\n print('\\t|--Test #{}'.format(test_name))\r\n test_dates = self.testing_dates[test_name]\r\n print('\\t\\t|--Performing Nested Cross-Validation')\r\n cross_validation = CrossValidate()\r\n cross_validation.output_names = self.output_names\r\n cross_validation.feature_names = self.feature_names\r\n cross_validation.feature_dict = self.feature_dict\r\n cross_validation.full_df = self.final_df_output\r\n cross_validation.cv_params = self.testing_dates\r\n cross_validation.test_name = test_name\r\n cross_validation.walk_forward_cv()\r\n self.optimal_params['Test #{}'.format(test_name)] = cross_validation.optimal_params_by_output\r\n self.cv_model_metadata['Test #{}'.format(test_name)] = cross_validation.cv_metadata_by_output\r\n \r\n print('\\t\\t|--Performing Out-Of-Sample Testing')\r\n prediction = Predict()\r\n prediction.output_names = self.output_names\r\n prediction.feature_names = self.feature_names\r\n prediction.feature_dict = self.feature_dict\r\n prediction.optimal_params_by_output = cross_validation.optimal_params_by_output\r\n prediction.cv_predictions_by_output = cross_validation.cv_predictions_by_output\r\n prediction.full_df = self.final_df_output\r\n prediction.pred_start = test_dates['pred_start']\r\n prediction.pred_end = test_dates['pred_end']\r\n prediction.run_prediction()\r\n self.full_predictions['Test #{}'.format(test_name)] = prediction.predictions_by_output\r\n self.pred_model_metadata['Test #{}'.format(test_name)] = prediction.pred_metadata_by_output\r\n \r\n print('\\nSaving model metadata...')\r\n with open(path.deployment_cv_results, 'w') as file:\r\n json.dump(self.optimal_params, file)\r\n with open(path.deployment_cv_metadata, 'w') as file:\r\n json.dump(self.cv_model_metadata, file)\r\n with open(path.deployment_pred_model_metadata, 'w') as file:\r\n json.dump(self.pred_model_metadata, file)\r\n with open(path.deployment_full_predictions, 'w') as file:\r\n json.dump(self.full_predictions, file)","def getErrRegression(mdlParams, indices, sess, loss_op, prediction, getBatch, placeholders, feed_list_inference):\r\n # Set up sizes\r\n loss = np.zeros([len(mdlParams[indices])])\r\n predictions = np.zeros([len(mdlParams[indices]),len(mdlParams['tar_range'])])\r\n targets = np.zeros([len(mdlParams[indices]),len(mdlParams['tar_range'])])\r\n numBatches = int(math.ceil(len(mdlParams[indices])/mdlParams['batchSize']))\r\n for k in range(numBatches):\r\n feed_list_inference['X'], feed_list_inference['Y'] = getBatch(mdlParams,indices,k)\r\n # Take care of last batch being smaller\r\n if int(math.ceil(len(mdlParams[indices]) / mdlParams['batchSize'])) - 1 == k :\r\n bSize = len(mdlParams[indices]) - mdlParams['batchSize'] * k\r\n else:\r\n bSize = mdlParams['batchSize']\r\n targets[mdlParams['batchSize']*k:(mdlParams['batchSize']*k+bSize),:] = feed_list_inference['Y'] \r\n loss[mdlParams['batchSize']*k:(mdlParams['batchSize']*k+bSize)], predictions[mdlParams['batchSize']*k:(mdlParams['batchSize']*k+bSize)] = sess.run([loss_op, prediction], feed_dict={placeholders[p]: feed_list_inference[p] for p in placeholders})\r\n # Transform targets and predictions\r\n if mdlParams['scale_targets']:\r\n targets = mdlParams['scaler'].inverse_transform(targets)\r\n predictions = mdlParams['scaler'].inverse_transform(predictions)\r\n # Error metrics\r\n # MAE\r\n mae = np.mean(np.abs(predictions-targets),1)\r\n mae_mean = np.mean(mae)\r\n mae_std = np.std(mae)\r\n # Relative MAE\r\n tar_std = np.std(targets,0)\r\n rmae_mean = np.mean(np.mean(np.abs(predictions-targets),0)/tar_std)\r\n rmae_std = np.mean(np.std(np.abs(predictions-targets),0)/tar_std)\r\n # Avg. Corr. Coeff.\r\n corr = np.corrcoef(np.transpose(predictions),np.transpose(targets))\r\n # Extract relevant components for aCC\r\n num_tar = len(mdlParams['tar_range'])\r\n acc = 0\r\n for k in range(num_tar):\r\n acc += corr[num_tar+k,k]\r\n acc /= num_tar\r\n return np.mean(loss), mae_mean, mae_std, rmae_mean, rmae_std, acc","def run_test_procedures(self):\r\n print('\\nDeploying prediction model...\\n')\r\n self.final_df_output = pd.read_json(path.data_final)\r\n self.final_df_output.sort_index(inplace=True)\r\n self.fill_testing_dates()\r\n self.perform_backtests()\r\n self.create_full_predictions_dataframe()\r\n print('\\nDeployment complete!')","def predict(self):\n # Input validation\n if 'halflife' not in self.cfg or 'min_periods' not in self.cfg:\n raise ValueError('SingleStockEWM: Model config requires both min_periods (periods backwards) and a '\n 'halflife (decay of historical values, periods to half original value) to run.')\n\n # ## Load up model configs\n halflife = self.cfg['halflife']\n min_periods = self.cfg['min_periods']\n\n # ## Estimates\n # Returns\n realized_returns = self.get('returns', data_type='realized', sampling_freq=self.cfg['returns']['sampling_freq'])\n realized_volumes = self.get('volumes', data_type='realized', sampling_freq=self.cfg['returns']['sampling_freq'])\n realized_sigmas = self.get('sigmas', data_type='realized', sampling_freq=self.cfg['returns']['sampling_freq'])\n logging.info(\"Typical variance of returns: %g\" % realized_returns.var().mean())\n\n self.set('returns', realized_returns.ewm(halflife=halflife, min_periods=min_periods).mean().shift(1).dropna(),\n 'predicted')\n\n # Volumes & sigmas\n self.set('volumes', realized_volumes.ewm(halflife=halflife, min_periods=min_periods).mean().shift(1).dropna(),\n 'predicted')\n self.set('sigmas', realized_sigmas.shift(1).dropna(), 'predicted')\n\n # Covariance\n if 'covariance' not in self.cfg:\n raise NotImplemented('Covariance section needs to be defined under SS EWM model config.')\n elif self.cfg['covariance']['method'] == 'SS':\n self.set('covariance', realized_returns.ewm(halflife=halflife, min_periods=min_periods).cov().\n shift(realized_returns.shape[1]).dropna(), 'predicted', self.cfg['covariance']['sampling_freq'])\n elif self.cfg['covariance']['method'] == 'FF5':\n # Fetch data\n ff_returns = self.get('ff_returns', 'realized', SamplingFrequency.DAY)\n realized_returns = self.get('returns', data_type='realized', sampling_freq=SamplingFrequency.DAY)\n\n update = self.cfg['covariance']['update'] if 'update' in self.cfg['covariance'] else 'monthly'\n if update == 'quarterly':\n update_freq = '3M'\n elif update == 'monthly':\n update_freq = 'M'\n elif update == 'biweekly':\n update_freq = '2W'\n elif update == 'weekly':\n update_freq = 'W'\n else:\n raise NotImplemented('Update freq under covariance only supports: month, biweekly, weekly.')\n\n # Generate computation frequency\n first_days = pd.date_range(start=realized_returns.index[max(self.cfg['min_periods'] + 1, 90)],\n end=realized_returns.index[-1],\n freq=update_freq)\n days_back = self.cfg['covariance']['train_days'] if 'train_days' in self.cfg['covariance'] else 90\n\n # Use ML regression to obtain factor loadings. Then factor covariance and stock idiosyncratic variances\n exposures, factor_sigma, idyos = {}, {}, {}\n\n # Every first day in each biweekly period\n cov_rscore = []\n for day in first_days:\n logging.info('Running for {}'.format(day.strftime('%Y %b %d')))\n\n # Grab asset returns for preceding train_days (90 by default)\n used_returns = realized_returns.loc[(realized_returns.index < day) &\n (realized_returns.index >= day - pd.Timedelta(str(days_back) + \" days\"))]\n used_ff_returns = ff_returns.loc[ff_returns.index.isin(used_returns.index)].iloc[:, :-1]\n\n # Multi linear regression to extract factor loadings\n mlr = linear_model.LinearRegression()\n mlr.fit(used_ff_returns, used_returns)\n mlr.predict(used_ff_returns)\n\n # Track performance of FF fit\n # rscore = metrics.r2_score(used_ff_returns, used_returns)\n cov_rscore.append(0)\n #p rint('predict_cov_FF5: mlr score = {s}'.format(s=rscore))\n\n # Factor covariance - on FF returns\n factor_sigma[day] = used_ff_returns.cov().fillna(0)\n # Exposures - factor loadings obtained from multi linear regression coefficients of stock on FF factors\n exposures[day] = pd.DataFrame(data=mlr.coef_, index=realized_returns.columns).fillna(0)\n # Stock idiosyncratic variances - stock var minus FF var, ensure >=0\n idyos[day] = pd.Series(np.diag(used_returns.cov().values -\n exposures[day].values @ factor_sigma[day].values @ exposures[\n day].values.T),\n index=realized_returns.columns).fillna(method='ffill')\n idyos[day][idyos[day] < 0] = 0\n\n self.set('factor_sigma', pd.concat(factor_sigma.values(), axis=0, keys=factor_sigma.keys()), 'predicted')\n self.set('exposures', pd.concat(exposures.values(), axis=0, keys=exposures.keys()), 'predicted')\n self.set('idyos', pd.DataFrame(idyos).T, 'predicted')\n self.set('cov_rscore', pd.DataFrame.from_dict({'date': first_days,\n 'rscore': cov_rscore,\n 'train_days': days_back}), 'predicted')\n\n else:\n raise NotImplemented('Covariance section needs to be defined under SSEWM moodel config and needs definition'\n ' of method: SS (single stock returns) or FF5 (Fama French 5 factor returns).')","def execute(train_ts, train_inputs, test_inputs, op_exec):\n if op_exec == \"mlp\":\n model = mod.mlp_model()\n model.fit(train_inputs, train_ts)\n forecast = model.forecast(test_inputs)\n return forecast\n elif op_exec == \"rf\":\n model = mod.rf_model()\n model.fit(train_inputs, train_ts)\n forecast = model.forecast(test_inputs)\n return forecast","def perform(self, node, (fup, fup_target, betas), outputs):\n betas_out = numpy.zeros_like(betas)\n betas_out[0] = 1\n\n M = numpy.float(len(betas))\n fup_sorted_arg = numpy.argsort(fup) \n fup_sorted = fup[fup_sorted_arg]\n idx = numpy.arange(1,M-1)\n \n for i, fup_targ in zip(idx, fup_target[1:-1]):\n ubound_i = numpy.searchsorted(fup_sorted, fup_targ)\n # for given fup target, find tighest lower and upper bounds in current array\n fup_lower, fup_upper = fup_sorted[ubound_i-1], fup_sorted[ubound_i]\n # find matching beta values\n b_lower_i, b_upper_i = fup_sorted_arg[ubound_i-1], fup_sorted_arg[ubound_i]\n beta_lower, beta_upper = betas[b_lower_i], betas[b_upper_i]\n\n db_df = (beta_upper - beta_lower) / (fup_upper - fup_lower)\n betas_out[i] = beta_lower + db_df * (fup_targ - fup_lower)\n \n temp = numpy.sort(betas_out)\n outputs[0][0] = temp[::-1]","def test_predict_proba():\n args = get_layer('predict', 'manual', 'temporal', True, False, window=2, step_size=3)\n run_layer(*args)","def perform_bayesian_optimization(self):\n bounds = {'hunits': (self.hunits_lower, self.hunits_upper),\n 'embedding_dim': (self.embedding_dim_lower, self.embedding_dim_upper)}\n optimizer = BayesianOptimization(f=self.lstm_score, pbounds=bounds, random_state=1)\n optimizer.maximize(init_points=2, n_iter=self.iterations)\n print(optimizer.max)\n print(optimizer.res)","def calc_misclass_all_betas(beta, train_feat, train_lab, test_feat, test_lab):\n n, d = beta.shape\n mis_train = []\n mis_test = []\n for i in range(n):\n mis_train.append(calc_misclass(beta[i], train_feat, train_lab))\n for i in range(n):\n mis_test.append(calc_misclass(beta[i], test_feat, test_lab))\n return mis_train, mis_test","def fit(self, epochs=1000, alpha=0.001, beta=0, verbose=False):\n X, y = self.data, self.labels\n\n for epoch in range(epochs):\n for n in range(X.shape[0]):\n\n # Feedforward\n x = X[n, :].reshape(X.shape[1], 1)\n prediction = self._sigmoid_m(np.matmul(self.w, x) + self.b)\n\n # Back propagation\n error = prediction - y[n, 0]\n self._backpropagation(\n alpha=alpha,\n error=error,\n layer_input=x,\n layer_output=prediction)","def get_beta(self, epoch, beta_start=None, beta_end=None):\r\n if beta_start is None:\r\n beta_start = self.beta_start\r\n if beta_end is None:\r\n beta_end = self.beta_end\r\n\r\n if epoch < beta_start:\r\n return self.beta0\r\n elif beta_start <= epoch <= beta_end:\r\n if self.schedule_type == 'constant':\r\n return self.beta0\r\n elif self.schedule_type == 'linear':\r\n return (self.beta0\r\n + (self.beta1 - self.beta0) * (epoch - beta_start)\r\n / (beta_end - beta_start))\r\n elif self.schedule_type == 'cosine':\r\n return self.cosine_annealing(epoch, beta_start, beta_end)\r\n elif self.schedule_type == 'cosine-restarts':\r\n cycle_len = (beta_end - beta_start) // self.cycles\r\n cycle_idx = (epoch - beta_start) // cycle_len\r\n start_epoch = beta_start + cycle_len * cycle_idx\r\n end_epoch = beta_start + cycle_len * (cycle_idx + 1)\r\n return self.cosine_annealing(epoch, start_epoch, end_epoch)\r\n else:\r\n raise ValueError('Unsupported schedule type {}'\r\n .format(self.schedule_type))\r\n else:\r\n return self.beta1","def test_model_outcome(predicted, actual, planned):\n if not isinstance(predicted, pd.DataFrame):\n predicted = pd.DataFrame(predicted, columns=[\"PREDICTED_TRIP_DURATION\"])\n if not isinstance(actual, pd.DataFrame):\n actual = pd.DataFrame(actual, columns=[\"ACTUAL_TRIP_DURATION\"])\n if not isinstance(planned, pd.DataFrame):\n planned = pd.DataFrame(planned, columns=[\"PLANNED_TRIP_DURATION\"])\n # Initialise the combined dataframe\n combined = pd.concat([predicted, actual, planned], axis=1)\n # Calculate the actual delay\n actual_delay = combined[\"PLANNED_TRIP_DURATION\"] - combined[\"ACTUAL_TRIP_DURATION\"]\n # Calculate the predicted delay\n predicted_delay = combined[\"PLANNED_TRIP_DURATION\"] - combined[\"PREDICTED_TRIP_DURATION\"]\n # Calculate the difference in delay\n delay_diff = actual_delay - predicted_delay\n # Combine the delays into a single dataframe\n combined_delay = pd.concat([pd.DataFrame(actual_delay, columns=['Actual_Delay']),\n pd.DataFrame(predicted_delay, columns=['Predicted_Delay']),\n pd.DataFrame(delay_diff, columns=['Difference_In_Delay'])], axis=1)\n # Obtain the index of the max and min values of the actual, predicted and difference delays\n actual_max_index = combined_delay[\"Actual_Delay\"].argmax()\n actual_min_index = combined_delay[\"Actual_Delay\"].argmin()\n predicted_max_index = combined_delay[\"Predicted_Delay\"].argmax()\n predicted_min_index = combined_delay[\"Predicted_Delay\"].argmin()\n delay_diff_max_index = combined_delay[\"Difference_In_Delay\"].argmax()\n delay_diff_min_index = combined_delay[\"Difference_In_Delay\"].argmin()\n # Get the Mean Absolute Error\n MAE = metrics.mean_absolute_error(combined[\"ACTUAL_TRIP_DURATION\"], combined[\"PREDICTED_TRIP_DURATION\"])\n # Get the R2 Score\n R2 = metrics.r2_score(combined[\"ACTUAL_TRIP_DURATION\"], combined[\"PREDICTED_TRIP_DURATION\"])\n # Get the Root Mean Squared Error\n RMSE = metrics.mean_squared_error(combined[\"ACTUAL_TRIP_DURATION\"], combined[\"PREDICTED_TRIP_DURATION\"],\n squared=False)\n # Get the Median Absolute Error\n MEDAE = metrics.median_absolute_error(combined[\"ACTUAL_TRIP_DURATION\"], combined[\"PREDICTED_TRIP_DURATION\"])\n # Get the Mean Squared Error Log Value\n MSLE = metrics.mean_squared_log_error(combined[\"ACTUAL_TRIP_DURATION\"], combined[\"PREDICTED_TRIP_DURATION\"])\n # Build Dictionary\n pass_val = {\"combined\": combined,\n \"combined_delay\": combined_delay,\n \"actual_max_index\": actual_max_index,\n \"actual_min_index\": actual_min_index,\n \"predicted_max_index\": predicted_max_index,\n \"predicted_min_index\": predicted_min_index,\n \"delay_diff_max_index\": delay_diff_max_index,\n \"delay_diff_min_index\": delay_diff_min_index,\n \"MAE\": MAE,\n \"R2\": R2,\n \"MEDAE\": MEDAE,\n \"RMSE\": RMSE,\n \"MSLE\": MSLE}\n # Return Dictionary\n return pass_val","def test_naive_forecaster_model_with_regressor_pyfunc_output(\n naive_forecaster_model_with_regressor, model_path, data_longley\n):\n _, _, _, X_test = data_longley\n\n flavor.save_model(sktime_model=naive_forecaster_model_with_regressor, path=model_path)\n loaded_pyfunc = flavor.pyfunc.load_model(model_uri=model_path)\n\n X_test_array = convert(X_test, \"pd.DataFrame\", \"np.ndarray\")\n\n model_predict = naive_forecaster_model_with_regressor.predict(fh=FH, X=X_test)\n predict_conf = pd.DataFrame([{\"fh\": FH, \"predict_method\": \"predict\", \"X\": X_test_array}])\n pyfunc_predict = loaded_pyfunc.predict(predict_conf)\n np.testing.assert_array_equal(model_predict, pyfunc_predict)\n\n model_predict_interval = naive_forecaster_model_with_regressor.predict_interval(\n fh=FH, coverage=COVERAGE, X=X_test\n )\n predict_interval_conf = pd.DataFrame(\n [\n {\n \"fh\": FH,\n \"predict_method\": \"predict_interval\",\n \"coverage\": COVERAGE,\n \"X\": X_test_array,\n }\n ]\n )\n pyfunc_predict_interval = loaded_pyfunc.predict(predict_interval_conf)\n np.testing.assert_array_equal(model_predict_interval.values, pyfunc_predict_interval.values)\n\n model_predict_quantiles = naive_forecaster_model_with_regressor.predict_quantiles(\n fh=FH, alpha=ALPHA, X=X_test\n )\n predict_quantiles_conf = pd.DataFrame(\n [\n {\n \"fh\": FH,\n \"predict_method\": \"predict_quantiles\",\n \"alpha\": ALPHA,\n \"X\": X_test_array,\n }\n ]\n )\n pyfunc_predict_quantiles = loaded_pyfunc.predict(predict_quantiles_conf)\n np.testing.assert_array_equal(model_predict_quantiles.values, pyfunc_predict_quantiles.values)\n\n model_predict_var = naive_forecaster_model_with_regressor.predict_var(fh=FH, cov=COV, X=X_test)\n predict_var_conf = pd.DataFrame(\n [\n {\n \"fh\": FH,\n \"predict_method\": \"predict_var\",\n \"cov\": COV,\n \"X\": X_test_array,\n }\n ]\n )\n pyfunc_predict_var = loaded_pyfunc.predict(predict_var_conf)\n np.testing.assert_array_equal(model_predict_var.values, pyfunc_predict_var.values)","def test_200_boosted_goal_difference_for_home_models_with_thresholds(self):\n\n def create_model_fn(fn_team: str):\n team_stat = Stats.n_sample_stats_for_team(cursor=db_in_cursor,\n team=fn_team,\n last_sample_date=self.model_date,\n n_samples=self.num_samples,\n normalize_by_matches=True)\n\n\n return FeatureModel(\n input_data=[self.home_boost + team_stat.goal_diff, team_stat.goal_diff],\n id=team_stat.team_name\n )\n\n\n for match_date in played_home_OR_away_before_dates:\n ####\n # Build model up to the day before the match\n ####\n self.home_boost = 0.72\n self.model_date = match_date - timedelta(days=1)\n self.num_samples = num_matches_in_season\n\n models: {str: FeatureModel} = FeatureModel.create_models_for_all_teams(\n model_making_fn=create_model_fn, entities=teams)\n\n self.persist_models(model_gen_date=self.model_date, model_description=self.shortDescription(), models=models)\n\n self.make_and_store_predictions_for_date(match_date=match_date, models=models, draw_range=(0.3, 0.9))","def AR_Model(x, y, last_week_flow):\n # Fitting AR model to training dataset\n model = LinearRegression().fit(x, y)\n\n # Printing model fitting parameters\n r_sq1 = model.score(x, y)\n # print('Coefficient of Determination = ', np.round(r_sq1, 2))\n\n # Predicting flows with fitted AR Model\n nextweek_prediction = model.predict(last_week_flow)\n\n # Output of this function will be printed as forecasted streamflow\n return nextweek_prediction","def do_rbfsvm(x_data, y_data, bst_model):\n\n bst_rbfsvm = SVC(C = bst_model.best_estimator_.C, gamma = bst_model.best_estimator_.gamma)\n bst_rbfsvm_fit = bst_lsvm.fit(x_data, y_data)\n return True","def test_f_1_is_equal_to_sklearn_fbeta(self):\n\n y_true = [1, 0, 1, 1, 0, 1]\n y_pred = [0, 0, 1, 1, 1, 1]\n tp = 3\n fp = 1\n fn = 1\n tn = 1\n beta = 1.0\n\n f_beta_custom = Metrics.f_beta(tp=tp, fp=fp, fn=fn, beta=beta)\n f_beta_sklearn = f1_score(y_true=y_true, y_pred=y_pred)\n\n self.assertEqual(f_beta_custom, f_beta_sklearn)","def forecast(self, stock):\n # Load the trained model\n model_handler = ModelHandler()\n model = model_handler.load_json_model(stock)\n\n # Importing the training set\n dataset = pd.read_csv(stock.csv_name)\n dates = dataset.iloc[len(dataset)-31:len(dataset)-1, 0].values\n dates = [dt.datetime.strptime(d, '%Y-%m-%d').date() for d in dates]\n\n # Create the test dataset\n dataset_test = dataset[len(dataset) - 30:]\n real_stock_price = dataset_test.iloc[:, 1:2].values\n dataset = dataset['Open']\n inputs = dataset[len(dataset) - len(dataset_test) - 60:].values\n inputs = inputs.reshape(-1, 1)\n\n # Feature Scaling\n sc = MinMaxScaler(feature_range=(0, 1))\n inputs = sc.fit_transform(inputs)\n\n x_test = []\n x_test.append(inputs[0:60, 0])\n predicted_values = []\n for i in range(1, 31):\n x_test_np = np.array(x_test)\n x_test_np = np.reshape(x_test_np, (x_test_np.shape[0], x_test_np.shape[1], 1))\n new_data = model.predict(x_test_np)\n predicted_values.append(new_data[0])\n x_test[0] = np.delete(x_test[0], 0)\n x_test[0] = np.concatenate([x_test[0], new_data[0]])\n\n predicted_values = sc.inverse_transform(predicted_values)\n plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))\n plt.gca().xaxis.set_major_locator(mdates.DayLocator())\n plt.plot(dates, real_stock_price, color='red', label=f'Actual {stock.ticker} Stock Price')\n plt.plot(dates, predicted_values, color='blue', label=f'Predicted {stock.ticker} Stock Price')\n plt.gcf().autofmt_xdate()\n plt.title(f'{stock.ticker} Stock Price Prediction')\n plt.xlabel('Time')\n plt.ylabel(f'{stock.ticker} Stock Price')\n plt.legend()\n plt.show()","def AR_Model(x, y, last_week_flow):\n # Fitting AR model to training dataset\n model = LinearRegression().fit(x, y)\n\n # Printing model fitting parameters\n r_sq1 = model.score(x, y)\n print('Coefficient of Determination = ', np.round(r_sq1, 2))\n\n # Predicting flows with fitted AR Model\n nextweek_prediction = model.predict(last_week_flow.values)\n\n # Output of this function will be printed as forecasted streamflow\n return nextweek_prediction","def error(beta_0: float, beta_1: float, x_i: float, y_i: float) -> float:\n return predict(beta_0, beta_1, x_i) - y_i","def main():\n\n args = parse_cmd_line_args()\n\n random_state = check_random_state(args.random_seed)\n\n X, mu, A, phases = generate_data(args.n_features, n_samples=args.n_samples,\n period=args.period, order=args.order,\n noise_variance=args.noise_variance,\n random_state=random_state)\n\n if args.plot_data:\n plot_data_timeseries(X)\n\n best_fit, best_weights = fit_fembv_varx(\n X, n_components=args.n_components,\n max_tv_norm=args.max_tv_norm,\n memory=args.memory, n_init=args.n_init,\n tolerance=args.tolerance,\n max_iterations=args.max_iterations,\n verbose=args.verbose, random_state=random_state)\n\n if args.plot_weights:\n plot_weights_timeseries(best_weights, phases)","def getRegressionOutput(self):\n\n # Construct train data\n X_tmp = np.empty(shape=(0, 14))\n for flightNum in range(len(self.routes)):\n # concatenate the buy or wait info to get the total datas\n y_train = self.y_train.reshape((self.y_train.shape[0],1))\n y_train_price = self.y_train_price.reshape((self.y_train_price.shape[0],1))\n\n X_train = np.concatenate((self.X_train, y_train, y_train_price), axis=1)\n\n # choose one route datas\n X_train = X_train[np.where(X_train[:, flightNum]==1)[0], :]\n\n # remove dummy variables\n # feature 8: departure date; feature 9: observed date state\n # feature 10: minimum price; feature 11: maximum price\n # feature 12: prediction(buy or wait); feature 13: current price\n X_train = X_train[:, 0:14]\n\n # group by the feature: departure date\n departureDates_train = np.unique(X_train[:, 8])\n\n # get the final datas, the observed data state should be from large to small(i.e. for time series)\n for departureDate in departureDates_train:\n indexs = np.where(X_train[:, 8]==departureDate)[0]\n datas = X_train[indexs, :]\n minPrice = min(datas[:, 10])\n datas[:, 12] = minPrice\n \"\"\"\n print departureDate\n print minPrice\n print datas\n \"\"\"\n X_tmp = np.concatenate((X_tmp, datas), axis=0)\n\n X_train = X_tmp[:, 0:12]\n y_train = X_tmp[:, 12]\n y_train_price = X_tmp[:, 13]\n y_train = y_train.reshape((y_train.shape[0], 1))\n y_train_price = y_train_price.reshape((y_train_price.shape[0], 1))\n\n\n X_train = np.concatenate((X_train, y_train_price), axis=1)\n np.save('inputReg/X_train', X_train)\n np.save('inputReg/y_train', y_train)\n np.save('inputReg/y_train_price', y_train_price)\n\n\n # Construct test data\n X_tmp = np.empty(shape=(0, 14))\n for flightNum in range(len(self.routes)):\n # concatenate the buy or wait info to get the total datas\n y_test = self.y_test.reshape((self.y_test.shape[0],1))\n y_test_price = self.y_test_price.reshape((self.y_test_price.shape[0],1))\n\n X_test = np.concatenate((self.X_test, y_test, y_test_price), axis=1)\n\n # choose one route datas\n X_test = X_test[np.where(X_test[:, flightNum]==1)[0], :]\n\n # remove dummy variables\n # feature 8: departure date; feature 9: observed date state\n # feature 10: minimum price; feature 11: maximum price\n # feature 12: prediction(buy or wait); feature 13: current price\n X_test = X_test[:, 0:14]\n\n # group by the feature: departure date\n departureDates_test = np.unique(X_test[:, 8])\n\n # get the final datas, the observed data state should be from large to small(i.e. for time series)\n for departureDate in departureDates_test:\n indexs = np.where(X_test[:, 8]==departureDate)[0]\n datas = X_test[indexs, :]\n minPrice = min(datas[:, 10])\n datas[:, 12] = minPrice\n \"\"\"\n print departureDate\n print minPrice\n print datas\n \"\"\"\n X_tmp = np.concatenate((X_tmp, datas), axis=0)\n\n X_test = X_tmp[:, 0:12]\n y_test = X_tmp[:, 12]\n y_test_price = X_tmp[:, 13]\n y_test = y_test.reshape((y_test.shape[0], 1))\n y_test_price = y_test_price.reshape((y_test_price.shape[0], 1))\n X_test = np.concatenate((X_test, y_test_price), axis=1)\n np.save('inputReg/X_test', X_test)\n np.save('inputReg/y_test', y_test)\n np.save('inputReg/y_test_price', y_test_price)","def Sarsa_lbda_w_bf_and_lin_FA(env, fvecs, idcs_per_action, weights, alpha,\n epsilon, gamma, num_actions, num_episodes, lbda):\n\n for episode in tqdm(range(num_episodes)):\n done = False\n \n state = env.reset()\n fvec_idx_per_tiling = fvecs.calc_feature_vec(state)\n Q_vals = init_Q_values(weights, fvec_idx_per_tiling, idcs_per_action,\n num_actions)\n action, Q_current = eps_greedy(Q_vals, epsilon, num_actions) \n curr_active_feat_idcs = fvec_idx_per_tiling + action*idcs_per_action \n \n z_trace = init_e_traces(len(weights)) ###\n \n step_count = 0\n while not done:\n step_count += 1\n if episode == (num_episodes -1):\n env.render(state[0])\n\n next_state, reward, done,__ = env.step(state, action)\n\n delta = reward\n for i in curr_active_feat_idcs:\n delta -= weights[i]\n z_trace[i] += 1 ### accumulating traces\n #z_trace[i] = 1 ### replacing traces\n if done:\n weights += alpha*delta*z_trace\n break\n\n fvec_idx_per_tiling = fvecs.calc_feature_vec(next_state)\n Q_vals = init_Q_values(weights, fvec_idx_per_tiling, idcs_per_action,\n num_actions)\n next_action, Q_next = eps_greedy(Q_vals, epsilon, num_actions)\n \n next_active_feat_idcs = fvec_idx_per_tiling + next_action*idcs_per_action### \n\n for i in next_active_feat_idcs:\n delta += gamma*weights[i]\n weights += alpha*delta*z_trace\n \n z_trace = gamma*lbda*z_trace\n state = next_state\n action = next_action \n curr_active_feat_idcs = next_active_feat_idcs\n \n env.plot_step_per_ep(episode, step_count)\n \n return","def run_trainig_and_testing(company = \"FB\", training_window = 60,\n trainig_period_y=8, testing_period_y=1,\n prediction_day=dt.datetime.now() ):\n # Load data for model\n training_data, testing_data = fetch_data(company, trainig_period_y, testing_period_y, prediction_day)\n # prepare data\n # scaler is object which will scale whole series values to defined feature_range interval\n scaler = MinMaxScaler(feature_range=(0,1))\n # we are insterested only in the closing values\n # optionaly we can use AdjClose -> adjusted for splits\n scaled_data = scaler.fit_transform(training_data['Close'].values.reshape(-1,1))\n\n # x is our learning data\n x_train, y_train = create_training_tensors(training_window, scaled_data)\n\n # build the model\n model = create_model((x_train.shape[1], 1))\n model.fit(x_train, y_train, epochs=5, batch_size=32)\n\n # for future we can save the model here and load for future\n\n \"\"\"\n TESTING MODEL ACCURACY\n \"\"\"\n\n # load test data\n\n tst_start_dt = dt.datetime(2020,1,1)\n tst_end_dt = dt.datetime(2021,1,1)\n\n\n actual_prices = testing_data['Close'].values\n total_dataset = pd.concat((training_data['Close'], testing_data['Close']), axis=0)\n\n model_inputs = total_dataset[len(total_dataset) - len(testing_data) - training_window:].values\n model_inputs = model_inputs.reshape(-1,1)\n model_inputs = scaler.fit_transform(model_inputs)\n\n # make prediction on test data\n\n x_test, y_test = create_training_tensors(training_window, model_inputs)\n\n predicted_prices = model.predict(x_test)\n # predicted prices are scaled to (0-1) so we need to rescale them to actual prices\n predicted_prices = scaler.inverse_transform(predicted_prices)\n\n plot_actual_vs_predicted(predicted_prices,actual_prices, company,\n x_labels=[date.strftime(\"%d-%m-%y\") for date in testing_data.index] )\n\n # predict next day\n real_data = [model_inputs[len(model_inputs) + 1 - training_window: len(model_inputs)+1, 0]]\n real_data = np.array(real_data)\n real_data = np.reshape(real_data, (real_data.shape[0], real_data.shape[1], 1))\n print(scaler.inverse_transform(real_data[-1]))\n\n prediction = model.predict(real_data)\n prediction = scaler.inverse_transform(prediction)\n print(f\"Next day value is: {prediction}\")\n\n print(\"===================================\")\n print(model.summary())\n print(\"===================================\")\n print(\"Evaluating model\")\n result = model.evaluate(x_test, y_test)\n print(dict(zip(model.metrics_names, result)))\n\n\n # TODO\n # experiment with next days predictions","def predict(x_tst, model):\n\n predictions = model.predict(x_tst)\n return predictions","def run_model(self, trend_growth='linear', trend_flexibility=0.05, changepoints=None,\n holidays=None, additional_features=None):\n\n # Prepare data to Prophet requirements\n ###################################################################\n # Create column ds with index (demand date)\n demand = self.demand\n demand['ds'] = demand.index\n\n # Adding additional features to the model as a regressor\n if additional_features is not None:\n # Select only the exog features in the list\n demand = demand[['y','ds'] + list(additional_features)]\n\n # Check holidays\n if holidays is not None:\n holidays = self.holidays_format(holidays)\n\n # Specify the carrying capacity (maximum) in a column 'cap'\n if self.market_share is not None:\n demand['cap'] = self.market_share[:len(demand)]\n # Saturating Minimum in a column 'floor'\n demand['floor'] = 0\n\n # Train model\n #####################################################################\n if self.freq is not 'D' or 'B':\n model = Prophet(growth=trend_growth, changepoint_prior_scale=trend_flexibility,\n changepoints=changepoints, holidays=holidays, weekly_seasonality=False)\n else:\n model = Prophet(growth=trend_growth, changepoint_prior_scale=trend_flexibility,\n changepoints=changepoints, holidays=holidays)\n\n # Adding regressors to the linear part of the model\n if additional_features is not None:\n for feature in additional_features:\n model.add_regressor(feature)\n\n # Fit\n model.fit(demand)\n\n # Predict Test\n ####################################################################\n\n # Dataframe to predict (including all history)\n # predict() from Prophet only receives dates as a column 'ds' (and other external variables)\n demand_predict = demand.drop(columns=['y'])\n\n # Dataframe with yhat (y predicted) and the components of the model\n forecast = model.predict(demand_predict)\n forecast.set_index('ds', inplace=True) # Set dates as index again\n\n # Diagnostics - Time series cross validation to measure forecast error using historical data\n train_period = int(len(demand) * 0.5) # Initialize with half of the sample\n test_ratio = 0.3\n horizon = int(train_period * test_ratio)\n\n error = self.evaluation(model, training_period=train_period, forecast_horizon=horizon)\n\n return forecast['yhat'], model, error","def forecast_loss_V(num_feats):\n\n def forecast_loss(y_true, y_pred):\n \"\"\"Forecasting loss function (ignores missing values).\"\"\"\n\n return K.sum(\n y_true[:, num_feats:] * (y_true[:, :num_feats] - y_pred) ** 2, axis=-1\n )\n return forecast_loss","def test_bayes_updates_good_data(self):\r\n # result for first -> fourth calculated by hand\r\n for obs, exp in zip(bayes_updates(self.test), self.result):\r\n self.assertFloatEqualAbs(obs, exp, 1e-11)","def beta_fct(M_p, F_xuv, R_p):\n\n M_EARTH= const.M_earth.cgs.value\n R_EARTH = const.R_earth.cgs.value\n\n if (type(F_xuv) == float) or (type(F_xuv) == np.float64):\n # if F_xuv is single value\n grav_pot = -const.G.cgs.value * (M_p*M_EARTH) / (R_p*R_EARTH)\n log_beta = max(0.0, -0.185 * np.log10(-grav_pot)\n \t\t\t\t\t+ 0.021 * np.log10(F_xuv) + 2.42)\n beta = 10**log_beta\n return beta\n\n elif len(F_xuv) > 1:\n # if F_xuv is a list\n betas = []\n for i in range(len(F_xuv)):\n grav_pot_i = -const.G.cgs.value \\\n * (M_p[i]*M_EARTH) / (R_p[i]*R_EARTH)\n log_beta_i = max(0.0, -0.185 * np.log10(-grav_pot_i)\n \t\t\t\t\t + 0.021 * np.log10(F_xuv[i]) + 2.42)\n beta_i = 10**log_beta_i\n betas.append(beta_i)\n betas = np.array(betas)\n return betas","def best_weigth(test, lstm_forecasts, arima_forecasts):\n errors = []\n for i in range(0, 100):\n alpha = i / 100\n beta = 1 - alpha\n weighted_forecasts = (alpha * lstm_forecasts + beta * arima_forecasts)\n meanError = ((test - weighted_forecasts)**2).mean() #mse\n errors.append(meanError)\n return np.array(errors).argmin() / 100","def stepwise_regression(train_per_list, cut_off_list, C_list,\n factors,non_factors, data_path, executable_path):\n sql_table = 'aggregated_ctr' #Data table\n sql_features = list(set(sum([fs.split('*') for fs in factors], [])))\n # remove cross terms\n\n factors+=['campaign_id','ad_account_id','pub_account_id', \n 'campaign_id*site', 'ad*pub_account_id']\n con_dict_mad={'host':'db.lqm.io','db':'madvertise_production',\n 'user':'readonly','passwd':'z0q909TVZj'}\n con_dict_dse={'host':'db.lqm.io','db':'dse','user':'dse','passwd':'dSe@lQm'}\n rtb_flag=[0,1]\n \n test_per_list= map(lambda x: ( add_hour(x[1], 1), add_hour(x[1], 3)), train_per_list)\n \n # test period is next 3 hours after end of training period\n # DATA RANGE IS INCLUSIVE => 00:00-02:00 = 3 HOURS\n MySQL_save_data_loop(con_dict_dse, sql_table,\n train_per_list, sql_features, rtb_flag, data_path)\n MySQL_save_data_loop(con_dict_dse, sql_table,\n test_per_list, sql_features, rtb_flag, data_path)\n \n model_type=0\n has_intercept = True # bias term in LR\n tol = 0.00000001\n\n # NB these filenames are HARDCODED in write_sparse routines\n weights_file = 'train_ais.txt'\n train_file = 'train_svm.txt'\n test_file = 'test_svm.txt'\n probability_file = 'preds_SummModel_py.txt'\n\n \n res_df_list=[]\n trial_factors=[]\n remaining_factors=factors[:]\n while len(remaining_factors):\n results = [] \n # we assume we cannot load all the data in memory\n # so we have to reload for every step of stepwise selection\n for train_per, test_per in zip(train_per_list, test_per_list):\n \n train_df=load_data(data_path,train_per)\n test_df=load_data(data_path,test_per)\n \n sc, click_no_click_df, weights, targets \\\n = libLinear_functions.create_sparse_cat(train_df, factors, non_factors)\n \n for cut_off in cut_off_list:\n sparse_train_all = libLinear_functions.create_sparse(sc, cut_off, click_no_click_df)\n sparse_test_all = sc.transform(test_df)\n for fac in remaining_factors:\n trial_factors.append(fac)\n trial_factors.sort(key=lambda x: sc.factors.index(x))\n # libsvm expects the indices in ascending order\n print (trial_factors) \n sparse_train=sc.select_factors(sparse_train_all, trial_factors)\n sparse_test=sc.select_factors(sparse_test_all, trial_factors)\n libLinear_functions.write_sparse(sc, sparse_train, weights, targets, data_path, len(trial_factors))\n libLinear_functions.write_sparse_test(sc, sparse_test, data_path, n_columns_used= len(trial_factors))\n\n for C in C_list:\n model_file = \\\n '{start}_{stop}_cut_{cut_off}_C_{C:0.3}.model'.format(\n start=date_name(train_per[0]),\n stop=date_name(train_per[1]),\n cut_off=cut_off, C=C)\n fit(executable_path, data_path, train_file,\n model_file, weights_file, model_type, reg_param=C, tol=tol,\n has_intercept=has_intercept)\n \n pCTR = libLinear_functions.predict(\n executable_path, data_path, test_file,\n model_file, probability_file)\n if type(pCTR) is pd.Series:\n amounts = pd.DataFrame({\n 'no_clicks':test_df['instances' ]-test_df['clicks'],\n 'clicks':test_df['clicks']})\n mean_log_loss, weighted_log_loss =\\\n libLinear_functions.log_loss_weighted(pCTR, amounts)\n results.append([train_per[:], tuple(trial_factors),fac, cut_off, C, mean_log_loss])\n # what to do if ERROR?\n trial_factors.remove(fac)\n res_df=pd.DataFrame(results,columns=['train_per','factors','add_factor','cut_off','C','mean_log_loss'])\n res_avg=res_df.groupby(['factors','add_factor','cut_off','C']).agg([np.mean,np.std])\n best_params=res_avg['mean_log_loss','mean'].argmin()\n best_fac=best_params[1]\n remaining_factors.remove(best_fac)\n trial_factors.append(best_fac)\n res_df_list.append(res_df)\n results_df=pd.concat(res_df_list)\n return results_df","def AFTER(df_train, df_test, lambd):\n\n # forecast errors\n errors = df_train.iloc[:, 1:].subtract(df_train.iloc[:, 0], axis=0)\n sq_errors = errors**2\n\n # combining weights\n nominator = np.exp((-lambd) * sq_errors.sum(axis=0))\n denominator = nominator.sum()\n comb_w = nominator / denominator\n\n # predictions\n df_pred = pd.DataFrame({\"AFTER\": df_test.dot(comb_w)})\n\n return df_pred","def fun(params, slope, data):\n x, y_true = data\n return y_true - model_fun(params, slope, x)","def _tstat_beta(self):\n return _handle_ab(self._tstat_all, self.use_const)[1]","def a_test_bbvi_elbo():\n model = ARIMAX(formula=\"y ~ x1\", data=data, ar=1, ma=1, family=Exponential())\n x = model.fit('BBVI',iterations=300, record_elbo=True, map_start=False)\n assert(x.elbo_records[-1]>x.elbo_records[0])","def back_testing(self, func=None):\n # preallocate real statistics dict\n ism_label = []\n \n if not self.moving_average:\n # iterate on all ism packages\n for _i, ism_pack in enumerate(self.ism_data):\n ism_code = ism_pack[0][0]\n interval = float(ism_pack[0][-5])\n df = self.back_testing_data[ism_code]\n # using close price only now, check data is/not empty\n if df.empty:\n res = np.nan\n continue\n else:\n run_price = df['ClosePrice_run'].sum()\n stop_price = df['ClosePrice_stop'].sum()\n res = ((stop_price-run_price)/run_price)*243/interval # 243 market days/year\n ism_label.append([ism_code, res])\n \n else: # using moving_average\n # iterate on all ism packages\n # for _i, ism_pack in enumerate(self.ism_data):\n # ism_code = ism_pack[0][0]\n # interval = int(ism_pack[0][-5])\n # df = self.back_testing_data[ism_code]\n # \n # # using close price only now\n # dim = int(len(df)/(L_shift+R_shift-1)) \n # close_price = np.array_split(df['ClosePrice'], dim)\n # f1 = lambda x: float(x[-1]-x[0]) # real return for single stock\n # f2 = lambda x: float(x[0]) # initial value of single stock\n # if L_shift+R_shift >= interval:\n # ls1 = np.zeros((L_shift+R_shift-interval,), dtype=np.float32)\n # ls2 = np.zeros_like(ls1)\n # for _j, par in enumerate(close_price):\n # # sum over different stocks in one ism_pack\n # par_rolling = par.rolling(interval)\n # ls1 += np.asarray(par_rolling.apply(f1).dropna())\n # ls2 += np.asarray(par_rolling.apply(f2).dropna())\n # ism_label[ism_code] = {}\n # ism_label[ism_code]['real_return'] = (ls1/ls2/interval)\n # else:\n # print(\"L_shift+R_shift < interval\")\n pass \n \n return ism_label","def big_analysis(beta0s=[0.5, 0.8, 1.1, 1.4, 1.7], ks=range(6), betaps=[1.2, 1.5, 2, 3]):","def run_models(X_train, X_test, Y_train, Y_test, models_list, dataframe_name):\n results = []\n for type, model in models_list:\n model.fit(X_train, Y_train)\n Y_predict = model.predict(X_test)\n accuracy = accuracy_score(Y_test, Y_predict)*100\n results.append([type, accuracy, dataframe_name])\n return results","def run_regression(train_embeds, train_labels, test_embeds, test_labels):\n np.random.seed(1)\n from sklearn.linear_model import SGDClassifier\n from sklearn.dummy import DummyClassifier\n from sklearn.metrics import f1_score\n dummy = DummyClassifier()\n dummy.fit(train_embeds, train_labels)\n log = SGDClassifier(loss=\"log\", n_jobs=10, tol=1e-3)\n log.fit(train_embeds, train_labels)\n print(\"F1 score:\", f1_score(test_labels, log.predict(test_embeds), average=\"micro\"))\n print(\"Random baseline f1 score:\", f1_score(test_labels, dummy.predict(test_embeds), average=\"micro\"))","def _analytic_forecast(\n self,\n parameters: NDArray,\n resids: NDArray,\n backcast: Union[float, NDArray],\n var_bounds: NDArray,\n start: int,\n horizon: int,\n ) -> VarianceForecast:","def main(feats_name, targets_name, model_name, n_boot, seed_start, output_filename, train_test_flag):\n\n #load feats and targets\n input_dict = {}\n input_dict['feats'] = 'data/%s' % (feats_name)\n input_dict['targets'] = 'data/%s' % (targets_name)\n #load the feats and targets\n df = pd.read_csv(\"%s\" % (input_dict['feats']))\n targets = pd.read_csv(\"%s\" % (input_dict['targets']))\n #drop columns not used for prediction\n drop_cols = [\"Unnamed: 0\",\"index\"]\n for dc in drop_cols:\n if dc in targets.columns:\n targets = targets.drop(dc,axis=1)\n if dc in df.columns:\n df = df.drop(dc,axis=1)\n #reduce to training or test set only if requested\n if (train_test_flag == 'train') and ('test_set' in df.columns):\n targets = targets[df['test_set'] == 0]\n df = df[df['test_set'] == 0]\n elif (train_test_flag == 'test') and ('test_set' in df.columns):\n targets = targets[df['test_set'] == 1]\n df = df[df['test_set'] == 1]\n df = df.drop('test_set', axis = 1)\n \n #broadcast the feats and targets\n df_b = sc.broadcast(df)\n targets_b = sc.broadcast(targets)\n\n #Set up the classifier. 3fold CV for selection of regularization term.\n if model_name == 'linear':\n model = LinearRegression(fit_intercept=True,\n normalize=False,\n copy_X=True,\n n_jobs=1) \n elif model_name == 'lasso':\n model = LassoCV(alphas = [.05,.1,.2],\n normalize = False,\n fit_intercept = True,\n verbose = False,\n copy_X = False,\n n_jobs = 3)\n elif model_name == 'ridge':\n model = RidgeCV(alphas = [.00001,.0001,.001,.01,.1,1,10,100,1000,10000],\n normalize = False,\n fit_intercept = True,\n verbose = 1,\n cv = 3)\n else:\n raise ValueError('model_name not recognized.')\n \n #Create an RDD that specifies prng seed to use\n samp_list = [(n,) for n in np.arange(seed_start, seed_start+n_boot)]\n samp_rdd = sc.parallelize(samp_list,n_boot) #create RDD with one partition for each row (second arg is number of partitions)\n #Create a function that takes a tuple as input and returns \n def func(tup):\n \"\"\"\n Takes as input a tuple containing an integer. The integer specifies the random seed that will be used to \n randomly sample, with replacement, observations from the feats set provided. The model is fitted to the \n sampled feats. Resulting best fit parameters, along with some other summary statistics and information are\n provided as input in a JSON string that will be written to the output file when all jobs are completed.\n \n Parameters\n ----------\n tup, rdd\n - series of tuples with different integer values defining the RNG seed to be used to sample observations\n \n Returns\n ----------\n tup[0], int\n - the seed that was used\n json.dumps(results_dict), str\n - dict in json format with the following keys:\n - alpha, the regularization term providing the best fit according to 3 fold cross-validation\n - random_state, the initial state used for fitting\n - training_feats, the name of the training_feats csv file\n - training_targets, the name of the target variable csv file\n - cv, the type of cross-validation used\n - sklearn_version, which version of sklearn was used\n - mse_min, the mean squared error for the test set on each fold\n - r2, the r-squared value (% var explained)\n - coef, parameter vector\n - intercept, intercept parameter\n - column_names, feature name corresponding to each parameter in the parameter vector\n \"\"\"\n #take a random sample with replacement\n np.random.seed(seed=tup[0]) #set the seed\n n_obs = np.shape(df_b.value)[0] #number of observations determines sample size\n samp = list(np.random.randint(0,high=n_obs,size=n_obs)) #draw the random sample with replacement\n #fit the model\n tic = time.time()\n results = model.fit(df_b.value.iloc[samp,:],np.ravel(targets_b.value.iloc[samp]))\n toc = tic - time.time()\n #save the results in a dict\n results_dict = {}\n results_dict['alpha'] = results.alpha_\n results_dict['random_state'] = results.random_state\n results_dict['training_feats'] = input_dict['feats']\n results_dict['training_targets'] = input_dict['targets']\n results_dict['cv'] = results.cv\n results_dict['sklearn_version'] = sklearn.__version__\n results_dict['mse_min'] = results.mse_path_.min()\n results_dict['r2'] = results.score(df_b.value.iloc[samp,:],np.ravel(targets_b.value.iloc[samp]))\n results_dict['coef'] = list(results.coef_)\n results_dict['intercept'] = results.intercept_\n results_dict['column_names'] = [i for i in df_b.value.columns]\n results_dict['fit_time'] = toc\n #convert results dict to json and save in tuple\n return(json.dumps(results_dict))\n\n #fit model in parallel\n results = samp_rdd.map(lambda p: func(p))\n #save to text file\n results.saveAsTextFile(output_filename)\n #stop the SparkContext.\n if not local_mode:\n sc.stop()"],"string":"[\n \"def linear_regression_forecasting(x_train,y_train,x_valid,y_valid,x_test,y_test):\\n y_train = y_train.reshape(TRAINING_BATCH_SIZE,N_PREDICTIONS*N_OUTPUT_FEATURES)\\n y_valid = y_valid.reshape(VALIDATION_BATCH_SIZE,N_PREDICTIONS*N_OUTPUT_FEATURES)\\n layer1 = keras.layers.Flatten(input_shape=[N_INPUT_STEPS,N_INPUT_FEATURES]) #input layer flattens each batch instance from [n_steps,n_input_features] to [n_steps*n_input_features]\\n layer2 = keras.layers.Dense(N_PREDICTIONS*N_OUTPUT_FEATURES) #fully connected layer solves combination of linear equations\\n model = keras.models.Sequential([layer1,layer2])\\n model.compile(loss=\\\"mse\\\",optimizer=\\\"adam\\\")\\n training_history = model.fit(x_train,y_train,epochs=N_EPOCHS,validation_data=(x_valid,y_valid),verbose=0)\\n y_pred = model.predict(x_test, TESTING_BATCH_SIZE)\\n y_pred = y_pred.reshape(TESTING_BATCH_SIZE,N_PREDICTIONS,N_OUTPUT_FEATURES)\\n return training_history.history, y_pred, model\",\n \"def _regression_loop(endog, exog, model, lasso_positive, alpha=50):\\n mod_result = _build_regression(exog, endog, model, lasso_positive, alpha=alpha)\\n beta = np.hstack([mod_result.intercept_, mod_result.coef_])\\n \\n return beta\",\n \"def Empirical_Bayes_Estimator(df_train, df_test):\\n\\n # number of individual forecasts and number of periods\\n K = df_test.shape[1]\\n T = df_train.shape[0]\\n\\n # define the prior weights (simple average, with intercept equal zero)\\n beta_0 = np.append(np.array(0), np.full(K, fill_value=1/K, dtype=float))\\n\\n # design matrix (intercept + individual forecasts)\\n F = df_train.iloc[:, 1:].values\\n F = np.insert(F, 0, 1, axis=1)\\n\\n # define y (observed values)\\n y = df_train.iloc[:, 0].values\\n\\n # OLS weights\\n beta_hat = np.dot(np.linalg.inv(np.dot(np.transpose(F), F)),\\n np.dot(np.transpose(F), y))\\n\\n # sigma\\n sigma_hat_sq = np.dot(\\n np.transpose(y - np.dot(F, beta_hat)),\\n y - np.dot(F, beta_hat)\\n ) / T\\n\\n # tau\\n num = np.dot(np.transpose(beta_hat - beta_0), beta_hat - beta_0)\\n denum = np.trace(np.linalg.inv(np.dot(np.transpose(F), F)))\\n tau_hat_sq = num / denum - sigma_hat_sq\\n\\n # combining weights\\n shrinkage = 1 - sigma_hat_sq / (sigma_hat_sq + tau_hat_sq)\\n beta_1_hat = beta_0 + shrinkage*(beta_hat - beta_0)\\n\\n # predictions\\n df_pred = pd.DataFrame(\\n {\\\"Empirical Bayes Estimator\\\":\\n beta_1_hat[0] + np.dot(df_test, beta_1_hat[1:])},\\n index=df_test.index)\\n\\n return df_pred\",\n \"def run_forecast(self, people, accounts, debts):\\n # We don't want to mutate the inputs, so create copies:\\n memo = {}\\n people = deepcopy(people, memo=memo)\\n accounts = deepcopy(accounts, memo=memo)\\n debts = deepcopy(debts, memo=memo)\\n\\n # Build Scenario first so that we have access to initial_year:\\n memo = {}\\n scenario = self.get_param(Parameter.SCENARIO, memo=memo)\\n initial_year = scenario.initial_year # extract for convenience\\n\\n # Retrieve the necessary strategies for building SubForecasts:\\n living_expenses_strategy = self.get_param(\\n Parameter.LIVING_EXPENSES_STRATEGY, memo=memo)\\n saving_strategy = self.get_param(\\n Parameter.SAVING_STRATEGY, memo=memo)\\n withdrawal_strategy = self.get_param(\\n Parameter.WITHDRAWAL_STRATEGY, memo=memo)\\n tax_treatment = self.get_param(\\n Parameter.TAX_TREATMENT, memo=memo)\\n\\n # Now build each of the SubForecast objects required by Forecast\\n income_forecast = IncomeForecast(\\n initial_year=initial_year,\\n people=people)\\n living_expenses_forecast = LivingExpensesForecast(\\n initial_year=initial_year,\\n people=people,\\n living_expenses_strategy=living_expenses_strategy)\\n saving_forecast = SavingForecast(\\n initial_year=initial_year,\\n retirement_accounts=accounts,\\n debt_accounts=debts,\\n transaction_strategy=saving_strategy)\\n withdrawal_forecast = WithdrawalForecast(\\n initial_year=initial_year,\\n people=people,\\n accounts=accounts,\\n transaction_strategy=withdrawal_strategy)\\n tax_forecast = TaxForecast(\\n initial_year=initial_year,\\n people=people,\\n tax_treatment=tax_treatment)\\n\\n # With these SubForecasts defined, building Forecast is trivial:\\n forecast = Forecast(\\n income_forecast=income_forecast,\\n living_expenses_forecast=living_expenses_forecast,\\n saving_forecast=saving_forecast,\\n withdrawal_forecast=withdrawal_forecast,\\n tax_forecast=tax_forecast,\\n scenario=scenario)\\n\\n # Forecasts run automatically on init, so we're done!\\n return forecast\",\n \"def run():\\n\\n df = read_input() # the parameters\\n df = add_time_period(df) # a feature\\n df = is_holiday(df) # a feature\\n df = scale_continous(df) # continous feature transformation\\n df = encode_dummy(df) # categorical feature transformation\\n df = order_columns(df) # ordering model inputs\\n model = load_model() # the multiple linear regression model\\n prediction = int(model.predict(df)) # form a prediction\\n return prediction # return the prediction\",\n \"def test_run_regressions(test_data, model):\\n answer = model._Run_Regressions(\\n test_data,\\n 100,\\n forecasts={\\\"arima\\\", \\\"holtwinters\\\", \\\"prophet\\\", \\\"arima_r\\\", \\\"sarima_r\\\"}\\n )\",\n \"def AdaBoost(df_train, df_test, phi):\\n\\n # number of periods\\n T = df_train.shape[0]\\n T_test = df_test.shape[0]\\n\\n # initialize observation weights\\n w = np.full(T, 1, dtype=float)\\n w_sum = np.sum(w)\\n\\n # matrix of individual forecasts\\n F = df_train.iloc[:, 1:]\\n\\n # dependent variable\\n y = df_train.iloc[:, 0]\\n\\n # prepare testing set\\n test_F = df_test.values\\n test_y_pred = np.full((T_test, 50), np.nan, dtype=float)\\n\\n # prepare MLP object\\n MLP = MLPRegressor(hidden_layer_sizes=2, activation=\\\"tanh\\\", solver=\\\"lbfgs\\\")\\n\\n # AdaBoost iteration steps\\n for i in range(50):\\n\\n # compute observation probabilities from weights\\n prob = w / w_sum\\n\\n # generate the training sample\\n train_index = np.random.choice(T, size=T, p=prob)\\n train_F = df_train.iloc[train_index, 1:]\\n # train_F_t = np.swapaxes(train_F, 0, 1)\\n train_y = df_train.iloc[train_index, 0]\\n\\n # training the model by OLS\\n # theta_hat = np.linalg.multi_dot(\\n # [np.linalg.inv(np.dot(train_F_t, train_F)), train_F_t,train_y]\\n # )\\n\\n # fit MLP\\n MLP_fit = MLP.fit(train_F, train_y)\\n\\n # predictions (on the original dataset)\\n # y_pred = np.dot(F, theta_hat)\\n # test_y_pred[:, i] = np.dot(test_F, theta_hat)\\n y_pred = MLP_fit.predict(F)\\n test_y_pred[:, i] = MLP_fit.predict(test_F)\\n\\n # absolute relative errors\\n ARE = np.abs((y - y_pred)/y)\\n\\n # threshold losses\\n L = (ARE > phi)*1\\n\\n # weighted average loss\\n L_bar = np.dot(prob, L)\\n\\n # I believe this is a necesarry condition for the AdaBoost algorithm\\n # to work (even though it is not discussed by the author), because,\\n # else if the error is too low, beta becomes > 1 and the model could\\n # get super-overfit on just few observations\\n # L_bar > 1/np.exp(1) ensures, that some weight updating always happens\\n # L_bar = max(L_bar, 1/np.exp(1))\\n L_bar = max(L_bar, 0.5)\\n # model confidence\\n beta = np.log(1/L_bar)\\n\\n # observation weights updating, absolute value for computational rsns.\\n w = np.abs(np.multiply(w, beta**(1-L)))\\n w_sum = np.sum(w)\\n\\n # forecast combination\\n pred = np.mean(test_y_pred, axis=1)\\n\\n # output\\n df_pred = pd.DataFrame(\\n {\\\"AdaBoost\\\": pred},\\n index=df_test.index\\n )\\n\\n return df_pred\",\n \"def predict():\\n model = LinearRegression().fit(input_data[['x']], input_data['y'])\\n future_vals = [[20], [21], [22]]\\n return None\",\n \"def rtest_predictoutcome():\\n\\n #define cohort size\\n npatients = 2\\n\\n #init healthy patients\\n simulator = AbbcEnvironment(patients=npatients)\\n\\n #simulate healthy patients for long term in short term increments\\n nstep = int(long_term/short_term)\\n\\n #define action taken : -1 means patients will be simulated as healthy\\n action = np.repeat(-1, npatients)\\n\\n #init episode list\\n episode = [simulator.state]\\n\\n #main simulation loop to generate episodes\\n for step in range(nstep):\\n episode += simulator.take_action(action=action, simtime=short_term)\\n\\n #episode length is 1+2*nstep consisting of intit state (5xnpat) followed by\\n # next state and reward (1xnpat) repeating each time step.\\n #print(episode)\\n #print(len(episode))\\n\\n #---semi gradient temporal difference (0) algorithm ---\\n #init hyperparameters\\n alpha = .1 #learning rate\\n #init Value function model\\n agent = AbbcAgent(discount=1.0)\\n #loop over episodes\\n for patient in range(npatients):\\n #state = [nstep]\\n #state += episode[0][:,patient] #get inital state\\n state = np.append(episode[0][:,patient],nstep).reshape((6,1)) #get inital state\\n\\n print(state)\\n #loop over time steps in episode\\n for k in range(1,nstep+1):\\n #get next state and reward\\n #nextstate = [nstep-k]\\n #nextstate = episode[k*2-1][:,patient]\\n nextstate = np.append(episode[k*2-1][:,patient],nstep-k).reshape((6,1))\\n\\n reward = episode[k*2][patient]\\n\\n #get magnitude for forces\\n magnitude = alpha * (reward + agent.discount * agent.get_value(nextstate)\\n - agent.get_value(state))\\n #compute forces\\n forces = computeforces(agent.prednet, state, 0, \\\"iden\\\")\\n\\n #update model\\n for layer in forces:\\n index = layer[\\\"layer\\\"]\\n agent.prednet[index][\\\"weight\\\"] += magnitude * layer[\\\"fweight\\\"]\\n agent.prednet[index][\\\"bias\\\"] += magnitude * layer[\\\"fbias\\\"]\\n\\n state = np.copy(nextstate)\\n\\n\\n #make predictions\\n state = np.append(episode[0][:,patient],nstep).reshape((6,1)) #get inital state\\n print(agent.get_value(state))\\n\\n #Value function approximates outcome return at time horizon.\\n assert(False)\\n\\n ##define action taken\\n #action = np.repeat(2, npatients)\\n ##main simulation loop\\n #for step in range(nstep):\\n # _, drugy_reward[step,:] = simulator.take_action(action=action, simtime=short_term)\",\n \"def forecast(self, requests):\\n responses = []\\n SOC = self.SOC \\n\\n for number in range(self.numHVAC): #loop through all HVACs\\n TinRB = self.TinInitialB[number]\\n TwallRB = self.TwallInitialB[number]\\n TmassRB = self.TmassInitialB[number]\\n TatticRB = self.TatticInitialB[number]\\n elementOnRB = self.elementOnB[number]\\n\\n TinR = self.TinInitial[number]\\n TwallR = self.TwallInitial[number]\\n TmassR = self.TmassInitial[number]\\n TatticR = self.TatticInitial[number]\\n\\n SOC_bR = self.SOCb[number]\\n SOCR = self.SOC[number]\\n\\n elementOnR = self.elementOn[number]\\n lockonR = self.lockon[number]\\n lockoffR = self.lockoff[number]\\n cycle_offR = self.cycle_off_base[number]\\n cycle_on_baseR = self.cycle_on_base[number]\\n cycle_off_gridR = self.cycle_off_grid[number]\\n cycle_on_gridR = self.cycle_on_grid[number]\\n\\n # Iterate and process each request in fleet_requests\\n for req in requests:\\n ts = req.ts_req\\n dt = int(req.sim_step.total_seconds())\\n p_req = req.P_req\\n q_req = req.Q_req\\n res = self.run(p_req, q_req, self.SOC, self.time, dt, ts)\\n\\n return res\\n \\n # reset the model\\n for number in range(self.numHVAC): #loop through all HVACs\\n self.SOC = SOCR \\n self.SOCb = SOC_bR \\n\\n self.TinInitialB[number] = TinRB\\n self.TwallInitialB[number] = TwallRB\\n self.TmassInitialB[number] = TmassRB\\n self.TatticInitialB[number] = TatticRB\\n\\n self.TinInitial[number] = TinR\\n self.TwallInitial[number] = TwallR\\n self.TmassInitial[number] = TmassR\\n self.TatticInitial[number] = TatticR\\n\\n self.elementOn[number] = elementOnR\\n self.elementOnB[number] = elementOnRB\\n\\n self.lockon[number] = lockonR\\n self.lockoff[number] = lockoffR\\n self.cycle_off_base[number] = cycle_offR\\n self.cycle_on_base[number] = cycle_on_baseR\\n self.cycle_off_grid[number] = cycle_off_gridR\\n self.cycle_on_grid[number] = cycle_on_gridR\\n\\n return responses\",\n \"def alpha_beta(returns, factor_returns):\\n\\n ret_index = returns.index\\n beta, alpha = sp.sp.stats.linregress(factor_returns.loc[ret_index].values,\\n returns.values)[:2]\\n\\n return alpha * APPROX_BDAYS_PER_YEAR, beta\",\n \"def fbeta(true_label, prediction):\\n true_label = np.asarray(true_label)\\n prediction = np.asarray(prediction)\\n return fbeta_score(true_label, prediction, beta=2, average='samples')\",\n \"def calc_beta(self) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:\\n snr_limit = 5\\n noisy_data = NoisyData(*self._get_args(), snr_limit)\\n beta = noisy_data.screen_data(self.processed_data['backscatter'])\\n beta_smooth = self._calc_beta_smooth(beta)\\n beta_smooth = noisy_data.screen_data(beta_smooth, is_smoothed=True)\\n return self.processed_data['backscatter'], beta, beta_smooth\",\n \"def predict(self, days=None, name=\\\"Main\\\"):\\n # Arguments\\n if name not in self._reghandler_dict:\\n raise UnExecutedError(f\\\"Scenario.fit(name={name})\\\")\\n # Prediction with regression model\\n handler = self._reghandler_dict[name]\\n df = handler.predict()\\n # -> end_date/parameter values\\n df.index = [date.strftime(self.DATE_FORMAT) for date in df.index]\\n df.index.name = \\\"end_date\\\"\\n # Days to predict\\n days = days or [len(df) - 1]\\n self._ensure_list(days, candidates=list(range(len(df))), name=\\\"days\\\")\\n phase_df = df.reset_index().loc[days, :]\\n # Set new future phases\\n for phase_dict in phase_df.to_dict(orient=\\\"records\\\"):\\n self.add(name=name, **phase_dict)\\n return self\",\n \"def predict(self, **kwargs):\\n\\n logging.debug(\\n \\\"Call predict() with parameters. \\\"\\n \\\"Forecast 1 step only, kwargs:{kwargs}\\\".format(kwargs=kwargs)\\n )\\n\\n X_test = self.df[-self.step :][self.feature_names]\\n X_test = self.scaler.transform(X_test)\\n y_predict = self.model.predict(X_test)\\n poly_now = self.y_train_season_obj[-1]\\n first_occ = np.where(self.y_train_season_obj == poly_now)\\n polynext = self.y_train_season_obj[first_occ[0][0] + self.step]\\n now = self.df[\\\"y\\\"][-self.step :]\\n return (now - poly_now) - y_predict + polynext\",\n \"def bias_var(x, y, z, first_poly = 4, complexity = 10, N = 100, method = 'OLS', seed = 42, lam = 0, train = 0.7, folds = 5):\\n\\n bias = np.zeros(complexity + 1)\\n variance = np.zeros(complexity + 1)\\n z_real = FrankeFunction(x, y)\\n\\n complx = np.arange(first_poly, first_poly + complexity + 1, 1)\\n\\n for i in range(complexity + 1):\\n print(i)\\n model = regression(x, y, z, k = first_poly + i, split = True, train = train, seed = seed)\\n\\n _, _, _, z_real_test = model.train_test(X = model.X_full, z = np.ravel(z_real), train = train, seed = seed)\\n\\n counter = 0\\n z_tildes = np.zeros((np.size(z_real_test), N))\\n for j in range(N):\\n\\n z_new = FrankeFunction(x, y) + np.random.normal(0, 1, size = x.shape)\\n _, _, z_train, _ = model.train_test(X = model.X_full, z = np.ravel(z_new), train = train)\\n if method == 'OLS':\\n beta = model.OLS(z = z_train)\\n elif method == 'Ridge':\\n beta = model.Ridge(lam = lam, z = z_train)\\n elif method == 'Lasso':\\n beta = model.Lasso(lam = lam, z = z_train)\\n\\n z_tilde = model.z_tilde(beta, X = model.X_test)\\n z_tildes[:, j] = np.ravel(z_tilde)\\n\\n\\n bias[i] = np.mean((np.ravel(z_real_test).reshape(-1, 1) - np.mean(z_tildes, axis = 1, keepdims = True))**2)\\n variance[i] = np.mean(np.var(z_tildes, axis = 1, keepdims = True))\\n\\n plt.title(method + ' with N = ' + str(N) + ' times pr complexity')\\n plt.plot(complx, bias, 'go--', label = 'Bias', color = 'blue')\\n plt.plot(complx, variance, 'go--', label = 'Variance', color = 'red')\\n #plt.ylim([np.min(errors_R2[2]*1.2), np.max(errors_R2[0]*1.2)])\\n plt.legend()\\n plt.xlabel('Polynomial maximum order', fontsize = 14)\\n plt.ylabel('Bias/variance', fontsize = 14)\\n plt.tight_layout()\\n plt.savefig(results_dir + 'bias_variance' + method + '.png')\\n\\n plt.show()\",\n \"def estimate_sample_beta(sample):\\n x_s, y_s = zip(*sample)\\n reg.fit(x_s, y_s)\\n betas = reg.weights_\\n return betas\",\n \"def eval_step_b(self, sess, task_b_data):\\r\\n fdict = self._prerun(sess, None, task_b_data)\\r\\n prediction_b, y_b = sess.run([self.prediction_b_all, self.labels_b_v_all],\\r\\n feed_dict=fdict)\\r\\n\\r\\n return prediction_b, y_b\",\n \"def RegressionMain(full_raw, target_col, feature_cols, test_size, model='Ridge', max_train_size=200, embargo_size=1, logpx=True, \\n resample_per='B', ewm_span=50, verbose=False, alpha_override=None, lasso_positive=False):\\n \\n # pre-process\\n cols = np.hstack((target_col, feature_cols))\\n data = full_raw[cols].copy(deep=True)\\n raw_clean = data.asfreq(resample_per).dropna(how='any')\\n if ewm_span is not None:\\n data = data.ewm(span=ewm_span).mean()\\n data = data.asfreq(resample_per).dropna(how='any')\\n if logpx:\\n data = data.apply(np.log)\\n raw_clean = raw_clean.apply(np.log)\\n \\n dates, betas = [],[]\\n \\n # get alpha to use in model fits\\n ## we only use first quarter of data to not cheat so hard\\n x_full = data[feature_cols]\\n y_full = data[target_col]\\n x_raw_clean = raw_clean[feature_cols]\\n \\n x_find_alpha = x_full.iloc[:int(data.shape[0]/4)]\\n y_find_alpha = y_full.iloc[:int(data.shape[0]/4)]\\n tss = TimeSeriesSplit(n_splits=20)\\n alpha_space = np.logspace(-6,2,25)\\n if model == 'Ridge':\\n cv = RidgeCV(alphas=alpha_space, cv=tss)\\n cv.fit(x_find_alpha, y_find_alpha)\\n alpha = cv.alpha_\\n \\n elif model == 'Lasso':\\n cv = LassoCV(alphas=alpha_space, cv=tss)\\n cv.fit(x_find_alpha, y_find_alpha)\\n alpha = cv.alpha_\\n \\n else:\\n alpha = 0.0001\\n if alpha_override is not None: alpha = alpha_override\\n if verbose: print(alpha)\\n \\n pred_full = pd.Series(name='Pred')\\n for train_idx, embargo_idx, test_idx in _embargo_ts_splitter(data, test_size, max_train_size=max_train_size,\\n embargo=embargo_size):\\n x_train = x_full.iloc[train_idx]\\n y_train = y_full.iloc[train_idx]\\n beta = _regression_loop(y_train, x_train, model, lasso_positive, alpha=alpha)\\n \\n x_test = x_raw_clean.iloc[test_idx]\\n pred = sm.add_constant(x_test).dot(beta).rename('Pred')\\n pred_full = pred_full.append(pred)\\n \\n # save to return params\\n # date associated with beta (for rebalancing) should be day after computation\\n dates.append(data.index[test_idx[0]])\\n betas.append(beta)\\n \\n #rescale if necessary\\n if logpx:\\n pred_full = pred_full.apply(np.exp)\\n \\n return pred_full, dates, betas\",\n \"def Componentwise_Boosting(df_train, df_test, nu):\\n\\n # number of individual forecasts and number of periods\\n K = df_test.shape[1]\\n T = df_train.shape[0]\\n T_test = df_test.shape[0]\\n\\n # variable of interest\\n y = df_train.iloc[:, 0].values[:, np.newaxis]\\n y_bar = np.mean(y)\\n\\n # individual forecasts\\n F = np.swapaxes(df_train.iloc[:, 1:].values, 0, 1)[:, :, np.newaxis]\\n F_t = np.swapaxes(F, 1, 2)\\n F_test = np.swapaxes(df_test.values, 0, 1)[:, :, np.newaxis]\\n\\n # 5-fold CV to determine optimal M\\n # length of training and testing sets\\n T_cv_test = int(T/5)\\n T_cv_train = T - T_cv_test\\n\\n # initialize vector to store the precision of fit\\n SSR_vec = np.full(1000, 0, dtype=float)\\n\\n # find the optimal number of boosting iterationts using 5-fold CV\\n # CV folds\\n for k in range(5):\\n\\n # definition of the test and training sample for the given CV round\\n cv_index = np.full(T, True, dtype=bool)\\n cv_index[(k*T_cv_test):((k+1)*T_cv_test)] = False\\n\\n y_cv = y[cv_index]\\n y_cv_bar = np.mean(y_cv)\\n y_cv_test = y[~cv_index]\\n\\n F_cv = F[:, cv_index, :]\\n F_cv_t = F_t[:, :, cv_index]\\n F_cv_test = F[:, ~cv_index, :]\\n\\n # initialization step\\n psi = np.tile(y_cv_bar, (T_cv_train, 1))\\n psi_test = np.tile(y_cv_bar, (T_cv_test, 1))\\n\\n # main steps\\n for m in range(1000):\\n\\n # compute the negative gradient vector\\n u = y_cv - psi\\n\\n # regress the negative gradient vector on the ind. forecasts\\n beta_hat = np.matmul(\\n 1/np.matmul(F_cv_t, F_cv),\\n np.matmul(F_cv_t, np.tile(u, (K, 1, 1)))\\n )\\n\\n # save the sums of the squared residuals\\n SSR = np.dot(np.ones(T_cv_train), (u - (beta_hat * F_cv))**2)\\n\\n # find the minimum SSR and its corresponding ind.forecast\\n k_star = np.argmin(SSR)\\n\\n # update\\n psi += nu * beta_hat[k_star, :, :] * F_cv[k_star, :, :]\\n psi_test += nu * beta_hat[k_star, :, :] * F_cv_test[\\n k_star, :, :]\\n\\n # save the precision of the fit\\n SSR_vec[m] += np.sum((y_cv_test - psi_test)**2)\\n\\n # find number of iterations for which the MSE is the lowest\\n M = np.argmin(SSR_vec) + 1\\n\\n # final Gradient Boosting with pre-determined number of iterations M\\n # initialization step\\n psi = np.tile(y_bar, (T, 1))\\n psi_test = np.tile(y_bar, (T_test, 1))\\n\\n # main steps\\n for m in range(M):\\n\\n # compute the negative gradient vector\\n u = y - psi\\n\\n # regress the negative gradient vector on the individual forecasts\\n beta_hat = np.matmul(\\n 1/np.matmul(F_t, F),\\n np.matmul(F_t, np.tile(u, (K, 1, 1)))\\n )\\n\\n # save the sums of the squared residuals\\n SSR = np.dot(np.ones(T), (u - (beta_hat * F))**2)\\n\\n # find the minimum SSR and its corresponding individual forecast\\n k_star = np.argmin(SSR)\\n\\n # update\\n psi += nu * beta_hat[k_star, :, :] * F[k_star, :, :]\\n psi_test += nu * beta_hat[k_star, :, :] * F_test[k_star, :, :]\\n\\n # predictions\\n df_pred = pd.DataFrame(\\n {\\\"Componentwise Boosting\\\": psi_test.flatten()},\\n index=df_test.index\\n )\\n\\n return df_pred\",\n \"def train_predict(self, xTrain, yTrain, xTest, yTest):\\n trainStats = {}\\n # TODO: DO SGD\\n #beta = np.linalg.inv(xTrain.transpose().dot(xTrain)).dot(xTrain.transpose()).dot(yTrain)\\n LinearRegression.beta = [[0],[0]]\\n intercept = 0\\n n = len(xTrain)\\n startall = time.time()\\n list = [x for x in range(0,len(xTrain))]\\n for i in range(self.mEpoch):\\n random.shuffle(list)\\n Dbetalist = []\\n Dinterceptlist = []\\n start = time.time()\\n for j in range(self.bs):\\n # Dbetalist = []\\n # Dinterceptlist = []\\n indexstart = len(list)/self.bs * j\\n indexend = len(list)/self.bs * j + len(list)/self.bs\\n subset = xTrain[int(indexstart):int(indexend)]\\n #print(subset)\\n ypredict = np.dot(subset,LinearRegression.beta) + intercept\\n subyTrain = yTrain[int(indexstart):int(indexend)]\\n Dbeta = (-2/n)*sum(np.dot(subset.transpose(),(subyTrain-ypredict)))\\n Dbetalist.append(Dbeta)\\n Dintercept = (-2/n)*sum(subyTrain-ypredict)\\n Dinterceptlist.append(Dintercept)\\n\\n end = time.time()\\n timeElapse = end - start\\n\\n avgDbeta = sum(Dbetalist) / len(Dbetalist)\\n avgDintercept = sum(Dinterceptlist) / len(Dinterceptlist)\\n #print(avgDbeta)\\n # print(avgDintercept)\\n LinearRegression.beta = LinearRegression.beta - self.lr * avgDbeta\\n #print(LinearRegression.beta)\\n # print(beta)\\n intercept = intercept - self.lr * avgDintercept\\n\\n value = {}\\n value['time'] = timeElapse\\n value['train-mse'] = LinearRegression.mse(LinearRegression, xTrain, yTrain)\\n value['test-mse'] = LinearRegression.mse(LinearRegression, xTest, yTest)\\n trainStats[i*self.bs] = value\\n\\n endall = time.time()\\n totaltime = endall - startall\\n\\n\\n return trainStats\",\n \"def cAPM_Constant(df_train, df_test, MaxRPT_r1, MaxRPT, no_rounds):\\n\\n # number of periods in samples\\n T = df_train.shape[0]\\n T_test = df_test.shape[0]\\n\\n # number of agents\\n K = df_test.shape[1]\\n\\n # individual forecasts\\n F = df_train.iloc[:, 1:].values\\n F_test = df_test.iloc[:, :].values\\n\\n # outcomes\\n outcomes = df_train.iloc[:, 0].values\\n\\n # initialize matrices containing all predictions and bets\\n pred_mat = np.full((no_rounds, K), np.nan, dtype=float)\\n bet_mat = np.full((no_rounds, K), np.nan, dtype=float)\\n\\n # budget initialization\\n budgets = np.full(K, 1, dtype=float)\\n\\n # training the market\\n for i in range(T):\\n\\n # first round betting\\n pred_mat[0, :] = F[i, :]\\n bet_mat[0, :] = budgets * MaxRPT_r1\\n budgets = budgets * (1-MaxRPT_r1)\\n\\n # rest of rounds betting\\n for j in range(1, no_rounds):\\n\\n pred_mat[j, :] = F[i, :]\\n bet_mat[j, :] = budgets * MaxRPT\\n budgets = budgets * (1-MaxRPT)\\n\\n # use IQR to determine outlier error threshold\\n abs_errors = np.abs(outcomes[i] - pred_mat)\\n Q3, Q1 = np.percentile(abs_errors, [75, 25])\\n IQR = Q3 - Q1\\n oet = Q3 + (IQR*1.5)\\n\\n # reward the agents based on accuracy of their predictions\\n accuracy = np.maximum(100 * (1 - abs_errors/oet), 1)\\n score = np.log(accuracy)\\n revenue = np.multiply(score, bet_mat)\\n budgets = budgets + np.sum(revenue, axis=0)\\n\\n # rescaling the budget (only for computational reasons)\\n budgets = budgets/np.sum(budgets)\\n\\n # using the market for the out-of-sample predictions\\n pred = np.full(T_test, np.nan, dtype=float)\\n for i in range(T_test):\\n\\n # reset test budget\\n budgets_test = np.copy(budgets)\\n\\n # first round betting\\n pred_mat[0, :] = F_test[i, :]\\n bet_mat[0, :] = budgets_test * MaxRPT_r1\\n budgets_test = budgets_test * (1-MaxRPT_r1)\\n\\n # rest of rounds betting\\n for j in range(1, no_rounds):\\n\\n pred_mat[j, :] = F_test[i, :]\\n bet_mat[j, :] = budgets_test * MaxRPT\\n budgets_test = budgets_test * (1-MaxRPT)\\n\\n # final round market prediction is the c-APM prediction\\n pred[i] = np.dot(\\n pred_mat[no_rounds-1, :],\\n bet_mat[no_rounds-1, :])/np.sum(bet_mat[no_rounds-1, :])\\n\\n # output\\n df_pred = pd.DataFrame(\\n {\\\"c-APM (Constant)\\\": pred},\\n index=df_test.index\\n )\\n\\n return df_pred\",\n \"def a_test2_bbvi():\\n model = ARIMAX(formula=\\\"y ~ x1 + x2\\\", data=data, ar=1, ma=1, family=Exponential())\\n x = model.fit('BBVI',iterations=100)\\n assert(len(model.latent_variables.z_list) == 5)\\n lvs = np.array([i.value for i in model.latent_variables.z_list])\\n assert(len(lvs[np.isnan(lvs)]) == 0)\",\n \"def a_test2_bbvi():\\n model = ARIMAX(formula=\\\"y ~ x1 + x2\\\", data=data, ar=1, ma=1, family=Exponential())\\n x = model.fit('BBVI',iterations=100)\\n assert(len(model.latent_variables.z_list) == 5)\\n lvs = np.array([i.value for i in model.latent_variables.z_list])\\n assert(len(lvs[np.isnan(lvs)]) == 0)\",\n \"def _simulation_forecast(\\n self,\\n parameters: NDArray,\\n resids: NDArray,\\n backcast: Union[float, NDArray],\\n var_bounds: NDArray,\\n start: int,\\n horizon: int,\\n simulations: int,\\n rng: RNGType,\\n ) -> VarianceForecast:\",\n \"def evaluate(self):\\n # define start index test set\\n start_test_set = int(len(self.X) * 2 / 3)\\n\\n # Different methods for cummulativa vs day-ahead forecasting\\n if self.forecast_horizon == 1:\\n # In sample\\n lin_residuals_in_sample = self.y - (self.betas[0] + np.dot(self.X, self.betas[1]))\\n self.rmse_in_sample = np.mean(lin_residuals_in_sample ** 2) ** 0.5\\n self.var_in_sample = np.var(self.y)\\n\\n # Out of sample\\n # Calculate MSE of wls-ev prediction\\n self.mse_wlsev = np.mean((self.y[start_test_set:] - self.ols_predict()) ** 2)\\n # Calculate MSE of benchmark prediction\\n self.mse_benchmark = np.mean((self.y[start_test_set:] - self.benchmark_predict()) ** 2)\\n else:\\n # In Sample with betas estimated on full time series\\n lin_residuals_in_sample = rolling_sum(self.y, self.forecast_horizon) - (\\n self.betas[0] + np.dot(self.X[:-(self.forecast_horizon-1)], self.betas[1]))\\n self.rmse_in_sample = np.mean(lin_residuals_in_sample ** 2) ** 0.5\\n self.var_in_sample = np.var(rolling_sum(self.y, self.forecast_horizon))\\n\\n # Out of sample\\n # calculate realized cummulative returns over forecast horizon sequences\\n cum_rets_realized = rolling_sum(self.y[start_test_set:], self.forecast_horizon)\\n # Calculate MSE of wls-ev prediction, only where realized values are available\\n self.mse_wlsev = np.mean((cum_rets_realized - self.ols_predict()[:-(self.forecast_horizon-1)]) ** 2)\\n # Calculate MSE of benchmark prediction, only where realized values are available\\n self.mse_benchmark = np.mean(\\n (cum_rets_realized - self.benchmark_predict()[:-(self.forecast_horizon-1)]) ** 2)\\n\\n # Calculate out of sample r-squared\\n self.oos_r_squared = 1 - (self.mse_wlsev / self.mse_benchmark)\\n # Calculate in sample r-squared\\n self.in_sample_r_squared = 1.0 - (self.rmse_in_sample ** 2) / self.var_in_sample\",\n \"def run_regression(auto_input,\\n variables: list = ['magasin', 'tilsig'],\\n regions: list = ['NO1', 'NO2', 'NO3', 'NO4', 'NO5', 'SE1', 'SE2', 'SE3', 'SE4'],\\n jupyter: bool = False,\\n backup: bool = False,\\n loop: bool = False,\\n write: bool = True,\\n week_nb: int = False,\\n year: int = False) -> None:\\n\\n start_tuning = utctime_now()\\n\\n for region in regions:\\n\\n if not region in ['NO1', 'NO2', 'NO3', 'NO4', 'NO5', 'SE1', 'SE2', 'SE3', 'SE4']:\\n sys.exit(\\\"Region must be one out of: 'NO1', 'NO2', 'NO3', 'NO4', 'NO5', 'SE1', 'SE2', 'SE3', 'SE4'\\\")\\n\\n for variable in variables:\\n\\n if not variable in ['magasin', 'tilsig']:\\n sys.exit(\\\"Variable must be either 'tilsig' or 'magasin'\\\")\\n\\n print('---------------------------------------------------------------')\\n print(' {}, {} '.format(region, variable))\\n print('---------------------------------------------------------------')\\n\\n \\n \\n df_week, MagKap = auto_input[variable]\\n \\n period, forecast_time, read_start = rs.get_timeperiods(variable, week_nb, year)\\n \\n reg_end = (pd.to_datetime(time.strftime(forecast_time), format=\\\"%Y.%m.%d\\\") - Timedelta(days=7)).strftime(\\n '%Y.%m.%d')\\n\\n if (0 <= today.weekday() <= 1) or (today.weekday() == 2 and today.hour < 14): # True for tipping\\n last_forecast = forecast_time\\n else:\\n last_forecast = forecast_time\\n\\n df_cleaned = deletingNaNs(df_week.loc[:last_forecast])\\n\\n if loop:\\n if variable == 'tilsig':\\n max_kandidater = 196\\n min_kandidater = 2\\n\\n else:\\n max_kandidater = 171\\n min_kandidater = 2\\n\\n max_weeks = 238 #288\\n min_weeks = 16\\n print('max ant. kandidater: {}, min ant. kandidater: {}'.format(max_kandidater, min_kandidater))\\n print('max ant. uker: {}, min ant. uker: {}'.format(max_weeks, min_weeks))\\n\\n start_time_loop = utctime_now()\\n fasit, fasit_key = rs.make_fasit(variable, region, reg_end, period)\\n print('Fasit er lest inn.\\\\n')\\n\\n if fasit[fasit_key][:reg_end].isnull().any():\\n print('OBS: Det mangler verdier på fasiten! Går videre til neste region i loopen..')\\n print(fasit[fasit_key][:reg_end].isnull())\\n continue\\n\\n sorted_r2 = get_R2_sorted(variable, df_cleaned, fasit, fasit_key)\\n\\n if loop:\\n max_p = 0.001 # Beste testresultat SvF\\n\\n # First loop: Tuning number of candidates for best possible R2 combined\\n df_ant_kandidater = pd.DataFrame(columns=columns)\\n for antall in range(min_kandidater, max_kandidater + 1, 1):\\n if antall > len(sorted_r2):\\n chosen_r2 = sorted_r2\\n else:\\n chosen_r2 = sorted_r2[:antall]\\n output = make_estimate(df_cleaned, fasit, fasit_key, last_forecast, first_period, max_p, chosen_r2,\\n loop=True)\\n df_ant_kandidater = df_ant_kandidater.append(\\n {columns[0]: output[0], columns[1]: output[1], columns[2]: output[2], columns[3]: output[3],\\n columns[4]: output[4], columns[5]: output[5], columns[6]: output[6]}, ignore_index=True)\\n if antall > len(sorted_r2):\\n print('Feilmelding: Ønsket antall kandidater overskrider maks (%i).\\\\n' % len(sorted_r2))\\n break\\n idx_max = df_ant_kandidater.r2_samlet.idxmax(skipna=True)\\n ant_kandidater_beste = int(df_ant_kandidater.ant_kandidater.values[idx_max])\\n print('Beste ant_kandidater loop 1: ', ant_kandidater_beste)\\n\\n # Second loop: tuning length of the short regression for best possible R2 combined, using the best number of\\n # candidates found in the First loop.\\n final_chosen_r2 = sorted_r2[:ant_kandidater_beste]\\n df_short_period = pd.DataFrame(columns=columns)\\n for short_period in range(min_weeks, max_weeks + 1, 1):\\n short_period = int(short_period)\\n output = make_estimate(df_cleaned, fasit, fasit_key, last_forecast, short_period, max_p,\\n final_chosen_r2, loop=True)\\n df_short_period = df_short_period.append(\\n {columns[0]: output[0], columns[1]: output[1], columns[2]: output[2], columns[3]: output[3],\\n columns[4]: output[4], columns[5]: output[5], columns[6]: output[6]}, ignore_index=True)\\n idx_max = df_short_period.r2_samlet.idxmax(skipna=True)\\n short_period_beste = int(df_short_period.short_period.values[idx_max])\\n print('Beste short_period loop 2: ', short_period_beste)\\n\\n # Getting the best input variables from loop and write to input_variables_from_tuning.txt\\n df_all_methods = pd.concat([df_ant_kandidater, df_short_period], ignore_index=True, sort=False)\\n idx_max = df_all_methods.r2_samlet.idxmax(skipna=True)\\n ant_kandidater_beste = int(df_all_methods.ant_kandidater.values[idx_max])\\n chosen_r2_beste = sorted_r2[:ant_kandidater_beste]\\n short_period_beste = df_all_methods.short_period.values[idx_max]\\n ws.write_input_variables_to_file(region, variable, max_p, ant_kandidater_beste, short_period_beste)\\n\\n print('\\\\nTuning for regionen tok %.0f minutter. \\\\n' % ((utctime_now() - start_time_loop) / 60))\\n\\n else:\\n # getting the best variables from input_variables_from_tuning.txt or input_variables_backup.txr\\n short_period_beste, max_p, ant_kandidater_beste, input_file = rs.get_input_variables_from_file(variable,region,backup)\\n chosen_r2_beste = sorted_r2[:ant_kandidater_beste]\\n print(\\\"Input variables was read from: \\\", input_file)\\n\\n # MAKE ESTIMATE AND SHOW RESULTS\\n input1 = make_estimate(df_cleaned, fasit, fasit_key, last_forecast, short_period_beste, max_p,\\n chosen_r2_beste, loop=False)\\n input2 = fasit_key, ant_kandidater_beste, max_p, reg_end, read_start\\n\\n\\n #WRITE RESULTS\\n if write:\\n # Write results from the regression to SMG.\\n fasit, long_results, short_results, df_tot, chosen_p, chosen_r2, r2_modelled, r2_modelled_long, prediction, tipping_ps, short_period, nb_weeks_tipping = input1\\n\\n # write to SMG:\\n ws.write_SMG_regresjon(variable, region, tipping_ps[-1:])\\n\\n # write to SMG, virtual:\\n ws.write_V_SMG_Regresjon(short_results, chosen_p, fasit_key, r2_modelled, r2_modelled_long, short_period_beste, MagKap)\\n\\n if jupyter:\\n ws.show_result_jupyter(input1, input2)\\n else:\\n ws.show_result(input1, input2, input_file)\\n\\n print('---------------------------------------------------------------')\\n print(' SLUTT ')\\n print('---------------------------------------------------------------')\\n print('\\\\nRegresjon for alle regioner og variabler brukte totalt %.0f minutter. \\\\n' % (\\n (utctime_now() - start_tuning) / 60))\",\n \"def _fit(self, y, X, fh):\\n _, forecasters = self._check_forecasters()\\n\\n # get training data for meta-model\\n if X is not None:\\n y_train, y_test, X_train, X_test = temporal_train_test_split(\\n y, X, test_size=self.test_size\\n )\\n else:\\n y_train, y_test = temporal_train_test_split(y, test_size=self.test_size)\\n X_train, X_test = None, None\\n\\n # fit ensemble models\\n fh_test = ForecastingHorizon(y_test.index, is_relative=False)\\n self._fit_forecasters(forecasters, y_train, X_train, fh_test)\\n\\n if self.method == \\\"feature-importance\\\":\\n self.regressor_ = check_regressor(\\n regressor=self.regressor, random_state=self.random_state\\n )\\n X_meta = pd.concat(self._predict_forecasters(fh_test, X_test), axis=1)\\n X_meta.columns = pd.RangeIndex(len(X_meta.columns))\\n\\n # fit meta-model (regressor) on predictions of ensemble models\\n # with y_test as endog/target\\n self.regressor_.fit(X=X_meta, y=y_test)\\n\\n # check if regressor is a sklearn.Pipeline\\n if isinstance(self.regressor_, Pipeline):\\n # extract regressor from pipeline to access its attributes\\n self.weights_ = _get_weights(self.regressor_.steps[-1][1])\\n else:\\n self.weights_ = _get_weights(self.regressor_)\\n\\n elif self.method == \\\"inverse-variance\\\":\\n # get in-sample forecasts\\n if self.regressor is not None:\\n Warning(f\\\"regressor will not be used because ${self.method} is set.\\\")\\n inv_var = np.array(\\n [\\n 1 / np.var(y_test - y_pred_test)\\n for y_pred_test in self._predict_forecasters(fh_test, X_test)\\n ]\\n )\\n # standardize the inverse variance\\n self.weights_ = list(inv_var / np.sum(inv_var))\\n else:\\n raise NotImplementedError(\\n f\\\"Given method {self.method} does not exist, \\\"\\n f\\\"please provide valid method parameter.\\\"\\n )\\n\\n self._fit_forecasters(forecasters, y, X, fh)\\n return self\",\n \"def compute_beta_vae_sklearn(ground_truth_data,\\n representation_function,\\n random_state,\\n batch_size=gin.REQUIRED,\\n num_train=gin.REQUIRED,\\n num_eval=gin.REQUIRED):\\n logging.info(\\\"Generating training set.\\\")\\n train_points, train_labels = _generate_training_batch(\\n ground_truth_data, representation_function, batch_size, num_train,\\n random_state)\\n\\n logging.info(\\\"Training sklearn model.\\\")\\n model = linear_model.LogisticRegression(random_state=random_state)\\n model.fit(train_points, train_labels)\\n\\n logging.info(\\\"Evaluate training set accuracy.\\\")\\n train_accuracy = model.score(train_points, train_labels)\\n train_accuracy = np.mean(model.predict(train_points) == train_labels)\\n logging.info(\\\"Training set accuracy: %.2g\\\", train_accuracy)\\n\\n logging.info(\\\"Generating evaluation set.\\\")\\n eval_points, eval_labels = _generate_training_batch(\\n ground_truth_data, representation_function, batch_size, num_eval,\\n random_state)\\n\\n logging.info(\\\"Evaluate evaluation set accuracy.\\\")\\n eval_accuracy = model.score(eval_points, eval_labels)\\n logging.info(\\\"Evaluation set accuracy: %.2g\\\", eval_accuracy)\\n scores_dict = {}\\n scores_dict[\\\"train_accuracy\\\"] = train_accuracy\\n scores_dict[\\\"eval_accuracy\\\"] = eval_accuracy\\n return scores_dict\",\n \"def beta_and_alpha(self):\\n # make scatter plot\\n sp_temp = self.daily_returns(self.sp.rename(columns={'Adj Close': '^GSPC'}))\\n symbol_temp = self.daily_returns(self.daily.rename(columns={'Adj Close': self.symbol}))\\n joined = sp_temp.merge(symbol_temp, on='Date')\\n\\n # beta and alpha\\n beta, alpha = np.polyfit(joined[\\\"^GSPC\\\"], joined[self.symbol], 1)\\n beta = round(beta, 3)\\n alpha = round(alpha, 5)\\n if alpha > 0:\\n self.buys += 1\\n self.debug += '\\\\nAlpha > 0: buys + {}'.format(alpha)\\n else:\\n self.debug += '\\\\nAlpha < 0: {}'.format(alpha)\\n\\n # assuming favorable market conditions. else, it would be sells + 1.\\n if beta > 1:\\n self.buys += 1\\n self.debug += '\\\\nBeta > 1: buys + {}'.format(beta)\\n else:\\n self.debug += '\\\\nBeta < 1: {}'.format(beta)\\n\\n # finish plotting scatter\\n if self.will_plot:\\n ax = joined.plot(title=self.symbol + ' vs The Market', kind = 'scatter', x='^GSPC', y=self.symbol)\\n ax.set_xlabel(\\\"S&P 500\\\")\\n plt.plot(joined[\\\"^GSPC\\\"], beta * joined['^GSPC'] + alpha, '-', color='r', label='Correlation')\\n\\n # plot expected beta (slope) of 1 and alpha (y- int.) of zero\\n plt.plot(joined[\\\"^GSPC\\\"], 1 * joined['^GSPC'] + 0, '-', color='gray', label='Beta of 1')\\n plt.plot(joined[\\\"^GSPC\\\"], 0 * joined['^GSPC'] + 0, '-', color='gray', label='Alpha of 0')\\n plt.legend(loc='best')\",\n \"def main():\\n np.set_printoptions(threshold=25)\\n ts_length = 1000\\n window_size = 50\\n\\n print('\\\\nSimple single timeseries vector prediction')\\n timeseries = np.arange(ts_length) # The timeseries f(t) = t\\n evaluate_timeseries(timeseries, window_size)\\n\\n print('\\\\nMultiple-input, multiple-output prediction')\\n timeseries = np.array([np.arange(ts_length), -np.arange(ts_length)]).T # The timeseries f(t) = [t, -t]\\n evaluate_timeseries(timeseries, window_size)\",\n \"def run_model(Xp_train=None,y_train=None,Xp_test=None,y_test=None,mt='bl', params={}):\\n models = { \\n \\\"bl\\\": lambda : None,\\n \\\"lr\\\": LinearRegression,\\n \\\"dtr\\\": DecisionTreeRegressor,\\n \\\"gbr\\\": GradientBoostingRegressor\\n }\\n\\n # Select Model, returns None for baseline\\n model = models[mt](**params)\\n\\n # Fit Model and make predictions\\n if mt != 'bl': # If model not baseline\\n # Dummy encoding , (each category of n levels or attributes is converted into n-1 dichotomous variables)\\n X_train = pd.get_dummies(Xp_train, columns=['type', 'duration','location'],drop_first=True)\\n X_test = pd.get_dummies(Xp_test, columns=['type', 'duration','location'],drop_first=True)\\n model.fit(X_train,y_train)\\n # Make Predictions\\n y_pred = model.predict(X_test)\\n #print X_test.shape\\n y_predtr = model.predict(X_train) \\n else: # Compute baseline\\n y_pred = np.median(y_train).repeat(len(y_test))\\n y_predtr = np.median(y_train).repeat(len(y_train))\\n \\n \\n\\n # Report metrics\\n print(\\\"Model name: %s\\\" % (model.__class__.__name__ if model else \\\"Price Median Baseline\\\"))\\n if mt != 'bl':\\n print(\\\"hyper-parameters: \\\" + \\\", \\\".join( \\\"{0}: {1}\\\".format(k,v) for (k,v) in params.items() ) )\\n print(\\\"Mean absolute error training set: %.2f\\\" % mean_absolute_error(y_train, y_predtr)) \\n print(\\\"Mean absolute error testing set: %.2f \\\\n\\\" % mean_absolute_error(y_test, y_pred))\",\n \"def delta_learning(self, inputs, targets, eta, val_inputs=None, val_targets=None,\\n early_stop_threshold=10e-5, early_stop_tolerance=100, epochs=10000):\\n pocket_mae = float('inf')\\n pocket_epoch = 0\\n pocket_weights = None\\n no_improvement = 0\\n\\n for epoch in tqdm(range(1, epochs + 1)):\\n self._rbf_forward(inputs)\\n\\n indices = np.arange(self.rbf_outputs.shape[0])\\n np.random.shuffle(indices)\\n\\n # one epoch\\n for i in indices:\\n output = self.rbf_outputs[i:i + 1] @ self.slp_weights\\n self.slp_weights += eta * (targets[i:i + 1] - output) * self.rbf_outputs[i:i + 1].T\\n\\n # use validation for early stopping if it is available\\n if val_inputs is not None and val_targets is not None:\\n error = self.evaluate_mae(val_inputs, val_targets)\\n else:\\n error = self.evaluate_mae(inputs, targets)\\n\\n if error + early_stop_threshold < pocket_mae:\\n no_improvement = 0\\n pocket_mae = error\\n pocket_epoch = epoch\\n pocket_weights = self.slp_weights.copy()\\n else:\\n no_improvement += 1\\n if no_improvement > early_stop_tolerance:\\n break\\n\\n self.slp_weights = pocket_weights\\n return pocket_mae, pocket_epoch\",\n \"def predict_sales() -> str:\\r\\n # HTML tables and list that are filled depending on the user input.\\r\\n html_gr_month_table = app.config[\\\"table_monthly_growth\\\"]\\r\\n html_gr_week_table = app.config[\\\"table_weekly_growth\\\"]\\r\\n html_csv_list = \\\"\\\"\\r\\n # Empty filename and path to sales forecast graph; also dep. on user input.\\r\\n graph_filename = \\\"\\\"\\r\\n graph_filepath = \\\"'File has not been created yet!'\\\"\\r\\n # Contains HTML form data inputted by the user submitted using POST.\\r\\n response = request.form\\r\\n num_months_input = response.get(\\\"num_months_input\\\")\\r\\n csv_filename_input = response.get(\\\"csv_filename_input\\\")\\r\\n # True if user inputted num of past months for prediction and uploaded CSV.\\r\\n if num_months_input is not None and csv_filename_input is not None:\\r\\n # Loads data from CSV into dataframe & removes rows with missing values\\r\\n file_name = csv_filename_input\\r\\n dataframe = load_csv_clean_to_dataframe(file_name)\\r\\n try:\\r\\n # Replaces index numbers of df with datetimes to get a time series.\\r\\n dataframe = convert_to_time_series(dataframe)\\r\\n except KeyError as error:\\r\\n error_message = (\\\"The following column name is missing \\\"\\r\\n \\\"in the data: \\\" + str(error))\\r\\n app.config[\\\"logger\\\"].error(error_message)\\r\\n return error_message\\r\\n # Calculates Quantity ordered averages for every past week.\\r\\n column_to_avg = \\\"Quantity ordered\\\"\\r\\n average_by = \\\"W\\\" # \\\"W\\\" = week; \\\"MS\\\" = month\\r\\n past_avgs_per_week = average_data(dataframe, column_to_avg, average_by)\\r\\n # Calculates Quantity ordered averages for every past month.\\r\\n average_by = \\\"MS\\\" # \\\"W\\\" = week; \\\"MS\\\" = month\\r\\n past_avgs_per_month = average_data(dataframe, column_to_avg,\\r\\n average_by)\\r\\n # Calculates past Quantity ordered average growth rates.\\r\\n growth_rates_per_week = past_avgs_per_week.pct_change()\\r\\n growth_rates_per_month = past_avgs_per_month.pct_change()\\r\\n # Creates HTML table containing past monthly sales growth rates.\\r\\n html_gr_month_table = update_growth_rate_table(growth_rates_per_month)\\r\\n app.config[\\\"table_monthly_growth\\\"] = html_gr_month_table\\r\\n # Creates HTML table containing past weekly sales growth rates.\\r\\n html_gr_week_table = update_growth_rate_table(growth_rates_per_week)\\r\\n app.config[\\\"table_weekly_growth\\\"] = html_gr_week_table\\r\\n # Filters dataframe for beer types.\\r\\n dunkel = dataframe.loc[dataframe[\\\"Recipe\\\"] == \\\"Organic Dunkel\\\"]\\r\\n pilsner = dataframe.loc[dataframe[\\\"Recipe\\\"] == \\\"Organic Pilsner\\\"]\\r\\n red_helles = dataframe.loc[dataframe[\\\"Recipe\\\"] == \\\"Organic Red Helles\\\"]\\r\\n # Monthly basis;\\r\\n # calculates dunkel's Quantity ordered sums for each month.\\r\\n column_to_sum = \\\"Quantity ordered\\\"\\r\\n sum_by = \\\"MS\\\" # \\\"W\\\" = week; \\\"MS\\\" = month\\r\\n dunk_order_sum_per_month = sum_data(dunkel, column_to_sum, sum_by)\\r\\n # Calculates pilsner's Quantity ordered sums for each month.\\r\\n pils_order_sum_per_month = sum_data(pilsner, column_to_sum, sum_by)\\r\\n # Calculates red_helles' Quantity ordered sums for each month.\\r\\n redh_order_sum_per_month = sum_data(red_helles, column_to_sum, sum_by)\\r\\n all_order_sum_month = {\\\"dunkers\\\": dunk_order_sum_per_month,\\r\\n \\\"pilsner\\\": pils_order_sum_per_month,\\r\\n \\\"red_helles\\\": redh_order_sum_per_month}\\r\\n # Clears monthly sales forecasts from the dictionary.\\r\\n app.config[\\\"monthly_sales_forecasts\\\"] = {}\\r\\n # Hyperparameters (hp) for seasonal ARIMA model.\\r\\n order = app.config[\\\"order\\\"]\\r\\n # Sets num_months_in. as num of past periods to be considered by model.\\r\\n app.config[\\\"seasonal_order\\\"][3] = int(num_months_input)\\r\\n seasonal_order = app.config[\\\"seasonal_order\\\"]\\r\\n # Fits ARIMA model and forecasts monthly sales for each beer type.\\r\\n try:\\r\\n for beer_type in all_order_sum_month:\\r\\n model = define_and_fit_model(all_order_sum_month[beer_type],\\r\\n False, order, seasonal_order)\\r\\n # Forecasts 12 months into the future with the fitted model.\\r\\n forecast = model.get_forecast(steps=12)\\r\\n # Stores monthly sales forecasts in dictionary by beer_type.\\r\\n app.config[\\\"monthly_sales_forecasts\\\"][beer_type] = forecast\\r\\n # Plots actual and predicted num of beers sold and saves plot.\\r\\n plot_size = (15, 10)\\r\\n plot_sales_forecast(forecast, all_order_sum_month[beer_type],\\r\\n plot_size, beer_type)\\r\\n except ValueError as error:\\r\\n error_message = (\\\"Wrong hyperparameters are set, the model \\\"\\r\\n + \\\"can not be fitted: \\\" + str(error))\\r\\n app.config[\\\"logger\\\"].error(error_message)\\r\\n return error_message\\r\\n # Gets filepath of this module and thus filepath to graph (png file).\\r\\n graph_filepath = os.path.dirname(os.path.abspath(__file__))\\r\\n graph_filename = \\\"monthly_forecast_and_past.png\\\"\\r\\n try:\\r\\n # Opens plotted graph.\\r\\n img = Image.open(graph_filename)\\r\\n img.show()\\r\\n except (FileNotFoundError, PermissionError) as error:\\r\\n app.config[\\\"logger\\\"].error(error)\\r\\n # Finds names of all uploaded CSV files in folder where this module is.\\r\\n all_uploaded_csv = []\\r\\n for csv_file in glob.glob(\\\"*.csv\\\"):\\r\\n all_uploaded_csv.append(csv_file)\\r\\n # Creates HTML drop-down list containing all uploaded CSV filenames.\\r\\n html_csv_list = update_csv_list(all_uploaded_csv)\\r\\n return (\\\"\\\"\\\"<style>\\r\\n h1, h2, h3 {\\r\\n font-family: arial, sans-serif;\\r\\n }\\r\\n table {\\r\\n font-family: arial, sans-serif;\\r\\n border-collapse: collapse;\\r\\n width: 100%;\\r\\n }\\r\\n td, th {\\r\\n border: 1px solid #dddddd;\\r\\n text-align: left;\\r\\n padding: 8px;\\r\\n }\\r\\n tr:nth-child(even) {\\r\\n background-color: #dddddd;\\r\\n }\\r\\n </style>\\r\\n <h2>Predict sales</h2>\\r\\n <form action=\\\"/predict_sales\\\" method=\\\"POST\\\">\\r\\n Please select an uploaded CSV file that should be used for the \\r\\n sales forecast (if the list is empty, no CSV file has been \\r\\n uploaded yet; to upload a file, please go back to the \\r\\n tracking screen).<br>\\r\\n <select name=\\\"csv_filename_input\\\">\\\"\\\"\\\"\\r\\n + html_csv_list\\r\\n + \\\"\\\"\\\"</select><br><br>\\r\\n Please enter how many <b>months</b> of past data should be\\r\\n used for the sales forecast:<br>\\r\\n (Note: If the number of past periods is higher than the number\\r\\n of periods in the past sales data, the nonexistent periods are\\r\\n recorded as 0 sales, which, if too many periods are 0,\\r\\n makes the prediction zero.\\r\\n <br>\\r\\n <input type=\\\"number\\\" name=\\\"num_months_input\\\" min=\\\"2\\\"\\r\\n required=\\\"required\\\">\\r\\n <br><br>\\r\\n <input type=\\\"submit\\\" value=\\\"Predict sales\\\">\\r\\n </form>\\r\\n In case the graph is not displayed, please open the file \\\"\\\"\\\"\\r\\n + graph_filename + \\\" manually using the path: \\\"\\r\\n + graph_filepath\\r\\n + \\\"\\\"\\\"<form action=\\\"/\\\" method=\\\"POST\\\">\\r\\n <input type=\\\"hidden\\\">\\r\\n <br>\\r\\n <input type=\\\"submit\\\" value=\\\"Go back to tracking screen\\\">\\r\\n </form> \\r\\n <h3>Average sales growth rates of the past months</h3>\\r\\n <table>\\r\\n <tr>\\r\\n <th>Date</th>\\r\\n <th>Monthly average</th>\\r\\n </tr>\\\"\\\"\\\" + html_gr_month_table + \\\"</table>\\\"\\r\\n + \\\"\\\"\\\"<h3>Average sales growth rates of the past weeks</h3>\\r\\n <table>\\r\\n <tr>\\r\\n <th>Date</th>\\r\\n <th>Weekly average</th>\\r\\n </tr>\\\"\\\"\\\" + html_gr_week_table + \\\"</table>\\\")\",\n \"def fbeta1(y_test, predictions, beta=1):\\n pr = precision1(y_test, predictions)\\n re = recall1(y_test, predictions)\\n\\n num = float((1+pow(beta,2))*pr*re)\\n den = float(pow(beta,2)*pr + re)\\n\\n if den != 0:\\n fbeta = num/den\\n else:\\n fbeta = 0.0\\n return fbeta\",\n \"def beta(G, responses = [], covariates = [], conditionants = []):\\n variables = covariates + conditionants \\n \\n means = mean(G, responses, variables)\\n \\n def collect(index):\\n return sp.collect(expr = sp.expand(means[index]), \\n syms = variables)\\n \\n collections = [collect(index) for index in range(len(means))]\\n \\n betas = sp.Matrix([collection.coeff(variables) \\n for collection, variables \\n in product(collections, variables)])\\n \\n betas.reshape(len(collections), len(variables)).T\\n \\n indices = variable_indices(G, values = covariates, \\n restrictions = variables, \\n sort = True)\\n \\n return betas[indices, :]\",\n \"def estim_F(Xn, Ret, continuous, h, Std, Std2, minH, minR, IS, lagBM): \\n # Copy to avoid modifying original df\\n Ret = Ret.copy(deep=True)\\n Xn = Xn.copy(deep=True)\\n \\n if lagBM != 0:\\n Xn.index = Xn.index + MonthEnd(lagBM)\\n \\n # Main standardization choice (not mentioned in KP paper)\\n if Std:\\n Xn = Xn / Xn.std()\\n\\n # Transform to \\\"annual\\\" *realized* returns if needed, \\n # and only if function is run in-sample\\n if h != 1 and IS:\\n Ret = changeFreq(Ret, new_frequency=h, continuous=continuous,\\n percent=False)\\n # else: Ret frequency already fine\\n\\n # Merge to enforce correct dates:\\n data = pd.merge(Xn, Ret.shift(-h), how='left', \\n left_index=True, right_index=True)\\n idxR = Xn.shape[1] # Index of 'RM' column\\n # => data.iloc[:,:idxR] = Xn\\n # data.iloc[:,idxR] = Ret.shift(-h)\\n \\n # First pass: Regress BMs on future returns (one time-series reg per BM)\\n ########################################################################\\n # Apply on columns of BMs:\\n phi = np.array(data.iloc[:, :idxR].apply(get_phi,\\n args=(data.iloc[:, idxR], minH,\\n minR),\\n axis=0))\\n \\n # Here is the second normalization made by KP \\n # (only for OOS, also not mentioned in their paper)\\n # But this one does not impact the results, in fact.\\n if Std2:\\n phi = phi / np.nanstd(phi)\\n\\n # Second pass: Regress BMs on phi (one cross-sec regression per t)\\n ###################################################################\\n data['F'] = data.iloc[:, :idxR].apply(get_ft,\\n args=(phi,),\\n axis=1)\\n return data['F']\",\n \"def run(self, function, beta):\\n if self.info_requested(Info.ok):\\n self.info_set(Info.ok, False)\\n\\n# step = function.step(beta)\\n\\n z = betanew = betaold = beta\\n\\n if self.info_requested(Info.time):\\n t = []\\n if self.info_requested(Info.fvalue):\\n f = []\\n if self.info_requested(Info.converged):\\n self.info_set(Info.converged, False)\\n\\n for i in xrange(1, max(self.min_iter, self.max_iter) + 1):\\n\\n if self.info_requested(Info.time):\\n tm = utils.time_cpu()\\n\\n z = betanew + ((i - 2.0) / (i + 1.0)) * (betanew - betaold)\\n\\n step = function.step(z)\\n\\n betaold = betanew\\n betanew = function.prox(z - step * function.grad(z),\\n step)\\n\\n if self.info_requested(Info.time):\\n t.append(utils.time_cpu() - tm)\\n if self.info_requested(Info.fvalue):\\n f.append(function.f(betanew))\\n\\n if self.conesta_stop is not None:\\n mu_min = self.conesta_stop[0]\\n# print \\\"mu_min:\\\", mu_min\\n mu_old = function.set_mu(mu_min)\\n# print \\\"mu_old:\\\", mu_old\\n stop_step = function.step(betanew)\\n# print \\\"step :\\\", step\\n # Take one ISTA step for use in the stopping criterion.\\n stop_z = function.prox(betanew - stop_step \\\\\\n * function.grad(betanew),\\n stop_step)\\n function.set_mu(mu_old)\\n# print \\\"err :\\\", maths.norm(betanew - z)\\n# print \\\"sc err:\\\", (1.0 / step) * maths.norm(betanew - z)\\n# print \\\"eps :\\\", self.eps\\n\\n if (1. / stop_step) * maths.norm(betanew - stop_z) < self.eps \\\\\\n and i >= self.min_iter:\\n\\n if self.info_requested(Info.converged):\\n self.info_set(Info.converged, True)\\n\\n break\\n\\n else:\\n if step > 0.0:\\n if (1.0 / step) * maths.norm(betanew - z) < self.eps \\\\\\n and i >= self.min_iter:\\n\\n if self.info_requested(Info.converged):\\n self.info_set(Info.converged, True)\\n\\n break\\n\\n else: # TODO: Fix this!\\n if maths.norm(betanew - z) < self.eps \\\\\\n and i >= self.min_iter:\\n\\n if self.info_requested(Info.converged):\\n self.info_set(Info.converged, True)\\n\\n break\\n\\n self.num_iter = i\\n\\n if self.info_requested(Info.num_iter):\\n self.info_set(Info.num_iter, i)\\n if self.info_requested(Info.time):\\n self.info_set(Info.time, t)\\n if self.info_requested(Info.fvalue):\\n self.info_set(Info.fvalue, f)\\n if self.info_requested(Info.ok):\\n self.info_set(Info.ok, True)\\n\\n return betanew\",\n \"def test_models_regression(model):\\n atom = ATOMRegressor(X_reg, y_reg, test_size=0.24, random_state=1)\\n atom.run(\\n models=model,\\n metric=\\\"neg_mean_absolute_error\\\",\\n n_calls=2,\\n n_initial_points=1,\\n bo_params={\\\"base_estimator\\\": \\\"gbrt\\\", \\\"cv\\\": 1},\\n )\\n assert not atom.errors\\n assert hasattr(atom, model)\",\n \"def MK(df_train, df_test):\\n\\n # number of periods\\n T = df_train.shape[0]\\n\\n # matrix of individual forecasts\\n F = df_train.iloc[:, 1:].values\\n F_test = df_test.iloc[:, :].values\\n\\n # number of agents\\n K = df_test.shape[1]\\n\\n # true outcomes\\n y = df_train.iloc[:, 0].values\\n\\n # initialize agent wealth (sum equal to 1 -> results in convex combination)\\n w = np.full(K, 1/K, dtype=float)\\n\\n # initialize standard deviations\\n sigma = np.full(K, 1, dtype=float)\\n\\n # run market for all observations (training the market)\\n for i in range(T):\\n\\n # individual predictions in the current market\\n ind_pred = F[i, :]\\n\\n # size of bet at the true outcome (kernel evaluated at true outcome)\\n kernel_val = sps.norm.pdf(y[i], loc=ind_pred, scale=sigma)\\n\\n # scale by the size of budget of each agent\\n w_kernel_val = np.multiply(w, kernel_val)\\n\\n # reward agents\\n w = w_kernel_val / np.sum(w_kernel_val)\\n\\n # update sigma\\n abs_error = np.abs(y[i] - ind_pred)\\n sigma = ((i+1)*sigma + abs_error) / (i + 2)\\n\\n # using the trained market for out of sample forecasts\\n # obtain the aggregated market prediction in each test market\\n market_pred = np.dot(F_test, w)\\n\\n # output\\n df_pred = pd.DataFrame(\\n {\\\"Market for Kernels\\\": market_pred},\\n index=df_test.index\\n )\\n\\n return df_pred\",\n \"def compute_backward_variables(self, normalized_logits, target):\\n\\n target_length = target.shape[0]\\n num_time_steps = normalized_logits.shape[0]\\n\\n\\t\\t######################\\n\\t\\t### YOUR CODE HERE ###\\n\\t\\t######################\\n\\n blank_label = normalized_logits.shape[1] - 1\\n l = add_blanks(target, blank_label)\\n target_length = l.shape[0]\\n\\n beta = np.zeros((target_length, num_time_steps))\\n\\n # init\\n beta[target_length - 1, num_time_steps - 1] = normalized_logits[num_time_steps - 1, l[-1]]\\n beta[target_length - 2, num_time_steps - 1] = normalized_logits[num_time_steps - 1, l[-2]]\\n\\n for s in xrange(target_length - 2):\\n beta[s, num_time_steps - 2] = 0.0\\n\\n # recursive case\\n for t in xrange(num_time_steps - 2, -1, -1):\\n for s in xrange(target_length - 3, -1, -1):\\n b_bar = beta[s, t+1] + beta[s+1, t+1]\\n \\n if l[s] == blank_label or l[s+2] == l[s]:\\n beta[s, t] = b_bar * normalized_logits[t, l[s]]\\n else:\\n beta[s, t] = (b_bar + beta[s+2, t+1]) * normalized_logits[t, l[2]]\\n\\n return beta\",\n \"def predict():\\n if (not request.json):\\n abort(400)\\n \\n product = {\\n 'brand': request.json['brand'],\\n 'category-1': request.json['category-1'],\\n 'category-2': request.json['category-2'],\\n 'category-3': request.json['category-3'],\\n 'colour': request.json['colour'],\\n 'fabric_type': request.json['fabric_type'],\\n 'ftp_acrylic': request.json['ftp_acrylic'],\\n 'ftp_cotton': request.json['ftp_cotton'],\\n 'ftp_elastane': request.json['ftp_elastane'],\\n 'ftp_linen': request.json['ftp_linen'],\\n 'ftp_other': request.json['ftp_other'],\\n 'ftp_polyamide': request.json['ftp_polyamide'],\\n 'ftp_polyester': request.json['ftp_polyester'],\\n 'ftp_polypropylene': request.json['ftp_polypropylene'],\\n 'ftp_silk': request.json['ftp_silk'],\\n 'ftp_viscose': request.json['ftp_viscose'],\\n 'ftp_wool': request.json['ftp_wool'],\\n 'gender': request.json['gender'],\\n 'label': request.json['label'],\\n 'made_in': request.json['made_in'],\\n 'season': request.json['season'],\\n 'size': request.json['size'],\\n 'unspsc_code': request.json['unspsc_code'],\\n 'weight': request.json['weight'],\\n 'ML-model': request.json['ML-model']\\n }\\n\\n product['co2_total'] = None\\n ml_model = product.pop('ML-model', None)\\n if (ml_model == None or ml_model == ''):\\n print('Loading default model: LGBM')\\n ml_model = 'lgbm_default'\\n else:\\n print(f'Loading model: {ml_model}')\\n model = load_model(ml_model)\\n print('Model loaded')\\n \\n pred_with_intervals = do_prediction_with_params(model, product, intervals=True)\\n \\n prediction = pred_with_intervals[0][0]\\n percentile_5 = pred_with_intervals[0][1] if len(pred_with_intervals[0]) == 3 and pred_with_intervals[0][1] is not None else None\\n percentile_95 = pred_with_intervals[0][2] if len(pred_with_intervals[0]) == 3 and pred_with_intervals[0][2] is not None else None\\n result = {\\n \\\"prediction\\\": prediction,\\n \\\"5-percentile\\\": percentile_5,\\n \\\"95-percentile\\\": percentile_95\\n }\\n \\n print('CO2e prediction complete, returning result')\\n print(result)\\n \\n resp = jsonify(result)\\n resp.status_code = 201\\n return resp\",\n \"def ANN(df_train, df_test):\\n\\n # number of individual forecasts and number of periods\\n K = df_test.shape[1]\\n T = df_train.shape[0]\\n T_all = df_train.shape[0] + df_test.shape[0]\\n\\n # matrix of individual forecasts (including train and test sets)\\n F = np.concatenate((df_train.iloc[:, 1:].values, df_test.values), axis=0)\\n\\n # matrix of standardized forecasts and the intercept\\n y = df_train.iloc[:, 0]\\n y_bar = np.mean(y)\\n y_std = np.std(y, ddof=1)\\n\\n Z = np.concatenate(\\n (np.full((T_all, 1), 1, dtype=float),\\n (F - y_bar) / y_std),\\n axis=1)\\n\\n # randomly draw 10 Gamma sets (elements from uniform (-1, 1))\\n len_Gamma = 10\\n Gamma = []\\n\\n for i in range(len_Gamma):\\n Gamma += [\\n (np.random.rand(K+1, 3) * 2) - 1\\n ]\\n\\n # prepare design matrices for the estimation\\n X_list = []\\n\\n # add intercepts\\n for i in range(len_Gamma * 2 * 3 + 1):\\n X_list += [np.full((T_all, 1), 1, dtype=float)]\\n\\n # add linear nodes to half of the ANNs + the first fully linear model\\n for i in range(len_Gamma * 3 + 1):\\n\\n X_list[i] = np.concatenate((X_list[i], F), axis=1)\\n\\n # add logistic nodes to all models (2 halves) except the first one\\n for h in range(2):\\n\\n for i in range(len_Gamma):\\n\\n for p in range(1, 4):\\n\\n logistic_nodes = 1 / (1 + np.exp(-np.dot(Z, Gamma[i][:, :p])))\\n\\n X_list[(h * len_Gamma + i) * 3 + p] = np.concatenate(\\n (X_list[(h * len_Gamma + i) * 3 + p], logistic_nodes),\\n axis=1)\\n\\n # search for the best model specification using the 5-fold CV\\n cv_sq_errors = np.full(len(X_list), 0, dtype=float)\\n\\n for i in range(5):\\n\\n # train and test indices\\n cv_y_train_index = np.full(T, True, dtype=bool)\\n cv_y_train_index[int(i * T/5):int((i+1) * T/5)] = False\\n\\n cv_X_train_index = np.append(cv_y_train_index,\\n np.full(T_all-T, False, dtype=bool))\\n cv_X_test_index = np.append(~cv_y_train_index,\\n np.full(T_all-T, False, dtype=bool))\\n\\n # variable to be forecast\\n cv_y_train = y[cv_y_train_index]\\n cv_y_test = y[~cv_y_train_index]\\n\\n # design matrix for each model separately\\n for j in range(len(X_list)):\\n\\n # set for training\\n cv_X_train = X_list[j][cv_X_train_index, :]\\n\\n # set for testing\\n cv_X_test = X_list[j][cv_X_test_index, :]\\n\\n # estimate the model\\n # first, check if the X'X matrix is regular\\n in_mat = np.dot(np.transpose(cv_X_train), cv_X_train)\\n if np.linalg.det(in_mat) == 0:\\n est_par = np.full(cv_X_train.shape[1], 0, dtype=float)\\n else:\\n est_par = np.dot(\\n np.linalg.inv(in_mat),\\n np.dot(np.transpose(cv_X_train), cv_y_train)\\n )\\n\\n # predict\\n cv_y_pred = np.dot(cv_X_test, est_par)\\n\\n # save the squared errors\\n cv_sq_errors[j] += np.sum((cv_y_test - cv_y_pred)**2)\\n\\n # turn the sum of squared errors to mean squared errors\\n cv_MSE = cv_sq_errors/T\\n\\n # find the ANN specification with the minimal CV MSE\\n best_spec_index = np.argmin(cv_MSE)\\n\\n # train the best ANN\\n X_train = X_list[best_spec_index][:T, :]\\n\\n est_par = np.dot(\\n np.linalg.inv(np.dot(np.transpose(X_train), X_train)),\\n np.dot(np.transpose(X_train), y)\\n )\\n\\n # final predictions\\n X_test = X_list[best_spec_index][T:, :]\\n\\n pred = np.dot(X_test, est_par)\\n\\n df_pred = pd.DataFrame(\\n {\\\"ANN\\\": pred},\\n index=df_test.index\\n )\\n\\n return df_pred\",\n \"def a_test_bbvi():\\n model = ARIMAX(formula=\\\"y ~ x1\\\", data=data, ar=1, ma=1, family=Exponential())\\n x = model.fit('BBVI',iterations=100)\\n assert(len(model.latent_variables.z_list) == 4)\\n lvs = np.array([i.value for i in model.latent_variables.z_list])\\n assert(len(lvs[np.isnan(lvs)]) == 0)\",\n \"def forecast_ARIMA(train, test, p, d, q, model_selection, prod_num):\\n model = ARIMA(train['STU'].dropna(), order=(p, d, q))\\n model_fit = model.fit(disp=0)\\n if model_selection:\\n forecast, st_errs, conf_int = model_fit.forecast(len(test), alpha=0.05)\\n else:\\n predictions_range = pd.date_range('2019-04-01', '2019-09-29', freq=\\\"W-MON\\\").to_series()\\n\\n df_predictions = pd.DataFrame(predictions_range, columns=[\\\"Week\\\"])\\n\\n forecast, st_errs, conf_int = model_fit.forecast(26, alpha=0.05)\\n df_predictions['Sales_Prediction'] = forecast\\n df_predictions['Week'] = pd.to_datetime(df_predictions[\\\"Week\\\"])\\n df_predictions.set_index(\\\"Week\\\", inplace=True)\\n\\n plt.figure(figsize=(12, 5))\\n plt.plot(train['STU'], label='training')\\n plt.plot(df_predictions['Sales_Prediction'], label='forecast')\\n plt.title('Forecast vs Actuals')\\n plt.legend(loc='upper left', fontsize=8)\\n plt.savefig(\\\"Product\\\" + f\\\"_{prod_num}\\\" + \\\".pdf\\\")\\n if model_selection:\\n MSE = mean_squared_error(test, forecast)\\n return MSE\\n return df_predictions\",\n \"def run(self, function, beta):\\n if self.info_requested(Info.ok):\\n self.info_set(Info.ok, False)\\n\\n step = function.step(beta)\\n\\n betanew = betaold = beta\\n\\n if self.info_requested(Info.time):\\n t = []\\n if self.info_requested(Info.fvalue):\\n f = []\\n if self.info_requested(Info.converged):\\n self.info_set(Info.converged, False)\\n\\n for i in xrange(1, self.max_iter + 1):\\n\\n if self.info_requested(Info.time):\\n tm = utils.time_cpu()\\n\\n step = function.step(betanew)\\n\\n betaold = betanew\\n betanew = function.prox(betaold - step * function.grad(betaold),\\n step)\\n\\n if self.info_requested(Info.time):\\n t.append(utils.time_cpu() - tm)\\n if self.info_requested(Info.fvalue):\\n f.append(function.f(betanew))\\n\\n if (1.0 / step) * maths.norm(betanew - betaold) < self.eps \\\\\\n and i >= self.min_iter:\\n\\n if self.info_requested(Info.converged):\\n self.info_set(Info.converged, True)\\n\\n break\\n\\n self.num_iter = i\\n\\n if self.info_requested(Info.num_iter):\\n self.info_set(Info.num_iter, i)\\n if self.info_requested(Info.time):\\n self.info_set(Info.time, t)\\n if self.info_requested(Info.fvalue):\\n self.info_set(Info.fvalue, f)\\n if self.info_requested(Info.ok):\\n self.info_set(Info.ok, True)\\n\\n return betanew\",\n \"def SGD_beta(X, y, eta=1e-4, gamma=0.01):\\n\\n\\t# Stochastic Gradient Descent, shuffle?\\n\\tbeta = np.random.randn(len(X[0]), 1)\\n\\tn = len(X)\\n\\tM = 10 #0.05*n \\t # Size of each minibatch, should be smaller than n\\n\\tm = int(n/M) \\t # Number of minibatches\\n\\tn_epochs = 500 \\t\\t # Nmber of epochs\\n\\n\\tacc = np.zeros(n_epochs+1)\\n\\tepoch_list = np.zeros(n_epochs+1)\\n\\n\\t#z_i = np.zeros(m)\\n\\t#model_i = np.zeros(m)\\n\\t#y_i = np.zeros(m)\\n\\n\\tfor epoch in range(1,n_epochs+1):\\n\\t\\tfor i in range(m):\\n\\n\\t\\t\\trandom_index = np.random.randint(m) #Pick the k-th minibatch at random\\n\\t\\t\\txi = X[random_index:random_index+1]\\n\\t\\t\\tyi = y[random_index:random_index+1]\\n\\n\\t\\t\\t#Compute the gradient using the data in minibatch Bk\\n\\t\\t\\tgrad_beta_C = beta_gradients(xi, yi, beta)\\n\\t\\t\\tbeta -= eta - gamma * grad_beta_C\\n\\n\\t\\t\\t#y_i[i] = yi\\n\\t\\t\\t#z_i[i] = xi@beta\\n\\t\\t\\t#model_i[i] = logistic_function(z_i[i])\\n\\n\\t\\t#acc[epoch] = accuracy(model_i, y_i)\\n\\t\\t#epoch_list[epoch] = epoch\\n\\n\\treturn beta\",\n \"def train(self, df, feature, max_range, extra=False, defender=False):\\n\\n df2 = self._train_preprocess(df, feature, extra)\\n\\n # No need for names anymore\\n if defender:\\n df2 = df2.drop([\\\"Player Id\\\"], axis=1)\\n\\n # Instantiate the models\\n self.rfrg = RandomForestRegressor(n_estimators=1000, n_jobs=-1, random_state=69420)\\n\\n if not defender:\\n self.gbrg = LGBMRegressor(n_estimators=1000, learning_rate=0.01)\\n\\n # Then, perform regression -> This is to see how it performs over weeks\\n mean_error1 = []\\n mean_error2 = []\\n\\n for week in range(max_range - 5, max_range):\\n train = df2[df2['week'] < week]\\n val = df2[df2['week'] == week]\\n\\n x_train, x_test = train.drop([feature], axis=1), val.drop([feature], axis=1)\\n y_train, y_test = train[feature].values, val[feature].values\\n\\n self.rfrg.fit(x_train, y_train)\\n preds1 = self.rfrg.predict(x_test)\\n error1 = rmsle(y_test, preds1)\\n print('Week %d - Error for Random Forest %.5f' % (week, error1))\\n\\n mean_error1.append(error1)\\n if not defender:\\n self.gbrg.fit(x_train, np.log1p(y_train))\\n preds2 = np.expm1(self.gbrg.predict(x_test))\\n error2 = rmsle(y_test, preds2)\\n print('Week %d - Error for Gradient Boosting %.5f' % (week, error2))\\n mean_error2.append(error2)\\n\\n print()\\n print()\\n print(\\\"Feature statistics:\\\")\\n print(f\\\"Min value for feature {feature}: {df[feature].min()}\\\")\\n print(f\\\"Max value for feature {feature}: {df[feature].max()}\\\")\\n print(f\\\"Mean value for feature {feature}: {df[feature].mean()}\\\")\\n print(f\\\"Standard deviation for feature {feature}: {df[feature].std()}\\\")\\n print()\\n print(\\\"Results\\\")\\n print('Mean Error for Random Forest = %.5f' % np.mean(mean_error1))\\n\\n # Note: the final model is trained on every week and stored in self.model!\\n final_xtrain = df2.drop([feature], axis=1)\\n final_ytrain = df2[feature].values\\n self.rfrg.fit(final_xtrain, final_ytrain)\\n\\n if not defender:\\n print('Mean Error for Gradient Boosting = %.5f' % np.mean(mean_error2))\\n self.gbrg.fit(final_xtrain, np.log1p(final_ytrain))\",\n \"def test_timescale_beta(self):\\n param_pairs = [(0, 1, self.rule.tau2)]\\n\\n nsteps = 10\\n self.conductor.out_fct = lambda i: 10*np.ones(self.Nc) if i == 0 \\\\\\n else np.zeros(self.Nc)\\n\\n sim = simulation.Simulation(self.conductor, self.student, self.tutor,\\n self.syns, self.rule, dt=self.dt)\\n W0 = np.copy(self.syns.W)\\n\\n for params in param_pairs:\\n self.rule.alpha = params[0]\\n self.rule.beta = params[1]\\n tau = params[2]\\n\\n self.tutor.out_fct = lambda i: (self.rule.theta + (10 if i == 0 else 0))*\\\\\\n np.ones(self.Ns)\\n\\n self.syns.W = np.copy(W0)\\n sim.run(self.dt)\\n\\n change0 = self.syns.W - W0\\n\\n self.assertGreater(np.linalg.norm(change0), 1e-10)\\n \\n self.tutor.out_fct = lambda i: (self.rule.theta + (10\\n if i == nsteps-1 else 0))*np.ones(self.Ns)\\n\\n self.syns.W = np.copy(W0)\\n sim.run(nsteps*self.dt)\\n\\n change1 = self.syns.W - W0\\n\\n change1_exp = change0*(1 - float(self.dt)/tau)**(nsteps-1)\\n\\n self.assertTrue(np.allclose(change1, change1_exp),\\n msg=\\\"Timescale not verified, alpha={}, beta={}.\\\".format(*params[:2]))\",\n \"def var_predict(temperature_data, n_forwards=(1, 3), n_lags=4, test_ratio=0.2):\\n data = temperature_data.transpose(1, 0, 2)[:, :, 0]\\n num_samples, num_output = data.shape\\n n_test = int(round(num_samples * test_ratio))\\n n_train = num_samples - n_test\\n train_data, test_data = data[:n_train, ...], data[n_train:, ...]\\n\\n scaler = StandardScaler(mean=train_data.mean(), std=train_data.std())\\n _data = scaler.transform(train_data)\\n var_model = VAR(_data)\\n var_result = var_model.fit(n_lags)\\n max_n_forwards = np.max(n_forwards)\\n # Do forecasting.\\n result = np.zeros(shape=(len(n_forwards), n_test, num_output))\\n start = n_train - n_lags - max_n_forwards + 1\\n for input_ind in range(start, num_samples - n_lags):\\n prediction = var_result.forecast(scaler.transform(data[input_ind: input_ind + n_lags]), max_n_forwards)\\n for i, n_forward in enumerate(n_forwards):\\n result_ind = input_ind - n_train + n_lags + n_forward - 1\\n if 0 <= result_ind < n_test:\\n result[i, result_ind, :] = prediction[n_forward - 1, :]\\n\\n predicts = []\\n for i, n_forward in enumerate(n_forwards):\\n predict = scaler.inverse_transform(result[i])\\n predicts.append(predict)\\n return predicts, test_data\",\n \"def predict(self, forecastyear, scenario=None):\\n\\n start_time = time()\\n\\n results = []\\n for m in self.model:\\n\\n # \\\"TARGET\\\" -> ISO-3 country code of destination country\\n R = {}\\n R['target'] = m['target']\\n\\n # Forecast year in the input query\\n R['year'] = forecastyear\\n\\n # Scenario in the input query\\n R['scenario'] = scenario if scenario else {}\\n\\n Xv = self.__adjust_features(m['feature'], scenario)\\n # logger.info(\\\"Return adjust features in {:3.2f} sec.\\\".format(time() - start_time))\\n\\n R['forecast'] = m['clf'].predict(Xv)[0]\\n # logger.info(\\\"Return forecast predict in {:3.2f} sec.\\\".format(time() - start_time))\\n\\n # Explanation of point forecast using SHAP values\\n # R['explanation'] = self.__get_explanations(m['explainer'], Xv, m['Xt'].columns)\\n\\n # Confidence intervals are now one-sided\\n # R['CI'] = [0.0, m['CI'].predict(Xv)[0]]\\n # logger.info(\\\"Return CI predict in {:3.2f} sec.\\\".format(time() - start_time))\\n\\n results.append(R)\\n\\n logger.debug(\\\"Return predict in {:3.2f} sec.\\\".format(time() - start_time))\\n\\n return results\",\n \"def get_alphas_and_betas(context, data):\\r\\n all_assets = context.portfolio.positions.keys()\\r\\n if context.index not in all_assets:\\r\\n all_assets.append(context.index)\\r\\n prices = data.history(all_assets, 'price', context.lookback, '1d')\\r\\n returns = prices.pct_change()[1:]\\r\\n # index_returns = returns[context.index]\\r\\n factors = {}\\r\\n for asset in context.portfolio.positions:\\r\\n try:\\r\\n y = returns[asset]\\r\\n factors[asset] = linreg(returns[context.index], y)\\r\\n except:\\r\\n log.warn(\\\"[Failed Beta Calculation] asset = %s\\\" % asset.symbol)\\r\\n return pd.DataFrame(factors, index=['alpha', 'beta'])\",\n \"def test_multiple_gradient_descent_with_backprop():\\n layer_list = [7, 7, 7, 2]\\n print(\\\"test\\\", \\\"layer_list\\\", layer_list)\\n\\n X, W, B = initialize_parameters_for_layers(layer_list, 5)\\n\\n alpha = 0.01\\n num_iterations = 2000\\n num_layers = len(layer_list) - 1\\n\\n Y = np.arange(10).reshape(2, 5)\\n W, B, yhat = gradient_descent_for_n_layers(num_layers, W, B, X, Y, alpha, num_iterations)\\n\\n print(\\\"test done.\\\") # final W and B are\\\", W, \\\"and\\\", B)\\n print(\\\"final yhat\\\", yhat)\",\n \"def backtesting_engine(symbol, strategy, fromdate, todate, args=None):\\n\\n # Backtesting Engine\\n cerebro = bt.Cerebro()\\n\\n # Add a Strategy if no Data Required for the model\\n if args is None:\\n cerebro.addstrategy(strategy)\\n # If the Strategy requires a Model and therefore data\\n elif args is not None:\\n cerebro.addstrategy(strategy, args)\\n\\n # Retrieve Data from Alpaca\\n data = bt.feeds.YahooFinanceData(\\n dataname=symbol,\\n fromdate=fromdate, # datetime.date(2015, 1, 1)\\n todate=todate, # datetime.datetime(2016, 1, 1)\\n reverse=False\\n )\\n\\n # Add Data to Backtesting Engine\\n cerebro.adddata(data)\\n\\n # Set Initial Portfolio Value\\n cerebro.broker.setcash(100000.0)\\n\\n # Add Analysis Tools\\n cerebro.addanalyzer(bt.analyzers.SharpeRatio, _name='sharpe')\\n cerebro.addanalyzer(bt.analyzers.Returns, _name='returns')\\n cerebro.addanalyzer(bt.analyzers.SQN, _name='sqn')\\n cerebro.addanalyzer(bt.analyzers.DrawDown, _name='drawdown')\\n cerebro.addanalyzer(bt.analyzers.PositionsValue, _name='posval')\\n cerebro.addanalyzer(bt.analyzers.PyFolio, _name='pyfolio')\\n\\n # Starting Portfolio Value\\n print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue())\\n\\n # Run the Backtesting Engine\\n backtest = cerebro.run()\\n\\n # Print Analysis and Final Portfolio Value\\n print(\\n 'Final Portfolio Value: %.2f' % cerebro.broker.getvalue()\\n )\\n print(\\n 'Return: ', backtest[0].analyzers.returns.get_analysis()\\n )\\n print(\\n 'Sharpe Ratio: ', backtest[0].analyzers.sharpe.get_analysis()\\n )\\n print(\\n 'System Quality Number: ', backtest[0].analyzers.sqn.get_analysis()\\n )\\n print(\\n 'Drawdown: ', backtest[0].analyzers.drawdown.get_analysis()\\n )\\n print(\\n 'Active Position Value: ', backtest[0].analyzers.posval.get_analysis()\\n )\\n print(\\n 'Pyfolio: ', backtest[0].analyzers.pyfolio.get_analysis()\\n )\\n\\n # Print Analysis and Final Portfolio Value\\n pyfoliozer = backtest[0].analyzers.getbyname('pyfolio')\\n returns, positions, transactions, gross_lev = pyfoliozer.get_pf_items()\\n\\n # See if we can add regular FB data to compare against returns of algo\\n pf.create_full_tear_sheet(\\n returns, positions=positions, transactions=transactions\\n )\",\n \"def arimax_by_month_forecast_fit_predict (y_train,y_test,months_ahead, months_list):\\n predictions_df= pd.DataFrame(np.random.randn(1, 2),\\\\\\n columns=['month_year', 'predicted_evictions'])\\n\\n now = datetime.datetime.now()\\n\\n model = pf.ARIMAX(data=y_train,formula='Eviction_Notice~1+CASANF0URN',ar=2, ma=2)\\n model.fit()\\n y_hat = model.predict(h=months_ahead,oos_data=y_test)\\n\\n predicted = y_hat.Eviction_Notice.values.tolist()\\n temp_df = pd.DataFrame({'predicted_evictions':predicted,'months_ahead':range(1,months_ahead+1)})\\n temp_df['month_year']=months_list\\n\\n predictions_df = predictions_df.append(temp_df, ignore_index=True)\\n predictions_df = predictions_df[1:]\\n predictions_df['month_year']=pd.to_datetime(predictions_df['month_year'])\\n\\n return predictions_df, y_hat\",\n \"def run_hbvedu(temp, prec, month, PE_m, T_m, snow_init, soil_init, s1_init, \\n s2_init, params):\\n # Number of simulation timesteps\\n num_timesteps = len(prec)\\n \\n # Unpack the model parameters\\n T_t = params['T_t']\\n DD = params['DD']\\n FC = params['FC']\\n Beta = params['Beta']\\n C = params['C']\\n PWP = params['PWP']\\n K_0 = params['K_0']\\n K_1 = params['K_1']\\n K_2 = params['K_2']\\n K_p = params['K_p']\\n L = params['L']\\n \\n # initialize empty arrays for all reservoirs and outflow\\n snow = np.zeros(num_timesteps, np.float64)\\n soil = np.zeros(num_timesteps, np.float64)\\n s1 = np.zeros(num_timesteps, np.float64)\\n s2 = np.zeros(num_timesteps, np.float64)\\n qsim = np.zeros(num_timesteps, np.float64)\\n\\n # set initial values\\n snow[0] = snow_init\\n soil[0] = soil_init\\n s1[0] = s1_init\\n s2[0] = s2_init\\n\\n # Start the model simulation as loop over all timesteps\\n for t in range(1, num_timesteps):\\n\\n # Check if temperature is below threshold\\n if temp[t] < T_t:\\n # accumulate snow\\n snow[t] = snow[t-1] + prec[t]\\n # no liquid water\\n liquid_water = 0\\n else:\\n # melt snow\\n snow[t] = max(0, snow[t-1] - DD * (temp[t] - T_t))\\n # add melted snow to available liquid water\\n liquid_water = prec[t] + min(snow[t-1], DD * (temp[t] - T_t))\\n\\n # calculate the effective precipitation\\n prec_eff = liquid_water * (soil[t-1] / FC) ** Beta\\n\\n # Calculate the potential evapotranspiration\\n pe = (1 + C * (temp[t] - T_m[month[t]])) * PE_m[month[t]]\\n\\n # Calculate the actual evapotranspiration\\n if soil[t-1] > PWP:\\n ea = pe\\n else:\\n ea = pe * (soil[t-1] / PWP)\\n\\n # calculate the actual level of the soil reservoir\\n soil[t] = soil[t-1] + liquid_water - prec_eff - ea\\n\\n # calculate the actual level of the near surface flow reservoir\\n s1[t] = (s1[t-1]\\n + prec_eff\\n - max(0, s1[t-1] - L) * K_0\\n - s1[t-1] * K_1\\n - s1[t-1] * K_p)\\n\\n # calculate the actual level of the base flow reservoir\\n s2[t] = (s2[t-1]\\n + s1[t-1] * K_p\\n - s2[t-1] * K_2)\\n\\n qsim[t] = ((max(0, s1[t-1] - L)) * K_0\\n + s1[t] * K_1\\n + s2[t] * K_2)\\n \\n return qsim, snow, soil, s1, s2\",\n \"def naive_forecasting(x_test, y_test):\\n y_pred = np.concatenate([x_test[:,-1].reshape((TESTING_BATCH_SIZE,1,N_INPUT_FEATURES)) for x in range(N_PREDICTIONS)],axis=1)\\n y_pred = y_pred[:,:,:N_OUTPUT_FEATURES]\\n return np.mean(keras.losses.mean_squared_error(y_test,y_pred)), y_pred\",\n \"def fbeta_score(self, y_true=None, y_pred=None, beta=1.0, labels=None, average=\\\"macro\\\", decimal=None, **kwargs):\\n y_true, y_pred, binary, representor, decimal = self.get_processed_data(y_true, y_pred, decimal)\\n matrix, imap, imap_count = cu.calculate_confusion_matrix(y_true, y_pred, labels, normalize=None)\\n metrics = cu.calculate_single_label_metric(matrix, imap, imap_count, beta=beta)\\n\\n list_fbeta = np.array([item[\\\"fbeta\\\"] for item in metrics.values()])\\n list_weights = np.array([item[\\\"n_true\\\"] for item in metrics.values()])\\n\\n if average == \\\"micro\\\":\\n tp_global = np.sum(np.diag(matrix))\\n fp_global = fn_global = np.sum(matrix) - tp_global\\n precision = tp_global / (tp_global + fp_global)\\n recall = tp_global / (tp_global + fn_global)\\n fbeta = ((1 + beta ** 2) * precision * recall) / (beta ** 2 * precision + recall)\\n elif average == \\\"macro\\\":\\n fbeta = np.mean(list_fbeta)\\n elif average == \\\"weighted\\\":\\n fbeta = np.dot(list_weights, list_fbeta) / np.sum(list_weights)\\n else:\\n fbeta = dict([(label, np.round(item[\\\"fbeta\\\"], decimal)) for label, item in metrics.items()])\\n return fbeta if type(fbeta) == dict else np.round(fbeta, decimal)\",\n \"def _process( self, raw_data, regression_var ):\\n\\n reg_idx = int( regression_var )\\n\\n Y = raw_data.ix[:,reg_idx]\\n X = raw_data.ix[:, raw_data.columns != raw_data.columns[reg_idx] ]\\n\\n col_names = list( raw_data.columns.values )\\n\\n Y = Y.as_matrix().astype(np.float64)\\n X = X.as_matrix().astype(np.float64)\\n\\n C = 0.75\\n\\n lamdbaMax = 2 * max( abs( np.dot( np.transpose( X ), Y ) ) ) / Y.size\\n\\n lambdaGrid = np.arange( 0, 100 )\\n lambdaGrid = np.apply_along_axis( lambda x: lamdbaMax / 1.3**x, 0, lambdaGrid )\\n\\n betas = glmnet_py.glmnetCoef( glmnet_py.glmnet( x = X.copy(), y = Y.copy(), lambdau = lambdaGrid ) )\\n\\n j = 0\\n t = 1\\n\\n while( t > 0 and j < 99 ):\\n j += 1\\n beta_j = betas[:,j]\\n lambda_j = lambdaGrid[j]\\n\\n for k in np.arange( 1, ( j - 1 ) ) :\\n beta_k = betas[:,k]\\n lambda_k = lambdaGrid[k]\\n t = t * (max(abs(beta_j - beta_k)) / (lambda_j + lambda_k) <= C)\\n\\n beta = betas[:,j]\\n\\n # fit['beta'] = fit['beta'] * (abs(fit['beta']) >= 3 * C * fit['lambdau'])\\n # beta = beta * (abs( beta ) >= 3 * C * lambdaGrid[j] )\\n\\n coefficients = beta\\n\\n nz_indices = coefficients.nonzero()[0]\\n support_coefs = coefficients[ nz_indices ]\\n\\n # cvglmnetCoef places the intercept term in the first position of the\\n # coefficients vector -- need to manually add Intercept name to vector of column names\\n col_names.insert( 0, \\\"Intercept\\\" )\\n col_names = [ col_names[idx] for idx in nz_indices ]\\n\\n return( pd.DataFrame( data = support_coefs , index = col_names ).to_json(orient='columns') )\",\n \"def perform_backtests(self):\\r\\n \\r\\n for test_name in self.testing_dates:\\r\\n print('\\\\t|--Test #{}'.format(test_name))\\r\\n test_dates = self.testing_dates[test_name]\\r\\n print('\\\\t\\\\t|--Performing Nested Cross-Validation')\\r\\n cross_validation = CrossValidate()\\r\\n cross_validation.output_names = self.output_names\\r\\n cross_validation.feature_names = self.feature_names\\r\\n cross_validation.feature_dict = self.feature_dict\\r\\n cross_validation.full_df = self.final_df_output\\r\\n cross_validation.cv_params = self.testing_dates\\r\\n cross_validation.test_name = test_name\\r\\n cross_validation.walk_forward_cv()\\r\\n self.optimal_params['Test #{}'.format(test_name)] = cross_validation.optimal_params_by_output\\r\\n self.cv_model_metadata['Test #{}'.format(test_name)] = cross_validation.cv_metadata_by_output\\r\\n \\r\\n print('\\\\t\\\\t|--Performing Out-Of-Sample Testing')\\r\\n prediction = Predict()\\r\\n prediction.output_names = self.output_names\\r\\n prediction.feature_names = self.feature_names\\r\\n prediction.feature_dict = self.feature_dict\\r\\n prediction.optimal_params_by_output = cross_validation.optimal_params_by_output\\r\\n prediction.cv_predictions_by_output = cross_validation.cv_predictions_by_output\\r\\n prediction.full_df = self.final_df_output\\r\\n prediction.pred_start = test_dates['pred_start']\\r\\n prediction.pred_end = test_dates['pred_end']\\r\\n prediction.run_prediction()\\r\\n self.full_predictions['Test #{}'.format(test_name)] = prediction.predictions_by_output\\r\\n self.pred_model_metadata['Test #{}'.format(test_name)] = prediction.pred_metadata_by_output\\r\\n \\r\\n print('\\\\nSaving model metadata...')\\r\\n with open(path.deployment_cv_results, 'w') as file:\\r\\n json.dump(self.optimal_params, file)\\r\\n with open(path.deployment_cv_metadata, 'w') as file:\\r\\n json.dump(self.cv_model_metadata, file)\\r\\n with open(path.deployment_pred_model_metadata, 'w') as file:\\r\\n json.dump(self.pred_model_metadata, file)\\r\\n with open(path.deployment_full_predictions, 'w') as file:\\r\\n json.dump(self.full_predictions, file)\",\n \"def getErrRegression(mdlParams, indices, sess, loss_op, prediction, getBatch, placeholders, feed_list_inference):\\r\\n # Set up sizes\\r\\n loss = np.zeros([len(mdlParams[indices])])\\r\\n predictions = np.zeros([len(mdlParams[indices]),len(mdlParams['tar_range'])])\\r\\n targets = np.zeros([len(mdlParams[indices]),len(mdlParams['tar_range'])])\\r\\n numBatches = int(math.ceil(len(mdlParams[indices])/mdlParams['batchSize']))\\r\\n for k in range(numBatches):\\r\\n feed_list_inference['X'], feed_list_inference['Y'] = getBatch(mdlParams,indices,k)\\r\\n # Take care of last batch being smaller\\r\\n if int(math.ceil(len(mdlParams[indices]) / mdlParams['batchSize'])) - 1 == k :\\r\\n bSize = len(mdlParams[indices]) - mdlParams['batchSize'] * k\\r\\n else:\\r\\n bSize = mdlParams['batchSize']\\r\\n targets[mdlParams['batchSize']*k:(mdlParams['batchSize']*k+bSize),:] = feed_list_inference['Y'] \\r\\n loss[mdlParams['batchSize']*k:(mdlParams['batchSize']*k+bSize)], predictions[mdlParams['batchSize']*k:(mdlParams['batchSize']*k+bSize)] = sess.run([loss_op, prediction], feed_dict={placeholders[p]: feed_list_inference[p] for p in placeholders})\\r\\n # Transform targets and predictions\\r\\n if mdlParams['scale_targets']:\\r\\n targets = mdlParams['scaler'].inverse_transform(targets)\\r\\n predictions = mdlParams['scaler'].inverse_transform(predictions)\\r\\n # Error metrics\\r\\n # MAE\\r\\n mae = np.mean(np.abs(predictions-targets),1)\\r\\n mae_mean = np.mean(mae)\\r\\n mae_std = np.std(mae)\\r\\n # Relative MAE\\r\\n tar_std = np.std(targets,0)\\r\\n rmae_mean = np.mean(np.mean(np.abs(predictions-targets),0)/tar_std)\\r\\n rmae_std = np.mean(np.std(np.abs(predictions-targets),0)/tar_std)\\r\\n # Avg. Corr. Coeff.\\r\\n corr = np.corrcoef(np.transpose(predictions),np.transpose(targets))\\r\\n # Extract relevant components for aCC\\r\\n num_tar = len(mdlParams['tar_range'])\\r\\n acc = 0\\r\\n for k in range(num_tar):\\r\\n acc += corr[num_tar+k,k]\\r\\n acc /= num_tar\\r\\n return np.mean(loss), mae_mean, mae_std, rmae_mean, rmae_std, acc\",\n \"def run_test_procedures(self):\\r\\n print('\\\\nDeploying prediction model...\\\\n')\\r\\n self.final_df_output = pd.read_json(path.data_final)\\r\\n self.final_df_output.sort_index(inplace=True)\\r\\n self.fill_testing_dates()\\r\\n self.perform_backtests()\\r\\n self.create_full_predictions_dataframe()\\r\\n print('\\\\nDeployment complete!')\",\n \"def predict(self):\\n # Input validation\\n if 'halflife' not in self.cfg or 'min_periods' not in self.cfg:\\n raise ValueError('SingleStockEWM: Model config requires both min_periods (periods backwards) and a '\\n 'halflife (decay of historical values, periods to half original value) to run.')\\n\\n # ## Load up model configs\\n halflife = self.cfg['halflife']\\n min_periods = self.cfg['min_periods']\\n\\n # ## Estimates\\n # Returns\\n realized_returns = self.get('returns', data_type='realized', sampling_freq=self.cfg['returns']['sampling_freq'])\\n realized_volumes = self.get('volumes', data_type='realized', sampling_freq=self.cfg['returns']['sampling_freq'])\\n realized_sigmas = self.get('sigmas', data_type='realized', sampling_freq=self.cfg['returns']['sampling_freq'])\\n logging.info(\\\"Typical variance of returns: %g\\\" % realized_returns.var().mean())\\n\\n self.set('returns', realized_returns.ewm(halflife=halflife, min_periods=min_periods).mean().shift(1).dropna(),\\n 'predicted')\\n\\n # Volumes & sigmas\\n self.set('volumes', realized_volumes.ewm(halflife=halflife, min_periods=min_periods).mean().shift(1).dropna(),\\n 'predicted')\\n self.set('sigmas', realized_sigmas.shift(1).dropna(), 'predicted')\\n\\n # Covariance\\n if 'covariance' not in self.cfg:\\n raise NotImplemented('Covariance section needs to be defined under SS EWM model config.')\\n elif self.cfg['covariance']['method'] == 'SS':\\n self.set('covariance', realized_returns.ewm(halflife=halflife, min_periods=min_periods).cov().\\n shift(realized_returns.shape[1]).dropna(), 'predicted', self.cfg['covariance']['sampling_freq'])\\n elif self.cfg['covariance']['method'] == 'FF5':\\n # Fetch data\\n ff_returns = self.get('ff_returns', 'realized', SamplingFrequency.DAY)\\n realized_returns = self.get('returns', data_type='realized', sampling_freq=SamplingFrequency.DAY)\\n\\n update = self.cfg['covariance']['update'] if 'update' in self.cfg['covariance'] else 'monthly'\\n if update == 'quarterly':\\n update_freq = '3M'\\n elif update == 'monthly':\\n update_freq = 'M'\\n elif update == 'biweekly':\\n update_freq = '2W'\\n elif update == 'weekly':\\n update_freq = 'W'\\n else:\\n raise NotImplemented('Update freq under covariance only supports: month, biweekly, weekly.')\\n\\n # Generate computation frequency\\n first_days = pd.date_range(start=realized_returns.index[max(self.cfg['min_periods'] + 1, 90)],\\n end=realized_returns.index[-1],\\n freq=update_freq)\\n days_back = self.cfg['covariance']['train_days'] if 'train_days' in self.cfg['covariance'] else 90\\n\\n # Use ML regression to obtain factor loadings. Then factor covariance and stock idiosyncratic variances\\n exposures, factor_sigma, idyos = {}, {}, {}\\n\\n # Every first day in each biweekly period\\n cov_rscore = []\\n for day in first_days:\\n logging.info('Running for {}'.format(day.strftime('%Y %b %d')))\\n\\n # Grab asset returns for preceding train_days (90 by default)\\n used_returns = realized_returns.loc[(realized_returns.index < day) &\\n (realized_returns.index >= day - pd.Timedelta(str(days_back) + \\\" days\\\"))]\\n used_ff_returns = ff_returns.loc[ff_returns.index.isin(used_returns.index)].iloc[:, :-1]\\n\\n # Multi linear regression to extract factor loadings\\n mlr = linear_model.LinearRegression()\\n mlr.fit(used_ff_returns, used_returns)\\n mlr.predict(used_ff_returns)\\n\\n # Track performance of FF fit\\n # rscore = metrics.r2_score(used_ff_returns, used_returns)\\n cov_rscore.append(0)\\n #p rint('predict_cov_FF5: mlr score = {s}'.format(s=rscore))\\n\\n # Factor covariance - on FF returns\\n factor_sigma[day] = used_ff_returns.cov().fillna(0)\\n # Exposures - factor loadings obtained from multi linear regression coefficients of stock on FF factors\\n exposures[day] = pd.DataFrame(data=mlr.coef_, index=realized_returns.columns).fillna(0)\\n # Stock idiosyncratic variances - stock var minus FF var, ensure >=0\\n idyos[day] = pd.Series(np.diag(used_returns.cov().values -\\n exposures[day].values @ factor_sigma[day].values @ exposures[\\n day].values.T),\\n index=realized_returns.columns).fillna(method='ffill')\\n idyos[day][idyos[day] < 0] = 0\\n\\n self.set('factor_sigma', pd.concat(factor_sigma.values(), axis=0, keys=factor_sigma.keys()), 'predicted')\\n self.set('exposures', pd.concat(exposures.values(), axis=0, keys=exposures.keys()), 'predicted')\\n self.set('idyos', pd.DataFrame(idyos).T, 'predicted')\\n self.set('cov_rscore', pd.DataFrame.from_dict({'date': first_days,\\n 'rscore': cov_rscore,\\n 'train_days': days_back}), 'predicted')\\n\\n else:\\n raise NotImplemented('Covariance section needs to be defined under SSEWM moodel config and needs definition'\\n ' of method: SS (single stock returns) or FF5 (Fama French 5 factor returns).')\",\n \"def execute(train_ts, train_inputs, test_inputs, op_exec):\\n if op_exec == \\\"mlp\\\":\\n model = mod.mlp_model()\\n model.fit(train_inputs, train_ts)\\n forecast = model.forecast(test_inputs)\\n return forecast\\n elif op_exec == \\\"rf\\\":\\n model = mod.rf_model()\\n model.fit(train_inputs, train_ts)\\n forecast = model.forecast(test_inputs)\\n return forecast\",\n \"def perform(self, node, (fup, fup_target, betas), outputs):\\n betas_out = numpy.zeros_like(betas)\\n betas_out[0] = 1\\n\\n M = numpy.float(len(betas))\\n fup_sorted_arg = numpy.argsort(fup) \\n fup_sorted = fup[fup_sorted_arg]\\n idx = numpy.arange(1,M-1)\\n \\n for i, fup_targ in zip(idx, fup_target[1:-1]):\\n ubound_i = numpy.searchsorted(fup_sorted, fup_targ)\\n # for given fup target, find tighest lower and upper bounds in current array\\n fup_lower, fup_upper = fup_sorted[ubound_i-1], fup_sorted[ubound_i]\\n # find matching beta values\\n b_lower_i, b_upper_i = fup_sorted_arg[ubound_i-1], fup_sorted_arg[ubound_i]\\n beta_lower, beta_upper = betas[b_lower_i], betas[b_upper_i]\\n\\n db_df = (beta_upper - beta_lower) / (fup_upper - fup_lower)\\n betas_out[i] = beta_lower + db_df * (fup_targ - fup_lower)\\n \\n temp = numpy.sort(betas_out)\\n outputs[0][0] = temp[::-1]\",\n \"def test_predict_proba():\\n args = get_layer('predict', 'manual', 'temporal', True, False, window=2, step_size=3)\\n run_layer(*args)\",\n \"def perform_bayesian_optimization(self):\\n bounds = {'hunits': (self.hunits_lower, self.hunits_upper),\\n 'embedding_dim': (self.embedding_dim_lower, self.embedding_dim_upper)}\\n optimizer = BayesianOptimization(f=self.lstm_score, pbounds=bounds, random_state=1)\\n optimizer.maximize(init_points=2, n_iter=self.iterations)\\n print(optimizer.max)\\n print(optimizer.res)\",\n \"def calc_misclass_all_betas(beta, train_feat, train_lab, test_feat, test_lab):\\n n, d = beta.shape\\n mis_train = []\\n mis_test = []\\n for i in range(n):\\n mis_train.append(calc_misclass(beta[i], train_feat, train_lab))\\n for i in range(n):\\n mis_test.append(calc_misclass(beta[i], test_feat, test_lab))\\n return mis_train, mis_test\",\n \"def fit(self, epochs=1000, alpha=0.001, beta=0, verbose=False):\\n X, y = self.data, self.labels\\n\\n for epoch in range(epochs):\\n for n in range(X.shape[0]):\\n\\n # Feedforward\\n x = X[n, :].reshape(X.shape[1], 1)\\n prediction = self._sigmoid_m(np.matmul(self.w, x) + self.b)\\n\\n # Back propagation\\n error = prediction - y[n, 0]\\n self._backpropagation(\\n alpha=alpha,\\n error=error,\\n layer_input=x,\\n layer_output=prediction)\",\n \"def get_beta(self, epoch, beta_start=None, beta_end=None):\\r\\n if beta_start is None:\\r\\n beta_start = self.beta_start\\r\\n if beta_end is None:\\r\\n beta_end = self.beta_end\\r\\n\\r\\n if epoch < beta_start:\\r\\n return self.beta0\\r\\n elif beta_start <= epoch <= beta_end:\\r\\n if self.schedule_type == 'constant':\\r\\n return self.beta0\\r\\n elif self.schedule_type == 'linear':\\r\\n return (self.beta0\\r\\n + (self.beta1 - self.beta0) * (epoch - beta_start)\\r\\n / (beta_end - beta_start))\\r\\n elif self.schedule_type == 'cosine':\\r\\n return self.cosine_annealing(epoch, beta_start, beta_end)\\r\\n elif self.schedule_type == 'cosine-restarts':\\r\\n cycle_len = (beta_end - beta_start) // self.cycles\\r\\n cycle_idx = (epoch - beta_start) // cycle_len\\r\\n start_epoch = beta_start + cycle_len * cycle_idx\\r\\n end_epoch = beta_start + cycle_len * (cycle_idx + 1)\\r\\n return self.cosine_annealing(epoch, start_epoch, end_epoch)\\r\\n else:\\r\\n raise ValueError('Unsupported schedule type {}'\\r\\n .format(self.schedule_type))\\r\\n else:\\r\\n return self.beta1\",\n \"def test_model_outcome(predicted, actual, planned):\\n if not isinstance(predicted, pd.DataFrame):\\n predicted = pd.DataFrame(predicted, columns=[\\\"PREDICTED_TRIP_DURATION\\\"])\\n if not isinstance(actual, pd.DataFrame):\\n actual = pd.DataFrame(actual, columns=[\\\"ACTUAL_TRIP_DURATION\\\"])\\n if not isinstance(planned, pd.DataFrame):\\n planned = pd.DataFrame(planned, columns=[\\\"PLANNED_TRIP_DURATION\\\"])\\n # Initialise the combined dataframe\\n combined = pd.concat([predicted, actual, planned], axis=1)\\n # Calculate the actual delay\\n actual_delay = combined[\\\"PLANNED_TRIP_DURATION\\\"] - combined[\\\"ACTUAL_TRIP_DURATION\\\"]\\n # Calculate the predicted delay\\n predicted_delay = combined[\\\"PLANNED_TRIP_DURATION\\\"] - combined[\\\"PREDICTED_TRIP_DURATION\\\"]\\n # Calculate the difference in delay\\n delay_diff = actual_delay - predicted_delay\\n # Combine the delays into a single dataframe\\n combined_delay = pd.concat([pd.DataFrame(actual_delay, columns=['Actual_Delay']),\\n pd.DataFrame(predicted_delay, columns=['Predicted_Delay']),\\n pd.DataFrame(delay_diff, columns=['Difference_In_Delay'])], axis=1)\\n # Obtain the index of the max and min values of the actual, predicted and difference delays\\n actual_max_index = combined_delay[\\\"Actual_Delay\\\"].argmax()\\n actual_min_index = combined_delay[\\\"Actual_Delay\\\"].argmin()\\n predicted_max_index = combined_delay[\\\"Predicted_Delay\\\"].argmax()\\n predicted_min_index = combined_delay[\\\"Predicted_Delay\\\"].argmin()\\n delay_diff_max_index = combined_delay[\\\"Difference_In_Delay\\\"].argmax()\\n delay_diff_min_index = combined_delay[\\\"Difference_In_Delay\\\"].argmin()\\n # Get the Mean Absolute Error\\n MAE = metrics.mean_absolute_error(combined[\\\"ACTUAL_TRIP_DURATION\\\"], combined[\\\"PREDICTED_TRIP_DURATION\\\"])\\n # Get the R2 Score\\n R2 = metrics.r2_score(combined[\\\"ACTUAL_TRIP_DURATION\\\"], combined[\\\"PREDICTED_TRIP_DURATION\\\"])\\n # Get the Root Mean Squared Error\\n RMSE = metrics.mean_squared_error(combined[\\\"ACTUAL_TRIP_DURATION\\\"], combined[\\\"PREDICTED_TRIP_DURATION\\\"],\\n squared=False)\\n # Get the Median Absolute Error\\n MEDAE = metrics.median_absolute_error(combined[\\\"ACTUAL_TRIP_DURATION\\\"], combined[\\\"PREDICTED_TRIP_DURATION\\\"])\\n # Get the Mean Squared Error Log Value\\n MSLE = metrics.mean_squared_log_error(combined[\\\"ACTUAL_TRIP_DURATION\\\"], combined[\\\"PREDICTED_TRIP_DURATION\\\"])\\n # Build Dictionary\\n pass_val = {\\\"combined\\\": combined,\\n \\\"combined_delay\\\": combined_delay,\\n \\\"actual_max_index\\\": actual_max_index,\\n \\\"actual_min_index\\\": actual_min_index,\\n \\\"predicted_max_index\\\": predicted_max_index,\\n \\\"predicted_min_index\\\": predicted_min_index,\\n \\\"delay_diff_max_index\\\": delay_diff_max_index,\\n \\\"delay_diff_min_index\\\": delay_diff_min_index,\\n \\\"MAE\\\": MAE,\\n \\\"R2\\\": R2,\\n \\\"MEDAE\\\": MEDAE,\\n \\\"RMSE\\\": RMSE,\\n \\\"MSLE\\\": MSLE}\\n # Return Dictionary\\n return pass_val\",\n \"def test_naive_forecaster_model_with_regressor_pyfunc_output(\\n naive_forecaster_model_with_regressor, model_path, data_longley\\n):\\n _, _, _, X_test = data_longley\\n\\n flavor.save_model(sktime_model=naive_forecaster_model_with_regressor, path=model_path)\\n loaded_pyfunc = flavor.pyfunc.load_model(model_uri=model_path)\\n\\n X_test_array = convert(X_test, \\\"pd.DataFrame\\\", \\\"np.ndarray\\\")\\n\\n model_predict = naive_forecaster_model_with_regressor.predict(fh=FH, X=X_test)\\n predict_conf = pd.DataFrame([{\\\"fh\\\": FH, \\\"predict_method\\\": \\\"predict\\\", \\\"X\\\": X_test_array}])\\n pyfunc_predict = loaded_pyfunc.predict(predict_conf)\\n np.testing.assert_array_equal(model_predict, pyfunc_predict)\\n\\n model_predict_interval = naive_forecaster_model_with_regressor.predict_interval(\\n fh=FH, coverage=COVERAGE, X=X_test\\n )\\n predict_interval_conf = pd.DataFrame(\\n [\\n {\\n \\\"fh\\\": FH,\\n \\\"predict_method\\\": \\\"predict_interval\\\",\\n \\\"coverage\\\": COVERAGE,\\n \\\"X\\\": X_test_array,\\n }\\n ]\\n )\\n pyfunc_predict_interval = loaded_pyfunc.predict(predict_interval_conf)\\n np.testing.assert_array_equal(model_predict_interval.values, pyfunc_predict_interval.values)\\n\\n model_predict_quantiles = naive_forecaster_model_with_regressor.predict_quantiles(\\n fh=FH, alpha=ALPHA, X=X_test\\n )\\n predict_quantiles_conf = pd.DataFrame(\\n [\\n {\\n \\\"fh\\\": FH,\\n \\\"predict_method\\\": \\\"predict_quantiles\\\",\\n \\\"alpha\\\": ALPHA,\\n \\\"X\\\": X_test_array,\\n }\\n ]\\n )\\n pyfunc_predict_quantiles = loaded_pyfunc.predict(predict_quantiles_conf)\\n np.testing.assert_array_equal(model_predict_quantiles.values, pyfunc_predict_quantiles.values)\\n\\n model_predict_var = naive_forecaster_model_with_regressor.predict_var(fh=FH, cov=COV, X=X_test)\\n predict_var_conf = pd.DataFrame(\\n [\\n {\\n \\\"fh\\\": FH,\\n \\\"predict_method\\\": \\\"predict_var\\\",\\n \\\"cov\\\": COV,\\n \\\"X\\\": X_test_array,\\n }\\n ]\\n )\\n pyfunc_predict_var = loaded_pyfunc.predict(predict_var_conf)\\n np.testing.assert_array_equal(model_predict_var.values, pyfunc_predict_var.values)\",\n \"def test_200_boosted_goal_difference_for_home_models_with_thresholds(self):\\n\\n def create_model_fn(fn_team: str):\\n team_stat = Stats.n_sample_stats_for_team(cursor=db_in_cursor,\\n team=fn_team,\\n last_sample_date=self.model_date,\\n n_samples=self.num_samples,\\n normalize_by_matches=True)\\n\\n\\n return FeatureModel(\\n input_data=[self.home_boost + team_stat.goal_diff, team_stat.goal_diff],\\n id=team_stat.team_name\\n )\\n\\n\\n for match_date in played_home_OR_away_before_dates:\\n ####\\n # Build model up to the day before the match\\n ####\\n self.home_boost = 0.72\\n self.model_date = match_date - timedelta(days=1)\\n self.num_samples = num_matches_in_season\\n\\n models: {str: FeatureModel} = FeatureModel.create_models_for_all_teams(\\n model_making_fn=create_model_fn, entities=teams)\\n\\n self.persist_models(model_gen_date=self.model_date, model_description=self.shortDescription(), models=models)\\n\\n self.make_and_store_predictions_for_date(match_date=match_date, models=models, draw_range=(0.3, 0.9))\",\n \"def AR_Model(x, y, last_week_flow):\\n # Fitting AR model to training dataset\\n model = LinearRegression().fit(x, y)\\n\\n # Printing model fitting parameters\\n r_sq1 = model.score(x, y)\\n # print('Coefficient of Determination = ', np.round(r_sq1, 2))\\n\\n # Predicting flows with fitted AR Model\\n nextweek_prediction = model.predict(last_week_flow)\\n\\n # Output of this function will be printed as forecasted streamflow\\n return nextweek_prediction\",\n \"def do_rbfsvm(x_data, y_data, bst_model):\\n\\n bst_rbfsvm = SVC(C = bst_model.best_estimator_.C, gamma = bst_model.best_estimator_.gamma)\\n bst_rbfsvm_fit = bst_lsvm.fit(x_data, y_data)\\n return True\",\n \"def test_f_1_is_equal_to_sklearn_fbeta(self):\\n\\n y_true = [1, 0, 1, 1, 0, 1]\\n y_pred = [0, 0, 1, 1, 1, 1]\\n tp = 3\\n fp = 1\\n fn = 1\\n tn = 1\\n beta = 1.0\\n\\n f_beta_custom = Metrics.f_beta(tp=tp, fp=fp, fn=fn, beta=beta)\\n f_beta_sklearn = f1_score(y_true=y_true, y_pred=y_pred)\\n\\n self.assertEqual(f_beta_custom, f_beta_sklearn)\",\n \"def forecast(self, stock):\\n # Load the trained model\\n model_handler = ModelHandler()\\n model = model_handler.load_json_model(stock)\\n\\n # Importing the training set\\n dataset = pd.read_csv(stock.csv_name)\\n dates = dataset.iloc[len(dataset)-31:len(dataset)-1, 0].values\\n dates = [dt.datetime.strptime(d, '%Y-%m-%d').date() for d in dates]\\n\\n # Create the test dataset\\n dataset_test = dataset[len(dataset) - 30:]\\n real_stock_price = dataset_test.iloc[:, 1:2].values\\n dataset = dataset['Open']\\n inputs = dataset[len(dataset) - len(dataset_test) - 60:].values\\n inputs = inputs.reshape(-1, 1)\\n\\n # Feature Scaling\\n sc = MinMaxScaler(feature_range=(0, 1))\\n inputs = sc.fit_transform(inputs)\\n\\n x_test = []\\n x_test.append(inputs[0:60, 0])\\n predicted_values = []\\n for i in range(1, 31):\\n x_test_np = np.array(x_test)\\n x_test_np = np.reshape(x_test_np, (x_test_np.shape[0], x_test_np.shape[1], 1))\\n new_data = model.predict(x_test_np)\\n predicted_values.append(new_data[0])\\n x_test[0] = np.delete(x_test[0], 0)\\n x_test[0] = np.concatenate([x_test[0], new_data[0]])\\n\\n predicted_values = sc.inverse_transform(predicted_values)\\n plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))\\n plt.gca().xaxis.set_major_locator(mdates.DayLocator())\\n plt.plot(dates, real_stock_price, color='red', label=f'Actual {stock.ticker} Stock Price')\\n plt.plot(dates, predicted_values, color='blue', label=f'Predicted {stock.ticker} Stock Price')\\n plt.gcf().autofmt_xdate()\\n plt.title(f'{stock.ticker} Stock Price Prediction')\\n plt.xlabel('Time')\\n plt.ylabel(f'{stock.ticker} Stock Price')\\n plt.legend()\\n plt.show()\",\n \"def AR_Model(x, y, last_week_flow):\\n # Fitting AR model to training dataset\\n model = LinearRegression().fit(x, y)\\n\\n # Printing model fitting parameters\\n r_sq1 = model.score(x, y)\\n print('Coefficient of Determination = ', np.round(r_sq1, 2))\\n\\n # Predicting flows with fitted AR Model\\n nextweek_prediction = model.predict(last_week_flow.values)\\n\\n # Output of this function will be printed as forecasted streamflow\\n return nextweek_prediction\",\n \"def error(beta_0: float, beta_1: float, x_i: float, y_i: float) -> float:\\n return predict(beta_0, beta_1, x_i) - y_i\",\n \"def main():\\n\\n args = parse_cmd_line_args()\\n\\n random_state = check_random_state(args.random_seed)\\n\\n X, mu, A, phases = generate_data(args.n_features, n_samples=args.n_samples,\\n period=args.period, order=args.order,\\n noise_variance=args.noise_variance,\\n random_state=random_state)\\n\\n if args.plot_data:\\n plot_data_timeseries(X)\\n\\n best_fit, best_weights = fit_fembv_varx(\\n X, n_components=args.n_components,\\n max_tv_norm=args.max_tv_norm,\\n memory=args.memory, n_init=args.n_init,\\n tolerance=args.tolerance,\\n max_iterations=args.max_iterations,\\n verbose=args.verbose, random_state=random_state)\\n\\n if args.plot_weights:\\n plot_weights_timeseries(best_weights, phases)\",\n \"def getRegressionOutput(self):\\n\\n # Construct train data\\n X_tmp = np.empty(shape=(0, 14))\\n for flightNum in range(len(self.routes)):\\n # concatenate the buy or wait info to get the total datas\\n y_train = self.y_train.reshape((self.y_train.shape[0],1))\\n y_train_price = self.y_train_price.reshape((self.y_train_price.shape[0],1))\\n\\n X_train = np.concatenate((self.X_train, y_train, y_train_price), axis=1)\\n\\n # choose one route datas\\n X_train = X_train[np.where(X_train[:, flightNum]==1)[0], :]\\n\\n # remove dummy variables\\n # feature 8: departure date; feature 9: observed date state\\n # feature 10: minimum price; feature 11: maximum price\\n # feature 12: prediction(buy or wait); feature 13: current price\\n X_train = X_train[:, 0:14]\\n\\n # group by the feature: departure date\\n departureDates_train = np.unique(X_train[:, 8])\\n\\n # get the final datas, the observed data state should be from large to small(i.e. for time series)\\n for departureDate in departureDates_train:\\n indexs = np.where(X_train[:, 8]==departureDate)[0]\\n datas = X_train[indexs, :]\\n minPrice = min(datas[:, 10])\\n datas[:, 12] = minPrice\\n \\\"\\\"\\\"\\n print departureDate\\n print minPrice\\n print datas\\n \\\"\\\"\\\"\\n X_tmp = np.concatenate((X_tmp, datas), axis=0)\\n\\n X_train = X_tmp[:, 0:12]\\n y_train = X_tmp[:, 12]\\n y_train_price = X_tmp[:, 13]\\n y_train = y_train.reshape((y_train.shape[0], 1))\\n y_train_price = y_train_price.reshape((y_train_price.shape[0], 1))\\n\\n\\n X_train = np.concatenate((X_train, y_train_price), axis=1)\\n np.save('inputReg/X_train', X_train)\\n np.save('inputReg/y_train', y_train)\\n np.save('inputReg/y_train_price', y_train_price)\\n\\n\\n # Construct test data\\n X_tmp = np.empty(shape=(0, 14))\\n for flightNum in range(len(self.routes)):\\n # concatenate the buy or wait info to get the total datas\\n y_test = self.y_test.reshape((self.y_test.shape[0],1))\\n y_test_price = self.y_test_price.reshape((self.y_test_price.shape[0],1))\\n\\n X_test = np.concatenate((self.X_test, y_test, y_test_price), axis=1)\\n\\n # choose one route datas\\n X_test = X_test[np.where(X_test[:, flightNum]==1)[0], :]\\n\\n # remove dummy variables\\n # feature 8: departure date; feature 9: observed date state\\n # feature 10: minimum price; feature 11: maximum price\\n # feature 12: prediction(buy or wait); feature 13: current price\\n X_test = X_test[:, 0:14]\\n\\n # group by the feature: departure date\\n departureDates_test = np.unique(X_test[:, 8])\\n\\n # get the final datas, the observed data state should be from large to small(i.e. for time series)\\n for departureDate in departureDates_test:\\n indexs = np.where(X_test[:, 8]==departureDate)[0]\\n datas = X_test[indexs, :]\\n minPrice = min(datas[:, 10])\\n datas[:, 12] = minPrice\\n \\\"\\\"\\\"\\n print departureDate\\n print minPrice\\n print datas\\n \\\"\\\"\\\"\\n X_tmp = np.concatenate((X_tmp, datas), axis=0)\\n\\n X_test = X_tmp[:, 0:12]\\n y_test = X_tmp[:, 12]\\n y_test_price = X_tmp[:, 13]\\n y_test = y_test.reshape((y_test.shape[0], 1))\\n y_test_price = y_test_price.reshape((y_test_price.shape[0], 1))\\n X_test = np.concatenate((X_test, y_test_price), axis=1)\\n np.save('inputReg/X_test', X_test)\\n np.save('inputReg/y_test', y_test)\\n np.save('inputReg/y_test_price', y_test_price)\",\n \"def Sarsa_lbda_w_bf_and_lin_FA(env, fvecs, idcs_per_action, weights, alpha,\\n epsilon, gamma, num_actions, num_episodes, lbda):\\n\\n for episode in tqdm(range(num_episodes)):\\n done = False\\n \\n state = env.reset()\\n fvec_idx_per_tiling = fvecs.calc_feature_vec(state)\\n Q_vals = init_Q_values(weights, fvec_idx_per_tiling, idcs_per_action,\\n num_actions)\\n action, Q_current = eps_greedy(Q_vals, epsilon, num_actions) \\n curr_active_feat_idcs = fvec_idx_per_tiling + action*idcs_per_action \\n \\n z_trace = init_e_traces(len(weights)) ###\\n \\n step_count = 0\\n while not done:\\n step_count += 1\\n if episode == (num_episodes -1):\\n env.render(state[0])\\n\\n next_state, reward, done,__ = env.step(state, action)\\n\\n delta = reward\\n for i in curr_active_feat_idcs:\\n delta -= weights[i]\\n z_trace[i] += 1 ### accumulating traces\\n #z_trace[i] = 1 ### replacing traces\\n if done:\\n weights += alpha*delta*z_trace\\n break\\n\\n fvec_idx_per_tiling = fvecs.calc_feature_vec(next_state)\\n Q_vals = init_Q_values(weights, fvec_idx_per_tiling, idcs_per_action,\\n num_actions)\\n next_action, Q_next = eps_greedy(Q_vals, epsilon, num_actions)\\n \\n next_active_feat_idcs = fvec_idx_per_tiling + next_action*idcs_per_action### \\n\\n for i in next_active_feat_idcs:\\n delta += gamma*weights[i]\\n weights += alpha*delta*z_trace\\n \\n z_trace = gamma*lbda*z_trace\\n state = next_state\\n action = next_action \\n curr_active_feat_idcs = next_active_feat_idcs\\n \\n env.plot_step_per_ep(episode, step_count)\\n \\n return\",\n \"def run_trainig_and_testing(company = \\\"FB\\\", training_window = 60,\\n trainig_period_y=8, testing_period_y=1,\\n prediction_day=dt.datetime.now() ):\\n # Load data for model\\n training_data, testing_data = fetch_data(company, trainig_period_y, testing_period_y, prediction_day)\\n # prepare data\\n # scaler is object which will scale whole series values to defined feature_range interval\\n scaler = MinMaxScaler(feature_range=(0,1))\\n # we are insterested only in the closing values\\n # optionaly we can use AdjClose -> adjusted for splits\\n scaled_data = scaler.fit_transform(training_data['Close'].values.reshape(-1,1))\\n\\n # x is our learning data\\n x_train, y_train = create_training_tensors(training_window, scaled_data)\\n\\n # build the model\\n model = create_model((x_train.shape[1], 1))\\n model.fit(x_train, y_train, epochs=5, batch_size=32)\\n\\n # for future we can save the model here and load for future\\n\\n \\\"\\\"\\\"\\n TESTING MODEL ACCURACY\\n \\\"\\\"\\\"\\n\\n # load test data\\n\\n tst_start_dt = dt.datetime(2020,1,1)\\n tst_end_dt = dt.datetime(2021,1,1)\\n\\n\\n actual_prices = testing_data['Close'].values\\n total_dataset = pd.concat((training_data['Close'], testing_data['Close']), axis=0)\\n\\n model_inputs = total_dataset[len(total_dataset) - len(testing_data) - training_window:].values\\n model_inputs = model_inputs.reshape(-1,1)\\n model_inputs = scaler.fit_transform(model_inputs)\\n\\n # make prediction on test data\\n\\n x_test, y_test = create_training_tensors(training_window, model_inputs)\\n\\n predicted_prices = model.predict(x_test)\\n # predicted prices are scaled to (0-1) so we need to rescale them to actual prices\\n predicted_prices = scaler.inverse_transform(predicted_prices)\\n\\n plot_actual_vs_predicted(predicted_prices,actual_prices, company,\\n x_labels=[date.strftime(\\\"%d-%m-%y\\\") for date in testing_data.index] )\\n\\n # predict next day\\n real_data = [model_inputs[len(model_inputs) + 1 - training_window: len(model_inputs)+1, 0]]\\n real_data = np.array(real_data)\\n real_data = np.reshape(real_data, (real_data.shape[0], real_data.shape[1], 1))\\n print(scaler.inverse_transform(real_data[-1]))\\n\\n prediction = model.predict(real_data)\\n prediction = scaler.inverse_transform(prediction)\\n print(f\\\"Next day value is: {prediction}\\\")\\n\\n print(\\\"===================================\\\")\\n print(model.summary())\\n print(\\\"===================================\\\")\\n print(\\\"Evaluating model\\\")\\n result = model.evaluate(x_test, y_test)\\n print(dict(zip(model.metrics_names, result)))\\n\\n\\n # TODO\\n # experiment with next days predictions\",\n \"def predict(x_tst, model):\\n\\n predictions = model.predict(x_tst)\\n return predictions\",\n \"def run_model(self, trend_growth='linear', trend_flexibility=0.05, changepoints=None,\\n holidays=None, additional_features=None):\\n\\n # Prepare data to Prophet requirements\\n ###################################################################\\n # Create column ds with index (demand date)\\n demand = self.demand\\n demand['ds'] = demand.index\\n\\n # Adding additional features to the model as a regressor\\n if additional_features is not None:\\n # Select only the exog features in the list\\n demand = demand[['y','ds'] + list(additional_features)]\\n\\n # Check holidays\\n if holidays is not None:\\n holidays = self.holidays_format(holidays)\\n\\n # Specify the carrying capacity (maximum) in a column 'cap'\\n if self.market_share is not None:\\n demand['cap'] = self.market_share[:len(demand)]\\n # Saturating Minimum in a column 'floor'\\n demand['floor'] = 0\\n\\n # Train model\\n #####################################################################\\n if self.freq is not 'D' or 'B':\\n model = Prophet(growth=trend_growth, changepoint_prior_scale=trend_flexibility,\\n changepoints=changepoints, holidays=holidays, weekly_seasonality=False)\\n else:\\n model = Prophet(growth=trend_growth, changepoint_prior_scale=trend_flexibility,\\n changepoints=changepoints, holidays=holidays)\\n\\n # Adding regressors to the linear part of the model\\n if additional_features is not None:\\n for feature in additional_features:\\n model.add_regressor(feature)\\n\\n # Fit\\n model.fit(demand)\\n\\n # Predict Test\\n ####################################################################\\n\\n # Dataframe to predict (including all history)\\n # predict() from Prophet only receives dates as a column 'ds' (and other external variables)\\n demand_predict = demand.drop(columns=['y'])\\n\\n # Dataframe with yhat (y predicted) and the components of the model\\n forecast = model.predict(demand_predict)\\n forecast.set_index('ds', inplace=True) # Set dates as index again\\n\\n # Diagnostics - Time series cross validation to measure forecast error using historical data\\n train_period = int(len(demand) * 0.5) # Initialize with half of the sample\\n test_ratio = 0.3\\n horizon = int(train_period * test_ratio)\\n\\n error = self.evaluation(model, training_period=train_period, forecast_horizon=horizon)\\n\\n return forecast['yhat'], model, error\",\n \"def forecast_loss_V(num_feats):\\n\\n def forecast_loss(y_true, y_pred):\\n \\\"\\\"\\\"Forecasting loss function (ignores missing values).\\\"\\\"\\\"\\n\\n return K.sum(\\n y_true[:, num_feats:] * (y_true[:, :num_feats] - y_pred) ** 2, axis=-1\\n )\\n return forecast_loss\",\n \"def test_bayes_updates_good_data(self):\\r\\n # result for first -> fourth calculated by hand\\r\\n for obs, exp in zip(bayes_updates(self.test), self.result):\\r\\n self.assertFloatEqualAbs(obs, exp, 1e-11)\",\n \"def beta_fct(M_p, F_xuv, R_p):\\n\\n M_EARTH= const.M_earth.cgs.value\\n R_EARTH = const.R_earth.cgs.value\\n\\n if (type(F_xuv) == float) or (type(F_xuv) == np.float64):\\n # if F_xuv is single value\\n grav_pot = -const.G.cgs.value * (M_p*M_EARTH) / (R_p*R_EARTH)\\n log_beta = max(0.0, -0.185 * np.log10(-grav_pot)\\n \\t\\t\\t\\t\\t+ 0.021 * np.log10(F_xuv) + 2.42)\\n beta = 10**log_beta\\n return beta\\n\\n elif len(F_xuv) > 1:\\n # if F_xuv is a list\\n betas = []\\n for i in range(len(F_xuv)):\\n grav_pot_i = -const.G.cgs.value \\\\\\n * (M_p[i]*M_EARTH) / (R_p[i]*R_EARTH)\\n log_beta_i = max(0.0, -0.185 * np.log10(-grav_pot_i)\\n \\t\\t\\t\\t\\t + 0.021 * np.log10(F_xuv[i]) + 2.42)\\n beta_i = 10**log_beta_i\\n betas.append(beta_i)\\n betas = np.array(betas)\\n return betas\",\n \"def best_weigth(test, lstm_forecasts, arima_forecasts):\\n errors = []\\n for i in range(0, 100):\\n alpha = i / 100\\n beta = 1 - alpha\\n weighted_forecasts = (alpha * lstm_forecasts + beta * arima_forecasts)\\n meanError = ((test - weighted_forecasts)**2).mean() #mse\\n errors.append(meanError)\\n return np.array(errors).argmin() / 100\",\n \"def stepwise_regression(train_per_list, cut_off_list, C_list,\\n factors,non_factors, data_path, executable_path):\\n sql_table = 'aggregated_ctr' #Data table\\n sql_features = list(set(sum([fs.split('*') for fs in factors], [])))\\n # remove cross terms\\n\\n factors+=['campaign_id','ad_account_id','pub_account_id', \\n 'campaign_id*site', 'ad*pub_account_id']\\n con_dict_mad={'host':'db.lqm.io','db':'madvertise_production',\\n 'user':'readonly','passwd':'z0q909TVZj'}\\n con_dict_dse={'host':'db.lqm.io','db':'dse','user':'dse','passwd':'dSe@lQm'}\\n rtb_flag=[0,1]\\n \\n test_per_list= map(lambda x: ( add_hour(x[1], 1), add_hour(x[1], 3)), train_per_list)\\n \\n # test period is next 3 hours after end of training period\\n # DATA RANGE IS INCLUSIVE => 00:00-02:00 = 3 HOURS\\n MySQL_save_data_loop(con_dict_dse, sql_table,\\n train_per_list, sql_features, rtb_flag, data_path)\\n MySQL_save_data_loop(con_dict_dse, sql_table,\\n test_per_list, sql_features, rtb_flag, data_path)\\n \\n model_type=0\\n has_intercept = True # bias term in LR\\n tol = 0.00000001\\n\\n # NB these filenames are HARDCODED in write_sparse routines\\n weights_file = 'train_ais.txt'\\n train_file = 'train_svm.txt'\\n test_file = 'test_svm.txt'\\n probability_file = 'preds_SummModel_py.txt'\\n\\n \\n res_df_list=[]\\n trial_factors=[]\\n remaining_factors=factors[:]\\n while len(remaining_factors):\\n results = [] \\n # we assume we cannot load all the data in memory\\n # so we have to reload for every step of stepwise selection\\n for train_per, test_per in zip(train_per_list, test_per_list):\\n \\n train_df=load_data(data_path,train_per)\\n test_df=load_data(data_path,test_per)\\n \\n sc, click_no_click_df, weights, targets \\\\\\n = libLinear_functions.create_sparse_cat(train_df, factors, non_factors)\\n \\n for cut_off in cut_off_list:\\n sparse_train_all = libLinear_functions.create_sparse(sc, cut_off, click_no_click_df)\\n sparse_test_all = sc.transform(test_df)\\n for fac in remaining_factors:\\n trial_factors.append(fac)\\n trial_factors.sort(key=lambda x: sc.factors.index(x))\\n # libsvm expects the indices in ascending order\\n print (trial_factors) \\n sparse_train=sc.select_factors(sparse_train_all, trial_factors)\\n sparse_test=sc.select_factors(sparse_test_all, trial_factors)\\n libLinear_functions.write_sparse(sc, sparse_train, weights, targets, data_path, len(trial_factors))\\n libLinear_functions.write_sparse_test(sc, sparse_test, data_path, n_columns_used= len(trial_factors))\\n\\n for C in C_list:\\n model_file = \\\\\\n '{start}_{stop}_cut_{cut_off}_C_{C:0.3}.model'.format(\\n start=date_name(train_per[0]),\\n stop=date_name(train_per[1]),\\n cut_off=cut_off, C=C)\\n fit(executable_path, data_path, train_file,\\n model_file, weights_file, model_type, reg_param=C, tol=tol,\\n has_intercept=has_intercept)\\n \\n pCTR = libLinear_functions.predict(\\n executable_path, data_path, test_file,\\n model_file, probability_file)\\n if type(pCTR) is pd.Series:\\n amounts = pd.DataFrame({\\n 'no_clicks':test_df['instances' ]-test_df['clicks'],\\n 'clicks':test_df['clicks']})\\n mean_log_loss, weighted_log_loss =\\\\\\n libLinear_functions.log_loss_weighted(pCTR, amounts)\\n results.append([train_per[:], tuple(trial_factors),fac, cut_off, C, mean_log_loss])\\n # what to do if ERROR?\\n trial_factors.remove(fac)\\n res_df=pd.DataFrame(results,columns=['train_per','factors','add_factor','cut_off','C','mean_log_loss'])\\n res_avg=res_df.groupby(['factors','add_factor','cut_off','C']).agg([np.mean,np.std])\\n best_params=res_avg['mean_log_loss','mean'].argmin()\\n best_fac=best_params[1]\\n remaining_factors.remove(best_fac)\\n trial_factors.append(best_fac)\\n res_df_list.append(res_df)\\n results_df=pd.concat(res_df_list)\\n return results_df\",\n \"def AFTER(df_train, df_test, lambd):\\n\\n # forecast errors\\n errors = df_train.iloc[:, 1:].subtract(df_train.iloc[:, 0], axis=0)\\n sq_errors = errors**2\\n\\n # combining weights\\n nominator = np.exp((-lambd) * sq_errors.sum(axis=0))\\n denominator = nominator.sum()\\n comb_w = nominator / denominator\\n\\n # predictions\\n df_pred = pd.DataFrame({\\\"AFTER\\\": df_test.dot(comb_w)})\\n\\n return df_pred\",\n \"def fun(params, slope, data):\\n x, y_true = data\\n return y_true - model_fun(params, slope, x)\",\n \"def _tstat_beta(self):\\n return _handle_ab(self._tstat_all, self.use_const)[1]\",\n \"def a_test_bbvi_elbo():\\n model = ARIMAX(formula=\\\"y ~ x1\\\", data=data, ar=1, ma=1, family=Exponential())\\n x = model.fit('BBVI',iterations=300, record_elbo=True, map_start=False)\\n assert(x.elbo_records[-1]>x.elbo_records[0])\",\n \"def back_testing(self, func=None):\\n # preallocate real statistics dict\\n ism_label = []\\n \\n if not self.moving_average:\\n # iterate on all ism packages\\n for _i, ism_pack in enumerate(self.ism_data):\\n ism_code = ism_pack[0][0]\\n interval = float(ism_pack[0][-5])\\n df = self.back_testing_data[ism_code]\\n # using close price only now, check data is/not empty\\n if df.empty:\\n res = np.nan\\n continue\\n else:\\n run_price = df['ClosePrice_run'].sum()\\n stop_price = df['ClosePrice_stop'].sum()\\n res = ((stop_price-run_price)/run_price)*243/interval # 243 market days/year\\n ism_label.append([ism_code, res])\\n \\n else: # using moving_average\\n # iterate on all ism packages\\n # for _i, ism_pack in enumerate(self.ism_data):\\n # ism_code = ism_pack[0][0]\\n # interval = int(ism_pack[0][-5])\\n # df = self.back_testing_data[ism_code]\\n # \\n # # using close price only now\\n # dim = int(len(df)/(L_shift+R_shift-1)) \\n # close_price = np.array_split(df['ClosePrice'], dim)\\n # f1 = lambda x: float(x[-1]-x[0]) # real return for single stock\\n # f2 = lambda x: float(x[0]) # initial value of single stock\\n # if L_shift+R_shift >= interval:\\n # ls1 = np.zeros((L_shift+R_shift-interval,), dtype=np.float32)\\n # ls2 = np.zeros_like(ls1)\\n # for _j, par in enumerate(close_price):\\n # # sum over different stocks in one ism_pack\\n # par_rolling = par.rolling(interval)\\n # ls1 += np.asarray(par_rolling.apply(f1).dropna())\\n # ls2 += np.asarray(par_rolling.apply(f2).dropna())\\n # ism_label[ism_code] = {}\\n # ism_label[ism_code]['real_return'] = (ls1/ls2/interval)\\n # else:\\n # print(\\\"L_shift+R_shift < interval\\\")\\n pass \\n \\n return ism_label\",\n \"def big_analysis(beta0s=[0.5, 0.8, 1.1, 1.4, 1.7], ks=range(6), betaps=[1.2, 1.5, 2, 3]):\",\n \"def run_models(X_train, X_test, Y_train, Y_test, models_list, dataframe_name):\\n results = []\\n for type, model in models_list:\\n model.fit(X_train, Y_train)\\n Y_predict = model.predict(X_test)\\n accuracy = accuracy_score(Y_test, Y_predict)*100\\n results.append([type, accuracy, dataframe_name])\\n return results\",\n \"def run_regression(train_embeds, train_labels, test_embeds, test_labels):\\n np.random.seed(1)\\n from sklearn.linear_model import SGDClassifier\\n from sklearn.dummy import DummyClassifier\\n from sklearn.metrics import f1_score\\n dummy = DummyClassifier()\\n dummy.fit(train_embeds, train_labels)\\n log = SGDClassifier(loss=\\\"log\\\", n_jobs=10, tol=1e-3)\\n log.fit(train_embeds, train_labels)\\n print(\\\"F1 score:\\\", f1_score(test_labels, log.predict(test_embeds), average=\\\"micro\\\"))\\n print(\\\"Random baseline f1 score:\\\", f1_score(test_labels, dummy.predict(test_embeds), average=\\\"micro\\\"))\",\n \"def _analytic_forecast(\\n self,\\n parameters: NDArray,\\n resids: NDArray,\\n backcast: Union[float, NDArray],\\n var_bounds: NDArray,\\n start: int,\\n horizon: int,\\n ) -> VarianceForecast:\",\n \"def main(feats_name, targets_name, model_name, n_boot, seed_start, output_filename, train_test_flag):\\n\\n #load feats and targets\\n input_dict = {}\\n input_dict['feats'] = 'data/%s' % (feats_name)\\n input_dict['targets'] = 'data/%s' % (targets_name)\\n #load the feats and targets\\n df = pd.read_csv(\\\"%s\\\" % (input_dict['feats']))\\n targets = pd.read_csv(\\\"%s\\\" % (input_dict['targets']))\\n #drop columns not used for prediction\\n drop_cols = [\\\"Unnamed: 0\\\",\\\"index\\\"]\\n for dc in drop_cols:\\n if dc in targets.columns:\\n targets = targets.drop(dc,axis=1)\\n if dc in df.columns:\\n df = df.drop(dc,axis=1)\\n #reduce to training or test set only if requested\\n if (train_test_flag == 'train') and ('test_set' in df.columns):\\n targets = targets[df['test_set'] == 0]\\n df = df[df['test_set'] == 0]\\n elif (train_test_flag == 'test') and ('test_set' in df.columns):\\n targets = targets[df['test_set'] == 1]\\n df = df[df['test_set'] == 1]\\n df = df.drop('test_set', axis = 1)\\n \\n #broadcast the feats and targets\\n df_b = sc.broadcast(df)\\n targets_b = sc.broadcast(targets)\\n\\n #Set up the classifier. 3fold CV for selection of regularization term.\\n if model_name == 'linear':\\n model = LinearRegression(fit_intercept=True,\\n normalize=False,\\n copy_X=True,\\n n_jobs=1) \\n elif model_name == 'lasso':\\n model = LassoCV(alphas = [.05,.1,.2],\\n normalize = False,\\n fit_intercept = True,\\n verbose = False,\\n copy_X = False,\\n n_jobs = 3)\\n elif model_name == 'ridge':\\n model = RidgeCV(alphas = [.00001,.0001,.001,.01,.1,1,10,100,1000,10000],\\n normalize = False,\\n fit_intercept = True,\\n verbose = 1,\\n cv = 3)\\n else:\\n raise ValueError('model_name not recognized.')\\n \\n #Create an RDD that specifies prng seed to use\\n samp_list = [(n,) for n in np.arange(seed_start, seed_start+n_boot)]\\n samp_rdd = sc.parallelize(samp_list,n_boot) #create RDD with one partition for each row (second arg is number of partitions)\\n #Create a function that takes a tuple as input and returns \\n def func(tup):\\n \\\"\\\"\\\"\\n Takes as input a tuple containing an integer. The integer specifies the random seed that will be used to \\n randomly sample, with replacement, observations from the feats set provided. The model is fitted to the \\n sampled feats. Resulting best fit parameters, along with some other summary statistics and information are\\n provided as input in a JSON string that will be written to the output file when all jobs are completed.\\n \\n Parameters\\n ----------\\n tup, rdd\\n - series of tuples with different integer values defining the RNG seed to be used to sample observations\\n \\n Returns\\n ----------\\n tup[0], int\\n - the seed that was used\\n json.dumps(results_dict), str\\n - dict in json format with the following keys:\\n - alpha, the regularization term providing the best fit according to 3 fold cross-validation\\n - random_state, the initial state used for fitting\\n - training_feats, the name of the training_feats csv file\\n - training_targets, the name of the target variable csv file\\n - cv, the type of cross-validation used\\n - sklearn_version, which version of sklearn was used\\n - mse_min, the mean squared error for the test set on each fold\\n - r2, the r-squared value (% var explained)\\n - coef, parameter vector\\n - intercept, intercept parameter\\n - column_names, feature name corresponding to each parameter in the parameter vector\\n \\\"\\\"\\\"\\n #take a random sample with replacement\\n np.random.seed(seed=tup[0]) #set the seed\\n n_obs = np.shape(df_b.value)[0] #number of observations determines sample size\\n samp = list(np.random.randint(0,high=n_obs,size=n_obs)) #draw the random sample with replacement\\n #fit the model\\n tic = time.time()\\n results = model.fit(df_b.value.iloc[samp,:],np.ravel(targets_b.value.iloc[samp]))\\n toc = tic - time.time()\\n #save the results in a dict\\n results_dict = {}\\n results_dict['alpha'] = results.alpha_\\n results_dict['random_state'] = results.random_state\\n results_dict['training_feats'] = input_dict['feats']\\n results_dict['training_targets'] = input_dict['targets']\\n results_dict['cv'] = results.cv\\n results_dict['sklearn_version'] = sklearn.__version__\\n results_dict['mse_min'] = results.mse_path_.min()\\n results_dict['r2'] = results.score(df_b.value.iloc[samp,:],np.ravel(targets_b.value.iloc[samp]))\\n results_dict['coef'] = list(results.coef_)\\n results_dict['intercept'] = results.intercept_\\n results_dict['column_names'] = [i for i in df_b.value.columns]\\n results_dict['fit_time'] = toc\\n #convert results dict to json and save in tuple\\n return(json.dumps(results_dict))\\n\\n #fit model in parallel\\n results = samp_rdd.map(lambda p: func(p))\\n #save to text file\\n results.saveAsTextFile(output_filename)\\n #stop the SparkContext.\\n if not local_mode:\\n sc.stop()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6112371","0.59843546","0.5966853","0.59554493","0.5932704","0.5894218","0.5783442","0.564491","0.56345904","0.56212074","0.56020945","0.558852","0.5585161","0.55805236","0.55623627","0.5552391","0.5545439","0.55443627","0.5535678","0.55313015","0.55157405","0.54837376","0.54676265","0.54676265","0.5465667","0.54364663","0.54348296","0.5426442","0.5409491","0.5389929","0.53837407","0.53750277","0.5356112","0.5352749","0.5348983","0.5339359","0.533665","0.5335829","0.5314019","0.5309738","0.53069437","0.53068334","0.52761054","0.52738464","0.52724147","0.5267917","0.5262574","0.52501667","0.5249881","0.5245097","0.521955","0.5218073","0.521578","0.5214182","0.52116466","0.51978177","0.5185029","0.5175564","0.5174357","0.51688063","0.5168144","0.51574594","0.5156817","0.514904","0.51436716","0.5140564","0.51387763","0.51357055","0.51333934","0.5127576","0.5124671","0.51225716","0.5119663","0.5117783","0.5117588","0.5106661","0.5106651","0.51052123","0.5102723","0.5102197","0.5101702","0.5096991","0.5094716","0.5088349","0.5086951","0.50849307","0.5077381","0.50743765","0.50724256","0.5069824","0.5068713","0.5067995","0.5066712","0.5065824","0.50637627","0.5056862","0.5056163","0.50545615","0.5052861","0.5050901"],"string":"[\n \"0.6112371\",\n \"0.59843546\",\n \"0.5966853\",\n \"0.59554493\",\n \"0.5932704\",\n \"0.5894218\",\n \"0.5783442\",\n \"0.564491\",\n \"0.56345904\",\n \"0.56212074\",\n \"0.56020945\",\n \"0.558852\",\n \"0.5585161\",\n \"0.55805236\",\n \"0.55623627\",\n \"0.5552391\",\n \"0.5545439\",\n \"0.55443627\",\n \"0.5535678\",\n \"0.55313015\",\n \"0.55157405\",\n \"0.54837376\",\n \"0.54676265\",\n \"0.54676265\",\n \"0.5465667\",\n \"0.54364663\",\n \"0.54348296\",\n \"0.5426442\",\n \"0.5409491\",\n \"0.5389929\",\n \"0.53837407\",\n \"0.53750277\",\n \"0.5356112\",\n \"0.5352749\",\n \"0.5348983\",\n \"0.5339359\",\n \"0.533665\",\n \"0.5335829\",\n \"0.5314019\",\n \"0.5309738\",\n \"0.53069437\",\n \"0.53068334\",\n \"0.52761054\",\n \"0.52738464\",\n \"0.52724147\",\n \"0.5267917\",\n \"0.5262574\",\n \"0.52501667\",\n \"0.5249881\",\n \"0.5245097\",\n \"0.521955\",\n \"0.5218073\",\n \"0.521578\",\n \"0.5214182\",\n \"0.52116466\",\n \"0.51978177\",\n \"0.5185029\",\n \"0.5175564\",\n \"0.5174357\",\n \"0.51688063\",\n \"0.5168144\",\n \"0.51574594\",\n \"0.5156817\",\n \"0.514904\",\n \"0.51436716\",\n \"0.5140564\",\n \"0.51387763\",\n \"0.51357055\",\n \"0.51333934\",\n \"0.5127576\",\n \"0.5124671\",\n \"0.51225716\",\n \"0.5119663\",\n \"0.5117783\",\n \"0.5117588\",\n \"0.5106661\",\n \"0.5106651\",\n \"0.51052123\",\n \"0.5102723\",\n \"0.5102197\",\n \"0.5101702\",\n \"0.5096991\",\n \"0.5094716\",\n \"0.5088349\",\n \"0.5086951\",\n \"0.50849307\",\n \"0.5077381\",\n \"0.50743765\",\n \"0.50724256\",\n \"0.5069824\",\n \"0.5068713\",\n \"0.5067995\",\n \"0.5066712\",\n \"0.5065824\",\n \"0.50637627\",\n \"0.5056862\",\n \"0.5056163\",\n \"0.50545615\",\n \"0.5052861\",\n \"0.5050901\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.53226084"},"document_rank":{"kind":"string","value":"38"}}},{"rowIdx":92583,"cells":{"query":{"kind":"string","value":"Gets the area cords for the dancefloor"},"document":{"kind":"string","value":"def get_dancefloor_area(self):\n cords = []\n\n x1 = self.coordinates[0]\n y1 = self.coordinates[1]\n x2 = self.coordinates[0] + self.width\n y2 = self.coordinates[1] + self.height\n if self.width <= 0:\n x1, x2 = x2, x1\n if self.height <= 0:\n y1, y2 = y2, y1\n\n for x in range(x1, x2):\n for y in range(y1, y2):\n if x % 20 == 0 and y % 20 == 0:\n cords.append([x, y])\n return cords"},"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_referenced_floor_area() -> np.ndarray:\n\n return envelope.get_referenced_floor_area()","def area(self):\n return _cantera.wall_area(self.__wall_id)","def get_area(self):\n ### Original\n from pyresample.spherical_geometry import get_polygon_area\n\n return get_polygon_area(self.corners)\n ### End Original\n #from .spherical import SphPolygon\n #shell()\n #log.info('RUNNING SPHERICAL in get_area')\n\n #return SphPolygon(self.corners).area","def find_area(self):\n min_lat_point = self.latitude_min\n max_lat_point = self.latitude_max\n min_lon_point = self.longitude_min\n max_lon_point = self.longitude_max\n self.rename_latitude()\n self.rename_longitude()\n all_lat_bounds = self.cube.coord('latitude').bounds\n all_lon_bounds = self.cube.coord('longitude').bounds\n # print(all_lat_bounds)\n # print(all_lon_bounds)\n for i, lat in enumerate(all_lat_bounds):\n for j, lon in enumerate(all_lon_bounds):\n lat_bounds = lat # 2D array of the lower and upper lat bounds\n lon_bounds = lon # 2D array of the lower and upper lon bounds\n if lat_bounds[0] <= min_lat_point < lat_bounds[1]:\n if lon_bounds[0] <= min_lon_point < lon_bounds[1]:\n nlat_min = i\n nlon_min = j\n else:\n pass\n else:\n pass\n\n for k, lat in enumerate(all_lat_bounds):\n for l, lon in enumerate(all_lon_bounds):\n lat_bounds = lat # 2D array of the lower and upper lat bounds\n lon_bounds = lon # 2D array of the lower and upper lon bounds\n if lat_bounds[0] <= max_lat_point < lat_bounds[1]:\n if lon_bounds[0] <= max_lon_point < lon_bounds[1]:\n nlat_max = k\n nlon_max = l\n else:\n pass\n else:\n pass\n\n area_subset = self.cube[:, nlat_min:nlat_max+1, nlon_min:nlon_max+1]\n # print(area_subset.coord('latitude').points)\n # print(area_subset.coord('longitude').points)\n area_mean = area_subset.collapsed(['latitude', 'longitude'],\n iris.analysis.MEAN)\n\n return area_mean","def get_area(geo_data):\n return geo_data[\"geometry\"].area","def areas(self):\n\n height_delta = (np.cos(self.polar_corners[:-1, :-1]) - np.cos(self.polar_corners[:-1, 1:]))\n azimuth_delta = (self.azimuthal_corners[1:, 1:] - self.azimuthal_corners[:-1, 1:])\n\n return height_delta * azimuth_delta","def area(self):\n area = 0\n\n for room in self.rooms:\n area += room.polygon.area()\n\n for wall in self.walls:\n area += wall.polygon.area()\n\n return area","def area(self):\n self.convert_window(\"Area\", \"square meters\", [\"acres\", \"ares\", \"circular inches\", \"hectares\", \"hides\", \"roods\", \"square centimeters\", \"square feet(US & UK)\", \"square feet(US survey)\", \"square inches\", \"square kilometers\", \"square meters\", \"square miles\", \"square millimeters\", \"square of timber\", \"square rods or poles\", \"square yards\", \"townships\"])","def area(self):\n return _property_op(arctern.ST_Area, self)","def get_area(self):\n return self._area","def get_artif_area(self):\n result = self.cities.all().aggregate(total=Sum(\"surface_artif\"))\n return result[\"total\"] or 0","def area(self):","def get_area(self):\n raise NotImplementedError()","def area(self):\n num_rows = self.row_end - self.row_start\n num_cols = self.col_end - self.col_start\n area = num_rows*num_cols\n return area","def calculate_areas(polygon):\n project = ft.partial(pj.transform,\n pj.Proj(init='epsg:4326'),\n pj.Proj('+proj=eck4 +lat_0=' + str(polygon.centroid.y) + ' +lon_0=' + str(polygon.centroid.x)))\n field_projected = transform(project, polygon)\n # convert from square meters to acres\n return uom.Uom(field_projected.area, uom.SquareMeter)","def area(self):\n raise NotImplementedError","def net_conditioned_building_area(self):\n if self._area_conditioned is None:\n if self.simulation_dir.exists():\n with sqlite3.connect(self.sql_file) as conn:\n sql_query = f\"\"\"\n SELECT t.Value\n FROM TabularDataWithStrings t\n WHERE TableName == 'Building Area' and ColumnName == 'Area' and RowName == 'Net Conditioned Building Area';\"\"\"\n (res,) = conn.execute(sql_query).fetchone()\n self._area_conditioned = float(res)\n else:\n area = 0\n zones = self.idfobjects[\"ZONE\"]\n zone: EpBunch\n for zone in zones:\n for surface in zone.zonesurfaces:\n if hasattr(surface, \"tilt\"):\n if surface.tilt == 180.0:\n part_of = int(\n zone.Part_of_Total_Floor_Area.upper() != \"NO\"\n )\n multiplier = float(\n zone.Multiplier if zone.Multiplier != \"\" else 1\n )\n\n area += surface.area * multiplier * part_of\n self._area_conditioned = area\n return self._area_conditioned","def area(\n self):\n pi = numpy.pi\n area0 = 4.0 * pi / 8.0\n areadiv = 4.0 ** self.depth\n area = area0 / areadiv * (180.0 / pi) ** 2\n return area","def area(self):\n\n return 'Hexagon area'","def cylinder_area(radius: number, height: number) -> number:\n area = 2*pi*radius*(radius+height)\n return area","def area(self):\n return self._ned_shape.area","def to_area(x, y, w, h, bottom_only=False):\n cover = []\n if bottom_only:\n for i in range(int(ceil(w))):\n cover.append((int(x)+i, int(y+ceil(h)-1)))\n else:\n for i in range(int(ceil(w))):\n for j in range(int(ceil(h))):\n cover.append((int(x)+i, int(y)+j))\n\n return cover","def area(self):\n ...","def trapezoid_area(lower, leg , upper):\n area = (((upper+lower)/2)*leg)\n return area","def getArea(self):\n seg = self._group_index\n groups = np.unique(seg)\n ng = len(groups)\n area = 0\n for i in range(ng):\n group_segments = np.where(groups[i] == seg)[0]\n nseg = len(group_segments) - 1\n for j in range(nseg):\n ind = group_segments[j]\n p0 = latlon2ecef(self._toplats[ind],\n self._toplons[ind],\n self._topdeps[ind])\n p1 = latlon2ecef(self._toplats[ind + 1],\n self._toplons[ind + 1],\n self._topdeps[ind + 1])\n p2 = latlon2ecef(self._botlats[ind + 1],\n self._botlons[ind + 1],\n self._botdeps[ind + 1])\n p3 = latlon2ecef(self._botlats[ind],\n self._botlons[ind],\n self._botdeps[ind])\n a = np.sqrt((p1[0] - p0[0])**2 +\n (p1[1] - p0[1])**2 +\n (p1[2] - p0[2])**2)\n b = np.sqrt((p2[0] - p0[0])**2 +\n (p2[1] - p0[1])**2 +\n (p2[2] - p0[2])**2)\n c = np.sqrt((p2[0] - p1[0])**2 +\n (p2[1] - p1[1])**2 +\n (p2[2] - p1[2])**2)\n s = (a + b + c) / 2\n A1 = np.sqrt(s * (s - a) * (s - b) * (s - c))\n a = np.sqrt((p0[0] - p3[0])**2 +\n (p0[1] - p3[1])**2 +\n (p0[2] - p3[2])**2)\n b = np.sqrt((p2[0] - p3[0])**2 +\n (p2[1] - p3[1])**2 +\n (p2[2] - p3[2])**2)\n c = np.sqrt((p0[0] - p2[0])**2 +\n (p0[1] - p2[1])**2 +\n (p0[2] - p2[2])**2)\n s = (a + b + c) / 2\n A2 = np.sqrt(s * (s - a) * (s - b) * (s - c))\n area = area + (A1 + A2) / 1000 / 1000\n return area","def area(self):\n\t\t#print (self.radius*self.radius*math.pi)\n\t\tcircle_area = (self.radius*self.radius*math.pi)\n\t\treturn circle_area","def area(self, tileID):\n pass","def _area(bounds):\n return (bounds[0, 1] - bounds[0, 0]) * (bounds[1, 1] - bounds[1, 0])","def council_area(self) -> str:\n return self._search_in_description(REGEXP_ATTR_COUNCIL_AREA)","def get_area_polygon_collection(self) -> List:\n\n ## first step, try to read colrow set if possible\n if os.path.isfile('area_collection.json'):\n with open('area_collection.json') as f:\n area_collection = json.load(f)\n return area_collection\n else:\n raise Exception('area_collection.json does not exist, try using server.util.register_area_polygon()')","def test_absolute_areas(self):\n\n assert len(self.test_shape.areas) == 4\n assert len(set([round(i) for i in self.test_shape.areas])) == 3\n assert self.test_shape.areas.count(pytest.approx(60 * math.pi * 2 * 1000)) == 2\n assert self.test_shape.areas.count(pytest.approx(50 * math.pi * 2 * 970)) == 1\n assert self.test_shape.areas.count(pytest.approx(50 * math.pi * 2 * 1030)) == 1","def area(self):\n return np.array([f.area() for f in self])","def area(self, by_spec=False):\n if by_spec:\n cell_area = {}\n for element in itertools.chain(self.polygons, self.paths, self.references):\n element_area = element.area(True)\n for ll in element_area.keys():\n if ll in cell_area:\n cell_area[ll] += element_area[ll]\n else:\n cell_area[ll] = element_area[ll]\n else:\n cell_area = 0\n for element in itertools.chain(self.polygons, self.paths, self.references):\n cell_area += element.area()\n return cell_area","def getArea(self):\n return None","def getArea(self):\n return None","def area(self):\n\n raise Exception(\"area() is not implemented\")","def area(self):\n\n raise Exception(\"area() is not implemented\")","def area(self, cc_index=None):\n if cc_index is not None:\n return self.areas[cc_index]\n return np.sum(self.areas)","def unconditioned_building_area(self):\n if self._area_unconditioned is None:\n if self.simulation_dir.exists():\n with sqlite3.connect(self.sql_file) as conn:\n sql_query = f\"\"\"\n SELECT t.Value\n FROM TabularDataWithStrings t\n WHERE TableName == 'Building Area' and \n ColumnName == 'Area' and RowName == 'Unconditioned Building Area';\"\"\"\n (res,) = conn.execute(sql_query).fetchone()\n self._area_unconditioned = float(res)\n else:\n area = 0\n zones = self.idfobjects[\"ZONE\"]\n zone: EpBunch\n for zone in zones:\n for surface in zone.zonesurfaces:\n if hasattr(surface, \"tilt\"):\n if surface.tilt == 180.0:\n part_of = int(\n zone.Part_of_Total_Floor_Area.upper() == \"NO\"\n )\n multiplier = float(\n zone.Multiplier if zone.Multiplier != \"\" else 1\n )\n\n area += surface.area * multiplier * part_of\n self._area_unconditioned = area\n return self._area_unconditioned","def area(self):\n return self.length*self.length","def area(self, by_spec=False):\n if by_spec:\n path_area = {}\n for poly, key in zip(self.polygons, zip(self.layers,\n self.datatypes)):\n poly_area = 0\n for ii in range(1, len(poly) - 1):\n poly_area += (poly[0][0] - poly[ii + 1][0]) * (\n poly[ii][1] - poly[0][1]) - (\n poly[0][1] - poly[ii + 1][1]) * (\n poly[ii][0] - poly[0][0])\n if key in path_area:\n path_area[key] += 0.5 * abs(poly_area)\n else:\n path_area[key] = 0.5 * abs(poly_area)\n else:\n path_area = 0\n for points in self.polygons:\n poly_area = 0\n for ii in range(1, len(points) - 1):\n poly_area += (points[0][0] - points[ii + 1][0]) * (\n points[ii][1] - points[0][1]) - (\n points[0][1] - points[ii + 1][1]) * (\n points[ii][0] - points[0][0])\n path_area += 0.5 * abs(poly_area)\n return path_area","def area(self):\n raise Exception('area() is not implemented')","def area(self):\n raise Exception('area() is not implemented')","def area(self):\n return (self.baselength1 + self.baselength2)*self.height/2","def area(self):\n raise Exception(\"area() is not implemented\")","def area(self):\n raise Exception(\"area() is not implemented\")","def extract_area(data,box):\n if box is None or box[0] is None or box[1] is None or box[1][0] - box[0][0] == 0 or box[1][1] - box[0][1] == 0:\n box = ((0,0),(10,10));\n area = ut.extract_area(data['frame'],*box,data['uc'],256);\n return area;","def Intarea( xc, yc, r, x0, x1, y0, y1):\n\n#\n# Shift the objects so that the circle is at the origin.\n#\n x0 = x0 - xc\n y0 = y0 - yc\n x1 = x1 - xc\n y1 = y1 - yc\n\n return Oneside( x1, y0, y1, r ) + Oneside( y1, -x1, -x0, r ) +\\\n Oneside( -x0, -y1, -y0, r ) + Oneside( -y0, x0, x1, r )","def calculateDetectorArea(self):\n area = 0.0\n r = self.geoParam['CylinderLightGuideRadius']\n while(r + self.geoParam['DetectorThickness'] < self.geoParam['CylinderRadius']):\n area -= math.pow(r,2)\n r += self.geoParam['DetectorThickness']\n area += math.pow(r,2)\n r += self.geoParam['DetectorSpacing']\n return math.pi*area","def area(length, hypotenuse):\n side = int(length)* hypotenuse\n return round(side*2, 2) # returns the rounded area of the roof.","def cone_area(radius: number, height: number) -> number:\n return pi*radius*(radius + sqrt(radius**2 + height**2))","def get_borders_of_operation_area(self):\n if len(self.edge_documents) == 0:\n self.retrieve_edge_documents()\n\n self.set_borders_of_operation_area()\n\n borders = {\n 'minimum_latitude': self.minimum_latitude,\n 'maximum_latitude': self.maximum_latitude,\n 'minimum_longitude': self.minimum_longitude,\n 'maximum_longitude': self.maximum_longitude\n }\n return borders","def area_equilat(side):\n\treturn side/2 * math.sqrt(side**2 - (side/2)**2)","def get_areas(\n heat_density_map: str,\n pixel_threshold: float,\n district_heating_zone_threshold: float,\n):\n # array_map units : MWh\n array_map, geo_transform = read_raster(raster=heat_density_map)\n\n # total_heat_demand units : GWh\n total_heat_demand = np.around(np.sum(array_map) / 1000, 2)\n\n # pixel_filtered_map units : MWh\n pixel_filtered_map = array_map * (array_map > pixel_threshold)\n\n # district_heating_zone_threshold units : MWh (after the conversion)\n district_heating_zone_threshold = district_heating_zone_threshold * 1000\n\n # district_heating_zone_threshold filter\n areas, filtered_map, total_potential, areas_potential = define_areas(\n pixel_filtered_map=pixel_filtered_map,\n district_heating_zone_threshold=district_heating_zone_threshold,\n )\n\n return (\n geo_transform,\n total_heat_demand,\n areas,\n filtered_map,\n total_potential,\n areas_potential,\n )","def area(self):\n\n raise Exception('Not implemented')","def regular_polygon_area(perimeter, apothem):\n return (perimeter * apothem) / 2","def name_area(lon, lat):\n \n point = Point(lon, lat)\n area = [point.within(polygon) for polygon in districts.geometry]\n \n return(districts[area].EDNAME.values)","def findArea(self):\n\n a, b = self.sides\n area = a * b\n print(f\"Are of rectangle is: {area}\")","def area(self) -> ir.FloatingValue:\n return ops.GeoArea(self).to_expr()","def get_floor_rogueaps(self, conn, floor_id: str, offset=0, limit=100, units=\"FEET\"):\n path = urlJoin(urls.ROGUE_LOCATION[\"GET_FLOOR_APS\"], floor_id)\n params = {\n \"offset\": offset,\n \"limit\": limit,\n \"units\": units\n }\n resp = conn.command(apiMethod=\"GET\", apiPath=path, apiParams=params)\n return resp","def area_code(self):\n return self.number[:3]","def area(lado):\n\treturn \"El area de un cuadrado es \"+ str(lado*lado)","def get_floor_area(a_mr: float, a_or: float, a_a: float, r_env: float) -> np.ndarray:\n\n # make envelope.FloorArea class\n floor_area = envelope.FloorArea(a_mr, a_or, a_a, r_env)\n\n # floor area of the evaluated house, m2 (12 rooms)\n return envelope.get_hc_floor_areas(floor_area)","def getArea(antenna):\n diameter = getDiameter(antenna)\n if antenna >=1 and antenna <= 6: \n efficiency = 0.5\n elif antenna >=7 and antenna <= 15: \n efficiency = 0.6\n elif antenna >=16 and antenna <= 23: \n efficiency = 0.6\n else:\n raise Exception,'Unregonized antenna type'\n area = math.pi * diameter*diameter / 4.0 * efficiency\n\n return area","def area(self):\n if len(self.exterior) < 3:\n raise Exception(\"Cannot compute the polygon's area because it contains less than three points.\")\n poly = self.to_shapely_polygon()\n return poly.area","def areas(cells):\n return numpy.array([polygon_area(cells[i])\n for i in range(len(cells.keys()))])","def area(self,\n type: str = \"wetted\",\n _sectional: bool = False,\n ) -> float:\n area = 0\n chords = [xsec.chord for xsec in self.xsecs]\n\n if type == \"wetted\":\n sectional_spans = self.span(_sectional=True)\n elif type == \"projected\":\n sectional_spans = self.span(type=\"y\", _sectional=True)\n else:\n raise ValueError(\"Bad value of `type`!\")\n sectional_chords = [\n (inner_chord + outer_chord) / 2\n for inner_chord, outer_chord in zip(\n chords[1:],\n chords[:-1]\n )\n ]\n sectional_areas = [\n span * chord\n for span, chord in zip(\n sectional_spans,\n sectional_chords\n )\n ]\n area = sum(sectional_areas)\n\n if _sectional:\n return sectional_areas\n\n if self.symmetric:\n area *= 2\n\n return area","def _getPblockArea(self, pblock_def):\n assert re.search(r'CLOCKREGION_X\\d+Y\\d+:CLOCKREGION_X\\d+Y\\d+', pblock_def), f'unexpected format of the slot name {pblock_def}'\n DL_x, DL_y, UR_x, UR_y = [int(val) for val in re.findall(r'[XY](\\d+)', pblock_def)] # DownLeft & UpRight\n\n # treat the pseudo SLR with 0 area\n UR_y = min(self.CR_NUM_VERTICAL-1, UR_y) \n\n area = {\n 'BRAM' : 0,\n 'DSP' : 0,\n 'FF' : 0,\n 'LUT' : 0,\n 'URAM' : 0\n }\n \n if DL_y > self.CR_NUM_VERTICAL-1:\n return area \n\n for item in ['BRAM', 'DSP', 'FF', 'LUT', 'URAM']:\n # the total area of one row\n area[item] = sum(self.CR_AREA[i][j][item] for i in range(DL_x, UR_x + 1) for j in range(DL_y, UR_y+1))\n\n return area","def area(self):\n return self.radius*self.radius*math.pi","def define_areas(\n pixel_filtered_map: np.ndarray, district_heating_zone_threshold: float\n):\n structure = np.ones((3, 3)).astype(int)\n expanded_map = binary_dilation(input=pixel_filtered_map, structure=structure)\n eroded_map = binary_erosion(input=expanded_map, structure=structure)\n labels_array, n_label = measurements.label(\n input=eroded_map,\n structure=structure,\n )\n\n # labels start from 1, therefore the array size is 'num_labels_array + 1'\n areas_potential = np.zeros((n_label + 1)).astype(float)\n if n_label > 0:\n end, start, sorted_array = get_browsing_indexes(\n labels_array=labels_array,\n pixel_filtered_map=pixel_filtered_map,\n n_label=n_label,\n )\n\n for i, (start_index, end_index) in enumerate(zip(start, end)):\n area = sorted_array[start_index:end_index, 3]\n area_potential = np.sum(area)\n if area_potential >= district_heating_zone_threshold:\n # i+1 because labeling starts from 1 and not from 0\n # factor 0.001 for conversion from MWh/ha to GWh/ha\n areas_potential[i + 1] = np.around(np.sum(area_potential) / 1000, 2)\n\n areas = areas_potential[labels_array]\n filtered_map = pixel_filtered_map * (areas > 0).astype(int)\n total_potential = np.sum(areas_potential)\n return areas, filtered_map, total_potential, areas_potential[1:]","def get_area(area=None):\n # Set global vars that can be accessed outside of function for debuging\n global sorted_areas, chosen_area, mask\n # Create a dicionary where keys --> brain areas and values --> list of neurons\n sorted_areas = sort_by_area()\n # Choose area of interest\n chosen_area = sorted_areas[area]\n # Create a mask to keep only rows of interest\n mask = np.isin(spiketimes[:,0], chosen_area)\n # Fetch only desired spiketimes\n area_spikes = spiketimes[mask]\n # Find number of neurons\n N_neurons = len(chosen_area)\n \n return area_spikes","def area(boxes):\n y_min, x_min, y_max, x_max = np.split(boxes, 4, axis=-1)\n return np.squeeze((y_max - y_min) * (x_max - x_min), [1])","def area(self):\n return math.pi*self._radius*self._radius","def area(self):\n if isinstance(self.crs, GeographicalCRS):\n major_axis = self.crs.ellipsoid.a\n minor_axis = self.crs.ellipsoid.b\n\n area = 0.0\n if major_axis == minor_axis: # Sphere\n for seg in self.segment_tuples:\n x1, y1 = seg[0]\n x2, y2 = seg[1]\n area += geodesy.spherical_area(major_axis, x1, y1, x2, y2)\n\n else:\n for seg in self.segment_tuples:\n x1, y1 = seg[0]\n x2, y2 = seg[1]\n area += geodesy.ellipsoidal_area(major_axis, minor_axis,\n x1, y1, x2, y2)\n\n else:\n # Cartesian coordinate systems\n x, y = self.coordinates\n x0 = np.min(x)\n area = (0.5*(x[0] + x[-1]) - x0) * (y[0] - y[-1])\n area += sum((0.5*(x[i+1]+x[i]) - x0) * (y[i+1] - y[i]) for i in range(len(x)-1))\n return abs(area) - sum(sub.area for sub in self.subs)","def surface_area_of_cube(side):\n return side","def area(base, height):\n\n return base * height","def area_cuadrado(lado):\n area = lado**2\n print \"El area es: \",area","def get_area_formula(cls):\n pass","def area(self):\n return (self.__radius ** 2 * math.pi)","def area(self):\n area = self.__length * self.__width\n\n return area","def surface_area(self):\n return [node.surface_area for node in self]","def area(self):\n return math.pi * math.pow(self.radius, 2)","def area(self, sez):\n # find area below water line\n area_h2o = (sez[-1][0]-sez[0][0])*sez[0][1]\n # find area bellow section\n area_sez = np.trapz(sez.T[1],x=sez.T[0])\n return area_h2o - area_sez","def extract_area(self, pixel_geometry):\n\t\tarea = self.__dataset__.extract_area(pixel_geometry, bands=[self.index])\n\t\treturn area","def test_geography_area(self):\n # SELECT ST_Area(poly) FROM geogapp_zipcode WHERE code='77002';\n z = Zipcode.objects.annotate(area=Area(\"poly\")).get(code=\"77002\")\n # Round to the nearest thousand as possible values (depending on\n # the database and geolib) include 5439084, 5439100, 5439101.\n rounded_value = z.area.sq_m\n rounded_value -= z.area.sq_m % 1000\n self.assertEqual(rounded_value, 5439000)","def island_perimeter(grid):\n LAND = 1\n WATER = 0\n perimeter = 0\n for y, row in enumerate(grid):\n for x, cell in enumerate(row):\n if cell == LAND:\n # print(\"land in [x= {:d} y= {:d}]\".format(x, y))\n # left\n if y == 0 or grid[y - 1][x] == WATER:\n perimeter += 1\n # right\n if y == len(grid) - 1 or grid[y + 1][x] == WATER:\n perimeter += 1\n # up\n if x == 0 or grid[y][x - 1] == WATER:\n perimeter += 1\n # down\n if x == len(row) - 1 or grid[y][x + 1] == WATER:\n perimeter += 1\n return perimeter","def area(self):\r\n return math.pi*(self.__radius**2)","def grid_area(ulon,ulat):\n R = 6371. * 1000. # radius of Earth in meters\n dlon = N.diff(ulon)\n dlat = N.diff(ulat)\n dx = N.outer(deg2rad * R * N.cos(deg2rad * ulat),dlon) # dx (meters)\n dy = 60. * 1852. * dlat # dy (meters)\n area = (dx[1:] + dx[:-1]) / 2. * dy[:,N.newaxis] # area of grid cells\n return area","def _area(self):\n self.area = 0.0\n for sail in self.sails:\n self.area += sail.area","def area(self):\n area = self.__width * self.__height\n return area","def get_area_formula(cls):\n dict_area = {'circle':\"πr^2\", 'square':'a**2', 'rectangle':'a*b',\n 'triangle':'1/2*a*h',\"equilateral triangle\":'1/2*a*h',\n 'regular pentagon':\"(a ** 2 * 1/2 * (5(5+2 * 1/2 *(5))))/4\"}\n for k,v in dict_area.items():\n if cls.__name__ == k:\n return v","def identify_habarea(indiv_xy_position, habarea_map): \n \n row=int(indiv_xy_position[0])\n col=int(indiv_xy_position[1])\n habarea=habarea_map[row][col]\n \n return habarea","def get_area(option_set):\n return option_set & AREA_MASK","def area(width, height):\n return width * height","def total_area(self) :\n area = 0\n for i in self.residues :\n area += i.solvent_acc_area\n return area","def get_floor_aps(self, conn, floor_id, offset=0, limit=100, units=\"FEET\"):\n path = urlJoin(urls.FLOOR_PLAN[\"GET_FLOOR_APS\"], floor_id, \"access_point_location\")\n params = {\n \"offset\": offset,\n \"limit\": limit,\n \"units\": units\n }\n resp = conn.command(apiMethod=\"GET\", apiPath=path, apiParams=params)\n return resp","def area_grid(self, dtype=np.float32):\n if self.rotation > 0:\n raise NotImplementedError(\n \"area_grid has not yet been implemented for rotated grids.\"\n )\n if self.crs.is_geographic:\n data = gis_utils.reggrid_area(self.ycoords.values, self.xcoords.values)\n elif self.crs.is_projected:\n ucf = rasterio.crs.CRS.from_user_input(self.crs).linear_units_factor[1]\n data = np.full(self.shape, abs(self.res[0] * self.res[0]) * ucf**2)\n da_area = xr.DataArray(\n data=data.astype(dtype), coords=self.coords, dims=self.dims\n )\n da_area.raster.set_nodata(0)\n da_area.raster.set_crs(self.crs)\n da_area.attrs.update(unit=\"m2\")\n return da_area.rename(\"area\")","def compInterArea(self, direction): \r\n if self.coord_sys == 'Cartesian' :\r\n if direction==0 or direction==3 :\r\n area = self.dy * self.dz\r\n elif direction==1 or direction==2 :\r\n area = self.dx * self.dz\r\n else :\r\n raise ValueError('direction must be one of: 0, 1, 2, 3')\r\n \r\n elif self.coord_sys == 'Cylindrical' :\r\n r1 = self.y_coord\r\n r2 = self.y_coord + self.dy;\r\n pi_alpha_360 = np.pi * self.m_dz / 360\r\n if direction==0 or direction==3 :\r\n area = pi_alpha_360 * (r2*r2 - r1*r1)\r\n elif direction==1 :\r\n area = self.dx * pi_alpha_360 * 2 * r1\r\n elif direction==2 :\r\n area = self.dx * pi_alpha_360 * 2 * r2\r\n else :\r\n raise ValueError('direction must be one of: 0, 1, 2, 3')\r\n \r\n else :\r\n raise ValueError('Coordinate system not implemented')\r\n \r\n return area","def area(self):\n\t\treturn self.height * self.height","def get_area_contents(self):\n types = set()\n\n # Iterates through self._area and adds values to return set\n for point in self._area:\n types.add(self._board[point[0]][point[1]])\n\n return types"],"string":"[\n \"def get_referenced_floor_area() -> np.ndarray:\\n\\n return envelope.get_referenced_floor_area()\",\n \"def area(self):\\n return _cantera.wall_area(self.__wall_id)\",\n \"def get_area(self):\\n ### Original\\n from pyresample.spherical_geometry import get_polygon_area\\n\\n return get_polygon_area(self.corners)\\n ### End Original\\n #from .spherical import SphPolygon\\n #shell()\\n #log.info('RUNNING SPHERICAL in get_area')\\n\\n #return SphPolygon(self.corners).area\",\n \"def find_area(self):\\n min_lat_point = self.latitude_min\\n max_lat_point = self.latitude_max\\n min_lon_point = self.longitude_min\\n max_lon_point = self.longitude_max\\n self.rename_latitude()\\n self.rename_longitude()\\n all_lat_bounds = self.cube.coord('latitude').bounds\\n all_lon_bounds = self.cube.coord('longitude').bounds\\n # print(all_lat_bounds)\\n # print(all_lon_bounds)\\n for i, lat in enumerate(all_lat_bounds):\\n for j, lon in enumerate(all_lon_bounds):\\n lat_bounds = lat # 2D array of the lower and upper lat bounds\\n lon_bounds = lon # 2D array of the lower and upper lon bounds\\n if lat_bounds[0] <= min_lat_point < lat_bounds[1]:\\n if lon_bounds[0] <= min_lon_point < lon_bounds[1]:\\n nlat_min = i\\n nlon_min = j\\n else:\\n pass\\n else:\\n pass\\n\\n for k, lat in enumerate(all_lat_bounds):\\n for l, lon in enumerate(all_lon_bounds):\\n lat_bounds = lat # 2D array of the lower and upper lat bounds\\n lon_bounds = lon # 2D array of the lower and upper lon bounds\\n if lat_bounds[0] <= max_lat_point < lat_bounds[1]:\\n if lon_bounds[0] <= max_lon_point < lon_bounds[1]:\\n nlat_max = k\\n nlon_max = l\\n else:\\n pass\\n else:\\n pass\\n\\n area_subset = self.cube[:, nlat_min:nlat_max+1, nlon_min:nlon_max+1]\\n # print(area_subset.coord('latitude').points)\\n # print(area_subset.coord('longitude').points)\\n area_mean = area_subset.collapsed(['latitude', 'longitude'],\\n iris.analysis.MEAN)\\n\\n return area_mean\",\n \"def get_area(geo_data):\\n return geo_data[\\\"geometry\\\"].area\",\n \"def areas(self):\\n\\n height_delta = (np.cos(self.polar_corners[:-1, :-1]) - np.cos(self.polar_corners[:-1, 1:]))\\n azimuth_delta = (self.azimuthal_corners[1:, 1:] - self.azimuthal_corners[:-1, 1:])\\n\\n return height_delta * azimuth_delta\",\n \"def area(self):\\n area = 0\\n\\n for room in self.rooms:\\n area += room.polygon.area()\\n\\n for wall in self.walls:\\n area += wall.polygon.area()\\n\\n return area\",\n \"def area(self):\\n self.convert_window(\\\"Area\\\", \\\"square meters\\\", [\\\"acres\\\", \\\"ares\\\", \\\"circular inches\\\", \\\"hectares\\\", \\\"hides\\\", \\\"roods\\\", \\\"square centimeters\\\", \\\"square feet(US & UK)\\\", \\\"square feet(US survey)\\\", \\\"square inches\\\", \\\"square kilometers\\\", \\\"square meters\\\", \\\"square miles\\\", \\\"square millimeters\\\", \\\"square of timber\\\", \\\"square rods or poles\\\", \\\"square yards\\\", \\\"townships\\\"])\",\n \"def area(self):\\n return _property_op(arctern.ST_Area, self)\",\n \"def get_area(self):\\n return self._area\",\n \"def get_artif_area(self):\\n result = self.cities.all().aggregate(total=Sum(\\\"surface_artif\\\"))\\n return result[\\\"total\\\"] or 0\",\n \"def area(self):\",\n \"def get_area(self):\\n raise NotImplementedError()\",\n \"def area(self):\\n num_rows = self.row_end - self.row_start\\n num_cols = self.col_end - self.col_start\\n area = num_rows*num_cols\\n return area\",\n \"def calculate_areas(polygon):\\n project = ft.partial(pj.transform,\\n pj.Proj(init='epsg:4326'),\\n pj.Proj('+proj=eck4 +lat_0=' + str(polygon.centroid.y) + ' +lon_0=' + str(polygon.centroid.x)))\\n field_projected = transform(project, polygon)\\n # convert from square meters to acres\\n return uom.Uom(field_projected.area, uom.SquareMeter)\",\n \"def area(self):\\n raise NotImplementedError\",\n \"def net_conditioned_building_area(self):\\n if self._area_conditioned is None:\\n if self.simulation_dir.exists():\\n with sqlite3.connect(self.sql_file) as conn:\\n sql_query = f\\\"\\\"\\\"\\n SELECT t.Value\\n FROM TabularDataWithStrings t\\n WHERE TableName == 'Building Area' and ColumnName == 'Area' and RowName == 'Net Conditioned Building Area';\\\"\\\"\\\"\\n (res,) = conn.execute(sql_query).fetchone()\\n self._area_conditioned = float(res)\\n else:\\n area = 0\\n zones = self.idfobjects[\\\"ZONE\\\"]\\n zone: EpBunch\\n for zone in zones:\\n for surface in zone.zonesurfaces:\\n if hasattr(surface, \\\"tilt\\\"):\\n if surface.tilt == 180.0:\\n part_of = int(\\n zone.Part_of_Total_Floor_Area.upper() != \\\"NO\\\"\\n )\\n multiplier = float(\\n zone.Multiplier if zone.Multiplier != \\\"\\\" else 1\\n )\\n\\n area += surface.area * multiplier * part_of\\n self._area_conditioned = area\\n return self._area_conditioned\",\n \"def area(\\n self):\\n pi = numpy.pi\\n area0 = 4.0 * pi / 8.0\\n areadiv = 4.0 ** self.depth\\n area = area0 / areadiv * (180.0 / pi) ** 2\\n return area\",\n \"def area(self):\\n\\n return 'Hexagon area'\",\n \"def cylinder_area(radius: number, height: number) -> number:\\n area = 2*pi*radius*(radius+height)\\n return area\",\n \"def area(self):\\n return self._ned_shape.area\",\n \"def to_area(x, y, w, h, bottom_only=False):\\n cover = []\\n if bottom_only:\\n for i in range(int(ceil(w))):\\n cover.append((int(x)+i, int(y+ceil(h)-1)))\\n else:\\n for i in range(int(ceil(w))):\\n for j in range(int(ceil(h))):\\n cover.append((int(x)+i, int(y)+j))\\n\\n return cover\",\n \"def area(self):\\n ...\",\n \"def trapezoid_area(lower, leg , upper):\\n area = (((upper+lower)/2)*leg)\\n return area\",\n \"def getArea(self):\\n seg = self._group_index\\n groups = np.unique(seg)\\n ng = len(groups)\\n area = 0\\n for i in range(ng):\\n group_segments = np.where(groups[i] == seg)[0]\\n nseg = len(group_segments) - 1\\n for j in range(nseg):\\n ind = group_segments[j]\\n p0 = latlon2ecef(self._toplats[ind],\\n self._toplons[ind],\\n self._topdeps[ind])\\n p1 = latlon2ecef(self._toplats[ind + 1],\\n self._toplons[ind + 1],\\n self._topdeps[ind + 1])\\n p2 = latlon2ecef(self._botlats[ind + 1],\\n self._botlons[ind + 1],\\n self._botdeps[ind + 1])\\n p3 = latlon2ecef(self._botlats[ind],\\n self._botlons[ind],\\n self._botdeps[ind])\\n a = np.sqrt((p1[0] - p0[0])**2 +\\n (p1[1] - p0[1])**2 +\\n (p1[2] - p0[2])**2)\\n b = np.sqrt((p2[0] - p0[0])**2 +\\n (p2[1] - p0[1])**2 +\\n (p2[2] - p0[2])**2)\\n c = np.sqrt((p2[0] - p1[0])**2 +\\n (p2[1] - p1[1])**2 +\\n (p2[2] - p1[2])**2)\\n s = (a + b + c) / 2\\n A1 = np.sqrt(s * (s - a) * (s - b) * (s - c))\\n a = np.sqrt((p0[0] - p3[0])**2 +\\n (p0[1] - p3[1])**2 +\\n (p0[2] - p3[2])**2)\\n b = np.sqrt((p2[0] - p3[0])**2 +\\n (p2[1] - p3[1])**2 +\\n (p2[2] - p3[2])**2)\\n c = np.sqrt((p0[0] - p2[0])**2 +\\n (p0[1] - p2[1])**2 +\\n (p0[2] - p2[2])**2)\\n s = (a + b + c) / 2\\n A2 = np.sqrt(s * (s - a) * (s - b) * (s - c))\\n area = area + (A1 + A2) / 1000 / 1000\\n return area\",\n \"def area(self):\\n\\t\\t#print (self.radius*self.radius*math.pi)\\n\\t\\tcircle_area = (self.radius*self.radius*math.pi)\\n\\t\\treturn circle_area\",\n \"def area(self, tileID):\\n pass\",\n \"def _area(bounds):\\n return (bounds[0, 1] - bounds[0, 0]) * (bounds[1, 1] - bounds[1, 0])\",\n \"def council_area(self) -> str:\\n return self._search_in_description(REGEXP_ATTR_COUNCIL_AREA)\",\n \"def get_area_polygon_collection(self) -> List:\\n\\n ## first step, try to read colrow set if possible\\n if os.path.isfile('area_collection.json'):\\n with open('area_collection.json') as f:\\n area_collection = json.load(f)\\n return area_collection\\n else:\\n raise Exception('area_collection.json does not exist, try using server.util.register_area_polygon()')\",\n \"def test_absolute_areas(self):\\n\\n assert len(self.test_shape.areas) == 4\\n assert len(set([round(i) for i in self.test_shape.areas])) == 3\\n assert self.test_shape.areas.count(pytest.approx(60 * math.pi * 2 * 1000)) == 2\\n assert self.test_shape.areas.count(pytest.approx(50 * math.pi * 2 * 970)) == 1\\n assert self.test_shape.areas.count(pytest.approx(50 * math.pi * 2 * 1030)) == 1\",\n \"def area(self):\\n return np.array([f.area() for f in self])\",\n \"def area(self, by_spec=False):\\n if by_spec:\\n cell_area = {}\\n for element in itertools.chain(self.polygons, self.paths, self.references):\\n element_area = element.area(True)\\n for ll in element_area.keys():\\n if ll in cell_area:\\n cell_area[ll] += element_area[ll]\\n else:\\n cell_area[ll] = element_area[ll]\\n else:\\n cell_area = 0\\n for element in itertools.chain(self.polygons, self.paths, self.references):\\n cell_area += element.area()\\n return cell_area\",\n \"def getArea(self):\\n return None\",\n \"def getArea(self):\\n return None\",\n \"def area(self):\\n\\n raise Exception(\\\"area() is not implemented\\\")\",\n \"def area(self):\\n\\n raise Exception(\\\"area() is not implemented\\\")\",\n \"def area(self, cc_index=None):\\n if cc_index is not None:\\n return self.areas[cc_index]\\n return np.sum(self.areas)\",\n \"def unconditioned_building_area(self):\\n if self._area_unconditioned is None:\\n if self.simulation_dir.exists():\\n with sqlite3.connect(self.sql_file) as conn:\\n sql_query = f\\\"\\\"\\\"\\n SELECT t.Value\\n FROM TabularDataWithStrings t\\n WHERE TableName == 'Building Area' and \\n ColumnName == 'Area' and RowName == 'Unconditioned Building Area';\\\"\\\"\\\"\\n (res,) = conn.execute(sql_query).fetchone()\\n self._area_unconditioned = float(res)\\n else:\\n area = 0\\n zones = self.idfobjects[\\\"ZONE\\\"]\\n zone: EpBunch\\n for zone in zones:\\n for surface in zone.zonesurfaces:\\n if hasattr(surface, \\\"tilt\\\"):\\n if surface.tilt == 180.0:\\n part_of = int(\\n zone.Part_of_Total_Floor_Area.upper() == \\\"NO\\\"\\n )\\n multiplier = float(\\n zone.Multiplier if zone.Multiplier != \\\"\\\" else 1\\n )\\n\\n area += surface.area * multiplier * part_of\\n self._area_unconditioned = area\\n return self._area_unconditioned\",\n \"def area(self):\\n return self.length*self.length\",\n \"def area(self, by_spec=False):\\n if by_spec:\\n path_area = {}\\n for poly, key in zip(self.polygons, zip(self.layers,\\n self.datatypes)):\\n poly_area = 0\\n for ii in range(1, len(poly) - 1):\\n poly_area += (poly[0][0] - poly[ii + 1][0]) * (\\n poly[ii][1] - poly[0][1]) - (\\n poly[0][1] - poly[ii + 1][1]) * (\\n poly[ii][0] - poly[0][0])\\n if key in path_area:\\n path_area[key] += 0.5 * abs(poly_area)\\n else:\\n path_area[key] = 0.5 * abs(poly_area)\\n else:\\n path_area = 0\\n for points in self.polygons:\\n poly_area = 0\\n for ii in range(1, len(points) - 1):\\n poly_area += (points[0][0] - points[ii + 1][0]) * (\\n points[ii][1] - points[0][1]) - (\\n points[0][1] - points[ii + 1][1]) * (\\n points[ii][0] - points[0][0])\\n path_area += 0.5 * abs(poly_area)\\n return path_area\",\n \"def area(self):\\n raise Exception('area() is not implemented')\",\n \"def area(self):\\n raise Exception('area() is not implemented')\",\n \"def area(self):\\n return (self.baselength1 + self.baselength2)*self.height/2\",\n \"def area(self):\\n raise Exception(\\\"area() is not implemented\\\")\",\n \"def area(self):\\n raise Exception(\\\"area() is not implemented\\\")\",\n \"def extract_area(data,box):\\n if box is None or box[0] is None or box[1] is None or box[1][0] - box[0][0] == 0 or box[1][1] - box[0][1] == 0:\\n box = ((0,0),(10,10));\\n area = ut.extract_area(data['frame'],*box,data['uc'],256);\\n return area;\",\n \"def Intarea( xc, yc, r, x0, x1, y0, y1):\\n\\n#\\n# Shift the objects so that the circle is at the origin.\\n#\\n x0 = x0 - xc\\n y0 = y0 - yc\\n x1 = x1 - xc\\n y1 = y1 - yc\\n\\n return Oneside( x1, y0, y1, r ) + Oneside( y1, -x1, -x0, r ) +\\\\\\n Oneside( -x0, -y1, -y0, r ) + Oneside( -y0, x0, x1, r )\",\n \"def calculateDetectorArea(self):\\n area = 0.0\\n r = self.geoParam['CylinderLightGuideRadius']\\n while(r + self.geoParam['DetectorThickness'] < self.geoParam['CylinderRadius']):\\n area -= math.pow(r,2)\\n r += self.geoParam['DetectorThickness']\\n area += math.pow(r,2)\\n r += self.geoParam['DetectorSpacing']\\n return math.pi*area\",\n \"def area(length, hypotenuse):\\n side = int(length)* hypotenuse\\n return round(side*2, 2) # returns the rounded area of the roof.\",\n \"def cone_area(radius: number, height: number) -> number:\\n return pi*radius*(radius + sqrt(radius**2 + height**2))\",\n \"def get_borders_of_operation_area(self):\\n if len(self.edge_documents) == 0:\\n self.retrieve_edge_documents()\\n\\n self.set_borders_of_operation_area()\\n\\n borders = {\\n 'minimum_latitude': self.minimum_latitude,\\n 'maximum_latitude': self.maximum_latitude,\\n 'minimum_longitude': self.minimum_longitude,\\n 'maximum_longitude': self.maximum_longitude\\n }\\n return borders\",\n \"def area_equilat(side):\\n\\treturn side/2 * math.sqrt(side**2 - (side/2)**2)\",\n \"def get_areas(\\n heat_density_map: str,\\n pixel_threshold: float,\\n district_heating_zone_threshold: float,\\n):\\n # array_map units : MWh\\n array_map, geo_transform = read_raster(raster=heat_density_map)\\n\\n # total_heat_demand units : GWh\\n total_heat_demand = np.around(np.sum(array_map) / 1000, 2)\\n\\n # pixel_filtered_map units : MWh\\n pixel_filtered_map = array_map * (array_map > pixel_threshold)\\n\\n # district_heating_zone_threshold units : MWh (after the conversion)\\n district_heating_zone_threshold = district_heating_zone_threshold * 1000\\n\\n # district_heating_zone_threshold filter\\n areas, filtered_map, total_potential, areas_potential = define_areas(\\n pixel_filtered_map=pixel_filtered_map,\\n district_heating_zone_threshold=district_heating_zone_threshold,\\n )\\n\\n return (\\n geo_transform,\\n total_heat_demand,\\n areas,\\n filtered_map,\\n total_potential,\\n areas_potential,\\n )\",\n \"def area(self):\\n\\n raise Exception('Not implemented')\",\n \"def regular_polygon_area(perimeter, apothem):\\n return (perimeter * apothem) / 2\",\n \"def name_area(lon, lat):\\n \\n point = Point(lon, lat)\\n area = [point.within(polygon) for polygon in districts.geometry]\\n \\n return(districts[area].EDNAME.values)\",\n \"def findArea(self):\\n\\n a, b = self.sides\\n area = a * b\\n print(f\\\"Are of rectangle is: {area}\\\")\",\n \"def area(self) -> ir.FloatingValue:\\n return ops.GeoArea(self).to_expr()\",\n \"def get_floor_rogueaps(self, conn, floor_id: str, offset=0, limit=100, units=\\\"FEET\\\"):\\n path = urlJoin(urls.ROGUE_LOCATION[\\\"GET_FLOOR_APS\\\"], floor_id)\\n params = {\\n \\\"offset\\\": offset,\\n \\\"limit\\\": limit,\\n \\\"units\\\": units\\n }\\n resp = conn.command(apiMethod=\\\"GET\\\", apiPath=path, apiParams=params)\\n return resp\",\n \"def area_code(self):\\n return self.number[:3]\",\n \"def area(lado):\\n\\treturn \\\"El area de un cuadrado es \\\"+ str(lado*lado)\",\n \"def get_floor_area(a_mr: float, a_or: float, a_a: float, r_env: float) -> np.ndarray:\\n\\n # make envelope.FloorArea class\\n floor_area = envelope.FloorArea(a_mr, a_or, a_a, r_env)\\n\\n # floor area of the evaluated house, m2 (12 rooms)\\n return envelope.get_hc_floor_areas(floor_area)\",\n \"def getArea(antenna):\\n diameter = getDiameter(antenna)\\n if antenna >=1 and antenna <= 6: \\n efficiency = 0.5\\n elif antenna >=7 and antenna <= 15: \\n efficiency = 0.6\\n elif antenna >=16 and antenna <= 23: \\n efficiency = 0.6\\n else:\\n raise Exception,'Unregonized antenna type'\\n area = math.pi * diameter*diameter / 4.0 * efficiency\\n\\n return area\",\n \"def area(self):\\n if len(self.exterior) < 3:\\n raise Exception(\\\"Cannot compute the polygon's area because it contains less than three points.\\\")\\n poly = self.to_shapely_polygon()\\n return poly.area\",\n \"def areas(cells):\\n return numpy.array([polygon_area(cells[i])\\n for i in range(len(cells.keys()))])\",\n \"def area(self,\\n type: str = \\\"wetted\\\",\\n _sectional: bool = False,\\n ) -> float:\\n area = 0\\n chords = [xsec.chord for xsec in self.xsecs]\\n\\n if type == \\\"wetted\\\":\\n sectional_spans = self.span(_sectional=True)\\n elif type == \\\"projected\\\":\\n sectional_spans = self.span(type=\\\"y\\\", _sectional=True)\\n else:\\n raise ValueError(\\\"Bad value of `type`!\\\")\\n sectional_chords = [\\n (inner_chord + outer_chord) / 2\\n for inner_chord, outer_chord in zip(\\n chords[1:],\\n chords[:-1]\\n )\\n ]\\n sectional_areas = [\\n span * chord\\n for span, chord in zip(\\n sectional_spans,\\n sectional_chords\\n )\\n ]\\n area = sum(sectional_areas)\\n\\n if _sectional:\\n return sectional_areas\\n\\n if self.symmetric:\\n area *= 2\\n\\n return area\",\n \"def _getPblockArea(self, pblock_def):\\n assert re.search(r'CLOCKREGION_X\\\\d+Y\\\\d+:CLOCKREGION_X\\\\d+Y\\\\d+', pblock_def), f'unexpected format of the slot name {pblock_def}'\\n DL_x, DL_y, UR_x, UR_y = [int(val) for val in re.findall(r'[XY](\\\\d+)', pblock_def)] # DownLeft & UpRight\\n\\n # treat the pseudo SLR with 0 area\\n UR_y = min(self.CR_NUM_VERTICAL-1, UR_y) \\n\\n area = {\\n 'BRAM' : 0,\\n 'DSP' : 0,\\n 'FF' : 0,\\n 'LUT' : 0,\\n 'URAM' : 0\\n }\\n \\n if DL_y > self.CR_NUM_VERTICAL-1:\\n return area \\n\\n for item in ['BRAM', 'DSP', 'FF', 'LUT', 'URAM']:\\n # the total area of one row\\n area[item] = sum(self.CR_AREA[i][j][item] for i in range(DL_x, UR_x + 1) for j in range(DL_y, UR_y+1))\\n\\n return area\",\n \"def area(self):\\n return self.radius*self.radius*math.pi\",\n \"def define_areas(\\n pixel_filtered_map: np.ndarray, district_heating_zone_threshold: float\\n):\\n structure = np.ones((3, 3)).astype(int)\\n expanded_map = binary_dilation(input=pixel_filtered_map, structure=structure)\\n eroded_map = binary_erosion(input=expanded_map, structure=structure)\\n labels_array, n_label = measurements.label(\\n input=eroded_map,\\n structure=structure,\\n )\\n\\n # labels start from 1, therefore the array size is 'num_labels_array + 1'\\n areas_potential = np.zeros((n_label + 1)).astype(float)\\n if n_label > 0:\\n end, start, sorted_array = get_browsing_indexes(\\n labels_array=labels_array,\\n pixel_filtered_map=pixel_filtered_map,\\n n_label=n_label,\\n )\\n\\n for i, (start_index, end_index) in enumerate(zip(start, end)):\\n area = sorted_array[start_index:end_index, 3]\\n area_potential = np.sum(area)\\n if area_potential >= district_heating_zone_threshold:\\n # i+1 because labeling starts from 1 and not from 0\\n # factor 0.001 for conversion from MWh/ha to GWh/ha\\n areas_potential[i + 1] = np.around(np.sum(area_potential) / 1000, 2)\\n\\n areas = areas_potential[labels_array]\\n filtered_map = pixel_filtered_map * (areas > 0).astype(int)\\n total_potential = np.sum(areas_potential)\\n return areas, filtered_map, total_potential, areas_potential[1:]\",\n \"def get_area(area=None):\\n # Set global vars that can be accessed outside of function for debuging\\n global sorted_areas, chosen_area, mask\\n # Create a dicionary where keys --> brain areas and values --> list of neurons\\n sorted_areas = sort_by_area()\\n # Choose area of interest\\n chosen_area = sorted_areas[area]\\n # Create a mask to keep only rows of interest\\n mask = np.isin(spiketimes[:,0], chosen_area)\\n # Fetch only desired spiketimes\\n area_spikes = spiketimes[mask]\\n # Find number of neurons\\n N_neurons = len(chosen_area)\\n \\n return area_spikes\",\n \"def area(boxes):\\n y_min, x_min, y_max, x_max = np.split(boxes, 4, axis=-1)\\n return np.squeeze((y_max - y_min) * (x_max - x_min), [1])\",\n \"def area(self):\\n return math.pi*self._radius*self._radius\",\n \"def area(self):\\n if isinstance(self.crs, GeographicalCRS):\\n major_axis = self.crs.ellipsoid.a\\n minor_axis = self.crs.ellipsoid.b\\n\\n area = 0.0\\n if major_axis == minor_axis: # Sphere\\n for seg in self.segment_tuples:\\n x1, y1 = seg[0]\\n x2, y2 = seg[1]\\n area += geodesy.spherical_area(major_axis, x1, y1, x2, y2)\\n\\n else:\\n for seg in self.segment_tuples:\\n x1, y1 = seg[0]\\n x2, y2 = seg[1]\\n area += geodesy.ellipsoidal_area(major_axis, minor_axis,\\n x1, y1, x2, y2)\\n\\n else:\\n # Cartesian coordinate systems\\n x, y = self.coordinates\\n x0 = np.min(x)\\n area = (0.5*(x[0] + x[-1]) - x0) * (y[0] - y[-1])\\n area += sum((0.5*(x[i+1]+x[i]) - x0) * (y[i+1] - y[i]) for i in range(len(x)-1))\\n return abs(area) - sum(sub.area for sub in self.subs)\",\n \"def surface_area_of_cube(side):\\n return side\",\n \"def area(base, height):\\n\\n return base * height\",\n \"def area_cuadrado(lado):\\n area = lado**2\\n print \\\"El area es: \\\",area\",\n \"def get_area_formula(cls):\\n pass\",\n \"def area(self):\\n return (self.__radius ** 2 * math.pi)\",\n \"def area(self):\\n area = self.__length * self.__width\\n\\n return area\",\n \"def surface_area(self):\\n return [node.surface_area for node in self]\",\n \"def area(self):\\n return math.pi * math.pow(self.radius, 2)\",\n \"def area(self, sez):\\n # find area below water line\\n area_h2o = (sez[-1][0]-sez[0][0])*sez[0][1]\\n # find area bellow section\\n area_sez = np.trapz(sez.T[1],x=sez.T[0])\\n return area_h2o - area_sez\",\n \"def extract_area(self, pixel_geometry):\\n\\t\\tarea = self.__dataset__.extract_area(pixel_geometry, bands=[self.index])\\n\\t\\treturn area\",\n \"def test_geography_area(self):\\n # SELECT ST_Area(poly) FROM geogapp_zipcode WHERE code='77002';\\n z = Zipcode.objects.annotate(area=Area(\\\"poly\\\")).get(code=\\\"77002\\\")\\n # Round to the nearest thousand as possible values (depending on\\n # the database and geolib) include 5439084, 5439100, 5439101.\\n rounded_value = z.area.sq_m\\n rounded_value -= z.area.sq_m % 1000\\n self.assertEqual(rounded_value, 5439000)\",\n \"def island_perimeter(grid):\\n LAND = 1\\n WATER = 0\\n perimeter = 0\\n for y, row in enumerate(grid):\\n for x, cell in enumerate(row):\\n if cell == LAND:\\n # print(\\\"land in [x= {:d} y= {:d}]\\\".format(x, y))\\n # left\\n if y == 0 or grid[y - 1][x] == WATER:\\n perimeter += 1\\n # right\\n if y == len(grid) - 1 or grid[y + 1][x] == WATER:\\n perimeter += 1\\n # up\\n if x == 0 or grid[y][x - 1] == WATER:\\n perimeter += 1\\n # down\\n if x == len(row) - 1 or grid[y][x + 1] == WATER:\\n perimeter += 1\\n return perimeter\",\n \"def area(self):\\r\\n return math.pi*(self.__radius**2)\",\n \"def grid_area(ulon,ulat):\\n R = 6371. * 1000. # radius of Earth in meters\\n dlon = N.diff(ulon)\\n dlat = N.diff(ulat)\\n dx = N.outer(deg2rad * R * N.cos(deg2rad * ulat),dlon) # dx (meters)\\n dy = 60. * 1852. * dlat # dy (meters)\\n area = (dx[1:] + dx[:-1]) / 2. * dy[:,N.newaxis] # area of grid cells\\n return area\",\n \"def _area(self):\\n self.area = 0.0\\n for sail in self.sails:\\n self.area += sail.area\",\n \"def area(self):\\n area = self.__width * self.__height\\n return area\",\n \"def get_area_formula(cls):\\n dict_area = {'circle':\\\"πr^2\\\", 'square':'a**2', 'rectangle':'a*b',\\n 'triangle':'1/2*a*h',\\\"equilateral triangle\\\":'1/2*a*h',\\n 'regular pentagon':\\\"(a ** 2 * 1/2 * (5(5+2 * 1/2 *(5))))/4\\\"}\\n for k,v in dict_area.items():\\n if cls.__name__ == k:\\n return v\",\n \"def identify_habarea(indiv_xy_position, habarea_map): \\n \\n row=int(indiv_xy_position[0])\\n col=int(indiv_xy_position[1])\\n habarea=habarea_map[row][col]\\n \\n return habarea\",\n \"def get_area(option_set):\\n return option_set & AREA_MASK\",\n \"def area(width, height):\\n return width * height\",\n \"def total_area(self) :\\n area = 0\\n for i in self.residues :\\n area += i.solvent_acc_area\\n return area\",\n \"def get_floor_aps(self, conn, floor_id, offset=0, limit=100, units=\\\"FEET\\\"):\\n path = urlJoin(urls.FLOOR_PLAN[\\\"GET_FLOOR_APS\\\"], floor_id, \\\"access_point_location\\\")\\n params = {\\n \\\"offset\\\": offset,\\n \\\"limit\\\": limit,\\n \\\"units\\\": units\\n }\\n resp = conn.command(apiMethod=\\\"GET\\\", apiPath=path, apiParams=params)\\n return resp\",\n \"def area_grid(self, dtype=np.float32):\\n if self.rotation > 0:\\n raise NotImplementedError(\\n \\\"area_grid has not yet been implemented for rotated grids.\\\"\\n )\\n if self.crs.is_geographic:\\n data = gis_utils.reggrid_area(self.ycoords.values, self.xcoords.values)\\n elif self.crs.is_projected:\\n ucf = rasterio.crs.CRS.from_user_input(self.crs).linear_units_factor[1]\\n data = np.full(self.shape, abs(self.res[0] * self.res[0]) * ucf**2)\\n da_area = xr.DataArray(\\n data=data.astype(dtype), coords=self.coords, dims=self.dims\\n )\\n da_area.raster.set_nodata(0)\\n da_area.raster.set_crs(self.crs)\\n da_area.attrs.update(unit=\\\"m2\\\")\\n return da_area.rename(\\\"area\\\")\",\n \"def compInterArea(self, direction): \\r\\n if self.coord_sys == 'Cartesian' :\\r\\n if direction==0 or direction==3 :\\r\\n area = self.dy * self.dz\\r\\n elif direction==1 or direction==2 :\\r\\n area = self.dx * self.dz\\r\\n else :\\r\\n raise ValueError('direction must be one of: 0, 1, 2, 3')\\r\\n \\r\\n elif self.coord_sys == 'Cylindrical' :\\r\\n r1 = self.y_coord\\r\\n r2 = self.y_coord + self.dy;\\r\\n pi_alpha_360 = np.pi * self.m_dz / 360\\r\\n if direction==0 or direction==3 :\\r\\n area = pi_alpha_360 * (r2*r2 - r1*r1)\\r\\n elif direction==1 :\\r\\n area = self.dx * pi_alpha_360 * 2 * r1\\r\\n elif direction==2 :\\r\\n area = self.dx * pi_alpha_360 * 2 * r2\\r\\n else :\\r\\n raise ValueError('direction must be one of: 0, 1, 2, 3')\\r\\n \\r\\n else :\\r\\n raise ValueError('Coordinate system not implemented')\\r\\n \\r\\n return area\",\n \"def area(self):\\n\\t\\treturn self.height * self.height\",\n \"def get_area_contents(self):\\n types = set()\\n\\n # Iterates through self._area and adds values to return set\\n for point in self._area:\\n types.add(self._board[point[0]][point[1]])\\n\\n return types\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.66182476","0.65240717","0.63324845","0.6230986","0.6223651","0.6190598","0.6087059","0.60808396","0.6071041","0.60708934","0.60700375","0.6048479","0.60449976","0.5992652","0.59492236","0.59431803","0.5929464","0.5906095","0.58992434","0.5882558","0.58609766","0.585914","0.58374965","0.57883537","0.5773193","0.5764371","0.57575196","0.57495534","0.5746205","0.5725353","0.5716219","0.5712816","0.5701441","0.56916904","0.56916904","0.56801754","0.56801754","0.56731963","0.56707966","0.5667539","0.5644624","0.5641574","0.5641574","0.5634794","0.56326455","0.56326455","0.5629595","0.5629124","0.56201285","0.5619837","0.5605769","0.560019","0.5591714","0.558599","0.5575681","0.55736196","0.5568897","0.55668026","0.5553568","0.5551365","0.5549598","0.5547737","0.5544563","0.5522318","0.5514238","0.5513602","0.55134153","0.5508595","0.5489711","0.5486305","0.5484699","0.54838806","0.5482821","0.54660517","0.54646426","0.545659","0.5456169","0.5454606","0.5453135","0.54488415","0.54453975","0.5443484","0.5443208","0.5436441","0.5428578","0.5426174","0.5413111","0.54111296","0.54100686","0.5409382","0.5406252","0.54044646","0.5398327","0.5393895","0.53861994","0.5377358","0.537156","0.536969","0.53694534","0.5366724"],"string":"[\n \"0.66182476\",\n \"0.65240717\",\n \"0.63324845\",\n \"0.6230986\",\n \"0.6223651\",\n \"0.6190598\",\n \"0.6087059\",\n \"0.60808396\",\n \"0.6071041\",\n \"0.60708934\",\n \"0.60700375\",\n \"0.6048479\",\n \"0.60449976\",\n \"0.5992652\",\n \"0.59492236\",\n \"0.59431803\",\n \"0.5929464\",\n \"0.5906095\",\n \"0.58992434\",\n \"0.5882558\",\n \"0.58609766\",\n \"0.585914\",\n \"0.58374965\",\n \"0.57883537\",\n \"0.5773193\",\n \"0.5764371\",\n \"0.57575196\",\n \"0.57495534\",\n \"0.5746205\",\n \"0.5725353\",\n \"0.5716219\",\n \"0.5712816\",\n \"0.5701441\",\n \"0.56916904\",\n \"0.56916904\",\n \"0.56801754\",\n \"0.56801754\",\n \"0.56731963\",\n \"0.56707966\",\n \"0.5667539\",\n \"0.5644624\",\n \"0.5641574\",\n \"0.5641574\",\n \"0.5634794\",\n \"0.56326455\",\n \"0.56326455\",\n \"0.5629595\",\n \"0.5629124\",\n \"0.56201285\",\n \"0.5619837\",\n \"0.5605769\",\n \"0.560019\",\n \"0.5591714\",\n \"0.558599\",\n \"0.5575681\",\n \"0.55736196\",\n \"0.5568897\",\n \"0.55668026\",\n \"0.5553568\",\n \"0.5551365\",\n \"0.5549598\",\n \"0.5547737\",\n \"0.5544563\",\n \"0.5522318\",\n \"0.5514238\",\n \"0.5513602\",\n \"0.55134153\",\n \"0.5508595\",\n \"0.5489711\",\n \"0.5486305\",\n \"0.5484699\",\n \"0.54838806\",\n \"0.5482821\",\n \"0.54660517\",\n \"0.54646426\",\n \"0.545659\",\n \"0.5456169\",\n \"0.5454606\",\n \"0.5453135\",\n \"0.54488415\",\n \"0.54453975\",\n \"0.5443484\",\n \"0.5443208\",\n \"0.5436441\",\n \"0.5428578\",\n \"0.5426174\",\n \"0.5413111\",\n \"0.54111296\",\n \"0.54100686\",\n \"0.5409382\",\n \"0.5406252\",\n \"0.54044646\",\n \"0.5398327\",\n \"0.5393895\",\n \"0.53861994\",\n \"0.5377358\",\n \"0.537156\",\n \"0.536969\",\n \"0.53694534\",\n \"0.5366724\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.8184448"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":92584,"cells":{"query":{"kind":"string","value":"This function sends a SUCCESS/FAILED response to the CloudFormation stack that invoked the Lambda function."},"document":{"kind":"string","value":"def send_cf_response(event, context, success=True, reason='Unknown'):\n data = {\n 'Status': 'SUCCESS' if success else 'FAILED',\n 'PhysicalResourceId': context.log_stream_name,\n 'StackId': event['StackId'],\n 'RequestId': event['RequestId'],\n 'LogicalResourceId': event['LogicalResourceId']\n }\n if not success:\n data['Reason'] = reason\n\n headers = {\n 'Content-Type': ''\n }\n\n logger.info(data)\n logger.info(f\"Sending CloudFormation response to {event['ResponseURL']}\")\n resp = requests.put(\n event['ResponseURL'],\n data=json.dumps(data),\n headers=headers,\n timeout=5\n )\n logger.info(resp.status_code)"},"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 lambda_handler(event, context):\n\n return {\n \"statusCode\": 200,\n \"body\": json.dumps({\n 'message': 'API deployed',\n })\n }","def lambda_handler(event, context):\n return {\n 'statusCode': 200,\n 'body': say_hello()\n }","def lambda_response(err, res=None):\n return {\n 'statusCode': '400' if err else '200',\n 'body': err.message if err else json.dumps(res),\n 'headers': {\n 'Content-Type': 'application/json',\n },\n }","def aws_lambda(f):\n def wrapper(event, context):\n logger = logging.getLogger(__name__)\n try:\n # if no return value is given by wrapped func,\n # return default status code 200 response.\n r = f(event, context)\n if r is None:\n r = {\n 'statusCode': 200,\n 'body': json.dumps({'input': event})\n }\n return r\n except Exception as e:\n # if exception is thrown, log exception,\n # return exception text,\n # and return status code associated with passed\n # exception type\n logger.info(\n 'Call to {} resulted in exception'.format(f.__name__), e)\n exc_type = type(e)\n # get exception type for code lookup and msg\n if exc_type is type:\n exc_type = e\n msg = e.__name__\n else:\n msg = str(e)\n # get default exception code for raised Exception.\n # default to code 500 if exception is not in codes dict.\n code = codes.get(exc_type, DEFAULT_EXCEPTION_CODE)\n return {\n 'statusCode': code,\n 'body': json.dumps({'input': event, 'message': msg})\n }\n\n wrapper.__name__ = f.__name__ + '_wrapper'\n return wrapper","def lambda_handler(event, context):\n # define initial status code and headers\n statusCode = 400\n try:\n # get the body params\n if type(event) == dict:\n event_body = event.get('body', event)\n else:\n event_body = json.loads(event).get('body', {})\n # generate and store the reservation response result from reservation handler function\n reservation_handler = ReservationHandler(EventBodyData=event_body)\n result = reservation_handler.sabre_reservation_handler()\n # define status code, headers and response\n if type(result) == dict:\n statusCode = result.get(\"statusCode\", statusCode)\n response = result.get(\"body\", \"\")\n else:\n response = result\n except Exception as E:\n response = str(E)\n\n # return the response\n return {\n 'statusCode': statusCode,\n 'body': response\n }","def checkError(invoke_response, message):\n\n if 'FunctionError' in invoke_response:\n err_message = invoke_response['Payload'].read()\n print(message)\n print(err_message)\n return {\n 'statusCode': 500,\n 'body': json.dumps(str(err_message))\n }\n return None","def hosted_function(payload):\n logger.info('hosted_function called')\n return 'OK', 201","def lambda_handler(event, context):\n\n # try:\n # ip = requests.get(\"http://checkip.amazonaws.com/\")\n # except requests.RequestException as e:\n # # Send some context about this error to Lambda Logs\n # print(e)\n\n # raise e\n\n print(\"starting 1\")\n #myBuckets = Buckets()\n #bucketsList = \"disabled\" #myBuckets.list()\n\n userTopic = Topic()\n snsResponse = userTopic.publish()\n print(\"done\")\n\n return {\n \"statusCode\": 200,\n \"body\": json.dumps({\n \"message\": \"hello world sns:{}\".format( snsResponse), \n # \"location\": ip.text.replace(\"\\n\", \"\")\n }),\n }","def lambda_handler(event, context):\r\n if event[\"httpMethod\"] == \"POST\":\r\n # Decrypt the recaptchaKey environment variable. If it's stored as plaintext recaptchaKey = environ['recaptchaKey'] will work perfectly\r\n recaptchaKey = boto3.client('kms').decrypt(CiphertextBlob=b64decode(environ['recaptchaKey']))['Plaintext'].decode('utf-8')\r\n # Remove the Access-Control-Allow-* headers if requests will always be from the same domain\r\n returnDict = {\r\n \"statusCode\": 500,\r\n 'headers': {\r\n \"Access-Control-Allow-Origin\": \"*\",\r\n \"Access-Control-Allow-Headers\": \"Content-Type\",\r\n \"Access-Control-Allow-Methods\": \"OPTIONS,POST,GET\"\r\n }\r\n }\r\n try:\r\n if not event[\"body\"]:\r\n returnDict[\"body\"] = json.dumps({\"Error\": \"Empty body\"})\r\n else:\r\n body = json.loads(event[\"body\"])\r\n responseFromGoogle = requests.post(\"https://www.google.com/recaptcha/api/siteverify\", {\"secret\": recaptchaKey, \r\n \"response\": body[\"g-recaptcha-response\"]})\r\n # Fallback - failure should be indicated by success being False not a HTTP error\r\n if responseFromGoogle.status_code != 200:\r\n returnDict[\"body\"] = json.dumps({\"Error\": \"ReCaptcha validation failed\"})\r\n # If the request failed\r\n elif responseFromGoogle.json()[\"success\"] == False: \r\n returnDict[\"statusCode\"] = 200\r\n returnDict[\"body\"] = json.dumps({\"Error\": \"ReCaptcha validation failed.\", \r\n \"error-codes\": responseFromGoogle.json().get(\"error-codes\", [])\r\n })\r\n elif responseFromGoogle.json()[\"score\"] < 0.5:\r\n returnDict[\"statusCode\"] = 200\r\n returnDict[\"body\"] = json.dumps({\"Error\": \"Score below threshhold\"})\r\n else:\r\n try:\r\n send_email(body)\r\n returnDict[\"statusCode\"] = 200\r\n returnDict[\"body\"] = json.dumps({\"message\": \"Email sent successfully\"})\r\n print(\"Email sent!\")\r\n except Exception as e:\r\n print(e.response['Error']['Message'])\r\n returnDict[\"body\"] = json.dumps({\"Error\": \"Unable to send email\"})\r\n except Exception as e:\r\n print(e) \r\n returnDict[\"body\"] = json.dumps({\"Error\": \"Exception occurred\"})\r\n finally:\r\n return returnDict","def lambda_handler(event, context):\n\n # Get Params\n pipeline = event['detail']['pipeline']\n executionId = event['detail']['execution-id']\n status = event['detail']['state']\n stage = event['detail']['stage']\n \n if stage.lower() == \"Source\".lower():\n return '200 OK'\n\n # Get Pipeline execution details.\n client = boto3.client('codepipeline')\n pipelineDetails = client.get_pipeline_execution(\n pipelineName=pipeline,\n pipelineExecutionId=executionId\n )\n\n # Format Slack Message.\n message = '*Commit*\\n' + pipelineDetails['pipelineExecution']['artifactRevisions'][0]['revisionSummary']\n message = message + '\\n*Link*\\n' + pipelineDetails['pipelineExecution']['artifactRevisions'][0]['revisionUrl']\n color = {\n 'SUCCEEDED': 'good',\n 'FAILED': 'danger',\n 'STARTED': '#4682B4'\n }.get(status, '#2a2a2a')\n\n # JSON Payload.\n payload = {\n 'channel': CHANNEL,\n 'attachments': [\n {\n 'color': color,\n 'text': message,\n 'fields': [\n {\n 'title': 'Stage',\n 'value': stage.title(),\n 'short': 'true'\n },\n {\n 'title': 'Status',\n 'value': status.title(),\n 'short': 'true'\n }\n ],\n }\n ]\n }\n # Construct the HTTP request that will be sent to the Slack API.\n request = urllib.request.Request(\n SLACK_URL,\n method='POST',\n data=json.dumps(payload).encode('utf-8')\n )\n # Add Headers.\n request.add_header('Authorization', 'Bearer ' + TOKEN)\n request.add_header('Content-Type', 'application/json')\n \n # Make the Request.\n urllib.request.urlopen(request).read()\n\n # Return Success.\n return '200 OK'","def lambda_handler(event, context):\n\n print(\"EVENT:\")\n print(event)\n\n # try:\n # ip = requests.get(\"http://checkip.amazonaws.com/\")\n # except requests.RequestException as e:\n # # Send some context about this error to Lambda Logs\n # print(e)\n\n # raise e\n\n\n recs = flow(event, s3)\n print(recs)\n\n return {\n \"statusCode\": 200,\n \"body\": json.dumps({\n \"message\": \"hello world\",\n # \"location\": ip.text.replace(\"\\n\", \"\")\n }),\n }","def lambda_handler(event, context):\n\n # Log the values received in the event argument\n logger.info(f'Request event: {event}')\n\n # Define default hard-coded return values\n response = {\n 'uid': 'Example function ID',\n 'return_val01': 'Return value #1',\n 'return_val02': 'Return Value #2',\n }\n\n # Retrieve type of invocation (GET, PUT, etc.)\n if 'http_verb' in event:\n operation = event['http_verb'].upper()\n if operation == 'PUT':\n # Return the values passed to the function\n response = {\n 'uid': event['functionID'],\n 'return_val01': event['parameters']['parm01'],\n 'return_val02': event['parameters']['parm02'],\n }\n\n logger.info(f'Response={response}')\n return response","def lambda_handler(event, context):\n try:\n # Extract the Job ID\n job_id = event['CodePipeline.job']['id']\n\n # Extract the Job Data\n job_data = event['CodePipeline.job']['data']\n\n # Extract the params\n params = get_user_params(job_data)\n\n # Get the lists of artifacts coming in and out of this function\n input_artifacts = job_data['inputArtifacts']\n output_artifacts = job_data['outputArtifacts']\n\n # Perform a build on the source (from source_artifact)\n # and write results to the build_artifact\n s3 = setup_s3_client(job_data)\n source_artifact = find_artifact(input_artifacts, params['source_artifact'])\n src_dir = get_zipped_artifact(s3, source_artifact)\n dest_dir = tempfile.mkdtemp()\n perform_build(os.path.join(src_dir, 'src'), dest_dir)\n build_artifact = find_artifact(output_artifacts, params['build_artifact'])\n put_zipped_artifact(s3, dest_dir, build_artifact)\n\n # Pick the template out of the source code and write it to the\n # template_artifact\n template_artifact = find_artifact(output_artifacts, params['template_artifact'])\n put_zipped_artifact(s3, os.path.join(src_dir, params['template_subdir_path']), template_artifact)\n\n shutil.rmtree(src_dir)\n shutil.rmtree(dest_dir)\n put_job_success(job_id, \"Built code: \" + \", template:\")\n\n except Exception as e:\n # If any other exceptions which we didn't expect are raised\n # then fail the job and log the exception message.\n print('Function failed due to exception.')\n print(e)\n traceback.print_exc()\n put_job_failure(job_id, 'Function exception: ' + str(e))\n\n print('Function complete.')\n return \"Complete.\"","def lambda_handler(event, context):\n\n try:\n created_item = create_new_table_item(event)\n return {\"statusCode\": 201, \"body\": json.dumps(f\"{created_item}\")}\n\n except BlankRequestBody as exception:\n logging.error(f\"{exception}\")\n return {\"statusCode\": 400, \"body\": json.dumps(MISSING_PARAMETERS_MESSAGE)}\n\n except ValidationError as exception:\n logging.error(f\"{exception}\")\n return {\"statusCode\": 400, \"body\": json.dumps(INCORRECT_PARAMETERS_MESSAGE)}\n\n except Exception as exception:\n logging.error(f\"{exception}\")\n return {\"statusCode\": 500, \"body\": json.dumps(\"Internal server error\")}","def lambda_handler(event, context):\n logging.info(event)\n current_time = datetime.datetime.utcnow().strftime(\"%Y-%m-%dT%H:%M:%SZ\")\n is_conversation_result = 'Details' in event\n if is_conversation_result:\n combine_bot_state_to_s3(event, current_time)\n else:\n save_bot_state_to_s3(event, current_time)\n\n # Generate response back to bot\n response = dict()\n if not is_conversation_result:\n response = {\n 'dialogAction': {\n 'type': 'Delegate',\n 'slots': event['currentIntent']['slots']\n }\n }\n logging.info(response)\n return response","def hello(event, context):\n\n result = {\n 'data': {\n 'message': 'Kanban-board!'\n },\n 'status': 200\n }\n return _get_response(result)\n\n # Use this code if you don't use the http event with the LAMBDA-PROXY\n # integration\n \"\"\"\n return {\n \"message\": \"Go Serverless v1.0! Your function executed successfully!\",\n \"event\": event\n }\n \"\"\"","def glue_failure_handler(event, context) -> None:\n\n # get lambda variables\n environment_level = os.environ[\"ENVIRONMENT\"]\n sns_topic_name = os.environ[\"PROCESSING_NOTIFICATION_SNS\"]\n\n # fetches the response in dict received from eventbridge rule when 'event pattern' matches\n # here failing a glue job is an event pattern. this is already stated in event rule 'glue_failure_rule'\n # the output from below will be like --> {\"version\": \"0\", \"id\": \"26c9301e-6aec-872d-4739-1aa6ebc66853\", \"detail-type\": \"Glue Job State Change\", \"source\": \"aws.glue\", \"account\": \"795038802291\", \"time\": \"2021-03-24T07:37:07Z\", \"region\": \"us-east-1\", \"resources\": [], \"detail\": {\"jobName\": \"rahul_create_warm_pool\", \"severity\": \"ERROR\", \"state\": \"FAILED\", \"jobRunId\": \"jr_bdb3f0d56b0f842c7337be54927deb6ddb78a1f37956d9817b0f11710602139f\", \"message\": \"Command failed with exit code 1\"}}\n event_response = json.dumps(event)\n\n # calling GlueFailureEvent class\n glue_failure_event = GlueFailureEvent(event_response)\n\n # get failure time (event occurrence)\n event_occurrence_time = glue_failure_event.time # returns ISO UTC time (ex: '2021-03-24T15:52:37Z')\n # convert ISO UTC time to regular UTC time and then to ET local time\n converted_time = event_occurrence_time.replace(\"T\",\" \").replace(\"Z\",\".0\")\n _converted_time = datetime.strptime(converted_time, \"%Y-%m-%d %H:%M:%S.%f\")\n local_time = convert_utc_to_local(_converted_time, DEFAULT_TIMEZONE)\n\n if glue_failure_event.detail['jobRunId'].endswith(\"attempt_1\"):\n # we dont want get notified for attempt1 run failure because this will always be failure in real time.\n # so its an attempt to avoid 2 failure alerts for each failed glue job event\n return None\n # form subject, message required for publishing SNS\n subject = f\"{environment_level}: Glue job Failure\"\n message = (\n f\"Glue job '{glue_failure_event.detail['jobName']}' has {glue_failure_event.detail['state']} at {local_time}. Below are the details:{os.linesep}{os.linesep}\"\n f\"Error : {glue_failure_event.detail['message']}{os.linesep}\"\n f\"{os.linesep}\"\n f\"JobRunId : {glue_failure_event.detail['jobRunId']}\"\n )\n\n send_ahub_email_notification(sns_topic_name, subject, message)","def deploy_resources(credentials, template, stack_name, stack_region, SNSfriendlyname, SNSemail, SNSdescription):\n\n datestamp = time.strftime(\"%d/%m/%Y\")\n client = boto3.client('cloudformation',\n aws_access_key_id=credentials['AccessKeyId'],\n aws_secret_access_key=credentials['SecretAccessKey'],\n aws_session_token=credentials['SessionToken'],\n region_name=stack_region)\n print(\"Creating stack \" + stack_name + \" in \" + stack_region)\n\n creating_stack = True\n while creating_stack is True:\n try:\n creating_stack = False\n create_stack_response = client.create_stack(\n StackName=stack_name,\n TemplateBody=template,\n Parameters=[\n {\n 'ParameterKey': 'SNSfriendlyname',\n 'ParameterValue': SNSfriendlyname\n },\n\n {\n 'ParameterKey': 'SNSemail',\n 'ParameterValue': SNSemail\n },\n {\n 'ParameterKey': 'SNSdescription',\n 'ParameterValue': SNSdescription\n }\n ],\n NotificationARNs=[],\n Capabilities=[\n 'CAPABILITY_NAMED_IAM',\n ],\n OnFailure='ROLLBACK',\n Tags=[\n {\n 'Key': 'ManagedResource',\n 'Value': 'True'\n },\n {\n 'Key': 'DeployDate',\n 'Value': datestamp\n }\n ]\n )\n except botocore.exceptions.ClientError as e:\n creating_stack = True\n print(e)\n print(\"Retrying...\")\n time.sleep(10)\n\n stack_building = True\n print(\"Stack creation in process...\")\n print(create_stack_response)\n while stack_building is True:\n event_list = client.describe_stack_events(StackName=stack_name).get(\"StackEvents\")\n stack_event = event_list[0]\n\n if (stack_event.get('ResourceType') == 'AWS::CloudFormation::Stack' and\n stack_event.get('ResourceStatus') == 'CREATE_COMPLETE'):\n stack_building = False\n print(\"Stack construction complete.\")\n elif (stack_event.get('ResourceType') == 'AWS::CloudFormation::Stack' and\n stack_event.get('ResourceStatus') == 'ROLLBACK_COMPLETE'):\n stack_building = False\n print(\"Stack construction failed.\")\n sys.exit(1)\n else:\n print(stack_event)\n print(\"Stack building . . .\")\n time.sleep(10)\n\n stack = client.describe_stacks(StackName=stack_name)\n return stack","def lambda_handler(event, context):\n\n params = ['comp_name', 'action', 'level', 'msg']\n missing = []\n for i in range(len(params)):\n if params[i] not in event:\n missing.append(params[i])\n \n if missing:\n err = \"Missing these parameters: \" + str(missing)\n print(err)\n \n return {\n \"statusCode\": 500,\n \"body\": json.dumps(err)\n }\n notify_snitch(event['comp_name'], event['action'], event['level'], event['msg'])\n \n return {\n \"statusCode\": 200,\n \"body\": json.dumps('Notified snitch')\n }","def lambda_handler(event, context):\n\n event_body = json.loads(event['body'])\n print(\"EVENT:\")\n print(event_body)\n\n\n # try:\n # ip = requests.get(\"http://checkip.amazonaws.com/\")\n # except requests.RequestException as e:\n # # Send some context about this error to Lambda Logs\n # print(e)\n\n # raise e\n\n recs = flow(event_body, textract, cache = True)\n rval = {\n \"statusCode\": 200,\n \"body\": json.dumps({\n \"message\" : \"hello world\",\n \"textract\" : recs\n # \"location\": ip.text.replace(\"\\n\", \"\")\n }),\n }\n\n return rval","def lambda_handler(event, context):\n\n # try:\n # ip = requests.get(\"http://checkip.amazonaws.com/\")\n # except requests.RequestException as e:\n # # Send some context about this error to Lambda Logs\n # print(e)\n\n # raise e\n\n try:\n response = s3.get_object(Bucket=BUCKET, Key=KEY)\n print('CONTENT TYPE:', response['ContentType'])\n print('response:')\n pprint.pprint(response)\n print('event')\n pprint.pprint(event)\n print('payload')\n pprint.pprint(event.get('payload'))\n # return json.loads(json.dumps(response, default=str))\n # defined by https://docs.aws.amazon.com/apigateway/latest/developerguide/set-up-lambda-proxy-integrations.html#api-gateway-simple-proxy-for-lambda-output-format\n return {\n 'statusCode': 200,\n 'isBase64Encoded': False,\n 'body': json.dumps(response, default=str)\n }\n # return response['ContentType']\n except Exception as e:\n print(e)\n print('Error getting object {} from bucket {}. Make sure they exist and your bucket is in the same region as this function.'.format(KEY, BUCKET))\n raise e","def func_wrapper(event, context):\n req = Request(event, context)\n\n try:\n resp = func(req)\n\n if not isinstance(resp, Response):\n message = (\n 'Invalid return value from handler. '\n 'It should be either Response or Exception'\n )\n raise TypeError(message)\n except ServerlessError as e:\n status_code = e.status_code\n message = e.message if e.message else e.__class__.__name__\n\n resp = to_error_response(message, e.errors, status_code)\n except Exception as e: # pylint: disable=W0703\n logger.exception(e)\n status_code = 500\n message = 'InternalServerError'\n errors = tuple()\n\n resp = to_error_response(message, errors, status_code)\n return resp.to_lambda_output()","def test_response_status():\n\n status_code = 400\n retval = apigateway.response(\"Message\", status_code)\n assert retval[\"statusCode\"] == status_code","def lambda_handler(event, context=None):\n response = {}\n try:\n response = middleware.IdentityAuthMiddleWare.process_request(event, response)\n except Exception as e:\n response[\"message\"] = e.message\n response[\"errors\"] = e.errors\n # removing request_dump data\n if \"request_dump\" in response[\"errors\"]:\n del response[\"errors\"][\"request_dump\"]\n for _k, _v in response[\"errors\"].items():\n response[\"errors\"][_k] = str(_v)\n return response","def lambda_handler(event, context):\n\n # Get details from the event.\n job = event[\"CodePipeline.job\"]\n input_bucket, input_key = get_input_artifact_location(job)\n output_bucket, output_key = get_output_artifact_location(job)\n user_params = get_user_parameters(job)\n assume_role_arn = user_params[\"AssumeRoleArn\"]\n image_parameter_name = user_params[\"ImageParameterName\"]\n stack_name = user_params[\"StackName\"]\n template_filename = user_params[\"TemplateFilename\"]\n\n # Create client in the pipeline account.\n pipeline_s3_client = get_artifact_s3_client(job)\n\n # Create clients in the target account.\n target_session = get_session(\n role_arn=assume_role_arn, session_name=\"prepare-ami-deployment\"\n )\n target_cfn_client = target_session.client(\"cloudformation\")\n target_ssm_client = target_session.client(\"ssm\")\n\n # Download the input artifact zip file, read manifest.json from it,\n # and get the AMI it references. Also look up the associated image name.\n with download_zip_file(\n s3_client=pipeline_s3_client, bucket=input_bucket, key=input_key\n ) as zip_file:\n image_detail_string = zip_file.read(\"imageDetail.json\").decode(\"utf-8\")\n log(\"IMAGE_DETAIL_STRING\", image_detail_string)\n image_detail = json.loads(image_detail_string)\n image = image_detail[\"ImageURI\"]\n log(\"IMAGE\", image)\n\n # Update the SSM parameters with the image,\n # to be used by the CloudFormation deployment stage of the pipeline.\n target_ssm_client.put_parameter(\n Name=image_parameter_name, Value=image, Type=\"String\", Overwrite=True\n )\n\n # Write the CloudFormation stack's template to the output artifact location,\n # to be used by the CloudFormation deployment stage of the pipeline.\n template = get_cloudformation_template(\n cfn_client=target_cfn_client, stack_name=stack_name\n )\n with create_zip_file({template_filename: template}) as zip_path:\n pipeline_s3_client.upload_file(zip_path, output_bucket, output_key)","def lambda_handler(event, context):\n # EOL char append function\n encode_data = lambda x: \"{data}{eol}\".format(data=json.dumps(x), eol=chr(10)).encode(\"UTF-8\")\n \n # Punk API call\n try:\n logger.debug(\"Requesting api: {api}\".format(api=os.environ[\"API_URL\"]))\n request = r.get(os.environ[\"API_URL\"])\n except Exception as e:\n logger.error(\"An error occured while requesting api: {api}\".format(api=os.environ[\"API_URL\"]))\n raise e\n \n # Send records to kinesis stream\n logger.debug(\"Sending data to stream: {stream}\".format(stream=os.environ[\"STREAM_NAME\"]))\n for data in request.json():\n client.put_record(\n StreamName=os.environ[\"STREAM_NAME\"],\n Data=encode_data(data),\n PartitionKey=\"key\"\n )\n\n return {\n 'statusCode': request.status_code,\n 'body': data\n }","def serverless_lambda_deploy(self, gateway_stack_name, lambda_stack_name, code_key):\n\n domain_name = self.get_stack_domain_name(gateway_stack_name)\n\n if not domain_name:\n print \"[WARN] Custom Domain not found. Depolying to $LATEST\"\n self.lambda_deploy_code(lambda_stack_name, code_key)\n return True\n\n current_live_stage = self.get_live_stack(domain_name)\n\n to_alias = None\n\n if current_live_stage == 'green':\n to_alias = 'blue'\n elif current_live_stage == 'blue':\n to_alias = 'green'\n\n lambdas = self.get_stack_lambdas(lambda_stack_name)\n print \"[INFO] Updating lambda code for %s \" % str(lambdas)\n\n for aws_lambda in lambdas:\n\n self.get_or_set_lambda_alias(aws_lambda)\n function_arn = self.get_lambda_arn(aws_lambda)\n self.update_lambda_code_key(function_arn, code_key)\n code_bucket, code_key = self.get_lambda_code_bucket_and_key(function_arn)\n\n print \"[INFO] Updating lambda code for %s with bucket: %s & key: %s\" % (\n aws_lambda, code_bucket, code_key)\n\n self.lambda_client.update_function_code(\n FunctionName=aws_lambda,\n S3Bucket=code_bucket,\n S3Key=code_key,\n # S3ObjectVersion='string', # TODO Soon!\n Publish=True\n )\n\n response = self.lambda_client.publish_version(\n FunctionName=aws_lambda,\n Description=datetime.now().strftime(\"%A, %d. %B %Y %I:%M%p %S\")\n )\n\n version = response[\"Version\"]\n\n if not to_alias or not version:\n print \"[FAIL] Unable to version deploy\"\n\n self.lambda_client.update_alias(\n FunctionName=aws_lambda,\n Name=to_alias,\n FunctionVersion=version\n )\n\n print \"[INFO] Function %s Updated to version %s linked to alias %s\" % (aws_lambda, version, to_alias)","def lambda_handler(func):\n logger = logging.getLogger(__name__)\n\n @wraps(func)\n def func_wrapper(event, context):\n \"\"\"\n This is what's invoked via lambda.\n\n Args:\n event (dict): AWS Lambda event\n context (LambdaContext): AWS Lambda context\n\n .. _AWS Lambda python programming model:\n http://docs.aws.amazon.com/lambda/latest/dg/python-programming-model-handler-types.html\n \"\"\"\n req = Request(event, context)\n\n try:\n resp = func(req)\n\n if not isinstance(resp, Response):\n message = (\n 'Invalid return value from handler. '\n 'It should be either Response or Exception'\n )\n raise TypeError(message)\n except ServerlessError as e:\n status_code = e.status_code\n message = e.message if e.message else e.__class__.__name__\n\n resp = to_error_response(message, e.errors, status_code)\n except Exception as e: # pylint: disable=W0703\n logger.exception(e)\n status_code = 500\n message = 'InternalServerError'\n errors = tuple()\n\n resp = to_error_response(message, errors, status_code)\n return resp.to_lambda_output()\n return func_wrapper","def lambda_handler(event, context):\n\n operations = {\n 'POST': main,\n }\n\n if event.get('httpMethod', False):\n operation = event['httpMethod']\n else:\n operation = \"not available\"\n\n payload = base64.b64decode(event['body'])\n try:\n payload = json.loads(payload)\n except TypeError:\n pass\n\n if operation in operations:\n return respond(None, operations[operation](payload))\n else:\n return respond(ValueError(f'Unsupported method {operation}'))","def lambda_handler(event, context):\n\n # S3 resource invocation\n s3_resource = boto3.resource('s3')\n # S3 bucket selection\n data_bucket_name = \"put_here_data_bucket_name\"\n # The SageMaker runtime is what allows us to invoke the endpoint that we've created.\n runtime = boto3.Session().client('sagemaker-runtime')\n\n request_body_dict = json.loads(event['body'])\n\n # Now we use the SageMaker runtime to invoke our endpoint, sending both ticker and start date if given\n if request_body_dict['start_date'] != \"\":\n response = runtime.invoke_endpoint(EndpointName='DeepAR-ml-spp', # The name of the endpoint we created\n ContentType='application/json', # The data format that is expected\n Body=encode_future_request(request_body=request_body_dict,\n s3_resource=s3_resource,\n s3_bucket=data_bucket_name, prefix='valid'))\n # or only ticker name if no start date has been provided\n elif request_body_dict['ticker_name'] != \"\":\n response = runtime.invoke_endpoint(EndpointName='DeepAR-ml-spp', # The name of the endpoint we created\n ContentType='application/json', # The data format that is expected\n Body=encode_request(ticker_name=request_body_dict['ticker_name'],\n s3_resource=s3_resource, s3_bucket=data_bucket_name,\n prefix='train'))\n\n # The response is an HTTP response whose body contains the result of our inference\n result = response['Body'].read().decode('utf-8')\n\n # print data for debug purposes\n print(result)\n\n return {\n 'statusCode': 200,\n 'headers': {'Content-Type': 'application/json', 'Access-Control-Allow-Origin': '*'},\n 'body': str(result)\n }","def lambda_handler(event, context) -> Dict:\n\n destination: Optional[str] = os.environ.get(\"DESTINATION\")\n assert destination, \"failed to get destination\"\n\n ec2_client = boto3.client(\"ec2\")\n try:\n vpcs_res: Dict = ec2_client.describe_vpcs()\n except ClientError as error:\n raise error\n\n for vpc in vpcs_res.get(\"Vpcs\", []):\n try:\n flow_logs_res: Dict = ec2_client.describe_flow_logs(\n Filters=[{\"Name\": \"resource-id\", \"Values\": [vpc.get(\"VpcId\")]}],\n )\n except ClientError as error:\n raise error\n\n flow_logs = flow_logs_res.get(\"FlowLogs\", [])\n\n if len(flow_logs) == 0:\n try:\n response = ec2_client.create_flow_logs(\n ResourceIds=[vpc.get(\"VpcId\")],\n ResourceType=\"VPC\",\n TrafficType=\"ALL\",\n LogDestinationType=\"s3\",\n LogDestination=destination,\n )\n except ClientError as error:\n raise error\n\n if response.get(\"Unsuccessful\", []) != []:\n raise Exception(f\"failed to create_flow_logs {response}\")\n\n print(f\"Created flow logs for {vpc.get('VpcId')}\")\n\n elif len(flow_logs) >= 1:\n exists = False\n for flow_log in flow_logs:\n if flow_log.get(\"LogDestination\", \"\") == destination:\n exists = True\n\n if not exists:\n try:\n ec2_client.create_flow_logs(\n ResourceIds=[vpc.get(\"VpcId\")],\n ResourceType=\"VPC\",\n TrafficType=\"ALL\",\n LogDestinationType=\"s3\",\n LogDestination=destination,\n )\n except ClientError as error:\n raise error\n print(f\"Created flow logs for {vpc.get('VpcId')}\")\n\n return {\"statusCode\": 200, \"body\": \"success\"}","def lambda_handler(event, context):\n print(event)\n print(context)\n storage_gateway_status()","def test4():\n event = {\n \"Records\": [\n {\n \"s3\": {\n \"s3SchemaVersion\": \"1.0\",\n \"configurationId\": \"b0efd5b1-cc92-47b4-8501-1c34f5eba235\",\n \"bucket\": {\n \"name\": \"/tmp/\"\n },\n \"object\": {\n \"key\": \"tic000147203645/tic000147203645_s0001-1-1_stlc.fits\"\n }\n }\n }\n ]\n}\n context = {}\n \n out = lambda_handler(event, context)\n \n assert out[\"statusCode\"] == 200","def test_create_message_with_succes(self, mock_client): \n\n event = {\n 'operation': 'createMessage', \n 'arguments': {\n 'template': 'my-sample-geofence-id',\n 'input': {\n 'service': 'APNS',\n 'action': 'OPEN_APP',\n 'title': 'Sample Title',\n 'body': 'This is a sample body'\n }\n }\n }\n\n response = {\n \"Arn\": f'arn:aws:mobiletargeting:eus-east-1:SOME_ACCOUNT_ID:templates/my-sample-geofence-id/PUSH',\n \"RequestID\": \"some-request-id\",\n \"Message\": 'some message' \n }\n\n mock_client().create_push_template.return_value = response\n response = manageMessages.handler(event, None)\n\n self.assertTrue(response)\n self.assertEqual(response['status'], 'MESSAGE_CREATED')","def test_wsgi_script_name_on_alb_event(self):\n lh = LambdaHandler(\"tests.test_wsgi_script_name_settings\")\n\n event = {\n \"requestContext\": {\n \"elb\": {\n \"targetGroupArn\": \"arn:aws:elasticloadbalancing:region:123456789012:targetgroup/my-target-group/6d0ecf831eec9f09\"\n }\n },\n \"httpMethod\": \"GET\",\n \"path\": \"/return/request/url\",\n \"queryStringParameters\": {},\n \"headers\": {\n \"accept\": \"text/html,application/xhtml+xml\",\n \"accept-language\": \"en-US,en;q=0.8\",\n \"content-type\": \"text/plain\",\n \"cookie\": \"cookies\",\n \"host\": \"1234567890.execute-api.us-east-1.amazonaws.com\",\n \"user-agent\": \"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_6)\",\n \"x-amzn-trace-id\": \"Root=1-5bdb40ca-556d8b0c50dc66f0511bf520\",\n \"x-forwarded-for\": \"72.21.198.66\",\n \"x-forwarded-port\": \"443\",\n \"x-forwarded-proto\": \"https\",\n },\n \"isBase64Encoded\": False,\n \"body\": \"\",\n }\n response = lh.handler(event, None)\n\n self.assertEqual(response[\"statusCode\"], 200)\n self.assertEqual(response[\"statusDescription\"], \"200 OK\")\n self.assertEqual(response[\"isBase64Encoded\"], False)\n self.assertEqual(\n response[\"body\"],\n \"https://1234567890.execute-api.us-east-1.amazonaws.com/return/request/url\",\n )","def lambda_handler(event, context):\n\n # try:\n # ip = requests.get(\"http://checkip.amazonaws.com/\")\n # except requests.RequestException as e:\n # # Send some context about this error to Lambda Logs\n # print(e)\n\n # raise e\n curr_time = str(int(datetime.now().strftime(\"%s\")) * 1000)\n client.put_item(TableName='demo-table', \n Item={\n 'doi': {'S': curr_time}, \n 'crossref_flag': {'BOOL': False},\n 'europepmc_flag': {'BOOL': False},\n 's3_flag': {'BOOL': False},\n 'indexing_flag': {'BOOL': False}\n })\n return {\n \"statusCode\": 200,\n \"body\": json.dumps({\n \"message\": \"hello world\",\n # \"location\": ip.text.replace(\"\\n\", \"\")\n }),\n }","def lambda_handler(event=None, context=None):\n logger.info('Lambda function invoked index()')\n\n # Get configuration from environment\n file_name_default = os.environ.get('FILE_NAME') or \"bamboo_employees\"\n api_key = os.environ.get('BAMBOO_TOKEN')\n url = os.environ.get('BAMBOO_API')\n\n # Parameters, take a file name if exists and remove it from the dict\n file_name = request.args.get('file_name') or file_name_default\n\n try:\n # Request data from Bamboo API\n headers = {'Accept': 'application/json'}\n auth = HTTPBasicAuth(api_key, 'x')\n response = requests.get(url=url, headers=headers, auth=auth)\n\n if response.status_code != requests.codes.ok:\n raise Exception('Error making the request to Bamboo\\r\\n')\n\n # Check the result\n result = json.loads(response.text)\n if 'employees' in result:\n # Generate the GeoJSON from API response\n employees = []\n for employee in result['employees']:\n # Bamboo does not provide explicit locations\n point = Point(None, None)\n employees.append(Feature(geometry=point, properties=employee))\n\n # Produce a GeoJSON Feature collection\n body = json.dumps(FeatureCollection(employees))\n attachment = 'attachment; filename={0}.json'.format(file_name)\n\n # Create a response with the proper headers\n # CARTO will use the file name property as the table name\n response = make_response(body)\n response.headers['Content-Type'] = 'application/json'\n response.headers['Content-Disposition'] = attachment\n else:\n raise Exception('No photos on your request')\n\n return response\n\n except Exception as e:\n response = make_response(e.message + \"\\r\\n\")\n response.headers['Content-Type'] = 'text/plain'\n response.status_code = 500\n return response","def redshift_lambda_handler(event, context):\n logging.debug('event: %s', event)\n\n detail = event['detail']\n event_name = detail['eventName']\n creator = get_creator(event)\n\n logger.info('Event type: %s', event_name)\n\n if is_err_detail(logger, detail):\n return False\n\n if event_name == 'CreateCluster':\n logger.debug('%s is creating cluster: %s',\n creator, detail['requestParameters']['clusterIdentifier'])\n\n # https://docs.aws.amazon.com/general/latest/gr/aws-arns-and-namespaces.html\n cluster_arn = 'arn:aws:redshift:' + detail['awsRegion'] + ':'\\\n + detail['userIdentity']['accountId'] + ':cluster:'\\\n + detail['requestParameters']['clusterIdentifier']\n short_msg = {\n \"EventName\": event_name,\n \"Creator\": creator,\n \"ResourceArn\": cluster_arn,\n \"TagStatus\": \"pending\",\n \"MaxRetries\": int(os.environ['SFN_MAX_RETRIES']),\n \"Retries\": 0\n }\n\n sfn = Boto3Wrapper.get_client('stepfunctions')\n response = sfn.start_execution(\n stateMachineArn=os.environ['SFN_ARN'],\n name=creator+'-'+event_name+'-'+detail['eventID'],\n input=json.dumps(short_msg)\n )\n\n logger.info('Step Functions start execution: %s', response)\n\n return True","def lambda_handler(event, context):\n\n # resolve backend api key from the secrets manager\n sm_client = boto3.client('secretsmanager')\n sm_resp = sm_client.get_secret_value(os.getenv('BACKEND_SERVICE_API_KEY_SECRET_ARN'))\n backend_api_key = json.dumps(sm_resp.get('SecretString')).get('key')\n\n # TODO implement further business logic\n return {\n 'statusCode': 200,\n 'body': json.dumps('Hello from Lambda!')\n }","def lambda_handler(event, context):\n logger.info(\"event.session.application.applicationId=\" +\n event['session']['application']['applicationId'])\n\n \"\"\"Check that this is being called by our skill\"\"\"\n logger.info(\"Calling app: \"+str(event['session']['application']['applicationId']))\n if (event['session']['application']['applicationId'] !=\n \"amzn1.ask.skill.\"+skill_id):\n logger.error(\"Invalid application ID\")\n raise ValueError(\"Invalid Application ID\")\n\n if event['session']['new']:\n on_session_started(event, {'requestId': event['request']['requestId']},\n event['session'])\n\n if event['request']['type'] == \"LaunchRequest\":\n return on_launch(event, event['request'], event['session'])\n elif event['request']['type'] == \"IntentRequest\":\n return on_intent(event, event['request'], event['session'])\n elif event['request']['type'] == \"SessionEndedRequest\":\n return on_session_ended(event, event['request'], event['session'])\n\n # Otherwise deal with it gracefully\n logger.info(\"Unexpected request type:\")\n logger.info(json.dumps(event))\n return build_response({}, build_speechlet_response(\"Leeds Bins\", \"Welcome to Leeds Bins. Now you can find out which waste bins to take out when. Try asking: what's my next collection.\", None, False))","def handler(event, context):\n\n status = cfnresponse.FAILED\n physical_resource_id = None\n response_data = {}\n try:\n\n # Start with the default parameters.\n response_data.update(event[\"ResourceProperties\"][\"DefaultParams\"])\n\n # Then override with any values from the secret.\n secret = secrets_client.get_secret_value(\n SecretId=event[\"ResourceProperties\"][\"SecretArn\"],\n VersionId=event[\"ResourceProperties\"][\"SecretVersionId\"],\n )\n secret_values = json.loads(secret[\"SecretString\"])\n response_data.update(secret_values)\n\n # Set the log stream prefix based on the image version or tag.\n if \":\" in response_data[\"IMAGE\"]:\n response_data[\"LOG_STREAM_PREFIX\"] = response_data[\"IMAGE\"].split(\":\")[-1]\n else:\n response_data[\"LOG_STREAM_PREFIX\"] = response_data[\"IMAGE\"].split(\"/\")[-1]\n\n # Use the existing desired count when updating an existing service,\n # because this value is managed by auto scaling. Otherwise,\n # start new services with the auto scaling minimum.\n if event[\"RequestType\"] == \"Update\":\n response_data[\"AUTOSCALING_DESIRED\"] = get_desired_count(\n cluster_name=event[\"ResourceProperties\"][\"ClusterName\"],\n service_name=event[\"ResourceProperties\"][\"ServiceName\"],\n )\n else:\n response_data[\"AUTOSCALING_DESIRED\"] = response_data[\"AUTOSCALING_MIN\"]\n\n status = cfnresponse.SUCCESS\n\n finally:\n cfnresponse.send(event, context, status, response_data, physical_resource_id)","def response(status, message, code):\n return make_response(jsonify({\n 'status': status,\n 'message': message\n })), code","def lambda_handler(Event, Context):\n if 'StateMachineArn' in Event.keys():\n step_function_arn = Event['StateMachineArn']\n r = step_function_client.start_execution(\n stateMachineArn=step_function_arn,\n input=json.dumps({\"last_updated\": \"\"}))\n\n else:\n stepfunctions = [os.getenv(\"CHARGEBEEDOWNLOADARN\"), os.getenv(\"EXCHANGERATESDOWNLOADARN\")]\n\n for stepfunction in stepfunctions:\n step_function_arn = stepfunction\n r = step_function_client.start_execution(\n stateMachineArn=step_function_arn,\n input=json.dumps({\"last_updated\": \"\"}))","def lambda_handler(event, context):\n return","def lambda_handler(event, context):\n return","def test3():\n event = {\n \"Records\": [\n {\n \"s3\": {\n \"s3SchemaVersion\": \"1.0\",\n \"configurationId\": \"b0efd5b1-cc92-47b4-8501-1c34f5eba235\",\n \"bucket\": {\n \"name\": \"/tmp/\"\n },\n \"object\": {\n \"key\": \"tic000129646247_s0001-1-1_stlc.fits\"\n }\n }\n }\n ]\n}\n context = {}\n \n out = lambda_handler(event, context)\n \n assert out[\"statusCode\"] == 200","def cloud_handler(event, context):\n # Payload & Headers initialization for GET API calls\n payload = {}\n headers = {}\n\n url = CORRECT_URL + \":\" + WRONG_PORT + API\n\n response = get_call_api(url, payload, headers)\n response_code = response.status_code\n try:\n response_data = response.json()\n except Exception as e:\n response_data = json.dumps(\"Error : {}\".format(e))\n\n return {\n \"url\": url,\n \"data\": response_data,\n \"status_code\": response_code\n }","def lambda_handler(event, context):\n logger.debug(event)\n\n product_list = PRODUCT_LIST\n\n return {\n \"statusCode\": 200,\n \"headers\": HEADERS,\n \"body\": json.dumps({\"products\": product_list}),\n }","def process_response(response):\n # Print it and exit with 1 if operation wasn't successful\n print(response['message'])\n if response['status'] != 'success':\n sys.exit(1)","def handler(event, context):\n try:\n # Retrieve environment variables\n dimension_name = getenv(\"CODEDEPLOY_DIMENSION_NAME\")\n metric_name = getenv(\"CODEDEPLOY_METRIC_NAME\")\n if not dimension_name or not metric_name:\n return \"CODEDEPLOY_DIMENSION_NAME or CODEDEPLOY_METRIC_NAME not set\"\n\n # Get deployment state from CodeDeploy event\n deployment_state = event[\"detail\"][\"state\"]\n print(f\"Deployment state: {deployment_state}\")\n\n # Pushing custom metric to CW\n response = boto3.client(\"cloudwatch\").put_metric_data(\n MetricData=[\n {\n \"MetricName\": metric_name,\n \"Dimensions\": [{\"Name\": dimension_name, \"Value\": deployment_state}],\n \"Unit\": \"None\",\n \"Value\": 1,\n \"Timestamp\": datetime.datetime.now(),\n },\n ],\n Namespace=\"CodeDeployDeploymentStates\",\n )\n print(f\"Response from CW service: {response}\")\n return response\n # pylint: disable=broad-except\n except Exception as excpt:\n print(f\"Execution failed... {excpt}\")\n return None","def lambda_handler(event, context):\n\n LOGGER.debug(\"Lambda invoked\")\n log_environment_variables()\n\n\n # 1) Initialize\n # Setup required AWS infrastructure\n create_infrastructure()\n\n # If configuration exists, retrieve from S3 - otherwise create a new one\n config = get_config()\n LOGGER.info(f\"config loaded: {config}\")\n\n # If previously_valid_urls exists, retrieve from S3, otherwise create a blank one\n previously_valid_urls = get_previously_valid_urls()\n LOGGER.info(f\"previously_valid_urls loaded: {list(previously_valid_urls)}\")\n\n\n # 2) Retrieve URL data\n # Generate URLs\n end_value_inclusive = config[\"start_value\"] + config[\"look_ahead\"] + 1\n requests = [{\"url\": config[\"url\"] % value, \"value\": value} for value in\n range(config[\"start_value\"], end_value_inclusive)]\n LOGGER.info(f\"Requesting values in range {config['start_value']} -> {end_value_inclusive} (inclusive)\")\n LOGGER.info(f\"First URL to request: {requests[0]['url'] if requests else 'None'}\")\n LOGGER.info(f\"Last URL to request: {requests[-1]['url'] if requests else 'None'}\")\n\n # Get all responses\n responses = get_responses(requests)\n\n # Create a list of requests that responded with status code 200\n valid_urls = [{\"url\": response[\"url\"], \"value\": response[\"value\"]} for response in responses\n if response[\"response\"].status == 200]\n\n\n # 3) Process URL data\n # Check valid_urls against previously_valid_urls and add to new_valid_urls\n new_valid_urls = []\n for valid_url in valid_urls:\n if valid_url[\"url\"] not in previously_valid_urls:\n LOGGER.debug(f\"New valid URL found: {valid_url['url']}\")\n new_valid_urls.append(valid_url)\n\n\n # 4) Send alerts based upon processed URL data\n # Send emails for new_valid_urls\n if new_valid_urls:\n LOGGER.info(\"New URLs found, sending email.\")\n send_email(config[\"from_email\"], config[\"to_email\"], config[\"email_subject\"],\n generate_email_body(new_valid_urls))\n\n # Once emails have sent (and threw no exception), update start_value if slide_window is True\n if config[\"slide_window\"]:\n greatest_valid_value = new_valid_urls[-1]['value']\n\n new_start_value = greatest_valid_value + 1\n LOGGER.info(f\"Updating start value from {config['start_value']} to {new_start_value}\")\n config[\"start_value\"] = new_start_value\n\n\n # 5) Save state\n # Save previously_valid_urls as list of just URL strings, no metadata is needed\n save_previously_valid_urls([valid_url[\"url\"] for valid_url in valid_urls])\n\n save_config(config)\n\n\n return {\n \"statusCode\": 200,\n \"body\": json.dumps({\n \"all_valid_urls\": valid_urls,\n \"new_valid_urls\": new_valid_urls,\n }),\n }","def lambda_handler(event, context):\n\n # return {\n # \"statusCode\": 200,\n # \"body\": json.dumps(\n # {\n # \"message\": \"hello world\",\n # }\n # ),\n # }\n src_bytes = base64.b64decode(event[\"body\"])\n src = cv2.imdecode(np.frombuffer(src_bytes, dtype=np.uint8), cv2.IMREAD_COLOR)\n gray = cv2.cvtColor(src, cv2.COLOR_BGR2GRAY)\n _, gray_bytes = cv2.imencode(\".jpg\", gray)\n\n return {\n \"statusCode\": 200,\n \"body\": json.dumps(base64.b64encode(gray_bytes).decode(\"UTF-8\")),\n }","def lambda_handler(event, context):\n\n client = boto3.client('events')\n event_to_put = {\n \"source\": \"aws-lambda-function\"\n }\n event_to_put.update(**event)\n try:\n response = client.put_events(\n Entries=[\n {\n 'Source': 'learn.eventbridge',\n 'Detail': json.dumps(event_to_put),\n 'DetailType': 'Learning Eventbridge',\n 'EventBusName': 'default'\n },\n ]\n )\n return {\n 'statusCode': 200,\n 'body': json.dumps('Event has been put on event bus successfully.')\n }\n except Exception as ex:\n return {\n 'statusCode': 500,\n 'body': json.dumps(str(ex))\n }","def lambda_handler(event, context):\n\n # try:\n # ip = requests.get(\"http://checkip.amazonaws.com/\")\n # except requests.RequestException as e:\n # # Send some context about this error to Lambda Logs\n # print(e)\n\n # raise e","def to_lambda_output(self):\n resp = {\n 'statusCode': self.status_code,\n 'body': self.body,\n 'headers': self.headers\n }\n\n return resp","def lambda_handler(event, context):\n\n # try:\n # ip = requests.get(\"http://checkip.amazonaws.com/\")\n # except requests.RequestException as e:\n # # Send some context about this error to Lambda Logs\n # print(e)\n\n # raise e\n print(event)\n method=event['httpMethod']\n print(f\"method={method}\")\n print(f\"table_name={table_name}\")\n myTriggerType='instrument_price'\n\n \n if method == \"DELETE\":\n #path=event['path']\n trigger_id=event['pathParameters']['trigger_id']\n print(f\"triggerId={trigger_id}\")\n\n try:\n #see https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/dynamodb.html#DynamoDB.Table.delete_item\n response = table.delete_item(\n Key={'PK':f\"TR#{myTriggerType}#{trigger_id}\", \"SK\":f\"TR#{myTriggerType}#{trigger_id}\"},\n ConditionExpression=And(Attr('PK').eq(Attr('SK')),Attr('triggerType').eq(myTriggerType)),\n )\n except ClientError as e:\n print(f\"clientError={e}\")\n if e.response['Error']['Code']=='ConditionalCheckFailedException':\n return iftttError(404,\"item not found\")\n raise\n print(f\"response={response}\")\n return {\n \"statusCode\": 200,\n \"body\":\"\",\n }\n \n elif method == \"POST\":\n body=json.loads(event['body'])\n trigger_id=body['trigger_identity']\n print(f\"triggerId={trigger_id}\")\n\n response = table.get_item(\n Key={'PK':f\"TR#{myTriggerType}#{trigger_id}\", \"SK\":f\"TR#{myTriggerType}#{trigger_id}\"},\n ProjectionExpression=\"triggerEvents, triggerType\",\n )\n print(f\"response={response}\")\n\n if \"Item\" not in response:\n #brand new \n print(f\"inserting {trigger_id}\")\n if 'triggerFields' not in body:\n return iftttError(400, \"triggerFields missing from request\")\n triggerFields=body['triggerFields']\n #todo validate trigger fields\n try:\n response = table.put_item(\n Item={\n 'PK':f\"TR#{myTriggerType}#{trigger_id}\", \n \"SK\":f\"TR#{myTriggerType}#{trigger_id}\",\n 'triggerId': trigger_id,\n #hacky string way to avoid having multiple columns\n 'triggerFields': json.dumps(triggerFields),\n 'triggerType': myTriggerType,\n },\n ConditionExpression=Or(Attr('triggerType').eq(myTriggerType),Attr('triggerType').not_exists())\n )\n except ClientError as e:\n print(f\"clientError={e}\")\n #somehow got created with someone elses triggerType\n if e.response['Error']['Code']=='ConditionalCheckFailedException':\n return iftttError(404,\"item not found\")\n raise\n print(\"response \",response)\n triggered=[]\n elif response['Item'].get(\"triggerType\",myTriggerType) != myTriggerType:\n #it exists but it is someone elses\n return iftttError(404,\"item not found\")\n else:\n item=response['Item']\n print(f\"found {item} \")\n #hacky string way to avoid having multiple columns\n #TODO: change this to use a Map? (will allow to add without overwrite)\n events = json.loads(item.get(\"triggerEvents\",\"[]\"))\n triggered= []\n for event in events:\n #TODO: implement limit (not needed now becasue I expect only up to one events)\n triggered.append(event['data'])\n \n return {\n \"statusCode\": 200,\n \"body\": json.dumps({\n \"data\": triggered,\n # \"location\": ip.text.replace(\"\\n\", \"\")\n }),\n }\n else :\n return iftttError(400, f\"unexpected httpMethod {method}\")","def lambda_handler(event, context):\n # get the image data\n if 'queryStringParameters' in event:\n image = event['queryStringParameters']['base64image']\n k = event['queryStringParameters']['k'] # number of similar images\n\n else:\n image = event['base64image']\n k = event['k'] # number of similar images\n \n # connect to elastic search\n es = connect_to_ES(ES_ENDPOINT)\n\n # get the image features from the sagemaker endpoint\n features = extract_features(image)\n uris = search_es(es, features, k)\n presigned_uris = create_presigned_url(uris, expiration=3600*24*2)\n\n return {\n 'statusCode': 200,\n 'headers':{'Content-Type':'application/json'},\n 'body': json.dumps({'images': presigned_uris})\n }","def lambda_handler(event, context):\r\n if 'session' in event:\r\n print(\"event.session.application.applicationId=\" +\r\n event['session']['application']['applicationId'])\r\n\r\n \"\"\"\r\n Uncomment this if statement and populate with your skill's application ID to\r\n prevent someone else from configuring a skill that sends requests to this\r\n function.\r\n \"\"\"\r\n if ('session' in event and (event['session']['application']['applicationId'] !=\r\n \"amzn1.ask.skill.57119d91-fb3c-487f-be53-4e7fac12fb83\")):\r\n raise ValueError(\"Invalid Application ID\")\r\n\r\n \"\"\"if event['session']['new']:\r\n on_session_started({'requestId': event['request']['requestId']},\r\n event['session'])\"\"\"\r\n\r\n if event['request']['type'] == \"LaunchRequest\":\r\n return on_launch(event['request'], event['session'])\r\n elif event['request']['type'] == \"IntentRequest\":\r\n return on_intent(event['request'], event['session'])\r\n elif event['request']['type'] == \"SessionEndedRequest\":\r\n return on_session_ended(event['request'], event['session'])\r\n elif event['request']['type'] == 'UPDATE':\r\n return saveCoffeeMachineStatus(event['request'])\r\n elif event['request']['type'] == \"GLASS\":\r\n return glassStatus(event['request'])\r\n elif event['request']['type'] == \"WATER\":\r\n return waterStatus(event['request'])\r\n elif event['request']['type'] == \"COFFEE\":\r\n return coffeeStatus(event['request'])\r\n elif event['request']['type'] == \"ON_OFF\":\r\n return on_off_status(event['request'])\r\n elif event['request']['type'] == \"ONLINE\":\r\n return online_status_f(event['request'])\r\n elif event['request']['type'] == 'BUSY':\r\n return busyStatus(event['request'])","def _send_immediate_response(self, success, message=\"\"):\r\n\r\n # Send the response indicating success/failure\r\n response_str = json.dumps(\r\n {'return_code': 0 if success else 1, 'content': message}\r\n )\r\n\r\n if self._is_grade_request():\r\n self.send_response(\r\n 200, content=response_str, headers={'Content-type': 'text/plain'}\r\n )\r\n self.log_message(\"XQueue: sent response {0}\".format(response_str))\r\n\r\n else:\r\n self.send_response(500)","def check_deployment(version_stack_name, app_name):\n\n print(\"Polling Target Group ({}) until a successful state is reached...\".format(version_stack_name))\n elbv2 = boto3.client('elbv2')\n waiter = elbv2.get_waiter('target_in_service')\n cloudformation = boto3.client('cloudformation')\n response = cloudformation.describe_stack_resources(\n StackName=version_stack_name,\n LogicalResourceId='ALBTargetGroup'\n )\n target_group = response['StackResources'][0]['PhysicalResourceId']\n start_time = datetime.datetime.now()\n try:\n waiter.wait(TargetGroupArn=target_group)\n except botocore.exceptions.WaiterError:\n print('Health check did not pass!')\n response = cloudformation.describe_stack_resources(\n StackName=version_stack_name,\n LogicalResourceId='ECSService'\n )\n service = response['StackResources'][0]['PhysicalResourceId']\n print('Outputting events for service {}:'.format(service))\n response = cloudformation.describe_stack_resources(\n StackName=\"ECS-{}\".format(app_name),\n LogicalResourceId='ECSCluster'\n )\n cluster = response['StackResources'][0]['PhysicalResourceId']\n ecs = boto3.client('ecs')\n response = ecs.describe_services(\n cluster=cluster,\n services=[service]\n )\n for event in [x['message'] for x in response['services'][0]['events']]:\n print(event)\n# print('Deleting CloudFormation stack...')\n# response = cloudformation.delete_stack(\n# StackName=\"MV-{realm}-{app_name}-{version}-{env}\".format(env=os.environ['ENV'], app_name=os.environ['ECS_APP_NAME'], version=os.environ['BUILD_VERSION'], realm=os.environ['REALM'])\n# )\n# waiter = cf.get_waiter('stack_delete_complete')\n# waiter.wait(\n# StackName=\"MV-{realm}-{app_name}-{version}-{env}\".format(env=os.environ['ENV'], app_name=os.environ['ECS_APP_NAME'], version=os.environ['BUILD_VERSION'], realm=os.environ['REALM'])\n# )\n# print('CloudFormation stack deleted.')\n elapsed_time = datetime.datetime.now() - start_time\n print('Health check passed in {}'.format(elapsed_time))\n print(\"Done.\")","def _error_response(self):\r\n response_dict = {'success': False, 'version': 1}\r\n self.send_response(\r\n 400, content=json.dumps(response_dict),\r\n headers={'Content-type': 'application/json'}\r\n )","def success(self, appstruct):\n \n return None","def lambda_handler(event, context):\n feature = session.query(m.Feature).first()\n return {\n \"statusCode\": 200,\n \"body\": json.dumps({\n \"message\": feature.title,\n }),\n }","def callback(request, uri, headers): # lint-amnesty, pylint: disable=unused-argument\n headers['CorrelationId'] = 'aefd38b7-c92c-445a-8c7a-487a3f0c7a9d'\n headers['RequestNo'] = '[787177]' # This is the format SAPSF returns for the transaction request number\n return 500, headers, 'Failure!'","def lambda_handler(event: Dict[str, Any], context: Any) -> str:\n print(\"Received event: \" + json.dumps(event, indent=2))\n\n migrated_ami_id: str = event[\"migrated_ami_id\"]\n kms_id: str = event[\"kms_id\"]\n region: str = event.get(\"region\", os.environ.get(\"AWS_REGION\"))\n role: str = event.get(\"role\")\n instance_name: str = event.get(\"name\", \"\")\n\n sts_client = boto3.client(\"sts\")\n\n print(f\"Assuming role: {role}\")\n assumed_role: Dict[str, Any] = sts_client.assume_role(RoleArn=role, RoleSessionName=\"CopyImageLambda\")\n\n credentials = assumed_role.get(\"Credentials\")\n\n ec2_client = boto3.client(\n \"ec2\",\n region_name=region,\n aws_access_key_id=credentials[\"AccessKeyId\"],\n aws_secret_access_key=credentials[\"SecretAccessKey\"],\n aws_session_token=credentials[\"SessionToken\"],\n )\n\n try:\n new_image: Dict[str, Any] = ec2_client.copy_image(\n SourceImageId=migrated_ami_id,\n SourceRegion=region,\n Name=f\"copied-{migrated_ami_id}\",\n Encrypted=True,\n KmsKeyId=kms_id,\n )\n except Exception as e:\n print(e)\n return \"\"\n\n MigrationStateHandler().update_state(state=\"IMAGE_COPYING\", machine_name=instance_name)\n return new_image.get(\"ImageId\", \"\")","def lambda_handler(event, context):\n import urllib.request\n import json\n\n allowed_domain = \"brianandkelly.ws\"\n\n # print(event)\n # Get the user info from Google for the recieved token...\n id_token = event['authorizationToken']\n google_token_helper_uri = \"https://www.googleapis.com/oauth2/v3/tokeninfo?id_token=\" + id_token\n\n result = json.loads(urllib.request.urlopen(google_token_helper_uri).read())\n\n domain = result['hd']\n user = result['sub']\n effect = 'Deny'\n\n if domain == allowed_domain:\n effect = 'Allow'\n \n print(\"Access request for user: \" + result['email'] + \" with effect: \" + effect + \" processed.\")\n\n respond = {\n \"principalId\": user,\n \"policyDocument\": {\n \"Version\": \"2012-10-17\",\n \"Statement\": [\n {\n \"Action\": \"execute-api:Invoke\",\n \"Effect\": effect,\n \"Resource\": \"arn:aws:execute-api:us-east-1:*:7k8o0sgjli/securityvideos/*\"\n }\n ]\n }\n }\n\n return respond","def test_delete_message_with_succes(self, mock_client): \n\n event = {\n 'operation': 'deleteMessage', \n 'arguments': {\n 'template': 'my-sample-geofence-id',\n }\n }\n\n response = {\n \"Arn\": f'arn:aws:mobiletargeting:eus-east-1:SOME_ACCOUNT_ID:templates/my-sample-geofence-id/PUSH',\n \"RequestID\": \"some-request-id\",\n \"Message\": 'some message' \n }\n\n mock_client().delete_push_template.return_value = response\n response = manageMessages.handler(event, None)\n\n self.assertTrue(response)\n self.assertEqual(response['status'], 'MESSAGE_DELETED')","def delete_stack(stack_name, Region, ACCESS_KEY, SECRET_KEY):\n cf_client = boto3.client('cloudformation',\n region_name=Region,\n aws_access_key_id=ACCESS_KEY,\n aws_secret_access_key=SECRET_KEY)\n\n #\n # Need to use the StackId when calling describe stacks as DELETE_COMPLETE is never returned using stack names\n #\n try:\n stack_data = cf_client.describe_stacks(StackName=stack_name)\n stack_id = stack_data['Stacks'][0]['StackId']\n response = cf_client.delete_stack(StackName=stack_id)\n\n if 'ResponseMetadata' in response and response['ResponseMetadata']['HTTPStatusCode'] < 300:\n logger.info(\"Got response: {0}\".format(response))\n\n while True:\n try:\n stack_data = cf_client.describe_stacks(StackName=stack_id)\n\n if stack_data['Stacks'][0]['StackStatus'] == 'ROLLBACK_IN_PROGRESS':\n print('Stack is rolling back - check the event logs')\n time.sleep(30)\n continue\n elif stack_data['Stacks'][0]['StackStatus'] == 'DELETE_IN_PROGRESS':\n print('Stack still deleting')\n time.sleep(30)\n continue\n elif stack_data['Stacks'][0]['StackStatus'] == 'DELETE_FAILED':\n print('Stack has failed to delete check your console')\n return False\n elif stack_data['Stacks'][0]['StackStatus'] == 'ROLLBACK_FAILED':\n print('Stack has failed to rollback check your console')\n return False\n elif stack_data['Stacks'][0]['StackStatus'] == 'DELETE_COMPLETE':\n print('Stack has been deleted')\n return True\n else:\n print('Please check the stack deletion status in your AWS console')\n return False\n except ClientError:\n print('Unable to find stack {}'.format(stack_name))\n return False\n except Exception as e:\n logger.info('Got exception {}'.format(e))\n return False\n\n else:\n logger.info(\"There was an Unexpected error. response: {0}\".format(response))\n return False\n\n except ClientError as error:\n print('Got exception {}'.format(error))\n except Exception as e:\n print('Got exception {}'.format(e))","def lambda_handler(event, context):\n\n markdown_files = sys.argv[1:]\n logging.info(f'Markdown files to clean = {markdown_files}')\n for file_to_clean in markdown_files:\n clean_file(file_to_clean)\n print(json.dumps(event))\n body = event['body']\n return {\n \"statusCode\": 200,\n \"body\": json.dumps({\n \"message\": \"hello world from aws.\",\n \"commit\": body\n # \"location\": ip.text.replace(\"\\n\", \"\")\n }),\n }","def _success(self, msg=\"\"):\n if msg:\n self.result[\"message\"] = msg\n self.module.exit_json(**self.result)","def delete_queues_handler(event, context):\n logger.info(\"Request: %s\", json.dumps(event))\n # In most other contexts that Lambda is used, the return value is\n # immediately available. However, CloudFormation doesn't use those standard\n # return values, and instead wants the agent to hit a URL to indicate\n # success or failure. If CloudFormation does not receive an explicit\n # notification, it can only rely on timeouts to detect failure, and that\n # can waste human time.\n #\n # We wrap all the logic in a try/except so that we always try to send\n # CloudFormation a status message.\n try:\n sqs_client = boto3.client('sqs')\n handler(event, context, sqs_client)\n send(event, context, SUCCESS)\n except Exception, e:\n logger.error(\"Exception: %r\", e)\n\t# There is no point in trying to notify if we for some reason already\n\t# failed to notify. CloudFormation will eventually time out.\n\tif not isinstance(e, urllib2.URLError): \n send(event, context, FAILED)\n raise","def _invokeLambdaFunction(lambdaArn:str, parameters:Dict):\n client = boto3.client('lambda')\n payload = json.dumps(parameters).encode()\n try:\n response = client.invoke(\n FunctionName = lambdaArn,\n InvocationType='RequestResponse',\n LogType='Tail',\n Payload=payload\n )\n except ClientError as e:\n raise LambdaInvocationError(e)\n return response","def handle_health(event, context):\n LOGGER.info(\"Received event in handle_health:\")\n response = {\"statusCode\": os.environ[\"STATUS\"]}\n return response","def lambda_handler(event, context): # pylint: disable=too-many-locals,too-many-branches,too-many-statements\r\n try: # pylint: disable=too-many-nested-blocks\r\n print(\"Execution started!\")\r\n #print(\"Event: \",event)\r\n # Bucket name and Full path for file - where file will be uploded\r\n source_bucket_name = event[\"detail\"][\"requestParameters\"][\"bucketName\"]\r\n source_key = urllib.parse.unquote_plus(\r\n event[\"detail\"][\"requestParameters\"][\"key\"], encoding='utf-8')\r\n \r\n print(\"file_path: \",source_key)\r\n #Loading master config\r\n print(\"Loading master_config\")\r\n audit_config = {}\r\n config_path = \"./config/\" + \\\r\n os.environ['CCM_ENV'] + \"/master_config.json\"\r\n config_content = open(config_path).read()\r\n config_json = json.loads(config_content)\r\n audit_config = config_json[\"audit_config\"]\r\n snow_params = config_json[\"ERROR_NOTIFICATION_SNOW_PARAMS\"]\r\n athena_query_param = config_json[\"ATHENA_QUERY_PARAMS\"]\r\n athena_table_params = config_json[\"ATHENA_TABLE_PARAMS\"]\r\n\r\n # Audit Parameters Based on the Invoking lambda and its operation involved\r\n audit_config[\"component_type_code\"] = \"ETL\"\r\n audit_config[\"component_name\"] = \"PCP Appflow\"\r\n audit_config[\"source_name\"] = \"Patient Connections Platform\"\r\n audit_config[\"target_name\"] = \"Consumer Consent Management\"\r\n audit_config[\"full_file_path\"] = \"s3://\" + \\\r\n source_bucket_name + \"/\" + source_key\r\n audit_config[\"file_version_id\"] = \"\"\r\n\r\n # Creates Job Entry in ABC Framework\r\n print(\"audit config::\", audit_config)\r\n process_execution_id = audit_helper.\\\r\n invoke_edb_abc_log_process_status_event_job_entry(audit_config)\r\n audit_config[\"process_execution_id\"] = process_execution_id\r\n print(\"process_execution_id ::\", process_execution_id)\r\n #print(\"source_key: \",source_key)\r\n s3_write = boto3.client('s3')\r\n record_dict = {}\r\n file_name = \"\"\r\n final_json = \"\"\r\n # prefix = \"\"\r\n # file_list = []\r\n # client = boto3.client(\"s3\")\r\n # result = client.list_objects(Bucket=source_bucket_name, Prefix=source_key, Delimiter='/')\r\n # #print(result)\r\n # for obj in result.get('CommonPrefixes'):\r\n # prefix = obj.get('Prefix')\r\n # #print(prefix)\r\n # file_list = list_files(client,source_bucket_name,prefix)\r\n # for file in file_list:\r\n # #print(file)\r\n json_read = read_s3_file(source_bucket_name, source_key)\r\n data = json.loads(json_read)\r\n #print(data)\r\n if data != '':\r\n record_dict = {k.lower(): v for k, v in data.items()}\r\n print(\"Record_Dict::\",record_dict)\r\n event_type_param = {}\r\n event_type_list = athena_table_params.keys()\r\n print(\"event_type_list\",event_type_list)\r\n for key in event_type_list:\r\n print(\"key\",key)\r\n if key in source_key:\r\n print(\"key\",key)\r\n event_type_param = athena_table_params[key]\r\n print(event_type_param)\r\n if \"changeeventheader\" in record_dict:\r\n if record_dict[\"changeeventheader\"][\"changeType\"] == \"CREATE\":\r\n #and record_dict[\"dtpc_affiliate__c\"] == 'US':\r\n recordid_create = record_dict[\"changeeventheader\"][\"recordIds\"][0]\r\n print(recordid_create)\r\n if recordid_create != '':\r\n last_modified_date = record_dict[\"lastmodifieddate\"].replace(\":\",\".\")\r\n create_json = json.dumps(record_dict)\r\n final_json = create_json\r\n file_name = recordid_create + \"-create-\" + str(last_modified_date)\r\n print(\"file_name: \",file_name)\r\n outbound_path = event_type_param[\"folder_path\"]\r\n final_source_key = outbound_path + '/' + file_name+\".json\"\r\n print(\"final_source_key :\", final_source_key)\r\n s3_write.put_object(\r\n Body=final_json, Bucket=source_bucket_name, Key=final_source_key)\r\n else:\r\n raise Exception(\"RecordId is missing: \", record_dict)\r\n elif record_dict[\"changeeventheader\"][\"changeType\"] == \"UPDATE\":\r\n record_ids_list = record_dict[\"changeeventheader\"][\"recordIds\"]\r\n if len(record_ids_list) != 0:\r\n for ele in record_ids_list:\r\n print(ele)\r\n element = \"'\" + ele + \"'\"\r\n payload_condition = event_type_param[\"recordid_condition\"]\r\n query = 'SELECT * FROM '+event_type_param[\"athena_create_table\"]+\\\r\n ' WHERE lastmodifieddate IN(SELECT max(lastmodifieddate) from '\\\r\n +event_type_param[\"athena_create_table\"]+\\\r\n ', UNNEST(\"'+payload_condition[0]+'\".\"'+payload_condition[1]+\\\r\n '\") AS ln(jsondata) WHERE jsondata IN ('+element+'));'\r\n print(query)\r\n athena_query_param['athena_query'] = query\r\n query_result_record_id = athena_helper.perform_athena_search\\\r\n (athena_query_param)\r\n print(\"Athena Query Result for Create Path:::\", query_result_record_id)\r\n update_json = create_complete_payload(data,query_result_record_id)\r\n print(\"update_json: \",update_json)\r\n if len(update_json) != 0:\r\n last_modified_date = record_dict[\"lastmodifieddate\"].replace\\\r\n (\":\",\".\")\r\n final_json = json.dumps(update_json)\r\n file_name = ele + \"-update-\" + str(last_modified_date)\r\n print(\"file_name: \",file_name)\r\n outbound_path = event_type_param[\"folder_path\"]\r\n final_source_key = outbound_path + '/' + file_name+\".json\"\r\n print(\"final_source_key :\", final_source_key)\r\n s3_write.put_object(\r\n Body=final_json, Bucket=source_bucket_name, \\\r\n Key=final_source_key)\r\n else:\r\n print(ele,\" does not have a create payload\")\r\n else:\r\n raise Exception(\"RecordId is missing: \", record_dict)\r\n else:\r\n raise Exception(\"ChangeEventHeader is missing: \", record_dict)\r\n else:\r\n raise Exception(\"Invalid Payload: \", record_dict)\r\n\r\n except (Exception) as err: # pylint: disable=line-too-long,broad-except\r\n print(\"Error occured: {0}\".format(str(err)))\r\n audit_type = \"error\"\r\n error_msg = sys.exc_info()\r\n exc_type = error_msg\r\n exc_obj = error_msg\r\n snow_params[\"flag\"] = \"FAIL\"\r\n snow_params[\"error_message\"] = str(exc_obj)\r\n snow_params[\"error_type\"] = str(exc_type)\r\n audit_config[\"exception_message\"] = str(exc_obj)\r\n if audit_config != {}:\r\n logging.exception(sys.exc_info())\r\n audit_helper.invoke_edb_abc_log_process_status_event(\r\n audit_type, audit_config) # pylint: disable=line-too-long\r\n audit_helper.raise_snow_incident(snow_params)","def handler(event, context):\n return awsgi.response(app, event, context)","def test_response_string():\n\n msg = \"This is a test\"\n retval = apigateway.response(msg)\n\n assert retval[\"body\"] == json.dumps({\"message\": msg})\n assert retval[\"statusCode\"] == 200","def notify_snitch(comp_name, action, level, msg):\n\n headers = json.loads(os.environ.get(\"headers\"))\n epoch = int(datetime.datetime.now().strftime('%s'))\n data = {\n \"source\": \"aws_lamba\",\n \"time\": epoch,\n \"event\": {\n \"log_level\": level,\n \"action\": action,\n \"time\": epoch,\n \"application_name\": \"aws_cleanup\",\n \"message\": msg,\n \"component_name\": comp_name\n }\n }\n try:\n json_data = json.dumps(data)\n response = str(requests.post(os.environ.get(\"snitch_endpoint\"), headers=headers, data=json_data, verify=False))[1:-1]\n\n if response != 'Response [200]':\n raise ConnectionError(\"Error code: \" + response)\n except Exception as e:\n messages = []\n messages.append(\"\"\"The AWS Lambda function: dev-png-aws-manage was unable to communicate with Snitch; <strong style=\"font-family: 'Helvetica Neue',Helvetica,Arial,sans-serif; box-sizing: border-box; font-size: 14px; margin: 0;\">\"\"\" + str(e) + \"\"\"</strong>\n\t\t\t\t\t\t\t\t\t</td>\n\t\t\t\t\t\t\t\t</tr><tr style=\"font-family: 'Helvetica Neue',Helvetica,Arial,sans-serif; box-sizing: border-box; font-size: 14px; margin: 0;\"><td class=\"content-block\" style=\"font-family: 'Helvetica Neue',Helvetica,Arial,sans-serif; box-sizing: border-box; font-size: 14px; vertical-align: top; margin: 0; padding: 0 0 20px;\" valign=\"top\">\n\t\t\t\t\t\t\t\t\t\tThis email was sent to alert you that Snitch requests are not working.\n <ul>\n <li>Without Snitch, any logging or monitoring is down and we cannot view events</li>\n </ul>\n \"\"\" + json_data)\n messages.append(\"The AWS Lambda function, dev-png-aws-manage was unable to communicate with Snitch; \" + str(e) + \"\\n This message was sent to alert you that Snitch requests are not working. Without Snitch any logging or monitoring is down and we cannot view events.\")\n \n lambda_client = boto3.client('lambda')\n email_data = {\n 'sender_mail': SENDER_EMAIL,\n 'email': EMAIL_RECIPIENT,\n 'subj': \"Snitch error\",\n 'heading': \"Alert: Could not send message to Snitch!\",\n 'messages': messages,\n 'region': os.environ.get(\"AWS_DEFAULT_REGION\")\n }\n invoke_email_response = lambda_client.invoke(\n FunctionName= os.environ.get(\"formatted_email\"),\n InvocationType= \"RequestResponse\",\n Payload= json.dumps(email_data)\n )\n err = checkError(invoke_email_response, \"Error sending email!\")\n if err:\n return err\n \n slack_data = {\n 'application_url': APP_URL,\n 'channel': CHANNEL,\n 'message': messages[1]\n }\n invoke_slack_response = lambda_client.invoke(\n FunctionName= os.environ.get(\"slack_message\"),\n InvocationType= \"RequestResponse\",\n Payload= json.dumps(slack_data)\n )\n err = checkError(invoke_slack_response, \"Error sending slack message!\")\n if err:\n return err","def test_success(self, put, get, auth, circuits_app, fn_cloud_foundry_action, fn_cloud_foundry_applications):\n auth.return_value = AuthenticationMock()\n put.return_value = give_response(201, GUIDS_MOCK[\"resources\"][0])\n get.return_value = give_response(200, GUIDS_MOCK)\n\n function_params = { \n \"fn_cloud_foundry_action\": fn_cloud_foundry_action,\n \"fn_cloud_foundry_applications\": fn_cloud_foundry_applications\n }\n results = call_fn_cloud_foundry_manage_applications_function(circuits_app, function_params)\n assert results[\"test1\"][\"success\"] == True","def lambda_handler(event, context):\n return dispatch(event)","def handler(event, context):\n alert_message = json.loads(event['Records'][0]['Sns']['Message'])\n alarm_name = alert_message['AlarmName']\n reason = alert_message['NewStateReason']\n new_state = alert_message['NewStateValue']\n color = \"good\" if new_state == 'OK' else \"danger\"\n\n region = os.getenv('AWS_DEFAULT_REGION')\n alert_url = f'https://console.aws.amazon.com/cloudwatch/home?region={region}#alarm:alarmFilter=ANY;name={alarm_name}'\n link = f\"<{alert_url}|{alarm_name}>\"\n\n secrets = json.loads(get_secret()['SecretString'])\n default_slack_channel = secrets['slack_alert_channel']\n alarm_description = json.loads(alert_message.get('AlarmDescription', '{}'))\n slack_channel = alarm_description.get(\"slack_channel\", default_slack_channel)\n description = alarm_description.get(\"description\")\n slack_message = '\\n'.join(\n [f\"New state: {new_state}\", f\"Description: {description}\", reason]\n )\n\n attachments = [{\n \"fallback\": f\"{link} {slack_message}\",\n \"title\": alarm_name,\n \"title_link\": alert_url,\n \"text\": slack_message,\n \"color\": color\n }]\n\n slack_url = secrets['slack_webhooks'][slack_channel]\n\n post_message_to_url(slack_url, {\"attachments\": attachments})","def handler(event, context=None):\n LOGGER.info(event)\n result = {'status':'400', 'messages':'Error'}\n bucket = event[\"Records\"][0][\"s3\"][\"bucket\"][\"name\"]\n key = unquote_plus(event[\"Records\"][0][\"s3\"][\"object\"][\"key\"].encode(\"utf8\"))\n obj = S3.get_object(Bucket=bucket, Key=key)\n document_file = (key, BytesIO(obj[\"Body\"].read()))\n lendi_document_id = obj[\"Metadata\"][\"lendi_document_id\"]\n master_document_id = obj[\"Metadata\"][\"master_document_id\"]\n workflow_id = get_workflow_id(master_document_id)\n if workflow_id:\n xtracta_response = upload_document_to_xtracta(workflow_id, document_file)\n else:\n logging.error(\"No workflow_id for master_document_id: %s\", master_document_id)\n return result\n if xtracta_response['status'] == '200':\n insert_document(xtracta_response['document_id'], lendi_document_id,\\\n workflow_id, 'preprocessing')\n else:\n logging.error(\"No xtracta_document_id for %s\", xtracta_response['message'])\n return xtracta_response\n return xtracta_response","def response_for_created_event(event, status_code):\n return make_response(jsonify({'event' : {\n 'event_id': event.event_id,\n 'event_name': event.event_name,\n 'event_location': event.event_location,\n 'event_eval_link': event.event_eval_link,\n 'event_time': event.event_time,\n 'created_on': event.event_created_on,\n 'modified_on': event.event_updated_on\n }, 'status': 'success'})), status_code","def lambda_handler(event, context):\n client = boto3.client('sns')\n topic_arn = os.environ['topic_arn']\n try:\n quarantine_instance_id = event['quarantine_instance_id']\n message = \"Quarantine Instance : {quarantine_instance_id} has been launched.\".format(quarantine_instance_id=quarantine_instance_id)\n subject = \"Quarantine Instance had been launched.\"\n client.publish(\n TopicArn=topic_arn,\n Message=message,\n Subject=subject\n )\n return message\n except KeyError as ke:\n print(\"Key not found : {ke}\".format(ke=ke))\n raise\n except:\n print(\"Error encountered while sending SNS notification about instance launch.\")\n raise","def lambda_function_as_expected(test_case: unittest.TestCase, function_name: str, handler: str,\n state: str = 'Active', runtime: str = 'python3.8', memory_size: int = 128,\n timeout: int = 10, env_vars: dict = None) -> None:\n lambda_function_response = aws_lambda.get_function(FunctionName=function_name)\n lambda_function: dict = lambda_function_response.get('Configuration')\n\n test_case.assertEqual(function_name, lambda_function.get('FunctionName'))\n test_case.assertEqual(state, lambda_function.get('State'))\n test_case.assertEqual(runtime, lambda_function.get('Runtime'))\n test_case.assertEqual(handler, lambda_function.get('Handler'))\n test_case.assertEqual(memory_size, lambda_function.get('MemorySize'))\n test_case.assertEqual(timeout, lambda_function.get('Timeout'))\n\n if env_vars is not None:\n test_case.assertDictEqual(env_vars, lambda_function.get('Environment').get('Variables'))","def lambda_handler(event, context):\n try:\n aq = Aquifer()\n aq.run()\n\n return \"Completed\"\n\n except (Exception, KeyboardInterrupt) as e:\n return \"Error occurred\"","def test_exception_handler_on_web_request(self):\n lh = LambdaHandler(\"tests.test_exception_handler_settings\")\n\n event = {\n \"body\": \"\",\n \"resource\": \"/{proxy+}\",\n \"requestContext\": {},\n \"queryStringParameters\": {},\n \"headers\": {\n \"Host\": \"1234567890.execute-api.us-east-1.amazonaws.com\",\n },\n \"pathParameters\": {\"proxy\": \"return/request/url\"},\n \"httpMethod\": \"GET\",\n \"stageVariables\": {},\n \"path\": \"/return/request/url\",\n }\n\n mocked_exception_handler.assert_not_called()\n response = lh.handler(event, None)\n\n self.assertEqual(response[\"statusCode\"], 500)\n mocked_exception_handler.assert_called()","def test_wsgi_script_name_on_test_request(self):\n lh = LambdaHandler(\"tests.test_wsgi_script_name_settings\")\n\n event = {\n \"body\": \"\",\n \"resource\": \"/{proxy+}\",\n \"requestContext\": {},\n \"queryStringParameters\": {},\n \"headers\": {},\n \"pathParameters\": {\"proxy\": \"return/request/url\"},\n \"httpMethod\": \"GET\",\n \"stageVariables\": {},\n \"path\": \"/return/request/url\",\n }\n response = lh.handler(event, None)\n\n self.assertEqual(response[\"statusCode\"], 200)\n self.assertEqual(response[\"body\"], \"https://zappa:80/return/request/url\")","def api_response(fn):\n def response(self, *args, **kw):\n logger.debug(\"Data: %s\" % (get_input()))\n data = fn(self, *args, **kw)\n count = len(data)\n if count > 0:\n return {'status': 'ok', 'code': 200, 'count': count, 'data': data}\n else:\n raise Error(NORESULT)\n return response","def lambda_handler(event, context):\n #print(\"event.session.application.applicationId=\" + event['session']['application']['applicationId'])\n\n #if event['session']['new']:\n # on_session_started({'requestId': event['request']['requestId']},event['session'])\n \n intent = None\n try:\n intent = Intent(**event)\n return handle_intent(intent)\n except Exception as ex:\n err = traceback.format_exc()\n print(err)\n return error_handler_func(intent,msg=str(err))","def lambda_handler(event, context):\n if DEBUG:\n print(\"event : {}\".format(json.dumps(event)))\n\n if event['session']['new']:\n if DEBUG:\n print(\"on_session_started requestId=\" + event['request']['requestId'] + \", sessionId=\" + event['session']['sessionId'])\n if event['request']['type'] == \"LaunchRequest\":\n if DEBUG:\n print(\"on_launch requestId=\" + event['request']['requestId'] + \", sessionId=\" + event['session']['sessionId'])\n return get_start_end_response(False)\n elif event['request']['type'] == \"IntentRequest\":\n if DEBUG:\n print(\"on_intent requestId=\" + event['request']['requestId'] + \", sessionId=\" + event['session']['sessionId'])\n return on_intent(event['request'], event['session'])\n elif event['request']['type'] == \"SessionEndedRequest\":\n if DEBUG:\n print(\"on_session_ended requestId=\" + event['request']['requestId'] + \", sessionId=\" + event['session']['sessionId'])","def handler(event, context):\n response = None\n try:\n if 'Records' in event:\n record = testSerializer(event['Records'][0]['dynamodb']['NewImage'])\n type = record['type']\n if type == \"PostCreatedEvent\":\n addPost(record)\n elif type == \"PostUpdatedEvent\":\n updatePost(record)\n elif type == \"PostDeletedEvent\":\n deletePost(record)\n elif type == \"PostVoteEvent\":\n votePost(record)\n response = None\n else:\n if event['httpMethod'] == \"GET\":\n response = respond(None, getPost(event))\n except Exception as error:\n logger.exception(error)\n response = {\n 'status': 500,\n 'error': {\n 'type': type(error).__name__,\n 'description': str(error),\n },\n }\n finally:\n return response","def lambda_handler(event, context):\n print(\"event.session.application.applicationId=\" +\n event['session']['application']['applicationId'])\n\n \"\"\"\n Uncomment this if statement and populate with your skill's application ID to\n prevent someone else from configuring a skill that sends requests to this\n function.\n \"\"\"\n # if (event['session']['application']['applicationId'] !=\n # \"amzn1.echo-sdk-ams.app.[unique-value-here]\"):\n # raise ValueError(\"Invalid Application ID\")\n\n if event['session']['new']:\n on_session_started({'requestId': event['request']['requestId']},\n event['session'])\n\n if event['request']['type'] == \"LaunchRequest\":\n return on_launch(event['request'], event['session'])\n elif event['request']['type'] == \"IntentRequest\":\n return on_intent(event['request'], event['session'])\n elif event['request']['type'] == \"SessionEndedRequest\":\n return on_session_ended(event['request'], event['session'])\n else:\n print (\"********************** Unknown Request\")","def on_response_validation_error(err):\n return jsonify(message='Bad response'), 500","def lambda_handler(event, context):\n params = parse_qs(event['body'])\n token = params['token'][0] if 'token' in params else ''\n\n if not verify_slack_token(token):\n logger.error(\"Request token (%s) does not match expected token\", token)\n return lambda_response(Exception('Invalid request token'))\n\n return gauges_app(params)","def send_returns(returns):\n #Build the header\n head = {'Content-Type':'application/json'}\n head.update(auth_head)\n r = pool.urlopen('POST',server_address+'/command_returns?client_name='+client_name,headers=head,body=json.dumps(returns))\n if r.data!=\"Processed\":\n print \"Something went horribly wrong on the other end. Here are the bits of information to retry later with...\"\n print json.dumps(returns)","def lambda_handler(event, context):\r\n print(\"Incoming request...\")\r\n\r\n \"\"\"\r\n Uncomment this if statement and populate with your skill's application ID to\r\n prevent someone else from configuring a skill that sends requests to this\r\n function.\r\n \"\"\"\r\n # if (event['session']['application']['applicationId'] !=\r\n # \"amzn1.echo-sdk-ams.app.[unique-value-here]\"):\r\n # raise ValueError(\"Invalid Application ID\")\r\n\r\n if event['session']['new']:\r\n on_session_started({'requestId': event['request']['requestId']},\r\n event['session'])\r\n\r\n if event['request']['type'] == \"LaunchRequest\":\r\n return on_launch(event['request'], event['session'])\r\n elif event['request']['type'] == \"IntentRequest\":\r\n return on_intent(event['request'], event['session'])\r\n elif event['request']['type'] == \"SessionEndedRequest\":\r\n return on_session_ended(event['request'], event['session'])","def exceptionhandler(e):\n response = e.get_response()\n response.data = json.dumps({\n \"code\" : e.code,\n \"name\": e.name,\n \"description\": e.description\n })\n response.content_type = \"application/json\"\n\n return response","def lambda_handler(event, context):\n for record in event[\"Records\"]:\n arn = record[\"Sns\"][\"TopicArn\"].split(\":\")\n message = json.loads(record[\"Sns\"][\"Message\"])\n message_handler(arn[3], message)\n return ''","def lambda_handler(event, context):\n # Boto is always available in AWS lambda, but may not be available in standalone mode\n# import boto3\n# from base64 import b64decode\n\n # To generate the encrypted values, go to AWS IAM Keys and Generate a key\n # Then grant decryption using the key to the IAM Role used for your lambda function.\n #\n # Use the command `aws kms encrypt --key-id alias/<key-alias> --plaintext <value-to-encrypt>\n # Put the encrypted value in the configuration dictionary below\n# encrypted_config = {\n# 'pagerduty_api_key': '<ENCRYPTED VALUE>',\n# 'schedule_ids': '<ENCRYPTED VALUE>'\n# }\n\n# kms = boto3.client('kms')\n# config = {x: kms.decrypt(CiphertextBlob=b64decode(y))['Plaintext'] for x, y in encrypted_config.iteritems()}\n on_call = OnCall(API_KEY, SCHEDULE_IDS)\n output = on_call.run()\n\n return { \"response_type\": \"in_channel\", \"text\": '\\n'.join(output) }","def lambda_handler(event, context):\n print('HANDLING EVENT')\n print(\"event.session.application.applicationId=\" +\n event['session']['application']['applicationId'])\n\n \"\"\"\n Uncomment this if statement and populate with your skill's application ID to\n prevent someone else from configuring a skill that sends requests to this\n function.\n \"\"\"\n # if (event['session']['application']['applicationId'] !=\n # \"amzn1.echo-sdk-ams.app.[unique-value-here]\"):\n # raise ValueError(\"Invalid Application ID\")\n\n\n if event['session']['new']:\n on_session_started({'requestId': event['request']['requestId']},\n event['session'])\n\n if event['request']['type'] == \"LaunchRequest\":\n return on_launch(event['request'], event['session'])\n elif event['request']['type'] == \"IntentRequest\":\n return on_intent(event['request'], event['session'])\n elif event['request']['type'] == \"SessionEndedRequest\":\n return on_session_ended(event['request'], event['session'])"],"string":"[\n \"def lambda_handler(event, context):\\n\\n return {\\n \\\"statusCode\\\": 200,\\n \\\"body\\\": json.dumps({\\n 'message': 'API deployed',\\n })\\n }\",\n \"def lambda_handler(event, context):\\n return {\\n 'statusCode': 200,\\n 'body': say_hello()\\n }\",\n \"def lambda_response(err, res=None):\\n return {\\n 'statusCode': '400' if err else '200',\\n 'body': err.message if err else json.dumps(res),\\n 'headers': {\\n 'Content-Type': 'application/json',\\n },\\n }\",\n \"def aws_lambda(f):\\n def wrapper(event, context):\\n logger = logging.getLogger(__name__)\\n try:\\n # if no return value is given by wrapped func,\\n # return default status code 200 response.\\n r = f(event, context)\\n if r is None:\\n r = {\\n 'statusCode': 200,\\n 'body': json.dumps({'input': event})\\n }\\n return r\\n except Exception as e:\\n # if exception is thrown, log exception,\\n # return exception text,\\n # and return status code associated with passed\\n # exception type\\n logger.info(\\n 'Call to {} resulted in exception'.format(f.__name__), e)\\n exc_type = type(e)\\n # get exception type for code lookup and msg\\n if exc_type is type:\\n exc_type = e\\n msg = e.__name__\\n else:\\n msg = str(e)\\n # get default exception code for raised Exception.\\n # default to code 500 if exception is not in codes dict.\\n code = codes.get(exc_type, DEFAULT_EXCEPTION_CODE)\\n return {\\n 'statusCode': code,\\n 'body': json.dumps({'input': event, 'message': msg})\\n }\\n\\n wrapper.__name__ = f.__name__ + '_wrapper'\\n return wrapper\",\n \"def lambda_handler(event, context):\\n # define initial status code and headers\\n statusCode = 400\\n try:\\n # get the body params\\n if type(event) == dict:\\n event_body = event.get('body', event)\\n else:\\n event_body = json.loads(event).get('body', {})\\n # generate and store the reservation response result from reservation handler function\\n reservation_handler = ReservationHandler(EventBodyData=event_body)\\n result = reservation_handler.sabre_reservation_handler()\\n # define status code, headers and response\\n if type(result) == dict:\\n statusCode = result.get(\\\"statusCode\\\", statusCode)\\n response = result.get(\\\"body\\\", \\\"\\\")\\n else:\\n response = result\\n except Exception as E:\\n response = str(E)\\n\\n # return the response\\n return {\\n 'statusCode': statusCode,\\n 'body': response\\n }\",\n \"def checkError(invoke_response, message):\\n\\n if 'FunctionError' in invoke_response:\\n err_message = invoke_response['Payload'].read()\\n print(message)\\n print(err_message)\\n return {\\n 'statusCode': 500,\\n 'body': json.dumps(str(err_message))\\n }\\n return None\",\n \"def hosted_function(payload):\\n logger.info('hosted_function called')\\n return 'OK', 201\",\n \"def lambda_handler(event, context):\\n\\n # try:\\n # ip = requests.get(\\\"http://checkip.amazonaws.com/\\\")\\n # except requests.RequestException as e:\\n # # Send some context about this error to Lambda Logs\\n # print(e)\\n\\n # raise e\\n\\n print(\\\"starting 1\\\")\\n #myBuckets = Buckets()\\n #bucketsList = \\\"disabled\\\" #myBuckets.list()\\n\\n userTopic = Topic()\\n snsResponse = userTopic.publish()\\n print(\\\"done\\\")\\n\\n return {\\n \\\"statusCode\\\": 200,\\n \\\"body\\\": json.dumps({\\n \\\"message\\\": \\\"hello world sns:{}\\\".format( snsResponse), \\n # \\\"location\\\": ip.text.replace(\\\"\\\\n\\\", \\\"\\\")\\n }),\\n }\",\n \"def lambda_handler(event, context):\\r\\n if event[\\\"httpMethod\\\"] == \\\"POST\\\":\\r\\n # Decrypt the recaptchaKey environment variable. If it's stored as plaintext recaptchaKey = environ['recaptchaKey'] will work perfectly\\r\\n recaptchaKey = boto3.client('kms').decrypt(CiphertextBlob=b64decode(environ['recaptchaKey']))['Plaintext'].decode('utf-8')\\r\\n # Remove the Access-Control-Allow-* headers if requests will always be from the same domain\\r\\n returnDict = {\\r\\n \\\"statusCode\\\": 500,\\r\\n 'headers': {\\r\\n \\\"Access-Control-Allow-Origin\\\": \\\"*\\\",\\r\\n \\\"Access-Control-Allow-Headers\\\": \\\"Content-Type\\\",\\r\\n \\\"Access-Control-Allow-Methods\\\": \\\"OPTIONS,POST,GET\\\"\\r\\n }\\r\\n }\\r\\n try:\\r\\n if not event[\\\"body\\\"]:\\r\\n returnDict[\\\"body\\\"] = json.dumps({\\\"Error\\\": \\\"Empty body\\\"})\\r\\n else:\\r\\n body = json.loads(event[\\\"body\\\"])\\r\\n responseFromGoogle = requests.post(\\\"https://www.google.com/recaptcha/api/siteverify\\\", {\\\"secret\\\": recaptchaKey, \\r\\n \\\"response\\\": body[\\\"g-recaptcha-response\\\"]})\\r\\n # Fallback - failure should be indicated by success being False not a HTTP error\\r\\n if responseFromGoogle.status_code != 200:\\r\\n returnDict[\\\"body\\\"] = json.dumps({\\\"Error\\\": \\\"ReCaptcha validation failed\\\"})\\r\\n # If the request failed\\r\\n elif responseFromGoogle.json()[\\\"success\\\"] == False: \\r\\n returnDict[\\\"statusCode\\\"] = 200\\r\\n returnDict[\\\"body\\\"] = json.dumps({\\\"Error\\\": \\\"ReCaptcha validation failed.\\\", \\r\\n \\\"error-codes\\\": responseFromGoogle.json().get(\\\"error-codes\\\", [])\\r\\n })\\r\\n elif responseFromGoogle.json()[\\\"score\\\"] < 0.5:\\r\\n returnDict[\\\"statusCode\\\"] = 200\\r\\n returnDict[\\\"body\\\"] = json.dumps({\\\"Error\\\": \\\"Score below threshhold\\\"})\\r\\n else:\\r\\n try:\\r\\n send_email(body)\\r\\n returnDict[\\\"statusCode\\\"] = 200\\r\\n returnDict[\\\"body\\\"] = json.dumps({\\\"message\\\": \\\"Email sent successfully\\\"})\\r\\n print(\\\"Email sent!\\\")\\r\\n except Exception as e:\\r\\n print(e.response['Error']['Message'])\\r\\n returnDict[\\\"body\\\"] = json.dumps({\\\"Error\\\": \\\"Unable to send email\\\"})\\r\\n except Exception as e:\\r\\n print(e) \\r\\n returnDict[\\\"body\\\"] = json.dumps({\\\"Error\\\": \\\"Exception occurred\\\"})\\r\\n finally:\\r\\n return returnDict\",\n \"def lambda_handler(event, context):\\n\\n # Get Params\\n pipeline = event['detail']['pipeline']\\n executionId = event['detail']['execution-id']\\n status = event['detail']['state']\\n stage = event['detail']['stage']\\n \\n if stage.lower() == \\\"Source\\\".lower():\\n return '200 OK'\\n\\n # Get Pipeline execution details.\\n client = boto3.client('codepipeline')\\n pipelineDetails = client.get_pipeline_execution(\\n pipelineName=pipeline,\\n pipelineExecutionId=executionId\\n )\\n\\n # Format Slack Message.\\n message = '*Commit*\\\\n' + pipelineDetails['pipelineExecution']['artifactRevisions'][0]['revisionSummary']\\n message = message + '\\\\n*Link*\\\\n' + pipelineDetails['pipelineExecution']['artifactRevisions'][0]['revisionUrl']\\n color = {\\n 'SUCCEEDED': 'good',\\n 'FAILED': 'danger',\\n 'STARTED': '#4682B4'\\n }.get(status, '#2a2a2a')\\n\\n # JSON Payload.\\n payload = {\\n 'channel': CHANNEL,\\n 'attachments': [\\n {\\n 'color': color,\\n 'text': message,\\n 'fields': [\\n {\\n 'title': 'Stage',\\n 'value': stage.title(),\\n 'short': 'true'\\n },\\n {\\n 'title': 'Status',\\n 'value': status.title(),\\n 'short': 'true'\\n }\\n ],\\n }\\n ]\\n }\\n # Construct the HTTP request that will be sent to the Slack API.\\n request = urllib.request.Request(\\n SLACK_URL,\\n method='POST',\\n data=json.dumps(payload).encode('utf-8')\\n )\\n # Add Headers.\\n request.add_header('Authorization', 'Bearer ' + TOKEN)\\n request.add_header('Content-Type', 'application/json')\\n \\n # Make the Request.\\n urllib.request.urlopen(request).read()\\n\\n # Return Success.\\n return '200 OK'\",\n \"def lambda_handler(event, context):\\n\\n print(\\\"EVENT:\\\")\\n print(event)\\n\\n # try:\\n # ip = requests.get(\\\"http://checkip.amazonaws.com/\\\")\\n # except requests.RequestException as e:\\n # # Send some context about this error to Lambda Logs\\n # print(e)\\n\\n # raise e\\n\\n\\n recs = flow(event, s3)\\n print(recs)\\n\\n return {\\n \\\"statusCode\\\": 200,\\n \\\"body\\\": json.dumps({\\n \\\"message\\\": \\\"hello world\\\",\\n # \\\"location\\\": ip.text.replace(\\\"\\\\n\\\", \\\"\\\")\\n }),\\n }\",\n \"def lambda_handler(event, context):\\n\\n # Log the values received in the event argument\\n logger.info(f'Request event: {event}')\\n\\n # Define default hard-coded return values\\n response = {\\n 'uid': 'Example function ID',\\n 'return_val01': 'Return value #1',\\n 'return_val02': 'Return Value #2',\\n }\\n\\n # Retrieve type of invocation (GET, PUT, etc.)\\n if 'http_verb' in event:\\n operation = event['http_verb'].upper()\\n if operation == 'PUT':\\n # Return the values passed to the function\\n response = {\\n 'uid': event['functionID'],\\n 'return_val01': event['parameters']['parm01'],\\n 'return_val02': event['parameters']['parm02'],\\n }\\n\\n logger.info(f'Response={response}')\\n return response\",\n \"def lambda_handler(event, context):\\n try:\\n # Extract the Job ID\\n job_id = event['CodePipeline.job']['id']\\n\\n # Extract the Job Data\\n job_data = event['CodePipeline.job']['data']\\n\\n # Extract the params\\n params = get_user_params(job_data)\\n\\n # Get the lists of artifacts coming in and out of this function\\n input_artifacts = job_data['inputArtifacts']\\n output_artifacts = job_data['outputArtifacts']\\n\\n # Perform a build on the source (from source_artifact)\\n # and write results to the build_artifact\\n s3 = setup_s3_client(job_data)\\n source_artifact = find_artifact(input_artifacts, params['source_artifact'])\\n src_dir = get_zipped_artifact(s3, source_artifact)\\n dest_dir = tempfile.mkdtemp()\\n perform_build(os.path.join(src_dir, 'src'), dest_dir)\\n build_artifact = find_artifact(output_artifacts, params['build_artifact'])\\n put_zipped_artifact(s3, dest_dir, build_artifact)\\n\\n # Pick the template out of the source code and write it to the\\n # template_artifact\\n template_artifact = find_artifact(output_artifacts, params['template_artifact'])\\n put_zipped_artifact(s3, os.path.join(src_dir, params['template_subdir_path']), template_artifact)\\n\\n shutil.rmtree(src_dir)\\n shutil.rmtree(dest_dir)\\n put_job_success(job_id, \\\"Built code: \\\" + \\\", template:\\\")\\n\\n except Exception as e:\\n # If any other exceptions which we didn't expect are raised\\n # then fail the job and log the exception message.\\n print('Function failed due to exception.')\\n print(e)\\n traceback.print_exc()\\n put_job_failure(job_id, 'Function exception: ' + str(e))\\n\\n print('Function complete.')\\n return \\\"Complete.\\\"\",\n \"def lambda_handler(event, context):\\n\\n try:\\n created_item = create_new_table_item(event)\\n return {\\\"statusCode\\\": 201, \\\"body\\\": json.dumps(f\\\"{created_item}\\\")}\\n\\n except BlankRequestBody as exception:\\n logging.error(f\\\"{exception}\\\")\\n return {\\\"statusCode\\\": 400, \\\"body\\\": json.dumps(MISSING_PARAMETERS_MESSAGE)}\\n\\n except ValidationError as exception:\\n logging.error(f\\\"{exception}\\\")\\n return {\\\"statusCode\\\": 400, \\\"body\\\": json.dumps(INCORRECT_PARAMETERS_MESSAGE)}\\n\\n except Exception as exception:\\n logging.error(f\\\"{exception}\\\")\\n return {\\\"statusCode\\\": 500, \\\"body\\\": json.dumps(\\\"Internal server error\\\")}\",\n \"def lambda_handler(event, context):\\n logging.info(event)\\n current_time = datetime.datetime.utcnow().strftime(\\\"%Y-%m-%dT%H:%M:%SZ\\\")\\n is_conversation_result = 'Details' in event\\n if is_conversation_result:\\n combine_bot_state_to_s3(event, current_time)\\n else:\\n save_bot_state_to_s3(event, current_time)\\n\\n # Generate response back to bot\\n response = dict()\\n if not is_conversation_result:\\n response = {\\n 'dialogAction': {\\n 'type': 'Delegate',\\n 'slots': event['currentIntent']['slots']\\n }\\n }\\n logging.info(response)\\n return response\",\n \"def hello(event, context):\\n\\n result = {\\n 'data': {\\n 'message': 'Kanban-board!'\\n },\\n 'status': 200\\n }\\n return _get_response(result)\\n\\n # Use this code if you don't use the http event with the LAMBDA-PROXY\\n # integration\\n \\\"\\\"\\\"\\n return {\\n \\\"message\\\": \\\"Go Serverless v1.0! Your function executed successfully!\\\",\\n \\\"event\\\": event\\n }\\n \\\"\\\"\\\"\",\n \"def glue_failure_handler(event, context) -> None:\\n\\n # get lambda variables\\n environment_level = os.environ[\\\"ENVIRONMENT\\\"]\\n sns_topic_name = os.environ[\\\"PROCESSING_NOTIFICATION_SNS\\\"]\\n\\n # fetches the response in dict received from eventbridge rule when 'event pattern' matches\\n # here failing a glue job is an event pattern. this is already stated in event rule 'glue_failure_rule'\\n # the output from below will be like --> {\\\"version\\\": \\\"0\\\", \\\"id\\\": \\\"26c9301e-6aec-872d-4739-1aa6ebc66853\\\", \\\"detail-type\\\": \\\"Glue Job State Change\\\", \\\"source\\\": \\\"aws.glue\\\", \\\"account\\\": \\\"795038802291\\\", \\\"time\\\": \\\"2021-03-24T07:37:07Z\\\", \\\"region\\\": \\\"us-east-1\\\", \\\"resources\\\": [], \\\"detail\\\": {\\\"jobName\\\": \\\"rahul_create_warm_pool\\\", \\\"severity\\\": \\\"ERROR\\\", \\\"state\\\": \\\"FAILED\\\", \\\"jobRunId\\\": \\\"jr_bdb3f0d56b0f842c7337be54927deb6ddb78a1f37956d9817b0f11710602139f\\\", \\\"message\\\": \\\"Command failed with exit code 1\\\"}}\\n event_response = json.dumps(event)\\n\\n # calling GlueFailureEvent class\\n glue_failure_event = GlueFailureEvent(event_response)\\n\\n # get failure time (event occurrence)\\n event_occurrence_time = glue_failure_event.time # returns ISO UTC time (ex: '2021-03-24T15:52:37Z')\\n # convert ISO UTC time to regular UTC time and then to ET local time\\n converted_time = event_occurrence_time.replace(\\\"T\\\",\\\" \\\").replace(\\\"Z\\\",\\\".0\\\")\\n _converted_time = datetime.strptime(converted_time, \\\"%Y-%m-%d %H:%M:%S.%f\\\")\\n local_time = convert_utc_to_local(_converted_time, DEFAULT_TIMEZONE)\\n\\n if glue_failure_event.detail['jobRunId'].endswith(\\\"attempt_1\\\"):\\n # we dont want get notified for attempt1 run failure because this will always be failure in real time.\\n # so its an attempt to avoid 2 failure alerts for each failed glue job event\\n return None\\n # form subject, message required for publishing SNS\\n subject = f\\\"{environment_level}: Glue job Failure\\\"\\n message = (\\n f\\\"Glue job '{glue_failure_event.detail['jobName']}' has {glue_failure_event.detail['state']} at {local_time}. Below are the details:{os.linesep}{os.linesep}\\\"\\n f\\\"Error : {glue_failure_event.detail['message']}{os.linesep}\\\"\\n f\\\"{os.linesep}\\\"\\n f\\\"JobRunId : {glue_failure_event.detail['jobRunId']}\\\"\\n )\\n\\n send_ahub_email_notification(sns_topic_name, subject, message)\",\n \"def deploy_resources(credentials, template, stack_name, stack_region, SNSfriendlyname, SNSemail, SNSdescription):\\n\\n datestamp = time.strftime(\\\"%d/%m/%Y\\\")\\n client = boto3.client('cloudformation',\\n aws_access_key_id=credentials['AccessKeyId'],\\n aws_secret_access_key=credentials['SecretAccessKey'],\\n aws_session_token=credentials['SessionToken'],\\n region_name=stack_region)\\n print(\\\"Creating stack \\\" + stack_name + \\\" in \\\" + stack_region)\\n\\n creating_stack = True\\n while creating_stack is True:\\n try:\\n creating_stack = False\\n create_stack_response = client.create_stack(\\n StackName=stack_name,\\n TemplateBody=template,\\n Parameters=[\\n {\\n 'ParameterKey': 'SNSfriendlyname',\\n 'ParameterValue': SNSfriendlyname\\n },\\n\\n {\\n 'ParameterKey': 'SNSemail',\\n 'ParameterValue': SNSemail\\n },\\n {\\n 'ParameterKey': 'SNSdescription',\\n 'ParameterValue': SNSdescription\\n }\\n ],\\n NotificationARNs=[],\\n Capabilities=[\\n 'CAPABILITY_NAMED_IAM',\\n ],\\n OnFailure='ROLLBACK',\\n Tags=[\\n {\\n 'Key': 'ManagedResource',\\n 'Value': 'True'\\n },\\n {\\n 'Key': 'DeployDate',\\n 'Value': datestamp\\n }\\n ]\\n )\\n except botocore.exceptions.ClientError as e:\\n creating_stack = True\\n print(e)\\n print(\\\"Retrying...\\\")\\n time.sleep(10)\\n\\n stack_building = True\\n print(\\\"Stack creation in process...\\\")\\n print(create_stack_response)\\n while stack_building is True:\\n event_list = client.describe_stack_events(StackName=stack_name).get(\\\"StackEvents\\\")\\n stack_event = event_list[0]\\n\\n if (stack_event.get('ResourceType') == 'AWS::CloudFormation::Stack' and\\n stack_event.get('ResourceStatus') == 'CREATE_COMPLETE'):\\n stack_building = False\\n print(\\\"Stack construction complete.\\\")\\n elif (stack_event.get('ResourceType') == 'AWS::CloudFormation::Stack' and\\n stack_event.get('ResourceStatus') == 'ROLLBACK_COMPLETE'):\\n stack_building = False\\n print(\\\"Stack construction failed.\\\")\\n sys.exit(1)\\n else:\\n print(stack_event)\\n print(\\\"Stack building . . .\\\")\\n time.sleep(10)\\n\\n stack = client.describe_stacks(StackName=stack_name)\\n return stack\",\n \"def lambda_handler(event, context):\\n\\n params = ['comp_name', 'action', 'level', 'msg']\\n missing = []\\n for i in range(len(params)):\\n if params[i] not in event:\\n missing.append(params[i])\\n \\n if missing:\\n err = \\\"Missing these parameters: \\\" + str(missing)\\n print(err)\\n \\n return {\\n \\\"statusCode\\\": 500,\\n \\\"body\\\": json.dumps(err)\\n }\\n notify_snitch(event['comp_name'], event['action'], event['level'], event['msg'])\\n \\n return {\\n \\\"statusCode\\\": 200,\\n \\\"body\\\": json.dumps('Notified snitch')\\n }\",\n \"def lambda_handler(event, context):\\n\\n event_body = json.loads(event['body'])\\n print(\\\"EVENT:\\\")\\n print(event_body)\\n\\n\\n # try:\\n # ip = requests.get(\\\"http://checkip.amazonaws.com/\\\")\\n # except requests.RequestException as e:\\n # # Send some context about this error to Lambda Logs\\n # print(e)\\n\\n # raise e\\n\\n recs = flow(event_body, textract, cache = True)\\n rval = {\\n \\\"statusCode\\\": 200,\\n \\\"body\\\": json.dumps({\\n \\\"message\\\" : \\\"hello world\\\",\\n \\\"textract\\\" : recs\\n # \\\"location\\\": ip.text.replace(\\\"\\\\n\\\", \\\"\\\")\\n }),\\n }\\n\\n return rval\",\n \"def lambda_handler(event, context):\\n\\n # try:\\n # ip = requests.get(\\\"http://checkip.amazonaws.com/\\\")\\n # except requests.RequestException as e:\\n # # Send some context about this error to Lambda Logs\\n # print(e)\\n\\n # raise e\\n\\n try:\\n response = s3.get_object(Bucket=BUCKET, Key=KEY)\\n print('CONTENT TYPE:', response['ContentType'])\\n print('response:')\\n pprint.pprint(response)\\n print('event')\\n pprint.pprint(event)\\n print('payload')\\n pprint.pprint(event.get('payload'))\\n # return json.loads(json.dumps(response, default=str))\\n # defined by https://docs.aws.amazon.com/apigateway/latest/developerguide/set-up-lambda-proxy-integrations.html#api-gateway-simple-proxy-for-lambda-output-format\\n return {\\n 'statusCode': 200,\\n 'isBase64Encoded': False,\\n 'body': json.dumps(response, default=str)\\n }\\n # return response['ContentType']\\n except Exception as e:\\n print(e)\\n print('Error getting object {} from bucket {}. Make sure they exist and your bucket is in the same region as this function.'.format(KEY, BUCKET))\\n raise e\",\n \"def func_wrapper(event, context):\\n req = Request(event, context)\\n\\n try:\\n resp = func(req)\\n\\n if not isinstance(resp, Response):\\n message = (\\n 'Invalid return value from handler. '\\n 'It should be either Response or Exception'\\n )\\n raise TypeError(message)\\n except ServerlessError as e:\\n status_code = e.status_code\\n message = e.message if e.message else e.__class__.__name__\\n\\n resp = to_error_response(message, e.errors, status_code)\\n except Exception as e: # pylint: disable=W0703\\n logger.exception(e)\\n status_code = 500\\n message = 'InternalServerError'\\n errors = tuple()\\n\\n resp = to_error_response(message, errors, status_code)\\n return resp.to_lambda_output()\",\n \"def test_response_status():\\n\\n status_code = 400\\n retval = apigateway.response(\\\"Message\\\", status_code)\\n assert retval[\\\"statusCode\\\"] == status_code\",\n \"def lambda_handler(event, context=None):\\n response = {}\\n try:\\n response = middleware.IdentityAuthMiddleWare.process_request(event, response)\\n except Exception as e:\\n response[\\\"message\\\"] = e.message\\n response[\\\"errors\\\"] = e.errors\\n # removing request_dump data\\n if \\\"request_dump\\\" in response[\\\"errors\\\"]:\\n del response[\\\"errors\\\"][\\\"request_dump\\\"]\\n for _k, _v in response[\\\"errors\\\"].items():\\n response[\\\"errors\\\"][_k] = str(_v)\\n return response\",\n \"def lambda_handler(event, context):\\n\\n # Get details from the event.\\n job = event[\\\"CodePipeline.job\\\"]\\n input_bucket, input_key = get_input_artifact_location(job)\\n output_bucket, output_key = get_output_artifact_location(job)\\n user_params = get_user_parameters(job)\\n assume_role_arn = user_params[\\\"AssumeRoleArn\\\"]\\n image_parameter_name = user_params[\\\"ImageParameterName\\\"]\\n stack_name = user_params[\\\"StackName\\\"]\\n template_filename = user_params[\\\"TemplateFilename\\\"]\\n\\n # Create client in the pipeline account.\\n pipeline_s3_client = get_artifact_s3_client(job)\\n\\n # Create clients in the target account.\\n target_session = get_session(\\n role_arn=assume_role_arn, session_name=\\\"prepare-ami-deployment\\\"\\n )\\n target_cfn_client = target_session.client(\\\"cloudformation\\\")\\n target_ssm_client = target_session.client(\\\"ssm\\\")\\n\\n # Download the input artifact zip file, read manifest.json from it,\\n # and get the AMI it references. Also look up the associated image name.\\n with download_zip_file(\\n s3_client=pipeline_s3_client, bucket=input_bucket, key=input_key\\n ) as zip_file:\\n image_detail_string = zip_file.read(\\\"imageDetail.json\\\").decode(\\\"utf-8\\\")\\n log(\\\"IMAGE_DETAIL_STRING\\\", image_detail_string)\\n image_detail = json.loads(image_detail_string)\\n image = image_detail[\\\"ImageURI\\\"]\\n log(\\\"IMAGE\\\", image)\\n\\n # Update the SSM parameters with the image,\\n # to be used by the CloudFormation deployment stage of the pipeline.\\n target_ssm_client.put_parameter(\\n Name=image_parameter_name, Value=image, Type=\\\"String\\\", Overwrite=True\\n )\\n\\n # Write the CloudFormation stack's template to the output artifact location,\\n # to be used by the CloudFormation deployment stage of the pipeline.\\n template = get_cloudformation_template(\\n cfn_client=target_cfn_client, stack_name=stack_name\\n )\\n with create_zip_file({template_filename: template}) as zip_path:\\n pipeline_s3_client.upload_file(zip_path, output_bucket, output_key)\",\n \"def lambda_handler(event, context):\\n # EOL char append function\\n encode_data = lambda x: \\\"{data}{eol}\\\".format(data=json.dumps(x), eol=chr(10)).encode(\\\"UTF-8\\\")\\n \\n # Punk API call\\n try:\\n logger.debug(\\\"Requesting api: {api}\\\".format(api=os.environ[\\\"API_URL\\\"]))\\n request = r.get(os.environ[\\\"API_URL\\\"])\\n except Exception as e:\\n logger.error(\\\"An error occured while requesting api: {api}\\\".format(api=os.environ[\\\"API_URL\\\"]))\\n raise e\\n \\n # Send records to kinesis stream\\n logger.debug(\\\"Sending data to stream: {stream}\\\".format(stream=os.environ[\\\"STREAM_NAME\\\"]))\\n for data in request.json():\\n client.put_record(\\n StreamName=os.environ[\\\"STREAM_NAME\\\"],\\n Data=encode_data(data),\\n PartitionKey=\\\"key\\\"\\n )\\n\\n return {\\n 'statusCode': request.status_code,\\n 'body': data\\n }\",\n \"def serverless_lambda_deploy(self, gateway_stack_name, lambda_stack_name, code_key):\\n\\n domain_name = self.get_stack_domain_name(gateway_stack_name)\\n\\n if not domain_name:\\n print \\\"[WARN] Custom Domain not found. Depolying to $LATEST\\\"\\n self.lambda_deploy_code(lambda_stack_name, code_key)\\n return True\\n\\n current_live_stage = self.get_live_stack(domain_name)\\n\\n to_alias = None\\n\\n if current_live_stage == 'green':\\n to_alias = 'blue'\\n elif current_live_stage == 'blue':\\n to_alias = 'green'\\n\\n lambdas = self.get_stack_lambdas(lambda_stack_name)\\n print \\\"[INFO] Updating lambda code for %s \\\" % str(lambdas)\\n\\n for aws_lambda in lambdas:\\n\\n self.get_or_set_lambda_alias(aws_lambda)\\n function_arn = self.get_lambda_arn(aws_lambda)\\n self.update_lambda_code_key(function_arn, code_key)\\n code_bucket, code_key = self.get_lambda_code_bucket_and_key(function_arn)\\n\\n print \\\"[INFO] Updating lambda code for %s with bucket: %s & key: %s\\\" % (\\n aws_lambda, code_bucket, code_key)\\n\\n self.lambda_client.update_function_code(\\n FunctionName=aws_lambda,\\n S3Bucket=code_bucket,\\n S3Key=code_key,\\n # S3ObjectVersion='string', # TODO Soon!\\n Publish=True\\n )\\n\\n response = self.lambda_client.publish_version(\\n FunctionName=aws_lambda,\\n Description=datetime.now().strftime(\\\"%A, %d. %B %Y %I:%M%p %S\\\")\\n )\\n\\n version = response[\\\"Version\\\"]\\n\\n if not to_alias or not version:\\n print \\\"[FAIL] Unable to version deploy\\\"\\n\\n self.lambda_client.update_alias(\\n FunctionName=aws_lambda,\\n Name=to_alias,\\n FunctionVersion=version\\n )\\n\\n print \\\"[INFO] Function %s Updated to version %s linked to alias %s\\\" % (aws_lambda, version, to_alias)\",\n \"def lambda_handler(func):\\n logger = logging.getLogger(__name__)\\n\\n @wraps(func)\\n def func_wrapper(event, context):\\n \\\"\\\"\\\"\\n This is what's invoked via lambda.\\n\\n Args:\\n event (dict): AWS Lambda event\\n context (LambdaContext): AWS Lambda context\\n\\n .. _AWS Lambda python programming model:\\n http://docs.aws.amazon.com/lambda/latest/dg/python-programming-model-handler-types.html\\n \\\"\\\"\\\"\\n req = Request(event, context)\\n\\n try:\\n resp = func(req)\\n\\n if not isinstance(resp, Response):\\n message = (\\n 'Invalid return value from handler. '\\n 'It should be either Response or Exception'\\n )\\n raise TypeError(message)\\n except ServerlessError as e:\\n status_code = e.status_code\\n message = e.message if e.message else e.__class__.__name__\\n\\n resp = to_error_response(message, e.errors, status_code)\\n except Exception as e: # pylint: disable=W0703\\n logger.exception(e)\\n status_code = 500\\n message = 'InternalServerError'\\n errors = tuple()\\n\\n resp = to_error_response(message, errors, status_code)\\n return resp.to_lambda_output()\\n return func_wrapper\",\n \"def lambda_handler(event, context):\\n\\n operations = {\\n 'POST': main,\\n }\\n\\n if event.get('httpMethod', False):\\n operation = event['httpMethod']\\n else:\\n operation = \\\"not available\\\"\\n\\n payload = base64.b64decode(event['body'])\\n try:\\n payload = json.loads(payload)\\n except TypeError:\\n pass\\n\\n if operation in operations:\\n return respond(None, operations[operation](payload))\\n else:\\n return respond(ValueError(f'Unsupported method {operation}'))\",\n \"def lambda_handler(event, context):\\n\\n # S3 resource invocation\\n s3_resource = boto3.resource('s3')\\n # S3 bucket selection\\n data_bucket_name = \\\"put_here_data_bucket_name\\\"\\n # The SageMaker runtime is what allows us to invoke the endpoint that we've created.\\n runtime = boto3.Session().client('sagemaker-runtime')\\n\\n request_body_dict = json.loads(event['body'])\\n\\n # Now we use the SageMaker runtime to invoke our endpoint, sending both ticker and start date if given\\n if request_body_dict['start_date'] != \\\"\\\":\\n response = runtime.invoke_endpoint(EndpointName='DeepAR-ml-spp', # The name of the endpoint we created\\n ContentType='application/json', # The data format that is expected\\n Body=encode_future_request(request_body=request_body_dict,\\n s3_resource=s3_resource,\\n s3_bucket=data_bucket_name, prefix='valid'))\\n # or only ticker name if no start date has been provided\\n elif request_body_dict['ticker_name'] != \\\"\\\":\\n response = runtime.invoke_endpoint(EndpointName='DeepAR-ml-spp', # The name of the endpoint we created\\n ContentType='application/json', # The data format that is expected\\n Body=encode_request(ticker_name=request_body_dict['ticker_name'],\\n s3_resource=s3_resource, s3_bucket=data_bucket_name,\\n prefix='train'))\\n\\n # The response is an HTTP response whose body contains the result of our inference\\n result = response['Body'].read().decode('utf-8')\\n\\n # print data for debug purposes\\n print(result)\\n\\n return {\\n 'statusCode': 200,\\n 'headers': {'Content-Type': 'application/json', 'Access-Control-Allow-Origin': '*'},\\n 'body': str(result)\\n }\",\n \"def lambda_handler(event, context) -> Dict:\\n\\n destination: Optional[str] = os.environ.get(\\\"DESTINATION\\\")\\n assert destination, \\\"failed to get destination\\\"\\n\\n ec2_client = boto3.client(\\\"ec2\\\")\\n try:\\n vpcs_res: Dict = ec2_client.describe_vpcs()\\n except ClientError as error:\\n raise error\\n\\n for vpc in vpcs_res.get(\\\"Vpcs\\\", []):\\n try:\\n flow_logs_res: Dict = ec2_client.describe_flow_logs(\\n Filters=[{\\\"Name\\\": \\\"resource-id\\\", \\\"Values\\\": [vpc.get(\\\"VpcId\\\")]}],\\n )\\n except ClientError as error:\\n raise error\\n\\n flow_logs = flow_logs_res.get(\\\"FlowLogs\\\", [])\\n\\n if len(flow_logs) == 0:\\n try:\\n response = ec2_client.create_flow_logs(\\n ResourceIds=[vpc.get(\\\"VpcId\\\")],\\n ResourceType=\\\"VPC\\\",\\n TrafficType=\\\"ALL\\\",\\n LogDestinationType=\\\"s3\\\",\\n LogDestination=destination,\\n )\\n except ClientError as error:\\n raise error\\n\\n if response.get(\\\"Unsuccessful\\\", []) != []:\\n raise Exception(f\\\"failed to create_flow_logs {response}\\\")\\n\\n print(f\\\"Created flow logs for {vpc.get('VpcId')}\\\")\\n\\n elif len(flow_logs) >= 1:\\n exists = False\\n for flow_log in flow_logs:\\n if flow_log.get(\\\"LogDestination\\\", \\\"\\\") == destination:\\n exists = True\\n\\n if not exists:\\n try:\\n ec2_client.create_flow_logs(\\n ResourceIds=[vpc.get(\\\"VpcId\\\")],\\n ResourceType=\\\"VPC\\\",\\n TrafficType=\\\"ALL\\\",\\n LogDestinationType=\\\"s3\\\",\\n LogDestination=destination,\\n )\\n except ClientError as error:\\n raise error\\n print(f\\\"Created flow logs for {vpc.get('VpcId')}\\\")\\n\\n return {\\\"statusCode\\\": 200, \\\"body\\\": \\\"success\\\"}\",\n \"def lambda_handler(event, context):\\n print(event)\\n print(context)\\n storage_gateway_status()\",\n \"def test4():\\n event = {\\n \\\"Records\\\": [\\n {\\n \\\"s3\\\": {\\n \\\"s3SchemaVersion\\\": \\\"1.0\\\",\\n \\\"configurationId\\\": \\\"b0efd5b1-cc92-47b4-8501-1c34f5eba235\\\",\\n \\\"bucket\\\": {\\n \\\"name\\\": \\\"/tmp/\\\"\\n },\\n \\\"object\\\": {\\n \\\"key\\\": \\\"tic000147203645/tic000147203645_s0001-1-1_stlc.fits\\\"\\n }\\n }\\n }\\n ]\\n}\\n context = {}\\n \\n out = lambda_handler(event, context)\\n \\n assert out[\\\"statusCode\\\"] == 200\",\n \"def test_create_message_with_succes(self, mock_client): \\n\\n event = {\\n 'operation': 'createMessage', \\n 'arguments': {\\n 'template': 'my-sample-geofence-id',\\n 'input': {\\n 'service': 'APNS',\\n 'action': 'OPEN_APP',\\n 'title': 'Sample Title',\\n 'body': 'This is a sample body'\\n }\\n }\\n }\\n\\n response = {\\n \\\"Arn\\\": f'arn:aws:mobiletargeting:eus-east-1:SOME_ACCOUNT_ID:templates/my-sample-geofence-id/PUSH',\\n \\\"RequestID\\\": \\\"some-request-id\\\",\\n \\\"Message\\\": 'some message' \\n }\\n\\n mock_client().create_push_template.return_value = response\\n response = manageMessages.handler(event, None)\\n\\n self.assertTrue(response)\\n self.assertEqual(response['status'], 'MESSAGE_CREATED')\",\n \"def test_wsgi_script_name_on_alb_event(self):\\n lh = LambdaHandler(\\\"tests.test_wsgi_script_name_settings\\\")\\n\\n event = {\\n \\\"requestContext\\\": {\\n \\\"elb\\\": {\\n \\\"targetGroupArn\\\": \\\"arn:aws:elasticloadbalancing:region:123456789012:targetgroup/my-target-group/6d0ecf831eec9f09\\\"\\n }\\n },\\n \\\"httpMethod\\\": \\\"GET\\\",\\n \\\"path\\\": \\\"/return/request/url\\\",\\n \\\"queryStringParameters\\\": {},\\n \\\"headers\\\": {\\n \\\"accept\\\": \\\"text/html,application/xhtml+xml\\\",\\n \\\"accept-language\\\": \\\"en-US,en;q=0.8\\\",\\n \\\"content-type\\\": \\\"text/plain\\\",\\n \\\"cookie\\\": \\\"cookies\\\",\\n \\\"host\\\": \\\"1234567890.execute-api.us-east-1.amazonaws.com\\\",\\n \\\"user-agent\\\": \\\"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_6)\\\",\\n \\\"x-amzn-trace-id\\\": \\\"Root=1-5bdb40ca-556d8b0c50dc66f0511bf520\\\",\\n \\\"x-forwarded-for\\\": \\\"72.21.198.66\\\",\\n \\\"x-forwarded-port\\\": \\\"443\\\",\\n \\\"x-forwarded-proto\\\": \\\"https\\\",\\n },\\n \\\"isBase64Encoded\\\": False,\\n \\\"body\\\": \\\"\\\",\\n }\\n response = lh.handler(event, None)\\n\\n self.assertEqual(response[\\\"statusCode\\\"], 200)\\n self.assertEqual(response[\\\"statusDescription\\\"], \\\"200 OK\\\")\\n self.assertEqual(response[\\\"isBase64Encoded\\\"], False)\\n self.assertEqual(\\n response[\\\"body\\\"],\\n \\\"https://1234567890.execute-api.us-east-1.amazonaws.com/return/request/url\\\",\\n )\",\n \"def lambda_handler(event, context):\\n\\n # try:\\n # ip = requests.get(\\\"http://checkip.amazonaws.com/\\\")\\n # except requests.RequestException as e:\\n # # Send some context about this error to Lambda Logs\\n # print(e)\\n\\n # raise e\\n curr_time = str(int(datetime.now().strftime(\\\"%s\\\")) * 1000)\\n client.put_item(TableName='demo-table', \\n Item={\\n 'doi': {'S': curr_time}, \\n 'crossref_flag': {'BOOL': False},\\n 'europepmc_flag': {'BOOL': False},\\n 's3_flag': {'BOOL': False},\\n 'indexing_flag': {'BOOL': False}\\n })\\n return {\\n \\\"statusCode\\\": 200,\\n \\\"body\\\": json.dumps({\\n \\\"message\\\": \\\"hello world\\\",\\n # \\\"location\\\": ip.text.replace(\\\"\\\\n\\\", \\\"\\\")\\n }),\\n }\",\n \"def lambda_handler(event=None, context=None):\\n logger.info('Lambda function invoked index()')\\n\\n # Get configuration from environment\\n file_name_default = os.environ.get('FILE_NAME') or \\\"bamboo_employees\\\"\\n api_key = os.environ.get('BAMBOO_TOKEN')\\n url = os.environ.get('BAMBOO_API')\\n\\n # Parameters, take a file name if exists and remove it from the dict\\n file_name = request.args.get('file_name') or file_name_default\\n\\n try:\\n # Request data from Bamboo API\\n headers = {'Accept': 'application/json'}\\n auth = HTTPBasicAuth(api_key, 'x')\\n response = requests.get(url=url, headers=headers, auth=auth)\\n\\n if response.status_code != requests.codes.ok:\\n raise Exception('Error making the request to Bamboo\\\\r\\\\n')\\n\\n # Check the result\\n result = json.loads(response.text)\\n if 'employees' in result:\\n # Generate the GeoJSON from API response\\n employees = []\\n for employee in result['employees']:\\n # Bamboo does not provide explicit locations\\n point = Point(None, None)\\n employees.append(Feature(geometry=point, properties=employee))\\n\\n # Produce a GeoJSON Feature collection\\n body = json.dumps(FeatureCollection(employees))\\n attachment = 'attachment; filename={0}.json'.format(file_name)\\n\\n # Create a response with the proper headers\\n # CARTO will use the file name property as the table name\\n response = make_response(body)\\n response.headers['Content-Type'] = 'application/json'\\n response.headers['Content-Disposition'] = attachment\\n else:\\n raise Exception('No photos on your request')\\n\\n return response\\n\\n except Exception as e:\\n response = make_response(e.message + \\\"\\\\r\\\\n\\\")\\n response.headers['Content-Type'] = 'text/plain'\\n response.status_code = 500\\n return response\",\n \"def redshift_lambda_handler(event, context):\\n logging.debug('event: %s', event)\\n\\n detail = event['detail']\\n event_name = detail['eventName']\\n creator = get_creator(event)\\n\\n logger.info('Event type: %s', event_name)\\n\\n if is_err_detail(logger, detail):\\n return False\\n\\n if event_name == 'CreateCluster':\\n logger.debug('%s is creating cluster: %s',\\n creator, detail['requestParameters']['clusterIdentifier'])\\n\\n # https://docs.aws.amazon.com/general/latest/gr/aws-arns-and-namespaces.html\\n cluster_arn = 'arn:aws:redshift:' + detail['awsRegion'] + ':'\\\\\\n + detail['userIdentity']['accountId'] + ':cluster:'\\\\\\n + detail['requestParameters']['clusterIdentifier']\\n short_msg = {\\n \\\"EventName\\\": event_name,\\n \\\"Creator\\\": creator,\\n \\\"ResourceArn\\\": cluster_arn,\\n \\\"TagStatus\\\": \\\"pending\\\",\\n \\\"MaxRetries\\\": int(os.environ['SFN_MAX_RETRIES']),\\n \\\"Retries\\\": 0\\n }\\n\\n sfn = Boto3Wrapper.get_client('stepfunctions')\\n response = sfn.start_execution(\\n stateMachineArn=os.environ['SFN_ARN'],\\n name=creator+'-'+event_name+'-'+detail['eventID'],\\n input=json.dumps(short_msg)\\n )\\n\\n logger.info('Step Functions start execution: %s', response)\\n\\n return True\",\n \"def lambda_handler(event, context):\\n\\n # resolve backend api key from the secrets manager\\n sm_client = boto3.client('secretsmanager')\\n sm_resp = sm_client.get_secret_value(os.getenv('BACKEND_SERVICE_API_KEY_SECRET_ARN'))\\n backend_api_key = json.dumps(sm_resp.get('SecretString')).get('key')\\n\\n # TODO implement further business logic\\n return {\\n 'statusCode': 200,\\n 'body': json.dumps('Hello from Lambda!')\\n }\",\n \"def lambda_handler(event, context):\\n logger.info(\\\"event.session.application.applicationId=\\\" +\\n event['session']['application']['applicationId'])\\n\\n \\\"\\\"\\\"Check that this is being called by our skill\\\"\\\"\\\"\\n logger.info(\\\"Calling app: \\\"+str(event['session']['application']['applicationId']))\\n if (event['session']['application']['applicationId'] !=\\n \\\"amzn1.ask.skill.\\\"+skill_id):\\n logger.error(\\\"Invalid application ID\\\")\\n raise ValueError(\\\"Invalid Application ID\\\")\\n\\n if event['session']['new']:\\n on_session_started(event, {'requestId': event['request']['requestId']},\\n event['session'])\\n\\n if event['request']['type'] == \\\"LaunchRequest\\\":\\n return on_launch(event, event['request'], event['session'])\\n elif event['request']['type'] == \\\"IntentRequest\\\":\\n return on_intent(event, event['request'], event['session'])\\n elif event['request']['type'] == \\\"SessionEndedRequest\\\":\\n return on_session_ended(event, event['request'], event['session'])\\n\\n # Otherwise deal with it gracefully\\n logger.info(\\\"Unexpected request type:\\\")\\n logger.info(json.dumps(event))\\n return build_response({}, build_speechlet_response(\\\"Leeds Bins\\\", \\\"Welcome to Leeds Bins. Now you can find out which waste bins to take out when. Try asking: what's my next collection.\\\", None, False))\",\n \"def handler(event, context):\\n\\n status = cfnresponse.FAILED\\n physical_resource_id = None\\n response_data = {}\\n try:\\n\\n # Start with the default parameters.\\n response_data.update(event[\\\"ResourceProperties\\\"][\\\"DefaultParams\\\"])\\n\\n # Then override with any values from the secret.\\n secret = secrets_client.get_secret_value(\\n SecretId=event[\\\"ResourceProperties\\\"][\\\"SecretArn\\\"],\\n VersionId=event[\\\"ResourceProperties\\\"][\\\"SecretVersionId\\\"],\\n )\\n secret_values = json.loads(secret[\\\"SecretString\\\"])\\n response_data.update(secret_values)\\n\\n # Set the log stream prefix based on the image version or tag.\\n if \\\":\\\" in response_data[\\\"IMAGE\\\"]:\\n response_data[\\\"LOG_STREAM_PREFIX\\\"] = response_data[\\\"IMAGE\\\"].split(\\\":\\\")[-1]\\n else:\\n response_data[\\\"LOG_STREAM_PREFIX\\\"] = response_data[\\\"IMAGE\\\"].split(\\\"/\\\")[-1]\\n\\n # Use the existing desired count when updating an existing service,\\n # because this value is managed by auto scaling. Otherwise,\\n # start new services with the auto scaling minimum.\\n if event[\\\"RequestType\\\"] == \\\"Update\\\":\\n response_data[\\\"AUTOSCALING_DESIRED\\\"] = get_desired_count(\\n cluster_name=event[\\\"ResourceProperties\\\"][\\\"ClusterName\\\"],\\n service_name=event[\\\"ResourceProperties\\\"][\\\"ServiceName\\\"],\\n )\\n else:\\n response_data[\\\"AUTOSCALING_DESIRED\\\"] = response_data[\\\"AUTOSCALING_MIN\\\"]\\n\\n status = cfnresponse.SUCCESS\\n\\n finally:\\n cfnresponse.send(event, context, status, response_data, physical_resource_id)\",\n \"def response(status, message, code):\\n return make_response(jsonify({\\n 'status': status,\\n 'message': message\\n })), code\",\n \"def lambda_handler(Event, Context):\\n if 'StateMachineArn' in Event.keys():\\n step_function_arn = Event['StateMachineArn']\\n r = step_function_client.start_execution(\\n stateMachineArn=step_function_arn,\\n input=json.dumps({\\\"last_updated\\\": \\\"\\\"}))\\n\\n else:\\n stepfunctions = [os.getenv(\\\"CHARGEBEEDOWNLOADARN\\\"), os.getenv(\\\"EXCHANGERATESDOWNLOADARN\\\")]\\n\\n for stepfunction in stepfunctions:\\n step_function_arn = stepfunction\\n r = step_function_client.start_execution(\\n stateMachineArn=step_function_arn,\\n input=json.dumps({\\\"last_updated\\\": \\\"\\\"}))\",\n \"def lambda_handler(event, context):\\n return\",\n \"def lambda_handler(event, context):\\n return\",\n \"def test3():\\n event = {\\n \\\"Records\\\": [\\n {\\n \\\"s3\\\": {\\n \\\"s3SchemaVersion\\\": \\\"1.0\\\",\\n \\\"configurationId\\\": \\\"b0efd5b1-cc92-47b4-8501-1c34f5eba235\\\",\\n \\\"bucket\\\": {\\n \\\"name\\\": \\\"/tmp/\\\"\\n },\\n \\\"object\\\": {\\n \\\"key\\\": \\\"tic000129646247_s0001-1-1_stlc.fits\\\"\\n }\\n }\\n }\\n ]\\n}\\n context = {}\\n \\n out = lambda_handler(event, context)\\n \\n assert out[\\\"statusCode\\\"] == 200\",\n \"def cloud_handler(event, context):\\n # Payload & Headers initialization for GET API calls\\n payload = {}\\n headers = {}\\n\\n url = CORRECT_URL + \\\":\\\" + WRONG_PORT + API\\n\\n response = get_call_api(url, payload, headers)\\n response_code = response.status_code\\n try:\\n response_data = response.json()\\n except Exception as e:\\n response_data = json.dumps(\\\"Error : {}\\\".format(e))\\n\\n return {\\n \\\"url\\\": url,\\n \\\"data\\\": response_data,\\n \\\"status_code\\\": response_code\\n }\",\n \"def lambda_handler(event, context):\\n logger.debug(event)\\n\\n product_list = PRODUCT_LIST\\n\\n return {\\n \\\"statusCode\\\": 200,\\n \\\"headers\\\": HEADERS,\\n \\\"body\\\": json.dumps({\\\"products\\\": product_list}),\\n }\",\n \"def process_response(response):\\n # Print it and exit with 1 if operation wasn't successful\\n print(response['message'])\\n if response['status'] != 'success':\\n sys.exit(1)\",\n \"def handler(event, context):\\n try:\\n # Retrieve environment variables\\n dimension_name = getenv(\\\"CODEDEPLOY_DIMENSION_NAME\\\")\\n metric_name = getenv(\\\"CODEDEPLOY_METRIC_NAME\\\")\\n if not dimension_name or not metric_name:\\n return \\\"CODEDEPLOY_DIMENSION_NAME or CODEDEPLOY_METRIC_NAME not set\\\"\\n\\n # Get deployment state from CodeDeploy event\\n deployment_state = event[\\\"detail\\\"][\\\"state\\\"]\\n print(f\\\"Deployment state: {deployment_state}\\\")\\n\\n # Pushing custom metric to CW\\n response = boto3.client(\\\"cloudwatch\\\").put_metric_data(\\n MetricData=[\\n {\\n \\\"MetricName\\\": metric_name,\\n \\\"Dimensions\\\": [{\\\"Name\\\": dimension_name, \\\"Value\\\": deployment_state}],\\n \\\"Unit\\\": \\\"None\\\",\\n \\\"Value\\\": 1,\\n \\\"Timestamp\\\": datetime.datetime.now(),\\n },\\n ],\\n Namespace=\\\"CodeDeployDeploymentStates\\\",\\n )\\n print(f\\\"Response from CW service: {response}\\\")\\n return response\\n # pylint: disable=broad-except\\n except Exception as excpt:\\n print(f\\\"Execution failed... {excpt}\\\")\\n return None\",\n \"def lambda_handler(event, context):\\n\\n LOGGER.debug(\\\"Lambda invoked\\\")\\n log_environment_variables()\\n\\n\\n # 1) Initialize\\n # Setup required AWS infrastructure\\n create_infrastructure()\\n\\n # If configuration exists, retrieve from S3 - otherwise create a new one\\n config = get_config()\\n LOGGER.info(f\\\"config loaded: {config}\\\")\\n\\n # If previously_valid_urls exists, retrieve from S3, otherwise create a blank one\\n previously_valid_urls = get_previously_valid_urls()\\n LOGGER.info(f\\\"previously_valid_urls loaded: {list(previously_valid_urls)}\\\")\\n\\n\\n # 2) Retrieve URL data\\n # Generate URLs\\n end_value_inclusive = config[\\\"start_value\\\"] + config[\\\"look_ahead\\\"] + 1\\n requests = [{\\\"url\\\": config[\\\"url\\\"] % value, \\\"value\\\": value} for value in\\n range(config[\\\"start_value\\\"], end_value_inclusive)]\\n LOGGER.info(f\\\"Requesting values in range {config['start_value']} -> {end_value_inclusive} (inclusive)\\\")\\n LOGGER.info(f\\\"First URL to request: {requests[0]['url'] if requests else 'None'}\\\")\\n LOGGER.info(f\\\"Last URL to request: {requests[-1]['url'] if requests else 'None'}\\\")\\n\\n # Get all responses\\n responses = get_responses(requests)\\n\\n # Create a list of requests that responded with status code 200\\n valid_urls = [{\\\"url\\\": response[\\\"url\\\"], \\\"value\\\": response[\\\"value\\\"]} for response in responses\\n if response[\\\"response\\\"].status == 200]\\n\\n\\n # 3) Process URL data\\n # Check valid_urls against previously_valid_urls and add to new_valid_urls\\n new_valid_urls = []\\n for valid_url in valid_urls:\\n if valid_url[\\\"url\\\"] not in previously_valid_urls:\\n LOGGER.debug(f\\\"New valid URL found: {valid_url['url']}\\\")\\n new_valid_urls.append(valid_url)\\n\\n\\n # 4) Send alerts based upon processed URL data\\n # Send emails for new_valid_urls\\n if new_valid_urls:\\n LOGGER.info(\\\"New URLs found, sending email.\\\")\\n send_email(config[\\\"from_email\\\"], config[\\\"to_email\\\"], config[\\\"email_subject\\\"],\\n generate_email_body(new_valid_urls))\\n\\n # Once emails have sent (and threw no exception), update start_value if slide_window is True\\n if config[\\\"slide_window\\\"]:\\n greatest_valid_value = new_valid_urls[-1]['value']\\n\\n new_start_value = greatest_valid_value + 1\\n LOGGER.info(f\\\"Updating start value from {config['start_value']} to {new_start_value}\\\")\\n config[\\\"start_value\\\"] = new_start_value\\n\\n\\n # 5) Save state\\n # Save previously_valid_urls as list of just URL strings, no metadata is needed\\n save_previously_valid_urls([valid_url[\\\"url\\\"] for valid_url in valid_urls])\\n\\n save_config(config)\\n\\n\\n return {\\n \\\"statusCode\\\": 200,\\n \\\"body\\\": json.dumps({\\n \\\"all_valid_urls\\\": valid_urls,\\n \\\"new_valid_urls\\\": new_valid_urls,\\n }),\\n }\",\n \"def lambda_handler(event, context):\\n\\n # return {\\n # \\\"statusCode\\\": 200,\\n # \\\"body\\\": json.dumps(\\n # {\\n # \\\"message\\\": \\\"hello world\\\",\\n # }\\n # ),\\n # }\\n src_bytes = base64.b64decode(event[\\\"body\\\"])\\n src = cv2.imdecode(np.frombuffer(src_bytes, dtype=np.uint8), cv2.IMREAD_COLOR)\\n gray = cv2.cvtColor(src, cv2.COLOR_BGR2GRAY)\\n _, gray_bytes = cv2.imencode(\\\".jpg\\\", gray)\\n\\n return {\\n \\\"statusCode\\\": 200,\\n \\\"body\\\": json.dumps(base64.b64encode(gray_bytes).decode(\\\"UTF-8\\\")),\\n }\",\n \"def lambda_handler(event, context):\\n\\n client = boto3.client('events')\\n event_to_put = {\\n \\\"source\\\": \\\"aws-lambda-function\\\"\\n }\\n event_to_put.update(**event)\\n try:\\n response = client.put_events(\\n Entries=[\\n {\\n 'Source': 'learn.eventbridge',\\n 'Detail': json.dumps(event_to_put),\\n 'DetailType': 'Learning Eventbridge',\\n 'EventBusName': 'default'\\n },\\n ]\\n )\\n return {\\n 'statusCode': 200,\\n 'body': json.dumps('Event has been put on event bus successfully.')\\n }\\n except Exception as ex:\\n return {\\n 'statusCode': 500,\\n 'body': json.dumps(str(ex))\\n }\",\n \"def lambda_handler(event, context):\\n\\n # try:\\n # ip = requests.get(\\\"http://checkip.amazonaws.com/\\\")\\n # except requests.RequestException as e:\\n # # Send some context about this error to Lambda Logs\\n # print(e)\\n\\n # raise e\",\n \"def to_lambda_output(self):\\n resp = {\\n 'statusCode': self.status_code,\\n 'body': self.body,\\n 'headers': self.headers\\n }\\n\\n return resp\",\n \"def lambda_handler(event, context):\\n\\n # try:\\n # ip = requests.get(\\\"http://checkip.amazonaws.com/\\\")\\n # except requests.RequestException as e:\\n # # Send some context about this error to Lambda Logs\\n # print(e)\\n\\n # raise e\\n print(event)\\n method=event['httpMethod']\\n print(f\\\"method={method}\\\")\\n print(f\\\"table_name={table_name}\\\")\\n myTriggerType='instrument_price'\\n\\n \\n if method == \\\"DELETE\\\":\\n #path=event['path']\\n trigger_id=event['pathParameters']['trigger_id']\\n print(f\\\"triggerId={trigger_id}\\\")\\n\\n try:\\n #see https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/dynamodb.html#DynamoDB.Table.delete_item\\n response = table.delete_item(\\n Key={'PK':f\\\"TR#{myTriggerType}#{trigger_id}\\\", \\\"SK\\\":f\\\"TR#{myTriggerType}#{trigger_id}\\\"},\\n ConditionExpression=And(Attr('PK').eq(Attr('SK')),Attr('triggerType').eq(myTriggerType)),\\n )\\n except ClientError as e:\\n print(f\\\"clientError={e}\\\")\\n if e.response['Error']['Code']=='ConditionalCheckFailedException':\\n return iftttError(404,\\\"item not found\\\")\\n raise\\n print(f\\\"response={response}\\\")\\n return {\\n \\\"statusCode\\\": 200,\\n \\\"body\\\":\\\"\\\",\\n }\\n \\n elif method == \\\"POST\\\":\\n body=json.loads(event['body'])\\n trigger_id=body['trigger_identity']\\n print(f\\\"triggerId={trigger_id}\\\")\\n\\n response = table.get_item(\\n Key={'PK':f\\\"TR#{myTriggerType}#{trigger_id}\\\", \\\"SK\\\":f\\\"TR#{myTriggerType}#{trigger_id}\\\"},\\n ProjectionExpression=\\\"triggerEvents, triggerType\\\",\\n )\\n print(f\\\"response={response}\\\")\\n\\n if \\\"Item\\\" not in response:\\n #brand new \\n print(f\\\"inserting {trigger_id}\\\")\\n if 'triggerFields' not in body:\\n return iftttError(400, \\\"triggerFields missing from request\\\")\\n triggerFields=body['triggerFields']\\n #todo validate trigger fields\\n try:\\n response = table.put_item(\\n Item={\\n 'PK':f\\\"TR#{myTriggerType}#{trigger_id}\\\", \\n \\\"SK\\\":f\\\"TR#{myTriggerType}#{trigger_id}\\\",\\n 'triggerId': trigger_id,\\n #hacky string way to avoid having multiple columns\\n 'triggerFields': json.dumps(triggerFields),\\n 'triggerType': myTriggerType,\\n },\\n ConditionExpression=Or(Attr('triggerType').eq(myTriggerType),Attr('triggerType').not_exists())\\n )\\n except ClientError as e:\\n print(f\\\"clientError={e}\\\")\\n #somehow got created with someone elses triggerType\\n if e.response['Error']['Code']=='ConditionalCheckFailedException':\\n return iftttError(404,\\\"item not found\\\")\\n raise\\n print(\\\"response \\\",response)\\n triggered=[]\\n elif response['Item'].get(\\\"triggerType\\\",myTriggerType) != myTriggerType:\\n #it exists but it is someone elses\\n return iftttError(404,\\\"item not found\\\")\\n else:\\n item=response['Item']\\n print(f\\\"found {item} \\\")\\n #hacky string way to avoid having multiple columns\\n #TODO: change this to use a Map? (will allow to add without overwrite)\\n events = json.loads(item.get(\\\"triggerEvents\\\",\\\"[]\\\"))\\n triggered= []\\n for event in events:\\n #TODO: implement limit (not needed now becasue I expect only up to one events)\\n triggered.append(event['data'])\\n \\n return {\\n \\\"statusCode\\\": 200,\\n \\\"body\\\": json.dumps({\\n \\\"data\\\": triggered,\\n # \\\"location\\\": ip.text.replace(\\\"\\\\n\\\", \\\"\\\")\\n }),\\n }\\n else :\\n return iftttError(400, f\\\"unexpected httpMethod {method}\\\")\",\n \"def lambda_handler(event, context):\\n # get the image data\\n if 'queryStringParameters' in event:\\n image = event['queryStringParameters']['base64image']\\n k = event['queryStringParameters']['k'] # number of similar images\\n\\n else:\\n image = event['base64image']\\n k = event['k'] # number of similar images\\n \\n # connect to elastic search\\n es = connect_to_ES(ES_ENDPOINT)\\n\\n # get the image features from the sagemaker endpoint\\n features = extract_features(image)\\n uris = search_es(es, features, k)\\n presigned_uris = create_presigned_url(uris, expiration=3600*24*2)\\n\\n return {\\n 'statusCode': 200,\\n 'headers':{'Content-Type':'application/json'},\\n 'body': json.dumps({'images': presigned_uris})\\n }\",\n \"def lambda_handler(event, context):\\r\\n if 'session' in event:\\r\\n print(\\\"event.session.application.applicationId=\\\" +\\r\\n event['session']['application']['applicationId'])\\r\\n\\r\\n \\\"\\\"\\\"\\r\\n Uncomment this if statement and populate with your skill's application ID to\\r\\n prevent someone else from configuring a skill that sends requests to this\\r\\n function.\\r\\n \\\"\\\"\\\"\\r\\n if ('session' in event and (event['session']['application']['applicationId'] !=\\r\\n \\\"amzn1.ask.skill.57119d91-fb3c-487f-be53-4e7fac12fb83\\\")):\\r\\n raise ValueError(\\\"Invalid Application ID\\\")\\r\\n\\r\\n \\\"\\\"\\\"if event['session']['new']:\\r\\n on_session_started({'requestId': event['request']['requestId']},\\r\\n event['session'])\\\"\\\"\\\"\\r\\n\\r\\n if event['request']['type'] == \\\"LaunchRequest\\\":\\r\\n return on_launch(event['request'], event['session'])\\r\\n elif event['request']['type'] == \\\"IntentRequest\\\":\\r\\n return on_intent(event['request'], event['session'])\\r\\n elif event['request']['type'] == \\\"SessionEndedRequest\\\":\\r\\n return on_session_ended(event['request'], event['session'])\\r\\n elif event['request']['type'] == 'UPDATE':\\r\\n return saveCoffeeMachineStatus(event['request'])\\r\\n elif event['request']['type'] == \\\"GLASS\\\":\\r\\n return glassStatus(event['request'])\\r\\n elif event['request']['type'] == \\\"WATER\\\":\\r\\n return waterStatus(event['request'])\\r\\n elif event['request']['type'] == \\\"COFFEE\\\":\\r\\n return coffeeStatus(event['request'])\\r\\n elif event['request']['type'] == \\\"ON_OFF\\\":\\r\\n return on_off_status(event['request'])\\r\\n elif event['request']['type'] == \\\"ONLINE\\\":\\r\\n return online_status_f(event['request'])\\r\\n elif event['request']['type'] == 'BUSY':\\r\\n return busyStatus(event['request'])\",\n \"def _send_immediate_response(self, success, message=\\\"\\\"):\\r\\n\\r\\n # Send the response indicating success/failure\\r\\n response_str = json.dumps(\\r\\n {'return_code': 0 if success else 1, 'content': message}\\r\\n )\\r\\n\\r\\n if self._is_grade_request():\\r\\n self.send_response(\\r\\n 200, content=response_str, headers={'Content-type': 'text/plain'}\\r\\n )\\r\\n self.log_message(\\\"XQueue: sent response {0}\\\".format(response_str))\\r\\n\\r\\n else:\\r\\n self.send_response(500)\",\n \"def check_deployment(version_stack_name, app_name):\\n\\n print(\\\"Polling Target Group ({}) until a successful state is reached...\\\".format(version_stack_name))\\n elbv2 = boto3.client('elbv2')\\n waiter = elbv2.get_waiter('target_in_service')\\n cloudformation = boto3.client('cloudformation')\\n response = cloudformation.describe_stack_resources(\\n StackName=version_stack_name,\\n LogicalResourceId='ALBTargetGroup'\\n )\\n target_group = response['StackResources'][0]['PhysicalResourceId']\\n start_time = datetime.datetime.now()\\n try:\\n waiter.wait(TargetGroupArn=target_group)\\n except botocore.exceptions.WaiterError:\\n print('Health check did not pass!')\\n response = cloudformation.describe_stack_resources(\\n StackName=version_stack_name,\\n LogicalResourceId='ECSService'\\n )\\n service = response['StackResources'][0]['PhysicalResourceId']\\n print('Outputting events for service {}:'.format(service))\\n response = cloudformation.describe_stack_resources(\\n StackName=\\\"ECS-{}\\\".format(app_name),\\n LogicalResourceId='ECSCluster'\\n )\\n cluster = response['StackResources'][0]['PhysicalResourceId']\\n ecs = boto3.client('ecs')\\n response = ecs.describe_services(\\n cluster=cluster,\\n services=[service]\\n )\\n for event in [x['message'] for x in response['services'][0]['events']]:\\n print(event)\\n# print('Deleting CloudFormation stack...')\\n# response = cloudformation.delete_stack(\\n# StackName=\\\"MV-{realm}-{app_name}-{version}-{env}\\\".format(env=os.environ['ENV'], app_name=os.environ['ECS_APP_NAME'], version=os.environ['BUILD_VERSION'], realm=os.environ['REALM'])\\n# )\\n# waiter = cf.get_waiter('stack_delete_complete')\\n# waiter.wait(\\n# StackName=\\\"MV-{realm}-{app_name}-{version}-{env}\\\".format(env=os.environ['ENV'], app_name=os.environ['ECS_APP_NAME'], version=os.environ['BUILD_VERSION'], realm=os.environ['REALM'])\\n# )\\n# print('CloudFormation stack deleted.')\\n elapsed_time = datetime.datetime.now() - start_time\\n print('Health check passed in {}'.format(elapsed_time))\\n print(\\\"Done.\\\")\",\n \"def _error_response(self):\\r\\n response_dict = {'success': False, 'version': 1}\\r\\n self.send_response(\\r\\n 400, content=json.dumps(response_dict),\\r\\n headers={'Content-type': 'application/json'}\\r\\n )\",\n \"def success(self, appstruct):\\n \\n return None\",\n \"def lambda_handler(event, context):\\n feature = session.query(m.Feature).first()\\n return {\\n \\\"statusCode\\\": 200,\\n \\\"body\\\": json.dumps({\\n \\\"message\\\": feature.title,\\n }),\\n }\",\n \"def callback(request, uri, headers): # lint-amnesty, pylint: disable=unused-argument\\n headers['CorrelationId'] = 'aefd38b7-c92c-445a-8c7a-487a3f0c7a9d'\\n headers['RequestNo'] = '[787177]' # This is the format SAPSF returns for the transaction request number\\n return 500, headers, 'Failure!'\",\n \"def lambda_handler(event: Dict[str, Any], context: Any) -> str:\\n print(\\\"Received event: \\\" + json.dumps(event, indent=2))\\n\\n migrated_ami_id: str = event[\\\"migrated_ami_id\\\"]\\n kms_id: str = event[\\\"kms_id\\\"]\\n region: str = event.get(\\\"region\\\", os.environ.get(\\\"AWS_REGION\\\"))\\n role: str = event.get(\\\"role\\\")\\n instance_name: str = event.get(\\\"name\\\", \\\"\\\")\\n\\n sts_client = boto3.client(\\\"sts\\\")\\n\\n print(f\\\"Assuming role: {role}\\\")\\n assumed_role: Dict[str, Any] = sts_client.assume_role(RoleArn=role, RoleSessionName=\\\"CopyImageLambda\\\")\\n\\n credentials = assumed_role.get(\\\"Credentials\\\")\\n\\n ec2_client = boto3.client(\\n \\\"ec2\\\",\\n region_name=region,\\n aws_access_key_id=credentials[\\\"AccessKeyId\\\"],\\n aws_secret_access_key=credentials[\\\"SecretAccessKey\\\"],\\n aws_session_token=credentials[\\\"SessionToken\\\"],\\n )\\n\\n try:\\n new_image: Dict[str, Any] = ec2_client.copy_image(\\n SourceImageId=migrated_ami_id,\\n SourceRegion=region,\\n Name=f\\\"copied-{migrated_ami_id}\\\",\\n Encrypted=True,\\n KmsKeyId=kms_id,\\n )\\n except Exception as e:\\n print(e)\\n return \\\"\\\"\\n\\n MigrationStateHandler().update_state(state=\\\"IMAGE_COPYING\\\", machine_name=instance_name)\\n return new_image.get(\\\"ImageId\\\", \\\"\\\")\",\n \"def lambda_handler(event, context):\\n import urllib.request\\n import json\\n\\n allowed_domain = \\\"brianandkelly.ws\\\"\\n\\n # print(event)\\n # Get the user info from Google for the recieved token...\\n id_token = event['authorizationToken']\\n google_token_helper_uri = \\\"https://www.googleapis.com/oauth2/v3/tokeninfo?id_token=\\\" + id_token\\n\\n result = json.loads(urllib.request.urlopen(google_token_helper_uri).read())\\n\\n domain = result['hd']\\n user = result['sub']\\n effect = 'Deny'\\n\\n if domain == allowed_domain:\\n effect = 'Allow'\\n \\n print(\\\"Access request for user: \\\" + result['email'] + \\\" with effect: \\\" + effect + \\\" processed.\\\")\\n\\n respond = {\\n \\\"principalId\\\": user,\\n \\\"policyDocument\\\": {\\n \\\"Version\\\": \\\"2012-10-17\\\",\\n \\\"Statement\\\": [\\n {\\n \\\"Action\\\": \\\"execute-api:Invoke\\\",\\n \\\"Effect\\\": effect,\\n \\\"Resource\\\": \\\"arn:aws:execute-api:us-east-1:*:7k8o0sgjli/securityvideos/*\\\"\\n }\\n ]\\n }\\n }\\n\\n return respond\",\n \"def test_delete_message_with_succes(self, mock_client): \\n\\n event = {\\n 'operation': 'deleteMessage', \\n 'arguments': {\\n 'template': 'my-sample-geofence-id',\\n }\\n }\\n\\n response = {\\n \\\"Arn\\\": f'arn:aws:mobiletargeting:eus-east-1:SOME_ACCOUNT_ID:templates/my-sample-geofence-id/PUSH',\\n \\\"RequestID\\\": \\\"some-request-id\\\",\\n \\\"Message\\\": 'some message' \\n }\\n\\n mock_client().delete_push_template.return_value = response\\n response = manageMessages.handler(event, None)\\n\\n self.assertTrue(response)\\n self.assertEqual(response['status'], 'MESSAGE_DELETED')\",\n \"def delete_stack(stack_name, Region, ACCESS_KEY, SECRET_KEY):\\n cf_client = boto3.client('cloudformation',\\n region_name=Region,\\n aws_access_key_id=ACCESS_KEY,\\n aws_secret_access_key=SECRET_KEY)\\n\\n #\\n # Need to use the StackId when calling describe stacks as DELETE_COMPLETE is never returned using stack names\\n #\\n try:\\n stack_data = cf_client.describe_stacks(StackName=stack_name)\\n stack_id = stack_data['Stacks'][0]['StackId']\\n response = cf_client.delete_stack(StackName=stack_id)\\n\\n if 'ResponseMetadata' in response and response['ResponseMetadata']['HTTPStatusCode'] < 300:\\n logger.info(\\\"Got response: {0}\\\".format(response))\\n\\n while True:\\n try:\\n stack_data = cf_client.describe_stacks(StackName=stack_id)\\n\\n if stack_data['Stacks'][0]['StackStatus'] == 'ROLLBACK_IN_PROGRESS':\\n print('Stack is rolling back - check the event logs')\\n time.sleep(30)\\n continue\\n elif stack_data['Stacks'][0]['StackStatus'] == 'DELETE_IN_PROGRESS':\\n print('Stack still deleting')\\n time.sleep(30)\\n continue\\n elif stack_data['Stacks'][0]['StackStatus'] == 'DELETE_FAILED':\\n print('Stack has failed to delete check your console')\\n return False\\n elif stack_data['Stacks'][0]['StackStatus'] == 'ROLLBACK_FAILED':\\n print('Stack has failed to rollback check your console')\\n return False\\n elif stack_data['Stacks'][0]['StackStatus'] == 'DELETE_COMPLETE':\\n print('Stack has been deleted')\\n return True\\n else:\\n print('Please check the stack deletion status in your AWS console')\\n return False\\n except ClientError:\\n print('Unable to find stack {}'.format(stack_name))\\n return False\\n except Exception as e:\\n logger.info('Got exception {}'.format(e))\\n return False\\n\\n else:\\n logger.info(\\\"There was an Unexpected error. response: {0}\\\".format(response))\\n return False\\n\\n except ClientError as error:\\n print('Got exception {}'.format(error))\\n except Exception as e:\\n print('Got exception {}'.format(e))\",\n \"def lambda_handler(event, context):\\n\\n markdown_files = sys.argv[1:]\\n logging.info(f'Markdown files to clean = {markdown_files}')\\n for file_to_clean in markdown_files:\\n clean_file(file_to_clean)\\n print(json.dumps(event))\\n body = event['body']\\n return {\\n \\\"statusCode\\\": 200,\\n \\\"body\\\": json.dumps({\\n \\\"message\\\": \\\"hello world from aws.\\\",\\n \\\"commit\\\": body\\n # \\\"location\\\": ip.text.replace(\\\"\\\\n\\\", \\\"\\\")\\n }),\\n }\",\n \"def _success(self, msg=\\\"\\\"):\\n if msg:\\n self.result[\\\"message\\\"] = msg\\n self.module.exit_json(**self.result)\",\n \"def delete_queues_handler(event, context):\\n logger.info(\\\"Request: %s\\\", json.dumps(event))\\n # In most other contexts that Lambda is used, the return value is\\n # immediately available. However, CloudFormation doesn't use those standard\\n # return values, and instead wants the agent to hit a URL to indicate\\n # success or failure. If CloudFormation does not receive an explicit\\n # notification, it can only rely on timeouts to detect failure, and that\\n # can waste human time.\\n #\\n # We wrap all the logic in a try/except so that we always try to send\\n # CloudFormation a status message.\\n try:\\n sqs_client = boto3.client('sqs')\\n handler(event, context, sqs_client)\\n send(event, context, SUCCESS)\\n except Exception, e:\\n logger.error(\\\"Exception: %r\\\", e)\\n\\t# There is no point in trying to notify if we for some reason already\\n\\t# failed to notify. CloudFormation will eventually time out.\\n\\tif not isinstance(e, urllib2.URLError): \\n send(event, context, FAILED)\\n raise\",\n \"def _invokeLambdaFunction(lambdaArn:str, parameters:Dict):\\n client = boto3.client('lambda')\\n payload = json.dumps(parameters).encode()\\n try:\\n response = client.invoke(\\n FunctionName = lambdaArn,\\n InvocationType='RequestResponse',\\n LogType='Tail',\\n Payload=payload\\n )\\n except ClientError as e:\\n raise LambdaInvocationError(e)\\n return response\",\n \"def handle_health(event, context):\\n LOGGER.info(\\\"Received event in handle_health:\\\")\\n response = {\\\"statusCode\\\": os.environ[\\\"STATUS\\\"]}\\n return response\",\n \"def lambda_handler(event, context): # pylint: disable=too-many-locals,too-many-branches,too-many-statements\\r\\n try: # pylint: disable=too-many-nested-blocks\\r\\n print(\\\"Execution started!\\\")\\r\\n #print(\\\"Event: \\\",event)\\r\\n # Bucket name and Full path for file - where file will be uploded\\r\\n source_bucket_name = event[\\\"detail\\\"][\\\"requestParameters\\\"][\\\"bucketName\\\"]\\r\\n source_key = urllib.parse.unquote_plus(\\r\\n event[\\\"detail\\\"][\\\"requestParameters\\\"][\\\"key\\\"], encoding='utf-8')\\r\\n \\r\\n print(\\\"file_path: \\\",source_key)\\r\\n #Loading master config\\r\\n print(\\\"Loading master_config\\\")\\r\\n audit_config = {}\\r\\n config_path = \\\"./config/\\\" + \\\\\\r\\n os.environ['CCM_ENV'] + \\\"/master_config.json\\\"\\r\\n config_content = open(config_path).read()\\r\\n config_json = json.loads(config_content)\\r\\n audit_config = config_json[\\\"audit_config\\\"]\\r\\n snow_params = config_json[\\\"ERROR_NOTIFICATION_SNOW_PARAMS\\\"]\\r\\n athena_query_param = config_json[\\\"ATHENA_QUERY_PARAMS\\\"]\\r\\n athena_table_params = config_json[\\\"ATHENA_TABLE_PARAMS\\\"]\\r\\n\\r\\n # Audit Parameters Based on the Invoking lambda and its operation involved\\r\\n audit_config[\\\"component_type_code\\\"] = \\\"ETL\\\"\\r\\n audit_config[\\\"component_name\\\"] = \\\"PCP Appflow\\\"\\r\\n audit_config[\\\"source_name\\\"] = \\\"Patient Connections Platform\\\"\\r\\n audit_config[\\\"target_name\\\"] = \\\"Consumer Consent Management\\\"\\r\\n audit_config[\\\"full_file_path\\\"] = \\\"s3://\\\" + \\\\\\r\\n source_bucket_name + \\\"/\\\" + source_key\\r\\n audit_config[\\\"file_version_id\\\"] = \\\"\\\"\\r\\n\\r\\n # Creates Job Entry in ABC Framework\\r\\n print(\\\"audit config::\\\", audit_config)\\r\\n process_execution_id = audit_helper.\\\\\\r\\n invoke_edb_abc_log_process_status_event_job_entry(audit_config)\\r\\n audit_config[\\\"process_execution_id\\\"] = process_execution_id\\r\\n print(\\\"process_execution_id ::\\\", process_execution_id)\\r\\n #print(\\\"source_key: \\\",source_key)\\r\\n s3_write = boto3.client('s3')\\r\\n record_dict = {}\\r\\n file_name = \\\"\\\"\\r\\n final_json = \\\"\\\"\\r\\n # prefix = \\\"\\\"\\r\\n # file_list = []\\r\\n # client = boto3.client(\\\"s3\\\")\\r\\n # result = client.list_objects(Bucket=source_bucket_name, Prefix=source_key, Delimiter='/')\\r\\n # #print(result)\\r\\n # for obj in result.get('CommonPrefixes'):\\r\\n # prefix = obj.get('Prefix')\\r\\n # #print(prefix)\\r\\n # file_list = list_files(client,source_bucket_name,prefix)\\r\\n # for file in file_list:\\r\\n # #print(file)\\r\\n json_read = read_s3_file(source_bucket_name, source_key)\\r\\n data = json.loads(json_read)\\r\\n #print(data)\\r\\n if data != '':\\r\\n record_dict = {k.lower(): v for k, v in data.items()}\\r\\n print(\\\"Record_Dict::\\\",record_dict)\\r\\n event_type_param = {}\\r\\n event_type_list = athena_table_params.keys()\\r\\n print(\\\"event_type_list\\\",event_type_list)\\r\\n for key in event_type_list:\\r\\n print(\\\"key\\\",key)\\r\\n if key in source_key:\\r\\n print(\\\"key\\\",key)\\r\\n event_type_param = athena_table_params[key]\\r\\n print(event_type_param)\\r\\n if \\\"changeeventheader\\\" in record_dict:\\r\\n if record_dict[\\\"changeeventheader\\\"][\\\"changeType\\\"] == \\\"CREATE\\\":\\r\\n #and record_dict[\\\"dtpc_affiliate__c\\\"] == 'US':\\r\\n recordid_create = record_dict[\\\"changeeventheader\\\"][\\\"recordIds\\\"][0]\\r\\n print(recordid_create)\\r\\n if recordid_create != '':\\r\\n last_modified_date = record_dict[\\\"lastmodifieddate\\\"].replace(\\\":\\\",\\\".\\\")\\r\\n create_json = json.dumps(record_dict)\\r\\n final_json = create_json\\r\\n file_name = recordid_create + \\\"-create-\\\" + str(last_modified_date)\\r\\n print(\\\"file_name: \\\",file_name)\\r\\n outbound_path = event_type_param[\\\"folder_path\\\"]\\r\\n final_source_key = outbound_path + '/' + file_name+\\\".json\\\"\\r\\n print(\\\"final_source_key :\\\", final_source_key)\\r\\n s3_write.put_object(\\r\\n Body=final_json, Bucket=source_bucket_name, Key=final_source_key)\\r\\n else:\\r\\n raise Exception(\\\"RecordId is missing: \\\", record_dict)\\r\\n elif record_dict[\\\"changeeventheader\\\"][\\\"changeType\\\"] == \\\"UPDATE\\\":\\r\\n record_ids_list = record_dict[\\\"changeeventheader\\\"][\\\"recordIds\\\"]\\r\\n if len(record_ids_list) != 0:\\r\\n for ele in record_ids_list:\\r\\n print(ele)\\r\\n element = \\\"'\\\" + ele + \\\"'\\\"\\r\\n payload_condition = event_type_param[\\\"recordid_condition\\\"]\\r\\n query = 'SELECT * FROM '+event_type_param[\\\"athena_create_table\\\"]+\\\\\\r\\n ' WHERE lastmodifieddate IN(SELECT max(lastmodifieddate) from '\\\\\\r\\n +event_type_param[\\\"athena_create_table\\\"]+\\\\\\r\\n ', UNNEST(\\\"'+payload_condition[0]+'\\\".\\\"'+payload_condition[1]+\\\\\\r\\n '\\\") AS ln(jsondata) WHERE jsondata IN ('+element+'));'\\r\\n print(query)\\r\\n athena_query_param['athena_query'] = query\\r\\n query_result_record_id = athena_helper.perform_athena_search\\\\\\r\\n (athena_query_param)\\r\\n print(\\\"Athena Query Result for Create Path:::\\\", query_result_record_id)\\r\\n update_json = create_complete_payload(data,query_result_record_id)\\r\\n print(\\\"update_json: \\\",update_json)\\r\\n if len(update_json) != 0:\\r\\n last_modified_date = record_dict[\\\"lastmodifieddate\\\"].replace\\\\\\r\\n (\\\":\\\",\\\".\\\")\\r\\n final_json = json.dumps(update_json)\\r\\n file_name = ele + \\\"-update-\\\" + str(last_modified_date)\\r\\n print(\\\"file_name: \\\",file_name)\\r\\n outbound_path = event_type_param[\\\"folder_path\\\"]\\r\\n final_source_key = outbound_path + '/' + file_name+\\\".json\\\"\\r\\n print(\\\"final_source_key :\\\", final_source_key)\\r\\n s3_write.put_object(\\r\\n Body=final_json, Bucket=source_bucket_name, \\\\\\r\\n Key=final_source_key)\\r\\n else:\\r\\n print(ele,\\\" does not have a create payload\\\")\\r\\n else:\\r\\n raise Exception(\\\"RecordId is missing: \\\", record_dict)\\r\\n else:\\r\\n raise Exception(\\\"ChangeEventHeader is missing: \\\", record_dict)\\r\\n else:\\r\\n raise Exception(\\\"Invalid Payload: \\\", record_dict)\\r\\n\\r\\n except (Exception) as err: # pylint: disable=line-too-long,broad-except\\r\\n print(\\\"Error occured: {0}\\\".format(str(err)))\\r\\n audit_type = \\\"error\\\"\\r\\n error_msg = sys.exc_info()\\r\\n exc_type = error_msg\\r\\n exc_obj = error_msg\\r\\n snow_params[\\\"flag\\\"] = \\\"FAIL\\\"\\r\\n snow_params[\\\"error_message\\\"] = str(exc_obj)\\r\\n snow_params[\\\"error_type\\\"] = str(exc_type)\\r\\n audit_config[\\\"exception_message\\\"] = str(exc_obj)\\r\\n if audit_config != {}:\\r\\n logging.exception(sys.exc_info())\\r\\n audit_helper.invoke_edb_abc_log_process_status_event(\\r\\n audit_type, audit_config) # pylint: disable=line-too-long\\r\\n audit_helper.raise_snow_incident(snow_params)\",\n \"def handler(event, context):\\n return awsgi.response(app, event, context)\",\n \"def test_response_string():\\n\\n msg = \\\"This is a test\\\"\\n retval = apigateway.response(msg)\\n\\n assert retval[\\\"body\\\"] == json.dumps({\\\"message\\\": msg})\\n assert retval[\\\"statusCode\\\"] == 200\",\n \"def notify_snitch(comp_name, action, level, msg):\\n\\n headers = json.loads(os.environ.get(\\\"headers\\\"))\\n epoch = int(datetime.datetime.now().strftime('%s'))\\n data = {\\n \\\"source\\\": \\\"aws_lamba\\\",\\n \\\"time\\\": epoch,\\n \\\"event\\\": {\\n \\\"log_level\\\": level,\\n \\\"action\\\": action,\\n \\\"time\\\": epoch,\\n \\\"application_name\\\": \\\"aws_cleanup\\\",\\n \\\"message\\\": msg,\\n \\\"component_name\\\": comp_name\\n }\\n }\\n try:\\n json_data = json.dumps(data)\\n response = str(requests.post(os.environ.get(\\\"snitch_endpoint\\\"), headers=headers, data=json_data, verify=False))[1:-1]\\n\\n if response != 'Response [200]':\\n raise ConnectionError(\\\"Error code: \\\" + response)\\n except Exception as e:\\n messages = []\\n messages.append(\\\"\\\"\\\"The AWS Lambda function: dev-png-aws-manage was unable to communicate with Snitch; <strong style=\\\"font-family: 'Helvetica Neue',Helvetica,Arial,sans-serif; box-sizing: border-box; font-size: 14px; margin: 0;\\\">\\\"\\\"\\\" + str(e) + \\\"\\\"\\\"</strong>\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t</td>\\n\\t\\t\\t\\t\\t\\t\\t\\t</tr><tr style=\\\"font-family: 'Helvetica Neue',Helvetica,Arial,sans-serif; box-sizing: border-box; font-size: 14px; margin: 0;\\\"><td class=\\\"content-block\\\" style=\\\"font-family: 'Helvetica Neue',Helvetica,Arial,sans-serif; box-sizing: border-box; font-size: 14px; vertical-align: top; margin: 0; padding: 0 0 20px;\\\" valign=\\\"top\\\">\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tThis email was sent to alert you that Snitch requests are not working.\\n <ul>\\n <li>Without Snitch, any logging or monitoring is down and we cannot view events</li>\\n </ul>\\n \\\"\\\"\\\" + json_data)\\n messages.append(\\\"The AWS Lambda function, dev-png-aws-manage was unable to communicate with Snitch; \\\" + str(e) + \\\"\\\\n This message was sent to alert you that Snitch requests are not working. Without Snitch any logging or monitoring is down and we cannot view events.\\\")\\n \\n lambda_client = boto3.client('lambda')\\n email_data = {\\n 'sender_mail': SENDER_EMAIL,\\n 'email': EMAIL_RECIPIENT,\\n 'subj': \\\"Snitch error\\\",\\n 'heading': \\\"Alert: Could not send message to Snitch!\\\",\\n 'messages': messages,\\n 'region': os.environ.get(\\\"AWS_DEFAULT_REGION\\\")\\n }\\n invoke_email_response = lambda_client.invoke(\\n FunctionName= os.environ.get(\\\"formatted_email\\\"),\\n InvocationType= \\\"RequestResponse\\\",\\n Payload= json.dumps(email_data)\\n )\\n err = checkError(invoke_email_response, \\\"Error sending email!\\\")\\n if err:\\n return err\\n \\n slack_data = {\\n 'application_url': APP_URL,\\n 'channel': CHANNEL,\\n 'message': messages[1]\\n }\\n invoke_slack_response = lambda_client.invoke(\\n FunctionName= os.environ.get(\\\"slack_message\\\"),\\n InvocationType= \\\"RequestResponse\\\",\\n Payload= json.dumps(slack_data)\\n )\\n err = checkError(invoke_slack_response, \\\"Error sending slack message!\\\")\\n if err:\\n return err\",\n \"def test_success(self, put, get, auth, circuits_app, fn_cloud_foundry_action, fn_cloud_foundry_applications):\\n auth.return_value = AuthenticationMock()\\n put.return_value = give_response(201, GUIDS_MOCK[\\\"resources\\\"][0])\\n get.return_value = give_response(200, GUIDS_MOCK)\\n\\n function_params = { \\n \\\"fn_cloud_foundry_action\\\": fn_cloud_foundry_action,\\n \\\"fn_cloud_foundry_applications\\\": fn_cloud_foundry_applications\\n }\\n results = call_fn_cloud_foundry_manage_applications_function(circuits_app, function_params)\\n assert results[\\\"test1\\\"][\\\"success\\\"] == True\",\n \"def lambda_handler(event, context):\\n return dispatch(event)\",\n \"def handler(event, context):\\n alert_message = json.loads(event['Records'][0]['Sns']['Message'])\\n alarm_name = alert_message['AlarmName']\\n reason = alert_message['NewStateReason']\\n new_state = alert_message['NewStateValue']\\n color = \\\"good\\\" if new_state == 'OK' else \\\"danger\\\"\\n\\n region = os.getenv('AWS_DEFAULT_REGION')\\n alert_url = f'https://console.aws.amazon.com/cloudwatch/home?region={region}#alarm:alarmFilter=ANY;name={alarm_name}'\\n link = f\\\"<{alert_url}|{alarm_name}>\\\"\\n\\n secrets = json.loads(get_secret()['SecretString'])\\n default_slack_channel = secrets['slack_alert_channel']\\n alarm_description = json.loads(alert_message.get('AlarmDescription', '{}'))\\n slack_channel = alarm_description.get(\\\"slack_channel\\\", default_slack_channel)\\n description = alarm_description.get(\\\"description\\\")\\n slack_message = '\\\\n'.join(\\n [f\\\"New state: {new_state}\\\", f\\\"Description: {description}\\\", reason]\\n )\\n\\n attachments = [{\\n \\\"fallback\\\": f\\\"{link} {slack_message}\\\",\\n \\\"title\\\": alarm_name,\\n \\\"title_link\\\": alert_url,\\n \\\"text\\\": slack_message,\\n \\\"color\\\": color\\n }]\\n\\n slack_url = secrets['slack_webhooks'][slack_channel]\\n\\n post_message_to_url(slack_url, {\\\"attachments\\\": attachments})\",\n \"def handler(event, context=None):\\n LOGGER.info(event)\\n result = {'status':'400', 'messages':'Error'}\\n bucket = event[\\\"Records\\\"][0][\\\"s3\\\"][\\\"bucket\\\"][\\\"name\\\"]\\n key = unquote_plus(event[\\\"Records\\\"][0][\\\"s3\\\"][\\\"object\\\"][\\\"key\\\"].encode(\\\"utf8\\\"))\\n obj = S3.get_object(Bucket=bucket, Key=key)\\n document_file = (key, BytesIO(obj[\\\"Body\\\"].read()))\\n lendi_document_id = obj[\\\"Metadata\\\"][\\\"lendi_document_id\\\"]\\n master_document_id = obj[\\\"Metadata\\\"][\\\"master_document_id\\\"]\\n workflow_id = get_workflow_id(master_document_id)\\n if workflow_id:\\n xtracta_response = upload_document_to_xtracta(workflow_id, document_file)\\n else:\\n logging.error(\\\"No workflow_id for master_document_id: %s\\\", master_document_id)\\n return result\\n if xtracta_response['status'] == '200':\\n insert_document(xtracta_response['document_id'], lendi_document_id,\\\\\\n workflow_id, 'preprocessing')\\n else:\\n logging.error(\\\"No xtracta_document_id for %s\\\", xtracta_response['message'])\\n return xtracta_response\\n return xtracta_response\",\n \"def response_for_created_event(event, status_code):\\n return make_response(jsonify({'event' : {\\n 'event_id': event.event_id,\\n 'event_name': event.event_name,\\n 'event_location': event.event_location,\\n 'event_eval_link': event.event_eval_link,\\n 'event_time': event.event_time,\\n 'created_on': event.event_created_on,\\n 'modified_on': event.event_updated_on\\n }, 'status': 'success'})), status_code\",\n \"def lambda_handler(event, context):\\n client = boto3.client('sns')\\n topic_arn = os.environ['topic_arn']\\n try:\\n quarantine_instance_id = event['quarantine_instance_id']\\n message = \\\"Quarantine Instance : {quarantine_instance_id} has been launched.\\\".format(quarantine_instance_id=quarantine_instance_id)\\n subject = \\\"Quarantine Instance had been launched.\\\"\\n client.publish(\\n TopicArn=topic_arn,\\n Message=message,\\n Subject=subject\\n )\\n return message\\n except KeyError as ke:\\n print(\\\"Key not found : {ke}\\\".format(ke=ke))\\n raise\\n except:\\n print(\\\"Error encountered while sending SNS notification about instance launch.\\\")\\n raise\",\n \"def lambda_function_as_expected(test_case: unittest.TestCase, function_name: str, handler: str,\\n state: str = 'Active', runtime: str = 'python3.8', memory_size: int = 128,\\n timeout: int = 10, env_vars: dict = None) -> None:\\n lambda_function_response = aws_lambda.get_function(FunctionName=function_name)\\n lambda_function: dict = lambda_function_response.get('Configuration')\\n\\n test_case.assertEqual(function_name, lambda_function.get('FunctionName'))\\n test_case.assertEqual(state, lambda_function.get('State'))\\n test_case.assertEqual(runtime, lambda_function.get('Runtime'))\\n test_case.assertEqual(handler, lambda_function.get('Handler'))\\n test_case.assertEqual(memory_size, lambda_function.get('MemorySize'))\\n test_case.assertEqual(timeout, lambda_function.get('Timeout'))\\n\\n if env_vars is not None:\\n test_case.assertDictEqual(env_vars, lambda_function.get('Environment').get('Variables'))\",\n \"def lambda_handler(event, context):\\n try:\\n aq = Aquifer()\\n aq.run()\\n\\n return \\\"Completed\\\"\\n\\n except (Exception, KeyboardInterrupt) as e:\\n return \\\"Error occurred\\\"\",\n \"def test_exception_handler_on_web_request(self):\\n lh = LambdaHandler(\\\"tests.test_exception_handler_settings\\\")\\n\\n event = {\\n \\\"body\\\": \\\"\\\",\\n \\\"resource\\\": \\\"/{proxy+}\\\",\\n \\\"requestContext\\\": {},\\n \\\"queryStringParameters\\\": {},\\n \\\"headers\\\": {\\n \\\"Host\\\": \\\"1234567890.execute-api.us-east-1.amazonaws.com\\\",\\n },\\n \\\"pathParameters\\\": {\\\"proxy\\\": \\\"return/request/url\\\"},\\n \\\"httpMethod\\\": \\\"GET\\\",\\n \\\"stageVariables\\\": {},\\n \\\"path\\\": \\\"/return/request/url\\\",\\n }\\n\\n mocked_exception_handler.assert_not_called()\\n response = lh.handler(event, None)\\n\\n self.assertEqual(response[\\\"statusCode\\\"], 500)\\n mocked_exception_handler.assert_called()\",\n \"def test_wsgi_script_name_on_test_request(self):\\n lh = LambdaHandler(\\\"tests.test_wsgi_script_name_settings\\\")\\n\\n event = {\\n \\\"body\\\": \\\"\\\",\\n \\\"resource\\\": \\\"/{proxy+}\\\",\\n \\\"requestContext\\\": {},\\n \\\"queryStringParameters\\\": {},\\n \\\"headers\\\": {},\\n \\\"pathParameters\\\": {\\\"proxy\\\": \\\"return/request/url\\\"},\\n \\\"httpMethod\\\": \\\"GET\\\",\\n \\\"stageVariables\\\": {},\\n \\\"path\\\": \\\"/return/request/url\\\",\\n }\\n response = lh.handler(event, None)\\n\\n self.assertEqual(response[\\\"statusCode\\\"], 200)\\n self.assertEqual(response[\\\"body\\\"], \\\"https://zappa:80/return/request/url\\\")\",\n \"def api_response(fn):\\n def response(self, *args, **kw):\\n logger.debug(\\\"Data: %s\\\" % (get_input()))\\n data = fn(self, *args, **kw)\\n count = len(data)\\n if count > 0:\\n return {'status': 'ok', 'code': 200, 'count': count, 'data': data}\\n else:\\n raise Error(NORESULT)\\n return response\",\n \"def lambda_handler(event, context):\\n #print(\\\"event.session.application.applicationId=\\\" + event['session']['application']['applicationId'])\\n\\n #if event['session']['new']:\\n # on_session_started({'requestId': event['request']['requestId']},event['session'])\\n \\n intent = None\\n try:\\n intent = Intent(**event)\\n return handle_intent(intent)\\n except Exception as ex:\\n err = traceback.format_exc()\\n print(err)\\n return error_handler_func(intent,msg=str(err))\",\n \"def lambda_handler(event, context):\\n if DEBUG:\\n print(\\\"event : {}\\\".format(json.dumps(event)))\\n\\n if event['session']['new']:\\n if DEBUG:\\n print(\\\"on_session_started requestId=\\\" + event['request']['requestId'] + \\\", sessionId=\\\" + event['session']['sessionId'])\\n if event['request']['type'] == \\\"LaunchRequest\\\":\\n if DEBUG:\\n print(\\\"on_launch requestId=\\\" + event['request']['requestId'] + \\\", sessionId=\\\" + event['session']['sessionId'])\\n return get_start_end_response(False)\\n elif event['request']['type'] == \\\"IntentRequest\\\":\\n if DEBUG:\\n print(\\\"on_intent requestId=\\\" + event['request']['requestId'] + \\\", sessionId=\\\" + event['session']['sessionId'])\\n return on_intent(event['request'], event['session'])\\n elif event['request']['type'] == \\\"SessionEndedRequest\\\":\\n if DEBUG:\\n print(\\\"on_session_ended requestId=\\\" + event['request']['requestId'] + \\\", sessionId=\\\" + event['session']['sessionId'])\",\n \"def handler(event, context):\\n response = None\\n try:\\n if 'Records' in event:\\n record = testSerializer(event['Records'][0]['dynamodb']['NewImage'])\\n type = record['type']\\n if type == \\\"PostCreatedEvent\\\":\\n addPost(record)\\n elif type == \\\"PostUpdatedEvent\\\":\\n updatePost(record)\\n elif type == \\\"PostDeletedEvent\\\":\\n deletePost(record)\\n elif type == \\\"PostVoteEvent\\\":\\n votePost(record)\\n response = None\\n else:\\n if event['httpMethod'] == \\\"GET\\\":\\n response = respond(None, getPost(event))\\n except Exception as error:\\n logger.exception(error)\\n response = {\\n 'status': 500,\\n 'error': {\\n 'type': type(error).__name__,\\n 'description': str(error),\\n },\\n }\\n finally:\\n return response\",\n \"def lambda_handler(event, context):\\n print(\\\"event.session.application.applicationId=\\\" +\\n event['session']['application']['applicationId'])\\n\\n \\\"\\\"\\\"\\n Uncomment this if statement and populate with your skill's application ID to\\n prevent someone else from configuring a skill that sends requests to this\\n function.\\n \\\"\\\"\\\"\\n # if (event['session']['application']['applicationId'] !=\\n # \\\"amzn1.echo-sdk-ams.app.[unique-value-here]\\\"):\\n # raise ValueError(\\\"Invalid Application ID\\\")\\n\\n if event['session']['new']:\\n on_session_started({'requestId': event['request']['requestId']},\\n event['session'])\\n\\n if event['request']['type'] == \\\"LaunchRequest\\\":\\n return on_launch(event['request'], event['session'])\\n elif event['request']['type'] == \\\"IntentRequest\\\":\\n return on_intent(event['request'], event['session'])\\n elif event['request']['type'] == \\\"SessionEndedRequest\\\":\\n return on_session_ended(event['request'], event['session'])\\n else:\\n print (\\\"********************** Unknown Request\\\")\",\n \"def on_response_validation_error(err):\\n return jsonify(message='Bad response'), 500\",\n \"def lambda_handler(event, context):\\n params = parse_qs(event['body'])\\n token = params['token'][0] if 'token' in params else ''\\n\\n if not verify_slack_token(token):\\n logger.error(\\\"Request token (%s) does not match expected token\\\", token)\\n return lambda_response(Exception('Invalid request token'))\\n\\n return gauges_app(params)\",\n \"def send_returns(returns):\\n #Build the header\\n head = {'Content-Type':'application/json'}\\n head.update(auth_head)\\n r = pool.urlopen('POST',server_address+'/command_returns?client_name='+client_name,headers=head,body=json.dumps(returns))\\n if r.data!=\\\"Processed\\\":\\n print \\\"Something went horribly wrong on the other end. Here are the bits of information to retry later with...\\\"\\n print json.dumps(returns)\",\n \"def lambda_handler(event, context):\\r\\n print(\\\"Incoming request...\\\")\\r\\n\\r\\n \\\"\\\"\\\"\\r\\n Uncomment this if statement and populate with your skill's application ID to\\r\\n prevent someone else from configuring a skill that sends requests to this\\r\\n function.\\r\\n \\\"\\\"\\\"\\r\\n # if (event['session']['application']['applicationId'] !=\\r\\n # \\\"amzn1.echo-sdk-ams.app.[unique-value-here]\\\"):\\r\\n # raise ValueError(\\\"Invalid Application ID\\\")\\r\\n\\r\\n if event['session']['new']:\\r\\n on_session_started({'requestId': event['request']['requestId']},\\r\\n event['session'])\\r\\n\\r\\n if event['request']['type'] == \\\"LaunchRequest\\\":\\r\\n return on_launch(event['request'], event['session'])\\r\\n elif event['request']['type'] == \\\"IntentRequest\\\":\\r\\n return on_intent(event['request'], event['session'])\\r\\n elif event['request']['type'] == \\\"SessionEndedRequest\\\":\\r\\n return on_session_ended(event['request'], event['session'])\",\n \"def exceptionhandler(e):\\n response = e.get_response()\\n response.data = json.dumps({\\n \\\"code\\\" : e.code,\\n \\\"name\\\": e.name,\\n \\\"description\\\": e.description\\n })\\n response.content_type = \\\"application/json\\\"\\n\\n return response\",\n \"def lambda_handler(event, context):\\n for record in event[\\\"Records\\\"]:\\n arn = record[\\\"Sns\\\"][\\\"TopicArn\\\"].split(\\\":\\\")\\n message = json.loads(record[\\\"Sns\\\"][\\\"Message\\\"])\\n message_handler(arn[3], message)\\n return ''\",\n \"def lambda_handler(event, context):\\n # Boto is always available in AWS lambda, but may not be available in standalone mode\\n# import boto3\\n# from base64 import b64decode\\n\\n # To generate the encrypted values, go to AWS IAM Keys and Generate a key\\n # Then grant decryption using the key to the IAM Role used for your lambda function.\\n #\\n # Use the command `aws kms encrypt --key-id alias/<key-alias> --plaintext <value-to-encrypt>\\n # Put the encrypted value in the configuration dictionary below\\n# encrypted_config = {\\n# 'pagerduty_api_key': '<ENCRYPTED VALUE>',\\n# 'schedule_ids': '<ENCRYPTED VALUE>'\\n# }\\n\\n# kms = boto3.client('kms')\\n# config = {x: kms.decrypt(CiphertextBlob=b64decode(y))['Plaintext'] for x, y in encrypted_config.iteritems()}\\n on_call = OnCall(API_KEY, SCHEDULE_IDS)\\n output = on_call.run()\\n\\n return { \\\"response_type\\\": \\\"in_channel\\\", \\\"text\\\": '\\\\n'.join(output) }\",\n \"def lambda_handler(event, context):\\n print('HANDLING EVENT')\\n print(\\\"event.session.application.applicationId=\\\" +\\n event['session']['application']['applicationId'])\\n\\n \\\"\\\"\\\"\\n Uncomment this if statement and populate with your skill's application ID to\\n prevent someone else from configuring a skill that sends requests to this\\n function.\\n \\\"\\\"\\\"\\n # if (event['session']['application']['applicationId'] !=\\n # \\\"amzn1.echo-sdk-ams.app.[unique-value-here]\\\"):\\n # raise ValueError(\\\"Invalid Application ID\\\")\\n\\n\\n if event['session']['new']:\\n on_session_started({'requestId': event['request']['requestId']},\\n event['session'])\\n\\n if event['request']['type'] == \\\"LaunchRequest\\\":\\n return on_launch(event['request'], event['session'])\\n elif event['request']['type'] == \\\"IntentRequest\\\":\\n return on_intent(event['request'], event['session'])\\n elif event['request']['type'] == \\\"SessionEndedRequest\\\":\\n return on_session_ended(event['request'], event['session'])\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7016714","0.6934398","0.65957916","0.6580948","0.65773124","0.6350327","0.62960196","0.6292117","0.62879604","0.626153","0.62377375","0.6202347","0.6150959","0.61024934","0.60883546","0.59804237","0.5948244","0.5916419","0.5903106","0.58938974","0.5876558","0.5845623","0.5842314","0.58216596","0.5821586","0.5779017","0.5755752","0.5741096","0.5720405","0.5715245","0.5691772","0.56613594","0.5645912","0.5640693","0.5639409","0.5637855","0.5617167","0.5616025","0.56091493","0.56054276","0.55930394","0.55870354","0.5494452","0.5492218","0.5492218","0.54883474","0.54704374","0.54580647","0.5451853","0.54407626","0.54341996","0.54284006","0.54190516","0.54180896","0.5415046","0.54109454","0.5408391","0.5406926","0.5393683","0.5386235","0.53806764","0.53706294","0.5345194","0.5341198","0.53405166","0.5338604","0.5334298","0.53338015","0.52882516","0.5283117","0.52813816","0.5275957","0.5261429","0.5252668","0.524088","0.52367663","0.5231905","0.5226586","0.52228516","0.52180123","0.52167076","0.52119005","0.5202619","0.51954156","0.5193577","0.5180372","0.5178533","0.517416","0.51735735","0.5171723","0.51706904","0.5170288","0.5168981","0.51689494","0.5162634","0.51620513","0.51608473","0.5158035","0.5149003","0.514593"],"string":"[\n \"0.7016714\",\n \"0.6934398\",\n \"0.65957916\",\n \"0.6580948\",\n \"0.65773124\",\n \"0.6350327\",\n \"0.62960196\",\n \"0.6292117\",\n \"0.62879604\",\n \"0.626153\",\n \"0.62377375\",\n \"0.6202347\",\n \"0.6150959\",\n \"0.61024934\",\n \"0.60883546\",\n \"0.59804237\",\n \"0.5948244\",\n \"0.5916419\",\n \"0.5903106\",\n \"0.58938974\",\n \"0.5876558\",\n \"0.5845623\",\n \"0.5842314\",\n \"0.58216596\",\n \"0.5821586\",\n \"0.5779017\",\n \"0.5755752\",\n \"0.5741096\",\n \"0.5720405\",\n \"0.5715245\",\n \"0.5691772\",\n \"0.56613594\",\n \"0.5645912\",\n \"0.5640693\",\n \"0.5639409\",\n \"0.5637855\",\n \"0.5617167\",\n \"0.5616025\",\n \"0.56091493\",\n \"0.56054276\",\n \"0.55930394\",\n \"0.55870354\",\n \"0.5494452\",\n \"0.5492218\",\n \"0.5492218\",\n \"0.54883474\",\n \"0.54704374\",\n \"0.54580647\",\n \"0.5451853\",\n \"0.54407626\",\n \"0.54341996\",\n \"0.54284006\",\n \"0.54190516\",\n \"0.54180896\",\n \"0.5415046\",\n \"0.54109454\",\n \"0.5408391\",\n \"0.5406926\",\n \"0.5393683\",\n \"0.5386235\",\n \"0.53806764\",\n \"0.53706294\",\n \"0.5345194\",\n \"0.5341198\",\n \"0.53405166\",\n \"0.5338604\",\n \"0.5334298\",\n \"0.53338015\",\n \"0.52882516\",\n \"0.5283117\",\n \"0.52813816\",\n \"0.5275957\",\n \"0.5261429\",\n \"0.5252668\",\n \"0.524088\",\n \"0.52367663\",\n \"0.5231905\",\n \"0.5226586\",\n \"0.52228516\",\n \"0.52180123\",\n \"0.52167076\",\n \"0.52119005\",\n \"0.5202619\",\n \"0.51954156\",\n \"0.5193577\",\n \"0.5180372\",\n \"0.5178533\",\n \"0.517416\",\n \"0.51735735\",\n \"0.5171723\",\n \"0.51706904\",\n \"0.5170288\",\n \"0.5168981\",\n \"0.51689494\",\n \"0.5162634\",\n \"0.51620513\",\n \"0.51608473\",\n \"0.5158035\",\n \"0.5149003\",\n \"0.514593\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7171703"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":92585,"cells":{"query":{"kind":"string","value":"Save the model to a json file."},"document":{"kind":"string","value":"def save_model(self, model_path):\n MODEL_FILE = 'simple_model.dat'\n\n model = {\n 'm': self.m.tolist(),\n 'b': self.b,\n 'FEAST_CORE_URL': FEAST_CORE_URL,\n 'FEAST_SERVING_URL': FEAST_SERVING_URL,\n 'ENTITY_ID': ENTITY_ID,\n }\n \n logging.info('Saving model to %s', model_path)\n\n with open(model_path, 'w+') as f:\n json.dump(model, f)"},"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 save_model(model):\n json_string = model.to_json()\n with open(MODEL_SAVE_JSON, 'w') as fp:\n fp.write(json_string)","def save_model(model, model_file):\n json_string = model.to_json()\n with open(model_file, 'w') as f:\n json.dump(json_string, f)","def save_model(self, model_file):\n m = {'b':self.b,\n 'w':self.w.tolist()}\n\n with open(model_file, 'w') as f:\n json.dump(m, f)","def save(self, filename):\n # serialize model to JSON\n model_json = self._model.to_json()\n with open('models/' + filename + \".json\", \"w\") as json_file:\n json_file.write(model_json)\n\n # serialize weights to HDF5\n self._model.save_weights('models/' + filename + \".h5\")\n print(\"Saved model to disk\")","def save(self, filename):\n content = self.to_dict()\n with open(filename, 'w') as f:\n json.dump(content, f)","def saveFile(self, filename=\"UQModelTest.json\"):\n sd = self.saveDict()\n with open(filename, \"w\") as f:\n json.dump(sd, f, indent=2)","def save_model(self, model):\n # serialize model to JSON\n model_json = model.to_json()\n os.makedirs(os.path.dirname(self.model_json_path), exist_ok=True)\n with open(self.model_json_path, \"w\") as json_file:\n json_file.write(model_json)\n\n # serialize weights to HDF5\n model.save_weights(self.model_weights_path)\n print(\"Saved model to disk\")","def _save(self):\n with open(self.file_path, 'w') as fid:\n json.dump(self.data, fid, indent=4, sort_keys=True)","def save_model_to_file(self, fn):\n js = json.loads(self.model.to_json())\n\n # Add this model's params\n js[\"rnn\"] = self.to_json()\n with open(fn, 'w') as fout:\n json.dump(js, fout)","def save_model(model, filename=\"model.json\"):\n model_json = model.to_json()\n with open(filename, \"w\") as json_file:\n json_file.write(model_json)\n model.save_weights(\"model.h5\")\n print(\"Saved model to disk\")","def save(self):\n d1 = {}\n with open(self.__file_path, mode=\"w\") as f:\n for k, v in self.__objects.items():\n d1[k] = v.to_dict()\n json.dump(d1, f)","def save(self):\n with open(self.file_path, 'w', encoding=Config.ENCODING) as file:\n json.dump(self.data, file, indent=2, ensure_ascii=False)","def save(self, json_path):\n with open(json_path, 'w') as f:\n json.dump(self.__dict__, f, indent = 4)","def save(self, path: str):\n with open(path, 'w', encoding='utf-8') as f:\n f.write(self.to_json())","def save(self):\n\n with open(FileStorage.__file_path, \"w\") as file:\n dictionary = {}\n for a, b in FileStorage.__objects.items():\n dictionary[a] = b.to_dict()\n ink = json.dumps(dictionary)\n file.write(ink)","def save(self, json_path):\n with open(json_path, 'w') as f:\n json.dump(self.__dict__, f, indent=4)","def save(self, json_path):\n with open(json_path, 'w') as f:\n json.dump(self.__dict__, f, indent=4)","def save(self, json_path):\n with open(json_path, 'w') as f:\n json.dump(self.__dict__, f, indent=4)","def save(self):\n\n\t\tdirectory = os.path.dirname(self.path)\n\n\t\tif not os.path.exists(directory):\n\t\t\tos.makedirs(directory)\n\n\t\twith open(self.path, \"w\") as f:\n\t\t\tf.write(\n\t\t\t\tjson.dumps(\n\t\t\t\t\tself.dump(),\n\t\t\t\t\tindent=4,\n\t\t\t\t\tsort_keys=True\n\t\t\t\t)\n\t\t\t)","def save(self, filepath):\n with open(filepath, 'w') as f:\n json.dump(self, f, indent=2)","def save(self):\n\n toStore = {\n key: obj.to_dict()\n for key, obj in FileStorage.__objects.items()\n }\n with open(FileStorage.__file_path, 'wt') as file:\n json.dump(toStore, file)","def save(self, filepath: str):\n with open(filepath, 'w') as fp:\n json.dump(\n self.dict(),\n fp,\n )","def save_model(self, filename):\r\n pass","def save_model(self, file=None):\n return None","def save_model_to_file(self, fn):\n js = json.loads(self.model.to_json())\n\n # Add this model's params\n js[\"rnn\"] = self.to_json()\n with open(fn, 'w') as fout:\n json.dump(js, fout)\n with open(os.path.join(self.model_dir, \"model.summary\"),'w') as summaryFile:\n self.model.summary(print_fn = lambda s: print(s, file=summaryFile))","def save(path_to_model):\n pass","def save_model(model, model_filepath):","def save(self):\n f = open(self.file.name, 'w')\n json.dump(self.data,\n f,\n indent=4)\n f.close()\n return True","def save_model(self, model_path: str):","def save(self, path):\n if not is_dry():\n with open(path, 'w') as f:\n json.dump(self.to_dict(), f, indent=4)\n return path","def save(self):\n # Ensure store path exists\n store_path = self.manager.store_path\n if not os.path.exists(store_path):\n os.makedirs(store_path)\n \n # Get filepath\n filename = self._filename\n \n # Write into file\n raw = self.to_json()\n self.service.log.store('Saving %s' % filename)\n f = open(filename, 'w')\n f.write(raw)\n f.close()","def save_data_file(self):\n with open(self.files['data'], 'w') as outfile:\n outfile.write(self.to_json())\n outfile.close()","def save_model(self, path):\n pass","def save(self, file):\n\n import json\n\n if hasattr(file, 'write'):\n f = file\n else:\n f = open(file, 'w')\n output = json.dumps(self, default=lambda v: v.__dict__)\n f.write(output)\n f.close()","def export_model(self, save_path: str, save_format: Optional[str] = None) -> None:","def save(self, file):\n with open(file, \"w+\") as f:\n json.dump(self.dict(), f)","def saveToFile(self, filename: str):\n with open(filename, 'w') as file:\n serialized = self.serialize()\n file.write(json.dumps(serialized, indent=4))\n self.print('saving to ', filename, ' was successful')\n\n self.has_been_modified = False","def save(self):\n with open(FileStorage.__file_path, 'w') as saves:\n copy_dict = {key: self.__objects[key].to_dict()\n for key in self.__objects}\n json.dump(copy_dict, saves)","def save_model(self, path='./model_checkpoint', name='tf_model'):\n json_config = self._model.to_json()\n with open(os.path.join(path, name + '.json'), 'w') as json_file:\n json_file.write(json_config)\n weights_path = os.path.join(path, name + '_weights.h5')\n self._model.save_weights(weights_path)","def save(self):\n a_copy = FileStorage.__objects\n obj_dict = {obj: a_copy[obj].to_dict() for obj in a_copy.keys()}\n with open(FileStorage.__file_path, \"w\") as f:\n json.dump(obj_dict, f)","def save_model(self, filename):\n\t\tpickle.dump(self, open(filename, 'wb'))\n\t\tprint('Model saved in',filename)","def save(self, model_file):\n pickle.dump(self, open(model_file, 'wb'))","def saveModel(self):\n with open(self.modelSaveFile, 'wb') as f:\n pickle.dump(self.values, f, pickle.HIGHEST_PROTOCOL)\n pickle.dump(self.policy, f, pickle.HIGHEST_PROTOCOL)","def write(self):\r\n\r\n with open(self.filename + \".json\", mode='w') as json_file:\r\n json.dump(self.data, json_file, separators=(',', ':'))","def save_model(self, fname):\n self.get_booster().save_model(fname)","def save(self, filename):\n data = {\"sizes\": self.sizes,\n \"weights\": [w.tolist() for w in self.weights],\n \"biases\": [b.tolist() for b in self.biases],\n \"cost\": str(self.cost.__name__)}\n f = open(filename, \"w\")\n json.dump(data, f)\n f.close()","def save_model(self):\n joblib.dump(self.pipeline, \"model.joblib\")","def save(self, filename):\n data = {\"sizes\": self.sizes,\n \"weights\": [w.tolist() for w in self.weights],\n \"biases\": [b.tolist() for b in self.biases]}\n f = open(filename, \"w\")\n json.dump(data, f)\n f.close()","def save_model(self, filename, overwrite=None):\n return\n pickle.dump(\n obj=self._model,\n file=open(filename, \"wb\")\n )","def save(self) -> None:\n with open(dict_path, 'w', encoding='utf-8') as dictionary_file:\n json.dump(self.data, dictionary_file, indent=2, separators=(',', ':'), ensure_ascii=False)","def save_json(self, file):\n with open(file, 'w', encoding='utf8') as f:\n json.dump(self, f, ensure_ascii=False)","def to_json_file(self, path):\n with open(path, 'w') as f:\n f.write(self.to_json())","def save(self, filename):\n with open(filename, \"w\") as f:\n m = {\n \"order\": self.order,\n \"pad\": self.pad,\n \"records\": {str(k): v for k, v in self.records.items()}\n }\n json.dump(m, f)","def save(self, data, outpath):\n with open(path, \"wt\") as open_file:\n json.dump(data, open_file, indent=4)","def Save(self, filename: str):\n data_object = {\n \"input_layer_count\" : self.input_layer_size,\n \"hidden_layer_count\" : self.hidden_layer_size,\n \"output_layer_count\" : self.output_layer_size,\n\n \"hidden_layer_biases\" : self.hidden_layer_biases.tolist(),\n \"output_layer_biases\" : self.output_layer_biases.tolist(),\n\n \"input_to_hidden_weights\" : self.input_to_hidden_weights.tolist(),\n \"hidden_to_output_weights\" : self.hidden_to_output_weights.tolist()\n }\n\n with open(filename, \"w\") as f:\n json.dump(data_object, f)","def save(self, filename=None):\n if filename is None:\n filename = \"morse_smale_complex.json\"\n with open(filename, \"w\") as fp:\n fp.write(self.to_json())","def save(self):\n with open(self.__file_path, \"w\", encoding=\"UTF-8\") as file:\n parsed_dict = {\n key: value.to_dict()\n for key, value in self.__objects.items()\n }\n save_data(parsed_dict, file)","def save_to_json_file(my_obj, filename):\n with open(filename, 'w') as file:\n json.dump(my_obj, file)","def save(self, to_file: Path)->None:\n self.model.save(to_file)","def save_model(model, model_filepath):\n dump(model, model_filepath)","def save_model(self, filename):\n self.model.save('models/' + str(filename))","def save_to_json_file(my_obj, filename):\n with open(filename, 'w+') as json_file:\n json.dump(my_obj, json_file)","def _save_model(self):\n with open(self.filepath, 'wb') as file:\n pickle.dump(self.cmodel, file)","def save(self, filename):\n self.graph.save(filename)\n with open(filename + \".json\", \"w\") as f:\n f.write(json.dumps(self.params))","def save_model(model):\n model.to_disk(\"../model/custom_ner_model\")","def save_to_file(cls, list_objs):\n list_dictionaries = []\n if list_objs is None:\n with open(cls.__name__ + \".json\", \"w\", encoding='utf-8') as file:\n file.write(Base.to_json_string(list_dictionaries))\n return\n for model in list_objs:\n list_dictionaries.append(model.to_dictionary())\n with open(cls.__name__ + \".json\", \"w\", encoding='utf-8') as file:\n file.write(Base.to_json_string(list_dictionaries))","def saveModel(model, file_name):\n with open(SAVE_PATH + file_name, \"wb\") as out_file:\n # wo do not want to save redundant data, so keys and vals are excluded\n pickle.dump(model, out_file)\n print(\"model save to\", SAVE_PATH + file_name)","def save_model(self, model, model_filepath):\n joblib.dump(model, model_filepath)","def save_model(self):\n joblib.dump(self.pipeline, 'model.joblib')\n print(colored('model.joblib saved locally', 'green'))","def save_to_json_file(my_obj, filename):\n with open(filename, \"w\") as myfile:\n return myfile.write(json.dumps(my_obj))","def save_(self):\n if not self._edited:\n return\n data = {'history': self.dump()}\n with open(os.path.join(os.path.dirname(self.arch_handler.dicomdir_path), self.SAVE_NAME), \"w\") as outfile:\n json.dump(data, outfile)\n self._edited = False","def save_model(self):\n pass","def save(self, folder):\n if self.is_predict_only:\n raise Exception(\"Model is predict only! save not supported!\")\n if not path.exists(folder):\n os.makedirs(folder)\n param = {\n \"model\": self.__class__.__name__,\n \"depth\": self.depth,\n \"nr_features\": self.nr_features,\n \"nr_codes\": self.nr_codes,\n \"nr_labels\": self.nr_labels,\n }\n param = self.append_meta(param)\n open(f\"{folder}/param.json\", \"w\", encoding=\"utf-8\").write(json.dumps(param, indent=True))\n for d in range(self.depth):\n local_folder = f\"{folder}/{d}.model\"\n self.model_chain[d].save(local_folder)","def save_model(self,\n file_path: str = \"models/world_model.h5\"\n ):\n self.model.save(file_path)","def save_to_json_file(my_obj, filename):\n import json\n with open(filename, 'w') as file:\n json.dump(my_obj, file)","def save_data(self, filename):\n with open(settings.DIR_PATH + '/' + filename, 'w', encoding='utf-8') as f:\n json.dump(self.data, f, indent=4)","def saveData(self):\n file_location = self.json_File_Location.replace(\".json\", \"_Update.json\")\n json_file = open(file_location, \"w+\")\n json_file.write(json.dumps(self.data, indent=4, separators=(', ', ' : ')))\n json_file.close()","def save_to_json_file(my_obj, filename):\n with open(filename, \"w\", encoding=\"utf-8\") as opening:\n json.dump(my_obj, opening)","def save_training(self):\n\n filename = str(hashlib.sha1(str(self.training_data).encode(\"utf-8\"))\n .hexdigest())\n path = \"./training/\" + filename + \".json\"\n\n data = {\n \"states\": self.states,\n \"transitions\": self.transitions,\n \"matrix\": self.matrix.tolist()\n }\n\n with open(path, \"w\") as outfile:\n json.dump(data, outfile)","def save_model(self, fpath):\n self._make_model_folder(fpath)\n self.model.save(os.path.join(fpath, U.MODEL_NAME), save_format=\"h5\")\n return","def save_to_json_file(my_obj, filename):\n with open(filename, mode=\"w\", encoding=\"utf-8\") as writer:\n json.dump(my_obj, writer)","def save_model(model, file_name):\n with open(file_name, 'wb') as file:\n pickle.dump(model, file)","def save(self, path):\n individual = self.population.fittest_individual()\n order = [int(l) for l in individual.label_order]\n fitness = individual.fitness\n data = {'name': self.ds.name,\n 'num_labels': len(order),\n 'order': order,\n 'fitness': fitness\n }\n with open(path, 'w') as f:\n json.dump(data, f)","def save_model(self, path):\n self._model.save(path)","def SaveJSON(self, filename):\n data = {\n 'files': self._files,\n 'ebuilds': self._ebuilds,\n }\n json.dump(data, open(filename, 'w'))","def save(self, settings=None):\n json_string = json.dumps(self.variables)\n with open(self.filepath, 'w', encoding='utf-8') as fh:\n fh.write(json_string)","def saveModel(self):\n self._model.save_weights('./my_model')\n return None","def save_to_json_file(my_obj, filename):\n with open(filename, mode='w') as file:\n file.write(json.dumps(my_obj))","def save_model(self):\n\n # =============================================================\n # Default : pickle the trained model. Change this (and the load\n # function, below) only if the library you used does not support\n # pickling.\n # self.Model_made.save(\"Model_made.h5\")\n # self.Model_claim.save(\"Model_claim.h5\")\n # Model_made = self.Model_made\n # Model_claim = self.Model_claim\n # self.Model_made = None\n # self.Model_claim = None\n with open('pricing_model.p', 'wb') as target:\n pickle.dump(self, target)\n\n # self.Model_made = Model_made\n # self.Model_claim = Model_claim\n\n # zipObj = ZipFile(\"model.zip\",\"w\")\n # zipObj.write(\"Model_made.h5\")\n # zipObj.write(\"Model_claim.h5\")\n # zipObj.write(\"pricing_model.p\")\n # zipObj.close()","def save(self, model_path: str) -> None:\n metadata_string = json.dumps({ \"classes\": self.classes })\n with open(os.path.join(model_path, \"metadata.json\"), \"w\") as metadata_file:\n metadata_file.write(metadata_string)\n with self.graph.as_default():\n with self.session.as_default():\n self.model.save_weights(os.path.join(model_path, \"weights.h5\"))","def save_to_json_file(my_obj, filename):\n with open(filename, 'w', encoding='utf-8') as file:\n return file.write(json.dumps(my_obj))","def save(self):\n if self._filename:\n with open(self._filename, 'w') as f:\n json.dump(self._dict, f, sort_keys=True, indent=4,\n separators=(',', ': '))\n else:\n raise PoseError(\"Please load map first\")","def save_to_json_file(my_obj, filename):\n with open(filename, 'w') as json_file:\n written = json_file.write(json.dumps(my_obj))\n return written","def serialize_model(model,model_dir): \n # Make output directory to store model\n pathlib.Path(model_dir).mkdir(parents=True, exist_ok=True)\n model_json = model.to_json()\n \n # Serialize model to JSON\n with open(model_dir + '/' + model_dir + \".json\", \"w\") as json_file:\n json_file.write(model_json)\n \n # Serialize weights to HDF5\n model.save_weights(model_dir +'/' + model_dir + \".h5\")\n print(\"Saved model to disk\")\n print(\"Model info stored within local directory: {model_name}/\")\n print(\"Model weights stored as: {model_name}/{model_name}.h5\")\n print(\"Model structure stored as: {model_name}/{model_name}.json\")","def to_json(outFile):\n\n with open(outFile, 'w') as outfile:\n json.dump(self._models, outfile)","def SaveToJSON(self):\n import json\n\n f = open(f\"/Cache/{self.symbol}.JSON\", \"w\")\n j = {\"name\": self.name, \"symbol\": self.symbol}\n\n f.write(\"{\\\"name\\\":\\\"\" + str(self.name) + \"\\\", \")\n f.write(json.dumps(j))\n f.close()\n\n print(\"Warning: SaveToJSON not fully implemented.\")","def save_to_json_file(my_obj, filename):\n import json\n with open(filename, mode='w', encoding='utf-8') as f:\n json.dump(my_obj, f)","def save_model(self):\n if self.model:\n self.model.save(self.config[\"model_path\"])","def save_model(self, model):\n # get model file name\n root_dir = os.path.split(os.path.realpath(__file__))[0]\n model_path = os.path.join(root_dir, '..', 'common', 'model', self._this_party, self._task_chain_id)\n if not os.path.exists(model_path):\n os.makedirs(model_path)\n model_file_name = os.path.join(model_path, self._task_id + '.model')\n\n # save model to disk\n dump(model, model_file_name)","def save(model: nn.Module, path):\n save_model(model, path)"],"string":"[\n \"def save_model(model):\\n json_string = model.to_json()\\n with open(MODEL_SAVE_JSON, 'w') as fp:\\n fp.write(json_string)\",\n \"def save_model(model, model_file):\\n json_string = model.to_json()\\n with open(model_file, 'w') as f:\\n json.dump(json_string, f)\",\n \"def save_model(self, model_file):\\n m = {'b':self.b,\\n 'w':self.w.tolist()}\\n\\n with open(model_file, 'w') as f:\\n json.dump(m, f)\",\n \"def save(self, filename):\\n # serialize model to JSON\\n model_json = self._model.to_json()\\n with open('models/' + filename + \\\".json\\\", \\\"w\\\") as json_file:\\n json_file.write(model_json)\\n\\n # serialize weights to HDF5\\n self._model.save_weights('models/' + filename + \\\".h5\\\")\\n print(\\\"Saved model to disk\\\")\",\n \"def save(self, filename):\\n content = self.to_dict()\\n with open(filename, 'w') as f:\\n json.dump(content, f)\",\n \"def saveFile(self, filename=\\\"UQModelTest.json\\\"):\\n sd = self.saveDict()\\n with open(filename, \\\"w\\\") as f:\\n json.dump(sd, f, indent=2)\",\n \"def save_model(self, model):\\n # serialize model to JSON\\n model_json = model.to_json()\\n os.makedirs(os.path.dirname(self.model_json_path), exist_ok=True)\\n with open(self.model_json_path, \\\"w\\\") as json_file:\\n json_file.write(model_json)\\n\\n # serialize weights to HDF5\\n model.save_weights(self.model_weights_path)\\n print(\\\"Saved model to disk\\\")\",\n \"def _save(self):\\n with open(self.file_path, 'w') as fid:\\n json.dump(self.data, fid, indent=4, sort_keys=True)\",\n \"def save_model_to_file(self, fn):\\n js = json.loads(self.model.to_json())\\n\\n # Add this model's params\\n js[\\\"rnn\\\"] = self.to_json()\\n with open(fn, 'w') as fout:\\n json.dump(js, fout)\",\n \"def save_model(model, filename=\\\"model.json\\\"):\\n model_json = model.to_json()\\n with open(filename, \\\"w\\\") as json_file:\\n json_file.write(model_json)\\n model.save_weights(\\\"model.h5\\\")\\n print(\\\"Saved model to disk\\\")\",\n \"def save(self):\\n d1 = {}\\n with open(self.__file_path, mode=\\\"w\\\") as f:\\n for k, v in self.__objects.items():\\n d1[k] = v.to_dict()\\n json.dump(d1, f)\",\n \"def save(self):\\n with open(self.file_path, 'w', encoding=Config.ENCODING) as file:\\n json.dump(self.data, file, indent=2, ensure_ascii=False)\",\n \"def save(self, json_path):\\n with open(json_path, 'w') as f:\\n json.dump(self.__dict__, f, indent = 4)\",\n \"def save(self, path: str):\\n with open(path, 'w', encoding='utf-8') as f:\\n f.write(self.to_json())\",\n \"def save(self):\\n\\n with open(FileStorage.__file_path, \\\"w\\\") as file:\\n dictionary = {}\\n for a, b in FileStorage.__objects.items():\\n dictionary[a] = b.to_dict()\\n ink = json.dumps(dictionary)\\n file.write(ink)\",\n \"def save(self, json_path):\\n with open(json_path, 'w') as f:\\n json.dump(self.__dict__, f, indent=4)\",\n \"def save(self, json_path):\\n with open(json_path, 'w') as f:\\n json.dump(self.__dict__, f, indent=4)\",\n \"def save(self, json_path):\\n with open(json_path, 'w') as f:\\n json.dump(self.__dict__, f, indent=4)\",\n \"def save(self):\\n\\n\\t\\tdirectory = os.path.dirname(self.path)\\n\\n\\t\\tif not os.path.exists(directory):\\n\\t\\t\\tos.makedirs(directory)\\n\\n\\t\\twith open(self.path, \\\"w\\\") as f:\\n\\t\\t\\tf.write(\\n\\t\\t\\t\\tjson.dumps(\\n\\t\\t\\t\\t\\tself.dump(),\\n\\t\\t\\t\\t\\tindent=4,\\n\\t\\t\\t\\t\\tsort_keys=True\\n\\t\\t\\t\\t)\\n\\t\\t\\t)\",\n \"def save(self, filepath):\\n with open(filepath, 'w') as f:\\n json.dump(self, f, indent=2)\",\n \"def save(self):\\n\\n toStore = {\\n key: obj.to_dict()\\n for key, obj in FileStorage.__objects.items()\\n }\\n with open(FileStorage.__file_path, 'wt') as file:\\n json.dump(toStore, file)\",\n \"def save(self, filepath: str):\\n with open(filepath, 'w') as fp:\\n json.dump(\\n self.dict(),\\n fp,\\n )\",\n \"def save_model(self, filename):\\r\\n pass\",\n \"def save_model(self, file=None):\\n return None\",\n \"def save_model_to_file(self, fn):\\n js = json.loads(self.model.to_json())\\n\\n # Add this model's params\\n js[\\\"rnn\\\"] = self.to_json()\\n with open(fn, 'w') as fout:\\n json.dump(js, fout)\\n with open(os.path.join(self.model_dir, \\\"model.summary\\\"),'w') as summaryFile:\\n self.model.summary(print_fn = lambda s: print(s, file=summaryFile))\",\n \"def save(path_to_model):\\n pass\",\n \"def save_model(model, model_filepath):\",\n \"def save(self):\\n f = open(self.file.name, 'w')\\n json.dump(self.data,\\n f,\\n indent=4)\\n f.close()\\n return True\",\n \"def save_model(self, model_path: str):\",\n \"def save(self, path):\\n if not is_dry():\\n with open(path, 'w') as f:\\n json.dump(self.to_dict(), f, indent=4)\\n return path\",\n \"def save(self):\\n # Ensure store path exists\\n store_path = self.manager.store_path\\n if not os.path.exists(store_path):\\n os.makedirs(store_path)\\n \\n # Get filepath\\n filename = self._filename\\n \\n # Write into file\\n raw = self.to_json()\\n self.service.log.store('Saving %s' % filename)\\n f = open(filename, 'w')\\n f.write(raw)\\n f.close()\",\n \"def save_data_file(self):\\n with open(self.files['data'], 'w') as outfile:\\n outfile.write(self.to_json())\\n outfile.close()\",\n \"def save_model(self, path):\\n pass\",\n \"def save(self, file):\\n\\n import json\\n\\n if hasattr(file, 'write'):\\n f = file\\n else:\\n f = open(file, 'w')\\n output = json.dumps(self, default=lambda v: v.__dict__)\\n f.write(output)\\n f.close()\",\n \"def export_model(self, save_path: str, save_format: Optional[str] = None) -> None:\",\n \"def save(self, file):\\n with open(file, \\\"w+\\\") as f:\\n json.dump(self.dict(), f)\",\n \"def saveToFile(self, filename: str):\\n with open(filename, 'w') as file:\\n serialized = self.serialize()\\n file.write(json.dumps(serialized, indent=4))\\n self.print('saving to ', filename, ' was successful')\\n\\n self.has_been_modified = False\",\n \"def save(self):\\n with open(FileStorage.__file_path, 'w') as saves:\\n copy_dict = {key: self.__objects[key].to_dict()\\n for key in self.__objects}\\n json.dump(copy_dict, saves)\",\n \"def save_model(self, path='./model_checkpoint', name='tf_model'):\\n json_config = self._model.to_json()\\n with open(os.path.join(path, name + '.json'), 'w') as json_file:\\n json_file.write(json_config)\\n weights_path = os.path.join(path, name + '_weights.h5')\\n self._model.save_weights(weights_path)\",\n \"def save(self):\\n a_copy = FileStorage.__objects\\n obj_dict = {obj: a_copy[obj].to_dict() for obj in a_copy.keys()}\\n with open(FileStorage.__file_path, \\\"w\\\") as f:\\n json.dump(obj_dict, f)\",\n \"def save_model(self, filename):\\n\\t\\tpickle.dump(self, open(filename, 'wb'))\\n\\t\\tprint('Model saved in',filename)\",\n \"def save(self, model_file):\\n pickle.dump(self, open(model_file, 'wb'))\",\n \"def saveModel(self):\\n with open(self.modelSaveFile, 'wb') as f:\\n pickle.dump(self.values, f, pickle.HIGHEST_PROTOCOL)\\n pickle.dump(self.policy, f, pickle.HIGHEST_PROTOCOL)\",\n \"def write(self):\\r\\n\\r\\n with open(self.filename + \\\".json\\\", mode='w') as json_file:\\r\\n json.dump(self.data, json_file, separators=(',', ':'))\",\n \"def save_model(self, fname):\\n self.get_booster().save_model(fname)\",\n \"def save(self, filename):\\n data = {\\\"sizes\\\": self.sizes,\\n \\\"weights\\\": [w.tolist() for w in self.weights],\\n \\\"biases\\\": [b.tolist() for b in self.biases],\\n \\\"cost\\\": str(self.cost.__name__)}\\n f = open(filename, \\\"w\\\")\\n json.dump(data, f)\\n f.close()\",\n \"def save_model(self):\\n joblib.dump(self.pipeline, \\\"model.joblib\\\")\",\n \"def save(self, filename):\\n data = {\\\"sizes\\\": self.sizes,\\n \\\"weights\\\": [w.tolist() for w in self.weights],\\n \\\"biases\\\": [b.tolist() for b in self.biases]}\\n f = open(filename, \\\"w\\\")\\n json.dump(data, f)\\n f.close()\",\n \"def save_model(self, filename, overwrite=None):\\n return\\n pickle.dump(\\n obj=self._model,\\n file=open(filename, \\\"wb\\\")\\n )\",\n \"def save(self) -> None:\\n with open(dict_path, 'w', encoding='utf-8') as dictionary_file:\\n json.dump(self.data, dictionary_file, indent=2, separators=(',', ':'), ensure_ascii=False)\",\n \"def save_json(self, file):\\n with open(file, 'w', encoding='utf8') as f:\\n json.dump(self, f, ensure_ascii=False)\",\n \"def to_json_file(self, path):\\n with open(path, 'w') as f:\\n f.write(self.to_json())\",\n \"def save(self, filename):\\n with open(filename, \\\"w\\\") as f:\\n m = {\\n \\\"order\\\": self.order,\\n \\\"pad\\\": self.pad,\\n \\\"records\\\": {str(k): v for k, v in self.records.items()}\\n }\\n json.dump(m, f)\",\n \"def save(self, data, outpath):\\n with open(path, \\\"wt\\\") as open_file:\\n json.dump(data, open_file, indent=4)\",\n \"def Save(self, filename: str):\\n data_object = {\\n \\\"input_layer_count\\\" : self.input_layer_size,\\n \\\"hidden_layer_count\\\" : self.hidden_layer_size,\\n \\\"output_layer_count\\\" : self.output_layer_size,\\n\\n \\\"hidden_layer_biases\\\" : self.hidden_layer_biases.tolist(),\\n \\\"output_layer_biases\\\" : self.output_layer_biases.tolist(),\\n\\n \\\"input_to_hidden_weights\\\" : self.input_to_hidden_weights.tolist(),\\n \\\"hidden_to_output_weights\\\" : self.hidden_to_output_weights.tolist()\\n }\\n\\n with open(filename, \\\"w\\\") as f:\\n json.dump(data_object, f)\",\n \"def save(self, filename=None):\\n if filename is None:\\n filename = \\\"morse_smale_complex.json\\\"\\n with open(filename, \\\"w\\\") as fp:\\n fp.write(self.to_json())\",\n \"def save(self):\\n with open(self.__file_path, \\\"w\\\", encoding=\\\"UTF-8\\\") as file:\\n parsed_dict = {\\n key: value.to_dict()\\n for key, value in self.__objects.items()\\n }\\n save_data(parsed_dict, file)\",\n \"def save_to_json_file(my_obj, filename):\\n with open(filename, 'w') as file:\\n json.dump(my_obj, file)\",\n \"def save(self, to_file: Path)->None:\\n self.model.save(to_file)\",\n \"def save_model(model, model_filepath):\\n dump(model, model_filepath)\",\n \"def save_model(self, filename):\\n self.model.save('models/' + str(filename))\",\n \"def save_to_json_file(my_obj, filename):\\n with open(filename, 'w+') as json_file:\\n json.dump(my_obj, json_file)\",\n \"def _save_model(self):\\n with open(self.filepath, 'wb') as file:\\n pickle.dump(self.cmodel, file)\",\n \"def save(self, filename):\\n self.graph.save(filename)\\n with open(filename + \\\".json\\\", \\\"w\\\") as f:\\n f.write(json.dumps(self.params))\",\n \"def save_model(model):\\n model.to_disk(\\\"../model/custom_ner_model\\\")\",\n \"def save_to_file(cls, list_objs):\\n list_dictionaries = []\\n if list_objs is None:\\n with open(cls.__name__ + \\\".json\\\", \\\"w\\\", encoding='utf-8') as file:\\n file.write(Base.to_json_string(list_dictionaries))\\n return\\n for model in list_objs:\\n list_dictionaries.append(model.to_dictionary())\\n with open(cls.__name__ + \\\".json\\\", \\\"w\\\", encoding='utf-8') as file:\\n file.write(Base.to_json_string(list_dictionaries))\",\n \"def saveModel(model, file_name):\\n with open(SAVE_PATH + file_name, \\\"wb\\\") as out_file:\\n # wo do not want to save redundant data, so keys and vals are excluded\\n pickle.dump(model, out_file)\\n print(\\\"model save to\\\", SAVE_PATH + file_name)\",\n \"def save_model(self, model, model_filepath):\\n joblib.dump(model, model_filepath)\",\n \"def save_model(self):\\n joblib.dump(self.pipeline, 'model.joblib')\\n print(colored('model.joblib saved locally', 'green'))\",\n \"def save_to_json_file(my_obj, filename):\\n with open(filename, \\\"w\\\") as myfile:\\n return myfile.write(json.dumps(my_obj))\",\n \"def save_(self):\\n if not self._edited:\\n return\\n data = {'history': self.dump()}\\n with open(os.path.join(os.path.dirname(self.arch_handler.dicomdir_path), self.SAVE_NAME), \\\"w\\\") as outfile:\\n json.dump(data, outfile)\\n self._edited = False\",\n \"def save_model(self):\\n pass\",\n \"def save(self, folder):\\n if self.is_predict_only:\\n raise Exception(\\\"Model is predict only! save not supported!\\\")\\n if not path.exists(folder):\\n os.makedirs(folder)\\n param = {\\n \\\"model\\\": self.__class__.__name__,\\n \\\"depth\\\": self.depth,\\n \\\"nr_features\\\": self.nr_features,\\n \\\"nr_codes\\\": self.nr_codes,\\n \\\"nr_labels\\\": self.nr_labels,\\n }\\n param = self.append_meta(param)\\n open(f\\\"{folder}/param.json\\\", \\\"w\\\", encoding=\\\"utf-8\\\").write(json.dumps(param, indent=True))\\n for d in range(self.depth):\\n local_folder = f\\\"{folder}/{d}.model\\\"\\n self.model_chain[d].save(local_folder)\",\n \"def save_model(self,\\n file_path: str = \\\"models/world_model.h5\\\"\\n ):\\n self.model.save(file_path)\",\n \"def save_to_json_file(my_obj, filename):\\n import json\\n with open(filename, 'w') as file:\\n json.dump(my_obj, file)\",\n \"def save_data(self, filename):\\n with open(settings.DIR_PATH + '/' + filename, 'w', encoding='utf-8') as f:\\n json.dump(self.data, f, indent=4)\",\n \"def saveData(self):\\n file_location = self.json_File_Location.replace(\\\".json\\\", \\\"_Update.json\\\")\\n json_file = open(file_location, \\\"w+\\\")\\n json_file.write(json.dumps(self.data, indent=4, separators=(', ', ' : ')))\\n json_file.close()\",\n \"def save_to_json_file(my_obj, filename):\\n with open(filename, \\\"w\\\", encoding=\\\"utf-8\\\") as opening:\\n json.dump(my_obj, opening)\",\n \"def save_training(self):\\n\\n filename = str(hashlib.sha1(str(self.training_data).encode(\\\"utf-8\\\"))\\n .hexdigest())\\n path = \\\"./training/\\\" + filename + \\\".json\\\"\\n\\n data = {\\n \\\"states\\\": self.states,\\n \\\"transitions\\\": self.transitions,\\n \\\"matrix\\\": self.matrix.tolist()\\n }\\n\\n with open(path, \\\"w\\\") as outfile:\\n json.dump(data, outfile)\",\n \"def save_model(self, fpath):\\n self._make_model_folder(fpath)\\n self.model.save(os.path.join(fpath, U.MODEL_NAME), save_format=\\\"h5\\\")\\n return\",\n \"def save_to_json_file(my_obj, filename):\\n with open(filename, mode=\\\"w\\\", encoding=\\\"utf-8\\\") as writer:\\n json.dump(my_obj, writer)\",\n \"def save_model(model, file_name):\\n with open(file_name, 'wb') as file:\\n pickle.dump(model, file)\",\n \"def save(self, path):\\n individual = self.population.fittest_individual()\\n order = [int(l) for l in individual.label_order]\\n fitness = individual.fitness\\n data = {'name': self.ds.name,\\n 'num_labels': len(order),\\n 'order': order,\\n 'fitness': fitness\\n }\\n with open(path, 'w') as f:\\n json.dump(data, f)\",\n \"def save_model(self, path):\\n self._model.save(path)\",\n \"def SaveJSON(self, filename):\\n data = {\\n 'files': self._files,\\n 'ebuilds': self._ebuilds,\\n }\\n json.dump(data, open(filename, 'w'))\",\n \"def save(self, settings=None):\\n json_string = json.dumps(self.variables)\\n with open(self.filepath, 'w', encoding='utf-8') as fh:\\n fh.write(json_string)\",\n \"def saveModel(self):\\n self._model.save_weights('./my_model')\\n return None\",\n \"def save_to_json_file(my_obj, filename):\\n with open(filename, mode='w') as file:\\n file.write(json.dumps(my_obj))\",\n \"def save_model(self):\\n\\n # =============================================================\\n # Default : pickle the trained model. Change this (and the load\\n # function, below) only if the library you used does not support\\n # pickling.\\n # self.Model_made.save(\\\"Model_made.h5\\\")\\n # self.Model_claim.save(\\\"Model_claim.h5\\\")\\n # Model_made = self.Model_made\\n # Model_claim = self.Model_claim\\n # self.Model_made = None\\n # self.Model_claim = None\\n with open('pricing_model.p', 'wb') as target:\\n pickle.dump(self, target)\\n\\n # self.Model_made = Model_made\\n # self.Model_claim = Model_claim\\n\\n # zipObj = ZipFile(\\\"model.zip\\\",\\\"w\\\")\\n # zipObj.write(\\\"Model_made.h5\\\")\\n # zipObj.write(\\\"Model_claim.h5\\\")\\n # zipObj.write(\\\"pricing_model.p\\\")\\n # zipObj.close()\",\n \"def save(self, model_path: str) -> None:\\n metadata_string = json.dumps({ \\\"classes\\\": self.classes })\\n with open(os.path.join(model_path, \\\"metadata.json\\\"), \\\"w\\\") as metadata_file:\\n metadata_file.write(metadata_string)\\n with self.graph.as_default():\\n with self.session.as_default():\\n self.model.save_weights(os.path.join(model_path, \\\"weights.h5\\\"))\",\n \"def save_to_json_file(my_obj, filename):\\n with open(filename, 'w', encoding='utf-8') as file:\\n return file.write(json.dumps(my_obj))\",\n \"def save(self):\\n if self._filename:\\n with open(self._filename, 'w') as f:\\n json.dump(self._dict, f, sort_keys=True, indent=4,\\n separators=(',', ': '))\\n else:\\n raise PoseError(\\\"Please load map first\\\")\",\n \"def save_to_json_file(my_obj, filename):\\n with open(filename, 'w') as json_file:\\n written = json_file.write(json.dumps(my_obj))\\n return written\",\n \"def serialize_model(model,model_dir): \\n # Make output directory to store model\\n pathlib.Path(model_dir).mkdir(parents=True, exist_ok=True)\\n model_json = model.to_json()\\n \\n # Serialize model to JSON\\n with open(model_dir + '/' + model_dir + \\\".json\\\", \\\"w\\\") as json_file:\\n json_file.write(model_json)\\n \\n # Serialize weights to HDF5\\n model.save_weights(model_dir +'/' + model_dir + \\\".h5\\\")\\n print(\\\"Saved model to disk\\\")\\n print(\\\"Model info stored within local directory: {model_name}/\\\")\\n print(\\\"Model weights stored as: {model_name}/{model_name}.h5\\\")\\n print(\\\"Model structure stored as: {model_name}/{model_name}.json\\\")\",\n \"def to_json(outFile):\\n\\n with open(outFile, 'w') as outfile:\\n json.dump(self._models, outfile)\",\n \"def SaveToJSON(self):\\n import json\\n\\n f = open(f\\\"/Cache/{self.symbol}.JSON\\\", \\\"w\\\")\\n j = {\\\"name\\\": self.name, \\\"symbol\\\": self.symbol}\\n\\n f.write(\\\"{\\\\\\\"name\\\\\\\":\\\\\\\"\\\" + str(self.name) + \\\"\\\\\\\", \\\")\\n f.write(json.dumps(j))\\n f.close()\\n\\n print(\\\"Warning: SaveToJSON not fully implemented.\\\")\",\n \"def save_to_json_file(my_obj, filename):\\n import json\\n with open(filename, mode='w', encoding='utf-8') as f:\\n json.dump(my_obj, f)\",\n \"def save_model(self):\\n if self.model:\\n self.model.save(self.config[\\\"model_path\\\"])\",\n \"def save_model(self, model):\\n # get model file name\\n root_dir = os.path.split(os.path.realpath(__file__))[0]\\n model_path = os.path.join(root_dir, '..', 'common', 'model', self._this_party, self._task_chain_id)\\n if not os.path.exists(model_path):\\n os.makedirs(model_path)\\n model_file_name = os.path.join(model_path, self._task_id + '.model')\\n\\n # save model to disk\\n dump(model, model_file_name)\",\n \"def save(model: nn.Module, path):\\n save_model(model, path)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.8499312","0.8433259","0.8345494","0.797389","0.7874161","0.787403","0.7827332","0.77711433","0.7760659","0.76583546","0.75438595","0.7513002","0.7510078","0.75076234","0.7502369","0.74815285","0.74815285","0.74815285","0.746824","0.7452417","0.74390745","0.74066806","0.7398361","0.73631644","0.73501045","0.7344737","0.73408437","0.73300827","0.73262584","0.7309967","0.7267294","0.7262496","0.72620386","0.725918","0.7242451","0.7228886","0.7210477","0.72054017","0.7194735","0.7194468","0.71867853","0.7166871","0.7143409","0.7123418","0.7106646","0.7079473","0.70708936","0.70656395","0.70652664","0.7057096","0.7045699","0.70229554","0.7019394","0.7014953","0.70096415","0.7001649","0.6999281","0.69722456","0.6967224","0.69599485","0.695238","0.69505954","0.69492525","0.6946745","0.6941663","0.6941498","0.69087154","0.69065064","0.6905562","0.6904342","0.69031537","0.68977994","0.6895973","0.689237","0.68915707","0.6883905","0.68789434","0.68725634","0.686832","0.6862221","0.68538934","0.68518287","0.6848215","0.6839827","0.6835903","0.6831785","0.6827469","0.6822144","0.6818873","0.6808634","0.6798069","0.67935765","0.6791968","0.6789878","0.67869896","0.6785669","0.6784749","0.6779866","0.677649","0.6774796"],"string":"[\n \"0.8499312\",\n \"0.8433259\",\n \"0.8345494\",\n \"0.797389\",\n \"0.7874161\",\n \"0.787403\",\n \"0.7827332\",\n \"0.77711433\",\n \"0.7760659\",\n \"0.76583546\",\n \"0.75438595\",\n \"0.7513002\",\n \"0.7510078\",\n \"0.75076234\",\n \"0.7502369\",\n \"0.74815285\",\n \"0.74815285\",\n \"0.74815285\",\n \"0.746824\",\n \"0.7452417\",\n \"0.74390745\",\n \"0.74066806\",\n \"0.7398361\",\n \"0.73631644\",\n \"0.73501045\",\n \"0.7344737\",\n \"0.73408437\",\n \"0.73300827\",\n \"0.73262584\",\n \"0.7309967\",\n \"0.7267294\",\n \"0.7262496\",\n \"0.72620386\",\n \"0.725918\",\n \"0.7242451\",\n \"0.7228886\",\n \"0.7210477\",\n \"0.72054017\",\n \"0.7194735\",\n \"0.7194468\",\n \"0.71867853\",\n \"0.7166871\",\n \"0.7143409\",\n \"0.7123418\",\n \"0.7106646\",\n \"0.7079473\",\n \"0.70708936\",\n \"0.70656395\",\n \"0.70652664\",\n \"0.7057096\",\n \"0.7045699\",\n \"0.70229554\",\n \"0.7019394\",\n \"0.7014953\",\n \"0.70096415\",\n \"0.7001649\",\n \"0.6999281\",\n \"0.69722456\",\n \"0.6967224\",\n \"0.69599485\",\n \"0.695238\",\n \"0.69505954\",\n \"0.69492525\",\n \"0.6946745\",\n \"0.6941663\",\n \"0.6941498\",\n \"0.69087154\",\n \"0.69065064\",\n \"0.6905562\",\n \"0.6904342\",\n \"0.69031537\",\n \"0.68977994\",\n \"0.6895973\",\n \"0.689237\",\n \"0.68915707\",\n \"0.6883905\",\n \"0.68789434\",\n \"0.68725634\",\n \"0.686832\",\n \"0.6862221\",\n \"0.68538934\",\n \"0.68518287\",\n \"0.6848215\",\n \"0.6839827\",\n \"0.6835903\",\n \"0.6831785\",\n \"0.6827469\",\n \"0.6822144\",\n \"0.6818873\",\n \"0.6808634\",\n \"0.6798069\",\n \"0.67935765\",\n \"0.6791968\",\n \"0.6789878\",\n \"0.67869896\",\n \"0.6785669\",\n \"0.6784749\",\n \"0.6779866\",\n \"0.677649\",\n \"0.6774796\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.77844816"},"document_rank":{"kind":"string","value":"7"}}},{"rowIdx":92586,"cells":{"query":{"kind":"string","value":"testuser1 has a 'testing' subnamespace, testuser2 tries to see its permissions but fails."},"document":{"kind":"string","value":"def testRandomUserCannotGETRandomUserSubNamespace(self):\n headers = {\n 'accept': 'application/json',\n }\n self.addBasicAuthHeader(headers, 'testuser2', 'secret')\n path = defaults.sep.join([defaults.namespaceCategoryName,\n 'testuser1', 'testing'])\n for action in permissions.actionsByCategory[\n defaults.namespaceCategoryName]:\n d = self.getPage(path, headers=headers,\n queryDict={'action': action})\n d.addErrback(self.checkErrorStatus, http.UNAUTHORIZED)\n self.failUnlessFailure(d, Error)\n yield d"},"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 _check_namespace_access(self, namespace, user):\n if not namespace.owners.filter(id=user.id).count():\n raise exceptions.PermissionDenied(\n 'The namespace listed on your filename must match one of '\n 'the namespaces you have access to.'\n )","def test_read_namespaced_role(self):\n pass","def test_get_permissions(self):\n pass","def test_replace_namespaced_role(self):\n pass","def test_access_levels(self):\n resource_id = 'protected-data'\n arn_prefix = \"arn:dcp:fus:us-east-1:dev:\"\n user = 'user_test_access_levels'\n group = 'group_test_access_levels'\n\n # create the resource to control\n resp = self.app.post(\n f'/v1/resource/{self.test_resource}/id/{resource_id}',\n headers=admin_headers)\n self.assertEqual(resp.status_code, 201)\n\n with self.subTest(\"Check that no one has access by listing who has access\"):\n resp = self.app.get(\n f'/v1/resource/{self.test_resource}/id/{resource_id}/members',\n headers=admin_headers)\n self.assertEqual(resp.status_code, 200)\n self.assertJSONEqual(resp.body, {'members': []})\n\n with self.subTest(\"Toggle user access.\"):\n # create a user\n resp = self.app.post(\n f'/v1/user',\n data=json.dumps({'user_id': user}),\n headers=admin_headers)\n self.assertEqual(resp.status_code, 201)\n\n # give a user access\n request_body = [\n {'member': user,\n 'member_type': 'user',\n 'access_level': 'read'}\n ]\n resp = self.app.put(\n f'/v1/resource/{self.test_resource}/id/{resource_id}/members',\n data=json.dumps(request_body),\n headers=admin_headers\n )\n self.assertEqual(resp.status_code, 200)\n\n # Check that the user has access by listing who has access\n resp = self.app.get(\n f'/v1/resource/{self.test_resource}/id/{resource_id}/members',\n headers=admin_headers)\n self.assertEqual(resp.status_code, 200)\n self.assertJSONEqual(resp.body, {'members': request_body})\n\n # Remove access for the user\n request_body = [\n {'member': user,\n 'member_type': 'user'}\n ]\n resp = self.app.put(\n f'/v1/resource/{self.test_resource}/id/{resource_id}/members',\n data=json.dumps(request_body),\n headers=admin_headers\n )\n self.assertEqual(resp.status_code, 200)\n\n # Check that the user does not have access by listing who has access\n resp = self.app.get(\n f'/v1/resource/{self.test_resource}/id/{resource_id}/members',\n headers=admin_headers)\n self.assertEqual(resp.status_code, 200)\n self.assertJSONEqual(resp.body, {'members': []})\n\n with self.subTest(\"Toggle group access.\"):\n # create a group\n resp = self.app.post(\n f'/v1/group',\n data=json.dumps({'group_id': group}),\n headers=admin_headers)\n self.assertEqual(resp.status_code, 201)\n\n # give a group access\n request_body = [\n {'member': group,\n 'member_type': 'group',\n 'access_level': 'read'}\n ]\n resp = self.app.put(\n f'/v1/resource/{self.test_resource}/id/{resource_id}/members',\n data=json.dumps(request_body),\n headers=admin_headers\n )\n self.assertEqual(resp.status_code, 200)\n\n # Check that the group has access by listing who has access\n resp = self.app.get(\n f'/v1/resource/{self.test_resource}/id/{resource_id}/members',\n headers=admin_headers)\n self.assertEqual(resp.status_code, 200)\n self.assertJSONEqual(resp.body, {'members': request_body})\n\n # Remove access for the group\n request_body = [\n {'member': group,\n 'member_type': 'group'}\n ]\n resp = self.app.put(\n f'/v1/resource/{self.test_resource}/id/{resource_id}/members',\n data=json.dumps(request_body),\n headers=admin_headers\n )\n self.assertEqual(resp.status_code, 200)\n\n # Check that the group does not have access by listing who has access\n resp = self.app.get(\n f'/v1/resource/{self.test_resource}/id/{resource_id}/members',\n headers=admin_headers)\n self.assertEqual(resp.status_code, 200)\n self.assertJSONEqual(resp.body, {'members': []})","def test_status_code_for_privileged_user(self):\n self.grant_permission()\n self.client.login(username=\"john\", password=\"pass\")\n response = self.client.get(self.get_url())\n self.assertEqual(response.status_code, self.status_has_permission)","def test_user_without_share(self):\n set_permission(Permission.EDIT, self.user1, self.collection)\n\n # Can not add permissions to users.\n data = {\"users\": {self.user2.pk: \"view\"}}\n resp = self._detail_permissions(self.collection.pk, data, self.user1)\n self.assertEqual(resp.status_code, status.HTTP_403_FORBIDDEN)\n\n # Can not add permissions to groups.\n data = {\"users\": {self.group.pk: \"view\"}}\n resp = self._detail_permissions(self.collection.pk, data, self.user1)\n self.assertEqual(resp.status_code, status.HTTP_403_FORBIDDEN)","def test_user(self):\n return True","def test_user_belonging_to_more_sites(self):\n self._create_simple_setup()\n foo_site = Site.objects.get(domain='foo.site.com')\n bar_site = Site.objects.get(domain='bar.site.com')\n writer_role = Role.objects.get(name='writer')\n bob = User.objects.get(username='bob')\n foo_master_page = Page.objects.get(\n title_set__title='master',\n site=foo_site)\n writer_role.grant_to_user(bob, foo_site, [foo_master_page])\n news_page = Page.objects.get(\n title_set__title='news',\n parent=foo_master_page)\n PagePermission.objects.create(user=bob, page=news_page)\n writer_role.ungrant_from_user(bob, foo_site)\n writer_users = writer_role.users(foo_site)\n self.assertNotIn(bob, writer_users)\n writer_users = writer_role.users(bar_site)\n self.assertIn(bob, writer_users)","def test_permission_import_ok(self):\n test_name = sys._getframe().f_code.co_name\n user = u'test_user\\u0250'\n self._execute('permission add ' + user + ' WIKI_VIEW')\n self._execute('permission add ' + user + ' TICKET_VIEW')\n rv, output = self._execute('permission export')\n self._execute('permission remove ' + user + ' *')\n rv, output = self._execute('permission import', input=output)\n self.assertEqual(0, rv)\n self.assertEqual('', output)\n rv, output = self._execute('permission list')\n self.assertEqual(0, rv)\n self.assertEqual(self.expected_results[test_name], output)","def test_patch_namespaced_role(self):\n pass","def test_create_namespaced_role(self):\n pass","def test_create_role_for_all_namespaces(self):\n pass","def test_list_namespaced_role(self):\n pass","def test_superuser():\n assert os.geteuid() == 0, \"Need ROOT access in order to run tests.\"","def test_get_namespaces_from_accounts(self):\n pass","def test_serve_user(self):\n pass","def test_access_control_is_superuser_as_user_raises_access_control_error(\n self,\n ):\n # Arrange\n mock_request = create_mock_request(user=self.user1)\n\n # Act # Assert\n with self.assertRaises(AccessControlError):\n access_control_api.is_superuser(\n mock_function, request=mock_request\n )","def testSuperUserPermission(self):\n self.login_user(self.superuser)\n response = self.client.get(self.url, self.args)\n self.assertEqual(response.status_code, 200)","def test_users(self):\n users = (\"root\", \"matlab\")\n for user in users:\n with self.subTest(username=user):\n self.assertTrue(self.host.user(user).exists)","def test_create_local_resource_access_review_for_all_namespaces(self):\n pass","def test_aiven_user_permissions(self):\n response = requests.get(f'{os.environ[\"AIVEN_API_URL\"]}/v1/me',\n headers={\n \"Authorization\": f'aivenv1 {os.environ[\"AIVEN_TOKEN\"]}'\n },\n verify=True\n )\n assert response.status_code == 200\n result = json.loads(response.text)\n assert \"operator\" in [\n value for key, value\n in result[\"user\"][\"project_membership\"].items()\n ]","def test_read_user(self):\n pass","def test_create_resource_access_review_for_all_namespaces(self):\n pass","def test_set_display_name_other_users_template_as_user_raises_access_control_error(\n self,\n ):\n mock_request = create_mock_request(user=self.user1)\n with self.assertRaises(AccessControlError):\n template_api.set_display_name(\n self.fixture.user2_template, \"new_name\", request=mock_request\n )","def test_filter_owner_permission(self):\n User = get_user_model()\n user1 = User.objects.create(username=\"test_user1\", email=\"user1@test.com\")\n obj = DescriptorSchema.objects.create(contributor=user1)\n obj.set_permission(Permission.VIEW, user1)\n\n data_template = {\n \"users\": {user1.id: \"view\"},\n \"groups\": {1: \"edit\", 2: \"NONE\"},\n }\n\n check_owner_permission(data_template, False, obj)\n\n # Check that only owner can set owner permission.\n data = deepcopy(data_template)\n data[\"users\"][1] = \"owner\"\n with self.assertRaises(exceptions.PermissionDenied):\n check_owner_permission(data, False, obj)\n check_owner_permission(data, True, obj)\n\n # Check that only owner can rewoke owner permission.\n obj.set_permission(Permission.OWNER, user1)\n data = deepcopy(data_template)\n data[\"users\"][1] = \"edit\"\n with self.assertRaises(exceptions.PermissionDenied):\n check_owner_permission(data, False, obj)\n check_owner_permission(data, True, obj)\n\n # Check that group can not be owner.\n obj.set_permission(Permission.VIEW, user1)\n data = deepcopy(data_template)\n data[\"groups\"][1] = \"owner\"\n with self.assertRaises(exceptions.ParseError):\n check_owner_permission(data, False, obj)\n with self.assertRaises(exceptions.ParseError):\n check_owner_permission(data, True, obj)","def testGetAccessAllowed(self):\n for user in (self.guest, self.contributor, self.delegate, self.owner, self.root):\n response = self.runGet(user)\n self.response_200(response)\n data = json.loads(response.content.decode(\"utf-8\"))\n self.assertEqual(len(data), 1)","def test_list_role_for_all_namespaces(self):\n pass","def test_access_negative(self, api):\n self.builder.add_user(api.get_user())\n r1 = api.access_user(api.get_user(), False)\n access_false = self.builder.get_access(api.get_user())\n self.builder.del_user(api.get_user())\n assert access_false == 0\n assert r1.status_code == 200","def test05_user_perms(self):\n print_ln('test_user_perms')\n \n try:\n uList = review.find_users(User(uid='py-user*'))\n for usr in uList: \n print_ln(\"Assigned perms user=\" + usr.uid)\n pList = review.user_perms(usr)\n for perm in pList: \n print_ln(\"Assigned perm obj name=\" + perm.obj_name + ', op name=' + perm.op_name + ', obj id=' + perm.obj_id, 1) \n except Exception as e:\n self.fail('test_user_perms failed, exception=' + e.msg)","def test_filter_user_permissions(self):\n data = {\n \"users\": {\n 1: \"view\",\n 2: \"NONE\",\n }\n }\n\n with self.assertRaises(exceptions.PermissionDenied):\n check_user_permissions(data, 1)\n\n with self.assertRaises(exceptions.PermissionDenied):\n check_user_permissions(data, 2)\n\n check_user_permissions(data, 3)","def test_create_namespaced_resource_access_review(self):\n pass","def test_set_user_ns_1(tmp_path):\n pc_path = copy_default_profile_collection(tmp_path)\n\n create_local_imports_files(pc_path)\n patch_first_startup_file(pc_path, patch_code)\n\n nspace = load_profile_collection(pc_path)\n assert len(nspace) > 0, \"Failed to load the profile collection\"\n assert \"f1\" in nspace, \"Test for local imports failed\"\n assert \"f2\" in nspace, \"Test for local imports failed\"\n\n # Test if the decorator `set_user_ns` does not change function type\n assert inspect.isgeneratorfunction(nspace[\"f1\"]) is False\n assert inspect.isgeneratorfunction(nspace[\"f2\"]) is True\n\n # Check if the extra arguments are removed from the function signature\n def check_signature(func):\n params = inspect.signature(func).parameters\n assert \"user_ns\" not in params\n assert \"ipython\" not in params\n\n check_signature(nspace[\"f1\"])\n check_signature(nspace[\"f1a\"])\n check_signature(nspace[\"f2\"])\n check_signature(nspace[\"f2a\"])\n\n assert nspace[\"value_f3\"] == 91\n assert nspace[\"value_f4\"] == 90\n\n # Test function\n global_user_namespace.set_user_namespace(user_ns=nspace, use_ipython=False)\n global_user_namespace.user_ns[\"v_from_namespace\"] = \"value-sent-to-func\"\n assert nspace[\"v_from_namespace\"] == \"value-sent-to-func\"\n\n result_func = nspace[\"f1\"](60)\n assert nspace[\"func_was_called\"] == \"func_was_called\"\n assert result_func[0] == 60\n assert result_func[1] == \"value-sent-to-func\"\n assert result_func[2] is False\n\n result_func = nspace[\"f1a\"](65)\n assert nspace[\"func_A_was_called\"] == \"func_was_called\"\n assert result_func[0] == 65\n assert result_func[1] == \"value-sent-to-func\"\n\n # Test generator\n global_user_namespace.user_ns[\"v_from_namespace\"] = \"value-sent-to-gen\"\n result_func = list(nspace[\"f2\"](110))[0]\n assert nspace[\"gen_was_called\"] == \"gen_was_called\"\n assert result_func[0] == 110\n assert result_func[1] == \"value-sent-to-gen\"\n assert result_func[2] is False\n\n result_func = list(nspace[\"f2a\"](115))[0]\n assert nspace[\"gen_A_was_called\"] == \"gen_was_called\"\n assert result_func[0] == 115\n assert result_func[1] == \"value-sent-to-gen\"","def test_permission_add_one_action_ok(self):\n test_name = sys._getframe().f_code.co_name\n self._execute('permission add test_user WIKI_VIEW')\n rv, output = self._execute('permission list')\n self.assertEqual(0, rv)\n self.assertEqual(self.expected_results[test_name], output)","def test_read_namespaced_policy(self):\n pass","def test_create_namespaced_local_resource_access_review(self):\n pass","def testGetAccessAllowed(self):\n for user in (self.guest, self.contributor, self.delegate, self.owner, self.root):\n response = self.runGet(user, sequencer=self.hiseq2000.sodar_uuid)\n self.response_200(response)\n data = json.loads(response.content.decode(\"utf-8\"))\n self.assertEqual(data[\"sodar_uuid\"], str(self.hiseq2000.sodar_uuid))","def test_install_rbac_1(test_name, cloud_provider, rke_client, kubectl):\n rke_template = 'cluster_install_rbac_1.yml.j2'\n nodes = cloud_provider.create_multiple_nodes(3, test_name)\n create_rke_cluster(rke_client, kubectl, nodes, rke_template)\n\n result = kubectl.create_resourse_from_yml(\n 'resources/k8s_ymls/daemonset_pods_per_node.yml', namespace='default')\n assert result.ok, result.stderr\n kubectl.create_ns('outside-role')\n result = kubectl.create_resourse_from_yml(\n 'resources/k8s_ymls/daemonset_pods_per_node.yml',\n namespace='outside-role')\n assert result.ok, result.stderr\n\n # Create role and rolebinding to user1 in namespace 'default'\n # namespace is coded in role.yml and rolebinding.yml\n result = kubectl.create_resourse_from_yml('resources/k8s_ymls/role.yml')\n assert result.ok, result.stderr\n result = kubectl.create_resourse_from_yml(\n 'resources/k8s_ymls/rolebinding.yml')\n assert result.ok, result.stderr\n\n # verify read in namespace\n admin_call_pods = kubectl.get_resource('pods', namespace='default')\n user_call_pods = kubectl.get_resource(\n 'pods', as_user='user1', namespace='default')\n\n # Make sure the number of pods returned with out user is the same as user\n # for this namespace\n assert len(admin_call_pods['items']) > 0, \"Pods should be greater than 0\"\n assert (len(admin_call_pods['items']) == len(user_call_pods['items'])), (\n \"Did not retrieve correct number of pods for 'user1'. Expected {0},\"\n \"Retrieved {1}\".format(\n len(admin_call_pods['items']), len(user_call_pods['items'])))\n\n # verify restrictions no pods return in get pods in different namespaces\n user_call_pods = kubectl.get_resource(\n 'pods', as_user='user1', namespace='outside-role')\n assert len(user_call_pods['items']) == 0, (\n \"Should not be able to get pods outside of defined user1 namespace\")\n\n # verify create fails as user for any namespace\n result = kubectl.run(test_name + '-pod2', image='nginx', as_user='user1',\n namespace='outside-role')\n assert result.ok is False, (\n \"'user1' should not be able to create pods in other namespaces:\\n{0}\"\n .format(result.stdout + result.stderr))\n assert \"cannot create\" in result.stdout + result.stderr\n\n result = kubectl.run(test_name + '-pod3', image='nginx', as_user='user1',\n namespace='default')\n assert result.ok is False, (\n \"'user1' should not be able to create pods in its own namespace:\\n{0}\"\n .format(result.stdout + result.stderr))\n assert \"cannot create\" in result.stdout + result.stderr\n\n for node in nodes:\n cloud_provider.delete_node(node)","def test_protect_owner(self):\n self.collection.set_permission(Permission.SHARE, self.user1)\n\n # User with share permission cannot grant ``owner`` permission\n data = {\"users\": {self.user2.pk: \"owner\"}}\n resp = self._detail_permissions(self.collection.pk, data, self.user1)\n self.assertEqual(resp.status_code, status.HTTP_403_FORBIDDEN)\n self.assertNotIn(\"owner\", self.collection.get_permissions(self.user2))\n self.assertFalse(PermissionModel.objects.filter(user=self.user2).exists())\n\n # User with share permission cannot revoke ``owner`` permission\n self.collection.set_permission(Permission.OWNER, self.user2)\n data = {\"users\": {self.user2.pk: \"editor\"}}\n resp = self._detail_permissions(self.collection.pk, data, self.user1)\n self.assertEqual(resp.status_code, status.HTTP_403_FORBIDDEN)\n self.assertEqual(\n self.collection.get_permissions(self.user2),\n [Permission.VIEW, Permission.EDIT, Permission.SHARE, Permission.OWNER],\n )\n self.collection.set_permission(Permission.NONE, self.user2)\n\n # Now let user1 be owner on collection.\n set_permission(Permission.OWNER, self.user1, self.collection)\n\n # ``owner`` permission cannot be assigned to a group\n data = {\"groups\": {self.group.pk: \"owner\"}}\n resp = self._detail_permissions(self.collection.pk, data, self.user1)\n self.assertEqual(resp.status_code, status.HTTP_400_BAD_REQUEST)\n self.assertFalse(PermissionModel.objects.filter(group=self.group).exists())\n\n # User with owner permission can grant ``owner`` permission\n data = {\"users\": {self.user2.pk: \"owner\"}}\n resp = self._detail_permissions(self.collection.pk, data, self.user1)\n self.assertEqual(resp.status_code, status.HTTP_200_OK)\n self.assertEqual(\n self.collection.get_permissions(self.user2),\n [Permission.VIEW, Permission.EDIT, Permission.SHARE, Permission.OWNER],\n )\n\n # User with owner permission can revoke ``owner`` permission\n data = {\"users\": {self.user2.pk: \"edit\"}}\n resp = self._detail_permissions(self.collection.pk, data, self.user1)\n self.assertEqual(resp.status_code, status.HTTP_200_OK)\n self.assertFalse(\n PermissionModel.objects.filter(\n user=self.user2, value=Permission.OWNER.value\n ).exists()\n )\n\n # User with owner permission cannot remove all owners\n data = {\"users\": {self.user1.pk: \"edit\", self.owner.pk: \"edit\"}}\n\n resp = self._detail_permissions(self.collection.pk, data, self.user1)\n self.assertEqual(resp.status_code, status.HTTP_400_BAD_REQUEST)\n self.assertEqual(resp.data[\"detail\"], \"Object must have at least one owner.\")\n\n owner_permissions = self.collection.permission_group.permissions.filter(\n value=Permission.OWNER.value\n )\n owner_count = owner_permissions.count()\n self.assertEqual(owner_count, 2)\n\n # User can delete his owner permission if there is at least one other owner\n self.assertTrue(owner_permissions.filter(user=self.user1).exists())\n data = {\"users\": {self.user1.pk: \"view\"}}\n resp = self._detail_permissions(self.collection.pk, data, self.user1)\n self.assertEqual(resp.status_code, status.HTTP_200_OK)\n self.assertFalse(owner_permissions.filter(user=self.user1.pk).exists())","def test_public_user(self):\n set_permission(Permission.SHARE, self.user1, self.collection)\n\n data = {\"public\": \"view\"}\n resp = self._detail_permissions(self.collection.pk, data, self.user1)\n self.assertEqual(resp.status_code, status.HTTP_200_OK)\n\n data = {\"public\": \"none\"}\n resp = self._detail_permissions(self.collection.pk, data, self.user1)\n self.assertEqual(resp.status_code, status.HTTP_200_OK)\n\n data = {\"public\": \"edit\"}\n resp = self._detail_permissions(self.collection.pk, data, self.user1)\n self.assertEqual(resp.status_code, status.HTTP_403_FORBIDDEN)\n\n data = {\"public\": \"share\"}\n resp = self._detail_permissions(self.collection.pk, data, self.user1)\n self.assertEqual(resp.status_code, status.HTTP_403_FORBIDDEN)\n\n data = {\"public\": \"owner\"}\n resp = self._detail_permissions(self.collection.pk, data, self.user1)\n self.assertEqual(resp.status_code, status.HTTP_403_FORBIDDEN)","def test_access_positive(self, api):\n self.builder.add_user(api.get_user())\n self.builder.upd_access(api.get_user(), False)\n r1 = api.access_user(api.get_user(), True)\n access_true = self.builder.get_access(api.get_user())\n self.builder.del_user(api.get_user())\n assert access_true == 1\n assert r1.status_code == 200","def test_get_namespaces_from_account(self):\n pass","def test_subroles(self):\n def check_roles(r):\n dev_holder = r.html.find(\n 'table', {'id': 'usergroup_admin'}).findAll('tr')[2]\n mem_holder = r.html.find(\n 'table', {'id': 'usergroup_admin'}).findAll('tr')[3]\n assert 'All users in Admin group' in dev_holder.text\n assert 'All users in Developer group' in mem_holder.text\n\n r = self.app.get('/admin/groups/')\n\n admin_holder = r.html.find(\n 'table', {'id': 'usergroup_admin'}).findAll('tr')[1]\n admin_id = admin_holder['data-group']\n # test that subroles are intact after user added\n with audits('add user test-user to Admin'):\n r = self.app.post('/admin/groups/add_user', params={\n 'role_id': admin_id,\n 'username': 'test-user'})\n r = self.app.get('/admin/groups/')\n check_roles(r)\n # test that subroles are intact after user deleted\n with audits('remove user test-user from Admin'):\n r = self.app.post('/admin/groups/remove_user', params={\n 'role_id': admin_id,\n 'username': 'test-user'})\n r = self.app.get('/admin/groups/')\n check_roles(r)","def test_share_inactive_user(self):\n george = self.george\n alva = self.alva\n john = self.john\n bikes = self.bikes\n\n self.assertEqual(\n bikes.raccess.get_effective_privilege(alva),\n PrivilegeCodes.NONE)\n\n # inactive users can't be granted access\n # set john to an inactive user\n john.is_active = False\n john.save()\n\n with self.assertRaises(PermissionDenied):\n george.uaccess.share_resource_with_user(\n bikes, john, PrivilegeCodes.CHANGE)\n\n john.is_active = True\n john.save()\n\n # inactive grantor can't grant access\n # let's first grant John access privilege\n george.uaccess.share_resource_with_user(\n bikes, john, PrivilegeCodes.CHANGE)\n\n self.assertEqual(\n bikes.raccess.get_effective_privilege(john),\n PrivilegeCodes.CHANGE)\n\n john.is_active = False\n john.save()\n\n with self.assertRaises(PermissionDenied):\n john.uaccess.share_resource_with_user(\n bikes, alva, PrivilegeCodes.VIEW)","def test_workspace_access(self):\n\n failed_logins = []\n\n for username in self.testdata.get_usernames():\n userdata = self.testdata.get_userdata_for(username)\n\n if 'mw-login' not in userdata.admin_properties.groups:\n continue\n\n ws = None\n\n try:\n ws = self.cm.access(host=self.hubname,\n username=userdata.username,\n password=userdata.password)\n\n sessiondir = ws.execute('echo $SESSIONDIR')\n\n except:\n failed_logins.append(userdata.username)\n\n finally:\n if ws is not None:\n ws.close()\n ws = None\n\n\n assert len(failed_logins) == 0, \\\n 'login failed for the following accounts: %s' % (failed_logins)","def test_permission_remove_all_actions_for_user(self):\n test_name = sys._getframe().f_code.co_name\n self._execute('permission remove anonymous *')\n rv, output = self._execute('permission list')\n self.assertEqual(0, rv)\n self.assertEqual(self.expected_results[test_name], output)","def test_permission_add_multiple_actions_ok(self):\n test_name = sys._getframe().f_code.co_name\n self._execute('permission add test_user LOG_VIEW FILE_VIEW')\n rv, output = self._execute('permission list')\n self.assertEqual(0, rv)\n self.assertEqual(self.expected_results[test_name], output)","def test_replace_user(self):\n pass","def test_get_other_users_template_as_staff_raises_access_control_error(\n self,\n ):\n mock_request = create_mock_request(user=self.staff_user1)\n with self.assertRaises(AccessControlError):\n template_api.get_by_id(\n self.fixture.user2_template.id, request=mock_request\n )","def test_user_settings_for_subscribing_other_users(self) -> None:\n user_profile = self.example_user(\"cordelia\")\n invitee_user_id = user_profile.id\n realm = user_profile.realm\n\n do_set_realm_property(\n realm, \"create_public_stream_policy\", Realm.POLICY_MEMBERS_ONLY, acting_user=None\n )\n do_set_realm_property(\n realm, \"invite_to_stream_policy\", Realm.POLICY_ADMINS_ONLY, acting_user=None\n )\n do_change_user_role(self.test_user, UserProfile.ROLE_MODERATOR, acting_user=None)\n result = self.common_subscribe_to_streams(\n self.test_user,\n [\"stream1\"],\n {\"principals\": orjson.dumps([invitee_user_id]).decode()},\n allow_fail=True,\n )\n self.assert_json_error(result, \"Insufficient permission\")\n\n do_change_user_role(self.test_user, UserProfile.ROLE_REALM_ADMINISTRATOR, acting_user=None)\n self.common_subscribe_to_streams(\n self.test_user, [\"stream1\"], {\"principals\": orjson.dumps([invitee_user_id]).decode()}\n )\n\n do_set_realm_property(\n realm, \"invite_to_stream_policy\", Realm.POLICY_MODERATORS_ONLY, acting_user=None\n )\n do_change_user_role(self.test_user, UserProfile.ROLE_MEMBER, acting_user=None)\n # Make sure that we are checking the permission with a full member,\n # as full member is the user just below moderator in the role hierarchy.\n self.assertFalse(self.test_user.is_provisional_member)\n result = self.common_subscribe_to_streams(\n self.test_user,\n [\"stream2\"],\n {\"principals\": orjson.dumps([invitee_user_id]).decode()},\n allow_fail=True,\n )\n self.assert_json_error(result, \"Insufficient permission\")\n\n do_change_user_role(self.test_user, UserProfile.ROLE_MODERATOR, acting_user=None)\n self.common_subscribe_to_streams(\n self.test_user, [\"stream2\"], {\"principals\": orjson.dumps([invitee_user_id]).decode()}\n )\n self.unsubscribe(user_profile, \"stream2\")\n\n do_set_realm_property(\n realm, \"invite_to_stream_policy\", Realm.POLICY_MEMBERS_ONLY, acting_user=None\n )\n do_change_user_role(self.test_user, UserProfile.ROLE_GUEST, acting_user=None)\n result = self.common_subscribe_to_streams(\n self.test_user,\n [\"stream2\"],\n {\"principals\": orjson.dumps([invitee_user_id]).decode()},\n allow_fail=True,\n )\n self.assert_json_error(result, \"Not allowed for guest users\")\n\n do_change_user_role(self.test_user, UserProfile.ROLE_MEMBER, acting_user=None)\n self.common_subscribe_to_streams(\n self.test_user,\n [\"stream2\"],\n {\"principals\": orjson.dumps([self.test_user.id, invitee_user_id]).decode()},\n )\n self.unsubscribe(user_profile, \"stream2\")\n\n do_set_realm_property(\n realm,\n \"invite_to_stream_policy\",\n Realm.POLICY_FULL_MEMBERS_ONLY,\n acting_user=None,\n )\n do_set_realm_property(realm, \"waiting_period_threshold\", 100000, acting_user=None)\n result = self.common_subscribe_to_streams(\n self.test_user,\n [\"stream2\"],\n {\"principals\": orjson.dumps([invitee_user_id]).decode()},\n allow_fail=True,\n )\n self.assert_json_error(result, \"Insufficient permission\")\n\n do_set_realm_property(realm, \"waiting_period_threshold\", 0, acting_user=None)\n self.common_subscribe_to_streams(\n self.test_user, [\"stream2\"], {\"principals\": orjson.dumps([invitee_user_id]).decode()}\n )","def test_move_secret_to_different_folder_with_member_permission_edit_on_source_folder_for_user_a(\n core_session,\n users_and_roles,\n create_secret_inside_folder,\n pas_general_secrets,\n cleanup_secrets_and_folders):\n folder_id_list, folder_name, secret_id_list = create_secret_inside_folder\n folders_list = cleanup_secrets_and_folders[1]\n params = pas_general_secrets\n prefix = guid()\n pas_power_user = users_and_roles.get_user('Privileged Access Service Power User')\n user_name = pas_power_user.get_login_name()\n user_id = pas_power_user.get_id()\n\n # API to get new session for User A\n pas_power_user_session = users_and_roles.get_session_for_user('Privileged Access Service Power User')\n assert pas_power_user_session.auth_details is not None, 'Failed to Login with PAS Power User'\n logger.info(f'User with PAS Power User Rights login successfully :user_Name: {user_name}'\n f' & Password: {pas_power_user.get_password()} ')\n\n # Api to give user permissions to folder\n user_permissions_result = give_user_permissions_to_folder(core_session, user_name, user_id, folder_id_list[0],\n 'View,Grant')\n assert user_permissions_result, f'Not Able to set user permissions to folder{user_permissions_result}'\n logger.info(f'User Permissions to folder: {user_permissions_result}')\n\n # Api to give member permissions to folder\n member_perm_result, member_perm_success = set_member_permissions_to_folder(core_session,\n user_name,\n 'View,Grant,Edit',\n user_id,\n folder_id_list[0])\n assert member_perm_success, f'Not Able to set member permissions to Folder{member_perm_result[\"Result\"]}'\n logger.info(f'Member permissions to folder:{member_perm_result}')\n\n # Api to create secret folder for User A\n secret_folder_success, secret_folder_parameters, secret_folder_id = create_folder(pas_power_user_session,\n prefix + params['name'],\n params['description'])\n\n logger.info(f' Folder created successfully: {secret_folder_success} & details are {secret_folder_parameters}')\n assert secret_folder_success is True, f'Failed to create a folder {secret_folder_id}'\n folders_list.append(secret_folder_id)\n\n # Api to move secret into another Folder\n result_move = move_secret(pas_power_user_session, secret_id_list[0], secret_folder_id)\n assert result_move['success'], f'Not Able to move the secret into Folder: {result_move[\"Result\"]}'\n logger.info(f'Moving secret with edit permissions to another folder:{result_move}')","def verify_that_the_acl_was_set_to_rtacltest2(driver):\n assert wait_on_element(driver, 5, f'//div[contains(text(),\"rt-acl-test-1\")]//button', 'clickable')\n driver.find_element_by_xpath(f'//div[contains(text(),\"rt-acl-test-1\")]//button').click()\n assert wait_on_element(driver, 5, f'//tr[contains(.,\"rt-acl-test-2\")]//mat-icon[text()=\"more_vert\"]', 'clickable')\n driver.find_element_by_xpath(f'//tr[contains(.,\"rt-acl-test-2\")]//mat-icon[text()=\"more_vert\"]').click()\n assert wait_on_element(driver, 5, '//button[normalize-space(text())=\"View Permissions\"]', 'clickable')\n driver.find_element_by_xpath('//button[normalize-space(text())=\"View Permissions\"]').click()\n assert wait_on_element(driver, 5, '//div[contains(text(),\"User - ericbsd\")]')","def test_permission_remove_unknown_user(self):\n test_name = sys._getframe().f_code.co_name\n rv, output = self._execute('permission remove joe TICKET_VIEW')\n self.assertEqual(2, rv)\n self.assertEqual(self.expected_results[test_name], output)","def test_set_display_name_other_users_template_as_staff_raises_access_control_error(\n self,\n ):\n mock_request = create_mock_request(user=self.staff_user1)\n with self.assertRaises(AccessControlError):\n template_api.set_display_name(\n self.fixture.user2_template, \"new_name\", request=mock_request\n )","def test_serve_users(self):\n pass","def test_set_get_acls_recursive(self):\n path_one = \"%s/one\" % (self.tests_path)\n path_two = \"%s/one/two\" % (self.tests_path)\n self.shell.onecmd(\"create %s 'hello'\" % (path_one))\n self.shell.onecmd(\"create %s 'goodbye'\" % (path_two))\n self.shell.onecmd(\"set_acls %s 'world:anyone:r digest:%s:cdrwa' true\" % (\n path_one, self.auth_digest))\n self.shell.onecmd(\"get_acls %s 0\" % (path_one))\n\n if PYTHON3:\n user_id = \"Id(scheme='digest', id='%s')\" % (self.auth_digest)\n else:\n user_id = \"Id(scheme=u'digest', id=u'%s')\" % (self.auth_digest)\n\n user_acl = \"ACL(perms=31, acl_list=['ALL'], id=%s)\" % (user_id)\n expected_output = \"\"\"/tests/one: ['WORLD_READ', %s]\n/tests/one/two: ['WORLD_READ', %s]\n\"\"\" % (user_acl, user_acl)\n\n self.assertEqual(expected_output, self.output.getvalue())","def test_user_can_change_superuser(self):\n self.assertTrue(self.story.user_can_change(self.superuser))","def test_read_namespaced_role_binding_restriction(self):\n pass","def test_if_allowed_for_superusers_permissions(self):\r\n res = self.client_superuser.get(reverse(LIST_USER_URL),data={})\r\n self.assertEqual(res.status_code, status.HTTP_200_OK)","def test_6_1_5_etc_group_dash_user(host):\n assert host.file(ETC_GROUP_DASH).user == 'root'","def test_has_perm_change(self):\n perm = \"change\"\n self.assertTrue(self.story.has_perm(self.user1, perm))\n self.assertFalse(self.story.has_perm(self.user2, perm))","def test_delete_namespaced_role(self):\n pass","def test_list_projects_for_user_with_inherited_user_project_grants(self):\n # Enable OS-INHERIT extension\n root_project = unit.new_project_ref(\n domain_id=CONF.identity.default_domain_id)\n root_project = self.resource_api.create_project(root_project['id'],\n root_project)\n leaf_project = unit.new_project_ref(\n domain_id=CONF.identity.default_domain_id,\n parent_id=root_project['id'])\n leaf_project = self.resource_api.create_project(leaf_project['id'],\n leaf_project)\n\n user = unit.new_user_ref(domain_id=CONF.identity.default_domain_id)\n user = self.identity_api.create_user(user)\n\n # Grant inherited user role\n self.assignment_api.create_grant(user_id=user['id'],\n project_id=root_project['id'],\n role_id=self.role_admin['id'],\n inherited_to_projects=True)\n # Grant non-inherited user role\n self.assignment_api.create_grant(user_id=user['id'],\n project_id=root_project['id'],\n role_id=self.role_member['id'])\n # Should get back both projects: because the direct role assignment for\n # the root project and inherited role assignment for leaf project\n user_projects = self.assignment_api.list_projects_for_user(user['id'])\n self.assertEqual(2, len(user_projects))\n self.assertIn(root_project, user_projects)\n self.assertIn(leaf_project, user_projects)\n\n # TODO(henry-nash): The test above uses list_projects_for_user\n # which may, in a subsequent patch, be re-implemented to call\n # list_role_assignments and then report only the distinct projects.\n #\n # The test plan below therefore mirrors this test, to ensure that\n # list_role_assignments works the same. Once list_projects_for_user\n # has been re-implemented then the manual tests above can be\n # refactored.\n test_plan = {\n # A domain with a project and sub-project, plus a user.\n # Also, create 2 roles.\n 'entities': {\n 'domains': {'id': CONF.identity.default_domain_id, 'users': 1,\n 'projects': {'project': 1}},\n 'roles': 2},\n # A direct role and an inherited role on the parent\n 'assignments': [{'user': 0, 'role': 0, 'project': 0},\n {'user': 0, 'role': 1, 'project': 0,\n 'inherited_to_projects': True}],\n 'tests': [\n # List all effective assignments for user[0] - should get back\n # one direct role plus one inherited role.\n {'params': {'user': 0, 'effective': True},\n 'results': [{'user': 0, 'role': 0, 'project': 0},\n {'user': 0, 'role': 1, 'project': 1,\n 'indirect': {'project': 0}}]}\n ]\n }\n self.execute_assignment_plan(test_plan)","def inner_test(user: models.User):\n pass","def test_set_get_bad_acl(self):\n path_one = \"%s/one\" % (self.tests_path)\n auth_id = \"username_password:user:user\"\n self.shell.onecmd(\"create %s 'hello'\" % (path_one))\n self.shell.onecmd(\"set_acls %s 'world:anyone:r %s'\" % (\n path_one, auth_id))\n expected_output = \"Failed to set ACLs: \"\n expected_output += \"Bad ACL: username_password:user:user. \"\n expected_output += \"Format is scheme:id:perms.\\n\"\n self.assertEqual(expected_output, self.output.getvalue())","def test_permissions(self, course_dir, groupshared):\n run_nbgrader([\"db\", \"assignment\", \"add\", \"ps1\"])\n run_nbgrader([\"db\", \"student\", \"add\", \"foo\"])\n with open(\"nbgrader_config.py\", \"a\") as fh:\n if groupshared:\n fh.write(\"\"\"c.CourseDirectory.groupshared = True\\n\"\"\")\n self._empty_notebook(join(course_dir, \"source\", \"ps1\", \"foo.ipynb\"))\n run_nbgrader([\"generate_assignment\", \"ps1\"])\n\n self._empty_notebook(join(course_dir, \"submitted\", \"foo\", \"ps1\", \"foo.ipynb\"))\n run_nbgrader([\"autograde\", \"ps1\"])\n run_nbgrader([\"generate_feedback\", \"ps1\"])\n\n if not groupshared:\n if sys.platform == 'win32':\n perms = '666'\n else:\n perms = '644'\n else:\n if sys.platform == 'win32':\n perms = '666'\n dirperms = '777'\n else:\n perms = '664'\n dirperms = '2775'\n\n assert isfile(join(course_dir, \"feedback\", \"foo\", \"ps1\", \"foo.html\"))\n if groupshared:\n # non-groupshared doesn't guarantee anything about directory perms\n assert self._get_permissions(join(course_dir, \"feedback\", \"foo\", \"ps1\")) == dirperms\n assert self._get_permissions(join(course_dir, \"feedback\", \"foo\", \"ps1\", \"foo.html\")) == perms","def test_global_user_namespace():\n ns = {\"ab\": 1, \"cd\": 2}\n global_user_namespace.set_user_namespace(user_ns=ns)\n assert global_user_namespace.user_ns == ns\n assert global_user_namespace.use_ipython is False\n\n global_user_namespace.set_user_namespace(user_ns={}, use_ipython=True)\n assert global_user_namespace.user_ns == {}\n assert global_user_namespace.use_ipython is True\n\n global_user_namespace.set_user_namespace(user_ns=ns, use_ipython=False)\n assert global_user_namespace.user_ns == ns\n assert global_user_namespace.use_ipython is False","def test_6_1_3_etc_group_user(host):\n assert host.file(ETC_PASSWD_DASH).user == 'root'","def test06_perm_users(self):\n print_ln('test_perm_users')\n \n try:\n pList = review.find_perms(Perm(obj_name='py-obj*', op_name='*'))\n for perm in pList: \n print_ln(\"Perm obj name=\" + perm.obj_name + ', op=' + perm.op_name + ', id=' + perm.obj_id)\n uList = review.perm_users(perm)\n for user in uList:\n print_ln(\"Assigned user=\" + user.uid, 1)\n except Exception as e:\n self.fail('test_perm_users failed, exception=' + e.msg)","def test_channel_removeowner_not_owner_permissions():\n clear()\n auth_register('randemail@gmail.com', 'password1234', 'Jane', 'Citizen')\n register_second_result = auth_register('randemail2@gmail.com', 'password1234', 'Jane', 'Citizen')\n register_third_result = auth_register('randemail3@gmail.com', 'password1234', 'Jane', 'Citizen')\n randChannel_id = channels_create(register_second_result['token'], 'Random Channel', True)\n with pytest.raises(AccessError):\n assert channel_removeowner(register_third_result['token'], randChannel_id['channel_id'], register_second_result['u_id'])","def testGetAccessAllowed(self):\n for user in (self.guest, self.contributor, self.delegate, self.owner, self.root):\n response = self.runGet(user, sequencer=self.hiseq2000.vendor_id)\n self.response_200(response)\n data = json.loads(response.content.decode(\"utf-8\"))\n self.assertEqual(data[\"sodar_uuid\"], str(self.hiseq2000.sodar_uuid))","def test_project_administrator(project):\n if is_server_administrator():\n return True\n if is_project_administrator(project):\n return True\n return False","def test_invalid_namespace(self):\r\n self.attempt_login(403, ns=\"http%3A%2F%2Fspecs.openid.net%2Fauth%2F2.0\")","def test_delete_other_users_template_as_user_raises_access_control_error(\n self,\n ):\n mock_request = create_mock_request(user=self.user1)\n with self.assertRaises(AccessControlError):\n template_api.delete(\n self.fixture.user2_template, request=mock_request\n )","def testGetAccessDenied(self):\n self.runGet(None)\n self.response_401()\n for user in (self.norole, self.unrelated_owner):\n self.runGet(user)\n self.response_403()","def setUp(self):\n\n from django.contrib.auth.models import Permission, User\n perms = Permission.objects.filter(codename__startswith='resource_')\n jedi = User.objects.get(username='jedi')\n sith = User.objects.get(username='sith')\n for perm in perms:\n if perm.codename.endswith('_get'):\n sith.user_permissions.add(perm)\n jedi.user_permissions.add(perm)\n jedi.save()\n sith.save()","def test_create_namespaced_role_binding_restriction(self):\n pass","def test_change_permission(self):\r\n self.assertTrue(self.creator_admin.has_change_permission(self.request))\r\n\r\n self.request.user = self.user\r\n self.assertFalse(self.creator_admin.has_change_permission(self.request))","def test_replace_namespaced_role_binding_restriction(self):\n pass","def test_permission_remove_action_for_all_users(self):\n test_name = sys._getframe().f_code.co_name\n self._execute('permission add anonymous TICKET_CREATE')\n self._execute('permission remove * TICKET_CREATE')\n rv, output = self._execute('permission list')\n self.assertEqual(0, rv)\n self.assertEqual(self.expected_results[test_name], output)","def test_06_self_cannot_upgrade_group(self):\n meowers = self.meowers\n cat = self.cat\n dog = self.dog\n cat.uaccess.share_group_with_user(meowers, dog, PrivilegeCodes.VIEW)\n self.assertFalse(dog in meowers.gaccess.edit_users)\n self.assertTrue(dog in meowers.gaccess.members)\n self.assertTrue(\n is_equal_to_as_set(\n [dog],\n dog.uaccess.get_group_unshare_users(meowers)))\n with self.assertRaises(PermissionDenied):\n dog.uaccess.share_group_with_user(\n meowers, dog, PrivilegeCodes.VIEW)\n with self.assertRaises(PermissionDenied):\n dog.uaccess.share_group_with_user(\n meowers, dog, PrivilegeCodes.CHANGE)\n self.assertTrue(dog in meowers.gaccess.members)\n self.assertTrue(\n is_equal_to_as_set(\n [dog],\n dog.uaccess.get_group_unshare_users(meowers)))","def test_read_namespaced_role_binding(self):\n pass","def test_subsystems(self):\n pass","def test_get_other_users_template_raises_access_control_error(self):\n mock_request = create_mock_request(user=self.user1)\n with self.assertRaises(AccessControlError):\n template_api.get_by_id(\n self.fixture.user2_template.id, request=mock_request\n )","def allowed_user_access_use(usera, userb):\n try:\n upa = usera.get_profile()\n upb = userb.get_profile()\n except AttributeError:\n return False\n\n return usera == userb or usera.has_perm(\"vnswww.userprofile_use_any\") or (usera.has_perm(\"vnswww.userprofile_use_org\") and upa.org == upb.org)","def test_0000_initiate_users(self):\n self.login(email=common.test_user_1_email, username=common.test_user_1_name)\n test_user_1 = self.test_db_util.get_user(common.test_user_1_email)\n assert test_user_1 is not None, 'Problem retrieving user with email %s from the database' % common.test_user_1_email\n self.test_db_util.get_private_role(test_user_1)\n self.login(email=common.admin_email, username=common.admin_username)\n admin_user = self.test_db_util.get_user(common.admin_email)\n assert admin_user is not None, 'Problem retrieving user with email %s from the database' % common.admin_email\n self.test_db_util.get_private_role(admin_user)","def test_0000_initiate_users(self):\n self.login(email=common.test_user_1_email, username=common.test_user_1_name)\n test_user_1 = self.test_db_util.get_user(common.test_user_1_email)\n assert test_user_1 is not None, 'Problem retrieving user with email %s from the database' % common.test_user_1_email\n self.test_db_util.get_private_role(test_user_1)\n self.login(email=common.admin_email, username=common.admin_username)\n admin_user = self.test_db_util.get_user(common.admin_email)\n assert admin_user is not None, 'Problem retrieving user with email %s from the database' % common.admin_email\n self.test_db_util.get_private_role(admin_user)","def test_get_namespace(self):\n pass","def test_if_an_user_c_access_administration_panel(client):\n response = client.get(\"/admin/\")\n assert response.status_code != 200","def test_get_role_inherit_only(self):\n self.assertEqual(\n self.project.get_role(self.user_alice), self.owner_as_cat\n )","def verify_that_the_acl_was_set_to_rtacltest1(driver):\n assert wait_on_element(driver, 5, f'//tr[contains(.,\"rt-acl-test-1\")]//mat-icon[text()=\"more_vert\"]', 'clickable')\n driver.find_element_by_xpath(f'//tr[contains(.,\"rt-acl-test-1\")]//mat-icon[text()=\"more_vert\"]').click()\n assert wait_on_element(driver, 5, '//button[normalize-space(text())=\"View Permissions\"]', 'clickable')\n driver.find_element_by_xpath('//button[normalize-space(text())=\"View Permissions\"]').click()\n assert wait_on_element(driver, 5, '//div[contains(text(),\"User - ericbsd\")]')","def test_util_has_perm_or_owns_sanity(self):\n me = User.objects.get(pk=118533)\n my_t = Thread.objects.filter(creator=me)[0]\n other_t = Thread.objects.exclude(creator=me)[0]\n perm = 'forums_forum.thread_edit_forum'\n allowed = access.has_perm_or_owns(me, perm, my_t, self.forum_1)\n eq_(allowed, True)\n allowed = access.has_perm_or_owns(me, perm, other_t, self.forum_1)\n eq_(allowed, False)","def test_create_policy_for_all_namespaces(self):\n pass","def test_custom_permissions(self, course_dir):\n run_nbgrader([\"db\", \"assignment\", \"add\", \"ps1\"])\n run_nbgrader([\"db\", \"student\", \"add\", \"foo\"])\n self._empty_notebook(join(course_dir, \"source\", \"ps1\", \"foo.ipynb\"))\n run_nbgrader([\"generate_assignment\", \"ps1\"])\n\n self._empty_notebook(join(course_dir, \"submitted\", \"foo\", \"ps1\", \"foo.ipynb\"))\n run_nbgrader([\"autograde\", \"ps1\"])\n run_nbgrader([\"generate_feedback\", \"ps1\", \"--GenerateFeedback.permissions=444\"])\n\n assert isfile(join(course_dir, \"feedback\", \"foo\", \"ps1\", \"foo.html\"))\n assert self._get_permissions(join(course_dir, \"feedback\", \"foo\", \"ps1\", \"foo.html\")) == '444'","def test_set_display_name_global_template_as_user_raises_access_control_error(\n self,\n ):\n mock_request = create_mock_request(user=self.user1)\n with self.assertRaises(AccessControlError):\n template_api.set_display_name(\n self.fixture.global_template, \"new_name\", request=mock_request\n )","def test_remove_user_from_course_group_permission_denied(self):\r\n add_users(self.global_admin, CourseInstructorRole(self.course_key), self.creator)\r\n another_staff = User.objects.create_user('another', 'teststaff+anothercourses@edx.org', 'foo')\r\n add_users(self.global_admin, CourseStaffRole(self.course_key), self.creator, self.staff, another_staff)\r\n with self.assertRaises(PermissionDenied):\r\n remove_users(self.staff, CourseStaffRole(self.course_key), another_staff)","def test_create_role_binding_restriction_for_all_namespaces(self):\n pass","def test_create_namespaced_policy(self):\n pass","def test_user_not_in_group_cannot_access(self):\n\n utils.create_user_and_authenticate(self)\n expected_url = reverse('my_groups_view')\n\n utils.test_cannot_access(self, self.url, expected_url)","def test_create_local_subject_access_review_for_all_namespaces(self):\n pass"],"string":"[\n \"def _check_namespace_access(self, namespace, user):\\n if not namespace.owners.filter(id=user.id).count():\\n raise exceptions.PermissionDenied(\\n 'The namespace listed on your filename must match one of '\\n 'the namespaces you have access to.'\\n )\",\n \"def test_read_namespaced_role(self):\\n pass\",\n \"def test_get_permissions(self):\\n pass\",\n \"def test_replace_namespaced_role(self):\\n pass\",\n \"def test_access_levels(self):\\n resource_id = 'protected-data'\\n arn_prefix = \\\"arn:dcp:fus:us-east-1:dev:\\\"\\n user = 'user_test_access_levels'\\n group = 'group_test_access_levels'\\n\\n # create the resource to control\\n resp = self.app.post(\\n f'/v1/resource/{self.test_resource}/id/{resource_id}',\\n headers=admin_headers)\\n self.assertEqual(resp.status_code, 201)\\n\\n with self.subTest(\\\"Check that no one has access by listing who has access\\\"):\\n resp = self.app.get(\\n f'/v1/resource/{self.test_resource}/id/{resource_id}/members',\\n headers=admin_headers)\\n self.assertEqual(resp.status_code, 200)\\n self.assertJSONEqual(resp.body, {'members': []})\\n\\n with self.subTest(\\\"Toggle user access.\\\"):\\n # create a user\\n resp = self.app.post(\\n f'/v1/user',\\n data=json.dumps({'user_id': user}),\\n headers=admin_headers)\\n self.assertEqual(resp.status_code, 201)\\n\\n # give a user access\\n request_body = [\\n {'member': user,\\n 'member_type': 'user',\\n 'access_level': 'read'}\\n ]\\n resp = self.app.put(\\n f'/v1/resource/{self.test_resource}/id/{resource_id}/members',\\n data=json.dumps(request_body),\\n headers=admin_headers\\n )\\n self.assertEqual(resp.status_code, 200)\\n\\n # Check that the user has access by listing who has access\\n resp = self.app.get(\\n f'/v1/resource/{self.test_resource}/id/{resource_id}/members',\\n headers=admin_headers)\\n self.assertEqual(resp.status_code, 200)\\n self.assertJSONEqual(resp.body, {'members': request_body})\\n\\n # Remove access for the user\\n request_body = [\\n {'member': user,\\n 'member_type': 'user'}\\n ]\\n resp = self.app.put(\\n f'/v1/resource/{self.test_resource}/id/{resource_id}/members',\\n data=json.dumps(request_body),\\n headers=admin_headers\\n )\\n self.assertEqual(resp.status_code, 200)\\n\\n # Check that the user does not have access by listing who has access\\n resp = self.app.get(\\n f'/v1/resource/{self.test_resource}/id/{resource_id}/members',\\n headers=admin_headers)\\n self.assertEqual(resp.status_code, 200)\\n self.assertJSONEqual(resp.body, {'members': []})\\n\\n with self.subTest(\\\"Toggle group access.\\\"):\\n # create a group\\n resp = self.app.post(\\n f'/v1/group',\\n data=json.dumps({'group_id': group}),\\n headers=admin_headers)\\n self.assertEqual(resp.status_code, 201)\\n\\n # give a group access\\n request_body = [\\n {'member': group,\\n 'member_type': 'group',\\n 'access_level': 'read'}\\n ]\\n resp = self.app.put(\\n f'/v1/resource/{self.test_resource}/id/{resource_id}/members',\\n data=json.dumps(request_body),\\n headers=admin_headers\\n )\\n self.assertEqual(resp.status_code, 200)\\n\\n # Check that the group has access by listing who has access\\n resp = self.app.get(\\n f'/v1/resource/{self.test_resource}/id/{resource_id}/members',\\n headers=admin_headers)\\n self.assertEqual(resp.status_code, 200)\\n self.assertJSONEqual(resp.body, {'members': request_body})\\n\\n # Remove access for the group\\n request_body = [\\n {'member': group,\\n 'member_type': 'group'}\\n ]\\n resp = self.app.put(\\n f'/v1/resource/{self.test_resource}/id/{resource_id}/members',\\n data=json.dumps(request_body),\\n headers=admin_headers\\n )\\n self.assertEqual(resp.status_code, 200)\\n\\n # Check that the group does not have access by listing who has access\\n resp = self.app.get(\\n f'/v1/resource/{self.test_resource}/id/{resource_id}/members',\\n headers=admin_headers)\\n self.assertEqual(resp.status_code, 200)\\n self.assertJSONEqual(resp.body, {'members': []})\",\n \"def test_status_code_for_privileged_user(self):\\n self.grant_permission()\\n self.client.login(username=\\\"john\\\", password=\\\"pass\\\")\\n response = self.client.get(self.get_url())\\n self.assertEqual(response.status_code, self.status_has_permission)\",\n \"def test_user_without_share(self):\\n set_permission(Permission.EDIT, self.user1, self.collection)\\n\\n # Can not add permissions to users.\\n data = {\\\"users\\\": {self.user2.pk: \\\"view\\\"}}\\n resp = self._detail_permissions(self.collection.pk, data, self.user1)\\n self.assertEqual(resp.status_code, status.HTTP_403_FORBIDDEN)\\n\\n # Can not add permissions to groups.\\n data = {\\\"users\\\": {self.group.pk: \\\"view\\\"}}\\n resp = self._detail_permissions(self.collection.pk, data, self.user1)\\n self.assertEqual(resp.status_code, status.HTTP_403_FORBIDDEN)\",\n \"def test_user(self):\\n return True\",\n \"def test_user_belonging_to_more_sites(self):\\n self._create_simple_setup()\\n foo_site = Site.objects.get(domain='foo.site.com')\\n bar_site = Site.objects.get(domain='bar.site.com')\\n writer_role = Role.objects.get(name='writer')\\n bob = User.objects.get(username='bob')\\n foo_master_page = Page.objects.get(\\n title_set__title='master',\\n site=foo_site)\\n writer_role.grant_to_user(bob, foo_site, [foo_master_page])\\n news_page = Page.objects.get(\\n title_set__title='news',\\n parent=foo_master_page)\\n PagePermission.objects.create(user=bob, page=news_page)\\n writer_role.ungrant_from_user(bob, foo_site)\\n writer_users = writer_role.users(foo_site)\\n self.assertNotIn(bob, writer_users)\\n writer_users = writer_role.users(bar_site)\\n self.assertIn(bob, writer_users)\",\n \"def test_permission_import_ok(self):\\n test_name = sys._getframe().f_code.co_name\\n user = u'test_user\\\\u0250'\\n self._execute('permission add ' + user + ' WIKI_VIEW')\\n self._execute('permission add ' + user + ' TICKET_VIEW')\\n rv, output = self._execute('permission export')\\n self._execute('permission remove ' + user + ' *')\\n rv, output = self._execute('permission import', input=output)\\n self.assertEqual(0, rv)\\n self.assertEqual('', output)\\n rv, output = self._execute('permission list')\\n self.assertEqual(0, rv)\\n self.assertEqual(self.expected_results[test_name], output)\",\n \"def test_patch_namespaced_role(self):\\n pass\",\n \"def test_create_namespaced_role(self):\\n pass\",\n \"def test_create_role_for_all_namespaces(self):\\n pass\",\n \"def test_list_namespaced_role(self):\\n pass\",\n \"def test_superuser():\\n assert os.geteuid() == 0, \\\"Need ROOT access in order to run tests.\\\"\",\n \"def test_get_namespaces_from_accounts(self):\\n pass\",\n \"def test_serve_user(self):\\n pass\",\n \"def test_access_control_is_superuser_as_user_raises_access_control_error(\\n self,\\n ):\\n # Arrange\\n mock_request = create_mock_request(user=self.user1)\\n\\n # Act # Assert\\n with self.assertRaises(AccessControlError):\\n access_control_api.is_superuser(\\n mock_function, request=mock_request\\n )\",\n \"def testSuperUserPermission(self):\\n self.login_user(self.superuser)\\n response = self.client.get(self.url, self.args)\\n self.assertEqual(response.status_code, 200)\",\n \"def test_users(self):\\n users = (\\\"root\\\", \\\"matlab\\\")\\n for user in users:\\n with self.subTest(username=user):\\n self.assertTrue(self.host.user(user).exists)\",\n \"def test_create_local_resource_access_review_for_all_namespaces(self):\\n pass\",\n \"def test_aiven_user_permissions(self):\\n response = requests.get(f'{os.environ[\\\"AIVEN_API_URL\\\"]}/v1/me',\\n headers={\\n \\\"Authorization\\\": f'aivenv1 {os.environ[\\\"AIVEN_TOKEN\\\"]}'\\n },\\n verify=True\\n )\\n assert response.status_code == 200\\n result = json.loads(response.text)\\n assert \\\"operator\\\" in [\\n value for key, value\\n in result[\\\"user\\\"][\\\"project_membership\\\"].items()\\n ]\",\n \"def test_read_user(self):\\n pass\",\n \"def test_create_resource_access_review_for_all_namespaces(self):\\n pass\",\n \"def test_set_display_name_other_users_template_as_user_raises_access_control_error(\\n self,\\n ):\\n mock_request = create_mock_request(user=self.user1)\\n with self.assertRaises(AccessControlError):\\n template_api.set_display_name(\\n self.fixture.user2_template, \\\"new_name\\\", request=mock_request\\n )\",\n \"def test_filter_owner_permission(self):\\n User = get_user_model()\\n user1 = User.objects.create(username=\\\"test_user1\\\", email=\\\"user1@test.com\\\")\\n obj = DescriptorSchema.objects.create(contributor=user1)\\n obj.set_permission(Permission.VIEW, user1)\\n\\n data_template = {\\n \\\"users\\\": {user1.id: \\\"view\\\"},\\n \\\"groups\\\": {1: \\\"edit\\\", 2: \\\"NONE\\\"},\\n }\\n\\n check_owner_permission(data_template, False, obj)\\n\\n # Check that only owner can set owner permission.\\n data = deepcopy(data_template)\\n data[\\\"users\\\"][1] = \\\"owner\\\"\\n with self.assertRaises(exceptions.PermissionDenied):\\n check_owner_permission(data, False, obj)\\n check_owner_permission(data, True, obj)\\n\\n # Check that only owner can rewoke owner permission.\\n obj.set_permission(Permission.OWNER, user1)\\n data = deepcopy(data_template)\\n data[\\\"users\\\"][1] = \\\"edit\\\"\\n with self.assertRaises(exceptions.PermissionDenied):\\n check_owner_permission(data, False, obj)\\n check_owner_permission(data, True, obj)\\n\\n # Check that group can not be owner.\\n obj.set_permission(Permission.VIEW, user1)\\n data = deepcopy(data_template)\\n data[\\\"groups\\\"][1] = \\\"owner\\\"\\n with self.assertRaises(exceptions.ParseError):\\n check_owner_permission(data, False, obj)\\n with self.assertRaises(exceptions.ParseError):\\n check_owner_permission(data, True, obj)\",\n \"def testGetAccessAllowed(self):\\n for user in (self.guest, self.contributor, self.delegate, self.owner, self.root):\\n response = self.runGet(user)\\n self.response_200(response)\\n data = json.loads(response.content.decode(\\\"utf-8\\\"))\\n self.assertEqual(len(data), 1)\",\n \"def test_list_role_for_all_namespaces(self):\\n pass\",\n \"def test_access_negative(self, api):\\n self.builder.add_user(api.get_user())\\n r1 = api.access_user(api.get_user(), False)\\n access_false = self.builder.get_access(api.get_user())\\n self.builder.del_user(api.get_user())\\n assert access_false == 0\\n assert r1.status_code == 200\",\n \"def test05_user_perms(self):\\n print_ln('test_user_perms')\\n \\n try:\\n uList = review.find_users(User(uid='py-user*'))\\n for usr in uList: \\n print_ln(\\\"Assigned perms user=\\\" + usr.uid)\\n pList = review.user_perms(usr)\\n for perm in pList: \\n print_ln(\\\"Assigned perm obj name=\\\" + perm.obj_name + ', op name=' + perm.op_name + ', obj id=' + perm.obj_id, 1) \\n except Exception as e:\\n self.fail('test_user_perms failed, exception=' + e.msg)\",\n \"def test_filter_user_permissions(self):\\n data = {\\n \\\"users\\\": {\\n 1: \\\"view\\\",\\n 2: \\\"NONE\\\",\\n }\\n }\\n\\n with self.assertRaises(exceptions.PermissionDenied):\\n check_user_permissions(data, 1)\\n\\n with self.assertRaises(exceptions.PermissionDenied):\\n check_user_permissions(data, 2)\\n\\n check_user_permissions(data, 3)\",\n \"def test_create_namespaced_resource_access_review(self):\\n pass\",\n \"def test_set_user_ns_1(tmp_path):\\n pc_path = copy_default_profile_collection(tmp_path)\\n\\n create_local_imports_files(pc_path)\\n patch_first_startup_file(pc_path, patch_code)\\n\\n nspace = load_profile_collection(pc_path)\\n assert len(nspace) > 0, \\\"Failed to load the profile collection\\\"\\n assert \\\"f1\\\" in nspace, \\\"Test for local imports failed\\\"\\n assert \\\"f2\\\" in nspace, \\\"Test for local imports failed\\\"\\n\\n # Test if the decorator `set_user_ns` does not change function type\\n assert inspect.isgeneratorfunction(nspace[\\\"f1\\\"]) is False\\n assert inspect.isgeneratorfunction(nspace[\\\"f2\\\"]) is True\\n\\n # Check if the extra arguments are removed from the function signature\\n def check_signature(func):\\n params = inspect.signature(func).parameters\\n assert \\\"user_ns\\\" not in params\\n assert \\\"ipython\\\" not in params\\n\\n check_signature(nspace[\\\"f1\\\"])\\n check_signature(nspace[\\\"f1a\\\"])\\n check_signature(nspace[\\\"f2\\\"])\\n check_signature(nspace[\\\"f2a\\\"])\\n\\n assert nspace[\\\"value_f3\\\"] == 91\\n assert nspace[\\\"value_f4\\\"] == 90\\n\\n # Test function\\n global_user_namespace.set_user_namespace(user_ns=nspace, use_ipython=False)\\n global_user_namespace.user_ns[\\\"v_from_namespace\\\"] = \\\"value-sent-to-func\\\"\\n assert nspace[\\\"v_from_namespace\\\"] == \\\"value-sent-to-func\\\"\\n\\n result_func = nspace[\\\"f1\\\"](60)\\n assert nspace[\\\"func_was_called\\\"] == \\\"func_was_called\\\"\\n assert result_func[0] == 60\\n assert result_func[1] == \\\"value-sent-to-func\\\"\\n assert result_func[2] is False\\n\\n result_func = nspace[\\\"f1a\\\"](65)\\n assert nspace[\\\"func_A_was_called\\\"] == \\\"func_was_called\\\"\\n assert result_func[0] == 65\\n assert result_func[1] == \\\"value-sent-to-func\\\"\\n\\n # Test generator\\n global_user_namespace.user_ns[\\\"v_from_namespace\\\"] = \\\"value-sent-to-gen\\\"\\n result_func = list(nspace[\\\"f2\\\"](110))[0]\\n assert nspace[\\\"gen_was_called\\\"] == \\\"gen_was_called\\\"\\n assert result_func[0] == 110\\n assert result_func[1] == \\\"value-sent-to-gen\\\"\\n assert result_func[2] is False\\n\\n result_func = list(nspace[\\\"f2a\\\"](115))[0]\\n assert nspace[\\\"gen_A_was_called\\\"] == \\\"gen_was_called\\\"\\n assert result_func[0] == 115\\n assert result_func[1] == \\\"value-sent-to-gen\\\"\",\n \"def test_permission_add_one_action_ok(self):\\n test_name = sys._getframe().f_code.co_name\\n self._execute('permission add test_user WIKI_VIEW')\\n rv, output = self._execute('permission list')\\n self.assertEqual(0, rv)\\n self.assertEqual(self.expected_results[test_name], output)\",\n \"def test_read_namespaced_policy(self):\\n pass\",\n \"def test_create_namespaced_local_resource_access_review(self):\\n pass\",\n \"def testGetAccessAllowed(self):\\n for user in (self.guest, self.contributor, self.delegate, self.owner, self.root):\\n response = self.runGet(user, sequencer=self.hiseq2000.sodar_uuid)\\n self.response_200(response)\\n data = json.loads(response.content.decode(\\\"utf-8\\\"))\\n self.assertEqual(data[\\\"sodar_uuid\\\"], str(self.hiseq2000.sodar_uuid))\",\n \"def test_install_rbac_1(test_name, cloud_provider, rke_client, kubectl):\\n rke_template = 'cluster_install_rbac_1.yml.j2'\\n nodes = cloud_provider.create_multiple_nodes(3, test_name)\\n create_rke_cluster(rke_client, kubectl, nodes, rke_template)\\n\\n result = kubectl.create_resourse_from_yml(\\n 'resources/k8s_ymls/daemonset_pods_per_node.yml', namespace='default')\\n assert result.ok, result.stderr\\n kubectl.create_ns('outside-role')\\n result = kubectl.create_resourse_from_yml(\\n 'resources/k8s_ymls/daemonset_pods_per_node.yml',\\n namespace='outside-role')\\n assert result.ok, result.stderr\\n\\n # Create role and rolebinding to user1 in namespace 'default'\\n # namespace is coded in role.yml and rolebinding.yml\\n result = kubectl.create_resourse_from_yml('resources/k8s_ymls/role.yml')\\n assert result.ok, result.stderr\\n result = kubectl.create_resourse_from_yml(\\n 'resources/k8s_ymls/rolebinding.yml')\\n assert result.ok, result.stderr\\n\\n # verify read in namespace\\n admin_call_pods = kubectl.get_resource('pods', namespace='default')\\n user_call_pods = kubectl.get_resource(\\n 'pods', as_user='user1', namespace='default')\\n\\n # Make sure the number of pods returned with out user is the same as user\\n # for this namespace\\n assert len(admin_call_pods['items']) > 0, \\\"Pods should be greater than 0\\\"\\n assert (len(admin_call_pods['items']) == len(user_call_pods['items'])), (\\n \\\"Did not retrieve correct number of pods for 'user1'. Expected {0},\\\"\\n \\\"Retrieved {1}\\\".format(\\n len(admin_call_pods['items']), len(user_call_pods['items'])))\\n\\n # verify restrictions no pods return in get pods in different namespaces\\n user_call_pods = kubectl.get_resource(\\n 'pods', as_user='user1', namespace='outside-role')\\n assert len(user_call_pods['items']) == 0, (\\n \\\"Should not be able to get pods outside of defined user1 namespace\\\")\\n\\n # verify create fails as user for any namespace\\n result = kubectl.run(test_name + '-pod2', image='nginx', as_user='user1',\\n namespace='outside-role')\\n assert result.ok is False, (\\n \\\"'user1' should not be able to create pods in other namespaces:\\\\n{0}\\\"\\n .format(result.stdout + result.stderr))\\n assert \\\"cannot create\\\" in result.stdout + result.stderr\\n\\n result = kubectl.run(test_name + '-pod3', image='nginx', as_user='user1',\\n namespace='default')\\n assert result.ok is False, (\\n \\\"'user1' should not be able to create pods in its own namespace:\\\\n{0}\\\"\\n .format(result.stdout + result.stderr))\\n assert \\\"cannot create\\\" in result.stdout + result.stderr\\n\\n for node in nodes:\\n cloud_provider.delete_node(node)\",\n \"def test_protect_owner(self):\\n self.collection.set_permission(Permission.SHARE, self.user1)\\n\\n # User with share permission cannot grant ``owner`` permission\\n data = {\\\"users\\\": {self.user2.pk: \\\"owner\\\"}}\\n resp = self._detail_permissions(self.collection.pk, data, self.user1)\\n self.assertEqual(resp.status_code, status.HTTP_403_FORBIDDEN)\\n self.assertNotIn(\\\"owner\\\", self.collection.get_permissions(self.user2))\\n self.assertFalse(PermissionModel.objects.filter(user=self.user2).exists())\\n\\n # User with share permission cannot revoke ``owner`` permission\\n self.collection.set_permission(Permission.OWNER, self.user2)\\n data = {\\\"users\\\": {self.user2.pk: \\\"editor\\\"}}\\n resp = self._detail_permissions(self.collection.pk, data, self.user1)\\n self.assertEqual(resp.status_code, status.HTTP_403_FORBIDDEN)\\n self.assertEqual(\\n self.collection.get_permissions(self.user2),\\n [Permission.VIEW, Permission.EDIT, Permission.SHARE, Permission.OWNER],\\n )\\n self.collection.set_permission(Permission.NONE, self.user2)\\n\\n # Now let user1 be owner on collection.\\n set_permission(Permission.OWNER, self.user1, self.collection)\\n\\n # ``owner`` permission cannot be assigned to a group\\n data = {\\\"groups\\\": {self.group.pk: \\\"owner\\\"}}\\n resp = self._detail_permissions(self.collection.pk, data, self.user1)\\n self.assertEqual(resp.status_code, status.HTTP_400_BAD_REQUEST)\\n self.assertFalse(PermissionModel.objects.filter(group=self.group).exists())\\n\\n # User with owner permission can grant ``owner`` permission\\n data = {\\\"users\\\": {self.user2.pk: \\\"owner\\\"}}\\n resp = self._detail_permissions(self.collection.pk, data, self.user1)\\n self.assertEqual(resp.status_code, status.HTTP_200_OK)\\n self.assertEqual(\\n self.collection.get_permissions(self.user2),\\n [Permission.VIEW, Permission.EDIT, Permission.SHARE, Permission.OWNER],\\n )\\n\\n # User with owner permission can revoke ``owner`` permission\\n data = {\\\"users\\\": {self.user2.pk: \\\"edit\\\"}}\\n resp = self._detail_permissions(self.collection.pk, data, self.user1)\\n self.assertEqual(resp.status_code, status.HTTP_200_OK)\\n self.assertFalse(\\n PermissionModel.objects.filter(\\n user=self.user2, value=Permission.OWNER.value\\n ).exists()\\n )\\n\\n # User with owner permission cannot remove all owners\\n data = {\\\"users\\\": {self.user1.pk: \\\"edit\\\", self.owner.pk: \\\"edit\\\"}}\\n\\n resp = self._detail_permissions(self.collection.pk, data, self.user1)\\n self.assertEqual(resp.status_code, status.HTTP_400_BAD_REQUEST)\\n self.assertEqual(resp.data[\\\"detail\\\"], \\\"Object must have at least one owner.\\\")\\n\\n owner_permissions = self.collection.permission_group.permissions.filter(\\n value=Permission.OWNER.value\\n )\\n owner_count = owner_permissions.count()\\n self.assertEqual(owner_count, 2)\\n\\n # User can delete his owner permission if there is at least one other owner\\n self.assertTrue(owner_permissions.filter(user=self.user1).exists())\\n data = {\\\"users\\\": {self.user1.pk: \\\"view\\\"}}\\n resp = self._detail_permissions(self.collection.pk, data, self.user1)\\n self.assertEqual(resp.status_code, status.HTTP_200_OK)\\n self.assertFalse(owner_permissions.filter(user=self.user1.pk).exists())\",\n \"def test_public_user(self):\\n set_permission(Permission.SHARE, self.user1, self.collection)\\n\\n data = {\\\"public\\\": \\\"view\\\"}\\n resp = self._detail_permissions(self.collection.pk, data, self.user1)\\n self.assertEqual(resp.status_code, status.HTTP_200_OK)\\n\\n data = {\\\"public\\\": \\\"none\\\"}\\n resp = self._detail_permissions(self.collection.pk, data, self.user1)\\n self.assertEqual(resp.status_code, status.HTTP_200_OK)\\n\\n data = {\\\"public\\\": \\\"edit\\\"}\\n resp = self._detail_permissions(self.collection.pk, data, self.user1)\\n self.assertEqual(resp.status_code, status.HTTP_403_FORBIDDEN)\\n\\n data = {\\\"public\\\": \\\"share\\\"}\\n resp = self._detail_permissions(self.collection.pk, data, self.user1)\\n self.assertEqual(resp.status_code, status.HTTP_403_FORBIDDEN)\\n\\n data = {\\\"public\\\": \\\"owner\\\"}\\n resp = self._detail_permissions(self.collection.pk, data, self.user1)\\n self.assertEqual(resp.status_code, status.HTTP_403_FORBIDDEN)\",\n \"def test_access_positive(self, api):\\n self.builder.add_user(api.get_user())\\n self.builder.upd_access(api.get_user(), False)\\n r1 = api.access_user(api.get_user(), True)\\n access_true = self.builder.get_access(api.get_user())\\n self.builder.del_user(api.get_user())\\n assert access_true == 1\\n assert r1.status_code == 200\",\n \"def test_get_namespaces_from_account(self):\\n pass\",\n \"def test_subroles(self):\\n def check_roles(r):\\n dev_holder = r.html.find(\\n 'table', {'id': 'usergroup_admin'}).findAll('tr')[2]\\n mem_holder = r.html.find(\\n 'table', {'id': 'usergroup_admin'}).findAll('tr')[3]\\n assert 'All users in Admin group' in dev_holder.text\\n assert 'All users in Developer group' in mem_holder.text\\n\\n r = self.app.get('/admin/groups/')\\n\\n admin_holder = r.html.find(\\n 'table', {'id': 'usergroup_admin'}).findAll('tr')[1]\\n admin_id = admin_holder['data-group']\\n # test that subroles are intact after user added\\n with audits('add user test-user to Admin'):\\n r = self.app.post('/admin/groups/add_user', params={\\n 'role_id': admin_id,\\n 'username': 'test-user'})\\n r = self.app.get('/admin/groups/')\\n check_roles(r)\\n # test that subroles are intact after user deleted\\n with audits('remove user test-user from Admin'):\\n r = self.app.post('/admin/groups/remove_user', params={\\n 'role_id': admin_id,\\n 'username': 'test-user'})\\n r = self.app.get('/admin/groups/')\\n check_roles(r)\",\n \"def test_share_inactive_user(self):\\n george = self.george\\n alva = self.alva\\n john = self.john\\n bikes = self.bikes\\n\\n self.assertEqual(\\n bikes.raccess.get_effective_privilege(alva),\\n PrivilegeCodes.NONE)\\n\\n # inactive users can't be granted access\\n # set john to an inactive user\\n john.is_active = False\\n john.save()\\n\\n with self.assertRaises(PermissionDenied):\\n george.uaccess.share_resource_with_user(\\n bikes, john, PrivilegeCodes.CHANGE)\\n\\n john.is_active = True\\n john.save()\\n\\n # inactive grantor can't grant access\\n # let's first grant John access privilege\\n george.uaccess.share_resource_with_user(\\n bikes, john, PrivilegeCodes.CHANGE)\\n\\n self.assertEqual(\\n bikes.raccess.get_effective_privilege(john),\\n PrivilegeCodes.CHANGE)\\n\\n john.is_active = False\\n john.save()\\n\\n with self.assertRaises(PermissionDenied):\\n john.uaccess.share_resource_with_user(\\n bikes, alva, PrivilegeCodes.VIEW)\",\n \"def test_workspace_access(self):\\n\\n failed_logins = []\\n\\n for username in self.testdata.get_usernames():\\n userdata = self.testdata.get_userdata_for(username)\\n\\n if 'mw-login' not in userdata.admin_properties.groups:\\n continue\\n\\n ws = None\\n\\n try:\\n ws = self.cm.access(host=self.hubname,\\n username=userdata.username,\\n password=userdata.password)\\n\\n sessiondir = ws.execute('echo $SESSIONDIR')\\n\\n except:\\n failed_logins.append(userdata.username)\\n\\n finally:\\n if ws is not None:\\n ws.close()\\n ws = None\\n\\n\\n assert len(failed_logins) == 0, \\\\\\n 'login failed for the following accounts: %s' % (failed_logins)\",\n \"def test_permission_remove_all_actions_for_user(self):\\n test_name = sys._getframe().f_code.co_name\\n self._execute('permission remove anonymous *')\\n rv, output = self._execute('permission list')\\n self.assertEqual(0, rv)\\n self.assertEqual(self.expected_results[test_name], output)\",\n \"def test_permission_add_multiple_actions_ok(self):\\n test_name = sys._getframe().f_code.co_name\\n self._execute('permission add test_user LOG_VIEW FILE_VIEW')\\n rv, output = self._execute('permission list')\\n self.assertEqual(0, rv)\\n self.assertEqual(self.expected_results[test_name], output)\",\n \"def test_replace_user(self):\\n pass\",\n \"def test_get_other_users_template_as_staff_raises_access_control_error(\\n self,\\n ):\\n mock_request = create_mock_request(user=self.staff_user1)\\n with self.assertRaises(AccessControlError):\\n template_api.get_by_id(\\n self.fixture.user2_template.id, request=mock_request\\n )\",\n \"def test_user_settings_for_subscribing_other_users(self) -> None:\\n user_profile = self.example_user(\\\"cordelia\\\")\\n invitee_user_id = user_profile.id\\n realm = user_profile.realm\\n\\n do_set_realm_property(\\n realm, \\\"create_public_stream_policy\\\", Realm.POLICY_MEMBERS_ONLY, acting_user=None\\n )\\n do_set_realm_property(\\n realm, \\\"invite_to_stream_policy\\\", Realm.POLICY_ADMINS_ONLY, acting_user=None\\n )\\n do_change_user_role(self.test_user, UserProfile.ROLE_MODERATOR, acting_user=None)\\n result = self.common_subscribe_to_streams(\\n self.test_user,\\n [\\\"stream1\\\"],\\n {\\\"principals\\\": orjson.dumps([invitee_user_id]).decode()},\\n allow_fail=True,\\n )\\n self.assert_json_error(result, \\\"Insufficient permission\\\")\\n\\n do_change_user_role(self.test_user, UserProfile.ROLE_REALM_ADMINISTRATOR, acting_user=None)\\n self.common_subscribe_to_streams(\\n self.test_user, [\\\"stream1\\\"], {\\\"principals\\\": orjson.dumps([invitee_user_id]).decode()}\\n )\\n\\n do_set_realm_property(\\n realm, \\\"invite_to_stream_policy\\\", Realm.POLICY_MODERATORS_ONLY, acting_user=None\\n )\\n do_change_user_role(self.test_user, UserProfile.ROLE_MEMBER, acting_user=None)\\n # Make sure that we are checking the permission with a full member,\\n # as full member is the user just below moderator in the role hierarchy.\\n self.assertFalse(self.test_user.is_provisional_member)\\n result = self.common_subscribe_to_streams(\\n self.test_user,\\n [\\\"stream2\\\"],\\n {\\\"principals\\\": orjson.dumps([invitee_user_id]).decode()},\\n allow_fail=True,\\n )\\n self.assert_json_error(result, \\\"Insufficient permission\\\")\\n\\n do_change_user_role(self.test_user, UserProfile.ROLE_MODERATOR, acting_user=None)\\n self.common_subscribe_to_streams(\\n self.test_user, [\\\"stream2\\\"], {\\\"principals\\\": orjson.dumps([invitee_user_id]).decode()}\\n )\\n self.unsubscribe(user_profile, \\\"stream2\\\")\\n\\n do_set_realm_property(\\n realm, \\\"invite_to_stream_policy\\\", Realm.POLICY_MEMBERS_ONLY, acting_user=None\\n )\\n do_change_user_role(self.test_user, UserProfile.ROLE_GUEST, acting_user=None)\\n result = self.common_subscribe_to_streams(\\n self.test_user,\\n [\\\"stream2\\\"],\\n {\\\"principals\\\": orjson.dumps([invitee_user_id]).decode()},\\n allow_fail=True,\\n )\\n self.assert_json_error(result, \\\"Not allowed for guest users\\\")\\n\\n do_change_user_role(self.test_user, UserProfile.ROLE_MEMBER, acting_user=None)\\n self.common_subscribe_to_streams(\\n self.test_user,\\n [\\\"stream2\\\"],\\n {\\\"principals\\\": orjson.dumps([self.test_user.id, invitee_user_id]).decode()},\\n )\\n self.unsubscribe(user_profile, \\\"stream2\\\")\\n\\n do_set_realm_property(\\n realm,\\n \\\"invite_to_stream_policy\\\",\\n Realm.POLICY_FULL_MEMBERS_ONLY,\\n acting_user=None,\\n )\\n do_set_realm_property(realm, \\\"waiting_period_threshold\\\", 100000, acting_user=None)\\n result = self.common_subscribe_to_streams(\\n self.test_user,\\n [\\\"stream2\\\"],\\n {\\\"principals\\\": orjson.dumps([invitee_user_id]).decode()},\\n allow_fail=True,\\n )\\n self.assert_json_error(result, \\\"Insufficient permission\\\")\\n\\n do_set_realm_property(realm, \\\"waiting_period_threshold\\\", 0, acting_user=None)\\n self.common_subscribe_to_streams(\\n self.test_user, [\\\"stream2\\\"], {\\\"principals\\\": orjson.dumps([invitee_user_id]).decode()}\\n )\",\n \"def test_move_secret_to_different_folder_with_member_permission_edit_on_source_folder_for_user_a(\\n core_session,\\n users_and_roles,\\n create_secret_inside_folder,\\n pas_general_secrets,\\n cleanup_secrets_and_folders):\\n folder_id_list, folder_name, secret_id_list = create_secret_inside_folder\\n folders_list = cleanup_secrets_and_folders[1]\\n params = pas_general_secrets\\n prefix = guid()\\n pas_power_user = users_and_roles.get_user('Privileged Access Service Power User')\\n user_name = pas_power_user.get_login_name()\\n user_id = pas_power_user.get_id()\\n\\n # API to get new session for User A\\n pas_power_user_session = users_and_roles.get_session_for_user('Privileged Access Service Power User')\\n assert pas_power_user_session.auth_details is not None, 'Failed to Login with PAS Power User'\\n logger.info(f'User with PAS Power User Rights login successfully :user_Name: {user_name}'\\n f' & Password: {pas_power_user.get_password()} ')\\n\\n # Api to give user permissions to folder\\n user_permissions_result = give_user_permissions_to_folder(core_session, user_name, user_id, folder_id_list[0],\\n 'View,Grant')\\n assert user_permissions_result, f'Not Able to set user permissions to folder{user_permissions_result}'\\n logger.info(f'User Permissions to folder: {user_permissions_result}')\\n\\n # Api to give member permissions to folder\\n member_perm_result, member_perm_success = set_member_permissions_to_folder(core_session,\\n user_name,\\n 'View,Grant,Edit',\\n user_id,\\n folder_id_list[0])\\n assert member_perm_success, f'Not Able to set member permissions to Folder{member_perm_result[\\\"Result\\\"]}'\\n logger.info(f'Member permissions to folder:{member_perm_result}')\\n\\n # Api to create secret folder for User A\\n secret_folder_success, secret_folder_parameters, secret_folder_id = create_folder(pas_power_user_session,\\n prefix + params['name'],\\n params['description'])\\n\\n logger.info(f' Folder created successfully: {secret_folder_success} & details are {secret_folder_parameters}')\\n assert secret_folder_success is True, f'Failed to create a folder {secret_folder_id}'\\n folders_list.append(secret_folder_id)\\n\\n # Api to move secret into another Folder\\n result_move = move_secret(pas_power_user_session, secret_id_list[0], secret_folder_id)\\n assert result_move['success'], f'Not Able to move the secret into Folder: {result_move[\\\"Result\\\"]}'\\n logger.info(f'Moving secret with edit permissions to another folder:{result_move}')\",\n \"def verify_that_the_acl_was_set_to_rtacltest2(driver):\\n assert wait_on_element(driver, 5, f'//div[contains(text(),\\\"rt-acl-test-1\\\")]//button', 'clickable')\\n driver.find_element_by_xpath(f'//div[contains(text(),\\\"rt-acl-test-1\\\")]//button').click()\\n assert wait_on_element(driver, 5, f'//tr[contains(.,\\\"rt-acl-test-2\\\")]//mat-icon[text()=\\\"more_vert\\\"]', 'clickable')\\n driver.find_element_by_xpath(f'//tr[contains(.,\\\"rt-acl-test-2\\\")]//mat-icon[text()=\\\"more_vert\\\"]').click()\\n assert wait_on_element(driver, 5, '//button[normalize-space(text())=\\\"View Permissions\\\"]', 'clickable')\\n driver.find_element_by_xpath('//button[normalize-space(text())=\\\"View Permissions\\\"]').click()\\n assert wait_on_element(driver, 5, '//div[contains(text(),\\\"User - ericbsd\\\")]')\",\n \"def test_permission_remove_unknown_user(self):\\n test_name = sys._getframe().f_code.co_name\\n rv, output = self._execute('permission remove joe TICKET_VIEW')\\n self.assertEqual(2, rv)\\n self.assertEqual(self.expected_results[test_name], output)\",\n \"def test_set_display_name_other_users_template_as_staff_raises_access_control_error(\\n self,\\n ):\\n mock_request = create_mock_request(user=self.staff_user1)\\n with self.assertRaises(AccessControlError):\\n template_api.set_display_name(\\n self.fixture.user2_template, \\\"new_name\\\", request=mock_request\\n )\",\n \"def test_serve_users(self):\\n pass\",\n \"def test_set_get_acls_recursive(self):\\n path_one = \\\"%s/one\\\" % (self.tests_path)\\n path_two = \\\"%s/one/two\\\" % (self.tests_path)\\n self.shell.onecmd(\\\"create %s 'hello'\\\" % (path_one))\\n self.shell.onecmd(\\\"create %s 'goodbye'\\\" % (path_two))\\n self.shell.onecmd(\\\"set_acls %s 'world:anyone:r digest:%s:cdrwa' true\\\" % (\\n path_one, self.auth_digest))\\n self.shell.onecmd(\\\"get_acls %s 0\\\" % (path_one))\\n\\n if PYTHON3:\\n user_id = \\\"Id(scheme='digest', id='%s')\\\" % (self.auth_digest)\\n else:\\n user_id = \\\"Id(scheme=u'digest', id=u'%s')\\\" % (self.auth_digest)\\n\\n user_acl = \\\"ACL(perms=31, acl_list=['ALL'], id=%s)\\\" % (user_id)\\n expected_output = \\\"\\\"\\\"/tests/one: ['WORLD_READ', %s]\\n/tests/one/two: ['WORLD_READ', %s]\\n\\\"\\\"\\\" % (user_acl, user_acl)\\n\\n self.assertEqual(expected_output, self.output.getvalue())\",\n \"def test_user_can_change_superuser(self):\\n self.assertTrue(self.story.user_can_change(self.superuser))\",\n \"def test_read_namespaced_role_binding_restriction(self):\\n pass\",\n \"def test_if_allowed_for_superusers_permissions(self):\\r\\n res = self.client_superuser.get(reverse(LIST_USER_URL),data={})\\r\\n self.assertEqual(res.status_code, status.HTTP_200_OK)\",\n \"def test_6_1_5_etc_group_dash_user(host):\\n assert host.file(ETC_GROUP_DASH).user == 'root'\",\n \"def test_has_perm_change(self):\\n perm = \\\"change\\\"\\n self.assertTrue(self.story.has_perm(self.user1, perm))\\n self.assertFalse(self.story.has_perm(self.user2, perm))\",\n \"def test_delete_namespaced_role(self):\\n pass\",\n \"def test_list_projects_for_user_with_inherited_user_project_grants(self):\\n # Enable OS-INHERIT extension\\n root_project = unit.new_project_ref(\\n domain_id=CONF.identity.default_domain_id)\\n root_project = self.resource_api.create_project(root_project['id'],\\n root_project)\\n leaf_project = unit.new_project_ref(\\n domain_id=CONF.identity.default_domain_id,\\n parent_id=root_project['id'])\\n leaf_project = self.resource_api.create_project(leaf_project['id'],\\n leaf_project)\\n\\n user = unit.new_user_ref(domain_id=CONF.identity.default_domain_id)\\n user = self.identity_api.create_user(user)\\n\\n # Grant inherited user role\\n self.assignment_api.create_grant(user_id=user['id'],\\n project_id=root_project['id'],\\n role_id=self.role_admin['id'],\\n inherited_to_projects=True)\\n # Grant non-inherited user role\\n self.assignment_api.create_grant(user_id=user['id'],\\n project_id=root_project['id'],\\n role_id=self.role_member['id'])\\n # Should get back both projects: because the direct role assignment for\\n # the root project and inherited role assignment for leaf project\\n user_projects = self.assignment_api.list_projects_for_user(user['id'])\\n self.assertEqual(2, len(user_projects))\\n self.assertIn(root_project, user_projects)\\n self.assertIn(leaf_project, user_projects)\\n\\n # TODO(henry-nash): The test above uses list_projects_for_user\\n # which may, in a subsequent patch, be re-implemented to call\\n # list_role_assignments and then report only the distinct projects.\\n #\\n # The test plan below therefore mirrors this test, to ensure that\\n # list_role_assignments works the same. Once list_projects_for_user\\n # has been re-implemented then the manual tests above can be\\n # refactored.\\n test_plan = {\\n # A domain with a project and sub-project, plus a user.\\n # Also, create 2 roles.\\n 'entities': {\\n 'domains': {'id': CONF.identity.default_domain_id, 'users': 1,\\n 'projects': {'project': 1}},\\n 'roles': 2},\\n # A direct role and an inherited role on the parent\\n 'assignments': [{'user': 0, 'role': 0, 'project': 0},\\n {'user': 0, 'role': 1, 'project': 0,\\n 'inherited_to_projects': True}],\\n 'tests': [\\n # List all effective assignments for user[0] - should get back\\n # one direct role plus one inherited role.\\n {'params': {'user': 0, 'effective': True},\\n 'results': [{'user': 0, 'role': 0, 'project': 0},\\n {'user': 0, 'role': 1, 'project': 1,\\n 'indirect': {'project': 0}}]}\\n ]\\n }\\n self.execute_assignment_plan(test_plan)\",\n \"def inner_test(user: models.User):\\n pass\",\n \"def test_set_get_bad_acl(self):\\n path_one = \\\"%s/one\\\" % (self.tests_path)\\n auth_id = \\\"username_password:user:user\\\"\\n self.shell.onecmd(\\\"create %s 'hello'\\\" % (path_one))\\n self.shell.onecmd(\\\"set_acls %s 'world:anyone:r %s'\\\" % (\\n path_one, auth_id))\\n expected_output = \\\"Failed to set ACLs: \\\"\\n expected_output += \\\"Bad ACL: username_password:user:user. \\\"\\n expected_output += \\\"Format is scheme:id:perms.\\\\n\\\"\\n self.assertEqual(expected_output, self.output.getvalue())\",\n \"def test_permissions(self, course_dir, groupshared):\\n run_nbgrader([\\\"db\\\", \\\"assignment\\\", \\\"add\\\", \\\"ps1\\\"])\\n run_nbgrader([\\\"db\\\", \\\"student\\\", \\\"add\\\", \\\"foo\\\"])\\n with open(\\\"nbgrader_config.py\\\", \\\"a\\\") as fh:\\n if groupshared:\\n fh.write(\\\"\\\"\\\"c.CourseDirectory.groupshared = True\\\\n\\\"\\\"\\\")\\n self._empty_notebook(join(course_dir, \\\"source\\\", \\\"ps1\\\", \\\"foo.ipynb\\\"))\\n run_nbgrader([\\\"generate_assignment\\\", \\\"ps1\\\"])\\n\\n self._empty_notebook(join(course_dir, \\\"submitted\\\", \\\"foo\\\", \\\"ps1\\\", \\\"foo.ipynb\\\"))\\n run_nbgrader([\\\"autograde\\\", \\\"ps1\\\"])\\n run_nbgrader([\\\"generate_feedback\\\", \\\"ps1\\\"])\\n\\n if not groupshared:\\n if sys.platform == 'win32':\\n perms = '666'\\n else:\\n perms = '644'\\n else:\\n if sys.platform == 'win32':\\n perms = '666'\\n dirperms = '777'\\n else:\\n perms = '664'\\n dirperms = '2775'\\n\\n assert isfile(join(course_dir, \\\"feedback\\\", \\\"foo\\\", \\\"ps1\\\", \\\"foo.html\\\"))\\n if groupshared:\\n # non-groupshared doesn't guarantee anything about directory perms\\n assert self._get_permissions(join(course_dir, \\\"feedback\\\", \\\"foo\\\", \\\"ps1\\\")) == dirperms\\n assert self._get_permissions(join(course_dir, \\\"feedback\\\", \\\"foo\\\", \\\"ps1\\\", \\\"foo.html\\\")) == perms\",\n \"def test_global_user_namespace():\\n ns = {\\\"ab\\\": 1, \\\"cd\\\": 2}\\n global_user_namespace.set_user_namespace(user_ns=ns)\\n assert global_user_namespace.user_ns == ns\\n assert global_user_namespace.use_ipython is False\\n\\n global_user_namespace.set_user_namespace(user_ns={}, use_ipython=True)\\n assert global_user_namespace.user_ns == {}\\n assert global_user_namespace.use_ipython is True\\n\\n global_user_namespace.set_user_namespace(user_ns=ns, use_ipython=False)\\n assert global_user_namespace.user_ns == ns\\n assert global_user_namespace.use_ipython is False\",\n \"def test_6_1_3_etc_group_user(host):\\n assert host.file(ETC_PASSWD_DASH).user == 'root'\",\n \"def test06_perm_users(self):\\n print_ln('test_perm_users')\\n \\n try:\\n pList = review.find_perms(Perm(obj_name='py-obj*', op_name='*'))\\n for perm in pList: \\n print_ln(\\\"Perm obj name=\\\" + perm.obj_name + ', op=' + perm.op_name + ', id=' + perm.obj_id)\\n uList = review.perm_users(perm)\\n for user in uList:\\n print_ln(\\\"Assigned user=\\\" + user.uid, 1)\\n except Exception as e:\\n self.fail('test_perm_users failed, exception=' + e.msg)\",\n \"def test_channel_removeowner_not_owner_permissions():\\n clear()\\n auth_register('randemail@gmail.com', 'password1234', 'Jane', 'Citizen')\\n register_second_result = auth_register('randemail2@gmail.com', 'password1234', 'Jane', 'Citizen')\\n register_third_result = auth_register('randemail3@gmail.com', 'password1234', 'Jane', 'Citizen')\\n randChannel_id = channels_create(register_second_result['token'], 'Random Channel', True)\\n with pytest.raises(AccessError):\\n assert channel_removeowner(register_third_result['token'], randChannel_id['channel_id'], register_second_result['u_id'])\",\n \"def testGetAccessAllowed(self):\\n for user in (self.guest, self.contributor, self.delegate, self.owner, self.root):\\n response = self.runGet(user, sequencer=self.hiseq2000.vendor_id)\\n self.response_200(response)\\n data = json.loads(response.content.decode(\\\"utf-8\\\"))\\n self.assertEqual(data[\\\"sodar_uuid\\\"], str(self.hiseq2000.sodar_uuid))\",\n \"def test_project_administrator(project):\\n if is_server_administrator():\\n return True\\n if is_project_administrator(project):\\n return True\\n return False\",\n \"def test_invalid_namespace(self):\\r\\n self.attempt_login(403, ns=\\\"http%3A%2F%2Fspecs.openid.net%2Fauth%2F2.0\\\")\",\n \"def test_delete_other_users_template_as_user_raises_access_control_error(\\n self,\\n ):\\n mock_request = create_mock_request(user=self.user1)\\n with self.assertRaises(AccessControlError):\\n template_api.delete(\\n self.fixture.user2_template, request=mock_request\\n )\",\n \"def testGetAccessDenied(self):\\n self.runGet(None)\\n self.response_401()\\n for user in (self.norole, self.unrelated_owner):\\n self.runGet(user)\\n self.response_403()\",\n \"def setUp(self):\\n\\n from django.contrib.auth.models import Permission, User\\n perms = Permission.objects.filter(codename__startswith='resource_')\\n jedi = User.objects.get(username='jedi')\\n sith = User.objects.get(username='sith')\\n for perm in perms:\\n if perm.codename.endswith('_get'):\\n sith.user_permissions.add(perm)\\n jedi.user_permissions.add(perm)\\n jedi.save()\\n sith.save()\",\n \"def test_create_namespaced_role_binding_restriction(self):\\n pass\",\n \"def test_change_permission(self):\\r\\n self.assertTrue(self.creator_admin.has_change_permission(self.request))\\r\\n\\r\\n self.request.user = self.user\\r\\n self.assertFalse(self.creator_admin.has_change_permission(self.request))\",\n \"def test_replace_namespaced_role_binding_restriction(self):\\n pass\",\n \"def test_permission_remove_action_for_all_users(self):\\n test_name = sys._getframe().f_code.co_name\\n self._execute('permission add anonymous TICKET_CREATE')\\n self._execute('permission remove * TICKET_CREATE')\\n rv, output = self._execute('permission list')\\n self.assertEqual(0, rv)\\n self.assertEqual(self.expected_results[test_name], output)\",\n \"def test_06_self_cannot_upgrade_group(self):\\n meowers = self.meowers\\n cat = self.cat\\n dog = self.dog\\n cat.uaccess.share_group_with_user(meowers, dog, PrivilegeCodes.VIEW)\\n self.assertFalse(dog in meowers.gaccess.edit_users)\\n self.assertTrue(dog in meowers.gaccess.members)\\n self.assertTrue(\\n is_equal_to_as_set(\\n [dog],\\n dog.uaccess.get_group_unshare_users(meowers)))\\n with self.assertRaises(PermissionDenied):\\n dog.uaccess.share_group_with_user(\\n meowers, dog, PrivilegeCodes.VIEW)\\n with self.assertRaises(PermissionDenied):\\n dog.uaccess.share_group_with_user(\\n meowers, dog, PrivilegeCodes.CHANGE)\\n self.assertTrue(dog in meowers.gaccess.members)\\n self.assertTrue(\\n is_equal_to_as_set(\\n [dog],\\n dog.uaccess.get_group_unshare_users(meowers)))\",\n \"def test_read_namespaced_role_binding(self):\\n pass\",\n \"def test_subsystems(self):\\n pass\",\n \"def test_get_other_users_template_raises_access_control_error(self):\\n mock_request = create_mock_request(user=self.user1)\\n with self.assertRaises(AccessControlError):\\n template_api.get_by_id(\\n self.fixture.user2_template.id, request=mock_request\\n )\",\n \"def allowed_user_access_use(usera, userb):\\n try:\\n upa = usera.get_profile()\\n upb = userb.get_profile()\\n except AttributeError:\\n return False\\n\\n return usera == userb or usera.has_perm(\\\"vnswww.userprofile_use_any\\\") or (usera.has_perm(\\\"vnswww.userprofile_use_org\\\") and upa.org == upb.org)\",\n \"def test_0000_initiate_users(self):\\n self.login(email=common.test_user_1_email, username=common.test_user_1_name)\\n test_user_1 = self.test_db_util.get_user(common.test_user_1_email)\\n assert test_user_1 is not None, 'Problem retrieving user with email %s from the database' % common.test_user_1_email\\n self.test_db_util.get_private_role(test_user_1)\\n self.login(email=common.admin_email, username=common.admin_username)\\n admin_user = self.test_db_util.get_user(common.admin_email)\\n assert admin_user is not None, 'Problem retrieving user with email %s from the database' % common.admin_email\\n self.test_db_util.get_private_role(admin_user)\",\n \"def test_0000_initiate_users(self):\\n self.login(email=common.test_user_1_email, username=common.test_user_1_name)\\n test_user_1 = self.test_db_util.get_user(common.test_user_1_email)\\n assert test_user_1 is not None, 'Problem retrieving user with email %s from the database' % common.test_user_1_email\\n self.test_db_util.get_private_role(test_user_1)\\n self.login(email=common.admin_email, username=common.admin_username)\\n admin_user = self.test_db_util.get_user(common.admin_email)\\n assert admin_user is not None, 'Problem retrieving user with email %s from the database' % common.admin_email\\n self.test_db_util.get_private_role(admin_user)\",\n \"def test_get_namespace(self):\\n pass\",\n \"def test_if_an_user_c_access_administration_panel(client):\\n response = client.get(\\\"/admin/\\\")\\n assert response.status_code != 200\",\n \"def test_get_role_inherit_only(self):\\n self.assertEqual(\\n self.project.get_role(self.user_alice), self.owner_as_cat\\n )\",\n \"def verify_that_the_acl_was_set_to_rtacltest1(driver):\\n assert wait_on_element(driver, 5, f'//tr[contains(.,\\\"rt-acl-test-1\\\")]//mat-icon[text()=\\\"more_vert\\\"]', 'clickable')\\n driver.find_element_by_xpath(f'//tr[contains(.,\\\"rt-acl-test-1\\\")]//mat-icon[text()=\\\"more_vert\\\"]').click()\\n assert wait_on_element(driver, 5, '//button[normalize-space(text())=\\\"View Permissions\\\"]', 'clickable')\\n driver.find_element_by_xpath('//button[normalize-space(text())=\\\"View Permissions\\\"]').click()\\n assert wait_on_element(driver, 5, '//div[contains(text(),\\\"User - ericbsd\\\")]')\",\n \"def test_util_has_perm_or_owns_sanity(self):\\n me = User.objects.get(pk=118533)\\n my_t = Thread.objects.filter(creator=me)[0]\\n other_t = Thread.objects.exclude(creator=me)[0]\\n perm = 'forums_forum.thread_edit_forum'\\n allowed = access.has_perm_or_owns(me, perm, my_t, self.forum_1)\\n eq_(allowed, True)\\n allowed = access.has_perm_or_owns(me, perm, other_t, self.forum_1)\\n eq_(allowed, False)\",\n \"def test_create_policy_for_all_namespaces(self):\\n pass\",\n \"def test_custom_permissions(self, course_dir):\\n run_nbgrader([\\\"db\\\", \\\"assignment\\\", \\\"add\\\", \\\"ps1\\\"])\\n run_nbgrader([\\\"db\\\", \\\"student\\\", \\\"add\\\", \\\"foo\\\"])\\n self._empty_notebook(join(course_dir, \\\"source\\\", \\\"ps1\\\", \\\"foo.ipynb\\\"))\\n run_nbgrader([\\\"generate_assignment\\\", \\\"ps1\\\"])\\n\\n self._empty_notebook(join(course_dir, \\\"submitted\\\", \\\"foo\\\", \\\"ps1\\\", \\\"foo.ipynb\\\"))\\n run_nbgrader([\\\"autograde\\\", \\\"ps1\\\"])\\n run_nbgrader([\\\"generate_feedback\\\", \\\"ps1\\\", \\\"--GenerateFeedback.permissions=444\\\"])\\n\\n assert isfile(join(course_dir, \\\"feedback\\\", \\\"foo\\\", \\\"ps1\\\", \\\"foo.html\\\"))\\n assert self._get_permissions(join(course_dir, \\\"feedback\\\", \\\"foo\\\", \\\"ps1\\\", \\\"foo.html\\\")) == '444'\",\n \"def test_set_display_name_global_template_as_user_raises_access_control_error(\\n self,\\n ):\\n mock_request = create_mock_request(user=self.user1)\\n with self.assertRaises(AccessControlError):\\n template_api.set_display_name(\\n self.fixture.global_template, \\\"new_name\\\", request=mock_request\\n )\",\n \"def test_remove_user_from_course_group_permission_denied(self):\\r\\n add_users(self.global_admin, CourseInstructorRole(self.course_key), self.creator)\\r\\n another_staff = User.objects.create_user('another', 'teststaff+anothercourses@edx.org', 'foo')\\r\\n add_users(self.global_admin, CourseStaffRole(self.course_key), self.creator, self.staff, another_staff)\\r\\n with self.assertRaises(PermissionDenied):\\r\\n remove_users(self.staff, CourseStaffRole(self.course_key), another_staff)\",\n \"def test_create_role_binding_restriction_for_all_namespaces(self):\\n pass\",\n \"def test_create_namespaced_policy(self):\\n pass\",\n \"def test_user_not_in_group_cannot_access(self):\\n\\n utils.create_user_and_authenticate(self)\\n expected_url = reverse('my_groups_view')\\n\\n utils.test_cannot_access(self, self.url, expected_url)\",\n \"def test_create_local_subject_access_review_for_all_namespaces(self):\\n pass\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6429766","0.6371066","0.6243876","0.6156751","0.6154213","0.61048347","0.6101933","0.60836434","0.60561347","0.6050891","0.6036248","0.60299695","0.6001029","0.5991596","0.59487164","0.5943904","0.5931206","0.5913553","0.5899936","0.58955866","0.58599865","0.58324295","0.5826943","0.58243126","0.5816168","0.57885206","0.5787178","0.57778776","0.57541406","0.57508767","0.5738326","0.5738303","0.5736674","0.57281953","0.57195455","0.57114196","0.57060724","0.5693707","0.5685624","0.56826144","0.5677131","0.56606764","0.56528044","0.5650119","0.5639864","0.56353635","0.56350255","0.56286746","0.562827","0.5615047","0.5608913","0.56008583","0.55972964","0.55956787","0.5591478","0.55908906","0.55872726","0.5573841","0.5567821","0.556701","0.5560722","0.5558196","0.55476177","0.5539693","0.55261904","0.55256426","0.5522591","0.5514735","0.5514552","0.5510627","0.5509516","0.55069065","0.5500761","0.548945","0.5482124","0.5479643","0.54790497","0.5472281","0.5469432","0.54644644","0.54636836","0.54563165","0.54558074","0.5454028","0.5451357","0.5449983","0.5449983","0.5446212","0.5442109","0.5441885","0.54341114","0.5430013","0.54246616","0.5423176","0.5422777","0.5420556","0.541811","0.54177177","0.54171354","0.54170406"],"string":"[\n \"0.6429766\",\n \"0.6371066\",\n \"0.6243876\",\n \"0.6156751\",\n \"0.6154213\",\n \"0.61048347\",\n \"0.6101933\",\n \"0.60836434\",\n \"0.60561347\",\n \"0.6050891\",\n \"0.6036248\",\n \"0.60299695\",\n \"0.6001029\",\n \"0.5991596\",\n \"0.59487164\",\n \"0.5943904\",\n \"0.5931206\",\n \"0.5913553\",\n \"0.5899936\",\n \"0.58955866\",\n \"0.58599865\",\n \"0.58324295\",\n \"0.5826943\",\n \"0.58243126\",\n \"0.5816168\",\n \"0.57885206\",\n \"0.5787178\",\n \"0.57778776\",\n \"0.57541406\",\n \"0.57508767\",\n \"0.5738326\",\n \"0.5738303\",\n \"0.5736674\",\n \"0.57281953\",\n \"0.57195455\",\n \"0.57114196\",\n \"0.57060724\",\n \"0.5693707\",\n \"0.5685624\",\n \"0.56826144\",\n \"0.5677131\",\n \"0.56606764\",\n \"0.56528044\",\n \"0.5650119\",\n \"0.5639864\",\n \"0.56353635\",\n \"0.56350255\",\n \"0.56286746\",\n \"0.562827\",\n \"0.5615047\",\n \"0.5608913\",\n \"0.56008583\",\n \"0.55972964\",\n \"0.55956787\",\n \"0.5591478\",\n \"0.55908906\",\n \"0.55872726\",\n \"0.5573841\",\n \"0.5567821\",\n \"0.556701\",\n \"0.5560722\",\n \"0.5558196\",\n \"0.55476177\",\n \"0.5539693\",\n \"0.55261904\",\n \"0.55256426\",\n \"0.5522591\",\n \"0.5514735\",\n \"0.5514552\",\n \"0.5510627\",\n \"0.5509516\",\n \"0.55069065\",\n \"0.5500761\",\n \"0.548945\",\n \"0.5482124\",\n \"0.5479643\",\n \"0.54790497\",\n \"0.5472281\",\n \"0.5469432\",\n \"0.54644644\",\n \"0.54636836\",\n \"0.54563165\",\n \"0.54558074\",\n \"0.5454028\",\n \"0.5451357\",\n \"0.5449983\",\n \"0.5449983\",\n \"0.5446212\",\n \"0.5442109\",\n \"0.5441885\",\n \"0.54341114\",\n \"0.5430013\",\n \"0.54246616\",\n \"0.5423176\",\n \"0.5422777\",\n \"0.5420556\",\n \"0.541811\",\n \"0.54177177\",\n \"0.54171354\",\n \"0.54170406\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.66301256"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":92587,"cells":{"query":{"kind":"string","value":"Convert all dicts to HashableDicts in nested structure."},"document":{"kind":"string","value":"def parse(cls, el):\n if isinstance(el, list):\n for i, x in enumerate(el):\n el[i] = HashableDict.parse(x)\n elif isinstance(el, dict):\n d = HashableDict()\n for k, v in el.iteritems():\n d[k] = HashableDict.parse(v)\n return d\n return el"},"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 flatten(self):\n flat = {}\n for d in self.dicts:\n flat.update(d)\n return flat","def _deep_asdict(self):\n return {\n \"collections\": {k: p._deep_asdict() for (k, p) in self.collections.items()},\n }","def get_hashable_entries(nested:dict):\n for key, value in nested.items():\n if isinstance(value, abc.Mapping):\n yield from get_hashable_entries(value)\n else:\n yield (key, value)","def to_dict(self):\r\n new_dict = {}\r\n for key, val in self.items():\r\n if isinstance(val, NestedDict):\r\n new_dict[key] = val.to_dict()\r\n else:\r\n new_dict[key] = val\r\n return new_dict","def make_dicts(self):\n self._dicts = [tree.to_dict() for tree in self.reaction_trees]\n self._update_route_dict(self._dicts, \"dict\")","def as_dict_of_dicts(self):\n dod = {}\n for parent in self.nodes():\n dod[parent] = {}\n for child in self.children(parent):\n dod[parent][child] = {\"branch_length\": self.branch_length(child)}\n return dod","def _normalize(self, schema: t.Dict) -> t.Dict:\n normalized_mapping: t.Dict = {}\n flattened_schema = flatten_schema(schema, depth=dict_depth(schema) - 1)\n\n for keys in flattened_schema:\n columns = nested_get(schema, *zip(keys, keys))\n\n if not isinstance(columns, dict):\n raise SchemaError(\n f\"Table {'.'.join(keys[:-1])} must match the schema's nesting level: {len(flattened_schema[0])}.\"\n )\n\n normalized_keys = [\n self._normalize_name(key, dialect=self.dialect, is_table=True) for key in keys\n ]\n for column_name, column_type in columns.items():\n nested_set(\n normalized_mapping,\n normalized_keys + [self._normalize_name(column_name, dialect=self.dialect)],\n column_type,\n )\n\n return normalized_mapping","def _deep_asdict(self):\n return {\n \"schema\": self.schema._asdict(),\n \"publications\": {\n k: p._deep_asdict() for (k, p) in self.publications.items()\n },\n }","def todict(self):\n d = dict()\n for k in self:\n v = dict.__getitem__(self, k)\n if isinstance(v, udict):\n v = v.todict()\n d[k] = v\n return d","def sub_dict(d):\n r = {}\n for k in d:\n if type(d[k]) in prims:\n r[k] = d[k]\n elif type(d[k]) is list:\n r[k] = sub_list(d[k])\n elif type(d[k]) is dict:\n r[k] = sub_dict(d[k])\n else:\n print \"Unknown Type: {}\".format(type(d[k]))\n return r","def to_dict(self):\n res = {}\n for (k, v) in self.__dict__.items():\n if v == None or v == False or v == [] or v == {}:\n # Skip empty/false members.\n continue\n elif isinstance(v, IDLDictNode) and not len(v):\n # Skip empty dict sub-nodes.\n continue\n elif isinstance(v, list):\n # Convert lists:\n new_v = []\n for sub_node in v:\n if isinstance(sub_node, IDLNode):\n # Convert sub-node:\n new_v.append(sub_node.to_dict())\n else:\n new_v.append(sub_node)\n v = new_v\n elif isinstance(v, IDLNode):\n # Convert sub-node:\n v = v.to_dict()\n res[k] = v\n return res","def _flatten_dict(x: Dict) ->Dict:\n new_dict = {}\n for key, value in x.items():\n if isinstance(value, dict):\n for k, v in value.items():\n new_dict[k] = v\n else:\n new_dict[key] = value\n return new_dict","def _deep_asdict(self):\n return {\n \"metadata\": self.metadata,\n \"artifacts\": {k: a._asdict() for (k, a) in self.artifacts.items()},\n }","def _flatten_dict(self, d: Mapping[str, Any]) -> Dict[str, Any]:\n nested = {k for k, v in d.items() if isinstance(v, (Mapping, Configuration))}\n if self._lowercase:\n result = {\n k.lower() + \".\" + ki: vi\n for k in nested\n for ki, vi in self._flatten_dict(d[k]).items()\n }\n result.update(\n (k.lower(), v)\n for k, v in d.items()\n if not isinstance(v, (Mapping, Configuration))\n )\n else:\n result = {\n k + \".\" + ki: vi\n for k in nested\n for ki, vi in self._flatten_dict(d[k]).items()\n }\n result.update(\n (k, v)\n for k, v in d.items()\n if not isinstance(v, (Mapping, Configuration))\n )\n return result","def as_dict(self):\n return dict((key, value) for key, value, depth in self.entries.itervalues())","def dumpd(obj):\n # return input directly if it is already dict\n if isinstance(obj, dict):\n return obj\n # return list of dict\n elif isinstance(obj, (list, tuple, set)):\n return [dumpd(o) for o in obj]\n # convert to dict\n data = {}\n for key in obj.__dict__.keys():\n if not key.startswith(\"_\"):\n # convert each items in list-like object\n if isinstance(getattr(obj, key, None), (list, tuple, set)):\n data[key] = []\n for v in getattr(obj, key, None):\n if hasattr(v, \"to_dict\"):\n data[key].append(v.to_dict())\n elif hasattr(v, \"__dict__\"):\n data[key].append(dumpd(v))\n else:\n data[key].append(v)\n # convert each items in dict\n elif isinstance(getattr(obj, key, None), dict):\n data[key] = {}\n for k, v in getattr(obj, key, None).items():\n if hasattr(v, \"to_dict\"):\n data[key][k] = v.to_dict()\n elif hasattr(v, \"__dict__\"):\n data[key][k] = dumpd(v)\n else:\n data[key][k] = v\n # convert object with `to_dict`\n elif hasattr(getattr(obj, key, None), \"to_dict\"):\n data[key] = getattr(obj, key).to_dict()\n # convert plain object\n elif hasattr(getattr(obj, key, None), \"__dict__\"):\n data[key] = dumpd(getattr(obj, key))\n else:\n data[key] = getattr(obj, key, None)\n return data","def to_dict(self, deep=True):\n return {var: data for var, data in self.items(deep)}","def dicts(self):\n if self._dicts is None:\n self.make_dicts()\n return self._dicts","def flatten_dict(d):\n def items():\n for key, value in d.items():\n if isinstance(value, dict):\n for subkey, subvalue in flatten_dict(value).items():\n yield subkey, subvalue\n else:\n yield key, value\n\n return dict(items())","def recursive_squeeze(dictlike):\n out = {}\n for k, v in dictlike.items():\n if isinstance(v, dict):\n out[k] = recursive_squeeze(v)\n else:\n out[k] = np.squeeze(v)\n return out","def _dictRoundTripNormalize(self, treedict):\n for key, value in list(treedict.items()):\n if isinstance(value, dict):\n self._dictRoundTripNormalize(value)\n\n # Expand treedict[(\"group\", \"attr_name\")]\n # to treedict[\"group\"][\"attr_name\"]\n for key, value in list(treedict.items()):\n if not isinstance(key, tuple):\n continue\n # Put the attribute inside the group\n grpname, attr = key\n if not grpname:\n continue\n group = treedict.setdefault(grpname, dict())\n if isinstance(group, dict):\n del treedict[key]\n group[(\"\", attr)] = value","def _flatten_dictionary(self, params, parent=None):\r\n data = OrderedDict()\r\n for key, val in params.items():\r\n full_key = parent + \"[\" + key + \"]\" if parent else key\r\n if isinstance(val, dict):\r\n data.update(self._flatten_dictionary(val, full_key))\r\n else:\r\n data[full_key] = val\r\n return data","def to_dict(self, target_dict=None):\n if target_dict is None:\n target_dict = self.__dict__\n\n result_dict = dict()\n\n def to_inner_dict(actual_value):\n if hasattr(actual_value, 'to_dict'):\n return actual_value.to_dict()\n else:\n return actual_value\n\n for key, value in target_dict.iteritems():\n if value is not None:\n if isinstance(value, dict):\n result_dict[key] = self.to_dict(target_dict=value)\n elif isinstance(value, list):\n temp = list()\n\n for item in value:\n temp.append(to_inner_dict(actual_value=item))\n result_dict[key] = temp\n else:\n result_dict[key] = to_inner_dict(actual_value=value)\n\n return result_dict","def nested_dict():\n return defaultdict(nested_dict)","def flatten(d):\n\n c = {}\n\n def _flatten(parents, items):\n for k, v in items:\n cur = parents + [k]\n if isinstance(v, list):\n _flatten(cur, enumerate(v))\n elif isinstance(v, dict):\n _flatten(cur, v.items())\n else:\n if v is None:\n cur.append('$NULL')\n v = ''\n name = str(cur[0]) + ''.join(['['+str(x)+']' for x in cur[1:]])\n c[name] = v\n \n _flatten([], d.items())\n\n return c","def to_dict(self, target_dict=None):\n if target_dict is None:\n target_dict = self.storage\n\n result_dict = dict()\n\n def to_inner_dict(actual_value):\n if hasattr(actual_value, 'to_dict'):\n return actual_value.to_dict()\n else:\n return actual_value\n\n for key, value in target_dict.iteritems():\n if value is not None:\n if isinstance(value, dict):\n result_dict[key] = self.to_dict(target_dict=value)\n elif isinstance(value, list):\n temp = list()\n\n for item in value:\n temp.append(to_inner_dict(actual_value=item))\n result_dict[key] = temp\n else:\n result_dict[key] = to_inner_dict(actual_value=value)\n\n return result_dict","def to_dict(self, target_dict=None):\n if target_dict is None:\n target_dict = self.storage\n\n result_dict = dict()\n\n def to_inner_dict(actual_value):\n if hasattr(actual_value, 'to_dict'):\n return actual_value.to_dict()\n else:\n return actual_value\n\n for key, value in target_dict.iteritems():\n if value is not None:\n if isinstance(value, dict):\n result_dict[key] = self.to_dict(target_dict=value)\n elif isinstance(value, list):\n temp = list()\n\n for item in value:\n temp.append(to_inner_dict(actual_value=item))\n result_dict[key] = temp\n else:\n result_dict[key] = to_inner_dict(actual_value=value)\n\n return result_dict","def safe_dict(d):\r\n if isinstance(d, dict):\r\n return dict([(k.encode('utf-8'), safe_dict(v)) for k, v in d.iteritems()])\r\n elif isinstance(d, list):\r\n return [safe_dict(x) for x in d]\r\n else:\r\n return d","def to_dict(self):\n out_dict = dict(self)\n for k, v in out_dict.items():\n if v is self:\n out_dict[k] = out_dict\n elif hasattr(v, 'to_dict'):\n out_dict[k] = v.to_dict()\n elif hasattr(v, 'to_list'):\n out_dict[k] = v.to_list()\n return out_dict","def flatten(self):\n flattened = {}\n for key, constructs in self.data.iteritems():\n if not constructs:\n continue\n construct_class = constructs[0].__class__ # we shouldn't ever mix\n if len(constructs) == 1:\n flattened[key] = construct_class(key, constructs[0].value, validate_value=False)\n else:\n flattened[key] = construct_class.and_(key, sorted(c.value for c in constructs))\n return flattened","def simplify_dict(d: Dict[str, Any]) -> Dict[str, Any]:\n return {\n k: [ast_to_testing_string(n) for n in v] if k == \"children\" else v\n for k, v in d.items()\n }","def realized(self):\n result = {\"Object\": True}\n for key in self.__dict__:\n if isinstance(self.__dict__[key], Object):\n result[key] = self.__dict__[key].asdict()\n else:\n result[key] = self.__dict__[key]\n return result","def to_representation(self, data):\n items = super(DictSerializer, self).to_representation(data)\n return {item[self.dict_key]: item for item in items}","def as_dict(self, flat=False):\n if not flat:\n return self.as_dict_nested()\n else:\n return self.as_dict_flat()","def _prepare_freeze(xs: Any) -> Any:\n if isinstance(xs, FrozenDict):\n # we can safely ref share the internal state of a FrozenDict\n # because it is immutable.\n return xs._dict # pylint: disable=protected-access\n if not isinstance(xs, dict):\n # return a leaf as is.\n return xs\n # recursively copy dictionary to avoid ref sharing\n return {key: _prepare_freeze(val) for key, val in xs.items()}","def _to_dict_tree(self):\n return DictTree(self.entries)","def to_dict(self):\n data = {}\n for key, value in self.__dict__.items():\n try:\n data[key] = value.to_dict()\n except AttributeError:\n data[key] = value\n return data","def flatten_dict(nested):\n flattened = {}\n for key, value in nested.items():\n if isinstance(value, Mapping):\n for subkey, subval in value.items():\n newkey = '.'.join([key, subkey])\n flattened[newkey] = subval\n flatten_dict(flattened)\n else:\n flattened[key] = value\n mappings = [isinstance(value, Mapping) for key, value in flattened.items()]\n if len(set(mappings)) == 1 and set(mappings).pop() is False:\n return flattened\n else:\n return flatten_dict(flattened)","def _flatten_lists(\n data: Union[Dict[str, Any], List[Any], Any]\n ) -> Union[Dict[str, Any], Any]:\n if not isinstance(data, dict):\n return data\n copy_data = cast(Dict[str, Any], data.copy())\n for attr, val in copy_data.items():\n if isinstance(val, list):\n if len(cast(List[Any], val)) == 1:\n # pull single values out of lists\n data[attr] = _flatten_lists(cast(Any, val[0]))\n else:\n data[attr] = [_flatten_lists(v) for v in cast(List[Any], val)]\n elif isinstance(val, dict):\n data[attr] = _flatten_lists(cast(Dict[str, Any], val))\n return data","def deep_normalize(d):\n if 'sudsobject' in str(d.__class__):\n d = deep_normalize(dict(d))\n elif isinstance(d, dict):\n for k,v in d.iteritems():\n if 'sudsobject' in str(v.__class__):\n #print k, v, '%s' % v.__class__\n r = deep_normalize(dict(v))\n d[k] = r\n elif isinstance(v, dict):\n r = deep_normalize(v)\n d[k] = r\n elif isinstance(v, (list, tuple, )):\n d[k] = [deep_normalize(i) for i in v]\n elif isinstance(v, datetime):\n # per problemi di permessi sugli oggetti datetime trasformo\n # in DateTime di Zope\n d[k] = DateTime(v.isoformat())\n elif isinstance(d, (list, tuple, )):\n d = [deep_normalize(i) for i in d]\n\n return d","def getFullDict(self):\n temp = copy.copy(self)\n\n for key in temp.dict:\n if temp.dict[key]['type'] == 'func' or temp.dict[key]['type'] == 'lambda':\n childDict = temp.dict[key][varTable].getFullDict()\n temp.dict[key][varTable] = childDict\n\n return temp.dict","def pivot_nested_dict(nested_dict):\r\n\r\n reverse_nest_dict = {} #Create an empty dictionary\r\n for k, v in nested_dict.items(): #Iterate through each pair of elements\r\n for k2, v2 in v.items(): #Iterate through pair of values\r\n try:\r\n reverse_nest_dict[k2][k] = v2\r\n except KeyError:\r\n reverse_nest_dict[k2] = { k : v2 }\r\n return reverse_nest_dict\r\n \r\n #Create a dictionary that produces a different nested dictionary which\r\n #contains the same values\r","def make_recursive(obj):\n if isinstance(obj, list):\n for i, l in enumerate(obj):\n obj[i] = AttrDict.make_recursive(l)\n elif isinstance(obj, dict):\n for k, v in obj.items():\n obj[k] = AttrDict.make_recursive(v)\n return AttrDict(obj)\n return obj","def to_dict(self):\n\n def transform_value(value):\n if isinstance(value, list):\n return [transform_value(v) for v in value]\n elif isinstance(value, abc.Mapping):\n return dict([\n (k, transform_value(v))\n for k, v in iteritems(value)])\n else:\n return value\n\n return transform_value(dict(self))","def to_dict(self) -> Dict:\n _dict = {}\n if hasattr(self, 'type') and self.type is not None:\n _dict['type'] = self.type\n if hasattr(self, 'roles') and self.roles is not None:\n _dict['roles'] = [x.to_dict() for x in self.roles]\n if hasattr(self, 'resources') and self.resources is not None:\n _dict['resources'] = [x.to_dict() for x in self.resources]\n return _dict","def rconvert(self):\n for k in self:\n if isinstance(self[k], dict):\n if not isinstance(self[k], AttrDict):\n self[k] = AttrDict(self[k])\n self[k].rconvert()\n return self","def dictize(self):\n dict = {}\n for node in self.sort():\n logger.debug(\"Dictize: id %s has name %s\" % (node._id, node.name))\n x = node._kwargs()\n dict[node._id]={\"klass\":node.__class__.__name__, \n \"kwargs\": x,\n \"children\":[child._id for child in node.children()]}\n return dict","def flatten_dictionaries(input):\n output = dict()\n if isinstance(input, list):\n for map in input:\n if not isinstance(map, dict):\n raise Exception('Tried to flatten a list of NON-dictionaries into a single dictionary. Whoops!')\n for key in map.keys(): #Add keys into output\n output[key]=map[key]\n else: #Not a list of dictionaries\n output = input;\n return output","def _join_dicts(dicts):\n if dicts is None: # pragma: no cover\n return\n assembled_dict = {k: v for D in dicts for k, v in D.items()}\n return assembled_dict","def dict_normalization(dict_, nested=False):\n dict_norm = dict()\n if not nested:\n if dict_.values():\n d_max = max(dict_.values())\n d_min = min(dict_.values())\n if d_max - d_min == 0:\n dict_norm = {key: 1 for key in dict_}\n else:\n dict_norm = {key: (dict_[key] - d_min) / (d_max - d_min) for key in dict_}\n else:\n for key_1 in dict_:\n if dict_[key_1]:\n dict_norm[key_1] = dict()\n else: continue\n d_max = max(dict_[key_1].values())\n d_min = min(dict_[key_1].values())\n for key_2 in dict_[key_1]:\n if d_max - d_min == 0:\n dict_norm[key_1][key_2] = 1 / len(dict_[key_1])\n else:\n dict_norm[key_1][key_2] = (dict_[key_1][key_2] - d_min) / (d_max - d_min)\n return dict_norm","def clean_dict(d):\n\n if not isinstance(d, (dict, list)):\n return d\n if isinstance(d, list):\n return [v for v in (clean_dict(v) for v in d) if v]\n return OrderedDict([(k, v) for k, v in ((k, clean_dict(v)) for k, v in list(d.items())) if v])","def as_dict(self):\n def append(d, key, value, is_iterative, is_primitive, is_enum):\n if value is None:\n if is_iterative:\n value = []\n elif is_primitive == False and is_enum == False:\n if is_iterative:\n value = map(lambda i : i.as_dict(), value)\n else:\n value = value.as_dict()\n d[key] = value\n\n # Populate a deep dictionary.\n d = dict()\n append(d, 'file', self.__file, False, False, False)\n append(d, 'file_reference', self.__file_reference, False, False, False)\n append(d, 'name', self.__name, False, True, False)\n return d","def pivot_nested_dict(nested_dict):\n # declare res as the return object which should be a dict\n res = dict()\n # traverse the pollsters\n for pollster in nested_dict:\n \t# travserse the states\n \tfor state in nested_dict[pollster]:\n \t\t# if first meet a state, we need to create a new dict\n \t\tif state not in res:\n \t\t\tres[state] = dict()\n \t\t# put the pollster value in the state dict\n \t\tres[state][pollster] = nested_dict[pollster][state]\n return res","def flatten(parsed):\r\n parsedList = [parsed] if isinstance(parsed, dict) else parsed\r\n result = []\r\n for dico in parsedList:\r\n #Sort keys between actual values and nested dicts\r\n listKeys = []\r\n standardKeys = []\r\n for key in dico:\r\n if isinstance(dico[key], list):\r\n listKeys.append(key)\r\n else:\r\n standardKeys.append(key)\r\n if not listKeys:\r\n #Terminal condition: no nested dict\r\n result.append(dico)\r\n else:\r\n partialResult = [{x:dico[x] for x in standardKeys}]\r\n for key in listKeys:\r\n #Create a dict with the keys from partialResult and\r\n #from the nested dicts\r\n recurs = Bricks.flatten(dico[key])\r\n partialResult = [{**x, **y} for x in partialResult for y in recurs]\r\n result.extend(partialResult)\r\n return result","def flatten(self) -> Dict[str, Any]:\n\n self_dict = asdict(self)\n experiment_created_record_dict = self_dict.pop(\"experiment_created_record\")\n generation_strategy_created_record_dict = self_dict.pop(\n \"generation_strategy_created_record\"\n )\n\n return {\n **self_dict,\n **experiment_created_record_dict,\n **generation_strategy_created_record_dict,\n }","def to_dict(self) -> Dict[str, Union[dict, str, int]]:\n d = {}\n\n for k, v in self.__dict__.items():\n if k in self._exempt or v is None:\n continue\n if '__dataclass_fields__' in dir(v):\n d[k] = asdict(v)\n elif inspect.isclass(v) and issubclass(v, Base):\n d[k] = v.to_dict()\n elif hasattr(v, 'value'): # we assume this is an Enum value.\n d[k] = v.value\n else:\n d[k] = v\n return d","def to_dict(self):\n result = {}\n\n for attr, _ in six.iteritems(self.swagger_types):\n value = getattr(self, attr)\n if isinstance(value, list):\n result[attr] = list(map(\n lambda x: x.to_dict() if hasattr(x, \"to_dict\") else x,\n value\n ))\n elif hasattr(value, \"to_dict\"):\n result[attr] = value.to_dict()\n elif isinstance(value, dict):\n result[attr] = dict(map(\n lambda item: (item[0], item[1].to_dict())\n if hasattr(item[1], \"to_dict\") else item,\n value.items()\n ))\n else:\n result[attr] = value\n if issubclass(DeterminantDisease, dict):\n for key, value in self.items():\n result[key] = value\n\n return result","def flatten_ls_of_dt(ls: list) -> dict:\n\n def _flatten(ls):\n if not isinstance(ls, list):\n return ls\n else:\n if dict not in [type(x) for x in ls]:\n return ls\n # for [dict, dict, nan] objects\n else:\n # convert nan to empty dict\n ls = [x if isinstance(x, dict) else {} for x in ls]\n # convert list of dicts to dict of lists\n dt = pd.DataFrame(ls).to_dict(orient=\"list\")\n # continue with recursion if needed\n for k, v in dt.items():\n dt[k] = _flatten(v)\n return dt\n\n return _flatten(ls)","def merge_dicts(base, changes):\n for k, v in changes.items():\n if isinstance(v, dict):\n merge_dicts(base.setdefault(k, {}), v)\n else:\n base.setdefault(k, v)","def to_dict(self, drop_null=True, camel=False):\n #return _to_dict(self, drop_null, camel)\n def to_dict(obj, drop_null, camel):\n \"\"\"Recursively constructs the dict.\"\"\"\n if isinstance(obj, (Body, BodyChild)):\n obj = obj.__dict__\n if isinstance(obj, dict):\n data = {}\n for attr, val in six.iteritems(obj):\n if camel:\n attr = _snake_to_camel(attr)\n valid_null = (isinstance(val, bool) or val == 0 or\n (val and to_dict(val, drop_null, camel)))\n if not drop_null or (drop_null and valid_null):\n data[attr] = to_dict(val, drop_null, camel)\n return data\n elif isinstance(obj, list):\n data = []\n for val in obj:\n valid_null = (isinstance(val, bool) or val == 0 or\n (val and to_dict(val, drop_null, camel)))\n if not drop_null or (drop_null and valid_null):\n data.append(to_dict(val, drop_null, camel))\n return data\n else:\n return obj\n return to_dict(self, drop_null, camel)","def merge_dict_recursive(target, src):\r\n for k in src.keys():\r\n if ((k in target and isinstance(target[k], dict) and\r\n isinstance(src[k], collections.Mapping))):\r\n merge_dict_recursive(target[k], src[k])\r\n else:\r\n target[k] = src[k]","def fuse_dicts(ds):\n ks = set((k for d in ds for k in d.keys()))\n fused_d = {k: [] for k in ks}\n for d in ds:\n for k in ks:\n fused_d[k].append(d[k] if k in d else None)\n return fused_d","def convert(data):\n return {k: [d[k] for d in data] for k in data[0].keys()}","def unflatten(\n d: Dict[str, Any],\n base: Dict[str, Any] = None,\n) -> Dict[str, Any]:\n if base is None:\n base = {}\n\n for key, value in d.items():\n root = base\n\n ###\n # If a dotted path is encountered, create nested dicts for all but\n # the last level, then change root to that last level, and key to\n # the final key in the path. This allows one final setitem at the bottom\n # of the loop.\n if '.' in key:\n *parts, key = key.split('.')\n\n for part in parts:\n root.setdefault(part, {})\n root = root[part]\n\n if isinstance(value, dict):\n value = unflatten(value, root.get(key, {}))\n\n root[key] = value\n\n return base","def serialize_dict(data):\n new_data = {}\n for (key, value) in data.items():\n if isinstance(value, dict):\n new_data[key] = serialize_dict(value)\n elif isinstance(value, list):\n new_data[key] = serialize_list(value)\n elif isinstance(value, int) or isinstance(value, float) or isinstance(value, str) or isinstance(value, bool):\n new_data[key] = value\n else:\n new_data[str(key)] = None\n\n return new_data","def flatten_dict(d, separator=':', _parent_key=''):\n items = []\n for k, v in d.items():\n new_key = _parent_key + separator + k if _parent_key else k\n if isinstance(v, collections.MutableMapping):\n items.extend(flatten_dict(v, separator=separator, _parent_key=new_key).items())\n else:\n items.append((new_key, v))\n return dict(items)","def flatten_dict(dict_input):\n flattened_dict = dict()\n\n for key, value in dict_input.items():\n if isinstance(value, dict):\n new_keys = sorted(value.keys())\n for new_key in new_keys:\n entry = {key + '_' + new_key: value[new_key]}\n flattened_dict.update(entry)\n else:\n entry = {key: value}\n flattened_dict.update(entry)\n\n return flattened_dict","def serialize_dict(container: Dict) -> Dict:\n for key, value in container.items():\n container[key] = serialize_obj(value)\n return container","def serialize_dict(container: Dict) -> Dict:\n for key, value in container.items():\n container[key] = serialize_obj(value)\n return container","def to_dict(self):\n result = {}\n\n for attr, _ in iteritems(self.pdb_types):\n value = getattr(self, attr)\n if isinstance(value, list):\n result[attr] = list(map(\n lambda x: x.to_dict() if hasattr(x, \"to_dict\") else x,\n value\n ))\n elif hasattr(value, \"to_dict\"):\n result[attr] = value.to_dict()\n elif isinstance(value, dict):\n result[attr] = dict(map(\n lambda item: (item[0], item[1].to_dict())\n if hasattr(item[1], \"to_dict\") else item,\n value.items()\n ))\n else:\n result[attr] = value\n\n return result","def to_dict(self):\n result = {}\n\n for attr, _ in iteritems(self.pdb_types):\n value = getattr(self, attr)\n if isinstance(value, list):\n result[attr] = list(map(\n lambda x: x.to_dict() if hasattr(x, \"to_dict\") else x,\n value\n ))\n elif hasattr(value, \"to_dict\"):\n result[attr] = value.to_dict()\n elif isinstance(value, dict):\n result[attr] = dict(map(\n lambda item: (item[0], item[1].to_dict())\n if hasattr(item[1], \"to_dict\") else item,\n value.items()\n ))\n else:\n result[attr] = value\n\n return result","def ensure_json_serializable(dict_, normalize_containers=False, verbose=0):\n dict_ = copy.deepcopy(dict_)\n\n def _norm_container(c):\n if isinstance(c, dict):\n # Cast to a normal dictionary\n if isinstance(c, OrderedDict):\n if type(c) is not OrderedDict:\n c = OrderedDict(c)\n else:\n if type(c) is not dict:\n c = dict(c)\n return c\n\n walker = ub.IndexableWalker(dict_)\n for prefix, value in walker:\n if isinstance(value, tuple):\n new_value = list(value)\n walker[prefix] = new_value\n elif isinstance(value, np.ndarray):\n new_value = value.tolist()\n walker[prefix] = new_value\n elif isinstance(value, (np.integer)):\n new_value = int(value)\n walker[prefix] = new_value\n elif isinstance(value, (np.floating)):\n new_value = float(value)\n walker[prefix] = new_value\n elif isinstance(value, (np.complexfloating)):\n new_value = complex(value)\n walker[prefix] = new_value\n elif isinstance(value, decimal.Decimal):\n new_value = float(value)\n walker[prefix] = new_value\n elif isinstance(value, fractions.Fraction):\n new_value = float(value)\n walker[prefix] = new_value\n elif isinstance(value, pathlib.Path):\n new_value = str(value)\n walker[prefix] = new_value\n elif hasattr(value, '__json__'):\n new_value = value.__json__()\n walker[prefix] = new_value\n elif normalize_containers:\n if isinstance(value, dict):\n new_value = _norm_container(value)\n walker[prefix] = new_value\n\n if normalize_containers:\n # normalize the outer layer\n dict_ = _norm_container(dict_)\n return dict_","def to_dict(self):\n result = {}\n\n for attr, _ in six.iteritems(self.swagger_types):\n value = getattr(self, attr)\n if isinstance(value, list):\n result[attr] = list(map(\n lambda x: x.to_dict() if hasattr(x, \"to_dict\") else x,\n value\n ))\n elif hasattr(value, \"to_dict\"):\n result[attr] = value.to_dict()\n elif isinstance(value, dict):\n result[attr] = dict(map(\n lambda item: (item[0], item[1].to_dict())\n if hasattr(item[1], \"to_dict\") else item,\n value.items()\n ))\n else:\n result[attr] = value\n if issubclass(Referent, dict):\n for key, value in self.items():\n result[key] = value\n\n return result","def to_dict(obj, drop_null, camel):\n if isinstance(obj, (Body, BodyChild)):\n obj = obj.__dict__\n if isinstance(obj, dict):\n data = {}\n for attr, val in six.iteritems(obj):\n if camel:\n attr = _snake_to_camel(attr)\n valid_null = (isinstance(val, bool) or val == 0 or\n (val and to_dict(val, drop_null, camel)))\n if not drop_null or (drop_null and valid_null):\n data[attr] = to_dict(val, drop_null, camel)\n return data\n elif isinstance(obj, list):\n data = []\n for val in obj:\n valid_null = (isinstance(val, bool) or val == 0 or\n (val and to_dict(val, drop_null, camel)))\n if not drop_null or (drop_null and valid_null):\n data.append(to_dict(val, drop_null, camel))\n return data\n else:\n return obj","def flatten_dict(d):\n\n def expand(key, value):\n if isinstance(value, dict):\n return [(key + '.' + k, v) for k, v in flatten_dict(value).items()]\n else:\n return [(key, value)]\n\n items = [item for k, v in d.items() for item in expand(k, v)]\n return dict(items)","def dictOfBetas(self, free=True, fixed=False):\n s = {}\n for e in self.children:\n d = e.dictOfBetas(free, fixed)\n s = dict(s, **d)\n return s","def to_dict(self):\n return {k: v.to_dict() if isinstance(v, AttrDict) else v\n for k, v in self.__dict__.items() if not k.startswith('_')}","def from_iterable(iterables):\n for it in iterables:\n for element in it:\n if isinstance(element, dict):\n for key in element:\n yield key\n else:\n yield element","def to_dict(self):\n result = {}\n\n for attr, _ in six.iteritems(self.swagger_types):\n value = getattr(self, attr)\n if isinstance(value, list):\n result[attr] = list(map(\n lambda x: x.to_dict() if hasattr(x, \"to_dict\") else x,\n value\n ))\n elif hasattr(value, \"to_dict\"):\n result[attr] = value.to_dict()\n elif isinstance(value, dict):\n result[attr] = dict(map(\n lambda item: (item[0], item[1].to_dict())\n if hasattr(item[1], \"to_dict\") else item,\n value.items()\n ))\n else:\n result[attr] = value\n if issubclass(V1alpha3GoTemplate, dict):\n for key, value in self.items():\n result[key] = value\n\n return result","def toDictionary(self):\n result = {}\n for key, method in self.__dict__.iteritems():\n if (key == '_Entity__instanced'): continue\n\n value = method()\n if (isinstance(value, Entity)):\n value = value.toDictionary()\n elif (isinstance(value, list)):\n expands = []\n for node in value:\n if (hasattr(node, 'toDictionary')):\n expands.append(node.toDictionary())\n else:\n expands.append(node)\n value = expands\n\n result[_toPropertyName(key)] = value\n return result","def as_dict(self):\n data = {}\n\n for (key, value) in self.param_defaults.items():\n\n # If the value is a list, we need to create a list to hold the\n # dicts created by an object supporting the as_dict() method,\n # i.e., if it inherits from HEREModel. If the item in the list\n # doesn't support the as_dict() method, then we assign the value\n # directly. An example being a list of Media objects contained\n # within a Status object.\n if isinstance(getattr(self, key, None), (list, tuple, set)):\n data[key] = list()\n for subobj in getattr(self, key, None):\n if getattr(subobj, \"as_dict\", None):\n data[key].append(subobj.as_dict())\n else:\n data[key].append(subobj)\n\n # Not a list, *but still a subclass of HEREModel* and\n # and we can assign the data[key] directly with the as_dict()\n # method of the object. An example being a Status object contained\n # within a User object.\n elif getattr(getattr(self, key, None), \"as_dict\", None):\n data[key] = getattr(self, key).as_dict()\n\n # If the value doesn't have an as_dict() method, i.e., it's not\n # something that subclasses HEREModel, then we can use direct\n # assigment.\n elif getattr(self, key, None):\n data[key] = getattr(self, key, None)\n return data","def from_container(cls, container_object):\n _dict = {}\n for key, value in container_object.items():\n if isinstance(value, dict):\n value = cls.from_container(value)\n if isinstance(value, list):\n for i in range(len(value)):\n # one level is all that is necessary for what we are doing.\n if isinstance(value[i], dict):\n value[i] = cls.from_container(value[i])\n _dict[key] = value\n return cls(_dict)","def _flatten_dict(self, obj, prefix=''):\n\n encoded_dict = QueryDict('').copy()\n\n if hasattr(obj, 'items'):\n for key, value in obj.items():\n\n item_key = '%(prefix)s%(key)s' % { 'prefix': prefix, 'key': key }\n\n # Flatten lists for formsets and model choice fields\n if isinstance(value, list):\n for i, item in enumerate(value):\n\n if isinstance(item, dict):\n\n # Flatten nested object to work with formsets\n item_prefix = '%(key)s-%(index)d-' % { 'key': key, 'index': i }\n encoded_dict.update(self._flatten_dict(item, prefix=item_prefix))\n\n # ID for use with model multi choice fields\n id_value = item.get('id', None)\n if id_value:\n encoded_dict.update({ key: id_value })\n\n else:\n\n # Value for use with model multi choice fields\n encoded_dict.update({ key: item })\n\n # ID for use with model choice fields\n elif isinstance(value, dict):\n encoded_dict[item_key] = value.get('id', value)\n\n # Keep JavaScript null as Python None\n elif value is None:\n encoded_dict[item_key] = None\n\n # Other values are used directly\n else:\n encoded_dict[item_key] = unicode(value)\n\n return encoded_dict","def feedparser_to_dict(self, nested_dict):\n if type(nested_dict) == bytes:\n return str(nested_dict)\n if type(nested_dict) == list:\n return [self.feedparser_to_dict(thing) for thing in nested_dict]\n if not type(nested_dict) == feedparser.FeedParserDict:\n return nested_dict\n return {k: self.feedparser_to_dict(v) for k, v in nested_dict.items()}","def to_dict(self):\n result = {}\n\n for attr, _ in six.iteritems(self.swagger_types):\n value = getattr(self, attr)\n if isinstance(value, list):\n result[attr] = list(map(\n lambda x: x.to_dict() if hasattr(x, \"to_dict\") else x,\n value\n ))\n elif hasattr(value, \"to_dict\"):\n result[attr] = value.to_dict()\n elif isinstance(value, dict):\n result[attr] = dict(map(\n lambda item: (item[0], item[1].to_dict())\n if hasattr(item[1], \"to_dict\") else item,\n value.items()\n ))\n else:\n result[attr] = value\n if issubclass(ActivityLogTreeNode, dict):\n for key, value in self.items():\n result[key] = value\n\n return result","def merge_dictionaries(d1, d2):\n\n if d2 is None: \n return\n\n for k, v in d2.items():\n if k not in d1:\n d1[k] = dict()\n if isinstance(v, dict):\n merge_dictionaries(d1[k], v)\n else:\n d1[k] = v","def mkdicttree(self, dictH):\n for key in dictH.keys():\n if os.path.isfile(key):\n sys.exit(\"Key: {key} cannot be a file. \"\n \"Directories only\".format(key=key))\n elif os.path.isabs(key) and not os.path.isdir(key):\n os.makedirs(key)\n\n if not os.path.isabs(key):\n sys.exit('Got: \"{f}\", expected absolute path'.format(f=key))\n elif os.path.isabs(key) and isinstance(dictH[key], dict):\n self.traverse_dict_and_add(rootDir=key, dictH=dictH[key])","def _deserialize_dict(data, boxed_type):\n return {k: _deserialize(v, boxed_type)\n for k, v in iteritems(data)}","def nest_dict(dct, keys):\n nested_dict = dct\n for key in reversed(keys):\n nested_dict = RecursiveDict({key: nested_dict})\n return nested_dict","def hash_dict(dct):\n h = hashlib.md5()\n\n def update(d):\n for k, v in d.items():\n h.update(hashable(k))\n if isinstance(v, dict):\n update(v)\n else:\n h.update(hashable(v))\n\n update(dct)\n return h.digest()","def flatten_dict(d, parent_key=\"\", sep=\"_\"):\n items = []\n for k, v in d.items():\n new_key = parent_key + sep + k if parent_key else k\n if isinstance(v, MutableMapping):\n items.extend(flatten_dict(v, new_key, sep=sep).items())\n else:\n items.append((new_key, v))\n return dict(items)","def convert_keys(data):\n if data is None:\n return OrderedDict({})\n return OrderedDict(\n {underscore(k): convert_keys(v) if isinstance(v, dict) or isinstance(v, Object) else v for k, v in\n data.items()})","def to_dict(self):\n return {\n k: v.to_dict() if isinstance(v, AttrDict) else v\n for k, v in self.__dict__.items()\n if not k.startswith(\"_\")\n }","def to_dict(self):\n result = {}\n\n for attr, _ in six.iteritems(self.swagger_types):\n value = getattr(self, attr)\n if isinstance(value, list):\n result[attr] = list(map(\n lambda x: x.to_dict() if hasattr(x, \"to_dict\") else x,\n value\n ))\n elif hasattr(value, \"to_dict\"):\n result[attr] = value.to_dict()\n elif isinstance(value, dict):\n result[attr] = dict(map(\n lambda item: (item[0], item[1].to_dict())\n if hasattr(item[1], \"to_dict\") else item,\n value.items()\n ))\n else:\n result[attr] = value\n if issubclass(AttachmentViewModel, dict):\n for key, value in self.items():\n result[key] = value\n\n return result","def walk_dict(dct):\n for k, v in dct.items():\n yield dct, k, v\n if isinstance(v, dict):\n for d_, k_, v_ in walk_dict(v):\n yield d_, k_, v_","def flatten_values(d):\n if isinstance(d, dict):\n for v in d.values():\n if isinstance(v, dict):\n yield from flatten_values(v)\n else:\n yield v\n else:\n yield d","def to_dict(self):\n\n output = dict()\n for nb_object_class in NetBoxObject.__subclasses__():\n\n output[nb_object_class.name] = list()\n\n for this_object in self.base_structure[nb_object_class.name]:\n output[nb_object_class.name].append(this_object.to_dict())\n\n return output","def to_dict(self, request) -> Dict[str, Any]:\n adict = self.__dict__.copy()\n adict[\"url\"] = self.href(adict[\"url\"], request)\n adict[\"img\"] = self.href(adict[\"img\"], request)\n if self.children:\n adict[\"children\"] = [child.to_dict(request) for child in self.children]\n return adict","def to_dict(self):\n result = {}\n\n for attr, _ in six.iteritems(self.swagger_types):\n value = getattr(self, attr)\n if isinstance(value, list):\n result[attr] = list(map(\n lambda x: x.to_dict() if hasattr(x, \"to_dict\") else x,\n value\n ))\n elif hasattr(value, \"to_dict\"):\n result[attr] = value.to_dict()\n elif isinstance(value, dict):\n result[attr] = dict(map(\n lambda item: (item[0], item[1].to_dict())\n if hasattr(item[1], \"to_dict\") else item,\n value.items()\n ))\n else:\n result[attr] = value\n if issubclass(KeyDerivationInstance, dict):\n for key, value in self.items():\n result[key] = value\n\n return result","def flatten(self) -> Dict[str, Any]:\n\n self_dict = asdict(self)\n experiment_completed_record_dict = self_dict.pop(\"experiment_completed_record\")\n\n return {\n **self_dict,\n **experiment_completed_record_dict,\n }","def nestedDictValues(d):\n for key in sorted(d.keys()):\n if isinstance(d[key], dict):\n yield from nestedDictValues(d[key])\n else:\n yield d[key]"],"string":"[\n \"def flatten(self):\\n flat = {}\\n for d in self.dicts:\\n flat.update(d)\\n return flat\",\n \"def _deep_asdict(self):\\n return {\\n \\\"collections\\\": {k: p._deep_asdict() for (k, p) in self.collections.items()},\\n }\",\n \"def get_hashable_entries(nested:dict):\\n for key, value in nested.items():\\n if isinstance(value, abc.Mapping):\\n yield from get_hashable_entries(value)\\n else:\\n yield (key, value)\",\n \"def to_dict(self):\\r\\n new_dict = {}\\r\\n for key, val in self.items():\\r\\n if isinstance(val, NestedDict):\\r\\n new_dict[key] = val.to_dict()\\r\\n else:\\r\\n new_dict[key] = val\\r\\n return new_dict\",\n \"def make_dicts(self):\\n self._dicts = [tree.to_dict() for tree in self.reaction_trees]\\n self._update_route_dict(self._dicts, \\\"dict\\\")\",\n \"def as_dict_of_dicts(self):\\n dod = {}\\n for parent in self.nodes():\\n dod[parent] = {}\\n for child in self.children(parent):\\n dod[parent][child] = {\\\"branch_length\\\": self.branch_length(child)}\\n return dod\",\n \"def _normalize(self, schema: t.Dict) -> t.Dict:\\n normalized_mapping: t.Dict = {}\\n flattened_schema = flatten_schema(schema, depth=dict_depth(schema) - 1)\\n\\n for keys in flattened_schema:\\n columns = nested_get(schema, *zip(keys, keys))\\n\\n if not isinstance(columns, dict):\\n raise SchemaError(\\n f\\\"Table {'.'.join(keys[:-1])} must match the schema's nesting level: {len(flattened_schema[0])}.\\\"\\n )\\n\\n normalized_keys = [\\n self._normalize_name(key, dialect=self.dialect, is_table=True) for key in keys\\n ]\\n for column_name, column_type in columns.items():\\n nested_set(\\n normalized_mapping,\\n normalized_keys + [self._normalize_name(column_name, dialect=self.dialect)],\\n column_type,\\n )\\n\\n return normalized_mapping\",\n \"def _deep_asdict(self):\\n return {\\n \\\"schema\\\": self.schema._asdict(),\\n \\\"publications\\\": {\\n k: p._deep_asdict() for (k, p) in self.publications.items()\\n },\\n }\",\n \"def todict(self):\\n d = dict()\\n for k in self:\\n v = dict.__getitem__(self, k)\\n if isinstance(v, udict):\\n v = v.todict()\\n d[k] = v\\n return d\",\n \"def sub_dict(d):\\n r = {}\\n for k in d:\\n if type(d[k]) in prims:\\n r[k] = d[k]\\n elif type(d[k]) is list:\\n r[k] = sub_list(d[k])\\n elif type(d[k]) is dict:\\n r[k] = sub_dict(d[k])\\n else:\\n print \\\"Unknown Type: {}\\\".format(type(d[k]))\\n return r\",\n \"def to_dict(self):\\n res = {}\\n for (k, v) in self.__dict__.items():\\n if v == None or v == False or v == [] or v == {}:\\n # Skip empty/false members.\\n continue\\n elif isinstance(v, IDLDictNode) and not len(v):\\n # Skip empty dict sub-nodes.\\n continue\\n elif isinstance(v, list):\\n # Convert lists:\\n new_v = []\\n for sub_node in v:\\n if isinstance(sub_node, IDLNode):\\n # Convert sub-node:\\n new_v.append(sub_node.to_dict())\\n else:\\n new_v.append(sub_node)\\n v = new_v\\n elif isinstance(v, IDLNode):\\n # Convert sub-node:\\n v = v.to_dict()\\n res[k] = v\\n return res\",\n \"def _flatten_dict(x: Dict) ->Dict:\\n new_dict = {}\\n for key, value in x.items():\\n if isinstance(value, dict):\\n for k, v in value.items():\\n new_dict[k] = v\\n else:\\n new_dict[key] = value\\n return new_dict\",\n \"def _deep_asdict(self):\\n return {\\n \\\"metadata\\\": self.metadata,\\n \\\"artifacts\\\": {k: a._asdict() for (k, a) in self.artifacts.items()},\\n }\",\n \"def _flatten_dict(self, d: Mapping[str, Any]) -> Dict[str, Any]:\\n nested = {k for k, v in d.items() if isinstance(v, (Mapping, Configuration))}\\n if self._lowercase:\\n result = {\\n k.lower() + \\\".\\\" + ki: vi\\n for k in nested\\n for ki, vi in self._flatten_dict(d[k]).items()\\n }\\n result.update(\\n (k.lower(), v)\\n for k, v in d.items()\\n if not isinstance(v, (Mapping, Configuration))\\n )\\n else:\\n result = {\\n k + \\\".\\\" + ki: vi\\n for k in nested\\n for ki, vi in self._flatten_dict(d[k]).items()\\n }\\n result.update(\\n (k, v)\\n for k, v in d.items()\\n if not isinstance(v, (Mapping, Configuration))\\n )\\n return result\",\n \"def as_dict(self):\\n return dict((key, value) for key, value, depth in self.entries.itervalues())\",\n \"def dumpd(obj):\\n # return input directly if it is already dict\\n if isinstance(obj, dict):\\n return obj\\n # return list of dict\\n elif isinstance(obj, (list, tuple, set)):\\n return [dumpd(o) for o in obj]\\n # convert to dict\\n data = {}\\n for key in obj.__dict__.keys():\\n if not key.startswith(\\\"_\\\"):\\n # convert each items in list-like object\\n if isinstance(getattr(obj, key, None), (list, tuple, set)):\\n data[key] = []\\n for v in getattr(obj, key, None):\\n if hasattr(v, \\\"to_dict\\\"):\\n data[key].append(v.to_dict())\\n elif hasattr(v, \\\"__dict__\\\"):\\n data[key].append(dumpd(v))\\n else:\\n data[key].append(v)\\n # convert each items in dict\\n elif isinstance(getattr(obj, key, None), dict):\\n data[key] = {}\\n for k, v in getattr(obj, key, None).items():\\n if hasattr(v, \\\"to_dict\\\"):\\n data[key][k] = v.to_dict()\\n elif hasattr(v, \\\"__dict__\\\"):\\n data[key][k] = dumpd(v)\\n else:\\n data[key][k] = v\\n # convert object with `to_dict`\\n elif hasattr(getattr(obj, key, None), \\\"to_dict\\\"):\\n data[key] = getattr(obj, key).to_dict()\\n # convert plain object\\n elif hasattr(getattr(obj, key, None), \\\"__dict__\\\"):\\n data[key] = dumpd(getattr(obj, key))\\n else:\\n data[key] = getattr(obj, key, None)\\n return data\",\n \"def to_dict(self, deep=True):\\n return {var: data for var, data in self.items(deep)}\",\n \"def dicts(self):\\n if self._dicts is None:\\n self.make_dicts()\\n return self._dicts\",\n \"def flatten_dict(d):\\n def items():\\n for key, value in d.items():\\n if isinstance(value, dict):\\n for subkey, subvalue in flatten_dict(value).items():\\n yield subkey, subvalue\\n else:\\n yield key, value\\n\\n return dict(items())\",\n \"def recursive_squeeze(dictlike):\\n out = {}\\n for k, v in dictlike.items():\\n if isinstance(v, dict):\\n out[k] = recursive_squeeze(v)\\n else:\\n out[k] = np.squeeze(v)\\n return out\",\n \"def _dictRoundTripNormalize(self, treedict):\\n for key, value in list(treedict.items()):\\n if isinstance(value, dict):\\n self._dictRoundTripNormalize(value)\\n\\n # Expand treedict[(\\\"group\\\", \\\"attr_name\\\")]\\n # to treedict[\\\"group\\\"][\\\"attr_name\\\"]\\n for key, value in list(treedict.items()):\\n if not isinstance(key, tuple):\\n continue\\n # Put the attribute inside the group\\n grpname, attr = key\\n if not grpname:\\n continue\\n group = treedict.setdefault(grpname, dict())\\n if isinstance(group, dict):\\n del treedict[key]\\n group[(\\\"\\\", attr)] = value\",\n \"def _flatten_dictionary(self, params, parent=None):\\r\\n data = OrderedDict()\\r\\n for key, val in params.items():\\r\\n full_key = parent + \\\"[\\\" + key + \\\"]\\\" if parent else key\\r\\n if isinstance(val, dict):\\r\\n data.update(self._flatten_dictionary(val, full_key))\\r\\n else:\\r\\n data[full_key] = val\\r\\n return data\",\n \"def to_dict(self, target_dict=None):\\n if target_dict is None:\\n target_dict = self.__dict__\\n\\n result_dict = dict()\\n\\n def to_inner_dict(actual_value):\\n if hasattr(actual_value, 'to_dict'):\\n return actual_value.to_dict()\\n else:\\n return actual_value\\n\\n for key, value in target_dict.iteritems():\\n if value is not None:\\n if isinstance(value, dict):\\n result_dict[key] = self.to_dict(target_dict=value)\\n elif isinstance(value, list):\\n temp = list()\\n\\n for item in value:\\n temp.append(to_inner_dict(actual_value=item))\\n result_dict[key] = temp\\n else:\\n result_dict[key] = to_inner_dict(actual_value=value)\\n\\n return result_dict\",\n \"def nested_dict():\\n return defaultdict(nested_dict)\",\n \"def flatten(d):\\n\\n c = {}\\n\\n def _flatten(parents, items):\\n for k, v in items:\\n cur = parents + [k]\\n if isinstance(v, list):\\n _flatten(cur, enumerate(v))\\n elif isinstance(v, dict):\\n _flatten(cur, v.items())\\n else:\\n if v is None:\\n cur.append('$NULL')\\n v = ''\\n name = str(cur[0]) + ''.join(['['+str(x)+']' for x in cur[1:]])\\n c[name] = v\\n \\n _flatten([], d.items())\\n\\n return c\",\n \"def to_dict(self, target_dict=None):\\n if target_dict is None:\\n target_dict = self.storage\\n\\n result_dict = dict()\\n\\n def to_inner_dict(actual_value):\\n if hasattr(actual_value, 'to_dict'):\\n return actual_value.to_dict()\\n else:\\n return actual_value\\n\\n for key, value in target_dict.iteritems():\\n if value is not None:\\n if isinstance(value, dict):\\n result_dict[key] = self.to_dict(target_dict=value)\\n elif isinstance(value, list):\\n temp = list()\\n\\n for item in value:\\n temp.append(to_inner_dict(actual_value=item))\\n result_dict[key] = temp\\n else:\\n result_dict[key] = to_inner_dict(actual_value=value)\\n\\n return result_dict\",\n \"def to_dict(self, target_dict=None):\\n if target_dict is None:\\n target_dict = self.storage\\n\\n result_dict = dict()\\n\\n def to_inner_dict(actual_value):\\n if hasattr(actual_value, 'to_dict'):\\n return actual_value.to_dict()\\n else:\\n return actual_value\\n\\n for key, value in target_dict.iteritems():\\n if value is not None:\\n if isinstance(value, dict):\\n result_dict[key] = self.to_dict(target_dict=value)\\n elif isinstance(value, list):\\n temp = list()\\n\\n for item in value:\\n temp.append(to_inner_dict(actual_value=item))\\n result_dict[key] = temp\\n else:\\n result_dict[key] = to_inner_dict(actual_value=value)\\n\\n return result_dict\",\n \"def safe_dict(d):\\r\\n if isinstance(d, dict):\\r\\n return dict([(k.encode('utf-8'), safe_dict(v)) for k, v in d.iteritems()])\\r\\n elif isinstance(d, list):\\r\\n return [safe_dict(x) for x in d]\\r\\n else:\\r\\n return d\",\n \"def to_dict(self):\\n out_dict = dict(self)\\n for k, v in out_dict.items():\\n if v is self:\\n out_dict[k] = out_dict\\n elif hasattr(v, 'to_dict'):\\n out_dict[k] = v.to_dict()\\n elif hasattr(v, 'to_list'):\\n out_dict[k] = v.to_list()\\n return out_dict\",\n \"def flatten(self):\\n flattened = {}\\n for key, constructs in self.data.iteritems():\\n if not constructs:\\n continue\\n construct_class = constructs[0].__class__ # we shouldn't ever mix\\n if len(constructs) == 1:\\n flattened[key] = construct_class(key, constructs[0].value, validate_value=False)\\n else:\\n flattened[key] = construct_class.and_(key, sorted(c.value for c in constructs))\\n return flattened\",\n \"def simplify_dict(d: Dict[str, Any]) -> Dict[str, Any]:\\n return {\\n k: [ast_to_testing_string(n) for n in v] if k == \\\"children\\\" else v\\n for k, v in d.items()\\n }\",\n \"def realized(self):\\n result = {\\\"Object\\\": True}\\n for key in self.__dict__:\\n if isinstance(self.__dict__[key], Object):\\n result[key] = self.__dict__[key].asdict()\\n else:\\n result[key] = self.__dict__[key]\\n return result\",\n \"def to_representation(self, data):\\n items = super(DictSerializer, self).to_representation(data)\\n return {item[self.dict_key]: item for item in items}\",\n \"def as_dict(self, flat=False):\\n if not flat:\\n return self.as_dict_nested()\\n else:\\n return self.as_dict_flat()\",\n \"def _prepare_freeze(xs: Any) -> Any:\\n if isinstance(xs, FrozenDict):\\n # we can safely ref share the internal state of a FrozenDict\\n # because it is immutable.\\n return xs._dict # pylint: disable=protected-access\\n if not isinstance(xs, dict):\\n # return a leaf as is.\\n return xs\\n # recursively copy dictionary to avoid ref sharing\\n return {key: _prepare_freeze(val) for key, val in xs.items()}\",\n \"def _to_dict_tree(self):\\n return DictTree(self.entries)\",\n \"def to_dict(self):\\n data = {}\\n for key, value in self.__dict__.items():\\n try:\\n data[key] = value.to_dict()\\n except AttributeError:\\n data[key] = value\\n return data\",\n \"def flatten_dict(nested):\\n flattened = {}\\n for key, value in nested.items():\\n if isinstance(value, Mapping):\\n for subkey, subval in value.items():\\n newkey = '.'.join([key, subkey])\\n flattened[newkey] = subval\\n flatten_dict(flattened)\\n else:\\n flattened[key] = value\\n mappings = [isinstance(value, Mapping) for key, value in flattened.items()]\\n if len(set(mappings)) == 1 and set(mappings).pop() is False:\\n return flattened\\n else:\\n return flatten_dict(flattened)\",\n \"def _flatten_lists(\\n data: Union[Dict[str, Any], List[Any], Any]\\n ) -> Union[Dict[str, Any], Any]:\\n if not isinstance(data, dict):\\n return data\\n copy_data = cast(Dict[str, Any], data.copy())\\n for attr, val in copy_data.items():\\n if isinstance(val, list):\\n if len(cast(List[Any], val)) == 1:\\n # pull single values out of lists\\n data[attr] = _flatten_lists(cast(Any, val[0]))\\n else:\\n data[attr] = [_flatten_lists(v) for v in cast(List[Any], val)]\\n elif isinstance(val, dict):\\n data[attr] = _flatten_lists(cast(Dict[str, Any], val))\\n return data\",\n \"def deep_normalize(d):\\n if 'sudsobject' in str(d.__class__):\\n d = deep_normalize(dict(d))\\n elif isinstance(d, dict):\\n for k,v in d.iteritems():\\n if 'sudsobject' in str(v.__class__):\\n #print k, v, '%s' % v.__class__\\n r = deep_normalize(dict(v))\\n d[k] = r\\n elif isinstance(v, dict):\\n r = deep_normalize(v)\\n d[k] = r\\n elif isinstance(v, (list, tuple, )):\\n d[k] = [deep_normalize(i) for i in v]\\n elif isinstance(v, datetime):\\n # per problemi di permessi sugli oggetti datetime trasformo\\n # in DateTime di Zope\\n d[k] = DateTime(v.isoformat())\\n elif isinstance(d, (list, tuple, )):\\n d = [deep_normalize(i) for i in d]\\n\\n return d\",\n \"def getFullDict(self):\\n temp = copy.copy(self)\\n\\n for key in temp.dict:\\n if temp.dict[key]['type'] == 'func' or temp.dict[key]['type'] == 'lambda':\\n childDict = temp.dict[key][varTable].getFullDict()\\n temp.dict[key][varTable] = childDict\\n\\n return temp.dict\",\n \"def pivot_nested_dict(nested_dict):\\r\\n\\r\\n reverse_nest_dict = {} #Create an empty dictionary\\r\\n for k, v in nested_dict.items(): #Iterate through each pair of elements\\r\\n for k2, v2 in v.items(): #Iterate through pair of values\\r\\n try:\\r\\n reverse_nest_dict[k2][k] = v2\\r\\n except KeyError:\\r\\n reverse_nest_dict[k2] = { k : v2 }\\r\\n return reverse_nest_dict\\r\\n \\r\\n #Create a dictionary that produces a different nested dictionary which\\r\\n #contains the same values\\r\",\n \"def make_recursive(obj):\\n if isinstance(obj, list):\\n for i, l in enumerate(obj):\\n obj[i] = AttrDict.make_recursive(l)\\n elif isinstance(obj, dict):\\n for k, v in obj.items():\\n obj[k] = AttrDict.make_recursive(v)\\n return AttrDict(obj)\\n return obj\",\n \"def to_dict(self):\\n\\n def transform_value(value):\\n if isinstance(value, list):\\n return [transform_value(v) for v in value]\\n elif isinstance(value, abc.Mapping):\\n return dict([\\n (k, transform_value(v))\\n for k, v in iteritems(value)])\\n else:\\n return value\\n\\n return transform_value(dict(self))\",\n \"def to_dict(self) -> Dict:\\n _dict = {}\\n if hasattr(self, 'type') and self.type is not None:\\n _dict['type'] = self.type\\n if hasattr(self, 'roles') and self.roles is not None:\\n _dict['roles'] = [x.to_dict() for x in self.roles]\\n if hasattr(self, 'resources') and self.resources is not None:\\n _dict['resources'] = [x.to_dict() for x in self.resources]\\n return _dict\",\n \"def rconvert(self):\\n for k in self:\\n if isinstance(self[k], dict):\\n if not isinstance(self[k], AttrDict):\\n self[k] = AttrDict(self[k])\\n self[k].rconvert()\\n return self\",\n \"def dictize(self):\\n dict = {}\\n for node in self.sort():\\n logger.debug(\\\"Dictize: id %s has name %s\\\" % (node._id, node.name))\\n x = node._kwargs()\\n dict[node._id]={\\\"klass\\\":node.__class__.__name__, \\n \\\"kwargs\\\": x,\\n \\\"children\\\":[child._id for child in node.children()]}\\n return dict\",\n \"def flatten_dictionaries(input):\\n output = dict()\\n if isinstance(input, list):\\n for map in input:\\n if not isinstance(map, dict):\\n raise Exception('Tried to flatten a list of NON-dictionaries into a single dictionary. Whoops!')\\n for key in map.keys(): #Add keys into output\\n output[key]=map[key]\\n else: #Not a list of dictionaries\\n output = input;\\n return output\",\n \"def _join_dicts(dicts):\\n if dicts is None: # pragma: no cover\\n return\\n assembled_dict = {k: v for D in dicts for k, v in D.items()}\\n return assembled_dict\",\n \"def dict_normalization(dict_, nested=False):\\n dict_norm = dict()\\n if not nested:\\n if dict_.values():\\n d_max = max(dict_.values())\\n d_min = min(dict_.values())\\n if d_max - d_min == 0:\\n dict_norm = {key: 1 for key in dict_}\\n else:\\n dict_norm = {key: (dict_[key] - d_min) / (d_max - d_min) for key in dict_}\\n else:\\n for key_1 in dict_:\\n if dict_[key_1]:\\n dict_norm[key_1] = dict()\\n else: continue\\n d_max = max(dict_[key_1].values())\\n d_min = min(dict_[key_1].values())\\n for key_2 in dict_[key_1]:\\n if d_max - d_min == 0:\\n dict_norm[key_1][key_2] = 1 / len(dict_[key_1])\\n else:\\n dict_norm[key_1][key_2] = (dict_[key_1][key_2] - d_min) / (d_max - d_min)\\n return dict_norm\",\n \"def clean_dict(d):\\n\\n if not isinstance(d, (dict, list)):\\n return d\\n if isinstance(d, list):\\n return [v for v in (clean_dict(v) for v in d) if v]\\n return OrderedDict([(k, v) for k, v in ((k, clean_dict(v)) for k, v in list(d.items())) if v])\",\n \"def as_dict(self):\\n def append(d, key, value, is_iterative, is_primitive, is_enum):\\n if value is None:\\n if is_iterative:\\n value = []\\n elif is_primitive == False and is_enum == False:\\n if is_iterative:\\n value = map(lambda i : i.as_dict(), value)\\n else:\\n value = value.as_dict()\\n d[key] = value\\n\\n # Populate a deep dictionary.\\n d = dict()\\n append(d, 'file', self.__file, False, False, False)\\n append(d, 'file_reference', self.__file_reference, False, False, False)\\n append(d, 'name', self.__name, False, True, False)\\n return d\",\n \"def pivot_nested_dict(nested_dict):\\n # declare res as the return object which should be a dict\\n res = dict()\\n # traverse the pollsters\\n for pollster in nested_dict:\\n \\t# travserse the states\\n \\tfor state in nested_dict[pollster]:\\n \\t\\t# if first meet a state, we need to create a new dict\\n \\t\\tif state not in res:\\n \\t\\t\\tres[state] = dict()\\n \\t\\t# put the pollster value in the state dict\\n \\t\\tres[state][pollster] = nested_dict[pollster][state]\\n return res\",\n \"def flatten(parsed):\\r\\n parsedList = [parsed] if isinstance(parsed, dict) else parsed\\r\\n result = []\\r\\n for dico in parsedList:\\r\\n #Sort keys between actual values and nested dicts\\r\\n listKeys = []\\r\\n standardKeys = []\\r\\n for key in dico:\\r\\n if isinstance(dico[key], list):\\r\\n listKeys.append(key)\\r\\n else:\\r\\n standardKeys.append(key)\\r\\n if not listKeys:\\r\\n #Terminal condition: no nested dict\\r\\n result.append(dico)\\r\\n else:\\r\\n partialResult = [{x:dico[x] for x in standardKeys}]\\r\\n for key in listKeys:\\r\\n #Create a dict with the keys from partialResult and\\r\\n #from the nested dicts\\r\\n recurs = Bricks.flatten(dico[key])\\r\\n partialResult = [{**x, **y} for x in partialResult for y in recurs]\\r\\n result.extend(partialResult)\\r\\n return result\",\n \"def flatten(self) -> Dict[str, Any]:\\n\\n self_dict = asdict(self)\\n experiment_created_record_dict = self_dict.pop(\\\"experiment_created_record\\\")\\n generation_strategy_created_record_dict = self_dict.pop(\\n \\\"generation_strategy_created_record\\\"\\n )\\n\\n return {\\n **self_dict,\\n **experiment_created_record_dict,\\n **generation_strategy_created_record_dict,\\n }\",\n \"def to_dict(self) -> Dict[str, Union[dict, str, int]]:\\n d = {}\\n\\n for k, v in self.__dict__.items():\\n if k in self._exempt or v is None:\\n continue\\n if '__dataclass_fields__' in dir(v):\\n d[k] = asdict(v)\\n elif inspect.isclass(v) and issubclass(v, Base):\\n d[k] = v.to_dict()\\n elif hasattr(v, 'value'): # we assume this is an Enum value.\\n d[k] = v.value\\n else:\\n d[k] = v\\n return d\",\n \"def to_dict(self):\\n result = {}\\n\\n for attr, _ in six.iteritems(self.swagger_types):\\n value = getattr(self, attr)\\n if isinstance(value, list):\\n result[attr] = list(map(\\n lambda x: x.to_dict() if hasattr(x, \\\"to_dict\\\") else x,\\n value\\n ))\\n elif hasattr(value, \\\"to_dict\\\"):\\n result[attr] = value.to_dict()\\n elif isinstance(value, dict):\\n result[attr] = dict(map(\\n lambda item: (item[0], item[1].to_dict())\\n if hasattr(item[1], \\\"to_dict\\\") else item,\\n value.items()\\n ))\\n else:\\n result[attr] = value\\n if issubclass(DeterminantDisease, dict):\\n for key, value in self.items():\\n result[key] = value\\n\\n return result\",\n \"def flatten_ls_of_dt(ls: list) -> dict:\\n\\n def _flatten(ls):\\n if not isinstance(ls, list):\\n return ls\\n else:\\n if dict not in [type(x) for x in ls]:\\n return ls\\n # for [dict, dict, nan] objects\\n else:\\n # convert nan to empty dict\\n ls = [x if isinstance(x, dict) else {} for x in ls]\\n # convert list of dicts to dict of lists\\n dt = pd.DataFrame(ls).to_dict(orient=\\\"list\\\")\\n # continue with recursion if needed\\n for k, v in dt.items():\\n dt[k] = _flatten(v)\\n return dt\\n\\n return _flatten(ls)\",\n \"def merge_dicts(base, changes):\\n for k, v in changes.items():\\n if isinstance(v, dict):\\n merge_dicts(base.setdefault(k, {}), v)\\n else:\\n base.setdefault(k, v)\",\n \"def to_dict(self, drop_null=True, camel=False):\\n #return _to_dict(self, drop_null, camel)\\n def to_dict(obj, drop_null, camel):\\n \\\"\\\"\\\"Recursively constructs the dict.\\\"\\\"\\\"\\n if isinstance(obj, (Body, BodyChild)):\\n obj = obj.__dict__\\n if isinstance(obj, dict):\\n data = {}\\n for attr, val in six.iteritems(obj):\\n if camel:\\n attr = _snake_to_camel(attr)\\n valid_null = (isinstance(val, bool) or val == 0 or\\n (val and to_dict(val, drop_null, camel)))\\n if not drop_null or (drop_null and valid_null):\\n data[attr] = to_dict(val, drop_null, camel)\\n return data\\n elif isinstance(obj, list):\\n data = []\\n for val in obj:\\n valid_null = (isinstance(val, bool) or val == 0 or\\n (val and to_dict(val, drop_null, camel)))\\n if not drop_null or (drop_null and valid_null):\\n data.append(to_dict(val, drop_null, camel))\\n return data\\n else:\\n return obj\\n return to_dict(self, drop_null, camel)\",\n \"def merge_dict_recursive(target, src):\\r\\n for k in src.keys():\\r\\n if ((k in target and isinstance(target[k], dict) and\\r\\n isinstance(src[k], collections.Mapping))):\\r\\n merge_dict_recursive(target[k], src[k])\\r\\n else:\\r\\n target[k] = src[k]\",\n \"def fuse_dicts(ds):\\n ks = set((k for d in ds for k in d.keys()))\\n fused_d = {k: [] for k in ks}\\n for d in ds:\\n for k in ks:\\n fused_d[k].append(d[k] if k in d else None)\\n return fused_d\",\n \"def convert(data):\\n return {k: [d[k] for d in data] for k in data[0].keys()}\",\n \"def unflatten(\\n d: Dict[str, Any],\\n base: Dict[str, Any] = None,\\n) -> Dict[str, Any]:\\n if base is None:\\n base = {}\\n\\n for key, value in d.items():\\n root = base\\n\\n ###\\n # If a dotted path is encountered, create nested dicts for all but\\n # the last level, then change root to that last level, and key to\\n # the final key in the path. This allows one final setitem at the bottom\\n # of the loop.\\n if '.' in key:\\n *parts, key = key.split('.')\\n\\n for part in parts:\\n root.setdefault(part, {})\\n root = root[part]\\n\\n if isinstance(value, dict):\\n value = unflatten(value, root.get(key, {}))\\n\\n root[key] = value\\n\\n return base\",\n \"def serialize_dict(data):\\n new_data = {}\\n for (key, value) in data.items():\\n if isinstance(value, dict):\\n new_data[key] = serialize_dict(value)\\n elif isinstance(value, list):\\n new_data[key] = serialize_list(value)\\n elif isinstance(value, int) or isinstance(value, float) or isinstance(value, str) or isinstance(value, bool):\\n new_data[key] = value\\n else:\\n new_data[str(key)] = None\\n\\n return new_data\",\n \"def flatten_dict(d, separator=':', _parent_key=''):\\n items = []\\n for k, v in d.items():\\n new_key = _parent_key + separator + k if _parent_key else k\\n if isinstance(v, collections.MutableMapping):\\n items.extend(flatten_dict(v, separator=separator, _parent_key=new_key).items())\\n else:\\n items.append((new_key, v))\\n return dict(items)\",\n \"def flatten_dict(dict_input):\\n flattened_dict = dict()\\n\\n for key, value in dict_input.items():\\n if isinstance(value, dict):\\n new_keys = sorted(value.keys())\\n for new_key in new_keys:\\n entry = {key + '_' + new_key: value[new_key]}\\n flattened_dict.update(entry)\\n else:\\n entry = {key: value}\\n flattened_dict.update(entry)\\n\\n return flattened_dict\",\n \"def serialize_dict(container: Dict) -> Dict:\\n for key, value in container.items():\\n container[key] = serialize_obj(value)\\n return container\",\n \"def serialize_dict(container: Dict) -> Dict:\\n for key, value in container.items():\\n container[key] = serialize_obj(value)\\n return container\",\n \"def to_dict(self):\\n result = {}\\n\\n for attr, _ in iteritems(self.pdb_types):\\n value = getattr(self, attr)\\n if isinstance(value, list):\\n result[attr] = list(map(\\n lambda x: x.to_dict() if hasattr(x, \\\"to_dict\\\") else x,\\n value\\n ))\\n elif hasattr(value, \\\"to_dict\\\"):\\n result[attr] = value.to_dict()\\n elif isinstance(value, dict):\\n result[attr] = dict(map(\\n lambda item: (item[0], item[1].to_dict())\\n if hasattr(item[1], \\\"to_dict\\\") else item,\\n value.items()\\n ))\\n else:\\n result[attr] = value\\n\\n return result\",\n \"def to_dict(self):\\n result = {}\\n\\n for attr, _ in iteritems(self.pdb_types):\\n value = getattr(self, attr)\\n if isinstance(value, list):\\n result[attr] = list(map(\\n lambda x: x.to_dict() if hasattr(x, \\\"to_dict\\\") else x,\\n value\\n ))\\n elif hasattr(value, \\\"to_dict\\\"):\\n result[attr] = value.to_dict()\\n elif isinstance(value, dict):\\n result[attr] = dict(map(\\n lambda item: (item[0], item[1].to_dict())\\n if hasattr(item[1], \\\"to_dict\\\") else item,\\n value.items()\\n ))\\n else:\\n result[attr] = value\\n\\n return result\",\n \"def ensure_json_serializable(dict_, normalize_containers=False, verbose=0):\\n dict_ = copy.deepcopy(dict_)\\n\\n def _norm_container(c):\\n if isinstance(c, dict):\\n # Cast to a normal dictionary\\n if isinstance(c, OrderedDict):\\n if type(c) is not OrderedDict:\\n c = OrderedDict(c)\\n else:\\n if type(c) is not dict:\\n c = dict(c)\\n return c\\n\\n walker = ub.IndexableWalker(dict_)\\n for prefix, value in walker:\\n if isinstance(value, tuple):\\n new_value = list(value)\\n walker[prefix] = new_value\\n elif isinstance(value, np.ndarray):\\n new_value = value.tolist()\\n walker[prefix] = new_value\\n elif isinstance(value, (np.integer)):\\n new_value = int(value)\\n walker[prefix] = new_value\\n elif isinstance(value, (np.floating)):\\n new_value = float(value)\\n walker[prefix] = new_value\\n elif isinstance(value, (np.complexfloating)):\\n new_value = complex(value)\\n walker[prefix] = new_value\\n elif isinstance(value, decimal.Decimal):\\n new_value = float(value)\\n walker[prefix] = new_value\\n elif isinstance(value, fractions.Fraction):\\n new_value = float(value)\\n walker[prefix] = new_value\\n elif isinstance(value, pathlib.Path):\\n new_value = str(value)\\n walker[prefix] = new_value\\n elif hasattr(value, '__json__'):\\n new_value = value.__json__()\\n walker[prefix] = new_value\\n elif normalize_containers:\\n if isinstance(value, dict):\\n new_value = _norm_container(value)\\n walker[prefix] = new_value\\n\\n if normalize_containers:\\n # normalize the outer layer\\n dict_ = _norm_container(dict_)\\n return dict_\",\n \"def to_dict(self):\\n result = {}\\n\\n for attr, _ in six.iteritems(self.swagger_types):\\n value = getattr(self, attr)\\n if isinstance(value, list):\\n result[attr] = list(map(\\n lambda x: x.to_dict() if hasattr(x, \\\"to_dict\\\") else x,\\n value\\n ))\\n elif hasattr(value, \\\"to_dict\\\"):\\n result[attr] = value.to_dict()\\n elif isinstance(value, dict):\\n result[attr] = dict(map(\\n lambda item: (item[0], item[1].to_dict())\\n if hasattr(item[1], \\\"to_dict\\\") else item,\\n value.items()\\n ))\\n else:\\n result[attr] = value\\n if issubclass(Referent, dict):\\n for key, value in self.items():\\n result[key] = value\\n\\n return result\",\n \"def to_dict(obj, drop_null, camel):\\n if isinstance(obj, (Body, BodyChild)):\\n obj = obj.__dict__\\n if isinstance(obj, dict):\\n data = {}\\n for attr, val in six.iteritems(obj):\\n if camel:\\n attr = _snake_to_camel(attr)\\n valid_null = (isinstance(val, bool) or val == 0 or\\n (val and to_dict(val, drop_null, camel)))\\n if not drop_null or (drop_null and valid_null):\\n data[attr] = to_dict(val, drop_null, camel)\\n return data\\n elif isinstance(obj, list):\\n data = []\\n for val in obj:\\n valid_null = (isinstance(val, bool) or val == 0 or\\n (val and to_dict(val, drop_null, camel)))\\n if not drop_null or (drop_null and valid_null):\\n data.append(to_dict(val, drop_null, camel))\\n return data\\n else:\\n return obj\",\n \"def flatten_dict(d):\\n\\n def expand(key, value):\\n if isinstance(value, dict):\\n return [(key + '.' + k, v) for k, v in flatten_dict(value).items()]\\n else:\\n return [(key, value)]\\n\\n items = [item for k, v in d.items() for item in expand(k, v)]\\n return dict(items)\",\n \"def dictOfBetas(self, free=True, fixed=False):\\n s = {}\\n for e in self.children:\\n d = e.dictOfBetas(free, fixed)\\n s = dict(s, **d)\\n return s\",\n \"def to_dict(self):\\n return {k: v.to_dict() if isinstance(v, AttrDict) else v\\n for k, v in self.__dict__.items() if not k.startswith('_')}\",\n \"def from_iterable(iterables):\\n for it in iterables:\\n for element in it:\\n if isinstance(element, dict):\\n for key in element:\\n yield key\\n else:\\n yield element\",\n \"def to_dict(self):\\n result = {}\\n\\n for attr, _ in six.iteritems(self.swagger_types):\\n value = getattr(self, attr)\\n if isinstance(value, list):\\n result[attr] = list(map(\\n lambda x: x.to_dict() if hasattr(x, \\\"to_dict\\\") else x,\\n value\\n ))\\n elif hasattr(value, \\\"to_dict\\\"):\\n result[attr] = value.to_dict()\\n elif isinstance(value, dict):\\n result[attr] = dict(map(\\n lambda item: (item[0], item[1].to_dict())\\n if hasattr(item[1], \\\"to_dict\\\") else item,\\n value.items()\\n ))\\n else:\\n result[attr] = value\\n if issubclass(V1alpha3GoTemplate, dict):\\n for key, value in self.items():\\n result[key] = value\\n\\n return result\",\n \"def toDictionary(self):\\n result = {}\\n for key, method in self.__dict__.iteritems():\\n if (key == '_Entity__instanced'): continue\\n\\n value = method()\\n if (isinstance(value, Entity)):\\n value = value.toDictionary()\\n elif (isinstance(value, list)):\\n expands = []\\n for node in value:\\n if (hasattr(node, 'toDictionary')):\\n expands.append(node.toDictionary())\\n else:\\n expands.append(node)\\n value = expands\\n\\n result[_toPropertyName(key)] = value\\n return result\",\n \"def as_dict(self):\\n data = {}\\n\\n for (key, value) in self.param_defaults.items():\\n\\n # If the value is a list, we need to create a list to hold the\\n # dicts created by an object supporting the as_dict() method,\\n # i.e., if it inherits from HEREModel. If the item in the list\\n # doesn't support the as_dict() method, then we assign the value\\n # directly. An example being a list of Media objects contained\\n # within a Status object.\\n if isinstance(getattr(self, key, None), (list, tuple, set)):\\n data[key] = list()\\n for subobj in getattr(self, key, None):\\n if getattr(subobj, \\\"as_dict\\\", None):\\n data[key].append(subobj.as_dict())\\n else:\\n data[key].append(subobj)\\n\\n # Not a list, *but still a subclass of HEREModel* and\\n # and we can assign the data[key] directly with the as_dict()\\n # method of the object. An example being a Status object contained\\n # within a User object.\\n elif getattr(getattr(self, key, None), \\\"as_dict\\\", None):\\n data[key] = getattr(self, key).as_dict()\\n\\n # If the value doesn't have an as_dict() method, i.e., it's not\\n # something that subclasses HEREModel, then we can use direct\\n # assigment.\\n elif getattr(self, key, None):\\n data[key] = getattr(self, key, None)\\n return data\",\n \"def from_container(cls, container_object):\\n _dict = {}\\n for key, value in container_object.items():\\n if isinstance(value, dict):\\n value = cls.from_container(value)\\n if isinstance(value, list):\\n for i in range(len(value)):\\n # one level is all that is necessary for what we are doing.\\n if isinstance(value[i], dict):\\n value[i] = cls.from_container(value[i])\\n _dict[key] = value\\n return cls(_dict)\",\n \"def _flatten_dict(self, obj, prefix=''):\\n\\n encoded_dict = QueryDict('').copy()\\n\\n if hasattr(obj, 'items'):\\n for key, value in obj.items():\\n\\n item_key = '%(prefix)s%(key)s' % { 'prefix': prefix, 'key': key }\\n\\n # Flatten lists for formsets and model choice fields\\n if isinstance(value, list):\\n for i, item in enumerate(value):\\n\\n if isinstance(item, dict):\\n\\n # Flatten nested object to work with formsets\\n item_prefix = '%(key)s-%(index)d-' % { 'key': key, 'index': i }\\n encoded_dict.update(self._flatten_dict(item, prefix=item_prefix))\\n\\n # ID for use with model multi choice fields\\n id_value = item.get('id', None)\\n if id_value:\\n encoded_dict.update({ key: id_value })\\n\\n else:\\n\\n # Value for use with model multi choice fields\\n encoded_dict.update({ key: item })\\n\\n # ID for use with model choice fields\\n elif isinstance(value, dict):\\n encoded_dict[item_key] = value.get('id', value)\\n\\n # Keep JavaScript null as Python None\\n elif value is None:\\n encoded_dict[item_key] = None\\n\\n # Other values are used directly\\n else:\\n encoded_dict[item_key] = unicode(value)\\n\\n return encoded_dict\",\n \"def feedparser_to_dict(self, nested_dict):\\n if type(nested_dict) == bytes:\\n return str(nested_dict)\\n if type(nested_dict) == list:\\n return [self.feedparser_to_dict(thing) for thing in nested_dict]\\n if not type(nested_dict) == feedparser.FeedParserDict:\\n return nested_dict\\n return {k: self.feedparser_to_dict(v) for k, v in nested_dict.items()}\",\n \"def to_dict(self):\\n result = {}\\n\\n for attr, _ in six.iteritems(self.swagger_types):\\n value = getattr(self, attr)\\n if isinstance(value, list):\\n result[attr] = list(map(\\n lambda x: x.to_dict() if hasattr(x, \\\"to_dict\\\") else x,\\n value\\n ))\\n elif hasattr(value, \\\"to_dict\\\"):\\n result[attr] = value.to_dict()\\n elif isinstance(value, dict):\\n result[attr] = dict(map(\\n lambda item: (item[0], item[1].to_dict())\\n if hasattr(item[1], \\\"to_dict\\\") else item,\\n value.items()\\n ))\\n else:\\n result[attr] = value\\n if issubclass(ActivityLogTreeNode, dict):\\n for key, value in self.items():\\n result[key] = value\\n\\n return result\",\n \"def merge_dictionaries(d1, d2):\\n\\n if d2 is None: \\n return\\n\\n for k, v in d2.items():\\n if k not in d1:\\n d1[k] = dict()\\n if isinstance(v, dict):\\n merge_dictionaries(d1[k], v)\\n else:\\n d1[k] = v\",\n \"def mkdicttree(self, dictH):\\n for key in dictH.keys():\\n if os.path.isfile(key):\\n sys.exit(\\\"Key: {key} cannot be a file. \\\"\\n \\\"Directories only\\\".format(key=key))\\n elif os.path.isabs(key) and not os.path.isdir(key):\\n os.makedirs(key)\\n\\n if not os.path.isabs(key):\\n sys.exit('Got: \\\"{f}\\\", expected absolute path'.format(f=key))\\n elif os.path.isabs(key) and isinstance(dictH[key], dict):\\n self.traverse_dict_and_add(rootDir=key, dictH=dictH[key])\",\n \"def _deserialize_dict(data, boxed_type):\\n return {k: _deserialize(v, boxed_type)\\n for k, v in iteritems(data)}\",\n \"def nest_dict(dct, keys):\\n nested_dict = dct\\n for key in reversed(keys):\\n nested_dict = RecursiveDict({key: nested_dict})\\n return nested_dict\",\n \"def hash_dict(dct):\\n h = hashlib.md5()\\n\\n def update(d):\\n for k, v in d.items():\\n h.update(hashable(k))\\n if isinstance(v, dict):\\n update(v)\\n else:\\n h.update(hashable(v))\\n\\n update(dct)\\n return h.digest()\",\n \"def flatten_dict(d, parent_key=\\\"\\\", sep=\\\"_\\\"):\\n items = []\\n for k, v in d.items():\\n new_key = parent_key + sep + k if parent_key else k\\n if isinstance(v, MutableMapping):\\n items.extend(flatten_dict(v, new_key, sep=sep).items())\\n else:\\n items.append((new_key, v))\\n return dict(items)\",\n \"def convert_keys(data):\\n if data is None:\\n return OrderedDict({})\\n return OrderedDict(\\n {underscore(k): convert_keys(v) if isinstance(v, dict) or isinstance(v, Object) else v for k, v in\\n data.items()})\",\n \"def to_dict(self):\\n return {\\n k: v.to_dict() if isinstance(v, AttrDict) else v\\n for k, v in self.__dict__.items()\\n if not k.startswith(\\\"_\\\")\\n }\",\n \"def to_dict(self):\\n result = {}\\n\\n for attr, _ in six.iteritems(self.swagger_types):\\n value = getattr(self, attr)\\n if isinstance(value, list):\\n result[attr] = list(map(\\n lambda x: x.to_dict() if hasattr(x, \\\"to_dict\\\") else x,\\n value\\n ))\\n elif hasattr(value, \\\"to_dict\\\"):\\n result[attr] = value.to_dict()\\n elif isinstance(value, dict):\\n result[attr] = dict(map(\\n lambda item: (item[0], item[1].to_dict())\\n if hasattr(item[1], \\\"to_dict\\\") else item,\\n value.items()\\n ))\\n else:\\n result[attr] = value\\n if issubclass(AttachmentViewModel, dict):\\n for key, value in self.items():\\n result[key] = value\\n\\n return result\",\n \"def walk_dict(dct):\\n for k, v in dct.items():\\n yield dct, k, v\\n if isinstance(v, dict):\\n for d_, k_, v_ in walk_dict(v):\\n yield d_, k_, v_\",\n \"def flatten_values(d):\\n if isinstance(d, dict):\\n for v in d.values():\\n if isinstance(v, dict):\\n yield from flatten_values(v)\\n else:\\n yield v\\n else:\\n yield d\",\n \"def to_dict(self):\\n\\n output = dict()\\n for nb_object_class in NetBoxObject.__subclasses__():\\n\\n output[nb_object_class.name] = list()\\n\\n for this_object in self.base_structure[nb_object_class.name]:\\n output[nb_object_class.name].append(this_object.to_dict())\\n\\n return output\",\n \"def to_dict(self, request) -> Dict[str, Any]:\\n adict = self.__dict__.copy()\\n adict[\\\"url\\\"] = self.href(adict[\\\"url\\\"], request)\\n adict[\\\"img\\\"] = self.href(adict[\\\"img\\\"], request)\\n if self.children:\\n adict[\\\"children\\\"] = [child.to_dict(request) for child in self.children]\\n return adict\",\n \"def to_dict(self):\\n result = {}\\n\\n for attr, _ in six.iteritems(self.swagger_types):\\n value = getattr(self, attr)\\n if isinstance(value, list):\\n result[attr] = list(map(\\n lambda x: x.to_dict() if hasattr(x, \\\"to_dict\\\") else x,\\n value\\n ))\\n elif hasattr(value, \\\"to_dict\\\"):\\n result[attr] = value.to_dict()\\n elif isinstance(value, dict):\\n result[attr] = dict(map(\\n lambda item: (item[0], item[1].to_dict())\\n if hasattr(item[1], \\\"to_dict\\\") else item,\\n value.items()\\n ))\\n else:\\n result[attr] = value\\n if issubclass(KeyDerivationInstance, dict):\\n for key, value in self.items():\\n result[key] = value\\n\\n return result\",\n \"def flatten(self) -> Dict[str, Any]:\\n\\n self_dict = asdict(self)\\n experiment_completed_record_dict = self_dict.pop(\\\"experiment_completed_record\\\")\\n\\n return {\\n **self_dict,\\n **experiment_completed_record_dict,\\n }\",\n \"def nestedDictValues(d):\\n for key in sorted(d.keys()):\\n if isinstance(d[key], dict):\\n yield from nestedDictValues(d[key])\\n else:\\n yield d[key]\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.65635574","0.64972985","0.63339704","0.633059","0.62674254","0.6173345","0.6043641","0.6025789","0.60111827","0.59066117","0.58889693","0.5853961","0.5845323","0.58159864","0.57850933","0.57364714","0.57173663","0.56847185","0.5674789","0.5667077","0.56441784","0.56200826","0.561702","0.55895966","0.5579133","0.55698496","0.55698496","0.5547054","0.55419517","0.552779","0.55214196","0.5520294","0.55200464","0.5517888","0.54900706","0.5473123","0.5465329","0.5462306","0.545429","0.5446337","0.5431364","0.54287857","0.54164636","0.54107946","0.5406712","0.5398774","0.53970915","0.539401","0.53837055","0.53743106","0.5371272","0.5368736","0.53650594","0.5361758","0.53555584","0.5352564","0.53442514","0.53364193","0.5328892","0.5325956","0.5323728","0.53228945","0.5311464","0.5300747","0.5296697","0.52863544","0.5286041","0.52717674","0.52717674","0.5270772","0.5270772","0.5263817","0.52621764","0.5248967","0.52489346","0.52334714","0.5232476","0.52323145","0.52291924","0.52256835","0.52170146","0.521074","0.5207351","0.5203817","0.52013886","0.5197233","0.5186795","0.5178403","0.5173268","0.51687956","0.5165648","0.5148331","0.51395434","0.5135349","0.5133663","0.5133654","0.5125546","0.5124355","0.51160026","0.5114566","0.51125467"],"string":"[\n \"0.65635574\",\n \"0.64972985\",\n \"0.63339704\",\n \"0.633059\",\n \"0.62674254\",\n \"0.6173345\",\n \"0.6043641\",\n \"0.6025789\",\n \"0.60111827\",\n \"0.59066117\",\n \"0.58889693\",\n \"0.5853961\",\n \"0.5845323\",\n \"0.58159864\",\n \"0.57850933\",\n \"0.57364714\",\n \"0.57173663\",\n \"0.56847185\",\n \"0.5674789\",\n \"0.5667077\",\n \"0.56441784\",\n \"0.56200826\",\n \"0.561702\",\n \"0.55895966\",\n \"0.5579133\",\n \"0.55698496\",\n \"0.55698496\",\n \"0.5547054\",\n \"0.55419517\",\n \"0.552779\",\n \"0.55214196\",\n \"0.5520294\",\n \"0.55200464\",\n \"0.5517888\",\n \"0.54900706\",\n \"0.5473123\",\n \"0.5465329\",\n \"0.5462306\",\n \"0.545429\",\n \"0.5446337\",\n \"0.5431364\",\n \"0.54287857\",\n \"0.54164636\",\n \"0.54107946\",\n \"0.5406712\",\n \"0.5398774\",\n \"0.53970915\",\n \"0.539401\",\n \"0.53837055\",\n \"0.53743106\",\n \"0.5371272\",\n \"0.5368736\",\n \"0.53650594\",\n \"0.5361758\",\n \"0.53555584\",\n \"0.5352564\",\n \"0.53442514\",\n \"0.53364193\",\n \"0.5328892\",\n \"0.5325956\",\n \"0.5323728\",\n \"0.53228945\",\n \"0.5311464\",\n \"0.5300747\",\n \"0.5296697\",\n \"0.52863544\",\n \"0.5286041\",\n \"0.52717674\",\n \"0.52717674\",\n \"0.5270772\",\n \"0.5270772\",\n \"0.5263817\",\n \"0.52621764\",\n \"0.5248967\",\n \"0.52489346\",\n \"0.52334714\",\n \"0.5232476\",\n \"0.52323145\",\n \"0.52291924\",\n \"0.52256835\",\n \"0.52170146\",\n \"0.521074\",\n \"0.5207351\",\n \"0.5203817\",\n \"0.52013886\",\n \"0.5197233\",\n \"0.5186795\",\n \"0.5178403\",\n \"0.5173268\",\n \"0.51687956\",\n \"0.5165648\",\n \"0.5148331\",\n \"0.51395434\",\n \"0.5135349\",\n \"0.5133663\",\n \"0.5133654\",\n \"0.5125546\",\n \"0.5124355\",\n \"0.51160026\",\n \"0.5114566\",\n \"0.51125467\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":92588,"cells":{"query":{"kind":"string","value":"Checks if two intervals are overlapping. Function requires intervals to be proper (end >= start). Works with any types of comparable by mathematical operators objects."},"document":{"kind":"string","value":"def ranges_overlap(start1, end1, start2, end2):\n return start1 <= end2 and end1 >= start2"},"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 dates_intervals_are_overlapped(start_1, end_1, start_2, end_2):\n return end_1 >= start_2 and end_2 >= start_1","def overlap(start1, end1, start2, end2):\n return not (end1 < start2 or end2 < start1)","def overlap(t1start, t1end, t2start, t2end):\n\n return (t1start <= t2start <= t1end) or (t2start <= t1start <= t2end)","def overlap(range1, range2):\n if range1[0] <= range2[1] and range2[0] <= range1[1]:\n return True\n return False","def overlap(start_idx1, end_idx1, start_idx2, end_idx2):\n head = min(end_idx1, end_idx2)\n tail = max(start_idx1, start_idx2)\n return head >= tail","def intersect(start1, stop1, start2, stop2):\n\tassert isinstance(start1, int)\n\tassert isinstance(stop2, int)\n\tassert isinstance(start2, int)\n\tassert isinstance(stop2, int)\n\tassert start1 <= stop1\n\tassert start2 <= stop2\n\t\n\t# if interval 1 is completely to the left of interval 2\n\tif stop1 < start2:\n\t\treturn False\n\t\n\t# if interval 1 is completely to the right of interval2\n\tif stop2 < start1:\n\t\treturn False\n\t\t\n\treturn True","def overlaps(self, begin, end=None):\n if end is not None:\n # An overlap means that some C exists that is inside both ranges:\n # begin <= C < end\n # and \n # self.begin <= C < self.end\n # See https://stackoverflow.com/questions/3269434/whats-the-most-efficient-way-to-test-two-integer-ranges-for-overlap/3269471#3269471\n return begin < self.end and end > self.begin\n try:\n return self.overlaps(begin.begin, begin.end)\n except:\n return self.contains_point(begin)","def overlaps(self, other):\n return self.start <= other.end and self.end >= other.start","def contains_interval(self, other):\n return (\n self.begin <= other.begin and\n self.end >= other.end\n )","def overlaps(self, other):\n\n if self.start.equal(other.start) or self.stop.equal(other.stop):\n return True\n elif self.start.before(other.start) and self.stop.after(other.start):\n return True\n elif other.stop.after(self.start) and other.stop.before(self.stop):\n return True\n else:\n return False","def iOverlap (a1, a2, b1, b2):\n if b1<=a1<=b2 or b1<=a2<=b2 or a1<=b1<=a2 or a1<=b2<=a2:\n return True\n elif a1>a2 or b1>b2:\n return False\n else:\n return False","def span_overlap(a: Tuple[int, int], b: Tuple[int, int]) -> bool:\n return not (a[0] > b[1] or a[1] < b[0])","def overlap(component1, component2):\n if component1[0].start <= component2[0].stop and component2[0].start <= component1[0].stop:\n if component1[1].start <= component2[1].stop and component2[1].start <= component1[1].stop:\n return True\n return False","def overlaps(x1, x2, y1, y2):\n\n return x1 <= y2 and y1 <= x2","def overlap(a, b):\n return not(a[2]<=b[0] or a[3]<=b[1] or a[0]>=b[2] or a[1]>=b[3])","def overlap(a: Pos, b: Pos, exact: bool = False) -> bool:\n if a == b:\n return True\n elif exact:\n return False\n s0, e0 = a\n s1, e1 = b\n if in_interval(s1, s0, e0):\n return True\n if in_interval(e1, s0, e0):\n return True\n if in_interval(s0, s1, e1):\n return True\n if in_interval(e0, s1, e1):\n return True\n return False","def overlaps(a, b, **kwargs):\n return lib.overlaps(a, b, **kwargs)","def check_interval_bounds(begin, end):\n if begin.get_midpoint() >= end.get_midpoint():\n return False\n\n if begin.get_radius() is not None and end.get_radius() is not None:\n if begin.get_midpoint() - begin.get_radius() > \\\n end.get_midpoint() - end.get_radius():\n return False\n\n return True","def check_overlap(a, b):\n if a[0] >= b[2] or a[1] >= b[3] or a[2] <= b[0] or a[3] <= b[1]:\n return False\n return True","def _range_contains(self, a, b):\n\t\treturn b[0] >= a[0] and b[-1] <= a[-1]","def overlaps(self, other):\n return _binary_op(arctern.ST_Overlaps, self, other).astype(bool, copy=False)","def overlap(p1: Tuple, p2: Tuple) -> bool:\n if (p2[1] - p1[0]) * (p2[0] - p1[1]) <= 0:\n return True\n else:\n return False","def overlaps(self, other): # -> bool:\n ...","def overlap(self, other):\n\t\toverlap = self.contains(other.startX, other.startY) or \\\n\t\t\tself.contains(other.startX, other.endY) or \\\n\t\t\tself.contains(other.endX, other.startY) or \\\n\t\t\tself.contains(other.endX, other.endY)\n\n\t\tintersectY1 = self.startY <= other.startY <= self.endY and \\\n\t\t\tself.startY <= other.endY <= self.endY and \\\n\t\t\t(other.startX <= self.startX <= other.endX or \\\n\t\t\tother.startX <= self.endX <= other.endX)\n\n\t\tintersectY2 = other.startY <= self.startY <= other.endY and \\\n\t\t\t other.startY <= self.endY <= other.endY and \\\n\t\t\t (self.startX <= other.startX <= self.endX or \\\n\t\t\t self.startX <= other.endX <= self.endX)\n\n\t\tintersectY = intersectY1 or intersectY2\n\n\t\tintersectX1 = self.startX <= other.startX <= self.endY and \\\n\t\t\tself.startX <= other.endX <= self.endX and \\\n\t\t (other.startY <= self.startY <= other.endY or \\\n\t\t\tother.startY <= self.endY <= other.endY)\n\n\t\tintersectX2 = other.startX <= self.startX <= other.endX and \\\n\t\t\tother.startX <= self.endX <= other.endX and \\\n\t\t (self.startY <= other.startY <= self.endY or \\\n\t\t\tself.startY <= other.endY <= self.endY)\n\n\t\tintersectX = intersectX1 or intersectX2\n\n\t\treturn overlap or intersectX or intersectY","def covers(self, other):\n return self._start <= other._start and self._end >= other._end","def do_overlap(r1, r2):\n r1_s, r1_e = r1\n r2_s, r2_e = r2\n\n return r1_s <= r2_s <= r1_e or r2_s <= r1_s <= r2_e","def overlaps(self, other: \"Availability\", strict: bool) -> bool:\n\n if not isinstance(other, Availability):\n raise Exception(\"Please provide an Availability object\")\n\n if strict:\n return (\n (self.start <= other.start < self.end)\n or (self.start < other.end <= self.end)\n or (other.start <= self.start < other.end)\n or (other.start < self.end <= other.end)\n )\n return (\n (self.start <= other.start <= self.end)\n or (self.start <= other.end <= self.end)\n or (other.start <= self.start <= other.end)\n or (other.start <= self.end <= other.end)\n )","def do_intervals_differ((x1, y1), (x2, y2)):\n\n assert y1 >= x1 and y2 >= x2\n return y1 < x2 or y2 < x1","def detect_overlap_1d(first, first_length, second, second_length):\n first_end = first + first_length - 1\n second_end = second + second_length - 1\n return second_end >= first and first_end >= second","def _overlap(x1, w1, x2, w2):\r\n if x1+w1 < x2-w2: return False\r\n if x1-w1 > x2+w2: return False\r\n\r\n return True","def overlaps(interval,intervals):\n return [x for x in intervals if interval.overlaps(x)]","def range_overlap(range1, range2):\n return range(max(range1[0], range2[0]), min(range1[1], range2[1]))","def intersects(self, other: \"DateRange\") -> bool:\n return (\n self.end_date - other.start_date\n >= timedelta(0)\n >= self.start_date - other.end_date\n )","def is_overlap(bb1, bb2):\n l1, t1, r1, b1 = bb1['x'], bb1['y'], bb1['x']+bb1['w'], bb1['y']+bb1['h']\n l2, t2, r2, b2 = bb2['x'], bb2['y'], bb2['x']+bb2['w'], bb2['y']+bb2['h']\n\n if r1 > l2 and r2 > l1 and b2 > t1 and b1 > t2:\n return True\n else:\n return False","def in_range(cls, lhs, rhs):\n return rhs[0] <= lhs <= rhs[1]","def overlaps(self,b):\n if b.chr != self.chr :return False\n if (self.start <= b.start and b.start <=self.end) or (self.start >= b.start and self.start <= b.end):\n return True\n else:\n return False","def overlaps(self, chrom, start, end, strand=None):\n if (self.chrom != chrom \n or min(self.end, end) - max(self.start, start) <= 0 \n or (strand is not None and self.strand != strand)): \n return False\n return True","def overlap(indices1, indices2):\n assert (len(indices1) == 2 and len(indices2) == 2)\n indices1 = sorted(indices1)\n indices2 = sorted(indices2)\n if (indices2[0] <= indices1[0] <= indices2[1]) or \\\n (indices2[0] <= indices1[1] <= indices2[1]) or \\\n (indices1[0] <= indices2[0] <= indices1[1]) or \\\n (indices1[0] <= indices2[1] <= indices1[1]):\n return True\n else:\n return False","def overlaps(self, other):\n\n if self.ll.x >= other.ur.x:\n return False\n \n if self.ll.y >= other.ur.y:\n return False\n \n if self.ur.x <= other.ll.x:\n return False\n \n if self.ur.y <= other.ll.y:\n return False\n \n return True","def contains(self, another_interval):\n if another_interval.left_endpoint < self.left_endpoint:\n return False\n if another_interval.left_endpoint == self.left_endpoint:\n if not self.left_closed and another_interval.left_closed:\n return False\n if another_interval.right_endpoint > self.right_endpoint:\n return False\n if another_interval.right_endpoint == self.right_endpoint:\n if not self.right_closed and another_interval.right_closed:\n return False\n return True","def interval_intersect(b, a, d, c):\n return (d <= a) and (b <= c) # int2 lower <= int1 upper AND int1 lower <= int2 upper","def time_overlap(d1, d2):\n gt1, gt2, vt1, vt2 = parse_date(d1[\"t1\"]), parse_date(d1[\"t2\"]), parse_date(d2[\"t1\"]), parse_date(d2[\"t2\"])\n return (gt1 != vt2) and (vt1 != gt2) and (gt1 <= vt2) and (vt1 <= gt2)","def has_overlap(vevent, start, end):\n event_start = vevent.dtstart.value\n event_end = vevent.dtend.value\n\n assert not is_naive(start), 'start dt is naive'\n assert not is_naive(end), 'end dt is naive'\n assert not is_naive(event_start), 'event_start dt is naive'\n assert not is_naive(event_end), 'event_end dt is naive'\n\n if start <= event_start <= end: # starts today\n return True\n if start <= event_end <= end: # ends today\n return True\n if event_start <= start and end <= event_end: # spans over today\n return True\n return False","def overlaps(self, period):\n\n start_datetime = self.start_time.to_python_datetime()\n end_datetime = self.end_time.to_python_datetime()\n\n return period.start_time < end_datetime and period.end_time > start_datetime","def does_overlap(self, start, stop):\n\n ranges = [list(range(key, self.map[key] + 1)) for key in self.map]\n all_coords = [item for sublist in ranges for item in sublist]\n # removing all_coords implementation until we write some tests\n for i in range(start, stop + 1):\n if i in all_coords:\n return True\n return False","def is_subspan(a: Tuple[int, int], b: Tuple[int, int]) -> bool:\n if a[0] >= b[0] and a[1] <= b[1]:\n return True\n else:\n return False","def overlaps(self, other):\n return (self.right > other.left and self.left < other.right and\n self.top < other.bottom and self.bottom > other.top)","def add_range(self, start, end) -> bool:\n start = _normalize_datetime(start)\n end = _normalize_datetime(end)\n assert end > start\n\n if self._start_time is None:\n self._start_time = start\n\n if start < self._start_time:\n delta = int((self._start_time - start).total_seconds() / 60)\n self._start_time = start\n self._backing_int = self._backing_int << delta\n\n start_idx = self._datetime_to_index(start)\n end_idx = self._datetime_to_index(end)\n idx_range = end_idx - start_idx\n range_mask = ((1 << (idx_range + 1)) - 1) << start_idx\n\n has_overlap = (self._backing_int & range_mask) > 0\n self._backing_int |= range_mask\n return has_overlap","def overlap(l1, l2):\n l1, l2 = list(l1), list(l2)\n l1.sort()\n l2.sort()\n lines = [l1, l2]\n lines.sort(key=lambda x: x[0])\n if lines[0][1] >= lines[1][0]:\n return True\n return False","def overlapping_ranges(\n ranges_1: Sequence[Tuple[int, int]],\n ranges_2: Sequence[Tuple[int, int]],\n) -> List[Tuple[int, int]]:\n return [\n (max(first[0], second[0]), min(first[1], second[1]))\n for first in ranges_1\n for second in ranges_2\n if max(first[0], second[0]) < min(first[1], second[1])\n ]","def __contains__(self, other):\n if isinstance(other, (sppasInterval,\n sppasPoint,\n float, int)) is False:\n raise AnnDataTypeError(other,\n \"sppasInterval, sppasPoint, float, int\")\n\n if isinstance(other, sppasInterval):\n return (self.__begin <= other.get_begin() and\n other.get_end() <= self.__end)\n\n return self.__begin <= other <= self.__end","def overlap(x,y):\n if (x[0]<=y[-1] and x[-1]>y[0]) or (y[0]<=x[-1] and y[-1]>x[0]):\n return 1\n else: return 0","def compute_time_overlap(appointment1, appointment2):\n assert appointment1.date_ == appointment2.date_\n print(\"Checking for time overlap on \\\"{}\\\"...\".\n format(appointment1.date_))\n print(\"Times to check: {}, {}\".\n format(appointment1.time_range_, appointment2.time_range_))\n\n latest_start = max(appointment1.start_time_, appointment2.start_time_)\n earliest_end = min(appointment1.end_time_, appointment2.end_time_)\n\n delta = (earliest_end - latest_start).seconds\n overlap = max(0, delta)\n if overlap == 0:\n print(\"No time overlap.\")\n return False\n\n print(\"\\033[93mFound time overlap.\\033[0m\")\n return True","def is_overlapping(box1, box2):\n if box1[2] <= box2[0]: # If box1 is to the left of box2\n return False\n elif box1[0] >= box2[2]: # If box1 is to the right of box2\n return False\n elif box1[3] <= box2[1]: # If box1 is below box2\n return False\n elif box1[1] >= box2[3]: # If box1 is above box2\n return False\n else:\n return True","def overlaps(self, right: GeoSpatialValue) -> ir.BooleanValue:\n return ops.GeoOverlaps(self, right).to_expr()","def _intersect_interval(self, other):\n interval = Intersection(self.interval, other.interval)\n return interval.inf, interval.sup","def range_overlap(ranges):\n max_left = 0.0\n min_right = 1.0\n for (left, right) in ranges:\n max_left = max(max_left, left)\n min_right = min(min_right, right)\n return (max_left, min_right)","def overlaps(a, b):\n\n dx = a.x - b.x\n dy = a.y - b.y\n try:\n radius = a.radius + b.radius\n except AttributeError:\n radius = getattr(a, 'radius', 0.5) + getattr(b, 'radius', 0.5)\n\n return dx * dx + dy * dy <= radius * radius","def interval_intersect(a, b, c, d):\n if (c <= b) and (a <= d):\n return True\n else:\n return False","def bbox_overlap(bbox_1: Sequence, bbox_2: Sequence) -> bool:\n if (bbox_1[0] > bbox_2[0]) or (bbox_1[1] > bbox_2[1]):\n return False\n if (bbox_1[2] < bbox_2[2]) or (bbox_1[3] < bbox_2[3]):\n return False\n\n return True","def range_matches(self, other):\n return (\n self.begin == other.begin and \n self.end == other.end\n )","def feat_overlap(f1, f2):\n f1start = int(f1[3])\n f1end = int(f1[4])\n f2start = int(f2[3])\n f2end = int(f2[4])\n\n if f1start <= f2end and f1end >= f2start:\n return True\n return False","def check_overlap(self, a, b):\n return utils.is_point_in_circle(b.get_pos(), a.get_pos(), a.radius)","def overlap(line1, line2):\n\tx1, x2 = line1\n\tx3, x4 = line2\n\tonLeft = min(x1, x2) <= min(x3, x4)\n\tif onLeft:\n\t\treturn max(max((x1, x2)) - min((x3, x4)), 0) > 0\n\treturn max(max((x3, x4)) - min((x1, x2)),0) > 0","def isMergeable(int1,int2):\n if set(int1.span)&set(int2.span) or int1.maxval+1==int2.minval:\n return True\n else:\n return False","def find_overlap_range(x1, lenght1, x2, length2):\n\n\n highest_start_point = max(x1, x2)\n lowest_end_point = min(x1 + lenght1, x2 + length2)\n \n if highest_start_point >= lowest_end_point:\n return None\n \n overlap_length = lowest_end_point - highest_start_point\n \n return (highest_start_point, overlap_length)","def test_mergeOverlapping(self):\n\n # test expected behavior for correctly formatted inputs\n int1 = interval('[1,2)')\n int2 = interval('(1,2]')\n int12 = interval('[1,2]')\n merged12 = mergeOverlapping([int1, int2])\n self.assertEqual([int12], merged12)\n int3 = interval('[3,3]')\n int13 = interval('[1,3]')\n intneg1 = interval('[-1,0)')\n int0 = interval('[0,1)')\n intneg13 = interval('[-1,3]')\n self.assertEqual([intneg13], mergeOverlapping([intneg1, int0, int13]))\n self.assertEqual([intneg1, int3], mergeOverlapping([intneg1, int3]))\n self.assertEqual([int13], mergeOverlapping([int12, int3]))\n int4 = interval('(3,4]')\n int58 = interval('[5,8]')\n intnothing = mergeOverlapping([])\n self.assertEqual([], intnothing)\n self.assertEqual([int13, int58], mergeOverlapping([int12, int3, int58]))\n self.assertEqual([int13, int58], mergeOverlapping([int58, int13]))\n self.assertEqual([int13], mergeOverlapping([int1, int2, int3]))\n self.assertEqual([int13], mergeOverlapping([int1, int2, int2, int3, int12]))\n self.assertEqual([int1], mergeOverlapping([int1]))\n\n # test expected behavior for incorrectly formatted inputs\n with self.assertRaises(ValueError):\n int1 = mergeOverlapping([int1, 4])\n with self.assertRaises(ValueError):\n int1 = mergeOverlapping([3, int1])\n with self.assertRaises(ValueError):\n int1 = mergeOverlapping([3, \"not an interval\"])\n with self.assertRaises(ValueError):\n int1 = mergeOverlapping([3, \"[1,3]\"])\n with self.assertRaises(ValueError):\n int1 = mergeOverlapping([[], \"\"])\n with self.assertRaises(ValueError):\n int1 = mergeOverlapping([[12, \"hi\"], \"interval\"])\n with self.assertRaises(ValueError):\n int1 = mergeOverlapping([int1, \"\"])\n with self.assertRaises(ValueError):\n int1 = mergeOverlapping([[], int2])\n print(\"merge overlapping list test complete\")","def covers_overlaps(self, bounds):\n bounds = tuple(float(b) for b in bounds)\n return self.numba_rtree.covers_overlaps(bounds)","def InIntervalUnion(i, intervals):\n if len(intervals) == 0:\n return True\n accept = False\n for interval in intervals:\n assert(len(interval) == 2)\n if (interval[0] <= i) and ((i <= interval[1]) or (interval[1] == 0)):\n accept = True\n return accept","def is_overlap_sorted_values(v1, v2, w1, w2):\r\n if (v2 < w1) or (v1 > w2):\r\n return False\r\n else:\r\n return True","def rOverlap (x1, y1, w1, h1, x2, y2, w2, h2):\n if x1<=x2<=(x1+w1) or y1<=y2<=(y1+h1):\n return True\n elif x1<=(x2+w2)<=(x1+w1):\n return True\n else:\n return False","def overlaps(self, region):\n region = as_region(region)\n\n if region.chromosome != self.chromosome:\n return False\n\n if self.end is None or region.start is None or region.start <= self.end:\n if self.start is None or region.end is None or region.end >= self.start:\n return True\n return False","def overlaps(self, other):\n pass","def is_overlapping(segment_time, previous_segments):\n \n segment_start, segment_end = segment_time\n overlap = False\n for previous_start, previous_end in previous_segments:\n if previous_start<=segment_start<=previous_end or previous_start<=segment_end<=previous_end:\n overlap = True\n\n return overlap","def get_overlap(a, b):\n return max(0, min(a[1], b[1]) - max(a[0], b[0]))","def contains(self, interval):\n first, last = self._intersect(interval)\n return first != last","def upper_contains_interval(self, other):\n if not self.contains_interval(other):\n return False\n for (i, j) in other.increasing_cover_relations():\n if not self.le(i, j):\n return False\n return True","def interval_intersect(a, b, c, d):\r\n return (c <= b) and (a <= d)","def _intersect(self, interval):\n first = self.intervals.bisect_left(interval)\n last = first\n while first > 0 and \\\n self.intervals[first - 1].upper > interval.lower:\n first -= 1\n while last < len(self.intervals) and \\\n self.intervals[last].lower < interval.upper:\n last += 1\n return first, last","def contains(self, other: \"Availability\") -> bool:\n return self.start <= other.start and self.end >= other.end","def overlaps(self, other):\n\n isOverlaps = False\n\n if self.ipv4 is not None:\n isOverlaps = self.ipv4.overlaps(other.ipv4) \n\n if isOverlaps is False:\n if self.ipv6 is not None:\n isOverlaps = self.ipv6.overlaps(other.ipv6) \n\n return isOverlaps","def overlap_rect(rec1, rec2):\n # true if rec2 is left of rec1\n a = rec2[2] <= rec1[0]\n \n # true if rec2 is right of rec1\n b = rec1[2] <= rec2[0]\n\n # true if rec2 is below rec1\n c = rec2[3] <= rec1[1]\n\n # true if rec2 is above rec1\n d = rec1[3] <= rec2[1]\n\n return not (a or b or c or d)","def have_overlap(self,\n entry1: Union[Annotation, int],\n entry2: Union[Annotation, int]) -> bool:\n entry1_: Annotation = self._entry_index[\n entry1] if isinstance(entry1, (int, np.integer)) else entry1\n entry2_: Annotation = self._entry_index[\n entry2] if isinstance(entry2, (int, np.integer)) else entry1\n\n if not isinstance(entry1_, Annotation):\n raise TypeError(f\"'entry1' should be an instance of Annotation,\"\n f\" but get {type(entry1)}\")\n\n if not isinstance(entry2_, Annotation):\n raise TypeError(f\"'entry2' should be an instance of Annotation,\"\n f\" but get {type(entry2)}\")\n\n return not (entry1_.span.begin >= entry2_.span.end or\n entry1_.span.end <= entry2_.span.begin)","def contains_interval(self, other):\n if other.size() != self.size():\n return False\n for (i, j) in self._cover_relations:\n if not other.le(i, j):\n return False\n return True","def test_overlap(self):\r\n rect1 = Rectangle(10, 20, 30, 40)\r\n rect2 = Rectangle(50, 60, 70, 80)\r\n\r\n # overlap should be commutative\r\n assert not rect1.overlap_with(rect2)\r\n assert not rect2.overlap_with(rect1)\r\n assert not Rectangle.overlap(rect1, rect2)\r\n assert not Rectangle.overlap(rect2, rect1)\r\n\r\n rect1 = Rectangle(-10, -20, 10, 60)\r\n rect2 = Rectangle(0, 50, 100, 200)\r\n assert rect1.overlap_with(rect2)\r\n assert rect2.overlap_with(rect1)\r\n assert Rectangle.overlap(rect1, rect2)\r\n assert Rectangle.overlap(rect2, rect1)\r\n\r\n # rectangles with only same boarder are not considered overlapped\r\n rect1 = Rectangle(-30, -10, -20, 0)\r\n rect2 = Rectangle(-20, -5, 30, 20)\r\n rect3 = Rectangle(-40, 0, 30, 20)\r\n assert not rect1.overlap_with(rect2)\r\n assert not rect1.overlap_with(rect3)\r\n assert not Rectangle.overlap(rect2, rect1)\r\n assert not Rectangle.overlap(rect3, rect1)","def overlap(id1, id2, th):\n first = [int(pos) for pos in id1[:-2].replace('-', ':').split(':')[1:]]\n second = [int(pos) for pos in id2[:-2].replace('-', ':').split(':')[1:]]\n if all([abs(first[0] - second[0]) <= th,\n abs(first[2] - second[2]) <= th,\n abs(first[5] - second[5]) <= th,\n first[1] == second[1],\n first[4] == second[4]]):\n return True\n else:\n return False","def check_overlap(lorentz_params_1, lorentz_params_2):\n if lorentz_params_1 is None or lorentz_params_2 is None:\n return False\n [low_lorentz, high_lorentz] = sorted(\n [lorentz_params_1, lorentz_params_2], key=lambda l: l[1])\n low_fit_range = find_single_fit_range(low_lorentz)\n high_fit_range = find_single_fit_range(high_lorentz)\n return low_fit_range[2] > high_fit_range[1]","def can_attend_meetings(intervals: List[List[int]]) -> bool:\n intervals.sort()\n for i in range(1, len(intervals)):\n if intervals[i][0] < intervals[i - 1][1]:\n return False\n return True","def overlap(id1, id2, th):\n first = [int(pos) for pos in id1[:-2].replace('-', ':').split(':')[1:]]\n second = [int(pos) for pos in id2[:-2].replace('-', ':').split(':')[1:]]\n if all([abs(first[0] - second[0]) <= th,\n abs(first[2] - second[2]) <= th,\n abs(first[3] - second[3]) <= th,\n first[1] == second[1],\n first[4] == second[4]]):\n return True\n else:\n return False","def _is_overlapping(last_tf_end, cur_tf_start, tolerance_months=0):\n return last_tf_end <= _substract_months(cur_tf_start, months=tolerance_months)","def is_overlapping_lane_seq(lane_seq1: Sequence[int], lane_seq2: Sequence[int]) -> bool:\n\n if lane_seq2[0] in lane_seq1[1:] and lane_seq1[-1] in lane_seq2[:-1]:\n return True\n elif set(lane_seq2) <= set(lane_seq1):\n return True\n return False","def overlaps(self, that):\n if (not isinstance(that, Annotation)):\n raise ValueError(\"Argument for intersects should be an annotation\")\n\n if (self.bbox.xmin >= that.bbox.xmax or that.bbox.xmin >= self.bbox.xmax):\n return False\n\n # the coordinates are inverted, so y0 is larger than y1\n if (self.bbox.ymin >= that.bbox.ymax or that.bbox.ymin >= self.bbox.ymax):\n return False\n\n return True","def overlap(t1, t2):\n t1 = dict(min=np.min(t1), max=np.max(t1))\n t2 = dict(min=np.min(t2), max=np.max(t2))\n for t in (t1, t2):\n t['dur'] = t['max'] - t['min']\n\n # Ensure t1 min < t2 min\n if t2['min'] < t1['min']:\n print('t2 starts earlier')\n t1, t2 = t2, t1\n \n # var names wrt t2\n min_inside = t2['min'] >= t1['min'] and t2['min'] <= t1['max']\n max_inside = t2['max'] <= t1['max']\n if min_inside and max_inside:\n # t2 completely contained by t1\n return (t2['min'], t2['max'])\n elif min_inside:\n # t2 partially contained by t1\n return (t2['min'], t1['max'])\n else:\n # no overlap\n return (None, None)","def is_between(value, start, end, including_start=False, including_end=False):\n if not including_start and not including_end: # not include both start and end\n if (start < value < end):\n return True\n elif (start > end) and (start < value <= (2**m - 1) or 0 <= value < end):\n return True\n elif (start == end) and (value != start):\n return True\n return False\n elif not including_start and including_end: # include end but not the start\n if value == end:\n return True\n elif (start < value <= end):\n return True\n elif (start > end) and ((start < value <= (2**m - 1)) or (0 <= value <= end)):\n return True\n elif (start == end) and (value != start):\n return True\n return False\n elif including_start and not including_end: # include start but not the end\n if value == start:\n return True\n elif (start <= value < end):\n return True\n elif (start > end) and (start <= value <= (2**m - 1) or 0 <= value < end):\n return True\n elif (start == end) and (value != end):\n return False\n return False\n else: # include both start and end\n if (start <= value <= end):\n return True\n elif (start > end) and (start <= value <= (2**m - 1) or 0 <= value <= end):\n return True\n elif start == end:\n return True\n return False","def is_overlap(box_1, box_2, iou_th):\n return box_1.iou(box_2) > iou_th","def lower_contains_interval(self, other):\n if not self.contains_interval(other):\n return False\n for (i, j) in other.decreasing_cover_relations():\n if not self.le(i, j):\n return False\n return True","def overlap(id1, id2, th):\n first = [int(pos) for pos in id1[:-2].replace('-', ':').split(':')[1:]]\n second = [int(pos) for pos in id2[:-2].replace('-', ':').split(':')[1:]]\n if all([abs(first[0] - second[0]) <= th,\n abs(first[3] - second[3]) <= th,\n first[1] == second[1],\n first[2] == second[2],\n first[5] == second[5],\n first[6] == second[6]]):\n return True\n else:\n return False","def _bbox_overlap(self, other):\n reg0 = self.bbox\n reg1 = other.bbox\n return (reg0[0] <= reg1[2] and reg1[0] <= reg0[2] and\n reg0[1] <= reg1[3] and reg1[1] <= reg0[3])","def overlap(id1, id2, th):\n first = [int(pos) for pos in id1[:-2].replace('-', ':').split(':')[1:]]\n second = [int(pos) for pos in id2[:-2].replace('-', ':').split(':')[1:]]\n if all([abs(first[0] - second[0]) <= th,\n first[1] == second[1],\n first[2] == second[2],\n abs(first[3] - second[3]) <= th]):\n return True\n else:\n return False","def doesNotOverlap( self, other):\n return not self.overlaps( other)"],"string":"[\n \"def dates_intervals_are_overlapped(start_1, end_1, start_2, end_2):\\n return end_1 >= start_2 and end_2 >= start_1\",\n \"def overlap(start1, end1, start2, end2):\\n return not (end1 < start2 or end2 < start1)\",\n \"def overlap(t1start, t1end, t2start, t2end):\\n\\n return (t1start <= t2start <= t1end) or (t2start <= t1start <= t2end)\",\n \"def overlap(range1, range2):\\n if range1[0] <= range2[1] and range2[0] <= range1[1]:\\n return True\\n return False\",\n \"def overlap(start_idx1, end_idx1, start_idx2, end_idx2):\\n head = min(end_idx1, end_idx2)\\n tail = max(start_idx1, start_idx2)\\n return head >= tail\",\n \"def intersect(start1, stop1, start2, stop2):\\n\\tassert isinstance(start1, int)\\n\\tassert isinstance(stop2, int)\\n\\tassert isinstance(start2, int)\\n\\tassert isinstance(stop2, int)\\n\\tassert start1 <= stop1\\n\\tassert start2 <= stop2\\n\\t\\n\\t# if interval 1 is completely to the left of interval 2\\n\\tif stop1 < start2:\\n\\t\\treturn False\\n\\t\\n\\t# if interval 1 is completely to the right of interval2\\n\\tif stop2 < start1:\\n\\t\\treturn False\\n\\t\\t\\n\\treturn True\",\n \"def overlaps(self, begin, end=None):\\n if end is not None:\\n # An overlap means that some C exists that is inside both ranges:\\n # begin <= C < end\\n # and \\n # self.begin <= C < self.end\\n # See https://stackoverflow.com/questions/3269434/whats-the-most-efficient-way-to-test-two-integer-ranges-for-overlap/3269471#3269471\\n return begin < self.end and end > self.begin\\n try:\\n return self.overlaps(begin.begin, begin.end)\\n except:\\n return self.contains_point(begin)\",\n \"def overlaps(self, other):\\n return self.start <= other.end and self.end >= other.start\",\n \"def contains_interval(self, other):\\n return (\\n self.begin <= other.begin and\\n self.end >= other.end\\n )\",\n \"def overlaps(self, other):\\n\\n if self.start.equal(other.start) or self.stop.equal(other.stop):\\n return True\\n elif self.start.before(other.start) and self.stop.after(other.start):\\n return True\\n elif other.stop.after(self.start) and other.stop.before(self.stop):\\n return True\\n else:\\n return False\",\n \"def iOverlap (a1, a2, b1, b2):\\n if b1<=a1<=b2 or b1<=a2<=b2 or a1<=b1<=a2 or a1<=b2<=a2:\\n return True\\n elif a1>a2 or b1>b2:\\n return False\\n else:\\n return False\",\n \"def span_overlap(a: Tuple[int, int], b: Tuple[int, int]) -> bool:\\n return not (a[0] > b[1] or a[1] < b[0])\",\n \"def overlap(component1, component2):\\n if component1[0].start <= component2[0].stop and component2[0].start <= component1[0].stop:\\n if component1[1].start <= component2[1].stop and component2[1].start <= component1[1].stop:\\n return True\\n return False\",\n \"def overlaps(x1, x2, y1, y2):\\n\\n return x1 <= y2 and y1 <= x2\",\n \"def overlap(a, b):\\n return not(a[2]<=b[0] or a[3]<=b[1] or a[0]>=b[2] or a[1]>=b[3])\",\n \"def overlap(a: Pos, b: Pos, exact: bool = False) -> bool:\\n if a == b:\\n return True\\n elif exact:\\n return False\\n s0, e0 = a\\n s1, e1 = b\\n if in_interval(s1, s0, e0):\\n return True\\n if in_interval(e1, s0, e0):\\n return True\\n if in_interval(s0, s1, e1):\\n return True\\n if in_interval(e0, s1, e1):\\n return True\\n return False\",\n \"def overlaps(a, b, **kwargs):\\n return lib.overlaps(a, b, **kwargs)\",\n \"def check_interval_bounds(begin, end):\\n if begin.get_midpoint() >= end.get_midpoint():\\n return False\\n\\n if begin.get_radius() is not None and end.get_radius() is not None:\\n if begin.get_midpoint() - begin.get_radius() > \\\\\\n end.get_midpoint() - end.get_radius():\\n return False\\n\\n return True\",\n \"def check_overlap(a, b):\\n if a[0] >= b[2] or a[1] >= b[3] or a[2] <= b[0] or a[3] <= b[1]:\\n return False\\n return True\",\n \"def _range_contains(self, a, b):\\n\\t\\treturn b[0] >= a[0] and b[-1] <= a[-1]\",\n \"def overlaps(self, other):\\n return _binary_op(arctern.ST_Overlaps, self, other).astype(bool, copy=False)\",\n \"def overlap(p1: Tuple, p2: Tuple) -> bool:\\n if (p2[1] - p1[0]) * (p2[0] - p1[1]) <= 0:\\n return True\\n else:\\n return False\",\n \"def overlaps(self, other): # -> bool:\\n ...\",\n \"def overlap(self, other):\\n\\t\\toverlap = self.contains(other.startX, other.startY) or \\\\\\n\\t\\t\\tself.contains(other.startX, other.endY) or \\\\\\n\\t\\t\\tself.contains(other.endX, other.startY) or \\\\\\n\\t\\t\\tself.contains(other.endX, other.endY)\\n\\n\\t\\tintersectY1 = self.startY <= other.startY <= self.endY and \\\\\\n\\t\\t\\tself.startY <= other.endY <= self.endY and \\\\\\n\\t\\t\\t(other.startX <= self.startX <= other.endX or \\\\\\n\\t\\t\\tother.startX <= self.endX <= other.endX)\\n\\n\\t\\tintersectY2 = other.startY <= self.startY <= other.endY and \\\\\\n\\t\\t\\t other.startY <= self.endY <= other.endY and \\\\\\n\\t\\t\\t (self.startX <= other.startX <= self.endX or \\\\\\n\\t\\t\\t self.startX <= other.endX <= self.endX)\\n\\n\\t\\tintersectY = intersectY1 or intersectY2\\n\\n\\t\\tintersectX1 = self.startX <= other.startX <= self.endY and \\\\\\n\\t\\t\\tself.startX <= other.endX <= self.endX and \\\\\\n\\t\\t (other.startY <= self.startY <= other.endY or \\\\\\n\\t\\t\\tother.startY <= self.endY <= other.endY)\\n\\n\\t\\tintersectX2 = other.startX <= self.startX <= other.endX and \\\\\\n\\t\\t\\tother.startX <= self.endX <= other.endX and \\\\\\n\\t\\t (self.startY <= other.startY <= self.endY or \\\\\\n\\t\\t\\tself.startY <= other.endY <= self.endY)\\n\\n\\t\\tintersectX = intersectX1 or intersectX2\\n\\n\\t\\treturn overlap or intersectX or intersectY\",\n \"def covers(self, other):\\n return self._start <= other._start and self._end >= other._end\",\n \"def do_overlap(r1, r2):\\n r1_s, r1_e = r1\\n r2_s, r2_e = r2\\n\\n return r1_s <= r2_s <= r1_e or r2_s <= r1_s <= r2_e\",\n \"def overlaps(self, other: \\\"Availability\\\", strict: bool) -> bool:\\n\\n if not isinstance(other, Availability):\\n raise Exception(\\\"Please provide an Availability object\\\")\\n\\n if strict:\\n return (\\n (self.start <= other.start < self.end)\\n or (self.start < other.end <= self.end)\\n or (other.start <= self.start < other.end)\\n or (other.start < self.end <= other.end)\\n )\\n return (\\n (self.start <= other.start <= self.end)\\n or (self.start <= other.end <= self.end)\\n or (other.start <= self.start <= other.end)\\n or (other.start <= self.end <= other.end)\\n )\",\n \"def do_intervals_differ((x1, y1), (x2, y2)):\\n\\n assert y1 >= x1 and y2 >= x2\\n return y1 < x2 or y2 < x1\",\n \"def detect_overlap_1d(first, first_length, second, second_length):\\n first_end = first + first_length - 1\\n second_end = second + second_length - 1\\n return second_end >= first and first_end >= second\",\n \"def _overlap(x1, w1, x2, w2):\\r\\n if x1+w1 < x2-w2: return False\\r\\n if x1-w1 > x2+w2: return False\\r\\n\\r\\n return True\",\n \"def overlaps(interval,intervals):\\n return [x for x in intervals if interval.overlaps(x)]\",\n \"def range_overlap(range1, range2):\\n return range(max(range1[0], range2[0]), min(range1[1], range2[1]))\",\n \"def intersects(self, other: \\\"DateRange\\\") -> bool:\\n return (\\n self.end_date - other.start_date\\n >= timedelta(0)\\n >= self.start_date - other.end_date\\n )\",\n \"def is_overlap(bb1, bb2):\\n l1, t1, r1, b1 = bb1['x'], bb1['y'], bb1['x']+bb1['w'], bb1['y']+bb1['h']\\n l2, t2, r2, b2 = bb2['x'], bb2['y'], bb2['x']+bb2['w'], bb2['y']+bb2['h']\\n\\n if r1 > l2 and r2 > l1 and b2 > t1 and b1 > t2:\\n return True\\n else:\\n return False\",\n \"def in_range(cls, lhs, rhs):\\n return rhs[0] <= lhs <= rhs[1]\",\n \"def overlaps(self,b):\\n if b.chr != self.chr :return False\\n if (self.start <= b.start and b.start <=self.end) or (self.start >= b.start and self.start <= b.end):\\n return True\\n else:\\n return False\",\n \"def overlaps(self, chrom, start, end, strand=None):\\n if (self.chrom != chrom \\n or min(self.end, end) - max(self.start, start) <= 0 \\n or (strand is not None and self.strand != strand)): \\n return False\\n return True\",\n \"def overlap(indices1, indices2):\\n assert (len(indices1) == 2 and len(indices2) == 2)\\n indices1 = sorted(indices1)\\n indices2 = sorted(indices2)\\n if (indices2[0] <= indices1[0] <= indices2[1]) or \\\\\\n (indices2[0] <= indices1[1] <= indices2[1]) or \\\\\\n (indices1[0] <= indices2[0] <= indices1[1]) or \\\\\\n (indices1[0] <= indices2[1] <= indices1[1]):\\n return True\\n else:\\n return False\",\n \"def overlaps(self, other):\\n\\n if self.ll.x >= other.ur.x:\\n return False\\n \\n if self.ll.y >= other.ur.y:\\n return False\\n \\n if self.ur.x <= other.ll.x:\\n return False\\n \\n if self.ur.y <= other.ll.y:\\n return False\\n \\n return True\",\n \"def contains(self, another_interval):\\n if another_interval.left_endpoint < self.left_endpoint:\\n return False\\n if another_interval.left_endpoint == self.left_endpoint:\\n if not self.left_closed and another_interval.left_closed:\\n return False\\n if another_interval.right_endpoint > self.right_endpoint:\\n return False\\n if another_interval.right_endpoint == self.right_endpoint:\\n if not self.right_closed and another_interval.right_closed:\\n return False\\n return True\",\n \"def interval_intersect(b, a, d, c):\\n return (d <= a) and (b <= c) # int2 lower <= int1 upper AND int1 lower <= int2 upper\",\n \"def time_overlap(d1, d2):\\n gt1, gt2, vt1, vt2 = parse_date(d1[\\\"t1\\\"]), parse_date(d1[\\\"t2\\\"]), parse_date(d2[\\\"t1\\\"]), parse_date(d2[\\\"t2\\\"])\\n return (gt1 != vt2) and (vt1 != gt2) and (gt1 <= vt2) and (vt1 <= gt2)\",\n \"def has_overlap(vevent, start, end):\\n event_start = vevent.dtstart.value\\n event_end = vevent.dtend.value\\n\\n assert not is_naive(start), 'start dt is naive'\\n assert not is_naive(end), 'end dt is naive'\\n assert not is_naive(event_start), 'event_start dt is naive'\\n assert not is_naive(event_end), 'event_end dt is naive'\\n\\n if start <= event_start <= end: # starts today\\n return True\\n if start <= event_end <= end: # ends today\\n return True\\n if event_start <= start and end <= event_end: # spans over today\\n return True\\n return False\",\n \"def overlaps(self, period):\\n\\n start_datetime = self.start_time.to_python_datetime()\\n end_datetime = self.end_time.to_python_datetime()\\n\\n return period.start_time < end_datetime and period.end_time > start_datetime\",\n \"def does_overlap(self, start, stop):\\n\\n ranges = [list(range(key, self.map[key] + 1)) for key in self.map]\\n all_coords = [item for sublist in ranges for item in sublist]\\n # removing all_coords implementation until we write some tests\\n for i in range(start, stop + 1):\\n if i in all_coords:\\n return True\\n return False\",\n \"def is_subspan(a: Tuple[int, int], b: Tuple[int, int]) -> bool:\\n if a[0] >= b[0] and a[1] <= b[1]:\\n return True\\n else:\\n return False\",\n \"def overlaps(self, other):\\n return (self.right > other.left and self.left < other.right and\\n self.top < other.bottom and self.bottom > other.top)\",\n \"def add_range(self, start, end) -> bool:\\n start = _normalize_datetime(start)\\n end = _normalize_datetime(end)\\n assert end > start\\n\\n if self._start_time is None:\\n self._start_time = start\\n\\n if start < self._start_time:\\n delta = int((self._start_time - start).total_seconds() / 60)\\n self._start_time = start\\n self._backing_int = self._backing_int << delta\\n\\n start_idx = self._datetime_to_index(start)\\n end_idx = self._datetime_to_index(end)\\n idx_range = end_idx - start_idx\\n range_mask = ((1 << (idx_range + 1)) - 1) << start_idx\\n\\n has_overlap = (self._backing_int & range_mask) > 0\\n self._backing_int |= range_mask\\n return has_overlap\",\n \"def overlap(l1, l2):\\n l1, l2 = list(l1), list(l2)\\n l1.sort()\\n l2.sort()\\n lines = [l1, l2]\\n lines.sort(key=lambda x: x[0])\\n if lines[0][1] >= lines[1][0]:\\n return True\\n return False\",\n \"def overlapping_ranges(\\n ranges_1: Sequence[Tuple[int, int]],\\n ranges_2: Sequence[Tuple[int, int]],\\n) -> List[Tuple[int, int]]:\\n return [\\n (max(first[0], second[0]), min(first[1], second[1]))\\n for first in ranges_1\\n for second in ranges_2\\n if max(first[0], second[0]) < min(first[1], second[1])\\n ]\",\n \"def __contains__(self, other):\\n if isinstance(other, (sppasInterval,\\n sppasPoint,\\n float, int)) is False:\\n raise AnnDataTypeError(other,\\n \\\"sppasInterval, sppasPoint, float, int\\\")\\n\\n if isinstance(other, sppasInterval):\\n return (self.__begin <= other.get_begin() and\\n other.get_end() <= self.__end)\\n\\n return self.__begin <= other <= self.__end\",\n \"def overlap(x,y):\\n if (x[0]<=y[-1] and x[-1]>y[0]) or (y[0]<=x[-1] and y[-1]>x[0]):\\n return 1\\n else: return 0\",\n \"def compute_time_overlap(appointment1, appointment2):\\n assert appointment1.date_ == appointment2.date_\\n print(\\\"Checking for time overlap on \\\\\\\"{}\\\\\\\"...\\\".\\n format(appointment1.date_))\\n print(\\\"Times to check: {}, {}\\\".\\n format(appointment1.time_range_, appointment2.time_range_))\\n\\n latest_start = max(appointment1.start_time_, appointment2.start_time_)\\n earliest_end = min(appointment1.end_time_, appointment2.end_time_)\\n\\n delta = (earliest_end - latest_start).seconds\\n overlap = max(0, delta)\\n if overlap == 0:\\n print(\\\"No time overlap.\\\")\\n return False\\n\\n print(\\\"\\\\033[93mFound time overlap.\\\\033[0m\\\")\\n return True\",\n \"def is_overlapping(box1, box2):\\n if box1[2] <= box2[0]: # If box1 is to the left of box2\\n return False\\n elif box1[0] >= box2[2]: # If box1 is to the right of box2\\n return False\\n elif box1[3] <= box2[1]: # If box1 is below box2\\n return False\\n elif box1[1] >= box2[3]: # If box1 is above box2\\n return False\\n else:\\n return True\",\n \"def overlaps(self, right: GeoSpatialValue) -> ir.BooleanValue:\\n return ops.GeoOverlaps(self, right).to_expr()\",\n \"def _intersect_interval(self, other):\\n interval = Intersection(self.interval, other.interval)\\n return interval.inf, interval.sup\",\n \"def range_overlap(ranges):\\n max_left = 0.0\\n min_right = 1.0\\n for (left, right) in ranges:\\n max_left = max(max_left, left)\\n min_right = min(min_right, right)\\n return (max_left, min_right)\",\n \"def overlaps(a, b):\\n\\n dx = a.x - b.x\\n dy = a.y - b.y\\n try:\\n radius = a.radius + b.radius\\n except AttributeError:\\n radius = getattr(a, 'radius', 0.5) + getattr(b, 'radius', 0.5)\\n\\n return dx * dx + dy * dy <= radius * radius\",\n \"def interval_intersect(a, b, c, d):\\n if (c <= b) and (a <= d):\\n return True\\n else:\\n return False\",\n \"def bbox_overlap(bbox_1: Sequence, bbox_2: Sequence) -> bool:\\n if (bbox_1[0] > bbox_2[0]) or (bbox_1[1] > bbox_2[1]):\\n return False\\n if (bbox_1[2] < bbox_2[2]) or (bbox_1[3] < bbox_2[3]):\\n return False\\n\\n return True\",\n \"def range_matches(self, other):\\n return (\\n self.begin == other.begin and \\n self.end == other.end\\n )\",\n \"def feat_overlap(f1, f2):\\n f1start = int(f1[3])\\n f1end = int(f1[4])\\n f2start = int(f2[3])\\n f2end = int(f2[4])\\n\\n if f1start <= f2end and f1end >= f2start:\\n return True\\n return False\",\n \"def check_overlap(self, a, b):\\n return utils.is_point_in_circle(b.get_pos(), a.get_pos(), a.radius)\",\n \"def overlap(line1, line2):\\n\\tx1, x2 = line1\\n\\tx3, x4 = line2\\n\\tonLeft = min(x1, x2) <= min(x3, x4)\\n\\tif onLeft:\\n\\t\\treturn max(max((x1, x2)) - min((x3, x4)), 0) > 0\\n\\treturn max(max((x3, x4)) - min((x1, x2)),0) > 0\",\n \"def isMergeable(int1,int2):\\n if set(int1.span)&set(int2.span) or int1.maxval+1==int2.minval:\\n return True\\n else:\\n return False\",\n \"def find_overlap_range(x1, lenght1, x2, length2):\\n\\n\\n highest_start_point = max(x1, x2)\\n lowest_end_point = min(x1 + lenght1, x2 + length2)\\n \\n if highest_start_point >= lowest_end_point:\\n return None\\n \\n overlap_length = lowest_end_point - highest_start_point\\n \\n return (highest_start_point, overlap_length)\",\n \"def test_mergeOverlapping(self):\\n\\n # test expected behavior for correctly formatted inputs\\n int1 = interval('[1,2)')\\n int2 = interval('(1,2]')\\n int12 = interval('[1,2]')\\n merged12 = mergeOverlapping([int1, int2])\\n self.assertEqual([int12], merged12)\\n int3 = interval('[3,3]')\\n int13 = interval('[1,3]')\\n intneg1 = interval('[-1,0)')\\n int0 = interval('[0,1)')\\n intneg13 = interval('[-1,3]')\\n self.assertEqual([intneg13], mergeOverlapping([intneg1, int0, int13]))\\n self.assertEqual([intneg1, int3], mergeOverlapping([intneg1, int3]))\\n self.assertEqual([int13], mergeOverlapping([int12, int3]))\\n int4 = interval('(3,4]')\\n int58 = interval('[5,8]')\\n intnothing = mergeOverlapping([])\\n self.assertEqual([], intnothing)\\n self.assertEqual([int13, int58], mergeOverlapping([int12, int3, int58]))\\n self.assertEqual([int13, int58], mergeOverlapping([int58, int13]))\\n self.assertEqual([int13], mergeOverlapping([int1, int2, int3]))\\n self.assertEqual([int13], mergeOverlapping([int1, int2, int2, int3, int12]))\\n self.assertEqual([int1], mergeOverlapping([int1]))\\n\\n # test expected behavior for incorrectly formatted inputs\\n with self.assertRaises(ValueError):\\n int1 = mergeOverlapping([int1, 4])\\n with self.assertRaises(ValueError):\\n int1 = mergeOverlapping([3, int1])\\n with self.assertRaises(ValueError):\\n int1 = mergeOverlapping([3, \\\"not an interval\\\"])\\n with self.assertRaises(ValueError):\\n int1 = mergeOverlapping([3, \\\"[1,3]\\\"])\\n with self.assertRaises(ValueError):\\n int1 = mergeOverlapping([[], \\\"\\\"])\\n with self.assertRaises(ValueError):\\n int1 = mergeOverlapping([[12, \\\"hi\\\"], \\\"interval\\\"])\\n with self.assertRaises(ValueError):\\n int1 = mergeOverlapping([int1, \\\"\\\"])\\n with self.assertRaises(ValueError):\\n int1 = mergeOverlapping([[], int2])\\n print(\\\"merge overlapping list test complete\\\")\",\n \"def covers_overlaps(self, bounds):\\n bounds = tuple(float(b) for b in bounds)\\n return self.numba_rtree.covers_overlaps(bounds)\",\n \"def InIntervalUnion(i, intervals):\\n if len(intervals) == 0:\\n return True\\n accept = False\\n for interval in intervals:\\n assert(len(interval) == 2)\\n if (interval[0] <= i) and ((i <= interval[1]) or (interval[1] == 0)):\\n accept = True\\n return accept\",\n \"def is_overlap_sorted_values(v1, v2, w1, w2):\\r\\n if (v2 < w1) or (v1 > w2):\\r\\n return False\\r\\n else:\\r\\n return True\",\n \"def rOverlap (x1, y1, w1, h1, x2, y2, w2, h2):\\n if x1<=x2<=(x1+w1) or y1<=y2<=(y1+h1):\\n return True\\n elif x1<=(x2+w2)<=(x1+w1):\\n return True\\n else:\\n return False\",\n \"def overlaps(self, region):\\n region = as_region(region)\\n\\n if region.chromosome != self.chromosome:\\n return False\\n\\n if self.end is None or region.start is None or region.start <= self.end:\\n if self.start is None or region.end is None or region.end >= self.start:\\n return True\\n return False\",\n \"def overlaps(self, other):\\n pass\",\n \"def is_overlapping(segment_time, previous_segments):\\n \\n segment_start, segment_end = segment_time\\n overlap = False\\n for previous_start, previous_end in previous_segments:\\n if previous_start<=segment_start<=previous_end or previous_start<=segment_end<=previous_end:\\n overlap = True\\n\\n return overlap\",\n \"def get_overlap(a, b):\\n return max(0, min(a[1], b[1]) - max(a[0], b[0]))\",\n \"def contains(self, interval):\\n first, last = self._intersect(interval)\\n return first != last\",\n \"def upper_contains_interval(self, other):\\n if not self.contains_interval(other):\\n return False\\n for (i, j) in other.increasing_cover_relations():\\n if not self.le(i, j):\\n return False\\n return True\",\n \"def interval_intersect(a, b, c, d):\\r\\n return (c <= b) and (a <= d)\",\n \"def _intersect(self, interval):\\n first = self.intervals.bisect_left(interval)\\n last = first\\n while first > 0 and \\\\\\n self.intervals[first - 1].upper > interval.lower:\\n first -= 1\\n while last < len(self.intervals) and \\\\\\n self.intervals[last].lower < interval.upper:\\n last += 1\\n return first, last\",\n \"def contains(self, other: \\\"Availability\\\") -> bool:\\n return self.start <= other.start and self.end >= other.end\",\n \"def overlaps(self, other):\\n\\n isOverlaps = False\\n\\n if self.ipv4 is not None:\\n isOverlaps = self.ipv4.overlaps(other.ipv4) \\n\\n if isOverlaps is False:\\n if self.ipv6 is not None:\\n isOverlaps = self.ipv6.overlaps(other.ipv6) \\n\\n return isOverlaps\",\n \"def overlap_rect(rec1, rec2):\\n # true if rec2 is left of rec1\\n a = rec2[2] <= rec1[0]\\n \\n # true if rec2 is right of rec1\\n b = rec1[2] <= rec2[0]\\n\\n # true if rec2 is below rec1\\n c = rec2[3] <= rec1[1]\\n\\n # true if rec2 is above rec1\\n d = rec1[3] <= rec2[1]\\n\\n return not (a or b or c or d)\",\n \"def have_overlap(self,\\n entry1: Union[Annotation, int],\\n entry2: Union[Annotation, int]) -> bool:\\n entry1_: Annotation = self._entry_index[\\n entry1] if isinstance(entry1, (int, np.integer)) else entry1\\n entry2_: Annotation = self._entry_index[\\n entry2] if isinstance(entry2, (int, np.integer)) else entry1\\n\\n if not isinstance(entry1_, Annotation):\\n raise TypeError(f\\\"'entry1' should be an instance of Annotation,\\\"\\n f\\\" but get {type(entry1)}\\\")\\n\\n if not isinstance(entry2_, Annotation):\\n raise TypeError(f\\\"'entry2' should be an instance of Annotation,\\\"\\n f\\\" but get {type(entry2)}\\\")\\n\\n return not (entry1_.span.begin >= entry2_.span.end or\\n entry1_.span.end <= entry2_.span.begin)\",\n \"def contains_interval(self, other):\\n if other.size() != self.size():\\n return False\\n for (i, j) in self._cover_relations:\\n if not other.le(i, j):\\n return False\\n return True\",\n \"def test_overlap(self):\\r\\n rect1 = Rectangle(10, 20, 30, 40)\\r\\n rect2 = Rectangle(50, 60, 70, 80)\\r\\n\\r\\n # overlap should be commutative\\r\\n assert not rect1.overlap_with(rect2)\\r\\n assert not rect2.overlap_with(rect1)\\r\\n assert not Rectangle.overlap(rect1, rect2)\\r\\n assert not Rectangle.overlap(rect2, rect1)\\r\\n\\r\\n rect1 = Rectangle(-10, -20, 10, 60)\\r\\n rect2 = Rectangle(0, 50, 100, 200)\\r\\n assert rect1.overlap_with(rect2)\\r\\n assert rect2.overlap_with(rect1)\\r\\n assert Rectangle.overlap(rect1, rect2)\\r\\n assert Rectangle.overlap(rect2, rect1)\\r\\n\\r\\n # rectangles with only same boarder are not considered overlapped\\r\\n rect1 = Rectangle(-30, -10, -20, 0)\\r\\n rect2 = Rectangle(-20, -5, 30, 20)\\r\\n rect3 = Rectangle(-40, 0, 30, 20)\\r\\n assert not rect1.overlap_with(rect2)\\r\\n assert not rect1.overlap_with(rect3)\\r\\n assert not Rectangle.overlap(rect2, rect1)\\r\\n assert not Rectangle.overlap(rect3, rect1)\",\n \"def overlap(id1, id2, th):\\n first = [int(pos) for pos in id1[:-2].replace('-', ':').split(':')[1:]]\\n second = [int(pos) for pos in id2[:-2].replace('-', ':').split(':')[1:]]\\n if all([abs(first[0] - second[0]) <= th,\\n abs(first[2] - second[2]) <= th,\\n abs(first[5] - second[5]) <= th,\\n first[1] == second[1],\\n first[4] == second[4]]):\\n return True\\n else:\\n return False\",\n \"def check_overlap(lorentz_params_1, lorentz_params_2):\\n if lorentz_params_1 is None or lorentz_params_2 is None:\\n return False\\n [low_lorentz, high_lorentz] = sorted(\\n [lorentz_params_1, lorentz_params_2], key=lambda l: l[1])\\n low_fit_range = find_single_fit_range(low_lorentz)\\n high_fit_range = find_single_fit_range(high_lorentz)\\n return low_fit_range[2] > high_fit_range[1]\",\n \"def can_attend_meetings(intervals: List[List[int]]) -> bool:\\n intervals.sort()\\n for i in range(1, len(intervals)):\\n if intervals[i][0] < intervals[i - 1][1]:\\n return False\\n return True\",\n \"def overlap(id1, id2, th):\\n first = [int(pos) for pos in id1[:-2].replace('-', ':').split(':')[1:]]\\n second = [int(pos) for pos in id2[:-2].replace('-', ':').split(':')[1:]]\\n if all([abs(first[0] - second[0]) <= th,\\n abs(first[2] - second[2]) <= th,\\n abs(first[3] - second[3]) <= th,\\n first[1] == second[1],\\n first[4] == second[4]]):\\n return True\\n else:\\n return False\",\n \"def _is_overlapping(last_tf_end, cur_tf_start, tolerance_months=0):\\n return last_tf_end <= _substract_months(cur_tf_start, months=tolerance_months)\",\n \"def is_overlapping_lane_seq(lane_seq1: Sequence[int], lane_seq2: Sequence[int]) -> bool:\\n\\n if lane_seq2[0] in lane_seq1[1:] and lane_seq1[-1] in lane_seq2[:-1]:\\n return True\\n elif set(lane_seq2) <= set(lane_seq1):\\n return True\\n return False\",\n \"def overlaps(self, that):\\n if (not isinstance(that, Annotation)):\\n raise ValueError(\\\"Argument for intersects should be an annotation\\\")\\n\\n if (self.bbox.xmin >= that.bbox.xmax or that.bbox.xmin >= self.bbox.xmax):\\n return False\\n\\n # the coordinates are inverted, so y0 is larger than y1\\n if (self.bbox.ymin >= that.bbox.ymax or that.bbox.ymin >= self.bbox.ymax):\\n return False\\n\\n return True\",\n \"def overlap(t1, t2):\\n t1 = dict(min=np.min(t1), max=np.max(t1))\\n t2 = dict(min=np.min(t2), max=np.max(t2))\\n for t in (t1, t2):\\n t['dur'] = t['max'] - t['min']\\n\\n # Ensure t1 min < t2 min\\n if t2['min'] < t1['min']:\\n print('t2 starts earlier')\\n t1, t2 = t2, t1\\n \\n # var names wrt t2\\n min_inside = t2['min'] >= t1['min'] and t2['min'] <= t1['max']\\n max_inside = t2['max'] <= t1['max']\\n if min_inside and max_inside:\\n # t2 completely contained by t1\\n return (t2['min'], t2['max'])\\n elif min_inside:\\n # t2 partially contained by t1\\n return (t2['min'], t1['max'])\\n else:\\n # no overlap\\n return (None, None)\",\n \"def is_between(value, start, end, including_start=False, including_end=False):\\n if not including_start and not including_end: # not include both start and end\\n if (start < value < end):\\n return True\\n elif (start > end) and (start < value <= (2**m - 1) or 0 <= value < end):\\n return True\\n elif (start == end) and (value != start):\\n return True\\n return False\\n elif not including_start and including_end: # include end but not the start\\n if value == end:\\n return True\\n elif (start < value <= end):\\n return True\\n elif (start > end) and ((start < value <= (2**m - 1)) or (0 <= value <= end)):\\n return True\\n elif (start == end) and (value != start):\\n return True\\n return False\\n elif including_start and not including_end: # include start but not the end\\n if value == start:\\n return True\\n elif (start <= value < end):\\n return True\\n elif (start > end) and (start <= value <= (2**m - 1) or 0 <= value < end):\\n return True\\n elif (start == end) and (value != end):\\n return False\\n return False\\n else: # include both start and end\\n if (start <= value <= end):\\n return True\\n elif (start > end) and (start <= value <= (2**m - 1) or 0 <= value <= end):\\n return True\\n elif start == end:\\n return True\\n return False\",\n \"def is_overlap(box_1, box_2, iou_th):\\n return box_1.iou(box_2) > iou_th\",\n \"def lower_contains_interval(self, other):\\n if not self.contains_interval(other):\\n return False\\n for (i, j) in other.decreasing_cover_relations():\\n if not self.le(i, j):\\n return False\\n return True\",\n \"def overlap(id1, id2, th):\\n first = [int(pos) for pos in id1[:-2].replace('-', ':').split(':')[1:]]\\n second = [int(pos) for pos in id2[:-2].replace('-', ':').split(':')[1:]]\\n if all([abs(first[0] - second[0]) <= th,\\n abs(first[3] - second[3]) <= th,\\n first[1] == second[1],\\n first[2] == second[2],\\n first[5] == second[5],\\n first[6] == second[6]]):\\n return True\\n else:\\n return False\",\n \"def _bbox_overlap(self, other):\\n reg0 = self.bbox\\n reg1 = other.bbox\\n return (reg0[0] <= reg1[2] and reg1[0] <= reg0[2] and\\n reg0[1] <= reg1[3] and reg1[1] <= reg0[3])\",\n \"def overlap(id1, id2, th):\\n first = [int(pos) for pos in id1[:-2].replace('-', ':').split(':')[1:]]\\n second = [int(pos) for pos in id2[:-2].replace('-', ':').split(':')[1:]]\\n if all([abs(first[0] - second[0]) <= th,\\n first[1] == second[1],\\n first[2] == second[2],\\n abs(first[3] - second[3]) <= th]):\\n return True\\n else:\\n return False\",\n \"def doesNotOverlap( self, other):\\n return not self.overlaps( other)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.8221766","0.8196568","0.80125463","0.7714692","0.76665205","0.7543957","0.75031185","0.74704397","0.7351677","0.73447514","0.72555596","0.72339934","0.72237253","0.71955794","0.7165406","0.71499693","0.70835686","0.7058538","0.70213556","0.69799066","0.69696075","0.6956145","0.6936365","0.6904479","0.6835199","0.68217903","0.6809983","0.6802336","0.67940176","0.6789984","0.6777085","0.6757624","0.67384326","0.67374766","0.67231387","0.6701639","0.6676791","0.6676415","0.6672443","0.66219234","0.66088367","0.6603227","0.6586157","0.65769875","0.65698797","0.6555726","0.6553744","0.65453786","0.6545142","0.65219945","0.6489998","0.6486483","0.6482657","0.6481758","0.64622104","0.64593947","0.64504504","0.64483315","0.64329374","0.64274025","0.64162594","0.6408872","0.6401466","0.6390156","0.63845074","0.63715804","0.63642037","0.63575053","0.6351741","0.6349306","0.63424575","0.6340626","0.6338834","0.6333376","0.6332664","0.63191783","0.63163745","0.63156307","0.6295272","0.62810165","0.62453413","0.62451977","0.6244225","0.62364155","0.62272453","0.62259614","0.6222353","0.62139666","0.6213341","0.6205741","0.6204015","0.6199244","0.61944646","0.61944354","0.619391","0.6193887","0.61818","0.61695594","0.61664516","0.6166293"],"string":"[\n \"0.8221766\",\n \"0.8196568\",\n \"0.80125463\",\n \"0.7714692\",\n \"0.76665205\",\n \"0.7543957\",\n \"0.75031185\",\n \"0.74704397\",\n \"0.7351677\",\n \"0.73447514\",\n \"0.72555596\",\n \"0.72339934\",\n \"0.72237253\",\n \"0.71955794\",\n \"0.7165406\",\n \"0.71499693\",\n \"0.70835686\",\n \"0.7058538\",\n \"0.70213556\",\n \"0.69799066\",\n \"0.69696075\",\n \"0.6956145\",\n \"0.6936365\",\n \"0.6904479\",\n \"0.6835199\",\n \"0.68217903\",\n \"0.6809983\",\n \"0.6802336\",\n \"0.67940176\",\n \"0.6789984\",\n \"0.6777085\",\n \"0.6757624\",\n \"0.67384326\",\n \"0.67374766\",\n \"0.67231387\",\n \"0.6701639\",\n \"0.6676791\",\n \"0.6676415\",\n \"0.6672443\",\n \"0.66219234\",\n \"0.66088367\",\n \"0.6603227\",\n \"0.6586157\",\n \"0.65769875\",\n \"0.65698797\",\n \"0.6555726\",\n \"0.6553744\",\n \"0.65453786\",\n \"0.6545142\",\n \"0.65219945\",\n \"0.6489998\",\n \"0.6486483\",\n \"0.6482657\",\n \"0.6481758\",\n \"0.64622104\",\n \"0.64593947\",\n \"0.64504504\",\n \"0.64483315\",\n \"0.64329374\",\n \"0.64274025\",\n \"0.64162594\",\n \"0.6408872\",\n \"0.6401466\",\n \"0.6390156\",\n \"0.63845074\",\n \"0.63715804\",\n \"0.63642037\",\n \"0.63575053\",\n \"0.6351741\",\n \"0.6349306\",\n \"0.63424575\",\n \"0.6340626\",\n \"0.6338834\",\n \"0.6333376\",\n \"0.6332664\",\n \"0.63191783\",\n \"0.63163745\",\n \"0.63156307\",\n \"0.6295272\",\n \"0.62810165\",\n \"0.62453413\",\n \"0.62451977\",\n \"0.6244225\",\n \"0.62364155\",\n \"0.62272453\",\n \"0.62259614\",\n \"0.6222353\",\n \"0.62139666\",\n \"0.6213341\",\n \"0.6205741\",\n \"0.6204015\",\n \"0.6199244\",\n \"0.61944646\",\n \"0.61944354\",\n \"0.619391\",\n \"0.6193887\",\n \"0.61818\",\n \"0.61695594\",\n \"0.61664516\",\n \"0.6166293\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.82069135"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":92589,"cells":{"query":{"kind":"string","value":"Returns (list of) sum of intervals. Intervals could be tuple of any orderable values (ex. integers, dates). If sets are overlapping, they are \"merged\" together and resulting interval is (min(start1, start2), max(end1, end2)). >>> sum_of_intervals([(1, 5), (4, 10), (7, 13), (15, 20), (21, 30)]) [(1, 13), (15, 20), (21, 30)]"},"document":{"kind":"string","value":"def sum_of_intervals(intervals):\n intervals_set = set()\n for v_start, v_end in intervals:\n intervals_set.add((v_start, True))\n intervals_set.add((v_end, False))\n intervals = list(intervals_set)\n intervals.sort(key=lambda a: a[0])\n\n current_start = None\n started = 0\n result = []\n for value, is_start in intervals:\n if is_start:\n if current_start is None:\n current_start = value\n started += 1\n else:\n started -= 1\n if started == 0:\n result.append((current_start, value))\n current_start = None\n return result"},"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 intervals(start, end, delta, intervals_right_closed=False):\r\n result = []\r\n interval_start, interval_end = None, None\r\n while True:\r\n interval_start = start if interval_end is None else interval_end\r\n interval_end = min(end, interval_start + delta)\r\n result.append(thalesians.tsa.intervals.Interval(\r\n interval_start, interval_end,\r\n not intervals_right_closed, intervals_right_closed))\r\n if interval_end == end: break\r\n return result","def intervals_between(self, start: dt.datetime, end: dt.datetime) -> List[Interval]:\n relevant_intervals: List[Interval] = []\n\n for interval in self.intervals:\n if start <= interval.start_time < end:\n relevant_intervals.append(interval)\n\n return relevant_intervals","def merge(intervals):\n intervals.sort(key=lambda x: x[0])\n # take the first interval\n merged = [intervals[0]]\n # loop through all the intervals\n for this_interval in intervals:\n if this_interval[0] <= merged[-1][1]:\n merged[-1] = (merged[-1][0], max(merged[-1][1], this_interval[1]))\n else:\n merged.append(this_interval)\n return merged","def time_interval(intervals: List[Tuple[int, int]]) -> int:\n\n start = []\n finish = []\n for elems in intervals:\n start.append(elems[0])\n finish.append(elems[1])\n\n # Sorting the start and end times separately\n start.sort()\n finish.sort()\n\n index1, index2 = 0, 0\n current_rooms = 0\n max_rooms = 0\n # The logic below is, we add each room when current finish time is greater than current start time\n # This informs us whether how many times are collapsing with each other at any time\n while (index1 < len(start)) and (index2 < len(finish)):\n if start[index1] < finish[index2]:\n current_rooms += 1\n index1 += 1\n else:\n index2 += 1\n max_rooms = max(max_rooms, current_rooms)\n current_rooms -= 1\n max_rooms = max(max_rooms, current_rooms)\n return max_rooms","def collapse_ranges (ranges):\n\n # FIXME: does tuple and set conversion really add anything?\n\n # Ranges must be unique: we do not count timings when they start and end at\n # exactly the same time. By using a set, we do not repeat ranges.\n # we convert to a list before return.\n final = set()\n\n # return empty list if given an empty list\n if not ranges:\n return final\n\n START = 0\n END = 1\n\n # sort ranges into a copy list, by their start time\n _ranges = sorted(ranges, key=lambda x: x[START])\n\n # sat 'base' to the earliest range (smallest starting point)\n base = _ranges[0]\n\n for _range in _ranges[1:]:\n\n # if range is empty, skip it\n if _range[START] == _range[END]:\n continue\n\n if _range[START] <= base[END]:\n # ranges overlap -- extend the base\n base[END] = max(base[END], _range[END])\n\n else:\n # ranges don't overlap -- move base to final, and current _range\n # becomes the new base\n final.add(tuple(base))\n base = _range\n\n # termination: push last base to final\n final.add(tuple(base))\n\n # Return final as list of list in case a mutable type is needed.\n return [list(b) for b in final]","def range_sum(self, left: int, right: int) -> int:\n if right < left:\n raise ValueError(\"Right needs to be >= left\")\n return self._prefix_sum(right) - self._prefix_sum(left - 1)","def joinIntervalsSum(myIntervals,start='start',end='end',score='readcount',sampleName=\".\",offset=0):\n \n if not myIntervals: return myIntervals\n non_overlapping = []\n sep = {'+':[],'-':[]}\n \n print \"Splitting intervals by strand\"\n for i in myIntervals:\n sep[i.strand].append(i)\n \n print \"Joining intervals...\"\n for strand in sep.keys():\n print strand\n intervals = sep[strand]\n intervals.sort()\n \n \n current = copy.copy(intervals[0])\n for x in intervals[1:]:\n next = copy.copy(x)\n if current.intersects(next, start=start, end=end,offset=offset):\n current.end = max(current.end,next.end)\n current.__dict__[score] = current.__dict__[score]+next.__dict__[score]\n else:\n current.name = sampleName\n non_overlapping.append(current)\n current = copy.copy(next)\n current.name=sampleName\n non_overlapping.append(current)\n print \"Sorting intervals\"\n non_overlapping.sort()\n print \"Done\"\n return non_overlapping","def sum_range(self, lower, upper):\n if upper>self.upper:\n upper=self.upper\n if lower<self.lower:\n lower = self.lower\n\n i_l = int(np.floor((lower-self.lower)/self._dx))\n i_u = int(np.floor((upper-self.lower)/self._dx))\n total = 0.0\n for i in range(i_l,i_u):\n total+= self.y[i]\n return total","def _interval_tuples(self, interval, entries):\n date_first, date_last = getters.get_min_max_dates(entries,\n (Transaction))\n\n if not date_first:\n return []\n\n interval_tuples = []\n while date_first <= date_last:\n next_date = get_next_interval(date_first, interval)\n interval_tuples.append((date_first, next_date))\n date_first = next_date\n\n return interval_tuples","def _build_intervals(self) -> List[Tuple[datetime.datetime, datetime.datetime]]:\n if self.granularity == 'HOUR':\n days = max(min((self.bounds[1] - self.bounds[0]).days,\n self.GRANULARITIES['HOUR'][1]),\n self.GRANULARITIES['HOUR'][0])\n interval_length = datetime.timedelta(days=days)\n offset = datetime.timedelta(hours=1)\n elif self.granularity == 'MONTH':\n # no need to split requests for monthly data\n days = max((self.bounds[1] - self.bounds[0]).days,\n self.GRANULARITIES['MONTH'][0])\n interval_length = datetime.timedelta(days=days)\n offset = datetime.timedelta(days=1)\n else:\n days = max(min((self.bounds[1] - self.bounds[0]).days,\n self.GRANULARITIES['DAY'][1]),\n self.GRANULARITIES['DAY'][0])\n interval_length = datetime.timedelta(days=days)\n offset = datetime.timedelta(days=1)\n\n time_pointer = self.bounds[1]\n intervals = []\n while time_pointer > self.bounds[0]:\n upper = time_pointer\n time_pointer -= interval_length\n intervals.append((time_pointer, upper))\n time_pointer -= offset\n return intervals","def apply_list_intervals(list_, intervals):\n return [list_[interval] for interval in intervals]","def overlaps(interval,intervals):\n return [x for x in intervals if interval.overlaps(x)]","def define_intervals(self):\n i = 5 # a step of increment\n interval_sum = self.min_step\n interval_list = [self.min_step]\n while interval_sum < self.max_step:\n interval_sum += i\n interval_list.append(interval_sum)\n # interval_list.append(self.max_step)\n # print(\"Intervals\", interval_list)\n return interval_list","def intervals(start, end, delta):\n intervals = []\n current = copy.deepcopy(start)\n while current < end:\n intervals.append((unix_to_iso(current.strftime('%s')),\n unix_to_iso((current + delta).strftime('%s'))))\n current += delta\n return intervals","def merge_genomic_intervals(intervals):\n # type: (Iterable[Tuple[str, int, int]]) -> Iterable[Tuple[str, int, int]]\n\n # Group intervals by chromosome.\n grouped_intervals = itertools.groupby(\n sorted(intervals), operator.itemgetter(0))\n\n # Now yield merged intervals per chromosome.\n for chrom, grp in grouped_intervals:\n chrom_intervals = [interval[1:] for interval in grp]\n for low, high in merge_intervals(chrom_intervals, is_sorted=True):\n yield chrom, low, high","def sum(self, start: int = 0, end: Optional[Any] = None) -> Any:\n return self.reduce(start, end)","def timedInterval(self, start, end=False):\n\n assert type(start) == float\n interval1 = min(\n enumerate(self.intervals), key=lambda x: abs(x[1].xmin - start))\n\n if end:\n assert type(end) == float\n interval2 = self.timedInterval(end)\n else:\n interval2 = interval1\n\n return (interval1[0], interval2[0] + 1)","def bin_intervals(intervals, bins, interval_range=None, smoothing_window=None,\n nan_replacement=None, zero_to_nan=False):\n if intervals is None:\n return []\n\n intervals = np.array(list(intervals))\n\n if interval_range is None:\n try:\n interval_range = (min(intervals[:, 0]), max(intervals[:, 1]))\n except (IndexError, TypeError):\n raise ValueError(\"intervals cannot be None or length 0 if not providing interval_range!\")\n\n bin_size = (interval_range[1] - interval_range[0] + 1) / bins\n logger.debug(\"Bin size: {}\".format(bin_size))\n\n return RegionBased._bin_intervals_equidist(intervals, bin_size, interval_range, bins=bins,\n smoothing_window=smoothing_window,\n nan_replacement=nan_replacement,\n zero_to_nan=zero_to_nan)","def getIntervals(t_start,t_end,interval):\n timestamp_start = pd.Timestamp(t_start)\n timestamp_end = pd.Timestamp(t_end)\n nro_elements=int(int((timestamp_end-timestamp_start).total_seconds())/(60*interval))\n list_dates = []\n list_dates.append(t_start)\n for i in range(nro_elements):\n date_time_str = list_dates[i]\n Current_Date = dt.datetime.strptime(date_time_str, '%Y-%m-%d %H:%M:%S')\n Next_Date = Current_Date + datetime.timedelta(minutes=interval)\n list_dates.append(str(Next_Date))\n return list_dates","def range_union(ranges):\n union = []\n for r in sorted(ranges, key=lambda r: r.start):\n if len(union) > 0 and union[-1].stop >= r.start:\n union[-1] = range(union[-1].start, max(union[-1].stop, r.stop))\n else:\n union.append(r)\n return union","def make_elementary_intervals(interval_lists):\n # copy so we don't destroy the original lists\n interval_lists = [[i for i in il] for il in interval_lists]\n elem_intervals = []\n while interval_lists:\n elem_intervals.append(min(intervals[0] for intervals in interval_lists))\n i = 0\n while i < len(interval_lists):\n intervals = interval_lists[i]\n if intervals[0] == elem_intervals[-1]:\n intervals.pop(0)\n if not intervals:\n interval_lists.pop(i)\n else:\n i += 1\n return elem_intervals","def sum_range(nums, start=0, end=None):\n n = len(nums)\n if end == None:\n nz = n\n elif end > n:\n nz = n\n else:\n nz = end\n\n return sum(nums[start:nz + 1])","def interval(self):\n return Intersection(*(a.interval for a in self.args))","def count_within(values, intervals):\n rvals = np.reshape(values, [-1, 1])\n intervals_m = np.array(intervals)\n rlow = intervals_m[:, 0].reshape(1, -1)\n rhigh = intervals_m[:, 1].reshape(1, -1)\n\n flags = (rlow <= rvals) * (rvals < rhigh)\n return np.sum(flags, axis=0)","def sum_tuples(t1, t2):\n return tuple(sum(t) for t in zip(t1, t2))","def calculate_intervals(tick_times: List[float]) -> List[float]:\n return [tick_times[i] - tick_times[i - 1] for i in range(1, len(tick_times))]","def sum(self, start=0, end=None):\n return super().reduce(start, end)","def calc_lenient_interval_stats(all_intervals: Collection[float]) -> Tuple[float, float, float]:\n\n # We need to be a little lenient on the min and max intervals: we are trying to achieve a constant rate rather than exact intervals.\n # To achieve this leniency, I'm going to sort the intervals and discard outliers (the highest and lowest 5% of values)\n discard_count = max(1, len(all_intervals) // 20)\n sorted_intervals = sorted(all_intervals)\n intervals = sorted_intervals[discard_count:-discard_count]\n min_interval = intervals[0]\n max_interval = intervals[-1]\n mean_interval = statistics.mean(intervals)\n return max_interval, mean_interval, min_interval","def find_overlap_range_list(points_list):\n highest_start_point = points_list[0][0]\n lowest_end_point = points_list[0][0] + points_list[0][1]\n\n for point in points_list[1:]:\n highest_start_point = max(highest_start_point, point[0])\n lowest_end_point = min(lowest_end_point, point[1]+point[0])\n\n if lowest_end_point <= highest_start_point:\n return None\n\n return [highest_start_point, lowest_end_point - highest_start_point]","def sum(self, values):\n return self.aggregate(values, \"sum\")","def summarize(requestContext, seriesList, intervalString, func='sum', alignToFrom=False):\n results = []\n delta = parseTimeOffset(intervalString)\n interval = delta.seconds + (delta.days * 86400)\n\n for series in seriesList:\n buckets = {}\n\n timestamps = range( int(series.start), int(series.end), int(series.step) )\n datapoints = zip(timestamps, series)\n\n for timestamp_, value in datapoints:\n if alignToFrom:\n bucketInterval = int((timestamp_ - series.start) / interval)\n else:\n bucketInterval = timestamp_ - (timestamp_ % interval)\n\n if bucketInterval not in buckets:\n buckets[bucketInterval] = []\n\n if value is not None:\n buckets[bucketInterval].append(value)\n\n if alignToFrom:\n newStart = series.start\n newEnd = series.end\n else:\n newStart = series.start - (series.start % interval)\n newEnd = series.end - (series.end % interval) + interval\n\n newValues = []\n for timestamp_ in range(newStart, newEnd, interval):\n if alignToFrom:\n newEnd = timestamp_\n bucketInterval = int((timestamp_ - series.start) / interval)\n else:\n bucketInterval = timestamp_ - (timestamp_ % interval)\n\n bucket = buckets.get(bucketInterval, [])\n\n if bucket:\n if func == 'avg':\n newValues.append( float(sum(bucket)) / float(len(bucket)) )\n elif func == 'last':\n newValues.append( bucket[len(bucket)-1] )\n elif func == 'max':\n newValues.append( max(bucket) )\n elif func == 'min':\n newValues.append( min(bucket) )\n else:\n newValues.append( sum(bucket) )\n else:\n newValues.append( None )\n\n if alignToFrom:\n newEnd += interval\n\n newName = \"summarize(%s, \\\"%s\\\", \\\"%s\\\"%s)\" % (series.name, intervalString, func, alignToFrom and \", true\" or \"\")\n newSeries = TimeSeries(newName, newStart, newEnd, interval, newValues)\n newSeries.pathExpression = newName\n results.append(newSeries)\n\n return results","def overlapping_ranges(\n ranges_1: Sequence[Tuple[int, int]],\n ranges_2: Sequence[Tuple[int, int]],\n) -> List[Tuple[int, int]]:\n return [\n (max(first[0], second[0]), min(first[1], second[1]))\n for first in ranges_1\n for second in ranges_2\n if max(first[0], second[0]) < min(first[1], second[1])\n ]","def sum_values(values):\n return (sum(values))","def sum_range(lower, upper):\n\n def copies(pmin, pmax):\n if lower <= pmin and pmax <= upper:\n return True\n elif pmax > upper:\n return False\n return copies(pmin + 50, pmax + 60) or copies(pmin + 130, pmax + 140)\n\n return copies(0, 0)","def calcOverlap(intervals):\n bp = 0 \n for i in intervals:\n bp += sum([overlapCases(i, j) for j in intervals])\n return(bp)","def total_calculate_sum(start=None, end=None):\n check_instance(start)\n check_instance(end)\n result = OrderedDict()\n query = '''SELECT organization.id, organization.name, org_type.name FROM organization INNER JOIN org_type ON\n organization.type = org_type.id'''\n organizations = org_cursor.execute(query).fetchall()\n if not start:\n query = '''SELECT datetime FROM ps_transaction LIMIT 1'''\n datetime_data = trans_cursor.execute(query).fetchone()[0]\n start = datetime.strptime(datetime_data, '%Y-%m-%d %H:%M:%S')\n if not end:\n end = datetime.now()\n if start > end:\n raise ValueError('Start date must be lower than end date.')\n query = '''SELECT * FROM ps_transaction WHERE datetime >= ? AND datetime <= ? AND type = 1 ORDER BY org_id'''\n transactions = trans_cursor.execute(query, (start, end, )).fetchall()\n for item in organizations:\n sum_org = 0\n for tr in transactions:\n if item[0] == tr[6]:\n sum_org += tr[-1]\n result[item] = sum_org / 100\n report_data = [[start, end]]\n report_data.append(['ID', 'Name', 'Type', 'Amount'])\n for key, value in result.items():\n report_data.append([key[0], key[1], key[2], value])\n return report_data","def cumulative_sum_range(magnitudes):\n cum_sums = np.cumsum(magnitudes)\n\n cum_sum_range = np.max(cum_sums) - np.min(cum_sums)\n\n return cum_sum_range","def fuseinterval(intervals, dist):\n # if we have only one interval return it\n if len(intervals) == 1:\n return intervals\n # sort based on the coordinate of the end of the interval\n intervals = sorted(intervals,\n key=lambda interval: interval[1])\n\n # go through interval, fuse the one that are adjacent\n fuseintervals = []\n previnterval = intervals[0]\n # check adjacent intervals and merge them, return a list of intervals\n for interval in intervals[1:]:\n # adjacency: end of previous + 1 = start of current interval\n if int(previnterval[1]+50) >= int(interval[0]):\n previnterval[1] = interval[1]\n else:\n fuseintervals.append(previnterval)\n previnterval = interval\n # last element has to be appended too\n fuseintervals.append(previnterval)\n # reverse sort the interval, so that by\n # removing the vector sequence, the coordinates are\n # still actual.\n fuseintervals = sorted(fuseintervals,\n key=lambda interval: interval[1], reverse=True)\n\n # check how many intervals\n return fuseintervals","def make_intervals(self):\n if not self:\n return []\n intervals = []\n self.sort()\n index_of_the_first = 0\n for i in range(len(self) - 1): # i: indexes from zero to len(self)\n if self[i] + 1 == self[i+1] or self[i] == self[i+1]:\n continue\n # elif self[i] == self[i+1]:\n # not_uniq.append( (self.count(self[i]), self[i]) )\n else:\n intervals.append((self[index_of_the_first], self[i]))\n index_of_the_first = i + 1\n # And now the last element:\n last_index = len(self) - 1\n intervals.append((self[index_of_the_first], self[last_index]))\n return intervals","def sum_range(lower, upper):\n\n def copies(pmin, pmax):\n if lower <= pmin and pmax <= upper:\n return True\n elif pmax > upper:\n return False\n return copies(pmin+50, pmax+60)\n\n return copies(0, 0)","def build_date_bins(start_date, end_date, interval):\n if interval > 1:\n bins = np.array([np.datetime64(datetime.datetime(start_date.year, start_date.month,\n start_date.day, start_date.hour,\n (start_date.minute // interval) * interval)\n + datetime.timedelta(minutes=interval) * i) for i\n in range(int((end_date - start_date).total_seconds() / 60 / interval))])\n else:\n bins = np.array([np.datetime64(datetime.datetime(start_date.year, start_date.month,\n start_date.day, start_date.hour,\n start_date.minute)\n + datetime.timedelta(minutes=interval) * i) for i\n in range(int((end_date - start_date).total_seconds() / 60))])\n return bins","def interval_totals(self, interval, account_name, accumulate=False):\n if isinstance(account_name, str):\n names = [account_name]\n else:\n names = account_name\n\n interval_tuples = self._interval_tuples(interval, self.entries)\n date_first, _ = getters.get_min_max_dates(self.entries, (Transaction))\n return [{\n 'begin_date': begin_date,\n 'end_date': end_date,\n 'totals': self._balances_totals(\n names, begin_date if not accumulate else date_first, end_date),\n } for begin_date, end_date in interval_tuples]","def test__get_intervals(self):\n # Setup\n data = pd.Series(['bar', 'foo', 'foo', 'tar'])\n\n # Run\n result = CategoricalTransformer._get_intervals(data)\n\n # Asserts\n expected_intervals = {\n 'foo': (0, 0.5, 0.25, 0.5 / 6),\n 'tar': (0.5, 0.75, 0.625, 0.25 / 6),\n 'bar': (0.75, 1, 0.875, 0.25 / 6)\n }\n assert result == expected_intervals","def summationReduce(lower, upper):\r\n if lower > upper:\r\n return 0\r\n else:\r\n return reduce(lambda x, y: x + y, range(lower, upper + 1))","def sum(self, start=0, end=None):\n return super(SumSegmentTree, self).reduce(start, end)","def sum(self, start=0, end=None):\n return super(SumSegmentTree, self).reduce(start, end)","def sum(self, start=0, end=None):\n return super(SumSegmentTree, self).reduce(start, end)","def sum(self, start=0, end=None):\n return super(SumSegmentTree, self).reduce(start, end)","def sum(self, start=0, end=None):\n return super(SumSegmentTree, self).reduce(start, end)","def two_sum(array):\n\n WIDTH = 10000\n out = set()\n for i in array:\n lower = bisect.bisect_left(array, -WIDTH - i)\n upper = bisect.bisect_right(array, WIDTH - i)\n out |= set([i + j for j in array[lower:upper]])\n return out","def findIntervals(intervals, length, weights, maximum, graph):\r\n intervals_used = []\r\n vertex = maximum\r\n while vertex > 1:\r\n for k in range(length):\r\n if vertex == intervals[k][1] and weights[k] != 0 \\\r\n and graph[vertex] == graph[intervals[k][0]] + weights[k]:\r\n intervals_used.insert(0, intervals[k]) # insertion at the beginning\r\n vertex = intervals[k][0] + 1 # +1 because of adjacent intervals\r\n vertex -= 1\r\n \r\n return intervals_used","def test_intervals(self):\n x = np.array([-5, -3, -2, -2, 100])\n self.assertEqual(\n npinterval.interval(x, 2/5),\n (-2, -2, 2, 4))\n self.assertEqual(\n npinterval.interval(x, 3/5),\n (-3, -2, 1, 4))\n self.assertEqual(\n npinterval.interval(x, 4/5),\n (-5, -2, 0, 4))","def mergeOverlapping(intlist):\n \n intlist.sort(key=lambda x: x.minval)\n newint=[intlist[0]]\n\n for elem in intlist[1:]:\n try:\n newint[-1]=mergeIntervals(elem,newint[-1])\n except:\n newint.append(elem)\n \n return newint","def count_within_bkd(values, intervals):\n return OrderedDict([\n (tuple(i), c)\n for i, c in zip(intervals, count_within(values, intervals))\n ])","def sumRange(self, i, j):\n return self.sums[j]-self.sums[i-1] if i>0 else self.sums[j]","def list_by_list(list_to_be_splited, list_with_intervals):\n intervals = []\n for x, val in enumerate(list_to_be_splited):\n for y in list_with_intervals:\n if y == val:\n intervals.append((x, val))\n return intervals","def sumRange(self, i, j):\n if not self.nums: return 0 # edge case\n return self.sum(j+1)-self.sum(i)","def enumerateIntervals(bounds, stride):\n def alignDown(chunk, x):\n return (x/chunk)*chunk\n def alignUp(chunk, x):\n return int(math.ceil(float(x)/chunk)*chunk)\n\n start, end = bounds\n roundStart = alignDown(stride, start)\n roundEnd = alignUp (stride, end)\n\n for s in xrange(roundStart, roundEnd, stride):\n roundWin = (s, s + stride)\n yield intersection(bounds, roundWin)","def range(self):\n lows, highs = [], []\n for graph in self._graphs.values():\n low, high = graph.range()\n lows.append(low)\n highs.append(high)\n return (min(lows), max(highs))","def solution(intervals):\n solution = Solution()\n output = solution.merge(intervals)\n\n print(output)","def get_intervals(l):\n intervals = len(l) * [0]\n # Initalize with 1\n intervals[0] = 1\n for k in range(1, len(l)):\n intervals[k] = (len(l[k]) + 1) * intervals[k - 1]\n\n return intervals","def sum_numbers(op_sum):\n collection = []\n for op in op_sum.operators:\n # Strip numeric coefficient off\n collected = False\n num = 1\n n_removed = 0\n op = collect_numbers(op)\n if op.tensors[0].name == \"$number\":\n num = op.tensors[0].content\n new_op = Operator(op.tensors[1:])\n else:\n num = 1\n new_op = op\n\n # Sum the numbers of equal operators\n for i, (collected_op, collected_num) in enumerate(collection):\n if collected_op == new_op:\n collected = True\n collection[i] = (new_op, num + collected_num)\n break\n if not collected:\n collection.append((new_op, num))\n return [(o, num) for o, num in collection if abs(num) > 10**(-10)]","def bin_intervals_equidistant(intervals, bin_size, interval_range=None, smoothing_window=None,\n nan_replacement=None, zero_to_nan=False):\n if intervals is None:\n return []\n\n intervals = np.array(list(intervals))\n\n if interval_range is None:\n try:\n interval_range = (min(intervals[:, 0]), max(intervals[:, 1]))\n except (IndexError, TypeError):\n raise ValueError(\"intervals cannot be None or length 0 if not providing interval_range!\")\n\n if isinstance(interval_range, GenomicRegion):\n interval_range = (interval_range.start, interval_range.end)\n\n return RegionBased._bin_intervals_equidist(intervals, bin_size, interval_range,\n smoothing_window=smoothing_window,\n nan_replacement=nan_replacement,\n zero_to_nan=zero_to_nan)","def sumRange(self, i, j):\r\n return self.dp[i][j]","def init_data_sum(self, intervals):\n self.data_sum_win = []\n self.data_sum_lose = []\n\n for i in range(len(intervals)):\n interval_size = len(intervals[i])\n self.data_sum_win.append([0]*interval_size)\n self.data_sum_lose.append([0]*interval_size)\n\n for i in range(len(self.group_col)):\n interval_size = len(self.group_col[i])\n self.data_sum_win.append([0]*interval_size)\n self.data_sum_lose.append([0]*interval_size)","def event_intervals(self, name=None, task=None,\n interval=None, match_exact=True):\n if name is None:\n intervals = \\\n IntervalList(sorted_items(self._tmw_intervals_by_name.values()))\n elif isinstance(name, string_types):\n if match_exact:\n intervals = self._tmw_intervals_by_name[name]\n else:\n intervals = IntervalList(sorted_items(value for key, value in\n self._tmw_intervals_by_name.iteritems() if name in key))\n else: # assume iterable (must match exact)\n intervals = IntervalList(sorted_items(value for key, value in\n self._tmw_intervals_by_name.iteritems() if key in name))\n intervals = intervals.slice(interval=interval)\n if task:\n intervals = IntervalList(filter(lambda it: it.event.task == task, intervals))\n\n return intervals","def meeting_rooms_ii(self, intervals):\n # Solution1: use TreeMap\n # put intervals into TreeMap, key-> intervals.start, val->intervals.end, use floor to find\n\n # Solution2: heap\n intervals.sort()\n hp = []\n for inter in intervals:\n if hp and hp[0] < inter[0]: heapq.heappop(hp)\n heapq.heappush(hp, inter[1])\n return len(hp)","def smartSummarize(requestContext, seriesList, intervalString, func='sum', alignToFrom=False):\n if alignToFrom:\n log.info(\"Deprecated parameter 'alignToFrom' is being ignored.\")\n\n results = []\n delta = parseTimeOffset(intervalString)\n interval = delta.seconds + (delta.days * 86400)\n\n # Adjust the start time to fit an entire day for intervals >= 1 day\n requestContext = requestContext.copy()\n s = requestContext['startTime']\n if interval >= DAY:\n requestContext['startTime'] = datetime(s.year, s.month, s.day)\n elif interval >= HOUR:\n requestContext['startTime'] = datetime(s.year, s.month, s.day, s.hour)\n elif interval >= MINUTE:\n requestContext['startTime'] = datetime(s.year, s.month, s.day, s.hour, s.minute)\n\n for i,series in enumerate(seriesList):\n # XXX: breaks with summarize(metric.{a,b})\n # each series.pathExpression == metric.{a,b}\n newSeries = evaluateTarget(requestContext, series.pathExpression)[0]\n series[0:len(series)] = newSeries\n series.start = newSeries.start\n series.end = newSeries.end\n series.step = newSeries.step\n\n for series in seriesList:\n buckets = {} # { timestamp: [values] }\n\n timestamps = range( int(series.start), int(series.end), int(series.step) )\n datapoints = zip(timestamps, series)\n\n # Populate buckets\n for timestamp_, value in datapoints:\n bucketInterval = int((timestamp_ - series.start) / interval)\n\n if bucketInterval not in buckets:\n buckets[bucketInterval] = []\n\n if value is not None:\n buckets[bucketInterval].append(value)\n\n\n newValues = []\n for timestamp_ in range(series.start, series.end, interval):\n bucketInterval = int((timestamp_ - series.start) / interval)\n bucket = buckets.get(bucketInterval, [])\n\n if bucket:\n if func == 'avg':\n newValues.append( float(sum(bucket)) / float(len(bucket)) )\n elif func == 'last':\n newValues.append( bucket[len(bucket)-1] )\n elif func == 'max':\n newValues.append( max(bucket) )\n elif func == 'min':\n newValues.append( min(bucket) )\n else:\n newValues.append( sum(bucket) )\n else:\n newValues.append( None )\n\n newName = \"smartSummarize(%s, \\\"%s\\\", \\\"%s\\\")\" % (series.name, intervalString, func)\n alignedEnd = series.start + (bucketInterval * interval) + interval\n newSeries = TimeSeries(newName, series.start, alignedEnd, interval, newValues)\n newSeries.pathExpression = newName\n results.append(newSeries)\n\n return results","def range_overlap(ranges):\n max_left = 0.0\n min_right = 1.0\n for (left, right) in ranges:\n max_left = max(max_left, left)\n min_right = min(min_right, right)\n return (max_left, min_right)","def summaryRanges(self, nums):\n\t\tc = 0\n\t\trangeStart = None\n\t\trangeNext = None\n\t\tif nums == []:\n\t\t\treturn []\n\t\tresult = []\n\t\tfor i, n in enumerate(nums):\n\t\t\tif rangeStart == None:\n\t\t\t\trangeStart = n\n\t\t\t\trangeNext = n\n\t\t\t\tcontinue\n\t\t\trangePrevious = rangeNext\n\t\t\trangeNext += 1\n\n\t\t\tif n != rangeNext:\n\t\t\t\tif rangePrevious == rangeStart:\n\t\t\t\t\tresult.append(\"{}\".format(rangeStart))\n\t\t\t\telse:\n\t\t\t\t\tresult.append(\"{}->{}\".format(rangeStart, rangePrevious))\n\n\t\t\t\trangeStart = n\n\t\t\t\trangePrevious = n\n\t\t\t\trangeNext = n\n\n\n\t\tif rangeStart == rangeNext:\n\t\t\tresult.append(\"{}\".format(rangeStart))\n\t\telse:\n\t\t\tresult.append(\"{}->{}\".format(rangeStart, rangeNext))\n\n\t\treturn result","def get_interval_stats(usages):\n\n # NOTE: with the current functionality in this particular method, we shouldn't need to worry about having to\n # localize anything; it makes no difference to the relevance of the intervals, especially as they're not mapped\n # to any particular dates on the graph or anything\n\n prev_time = None\n interval_data = {'timespans': [],\n 'total': datetime.timedelta(0),\n 'longest': datetime.timedelta(0),\n 'shortest': datetime.timedelta.max,\n 'average': None,}\n\n for use in usages:\n if prev_time is not None:\n # current_delta = datetime.timedelta\n current_delta = use.timestamp - prev_time\n interval_data['timespans'].append(round_timedelta(current_delta, datetime.timedelta(seconds=1)))\n\n prev_time = use.timestamp\n\n for span in interval_data['timespans']:\n if interval_data['longest'] < span:\n interval_data['longest'] = span\n\n if interval_data['shortest'] > span:\n interval_data['shortest'] = span\n\n interval_data['total'] += span\n\n # errors here if there are 0 or 1 usages, obviously\n interval_data['average'] = round_timedelta((interval_data['total'] / (len(usages) - 1)),\n datetime.timedelta(seconds=1))\n\n return interval_data","def distance_between_intervals(s1,s2,interval):\n class __distance_interval:\n def __init__(self,first_pos, last_pos, first, second):\n self.distance = last_pos - first_pos\n self.first = first\n self.second = second\n self.start = first_pos\n self.end = last_pos\n\n for first, second in by_two_intervals(s1,s2,interval):\n # yield pos\n f1,f2 = first\n first_pos = f1.last_pos if f1.last_pos < f2.last_pos else f2.last_pos\n l1, l2 = second\n last_pos = l1.pos if l1.pos > l2.pos else l2.pos\n yield __distance_interval(first_pos,last_pos,first,second)","def sumRange(self, i, j):\r\n # Sum of the range can be obtained by subtracting dp[j] - dp[i-1]\r\n return self.dp[j] if i == 0 else self.dp[j] - self.dp[i-1]","def findInterval(intervals,interval):\n low,ind = algorithms.binsearch(intervals,interval.start-1,lambda a,b: cmp(a.start,b))\n return (low,ind)","def interval(start, stop=None, step=1):\n if stop is None:\n start, stop = 0, start\n result = []\n i = start\n while i < stop:\n result.append(i)\n i += step\n return result","def InIntervalUnion(i, intervals):\n if len(intervals) == 0:\n return True\n accept = False\n for interval in intervals:\n assert(len(interval) == 2)\n if (interval[0] <= i) and ((i <= interval[1]) or (interval[1] == 0)):\n accept = True\n return accept","def segment_sum(self, left, right):\n if self.empty():\n return 0\n less, _ = self.root.split_sums(left)\n _, greater = self.root.split_sums(right)\n return self.sum - less - greater","def ll_sum(x):\n xlist = []\n for i in x:\n for num in i:\n xlist.append(num)\n return sum(xlist)","def evaluate(self, seq, begin, end, *args):\n return reduce(operator.add, args, [])","def calculate_ranges(a, b):\n try:\n ranges = list(range(0, a, a//b))\n if ranges[-1] != a:\n ranges.append(a)\n return ranges\n except ValueError:\n return [0, a]","def total_calculate_sum_v2(org_id=None, start=None, end=None):\n check_instance(start)\n check_instance(end)\n result = OrderedDict()\n query = '''SELECT organization.id, organization.name, org_type.name FROM organization INNER JOIN org_type ON\n organization.type = org_type.id'''\n if org_id is not None:\n query += ' WHERE organization.id={}'.format(org_id)\n organizations = org_cursor.execute(query).fetchall()\n if not start:\n query = '''SELECT datetime FROM ps_transaction LIMIT 1'''\n datetime_data = trans_cursor.execute(query).fetchone()[0]\n start = datetime.strptime(datetime_data, '%Y-%m-%d %H:%M:%S')\n if not end:\n end = datetime.now()\n if start > end:\n raise ValueError('Start date must be lower than end date.')\n query = '''SELECT * FROM ps_transaction WHERE datetime >= ? AND datetime <= ? AND type = 1 ORDER BY org_id'''\n transactions = trans_cursor.execute(query, (start, end, )).fetchall()\n for item in organizations:\n trs = [tr for tr in transactions if item[0] == tr[6]]\n sum_org = sum([tr[-1] for tr in trs])\n result[item] = sum_org / 100\n total = [['Organization name', 'Organization type', 'Indebtedness']]\n for key, value in result.items():\n # print('{}. {} ({}): {} рублей.'.format(key[0], key[1], key[2], value))\n total.append([key[1], key[2], value])\n return total","def summationLoop(lower, upper):\r\n sum = 0\r\n for i in range(lower, upper + 1):\r\n sum += i\r\n return sum","def timeseries_list_sum(data, series_list, field_spec):\n return TimeSeries.timeseries_list_reduce(data, series_list, Event.sum, field_spec)","def _merge_ranges(ranges: Iterable[Range]) -> _LinkedList[Range]:\n # sort our list of ranges, first\n ranges = _LinkedList(sorted(ranges))\n # # determine if we need to do anything\n # if len(ranges) < 2:\n # return\n # try to merge each range with the one after it, up until the end of the list\n node = ranges.first\n while node and node.next:\n prev_range = node.value\n next_range = node.next.value\n new_range = prev_range.union(next_range)\n if new_range is not None: # TODO python 3.8 refactoring - this is a great place for :=\n node.value = new_range\n ranges.pop_after(node)\n else:\n node = node.next\n return ranges","def sum(values):\n total = 0\n for i in values:\n total += i\n return total","def gen_ranges(starts, ends):\n if starts.size != ends.size:\n raise ValueError(\"starts and ends must be same size\")\n if not ((ends - starts) > 0).all():\n raise ValueError(\"all ends must be greater than starts\")\n lengths = ends - starts\n segs = ak.cumsum(lengths) - lengths\n totlen = lengths.sum()\n slices = ak.ones(totlen, dtype=ak.int64)\n diffs = ak.concatenate((ak.array([starts[0]]), \n starts[1:] - starts[:-1] - lengths[:-1] + 1))\n slices[segs] = diffs\n return segs, ak.cumsum(slices)","def get_ranges(self, tchain, kw):\n (lo, hi) = (\"min\", \"max\")\n ran = None\n for t in tchain:\n rstmt = t.search_one(kw)\n if rstmt is None: continue\n ran = [ i.split(\"..\") for i in rstmt.arg.split(\"|\") ]\n if ran[0][0] != 'min': lo = ran[0][0]\n if ran[-1][-1] != 'max': hi = ran[-1][-1]\n if ran is None: return None\n if len(ran) == 1:\n return [(lo, hi)]\n else:\n return [(lo, ran[0][-1])] + ran[1:-1] + [(ran[-1][0], hi)]","def sum(iterable: typing.Iterable, start: float = 0) -> float:\n return sum(iterable, start)","def parse_range(seq: str) -> list[int]:\n seq = seq.split(\",\")\n acc = []\n for i in seq:\n m = re.match(r\" *(?P<start>\\d+) *(- *(?P<end>\\d+))? *\", i)\n\n if not m:\n continue\n\n a = [m.group(\"start\"), m.group(\"end\")]\n a = [int(x) for x in a if x]\n\n if len(a) > 1:\n a = range(int(a[0]), int(a[1] + 1))\n\n acc.append(a)\n\n return list(\n set([x for x in list(itertools.chain.from_iterable(acc)) if x])\n )","def sum_app(*numbers_to_sum: typing.Tuple[int, ...]):\n return sum(numbers_to_sum)","def _input_intervals():\n last_timestamp = self._trace.interval.start\n for ir_event in filter_by_task(all_tasks, 'name', 'InputReader', 'any'):\n if last_timestamp <= ir_event.interval.end:\n yield Interval(last_timestamp, ir_event.interval.end)\n last_timestamp = ir_event.interval.end","def _combine_ind_ranges(ind_ranges_to_merge):\n ind_ranges_to_merge = sorted(ind_ranges_to_merge)\n stack = []\n result = []\n for curr in ind_ranges_to_merge:\n if len(stack) == 0:\n stack.append(curr)\n elif stack[-1][-1] >= curr[0]:\n prev = stack.pop()\n merged = sorted(list(set(prev + curr)))\n stack.append(merged)\n else:\n prev = stack.pop()\n result.append(prev)\n stack.append(curr)\n result += stack\n return result","def _intervals(parts, duration) -> list:\n part_duration = duration / parts\n return [(floor(i * part_duration), floor((i + 1) * part_duration)) for i in range(parts)]","def count_hours(ranges, range_start=None, range_stop=None):\n buckets = [0.0] * (24 * 7)\n one_hour = timedelta(0, 60 * 60)\n\n first = None\n last = None\n\n for start, stop in ranges:\n if ((range_start is not None and start < range_start) or\n (range_stop is not None and stop > range_stop)):\n continue\n\n if first is None:\n first = start\n last = stop\n\n open_ref = datetime(start.year, start.month, start.day, start.hour)\n open_ref += one_hour\n open_ref = min(open_ref, stop)\n open_frac = (open_ref - start) / one_hour\n buckets[24 * start.weekday() + start.hour] += open_frac\n\n if start.date() != stop.date() or start.hour != stop.hour:\n stop_ref = datetime(stop.year, stop.month, stop.day, stop.hour)\n stop_frac = (stop - stop_ref) / one_hour\n buckets[24 * stop.weekday() + stop.hour] += stop_frac\n\n start_hour = 24 * open_ref.weekday() + open_ref.hour\n stop_hour = 24 * stop.weekday() + stop.hour\n if stop_hour < start_hour - 1:\n stop_hour += 24 * 7\n\n for hour in range(start_hour, stop_hour):\n buckets[hour % (24 * 7)] += 1\n\n return buckets, first, last","def interval_MAX_SMT(intervals):\n lower_indices = np.argsort(intervals[:, 0])\n lower_sorted = intervals[lower_indices, 0]\n\n upper_indices = np.argsort(intervals[:, 1])\n upper_sorted = intervals[upper_indices, 1]\n\n best_lower, best_upper = 0, 0\n upper_i = 0\n best_met = -1\n n_met = 0\n for lower_i, lower in enumerate(lower_sorted):\n # First, we update upper -- everything in this loop is an interval\n # we were meeting before but not anymore.\n while upper_sorted[upper_i] < lower:\n n_met -= 1\n upper_i += 1\n # We now meet the interval that this lower is from.\n n_met += 1\n if n_met > best_met:\n best_lower, best_upper = lower, upper_sorted[upper_i]\n best_met = n_met\n elif (len(lower_sorted) - lower_i) < (best_met - n_met):\n # Each iteration adds *at most* 1 to n_met. For us to even have\n # a chance of updating best_met, then, we will have to do at\n # least (best_met - n_met) more iterations.\n break\n return best_lower, best_upper, best_met","def Str2IntervalUnion(s):\n try:\n tokens = s.split(',')\n intervals = []\n for token in tokens:\n a_str = token\n b_str = token\n i_separator = token.find('-')\n if i_separator == -1:\n i_separator = token.find('*')\n if i_separator == -1:\n i_separator = token.find(':')\n if i_separator != -1:\n a_str = token[:i_separator]\n b_str = token[i_separator+1:]\n if (len(a_str) == 0) or (a_str in ('-','*',':')):\n a = 0\n else:\n a = int(a_str)\n if (len(b_str) == 0) or (b_str in ('-','*',':')):\n b = 0\n else:\n b = int(b_str)\n intervals.append((a,b))\n return intervals\n except (ValueError) as err:\n sys.stderr.write('Error: Incorrect format in numeric interval: \"'+s+'\"\\n')\n sys.exit(-1)","def SUM(*args):\n return _group_function(lambda x, y: x + y, *args)","def intervals_for(self, day: dt.date) -> List[Interval]:\n relevant_intervals: List[Interval] = []\n\n for interval in self.intervals:\n if interval.start_time.date() == day:\n relevant_intervals.append(interval)\n\n return relevant_intervals","def sum_values(self):\n raise NotImplementedError","def total_range_size(self) -> int:\n if not len(self):\n return 0\n regions = merge(self.data, bp=1)\n return regions.end.sum() - regions.start.sum()"],"string":"[\n \"def intervals(start, end, delta, intervals_right_closed=False):\\r\\n result = []\\r\\n interval_start, interval_end = None, None\\r\\n while True:\\r\\n interval_start = start if interval_end is None else interval_end\\r\\n interval_end = min(end, interval_start + delta)\\r\\n result.append(thalesians.tsa.intervals.Interval(\\r\\n interval_start, interval_end,\\r\\n not intervals_right_closed, intervals_right_closed))\\r\\n if interval_end == end: break\\r\\n return result\",\n \"def intervals_between(self, start: dt.datetime, end: dt.datetime) -> List[Interval]:\\n relevant_intervals: List[Interval] = []\\n\\n for interval in self.intervals:\\n if start <= interval.start_time < end:\\n relevant_intervals.append(interval)\\n\\n return relevant_intervals\",\n \"def merge(intervals):\\n intervals.sort(key=lambda x: x[0])\\n # take the first interval\\n merged = [intervals[0]]\\n # loop through all the intervals\\n for this_interval in intervals:\\n if this_interval[0] <= merged[-1][1]:\\n merged[-1] = (merged[-1][0], max(merged[-1][1], this_interval[1]))\\n else:\\n merged.append(this_interval)\\n return merged\",\n \"def time_interval(intervals: List[Tuple[int, int]]) -> int:\\n\\n start = []\\n finish = []\\n for elems in intervals:\\n start.append(elems[0])\\n finish.append(elems[1])\\n\\n # Sorting the start and end times separately\\n start.sort()\\n finish.sort()\\n\\n index1, index2 = 0, 0\\n current_rooms = 0\\n max_rooms = 0\\n # The logic below is, we add each room when current finish time is greater than current start time\\n # This informs us whether how many times are collapsing with each other at any time\\n while (index1 < len(start)) and (index2 < len(finish)):\\n if start[index1] < finish[index2]:\\n current_rooms += 1\\n index1 += 1\\n else:\\n index2 += 1\\n max_rooms = max(max_rooms, current_rooms)\\n current_rooms -= 1\\n max_rooms = max(max_rooms, current_rooms)\\n return max_rooms\",\n \"def collapse_ranges (ranges):\\n\\n # FIXME: does tuple and set conversion really add anything?\\n\\n # Ranges must be unique: we do not count timings when they start and end at\\n # exactly the same time. By using a set, we do not repeat ranges.\\n # we convert to a list before return.\\n final = set()\\n\\n # return empty list if given an empty list\\n if not ranges:\\n return final\\n\\n START = 0\\n END = 1\\n\\n # sort ranges into a copy list, by their start time\\n _ranges = sorted(ranges, key=lambda x: x[START])\\n\\n # sat 'base' to the earliest range (smallest starting point)\\n base = _ranges[0]\\n\\n for _range in _ranges[1:]:\\n\\n # if range is empty, skip it\\n if _range[START] == _range[END]:\\n continue\\n\\n if _range[START] <= base[END]:\\n # ranges overlap -- extend the base\\n base[END] = max(base[END], _range[END])\\n\\n else:\\n # ranges don't overlap -- move base to final, and current _range\\n # becomes the new base\\n final.add(tuple(base))\\n base = _range\\n\\n # termination: push last base to final\\n final.add(tuple(base))\\n\\n # Return final as list of list in case a mutable type is needed.\\n return [list(b) for b in final]\",\n \"def range_sum(self, left: int, right: int) -> int:\\n if right < left:\\n raise ValueError(\\\"Right needs to be >= left\\\")\\n return self._prefix_sum(right) - self._prefix_sum(left - 1)\",\n \"def joinIntervalsSum(myIntervals,start='start',end='end',score='readcount',sampleName=\\\".\\\",offset=0):\\n \\n if not myIntervals: return myIntervals\\n non_overlapping = []\\n sep = {'+':[],'-':[]}\\n \\n print \\\"Splitting intervals by strand\\\"\\n for i in myIntervals:\\n sep[i.strand].append(i)\\n \\n print \\\"Joining intervals...\\\"\\n for strand in sep.keys():\\n print strand\\n intervals = sep[strand]\\n intervals.sort()\\n \\n \\n current = copy.copy(intervals[0])\\n for x in intervals[1:]:\\n next = copy.copy(x)\\n if current.intersects(next, start=start, end=end,offset=offset):\\n current.end = max(current.end,next.end)\\n current.__dict__[score] = current.__dict__[score]+next.__dict__[score]\\n else:\\n current.name = sampleName\\n non_overlapping.append(current)\\n current = copy.copy(next)\\n current.name=sampleName\\n non_overlapping.append(current)\\n print \\\"Sorting intervals\\\"\\n non_overlapping.sort()\\n print \\\"Done\\\"\\n return non_overlapping\",\n \"def sum_range(self, lower, upper):\\n if upper>self.upper:\\n upper=self.upper\\n if lower<self.lower:\\n lower = self.lower\\n\\n i_l = int(np.floor((lower-self.lower)/self._dx))\\n i_u = int(np.floor((upper-self.lower)/self._dx))\\n total = 0.0\\n for i in range(i_l,i_u):\\n total+= self.y[i]\\n return total\",\n \"def _interval_tuples(self, interval, entries):\\n date_first, date_last = getters.get_min_max_dates(entries,\\n (Transaction))\\n\\n if not date_first:\\n return []\\n\\n interval_tuples = []\\n while date_first <= date_last:\\n next_date = get_next_interval(date_first, interval)\\n interval_tuples.append((date_first, next_date))\\n date_first = next_date\\n\\n return interval_tuples\",\n \"def _build_intervals(self) -> List[Tuple[datetime.datetime, datetime.datetime]]:\\n if self.granularity == 'HOUR':\\n days = max(min((self.bounds[1] - self.bounds[0]).days,\\n self.GRANULARITIES['HOUR'][1]),\\n self.GRANULARITIES['HOUR'][0])\\n interval_length = datetime.timedelta(days=days)\\n offset = datetime.timedelta(hours=1)\\n elif self.granularity == 'MONTH':\\n # no need to split requests for monthly data\\n days = max((self.bounds[1] - self.bounds[0]).days,\\n self.GRANULARITIES['MONTH'][0])\\n interval_length = datetime.timedelta(days=days)\\n offset = datetime.timedelta(days=1)\\n else:\\n days = max(min((self.bounds[1] - self.bounds[0]).days,\\n self.GRANULARITIES['DAY'][1]),\\n self.GRANULARITIES['DAY'][0])\\n interval_length = datetime.timedelta(days=days)\\n offset = datetime.timedelta(days=1)\\n\\n time_pointer = self.bounds[1]\\n intervals = []\\n while time_pointer > self.bounds[0]:\\n upper = time_pointer\\n time_pointer -= interval_length\\n intervals.append((time_pointer, upper))\\n time_pointer -= offset\\n return intervals\",\n \"def apply_list_intervals(list_, intervals):\\n return [list_[interval] for interval in intervals]\",\n \"def overlaps(interval,intervals):\\n return [x for x in intervals if interval.overlaps(x)]\",\n \"def define_intervals(self):\\n i = 5 # a step of increment\\n interval_sum = self.min_step\\n interval_list = [self.min_step]\\n while interval_sum < self.max_step:\\n interval_sum += i\\n interval_list.append(interval_sum)\\n # interval_list.append(self.max_step)\\n # print(\\\"Intervals\\\", interval_list)\\n return interval_list\",\n \"def intervals(start, end, delta):\\n intervals = []\\n current = copy.deepcopy(start)\\n while current < end:\\n intervals.append((unix_to_iso(current.strftime('%s')),\\n unix_to_iso((current + delta).strftime('%s'))))\\n current += delta\\n return intervals\",\n \"def merge_genomic_intervals(intervals):\\n # type: (Iterable[Tuple[str, int, int]]) -> Iterable[Tuple[str, int, int]]\\n\\n # Group intervals by chromosome.\\n grouped_intervals = itertools.groupby(\\n sorted(intervals), operator.itemgetter(0))\\n\\n # Now yield merged intervals per chromosome.\\n for chrom, grp in grouped_intervals:\\n chrom_intervals = [interval[1:] for interval in grp]\\n for low, high in merge_intervals(chrom_intervals, is_sorted=True):\\n yield chrom, low, high\",\n \"def sum(self, start: int = 0, end: Optional[Any] = None) -> Any:\\n return self.reduce(start, end)\",\n \"def timedInterval(self, start, end=False):\\n\\n assert type(start) == float\\n interval1 = min(\\n enumerate(self.intervals), key=lambda x: abs(x[1].xmin - start))\\n\\n if end:\\n assert type(end) == float\\n interval2 = self.timedInterval(end)\\n else:\\n interval2 = interval1\\n\\n return (interval1[0], interval2[0] + 1)\",\n \"def bin_intervals(intervals, bins, interval_range=None, smoothing_window=None,\\n nan_replacement=None, zero_to_nan=False):\\n if intervals is None:\\n return []\\n\\n intervals = np.array(list(intervals))\\n\\n if interval_range is None:\\n try:\\n interval_range = (min(intervals[:, 0]), max(intervals[:, 1]))\\n except (IndexError, TypeError):\\n raise ValueError(\\\"intervals cannot be None or length 0 if not providing interval_range!\\\")\\n\\n bin_size = (interval_range[1] - interval_range[0] + 1) / bins\\n logger.debug(\\\"Bin size: {}\\\".format(bin_size))\\n\\n return RegionBased._bin_intervals_equidist(intervals, bin_size, interval_range, bins=bins,\\n smoothing_window=smoothing_window,\\n nan_replacement=nan_replacement,\\n zero_to_nan=zero_to_nan)\",\n \"def getIntervals(t_start,t_end,interval):\\n timestamp_start = pd.Timestamp(t_start)\\n timestamp_end = pd.Timestamp(t_end)\\n nro_elements=int(int((timestamp_end-timestamp_start).total_seconds())/(60*interval))\\n list_dates = []\\n list_dates.append(t_start)\\n for i in range(nro_elements):\\n date_time_str = list_dates[i]\\n Current_Date = dt.datetime.strptime(date_time_str, '%Y-%m-%d %H:%M:%S')\\n Next_Date = Current_Date + datetime.timedelta(minutes=interval)\\n list_dates.append(str(Next_Date))\\n return list_dates\",\n \"def range_union(ranges):\\n union = []\\n for r in sorted(ranges, key=lambda r: r.start):\\n if len(union) > 0 and union[-1].stop >= r.start:\\n union[-1] = range(union[-1].start, max(union[-1].stop, r.stop))\\n else:\\n union.append(r)\\n return union\",\n \"def make_elementary_intervals(interval_lists):\\n # copy so we don't destroy the original lists\\n interval_lists = [[i for i in il] for il in interval_lists]\\n elem_intervals = []\\n while interval_lists:\\n elem_intervals.append(min(intervals[0] for intervals in interval_lists))\\n i = 0\\n while i < len(interval_lists):\\n intervals = interval_lists[i]\\n if intervals[0] == elem_intervals[-1]:\\n intervals.pop(0)\\n if not intervals:\\n interval_lists.pop(i)\\n else:\\n i += 1\\n return elem_intervals\",\n \"def sum_range(nums, start=0, end=None):\\n n = len(nums)\\n if end == None:\\n nz = n\\n elif end > n:\\n nz = n\\n else:\\n nz = end\\n\\n return sum(nums[start:nz + 1])\",\n \"def interval(self):\\n return Intersection(*(a.interval for a in self.args))\",\n \"def count_within(values, intervals):\\n rvals = np.reshape(values, [-1, 1])\\n intervals_m = np.array(intervals)\\n rlow = intervals_m[:, 0].reshape(1, -1)\\n rhigh = intervals_m[:, 1].reshape(1, -1)\\n\\n flags = (rlow <= rvals) * (rvals < rhigh)\\n return np.sum(flags, axis=0)\",\n \"def sum_tuples(t1, t2):\\n return tuple(sum(t) for t in zip(t1, t2))\",\n \"def calculate_intervals(tick_times: List[float]) -> List[float]:\\n return [tick_times[i] - tick_times[i - 1] for i in range(1, len(tick_times))]\",\n \"def sum(self, start=0, end=None):\\n return super().reduce(start, end)\",\n \"def calc_lenient_interval_stats(all_intervals: Collection[float]) -> Tuple[float, float, float]:\\n\\n # We need to be a little lenient on the min and max intervals: we are trying to achieve a constant rate rather than exact intervals.\\n # To achieve this leniency, I'm going to sort the intervals and discard outliers (the highest and lowest 5% of values)\\n discard_count = max(1, len(all_intervals) // 20)\\n sorted_intervals = sorted(all_intervals)\\n intervals = sorted_intervals[discard_count:-discard_count]\\n min_interval = intervals[0]\\n max_interval = intervals[-1]\\n mean_interval = statistics.mean(intervals)\\n return max_interval, mean_interval, min_interval\",\n \"def find_overlap_range_list(points_list):\\n highest_start_point = points_list[0][0]\\n lowest_end_point = points_list[0][0] + points_list[0][1]\\n\\n for point in points_list[1:]:\\n highest_start_point = max(highest_start_point, point[0])\\n lowest_end_point = min(lowest_end_point, point[1]+point[0])\\n\\n if lowest_end_point <= highest_start_point:\\n return None\\n\\n return [highest_start_point, lowest_end_point - highest_start_point]\",\n \"def sum(self, values):\\n return self.aggregate(values, \\\"sum\\\")\",\n \"def summarize(requestContext, seriesList, intervalString, func='sum', alignToFrom=False):\\n results = []\\n delta = parseTimeOffset(intervalString)\\n interval = delta.seconds + (delta.days * 86400)\\n\\n for series in seriesList:\\n buckets = {}\\n\\n timestamps = range( int(series.start), int(series.end), int(series.step) )\\n datapoints = zip(timestamps, series)\\n\\n for timestamp_, value in datapoints:\\n if alignToFrom:\\n bucketInterval = int((timestamp_ - series.start) / interval)\\n else:\\n bucketInterval = timestamp_ - (timestamp_ % interval)\\n\\n if bucketInterval not in buckets:\\n buckets[bucketInterval] = []\\n\\n if value is not None:\\n buckets[bucketInterval].append(value)\\n\\n if alignToFrom:\\n newStart = series.start\\n newEnd = series.end\\n else:\\n newStart = series.start - (series.start % interval)\\n newEnd = series.end - (series.end % interval) + interval\\n\\n newValues = []\\n for timestamp_ in range(newStart, newEnd, interval):\\n if alignToFrom:\\n newEnd = timestamp_\\n bucketInterval = int((timestamp_ - series.start) / interval)\\n else:\\n bucketInterval = timestamp_ - (timestamp_ % interval)\\n\\n bucket = buckets.get(bucketInterval, [])\\n\\n if bucket:\\n if func == 'avg':\\n newValues.append( float(sum(bucket)) / float(len(bucket)) )\\n elif func == 'last':\\n newValues.append( bucket[len(bucket)-1] )\\n elif func == 'max':\\n newValues.append( max(bucket) )\\n elif func == 'min':\\n newValues.append( min(bucket) )\\n else:\\n newValues.append( sum(bucket) )\\n else:\\n newValues.append( None )\\n\\n if alignToFrom:\\n newEnd += interval\\n\\n newName = \\\"summarize(%s, \\\\\\\"%s\\\\\\\", \\\\\\\"%s\\\\\\\"%s)\\\" % (series.name, intervalString, func, alignToFrom and \\\", true\\\" or \\\"\\\")\\n newSeries = TimeSeries(newName, newStart, newEnd, interval, newValues)\\n newSeries.pathExpression = newName\\n results.append(newSeries)\\n\\n return results\",\n \"def overlapping_ranges(\\n ranges_1: Sequence[Tuple[int, int]],\\n ranges_2: Sequence[Tuple[int, int]],\\n) -> List[Tuple[int, int]]:\\n return [\\n (max(first[0], second[0]), min(first[1], second[1]))\\n for first in ranges_1\\n for second in ranges_2\\n if max(first[0], second[0]) < min(first[1], second[1])\\n ]\",\n \"def sum_values(values):\\n return (sum(values))\",\n \"def sum_range(lower, upper):\\n\\n def copies(pmin, pmax):\\n if lower <= pmin and pmax <= upper:\\n return True\\n elif pmax > upper:\\n return False\\n return copies(pmin + 50, pmax + 60) or copies(pmin + 130, pmax + 140)\\n\\n return copies(0, 0)\",\n \"def calcOverlap(intervals):\\n bp = 0 \\n for i in intervals:\\n bp += sum([overlapCases(i, j) for j in intervals])\\n return(bp)\",\n \"def total_calculate_sum(start=None, end=None):\\n check_instance(start)\\n check_instance(end)\\n result = OrderedDict()\\n query = '''SELECT organization.id, organization.name, org_type.name FROM organization INNER JOIN org_type ON\\n organization.type = org_type.id'''\\n organizations = org_cursor.execute(query).fetchall()\\n if not start:\\n query = '''SELECT datetime FROM ps_transaction LIMIT 1'''\\n datetime_data = trans_cursor.execute(query).fetchone()[0]\\n start = datetime.strptime(datetime_data, '%Y-%m-%d %H:%M:%S')\\n if not end:\\n end = datetime.now()\\n if start > end:\\n raise ValueError('Start date must be lower than end date.')\\n query = '''SELECT * FROM ps_transaction WHERE datetime >= ? AND datetime <= ? AND type = 1 ORDER BY org_id'''\\n transactions = trans_cursor.execute(query, (start, end, )).fetchall()\\n for item in organizations:\\n sum_org = 0\\n for tr in transactions:\\n if item[0] == tr[6]:\\n sum_org += tr[-1]\\n result[item] = sum_org / 100\\n report_data = [[start, end]]\\n report_data.append(['ID', 'Name', 'Type', 'Amount'])\\n for key, value in result.items():\\n report_data.append([key[0], key[1], key[2], value])\\n return report_data\",\n \"def cumulative_sum_range(magnitudes):\\n cum_sums = np.cumsum(magnitudes)\\n\\n cum_sum_range = np.max(cum_sums) - np.min(cum_sums)\\n\\n return cum_sum_range\",\n \"def fuseinterval(intervals, dist):\\n # if we have only one interval return it\\n if len(intervals) == 1:\\n return intervals\\n # sort based on the coordinate of the end of the interval\\n intervals = sorted(intervals,\\n key=lambda interval: interval[1])\\n\\n # go through interval, fuse the one that are adjacent\\n fuseintervals = []\\n previnterval = intervals[0]\\n # check adjacent intervals and merge them, return a list of intervals\\n for interval in intervals[1:]:\\n # adjacency: end of previous + 1 = start of current interval\\n if int(previnterval[1]+50) >= int(interval[0]):\\n previnterval[1] = interval[1]\\n else:\\n fuseintervals.append(previnterval)\\n previnterval = interval\\n # last element has to be appended too\\n fuseintervals.append(previnterval)\\n # reverse sort the interval, so that by\\n # removing the vector sequence, the coordinates are\\n # still actual.\\n fuseintervals = sorted(fuseintervals,\\n key=lambda interval: interval[1], reverse=True)\\n\\n # check how many intervals\\n return fuseintervals\",\n \"def make_intervals(self):\\n if not self:\\n return []\\n intervals = []\\n self.sort()\\n index_of_the_first = 0\\n for i in range(len(self) - 1): # i: indexes from zero to len(self)\\n if self[i] + 1 == self[i+1] or self[i] == self[i+1]:\\n continue\\n # elif self[i] == self[i+1]:\\n # not_uniq.append( (self.count(self[i]), self[i]) )\\n else:\\n intervals.append((self[index_of_the_first], self[i]))\\n index_of_the_first = i + 1\\n # And now the last element:\\n last_index = len(self) - 1\\n intervals.append((self[index_of_the_first], self[last_index]))\\n return intervals\",\n \"def sum_range(lower, upper):\\n\\n def copies(pmin, pmax):\\n if lower <= pmin and pmax <= upper:\\n return True\\n elif pmax > upper:\\n return False\\n return copies(pmin+50, pmax+60)\\n\\n return copies(0, 0)\",\n \"def build_date_bins(start_date, end_date, interval):\\n if interval > 1:\\n bins = np.array([np.datetime64(datetime.datetime(start_date.year, start_date.month,\\n start_date.day, start_date.hour,\\n (start_date.minute // interval) * interval)\\n + datetime.timedelta(minutes=interval) * i) for i\\n in range(int((end_date - start_date).total_seconds() / 60 / interval))])\\n else:\\n bins = np.array([np.datetime64(datetime.datetime(start_date.year, start_date.month,\\n start_date.day, start_date.hour,\\n start_date.minute)\\n + datetime.timedelta(minutes=interval) * i) for i\\n in range(int((end_date - start_date).total_seconds() / 60))])\\n return bins\",\n \"def interval_totals(self, interval, account_name, accumulate=False):\\n if isinstance(account_name, str):\\n names = [account_name]\\n else:\\n names = account_name\\n\\n interval_tuples = self._interval_tuples(interval, self.entries)\\n date_first, _ = getters.get_min_max_dates(self.entries, (Transaction))\\n return [{\\n 'begin_date': begin_date,\\n 'end_date': end_date,\\n 'totals': self._balances_totals(\\n names, begin_date if not accumulate else date_first, end_date),\\n } for begin_date, end_date in interval_tuples]\",\n \"def test__get_intervals(self):\\n # Setup\\n data = pd.Series(['bar', 'foo', 'foo', 'tar'])\\n\\n # Run\\n result = CategoricalTransformer._get_intervals(data)\\n\\n # Asserts\\n expected_intervals = {\\n 'foo': (0, 0.5, 0.25, 0.5 / 6),\\n 'tar': (0.5, 0.75, 0.625, 0.25 / 6),\\n 'bar': (0.75, 1, 0.875, 0.25 / 6)\\n }\\n assert result == expected_intervals\",\n \"def summationReduce(lower, upper):\\r\\n if lower > upper:\\r\\n return 0\\r\\n else:\\r\\n return reduce(lambda x, y: x + y, range(lower, upper + 1))\",\n \"def sum(self, start=0, end=None):\\n return super(SumSegmentTree, self).reduce(start, end)\",\n \"def sum(self, start=0, end=None):\\n return super(SumSegmentTree, self).reduce(start, end)\",\n \"def sum(self, start=0, end=None):\\n return super(SumSegmentTree, self).reduce(start, end)\",\n \"def sum(self, start=0, end=None):\\n return super(SumSegmentTree, self).reduce(start, end)\",\n \"def sum(self, start=0, end=None):\\n return super(SumSegmentTree, self).reduce(start, end)\",\n \"def two_sum(array):\\n\\n WIDTH = 10000\\n out = set()\\n for i in array:\\n lower = bisect.bisect_left(array, -WIDTH - i)\\n upper = bisect.bisect_right(array, WIDTH - i)\\n out |= set([i + j for j in array[lower:upper]])\\n return out\",\n \"def findIntervals(intervals, length, weights, maximum, graph):\\r\\n intervals_used = []\\r\\n vertex = maximum\\r\\n while vertex > 1:\\r\\n for k in range(length):\\r\\n if vertex == intervals[k][1] and weights[k] != 0 \\\\\\r\\n and graph[vertex] == graph[intervals[k][0]] + weights[k]:\\r\\n intervals_used.insert(0, intervals[k]) # insertion at the beginning\\r\\n vertex = intervals[k][0] + 1 # +1 because of adjacent intervals\\r\\n vertex -= 1\\r\\n \\r\\n return intervals_used\",\n \"def test_intervals(self):\\n x = np.array([-5, -3, -2, -2, 100])\\n self.assertEqual(\\n npinterval.interval(x, 2/5),\\n (-2, -2, 2, 4))\\n self.assertEqual(\\n npinterval.interval(x, 3/5),\\n (-3, -2, 1, 4))\\n self.assertEqual(\\n npinterval.interval(x, 4/5),\\n (-5, -2, 0, 4))\",\n \"def mergeOverlapping(intlist):\\n \\n intlist.sort(key=lambda x: x.minval)\\n newint=[intlist[0]]\\n\\n for elem in intlist[1:]:\\n try:\\n newint[-1]=mergeIntervals(elem,newint[-1])\\n except:\\n newint.append(elem)\\n \\n return newint\",\n \"def count_within_bkd(values, intervals):\\n return OrderedDict([\\n (tuple(i), c)\\n for i, c in zip(intervals, count_within(values, intervals))\\n ])\",\n \"def sumRange(self, i, j):\\n return self.sums[j]-self.sums[i-1] if i>0 else self.sums[j]\",\n \"def list_by_list(list_to_be_splited, list_with_intervals):\\n intervals = []\\n for x, val in enumerate(list_to_be_splited):\\n for y in list_with_intervals:\\n if y == val:\\n intervals.append((x, val))\\n return intervals\",\n \"def sumRange(self, i, j):\\n if not self.nums: return 0 # edge case\\n return self.sum(j+1)-self.sum(i)\",\n \"def enumerateIntervals(bounds, stride):\\n def alignDown(chunk, x):\\n return (x/chunk)*chunk\\n def alignUp(chunk, x):\\n return int(math.ceil(float(x)/chunk)*chunk)\\n\\n start, end = bounds\\n roundStart = alignDown(stride, start)\\n roundEnd = alignUp (stride, end)\\n\\n for s in xrange(roundStart, roundEnd, stride):\\n roundWin = (s, s + stride)\\n yield intersection(bounds, roundWin)\",\n \"def range(self):\\n lows, highs = [], []\\n for graph in self._graphs.values():\\n low, high = graph.range()\\n lows.append(low)\\n highs.append(high)\\n return (min(lows), max(highs))\",\n \"def solution(intervals):\\n solution = Solution()\\n output = solution.merge(intervals)\\n\\n print(output)\",\n \"def get_intervals(l):\\n intervals = len(l) * [0]\\n # Initalize with 1\\n intervals[0] = 1\\n for k in range(1, len(l)):\\n intervals[k] = (len(l[k]) + 1) * intervals[k - 1]\\n\\n return intervals\",\n \"def sum_numbers(op_sum):\\n collection = []\\n for op in op_sum.operators:\\n # Strip numeric coefficient off\\n collected = False\\n num = 1\\n n_removed = 0\\n op = collect_numbers(op)\\n if op.tensors[0].name == \\\"$number\\\":\\n num = op.tensors[0].content\\n new_op = Operator(op.tensors[1:])\\n else:\\n num = 1\\n new_op = op\\n\\n # Sum the numbers of equal operators\\n for i, (collected_op, collected_num) in enumerate(collection):\\n if collected_op == new_op:\\n collected = True\\n collection[i] = (new_op, num + collected_num)\\n break\\n if not collected:\\n collection.append((new_op, num))\\n return [(o, num) for o, num in collection if abs(num) > 10**(-10)]\",\n \"def bin_intervals_equidistant(intervals, bin_size, interval_range=None, smoothing_window=None,\\n nan_replacement=None, zero_to_nan=False):\\n if intervals is None:\\n return []\\n\\n intervals = np.array(list(intervals))\\n\\n if interval_range is None:\\n try:\\n interval_range = (min(intervals[:, 0]), max(intervals[:, 1]))\\n except (IndexError, TypeError):\\n raise ValueError(\\\"intervals cannot be None or length 0 if not providing interval_range!\\\")\\n\\n if isinstance(interval_range, GenomicRegion):\\n interval_range = (interval_range.start, interval_range.end)\\n\\n return RegionBased._bin_intervals_equidist(intervals, bin_size, interval_range,\\n smoothing_window=smoothing_window,\\n nan_replacement=nan_replacement,\\n zero_to_nan=zero_to_nan)\",\n \"def sumRange(self, i, j):\\r\\n return self.dp[i][j]\",\n \"def init_data_sum(self, intervals):\\n self.data_sum_win = []\\n self.data_sum_lose = []\\n\\n for i in range(len(intervals)):\\n interval_size = len(intervals[i])\\n self.data_sum_win.append([0]*interval_size)\\n self.data_sum_lose.append([0]*interval_size)\\n\\n for i in range(len(self.group_col)):\\n interval_size = len(self.group_col[i])\\n self.data_sum_win.append([0]*interval_size)\\n self.data_sum_lose.append([0]*interval_size)\",\n \"def event_intervals(self, name=None, task=None,\\n interval=None, match_exact=True):\\n if name is None:\\n intervals = \\\\\\n IntervalList(sorted_items(self._tmw_intervals_by_name.values()))\\n elif isinstance(name, string_types):\\n if match_exact:\\n intervals = self._tmw_intervals_by_name[name]\\n else:\\n intervals = IntervalList(sorted_items(value for key, value in\\n self._tmw_intervals_by_name.iteritems() if name in key))\\n else: # assume iterable (must match exact)\\n intervals = IntervalList(sorted_items(value for key, value in\\n self._tmw_intervals_by_name.iteritems() if key in name))\\n intervals = intervals.slice(interval=interval)\\n if task:\\n intervals = IntervalList(filter(lambda it: it.event.task == task, intervals))\\n\\n return intervals\",\n \"def meeting_rooms_ii(self, intervals):\\n # Solution1: use TreeMap\\n # put intervals into TreeMap, key-> intervals.start, val->intervals.end, use floor to find\\n\\n # Solution2: heap\\n intervals.sort()\\n hp = []\\n for inter in intervals:\\n if hp and hp[0] < inter[0]: heapq.heappop(hp)\\n heapq.heappush(hp, inter[1])\\n return len(hp)\",\n \"def smartSummarize(requestContext, seriesList, intervalString, func='sum', alignToFrom=False):\\n if alignToFrom:\\n log.info(\\\"Deprecated parameter 'alignToFrom' is being ignored.\\\")\\n\\n results = []\\n delta = parseTimeOffset(intervalString)\\n interval = delta.seconds + (delta.days * 86400)\\n\\n # Adjust the start time to fit an entire day for intervals >= 1 day\\n requestContext = requestContext.copy()\\n s = requestContext['startTime']\\n if interval >= DAY:\\n requestContext['startTime'] = datetime(s.year, s.month, s.day)\\n elif interval >= HOUR:\\n requestContext['startTime'] = datetime(s.year, s.month, s.day, s.hour)\\n elif interval >= MINUTE:\\n requestContext['startTime'] = datetime(s.year, s.month, s.day, s.hour, s.minute)\\n\\n for i,series in enumerate(seriesList):\\n # XXX: breaks with summarize(metric.{a,b})\\n # each series.pathExpression == metric.{a,b}\\n newSeries = evaluateTarget(requestContext, series.pathExpression)[0]\\n series[0:len(series)] = newSeries\\n series.start = newSeries.start\\n series.end = newSeries.end\\n series.step = newSeries.step\\n\\n for series in seriesList:\\n buckets = {} # { timestamp: [values] }\\n\\n timestamps = range( int(series.start), int(series.end), int(series.step) )\\n datapoints = zip(timestamps, series)\\n\\n # Populate buckets\\n for timestamp_, value in datapoints:\\n bucketInterval = int((timestamp_ - series.start) / interval)\\n\\n if bucketInterval not in buckets:\\n buckets[bucketInterval] = []\\n\\n if value is not None:\\n buckets[bucketInterval].append(value)\\n\\n\\n newValues = []\\n for timestamp_ in range(series.start, series.end, interval):\\n bucketInterval = int((timestamp_ - series.start) / interval)\\n bucket = buckets.get(bucketInterval, [])\\n\\n if bucket:\\n if func == 'avg':\\n newValues.append( float(sum(bucket)) / float(len(bucket)) )\\n elif func == 'last':\\n newValues.append( bucket[len(bucket)-1] )\\n elif func == 'max':\\n newValues.append( max(bucket) )\\n elif func == 'min':\\n newValues.append( min(bucket) )\\n else:\\n newValues.append( sum(bucket) )\\n else:\\n newValues.append( None )\\n\\n newName = \\\"smartSummarize(%s, \\\\\\\"%s\\\\\\\", \\\\\\\"%s\\\\\\\")\\\" % (series.name, intervalString, func)\\n alignedEnd = series.start + (bucketInterval * interval) + interval\\n newSeries = TimeSeries(newName, series.start, alignedEnd, interval, newValues)\\n newSeries.pathExpression = newName\\n results.append(newSeries)\\n\\n return results\",\n \"def range_overlap(ranges):\\n max_left = 0.0\\n min_right = 1.0\\n for (left, right) in ranges:\\n max_left = max(max_left, left)\\n min_right = min(min_right, right)\\n return (max_left, min_right)\",\n \"def summaryRanges(self, nums):\\n\\t\\tc = 0\\n\\t\\trangeStart = None\\n\\t\\trangeNext = None\\n\\t\\tif nums == []:\\n\\t\\t\\treturn []\\n\\t\\tresult = []\\n\\t\\tfor i, n in enumerate(nums):\\n\\t\\t\\tif rangeStart == None:\\n\\t\\t\\t\\trangeStart = n\\n\\t\\t\\t\\trangeNext = n\\n\\t\\t\\t\\tcontinue\\n\\t\\t\\trangePrevious = rangeNext\\n\\t\\t\\trangeNext += 1\\n\\n\\t\\t\\tif n != rangeNext:\\n\\t\\t\\t\\tif rangePrevious == rangeStart:\\n\\t\\t\\t\\t\\tresult.append(\\\"{}\\\".format(rangeStart))\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\tresult.append(\\\"{}->{}\\\".format(rangeStart, rangePrevious))\\n\\n\\t\\t\\t\\trangeStart = n\\n\\t\\t\\t\\trangePrevious = n\\n\\t\\t\\t\\trangeNext = n\\n\\n\\n\\t\\tif rangeStart == rangeNext:\\n\\t\\t\\tresult.append(\\\"{}\\\".format(rangeStart))\\n\\t\\telse:\\n\\t\\t\\tresult.append(\\\"{}->{}\\\".format(rangeStart, rangeNext))\\n\\n\\t\\treturn result\",\n \"def get_interval_stats(usages):\\n\\n # NOTE: with the current functionality in this particular method, we shouldn't need to worry about having to\\n # localize anything; it makes no difference to the relevance of the intervals, especially as they're not mapped\\n # to any particular dates on the graph or anything\\n\\n prev_time = None\\n interval_data = {'timespans': [],\\n 'total': datetime.timedelta(0),\\n 'longest': datetime.timedelta(0),\\n 'shortest': datetime.timedelta.max,\\n 'average': None,}\\n\\n for use in usages:\\n if prev_time is not None:\\n # current_delta = datetime.timedelta\\n current_delta = use.timestamp - prev_time\\n interval_data['timespans'].append(round_timedelta(current_delta, datetime.timedelta(seconds=1)))\\n\\n prev_time = use.timestamp\\n\\n for span in interval_data['timespans']:\\n if interval_data['longest'] < span:\\n interval_data['longest'] = span\\n\\n if interval_data['shortest'] > span:\\n interval_data['shortest'] = span\\n\\n interval_data['total'] += span\\n\\n # errors here if there are 0 or 1 usages, obviously\\n interval_data['average'] = round_timedelta((interval_data['total'] / (len(usages) - 1)),\\n datetime.timedelta(seconds=1))\\n\\n return interval_data\",\n \"def distance_between_intervals(s1,s2,interval):\\n class __distance_interval:\\n def __init__(self,first_pos, last_pos, first, second):\\n self.distance = last_pos - first_pos\\n self.first = first\\n self.second = second\\n self.start = first_pos\\n self.end = last_pos\\n\\n for first, second in by_two_intervals(s1,s2,interval):\\n # yield pos\\n f1,f2 = first\\n first_pos = f1.last_pos if f1.last_pos < f2.last_pos else f2.last_pos\\n l1, l2 = second\\n last_pos = l1.pos if l1.pos > l2.pos else l2.pos\\n yield __distance_interval(first_pos,last_pos,first,second)\",\n \"def sumRange(self, i, j):\\r\\n # Sum of the range can be obtained by subtracting dp[j] - dp[i-1]\\r\\n return self.dp[j] if i == 0 else self.dp[j] - self.dp[i-1]\",\n \"def findInterval(intervals,interval):\\n low,ind = algorithms.binsearch(intervals,interval.start-1,lambda a,b: cmp(a.start,b))\\n return (low,ind)\",\n \"def interval(start, stop=None, step=1):\\n if stop is None:\\n start, stop = 0, start\\n result = []\\n i = start\\n while i < stop:\\n result.append(i)\\n i += step\\n return result\",\n \"def InIntervalUnion(i, intervals):\\n if len(intervals) == 0:\\n return True\\n accept = False\\n for interval in intervals:\\n assert(len(interval) == 2)\\n if (interval[0] <= i) and ((i <= interval[1]) or (interval[1] == 0)):\\n accept = True\\n return accept\",\n \"def segment_sum(self, left, right):\\n if self.empty():\\n return 0\\n less, _ = self.root.split_sums(left)\\n _, greater = self.root.split_sums(right)\\n return self.sum - less - greater\",\n \"def ll_sum(x):\\n xlist = []\\n for i in x:\\n for num in i:\\n xlist.append(num)\\n return sum(xlist)\",\n \"def evaluate(self, seq, begin, end, *args):\\n return reduce(operator.add, args, [])\",\n \"def calculate_ranges(a, b):\\n try:\\n ranges = list(range(0, a, a//b))\\n if ranges[-1] != a:\\n ranges.append(a)\\n return ranges\\n except ValueError:\\n return [0, a]\",\n \"def total_calculate_sum_v2(org_id=None, start=None, end=None):\\n check_instance(start)\\n check_instance(end)\\n result = OrderedDict()\\n query = '''SELECT organization.id, organization.name, org_type.name FROM organization INNER JOIN org_type ON\\n organization.type = org_type.id'''\\n if org_id is not None:\\n query += ' WHERE organization.id={}'.format(org_id)\\n organizations = org_cursor.execute(query).fetchall()\\n if not start:\\n query = '''SELECT datetime FROM ps_transaction LIMIT 1'''\\n datetime_data = trans_cursor.execute(query).fetchone()[0]\\n start = datetime.strptime(datetime_data, '%Y-%m-%d %H:%M:%S')\\n if not end:\\n end = datetime.now()\\n if start > end:\\n raise ValueError('Start date must be lower than end date.')\\n query = '''SELECT * FROM ps_transaction WHERE datetime >= ? AND datetime <= ? AND type = 1 ORDER BY org_id'''\\n transactions = trans_cursor.execute(query, (start, end, )).fetchall()\\n for item in organizations:\\n trs = [tr for tr in transactions if item[0] == tr[6]]\\n sum_org = sum([tr[-1] for tr in trs])\\n result[item] = sum_org / 100\\n total = [['Organization name', 'Organization type', 'Indebtedness']]\\n for key, value in result.items():\\n # print('{}. {} ({}): {} рублей.'.format(key[0], key[1], key[2], value))\\n total.append([key[1], key[2], value])\\n return total\",\n \"def summationLoop(lower, upper):\\r\\n sum = 0\\r\\n for i in range(lower, upper + 1):\\r\\n sum += i\\r\\n return sum\",\n \"def timeseries_list_sum(data, series_list, field_spec):\\n return TimeSeries.timeseries_list_reduce(data, series_list, Event.sum, field_spec)\",\n \"def _merge_ranges(ranges: Iterable[Range]) -> _LinkedList[Range]:\\n # sort our list of ranges, first\\n ranges = _LinkedList(sorted(ranges))\\n # # determine if we need to do anything\\n # if len(ranges) < 2:\\n # return\\n # try to merge each range with the one after it, up until the end of the list\\n node = ranges.first\\n while node and node.next:\\n prev_range = node.value\\n next_range = node.next.value\\n new_range = prev_range.union(next_range)\\n if new_range is not None: # TODO python 3.8 refactoring - this is a great place for :=\\n node.value = new_range\\n ranges.pop_after(node)\\n else:\\n node = node.next\\n return ranges\",\n \"def sum(values):\\n total = 0\\n for i in values:\\n total += i\\n return total\",\n \"def gen_ranges(starts, ends):\\n if starts.size != ends.size:\\n raise ValueError(\\\"starts and ends must be same size\\\")\\n if not ((ends - starts) > 0).all():\\n raise ValueError(\\\"all ends must be greater than starts\\\")\\n lengths = ends - starts\\n segs = ak.cumsum(lengths) - lengths\\n totlen = lengths.sum()\\n slices = ak.ones(totlen, dtype=ak.int64)\\n diffs = ak.concatenate((ak.array([starts[0]]), \\n starts[1:] - starts[:-1] - lengths[:-1] + 1))\\n slices[segs] = diffs\\n return segs, ak.cumsum(slices)\",\n \"def get_ranges(self, tchain, kw):\\n (lo, hi) = (\\\"min\\\", \\\"max\\\")\\n ran = None\\n for t in tchain:\\n rstmt = t.search_one(kw)\\n if rstmt is None: continue\\n ran = [ i.split(\\\"..\\\") for i in rstmt.arg.split(\\\"|\\\") ]\\n if ran[0][0] != 'min': lo = ran[0][0]\\n if ran[-1][-1] != 'max': hi = ran[-1][-1]\\n if ran is None: return None\\n if len(ran) == 1:\\n return [(lo, hi)]\\n else:\\n return [(lo, ran[0][-1])] + ran[1:-1] + [(ran[-1][0], hi)]\",\n \"def sum(iterable: typing.Iterable, start: float = 0) -> float:\\n return sum(iterable, start)\",\n \"def parse_range(seq: str) -> list[int]:\\n seq = seq.split(\\\",\\\")\\n acc = []\\n for i in seq:\\n m = re.match(r\\\" *(?P<start>\\\\d+) *(- *(?P<end>\\\\d+))? *\\\", i)\\n\\n if not m:\\n continue\\n\\n a = [m.group(\\\"start\\\"), m.group(\\\"end\\\")]\\n a = [int(x) for x in a if x]\\n\\n if len(a) > 1:\\n a = range(int(a[0]), int(a[1] + 1))\\n\\n acc.append(a)\\n\\n return list(\\n set([x for x in list(itertools.chain.from_iterable(acc)) if x])\\n )\",\n \"def sum_app(*numbers_to_sum: typing.Tuple[int, ...]):\\n return sum(numbers_to_sum)\",\n \"def _input_intervals():\\n last_timestamp = self._trace.interval.start\\n for ir_event in filter_by_task(all_tasks, 'name', 'InputReader', 'any'):\\n if last_timestamp <= ir_event.interval.end:\\n yield Interval(last_timestamp, ir_event.interval.end)\\n last_timestamp = ir_event.interval.end\",\n \"def _combine_ind_ranges(ind_ranges_to_merge):\\n ind_ranges_to_merge = sorted(ind_ranges_to_merge)\\n stack = []\\n result = []\\n for curr in ind_ranges_to_merge:\\n if len(stack) == 0:\\n stack.append(curr)\\n elif stack[-1][-1] >= curr[0]:\\n prev = stack.pop()\\n merged = sorted(list(set(prev + curr)))\\n stack.append(merged)\\n else:\\n prev = stack.pop()\\n result.append(prev)\\n stack.append(curr)\\n result += stack\\n return result\",\n \"def _intervals(parts, duration) -> list:\\n part_duration = duration / parts\\n return [(floor(i * part_duration), floor((i + 1) * part_duration)) for i in range(parts)]\",\n \"def count_hours(ranges, range_start=None, range_stop=None):\\n buckets = [0.0] * (24 * 7)\\n one_hour = timedelta(0, 60 * 60)\\n\\n first = None\\n last = None\\n\\n for start, stop in ranges:\\n if ((range_start is not None and start < range_start) or\\n (range_stop is not None and stop > range_stop)):\\n continue\\n\\n if first is None:\\n first = start\\n last = stop\\n\\n open_ref = datetime(start.year, start.month, start.day, start.hour)\\n open_ref += one_hour\\n open_ref = min(open_ref, stop)\\n open_frac = (open_ref - start) / one_hour\\n buckets[24 * start.weekday() + start.hour] += open_frac\\n\\n if start.date() != stop.date() or start.hour != stop.hour:\\n stop_ref = datetime(stop.year, stop.month, stop.day, stop.hour)\\n stop_frac = (stop - stop_ref) / one_hour\\n buckets[24 * stop.weekday() + stop.hour] += stop_frac\\n\\n start_hour = 24 * open_ref.weekday() + open_ref.hour\\n stop_hour = 24 * stop.weekday() + stop.hour\\n if stop_hour < start_hour - 1:\\n stop_hour += 24 * 7\\n\\n for hour in range(start_hour, stop_hour):\\n buckets[hour % (24 * 7)] += 1\\n\\n return buckets, first, last\",\n \"def interval_MAX_SMT(intervals):\\n lower_indices = np.argsort(intervals[:, 0])\\n lower_sorted = intervals[lower_indices, 0]\\n\\n upper_indices = np.argsort(intervals[:, 1])\\n upper_sorted = intervals[upper_indices, 1]\\n\\n best_lower, best_upper = 0, 0\\n upper_i = 0\\n best_met = -1\\n n_met = 0\\n for lower_i, lower in enumerate(lower_sorted):\\n # First, we update upper -- everything in this loop is an interval\\n # we were meeting before but not anymore.\\n while upper_sorted[upper_i] < lower:\\n n_met -= 1\\n upper_i += 1\\n # We now meet the interval that this lower is from.\\n n_met += 1\\n if n_met > best_met:\\n best_lower, best_upper = lower, upper_sorted[upper_i]\\n best_met = n_met\\n elif (len(lower_sorted) - lower_i) < (best_met - n_met):\\n # Each iteration adds *at most* 1 to n_met. For us to even have\\n # a chance of updating best_met, then, we will have to do at\\n # least (best_met - n_met) more iterations.\\n break\\n return best_lower, best_upper, best_met\",\n \"def Str2IntervalUnion(s):\\n try:\\n tokens = s.split(',')\\n intervals = []\\n for token in tokens:\\n a_str = token\\n b_str = token\\n i_separator = token.find('-')\\n if i_separator == -1:\\n i_separator = token.find('*')\\n if i_separator == -1:\\n i_separator = token.find(':')\\n if i_separator != -1:\\n a_str = token[:i_separator]\\n b_str = token[i_separator+1:]\\n if (len(a_str) == 0) or (a_str in ('-','*',':')):\\n a = 0\\n else:\\n a = int(a_str)\\n if (len(b_str) == 0) or (b_str in ('-','*',':')):\\n b = 0\\n else:\\n b = int(b_str)\\n intervals.append((a,b))\\n return intervals\\n except (ValueError) as err:\\n sys.stderr.write('Error: Incorrect format in numeric interval: \\\"'+s+'\\\"\\\\n')\\n sys.exit(-1)\",\n \"def SUM(*args):\\n return _group_function(lambda x, y: x + y, *args)\",\n \"def intervals_for(self, day: dt.date) -> List[Interval]:\\n relevant_intervals: List[Interval] = []\\n\\n for interval in self.intervals:\\n if interval.start_time.date() == day:\\n relevant_intervals.append(interval)\\n\\n return relevant_intervals\",\n \"def sum_values(self):\\n raise NotImplementedError\",\n \"def total_range_size(self) -> int:\\n if not len(self):\\n return 0\\n regions = merge(self.data, bp=1)\\n return regions.end.sum() - regions.start.sum()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.59077203","0.563408","0.5407047","0.5392709","0.52991736","0.52946335","0.52101135","0.5203491","0.5171669","0.51691556","0.5138005","0.5121623","0.51164556","0.506967","0.506666","0.5054887","0.5036717","0.50176084","0.5004771","0.49338767","0.49328962","0.49259388","0.49177232","0.49176416","0.49149784","0.49081427","0.49034107","0.48887822","0.48705766","0.4859334","0.48489273","0.48409006","0.48226082","0.48095515","0.4792696","0.47803158","0.47689214","0.47575027","0.47507736","0.47501996","0.47322023","0.47189817","0.47183925","0.47166225","0.47138652","0.47138652","0.47138652","0.47138652","0.47138652","0.47116074","0.47115844","0.46950477","0.46899012","0.46887788","0.4672992","0.46700454","0.46664268","0.4644474","0.4638641","0.4635946","0.46333244","0.46300715","0.46237323","0.46160933","0.46160904","0.4587366","0.45862985","0.45696855","0.45682308","0.456427","0.4548151","0.45452073","0.45412612","0.45392963","0.4523214","0.45201287","0.4511256","0.45112294","0.45027933","0.45012525","0.4495628","0.44883582","0.4484549","0.44844916","0.44816697","0.44782645","0.44761157","0.44752362","0.4471844","0.44679984","0.44635326","0.44588137","0.4449678","0.44395676","0.44375485","0.44364715","0.4434997","0.44336367","0.44277787","0.44253296"],"string":"[\n \"0.59077203\",\n \"0.563408\",\n \"0.5407047\",\n \"0.5392709\",\n \"0.52991736\",\n \"0.52946335\",\n \"0.52101135\",\n \"0.5203491\",\n \"0.5171669\",\n \"0.51691556\",\n \"0.5138005\",\n \"0.5121623\",\n \"0.51164556\",\n \"0.506967\",\n \"0.506666\",\n \"0.5054887\",\n \"0.5036717\",\n \"0.50176084\",\n \"0.5004771\",\n \"0.49338767\",\n \"0.49328962\",\n \"0.49259388\",\n \"0.49177232\",\n \"0.49176416\",\n \"0.49149784\",\n \"0.49081427\",\n \"0.49034107\",\n \"0.48887822\",\n \"0.48705766\",\n \"0.4859334\",\n \"0.48489273\",\n \"0.48409006\",\n \"0.48226082\",\n \"0.48095515\",\n \"0.4792696\",\n \"0.47803158\",\n \"0.47689214\",\n \"0.47575027\",\n \"0.47507736\",\n \"0.47501996\",\n \"0.47322023\",\n \"0.47189817\",\n \"0.47183925\",\n \"0.47166225\",\n \"0.47138652\",\n \"0.47138652\",\n \"0.47138652\",\n \"0.47138652\",\n \"0.47138652\",\n \"0.47116074\",\n \"0.47115844\",\n \"0.46950477\",\n \"0.46899012\",\n \"0.46887788\",\n \"0.4672992\",\n \"0.46700454\",\n \"0.46664268\",\n \"0.4644474\",\n \"0.4638641\",\n \"0.4635946\",\n \"0.46333244\",\n \"0.46300715\",\n \"0.46237323\",\n \"0.46160933\",\n \"0.46160904\",\n \"0.4587366\",\n \"0.45862985\",\n \"0.45696855\",\n \"0.45682308\",\n \"0.456427\",\n \"0.4548151\",\n \"0.45452073\",\n \"0.45412612\",\n \"0.45392963\",\n \"0.4523214\",\n \"0.45201287\",\n \"0.4511256\",\n \"0.45112294\",\n \"0.45027933\",\n \"0.45012525\",\n \"0.4495628\",\n \"0.44883582\",\n \"0.4484549\",\n \"0.44844916\",\n \"0.44816697\",\n \"0.44782645\",\n \"0.44761157\",\n \"0.44752362\",\n \"0.4471844\",\n \"0.44679984\",\n \"0.44635326\",\n \"0.44588137\",\n \"0.4449678\",\n \"0.44395676\",\n \"0.44375485\",\n \"0.44364715\",\n \"0.4434997\",\n \"0.44336367\",\n \"0.44277787\",\n \"0.44253296\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7316026"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":92590,"cells":{"query":{"kind":"string","value":"Normalize decimal without scientific notation (remove exponent and trailing zeros)."},"document":{"kind":"string","value":"def normalize_decimal(d):\n return d.quantize(Decimal(1)) if d == d.to_integral() else d.normalize()"},"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 remove_exponent(val):\n context = decimal.Context(prec=self.max_digits)\n return val.quantize(decimal.Decimal(1), context=context) if val == val.to_integral() else val.normalize(context)","def normalize(amount, min=2, max=4):\n if not amount:\n return amount\n\n # To Decimal, round to highest desired precision\n d = round(Decimal(amount), max)\n s = str(d)\n\n # Truncate as many extra zeros as we are allowed to\n for i in range(max-min):\n if s[-1] == '0':\n s = s[:-1]\n\n if s[-1] == '.':\n s = s[:-1]\n\n return s","def remove_zeroes(s: str) -> str:\n tup = s.split(\"e\")\n if len(tup) == 2:\n mantissa = tup[0].rstrip(\"0\").rstrip(\".\")\n exponent = int(tup[1])\n if exponent:\n s = \"%se%d\" % (mantissa, exponent)\n else:\n s = mantissa\n return s","def normalize(value):\n while value > 1:\n value = value / 10\n return value","def reformat(number):\n if number.find('E') == -1:\n exponent = \"-101\"\n mantissa = number.split(exponent)\n return float(mantissa[0])*10**float(exponent)\n else:\n mantissa, exponent = number.split('E')\n\n return float(mantissa)*10**float(exponent)","def normalize_price(price: str) -> float:\n return float(price.strip().replace(',', ''))","def normalisePrice(raw):\n if raw:\n return Decimal(str(raw)).to_eng_string()\n else:\n return None","def normalize(x):\n return (x - math_ops.reduce_min(x)) / (math_ops.reduce_max(x) - math_ops.reduce_min(x))","def clean_value(self, value):\n return float(value.replace('.', '').replace(',', '.'))","def normalize(v):\n\n return v * (1.0 / magnitude(v))","def truncate(input_float, num_decimals):\n s = '{}'.format(input_float)\n if 'e' in s or 'E' in s:\n return '{0:.{1}f}'.format(input_float, num_decimals)\n i, p, d = s.partition('.')\n return '.'.join([i, (d + '0' * num_decimals)[:num_decimals]])","def normalize(x):\n # TODO: Implement Function\n \n return x/255","def short_float_fmt(x):\n return '{0:f}'.format(x).rstrip('0').rstrip('.')","def normalize(self):\n total = float(self.totalCount())\n if total != 0:\n self.divideAll(total)","def normalize(string):\n string = string.replace('E', 'e')\n string = re.sub('(?<!e)-', ' -', string)\n string = re.sub('[ \\n\\r\\t,]+', ' ', string)\n string = re.sub(r'(\\.[0-9-]+)(?=\\.)', r'\\1 ', string)\n return string.strip()","def normalize(x):\r\n return x/norm(x)","def normalize(volume):\n max = np.amax(volume)\n if max == 0:#Fixes dividing by 0 error if nothing in the volume\n return volume.astype(np.uint8)\n\n normalized = volume * (255.0 / max)\n normalized = np.round(normalized).astype(np.uint8)\n return normalized","def normalize(self, factor):","def normalize_emission(self):\n self._e /= self._e.sum(0)","def vis_normalize(a, s=0.1):\n return s * (a - a.mean()) / (max(a.std(), 1e-4)) + 0.5","def normalize_m11(x):\n return x / 127.5 - 1","def correct_sci(self, val):\n try:\n dividend = float(val.split('E-')[0])\n divisor = float('1' + int(val.split('E-')[1]) * '0')\n return str('%f' % (dividend / divisor))\n except:\n return val","def _normalize(self, inp):\n \n return inp/inp.sum()","def normalize(x):\n return (x + 1e-10) / (K.sqrt(K.mean(K.square(x))) + 1e-10)","def normalise_num(version_num, normal_len):\n version_num = str(version_num).replace(\".\", \"\")\n if len(version_num) < normal_len:\n for i in range(len(version_num), normal_len):\n version_num = version_num + \"0\"\n return int(version_num) if version_num.isdigit() else 0","def normalize_01(x):\n return x / 255.0","def scientific(x):\n return '{:.2e}'.format(x)","def _normalize_(x: np.array) -> np.array:\n if x.max() != 0:\n x = x / x.max()\n return np.clip(x, 0, 1)# ensure that no values are >1\n else:\n raise ZeroDivisionError('Image Normalization')","def convert_to_floating(string):\n return \"\".join([\"\\u00b7\" if char==\".\" else char for char in string])","def normalize(knot_vector, decimals=18):\n try:\n if knot_vector is None or len(knot_vector) == 0:\n raise ValueError(\"Input knot vector cannot be empty\")\n except TypeError as e:\n print(\"An error occurred: {}\".format(e.args[-1]))\n raise TypeError(\"Knot vector must be a list or tuple\")\n except Exception:\n raise\n\n first_knot = float(knot_vector[0])\n last_knot = float(knot_vector[-1])\n denominator = last_knot - first_knot\n\n knot_vector_out = [float((\"{:.\" + str(decimals) + \"f}\").format((float(kv) - first_knot) / denominator))\n for kv in knot_vector]\n\n return knot_vector_out","def HighPrecisionE(number):\n\n return \"%.22e\" % number","def _normalize(x):\n tol = 1e-10\n dims = x.shape\n\n x = x.flatten()\n inverse = (np.sum(x**2) + tol) ** -.5\n x = x * inverse\n x = np.reshape(x, dims)\n\n return x","def normalize(x):\n a = 0\n b = 1\n scale_min = 0\n scale_max = 255\n return a + ( ( (x - scale_min)*(b - a) )/( scale_max - scale_min ) )","def pretty_float(x):\n return ('%f' % x).rstrip('0').rstrip('.')","def normalize_price_values(df):\r\n\r\n\tdf_normalize_dict = dict()\r\n\r\n\tfor i, col in enumerate(df.columns):\r\n\t\tcol_array = np.array(df[col])\r\n\t\tdf_normalize_dict[\"Normalized\" + col] = preprocessing.normalize([col_array])[0]\r\n\r\n\tdf_normalize = pd.DataFrame(df_normalize_dict, index=df.index)\r\n\tpd.set_option('precision', 2) # set output display precision in 2 decimal places\r\n\r\n\treturn df_normalize","def floatify(string):\n temporary_string = \"0.\" + string[:-1]\n return float(temporary_string)","def decimalize(n):\n return '{:.2f}'.format(n)","def drop_trailing(amount, decimals=2):\n if not amount:\n return amount\n\n s = str(float(amount)).rstrip('0')\n if decimals == 0:\n return s.rstrip('.')\n\n num_decimals = len(s.split('.')[1])\n num_to_add = decimals - num_decimals\n if num_to_add <= 0:\n return s\n\n return s + '0' * num_to_add","def _nice_sci(self, val, mdigits, force_sign=False):\n if val != 0:\n e = int(floor(log10(abs(val))))\n else:\n e = 0\n m = val / float(10**e)\n m_str = str(m)\n\n # Safely truncating the mantissa is somewhat tricky. The minimum\n # length of the mantissa is everything up to (but not including) the\n # period. If the m_str doesn't have a decimal point, then we have to\n # ignore mdigits.\n if mdigits > 0 and \".\" in m_str:\n max_len = max(m_str.index(\".\"), mdigits)\n m_str = m_str[:max_len]\n\n # Strip off a trailing decimal\n if m_str[-1] == \".\":\n m_str = m_str[:-1]\n\n # It's not sufficient just to truncate the string; we need to\n # handle proper rounding\n\n else:\n # Always strip off a trailing decimal\n if m_str[-1] == \".\":\n m_str = m_str[:-1]\n\n if force_sign and not m_str.startswith(\"-\"):\n m_str = \"+\" + m_str\n\n if e != 0:\n # Clean up the exponent\n e_str = str(e)\n\n if e_str.startswith(\"+\") and not force_sign:\n e_str = e_str[1:]\n m_str += \"e\" + e_str\n\n return m_str","def normalize(x):\n # TODO: Implement Function\n data_max = np.max(x)\n data_min = np.min(x)\n x = (x - data_min) / (data_max - data_min)\n return x","def _to_float(self, s: str) -> float:\n return int(s[:-1]) / 1e9 if s.endswith('n') else float(s[:-1])","def standard_form(num: Decimal):\n num = Decimal(num)\n\n if num >= 1:\n power = 0\n while num > 1:\n power += 1\n num /= 10\n\n else:\n power = 0\n while num < 1:\n power -= 1\n num *= 10\n if num > 1:\n break\n power += 1\n\n return num, power","def normalize(data):\n data_range = data.max() - data.min()\n #if data_range == 0.:\n # sys.exit(\"data.max() - data.min() == 0. !\")\n if stddev != 0.:\n data = (data - data.min()) / data_range\n\n return data","def data_normalize (self, data):\r\n data = data + (2**15)\r\n data = data / ((2**16) - 1)\r\n data = 2 * data\r\n data = data - 1\r\n\r\n return data","def clean_pcts(x):\n # if not enough data, will be '-' with investing.com\n if x == '-' or pd.isnull(x):\n return np.nan\n elif x == 'unch':\n return float(0)\n elif type(x) == float:\n return x\n\n new_x = x.replace('+', '')\n new_x = new_x.replace('%', '')\n new_x = float(new_x) / 100\n return new_x","def _normalize(a: np.ndarray, u: float=0, s: float=1) -> np.ndarray:\n a_norm = (a - np.mean(a)) / (np.std(a) + STABILITY)\n a_rescaled = a_norm * s + u\n\n return a_rescaled","def normalize(self) -> NoReturn:\n self._ionic_fractions = self._ionic_fractions / np.sum(self._ionic_fractions)","def test_normal_decimal_input(self):\r\n ws_leader = \"S. O'Neal (14.9)\"\r\n res = treat_input(ws_leader, type=\"float\")\r\n assert res == 14.9","def normalize(volume):\n\n MIN_BOUND = 0\n MAX_BOUND = 256.0\n volume = (volume - MIN_BOUND) /(MAX_BOUND - MIN_BOUND)\n volume[volume > 1] = 1 #Clip everything larger than 1 and 0\n volume[volume < 0] = 0\n volume = (volume*255).astype('uint8')\n\n return volume","def normalize(volume):\n\n MIN_BOUND = 0\n MAX_BOUND = 256.0\n volume = (volume - MIN_BOUND) /(MAX_BOUND - MIN_BOUND)\n volume[volume > 1] = 1 #Clip everything larger than 1 and 0\n volume[volume < 0] = 0\n volume = (volume*255).astype('uint8')\n\n return volume","def normalize_loan_amount(value):\n normalized_value = value.lower()\n if 'k' in normalized_value:\n normalized_value = normalized_value.replace('k', '000')\n normalized_value = normalized_value.replace('.', '')\n\n normalized_value = normalized_value.replace('$', '')\n normalized_value = normalized_value.replace(',', '')\n\n try: \n return Decimal(normalized_value)\n except: InvalidOperation\n \n return None","def truncate(x, digits):\n if x==0:\n return '0'\n if digits==0:\n return str(int(round(x)))\n FORMAT = \"\"\".%sf\"\"\"\n format_x = FORMAT % str(int(digits))\n return format(x, format_x).rstrip('0').rstrip('.')","def normalize(self):\n total = self.total()\n for x in self.d:\n self.d[x] /= total\n return total","def normalize(X, m, s):\n return (X - m)/s","def normalize(X, m, s):\n return (X - m) / s","def decimal_precision_from_scientific_notation(decimal_value: Decimal) -> int:\n return strict_integer_validator(\n None,\n -decimal_value.log10()\n )","def pad_normalize(s):\n\n if len(s) < length:\n s += '0' * (length-len(s))\n else:\n s = s[:length]\n return [float(int(s[i], 16)/255) for i in range(0, length)]","def convert_to_scientific_notation(number):\n\n number = \"%.2e\" % number\n if \"+\" in number:\n positive_exponent = True\n number, exponent = number.split(\"+\")\n else:\n positive_exponent = False\n number, exponent = number.split(\"-\")\n\n exponent = str(int(exponent)) # Removes leading zeros\n\n if positive_exponent:\n return number + exponent\n else:\n return number + \"-\" + exponent","def normalize(dictionary, num):\n for key in dictionary.keys():\n dictionary[key] = float(dictionary[key])/num\n return dictionary","def normalize(X, m, s):\n return ((X - m) / s)","def normalize(values):\n\n\tmax_value = float(max(map(abs, values)) or 1)\n\treturn [val / max_value for val in values]","def del_decim_zeros(num):\r\n \r\n if \".\" in num: \r\n \r\n num_zeros = num_zeros_end(num) \r\n\r\n if num_zeros != 0: \r\n num = num[:-num_zeros] \r\n \r\n if num[-1] == \".\": \r\n num = num[:-1]\r\n \r\n return num\r\n\r\n else:\r\n return num","def normalize(values):\n return (values - np.mean(values)) / np.std(values)","def z_normalize(ts):\n\n ts -= np.mean(ts)\n std = np.std(ts)\n\n if std == 0:\n raise ValueError(\"The Standard Deviation cannot be zero\")\n\n #ts /= std\n return ts / std","def normalize(a, new_max=1.0):\n a = (a - a.min())\n a = a/a.max()\n a *= new_max\n return a","def normalise_series(to_normalise: pd.Series) -> pd.Series:\n \n # return (to_normalise - to_normalise.mean()) / to_normalise.std() # 0 mean and unit standard deviation\n return to_normalise / to_normalise.std() # positive and unit standard deviation","def round_decimal(x, digits=0):\n x = decimal.Decimal(str(x))\n if digits == 0:\n return int(x.quantize(decimal.Decimal(\"1\"), rounding='ROUND_HALF_UP'))\n if digits > 1:\n string = '1e' + str(-1 * digits)\n else:\n string = '1e' + str(-1 * digits)\n return float(x.quantize(decimal.Decimal(string), rounding='ROUND_HALF_UP'))","def sci_notation_to_float(n):\n\n if 'e' in n:\n exponent = float(n[n.find('e') + 1:])\n number = float(n[:n.find('-') - 1])\n number *= 10**exponent\n \n return number\n\n return n","def scinotation(self, num):\n num = num.replace(\"D\", \"e\")\n return f\"{decimal.Decimal(num):.9e}\"","def clean_currency(x: str):\n # cprint(f\"### Function Name:-> {inspect.stack()[0][3]} ###\", 'yellow', 'on_grey', attrs=['bold'])\n try:\n # x = str(x)\n if isinstance(x, str):\n if x.startswith(\"$\"):\n return x.replace('$', '').replace(',', '')\n # return float(x)\n return x\n except Exception as ex:\n cprint(traceback.format_exc(), 'red')\n log_exception(traceback.format_exc())","def set_normalize(self, new_normalize=False):\n self.normalize = new_normalize","def normalize(audio):\n norm = audio/max(audio)\n return norm","def normalize(my_vector):\n my_vector = np.array(my_vector)\n size = len(my_vector)\n\n sum_ = sum(my_vector)\n if sum_ != 0.0:\n for i in range(size):\n my_vector[i] = my_vector[i] / sum_\n return my_vector","def _normalize(self):\n\n n = len(self.e2[0])\n E = []\n\n for e2 in self.e2:\n if len(e2) != n:\n print 'WARNING: non consistent length in error statistics!!!'\n E.append(np.nansum(np.sqrt(e2))) # temporal aggregation\n\n E = np.asarray(E)\n EM = E.mean() # take square root, as e2 is still the squared error!\n self.e_norm = (E - EM) / EM # see Glecker et al, eq.2","def __float__(self):\n\t\toutput = 0.0\n\n\t\tfor index,value in enumerate(self):\n\t\t\tif value > 0:\n\t\t\t\toutput+=float(value) * (10 ** -index)\n\n\t\treturn output","def normalize_series(series):\n return (series - series.mean()) / (series.max() - series.min())","def normalize_input(inputs: [float]) -> [float]:","def destring(val):\r\n\r\n if isinstance(val, str):\r\n if val.lstrip('-').isdecimal():\r\n return int(val)\r\n\r\n if val.lstrip('-').replace('.', '', 1).isdecimal():\r\n return float(val)\r\n\r\n return val","def normalization(sample):\n sample = sample + 100\n # 2^20 = 1048576\n return np.log2(sample * 1048576/np.sum(sample))","def normalize(dataset):\n return normalize_standard_deviation(normalize_mean(dataset))","def unit_scale(x, eps=1e-8):\n\tx = x.copy()\n\tx -= x.min()\n\tx *= 1.0 / (x.max() + eps)\n\treturn x","def normalize(X):\n # z-score\n mean = np.mean(X, axis=(0, 1, 2, 3))\n std = np.std(X, axis=(0, 1, 2, 3))\n # avoid dividing zero by adding a very small number\n X = (X - mean) / (std + 1e-7)\n\n return X","def normalize2digits(dictTwoDigt, alleleStatFile):\n\talleleDict={}\n\tdictTwoDigtNorm={}\n\n\tfh=open(alleleStatFile, \"r\")\n\tfor line in fh:\n\t\td2allele, total=line.strip(\"\\n\").split(\"\\t\")\n\t\talleleDict[d2allele]=float(total)\t\t\n\t\t\n\tfor key in dictTwoDigt.keys():\n\t\ttry:\n\t\t\tdictTwoDigtNorm[key]=round(float(dictTwoDigt[key])/float(alleleDict[key]),0)\n\t\texcept:\n\t\t\tpass\n\treturn dictTwoDigtNorm","def normalize(arr: np.ndarray) -> np.ndarray:\n if max(arr) - min(arr) == 0:\n logger.warning(\n \"Normalize averted a div/0, the input data was:\\n {0}\".format(arr)\n )\n return np.ones(len(arr))\n return (arr - min(arr)) / (max(arr) - min(arr))","def _normalize(self, x):\n # TODO: imagenet normalization\n\n return x","def test_default_cleaning_style(currency_df):\n result = currency_df.currency_column_to_numeric(\n \"d_col\",\n )\n expected = pd.DataFrame(\n {\n \"a_col\": [\" 24.56\", \"-\", \"(12.12)\", \"1,000,000\"],\n \"d_col\": [np.nan, np.nan, 1.23, -1_000],\n }\n )\n assert_frame_equal(result, expected)","def _fix_surprising_number(val, s):\n if (\n isinstance(val, (int, float)) and \"!!\" not in s\n and _contains_non_numeric_chars(s)\n ):\n return s\n return val","def alpha_dec_normalize(dec):\n\n temp = float(dec)\n if temp < 0.0 or temp > 1.0:\n dec = fmt_float(clamp(float(temp), 0.0, 1.0), 3)\n alpha_dec = dec\n alpha = \"%02X\" % round_int(float(alpha_dec) * 255.0)\n return alpha, alpha_dec","def normalize(p):\n return p / mod(p)","def test_default_cleaning_style_with_remove(currency_df):\n result = currency_df.currency_column_to_numeric(\n \"d_col\",\n cast_non_numeric={\"non-existent-col\": 10},\n remove_non_numeric=True,\n )\n expected = pd.DataFrame(\n {\n \"a_col\": [\" 24.56\", \"(12.12)\", \"1,000,000\"],\n \"d_col\": [np.nan, 1.23, -1_000],\n },\n index=[0, 2, 3],\n )\n assert_frame_equal(result, expected)","def normalize(data):\n min = np.min(data)\n if min:\n data = data + min\n return old_div(data,np.max(data))\n else: # if min is 0\n return old_div(data,np.max(data))","def normalize_interest_rate(value):\n if '%' in value:\n value = value.replace('%', '')\n\n try : \n return Decimal(value)/100\n except: InvalidOperation\n\n return None","def standardization(a, p):\r\n return a * 10 / 100 * p * p","def standardize(data):\n stddev = data.std()\n #if stddev == 0.:\n # sys.exit(\"data.std() == 0. !\")\n if stddev != 0.:\n data = (data - data.mean()) / (data.std())\n\n return data","def normalize_contents(object_contents):\n normalized = re.sub(r'(\\d*.\\d*?)0+(\\s)', r'\\g<1>0\\2', object_contents) # remove trailing zeroes\n if not re.match(r'\\n', object_contents[-1]): # add trailing newline if necessary\n normalized += '\\n'\n return normalized","def normalize(self):\n return (1. / abs(self)) * self","def _compute_normalization(self, normalize=True):\n if normalize:\n if self._img_norm is None:\n if np.sum(self._data) == 0:\n self._img_norm = 1\n else:\n self._img_norm = self._compute_raw_image_norm()\n\n if self._img_norm != 0.0 and np.isfinite(self._img_norm):\n self._data /= (self._img_norm * self._normalization_correction)\n self._normalization_status = 0\n else:\n self._normalization_status = 1\n self._img_norm = 1\n warnings.warn('Overflow encountered while computing '\n 'normalization constant. Normalization '\n 'constant will be set to 1.', NonNormalizable)\n else:\n self._normalization_status = 2","def trunc(x):\n return 0","def max_normalize(val, old_max):\n new_max = 100.0\n normalized = (val / old_max) * new_max\n # Cap at new_max\n if normalized > new_max:\n return new_max\n return normalized","def power_normalize(xx, alpha = 0.5):\r\n\treturn np.sign(xx) * np.abs(xx) ** alpha"],"string":"[\n \"def remove_exponent(val):\\n context = decimal.Context(prec=self.max_digits)\\n return val.quantize(decimal.Decimal(1), context=context) if val == val.to_integral() else val.normalize(context)\",\n \"def normalize(amount, min=2, max=4):\\n if not amount:\\n return amount\\n\\n # To Decimal, round to highest desired precision\\n d = round(Decimal(amount), max)\\n s = str(d)\\n\\n # Truncate as many extra zeros as we are allowed to\\n for i in range(max-min):\\n if s[-1] == '0':\\n s = s[:-1]\\n\\n if s[-1] == '.':\\n s = s[:-1]\\n\\n return s\",\n \"def remove_zeroes(s: str) -> str:\\n tup = s.split(\\\"e\\\")\\n if len(tup) == 2:\\n mantissa = tup[0].rstrip(\\\"0\\\").rstrip(\\\".\\\")\\n exponent = int(tup[1])\\n if exponent:\\n s = \\\"%se%d\\\" % (mantissa, exponent)\\n else:\\n s = mantissa\\n return s\",\n \"def normalize(value):\\n while value > 1:\\n value = value / 10\\n return value\",\n \"def reformat(number):\\n if number.find('E') == -1:\\n exponent = \\\"-101\\\"\\n mantissa = number.split(exponent)\\n return float(mantissa[0])*10**float(exponent)\\n else:\\n mantissa, exponent = number.split('E')\\n\\n return float(mantissa)*10**float(exponent)\",\n \"def normalize_price(price: str) -> float:\\n return float(price.strip().replace(',', ''))\",\n \"def normalisePrice(raw):\\n if raw:\\n return Decimal(str(raw)).to_eng_string()\\n else:\\n return None\",\n \"def normalize(x):\\n return (x - math_ops.reduce_min(x)) / (math_ops.reduce_max(x) - math_ops.reduce_min(x))\",\n \"def clean_value(self, value):\\n return float(value.replace('.', '').replace(',', '.'))\",\n \"def normalize(v):\\n\\n return v * (1.0 / magnitude(v))\",\n \"def truncate(input_float, num_decimals):\\n s = '{}'.format(input_float)\\n if 'e' in s or 'E' in s:\\n return '{0:.{1}f}'.format(input_float, num_decimals)\\n i, p, d = s.partition('.')\\n return '.'.join([i, (d + '0' * num_decimals)[:num_decimals]])\",\n \"def normalize(x):\\n # TODO: Implement Function\\n \\n return x/255\",\n \"def short_float_fmt(x):\\n return '{0:f}'.format(x).rstrip('0').rstrip('.')\",\n \"def normalize(self):\\n total = float(self.totalCount())\\n if total != 0:\\n self.divideAll(total)\",\n \"def normalize(string):\\n string = string.replace('E', 'e')\\n string = re.sub('(?<!e)-', ' -', string)\\n string = re.sub('[ \\\\n\\\\r\\\\t,]+', ' ', string)\\n string = re.sub(r'(\\\\.[0-9-]+)(?=\\\\.)', r'\\\\1 ', string)\\n return string.strip()\",\n \"def normalize(x):\\r\\n return x/norm(x)\",\n \"def normalize(volume):\\n max = np.amax(volume)\\n if max == 0:#Fixes dividing by 0 error if nothing in the volume\\n return volume.astype(np.uint8)\\n\\n normalized = volume * (255.0 / max)\\n normalized = np.round(normalized).astype(np.uint8)\\n return normalized\",\n \"def normalize(self, factor):\",\n \"def normalize_emission(self):\\n self._e /= self._e.sum(0)\",\n \"def vis_normalize(a, s=0.1):\\n return s * (a - a.mean()) / (max(a.std(), 1e-4)) + 0.5\",\n \"def normalize_m11(x):\\n return x / 127.5 - 1\",\n \"def correct_sci(self, val):\\n try:\\n dividend = float(val.split('E-')[0])\\n divisor = float('1' + int(val.split('E-')[1]) * '0')\\n return str('%f' % (dividend / divisor))\\n except:\\n return val\",\n \"def _normalize(self, inp):\\n \\n return inp/inp.sum()\",\n \"def normalize(x):\\n return (x + 1e-10) / (K.sqrt(K.mean(K.square(x))) + 1e-10)\",\n \"def normalise_num(version_num, normal_len):\\n version_num = str(version_num).replace(\\\".\\\", \\\"\\\")\\n if len(version_num) < normal_len:\\n for i in range(len(version_num), normal_len):\\n version_num = version_num + \\\"0\\\"\\n return int(version_num) if version_num.isdigit() else 0\",\n \"def normalize_01(x):\\n return x / 255.0\",\n \"def scientific(x):\\n return '{:.2e}'.format(x)\",\n \"def _normalize_(x: np.array) -> np.array:\\n if x.max() != 0:\\n x = x / x.max()\\n return np.clip(x, 0, 1)# ensure that no values are >1\\n else:\\n raise ZeroDivisionError('Image Normalization')\",\n \"def convert_to_floating(string):\\n return \\\"\\\".join([\\\"\\\\u00b7\\\" if char==\\\".\\\" else char for char in string])\",\n \"def normalize(knot_vector, decimals=18):\\n try:\\n if knot_vector is None or len(knot_vector) == 0:\\n raise ValueError(\\\"Input knot vector cannot be empty\\\")\\n except TypeError as e:\\n print(\\\"An error occurred: {}\\\".format(e.args[-1]))\\n raise TypeError(\\\"Knot vector must be a list or tuple\\\")\\n except Exception:\\n raise\\n\\n first_knot = float(knot_vector[0])\\n last_knot = float(knot_vector[-1])\\n denominator = last_knot - first_knot\\n\\n knot_vector_out = [float((\\\"{:.\\\" + str(decimals) + \\\"f}\\\").format((float(kv) - first_knot) / denominator))\\n for kv in knot_vector]\\n\\n return knot_vector_out\",\n \"def HighPrecisionE(number):\\n\\n return \\\"%.22e\\\" % number\",\n \"def _normalize(x):\\n tol = 1e-10\\n dims = x.shape\\n\\n x = x.flatten()\\n inverse = (np.sum(x**2) + tol) ** -.5\\n x = x * inverse\\n x = np.reshape(x, dims)\\n\\n return x\",\n \"def normalize(x):\\n a = 0\\n b = 1\\n scale_min = 0\\n scale_max = 255\\n return a + ( ( (x - scale_min)*(b - a) )/( scale_max - scale_min ) )\",\n \"def pretty_float(x):\\n return ('%f' % x).rstrip('0').rstrip('.')\",\n \"def normalize_price_values(df):\\r\\n\\r\\n\\tdf_normalize_dict = dict()\\r\\n\\r\\n\\tfor i, col in enumerate(df.columns):\\r\\n\\t\\tcol_array = np.array(df[col])\\r\\n\\t\\tdf_normalize_dict[\\\"Normalized\\\" + col] = preprocessing.normalize([col_array])[0]\\r\\n\\r\\n\\tdf_normalize = pd.DataFrame(df_normalize_dict, index=df.index)\\r\\n\\tpd.set_option('precision', 2) # set output display precision in 2 decimal places\\r\\n\\r\\n\\treturn df_normalize\",\n \"def floatify(string):\\n temporary_string = \\\"0.\\\" + string[:-1]\\n return float(temporary_string)\",\n \"def decimalize(n):\\n return '{:.2f}'.format(n)\",\n \"def drop_trailing(amount, decimals=2):\\n if not amount:\\n return amount\\n\\n s = str(float(amount)).rstrip('0')\\n if decimals == 0:\\n return s.rstrip('.')\\n\\n num_decimals = len(s.split('.')[1])\\n num_to_add = decimals - num_decimals\\n if num_to_add <= 0:\\n return s\\n\\n return s + '0' * num_to_add\",\n \"def _nice_sci(self, val, mdigits, force_sign=False):\\n if val != 0:\\n e = int(floor(log10(abs(val))))\\n else:\\n e = 0\\n m = val / float(10**e)\\n m_str = str(m)\\n\\n # Safely truncating the mantissa is somewhat tricky. The minimum\\n # length of the mantissa is everything up to (but not including) the\\n # period. If the m_str doesn't have a decimal point, then we have to\\n # ignore mdigits.\\n if mdigits > 0 and \\\".\\\" in m_str:\\n max_len = max(m_str.index(\\\".\\\"), mdigits)\\n m_str = m_str[:max_len]\\n\\n # Strip off a trailing decimal\\n if m_str[-1] == \\\".\\\":\\n m_str = m_str[:-1]\\n\\n # It's not sufficient just to truncate the string; we need to\\n # handle proper rounding\\n\\n else:\\n # Always strip off a trailing decimal\\n if m_str[-1] == \\\".\\\":\\n m_str = m_str[:-1]\\n\\n if force_sign and not m_str.startswith(\\\"-\\\"):\\n m_str = \\\"+\\\" + m_str\\n\\n if e != 0:\\n # Clean up the exponent\\n e_str = str(e)\\n\\n if e_str.startswith(\\\"+\\\") and not force_sign:\\n e_str = e_str[1:]\\n m_str += \\\"e\\\" + e_str\\n\\n return m_str\",\n \"def normalize(x):\\n # TODO: Implement Function\\n data_max = np.max(x)\\n data_min = np.min(x)\\n x = (x - data_min) / (data_max - data_min)\\n return x\",\n \"def _to_float(self, s: str) -> float:\\n return int(s[:-1]) / 1e9 if s.endswith('n') else float(s[:-1])\",\n \"def standard_form(num: Decimal):\\n num = Decimal(num)\\n\\n if num >= 1:\\n power = 0\\n while num > 1:\\n power += 1\\n num /= 10\\n\\n else:\\n power = 0\\n while num < 1:\\n power -= 1\\n num *= 10\\n if num > 1:\\n break\\n power += 1\\n\\n return num, power\",\n \"def normalize(data):\\n data_range = data.max() - data.min()\\n #if data_range == 0.:\\n # sys.exit(\\\"data.max() - data.min() == 0. !\\\")\\n if stddev != 0.:\\n data = (data - data.min()) / data_range\\n\\n return data\",\n \"def data_normalize (self, data):\\r\\n data = data + (2**15)\\r\\n data = data / ((2**16) - 1)\\r\\n data = 2 * data\\r\\n data = data - 1\\r\\n\\r\\n return data\",\n \"def clean_pcts(x):\\n # if not enough data, will be '-' with investing.com\\n if x == '-' or pd.isnull(x):\\n return np.nan\\n elif x == 'unch':\\n return float(0)\\n elif type(x) == float:\\n return x\\n\\n new_x = x.replace('+', '')\\n new_x = new_x.replace('%', '')\\n new_x = float(new_x) / 100\\n return new_x\",\n \"def _normalize(a: np.ndarray, u: float=0, s: float=1) -> np.ndarray:\\n a_norm = (a - np.mean(a)) / (np.std(a) + STABILITY)\\n a_rescaled = a_norm * s + u\\n\\n return a_rescaled\",\n \"def normalize(self) -> NoReturn:\\n self._ionic_fractions = self._ionic_fractions / np.sum(self._ionic_fractions)\",\n \"def test_normal_decimal_input(self):\\r\\n ws_leader = \\\"S. O'Neal (14.9)\\\"\\r\\n res = treat_input(ws_leader, type=\\\"float\\\")\\r\\n assert res == 14.9\",\n \"def normalize(volume):\\n\\n MIN_BOUND = 0\\n MAX_BOUND = 256.0\\n volume = (volume - MIN_BOUND) /(MAX_BOUND - MIN_BOUND)\\n volume[volume > 1] = 1 #Clip everything larger than 1 and 0\\n volume[volume < 0] = 0\\n volume = (volume*255).astype('uint8')\\n\\n return volume\",\n \"def normalize(volume):\\n\\n MIN_BOUND = 0\\n MAX_BOUND = 256.0\\n volume = (volume - MIN_BOUND) /(MAX_BOUND - MIN_BOUND)\\n volume[volume > 1] = 1 #Clip everything larger than 1 and 0\\n volume[volume < 0] = 0\\n volume = (volume*255).astype('uint8')\\n\\n return volume\",\n \"def normalize_loan_amount(value):\\n normalized_value = value.lower()\\n if 'k' in normalized_value:\\n normalized_value = normalized_value.replace('k', '000')\\n normalized_value = normalized_value.replace('.', '')\\n\\n normalized_value = normalized_value.replace('$', '')\\n normalized_value = normalized_value.replace(',', '')\\n\\n try: \\n return Decimal(normalized_value)\\n except: InvalidOperation\\n \\n return None\",\n \"def truncate(x, digits):\\n if x==0:\\n return '0'\\n if digits==0:\\n return str(int(round(x)))\\n FORMAT = \\\"\\\"\\\".%sf\\\"\\\"\\\"\\n format_x = FORMAT % str(int(digits))\\n return format(x, format_x).rstrip('0').rstrip('.')\",\n \"def normalize(self):\\n total = self.total()\\n for x in self.d:\\n self.d[x] /= total\\n return total\",\n \"def normalize(X, m, s):\\n return (X - m)/s\",\n \"def normalize(X, m, s):\\n return (X - m) / s\",\n \"def decimal_precision_from_scientific_notation(decimal_value: Decimal) -> int:\\n return strict_integer_validator(\\n None,\\n -decimal_value.log10()\\n )\",\n \"def pad_normalize(s):\\n\\n if len(s) < length:\\n s += '0' * (length-len(s))\\n else:\\n s = s[:length]\\n return [float(int(s[i], 16)/255) for i in range(0, length)]\",\n \"def convert_to_scientific_notation(number):\\n\\n number = \\\"%.2e\\\" % number\\n if \\\"+\\\" in number:\\n positive_exponent = True\\n number, exponent = number.split(\\\"+\\\")\\n else:\\n positive_exponent = False\\n number, exponent = number.split(\\\"-\\\")\\n\\n exponent = str(int(exponent)) # Removes leading zeros\\n\\n if positive_exponent:\\n return number + exponent\\n else:\\n return number + \\\"-\\\" + exponent\",\n \"def normalize(dictionary, num):\\n for key in dictionary.keys():\\n dictionary[key] = float(dictionary[key])/num\\n return dictionary\",\n \"def normalize(X, m, s):\\n return ((X - m) / s)\",\n \"def normalize(values):\\n\\n\\tmax_value = float(max(map(abs, values)) or 1)\\n\\treturn [val / max_value for val in values]\",\n \"def del_decim_zeros(num):\\r\\n \\r\\n if \\\".\\\" in num: \\r\\n \\r\\n num_zeros = num_zeros_end(num) \\r\\n\\r\\n if num_zeros != 0: \\r\\n num = num[:-num_zeros] \\r\\n \\r\\n if num[-1] == \\\".\\\": \\r\\n num = num[:-1]\\r\\n \\r\\n return num\\r\\n\\r\\n else:\\r\\n return num\",\n \"def normalize(values):\\n return (values - np.mean(values)) / np.std(values)\",\n \"def z_normalize(ts):\\n\\n ts -= np.mean(ts)\\n std = np.std(ts)\\n\\n if std == 0:\\n raise ValueError(\\\"The Standard Deviation cannot be zero\\\")\\n\\n #ts /= std\\n return ts / std\",\n \"def normalize(a, new_max=1.0):\\n a = (a - a.min())\\n a = a/a.max()\\n a *= new_max\\n return a\",\n \"def normalise_series(to_normalise: pd.Series) -> pd.Series:\\n \\n # return (to_normalise - to_normalise.mean()) / to_normalise.std() # 0 mean and unit standard deviation\\n return to_normalise / to_normalise.std() # positive and unit standard deviation\",\n \"def round_decimal(x, digits=0):\\n x = decimal.Decimal(str(x))\\n if digits == 0:\\n return int(x.quantize(decimal.Decimal(\\\"1\\\"), rounding='ROUND_HALF_UP'))\\n if digits > 1:\\n string = '1e' + str(-1 * digits)\\n else:\\n string = '1e' + str(-1 * digits)\\n return float(x.quantize(decimal.Decimal(string), rounding='ROUND_HALF_UP'))\",\n \"def sci_notation_to_float(n):\\n\\n if 'e' in n:\\n exponent = float(n[n.find('e') + 1:])\\n number = float(n[:n.find('-') - 1])\\n number *= 10**exponent\\n \\n return number\\n\\n return n\",\n \"def scinotation(self, num):\\n num = num.replace(\\\"D\\\", \\\"e\\\")\\n return f\\\"{decimal.Decimal(num):.9e}\\\"\",\n \"def clean_currency(x: str):\\n # cprint(f\\\"### Function Name:-> {inspect.stack()[0][3]} ###\\\", 'yellow', 'on_grey', attrs=['bold'])\\n try:\\n # x = str(x)\\n if isinstance(x, str):\\n if x.startswith(\\\"$\\\"):\\n return x.replace('$', '').replace(',', '')\\n # return float(x)\\n return x\\n except Exception as ex:\\n cprint(traceback.format_exc(), 'red')\\n log_exception(traceback.format_exc())\",\n \"def set_normalize(self, new_normalize=False):\\n self.normalize = new_normalize\",\n \"def normalize(audio):\\n norm = audio/max(audio)\\n return norm\",\n \"def normalize(my_vector):\\n my_vector = np.array(my_vector)\\n size = len(my_vector)\\n\\n sum_ = sum(my_vector)\\n if sum_ != 0.0:\\n for i in range(size):\\n my_vector[i] = my_vector[i] / sum_\\n return my_vector\",\n \"def _normalize(self):\\n\\n n = len(self.e2[0])\\n E = []\\n\\n for e2 in self.e2:\\n if len(e2) != n:\\n print 'WARNING: non consistent length in error statistics!!!'\\n E.append(np.nansum(np.sqrt(e2))) # temporal aggregation\\n\\n E = np.asarray(E)\\n EM = E.mean() # take square root, as e2 is still the squared error!\\n self.e_norm = (E - EM) / EM # see Glecker et al, eq.2\",\n \"def __float__(self):\\n\\t\\toutput = 0.0\\n\\n\\t\\tfor index,value in enumerate(self):\\n\\t\\t\\tif value > 0:\\n\\t\\t\\t\\toutput+=float(value) * (10 ** -index)\\n\\n\\t\\treturn output\",\n \"def normalize_series(series):\\n return (series - series.mean()) / (series.max() - series.min())\",\n \"def normalize_input(inputs: [float]) -> [float]:\",\n \"def destring(val):\\r\\n\\r\\n if isinstance(val, str):\\r\\n if val.lstrip('-').isdecimal():\\r\\n return int(val)\\r\\n\\r\\n if val.lstrip('-').replace('.', '', 1).isdecimal():\\r\\n return float(val)\\r\\n\\r\\n return val\",\n \"def normalization(sample):\\n sample = sample + 100\\n # 2^20 = 1048576\\n return np.log2(sample * 1048576/np.sum(sample))\",\n \"def normalize(dataset):\\n return normalize_standard_deviation(normalize_mean(dataset))\",\n \"def unit_scale(x, eps=1e-8):\\n\\tx = x.copy()\\n\\tx -= x.min()\\n\\tx *= 1.0 / (x.max() + eps)\\n\\treturn x\",\n \"def normalize(X):\\n # z-score\\n mean = np.mean(X, axis=(0, 1, 2, 3))\\n std = np.std(X, axis=(0, 1, 2, 3))\\n # avoid dividing zero by adding a very small number\\n X = (X - mean) / (std + 1e-7)\\n\\n return X\",\n \"def normalize2digits(dictTwoDigt, alleleStatFile):\\n\\talleleDict={}\\n\\tdictTwoDigtNorm={}\\n\\n\\tfh=open(alleleStatFile, \\\"r\\\")\\n\\tfor line in fh:\\n\\t\\td2allele, total=line.strip(\\\"\\\\n\\\").split(\\\"\\\\t\\\")\\n\\t\\talleleDict[d2allele]=float(total)\\t\\t\\n\\t\\t\\n\\tfor key in dictTwoDigt.keys():\\n\\t\\ttry:\\n\\t\\t\\tdictTwoDigtNorm[key]=round(float(dictTwoDigt[key])/float(alleleDict[key]),0)\\n\\t\\texcept:\\n\\t\\t\\tpass\\n\\treturn dictTwoDigtNorm\",\n \"def normalize(arr: np.ndarray) -> np.ndarray:\\n if max(arr) - min(arr) == 0:\\n logger.warning(\\n \\\"Normalize averted a div/0, the input data was:\\\\n {0}\\\".format(arr)\\n )\\n return np.ones(len(arr))\\n return (arr - min(arr)) / (max(arr) - min(arr))\",\n \"def _normalize(self, x):\\n # TODO: imagenet normalization\\n\\n return x\",\n \"def test_default_cleaning_style(currency_df):\\n result = currency_df.currency_column_to_numeric(\\n \\\"d_col\\\",\\n )\\n expected = pd.DataFrame(\\n {\\n \\\"a_col\\\": [\\\" 24.56\\\", \\\"-\\\", \\\"(12.12)\\\", \\\"1,000,000\\\"],\\n \\\"d_col\\\": [np.nan, np.nan, 1.23, -1_000],\\n }\\n )\\n assert_frame_equal(result, expected)\",\n \"def _fix_surprising_number(val, s):\\n if (\\n isinstance(val, (int, float)) and \\\"!!\\\" not in s\\n and _contains_non_numeric_chars(s)\\n ):\\n return s\\n return val\",\n \"def alpha_dec_normalize(dec):\\n\\n temp = float(dec)\\n if temp < 0.0 or temp > 1.0:\\n dec = fmt_float(clamp(float(temp), 0.0, 1.0), 3)\\n alpha_dec = dec\\n alpha = \\\"%02X\\\" % round_int(float(alpha_dec) * 255.0)\\n return alpha, alpha_dec\",\n \"def normalize(p):\\n return p / mod(p)\",\n \"def test_default_cleaning_style_with_remove(currency_df):\\n result = currency_df.currency_column_to_numeric(\\n \\\"d_col\\\",\\n cast_non_numeric={\\\"non-existent-col\\\": 10},\\n remove_non_numeric=True,\\n )\\n expected = pd.DataFrame(\\n {\\n \\\"a_col\\\": [\\\" 24.56\\\", \\\"(12.12)\\\", \\\"1,000,000\\\"],\\n \\\"d_col\\\": [np.nan, 1.23, -1_000],\\n },\\n index=[0, 2, 3],\\n )\\n assert_frame_equal(result, expected)\",\n \"def normalize(data):\\n min = np.min(data)\\n if min:\\n data = data + min\\n return old_div(data,np.max(data))\\n else: # if min is 0\\n return old_div(data,np.max(data))\",\n \"def normalize_interest_rate(value):\\n if '%' in value:\\n value = value.replace('%', '')\\n\\n try : \\n return Decimal(value)/100\\n except: InvalidOperation\\n\\n return None\",\n \"def standardization(a, p):\\r\\n return a * 10 / 100 * p * p\",\n \"def standardize(data):\\n stddev = data.std()\\n #if stddev == 0.:\\n # sys.exit(\\\"data.std() == 0. !\\\")\\n if stddev != 0.:\\n data = (data - data.mean()) / (data.std())\\n\\n return data\",\n \"def normalize_contents(object_contents):\\n normalized = re.sub(r'(\\\\d*.\\\\d*?)0+(\\\\s)', r'\\\\g<1>0\\\\2', object_contents) # remove trailing zeroes\\n if not re.match(r'\\\\n', object_contents[-1]): # add trailing newline if necessary\\n normalized += '\\\\n'\\n return normalized\",\n \"def normalize(self):\\n return (1. / abs(self)) * self\",\n \"def _compute_normalization(self, normalize=True):\\n if normalize:\\n if self._img_norm is None:\\n if np.sum(self._data) == 0:\\n self._img_norm = 1\\n else:\\n self._img_norm = self._compute_raw_image_norm()\\n\\n if self._img_norm != 0.0 and np.isfinite(self._img_norm):\\n self._data /= (self._img_norm * self._normalization_correction)\\n self._normalization_status = 0\\n else:\\n self._normalization_status = 1\\n self._img_norm = 1\\n warnings.warn('Overflow encountered while computing '\\n 'normalization constant. Normalization '\\n 'constant will be set to 1.', NonNormalizable)\\n else:\\n self._normalization_status = 2\",\n \"def trunc(x):\\n return 0\",\n \"def max_normalize(val, old_max):\\n new_max = 100.0\\n normalized = (val / old_max) * new_max\\n # Cap at new_max\\n if normalized > new_max:\\n return new_max\\n return normalized\",\n \"def power_normalize(xx, alpha = 0.5):\\r\\n\\treturn np.sign(xx) * np.abs(xx) ** alpha\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.71156347","0.6817118","0.6672521","0.6446351","0.62960124","0.61859643","0.6139326","0.5954451","0.5883746","0.5880353","0.58401924","0.5834147","0.57599866","0.5726349","0.57119143","0.5709601","0.5705417","0.5702603","0.5699816","0.56875485","0.5680764","0.5663564","0.56605935","0.56496155","0.564731","0.5644259","0.5627586","0.5616866","0.5614204","0.56141794","0.55816454","0.5576325","0.556805","0.55674607","0.55561566","0.55554163","0.5521846","0.55212265","0.5514497","0.551355","0.5513052","0.5511447","0.55043775","0.55015635","0.5500092","0.5497651","0.5488438","0.54834163","0.5481534","0.5481534","0.5473951","0.54663396","0.5464076","0.54509795","0.54462284","0.5445179","0.54404336","0.54304326","0.5430416","0.5420915","0.5420122","0.54033047","0.5399684","0.5382407","0.5363467","0.53630275","0.5346651","0.5340144","0.53384393","0.53358215","0.53242236","0.5314353","0.531322","0.5310414","0.5309272","0.5305213","0.52980995","0.528682","0.52863646","0.528531","0.527966","0.5279526","0.5276659","0.52652293","0.52639794","0.5262266","0.525826","0.5257073","0.52568454","0.5246379","0.5234039","0.5232348","0.5231853","0.52315","0.522122","0.52185804","0.52163","0.521451","0.5211796","0.52098"],"string":"[\n \"0.71156347\",\n \"0.6817118\",\n \"0.6672521\",\n \"0.6446351\",\n \"0.62960124\",\n \"0.61859643\",\n \"0.6139326\",\n \"0.5954451\",\n \"0.5883746\",\n \"0.5880353\",\n \"0.58401924\",\n \"0.5834147\",\n \"0.57599866\",\n \"0.5726349\",\n \"0.57119143\",\n \"0.5709601\",\n \"0.5705417\",\n \"0.5702603\",\n \"0.5699816\",\n \"0.56875485\",\n \"0.5680764\",\n \"0.5663564\",\n \"0.56605935\",\n \"0.56496155\",\n \"0.564731\",\n \"0.5644259\",\n \"0.5627586\",\n \"0.5616866\",\n \"0.5614204\",\n \"0.56141794\",\n \"0.55816454\",\n \"0.5576325\",\n \"0.556805\",\n \"0.55674607\",\n \"0.55561566\",\n \"0.55554163\",\n \"0.5521846\",\n \"0.55212265\",\n \"0.5514497\",\n \"0.551355\",\n \"0.5513052\",\n \"0.5511447\",\n \"0.55043775\",\n \"0.55015635\",\n \"0.5500092\",\n \"0.5497651\",\n \"0.5488438\",\n \"0.54834163\",\n \"0.5481534\",\n \"0.5481534\",\n \"0.5473951\",\n \"0.54663396\",\n \"0.5464076\",\n \"0.54509795\",\n \"0.54462284\",\n \"0.5445179\",\n \"0.54404336\",\n \"0.54304326\",\n \"0.5430416\",\n \"0.5420915\",\n \"0.5420122\",\n \"0.54033047\",\n \"0.5399684\",\n \"0.5382407\",\n \"0.5363467\",\n \"0.53630275\",\n \"0.5346651\",\n \"0.5340144\",\n \"0.53384393\",\n \"0.53358215\",\n \"0.53242236\",\n \"0.5314353\",\n \"0.531322\",\n \"0.5310414\",\n \"0.5309272\",\n \"0.5305213\",\n \"0.52980995\",\n \"0.528682\",\n \"0.52863646\",\n \"0.528531\",\n \"0.527966\",\n \"0.5279526\",\n \"0.5276659\",\n \"0.52652293\",\n \"0.52639794\",\n \"0.5262266\",\n \"0.525826\",\n \"0.5257073\",\n \"0.52568454\",\n \"0.5246379\",\n \"0.5234039\",\n \"0.5232348\",\n \"0.5231853\",\n \"0.52315\",\n \"0.522122\",\n \"0.52185804\",\n \"0.52163\",\n \"0.521451\",\n \"0.5211796\",\n \"0.52098\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7325013"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":92591,"cells":{"query":{"kind":"string","value":"Use IPMITOOL to blink a chassis LED"},"document":{"kind":"string","value":"def blink_light(hostname, on=True):\n\n if on:\n opt = \"force\"\n else:\n opt = \"0\"\n\n LOGGER.info(\"Blinking chassis LED for %s\", hostname)\n\n command = \"ipmitool -U {} -P {} -H {} chassis identify {}\".format(\n USER, PASS, hostname, opt)\n\n subprocess.check_call(command, shell=True)"},"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":["async def blink(my_board, pin):\n\n # set the pin mode\n await my_board.set_pin_mode_digital_output(pin)\n\n # toggle the pin 4 times and exit\n for x in range(4):\n print('ON')\n await my_board.digital_write(pin, 1)\n await asyncio.sleep(1)\n print('OFF')\n await my_board.digital_write(pin, 0)\n await asyncio.sleep(1)","def irq_handler():\n # open an LED session\n with LEDs() as LED:\n # specify the LED which you want to control\n led = Led.LED1\n # specify the LED status\n led_on_off = True\n # writes values 10 times, which makes LED1 flash for 3 seconds\n for x in range(0, 10):\n # turn LED0 on or off\n LED.write(led, led_on_off)\n # add a short delay\n time.sleep(0.3)\n # if the LED is on, set the parameter to off\n # if the LED is off, set the parameter to on\n led_on_off = not led_on_off","def blink(self):\n self.displaycontrol |= self.LCD_BLINKON\n self.write_lcd(self.LCD_DATA_E1, self.LCD_DISPLAYCONTROL | self.displaycontrol)\n self.write_lcd(self.LCD_DATA_E2, self.LCD_DISPLAYCONTROL | self.displaycontrol)","def setLED(self):\n newValue = 0\n while newValue != '':\n newValue = input('Enter 0 or 1 to turn LED on or off or enter to exit.\\n')\n \n if newValue == '0':\n self.board.write(b'0')\n time.sleep(1)\n elif newValue == '1':\n self.board.write(b'1')\n time.sleep(1)\n else:\n time.sleep(1)","def ControlLights(state):\n for led in (RED,YELLOW,GREEN):\n GPIO.output(LED[led],state[led])\n time.sleep(FLASH_TIME)","def led_on(args):\n _check_mcu()\n led_on_bytes = CMD_MODULE_ID_LEDS | 0x00\n i2c.write_bytes_to_address(MCU_MOUTH_ADDRESS, led_on_bytes)","def light_standby():\n for led in leds:\n led.on()\n\n rgb_driver.pulse(on_color=(scale[\"R\"], scale[\"G\"], scale[\"B\"]), off_color=(0,0,0))","def bulb_blink_call():\n times = blinkSlider.get()\n bulb_blink(times)","def led_off(args):\n _check_mcu()\n led_on_bytes = CMD_MODULE_ID_LEDS | 0x01\n i2c.write_bytes_to_address(MCU_MOUTH_ADDRESS, led_on_bytes)","def switch_on(self):\n if threading.current_thread() != self._blinking_thread:\n self._blinking_thread.unregister(self)\n GPIO.output(self.pin, GPIO.HIGH)","def blink_leds(self):\n led_state = int(self.time_counter) % 2\n self.leds[0].set(led_state)\n self.leds[1].set(int(not(led_state)))","def blink(self,inter):\n if 0 <= inter <= 255:\n self.send(\"\\x1f\\x45%c\" % inter)\n else:\n raise ValueError('blink values have to be between 0 and 255')","def ToggleBlink(self):\n self.displaycontrol ^= self.LCD_BLINKON\n self.write(self.LCD_DATA_E1, self.LCD_DISPLAYCONTROL | self.displaycontrol)\n self.write(self.LCD_DATA_E2, self.LCD_DISPLAYCONTROL | self.displaycontrol)","def switch(ind, status):\n print(\"Switching :\", ind, \">>\", status == 'on')\n GPIO.output(ind, status == 'on')","def bulb_blink(times=5, speed=0.25):\n for i in range(times):\n bulb_toggle()\n time.sleep(speed)\n bulb_toggle()\n time.sleep(speed)","def turn_output_on(self):\n self.instr.write('RF1')\n time.sleep(self.sleep_time)","def turnLightOff(ID):\n dislin.litmod(ID, 'OFF')","def control_lights(state):\n for led in (RED, AMBER, GREEN):\n GPIO.output(LED[led],state[led])","def do_red(self,command):\n if \"on\" in command:\n print 'Red ON'\n GPIO.output(7,GPIO.HIGH)\n elif \"off\" in command:\n print 'Red OFF'\n GPIO.output(7,GPIO.LOW)\n elif \"flash\" in command:\n print 'Flashing green'\n FlashPin(pin=7,count=5,delay=0.1)\n else:\n print \"ERROR! MF!\"","def blink(self, duration: int=1, intensity: int=0xff):\n # Turn LED on\n self.intensity(max(0, min(intensity, 0xff)))\n # Turn LED off (after a delay)\n upyt.sched.loop.call_later_ms(duration, self.off)","def off(\n id: int = typer.Argument(1),\n ip: str = typer.Option(..., \"--ip\", \"-i\", envvar=\"HUE_BRIDGE_IP\"),\n user: str = typer.Option(..., \"--user\", \"-u\", envvar=\"HUE_BRIDGE_USER\"),\n):\n light = Light(id, ip=ip, user=user)\n resp = asyncio.run(light.power_off())\n console.print(f\"[{ip}] Light {id} Off:\\n{json.dumps(resp, indent=2)}\")","def set_led(self, on=True):\n if on:\n GPIO.output(self.LED, GPIO.HIGH)\n else:\n GPIO.output(self.LED, GPIO.LOW)","def turn_on(self):\n GPIO.output(self.gpio, True) # turn on light","def led_duty_cycle(val):\n set_tmr_ocr(TMR1, OCRxB, val)","def power_down(self):\n t_end = time.time() + 3\n while time.time() < t_end:\n self.light_led(4)\n self.light_led(6)","def all_off():\n print(\"Climate is within set parameters; toggling systems off if any are on\")\n GPIO.output(HEATPIN, RELAYOFF)\n GPIO.output(COOLPIN, RELAYOFF)\n GPIO.output(FANPIN, RELAYOFF)\n time.sleep(30)","def set_led(self, value):\n GPIO.output(LED_PIN, value)","def turn_on(self):\n self._interrupt_flash()\n if not self.on:\n GPIO.output(self.pin, GPIO.HIGH)\n self.on = True","def do_green(self,command):\n if \"on\" in command:\n print 'Green ON'\n GPIO.output(22,GPIO.HIGH)\n elif \"off\" in command:\n print 'Green OFF'\n GPIO.output(22,GPIO.LOW)\n elif \"flash\" in command:\n print 'Flashing green'\n FlashPin(pin=22,count=5,delay=0.1)\n else:\n print \"ERROR! MF!\"","def toggle(\n id: int = typer.Argument(1),\n ip: str = typer.Option(..., \"--ip\", \"-i\", envvar=\"HUE_BRIDGE_IP\"),\n user: str = typer.Option(..., \"--user\", \"-u\", envvar=\"HUE_BRIDGE_USER\"),\n):\n light = Light(id, ip=ip, user=user)\n resp = asyncio.run(light.toggle())\n console.print(f\"[{ip}] Light {id} Toggle:\\n{json.dumps(resp, indent=2)}\")","def config_led(my_bus):\n try:\n my_bus.write_i2c_block_data(LED_DEVICE_ADDRESS, 0x2F, [0xFF]) # system setup\n my_bus.write_i2c_block_data(LED_DEVICE_ADDRESS, 0x89, [0xFF]) # display on\n except IOError:\n t = 1\n print(\"got IOError. try again in\", t, \"second\")\n time.sleep(t)","def LED_flash(self, state):\n command = bytearray(b\"l\")\n if state == \"on\":\n command += bytes([1])\n command += b\"\\n\"\n elif state == \"off\":\n command += bytes([0])\n command += b\"\\n\"\n self.send(command)","async def light(self) -> None:\n self.lit = True\n await self.run_command(\"miner fault_light on\")\n print(\"light \" + self.ip)","def led_toggle(self):\n if self.state == GPIO.LOW:\n self.state = GPIO.HIGH\n else:\n self.state = GPIO.LOW\n return self.update_status()","def turnLightOn(ID):\n dislin.litmod(ID, 'ON')","def flash_all_leds(self, k):\n t_end = time.time() + k\n while time.time() < t_end:\n for i in range(300):\n self.light_led(0)\n self.light_led(1)\n self.light_led(2)\n self.light_led(3)\n self.light_led(4)\n self.light_led(5)\n self.light_led(6)\n time.sleep(0.5)\n self.light_led(6)","def led(self, value):\n self._write(MX_LED, value)","def pibooth_reset(cfg, hard):","def init_leds():\n GPIO.setmode(GPIO.BCM)\n GPIO.setup(IR1, GPIO.OUT, initial=GPIO.HIGH)\n GPIO.setup(IR2, GPIO.OUT, initial=GPIO.HIGH)\n GPIO.setup(IR3, GPIO.OUT, initial=GPIO.HIGH)\n GPIO.setup(IR4, GPIO.OUT, initial=GPIO.HIGH)\n thread = threading.Thread(target=warnings)\n thread.daemon = False\n thread.start()\n return thread","def ledFlash(strip, color, t = 1):\r\n utime.sleep(t)\r\n setStrip(strip, color)\r\n utime.sleep(t)\r\n setStrip(strip, LED_COLOR_OFF)","def turnLightingSystemOn():\n dislin.light('ON')","def _led_disable():\n # type: () -> None\n GPIO.output(LED_nOE, GPIO.HIGH)","def cycle(self, include_ethernet=False):\n if not self.healthy:\n self.health_check()\n self.off()\n time.sleep(2) # Small delay before calling power_on\n self.on()","def toggle_lights(bridge):\n if check_any_light_on(bridge):\n turn_off_lights(bridge)\n else:\n turn_on_lights(bridge)","def init_led():\r\n global led\r\n led = LED(LED_GPIO_PIN)\r\n led.off()","def power_up(self):\n t_end = time.time() + 3\n while time.time() < t_end:\n self.light_led(5)\n self.light_led(6)","def on(\n id: int = typer.Argument(1),\n ip: str = typer.Option(..., \"--ip\", \"-i\", envvar=\"HUE_BRIDGE_IP\"),\n user: str = typer.Option(..., \"--user\", \"-u\", envvar=\"HUE_BRIDGE_USER\"),\n):\n light = Light(id, ip=ip, user=user)\n resp = asyncio.run(light.power_on())\n console.print(f\"[{ip}] Light {id} On:\\n{json.dumps(resp, indent=2)}\")","def led(red: int, green: int, blue: int, /) -> None:","def ON(self):\n GPIO.setmode(GPIO.BOARD)\n GPIO.setup(self.PIN, GPIO.OUT)\n GPIO.output(self.PIN, True)\n self.STATUS = \"ON\"","def switch_relay(sensor):\n relay_results=select_sql(\"SELECT relay,board FROM sensor_master WHERE sensors = '{0}'\".format(sensor))\n for relay in relay_results:\n relay_no=int(relay[0])\n relay_board=int(relay[1])\n bv4627 = i2c_raw.i2c(relay_board, 1)\n logging.info(\"switching on relay \"+str(relay_no)+\" on board \"+str(relay_board))\n bv4627.write(bytearray([relay_no,1,0,1]))\n time.sleep(.4)\n logging.info(\"switching off relay \"+str(relay_no)+\" on board \"+str(relay_board))\n bv4627.write(bytearray([relay_no,0,0,1]))","def led_heartbeat(args):\n _check_mcu()\n led_heartbeat_bytes = CMD_MODULE_ID_LEDS | 0x02\n i2c.write_bytes_to_address(MCU_MOUTH_ADDRESS, led_heartbeat_bytes)","def led(color: int, /) -> None:","def switch_off(self):\n if threading.current_thread() != self._blinking_thread:\n self._blinking_thread.unregister(self)\n GPIO.output(self.pin, GPIO.LOW)","def lightoff(update: Update, context: CallbackContext) -> None:\n if __sauna.control.getPortValue(\"Light Sensor\") == 1:\n # TODO Mit Stromstossrelais ist dieser Code richtig\n # __sauna.control.togglePortValue(\"Light Switch\")\n update.message.reply_text(\"Light is off\")\n else:\n update.message.reply_text(\"Light was already off\")\n\n __sauna.control.resetPortValue(\"Light Switch\")\n val = __sauna.control.getPortValue(\"Light Switch\")\n update.message.reply_text(\"Light Switch := \" + str(val))","def toggle(light_id):\n if light_id == \"alloff\":\n pidomCtrl.pulse(\"alloff\")\n elif light_id == \"outside\":\n pidomCtrl.pulse(\"outside\")\n elif light_id == \"stairs\":\n pidomCtrl.pulse(\"stairs\")\n elif light_id == \"frontdoorgroupoff\":\n pidomCtrl.pulse(\"persistedoff\")\n elif light_id == \"persistedon\":\n pidomCtrl.pulse(\"frontdoorgroupon\")","def sleep(self):\r\n if not self.backlight:\r\n return\r\n\r\n self.backlight.power = False","def set_all_pwm(self, on, off):\n self.i2cBus.write_byte_data(self.address, ALL_LED_ON_L, on & 0xFF)\n self.i2cBus.write_byte_data(self.address, ALL_LED_ON_H, on >> 8)\n self.i2cBus.write_byte_data(self.address, ALL_LED_OFF_L, off & 0xFF)\n self.i2cBus.write_byte_data(self.address, ALL_LED_OFF_H, off >> 8)","async def rainbow(self, params):\n wait_ms = params.get('wait_ms', 2)\n try:\n while True:\n await self.lights.rainbow_cycle(wait_ms=wait_ms)\n except KeyboardInterrupt:\n pass","def set_led(self, *args, **kw):\n return self.execute_command('set_led', *args, **kw)","def emitters_on(self):\n self.wp.digitalWrite(self.LEDON_PIN, self.wp.HIGH)\n self.wp.delayMicroseconds(20)","def turn_off(self, **kwargs: Any) -> None:\n self._device.power_on = False\n _LOGGER.debug(\"Turn off light %s\", self._device.ip)","def _iotool_enable_yellow_command(self):\n return self._iotool.commands.set_low(self._spconfig.IOTOOL_GREEN_YELLOW_SWITCH_PIN)","def brief_led_flash(self):\n self._ubx.send('CFG-TP5', pulseLenRatioLock=990000)","def blink_led(controller_id, physical_drive_id):\n try:\n phy_drv = controller.get_physical_drive(controller_id, physical_drive_id)\n return phy_drv.blink_led()\n except:\n return {\"controller\": controller_id,\n \"physical_drive\": physical_drive_id,\n \"status\": \"Failed to blink the led\"}","def set_led_selector(self):\n\t\t\"\"\"For LED selector\"\"\"\n\t\tDATA0 = (PCA9530_2C_1_LS_LED_PWM0 << 2 | PCA9530_2C_1_LS_LED_PWM0)\n\t\tbus.write_byte_data(PCA9530_2C_1_DEFAULT_ADDRESS, PCA9530_2C_1_REG_PWM0, DATA0)","def toggle_connect( self ):\n # show waiting for communication\n self.lbl_status.setText( 'Waiting...' )\n self.lbl_statusLight.setPixmap( self.img_yellowLight )\n self.repaint()\n \n # create laser controller if doesn't already exist, connect\n if self.inst is None:\n try:\n self.inst = pac.Ammeter( self.port, timeout = 30 )\n self.inst.connect()\n \n except Exception as err:\n self.update_connected_ui( False )\n \n warning = QMessageBox()\n warning.setWindowTitle( 'Picoammeter Controller Error' )\n warning.setText( 'Could not connect\\n{}'.format( err ) )\n warning.exec()\n \n else:\n self.delete_controller()\n \n # update ui\n if self.inst is not None:\n self.update_connected_ui( self.inst.connected )\n \n else:\n self.update_connected_ui( False )","def toggle(self):\n self._interrupt_flash()\n GPIO.output(self.pin, GPIO.LOW if self.on else GPIO.HIGH)\n self.on = not self.on","def fadeLED( gpio, startVal, stopVal ):\n\t#convert passed values into usable format for pi-blaster (i.e 0 - 1)\n\tRGBstartVal = startVal / 255\n\tRGBstopVal = stopVal / 255\n\t#debug\n\tprint RGBstartVal, startVal, RGBstopVal, stopVal;\n\t#set the current LED values to the start value\n\tcurrentVal = RGBstartVal\n\tif RGBstartVal < RGBstopVal:\n\t\twhile currentVal < RGBstopVal:\n\t\t\tos.system(\"echo \\\"{0}={1}\\\" > /dev/pi-blaster\" .format(gpio,currentVal))\n\t\t\tcurrentVal = currentVal + STEP;\n\t\t\ttime.sleep(FADESPEED)\n\t\t\tprint currentVal\n\telif RGBstartVal > RGBstopVal:\n\t\t while currentVal > RGBstopVal:\n\t\t\tos.system(\"echo \\\"{0}={1}\\\" > /dev/pi-blaster\" .format(gpio,currentVal))\n currentVal = currentVal - STEP;\n time.sleep(FADESPEED)\n print currentVal\n\treturn;","def terminate():\n leds.on()\n time.sleep(1)\n leds.off()\n\n GPIO.cleanup()","def app_principal_led():\n \"\"\"\n import serial\n ser = serial.Serial(0) # open first serial port\n print ser.portstr # check which port was really used\n ser.write(\"hello\") # write a string\n ser.close() # close port \n \"\"\"\n\n\n start = mpa.ModuloPyArduino()\n p, v = start.config_arduino()\n con = start.set_conection(p, v)\n\n\n print \"\\n Status of conection: \", con\n if con != 0:\n start.serial_loop_app(con, 1)\n else:\n pass\n\n con.close()","def __init__(self, red_pin, green_pin, blue_pin):\n #self.red = gpiozero.PWMLED(red_pin, frequency=80, initial_value=1)\n #self.green = gpiozero.PWMLED(green_pin, frequency=80, initial_value=1)\n #self.blue = gpiozero.PWMLED(blue_pin, frequency=80, initial_value=1)\n self.red = gpiozero.LED(red_pin)\n self.green = gpiozero.LED(green_pin)\n self.blue = gpiozero.LED(blue_pin)\n self.leds = [self.red, self.green, self.blue]\n self.off()","def turn_on(self):\n self._remote.power(1)","async def async_turn_on(self):\n path = \"/interface\"\n param = \"default-name\"\n if \"-\" in self._data[\"port-mac-address\"]:\n param = \"name\"\n value = self._data[param]\n mod_param = \"disabled\"\n mod_value = False\n self._ctrl.set_value(path, param, value, mod_param, mod_value)\n\n if self._data[\"poe-out\"] == \"off\":\n path = \"/interface/ethernet\"\n self._ctrl.set_value(path, param, value, \"poe-out\", \"auto-on\")\n\n await self._ctrl.force_update()","async def unlight(self) -> None:\n self.lit = False\n await self.run_command(\"miner fault_light off\")\n print(\"unlight\" + self.ip)","def idle(self):\n self.pi.set_servo_pulsewidth(self.gpio, 0)","def set_PIenable(self,highlow): \n GPIO.output(self.chanlist[4], highlow)","def power_off(self, port):\n port = int(port)\n self._validate_port(\"power_off\", port)\n self.set_mode(OFF, port)","def lcd_off(args):\n _check_mcu()\n lcd_off_bytes = CMD_MODULE_ID_LCD | 0x22\n i2c.write_bytes_to_address(MCU_MOUTH_ADDRESS, lcd_off_bytes)","def power(self, on_off, bulb_index=-1):\n\n # TODO: Throw an error if value not in range\n\n on_off = int(on_off)\n if bulb_index == -1:\n for bulb in self.bulbs:\n if on_off == PowerStates.ON:\n bulb.turn_on(effect=\"smooth\", duration=1000)\n if on_off == PowerStates.OFF:\n bulb.turn_off(effect=\"smooth\", duration=1000)\n if on_off == PowerStates.SWITCH:\n bulb.toggle(effect=\"smooth\", duration=1000)\n elif bulb_index <= len(self.bulbs):\n if on_off == PowerStates.ON:\n self.bulbs[bulb_index].turn_on(effect=\"smooth\", duration=1000)\n if on_off == PowerStates.OFF:\n self.bulbs[bulb_index].turn_off(effect=\"smooth\", duration=1000)\n if on_off == PowerStates.SWITCH:\n self.bulbs[bulb_index].toggle(effect=\"smooth\", duration=1000)","def demo(s_delay=2):\n for i in flags:\n print(i)\n show_leds(flag=i)\n show_display(flag=i)\n utime.sleep_ms(s_delay * 1000)\n leds.clear()\n with display.open() as disp:\n disp.clear().update()\n disp.close()","def fire():\n print(\"FIRING\")\n GPIO.output(PIN1, 0)\n GPIO.output(PIN2, 0)\n GPIO.output(TRIGGER, 1)","def twinkle_all_leds(self, k):\n t_end = time.time() + k\n while time.time() < t_end:\n self.light_led(0)\n time.sleep(0.2)\n self.light_led(1)\n time.sleep(0.2)\n self.light_led(2)\n time.sleep(0.2)\n self.light_led(3)\n time.sleep(0.2)\n self.light_led(4)\n time.sleep(0.2)\n self.light_led(5)\n time.sleep(0.2)\n self.light_led(6)","def _iotool_enable_green_command(self):\n return self._iotool.commands.set_high(self._spconfig.IOTOOL_GREEN_YELLOW_SWITCH_PIN)","def turnLightingSystemOff():\n dislin.light('OFF')","def update_leds():\n # zaświeć\n for i in range(current_leds):\n volume_leds[i].configure(background=TURN_ON_COLOR)\n\n # zgaś\n for i in range(current_leds, LEDS):\n volume_leds[i].configure(background=TURN_OFF_COLOR)","def turn_off(self):\n self._interrupt_flash()\n if self.on:\n GPIO.output(self.pin, GPIO.LOW)\n self.on = False","def _led_enable():\n # type: () -> None\n GPIO.output(LED_nOE, GPIO.LOW)","async def async_set_wifi_led_off(self):\n return","def update(self):\n\t\tfor x in range(self.leds):\n\t\t\tself.spi.write(self.buffer[x])\n\t\t\t#self.spi.flush()\n\t\t\t\n\t\tself.spi.write(bytearray(b'\\x00'))\n\t\tself.spi.flush()","def test():\n\tleds = [KeyboardLed(number, indicator) for number, indicator in get_available_leds()]\n\timport time\n\tfor i in range(3):\n\t\ttime.sleep(0.2)\n\t\tflash_leds_synced(leds)","def wr(self, port, val):\n hw = self.device.peripherals[port]\n hw.ODR.wr(val)","def firethelights():\n for i in enumerate(PROTOCOL):\n elem = []\n elem = PROTOCOL[i]\n colsend = elem[0]\n timer = elem[1]\n commandstring = \"sudo python /home/pi/rpi_ws281x/python/examples/neopixel_args.py \"\n commandstring = commandstring+str(colsend[1])+\" \"+str(colsend[0])+\" \"+str(colsend[2])+\" \"\n os.system(commandstring+str(timer))\n os.system(commandstring+\"0 0 0 1\")\n print \"End of Protocol\"","def turn_on(self):\n self.write(\"OUT1\\n\")","def set_pwm(self, channel, on, off):\n self.i2cBus.write_byte_data(self.address, LED0_ON_L + 4 * channel, on & 0xFF)\n self.i2cBus.write_byte_data(self.address, LED0_ON_H + 4 * channel, on >> 8)\n self.i2cBus.write_byte_data(self.address, LED0_OFF_L + 4 * channel, int(off) & 0xFF)\n self.i2cBus.write_byte_data(self.address, LED0_OFF_H + 4 * channel, int(off) >> 8)","def wifi_on(self):\n self._clear_read_buffer()\n self._write_cmd(\"PE01\")\n time.sleep(100e-3)","def blink_power(cycles=1, period=.25, persist=False):\n for i in range(cycles):\n toggle_power()\n time.sleep(period)\n if not (i == cycles - 1 and persist):\n toggle_power()\n time.sleep(period)","def turn_off(self):\n GPIO.output(self.gpio, False) # turn off light","def togglePWMPower(self):\n # PCPWM1 is located at position 6\n mask = 1 << 6\n self._injectFault(\"PCONP\", 0x400FC0C4, mask)","def test_turn_off(power_supply):\n power_supply.Init()\n assert power_supply.state() != tango.DevState.OFF\n power_supply.turn_off()\n assert power_supply.state() == tango.DevState.OFF","def test_blink(self):\n display = get_display(1)\n display.register_state(main.Fade)\n prev = display.strand[0]\n for i in range(1000):\n display.tick()\n assert(all(0 <= display.strand[0][i] <= 255 for i in range(3)))\n assert display.strand[0] != prev\n prev = display.strand[0]"],"string":"[\n \"async def blink(my_board, pin):\\n\\n # set the pin mode\\n await my_board.set_pin_mode_digital_output(pin)\\n\\n # toggle the pin 4 times and exit\\n for x in range(4):\\n print('ON')\\n await my_board.digital_write(pin, 1)\\n await asyncio.sleep(1)\\n print('OFF')\\n await my_board.digital_write(pin, 0)\\n await asyncio.sleep(1)\",\n \"def irq_handler():\\n # open an LED session\\n with LEDs() as LED:\\n # specify the LED which you want to control\\n led = Led.LED1\\n # specify the LED status\\n led_on_off = True\\n # writes values 10 times, which makes LED1 flash for 3 seconds\\n for x in range(0, 10):\\n # turn LED0 on or off\\n LED.write(led, led_on_off)\\n # add a short delay\\n time.sleep(0.3)\\n # if the LED is on, set the parameter to off\\n # if the LED is off, set the parameter to on\\n led_on_off = not led_on_off\",\n \"def blink(self):\\n self.displaycontrol |= self.LCD_BLINKON\\n self.write_lcd(self.LCD_DATA_E1, self.LCD_DISPLAYCONTROL | self.displaycontrol)\\n self.write_lcd(self.LCD_DATA_E2, self.LCD_DISPLAYCONTROL | self.displaycontrol)\",\n \"def setLED(self):\\n newValue = 0\\n while newValue != '':\\n newValue = input('Enter 0 or 1 to turn LED on or off or enter to exit.\\\\n')\\n \\n if newValue == '0':\\n self.board.write(b'0')\\n time.sleep(1)\\n elif newValue == '1':\\n self.board.write(b'1')\\n time.sleep(1)\\n else:\\n time.sleep(1)\",\n \"def ControlLights(state):\\n for led in (RED,YELLOW,GREEN):\\n GPIO.output(LED[led],state[led])\\n time.sleep(FLASH_TIME)\",\n \"def led_on(args):\\n _check_mcu()\\n led_on_bytes = CMD_MODULE_ID_LEDS | 0x00\\n i2c.write_bytes_to_address(MCU_MOUTH_ADDRESS, led_on_bytes)\",\n \"def light_standby():\\n for led in leds:\\n led.on()\\n\\n rgb_driver.pulse(on_color=(scale[\\\"R\\\"], scale[\\\"G\\\"], scale[\\\"B\\\"]), off_color=(0,0,0))\",\n \"def bulb_blink_call():\\n times = blinkSlider.get()\\n bulb_blink(times)\",\n \"def led_off(args):\\n _check_mcu()\\n led_on_bytes = CMD_MODULE_ID_LEDS | 0x01\\n i2c.write_bytes_to_address(MCU_MOUTH_ADDRESS, led_on_bytes)\",\n \"def switch_on(self):\\n if threading.current_thread() != self._blinking_thread:\\n self._blinking_thread.unregister(self)\\n GPIO.output(self.pin, GPIO.HIGH)\",\n \"def blink_leds(self):\\n led_state = int(self.time_counter) % 2\\n self.leds[0].set(led_state)\\n self.leds[1].set(int(not(led_state)))\",\n \"def blink(self,inter):\\n if 0 <= inter <= 255:\\n self.send(\\\"\\\\x1f\\\\x45%c\\\" % inter)\\n else:\\n raise ValueError('blink values have to be between 0 and 255')\",\n \"def ToggleBlink(self):\\n self.displaycontrol ^= self.LCD_BLINKON\\n self.write(self.LCD_DATA_E1, self.LCD_DISPLAYCONTROL | self.displaycontrol)\\n self.write(self.LCD_DATA_E2, self.LCD_DISPLAYCONTROL | self.displaycontrol)\",\n \"def switch(ind, status):\\n print(\\\"Switching :\\\", ind, \\\">>\\\", status == 'on')\\n GPIO.output(ind, status == 'on')\",\n \"def bulb_blink(times=5, speed=0.25):\\n for i in range(times):\\n bulb_toggle()\\n time.sleep(speed)\\n bulb_toggle()\\n time.sleep(speed)\",\n \"def turn_output_on(self):\\n self.instr.write('RF1')\\n time.sleep(self.sleep_time)\",\n \"def turnLightOff(ID):\\n dislin.litmod(ID, 'OFF')\",\n \"def control_lights(state):\\n for led in (RED, AMBER, GREEN):\\n GPIO.output(LED[led],state[led])\",\n \"def do_red(self,command):\\n if \\\"on\\\" in command:\\n print 'Red ON'\\n GPIO.output(7,GPIO.HIGH)\\n elif \\\"off\\\" in command:\\n print 'Red OFF'\\n GPIO.output(7,GPIO.LOW)\\n elif \\\"flash\\\" in command:\\n print 'Flashing green'\\n FlashPin(pin=7,count=5,delay=0.1)\\n else:\\n print \\\"ERROR! MF!\\\"\",\n \"def blink(self, duration: int=1, intensity: int=0xff):\\n # Turn LED on\\n self.intensity(max(0, min(intensity, 0xff)))\\n # Turn LED off (after a delay)\\n upyt.sched.loop.call_later_ms(duration, self.off)\",\n \"def off(\\n id: int = typer.Argument(1),\\n ip: str = typer.Option(..., \\\"--ip\\\", \\\"-i\\\", envvar=\\\"HUE_BRIDGE_IP\\\"),\\n user: str = typer.Option(..., \\\"--user\\\", \\\"-u\\\", envvar=\\\"HUE_BRIDGE_USER\\\"),\\n):\\n light = Light(id, ip=ip, user=user)\\n resp = asyncio.run(light.power_off())\\n console.print(f\\\"[{ip}] Light {id} Off:\\\\n{json.dumps(resp, indent=2)}\\\")\",\n \"def set_led(self, on=True):\\n if on:\\n GPIO.output(self.LED, GPIO.HIGH)\\n else:\\n GPIO.output(self.LED, GPIO.LOW)\",\n \"def turn_on(self):\\n GPIO.output(self.gpio, True) # turn on light\",\n \"def led_duty_cycle(val):\\n set_tmr_ocr(TMR1, OCRxB, val)\",\n \"def power_down(self):\\n t_end = time.time() + 3\\n while time.time() < t_end:\\n self.light_led(4)\\n self.light_led(6)\",\n \"def all_off():\\n print(\\\"Climate is within set parameters; toggling systems off if any are on\\\")\\n GPIO.output(HEATPIN, RELAYOFF)\\n GPIO.output(COOLPIN, RELAYOFF)\\n GPIO.output(FANPIN, RELAYOFF)\\n time.sleep(30)\",\n \"def set_led(self, value):\\n GPIO.output(LED_PIN, value)\",\n \"def turn_on(self):\\n self._interrupt_flash()\\n if not self.on:\\n GPIO.output(self.pin, GPIO.HIGH)\\n self.on = True\",\n \"def do_green(self,command):\\n if \\\"on\\\" in command:\\n print 'Green ON'\\n GPIO.output(22,GPIO.HIGH)\\n elif \\\"off\\\" in command:\\n print 'Green OFF'\\n GPIO.output(22,GPIO.LOW)\\n elif \\\"flash\\\" in command:\\n print 'Flashing green'\\n FlashPin(pin=22,count=5,delay=0.1)\\n else:\\n print \\\"ERROR! MF!\\\"\",\n \"def toggle(\\n id: int = typer.Argument(1),\\n ip: str = typer.Option(..., \\\"--ip\\\", \\\"-i\\\", envvar=\\\"HUE_BRIDGE_IP\\\"),\\n user: str = typer.Option(..., \\\"--user\\\", \\\"-u\\\", envvar=\\\"HUE_BRIDGE_USER\\\"),\\n):\\n light = Light(id, ip=ip, user=user)\\n resp = asyncio.run(light.toggle())\\n console.print(f\\\"[{ip}] Light {id} Toggle:\\\\n{json.dumps(resp, indent=2)}\\\")\",\n \"def config_led(my_bus):\\n try:\\n my_bus.write_i2c_block_data(LED_DEVICE_ADDRESS, 0x2F, [0xFF]) # system setup\\n my_bus.write_i2c_block_data(LED_DEVICE_ADDRESS, 0x89, [0xFF]) # display on\\n except IOError:\\n t = 1\\n print(\\\"got IOError. try again in\\\", t, \\\"second\\\")\\n time.sleep(t)\",\n \"def LED_flash(self, state):\\n command = bytearray(b\\\"l\\\")\\n if state == \\\"on\\\":\\n command += bytes([1])\\n command += b\\\"\\\\n\\\"\\n elif state == \\\"off\\\":\\n command += bytes([0])\\n command += b\\\"\\\\n\\\"\\n self.send(command)\",\n \"async def light(self) -> None:\\n self.lit = True\\n await self.run_command(\\\"miner fault_light on\\\")\\n print(\\\"light \\\" + self.ip)\",\n \"def led_toggle(self):\\n if self.state == GPIO.LOW:\\n self.state = GPIO.HIGH\\n else:\\n self.state = GPIO.LOW\\n return self.update_status()\",\n \"def turnLightOn(ID):\\n dislin.litmod(ID, 'ON')\",\n \"def flash_all_leds(self, k):\\n t_end = time.time() + k\\n while time.time() < t_end:\\n for i in range(300):\\n self.light_led(0)\\n self.light_led(1)\\n self.light_led(2)\\n self.light_led(3)\\n self.light_led(4)\\n self.light_led(5)\\n self.light_led(6)\\n time.sleep(0.5)\\n self.light_led(6)\",\n \"def led(self, value):\\n self._write(MX_LED, value)\",\n \"def pibooth_reset(cfg, hard):\",\n \"def init_leds():\\n GPIO.setmode(GPIO.BCM)\\n GPIO.setup(IR1, GPIO.OUT, initial=GPIO.HIGH)\\n GPIO.setup(IR2, GPIO.OUT, initial=GPIO.HIGH)\\n GPIO.setup(IR3, GPIO.OUT, initial=GPIO.HIGH)\\n GPIO.setup(IR4, GPIO.OUT, initial=GPIO.HIGH)\\n thread = threading.Thread(target=warnings)\\n thread.daemon = False\\n thread.start()\\n return thread\",\n \"def ledFlash(strip, color, t = 1):\\r\\n utime.sleep(t)\\r\\n setStrip(strip, color)\\r\\n utime.sleep(t)\\r\\n setStrip(strip, LED_COLOR_OFF)\",\n \"def turnLightingSystemOn():\\n dislin.light('ON')\",\n \"def _led_disable():\\n # type: () -> None\\n GPIO.output(LED_nOE, GPIO.HIGH)\",\n \"def cycle(self, include_ethernet=False):\\n if not self.healthy:\\n self.health_check()\\n self.off()\\n time.sleep(2) # Small delay before calling power_on\\n self.on()\",\n \"def toggle_lights(bridge):\\n if check_any_light_on(bridge):\\n turn_off_lights(bridge)\\n else:\\n turn_on_lights(bridge)\",\n \"def init_led():\\r\\n global led\\r\\n led = LED(LED_GPIO_PIN)\\r\\n led.off()\",\n \"def power_up(self):\\n t_end = time.time() + 3\\n while time.time() < t_end:\\n self.light_led(5)\\n self.light_led(6)\",\n \"def on(\\n id: int = typer.Argument(1),\\n ip: str = typer.Option(..., \\\"--ip\\\", \\\"-i\\\", envvar=\\\"HUE_BRIDGE_IP\\\"),\\n user: str = typer.Option(..., \\\"--user\\\", \\\"-u\\\", envvar=\\\"HUE_BRIDGE_USER\\\"),\\n):\\n light = Light(id, ip=ip, user=user)\\n resp = asyncio.run(light.power_on())\\n console.print(f\\\"[{ip}] Light {id} On:\\\\n{json.dumps(resp, indent=2)}\\\")\",\n \"def led(red: int, green: int, blue: int, /) -> None:\",\n \"def ON(self):\\n GPIO.setmode(GPIO.BOARD)\\n GPIO.setup(self.PIN, GPIO.OUT)\\n GPIO.output(self.PIN, True)\\n self.STATUS = \\\"ON\\\"\",\n \"def switch_relay(sensor):\\n relay_results=select_sql(\\\"SELECT relay,board FROM sensor_master WHERE sensors = '{0}'\\\".format(sensor))\\n for relay in relay_results:\\n relay_no=int(relay[0])\\n relay_board=int(relay[1])\\n bv4627 = i2c_raw.i2c(relay_board, 1)\\n logging.info(\\\"switching on relay \\\"+str(relay_no)+\\\" on board \\\"+str(relay_board))\\n bv4627.write(bytearray([relay_no,1,0,1]))\\n time.sleep(.4)\\n logging.info(\\\"switching off relay \\\"+str(relay_no)+\\\" on board \\\"+str(relay_board))\\n bv4627.write(bytearray([relay_no,0,0,1]))\",\n \"def led_heartbeat(args):\\n _check_mcu()\\n led_heartbeat_bytes = CMD_MODULE_ID_LEDS | 0x02\\n i2c.write_bytes_to_address(MCU_MOUTH_ADDRESS, led_heartbeat_bytes)\",\n \"def led(color: int, /) -> None:\",\n \"def switch_off(self):\\n if threading.current_thread() != self._blinking_thread:\\n self._blinking_thread.unregister(self)\\n GPIO.output(self.pin, GPIO.LOW)\",\n \"def lightoff(update: Update, context: CallbackContext) -> None:\\n if __sauna.control.getPortValue(\\\"Light Sensor\\\") == 1:\\n # TODO Mit Stromstossrelais ist dieser Code richtig\\n # __sauna.control.togglePortValue(\\\"Light Switch\\\")\\n update.message.reply_text(\\\"Light is off\\\")\\n else:\\n update.message.reply_text(\\\"Light was already off\\\")\\n\\n __sauna.control.resetPortValue(\\\"Light Switch\\\")\\n val = __sauna.control.getPortValue(\\\"Light Switch\\\")\\n update.message.reply_text(\\\"Light Switch := \\\" + str(val))\",\n \"def toggle(light_id):\\n if light_id == \\\"alloff\\\":\\n pidomCtrl.pulse(\\\"alloff\\\")\\n elif light_id == \\\"outside\\\":\\n pidomCtrl.pulse(\\\"outside\\\")\\n elif light_id == \\\"stairs\\\":\\n pidomCtrl.pulse(\\\"stairs\\\")\\n elif light_id == \\\"frontdoorgroupoff\\\":\\n pidomCtrl.pulse(\\\"persistedoff\\\")\\n elif light_id == \\\"persistedon\\\":\\n pidomCtrl.pulse(\\\"frontdoorgroupon\\\")\",\n \"def sleep(self):\\r\\n if not self.backlight:\\r\\n return\\r\\n\\r\\n self.backlight.power = False\",\n \"def set_all_pwm(self, on, off):\\n self.i2cBus.write_byte_data(self.address, ALL_LED_ON_L, on & 0xFF)\\n self.i2cBus.write_byte_data(self.address, ALL_LED_ON_H, on >> 8)\\n self.i2cBus.write_byte_data(self.address, ALL_LED_OFF_L, off & 0xFF)\\n self.i2cBus.write_byte_data(self.address, ALL_LED_OFF_H, off >> 8)\",\n \"async def rainbow(self, params):\\n wait_ms = params.get('wait_ms', 2)\\n try:\\n while True:\\n await self.lights.rainbow_cycle(wait_ms=wait_ms)\\n except KeyboardInterrupt:\\n pass\",\n \"def set_led(self, *args, **kw):\\n return self.execute_command('set_led', *args, **kw)\",\n \"def emitters_on(self):\\n self.wp.digitalWrite(self.LEDON_PIN, self.wp.HIGH)\\n self.wp.delayMicroseconds(20)\",\n \"def turn_off(self, **kwargs: Any) -> None:\\n self._device.power_on = False\\n _LOGGER.debug(\\\"Turn off light %s\\\", self._device.ip)\",\n \"def _iotool_enable_yellow_command(self):\\n return self._iotool.commands.set_low(self._spconfig.IOTOOL_GREEN_YELLOW_SWITCH_PIN)\",\n \"def brief_led_flash(self):\\n self._ubx.send('CFG-TP5', pulseLenRatioLock=990000)\",\n \"def blink_led(controller_id, physical_drive_id):\\n try:\\n phy_drv = controller.get_physical_drive(controller_id, physical_drive_id)\\n return phy_drv.blink_led()\\n except:\\n return {\\\"controller\\\": controller_id,\\n \\\"physical_drive\\\": physical_drive_id,\\n \\\"status\\\": \\\"Failed to blink the led\\\"}\",\n \"def set_led_selector(self):\\n\\t\\t\\\"\\\"\\\"For LED selector\\\"\\\"\\\"\\n\\t\\tDATA0 = (PCA9530_2C_1_LS_LED_PWM0 << 2 | PCA9530_2C_1_LS_LED_PWM0)\\n\\t\\tbus.write_byte_data(PCA9530_2C_1_DEFAULT_ADDRESS, PCA9530_2C_1_REG_PWM0, DATA0)\",\n \"def toggle_connect( self ):\\n # show waiting for communication\\n self.lbl_status.setText( 'Waiting...' )\\n self.lbl_statusLight.setPixmap( self.img_yellowLight )\\n self.repaint()\\n \\n # create laser controller if doesn't already exist, connect\\n if self.inst is None:\\n try:\\n self.inst = pac.Ammeter( self.port, timeout = 30 )\\n self.inst.connect()\\n \\n except Exception as err:\\n self.update_connected_ui( False )\\n \\n warning = QMessageBox()\\n warning.setWindowTitle( 'Picoammeter Controller Error' )\\n warning.setText( 'Could not connect\\\\n{}'.format( err ) )\\n warning.exec()\\n \\n else:\\n self.delete_controller()\\n \\n # update ui\\n if self.inst is not None:\\n self.update_connected_ui( self.inst.connected )\\n \\n else:\\n self.update_connected_ui( False )\",\n \"def toggle(self):\\n self._interrupt_flash()\\n GPIO.output(self.pin, GPIO.LOW if self.on else GPIO.HIGH)\\n self.on = not self.on\",\n \"def fadeLED( gpio, startVal, stopVal ):\\n\\t#convert passed values into usable format for pi-blaster (i.e 0 - 1)\\n\\tRGBstartVal = startVal / 255\\n\\tRGBstopVal = stopVal / 255\\n\\t#debug\\n\\tprint RGBstartVal, startVal, RGBstopVal, stopVal;\\n\\t#set the current LED values to the start value\\n\\tcurrentVal = RGBstartVal\\n\\tif RGBstartVal < RGBstopVal:\\n\\t\\twhile currentVal < RGBstopVal:\\n\\t\\t\\tos.system(\\\"echo \\\\\\\"{0}={1}\\\\\\\" > /dev/pi-blaster\\\" .format(gpio,currentVal))\\n\\t\\t\\tcurrentVal = currentVal + STEP;\\n\\t\\t\\ttime.sleep(FADESPEED)\\n\\t\\t\\tprint currentVal\\n\\telif RGBstartVal > RGBstopVal:\\n\\t\\t while currentVal > RGBstopVal:\\n\\t\\t\\tos.system(\\\"echo \\\\\\\"{0}={1}\\\\\\\" > /dev/pi-blaster\\\" .format(gpio,currentVal))\\n currentVal = currentVal - STEP;\\n time.sleep(FADESPEED)\\n print currentVal\\n\\treturn;\",\n \"def terminate():\\n leds.on()\\n time.sleep(1)\\n leds.off()\\n\\n GPIO.cleanup()\",\n \"def app_principal_led():\\n \\\"\\\"\\\"\\n import serial\\n ser = serial.Serial(0) # open first serial port\\n print ser.portstr # check which port was really used\\n ser.write(\\\"hello\\\") # write a string\\n ser.close() # close port \\n \\\"\\\"\\\"\\n\\n\\n start = mpa.ModuloPyArduino()\\n p, v = start.config_arduino()\\n con = start.set_conection(p, v)\\n\\n\\n print \\\"\\\\n Status of conection: \\\", con\\n if con != 0:\\n start.serial_loop_app(con, 1)\\n else:\\n pass\\n\\n con.close()\",\n \"def __init__(self, red_pin, green_pin, blue_pin):\\n #self.red = gpiozero.PWMLED(red_pin, frequency=80, initial_value=1)\\n #self.green = gpiozero.PWMLED(green_pin, frequency=80, initial_value=1)\\n #self.blue = gpiozero.PWMLED(blue_pin, frequency=80, initial_value=1)\\n self.red = gpiozero.LED(red_pin)\\n self.green = gpiozero.LED(green_pin)\\n self.blue = gpiozero.LED(blue_pin)\\n self.leds = [self.red, self.green, self.blue]\\n self.off()\",\n \"def turn_on(self):\\n self._remote.power(1)\",\n \"async def async_turn_on(self):\\n path = \\\"/interface\\\"\\n param = \\\"default-name\\\"\\n if \\\"-\\\" in self._data[\\\"port-mac-address\\\"]:\\n param = \\\"name\\\"\\n value = self._data[param]\\n mod_param = \\\"disabled\\\"\\n mod_value = False\\n self._ctrl.set_value(path, param, value, mod_param, mod_value)\\n\\n if self._data[\\\"poe-out\\\"] == \\\"off\\\":\\n path = \\\"/interface/ethernet\\\"\\n self._ctrl.set_value(path, param, value, \\\"poe-out\\\", \\\"auto-on\\\")\\n\\n await self._ctrl.force_update()\",\n \"async def unlight(self) -> None:\\n self.lit = False\\n await self.run_command(\\\"miner fault_light off\\\")\\n print(\\\"unlight\\\" + self.ip)\",\n \"def idle(self):\\n self.pi.set_servo_pulsewidth(self.gpio, 0)\",\n \"def set_PIenable(self,highlow): \\n GPIO.output(self.chanlist[4], highlow)\",\n \"def power_off(self, port):\\n port = int(port)\\n self._validate_port(\\\"power_off\\\", port)\\n self.set_mode(OFF, port)\",\n \"def lcd_off(args):\\n _check_mcu()\\n lcd_off_bytes = CMD_MODULE_ID_LCD | 0x22\\n i2c.write_bytes_to_address(MCU_MOUTH_ADDRESS, lcd_off_bytes)\",\n \"def power(self, on_off, bulb_index=-1):\\n\\n # TODO: Throw an error if value not in range\\n\\n on_off = int(on_off)\\n if bulb_index == -1:\\n for bulb in self.bulbs:\\n if on_off == PowerStates.ON:\\n bulb.turn_on(effect=\\\"smooth\\\", duration=1000)\\n if on_off == PowerStates.OFF:\\n bulb.turn_off(effect=\\\"smooth\\\", duration=1000)\\n if on_off == PowerStates.SWITCH:\\n bulb.toggle(effect=\\\"smooth\\\", duration=1000)\\n elif bulb_index <= len(self.bulbs):\\n if on_off == PowerStates.ON:\\n self.bulbs[bulb_index].turn_on(effect=\\\"smooth\\\", duration=1000)\\n if on_off == PowerStates.OFF:\\n self.bulbs[bulb_index].turn_off(effect=\\\"smooth\\\", duration=1000)\\n if on_off == PowerStates.SWITCH:\\n self.bulbs[bulb_index].toggle(effect=\\\"smooth\\\", duration=1000)\",\n \"def demo(s_delay=2):\\n for i in flags:\\n print(i)\\n show_leds(flag=i)\\n show_display(flag=i)\\n utime.sleep_ms(s_delay * 1000)\\n leds.clear()\\n with display.open() as disp:\\n disp.clear().update()\\n disp.close()\",\n \"def fire():\\n print(\\\"FIRING\\\")\\n GPIO.output(PIN1, 0)\\n GPIO.output(PIN2, 0)\\n GPIO.output(TRIGGER, 1)\",\n \"def twinkle_all_leds(self, k):\\n t_end = time.time() + k\\n while time.time() < t_end:\\n self.light_led(0)\\n time.sleep(0.2)\\n self.light_led(1)\\n time.sleep(0.2)\\n self.light_led(2)\\n time.sleep(0.2)\\n self.light_led(3)\\n time.sleep(0.2)\\n self.light_led(4)\\n time.sleep(0.2)\\n self.light_led(5)\\n time.sleep(0.2)\\n self.light_led(6)\",\n \"def _iotool_enable_green_command(self):\\n return self._iotool.commands.set_high(self._spconfig.IOTOOL_GREEN_YELLOW_SWITCH_PIN)\",\n \"def turnLightingSystemOff():\\n dislin.light('OFF')\",\n \"def update_leds():\\n # zaświeć\\n for i in range(current_leds):\\n volume_leds[i].configure(background=TURN_ON_COLOR)\\n\\n # zgaś\\n for i in range(current_leds, LEDS):\\n volume_leds[i].configure(background=TURN_OFF_COLOR)\",\n \"def turn_off(self):\\n self._interrupt_flash()\\n if self.on:\\n GPIO.output(self.pin, GPIO.LOW)\\n self.on = False\",\n \"def _led_enable():\\n # type: () -> None\\n GPIO.output(LED_nOE, GPIO.LOW)\",\n \"async def async_set_wifi_led_off(self):\\n return\",\n \"def update(self):\\n\\t\\tfor x in range(self.leds):\\n\\t\\t\\tself.spi.write(self.buffer[x])\\n\\t\\t\\t#self.spi.flush()\\n\\t\\t\\t\\n\\t\\tself.spi.write(bytearray(b'\\\\x00'))\\n\\t\\tself.spi.flush()\",\n \"def test():\\n\\tleds = [KeyboardLed(number, indicator) for number, indicator in get_available_leds()]\\n\\timport time\\n\\tfor i in range(3):\\n\\t\\ttime.sleep(0.2)\\n\\t\\tflash_leds_synced(leds)\",\n \"def wr(self, port, val):\\n hw = self.device.peripherals[port]\\n hw.ODR.wr(val)\",\n \"def firethelights():\\n for i in enumerate(PROTOCOL):\\n elem = []\\n elem = PROTOCOL[i]\\n colsend = elem[0]\\n timer = elem[1]\\n commandstring = \\\"sudo python /home/pi/rpi_ws281x/python/examples/neopixel_args.py \\\"\\n commandstring = commandstring+str(colsend[1])+\\\" \\\"+str(colsend[0])+\\\" \\\"+str(colsend[2])+\\\" \\\"\\n os.system(commandstring+str(timer))\\n os.system(commandstring+\\\"0 0 0 1\\\")\\n print \\\"End of Protocol\\\"\",\n \"def turn_on(self):\\n self.write(\\\"OUT1\\\\n\\\")\",\n \"def set_pwm(self, channel, on, off):\\n self.i2cBus.write_byte_data(self.address, LED0_ON_L + 4 * channel, on & 0xFF)\\n self.i2cBus.write_byte_data(self.address, LED0_ON_H + 4 * channel, on >> 8)\\n self.i2cBus.write_byte_data(self.address, LED0_OFF_L + 4 * channel, int(off) & 0xFF)\\n self.i2cBus.write_byte_data(self.address, LED0_OFF_H + 4 * channel, int(off) >> 8)\",\n \"def wifi_on(self):\\n self._clear_read_buffer()\\n self._write_cmd(\\\"PE01\\\")\\n time.sleep(100e-3)\",\n \"def blink_power(cycles=1, period=.25, persist=False):\\n for i in range(cycles):\\n toggle_power()\\n time.sleep(period)\\n if not (i == cycles - 1 and persist):\\n toggle_power()\\n time.sleep(period)\",\n \"def turn_off(self):\\n GPIO.output(self.gpio, False) # turn off light\",\n \"def togglePWMPower(self):\\n # PCPWM1 is located at position 6\\n mask = 1 << 6\\n self._injectFault(\\\"PCONP\\\", 0x400FC0C4, mask)\",\n \"def test_turn_off(power_supply):\\n power_supply.Init()\\n assert power_supply.state() != tango.DevState.OFF\\n power_supply.turn_off()\\n assert power_supply.state() == tango.DevState.OFF\",\n \"def test_blink(self):\\n display = get_display(1)\\n display.register_state(main.Fade)\\n prev = display.strand[0]\\n for i in range(1000):\\n display.tick()\\n assert(all(0 <= display.strand[0][i] <= 255 for i in range(3)))\\n assert display.strand[0] != prev\\n prev = display.strand[0]\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.73258066","0.69174576","0.6745456","0.6721252","0.66942364","0.66491276","0.6608069","0.65577483","0.64461106","0.63938886","0.63897306","0.6379322","0.637831","0.63509107","0.63327783","0.626326","0.62223494","0.61845493","0.6181832","0.6151551","0.6127639","0.6107242","0.60886776","0.6086557","0.60826486","0.60745955","0.60706264","0.604207","0.6007166","0.5992268","0.59822756","0.5979479","0.5973586","0.59735477","0.5962522","0.5938447","0.59382135","0.59379417","0.5925745","0.5924961","0.5910782","0.58975947","0.5891811","0.589139","0.5887872","0.58829546","0.5874237","0.5864217","0.5862812","0.5857488","0.5855995","0.58263224","0.5818318","0.5817746","0.5801933","0.5793397","0.57894826","0.5786428","0.5781919","0.57776815","0.57731014","0.5771445","0.5769093","0.5765215","0.5757345","0.57487935","0.5742423","0.57338107","0.5732644","0.5720484","0.5720265","0.5718687","0.57140875","0.5707631","0.5707115","0.56918436","0.56902766","0.56890243","0.56875825","0.56816745","0.56796616","0.56690186","0.56685483","0.56676733","0.5654784","0.56537646","0.56501627","0.56418043","0.5641379","0.56381744","0.563806","0.5637018","0.5627569","0.56272715","0.56264746","0.56186897","0.56115854","0.5610434","0.56087065","0.5606488"],"string":"[\n \"0.73258066\",\n \"0.69174576\",\n \"0.6745456\",\n \"0.6721252\",\n \"0.66942364\",\n \"0.66491276\",\n \"0.6608069\",\n \"0.65577483\",\n \"0.64461106\",\n \"0.63938886\",\n \"0.63897306\",\n \"0.6379322\",\n \"0.637831\",\n \"0.63509107\",\n \"0.63327783\",\n \"0.626326\",\n \"0.62223494\",\n \"0.61845493\",\n \"0.6181832\",\n \"0.6151551\",\n \"0.6127639\",\n \"0.6107242\",\n \"0.60886776\",\n \"0.6086557\",\n \"0.60826486\",\n \"0.60745955\",\n \"0.60706264\",\n \"0.604207\",\n \"0.6007166\",\n \"0.5992268\",\n \"0.59822756\",\n \"0.5979479\",\n \"0.5973586\",\n \"0.59735477\",\n \"0.5962522\",\n \"0.5938447\",\n \"0.59382135\",\n \"0.59379417\",\n \"0.5925745\",\n \"0.5924961\",\n \"0.5910782\",\n \"0.58975947\",\n \"0.5891811\",\n \"0.589139\",\n \"0.5887872\",\n \"0.58829546\",\n \"0.5874237\",\n \"0.5864217\",\n \"0.5862812\",\n \"0.5857488\",\n \"0.5855995\",\n \"0.58263224\",\n \"0.5818318\",\n \"0.5817746\",\n \"0.5801933\",\n \"0.5793397\",\n \"0.57894826\",\n \"0.5786428\",\n \"0.5781919\",\n \"0.57776815\",\n \"0.57731014\",\n \"0.5771445\",\n \"0.5769093\",\n \"0.5765215\",\n \"0.5757345\",\n \"0.57487935\",\n \"0.5742423\",\n \"0.57338107\",\n \"0.5732644\",\n \"0.5720484\",\n \"0.5720265\",\n \"0.5718687\",\n \"0.57140875\",\n \"0.5707631\",\n \"0.5707115\",\n \"0.56918436\",\n \"0.56902766\",\n \"0.56890243\",\n \"0.56875825\",\n \"0.56816745\",\n \"0.56796616\",\n \"0.56690186\",\n \"0.56685483\",\n \"0.56676733\",\n \"0.5654784\",\n \"0.56537646\",\n \"0.56501627\",\n \"0.56418043\",\n \"0.5641379\",\n \"0.56381744\",\n \"0.563806\",\n \"0.5637018\",\n \"0.5627569\",\n \"0.56272715\",\n \"0.56264746\",\n \"0.56186897\",\n \"0.56115854\",\n \"0.5610434\",\n \"0.56087065\",\n \"0.5606488\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7913171"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":92592,"cells":{"query":{"kind":"string","value":"Get the MAC for eth0 using IPMI"},"document":{"kind":"string","value":"def get_eth0_mac_address(hostname):\n\n command = \"ipmitool -H {} -U ADMIN -P ADMIN raw 0x30 0x21\".format(hostname)\n\n try:\n result = subprocess.check_output(command, shell=True)\n except subprocess.CalledProcessError as exc:\n LOGGER.exception(\"Failed to get eth0 mac for %s\", hostname)\n\n # Remove space and newline\n result = result.strip()\n mac = \":\".join(result.split()[4:]) # No verification :-(\n\n return mac"},"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_mac_address():\n eth0_interface = 'eth0'\n addresses = netifaces.ifaddresses(eth0_interface)[netifaces.AF_LINK][0]\n mac_address = addresses['addr']\n return mac_address","def getMac(self):\n # Import netifaces here to prevent error importing this module in setup.py\n import netifaces\n interfaces = ['eth0', 'wlan0']\n try:\n interfaces.append(netifaces.gateways()['default'][netifaces.AF_INET][1])\n except:\n pass\n for interface in interfaces:\n try:\n return netifaces.ifaddresses(interface)[netifaces.AF_LINK][0]['addr']\n except ValueError:\n pass\n except:\n exception('Error getting MAC address')\n return None","def get_mac():\n\n interface = [x for x in netifaces.interfaces() if 'wlan' in x or 'wlp' in x][0]\n return netifaces.ifaddresses(interface)[netifaces.AF_LINK][0]['addr']","def get_mac_address(ifname):\n try:\n return open('/sys/class/net/' + ifname + '/address') \\\n .readline().strip()\n except:\n SysTools.logger.error(\"Failed to get mac-address of %s\", ifname)\n return \"00:00:00:00:00:00\"","def _get_mac_address():\n if not sys.platform.startswith('linux'):\n raise RuntimeError(\n 'Cannot get the MAC address on non-Linux platforms'\n )\n ifname = get_default_iface_name_linux()\n s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\n info = fcntl.ioctl(s.fileno(), 0x8927,\n struct.pack('256s', bytes(ifname, 'utf-8')[:15]))\n return ''.join('%02x' % b for b in info[18:24])","def get_mac(self, node_id):\n nc = '%02x' % self.node_class\n nr_iface = '%02x' % self.nr_host_interface\n node_id = '%08x' % node_id\n\n return '%s:%s:%s:%s:%s:%s' % (nc, nr_iface, node_id[0:2], node_id[2:4], node_id[4:6], node_id[6:8])","def mac_from_vm(vm: libvirt.virDomain = None) -> str:\n doc = minidom.parseString(vm.XMLDesc())\n interfaces = doc.getElementsByTagName('mac')\n return interfaces[0].getAttribute('address')","def get_mac_address(hostname):\n\n url = \"https://{}/redfish/v1/Managers/1/EthernetInterfaces/1/\".format(hostname)\n eth_dict = requests.get(url, auth=(USER,PASS),verify=False).json()\n mac_address = eth_dict['MACAddress']\n\n LOGGER.info(\"IPMI BMC %s reports MAC address as %s\", hostnameh, mac_address)\n\n return mac_address","def get_mac(ip):\n p = Ether(dst=\"ff:ff:ff:ff:ff:ff\") / ARP(op=1, pdst=ip)\n result = srp(p, verbose=False, iface=IFACE)[0]\n return result[0][1].hwsrc if result else None","def get_current_mac(interface):\r\n ifconfig_result = subprocess.check_output([\"sudo\", \"ifconfig\", interface])\r\n mac_result = re.search(r\"\\w\\w:\\w\\w:\\w\\w:\\w\\w:\\w\\w:\\w\\w\", ifconfig_result.decode('utf-8'))\r\n if mac_result:\r\n return mac_result.group(0)\r\n else:\r\n print(\"Could not read MAC-address\")","def get_eth0_macs():\n\n\n mac_dict = {\n 'mac_to_host': {},\n 'host_to_mac': {}\n }\n\n # Host Range\n for i in range(1, 95):\n\n if i == 47: # 47 is broken\n continue\n\n hostname = \"sm{}-ipmi\".format(i)\n realname = \"sm{}\".format(i)\n mac = get_eth0_mac_address(hostname).lower()\n mac = emulab_mac(mac)\n\n mac_dict['host_to_mac'][realname] = mac\n mac_dict['mac_to_host'][mac] = realname\n\n with open(\"macs.json\", \"w\") as mac_json:\n\n json.dump(mac_dict, mac_json, indent=4)","def get_mac_address(self):\n\t\treturn call_sdk_function('PrlSrvCfgNet_GetMacAddress', self.handle)","def get_mac(ip):\n ans, _ = srp(Ether(dst='ff:ff:ff:ff:ff:ff')/ARP(pdst=ip), timeout=3, verbose=0)\n if ans:\n return ans[0][1].src\n else:\n return None","def _get_mac(self):\n return self.__mac","def get_my_mac():\n\n mac_set = get_my_mac_set(iface_filter=get_default_route()[1])\n return mac_set.pop()","def get_mac_address(self):\n\t\treturn call_sdk_function('PrlVmDevNet_GetMacAddress', self.handle)","def get_mac(self) -> str:\n self.sendline(\"iw {} info\".format(self.iface_dut))\n # We are looking for MAC definition of STA\n # wdev 0x1\n # addr 96:4e:c9:cc:7a:2c\n # type managed\n self.expect(\"addr (?P<mac>..:..:..:..:..:..)\\r\\n\\t(type|ssid)\")\n return self.match.group('mac')","def get_mac_string():\n mac_int = getnode()\n mac_str = ':'.join((\"%012x\" % mac_int)[i:i + 2] for i in range(0, 12, 2))\n return mac_str","def get_mac_address(self, result, host):\n if \"mac\" in result['scan'][host][\"addresses\"]:\n return result['scan'][host][\"addresses\"][\"mac\"]\n else:\n return \"\"","def get_mac(self) :\n\t\ttry :\n\t\t\treturn self.p_fields.f128\n\t\texcept :\n\t\t\treturn None","def set_mac_address(self, iface):\n if os.path.exists(\"/sys/class/net/%s\" % iface):\n return open(\"/sys/class/net/%s/address\" % iface).read().strip()\n return \"none\"","def set_mac_address(self, iface):\n if os.path.exists(\"/sys/class/net/%s\" % iface):\n return open(\"/sys/class/net/%s/address\" % iface).read().strip()\n return \"none\"","def get_eos_mac(nbr, nbr_intf):\n if \"port-channel\" in nbr_intf.lower():\n # convert Port-Channel1 to po1\n shell_intf = \"po\" + nbr_intf[-1]\n else:\n # convert Ethernet1 to eth1\n shell_intf = \"eth\" + nbr_intf[-1]\n\n output = nbr['host'].command(\"ip addr show dev %s\" % shell_intf)\n # 8: Ethernet0: <BROADCAST,MULTICAST,UP,LOWER_UP> mtu 9100 ...\n # link/ether a6:69:05:fd:da:5f brd ff:ff:ff:ff:ff:ff\n\n mac = output['stdout_lines'][1].split()[1]\n return {'mac': mac, \"shell_intf\": shell_intf}","def get_sonic_mac(host, asicnum, port):\n if host.facts[\"num_asic\"] == 1:\n cmd = \"sudo ip link show {}\".format(port)\n else:\n ns = \"asic\" + str(asicnum)\n cmd = \"sudo ip netns exec {} ip link show {}\".format(ns, port)\n output = host.command(cmd)\n mac = output['stdout_lines'][1].split()[1]\n logger.info(\"host: %s, asic: %d, port: %s, mac: %s\", host.hostname, asicnum, port, mac)\n return mac","def get_mac(ip):\n arp_request = scapy.ARP(pdst=ip)\n broadcast = scapy.Ether(dst=\"ff:ff:ff:ff:ff:ff\")\n arp_request_broadcast = broadcast / arp_request\n answered_list = scapy.srp(\n arp_request_broadcast,\n timeout=5,\n verbose=False)[0]\n return answered_list[0][1].hwsrc","def mac(self):\n mac = hexlify(WLAN().config('mac'), ':').decode()\n return mac.upper() # MAC-address in upper case","def fusion_api_get_appliance_interface_mac(self, device, api=None, headers=None):\n return self.interfaces.get_device_mac(device, api, headers)","def _get_server_hardware_mac_from_ilo(server_hardware):\n try:\n client = get_ilorest_client(server_hardware)\n ilo_path = \"/rest/v1/systems/1\"\n hardware = jsonutils.loads(client.get(ilo_path).text)\n hardware_mac = hardware['HostCorrelation']['HostMACAddress'][0]\n except redfish.JsonDecodingError as exc:\n LOG.error(\"Failed in JSON object getting path: %s\", ilo_path)\n raise exception.OneViewError(error=exc)\n except (ValueError, TypeError, IndexError) as exc:\n LOG.error(\n \"Failed to get mac from server hardware %(server_hardware)s \"\n \"via iLO. Error: %(message)s\", {\n \"server_hardware\": server_hardware.get(\"uri\"),\n \"message\": exc\n }\n )\n raise exception.OneViewError(error=exc)\n\n return hardware_mac","def mac(self):\n return self.device.settings[\"device\"][\"mac\"]","def mac(self):\n return self.device.settings[\"device\"][\"mac\"]","def getMacAddress(self, wlanInterface):\n self.wlanInterface = wlanInterface\n self.storeMacAddress=[]\n s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\n info = fcntl.ioctl(s.fileno(), 0x8927, struct.pack('256s', '%s'[:15]) % str(self.wlanInterface))\n self.storeMacAddress.append(''.join(['%02x:' % ord(char) for char in info[18:24]])[:-1])\n return self.storeMacAddress","def get_network_device_mac(devices, user, passwd, base_api_url):\n network_device_mac_address = ''\n\n if devices:\n selected_network_device = generate_network_devices_menu(devices, purpose='DHCP')\n network_device_mac_address = get_mac_address(selected_network_device, base_api_url, user, passwd)\n\n if network_device_mac_address:\n logging.info('device {} mac address is {}'.format(selected_network_device, network_device_mac_address))\n \n return network_device_mac_address","def mac(self) -> str:\n return self.camera_info[\"wifi_mac\"]","def get_mac(self) -> str:\n hex_mac = hexlify(self.message)[160:172].decode().upper()\n return (\n hex_mac[0:2]\n + \":\"\n + hex_mac[2:4]\n + \":\"\n + hex_mac[4:6]\n + \":\"\n + hex_mac[6:8]\n + \":\"\n + hex_mac[8:10]\n + \":\"\n + hex_mac[10:12]\n )","def __get_mac_address(self):\n str_hex_mac = uuid.UUID(int=uuid.getnode()).hex[-12:]\n return str_hex_mac","def mac_address(self):\n if self._mac_address is None:\n expression = expressions.WPA_MAC\n name = expressions.MAC_ADDRESS_NAME\n command = self.status_command\n self._mac_address = self._match(expression,\n name,\n command)\n return self._mac_address","def get_adapter_mac(cls, client_object):\n parsed_data = DefaultCRUDImpl.get_adapter_info(\n client_object)\n for record in parsed_data['table']:\n if record['dev'] == client_object.name:\n return record['mac']\n pylogger.warning('Did not find a MAC address for adapter %r on %r' %\n (client_object.name, client_object.ip))","def generate_ethmac(peripheral, **kwargs):\n buf = kwargs['buffer']()\n\n result = \"\"\"\nethmac: Network.LiteX_Ethernet @ {{\n sysbus <{}, +0x100>;\n sysbus new Bus.BusMultiRegistration {{ address: {};\n size: {};\n region: \"buffer\" }}\n}}\n\"\"\".format(peripheral['address'], buf['address'], buf['size'])\n\n if 'interrupt' in peripheral['constants']:\n result += ' -> cpu@{}\\n'.format(\n peripheral['constants']['interrupt'])\n\n return result","def get_sys_mac_address():\n mac = \"00:00:00:00:00:00\"\n try:\n if os.path.exists(SysTools.SYS_MAC_FILE):\n with open(SysTools.SYS_MAC_FILE, 'r') as fd:\n mac = fd.readline().strip()\n except:\n SysTools.logger.error(\"Failed to get sys-mac-address from %s\",\n SysTools.SYS_MAC_FILE)\n return mac","def mac_address(self) -> str:\n return self._device.mac","def get_mac_address(selected_network_device, base_api_url, user, passwd): \n url = '{}/{}'.format(base_api_url, selected_network_device)\n device_mac_address = ''\n try:\n response = requests.get(url, verify=False, auth=(user, passwd),\n timeout=5)\n except requests.exceptions.ConnectionTimeout:\n logging.error('failed to establish connection to get mac address')\n\n try:\n network_device_info = response.json()\n except ValueError:\n logging.error('check URL, iDRAC user and password may be invalid')\n logging.info('{}'.format(url))\n\n try:\n device_mac_address = network_device_info[u'MACAddress']\n except KeyError:\n logging.error('No MAC Address found for network devices')\n logging.info('{}'.format(selected_network_device))\n\n return device_mac_address","def get_mac_address(self):\n self.__not_implemented()","def generate_mac_addr(self):\n\t\tcall_sdk_function('PrlVmDevNet_GenerateMacAddr', self.handle)","def get_mac_address_table(self):\n\n mac_address_table = []\n command = '/interface bridge host print terse'\n\n output = self._send_command(command)\n\n for host in parse_terse_output(output):\n mac_address_table.append({\n 'mac': cast_mac(host.get('mac-address')),\n 'interface': host.get('interface'),\n 'vlan': -1,\n 'static': True if 'D' not in host.get('_flags') else False,\n 'active': True if 'X' not in host.get('_flags') else False,\n 'moves': -1,\n 'last_move': -1.0\n })\n\n return mac_address_table","def sw_mac(self):\n return self.raw.get('sw_mac')","def get_base_mac(self):\n raise NotImplementedError","def get_bound_card_mac(self):\n\t\treturn call_sdk_function('PrlVirtNet_GetBoundCardMac', self.handle)","def intGet(): \n macchanger, ip, iwconfig = pathGet()\n interfaces = []\n a = str(subprocess.check_output(\"{} link show\".format(ip), shell=True))\n ints = a.split(': ')\n for i in range(len(ints)):\n if len(ints[i].split()) == 1:\n if ints[i] not in [\"1\", \"lo\", \"b'1\"]:\n interface = {'name':str(ints[i])}\n interfaces.append(interface)\n # Get interface properties\n for interface in interfaces:\n name = interface['name']\n macs = subprocess.check_output(\"{} -s {}\".format(macchanger, name), shell=True).decode(\"utf-8\")\n interface['cMac'] = macs.split()[2]\n interface['cVend'] = macs.split(\"(\")[1].split(\")\")[0]\n interface['pMac'] = macs.split(\"\\n\")[1].split()[2]\n interface['pVend'] = macs.split(\"\\n\")[1].split(\"(\")[1].split(\")\")[0]\n try:\n mon = subprocess.check_output(\"{} {} 2> /dev/null\".format(iwconfig, name), shell=True).split()\n mon1 = mon[3].decode(\"utf-8\").split(':')[1]\n if mon1 == 'off/any':\n mon1 = mon[4].decode(\"utf-8\").split(':')[1]\n interface['mon'] = mon1\n except:\n interface['mon'] = 'Wired'\n return(interfaces)","def sx_router_neigh_get_mac(handle, rif, addr): \n try:\n neigh_entry_cnt_p = new_uint32_t_p()\n neigh_entry_list_p = new_sx_neigh_get_entry_t_arr(1)\n\n filter_p = new_sx_neigh_filter_t_p()\n neigh_filter = sx_neigh_filter_t()\n neigh_filter.filter_by_rif = SX_KEY_FILTER_FIELD_NOT_VALID\n neigh_filter.rif = 0\n sx_neigh_filter_t_p_assign(filter_p, neigh_filter)\n \n rc = sx_api_router_neigh_get(handle, SX_ACCESS_CMD_GET, rif, addr, filter_p, neigh_entry_list_p, neigh_entry_cnt_p)\n if rc == SX_STATUS_ENTRY_NOT_FOUND:\n return None \n sx_check_rc(rc)\n\n neighbor_entry = sx_neigh_get_entry_t_arr_getitem(neigh_entry_list_p, 0)\n \n return neighbor_entry.neigh_data.mac_addr.to_str()\n \n finally:\n delete_sx_neigh_filter_t_p(filter_p)\n delete_sx_neigh_get_entry_t_arr(neigh_entry_list_p) \n delete_uint32_t_p(neigh_entry_cnt_p)","def find_my_IP_and_MAC():\n mac = ':'.join(re.findall('..', '%012x' % getnode()))\n # I write IP and not domain cause i want to save time.\n p = sr1(IP(dst=\"google.com\", ttl=0) / ICMP() / \"XXXXXXXXXXX\",verbose=0,timeout=5) #verbose = withuot output\n return mac,p.dst","def mac_address(self):\n return self._mac_address","def eth_addr(a):\n if isinstance(a, bytes):\n a = a.decode(\"latin\")\n string = \"%.2x:%.2x:%.2x:%.2x:%.2x:%.2x\"\n mac = string % (ord(a[0]), ord(a[1]), ord(a[2]),\n ord(a[3]), ord(a[4]), ord(a[5]))\n return mac","def get_hwaddr(ip):\n if not ip:\n raise HWAddrError('ip argument is None')\n\n r, u = sr(ARP(op=ARP.who_has, pdst=ip), timeout=1, verbose=False)\n \n if r.res:\n return r.res[0][1][ARP].hwsrc\n else:\n raise HWAddrError('Error getting hardware address.')","def _generate_mac(topology_id):\n tid = int(topology_id)\n global mac_counter\n global used_macs\n base = '52:54:00:00:00:00'\n ba = base.split(':')\n ba[2] = '%02x' % int(tid / 256)\n ba[3] = '%02x' % int(tid % 256)\n ba[4] = '%02x' % int(len(used_macs[topology_id]) / 256)\n ba[5] = '%02x' % int(mac_counter)\n\n mac_counter += 1\n\n mac_counter = mac_counter % 256\n return ':'.join(ba)","def sys_mac(self):\n try:\n with open(os.path.join(self.sys_class_path, \"address\")) as f:\n sys_mac = f.read().strip('\\0').strip()\n except Exception:\n sys.stderr.write(\"Failed to read address for entry: %s\\n\" %\n (self._orig_line))\n raise\n\n return sys_mac","def macFor(cls, board):\n return cls.MAC_PREFIX + '{:02X}'.format(int(board))","def macaddr(index):\n hname = name.encode(\"utf-8\") if not isinstance(name, bytes) else name\n mac_ext = hashlib.md5(hname).hexdigest() # pylint: disable=E1101\n return \"52:54:00:{0}:{1}:{2:02x}\".format(mac_ext[0:2], mac_ext[2:4], int(mac_ext[4:6], 16) ^ index)","def _get_server_hardware_mac(server_hardware):\n sh_physical_port = None\n\n if server_hardware.get('portMap'):\n for device in server_hardware.get(\n 'portMap', {}).get('deviceSlots', ()):\n for physical_port in device.get('physicalPorts', ()):\n if physical_port.get('type') == 'Ethernet':\n sh_physical_port = physical_port\n break\n if sh_physical_port:\n for virtual_port in sh_physical_port.get('virtualPorts', ()):\n # NOTE(nicodemos): Ironic oneview drivers needs to use a\n # port that type is Ethernet and function identifier 'a' for\n # this FlexNIC to be able to make a deploy using PXE.\n if virtual_port.get('portFunction') == 'a':\n return virtual_port.get('mac', ()).lower()\n raise exception.OneViewError(\n _(\"There is no Ethernet port on the Server Hardware: %s\") %\n server_hardware.get('uri'))\n else:\n raise exception.OneViewError(\n _(\"The Server Hardware: %s doesn't have a list of adapters/slots, \"\n \"their ports and attributes. This information is available only \"\n \"for blade servers. Is this a rack server?\") %\n server_hardware.get('uri'))","def mac_string(self) :\n\t\tif not getattr(self,'pkg02') :\n\t\t\treturn None \n\t\tif self.pkg02[:2] not in ['01','02','04','05' ] :\n\t\t\treturn None\n\t\treturn self.pkg02 + ' ' + self.p_fields.mac_string()","def mac_address(self):\n mac = [\n self.random.randint(0x00, 0xff),\n self.random.randint(0x00, 0xff),\n self.random.randint(0x00, 0xff),\n self.random.randint(0x00, 0xff),\n self.random.randint(0x00, 0xff),\n self.random.randint(0x00, 0xff)\n ]\n return ':'.join(map(lambda x: f\"{x:02X}\", mac))","def get_random_mac():\r\n mac = [0x00, 0x16, 0x3e,\r\n random.randint(0x00, 0x7f),\r\n random.randint(0x00, 0xff),\r\n random.randint(0x00, 0xff)]\r\n return ':'.join(map(lambda x: \"%02x\" % x, mac))","def setMAC( self, intf, mac ):\n result = self.cmd( 'ifconfig', intf, 'down' )\n result += self.cmd( 'ifconfig', intf, 'hw', 'ether', mac )\n result += self.cmd( 'ifconfig', intf, 'up' )\n return result","def spoof(self, mac, air=False):\n\t\t\n\t\tif air:\n\t\t\tos.system(\n\t\t\t\t'sudo '\n\t\t\t\t'/System/Library/PrivateFrameworks'\n\t\t\t\t'/Apple80211.framework/Versions'\n\t\t\t\t'/Current/Resources/airport -z'\n\t\t\t)\n\t\t\n\t\t_status = os.system('sudo ifconfig %s ether %s' % (self.id, mac))\n\t\t\n\t\treturn 'Interface %s (%s) => (%s)' % (self.id, self.mac, mac)","def get_probable_router_mac(self):\n return self.probable_router_mac","def mac(self):\n if not self.is_rule:\n raise NotRuleError(\"No 'ATTR{address}' field.\")\n\n if \"ATTR{address}\" not in self._fields:\n raise NotRule70Error(\"No 'ATTR{address}' field.\")\n\n return self._fields[\"ATTR{address}\"]","def _get_interface_ip(mac_addr):\n interfaces = netifaces.interfaces()\n for iface in interfaces:\n addresses = netifaces.ifaddresses(iface)\n link_addresses = addresses.get(netifaces.AF_LINK, [])\n for link_addr in link_addresses:\n if link_addr.get('addr') == mac_addr:\n ip_addresses = addresses.get(netifaces.AF_INET)\n if ip_addresses:\n # NOTE: return first address, ironic API does not\n # support multiple\n return ip_addresses[0].get('addr')\n else:\n break","def _get_interface_ip(mac_addr):\n interfaces = netifaces.interfaces()\n for iface in interfaces:\n addresses = netifaces.ifaddresses(iface)\n link_addresses = addresses.get(netifaces.AF_LINK, [])\n for link_addr in link_addresses:\n if link_addr.get('addr') == mac_addr:\n ip_addresses = addresses.get(netifaces.AF_INET)\n if ip_addresses:\n # NOTE: return first address, ironic API does not\n # support multiple\n return ip_addresses[0].get('addr')\n else:\n break","def get_mac_addr(bytes):\n bytes_str = map('{:02x}'.format, bytes)\n mac_addr = ':'.join(bytes_str).upper()\n return mac_addr","def bmc_mac_address(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"bmc_mac_address\")","def get_server_ip_mac(self, server_id):\n port_list = self.list_port()\n if not isinstance(port_list, list):\n return\n interface_list = []\n for port in port_list:\n if port[\"device_id\"] == server_id:\n port_info = {}\n port_info['mac'] = port['mac_address']\n port_info['ip_address'] = port['fixed_ips'][0]['ip_address']\n interface_list.append(port_info)\n\n LOG_OBJ.info(\"VM Interface Info : %s \" % interface_list)\n return interface_list","def get_macaddr(self):\n return self.macaddr","def _get_ethernet_tag(self):\n return self.__ethernet_tag","def get_mac_from_raw_query(request_raw_query: str):\n mac = \"\"\n raw_query_list = request_raw_query.split(\"&\")\n for param in raw_query_list:\n if \"mac=\" in param:\n mac = param.replace(\"mac=\", \"\")\n if not mac:\n raise AttributeError(\"%s is not parsable\" % request_raw_query)\n return mac.replace(\"-\", \":\")","def get_hwaddress(ifname):\n # It might be possible that more than one link-layer address is associated\n # with the interface. If this is the case, we simply return the first.\n # If there's no such interface or the interface has no MAC address,\n # we notify caller via ValueError.\n macdict = getmacaddrs()\n try:\n addr = macdict[ifname][0]\n except (IndexError, KeyError):\n raise ValueError(\"%s\" % ifname)\n return addr","def mac_aton(str):\n macbytes = [int(i, 16) for i in str.split(':')]\n return struct.pack('6B', *macbytes)","def random_mac():\n return '\"02:%02x:%02x:%02x:%02x:%02x\"' % (random.randint(0,255),\n random.randint(0,255),\n random.randint(0,255),\n random.randint(0,255),\n random.randint(0,255))","def boot_mac_address(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"boot_mac_address\")","def get_rand_mac(self):\n random_mac = []\n\n # add manufacturer\n random_mac.append(self.get_rand_manufact())\n\n # generate the last 24 bits of the random hex\n for i in range(0, 3):\n rand_digit1 = self.get_rand_hex_digit()\n rand_digit2 = self.get_rand_hex_digit()\n random_mac.append(rand_digit1 + rand_digit2)\n\n return ':'.join(random_mac)","def get_mac(self):\n bluetooth_mac_address = ''\n is_general_visible = False\n if self.phone_info.phone_type == PhoneType.IOS:\n try:\n try:\n # verify that General Button is visible\n self.find_element(self.driver.appium_driver,\n 'self.ios_locators.GENERAL_NAVIGATION_BUTTON_ByXPATH',\n 0).is_displayed()\n is_general_visible = True\n except:\n logger.debug(\"General Button is currently not visible \")\n\n if is_general_visible:\n pass\n else:\n\n self.find_element(self.driver.appium_driver,\n self.general_button_settings).click()\n self.wait_till_element_to_be_visible(\n self.driver.appium_driver, self.status_button, 10)\n self.find_element(self.driver.appium_driver,\n self.status_button, 2).click()\n bluetooth_mac_address = self.find_element(\n self.driver.appium_driver, self.bluetooth_mac_addess,\n 2).text\n logger.debug(\"Bluetooth Mac Address:\" + bluetooth_mac_address)\n except Exception as e:\n logger.error(\"Bluetooth Mac Address is not Visible\")\n logger.debug(repr(e))\n elif self.phone_info.phone_type == PhoneType.ANDROID:\n try:\n\n self.find_element(self.driver.appium_driver,\n self.general_button_settings).click()\n self.wait_till_element_to_be_visible(self.driver.appium_driver,\n self.status_button, 10)\n self.find_element(self.driver.appium_driver,\n self.status_button).click()\n\n self.testcase_action = 'STEP -: Get Bluetooth Mac Address'\n bluetooth_mac_address = self.find_element(\n self.driver.appium_driver, self.bluetooth_mac_addess,\n 2).text\n logger.debug(\"Bluetooth Mac Address:\" + bluetooth_mac_address)\n except Exception as e:\n logger.warning(\"Mac address is not available\")\n logger.debug(repr(e))\n return bluetooth_mac_address","def person_mac(self):\n return self._person_mac","def get_my_mac_set(iface_filter=None):\n\n out_set = set()\n if sys.platform.startswith(\"win\"):\n from scapy.arch.windows import NetworkInterface\n if type(iface_filter) == NetworkInterface:\n out_set.add(iface_filter.mac)\n\n for iface in sc.get_if_list():\n if iface_filter is not None and iface != iface_filter:\n continue\n try:\n mac = sc.get_if_hwaddr(iface)\n except Exception as e:\n continue\n else:\n out_set.add(mac)\n\n return out_set","def mac_ntop(binary):\n x = b\":\".join(binascii.hexlify(binary)[i : i + 2] for i in range(0, 12, 2))\n return str(x.decode(\"ascii\"))","def get_random_mac():\n\t\n\t# use the Dlink range\n\tmac = \"00:05:5D\"\n\t\n\tfor i in range(0,3):\n\t\tmac += \":%s\" % hex(random.randrange(0,256))[2:]\n\t\t\n\t\t\n\treturn mac","def getmacaddrs():\n # Unpack just for the sake of being meaningful.\n ifaddrs, sockaddr_dl, sockaddr = PLATFORM_LOOKUP[PLATFORM]\n ptr = c_void_p(None)\n result = LIBC.getifaddrs(pointer(ptr))\n if result != 0:\n return {}\n ifa = ifaddrs.from_address(ptr.value)\n result = {}\n\n while True:\n name = ifa.ifa_name\n if name not in result:\n result[name] = []\n # Some interface (such as a TUN virtual network) doesn't give us\n # ifa_addr at all and we can usually skip them because they're hardly\n # relevant for our usage case.\n if ifa.ifa_addr:\n sa = sockaddr.from_address(ifa.ifa_addr)\n if sa.sa_family == AF_LINK:\n si = sockaddr_dl.from_address(ifa.ifa_addr)\n addr = \"%s\" % si\n if addr:\n result[name].append(addr)\n if ifa.ifa_next:\n ifa = ifaddrs.from_address(ifa.ifa_next)\n else:\n break\n\n LIBC.freeifaddrs(ptr)\n return result","def MacAddress(self):\n if self.force_auto_sync:\n self.get('MacAddress')\n return self._MacAddress","def _arp(ip_address):\n cmd = ['arp', '-n', ip_address]\n arp = subprocess.Popen(cmd, stdout=subprocess.PIPE)\n out, _ = arp.communicate()\n match = re.search(r'(([0-9A-Fa-f]{1,2}\\:){5}[0-9A-Fa-f]{1,2})', str(out))\n if match:\n return match.group(0)\n _LOGGER.info('No MAC address found for %s', ip_address)\n return None","def _getMac( self, bSeed ):\n\n\t\tbMac = ( bSeed >> 24 ) & 0xFF\n\t\treturn '%02X' % bMac","def mac_ntoa(mac):\n return '%.2x:%.2x:%.2x:%.2x:%.2x:%.2x' % tuple(map(ord, list(mac)))","def _get_mac_address(self, mac_numbers):\n\n mac = \"\"\n for num in mac_numbers:\n num = self._convert_to_hex(num)\n mac = ':'.join((mac, num))\n mac = mac[1:]\n return mac","def get_device_info(self, mac_address):\n try:\n out = self.get_output(\"info \" + mac_address)\n except BluetoothctlError, e:\n print(e)\n return None\n else:\n return out","def get_arp_table():\n IP = ''\n login = ''\n password = ''\n telnet = pexpect.spawn('telnet {}'.format(IP), timeout=30)\n telnet.expect('Username:')\n telnet.sendline(login)\n telnet.expect('Password:')\n telnet.sendline(password)\n telnet.expect('#')\n telnet.sendline('terminal length 0')\n telnet.expect('#')\n telnet.sendline('show arp')\n telnet.expect('#')\n arp_table = telnet.before.decode('utf-8')\n telnet.close()\n return arp_table","def generate_next_mac(topology_id):\n\n if configuration.deployment_backend == 'openstack':\n # we just don't need this for openstack at all, just return a string that will\n # never get used\n return '52:54:11:22:33:44'\n\n global used_macs\n if topology_id not in used_macs:\n used_macs[topology_id] = list()\n\n macs_for_topology = used_macs[topology_id]\n mac = _generate_mac(topology_id)\n\n while mac in macs_for_topology:\n logger.info('this mac %s has already been used!' % mac)\n mac = _generate_mac(topology_id)\n\n macs_for_topology.append(mac)\n return mac","def _mac_addr(address):\n return ':'.join('%02x' % ord(b) for b in address)","def fusion_api_get_appliance_interface_attribute(self, device='eth0', attribute='macAddress', api=None, headers=None):\n return self.interfaces.get_device_attribute(device, attribute, api, headers)","def get_dhcp_client_iaid(mac_address):\n hwaddr = list(int(byte, 16) for byte in mac_address.split(':'))\n return hwaddr[2] << 24 | hwaddr[3] << 16 | hwaddr[4] << 8 | hwaddr[5]","def _get_rmac(self):\n return self.__rmac","def generate_mac():\n rand_str = generate_name(choices=\"0123456789abcdef\", length=12)\n return \":\".join(re.findall(\"..\", rand_str))","def generate_mac():\n rand_str = generate_name(choices=\"0123456789abcdef\", length=12)\n return \":\".join(re.findall(\"..\", rand_str))","def unique_id(self):\n return self._device.mac","def get_ip_address(interface):\n try:\n import netifaces\n detail = netifaces.ifaddresses(interface)\n if netifaces.AF_INET in detail:\n ip = detail[netifaces.AF_INET][0]['addr']\n else:\n ip = 'unavailable'\n if netifaces.AF_LINK in detail:\n mac = detail[netifaces.AF_LINK][0]['addr']\n else:\n mac = 'unknown'\n except ImportError:\n import socket\n import fcntl\n import struct\n s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\n ip = socket.inet_ntoa(fcntl.ioctl(s.fileno(), 0x8915, struct.pack('256s', interface[:15]))[20:24])\n mac = fcntl.ioctl(s.fileno(), 0x8927, struct.pack('256s', interface[:15]))\n return ip, mac"],"string":"[\n \"def get_mac_address():\\n eth0_interface = 'eth0'\\n addresses = netifaces.ifaddresses(eth0_interface)[netifaces.AF_LINK][0]\\n mac_address = addresses['addr']\\n return mac_address\",\n \"def getMac(self):\\n # Import netifaces here to prevent error importing this module in setup.py\\n import netifaces\\n interfaces = ['eth0', 'wlan0']\\n try:\\n interfaces.append(netifaces.gateways()['default'][netifaces.AF_INET][1])\\n except:\\n pass\\n for interface in interfaces:\\n try:\\n return netifaces.ifaddresses(interface)[netifaces.AF_LINK][0]['addr']\\n except ValueError:\\n pass\\n except:\\n exception('Error getting MAC address')\\n return None\",\n \"def get_mac():\\n\\n interface = [x for x in netifaces.interfaces() if 'wlan' in x or 'wlp' in x][0]\\n return netifaces.ifaddresses(interface)[netifaces.AF_LINK][0]['addr']\",\n \"def get_mac_address(ifname):\\n try:\\n return open('/sys/class/net/' + ifname + '/address') \\\\\\n .readline().strip()\\n except:\\n SysTools.logger.error(\\\"Failed to get mac-address of %s\\\", ifname)\\n return \\\"00:00:00:00:00:00\\\"\",\n \"def _get_mac_address():\\n if not sys.platform.startswith('linux'):\\n raise RuntimeError(\\n 'Cannot get the MAC address on non-Linux platforms'\\n )\\n ifname = get_default_iface_name_linux()\\n s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\\n info = fcntl.ioctl(s.fileno(), 0x8927,\\n struct.pack('256s', bytes(ifname, 'utf-8')[:15]))\\n return ''.join('%02x' % b for b in info[18:24])\",\n \"def get_mac(self, node_id):\\n nc = '%02x' % self.node_class\\n nr_iface = '%02x' % self.nr_host_interface\\n node_id = '%08x' % node_id\\n\\n return '%s:%s:%s:%s:%s:%s' % (nc, nr_iface, node_id[0:2], node_id[2:4], node_id[4:6], node_id[6:8])\",\n \"def mac_from_vm(vm: libvirt.virDomain = None) -> str:\\n doc = minidom.parseString(vm.XMLDesc())\\n interfaces = doc.getElementsByTagName('mac')\\n return interfaces[0].getAttribute('address')\",\n \"def get_mac_address(hostname):\\n\\n url = \\\"https://{}/redfish/v1/Managers/1/EthernetInterfaces/1/\\\".format(hostname)\\n eth_dict = requests.get(url, auth=(USER,PASS),verify=False).json()\\n mac_address = eth_dict['MACAddress']\\n\\n LOGGER.info(\\\"IPMI BMC %s reports MAC address as %s\\\", hostnameh, mac_address)\\n\\n return mac_address\",\n \"def get_mac(ip):\\n p = Ether(dst=\\\"ff:ff:ff:ff:ff:ff\\\") / ARP(op=1, pdst=ip)\\n result = srp(p, verbose=False, iface=IFACE)[0]\\n return result[0][1].hwsrc if result else None\",\n \"def get_current_mac(interface):\\r\\n ifconfig_result = subprocess.check_output([\\\"sudo\\\", \\\"ifconfig\\\", interface])\\r\\n mac_result = re.search(r\\\"\\\\w\\\\w:\\\\w\\\\w:\\\\w\\\\w:\\\\w\\\\w:\\\\w\\\\w:\\\\w\\\\w\\\", ifconfig_result.decode('utf-8'))\\r\\n if mac_result:\\r\\n return mac_result.group(0)\\r\\n else:\\r\\n print(\\\"Could not read MAC-address\\\")\",\n \"def get_eth0_macs():\\n\\n\\n mac_dict = {\\n 'mac_to_host': {},\\n 'host_to_mac': {}\\n }\\n\\n # Host Range\\n for i in range(1, 95):\\n\\n if i == 47: # 47 is broken\\n continue\\n\\n hostname = \\\"sm{}-ipmi\\\".format(i)\\n realname = \\\"sm{}\\\".format(i)\\n mac = get_eth0_mac_address(hostname).lower()\\n mac = emulab_mac(mac)\\n\\n mac_dict['host_to_mac'][realname] = mac\\n mac_dict['mac_to_host'][mac] = realname\\n\\n with open(\\\"macs.json\\\", \\\"w\\\") as mac_json:\\n\\n json.dump(mac_dict, mac_json, indent=4)\",\n \"def get_mac_address(self):\\n\\t\\treturn call_sdk_function('PrlSrvCfgNet_GetMacAddress', self.handle)\",\n \"def get_mac(ip):\\n ans, _ = srp(Ether(dst='ff:ff:ff:ff:ff:ff')/ARP(pdst=ip), timeout=3, verbose=0)\\n if ans:\\n return ans[0][1].src\\n else:\\n return None\",\n \"def _get_mac(self):\\n return self.__mac\",\n \"def get_my_mac():\\n\\n mac_set = get_my_mac_set(iface_filter=get_default_route()[1])\\n return mac_set.pop()\",\n \"def get_mac_address(self):\\n\\t\\treturn call_sdk_function('PrlVmDevNet_GetMacAddress', self.handle)\",\n \"def get_mac(self) -> str:\\n self.sendline(\\\"iw {} info\\\".format(self.iface_dut))\\n # We are looking for MAC definition of STA\\n # wdev 0x1\\n # addr 96:4e:c9:cc:7a:2c\\n # type managed\\n self.expect(\\\"addr (?P<mac>..:..:..:..:..:..)\\\\r\\\\n\\\\t(type|ssid)\\\")\\n return self.match.group('mac')\",\n \"def get_mac_string():\\n mac_int = getnode()\\n mac_str = ':'.join((\\\"%012x\\\" % mac_int)[i:i + 2] for i in range(0, 12, 2))\\n return mac_str\",\n \"def get_mac_address(self, result, host):\\n if \\\"mac\\\" in result['scan'][host][\\\"addresses\\\"]:\\n return result['scan'][host][\\\"addresses\\\"][\\\"mac\\\"]\\n else:\\n return \\\"\\\"\",\n \"def get_mac(self) :\\n\\t\\ttry :\\n\\t\\t\\treturn self.p_fields.f128\\n\\t\\texcept :\\n\\t\\t\\treturn None\",\n \"def set_mac_address(self, iface):\\n if os.path.exists(\\\"/sys/class/net/%s\\\" % iface):\\n return open(\\\"/sys/class/net/%s/address\\\" % iface).read().strip()\\n return \\\"none\\\"\",\n \"def set_mac_address(self, iface):\\n if os.path.exists(\\\"/sys/class/net/%s\\\" % iface):\\n return open(\\\"/sys/class/net/%s/address\\\" % iface).read().strip()\\n return \\\"none\\\"\",\n \"def get_eos_mac(nbr, nbr_intf):\\n if \\\"port-channel\\\" in nbr_intf.lower():\\n # convert Port-Channel1 to po1\\n shell_intf = \\\"po\\\" + nbr_intf[-1]\\n else:\\n # convert Ethernet1 to eth1\\n shell_intf = \\\"eth\\\" + nbr_intf[-1]\\n\\n output = nbr['host'].command(\\\"ip addr show dev %s\\\" % shell_intf)\\n # 8: Ethernet0: <BROADCAST,MULTICAST,UP,LOWER_UP> mtu 9100 ...\\n # link/ether a6:69:05:fd:da:5f brd ff:ff:ff:ff:ff:ff\\n\\n mac = output['stdout_lines'][1].split()[1]\\n return {'mac': mac, \\\"shell_intf\\\": shell_intf}\",\n \"def get_sonic_mac(host, asicnum, port):\\n if host.facts[\\\"num_asic\\\"] == 1:\\n cmd = \\\"sudo ip link show {}\\\".format(port)\\n else:\\n ns = \\\"asic\\\" + str(asicnum)\\n cmd = \\\"sudo ip netns exec {} ip link show {}\\\".format(ns, port)\\n output = host.command(cmd)\\n mac = output['stdout_lines'][1].split()[1]\\n logger.info(\\\"host: %s, asic: %d, port: %s, mac: %s\\\", host.hostname, asicnum, port, mac)\\n return mac\",\n \"def get_mac(ip):\\n arp_request = scapy.ARP(pdst=ip)\\n broadcast = scapy.Ether(dst=\\\"ff:ff:ff:ff:ff:ff\\\")\\n arp_request_broadcast = broadcast / arp_request\\n answered_list = scapy.srp(\\n arp_request_broadcast,\\n timeout=5,\\n verbose=False)[0]\\n return answered_list[0][1].hwsrc\",\n \"def mac(self):\\n mac = hexlify(WLAN().config('mac'), ':').decode()\\n return mac.upper() # MAC-address in upper case\",\n \"def fusion_api_get_appliance_interface_mac(self, device, api=None, headers=None):\\n return self.interfaces.get_device_mac(device, api, headers)\",\n \"def _get_server_hardware_mac_from_ilo(server_hardware):\\n try:\\n client = get_ilorest_client(server_hardware)\\n ilo_path = \\\"/rest/v1/systems/1\\\"\\n hardware = jsonutils.loads(client.get(ilo_path).text)\\n hardware_mac = hardware['HostCorrelation']['HostMACAddress'][0]\\n except redfish.JsonDecodingError as exc:\\n LOG.error(\\\"Failed in JSON object getting path: %s\\\", ilo_path)\\n raise exception.OneViewError(error=exc)\\n except (ValueError, TypeError, IndexError) as exc:\\n LOG.error(\\n \\\"Failed to get mac from server hardware %(server_hardware)s \\\"\\n \\\"via iLO. Error: %(message)s\\\", {\\n \\\"server_hardware\\\": server_hardware.get(\\\"uri\\\"),\\n \\\"message\\\": exc\\n }\\n )\\n raise exception.OneViewError(error=exc)\\n\\n return hardware_mac\",\n \"def mac(self):\\n return self.device.settings[\\\"device\\\"][\\\"mac\\\"]\",\n \"def mac(self):\\n return self.device.settings[\\\"device\\\"][\\\"mac\\\"]\",\n \"def getMacAddress(self, wlanInterface):\\n self.wlanInterface = wlanInterface\\n self.storeMacAddress=[]\\n s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\\n info = fcntl.ioctl(s.fileno(), 0x8927, struct.pack('256s', '%s'[:15]) % str(self.wlanInterface))\\n self.storeMacAddress.append(''.join(['%02x:' % ord(char) for char in info[18:24]])[:-1])\\n return self.storeMacAddress\",\n \"def get_network_device_mac(devices, user, passwd, base_api_url):\\n network_device_mac_address = ''\\n\\n if devices:\\n selected_network_device = generate_network_devices_menu(devices, purpose='DHCP')\\n network_device_mac_address = get_mac_address(selected_network_device, base_api_url, user, passwd)\\n\\n if network_device_mac_address:\\n logging.info('device {} mac address is {}'.format(selected_network_device, network_device_mac_address))\\n \\n return network_device_mac_address\",\n \"def mac(self) -> str:\\n return self.camera_info[\\\"wifi_mac\\\"]\",\n \"def get_mac(self) -> str:\\n hex_mac = hexlify(self.message)[160:172].decode().upper()\\n return (\\n hex_mac[0:2]\\n + \\\":\\\"\\n + hex_mac[2:4]\\n + \\\":\\\"\\n + hex_mac[4:6]\\n + \\\":\\\"\\n + hex_mac[6:8]\\n + \\\":\\\"\\n + hex_mac[8:10]\\n + \\\":\\\"\\n + hex_mac[10:12]\\n )\",\n \"def __get_mac_address(self):\\n str_hex_mac = uuid.UUID(int=uuid.getnode()).hex[-12:]\\n return str_hex_mac\",\n \"def mac_address(self):\\n if self._mac_address is None:\\n expression = expressions.WPA_MAC\\n name = expressions.MAC_ADDRESS_NAME\\n command = self.status_command\\n self._mac_address = self._match(expression,\\n name,\\n command)\\n return self._mac_address\",\n \"def get_adapter_mac(cls, client_object):\\n parsed_data = DefaultCRUDImpl.get_adapter_info(\\n client_object)\\n for record in parsed_data['table']:\\n if record['dev'] == client_object.name:\\n return record['mac']\\n pylogger.warning('Did not find a MAC address for adapter %r on %r' %\\n (client_object.name, client_object.ip))\",\n \"def generate_ethmac(peripheral, **kwargs):\\n buf = kwargs['buffer']()\\n\\n result = \\\"\\\"\\\"\\nethmac: Network.LiteX_Ethernet @ {{\\n sysbus <{}, +0x100>;\\n sysbus new Bus.BusMultiRegistration {{ address: {};\\n size: {};\\n region: \\\"buffer\\\" }}\\n}}\\n\\\"\\\"\\\".format(peripheral['address'], buf['address'], buf['size'])\\n\\n if 'interrupt' in peripheral['constants']:\\n result += ' -> cpu@{}\\\\n'.format(\\n peripheral['constants']['interrupt'])\\n\\n return result\",\n \"def get_sys_mac_address():\\n mac = \\\"00:00:00:00:00:00\\\"\\n try:\\n if os.path.exists(SysTools.SYS_MAC_FILE):\\n with open(SysTools.SYS_MAC_FILE, 'r') as fd:\\n mac = fd.readline().strip()\\n except:\\n SysTools.logger.error(\\\"Failed to get sys-mac-address from %s\\\",\\n SysTools.SYS_MAC_FILE)\\n return mac\",\n \"def mac_address(self) -> str:\\n return self._device.mac\",\n \"def get_mac_address(selected_network_device, base_api_url, user, passwd): \\n url = '{}/{}'.format(base_api_url, selected_network_device)\\n device_mac_address = ''\\n try:\\n response = requests.get(url, verify=False, auth=(user, passwd),\\n timeout=5)\\n except requests.exceptions.ConnectionTimeout:\\n logging.error('failed to establish connection to get mac address')\\n\\n try:\\n network_device_info = response.json()\\n except ValueError:\\n logging.error('check URL, iDRAC user and password may be invalid')\\n logging.info('{}'.format(url))\\n\\n try:\\n device_mac_address = network_device_info[u'MACAddress']\\n except KeyError:\\n logging.error('No MAC Address found for network devices')\\n logging.info('{}'.format(selected_network_device))\\n\\n return device_mac_address\",\n \"def get_mac_address(self):\\n self.__not_implemented()\",\n \"def generate_mac_addr(self):\\n\\t\\tcall_sdk_function('PrlVmDevNet_GenerateMacAddr', self.handle)\",\n \"def get_mac_address_table(self):\\n\\n mac_address_table = []\\n command = '/interface bridge host print terse'\\n\\n output = self._send_command(command)\\n\\n for host in parse_terse_output(output):\\n mac_address_table.append({\\n 'mac': cast_mac(host.get('mac-address')),\\n 'interface': host.get('interface'),\\n 'vlan': -1,\\n 'static': True if 'D' not in host.get('_flags') else False,\\n 'active': True if 'X' not in host.get('_flags') else False,\\n 'moves': -1,\\n 'last_move': -1.0\\n })\\n\\n return mac_address_table\",\n \"def sw_mac(self):\\n return self.raw.get('sw_mac')\",\n \"def get_base_mac(self):\\n raise NotImplementedError\",\n \"def get_bound_card_mac(self):\\n\\t\\treturn call_sdk_function('PrlVirtNet_GetBoundCardMac', self.handle)\",\n \"def intGet(): \\n macchanger, ip, iwconfig = pathGet()\\n interfaces = []\\n a = str(subprocess.check_output(\\\"{} link show\\\".format(ip), shell=True))\\n ints = a.split(': ')\\n for i in range(len(ints)):\\n if len(ints[i].split()) == 1:\\n if ints[i] not in [\\\"1\\\", \\\"lo\\\", \\\"b'1\\\"]:\\n interface = {'name':str(ints[i])}\\n interfaces.append(interface)\\n # Get interface properties\\n for interface in interfaces:\\n name = interface['name']\\n macs = subprocess.check_output(\\\"{} -s {}\\\".format(macchanger, name), shell=True).decode(\\\"utf-8\\\")\\n interface['cMac'] = macs.split()[2]\\n interface['cVend'] = macs.split(\\\"(\\\")[1].split(\\\")\\\")[0]\\n interface['pMac'] = macs.split(\\\"\\\\n\\\")[1].split()[2]\\n interface['pVend'] = macs.split(\\\"\\\\n\\\")[1].split(\\\"(\\\")[1].split(\\\")\\\")[0]\\n try:\\n mon = subprocess.check_output(\\\"{} {} 2> /dev/null\\\".format(iwconfig, name), shell=True).split()\\n mon1 = mon[3].decode(\\\"utf-8\\\").split(':')[1]\\n if mon1 == 'off/any':\\n mon1 = mon[4].decode(\\\"utf-8\\\").split(':')[1]\\n interface['mon'] = mon1\\n except:\\n interface['mon'] = 'Wired'\\n return(interfaces)\",\n \"def sx_router_neigh_get_mac(handle, rif, addr): \\n try:\\n neigh_entry_cnt_p = new_uint32_t_p()\\n neigh_entry_list_p = new_sx_neigh_get_entry_t_arr(1)\\n\\n filter_p = new_sx_neigh_filter_t_p()\\n neigh_filter = sx_neigh_filter_t()\\n neigh_filter.filter_by_rif = SX_KEY_FILTER_FIELD_NOT_VALID\\n neigh_filter.rif = 0\\n sx_neigh_filter_t_p_assign(filter_p, neigh_filter)\\n \\n rc = sx_api_router_neigh_get(handle, SX_ACCESS_CMD_GET, rif, addr, filter_p, neigh_entry_list_p, neigh_entry_cnt_p)\\n if rc == SX_STATUS_ENTRY_NOT_FOUND:\\n return None \\n sx_check_rc(rc)\\n\\n neighbor_entry = sx_neigh_get_entry_t_arr_getitem(neigh_entry_list_p, 0)\\n \\n return neighbor_entry.neigh_data.mac_addr.to_str()\\n \\n finally:\\n delete_sx_neigh_filter_t_p(filter_p)\\n delete_sx_neigh_get_entry_t_arr(neigh_entry_list_p) \\n delete_uint32_t_p(neigh_entry_cnt_p)\",\n \"def find_my_IP_and_MAC():\\n mac = ':'.join(re.findall('..', '%012x' % getnode()))\\n # I write IP and not domain cause i want to save time.\\n p = sr1(IP(dst=\\\"google.com\\\", ttl=0) / ICMP() / \\\"XXXXXXXXXXX\\\",verbose=0,timeout=5) #verbose = withuot output\\n return mac,p.dst\",\n \"def mac_address(self):\\n return self._mac_address\",\n \"def eth_addr(a):\\n if isinstance(a, bytes):\\n a = a.decode(\\\"latin\\\")\\n string = \\\"%.2x:%.2x:%.2x:%.2x:%.2x:%.2x\\\"\\n mac = string % (ord(a[0]), ord(a[1]), ord(a[2]),\\n ord(a[3]), ord(a[4]), ord(a[5]))\\n return mac\",\n \"def get_hwaddr(ip):\\n if not ip:\\n raise HWAddrError('ip argument is None')\\n\\n r, u = sr(ARP(op=ARP.who_has, pdst=ip), timeout=1, verbose=False)\\n \\n if r.res:\\n return r.res[0][1][ARP].hwsrc\\n else:\\n raise HWAddrError('Error getting hardware address.')\",\n \"def _generate_mac(topology_id):\\n tid = int(topology_id)\\n global mac_counter\\n global used_macs\\n base = '52:54:00:00:00:00'\\n ba = base.split(':')\\n ba[2] = '%02x' % int(tid / 256)\\n ba[3] = '%02x' % int(tid % 256)\\n ba[4] = '%02x' % int(len(used_macs[topology_id]) / 256)\\n ba[5] = '%02x' % int(mac_counter)\\n\\n mac_counter += 1\\n\\n mac_counter = mac_counter % 256\\n return ':'.join(ba)\",\n \"def sys_mac(self):\\n try:\\n with open(os.path.join(self.sys_class_path, \\\"address\\\")) as f:\\n sys_mac = f.read().strip('\\\\0').strip()\\n except Exception:\\n sys.stderr.write(\\\"Failed to read address for entry: %s\\\\n\\\" %\\n (self._orig_line))\\n raise\\n\\n return sys_mac\",\n \"def macFor(cls, board):\\n return cls.MAC_PREFIX + '{:02X}'.format(int(board))\",\n \"def macaddr(index):\\n hname = name.encode(\\\"utf-8\\\") if not isinstance(name, bytes) else name\\n mac_ext = hashlib.md5(hname).hexdigest() # pylint: disable=E1101\\n return \\\"52:54:00:{0}:{1}:{2:02x}\\\".format(mac_ext[0:2], mac_ext[2:4], int(mac_ext[4:6], 16) ^ index)\",\n \"def _get_server_hardware_mac(server_hardware):\\n sh_physical_port = None\\n\\n if server_hardware.get('portMap'):\\n for device in server_hardware.get(\\n 'portMap', {}).get('deviceSlots', ()):\\n for physical_port in device.get('physicalPorts', ()):\\n if physical_port.get('type') == 'Ethernet':\\n sh_physical_port = physical_port\\n break\\n if sh_physical_port:\\n for virtual_port in sh_physical_port.get('virtualPorts', ()):\\n # NOTE(nicodemos): Ironic oneview drivers needs to use a\\n # port that type is Ethernet and function identifier 'a' for\\n # this FlexNIC to be able to make a deploy using PXE.\\n if virtual_port.get('portFunction') == 'a':\\n return virtual_port.get('mac', ()).lower()\\n raise exception.OneViewError(\\n _(\\\"There is no Ethernet port on the Server Hardware: %s\\\") %\\n server_hardware.get('uri'))\\n else:\\n raise exception.OneViewError(\\n _(\\\"The Server Hardware: %s doesn't have a list of adapters/slots, \\\"\\n \\\"their ports and attributes. This information is available only \\\"\\n \\\"for blade servers. Is this a rack server?\\\") %\\n server_hardware.get('uri'))\",\n \"def mac_string(self) :\\n\\t\\tif not getattr(self,'pkg02') :\\n\\t\\t\\treturn None \\n\\t\\tif self.pkg02[:2] not in ['01','02','04','05' ] :\\n\\t\\t\\treturn None\\n\\t\\treturn self.pkg02 + ' ' + self.p_fields.mac_string()\",\n \"def mac_address(self):\\n mac = [\\n self.random.randint(0x00, 0xff),\\n self.random.randint(0x00, 0xff),\\n self.random.randint(0x00, 0xff),\\n self.random.randint(0x00, 0xff),\\n self.random.randint(0x00, 0xff),\\n self.random.randint(0x00, 0xff)\\n ]\\n return ':'.join(map(lambda x: f\\\"{x:02X}\\\", mac))\",\n \"def get_random_mac():\\r\\n mac = [0x00, 0x16, 0x3e,\\r\\n random.randint(0x00, 0x7f),\\r\\n random.randint(0x00, 0xff),\\r\\n random.randint(0x00, 0xff)]\\r\\n return ':'.join(map(lambda x: \\\"%02x\\\" % x, mac))\",\n \"def setMAC( self, intf, mac ):\\n result = self.cmd( 'ifconfig', intf, 'down' )\\n result += self.cmd( 'ifconfig', intf, 'hw', 'ether', mac )\\n result += self.cmd( 'ifconfig', intf, 'up' )\\n return result\",\n \"def spoof(self, mac, air=False):\\n\\t\\t\\n\\t\\tif air:\\n\\t\\t\\tos.system(\\n\\t\\t\\t\\t'sudo '\\n\\t\\t\\t\\t'/System/Library/PrivateFrameworks'\\n\\t\\t\\t\\t'/Apple80211.framework/Versions'\\n\\t\\t\\t\\t'/Current/Resources/airport -z'\\n\\t\\t\\t)\\n\\t\\t\\n\\t\\t_status = os.system('sudo ifconfig %s ether %s' % (self.id, mac))\\n\\t\\t\\n\\t\\treturn 'Interface %s (%s) => (%s)' % (self.id, self.mac, mac)\",\n \"def get_probable_router_mac(self):\\n return self.probable_router_mac\",\n \"def mac(self):\\n if not self.is_rule:\\n raise NotRuleError(\\\"No 'ATTR{address}' field.\\\")\\n\\n if \\\"ATTR{address}\\\" not in self._fields:\\n raise NotRule70Error(\\\"No 'ATTR{address}' field.\\\")\\n\\n return self._fields[\\\"ATTR{address}\\\"]\",\n \"def _get_interface_ip(mac_addr):\\n interfaces = netifaces.interfaces()\\n for iface in interfaces:\\n addresses = netifaces.ifaddresses(iface)\\n link_addresses = addresses.get(netifaces.AF_LINK, [])\\n for link_addr in link_addresses:\\n if link_addr.get('addr') == mac_addr:\\n ip_addresses = addresses.get(netifaces.AF_INET)\\n if ip_addresses:\\n # NOTE: return first address, ironic API does not\\n # support multiple\\n return ip_addresses[0].get('addr')\\n else:\\n break\",\n \"def _get_interface_ip(mac_addr):\\n interfaces = netifaces.interfaces()\\n for iface in interfaces:\\n addresses = netifaces.ifaddresses(iface)\\n link_addresses = addresses.get(netifaces.AF_LINK, [])\\n for link_addr in link_addresses:\\n if link_addr.get('addr') == mac_addr:\\n ip_addresses = addresses.get(netifaces.AF_INET)\\n if ip_addresses:\\n # NOTE: return first address, ironic API does not\\n # support multiple\\n return ip_addresses[0].get('addr')\\n else:\\n break\",\n \"def get_mac_addr(bytes):\\n bytes_str = map('{:02x}'.format, bytes)\\n mac_addr = ':'.join(bytes_str).upper()\\n return mac_addr\",\n \"def bmc_mac_address(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"bmc_mac_address\\\")\",\n \"def get_server_ip_mac(self, server_id):\\n port_list = self.list_port()\\n if not isinstance(port_list, list):\\n return\\n interface_list = []\\n for port in port_list:\\n if port[\\\"device_id\\\"] == server_id:\\n port_info = {}\\n port_info['mac'] = port['mac_address']\\n port_info['ip_address'] = port['fixed_ips'][0]['ip_address']\\n interface_list.append(port_info)\\n\\n LOG_OBJ.info(\\\"VM Interface Info : %s \\\" % interface_list)\\n return interface_list\",\n \"def get_macaddr(self):\\n return self.macaddr\",\n \"def _get_ethernet_tag(self):\\n return self.__ethernet_tag\",\n \"def get_mac_from_raw_query(request_raw_query: str):\\n mac = \\\"\\\"\\n raw_query_list = request_raw_query.split(\\\"&\\\")\\n for param in raw_query_list:\\n if \\\"mac=\\\" in param:\\n mac = param.replace(\\\"mac=\\\", \\\"\\\")\\n if not mac:\\n raise AttributeError(\\\"%s is not parsable\\\" % request_raw_query)\\n return mac.replace(\\\"-\\\", \\\":\\\")\",\n \"def get_hwaddress(ifname):\\n # It might be possible that more than one link-layer address is associated\\n # with the interface. If this is the case, we simply return the first.\\n # If there's no such interface or the interface has no MAC address,\\n # we notify caller via ValueError.\\n macdict = getmacaddrs()\\n try:\\n addr = macdict[ifname][0]\\n except (IndexError, KeyError):\\n raise ValueError(\\\"%s\\\" % ifname)\\n return addr\",\n \"def mac_aton(str):\\n macbytes = [int(i, 16) for i in str.split(':')]\\n return struct.pack('6B', *macbytes)\",\n \"def random_mac():\\n return '\\\"02:%02x:%02x:%02x:%02x:%02x\\\"' % (random.randint(0,255),\\n random.randint(0,255),\\n random.randint(0,255),\\n random.randint(0,255),\\n random.randint(0,255))\",\n \"def boot_mac_address(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"boot_mac_address\\\")\",\n \"def get_rand_mac(self):\\n random_mac = []\\n\\n # add manufacturer\\n random_mac.append(self.get_rand_manufact())\\n\\n # generate the last 24 bits of the random hex\\n for i in range(0, 3):\\n rand_digit1 = self.get_rand_hex_digit()\\n rand_digit2 = self.get_rand_hex_digit()\\n random_mac.append(rand_digit1 + rand_digit2)\\n\\n return ':'.join(random_mac)\",\n \"def get_mac(self):\\n bluetooth_mac_address = ''\\n is_general_visible = False\\n if self.phone_info.phone_type == PhoneType.IOS:\\n try:\\n try:\\n # verify that General Button is visible\\n self.find_element(self.driver.appium_driver,\\n 'self.ios_locators.GENERAL_NAVIGATION_BUTTON_ByXPATH',\\n 0).is_displayed()\\n is_general_visible = True\\n except:\\n logger.debug(\\\"General Button is currently not visible \\\")\\n\\n if is_general_visible:\\n pass\\n else:\\n\\n self.find_element(self.driver.appium_driver,\\n self.general_button_settings).click()\\n self.wait_till_element_to_be_visible(\\n self.driver.appium_driver, self.status_button, 10)\\n self.find_element(self.driver.appium_driver,\\n self.status_button, 2).click()\\n bluetooth_mac_address = self.find_element(\\n self.driver.appium_driver, self.bluetooth_mac_addess,\\n 2).text\\n logger.debug(\\\"Bluetooth Mac Address:\\\" + bluetooth_mac_address)\\n except Exception as e:\\n logger.error(\\\"Bluetooth Mac Address is not Visible\\\")\\n logger.debug(repr(e))\\n elif self.phone_info.phone_type == PhoneType.ANDROID:\\n try:\\n\\n self.find_element(self.driver.appium_driver,\\n self.general_button_settings).click()\\n self.wait_till_element_to_be_visible(self.driver.appium_driver,\\n self.status_button, 10)\\n self.find_element(self.driver.appium_driver,\\n self.status_button).click()\\n\\n self.testcase_action = 'STEP -: Get Bluetooth Mac Address'\\n bluetooth_mac_address = self.find_element(\\n self.driver.appium_driver, self.bluetooth_mac_addess,\\n 2).text\\n logger.debug(\\\"Bluetooth Mac Address:\\\" + bluetooth_mac_address)\\n except Exception as e:\\n logger.warning(\\\"Mac address is not available\\\")\\n logger.debug(repr(e))\\n return bluetooth_mac_address\",\n \"def person_mac(self):\\n return self._person_mac\",\n \"def get_my_mac_set(iface_filter=None):\\n\\n out_set = set()\\n if sys.platform.startswith(\\\"win\\\"):\\n from scapy.arch.windows import NetworkInterface\\n if type(iface_filter) == NetworkInterface:\\n out_set.add(iface_filter.mac)\\n\\n for iface in sc.get_if_list():\\n if iface_filter is not None and iface != iface_filter:\\n continue\\n try:\\n mac = sc.get_if_hwaddr(iface)\\n except Exception as e:\\n continue\\n else:\\n out_set.add(mac)\\n\\n return out_set\",\n \"def mac_ntop(binary):\\n x = b\\\":\\\".join(binascii.hexlify(binary)[i : i + 2] for i in range(0, 12, 2))\\n return str(x.decode(\\\"ascii\\\"))\",\n \"def get_random_mac():\\n\\t\\n\\t# use the Dlink range\\n\\tmac = \\\"00:05:5D\\\"\\n\\t\\n\\tfor i in range(0,3):\\n\\t\\tmac += \\\":%s\\\" % hex(random.randrange(0,256))[2:]\\n\\t\\t\\n\\t\\t\\n\\treturn mac\",\n \"def getmacaddrs():\\n # Unpack just for the sake of being meaningful.\\n ifaddrs, sockaddr_dl, sockaddr = PLATFORM_LOOKUP[PLATFORM]\\n ptr = c_void_p(None)\\n result = LIBC.getifaddrs(pointer(ptr))\\n if result != 0:\\n return {}\\n ifa = ifaddrs.from_address(ptr.value)\\n result = {}\\n\\n while True:\\n name = ifa.ifa_name\\n if name not in result:\\n result[name] = []\\n # Some interface (such as a TUN virtual network) doesn't give us\\n # ifa_addr at all and we can usually skip them because they're hardly\\n # relevant for our usage case.\\n if ifa.ifa_addr:\\n sa = sockaddr.from_address(ifa.ifa_addr)\\n if sa.sa_family == AF_LINK:\\n si = sockaddr_dl.from_address(ifa.ifa_addr)\\n addr = \\\"%s\\\" % si\\n if addr:\\n result[name].append(addr)\\n if ifa.ifa_next:\\n ifa = ifaddrs.from_address(ifa.ifa_next)\\n else:\\n break\\n\\n LIBC.freeifaddrs(ptr)\\n return result\",\n \"def MacAddress(self):\\n if self.force_auto_sync:\\n self.get('MacAddress')\\n return self._MacAddress\",\n \"def _arp(ip_address):\\n cmd = ['arp', '-n', ip_address]\\n arp = subprocess.Popen(cmd, stdout=subprocess.PIPE)\\n out, _ = arp.communicate()\\n match = re.search(r'(([0-9A-Fa-f]{1,2}\\\\:){5}[0-9A-Fa-f]{1,2})', str(out))\\n if match:\\n return match.group(0)\\n _LOGGER.info('No MAC address found for %s', ip_address)\\n return None\",\n \"def _getMac( self, bSeed ):\\n\\n\\t\\tbMac = ( bSeed >> 24 ) & 0xFF\\n\\t\\treturn '%02X' % bMac\",\n \"def mac_ntoa(mac):\\n return '%.2x:%.2x:%.2x:%.2x:%.2x:%.2x' % tuple(map(ord, list(mac)))\",\n \"def _get_mac_address(self, mac_numbers):\\n\\n mac = \\\"\\\"\\n for num in mac_numbers:\\n num = self._convert_to_hex(num)\\n mac = ':'.join((mac, num))\\n mac = mac[1:]\\n return mac\",\n \"def get_device_info(self, mac_address):\\n try:\\n out = self.get_output(\\\"info \\\" + mac_address)\\n except BluetoothctlError, e:\\n print(e)\\n return None\\n else:\\n return out\",\n \"def get_arp_table():\\n IP = ''\\n login = ''\\n password = ''\\n telnet = pexpect.spawn('telnet {}'.format(IP), timeout=30)\\n telnet.expect('Username:')\\n telnet.sendline(login)\\n telnet.expect('Password:')\\n telnet.sendline(password)\\n telnet.expect('#')\\n telnet.sendline('terminal length 0')\\n telnet.expect('#')\\n telnet.sendline('show arp')\\n telnet.expect('#')\\n arp_table = telnet.before.decode('utf-8')\\n telnet.close()\\n return arp_table\",\n \"def generate_next_mac(topology_id):\\n\\n if configuration.deployment_backend == 'openstack':\\n # we just don't need this for openstack at all, just return a string that will\\n # never get used\\n return '52:54:11:22:33:44'\\n\\n global used_macs\\n if topology_id not in used_macs:\\n used_macs[topology_id] = list()\\n\\n macs_for_topology = used_macs[topology_id]\\n mac = _generate_mac(topology_id)\\n\\n while mac in macs_for_topology:\\n logger.info('this mac %s has already been used!' % mac)\\n mac = _generate_mac(topology_id)\\n\\n macs_for_topology.append(mac)\\n return mac\",\n \"def _mac_addr(address):\\n return ':'.join('%02x' % ord(b) for b in address)\",\n \"def fusion_api_get_appliance_interface_attribute(self, device='eth0', attribute='macAddress', api=None, headers=None):\\n return self.interfaces.get_device_attribute(device, attribute, api, headers)\",\n \"def get_dhcp_client_iaid(mac_address):\\n hwaddr = list(int(byte, 16) for byte in mac_address.split(':'))\\n return hwaddr[2] << 24 | hwaddr[3] << 16 | hwaddr[4] << 8 | hwaddr[5]\",\n \"def _get_rmac(self):\\n return self.__rmac\",\n \"def generate_mac():\\n rand_str = generate_name(choices=\\\"0123456789abcdef\\\", length=12)\\n return \\\":\\\".join(re.findall(\\\"..\\\", rand_str))\",\n \"def generate_mac():\\n rand_str = generate_name(choices=\\\"0123456789abcdef\\\", length=12)\\n return \\\":\\\".join(re.findall(\\\"..\\\", rand_str))\",\n \"def unique_id(self):\\n return self._device.mac\",\n \"def get_ip_address(interface):\\n try:\\n import netifaces\\n detail = netifaces.ifaddresses(interface)\\n if netifaces.AF_INET in detail:\\n ip = detail[netifaces.AF_INET][0]['addr']\\n else:\\n ip = 'unavailable'\\n if netifaces.AF_LINK in detail:\\n mac = detail[netifaces.AF_LINK][0]['addr']\\n else:\\n mac = 'unknown'\\n except ImportError:\\n import socket\\n import fcntl\\n import struct\\n s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\\n ip = socket.inet_ntoa(fcntl.ioctl(s.fileno(), 0x8915, struct.pack('256s', interface[:15]))[20:24])\\n mac = fcntl.ioctl(s.fileno(), 0x8927, struct.pack('256s', interface[:15]))\\n return ip, mac\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.78554046","0.78055936","0.7448746","0.7251684","0.72502637","0.72043616","0.7181221","0.71635437","0.70859075","0.69643205","0.69631994","0.6941659","0.69308925","0.6928495","0.68998766","0.6882689","0.6879579","0.68495977","0.68219835","0.67996645","0.6756655","0.6756655","0.67357373","0.6721518","0.66620785","0.660852","0.6595686","0.65595657","0.655757","0.655757","0.6550994","0.65407354","0.6479743","0.646247","0.6453378","0.645077","0.64303225","0.64140564","0.63751566","0.63669","0.63279974","0.6303838","0.6230934","0.621747","0.62085325","0.61586696","0.6146244","0.61389816","0.61139846","0.61130697","0.6091632","0.60792637","0.60707414","0.6060471","0.6054021","0.60313296","0.5977413","0.5971289","0.5967008","0.5962272","0.59434515","0.5927739","0.5890729","0.58856946","0.58854824","0.58726627","0.58726627","0.58442944","0.5839429","0.58105505","0.5808298","0.58054","0.57826865","0.5770043","0.576497","0.5764456","0.5761744","0.57388616","0.5733923","0.57321376","0.5708758","0.5695926","0.56756514","0.5673807","0.56678617","0.56670505","0.56591254","0.565275","0.5622219","0.56167185","0.561206","0.55831337","0.5578144","0.5567895","0.5565372","0.5541353","0.5539427","0.5539427","0.5537598","0.5500947"],"string":"[\n \"0.78554046\",\n \"0.78055936\",\n \"0.7448746\",\n \"0.7251684\",\n \"0.72502637\",\n \"0.72043616\",\n \"0.7181221\",\n \"0.71635437\",\n \"0.70859075\",\n \"0.69643205\",\n \"0.69631994\",\n \"0.6941659\",\n \"0.69308925\",\n \"0.6928495\",\n \"0.68998766\",\n \"0.6882689\",\n \"0.6879579\",\n \"0.68495977\",\n \"0.68219835\",\n \"0.67996645\",\n \"0.6756655\",\n \"0.6756655\",\n \"0.67357373\",\n \"0.6721518\",\n \"0.66620785\",\n \"0.660852\",\n \"0.6595686\",\n \"0.65595657\",\n \"0.655757\",\n \"0.655757\",\n \"0.6550994\",\n \"0.65407354\",\n \"0.6479743\",\n \"0.646247\",\n \"0.6453378\",\n \"0.645077\",\n \"0.64303225\",\n \"0.64140564\",\n \"0.63751566\",\n \"0.63669\",\n \"0.63279974\",\n \"0.6303838\",\n \"0.6230934\",\n \"0.621747\",\n \"0.62085325\",\n \"0.61586696\",\n \"0.6146244\",\n \"0.61389816\",\n \"0.61139846\",\n \"0.61130697\",\n \"0.6091632\",\n \"0.60792637\",\n \"0.60707414\",\n \"0.6060471\",\n \"0.6054021\",\n \"0.60313296\",\n \"0.5977413\",\n \"0.5971289\",\n \"0.5967008\",\n \"0.5962272\",\n \"0.59434515\",\n \"0.5927739\",\n \"0.5890729\",\n \"0.58856946\",\n \"0.58854824\",\n \"0.58726627\",\n \"0.58726627\",\n \"0.58442944\",\n \"0.5839429\",\n \"0.58105505\",\n \"0.5808298\",\n \"0.58054\",\n \"0.57826865\",\n \"0.5770043\",\n \"0.576497\",\n \"0.5764456\",\n \"0.5761744\",\n \"0.57388616\",\n \"0.5733923\",\n \"0.57321376\",\n \"0.5708758\",\n \"0.5695926\",\n \"0.56756514\",\n \"0.5673807\",\n \"0.56678617\",\n \"0.56670505\",\n \"0.56591254\",\n \"0.565275\",\n \"0.5622219\",\n \"0.56167185\",\n \"0.561206\",\n \"0.55831337\",\n \"0.5578144\",\n \"0.5567895\",\n \"0.5565372\",\n \"0.5541353\",\n \"0.5539427\",\n \"0.5539427\",\n \"0.5537598\",\n \"0.5500947\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.8025036"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":92593,"cells":{"query":{"kind":"string","value":"Get the BMC Mac from RedFish"},"document":{"kind":"string","value":"def get_mac_address(hostname):\n\n url = \"https://{}/redfish/v1/Managers/1/EthernetInterfaces/1/\".format(hostname)\n eth_dict = requests.get(url, auth=(USER,PASS),verify=False).json()\n mac_address = eth_dict['MACAddress']\n\n LOGGER.info(\"IPMI BMC %s reports MAC address as %s\", hostnameh, mac_address)\n\n return mac_address"},"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_mac(self):\n return self.__mac","def get_sonic_mac(host, asicnum, port):\n if host.facts[\"num_asic\"] == 1:\n cmd = \"sudo ip link show {}\".format(port)\n else:\n ns = \"asic\" + str(asicnum)\n cmd = \"sudo ip netns exec {} ip link show {}\".format(ns, port)\n output = host.command(cmd)\n mac = output['stdout_lines'][1].split()[1]\n logger.info(\"host: %s, asic: %d, port: %s, mac: %s\", host.hostname, asicnum, port, mac)\n return mac","def get_my_mac():\n\n mac_set = get_my_mac_set(iface_filter=get_default_route()[1])\n return mac_set.pop()","def get_mac_string():\n mac_int = getnode()\n mac_str = ':'.join((\"%012x\" % mac_int)[i:i + 2] for i in range(0, 12, 2))\n return mac_str","def get_mac(self) :\n\t\ttry :\n\t\t\treturn self.p_fields.f128\n\t\texcept :\n\t\t\treturn None","def get_mac(self) -> str:\n self.sendline(\"iw {} info\".format(self.iface_dut))\n # We are looking for MAC definition of STA\n # wdev 0x1\n # addr 96:4e:c9:cc:7a:2c\n # type managed\n self.expect(\"addr (?P<mac>..:..:..:..:..:..)\\r\\n\\t(type|ssid)\")\n return self.match.group('mac')","def _get_rmac(self):\n return self.__rmac","def mac(self):\n return self.device.settings[\"device\"][\"mac\"]","def mac(self):\n return self.device.settings[\"device\"][\"mac\"]","def sw_mac(self):\n return self.raw.get('sw_mac')","def get_mac(self) -> str:\n hex_mac = hexlify(self.message)[160:172].decode().upper()\n return (\n hex_mac[0:2]\n + \":\"\n + hex_mac[2:4]\n + \":\"\n + hex_mac[4:6]\n + \":\"\n + hex_mac[6:8]\n + \":\"\n + hex_mac[8:10]\n + \":\"\n + hex_mac[10:12]\n )","def getMac(self):\n # Import netifaces here to prevent error importing this module in setup.py\n import netifaces\n interfaces = ['eth0', 'wlan0']\n try:\n interfaces.append(netifaces.gateways()['default'][netifaces.AF_INET][1])\n except:\n pass\n for interface in interfaces:\n try:\n return netifaces.ifaddresses(interface)[netifaces.AF_LINK][0]['addr']\n except ValueError:\n pass\n except:\n exception('Error getting MAC address')\n return None","def _getMac( self, bSeed ):\n\n\t\tbMac = ( bSeed >> 24 ) & 0xFF\n\t\treturn '%02X' % bMac","def getMac(teff, logg):\n # For Dwarfs: Doyle et al. 2014\n # 5200 < teff < 6400\n # 4.0 < logg < 4.6\n if logg > 3.5:\n mac = 3.21 + (2.33 * (teff - 5777.) * (10**(-3))) + (2.00 * ((teff - 5777.)**2) * (10**(-6))) - (2.00 * (logg - 4.44))\n # For subgiants and giants: Hekker & Melendez 2007\n elif 2.9 <= logg <= 3.5: # subgiants\n mac = -8.426 + (0.00241*teff)\n elif 1.5 <= logg < 2.9: # giants\n mac = -3.953 + (0.00195*teff)\n elif logg < 1.5: # bright giants\n mac = -0.214 + (0.00158*teff)\n\n # For negative values, keep a minimum of 0.3 km/s\n if mac < 0.30:\n mac = 0.30\n return round(mac, 2)","def sys_mac(self):\n try:\n with open(os.path.join(self.sys_class_path, \"address\")) as f:\n sys_mac = f.read().strip('\\0').strip()\n except Exception:\n sys.stderr.write(\"Failed to read address for entry: %s\\n\" %\n (self._orig_line))\n raise\n\n return sys_mac","def get_bmc(node):\n if get_cluster_vendor() == \"sgi\":\n return sgi_cluster.get_bmc()\n elif get_cluster_vendor() == \"ibm\": \n return ibm_cluster.get_bmc()\n return None","def mac_string(self) :\n\t\tif not getattr(self,'pkg02') :\n\t\t\treturn None \n\t\tif self.pkg02[:2] not in ['01','02','04','05' ] :\n\t\t\treturn None\n\t\treturn self.pkg02 + ' ' + self.p_fields.mac_string()","def get_bound_card_mac(self):\n\t\treturn call_sdk_function('PrlVirtNet_GetBoundCardMac', self.handle)","def get_current_mac(interface):\r\n ifconfig_result = subprocess.check_output([\"sudo\", \"ifconfig\", interface])\r\n mac_result = re.search(r\"\\w\\w:\\w\\w:\\w\\w:\\w\\w:\\w\\w:\\w\\w\", ifconfig_result.decode('utf-8'))\r\n if mac_result:\r\n return mac_result.group(0)\r\n else:\r\n print(\"Could not read MAC-address\")","def bmc_mac_address(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"bmc_mac_address\")","def mac(self) -> str:\n return self.camera_info[\"wifi_mac\"]","def get_vendor(mac):\r\n return p.get_manuf(mac) or 'None'","def get_sys_mac_address():\n mac = \"00:00:00:00:00:00\"\n try:\n if os.path.exists(SysTools.SYS_MAC_FILE):\n with open(SysTools.SYS_MAC_FILE, 'r') as fd:\n mac = fd.readline().strip()\n except:\n SysTools.logger.error(\"Failed to get sys-mac-address from %s\",\n SysTools.SYS_MAC_FILE)\n return mac","def _get_server_hardware_mac(server_hardware):\n sh_physical_port = None\n\n if server_hardware.get('portMap'):\n for device in server_hardware.get(\n 'portMap', {}).get('deviceSlots', ()):\n for physical_port in device.get('physicalPorts', ()):\n if physical_port.get('type') == 'Ethernet':\n sh_physical_port = physical_port\n break\n if sh_physical_port:\n for virtual_port in sh_physical_port.get('virtualPorts', ()):\n # NOTE(nicodemos): Ironic oneview drivers needs to use a\n # port that type is Ethernet and function identifier 'a' for\n # this FlexNIC to be able to make a deploy using PXE.\n if virtual_port.get('portFunction') == 'a':\n return virtual_port.get('mac', ()).lower()\n raise exception.OneViewError(\n _(\"There is no Ethernet port on the Server Hardware: %s\") %\n server_hardware.get('uri'))\n else:\n raise exception.OneViewError(\n _(\"The Server Hardware: %s doesn't have a list of adapters/slots, \"\n \"their ports and attributes. This information is available only \"\n \"for blade servers. Is this a rack server?\") %\n server_hardware.get('uri'))","def get_base_mac(self):\n raise NotImplementedError","def mac(self):\n mac = hexlify(WLAN().config('mac'), ':').decode()\n return mac.upper() # MAC-address in upper case","def get_mac_address(self):\n\t\treturn call_sdk_function('PrlSrvCfgNet_GetMacAddress', self.handle)","def emulab_mac(mac):\n\n return \"\".join(mac.lower().split(':'))","def get_mac(self, node_id):\n nc = '%02x' % self.node_class\n nr_iface = '%02x' % self.nr_host_interface\n node_id = '%08x' % node_id\n\n return '%s:%s:%s:%s:%s:%s' % (nc, nr_iface, node_id[0:2], node_id[2:4], node_id[4:6], node_id[6:8])","def get_rand_mac(self):\n random_mac = []\n\n # add manufacturer\n random_mac.append(self.get_rand_manufact())\n\n # generate the last 24 bits of the random hex\n for i in range(0, 3):\n rand_digit1 = self.get_rand_hex_digit()\n rand_digit2 = self.get_rand_hex_digit()\n random_mac.append(rand_digit1 + rand_digit2)\n\n return ':'.join(random_mac)","def _get_server_hardware_mac_from_ilo(server_hardware):\n try:\n client = get_ilorest_client(server_hardware)\n ilo_path = \"/rest/v1/systems/1\"\n hardware = jsonutils.loads(client.get(ilo_path).text)\n hardware_mac = hardware['HostCorrelation']['HostMACAddress'][0]\n except redfish.JsonDecodingError as exc:\n LOG.error(\"Failed in JSON object getting path: %s\", ilo_path)\n raise exception.OneViewError(error=exc)\n except (ValueError, TypeError, IndexError) as exc:\n LOG.error(\n \"Failed to get mac from server hardware %(server_hardware)s \"\n \"via iLO. Error: %(message)s\", {\n \"server_hardware\": server_hardware.get(\"uri\"),\n \"message\": exc\n }\n )\n raise exception.OneViewError(error=exc)\n\n return hardware_mac","def getMacName(self):\r\n return CipherSuite.canonicalMacName(self.cipherSuite)","def get_mac_address(self, result, host):\n if \"mac\" in result['scan'][host][\"addresses\"]:\n return result['scan'][host][\"addresses\"][\"mac\"]\n else:\n return \"\"","def get_mac(self):\n bluetooth_mac_address = ''\n is_general_visible = False\n if self.phone_info.phone_type == PhoneType.IOS:\n try:\n try:\n # verify that General Button is visible\n self.find_element(self.driver.appium_driver,\n 'self.ios_locators.GENERAL_NAVIGATION_BUTTON_ByXPATH',\n 0).is_displayed()\n is_general_visible = True\n except:\n logger.debug(\"General Button is currently not visible \")\n\n if is_general_visible:\n pass\n else:\n\n self.find_element(self.driver.appium_driver,\n self.general_button_settings).click()\n self.wait_till_element_to_be_visible(\n self.driver.appium_driver, self.status_button, 10)\n self.find_element(self.driver.appium_driver,\n self.status_button, 2).click()\n bluetooth_mac_address = self.find_element(\n self.driver.appium_driver, self.bluetooth_mac_addess,\n 2).text\n logger.debug(\"Bluetooth Mac Address:\" + bluetooth_mac_address)\n except Exception as e:\n logger.error(\"Bluetooth Mac Address is not Visible\")\n logger.debug(repr(e))\n elif self.phone_info.phone_type == PhoneType.ANDROID:\n try:\n\n self.find_element(self.driver.appium_driver,\n self.general_button_settings).click()\n self.wait_till_element_to_be_visible(self.driver.appium_driver,\n self.status_button, 10)\n self.find_element(self.driver.appium_driver,\n self.status_button).click()\n\n self.testcase_action = 'STEP -: Get Bluetooth Mac Address'\n bluetooth_mac_address = self.find_element(\n self.driver.appium_driver, self.bluetooth_mac_addess,\n 2).text\n logger.debug(\"Bluetooth Mac Address:\" + bluetooth_mac_address)\n except Exception as e:\n logger.warning(\"Mac address is not available\")\n logger.debug(repr(e))\n return bluetooth_mac_address","def set_bios_bootmode_uefi(ip, login_account, login_password, system_id):\n result = {}\n login_host = \"https://\" + ip\n try:\n # Connect using the BMC address, account name, and password\n # Create a REDFISH object\n REDFISH_OBJ = redfish.redfish_client(base_url=login_host, username=login_account, timeout=utils.g_timeout,\n password=login_password, default_prefix='/redfish/v1', cafile=utils.g_CAFILE)\n # Login into the server and create a session\n REDFISH_OBJ.login(auth=utils.g_AUTH)\n except:\n traceback.print_exc()\n result = {'ret': False, 'msg': \"Please check the username, password, IP is correct\"}\n return result\n\n # GET the ComputerSystem resource\n system = utils.get_system_url(\"/redfish/v1\", system_id, REDFISH_OBJ)\n if not system:\n result = {'ret': False, 'msg': \"This system id is not exist or system member is None\"}\n REDFISH_OBJ.logout()\n return result\n for i in range(len(system)):\n system_url = system[i]\n response_system_url = REDFISH_OBJ.get(system_url, None)\n if response_system_url.status != 200:\n error_message = utils.get_extended_error(response_system_url)\n result = {'ret': False, 'msg': \"Url '%s' response Error code %s \\nerror_message: %s\" % (system_url, response_system_url.status, error_message)}\n REDFISH_OBJ.logout()\n return result\n else:\n # Get the bios resource\n bios_url = response_system_url.dict['Bios']['@odata.id']\n response_bios_url = REDFISH_OBJ.get(bios_url, None)\n if response_bios_url.status != 200:\n error_message = utils.get_extended_error(response_bios_url)\n result = {'ret': False, 'msg': \"Url '%s' response Error code %s \\nerror_message: %s\" % (bios_url, response_bios_url.status, error_message)}\n REDFISH_OBJ.logout()\n return result\n else: # Get bios success\n # Seek boot mode from bios attributes\n attribute_bootmode = None\n attributes = response_bios_url.dict['Attributes']\n for attribute in attributes:\n if attribute == \"BootMode\" or attribute == \"SystemBootMode\":\n attribute_bootmode = attribute\n break\n if attribute_bootmode == None:\n for attribute in attributes:\n if \"SystemBootMode\" in attribute:\n attribute_bootmode = attribute\n break\n if attribute_bootmode == None:\n for attribute in attributes:\n if \"Boot\" in attribute and \"Mode\" in attribute:\n attribute_bootmode = attribute\n break\n if attribute_bootmode == None:\n result = {'ret': False, 'msg': \"Can not found BootMode attribute in response of url %s\" %(bios_url)}\n REDFISH_OBJ.logout()\n return result\n\n # Get boot mode setting guide from bios registry\n WarningText = None\n ValueName = None\n bios_registry_url = \"/redfish/v1/Registries/\" + response_bios_url.dict['AttributeRegistry']\n response_bios_registry_url = REDFISH_OBJ.get(bios_registry_url, None)\n if response_bios_registry_url.status == 200:\n locations = response_bios_registry_url.dict['Location']\n bios_regjson_url = None\n for location in locations:\n if 'en' in location['Language']:\n bios_regjson_url = location['Uri']\n break\n if bios_regjson_url:\n response_bios_regjson_url = REDFISH_OBJ.get(bios_regjson_url, None)\n if response_bios_regjson_url.status == 200:\n regattributes = response_bios_regjson_url.dict['RegistryEntries']['Attributes']\n for regattribute in regattributes:\n if regattribute['AttributeName'] == attribute_bootmode:\n if 'WarningText' in regattribute:\n WarningText = regattribute['WarningText']\n for value in regattribute['Value']:\n if 'legacy' in value['ValueName'].lower():\n continue\n if 'uefi' in value['ValueName'].lower():\n ValueName = value['ValueName']\n break\n ValueName = value['ValueName']\n break\n \n # Perform patch to set\n if ValueName == None:\n ValueName = \"UEFIMode\"\n pending_url = response_bios_url.dict['@Redfish.Settings']['SettingsObject']['@odata.id']\n parameter = {attribute_bootmode: ValueName}\n attribute = {\"Attributes\": parameter}\n headers = {\"If-Match\": '*'}\n response_pending_url = REDFISH_OBJ.patch(pending_url, body=attribute, headers=headers)\n if response_pending_url.status in [200,204]:\n if WarningText:\n result = {'ret': True, 'msg': 'set bios bootmode uefi successful. WarningText: %s'% (WarningText) }\n else:\n result = {'ret': True, 'msg': 'set bios bootmode uefi successful'}\n elif response_pending_url.status == 405:\n result = {'ret': False, 'msg': \"Resource not supported\"}\n else:\n error_message = utils.get_extended_error(response_pending_url)\n result = {'ret': False, 'msg': \"Url '%s' response Error code %s \\nerror_message: %s\" % (\n pending_url, response_pending_url.status, error_message)}\n\n # Logout of the current session\n try:\n REDFISH_OBJ.logout()\n except:\n pass\n return result","def get_mac():\n\n interface = [x for x in netifaces.interfaces() if 'wlan' in x or 'wlp' in x][0]\n return netifaces.ifaddresses(interface)[netifaces.AF_LINK][0]['addr']","def random_mac():\n return '\"02:%02x:%02x:%02x:%02x:%02x\"' % (random.randint(0,255),\n random.randint(0,255),\n random.randint(0,255),\n random.randint(0,255),\n random.randint(0,255))","def GetMacVendor(macAddress):\n\turlMac = \"https://macvendors.co/api/%s/pipe\" % macAddress\n\tif macAddress in [\"\",\"FF-FF-FF-FF-FF-FF\"]:\n\t\treturn None\n\n\ttry:\n\t\t#sys.stderr.write(\"urlMac=%s\\n\"%urlMac)\n\n\t\timport urllib2\n\t\treq = urllib2.Request(urlMac)\n\t\treq.add_header('User-Agent', \"API Browser\")\n\t\tresp = urllib2.urlopen(req)\n\t\tcontent = resp.readlines()[0]\n\n\t\t#sys.stderr.write(\"content=%s\\n\"%content)\n\t\t#sys.stderr.write(\"content=%s\\n\"%str(type(content)))\n\t\tsplitMac = content.split(\"|\")\n\t\t#sys.stderr.write(\"splitMac[0]=%s\\n\"%splitMac[0])\n\t\treturn splitMac[0]\n\texcept:\n\t\texc = sys.exc_info()[1]\n\t\t#sys.stderr.write(\"Caught %s\\n\"%str(exc))\n\t\t# Any error returns a none strng: Thisinformation is not that important.\n\t\treturn \"Cannot determine vendor\"","def __get_mac_address(self):\n str_hex_mac = uuid.UUID(int=uuid.getnode()).hex[-12:]\n return str_hex_mac","def macFor(cls, board):\n return cls.MAC_PREFIX + '{:02X}'.format(int(board))","def get_mac_address():\n eth0_interface = 'eth0'\n addresses = netifaces.ifaddresses(eth0_interface)[netifaces.AF_LINK][0]\n mac_address = addresses['addr']\n return mac_address","def get_mac_address(self):\n\t\treturn call_sdk_function('PrlVmDevNet_GetMacAddress', self.handle)","def fusion_api_get_appliance_interface_mac(self, device, api=None, headers=None):\n return self.interfaces.get_device_mac(device, api, headers)","def generate_mac():\n rand_str = generate_name(choices=\"0123456789abcdef\", length=12)\n return \":\".join(re.findall(\"..\", rand_str))","def generate_mac():\n rand_str = generate_name(choices=\"0123456789abcdef\", length=12)\n return \":\".join(re.findall(\"..\", rand_str))","def mcpc():\n global simulator\n if simulator is None:\n print \"Program is not started.\"\n return None\n return simulator.get_pc()","def good_mac(mac):\r\n return mac.upper().replace('-', ':')","def get_adapter_mac(cls, client_object):\n parsed_data = DefaultCRUDImpl.get_adapter_info(\n client_object)\n for record in parsed_data['table']:\n if record['dev'] == client_object.name:\n return record['mac']\n pylogger.warning('Did not find a MAC address for adapter %r on %r' %\n (client_object.name, client_object.ip))","def get_random_mac():\n\t\n\t# use the Dlink range\n\tmac = \"00:05:5D\"\n\t\n\tfor i in range(0,3):\n\t\tmac += \":%s\" % hex(random.randrange(0,256))[2:]\n\t\t\n\t\t\n\treturn mac","def get_mac(ip):\n arp_request = scapy.ARP(pdst=ip)\n broadcast = scapy.Ether(dst=\"ff:ff:ff:ff:ff:ff\")\n arp_request_broadcast = broadcast / arp_request\n answered_list = scapy.srp(\n arp_request_broadcast,\n timeout=5,\n verbose=False)[0]\n return answered_list[0][1].hwsrc","def get_mac(ip):\n ans, _ = srp(Ether(dst='ff:ff:ff:ff:ff:ff')/ARP(pdst=ip), timeout=3, verbose=0)\n if ans:\n return ans[0][1].src\n else:\n return None","def _get_mac_address():\n if not sys.platform.startswith('linux'):\n raise RuntimeError(\n 'Cannot get the MAC address on non-Linux platforms'\n )\n ifname = get_default_iface_name_linux()\n s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\n info = fcntl.ioctl(s.fileno(), 0x8927,\n struct.pack('256s', bytes(ifname, 'utf-8')[:15]))\n return ''.join('%02x' % b for b in info[18:24])","def get_ffdc_bmc_cmd(self, i_type):\n return FFDC_BMC_CMD[i_type].items()","def intGet(): \n macchanger, ip, iwconfig = pathGet()\n interfaces = []\n a = str(subprocess.check_output(\"{} link show\".format(ip), shell=True))\n ints = a.split(': ')\n for i in range(len(ints)):\n if len(ints[i].split()) == 1:\n if ints[i] not in [\"1\", \"lo\", \"b'1\"]:\n interface = {'name':str(ints[i])}\n interfaces.append(interface)\n # Get interface properties\n for interface in interfaces:\n name = interface['name']\n macs = subprocess.check_output(\"{} -s {}\".format(macchanger, name), shell=True).decode(\"utf-8\")\n interface['cMac'] = macs.split()[2]\n interface['cVend'] = macs.split(\"(\")[1].split(\")\")[0]\n interface['pMac'] = macs.split(\"\\n\")[1].split()[2]\n interface['pVend'] = macs.split(\"\\n\")[1].split(\"(\")[1].split(\")\")[0]\n try:\n mon = subprocess.check_output(\"{} {} 2> /dev/null\".format(iwconfig, name), shell=True).split()\n mon1 = mon[3].decode(\"utf-8\").split(':')[1]\n if mon1 == 'off/any':\n mon1 = mon[4].decode(\"utf-8\").split(':')[1]\n interface['mon'] = mon1\n except:\n interface['mon'] = 'Wired'\n return(interfaces)","def mac_from_vm(vm: libvirt.virDomain = None) -> str:\n doc = minidom.parseString(vm.XMLDesc())\n interfaces = doc.getElementsByTagName('mac')\n return interfaces[0].getAttribute('address')","def get_random_mac():\r\n mac = [0x00, 0x16, 0x3e,\r\n random.randint(0x00, 0x7f),\r\n random.randint(0x00, 0xff),\r\n random.randint(0x00, 0xff)]\r\n return ':'.join(map(lambda x: \"%02x\" % x, mac))","def regmac(mac):\n return len(mac.split(\":\")[1]) == 12 and mac.split(\":\")[1] or None","def get_mac_address(selected_network_device, base_api_url, user, passwd): \n url = '{}/{}'.format(base_api_url, selected_network_device)\n device_mac_address = ''\n try:\n response = requests.get(url, verify=False, auth=(user, passwd),\n timeout=5)\n except requests.exceptions.ConnectionTimeout:\n logging.error('failed to establish connection to get mac address')\n\n try:\n network_device_info = response.json()\n except ValueError:\n logging.error('check URL, iDRAC user and password may be invalid')\n logging.info('{}'.format(url))\n\n try:\n device_mac_address = network_device_info[u'MACAddress']\n except KeyError:\n logging.error('No MAC Address found for network devices')\n logging.info('{}'.format(selected_network_device))\n\n return device_mac_address","def get_eos_mac(nbr, nbr_intf):\n if \"port-channel\" in nbr_intf.lower():\n # convert Port-Channel1 to po1\n shell_intf = \"po\" + nbr_intf[-1]\n else:\n # convert Ethernet1 to eth1\n shell_intf = \"eth\" + nbr_intf[-1]\n\n output = nbr['host'].command(\"ip addr show dev %s\" % shell_intf)\n # 8: Ethernet0: <BROADCAST,MULTICAST,UP,LOWER_UP> mtu 9100 ...\n # link/ether a6:69:05:fd:da:5f brd ff:ff:ff:ff:ff:ff\n\n mac = output['stdout_lines'][1].split()[1]\n return {'mac': mac, \"shell_intf\": shell_intf}","def find_my_IP_and_MAC():\n mac = ':'.join(re.findall('..', '%012x' % getnode()))\n # I write IP and not domain cause i want to save time.\n p = sr1(IP(dst=\"google.com\", ttl=0) / ICMP() / \"XXXXXXXXXXX\",verbose=0,timeout=5) #verbose = withuot output\n return mac,p.dst","def dump_macs(self, context):\n nmeta = self._nmeta\n dpid = self.dpid\n macs = []\n db_cursor = nmeta.dbidmac.find({'dpid': dpid, 'context': context})\n if db_cursor:\n for document in db_cursor:\n mac = document['mac']\n if mac:\n macs.append(mac)\n return macs","def get_mac_from_raw_query(request_raw_query: str):\n mac = \"\"\n raw_query_list = request_raw_query.split(\"&\")\n for param in raw_query_list:\n if \"mac=\" in param:\n mac = param.replace(\"mac=\", \"\")\n if not mac:\n raise AttributeError(\"%s is not parsable\" % request_raw_query)\n return mac.replace(\"-\", \":\")","def get_mac(ip):\n p = Ether(dst=\"ff:ff:ff:ff:ff:ff\") / ARP(op=1, pdst=ip)\n result = srp(p, verbose=False, iface=IFACE)[0]\n return result[0][1].hwsrc if result else None","def get_network_device_mac(devices, user, passwd, base_api_url):\n network_device_mac_address = ''\n\n if devices:\n selected_network_device = generate_network_devices_menu(devices, purpose='DHCP')\n network_device_mac_address = get_mac_address(selected_network_device, base_api_url, user, passwd)\n\n if network_device_mac_address:\n logging.info('device {} mac address is {}'.format(selected_network_device, network_device_mac_address))\n \n return network_device_mac_address","def connectgrimac(topology,\n hostmac,\n siteport,\n sitevlan,\n sitepop,\n remotesiteport,\n remotesitevlan,\n vc,\n meter=3,\n host_rewritemac=None):\n # Get both endpoints of the GRI\n (e1,e2) = vcendpoints(vc)\n core1 = topology.loadResource(e1[1])\n if 'Role' not in core1.properties.keys() or core1.properties['Role'] != 'CoreRouter' or 'Pop' not in core1.properties.keys():\n print core1.resourceName, \"is not a core router\"\n return None\n core2 = topology.loadResource(e2[1])\n if 'Role' not in core2.properties.keys() or core2.properties['Role'] != 'CoreRouter' or 'Pop' not in core2.properties.keys():\n print core1.resourceName, \"is not a core router\"\n return None\n pop1name = core1.properties['Pop']\n pop2name = core2.properties['Pop']\n\n remotepopname = None\n if sitepop.resourceName == pop1name:\n remotepopname = pop2name\n elif sitepop.resourceName == pop2name:\n remotepopname = pop1name\n if remotepopname == None:\n print \"vc\", vc, \"does not provide connectivity for\", hostmac, \"from\", remotepop.resourceName, \"to\", sitepop.resourceName\n return None\n\n remotepop = topology.loadResource(remotepopname)\n if remotepop == None:\n print \"Unable to retrieve remote POP\", remotepopname\n return None\n\n res = connectmac(localpop= sitepop,\n remotepop= remotepop,\n localsiteport= siteport,\n localsitevlan= sitevlan,\n remotesiteport= remotesiteport,\n remotesitevlan= remotesitevlan,\n hostmac= hostmac,\n vc= vc,\n meter = meter,\n host_rewritemac= host_rewritemac)\n return res","def pc():\n global simulator\n if simulator is None:\n print \"Program is not started.\"\n return None\n return mcpc()[0]","def mrdc(self) -> str:\n return self._device_info[\"MRDC\"]","def macOS(self) -> str:\n\n result = subprocess.run([\"sw_vers\", \"-productVersion\"], stdout=subprocess.PIPE)\n\n return result.stdout.decode(\"utf-8\").strip()","def from_mac(self):\n reason = \"[!] Mac Absolute timestamps are 9 digits, commonly followed by a decimal and up to 6 digits for milliseconds\"\n ts_type = self.ts_types['mac']\n try:\n if \".\" not in self.mac or not ((len(mac.split(\".\")[0]) == 9) and (len(self.mac.split(\".\")[1]) in range(0, 7))) or not ''.join(self.mac.split(\".\")).isdigit():\n self.in_mac = indiv_output = combined_output = False\n pass\n else:\n dt_obj = self.epoch_2001 + timedelta(seconds=float(self.mac))\n self.in_mac = dt_obj.strftime('%Y-%m-%d %H:%M:%S.%f')\n indiv_output = str(\"{} {} UTC\".format(ts_type, self.in_mac))\n combined_output = str(\"{}{}\\t\\t{} UTC{}\".format(self.left_color, ts_type, self.in_mac, self.right_color))\n except Exception:\n exc_type, exc_obj, exc_tb = sys.exc_info()\n print(str(exc_type) + \" - \" + str(exc_obj) + \" - line \" + str(exc_tb.tb_lineno))\n self.in_mac = indiv_output = combined_output = False\n return self.in_mac, indiv_output, combined_output, reason","def get_cmx_ap_mac(campus, building, floor, ap_name):\n\n url = CMX_URL + '/api/config/v1/maps/info/' + campus + '/' + building + '/' + floor\n header = {'content-type': 'application/json', 'accept': 'application/json'}\n response = requests.get(url, headers=header, auth=CMX_AUTH, verify=False)\n aps_list = response.json()['accessPoints']\n for ap in aps_list:\n if ap['name'] == ap_name:\n ap_mac = ap['radioMacAddress']\n return ap_mac","def mac_address(self) -> str:\n return self._device.mac","def load_macaroon(macaroon: str) -> str:\n\n # if the macaroon is a file path, load it\n if macaroon.split(\".\")[-1] == \"macaroon\":\n with open(macaroon, \"rb\") as f:\n macaroon_bytes = f.read()\n return macaroon_bytes.hex()\n else:\n # convert the bas64 macaroon to hex\n try:\n macaroon = base64.b64decode(macaroon).hex()\n except:\n pass\n return macaroon","def person_mac(self):\n return self._person_mac","def get_canmic_block(self, appip, port, appuname, apppassw, cmd):\n client = paramiko.SSHClient()\n client.load_system_host_keys()\n client.set_missing_host_key_policy(paramiko.AutoAddPolicy())\n port = 22 # SSH Port to establish connection\n client.connect(appip, port, appuname, apppassw)\n channel = client.invoke_shell()\n stdin = channel.makefile('wb')\n stdout = channel.makefile('rb')\n logging.info(\"cmd %s\" % cmd)\n input, output, error = client.exec_command(cmd)\n data = output.read()\n d = data.split(\"\\n\")\n mdata = []\n\n for k in d:\n head, sep, tail = k.partition('|')\n head = head[10:].split(\" \")\n mdata = mdata + head\n return filter(None, mdata)","def get_node_platform_and_mac(port):\n if not port:\n sys.exit(\"<port> argument not provided. Cannot read MAC address from node.\")\n sys.stdout = mystdout = StringIO()\n command = ['--port', port, 'chip_id']\n log.info(\"Running esptool command to get node\\\n platform and mac from device\")\n esptool.main(command)\n sys.stdout = sys.__stdout__\n # Finding chip type from output.\n node_platform = next(filter(lambda line: 'Detecting chip type' in line,\n mystdout.getvalue().splitlines()))\n # Finds the first occurence of the line\n # with the MAC Address from the output.\n mac = next(filter(lambda line: 'MAC: ' in line,\n mystdout.getvalue().splitlines()))\n mac_addr = mac.split('MAC: ')[1].replace(':', '').upper()\n platform = node_platform.split()[-1].lower().replace('-', '')\n print(\"Node platform detected is: \", platform)\n print(\"MAC address is: \", mac_addr)\n log.debug(\"MAC address received: \" + mac_addr)\n log.debug(\"Node platform is: \" + platform)\n return platform, mac_addr","def get_device_info(self, mac_address):\n try:\n out = self.get_output(\"info \" + mac_address)\n except BluetoothctlError, e:\n print(e)\n return None\n else:\n return out","def get_mac_address_table(self):\n\n mac_address_table = []\n command = '/interface bridge host print terse'\n\n output = self._send_command(command)\n\n for host in parse_terse_output(output):\n mac_address_table.append({\n 'mac': cast_mac(host.get('mac-address')),\n 'interface': host.get('interface'),\n 'vlan': -1,\n 'static': True if 'D' not in host.get('_flags') else False,\n 'active': True if 'X' not in host.get('_flags') else False,\n 'moves': -1,\n 'last_move': -1.0\n })\n\n return mac_address_table","def gather_system_versions(self):\n # Get Mac model ID\n self.hw_version = str(\n IORegistryEntryCreateCFProperty(\n IOServiceGetMatchingService(\n 0,\n IOServiceMatching(\"IOPlatformExpertDevice\")),\n \"model\",\n None,\n 0)).replace(\n \"\\x00\",\n \"\")\n\n if \"imacpro\" in self.hw_version.lower():\n # iMac Pro stores it's EFI data different due it's new architecture\n # so grab the EFI & SMC ROM versions appropriately\n raw_efi_list = []\n raw_rom_info = str(\n IORegistryEntryCreateCFProperty(\n IORegistryEntryFromPath(\n 0,\n \"IODeviceTree:/rom\"),\n \"apple-rom-info\",\n None,\n 0))\n for data in raw_rom_info.split(\"\\n\"):\n if data.strip().startswith(\"BIOS ID\"):\n raw_efi_list = data.split(\":\")[1].strip().split(\".\")\n break\n else:\n self.message(\n \"[-] Could not find raw EFI data to determine EFI versions. Exiting....\")\n return False\n\n self.efi_version = \"%s.%s.%s\" % (\n raw_efi_list[0], raw_efi_list[2], raw_efi_list[3])\n # Can't currently find the SMC version like this on imac pros ....\n # self.smc_version = str(IORegistryEntryCreateCFProperty(IOServiceGetMatchingService(0, IOServiceMatching(\"AppleSMC\")), \"smc-version\", None, 0))\n self.smc_version = \"\"\n else:\n # EFI & SMC ROM versions\n self.smc_version = str(\n IORegistryEntryCreateCFProperty(\n IOServiceGetMatchingService(\n 0,\n IOServiceMatching(\"AppleSMC\")),\n \"smc-version\",\n None,\n 0))\n raw_efi = str(\n IORegistryEntryCreateCFProperty(\n IORegistryEntryFromPath(\n 0,\n \"IODeviceTree:/rom\"),\n \"version\",\n None,\n 0)).replace(\n \"\\x00\",\n \"\").split(\".\")\n self.efi_version = \"%s.%s.%s\" % (\n raw_efi[0], raw_efi[2], raw_efi[3])\n\n # Set the salt to be the MAC address of the system, using the MAC as a salt in this manner\n # helps ensure that the hashed sysuuid is pseudonymous. We don't want to know the sysuuid's\n # value, but we do want it to be unique however. The Salt value is\n # never submitted to the API\n salt = hex(getnode())\n sys_uuid = str(\n IORegistryEntryCreateCFProperty(\n IOServiceGetMatchingService(\n 0,\n IOServiceMatching(\"IOPlatformExpertDevice\")),\n \"IOPlatformUUID\",\n None,\n 0)).replace(\n \"\\x00\",\n \"\")\n self.h_sys_uuid = hashlib.sha256(salt + sys_uuid).hexdigest()\n\n # Get the Board-ID, this is how EFI files are matched to running\n # hardware - Nastee\n self.board_id = str(\n IORegistryEntryCreateCFProperty(\n IOServiceGetMatchingService(\n 0,\n IOServiceMatching(\"IOPlatformExpertDevice\")),\n \"board-id\",\n None,\n 0)).replace(\n \"\\x00\",\n \"\")\n\n # Get OS version\n self.os_version = commands.getoutput(\"sw_vers -productVersion\")\n\n # Get build number\n self.build_num = commands.getoutput(\"sw_vers -buildVersion\")\n\n # Carve out the major version as we use this a bunch\n # self.os_maj_ver = \".\".join(self.os_version.split(\".\")[:2])\n\n # Add gathered info to the dictionary to query the API with\n self.endpoints_to_check[\"127.0.0.1\"] = {\n \"hashed_uuid\": self.h_sys_uuid,\n \"hw_ver\": self.hw_version,\n \"rom_ver\": self.efi_version,\n \"smc_ver\": self.smc_version,\n \"board_id\": self.board_id,\n \"os_ver\": self.os_version,\n \"build_num\": self.build_num}\n\n return True","def unique_id(self):\n return self._device.mac","def get_eprom_type():\n return command(\"S\")","def from_mac(self):\n try:\n dt_obj = self.epoch_2001 + timedelta(seconds=int(mac))\n self.in_mac = dt_obj.strftime('%Y-%m-%d %H:%M:%S.%f')\n except Exception as e:\n if not args.log:\n pass\n else:\n logging.error(str(type(e)) + \",\" + str(e))\n self.in_mac = False\n return self.in_mac","def boot_mac_address(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"boot_mac_address\")","def _get_http_mac_path(mac, delimiter='-', client_id=None):\n mac_file_name = mac.replace(':', delimiter).lower()\n hw_type = '01-'\n if client_id:\n hw_type = '20-'\n mac_file_name = hw_type + mac_file_name\n return os.path.join(get_http_boot_dir(), PXE_CFG_DIR_NAME, mac_file_name)","def get_desktop():\n l=get_pids(('kwin','ksmserver',))\n if l: kde=l[0]\n else: kde=None\n l=get_pids(('gnome-session',))\n if l: gnome=l[0]\n else: gnome=None\n if kde:\n if not gnome or kde<gnome: return 1\n else: return 0\n if gnome: return 0\n else: return -1","def machine():\n return uname().machine","def machine():\n return uname().machine","def mcc(self):\n return self._mcc","def pathGet():\n macchanger = subprocess.check_output(\"which macchanger\", shell=True)[:-1].decode(\"utf-8\")\n ip = subprocess.check_output(\"which ip\", shell=True)[:-1].decode(\"utf-8\")\n iwconfig = subprocess.check_output(\"which iwconfig\", shell=True)[:-1].decode(\"utf-8\")\n if macchanger == '':\n return(1)\n if ip == '':\n return(2)\n if iwconfig == '':\n return(3)\n else:\n return( macchanger, ip, iwconfig)","def get_device(mac, devices=None):\n print('get_device:', get_device)\n if not mac:\n return None\n if not devices:\n devices = discover_wemo()\n\n normal_mac = normalize_mac_address(mac)\n\n for dev in devices:\n if dev.mac and normalize_mac_address(dev.mac) == normal_mac:\n print('get_device dev:', dev)\n return dev\n\n return None","def connectmac(localpop,\n remotepop,\n localsiteport,\n localsitevlan,\n remotesiteport,\n remotesitevlan,\n hostmac,\n vc,\n meter=3,\n host_rewritemac = None):\n core = Container.fromAnchor(localpop.properties['CoreRouter'])\n corename = core.resourceName\n (corename,coredom,coreport,corevlan) = getvcnode(vc,corename)\n remotecore = Container.fromAnchor(remotepop.properties['CoreRouter'])\n remotecorename = remotecore.resourceName\n (remotecorename,remotecoredom,remotecoreport,remotecorevlan) = getvcnode(vc,remotecorename)\n\n # Find hwswitch/port - core/port\n topology = Container.getContainer(localpop.properties['HwSwitch']['containerName'])\n\n hwswitch = Container.fromAnchor(localpop.properties['HwSwitch'])\n hwport_tocore = getgriport(topology, hwswitch, core, coreport)\n # Find remotehwswitch/port - remotecore/port\n remotehwswitch = Container.fromAnchor(remotepop.properties['HwSwitch'])\n remotehwport_tocore = getgriport(topology, remotehwswitch, remotecore, remotecoreport)\n\n # Find the port on the HwSwitch that is connected to the host's port\n hwport_tosite = localsiteport\n\n fh1 = connectdataplanemac(hostmac= hostmac,\n hostvlan= localsitevlan,\n sw= hwswitch,\n tohostport= hwport_tosite,\n tocoreport= hwport_tocore,\n tocorevlan= corevlan,\n vc= vc,\n meter= meter,\n host_rewritemac= host_rewritemac)\n if fh1 == None:\n return None\n\n # Find the port on the remote HwSwitch that is connected to the remote host's port\n remotehwport_tosite = remotesiteport\n\n fh2 = connectremoteplanemac(remotesw = remotehwswitch,\n hostmac= hostmac,\n hostvlan= localsitevlan,\n remotehost_port= remotehwport_tosite,\n remotehost_vlan= remotesitevlan,\n remotehwport_tocore= remotehwport_tocore,\n corevlan= corevlan,\n vc= vc,\n meter= meter,\n host_rewritemac= host_rewritemac)\n if fh2 == None:\n SCC.deleteforward(fh1)\n return None\n\n fhs = (fh1, fh2)\n return fhs","def sx_router_neigh_get_mac(handle, rif, addr): \n try:\n neigh_entry_cnt_p = new_uint32_t_p()\n neigh_entry_list_p = new_sx_neigh_get_entry_t_arr(1)\n\n filter_p = new_sx_neigh_filter_t_p()\n neigh_filter = sx_neigh_filter_t()\n neigh_filter.filter_by_rif = SX_KEY_FILTER_FIELD_NOT_VALID\n neigh_filter.rif = 0\n sx_neigh_filter_t_p_assign(filter_p, neigh_filter)\n \n rc = sx_api_router_neigh_get(handle, SX_ACCESS_CMD_GET, rif, addr, filter_p, neigh_entry_list_p, neigh_entry_cnt_p)\n if rc == SX_STATUS_ENTRY_NOT_FOUND:\n return None \n sx_check_rc(rc)\n\n neighbor_entry = sx_neigh_get_entry_t_arr_getitem(neigh_entry_list_p, 0)\n \n return neighbor_entry.neigh_data.mac_addr.to_str()\n \n finally:\n delete_sx_neigh_filter_t_p(filter_p)\n delete_sx_neigh_get_entry_t_arr(neigh_entry_list_p) \n delete_uint32_t_p(neigh_entry_cnt_p)","def dest_mac(self):\n return self._dest_mac","def subcmd_getbmc_main(args, parameter_info):\n \n from get_bmc_inventory import get_bmc_inventory\n result = get_bmc_inventory(parameter_info['ip'], parameter_info['user'], parameter_info['passwd'], parameter_info['sysid'])\n \n if result['ret'] is True:\n del result['ret']\n sys.stdout.write(json.dumps(result['entries'], sort_keys=True, indent=2))\n else:\n sys.stderr.write(result['msg'])","def mac(self):\n if not self.is_rule:\n raise NotRuleError(\"No 'ATTR{address}' field.\")\n\n if \"ATTR{address}\" not in self._fields:\n raise NotRule70Error(\"No 'ATTR{address}' field.\")\n\n return self._fields[\"ATTR{address}\"]","def mac_address(self):\n if self._mac_address is None:\n expression = expressions.WPA_MAC\n name = expressions.MAC_ADDRESS_NAME\n command = self.status_command\n self._mac_address = self._match(expression,\n name,\n command)\n return self._mac_address","def mac_add():\n brew_installed = shutil.which('brew')\n if not brew_installed:\n print('Installing brew')\n url = 'https://raw.githubusercontent.com/Homebrew/install/master/install'\n command_line = \"/usr/bin/ruby -e \\\"$(curl -fsSL %s)\\\"\" % url\n subprocess.check_call(command_line, shell=True)\n else:\n print('brew present')\n gmsh_installed = shutil.which('gmsh')\n if not gmsh_installed:\n print('Installing gmsh')\n folder_path = os.path.dirname(os.path.abspath(__file__))\n dmginstall_path = os.path.join(folder_path, 'dmginstall.sh')\n url = 'http://gmsh.info/bin/MacOSX/gmsh-3.0.5-MacOSX.dmg'\n command_line = '%s %s' % (dmginstall_path, url)\n print('command_line=%s' % command_line)\n subprocess.check_call(command_line, shell=True)\n gmsh_path = '/Applications/Gmsh.app/Contents/MacOS/gmsh'\n command_line = \"ln -s %s /usr/local/bin/gmsh\" % gmsh_path\n subprocess.check_call(command_line, shell=True)\n else:\n print('gmsh present')\n ccx_installed = shutil.which('ccx')\n if not ccx_installed:\n mac_add_ccx()\n else:\n print('calculix (ccx) present')","def _generate_machine_id(self):\r\n mach_id = \"machine_\"\r\n try:\r\n gws = netifaces.gateways() # get all gateways\r\n default = gws['default'] # get the default gw\r\n adapter = default[2][1] # get the adapter identifier\r\n real_adapter = netifaces.ifaddresses(adapter) # get the adapter\r\n link_info = real_adapter[netifaces.AF_LINK]\r\n mac = link_info[0]['addr']\r\n mac = re.sub('[:]', '', mac)\r\n except:\r\n mac = \"unsup\"\r\n self.logger.error(\"Getting mac of internet card is not supported, needs netifaces >= 0.10\")\r\n self.machine_id = mach_id + mac","def kern():\n return platform.uname()","def getControllers():\n \n # Check enabled bluetooth service\n changeBluetoothService(enable=True)\n \n #proc = os.popen('bluetoothctl list').read()\n blueListStdout = execCommand('bluetoothctl list')\n \n # Get controller's MAC and name to list\n cntList = list()\n for line in blueListStdout.splitlines():\n lst = line.split()\n del lst[0]\n del lst[-1]\n \n if lst:\n cntList.append(lst)\n \n return cntList","def __init__(self, mac):\n self.go_on = True\n self.mac = mac"],"string":"[\n \"def _get_mac(self):\\n return self.__mac\",\n \"def get_sonic_mac(host, asicnum, port):\\n if host.facts[\\\"num_asic\\\"] == 1:\\n cmd = \\\"sudo ip link show {}\\\".format(port)\\n else:\\n ns = \\\"asic\\\" + str(asicnum)\\n cmd = \\\"sudo ip netns exec {} ip link show {}\\\".format(ns, port)\\n output = host.command(cmd)\\n mac = output['stdout_lines'][1].split()[1]\\n logger.info(\\\"host: %s, asic: %d, port: %s, mac: %s\\\", host.hostname, asicnum, port, mac)\\n return mac\",\n \"def get_my_mac():\\n\\n mac_set = get_my_mac_set(iface_filter=get_default_route()[1])\\n return mac_set.pop()\",\n \"def get_mac_string():\\n mac_int = getnode()\\n mac_str = ':'.join((\\\"%012x\\\" % mac_int)[i:i + 2] for i in range(0, 12, 2))\\n return mac_str\",\n \"def get_mac(self) :\\n\\t\\ttry :\\n\\t\\t\\treturn self.p_fields.f128\\n\\t\\texcept :\\n\\t\\t\\treturn None\",\n \"def get_mac(self) -> str:\\n self.sendline(\\\"iw {} info\\\".format(self.iface_dut))\\n # We are looking for MAC definition of STA\\n # wdev 0x1\\n # addr 96:4e:c9:cc:7a:2c\\n # type managed\\n self.expect(\\\"addr (?P<mac>..:..:..:..:..:..)\\\\r\\\\n\\\\t(type|ssid)\\\")\\n return self.match.group('mac')\",\n \"def _get_rmac(self):\\n return self.__rmac\",\n \"def mac(self):\\n return self.device.settings[\\\"device\\\"][\\\"mac\\\"]\",\n \"def mac(self):\\n return self.device.settings[\\\"device\\\"][\\\"mac\\\"]\",\n \"def sw_mac(self):\\n return self.raw.get('sw_mac')\",\n \"def get_mac(self) -> str:\\n hex_mac = hexlify(self.message)[160:172].decode().upper()\\n return (\\n hex_mac[0:2]\\n + \\\":\\\"\\n + hex_mac[2:4]\\n + \\\":\\\"\\n + hex_mac[4:6]\\n + \\\":\\\"\\n + hex_mac[6:8]\\n + \\\":\\\"\\n + hex_mac[8:10]\\n + \\\":\\\"\\n + hex_mac[10:12]\\n )\",\n \"def getMac(self):\\n # Import netifaces here to prevent error importing this module in setup.py\\n import netifaces\\n interfaces = ['eth0', 'wlan0']\\n try:\\n interfaces.append(netifaces.gateways()['default'][netifaces.AF_INET][1])\\n except:\\n pass\\n for interface in interfaces:\\n try:\\n return netifaces.ifaddresses(interface)[netifaces.AF_LINK][0]['addr']\\n except ValueError:\\n pass\\n except:\\n exception('Error getting MAC address')\\n return None\",\n \"def _getMac( self, bSeed ):\\n\\n\\t\\tbMac = ( bSeed >> 24 ) & 0xFF\\n\\t\\treturn '%02X' % bMac\",\n \"def getMac(teff, logg):\\n # For Dwarfs: Doyle et al. 2014\\n # 5200 < teff < 6400\\n # 4.0 < logg < 4.6\\n if logg > 3.5:\\n mac = 3.21 + (2.33 * (teff - 5777.) * (10**(-3))) + (2.00 * ((teff - 5777.)**2) * (10**(-6))) - (2.00 * (logg - 4.44))\\n # For subgiants and giants: Hekker & Melendez 2007\\n elif 2.9 <= logg <= 3.5: # subgiants\\n mac = -8.426 + (0.00241*teff)\\n elif 1.5 <= logg < 2.9: # giants\\n mac = -3.953 + (0.00195*teff)\\n elif logg < 1.5: # bright giants\\n mac = -0.214 + (0.00158*teff)\\n\\n # For negative values, keep a minimum of 0.3 km/s\\n if mac < 0.30:\\n mac = 0.30\\n return round(mac, 2)\",\n \"def sys_mac(self):\\n try:\\n with open(os.path.join(self.sys_class_path, \\\"address\\\")) as f:\\n sys_mac = f.read().strip('\\\\0').strip()\\n except Exception:\\n sys.stderr.write(\\\"Failed to read address for entry: %s\\\\n\\\" %\\n (self._orig_line))\\n raise\\n\\n return sys_mac\",\n \"def get_bmc(node):\\n if get_cluster_vendor() == \\\"sgi\\\":\\n return sgi_cluster.get_bmc()\\n elif get_cluster_vendor() == \\\"ibm\\\": \\n return ibm_cluster.get_bmc()\\n return None\",\n \"def mac_string(self) :\\n\\t\\tif not getattr(self,'pkg02') :\\n\\t\\t\\treturn None \\n\\t\\tif self.pkg02[:2] not in ['01','02','04','05' ] :\\n\\t\\t\\treturn None\\n\\t\\treturn self.pkg02 + ' ' + self.p_fields.mac_string()\",\n \"def get_bound_card_mac(self):\\n\\t\\treturn call_sdk_function('PrlVirtNet_GetBoundCardMac', self.handle)\",\n \"def get_current_mac(interface):\\r\\n ifconfig_result = subprocess.check_output([\\\"sudo\\\", \\\"ifconfig\\\", interface])\\r\\n mac_result = re.search(r\\\"\\\\w\\\\w:\\\\w\\\\w:\\\\w\\\\w:\\\\w\\\\w:\\\\w\\\\w:\\\\w\\\\w\\\", ifconfig_result.decode('utf-8'))\\r\\n if mac_result:\\r\\n return mac_result.group(0)\\r\\n else:\\r\\n print(\\\"Could not read MAC-address\\\")\",\n \"def bmc_mac_address(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"bmc_mac_address\\\")\",\n \"def mac(self) -> str:\\n return self.camera_info[\\\"wifi_mac\\\"]\",\n \"def get_vendor(mac):\\r\\n return p.get_manuf(mac) or 'None'\",\n \"def get_sys_mac_address():\\n mac = \\\"00:00:00:00:00:00\\\"\\n try:\\n if os.path.exists(SysTools.SYS_MAC_FILE):\\n with open(SysTools.SYS_MAC_FILE, 'r') as fd:\\n mac = fd.readline().strip()\\n except:\\n SysTools.logger.error(\\\"Failed to get sys-mac-address from %s\\\",\\n SysTools.SYS_MAC_FILE)\\n return mac\",\n \"def _get_server_hardware_mac(server_hardware):\\n sh_physical_port = None\\n\\n if server_hardware.get('portMap'):\\n for device in server_hardware.get(\\n 'portMap', {}).get('deviceSlots', ()):\\n for physical_port in device.get('physicalPorts', ()):\\n if physical_port.get('type') == 'Ethernet':\\n sh_physical_port = physical_port\\n break\\n if sh_physical_port:\\n for virtual_port in sh_physical_port.get('virtualPorts', ()):\\n # NOTE(nicodemos): Ironic oneview drivers needs to use a\\n # port that type is Ethernet and function identifier 'a' for\\n # this FlexNIC to be able to make a deploy using PXE.\\n if virtual_port.get('portFunction') == 'a':\\n return virtual_port.get('mac', ()).lower()\\n raise exception.OneViewError(\\n _(\\\"There is no Ethernet port on the Server Hardware: %s\\\") %\\n server_hardware.get('uri'))\\n else:\\n raise exception.OneViewError(\\n _(\\\"The Server Hardware: %s doesn't have a list of adapters/slots, \\\"\\n \\\"their ports and attributes. This information is available only \\\"\\n \\\"for blade servers. Is this a rack server?\\\") %\\n server_hardware.get('uri'))\",\n \"def get_base_mac(self):\\n raise NotImplementedError\",\n \"def mac(self):\\n mac = hexlify(WLAN().config('mac'), ':').decode()\\n return mac.upper() # MAC-address in upper case\",\n \"def get_mac_address(self):\\n\\t\\treturn call_sdk_function('PrlSrvCfgNet_GetMacAddress', self.handle)\",\n \"def emulab_mac(mac):\\n\\n return \\\"\\\".join(mac.lower().split(':'))\",\n \"def get_mac(self, node_id):\\n nc = '%02x' % self.node_class\\n nr_iface = '%02x' % self.nr_host_interface\\n node_id = '%08x' % node_id\\n\\n return '%s:%s:%s:%s:%s:%s' % (nc, nr_iface, node_id[0:2], node_id[2:4], node_id[4:6], node_id[6:8])\",\n \"def get_rand_mac(self):\\n random_mac = []\\n\\n # add manufacturer\\n random_mac.append(self.get_rand_manufact())\\n\\n # generate the last 24 bits of the random hex\\n for i in range(0, 3):\\n rand_digit1 = self.get_rand_hex_digit()\\n rand_digit2 = self.get_rand_hex_digit()\\n random_mac.append(rand_digit1 + rand_digit2)\\n\\n return ':'.join(random_mac)\",\n \"def _get_server_hardware_mac_from_ilo(server_hardware):\\n try:\\n client = get_ilorest_client(server_hardware)\\n ilo_path = \\\"/rest/v1/systems/1\\\"\\n hardware = jsonutils.loads(client.get(ilo_path).text)\\n hardware_mac = hardware['HostCorrelation']['HostMACAddress'][0]\\n except redfish.JsonDecodingError as exc:\\n LOG.error(\\\"Failed in JSON object getting path: %s\\\", ilo_path)\\n raise exception.OneViewError(error=exc)\\n except (ValueError, TypeError, IndexError) as exc:\\n LOG.error(\\n \\\"Failed to get mac from server hardware %(server_hardware)s \\\"\\n \\\"via iLO. Error: %(message)s\\\", {\\n \\\"server_hardware\\\": server_hardware.get(\\\"uri\\\"),\\n \\\"message\\\": exc\\n }\\n )\\n raise exception.OneViewError(error=exc)\\n\\n return hardware_mac\",\n \"def getMacName(self):\\r\\n return CipherSuite.canonicalMacName(self.cipherSuite)\",\n \"def get_mac_address(self, result, host):\\n if \\\"mac\\\" in result['scan'][host][\\\"addresses\\\"]:\\n return result['scan'][host][\\\"addresses\\\"][\\\"mac\\\"]\\n else:\\n return \\\"\\\"\",\n \"def get_mac(self):\\n bluetooth_mac_address = ''\\n is_general_visible = False\\n if self.phone_info.phone_type == PhoneType.IOS:\\n try:\\n try:\\n # verify that General Button is visible\\n self.find_element(self.driver.appium_driver,\\n 'self.ios_locators.GENERAL_NAVIGATION_BUTTON_ByXPATH',\\n 0).is_displayed()\\n is_general_visible = True\\n except:\\n logger.debug(\\\"General Button is currently not visible \\\")\\n\\n if is_general_visible:\\n pass\\n else:\\n\\n self.find_element(self.driver.appium_driver,\\n self.general_button_settings).click()\\n self.wait_till_element_to_be_visible(\\n self.driver.appium_driver, self.status_button, 10)\\n self.find_element(self.driver.appium_driver,\\n self.status_button, 2).click()\\n bluetooth_mac_address = self.find_element(\\n self.driver.appium_driver, self.bluetooth_mac_addess,\\n 2).text\\n logger.debug(\\\"Bluetooth Mac Address:\\\" + bluetooth_mac_address)\\n except Exception as e:\\n logger.error(\\\"Bluetooth Mac Address is not Visible\\\")\\n logger.debug(repr(e))\\n elif self.phone_info.phone_type == PhoneType.ANDROID:\\n try:\\n\\n self.find_element(self.driver.appium_driver,\\n self.general_button_settings).click()\\n self.wait_till_element_to_be_visible(self.driver.appium_driver,\\n self.status_button, 10)\\n self.find_element(self.driver.appium_driver,\\n self.status_button).click()\\n\\n self.testcase_action = 'STEP -: Get Bluetooth Mac Address'\\n bluetooth_mac_address = self.find_element(\\n self.driver.appium_driver, self.bluetooth_mac_addess,\\n 2).text\\n logger.debug(\\\"Bluetooth Mac Address:\\\" + bluetooth_mac_address)\\n except Exception as e:\\n logger.warning(\\\"Mac address is not available\\\")\\n logger.debug(repr(e))\\n return bluetooth_mac_address\",\n \"def set_bios_bootmode_uefi(ip, login_account, login_password, system_id):\\n result = {}\\n login_host = \\\"https://\\\" + ip\\n try:\\n # Connect using the BMC address, account name, and password\\n # Create a REDFISH object\\n REDFISH_OBJ = redfish.redfish_client(base_url=login_host, username=login_account, timeout=utils.g_timeout,\\n password=login_password, default_prefix='/redfish/v1', cafile=utils.g_CAFILE)\\n # Login into the server and create a session\\n REDFISH_OBJ.login(auth=utils.g_AUTH)\\n except:\\n traceback.print_exc()\\n result = {'ret': False, 'msg': \\\"Please check the username, password, IP is correct\\\"}\\n return result\\n\\n # GET the ComputerSystem resource\\n system = utils.get_system_url(\\\"/redfish/v1\\\", system_id, REDFISH_OBJ)\\n if not system:\\n result = {'ret': False, 'msg': \\\"This system id is not exist or system member is None\\\"}\\n REDFISH_OBJ.logout()\\n return result\\n for i in range(len(system)):\\n system_url = system[i]\\n response_system_url = REDFISH_OBJ.get(system_url, None)\\n if response_system_url.status != 200:\\n error_message = utils.get_extended_error(response_system_url)\\n result = {'ret': False, 'msg': \\\"Url '%s' response Error code %s \\\\nerror_message: %s\\\" % (system_url, response_system_url.status, error_message)}\\n REDFISH_OBJ.logout()\\n return result\\n else:\\n # Get the bios resource\\n bios_url = response_system_url.dict['Bios']['@odata.id']\\n response_bios_url = REDFISH_OBJ.get(bios_url, None)\\n if response_bios_url.status != 200:\\n error_message = utils.get_extended_error(response_bios_url)\\n result = {'ret': False, 'msg': \\\"Url '%s' response Error code %s \\\\nerror_message: %s\\\" % (bios_url, response_bios_url.status, error_message)}\\n REDFISH_OBJ.logout()\\n return result\\n else: # Get bios success\\n # Seek boot mode from bios attributes\\n attribute_bootmode = None\\n attributes = response_bios_url.dict['Attributes']\\n for attribute in attributes:\\n if attribute == \\\"BootMode\\\" or attribute == \\\"SystemBootMode\\\":\\n attribute_bootmode = attribute\\n break\\n if attribute_bootmode == None:\\n for attribute in attributes:\\n if \\\"SystemBootMode\\\" in attribute:\\n attribute_bootmode = attribute\\n break\\n if attribute_bootmode == None:\\n for attribute in attributes:\\n if \\\"Boot\\\" in attribute and \\\"Mode\\\" in attribute:\\n attribute_bootmode = attribute\\n break\\n if attribute_bootmode == None:\\n result = {'ret': False, 'msg': \\\"Can not found BootMode attribute in response of url %s\\\" %(bios_url)}\\n REDFISH_OBJ.logout()\\n return result\\n\\n # Get boot mode setting guide from bios registry\\n WarningText = None\\n ValueName = None\\n bios_registry_url = \\\"/redfish/v1/Registries/\\\" + response_bios_url.dict['AttributeRegistry']\\n response_bios_registry_url = REDFISH_OBJ.get(bios_registry_url, None)\\n if response_bios_registry_url.status == 200:\\n locations = response_bios_registry_url.dict['Location']\\n bios_regjson_url = None\\n for location in locations:\\n if 'en' in location['Language']:\\n bios_regjson_url = location['Uri']\\n break\\n if bios_regjson_url:\\n response_bios_regjson_url = REDFISH_OBJ.get(bios_regjson_url, None)\\n if response_bios_regjson_url.status == 200:\\n regattributes = response_bios_regjson_url.dict['RegistryEntries']['Attributes']\\n for regattribute in regattributes:\\n if regattribute['AttributeName'] == attribute_bootmode:\\n if 'WarningText' in regattribute:\\n WarningText = regattribute['WarningText']\\n for value in regattribute['Value']:\\n if 'legacy' in value['ValueName'].lower():\\n continue\\n if 'uefi' in value['ValueName'].lower():\\n ValueName = value['ValueName']\\n break\\n ValueName = value['ValueName']\\n break\\n \\n # Perform patch to set\\n if ValueName == None:\\n ValueName = \\\"UEFIMode\\\"\\n pending_url = response_bios_url.dict['@Redfish.Settings']['SettingsObject']['@odata.id']\\n parameter = {attribute_bootmode: ValueName}\\n attribute = {\\\"Attributes\\\": parameter}\\n headers = {\\\"If-Match\\\": '*'}\\n response_pending_url = REDFISH_OBJ.patch(pending_url, body=attribute, headers=headers)\\n if response_pending_url.status in [200,204]:\\n if WarningText:\\n result = {'ret': True, 'msg': 'set bios bootmode uefi successful. WarningText: %s'% (WarningText) }\\n else:\\n result = {'ret': True, 'msg': 'set bios bootmode uefi successful'}\\n elif response_pending_url.status == 405:\\n result = {'ret': False, 'msg': \\\"Resource not supported\\\"}\\n else:\\n error_message = utils.get_extended_error(response_pending_url)\\n result = {'ret': False, 'msg': \\\"Url '%s' response Error code %s \\\\nerror_message: %s\\\" % (\\n pending_url, response_pending_url.status, error_message)}\\n\\n # Logout of the current session\\n try:\\n REDFISH_OBJ.logout()\\n except:\\n pass\\n return result\",\n \"def get_mac():\\n\\n interface = [x for x in netifaces.interfaces() if 'wlan' in x or 'wlp' in x][0]\\n return netifaces.ifaddresses(interface)[netifaces.AF_LINK][0]['addr']\",\n \"def random_mac():\\n return '\\\"02:%02x:%02x:%02x:%02x:%02x\\\"' % (random.randint(0,255),\\n random.randint(0,255),\\n random.randint(0,255),\\n random.randint(0,255),\\n random.randint(0,255))\",\n \"def GetMacVendor(macAddress):\\n\\turlMac = \\\"https://macvendors.co/api/%s/pipe\\\" % macAddress\\n\\tif macAddress in [\\\"\\\",\\\"FF-FF-FF-FF-FF-FF\\\"]:\\n\\t\\treturn None\\n\\n\\ttry:\\n\\t\\t#sys.stderr.write(\\\"urlMac=%s\\\\n\\\"%urlMac)\\n\\n\\t\\timport urllib2\\n\\t\\treq = urllib2.Request(urlMac)\\n\\t\\treq.add_header('User-Agent', \\\"API Browser\\\")\\n\\t\\tresp = urllib2.urlopen(req)\\n\\t\\tcontent = resp.readlines()[0]\\n\\n\\t\\t#sys.stderr.write(\\\"content=%s\\\\n\\\"%content)\\n\\t\\t#sys.stderr.write(\\\"content=%s\\\\n\\\"%str(type(content)))\\n\\t\\tsplitMac = content.split(\\\"|\\\")\\n\\t\\t#sys.stderr.write(\\\"splitMac[0]=%s\\\\n\\\"%splitMac[0])\\n\\t\\treturn splitMac[0]\\n\\texcept:\\n\\t\\texc = sys.exc_info()[1]\\n\\t\\t#sys.stderr.write(\\\"Caught %s\\\\n\\\"%str(exc))\\n\\t\\t# Any error returns a none strng: Thisinformation is not that important.\\n\\t\\treturn \\\"Cannot determine vendor\\\"\",\n \"def __get_mac_address(self):\\n str_hex_mac = uuid.UUID(int=uuid.getnode()).hex[-12:]\\n return str_hex_mac\",\n \"def macFor(cls, board):\\n return cls.MAC_PREFIX + '{:02X}'.format(int(board))\",\n \"def get_mac_address():\\n eth0_interface = 'eth0'\\n addresses = netifaces.ifaddresses(eth0_interface)[netifaces.AF_LINK][0]\\n mac_address = addresses['addr']\\n return mac_address\",\n \"def get_mac_address(self):\\n\\t\\treturn call_sdk_function('PrlVmDevNet_GetMacAddress', self.handle)\",\n \"def fusion_api_get_appliance_interface_mac(self, device, api=None, headers=None):\\n return self.interfaces.get_device_mac(device, api, headers)\",\n \"def generate_mac():\\n rand_str = generate_name(choices=\\\"0123456789abcdef\\\", length=12)\\n return \\\":\\\".join(re.findall(\\\"..\\\", rand_str))\",\n \"def generate_mac():\\n rand_str = generate_name(choices=\\\"0123456789abcdef\\\", length=12)\\n return \\\":\\\".join(re.findall(\\\"..\\\", rand_str))\",\n \"def mcpc():\\n global simulator\\n if simulator is None:\\n print \\\"Program is not started.\\\"\\n return None\\n return simulator.get_pc()\",\n \"def good_mac(mac):\\r\\n return mac.upper().replace('-', ':')\",\n \"def get_adapter_mac(cls, client_object):\\n parsed_data = DefaultCRUDImpl.get_adapter_info(\\n client_object)\\n for record in parsed_data['table']:\\n if record['dev'] == client_object.name:\\n return record['mac']\\n pylogger.warning('Did not find a MAC address for adapter %r on %r' %\\n (client_object.name, client_object.ip))\",\n \"def get_random_mac():\\n\\t\\n\\t# use the Dlink range\\n\\tmac = \\\"00:05:5D\\\"\\n\\t\\n\\tfor i in range(0,3):\\n\\t\\tmac += \\\":%s\\\" % hex(random.randrange(0,256))[2:]\\n\\t\\t\\n\\t\\t\\n\\treturn mac\",\n \"def get_mac(ip):\\n arp_request = scapy.ARP(pdst=ip)\\n broadcast = scapy.Ether(dst=\\\"ff:ff:ff:ff:ff:ff\\\")\\n arp_request_broadcast = broadcast / arp_request\\n answered_list = scapy.srp(\\n arp_request_broadcast,\\n timeout=5,\\n verbose=False)[0]\\n return answered_list[0][1].hwsrc\",\n \"def get_mac(ip):\\n ans, _ = srp(Ether(dst='ff:ff:ff:ff:ff:ff')/ARP(pdst=ip), timeout=3, verbose=0)\\n if ans:\\n return ans[0][1].src\\n else:\\n return None\",\n \"def _get_mac_address():\\n if not sys.platform.startswith('linux'):\\n raise RuntimeError(\\n 'Cannot get the MAC address on non-Linux platforms'\\n )\\n ifname = get_default_iface_name_linux()\\n s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\\n info = fcntl.ioctl(s.fileno(), 0x8927,\\n struct.pack('256s', bytes(ifname, 'utf-8')[:15]))\\n return ''.join('%02x' % b for b in info[18:24])\",\n \"def get_ffdc_bmc_cmd(self, i_type):\\n return FFDC_BMC_CMD[i_type].items()\",\n \"def intGet(): \\n macchanger, ip, iwconfig = pathGet()\\n interfaces = []\\n a = str(subprocess.check_output(\\\"{} link show\\\".format(ip), shell=True))\\n ints = a.split(': ')\\n for i in range(len(ints)):\\n if len(ints[i].split()) == 1:\\n if ints[i] not in [\\\"1\\\", \\\"lo\\\", \\\"b'1\\\"]:\\n interface = {'name':str(ints[i])}\\n interfaces.append(interface)\\n # Get interface properties\\n for interface in interfaces:\\n name = interface['name']\\n macs = subprocess.check_output(\\\"{} -s {}\\\".format(macchanger, name), shell=True).decode(\\\"utf-8\\\")\\n interface['cMac'] = macs.split()[2]\\n interface['cVend'] = macs.split(\\\"(\\\")[1].split(\\\")\\\")[0]\\n interface['pMac'] = macs.split(\\\"\\\\n\\\")[1].split()[2]\\n interface['pVend'] = macs.split(\\\"\\\\n\\\")[1].split(\\\"(\\\")[1].split(\\\")\\\")[0]\\n try:\\n mon = subprocess.check_output(\\\"{} {} 2> /dev/null\\\".format(iwconfig, name), shell=True).split()\\n mon1 = mon[3].decode(\\\"utf-8\\\").split(':')[1]\\n if mon1 == 'off/any':\\n mon1 = mon[4].decode(\\\"utf-8\\\").split(':')[1]\\n interface['mon'] = mon1\\n except:\\n interface['mon'] = 'Wired'\\n return(interfaces)\",\n \"def mac_from_vm(vm: libvirt.virDomain = None) -> str:\\n doc = minidom.parseString(vm.XMLDesc())\\n interfaces = doc.getElementsByTagName('mac')\\n return interfaces[0].getAttribute('address')\",\n \"def get_random_mac():\\r\\n mac = [0x00, 0x16, 0x3e,\\r\\n random.randint(0x00, 0x7f),\\r\\n random.randint(0x00, 0xff),\\r\\n random.randint(0x00, 0xff)]\\r\\n return ':'.join(map(lambda x: \\\"%02x\\\" % x, mac))\",\n \"def regmac(mac):\\n return len(mac.split(\\\":\\\")[1]) == 12 and mac.split(\\\":\\\")[1] or None\",\n \"def get_mac_address(selected_network_device, base_api_url, user, passwd): \\n url = '{}/{}'.format(base_api_url, selected_network_device)\\n device_mac_address = ''\\n try:\\n response = requests.get(url, verify=False, auth=(user, passwd),\\n timeout=5)\\n except requests.exceptions.ConnectionTimeout:\\n logging.error('failed to establish connection to get mac address')\\n\\n try:\\n network_device_info = response.json()\\n except ValueError:\\n logging.error('check URL, iDRAC user and password may be invalid')\\n logging.info('{}'.format(url))\\n\\n try:\\n device_mac_address = network_device_info[u'MACAddress']\\n except KeyError:\\n logging.error('No MAC Address found for network devices')\\n logging.info('{}'.format(selected_network_device))\\n\\n return device_mac_address\",\n \"def get_eos_mac(nbr, nbr_intf):\\n if \\\"port-channel\\\" in nbr_intf.lower():\\n # convert Port-Channel1 to po1\\n shell_intf = \\\"po\\\" + nbr_intf[-1]\\n else:\\n # convert Ethernet1 to eth1\\n shell_intf = \\\"eth\\\" + nbr_intf[-1]\\n\\n output = nbr['host'].command(\\\"ip addr show dev %s\\\" % shell_intf)\\n # 8: Ethernet0: <BROADCAST,MULTICAST,UP,LOWER_UP> mtu 9100 ...\\n # link/ether a6:69:05:fd:da:5f brd ff:ff:ff:ff:ff:ff\\n\\n mac = output['stdout_lines'][1].split()[1]\\n return {'mac': mac, \\\"shell_intf\\\": shell_intf}\",\n \"def find_my_IP_and_MAC():\\n mac = ':'.join(re.findall('..', '%012x' % getnode()))\\n # I write IP and not domain cause i want to save time.\\n p = sr1(IP(dst=\\\"google.com\\\", ttl=0) / ICMP() / \\\"XXXXXXXXXXX\\\",verbose=0,timeout=5) #verbose = withuot output\\n return mac,p.dst\",\n \"def dump_macs(self, context):\\n nmeta = self._nmeta\\n dpid = self.dpid\\n macs = []\\n db_cursor = nmeta.dbidmac.find({'dpid': dpid, 'context': context})\\n if db_cursor:\\n for document in db_cursor:\\n mac = document['mac']\\n if mac:\\n macs.append(mac)\\n return macs\",\n \"def get_mac_from_raw_query(request_raw_query: str):\\n mac = \\\"\\\"\\n raw_query_list = request_raw_query.split(\\\"&\\\")\\n for param in raw_query_list:\\n if \\\"mac=\\\" in param:\\n mac = param.replace(\\\"mac=\\\", \\\"\\\")\\n if not mac:\\n raise AttributeError(\\\"%s is not parsable\\\" % request_raw_query)\\n return mac.replace(\\\"-\\\", \\\":\\\")\",\n \"def get_mac(ip):\\n p = Ether(dst=\\\"ff:ff:ff:ff:ff:ff\\\") / ARP(op=1, pdst=ip)\\n result = srp(p, verbose=False, iface=IFACE)[0]\\n return result[0][1].hwsrc if result else None\",\n \"def get_network_device_mac(devices, user, passwd, base_api_url):\\n network_device_mac_address = ''\\n\\n if devices:\\n selected_network_device = generate_network_devices_menu(devices, purpose='DHCP')\\n network_device_mac_address = get_mac_address(selected_network_device, base_api_url, user, passwd)\\n\\n if network_device_mac_address:\\n logging.info('device {} mac address is {}'.format(selected_network_device, network_device_mac_address))\\n \\n return network_device_mac_address\",\n \"def connectgrimac(topology,\\n hostmac,\\n siteport,\\n sitevlan,\\n sitepop,\\n remotesiteport,\\n remotesitevlan,\\n vc,\\n meter=3,\\n host_rewritemac=None):\\n # Get both endpoints of the GRI\\n (e1,e2) = vcendpoints(vc)\\n core1 = topology.loadResource(e1[1])\\n if 'Role' not in core1.properties.keys() or core1.properties['Role'] != 'CoreRouter' or 'Pop' not in core1.properties.keys():\\n print core1.resourceName, \\\"is not a core router\\\"\\n return None\\n core2 = topology.loadResource(e2[1])\\n if 'Role' not in core2.properties.keys() or core2.properties['Role'] != 'CoreRouter' or 'Pop' not in core2.properties.keys():\\n print core1.resourceName, \\\"is not a core router\\\"\\n return None\\n pop1name = core1.properties['Pop']\\n pop2name = core2.properties['Pop']\\n\\n remotepopname = None\\n if sitepop.resourceName == pop1name:\\n remotepopname = pop2name\\n elif sitepop.resourceName == pop2name:\\n remotepopname = pop1name\\n if remotepopname == None:\\n print \\\"vc\\\", vc, \\\"does not provide connectivity for\\\", hostmac, \\\"from\\\", remotepop.resourceName, \\\"to\\\", sitepop.resourceName\\n return None\\n\\n remotepop = topology.loadResource(remotepopname)\\n if remotepop == None:\\n print \\\"Unable to retrieve remote POP\\\", remotepopname\\n return None\\n\\n res = connectmac(localpop= sitepop,\\n remotepop= remotepop,\\n localsiteport= siteport,\\n localsitevlan= sitevlan,\\n remotesiteport= remotesiteport,\\n remotesitevlan= remotesitevlan,\\n hostmac= hostmac,\\n vc= vc,\\n meter = meter,\\n host_rewritemac= host_rewritemac)\\n return res\",\n \"def pc():\\n global simulator\\n if simulator is None:\\n print \\\"Program is not started.\\\"\\n return None\\n return mcpc()[0]\",\n \"def mrdc(self) -> str:\\n return self._device_info[\\\"MRDC\\\"]\",\n \"def macOS(self) -> str:\\n\\n result = subprocess.run([\\\"sw_vers\\\", \\\"-productVersion\\\"], stdout=subprocess.PIPE)\\n\\n return result.stdout.decode(\\\"utf-8\\\").strip()\",\n \"def from_mac(self):\\n reason = \\\"[!] Mac Absolute timestamps are 9 digits, commonly followed by a decimal and up to 6 digits for milliseconds\\\"\\n ts_type = self.ts_types['mac']\\n try:\\n if \\\".\\\" not in self.mac or not ((len(mac.split(\\\".\\\")[0]) == 9) and (len(self.mac.split(\\\".\\\")[1]) in range(0, 7))) or not ''.join(self.mac.split(\\\".\\\")).isdigit():\\n self.in_mac = indiv_output = combined_output = False\\n pass\\n else:\\n dt_obj = self.epoch_2001 + timedelta(seconds=float(self.mac))\\n self.in_mac = dt_obj.strftime('%Y-%m-%d %H:%M:%S.%f')\\n indiv_output = str(\\\"{} {} UTC\\\".format(ts_type, self.in_mac))\\n combined_output = str(\\\"{}{}\\\\t\\\\t{} UTC{}\\\".format(self.left_color, ts_type, self.in_mac, self.right_color))\\n except Exception:\\n exc_type, exc_obj, exc_tb = sys.exc_info()\\n print(str(exc_type) + \\\" - \\\" + str(exc_obj) + \\\" - line \\\" + str(exc_tb.tb_lineno))\\n self.in_mac = indiv_output = combined_output = False\\n return self.in_mac, indiv_output, combined_output, reason\",\n \"def get_cmx_ap_mac(campus, building, floor, ap_name):\\n\\n url = CMX_URL + '/api/config/v1/maps/info/' + campus + '/' + building + '/' + floor\\n header = {'content-type': 'application/json', 'accept': 'application/json'}\\n response = requests.get(url, headers=header, auth=CMX_AUTH, verify=False)\\n aps_list = response.json()['accessPoints']\\n for ap in aps_list:\\n if ap['name'] == ap_name:\\n ap_mac = ap['radioMacAddress']\\n return ap_mac\",\n \"def mac_address(self) -> str:\\n return self._device.mac\",\n \"def load_macaroon(macaroon: str) -> str:\\n\\n # if the macaroon is a file path, load it\\n if macaroon.split(\\\".\\\")[-1] == \\\"macaroon\\\":\\n with open(macaroon, \\\"rb\\\") as f:\\n macaroon_bytes = f.read()\\n return macaroon_bytes.hex()\\n else:\\n # convert the bas64 macaroon to hex\\n try:\\n macaroon = base64.b64decode(macaroon).hex()\\n except:\\n pass\\n return macaroon\",\n \"def person_mac(self):\\n return self._person_mac\",\n \"def get_canmic_block(self, appip, port, appuname, apppassw, cmd):\\n client = paramiko.SSHClient()\\n client.load_system_host_keys()\\n client.set_missing_host_key_policy(paramiko.AutoAddPolicy())\\n port = 22 # SSH Port to establish connection\\n client.connect(appip, port, appuname, apppassw)\\n channel = client.invoke_shell()\\n stdin = channel.makefile('wb')\\n stdout = channel.makefile('rb')\\n logging.info(\\\"cmd %s\\\" % cmd)\\n input, output, error = client.exec_command(cmd)\\n data = output.read()\\n d = data.split(\\\"\\\\n\\\")\\n mdata = []\\n\\n for k in d:\\n head, sep, tail = k.partition('|')\\n head = head[10:].split(\\\" \\\")\\n mdata = mdata + head\\n return filter(None, mdata)\",\n \"def get_node_platform_and_mac(port):\\n if not port:\\n sys.exit(\\\"<port> argument not provided. Cannot read MAC address from node.\\\")\\n sys.stdout = mystdout = StringIO()\\n command = ['--port', port, 'chip_id']\\n log.info(\\\"Running esptool command to get node\\\\\\n platform and mac from device\\\")\\n esptool.main(command)\\n sys.stdout = sys.__stdout__\\n # Finding chip type from output.\\n node_platform = next(filter(lambda line: 'Detecting chip type' in line,\\n mystdout.getvalue().splitlines()))\\n # Finds the first occurence of the line\\n # with the MAC Address from the output.\\n mac = next(filter(lambda line: 'MAC: ' in line,\\n mystdout.getvalue().splitlines()))\\n mac_addr = mac.split('MAC: ')[1].replace(':', '').upper()\\n platform = node_platform.split()[-1].lower().replace('-', '')\\n print(\\\"Node platform detected is: \\\", platform)\\n print(\\\"MAC address is: \\\", mac_addr)\\n log.debug(\\\"MAC address received: \\\" + mac_addr)\\n log.debug(\\\"Node platform is: \\\" + platform)\\n return platform, mac_addr\",\n \"def get_device_info(self, mac_address):\\n try:\\n out = self.get_output(\\\"info \\\" + mac_address)\\n except BluetoothctlError, e:\\n print(e)\\n return None\\n else:\\n return out\",\n \"def get_mac_address_table(self):\\n\\n mac_address_table = []\\n command = '/interface bridge host print terse'\\n\\n output = self._send_command(command)\\n\\n for host in parse_terse_output(output):\\n mac_address_table.append({\\n 'mac': cast_mac(host.get('mac-address')),\\n 'interface': host.get('interface'),\\n 'vlan': -1,\\n 'static': True if 'D' not in host.get('_flags') else False,\\n 'active': True if 'X' not in host.get('_flags') else False,\\n 'moves': -1,\\n 'last_move': -1.0\\n })\\n\\n return mac_address_table\",\n \"def gather_system_versions(self):\\n # Get Mac model ID\\n self.hw_version = str(\\n IORegistryEntryCreateCFProperty(\\n IOServiceGetMatchingService(\\n 0,\\n IOServiceMatching(\\\"IOPlatformExpertDevice\\\")),\\n \\\"model\\\",\\n None,\\n 0)).replace(\\n \\\"\\\\x00\\\",\\n \\\"\\\")\\n\\n if \\\"imacpro\\\" in self.hw_version.lower():\\n # iMac Pro stores it's EFI data different due it's new architecture\\n # so grab the EFI & SMC ROM versions appropriately\\n raw_efi_list = []\\n raw_rom_info = str(\\n IORegistryEntryCreateCFProperty(\\n IORegistryEntryFromPath(\\n 0,\\n \\\"IODeviceTree:/rom\\\"),\\n \\\"apple-rom-info\\\",\\n None,\\n 0))\\n for data in raw_rom_info.split(\\\"\\\\n\\\"):\\n if data.strip().startswith(\\\"BIOS ID\\\"):\\n raw_efi_list = data.split(\\\":\\\")[1].strip().split(\\\".\\\")\\n break\\n else:\\n self.message(\\n \\\"[-] Could not find raw EFI data to determine EFI versions. Exiting....\\\")\\n return False\\n\\n self.efi_version = \\\"%s.%s.%s\\\" % (\\n raw_efi_list[0], raw_efi_list[2], raw_efi_list[3])\\n # Can't currently find the SMC version like this on imac pros ....\\n # self.smc_version = str(IORegistryEntryCreateCFProperty(IOServiceGetMatchingService(0, IOServiceMatching(\\\"AppleSMC\\\")), \\\"smc-version\\\", None, 0))\\n self.smc_version = \\\"\\\"\\n else:\\n # EFI & SMC ROM versions\\n self.smc_version = str(\\n IORegistryEntryCreateCFProperty(\\n IOServiceGetMatchingService(\\n 0,\\n IOServiceMatching(\\\"AppleSMC\\\")),\\n \\\"smc-version\\\",\\n None,\\n 0))\\n raw_efi = str(\\n IORegistryEntryCreateCFProperty(\\n IORegistryEntryFromPath(\\n 0,\\n \\\"IODeviceTree:/rom\\\"),\\n \\\"version\\\",\\n None,\\n 0)).replace(\\n \\\"\\\\x00\\\",\\n \\\"\\\").split(\\\".\\\")\\n self.efi_version = \\\"%s.%s.%s\\\" % (\\n raw_efi[0], raw_efi[2], raw_efi[3])\\n\\n # Set the salt to be the MAC address of the system, using the MAC as a salt in this manner\\n # helps ensure that the hashed sysuuid is pseudonymous. We don't want to know the sysuuid's\\n # value, but we do want it to be unique however. The Salt value is\\n # never submitted to the API\\n salt = hex(getnode())\\n sys_uuid = str(\\n IORegistryEntryCreateCFProperty(\\n IOServiceGetMatchingService(\\n 0,\\n IOServiceMatching(\\\"IOPlatformExpertDevice\\\")),\\n \\\"IOPlatformUUID\\\",\\n None,\\n 0)).replace(\\n \\\"\\\\x00\\\",\\n \\\"\\\")\\n self.h_sys_uuid = hashlib.sha256(salt + sys_uuid).hexdigest()\\n\\n # Get the Board-ID, this is how EFI files are matched to running\\n # hardware - Nastee\\n self.board_id = str(\\n IORegistryEntryCreateCFProperty(\\n IOServiceGetMatchingService(\\n 0,\\n IOServiceMatching(\\\"IOPlatformExpertDevice\\\")),\\n \\\"board-id\\\",\\n None,\\n 0)).replace(\\n \\\"\\\\x00\\\",\\n \\\"\\\")\\n\\n # Get OS version\\n self.os_version = commands.getoutput(\\\"sw_vers -productVersion\\\")\\n\\n # Get build number\\n self.build_num = commands.getoutput(\\\"sw_vers -buildVersion\\\")\\n\\n # Carve out the major version as we use this a bunch\\n # self.os_maj_ver = \\\".\\\".join(self.os_version.split(\\\".\\\")[:2])\\n\\n # Add gathered info to the dictionary to query the API with\\n self.endpoints_to_check[\\\"127.0.0.1\\\"] = {\\n \\\"hashed_uuid\\\": self.h_sys_uuid,\\n \\\"hw_ver\\\": self.hw_version,\\n \\\"rom_ver\\\": self.efi_version,\\n \\\"smc_ver\\\": self.smc_version,\\n \\\"board_id\\\": self.board_id,\\n \\\"os_ver\\\": self.os_version,\\n \\\"build_num\\\": self.build_num}\\n\\n return True\",\n \"def unique_id(self):\\n return self._device.mac\",\n \"def get_eprom_type():\\n return command(\\\"S\\\")\",\n \"def from_mac(self):\\n try:\\n dt_obj = self.epoch_2001 + timedelta(seconds=int(mac))\\n self.in_mac = dt_obj.strftime('%Y-%m-%d %H:%M:%S.%f')\\n except Exception as e:\\n if not args.log:\\n pass\\n else:\\n logging.error(str(type(e)) + \\\",\\\" + str(e))\\n self.in_mac = False\\n return self.in_mac\",\n \"def boot_mac_address(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"boot_mac_address\\\")\",\n \"def _get_http_mac_path(mac, delimiter='-', client_id=None):\\n mac_file_name = mac.replace(':', delimiter).lower()\\n hw_type = '01-'\\n if client_id:\\n hw_type = '20-'\\n mac_file_name = hw_type + mac_file_name\\n return os.path.join(get_http_boot_dir(), PXE_CFG_DIR_NAME, mac_file_name)\",\n \"def get_desktop():\\n l=get_pids(('kwin','ksmserver',))\\n if l: kde=l[0]\\n else: kde=None\\n l=get_pids(('gnome-session',))\\n if l: gnome=l[0]\\n else: gnome=None\\n if kde:\\n if not gnome or kde<gnome: return 1\\n else: return 0\\n if gnome: return 0\\n else: return -1\",\n \"def machine():\\n return uname().machine\",\n \"def machine():\\n return uname().machine\",\n \"def mcc(self):\\n return self._mcc\",\n \"def pathGet():\\n macchanger = subprocess.check_output(\\\"which macchanger\\\", shell=True)[:-1].decode(\\\"utf-8\\\")\\n ip = subprocess.check_output(\\\"which ip\\\", shell=True)[:-1].decode(\\\"utf-8\\\")\\n iwconfig = subprocess.check_output(\\\"which iwconfig\\\", shell=True)[:-1].decode(\\\"utf-8\\\")\\n if macchanger == '':\\n return(1)\\n if ip == '':\\n return(2)\\n if iwconfig == '':\\n return(3)\\n else:\\n return( macchanger, ip, iwconfig)\",\n \"def get_device(mac, devices=None):\\n print('get_device:', get_device)\\n if not mac:\\n return None\\n if not devices:\\n devices = discover_wemo()\\n\\n normal_mac = normalize_mac_address(mac)\\n\\n for dev in devices:\\n if dev.mac and normalize_mac_address(dev.mac) == normal_mac:\\n print('get_device dev:', dev)\\n return dev\\n\\n return None\",\n \"def connectmac(localpop,\\n remotepop,\\n localsiteport,\\n localsitevlan,\\n remotesiteport,\\n remotesitevlan,\\n hostmac,\\n vc,\\n meter=3,\\n host_rewritemac = None):\\n core = Container.fromAnchor(localpop.properties['CoreRouter'])\\n corename = core.resourceName\\n (corename,coredom,coreport,corevlan) = getvcnode(vc,corename)\\n remotecore = Container.fromAnchor(remotepop.properties['CoreRouter'])\\n remotecorename = remotecore.resourceName\\n (remotecorename,remotecoredom,remotecoreport,remotecorevlan) = getvcnode(vc,remotecorename)\\n\\n # Find hwswitch/port - core/port\\n topology = Container.getContainer(localpop.properties['HwSwitch']['containerName'])\\n\\n hwswitch = Container.fromAnchor(localpop.properties['HwSwitch'])\\n hwport_tocore = getgriport(topology, hwswitch, core, coreport)\\n # Find remotehwswitch/port - remotecore/port\\n remotehwswitch = Container.fromAnchor(remotepop.properties['HwSwitch'])\\n remotehwport_tocore = getgriport(topology, remotehwswitch, remotecore, remotecoreport)\\n\\n # Find the port on the HwSwitch that is connected to the host's port\\n hwport_tosite = localsiteport\\n\\n fh1 = connectdataplanemac(hostmac= hostmac,\\n hostvlan= localsitevlan,\\n sw= hwswitch,\\n tohostport= hwport_tosite,\\n tocoreport= hwport_tocore,\\n tocorevlan= corevlan,\\n vc= vc,\\n meter= meter,\\n host_rewritemac= host_rewritemac)\\n if fh1 == None:\\n return None\\n\\n # Find the port on the remote HwSwitch that is connected to the remote host's port\\n remotehwport_tosite = remotesiteport\\n\\n fh2 = connectremoteplanemac(remotesw = remotehwswitch,\\n hostmac= hostmac,\\n hostvlan= localsitevlan,\\n remotehost_port= remotehwport_tosite,\\n remotehost_vlan= remotesitevlan,\\n remotehwport_tocore= remotehwport_tocore,\\n corevlan= corevlan,\\n vc= vc,\\n meter= meter,\\n host_rewritemac= host_rewritemac)\\n if fh2 == None:\\n SCC.deleteforward(fh1)\\n return None\\n\\n fhs = (fh1, fh2)\\n return fhs\",\n \"def sx_router_neigh_get_mac(handle, rif, addr): \\n try:\\n neigh_entry_cnt_p = new_uint32_t_p()\\n neigh_entry_list_p = new_sx_neigh_get_entry_t_arr(1)\\n\\n filter_p = new_sx_neigh_filter_t_p()\\n neigh_filter = sx_neigh_filter_t()\\n neigh_filter.filter_by_rif = SX_KEY_FILTER_FIELD_NOT_VALID\\n neigh_filter.rif = 0\\n sx_neigh_filter_t_p_assign(filter_p, neigh_filter)\\n \\n rc = sx_api_router_neigh_get(handle, SX_ACCESS_CMD_GET, rif, addr, filter_p, neigh_entry_list_p, neigh_entry_cnt_p)\\n if rc == SX_STATUS_ENTRY_NOT_FOUND:\\n return None \\n sx_check_rc(rc)\\n\\n neighbor_entry = sx_neigh_get_entry_t_arr_getitem(neigh_entry_list_p, 0)\\n \\n return neighbor_entry.neigh_data.mac_addr.to_str()\\n \\n finally:\\n delete_sx_neigh_filter_t_p(filter_p)\\n delete_sx_neigh_get_entry_t_arr(neigh_entry_list_p) \\n delete_uint32_t_p(neigh_entry_cnt_p)\",\n \"def dest_mac(self):\\n return self._dest_mac\",\n \"def subcmd_getbmc_main(args, parameter_info):\\n \\n from get_bmc_inventory import get_bmc_inventory\\n result = get_bmc_inventory(parameter_info['ip'], parameter_info['user'], parameter_info['passwd'], parameter_info['sysid'])\\n \\n if result['ret'] is True:\\n del result['ret']\\n sys.stdout.write(json.dumps(result['entries'], sort_keys=True, indent=2))\\n else:\\n sys.stderr.write(result['msg'])\",\n \"def mac(self):\\n if not self.is_rule:\\n raise NotRuleError(\\\"No 'ATTR{address}' field.\\\")\\n\\n if \\\"ATTR{address}\\\" not in self._fields:\\n raise NotRule70Error(\\\"No 'ATTR{address}' field.\\\")\\n\\n return self._fields[\\\"ATTR{address}\\\"]\",\n \"def mac_address(self):\\n if self._mac_address is None:\\n expression = expressions.WPA_MAC\\n name = expressions.MAC_ADDRESS_NAME\\n command = self.status_command\\n self._mac_address = self._match(expression,\\n name,\\n command)\\n return self._mac_address\",\n \"def mac_add():\\n brew_installed = shutil.which('brew')\\n if not brew_installed:\\n print('Installing brew')\\n url = 'https://raw.githubusercontent.com/Homebrew/install/master/install'\\n command_line = \\\"/usr/bin/ruby -e \\\\\\\"$(curl -fsSL %s)\\\\\\\"\\\" % url\\n subprocess.check_call(command_line, shell=True)\\n else:\\n print('brew present')\\n gmsh_installed = shutil.which('gmsh')\\n if not gmsh_installed:\\n print('Installing gmsh')\\n folder_path = os.path.dirname(os.path.abspath(__file__))\\n dmginstall_path = os.path.join(folder_path, 'dmginstall.sh')\\n url = 'http://gmsh.info/bin/MacOSX/gmsh-3.0.5-MacOSX.dmg'\\n command_line = '%s %s' % (dmginstall_path, url)\\n print('command_line=%s' % command_line)\\n subprocess.check_call(command_line, shell=True)\\n gmsh_path = '/Applications/Gmsh.app/Contents/MacOS/gmsh'\\n command_line = \\\"ln -s %s /usr/local/bin/gmsh\\\" % gmsh_path\\n subprocess.check_call(command_line, shell=True)\\n else:\\n print('gmsh present')\\n ccx_installed = shutil.which('ccx')\\n if not ccx_installed:\\n mac_add_ccx()\\n else:\\n print('calculix (ccx) present')\",\n \"def _generate_machine_id(self):\\r\\n mach_id = \\\"machine_\\\"\\r\\n try:\\r\\n gws = netifaces.gateways() # get all gateways\\r\\n default = gws['default'] # get the default gw\\r\\n adapter = default[2][1] # get the adapter identifier\\r\\n real_adapter = netifaces.ifaddresses(adapter) # get the adapter\\r\\n link_info = real_adapter[netifaces.AF_LINK]\\r\\n mac = link_info[0]['addr']\\r\\n mac = re.sub('[:]', '', mac)\\r\\n except:\\r\\n mac = \\\"unsup\\\"\\r\\n self.logger.error(\\\"Getting mac of internet card is not supported, needs netifaces >= 0.10\\\")\\r\\n self.machine_id = mach_id + mac\",\n \"def kern():\\n return platform.uname()\",\n \"def getControllers():\\n \\n # Check enabled bluetooth service\\n changeBluetoothService(enable=True)\\n \\n #proc = os.popen('bluetoothctl list').read()\\n blueListStdout = execCommand('bluetoothctl list')\\n \\n # Get controller's MAC and name to list\\n cntList = list()\\n for line in blueListStdout.splitlines():\\n lst = line.split()\\n del lst[0]\\n del lst[-1]\\n \\n if lst:\\n cntList.append(lst)\\n \\n return cntList\",\n \"def __init__(self, mac):\\n self.go_on = True\\n self.mac = mac\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6588843","0.6586241","0.6426141","0.63516474","0.62712574","0.61390996","0.61328363","0.6111466","0.6111466","0.6086921","0.6073458","0.6061298","0.6035936","0.6028136","0.60262287","0.59856164","0.59211636","0.59168446","0.59006864","0.58995175","0.5891991","0.5856686","0.5815912","0.57743233","0.572335","0.57209986","0.571632","0.5684447","0.5672465","0.5537294","0.55359674","0.5516385","0.548497","0.5475715","0.54528373","0.5416508","0.5411127","0.53775555","0.5359678","0.53570694","0.53470904","0.53291273","0.5325388","0.5317727","0.5317727","0.53168434","0.5310998","0.5308608","0.5306175","0.52821755","0.52726525","0.5271809","0.5260052","0.5254818","0.52521276","0.5243697","0.52003896","0.51911736","0.51889","0.5187756","0.51788384","0.51688987","0.5159273","0.5114941","0.5111905","0.50789917","0.50671387","0.50634867","0.5038223","0.5030617","0.50288504","0.50035965","0.49901637","0.4987141","0.49864903","0.49863756","0.49770045","0.49769628","0.49759355","0.49617606","0.49613717","0.49330652","0.4933052","0.49306107","0.4925064","0.4925064","0.49237862","0.49077705","0.4892899","0.48925212","0.48922342","0.48878968","0.4886506","0.48802605","0.48737842","0.48591524","0.48582923","0.48540887","0.4844009","0.48296878"],"string":"[\n \"0.6588843\",\n \"0.6586241\",\n \"0.6426141\",\n \"0.63516474\",\n \"0.62712574\",\n \"0.61390996\",\n \"0.61328363\",\n \"0.6111466\",\n \"0.6111466\",\n \"0.6086921\",\n \"0.6073458\",\n \"0.6061298\",\n \"0.6035936\",\n \"0.6028136\",\n \"0.60262287\",\n \"0.59856164\",\n \"0.59211636\",\n \"0.59168446\",\n \"0.59006864\",\n \"0.58995175\",\n \"0.5891991\",\n \"0.5856686\",\n \"0.5815912\",\n \"0.57743233\",\n \"0.572335\",\n \"0.57209986\",\n \"0.571632\",\n \"0.5684447\",\n \"0.5672465\",\n \"0.5537294\",\n \"0.55359674\",\n \"0.5516385\",\n \"0.548497\",\n \"0.5475715\",\n \"0.54528373\",\n \"0.5416508\",\n \"0.5411127\",\n \"0.53775555\",\n \"0.5359678\",\n \"0.53570694\",\n \"0.53470904\",\n \"0.53291273\",\n \"0.5325388\",\n \"0.5317727\",\n \"0.5317727\",\n \"0.53168434\",\n \"0.5310998\",\n \"0.5308608\",\n \"0.5306175\",\n \"0.52821755\",\n \"0.52726525\",\n \"0.5271809\",\n \"0.5260052\",\n \"0.5254818\",\n \"0.52521276\",\n \"0.5243697\",\n \"0.52003896\",\n \"0.51911736\",\n \"0.51889\",\n \"0.5187756\",\n \"0.51788384\",\n \"0.51688987\",\n \"0.5159273\",\n \"0.5114941\",\n \"0.5111905\",\n \"0.50789917\",\n \"0.50671387\",\n \"0.50634867\",\n \"0.5038223\",\n \"0.5030617\",\n \"0.50288504\",\n \"0.50035965\",\n \"0.49901637\",\n \"0.4987141\",\n \"0.49864903\",\n \"0.49863756\",\n \"0.49770045\",\n \"0.49769628\",\n \"0.49759355\",\n \"0.49617606\",\n \"0.49613717\",\n \"0.49330652\",\n \"0.4933052\",\n \"0.49306107\",\n \"0.4925064\",\n \"0.4925064\",\n \"0.49237862\",\n \"0.49077705\",\n \"0.4892899\",\n \"0.48925212\",\n \"0.48922342\",\n \"0.48878968\",\n \"0.4886506\",\n \"0.48802605\",\n \"0.48737842\",\n \"0.48591524\",\n \"0.48582923\",\n \"0.48540887\",\n \"0.4844009\",\n \"0.48296878\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5884323"},"document_rank":{"kind":"string","value":"21"}}},{"rowIdx":92594,"cells":{"query":{"kind":"string","value":"Prompt and convert cab and ru to a hostname"},"document":{"kind":"string","value":"def location_to_index():\n\n while True:\n cabinet = int(raw_input(\"Cabinet: \"))\n ru = int(raw_input(\"Rack U: \"))\n\n index = ((cabinet - 1) * 32) + ru\n\n correct = raw_input(\"Does index {} sound right (y/n)? \".format(index))\n\n if 'y' in correct:\n break\n\n\n LOGGER.info(\"Mapping Cabinet: %s, Rack U: %s to %s\", cabinet, ru, index)\n\n return index"},"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_host_name():\r\n\tglobal HOST_NAME\r\n\r\n\twhile True:\r\n\t\tname_of_host = input(\"What is your name (As it appears in zoom without the '(Host, me)' part)? \")\r\n\t\tHOST_NAME = name_of_host + \" (Host, me)\"\r\n\t\tcorrect = input(f\"Host name set as {HOST_NAME}, is this correct? [Y/N]: \")\r\n\t\tif correct.upper() == \"Y\":\r\n\t\t\treturn","def _get_hostname():\n return click.prompt(\"What is the Pulp host's hostname?\", type=click.STRING)","def InputMacAddress(prompt):\n while 1:\n SetPatternColor()\n print prompt,\n SetPatternColor(1)\n val = raw_input().strip()\n SetPatternColor()\n try:\n #if len(val) == 12 and int(val,16):\n if (len(val) == 12 or len(val) == 10): \n return val.upper()\n except ValueError:\n pass\n #SetPattern(0)\n SetPatternColor(0)\n print \"ERROR %s!!\"%prompt","def ip2host(self, irc, msg, args, ip):\n \n try:\n hostname = socket.gethostbyaddr(ip)\n hostname = hostname[0]\n if hostname:\n record = self._record_by_addr(ip)\n if record:\n reply = u'%s (%s)' % (hostname, self._geoip_city_check(record))\n else:\n reply = u'geoIP Fehler!'\n except:\n reply = u'gethostbyaddr() Error'\n \n irc.reply(reply.encode('utf-8'))","def host2ip(self, irc, msg, args, hostname):\n \n try:\n ip = socket.gethostbyname(hostname)\n if ip:\n record = self._record_by_addr(ip)\n if record:\n reply = u'%s (%s)' % (ip, self._geoip_city_check(record))\n else:\n reply = u'geoIP Fehler!'\n \n except:\n reply = u'gethostbyname() Error'\n \n irc.reply(reply.encode('utf-8'))","def ask_for_device():\n return input(\"Device name or ip address : \")","def countryInput(self, prompt):\n while True:\n name = input(prompt)\n if name not in self.travel_db.countries:\n print(\"Invalid country name. Please make sure name is capitalized.\")\n else:\n return name","def ask_username(ip):\n username_question = [\n {\n 'type': 'input',\n 'message': 'Enter the username of the machine with yhe IP address ' + ip + ':',\n 'name': 'username',\n 'validate': NotEmpty\n }\n ]\n\n username_answer = prompt(username_question, style=style)\n user = username_answer[\"username\"]\n return user","def format_host(host):\n\n host = strip_suffix(host, \".lan.urlab.be\")\n host = strip_suffix(host, \".lan\")\n host = strip_suffix(host, \".local\")\n host = strip_suffix(host, \"iPodtouch\")\n host = strip_suffix(host, \"-PC\")\n host = strip_suffix(host, \"-pc\")\n\n host = strip_prefix(host, \"pc-\")\n host = strip_prefix(host, \"PC-\")\n host = strip_prefix(host, \"DESKTOP-\")\n host = strip_prefix(host, \"LAPTOP-\")\n host = strip_prefix(host, \"iPod-de-\")\n host = strip_prefix(host, \"iPadde\")\n\n return host","def prompt_ip(prompt):\n response = \"\"\n while not is_valid_ip(response):\n response = prompt_base(prompt)\n return response","def ask_for_domain():\n found = False\n\n # Keep asking until a valid domain has been entered by the user\n while not found:\n domain = u\"https://\" + input(u\"\\nEnter the Canvas domain of your institution:\\n$ https://\")\n found = helpers.validate_domain(domain)\n\n return domain","def host():\n\n print(\"\"\" Bienvenue sur l'application Pur Beurre\n --------------------------------------------\n 1: Quel aliment souhaitez-vous remplacer ?\n 2: Retrouver mes aliments substitués\n 3: Quitter\"\"\")\n\n while True:\n try:\n choice = int(input(\"Entrez votre choix: \\n\"))\n if choice in range(1, 4):\n break\n except ValueError:\n continue\n\n return choice","def snap_part_to_host_input(part):\n adc, name = part.split('>')\n # convert the string adc (eg \"e2\") into a channel number 0-2\n adc_num = int(adc[1:]) // 2 # divide by 2 because ADC is in demux 2\n # convert the name into something which should be a hostname.\n # name has the form SNPA0000123. Hostnames have the form herasnapA123\n # Edit 16 Sep 2019 -- This isn't true anymore. CM names and hostnames are now the same\n hostname = name#\"herasnap%s\" % (name[3] + name[4:].lstrip('0'))\n try:\n true_name, aliases, addresses = socket.gethostbyaddr(hostname)\n except:\n logger.error('Failed to gethostbyname for host %s' % hostname)\n # assume that the one we want is the last thing in the hosts file line\n return aliases[-1], adc_num","def hostname(name: str = \"\") -> str:\n ...","def __connect_failed__(self):\n # Ask the user what to do with the error\n choice = input(\"[A]bort, [C]hange address and port, or [R]etry?\")\n if (choice.lower() == \"a\"):\n exit()\n elif (choice.lower() == \"c\"):\n address = input(\"Please enter the address:\")\n port_number = input(\"Please enter the port:\")","def hostname_to_site(hostname: str) -> str:\n if hostname == \"biothings.ncats.io\" or hostname.endswith(\"transltr.io\"):\n return \"ncats\"\n\n return \"pending\"","def _aa_host_name(self):\n self.is_option = True\n self.is_statement = False\n self.has_validator = True\n if not (self.value.startswith('\"') and self.value.endswith('\"')):\n self.value = '\"' + self.value + '\"'\n validate_name(self.value.strip('\"'))","def change_host(self, str_cef, address):\r\n tmp = str_cef\r\n if str_cef.find(\"CEF:\",0,len(str_cef)) != -1:\r\n tmp = str_cef[:21] + str(address[0]) + str_cef[25:]\r\n\r\n elif str_cef.find(\"|\",0,len(str_cef)) != -1:\r\n st = str_cef.find(\"|\",0,len(str_cef))\r\n st = str_cef.find(\"|\",st+1,len(str_cef))\r\n tmp = str_cef[:st+1]+str(address[0])+\"|\"+str_cef[st+1:]\r\n return tmp","def handle_hostname(bot, ievent):\n try:\n item = ievent.args[0]\n except IndexError:\n ievent.missing('<ipnr>')\n return\n try:\n hostname = socket.gethostbyaddr(item)\n ievent.reply(hostname[0])\n except:\n ievent.reply(\"can't match \" + str(item))","def _host(name):\n\n\ttry:\n\t\tsocket.gethostbyname(name)\n\t\treturn name\n\texcept socket.error:\n\t\traise argparse.ArgumentTypeError(\"Invalid hostname: \" + name)\n\ttry:\n\t\tsocket.inet_aton(name)\n\t\treturn name\n\texcept socket.error:\n\t\traise argparse.ArgumentTypeError(\"Invalid ip address: \" + name)","def cliquer(self):\n try:\n if self.input.get() != \"\":\n human = HumanPlayer(self.input.get())\n cpu = CPUPlayer(\"La Machine\", var_choix.get(), 15)\n self.input.destroy()\n self.bouton_cliquer[\"command\"] = self.jouer(human, cpu)\n except:\n input(\"Vous devez fournir un nom !\")","def searchCountry(host):\n process = subprocess.Popen(\"geoiplookup \"+host,stdout=subprocess.PIPE, shell=True)\n (output, err) = process.communicate()\n secondPart = output.split(\"GeoIP Country Edition: \", 1)[1]\n country = secondPart.split(\"\\nGeoIP City Edition\", 1)[0]\n return country","def checkGetHostByName(self, result):\n self.assertEquals(result, '127.0.0.1')","def _what_is_username(self):\n prompt = \"-?- Send to: \"\n sn = self._input(prompt)\n return sn","async def _parse_hostname(self, output, cb_token) -> None:\n if not output:\n self.hostname = \"-\"\n return\n\n if output[0][\"status\"] == 0:\n data = output[1][\"data\"]\n try:\n jout = json.loads(data)\n self.hostname = jout[\"hostname\"]\n except:\n self.hostname = \"-\"","def prep(user, host, resource):\n\n if user:\n try:\n user = nodeprep.prepare(unicode(user))\n except UnicodeError:\n raise InvalidFormat(\"Invalid character in username\")\n else:\n user = None\n\n if not host:\n raise InvalidFormat(\"Server address required.\")\n else:\n try:\n host = nameprep.prepare(unicode(host))\n except UnicodeError:\n raise InvalidFormat(\"Invalid character in hostname\")\n\n if resource:\n try:\n resource = resourceprep.prepare(unicode(resource))\n except UnicodeError:\n raise InvalidFormat(\"Invalid character in resource\")\n else:\n resource = None\n\n return (user, host, resource)","def test_strip_prompt():\n string = \"\"\"MyRouter version 1.25.9\nmyhostname>\"\"\"\n connection = FakeBaseConnection(RESPONSE_RETURN=\"\\n\", base_prompt=\"myhostname>\")\n result = connection.strip_prompt(string)\n assert result == \"MyRouter version 1.25.9\"","def passPrompt(title, prompt):\n answer = tkSimpleDialog.askstring(title, prompt, show=\"*\")\n print answer","def user_question():\n return input('What would you like? (espresso/latte/cappuccino): ')","def handle_commands_preset(self,cl,addr) :\n self.curDir = ['CTF','Challenges','tempUser'+str(random.randint(100,999))]\n try :\n client = cl\n if self.curDir != [] : \n userp = \"temp-user-\"+addr[0].replace('.','-')+\"@ieeectf:~/{}$ \".format('/'.join(self.curDir))\n else :\n userp = \"temp-user-\"+addr[0].replace('.','-')+\"@ieeectf:~$ \"\n self.userp = userp.encode()\n client.send(\"\"\"\nCustom Shell Server With Limited Functionality\n\nNew User Login from {} at {}\n \\n\"\"\".format(addr[0],time.ctime()).encode())\n shellin = \"\" \n while True:\n if self.curDir != [] : \n userp = \"temp-user-\"+addr[0].replace('.','-')+\"@ieeectf:~/{}$ \".format('/'.join(self.curDir))\n else :\n userp = \"temp-user-\"+addr[0].replace('.','-')+\"@ieeectf:~$ \"\n self.userp = userp.encode()\n client.send(self.userp)\n shellin = client.recv(2048).decode().strip('\\n')\n if shellin == \"exit\" or shellin == \"exit \" or shellin ==\"exit \" or shellin ==\"exit \" :\n break\n elif shellin == \"\" :\n continue\n elif shellin.split()[0] in self.denied :\n client.send(self.err.format(shellin.split()[0]).encode())\n else :\n self.handle_extended_commands(client,addr,shellin)\n continue\n client.close()\n except Exception as E:\n print(E)\n print(Log(\"Connection with {} Terminated\".format(addr)))","def askPass( user, host ):\n prompt = \"Password for user {} on host {}: \".format( user, host )\n password = getpass.getpass( prompt )\n return password","def name_prompt(input_msg):\n while True:\n donor_name = input(input_msg)\n try:\n if not \"\".join(donor_name.split()).isalpha():\n raise Exception(\"The name should be all Alphabetic characters:\")\n return donor_name.title()\n except Exception as e:\n print(\"INVALID Name: \",e)","def prompt_base(prompt):\n return input(prompt + \": \")","def _grab_host(self):\r\n host = \"\"\r\n while True:\r\n while self._char != -1 and not self._char in \".\\x00\\t\\r\\n\":\r\n host += self._char\r\n self._get_char()\r\n if self._char == \".\":\r\n host += \".\"\r\n self._get_char()\r\n else:\r\n break\r\n return host","def handle_dns(bot, ievent):\n if not ievent.args:\n ievent.missing('<host | ip>')\n else:\n is_a = None\n result = None\n # If we support IPv6 ...\n if socket.has_ipv6:\n # ... then check if this is an IPv6 ip\n try:\n socket.inet_pton(socket.AF_INET6, ievent.args[0])\n is_a = 'ipv6'\n except socket.error:\n pass\n # Ah not an IPv6 ip ...\n if not is_a:\n # ... maybe IPv4 ?\n try:\n socket.inet_pton(socket.AF_INET, ievent.args[0])\n is_a = 'ipv4'\n except socket.error:\n pass\n # Not an ip, must be a hostname then\n if not is_a:\n is_a = 'host'\n # If it was an ip ...\n if is_a in ['ipv4', 'ipv6']:\n try:\n # ... try to resolve it\n result = socket.gethostbyaddr(ievent.args[0])\n if result[1]:\n result = 'primary: %s, aliases: %s' % \\\n (result[0], ', '.join(result[1]))\n else:\n result = result[0]\n ievent.reply('%s ip %s resolves to %s' % \\\n (is_a, ievent.args[0], result))\n except Exception, e:\n ievent.reply('could not resolve %s address %s: %s' % \\\n (is_a, ievent.args[0], e[1]))\n # Oh it's a host, lets resolve that\n elif is_a == 'host':\n try:\n result = []\n for info in socket.getaddrinfo(ievent.args[0], None):\n if info[0] in [socket.AF_INET, socket.AF_INET6] and \\\n info[1] == socket.SOCK_STREAM:\n ip = info[4][0]\n if not ip in result:\n result.append(ip)\n if not result:\n ievent.reply('could not resolve hostname %s: not found' % \\\nievent.args[0])\n else:\n ievent.reply('%s resolves to: %s' % (ievent.args[0], \\\n', '.join(result)))\n except Exception, e:\n ievent.reply('could not resolve hostname %s: %s' % \\\n (ievent.args[0], e[1]))\n else:\n ievent.reply('lookup failed, no valid data found')","def test_address(self):\n result = irc.dccDescribe(\"CHAT arg 3232235522 6666\")\n self.assertEqual(result, \"CHAT for host 192.168.0.2, port 6666\")","def gethostbycondorname(name):\n\n m = htcondor_ip_name_re.match(name)\n if m is not None:\n return m.group(1).replace('-', '.')\n else:\n return socket.gethostbyname(name)","def getHost():","def getHost():","def prompt_user_passwd(host, realm):\n return ('', '')","def pedir_palabra(msg, min, max):\n while True:\n txt = str(raw_input(msg))\n if not txt.isalpha() :\n show_msg(\"Oops! Parece que eso no era una palabra valida\")\n continue\n if len(txt) > max:\n show_msg(\"Palabra muy larga (Max %d caracteres)\"%max)\n continue\n elif len(txt) < min :\n show_msg(\"Palabra muy corta (Min %d caracteres)\"%min)\n continue\n else:\n return txt.lower()","def prompt(self):\r\n super().prompt_number()\r\n self.email = str(input(\"Email: \"))","def test_detectCanonicalNameLoop(self):\n servers = {\n ('1.1.2.3', 53): {\n ('example.com', A): {\n 'answers': [('example.com', Record_CNAME('example.net')),\n ('example.net', Record_CNAME('example.com'))],\n },\n },\n }\n resolver = self._getResolver(servers)\n d = resolver.lookupAddress('example.com')\n return self.assertFailure(d, ResolverError)","def process_input(server_msg):\n global username\n original=server_msg\n server_msg=server_msg.split(\" \") #separa por args\n\n if(server_msg[0]==\"DISPLAY\"):\n print(original[8::].replace(';', '\\n'))\n\n\n\n elif(server_msg[0]==\"SUC\"):\n if(len(server_msg)>1):\n if(server_msg[1]==\"REG_OK\"):\n username='[' + server_msg[2].lower() + ']'\n print(REG_OK)\n elif(server_msg[1]=='INVITE_OK'):\n print(INVITE_OK)\n elif(server_msg[1]=='REJECT'):\n print(REJECT.format(server_msg[2]))\n elif(server_msg[1]=='DISCONNECT'):\n print(DISCONNECT)\n username=''\n\n elif(server_msg[0]=='GAME'):\n if(server_msg[1]=='START'):\n print(START.format(server_msg[2]))\n elif(server_msg[1]=='WIN'):\n print(WIN)\n elif(server_msg[1]=='LOSE'):\n print(LOSE)\n elif(server_msg[1]=='TIE'):\n print(TIE)\n elif(server_msg[1]=='FOLD'):\n print(FOLD.format(server_msg[2]))\n\n\n elif(server_msg[0]==\"ERR\"):\n if(server_msg[1]==\"BAD_REQUEST\"):\n print(BAD_REQUEST)\n elif(server_msg[1]==\"REG_FAIL\"):\n print(REG_FAIL)\n elif(server_msg[1]==\"USER_REGISTERED\"):\n print(USER_REGISTERED)\n elif(server_msg[1]=='USER_BUSY'):\n print(USER_BUSY)\n elif(server_msg[1]=='USER_UNKNOWN'):\n print(USER_UNKNOWN)\n elif(server_msg[1]=='IMBUSY'):\n print(IMBUSY)\n elif(server_msg[1]=='NO_USER'):\n print(NO_USER)\n elif(server_msg[1]=='NO_ENV'):\n print(NO_ENV)\n elif(server_msg[1]=='NOT_IN_GAME'):\n print(NOT_IN_GAME)\n elif(server_msg[1]=='NO_TURN'):\n print(NO_TURN)\n elif(server_msg[1]=='REG_FAIL'):\n print(REG_FAIL)\n elif(server_msg[1]=='INVALID_PLAY'):\n print(INVALID_PLAY.format(server_msg[2], server_msg[3], server_msg[4]))\n elif(server_msg[1]=='BAD_FORMAT'):\n print(BAD_FORMAT)\n elif(server_msg[1]=='INVALID_COOR'):\n print(INVALID_COOR)\n elif(server_msg[1]=='USER_DISCONECTED'):\n print(USER_DISCONECTED)\n elif(server_msg[1]=='YSELF'):\n print(YSELF)\n elif(server_msg[1]=='NO_INV'):\n print(NO_INV)\n\n \n elif(server_msg[0]==\"INVITE\"):\n print(INVITE_REC.format(server_msg[1]))\n\n\n\n elif(server_msg[0]==\"BOARD\"):\n if(server_msg[1]=='1'):\n print(\"\\n\\nYour turn to play\\n\")\n elif(server_msg[1]=='0'):\n print(\"\\n\\nWaiting for oponent...\\n\")\n board=eval(original[8::])\n for i in range(len(board)):\n for k in range(len(board[i])):\n if(board[i][k]==0):\n board[i][k]=' '\n elif(board[i][k]==1):\n board[i][k]='X'\n else:\n board[i][k]='O'\n try:\n spacer=\"\"\n rows, columns = os.popen('stty size', 'r').read().split()\n rows = int(rows)\n columns = int(columns)\n tmp=int((columns-13)/2)\n for i in range(tmp):\n spacer+=\" \"\n except:\n pass\n print(\"\\n{} 0 1 2 \\n\".format(spacer)\\\n +\"{}0 {} {} {} {} {} \\n\".format(spacer,board[0][0],VLINE, board[0][1],VLINE, board[0][2])\\\n +spacer+HLINE\\\n +\"{}1 {} {} {} {} {} \\n\".format(spacer,board[1][0],VLINE, board[1][1],VLINE, board[1][2])\\\n +spacer+HLINE\\\n +\"{}2 {} {} {} {} {} \\n\".format(spacer,board[2][0],VLINE, board[2][1],VLINE, board[2][2]))\n\n elif(server_msg[0]==\"LIST\"):\n all_users=eval(original[5::])\n print(\"USER\\t|\\tSTATUS\")\n for i in all_users:\n print(\"{}\\t|\\t{}\".format(i[0], \"available\" if i[1] == 0 else \"unavailable\"))\n print(\"\\n\")\n\n elif(server_msg[0]==\"SERVER_OFF\"):\n print('\\n' + SERVER_OFF + '\\n\\n')\n exit_sig()\n sys.stdout.flush()","def addr_entry(key):\n\n # getting the input\n rawline = input(\n 'please enter ' + key + ': '\n )\n\n # get rid of trailing whitespace\n line = rawline.strip()\n\n # replace tabs\n line = line.replace('\\t', ' ')\n\n # remove forbidden strings\n trans_tab = dict.fromkeys(\n map(ord, '\\:.\\\"\\'!@#$\\\\\\/'), None\n )\n\n line = line.translate(trans_tab)\n\n if rawline != line:\n print(\n 'Warning: string has been changed: \\''\n + rawline\n + '\\' to \\''\n + line\n + '\\'.'\n )\n return line","def localizeForHostName(filename): \n hostname = socket.gethostname()\n if hostname in filename:\n updated_filename = filename.replace(hostname, '')\n return updated_filename.strip('-')\n return filename","def hostname(self, code):\n return self.langs[code]","def input_desc():\r\n desc = input(\"Entrez ici les remarques générales du directeur du tournoi: \")\r\n return desc","def ask(self):\n subprocess.run([\"say\", \"-v\", \"Kyoko\", str(self.answer)])","def get_address():\r\n address = input(\"What is the customer's address?: \")\r\n\r\n return address","def test_sanitized_hostname(self):\n value = \" ../ ../some/dubious/hostname \"\n response = clean.hostname(value)\n assert response == \"somedubioushostname\"","def ask_for_interface():\n return input(\"Interface name : \")","def user(inp):\n user = inp.text.lower().replace(' ', '-')\n return 'http://www.wikidot.com/user:info/' + user","def convert_diplomaticletters():\n\n local('cd import_scripts;../bin/python import_diplomaticletters.py import')","def local_task_entry(text=\"> \"):\n task_completer = localTaskCompleter()\n while True:\n result = prompt(text, completer=task_completer)\n if result not in task_completer.tasks:\n print(\"Invalid task, try again.\")\n else:\n return result","def via_host_name(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"via_host_name\")","def via_host_name(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"via_host_name\")","def inask(question: str) -> str:\n answer = input(question)\n return answer","def prompt_for_credentials(realm, uri, user, password):\n\n try:\n if realm is None:\n realm = raw_input('Realm: ')\n if uri is None:\n uri = raw_input('URI: ')\n if user is None:\n user = raw_input('Username: ')\n if password is None:\n password = getpass.getpass()\n\n return realm, uri, user, password\n except KeyboardInterrupt:\n print\n return None, None, None, None","def getURL():\n url = input('Paste URL: ')\n\n return url","def UserHost(user, host):\n if user:\n return user + '@' + host\n else:\n return host","def main():\n with Scrapli(**MY_DEVICE) as conn:\n print(conn.get_prompt())\n print(conn.send_command(\"show run | i hostname\").result)","def requestInput(st):\n return input(st+\": \")","def CiscoLoopback():\n cisco = {}\n print \"***Cisco router configuration***\"\n as_number = raw_input(\"\\tInsert BGP AS number: \")\n while True:\n cisco_loopback = {}\n loopback_name = raw_input(\"\\tInsert loopback name (Press 'q' to quit): \")\n if loopback_name is not 'q':\n loopback_address = raw_input(\"\\tLoopback address (eg. x.x.x.x): \")\n loopback_network = raw_input(\"\\tLoopback network (eg. x.x.x.0): \")\n loopback_mask = raw_input(\"\\tLoopback mask (eg. 255.255.255.0): \")\n cisco_loopback[\"address\"] = loopback_address\n cisco_loopback[\"network\"] = loopback_network\n cisco_loopback[\"mask\"] = loopback_mask\n cisco[loopback_name] = cisco_loopback\n elif loopback_name == 'q':\n break\n else:\n print \"You just typed something incorrect!\"\n return (cisco, as_number)","def ask_user_input(prompt: str) -> str:\n return input(prompt)","def parse_command_line(description):\n ip = socket.gethostbyname(socket.gethostname())\n parser = argparse.ArgumentParser(description=description)\n parser.add_argument('host', metavar='host', nargs='?', help='IP or hostname', default=ip)\n parser.add_argument('-p', metavar='port', type=int, default=7000,\n help='TCP port (default 7000)')\n parser.add_argument('-a', metavar='cafile', default=None,\n help='Set up a basic encrypted Connection')\n args = parser.parse_args()\n address = (args.host, args.p, args.a)\n return address","def register_username(self):\n while True:\n message = self.receive()\n if message == Message.OK:\n break\n print(message.decode(\"UTF-8\"))\n while True:\n self.username = input(\"Username: \")\n if len(self.username) > 0:\n self.send(self.username.encode(\"UTF-8\"))\n break\n else:\n print(\"Empty username!\")","def ssh_connection(hostname, username):\n\n #We testing if the username can to connect to the hostname\n if username == \"company1\":\n if hostname in open(\"./servers_list_company1.txt\", \"r\").read():\n pass\n else:\n return 1\n elif username == \"company2\":\n if hostname in open(\"./servers_list_company2.txt\", \"r\").read():\n pass\n else:\n return 1\n else:\n return 1\n\n #Connexion au serveur (nb, il faut que l'échange de clé ssh est eu lieu)\n try:\n ssh = paramiko.SSHClient()\n ssh.set_missing_host_key_policy(paramiko.AutoAddPolicy())\n ssh.connect(hostname, username=\"postfix\", timeout=4)\n print \"Connected to %s\" % hostname\n except paramiko.AuthenticationException:\n return 1\n except:\n return 1\n\n return ssh","def click_advanced_and_input_the_computer_account_ou_truenas_servers(driver, ca_ou):\n if is_element_present(driver, '//button[@ix-auto=\"button__ADVANCED OPTIONS\"]'):\n driver.find_element_by_xpath('//button[@ix-auto=\"button__ADVANCED OPTIONS\"]').click()\n assert wait_on_element(driver, 7, '//input[@ix-auto=\"input__Computer Account OU\"]', 'inputable')\n driver.find_element_by_xpath('//input[@ix-auto=\"input__Computer Account OU\"]').clear()\n driver.find_element_by_xpath('//input[@ix-auto=\"input__Computer Account OU\"]').send_keys(ca_ou)","def location_prompt(location, msg='WEAthER'):\n from x84.bbs import getterminal, echo, getch\n term = getterminal()\n echo(u''.join((u'\\r\\n\\r\\n',\n term.yellow(u'diSPlAY %s fOR ' % (msg,)),\n term.bold('%(city)s, %(state)s' % location),\n term.yellow(' ? '),\n term.bold_yellow(u'['),\n term.underline_yellow(u'yn'),\n term.bold_yellow(u']'),\n u': '),))\n while True:\n inp = getch()\n if inp is None or inp in (u'n', u'N', 'q', 'Q', term.KEY_EXIT):\n return False\n if inp in (u'y', u'Y', u' ', term.KEY_ENTER):\n return True","def main():\n # input the dna\n dna = str(input('Please give me a DNA strand and I\\'ll find the complement: '))\n # check the input format\n while not is_dna(dna):\n print('Illegal format')\n dna = str(input('Please give me a DNA strand (A, T, C, G only) and I\\'ll find the complement: '))\n\n # find the complement strand\n ans = build_complement(dna)\n print('The complement of ' + str(dna.upper()) + ' is ' + str(ans))","def get_host_by_name(self, hostname: str) -> Union[str, Literal[False]]:\n self._read_line()\n if self._debug:\n print(\"*** Get host by name\")\n if isinstance(hostname, str):\n hostname = bytes(hostname, \"utf-8\")\n\n self._uart_write(b'AT+CDNSGIP=\"' + hostname + b'\"\\r\\n')\n self._read_line(10000) # Read the +CDNSGIP, takes a while\n\n if not self._parse_reply(b\"+CDNSGIP: \", idx=2):\n return False\n return self._buf","def set_hostname(dut, host_name):\n cmd = \"sudo hostname {}\".format(host_name)\n st.config(dut, cmd)\n return","def choose_server(self, domain):\n try:\n domain = domain.encode('idna').decode('utf-8')\n except TypeError:\n domain = domain.decode('utf-8').encode('idna').decode('utf-8')\n except AttributeError:\n domain = domain.decode('utf-8').encode('idna').decode('utf-8')\n if domain.endswith(\"-NORID\"):\n return NICClient.NORIDHOST\n if domain.endswith(\"id\"):\n return NICClient.PANDIHOST\n if domain.endswith(\"hr\"):\n return NICClient.HR_HOST\n\n domain = domain.split('.')\n if len(domain) < 2:\n return None\n tld = domain[-1]\n if tld[0].isdigit():\n return NICClient.ANICHOST\n elif tld == 'ai':\n return NICClient.AI_HOST\n elif tld == 'app':\n return NICClient.APP_HOST\n elif tld == 'dev':\n return NICClient.DEV_HOST\n elif tld == 'games':\n return NICClient.GAMES_HOST\n elif tld == 'page':\n return NICClient.PAGE_HOST\n elif tld == 'money':\n return NICClient.MONEY_HOST\n elif tld == 'online':\n return NICClient.ONLINE_HOST\n elif tld == 'cl':\n return NICClient.CL_HOST\n elif tld == 'ar':\n return NICClient.AR_HOST\n elif tld == 'by':\n return NICClient.BY_HOST\n elif tld == 'cr':\n return NICClient.CR_HOST\n elif tld == 'ca':\n return NICClient.CA_HOST\n elif tld == 'do':\n return NICClient.DO_HOST\n elif tld == 'de':\n return NICClient.DE_HOST\n elif tld == 'hk':\n return NICClient.HK_HOST\n elif tld == 'hn':\n return NICClient.HN_HOST\n elif tld == 'jobs':\n return NICClient.JOBS_HOST\n elif tld == 'lat':\n return NICClient.LAT_HOST\n elif tld == 'li':\n return NICClient.LI_HOST\n elif tld == 'mx':\n return NICClient.MX_HOST\n elif tld == 'pe':\n return NICClient.PE_HOST\n elif tld == 'ist':\n return NICClient.IST_HOST\n elif tld == 'kz':\n return NICClient.KZ_HOST\n elif tld == 'abogado':\n return NICClient.ABOGADO_HOST\n elif tld == 'accountant':\n return NICClient.ACCOUNTANT_HOST\n elif tld == 'aero':\n return NICClient.AERO_HOST\n elif tld == 'ag':\n return NICClient.AG_HOST\n elif tld == 'ai':\n return NICClient.AI_HOST\n elif tld == 'allfinanz':\n return NICClient.ALLFINANZ_HOST\n elif tld == 'alsace':\n return NICClient.ALSACE_HOST\n elif tld == 'am':\n return NICClient.AM_HOST\n elif tld == 'amsterdam':\n return NICClient.AMSTERDAM_HOST\n elif tld == 'aquarelle':\n return NICClient.AQUARELLE_HOST\n elif tld == 'as':\n return NICClient.AS_HOST\n elif tld == 'asia':\n return NICClient.ASIA_HOST\n elif tld == 'au':\n return NICClient.AU_HOST\n elif tld == 'aw':\n return NICClient.AW_HOST\n elif tld == 'ax':\n return NICClient.AX_HOST\n elif tld == 'bank':\n return NICClient.BANK_HOST\n elif tld == 'bar':\n return NICClient.BAR_HOST\n elif tld == 'barclaycard':\n return NICClient.BARCLAYCARD_HOST\n elif tld == 'barclays':\n return NICClient.BARCLAYS_HOST\n elif tld == 'bayern':\n return NICClient.BAYERN_HOST\n elif tld == 'beer':\n return NICClient.BEER_HOST\n elif tld == 'berlin':\n return NICClient.BERLIN_HOST\n elif tld == 'bi':\n return NICClient.BI_HOST\n elif tld == 'bid':\n return NICClient.BID_HOST\n elif tld == 'bio':\n return NICClient.BIO_HOST\n elif tld == 'bmw':\n return NICClient.BMW_HOST\n elif tld == 'biz':\n return NICClient.BIZ_HOST\n elif tld == 'bj':\n return NICClient.BJ_HOST\n elif tld == 'blog':\n return NICClient.BLOG_HOST\n elif tld == 'brussels':\n return NICClient.BRUSSELS_HOST\n elif tld == 'budapest':\n return NICClient.BUDAPEST_HOST\n elif tld == 'build':\n return NICClient.BUILD_HOST\n elif tld == 'buzz':\n return NICClient.BUZZ_HOST\n elif tld == 'bw':\n return NICClient.BW_HOST\n elif tld == 'by':\n return NICClient.BY_HOST\n elif tld == 'bzh':\n return NICClient.BZH_HOST\n elif tld == 'ca':\n return NICClient.CA_HOST\n elif tld == 'cam':\n return NICClient.CAM_HOST\n elif tld == 'cancerresearch':\n return NICClient.CANCERRESEARCH_HOST\n elif tld == 'capetown':\n return NICClient.CAPETOWN_HOST\n elif tld == 'career':\n return NICClient.CAREER_HOST\n elif tld == 'casa':\n return NICClient.CASA_HOST\n elif tld == 'cat':\n return NICClient.CAT_HOST\n elif tld == 'cc':\n return NICClient.CC_HOST\n elif tld == 'ch':\n return NICClient.CH_HOST\n elif tld == 'ci':\n return NICClient.CI_HOST\n elif tld == 'cl':\n return NICClient.CL_HOST\n elif tld == 'cloud':\n return NICClient.CLOUD_HOST\n elif tld == 'club':\n return NICClient.CLUB_HOST\n elif tld == 'cm':\n return NICClient.CM_HOST\n elif tld == 'cologne':\n return NICClient.COLOGNE_HOST\n elif tld == 'cooking':\n return NICClient.COOKING_HOST\n elif tld == 'coop':\n return NICClient.COOP_HOST\n elif tld == 'cricket':\n return NICClient.CRICKET_HOST\n elif tld == 'cuisinella':\n return NICClient.CUISINELLA_HOST\n elif tld == 'cx':\n return NICClient.CX_HOST\n elif tld == 'cymru':\n return NICClient.CYMRU_HOST\n elif tld == 'cz':\n return NICClient.CZ_HOST\n elif tld == 'date':\n return NICClient.DATE_HOST\n elif tld == 'de':\n return NICClient.DE_HOST\n elif tld == 'desi':\n return NICClient.DESI_HOST\n elif tld == 'dk':\n return NICClient.DK_HOST\n elif tld == 'dm':\n return NICClient.DM_HOST\n elif tld == 'do':\n return NICClient.DO_HOST\n elif tld == 'download':\n return NICClient.DOWNLOAD_HOST\n elif tld == 'durban':\n return NICClient.DURBAN_HOST\n elif tld == 'dvag':\n return NICClient.DVAG_HOST\n elif tld == 'ee':\n return NICClient.EE_HOST\n elif tld == 'eu':\n return NICClient.EU_HOST\n elif tld == 'eurovision':\n return NICClient.EUROVISION_HOST\n elif tld == 'eus':\n return NICClient.EUS_HOST\n elif tld == 'faith':\n return NICClient.FAITH_HOST\n elif tld == 'fashion':\n return NICClient.FASHION_HOST\n elif tld == 'fi':\n return NICClient.FI_HOST\n elif tld == 'film':\n return NICClient.FILM_HOST\n elif tld == 'firmdale':\n return NICClient.FIRMDALE_HOST\n elif tld == 'fishing':\n return NICClient.FISHING_HOST\n elif tld == 'fit':\n return NICClient.FIT_HOST\n elif tld == 'flsmidth':\n return NICClient.FLSMIDTH_HOST\n elif tld == 'frl':\n return NICClient.FRL_HOST\n elif tld == 'frogans':\n return NICClient.FROGANS_HOST\n elif tld == 'ga':\n return NICClient.GA_HOST\n elif tld == 'gal':\n return NICClient.GAL_HOST\n elif tld == 'games':\n return NICClient.GAMES_HOST\n elif tld == 'garden':\n return NICClient.GARDEN_HOST\n elif tld == 'gd':\n return NICClient.GD_HOST\n elif tld == 'gdn':\n return NICClient.GDN_HOST\n elif tld == 'gent':\n return NICClient.GENT_HOST\n elif tld == 'gg':\n return NICClient.GG_HOST\n elif tld == 'gl':\n return NICClient.GL_HOST\n elif tld == 'global':\n return NICClient.GLOBAL_HOST\n elif tld == 'gmx':\n return NICClient.GMX_HOST\n elif tld == 'gold':\n return NICClient.GOLD_HOST\n elif tld == 'gop':\n return NICClient.GOP_HOST\n elif tld == 'gov':\n return NICClient.GOV_HOST\n elif tld == 'gq':\n return NICClient.GQ_HOST\n elif tld == 'gy':\n return NICClient.GY_HOST\n elif tld == 'hamburg':\n return NICClient.HAMBURG_HOST\n elif tld == 'hn':\n return NICClient.HN_HOST\n elif tld == 'horse':\n return NICClient.HORSE_HOST\n elif tld == 'hr':\n return NICClient.HR_HOST\n elif tld == 'ht':\n return NICClient.HT_HOST\n elif tld == 'hu':\n return NICClient.HU_HOST\n elif tld == 'ibm':\n return NICClient.IBM_HOST\n elif tld == 'ie':\n return NICClient.IE_HOST\n elif tld == 'ifm':\n return NICClient.IFM_HOST\n elif tld == 'im':\n return NICClient.IM_HOST\n elif tld == 'int':\n return NICClient.INT_HOST\n elif tld == 'io':\n return NICClient.IO_HOST\n elif tld == 'is':\n return NICClient.IS_HOST\n elif tld == 'it':\n return NICClient.IT_HOST\n elif tld == 'java':\n return NICClient.JAVA_HOST\n elif tld == 'je':\n return NICClient.JE_HOST\n elif tld == 'jetzt':\n return NICClient.JETZT_HOST\n elif tld == 'jobs':\n return NICClient.JOBS_HOST\n elif tld == 'joburg':\n return NICClient.JOBURG_HOST\n elif tld == 'ki':\n return NICClient.KI_HOST\n elif tld == 'kiwi':\n return NICClient.KIWI_HOST\n elif tld == 'koeln':\n return NICClient.KOELN_HOST\n elif tld == 'ky':\n return NICClient.KY_HOST\n elif tld == 'la':\n return NICClient.LA_HOST\n elif tld == 'lacaixa':\n return NICClient.LACAIXA_HOST\n elif tld == 'lat':\n return NICClient.LAT_HOST\n elif tld == 'latrobe':\n return NICClient.LATROBE_HOST\n elif tld == 'leclerc':\n return NICClient.LECLERC_HOST\n elif tld == 'li':\n return NICClient.LI_HOST\n elif tld == 'live':\n return NICClient.LIVE_HOST\n elif tld == 'loan':\n return NICClient.LOAN_HOST\n elif tld == 'london':\n return NICClient.LONDON_HOST\n elif tld == 'lt':\n return NICClient.LT_HOST\n elif tld == 'lu':\n return NICClient.LU_HOST\n elif tld == 'luxe':\n return NICClient.LUXE_HOST\n elif tld == 'luxury':\n return NICClient.LUXURY_HOST\n elif tld == 'ma':\n return NICClient.MA_HOST\n elif tld == 'madrid':\n return NICClient.MADRID_HOST\n elif tld == 'mango':\n return NICClient.MANGO_HOST\n elif tld == 'md':\n return NICClient.MD_HOST\n elif tld == 'me':\n return NICClient.ME_HOST\n elif tld == 'men':\n return NICClient.MEN_HOST\n elif tld == 'menu':\n return NICClient.MENU_HOST\n elif tld == 'mg':\n return NICClient.MG_HOST\n elif tld == 'miami':\n return NICClient.MIAMI_HOST\n elif tld == 'mini':\n return NICClient.MINI_HOST\n elif tld == 'ml':\n return NICClient.ML_HOST\n elif tld == 'mo':\n return NICClient.MO_HOST\n elif tld == 'moe':\n return NICClient.MOE_HOST\n elif tld == 'monash':\n return NICClient.MONASH_HOST\n elif tld == 'moscow':\n return NICClient.MOSCOW_HOST\n elif tld == 'ms':\n return NICClient.MS_HOST\n elif tld == 'mu':\n return NICClient.MU_HOST\n elif tld == 'museum':\n return NICClient.MUSEUM_HOST\n elif tld == 'na':\n return NICClient.NA_HOST\n elif tld == 'name':\n return NICClient.NAME_HOST\n elif tld == 'nc':\n return NICClient.NC_HOST\n elif tld == 'news':\n return NICClient.NEWS_HOST\n elif tld == 'nf':\n return NICClient.NF_HOST\n elif tld == 'nl':\n return NICClient.NL_HOST\n elif tld == 'no':\n return NICClient.NO_HOST\n elif tld == 'nrw':\n return NICClient.NRW_HOST\n elif tld == 'nu':\n return NICClient.NU_HOST\n elif tld == 'nyc':\n return NICClient.NYC_HOST\n elif tld == 'one':\n return NICClient.ONE_HOST\n elif tld == 'online':\n return NICClient.ONLINE_HOST\n elif tld == 'ooo':\n return NICClient.OOO_HOST\n elif tld == 'ovh':\n return NICClient.OVH_HOST\n elif tld == 'paris':\n return NICClient.PARIS_HOST\n elif tld == 'party':\n return NICClient.PARTY_HOST\n elif tld == 'pf':\n return NICClient.PF_HOST\n elif tld == 'physio':\n return NICClient.PHYSIO_HOST\n elif tld == 'plus':\n return NICClient.PLUS_HOST\n elif tld == 'pm':\n return NICClient.PM_HOST\n elif tld == 'pohl':\n return NICClient.POHL_HOST\n elif tld == 'post':\n return NICClient.POST_HOST\n elif tld == 'qpon':\n return NICClient.QPON_HOST\n elif tld == 'quebec':\n return NICClient.QUEBEC_HOST\n elif tld == 'racing':\n return NICClient.RACING_HOST\n elif tld == 're':\n return NICClient.RE_HOST\n elif tld == 'reise':\n return NICClient.REISE_HOST\n elif tld == 'review':\n return NICClient.REVIEW_HOST\n elif tld == 'rodeo':\n return NICClient.RODEO_HOST\n elif tld == 'ruhr':\n return NICClient.RUHR_HOST\n elif tld == 'samsung':\n return NICClient.SAMSUNG_HOST\n elif tld == 'saarland':\n return NICClient.SAARLAND_HOST\n elif tld == 'sb':\n return NICClient.SB_HOST\n elif tld == 'sca':\n return NICClient.SCA_HOST\n elif tld == 'scb':\n return NICClient.SCB_HOST\n elif tld == 'schmidt':\n return NICClient.SCHMIDT_HOST\n elif tld == 'science':\n return NICClient.SCIENCE_HOST\n elif tld == 'scot':\n return NICClient.SCOT_HOST\n elif tld == 'se':\n return NICClient.SE_HOST\n elif tld == 'sh':\n return NICClient.SH_HOST\n elif tld == 'si':\n return NICClient.SI_HOST\n elif tld == 'sk':\n return NICClient.SK_HOST\n elif tld == 'sky':\n return NICClient.SKY_HOST\n elif tld == 'sm':\n return NICClient.SM_HOST\n elif tld == 'sn':\n return NICClient.SN_HOST\n elif tld == 'so':\n return NICClient.SO_HOST\n elif tld == 'spiegel':\n return NICClient.SPIEGEL_HOST\n elif tld == 'st':\n return NICClient.ST_HOST\n elif tld == 'stream':\n return NICClient.STREAM_HOST\n elif tld == 'study':\n return NICClient.STUDY_HOST\n elif tld == 'sucks':\n return NICClient.SUCKS_HOST\n elif tld == 'surf':\n return NICClient.SURF_HOST\n elif tld == 'sx':\n return NICClient.SX_HOST\n elif tld == 'sydney':\n return NICClient.SYDNEY_HOST\n elif tld == 'taipei':\n return NICClient.TAIPEI_HOST\n elif tld == 'tatar':\n return NICClient.TATAR_HOST\n elif tld == 'tc':\n return NICClient.TC_HOST\n elif tld == 'tel':\n return NICClient.TEL_HOST\n elif tld == 'tf':\n return NICClient.TF_HOST\n elif tld == 'tirol':\n return NICClient.TIROL_HOST\n elif tld == 'tk':\n return NICClient.TK_HOST\n elif tld == 'tl':\n return NICClient.TL_HOST\n elif tld == 'tm':\n return NICClient.TM_HOST\n elif tld == 'top':\n return NICClient.TOP_HOST\n elif tld == 'tr':\n return NICClient.TR_HOST\n elif tld == 'trade':\n return NICClient.TRADE_HOST\n elif tld == 'travel':\n return NICClient.TRAVEL_HOST\n elif tld == 'trust':\n return NICClient.TRUST_HOST\n elif tld == 'tui':\n return NICClient.TUI_HOST\n elif tld == 'tv':\n return NICClient.TV_HOST\n elif tld == 'co.ua':\n return NICClient.CO.UA_HOST\n elif tld == 'uno':\n return NICClient.UNO_HOST\n elif tld == 'us':\n return NICClient.US_HOST\n elif tld == 'uz':\n return NICClient.UZ_HOST\n elif tld == 'versicherung':\n return NICClient.VERSICHERUNG_HOST\n elif tld == 'vg':\n return NICClient.VG_HOST\n elif tld == 'vip':\n return NICClient.VIP_HOST\n elif tld == 'vlaanderen':\n return NICClient.VLAANDEREN_HOST\n elif tld == 'vodka':\n return NICClient.VODKA_HOST\n elif tld == 'voting':\n return NICClient.VOTING_HOST\n elif tld == 'wales':\n return NICClient.WALES_HOST\n elif tld == 'webcam':\n return NICClient.WEBCAM_HOST\n elif tld == 'wed':\n return NICClient.WED_HOST\n elif tld == 'wedding':\n return NICClient.WEDDING_HOST\n elif tld == 'wf':\n return NICClient.WF_HOST\n elif tld == 'whoswho':\n return NICClient.WHOSWHO_HOST\n elif tld == 'wien':\n return NICClient.WIEN_HOST\n elif tld == 'win':\n return NICClient.WIN_HOST\n elif tld == 'work':\n return NICClient.WORK_HOST\n elif tld == 'ws':\n return NICClient.WS_HOST\n elif tld == 'wtc':\n return NICClient.WTC_HOST\n elif tld == 'xxx':\n return NICClient.XXX_HOST\n elif tld == 'yoga':\n return NICClient.YOGA_HOST\n elif tld == 'yt':\n return NICClient.YT_HOST\n elif tld == 'zm':\n return NICClient.ZM_HOST\n else:\n return tld + NICClient.QNICHOST_TAIL","def enter_username(self):","def set_real_name(user: User, real_name: str=\"\") -> Result:\n current, *rest = user.pw_gecos.split(\",\")\n if current == real_name:\n return Result(State.unchanged)\n command([\"/usr/bin/chfn\", \"--full-name\", real_name, user.pw_name])\n user.pw_gecos = \",\".join([real_name, *rest])\n return Result(State.success)","def _get_local_ca_host():\n beamline_id = os.uname()[1][:4]\n if not beamline_id.startswith('xf'):\n raise LocationError('You are not on a registered beamline computer. '\n 'Unable to guess which channel archiver to use. '\n 'Please specify the channel archiver you wish to'\n 'obtain data from.')\n return 'http://' + beamline_id + '-ca/cgi-bin/ArchiveDataServer.cgi'","def get_string(opt=\"encode\"):\n text = input(f\"Enter string to {opt}: \")\n return text","async def chutiya (fuks):\n if not fuks.text[0].isalpha() and fuks.text[0] not in (\"/\", \"#\", \"@\", \"!\"):\n index = random.randint(0, len(memes.CHU_STRINGS) - 1)\n reply_text = memes.FUK_STRINGS[index]\n await fuks.edit(reply_text)","def test_returnCanonicalName(self):\n servers = {\n ('1.1.2.3', 53): {\n (b'example.com', A): {\n 'answers': [(b'example.com', Record_CNAME(b'example.net')),\n (b'example.net', Record_A('10.0.0.7'))],\n },\n },\n }\n resolver = self._getResolver(servers)\n d = resolver.lookupAddress(b'example.com')\n d.addCallback(lambda results: results[0]) # Get the answer section\n d.addCallback(\n self.assertEqual,\n [RRHeader(b'example.com', CNAME, payload=Record_CNAME(b'example.net')),\n RRHeader(b'example.net', A, payload=Record_A('10.0.0.7'))])\n return d","def changer_rep():\r\n\troot = tkinter.Tk()\r\n\ttext=tkinter.Text(root, wrap = 'none')\r\n\ttext.insert('1.0', \"Indiquer le nom du nouveau répertoire : \\n\")\r\n\ttext.pack()\r\n\tv = tkinter.StringVar()\r\n\treq=tkinter.Entry(root, textvariable=v, validate='all')\t\r\n\treq.pack()\t\r\n\tvalidate = tkinter.Button(root, text='valider', command=root.quit)\r\n\tvalidate.pack()\r\n\troot.mainloop()\r\n\texecution(v.get())","def prompt_user_passwd(self, host, realm):\n return None, None","async def chutiya(chus):\n if not chus.text[0].isalpha() and chus.text[0] not in (\"/\", \"#\", \"@\", \"!\"):\n index = random.randint(0, len(CHU_STRINGS) - 1)\n reply_text = CHU_STRINGS[index]\n await chus.edit(reply_text)","def getRequestHostname():","def on_the_network_global_configuration_page_change_the_first_nameserver_to_nameserver1(driver, nameserver1):\n global nameserver_1\n nameserver_1 = nameserver1\n assert wait_on_element(driver, 7, '//h4[contains(.,\"Hostname and Domain\")]')\n assert wait_on_element(driver, 5, '//input[@ix-auto=\"input__Nameserver 1\"]', 'inputable')\n driver.find_element_by_xpath('//input[@ix-auto=\"input__Nameserver 1\"]').clear()\n driver.find_element_by_xpath('//input[@ix-auto=\"input__Nameserver 1\"]').send_keys(nameserver1)","def getUsername():\n return input(\"Username\")","def create_host(self, wwpns, hostname):\n\n if not wwpns or len(wwpns) == 0 or not hostname or len(hostname) == 0:\n ex_args = {'wwpns': wwpns,\n 'hostname': hostname}\n raise SVCCreateHostParameterError(**ex_args)\n\n ports = ':'.join(wwpns)\n # get the host shortname.\n hostname_str = hostname.split('.')[0]\n LOG.debug(\"enter: create_host(): wwpns=%(wwpns)s\"\n \" hostname=%(hostname)s\"\n % {'wwpns': ports, 'hostname': hostname_str})\n\n rand_id = str(random.randint(0, 99999999)).zfill(8)\n host_name = '%s-%s' % (self._hostname_prefix(hostname_str), rand_id)\n\n cmd = 'mkhost -name %(host_name)s -hbawwpn %(ports)s -force' % locals()\n\n output, err_output = self._svc_command(cmd)\n\n if err_output:\n # err_output should be a list type\n if isinstance(err_output, types.ListType):\n err_msg = err_output[0]\n else:\n err_msg = err_output\n err_code = err_msg.split()[0]\n\n if err_code and err_code == 'CMMVC6035E':\n # host has been defined on the storage, but we don't see it.\n # return None and ask caller to run cfgdev to relogin to SAN\n # and retry get_host_from_wwpns().\n return None\n\n msg = (_(\"create_host() failure cmd=%(cmd)s, error:%(err_output)s.\"\n \" Make sure host and storage are zoned properly and check\"\n \" SAN fabric connectivity\") % locals())\n\n LOG.exception(msg)\n ex_args = {'host_name': hostname_str,\n 'err_output': err_output}\n raise SVCCreateHostFailed(**ex_args)\n\n return host_name","def host_name(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"host_name\")","def get_hostname_helper(self, sock, nameservers, hostname):\n hostname = hostname.rstrip(\".\") #framework can't handle \"\" or anything ending with a dot\n\n aliases = []\n addresses = []\n\n #1. See if the answer is in local information, and if so return it to the client.\n if self.caching:\n for alias in self.cache.lookup(hostname, Type.CNAME, Class.IN):\n aliases.append(alias.rdata.data)\n for address in self.cache.lookup(hostname, Type.A, Class.IN):\n addresses.append(address.rdata.data)\n \n if aliases != []:\n return hostname, aliases, addresses\n\n #3. Send them queries until one returns a response.\n # Create and send query\n \n while True:\n response = self._get_single_A(sock, nameservers, hostname)\n \n if response == None:\n break\n \n for answer in [ans for ans in response.answers + response.additionals if ans.type_ == Type.A and ans.name == hostname]:\n addresses.append(answer.rdata.data)\n for additional in [ans for ans in response.additionals if ans.type_ == Type.CNAME and ans.name == hostname]:\n aliases.append(additional.rdata.data)\n if addresses != []:\n break\n \n ns2 = nameservers\n nameservers, remaining = self.extract_ip(response.authorities, response.additionals, hostname)\n if nameservers == []:\n nameservers = self.get_ns(sock, ns2, remaining)\n return hostname, aliases, addresses","def setup(self):\n for k, v in self.data.items():\n kq = (k[0].upper() + k[1:].replace(\"_\", \" \")).replace(\"url\", \"URL\")\n option = input(f\"🎤 {kq} [{v}]: \").strip() or v.strip()\n option = self._validate_option(k, option)\n self.data[k] = option","def login(host):\n\n\n \"\"\" change this settings to make use. \"\"\"\n gateway_user = \"lonli\"\n gateway_ip = \"127.0.0.1\"\n gateway_port = \"22\"\n gateway_key = \"/home/lonli/.ssh/id_rsa\"\n\n \"\"\" change abbove settings to make use. \"\"\"\n\n\n if host:\n try:\n subprocess.check_output([\"ssh\", \"-p\", gateway_port, \"-i\", gateway_key,\n \"{0}@{1}\".format(gateway_user, gateway_ip), \"grep {0} ~/.ssh/config\".format(host)])\n except subprocess.CalledProcessError as e:\n print(\"'{0}' does not exists in the configuratian of the gateway!\".format(host), file=sys.stderr)\n return\n\n to_gateway = \"ssh -p {0} -i {1} {2}@{3}\".format(gateway_port, gateway_key, gateway_user, gateway_ip)\n ssh = pexpect.spawn(to_gateway)\n if host:\n\n \n \"\"\" change this settings to make use. \"\"\"\n exps = [\n (\"lonli@arch\", 'echo -n \"Enter diretory : \" && read && [ -d \"${REPLY}\" ] && cd ${REPLY}'),\n (\"Enter diretory : \", \"/tmp\"),\n (\"/tmp\", \"pwd\"),\n ]\n \"\"\" change abbove session to make use. \"\"\"\n\n\n for p, s in exps:\n # print(\"expect : {0}, then send : {1}\".format(p, s))\n ssh.expect(p)\n ssh.sendline(s)\n winch_handler = sigwinch_handler(ssh)\n signal.signal(signal.SIGWINCH, winch_handler)\n winch_handler(None, None)\n ssh.interact()","def fix_location(in_text):\n prompt = ConversionPrompt(\n 'I', 'O',\n (\"lenox ma\", \"Lenox, MA\"),\n (\"london\", \"London, U.K.\"),\n (\"chicago\", \"Chicago, IL\"),\n (\"dallas, tx\", \"Dallas, TX\"),\n engine='babbage'\n )\n return prompt.convert(in_text)","def get_ascii_input(self, varname, deflt=None):\n try:\n value = self.request.var(varname, deflt)\n if value is not None:\n value.decode(\"ascii\")\n return value\n except UnicodeDecodeError:\n raise MKUserError(varname, _(\"The given text must only contain ASCII characters.\"))","def url_fix_host(urlparts):\n # if not urlparts[1]:\n # urlparts[2] = urllib.unquote(urlparts[2])\n # return False\n userpass, netloc = urllib.splituser(urlparts[1])\n if userpass:\n userpass = urllib.unquote(userpass)\n netloc, is_idn = idna_encode(urllib.unquote(netloc).lower())\n # a leading backslash in path causes urlsplit() to add the\n # path components up to the first slash to host\n # try to find this case...\n i = netloc.find(\"\\\\\")\n if i != -1:\n # ...and fix it by prepending the misplaced components to the path\n comps = netloc[i:] # note: still has leading backslash\n if not urlparts[2] or urlparts[2] == '/':\n urlparts[2] = comps\n else:\n urlparts[2] = \"%s%s\" % (comps, urllib.unquote(urlparts[2]))\n netloc = netloc[:i]\n else:\n # a leading ? in path causes urlsplit() to add the query to the\n # host name\n i = netloc.find(\"?\")\n if i != -1:\n netloc, urlparts[3] = netloc.split('?', 1)\n # path\n urlparts[2] = urllib.unquote(urlparts[2])\n if userpass and userpass != ':':\n # append AT for easy concatenation\n userpass += \"@\"\n else:\n userpass = \"\"\n\n if urlparts[0] in default_ports:\n dport = default_ports[urlparts[0]]\n host, port = splitport(netloc, port=dport)\n\n host = host.rstrip('. ')\n if port != dport:\n host = \"%s:%d\" % (host, port)\n netloc = host\n urlparts[1] = userpass + netloc\n return is_idn","def prompt_username():\n msg = \"Enter Username: \"\n username = raw_input(msg)\n return username","def __init__(self, prompt, ip_address, port):\n self._ip_address = ip_address\n self._port = int(port)\n self._sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n self._sock.connect((self._ip_address, self._port))\n self._prompt = prompt\n while True:\n reply = self._receive(False)\n if reply is not None:\n if to_str(reply) == self._prompt:\n break\n else:\n break","def main():\n username = str(input())\n print(str.lower(username))","def _parse_host(host: str) -> str:\n urlparse_host = urlsplit(host).hostname\n if urlparse_host:\n # In this case, host = https://xx.cloud.databricks.com\n return urlparse_host\n else:\n # In this case, host = xx.cloud.databricks.com\n return host","def test_alt_name_request(self):\n oim = OIM()\n hostname = 'test.' + DOMAIN\n san = 'test-san.' + DOMAIN\n san2 = 'test-san2.' + DOMAIN\n rc, _, _, msg = oim.request('--hostname', hostname,\n '--altname', san,\n '--altname', san2)\n self.assertEqual(rc, 0, \"Failed to request certificate\\n%s\" % msg)\n self.assert_(oim.reqid != '', msg)","def letters(string, user_input):\n\n string = upper(string) # pass the string to the upper function to capitalize it\n options = [] # The array where the options given by the user will be stored\n new_string = '' # Empty string to append to\n\n for char in user_input: # Itterate over words in the options passed by the user \n options.append(char) # Append them to the options list\n\n for char in string: # Itterate over each character in the original string\n if char == 'A' and 'A' in options: # If the character is \"A\" and given by the user\n char = '4' # Repalce it with \"4\"\n if char == 'E' and 'E' in options: # If the character is \"E\" and given by the user\n char = '3' # Repalce it with \"3\"\n if char == 'S' and 'S' in options: # If the character is \"A\" and given by the user\n char = '5' # Repalce it with \"5\"\n if char == 'B' and 'B' in options: # If the character is \"B\" and given by the user\n char = '13' # Repalce it with \"13\"\n if char == 'O' and 'O' in options: # If the character is \"O\" and given by the user\n char = '0' # Repalce it with \"0\"\n if char == 'I' and 'I' in options: # If the character is \"I\" and given by the user\n char = '1' # Repalce it with \"1\"\n if char == 'V' and 'V' in options: # If the character is \"V\" and given by the user\n char = '\\/' # Repalce it with \"\\/\"\n if char == 'W' and 'W' in options: # If the character is \"W\" and given by the user\n char = '\\/\\/' # Repalce it with \"\\/\\/\"\n new_string += char # Append all the characters to the empty string\n\n return new_string # Return the new string","def test_correct_sheme_host_sent_with_request(self):\n req = self.httpbin.get_my_ip(dry_run=True)\n self.assertIn(self.httpbin.client['host'], urlparse(req.prepared_request.url).netloc)\n self.assertIn(self.httpbin.client['scheme'], urlparse(req.prepared_request.url).scheme)\n self.assertIn(self.httpbin.client['get_my_ip']['path'], urlparse(req.prepared_request.url).path)"],"string":"[\n \"def get_host_name():\\r\\n\\tglobal HOST_NAME\\r\\n\\r\\n\\twhile True:\\r\\n\\t\\tname_of_host = input(\\\"What is your name (As it appears in zoom without the '(Host, me)' part)? \\\")\\r\\n\\t\\tHOST_NAME = name_of_host + \\\" (Host, me)\\\"\\r\\n\\t\\tcorrect = input(f\\\"Host name set as {HOST_NAME}, is this correct? [Y/N]: \\\")\\r\\n\\t\\tif correct.upper() == \\\"Y\\\":\\r\\n\\t\\t\\treturn\",\n \"def _get_hostname():\\n return click.prompt(\\\"What is the Pulp host's hostname?\\\", type=click.STRING)\",\n \"def InputMacAddress(prompt):\\n while 1:\\n SetPatternColor()\\n print prompt,\\n SetPatternColor(1)\\n val = raw_input().strip()\\n SetPatternColor()\\n try:\\n #if len(val) == 12 and int(val,16):\\n if (len(val) == 12 or len(val) == 10): \\n return val.upper()\\n except ValueError:\\n pass\\n #SetPattern(0)\\n SetPatternColor(0)\\n print \\\"ERROR %s!!\\\"%prompt\",\n \"def ip2host(self, irc, msg, args, ip):\\n \\n try:\\n hostname = socket.gethostbyaddr(ip)\\n hostname = hostname[0]\\n if hostname:\\n record = self._record_by_addr(ip)\\n if record:\\n reply = u'%s (%s)' % (hostname, self._geoip_city_check(record))\\n else:\\n reply = u'geoIP Fehler!'\\n except:\\n reply = u'gethostbyaddr() Error'\\n \\n irc.reply(reply.encode('utf-8'))\",\n \"def host2ip(self, irc, msg, args, hostname):\\n \\n try:\\n ip = socket.gethostbyname(hostname)\\n if ip:\\n record = self._record_by_addr(ip)\\n if record:\\n reply = u'%s (%s)' % (ip, self._geoip_city_check(record))\\n else:\\n reply = u'geoIP Fehler!'\\n \\n except:\\n reply = u'gethostbyname() Error'\\n \\n irc.reply(reply.encode('utf-8'))\",\n \"def ask_for_device():\\n return input(\\\"Device name or ip address : \\\")\",\n \"def countryInput(self, prompt):\\n while True:\\n name = input(prompt)\\n if name not in self.travel_db.countries:\\n print(\\\"Invalid country name. Please make sure name is capitalized.\\\")\\n else:\\n return name\",\n \"def ask_username(ip):\\n username_question = [\\n {\\n 'type': 'input',\\n 'message': 'Enter the username of the machine with yhe IP address ' + ip + ':',\\n 'name': 'username',\\n 'validate': NotEmpty\\n }\\n ]\\n\\n username_answer = prompt(username_question, style=style)\\n user = username_answer[\\\"username\\\"]\\n return user\",\n \"def format_host(host):\\n\\n host = strip_suffix(host, \\\".lan.urlab.be\\\")\\n host = strip_suffix(host, \\\".lan\\\")\\n host = strip_suffix(host, \\\".local\\\")\\n host = strip_suffix(host, \\\"iPodtouch\\\")\\n host = strip_suffix(host, \\\"-PC\\\")\\n host = strip_suffix(host, \\\"-pc\\\")\\n\\n host = strip_prefix(host, \\\"pc-\\\")\\n host = strip_prefix(host, \\\"PC-\\\")\\n host = strip_prefix(host, \\\"DESKTOP-\\\")\\n host = strip_prefix(host, \\\"LAPTOP-\\\")\\n host = strip_prefix(host, \\\"iPod-de-\\\")\\n host = strip_prefix(host, \\\"iPadde\\\")\\n\\n return host\",\n \"def prompt_ip(prompt):\\n response = \\\"\\\"\\n while not is_valid_ip(response):\\n response = prompt_base(prompt)\\n return response\",\n \"def ask_for_domain():\\n found = False\\n\\n # Keep asking until a valid domain has been entered by the user\\n while not found:\\n domain = u\\\"https://\\\" + input(u\\\"\\\\nEnter the Canvas domain of your institution:\\\\n$ https://\\\")\\n found = helpers.validate_domain(domain)\\n\\n return domain\",\n \"def host():\\n\\n print(\\\"\\\"\\\" Bienvenue sur l'application Pur Beurre\\n --------------------------------------------\\n 1: Quel aliment souhaitez-vous remplacer ?\\n 2: Retrouver mes aliments substitués\\n 3: Quitter\\\"\\\"\\\")\\n\\n while True:\\n try:\\n choice = int(input(\\\"Entrez votre choix: \\\\n\\\"))\\n if choice in range(1, 4):\\n break\\n except ValueError:\\n continue\\n\\n return choice\",\n \"def snap_part_to_host_input(part):\\n adc, name = part.split('>')\\n # convert the string adc (eg \\\"e2\\\") into a channel number 0-2\\n adc_num = int(adc[1:]) // 2 # divide by 2 because ADC is in demux 2\\n # convert the name into something which should be a hostname.\\n # name has the form SNPA0000123. Hostnames have the form herasnapA123\\n # Edit 16 Sep 2019 -- This isn't true anymore. CM names and hostnames are now the same\\n hostname = name#\\\"herasnap%s\\\" % (name[3] + name[4:].lstrip('0'))\\n try:\\n true_name, aliases, addresses = socket.gethostbyaddr(hostname)\\n except:\\n logger.error('Failed to gethostbyname for host %s' % hostname)\\n # assume that the one we want is the last thing in the hosts file line\\n return aliases[-1], adc_num\",\n \"def hostname(name: str = \\\"\\\") -> str:\\n ...\",\n \"def __connect_failed__(self):\\n # Ask the user what to do with the error\\n choice = input(\\\"[A]bort, [C]hange address and port, or [R]etry?\\\")\\n if (choice.lower() == \\\"a\\\"):\\n exit()\\n elif (choice.lower() == \\\"c\\\"):\\n address = input(\\\"Please enter the address:\\\")\\n port_number = input(\\\"Please enter the port:\\\")\",\n \"def hostname_to_site(hostname: str) -> str:\\n if hostname == \\\"biothings.ncats.io\\\" or hostname.endswith(\\\"transltr.io\\\"):\\n return \\\"ncats\\\"\\n\\n return \\\"pending\\\"\",\n \"def _aa_host_name(self):\\n self.is_option = True\\n self.is_statement = False\\n self.has_validator = True\\n if not (self.value.startswith('\\\"') and self.value.endswith('\\\"')):\\n self.value = '\\\"' + self.value + '\\\"'\\n validate_name(self.value.strip('\\\"'))\",\n \"def change_host(self, str_cef, address):\\r\\n tmp = str_cef\\r\\n if str_cef.find(\\\"CEF:\\\",0,len(str_cef)) != -1:\\r\\n tmp = str_cef[:21] + str(address[0]) + str_cef[25:]\\r\\n\\r\\n elif str_cef.find(\\\"|\\\",0,len(str_cef)) != -1:\\r\\n st = str_cef.find(\\\"|\\\",0,len(str_cef))\\r\\n st = str_cef.find(\\\"|\\\",st+1,len(str_cef))\\r\\n tmp = str_cef[:st+1]+str(address[0])+\\\"|\\\"+str_cef[st+1:]\\r\\n return tmp\",\n \"def handle_hostname(bot, ievent):\\n try:\\n item = ievent.args[0]\\n except IndexError:\\n ievent.missing('<ipnr>')\\n return\\n try:\\n hostname = socket.gethostbyaddr(item)\\n ievent.reply(hostname[0])\\n except:\\n ievent.reply(\\\"can't match \\\" + str(item))\",\n \"def _host(name):\\n\\n\\ttry:\\n\\t\\tsocket.gethostbyname(name)\\n\\t\\treturn name\\n\\texcept socket.error:\\n\\t\\traise argparse.ArgumentTypeError(\\\"Invalid hostname: \\\" + name)\\n\\ttry:\\n\\t\\tsocket.inet_aton(name)\\n\\t\\treturn name\\n\\texcept socket.error:\\n\\t\\traise argparse.ArgumentTypeError(\\\"Invalid ip address: \\\" + name)\",\n \"def cliquer(self):\\n try:\\n if self.input.get() != \\\"\\\":\\n human = HumanPlayer(self.input.get())\\n cpu = CPUPlayer(\\\"La Machine\\\", var_choix.get(), 15)\\n self.input.destroy()\\n self.bouton_cliquer[\\\"command\\\"] = self.jouer(human, cpu)\\n except:\\n input(\\\"Vous devez fournir un nom !\\\")\",\n \"def searchCountry(host):\\n process = subprocess.Popen(\\\"geoiplookup \\\"+host,stdout=subprocess.PIPE, shell=True)\\n (output, err) = process.communicate()\\n secondPart = output.split(\\\"GeoIP Country Edition: \\\", 1)[1]\\n country = secondPart.split(\\\"\\\\nGeoIP City Edition\\\", 1)[0]\\n return country\",\n \"def checkGetHostByName(self, result):\\n self.assertEquals(result, '127.0.0.1')\",\n \"def _what_is_username(self):\\n prompt = \\\"-?- Send to: \\\"\\n sn = self._input(prompt)\\n return sn\",\n \"async def _parse_hostname(self, output, cb_token) -> None:\\n if not output:\\n self.hostname = \\\"-\\\"\\n return\\n\\n if output[0][\\\"status\\\"] == 0:\\n data = output[1][\\\"data\\\"]\\n try:\\n jout = json.loads(data)\\n self.hostname = jout[\\\"hostname\\\"]\\n except:\\n self.hostname = \\\"-\\\"\",\n \"def prep(user, host, resource):\\n\\n if user:\\n try:\\n user = nodeprep.prepare(unicode(user))\\n except UnicodeError:\\n raise InvalidFormat(\\\"Invalid character in username\\\")\\n else:\\n user = None\\n\\n if not host:\\n raise InvalidFormat(\\\"Server address required.\\\")\\n else:\\n try:\\n host = nameprep.prepare(unicode(host))\\n except UnicodeError:\\n raise InvalidFormat(\\\"Invalid character in hostname\\\")\\n\\n if resource:\\n try:\\n resource = resourceprep.prepare(unicode(resource))\\n except UnicodeError:\\n raise InvalidFormat(\\\"Invalid character in resource\\\")\\n else:\\n resource = None\\n\\n return (user, host, resource)\",\n \"def test_strip_prompt():\\n string = \\\"\\\"\\\"MyRouter version 1.25.9\\nmyhostname>\\\"\\\"\\\"\\n connection = FakeBaseConnection(RESPONSE_RETURN=\\\"\\\\n\\\", base_prompt=\\\"myhostname>\\\")\\n result = connection.strip_prompt(string)\\n assert result == \\\"MyRouter version 1.25.9\\\"\",\n \"def passPrompt(title, prompt):\\n answer = tkSimpleDialog.askstring(title, prompt, show=\\\"*\\\")\\n print answer\",\n \"def user_question():\\n return input('What would you like? (espresso/latte/cappuccino): ')\",\n \"def handle_commands_preset(self,cl,addr) :\\n self.curDir = ['CTF','Challenges','tempUser'+str(random.randint(100,999))]\\n try :\\n client = cl\\n if self.curDir != [] : \\n userp = \\\"temp-user-\\\"+addr[0].replace('.','-')+\\\"@ieeectf:~/{}$ \\\".format('/'.join(self.curDir))\\n else :\\n userp = \\\"temp-user-\\\"+addr[0].replace('.','-')+\\\"@ieeectf:~$ \\\"\\n self.userp = userp.encode()\\n client.send(\\\"\\\"\\\"\\nCustom Shell Server With Limited Functionality\\n\\nNew User Login from {} at {}\\n \\\\n\\\"\\\"\\\".format(addr[0],time.ctime()).encode())\\n shellin = \\\"\\\" \\n while True:\\n if self.curDir != [] : \\n userp = \\\"temp-user-\\\"+addr[0].replace('.','-')+\\\"@ieeectf:~/{}$ \\\".format('/'.join(self.curDir))\\n else :\\n userp = \\\"temp-user-\\\"+addr[0].replace('.','-')+\\\"@ieeectf:~$ \\\"\\n self.userp = userp.encode()\\n client.send(self.userp)\\n shellin = client.recv(2048).decode().strip('\\\\n')\\n if shellin == \\\"exit\\\" or shellin == \\\"exit \\\" or shellin ==\\\"exit \\\" or shellin ==\\\"exit \\\" :\\n break\\n elif shellin == \\\"\\\" :\\n continue\\n elif shellin.split()[0] in self.denied :\\n client.send(self.err.format(shellin.split()[0]).encode())\\n else :\\n self.handle_extended_commands(client,addr,shellin)\\n continue\\n client.close()\\n except Exception as E:\\n print(E)\\n print(Log(\\\"Connection with {} Terminated\\\".format(addr)))\",\n \"def askPass( user, host ):\\n prompt = \\\"Password for user {} on host {}: \\\".format( user, host )\\n password = getpass.getpass( prompt )\\n return password\",\n \"def name_prompt(input_msg):\\n while True:\\n donor_name = input(input_msg)\\n try:\\n if not \\\"\\\".join(donor_name.split()).isalpha():\\n raise Exception(\\\"The name should be all Alphabetic characters:\\\")\\n return donor_name.title()\\n except Exception as e:\\n print(\\\"INVALID Name: \\\",e)\",\n \"def prompt_base(prompt):\\n return input(prompt + \\\": \\\")\",\n \"def _grab_host(self):\\r\\n host = \\\"\\\"\\r\\n while True:\\r\\n while self._char != -1 and not self._char in \\\".\\\\x00\\\\t\\\\r\\\\n\\\":\\r\\n host += self._char\\r\\n self._get_char()\\r\\n if self._char == \\\".\\\":\\r\\n host += \\\".\\\"\\r\\n self._get_char()\\r\\n else:\\r\\n break\\r\\n return host\",\n \"def handle_dns(bot, ievent):\\n if not ievent.args:\\n ievent.missing('<host | ip>')\\n else:\\n is_a = None\\n result = None\\n # If we support IPv6 ...\\n if socket.has_ipv6:\\n # ... then check if this is an IPv6 ip\\n try:\\n socket.inet_pton(socket.AF_INET6, ievent.args[0])\\n is_a = 'ipv6'\\n except socket.error:\\n pass\\n # Ah not an IPv6 ip ...\\n if not is_a:\\n # ... maybe IPv4 ?\\n try:\\n socket.inet_pton(socket.AF_INET, ievent.args[0])\\n is_a = 'ipv4'\\n except socket.error:\\n pass\\n # Not an ip, must be a hostname then\\n if not is_a:\\n is_a = 'host'\\n # If it was an ip ...\\n if is_a in ['ipv4', 'ipv6']:\\n try:\\n # ... try to resolve it\\n result = socket.gethostbyaddr(ievent.args[0])\\n if result[1]:\\n result = 'primary: %s, aliases: %s' % \\\\\\n (result[0], ', '.join(result[1]))\\n else:\\n result = result[0]\\n ievent.reply('%s ip %s resolves to %s' % \\\\\\n (is_a, ievent.args[0], result))\\n except Exception, e:\\n ievent.reply('could not resolve %s address %s: %s' % \\\\\\n (is_a, ievent.args[0], e[1]))\\n # Oh it's a host, lets resolve that\\n elif is_a == 'host':\\n try:\\n result = []\\n for info in socket.getaddrinfo(ievent.args[0], None):\\n if info[0] in [socket.AF_INET, socket.AF_INET6] and \\\\\\n info[1] == socket.SOCK_STREAM:\\n ip = info[4][0]\\n if not ip in result:\\n result.append(ip)\\n if not result:\\n ievent.reply('could not resolve hostname %s: not found' % \\\\\\nievent.args[0])\\n else:\\n ievent.reply('%s resolves to: %s' % (ievent.args[0], \\\\\\n', '.join(result)))\\n except Exception, e:\\n ievent.reply('could not resolve hostname %s: %s' % \\\\\\n (ievent.args[0], e[1]))\\n else:\\n ievent.reply('lookup failed, no valid data found')\",\n \"def test_address(self):\\n result = irc.dccDescribe(\\\"CHAT arg 3232235522 6666\\\")\\n self.assertEqual(result, \\\"CHAT for host 192.168.0.2, port 6666\\\")\",\n \"def gethostbycondorname(name):\\n\\n m = htcondor_ip_name_re.match(name)\\n if m is not None:\\n return m.group(1).replace('-', '.')\\n else:\\n return socket.gethostbyname(name)\",\n \"def getHost():\",\n \"def getHost():\",\n \"def prompt_user_passwd(host, realm):\\n return ('', '')\",\n \"def pedir_palabra(msg, min, max):\\n while True:\\n txt = str(raw_input(msg))\\n if not txt.isalpha() :\\n show_msg(\\\"Oops! Parece que eso no era una palabra valida\\\")\\n continue\\n if len(txt) > max:\\n show_msg(\\\"Palabra muy larga (Max %d caracteres)\\\"%max)\\n continue\\n elif len(txt) < min :\\n show_msg(\\\"Palabra muy corta (Min %d caracteres)\\\"%min)\\n continue\\n else:\\n return txt.lower()\",\n \"def prompt(self):\\r\\n super().prompt_number()\\r\\n self.email = str(input(\\\"Email: \\\"))\",\n \"def test_detectCanonicalNameLoop(self):\\n servers = {\\n ('1.1.2.3', 53): {\\n ('example.com', A): {\\n 'answers': [('example.com', Record_CNAME('example.net')),\\n ('example.net', Record_CNAME('example.com'))],\\n },\\n },\\n }\\n resolver = self._getResolver(servers)\\n d = resolver.lookupAddress('example.com')\\n return self.assertFailure(d, ResolverError)\",\n \"def process_input(server_msg):\\n global username\\n original=server_msg\\n server_msg=server_msg.split(\\\" \\\") #separa por args\\n\\n if(server_msg[0]==\\\"DISPLAY\\\"):\\n print(original[8::].replace(';', '\\\\n'))\\n\\n\\n\\n elif(server_msg[0]==\\\"SUC\\\"):\\n if(len(server_msg)>1):\\n if(server_msg[1]==\\\"REG_OK\\\"):\\n username='[' + server_msg[2].lower() + ']'\\n print(REG_OK)\\n elif(server_msg[1]=='INVITE_OK'):\\n print(INVITE_OK)\\n elif(server_msg[1]=='REJECT'):\\n print(REJECT.format(server_msg[2]))\\n elif(server_msg[1]=='DISCONNECT'):\\n print(DISCONNECT)\\n username=''\\n\\n elif(server_msg[0]=='GAME'):\\n if(server_msg[1]=='START'):\\n print(START.format(server_msg[2]))\\n elif(server_msg[1]=='WIN'):\\n print(WIN)\\n elif(server_msg[1]=='LOSE'):\\n print(LOSE)\\n elif(server_msg[1]=='TIE'):\\n print(TIE)\\n elif(server_msg[1]=='FOLD'):\\n print(FOLD.format(server_msg[2]))\\n\\n\\n elif(server_msg[0]==\\\"ERR\\\"):\\n if(server_msg[1]==\\\"BAD_REQUEST\\\"):\\n print(BAD_REQUEST)\\n elif(server_msg[1]==\\\"REG_FAIL\\\"):\\n print(REG_FAIL)\\n elif(server_msg[1]==\\\"USER_REGISTERED\\\"):\\n print(USER_REGISTERED)\\n elif(server_msg[1]=='USER_BUSY'):\\n print(USER_BUSY)\\n elif(server_msg[1]=='USER_UNKNOWN'):\\n print(USER_UNKNOWN)\\n elif(server_msg[1]=='IMBUSY'):\\n print(IMBUSY)\\n elif(server_msg[1]=='NO_USER'):\\n print(NO_USER)\\n elif(server_msg[1]=='NO_ENV'):\\n print(NO_ENV)\\n elif(server_msg[1]=='NOT_IN_GAME'):\\n print(NOT_IN_GAME)\\n elif(server_msg[1]=='NO_TURN'):\\n print(NO_TURN)\\n elif(server_msg[1]=='REG_FAIL'):\\n print(REG_FAIL)\\n elif(server_msg[1]=='INVALID_PLAY'):\\n print(INVALID_PLAY.format(server_msg[2], server_msg[3], server_msg[4]))\\n elif(server_msg[1]=='BAD_FORMAT'):\\n print(BAD_FORMAT)\\n elif(server_msg[1]=='INVALID_COOR'):\\n print(INVALID_COOR)\\n elif(server_msg[1]=='USER_DISCONECTED'):\\n print(USER_DISCONECTED)\\n elif(server_msg[1]=='YSELF'):\\n print(YSELF)\\n elif(server_msg[1]=='NO_INV'):\\n print(NO_INV)\\n\\n \\n elif(server_msg[0]==\\\"INVITE\\\"):\\n print(INVITE_REC.format(server_msg[1]))\\n\\n\\n\\n elif(server_msg[0]==\\\"BOARD\\\"):\\n if(server_msg[1]=='1'):\\n print(\\\"\\\\n\\\\nYour turn to play\\\\n\\\")\\n elif(server_msg[1]=='0'):\\n print(\\\"\\\\n\\\\nWaiting for oponent...\\\\n\\\")\\n board=eval(original[8::])\\n for i in range(len(board)):\\n for k in range(len(board[i])):\\n if(board[i][k]==0):\\n board[i][k]=' '\\n elif(board[i][k]==1):\\n board[i][k]='X'\\n else:\\n board[i][k]='O'\\n try:\\n spacer=\\\"\\\"\\n rows, columns = os.popen('stty size', 'r').read().split()\\n rows = int(rows)\\n columns = int(columns)\\n tmp=int((columns-13)/2)\\n for i in range(tmp):\\n spacer+=\\\" \\\"\\n except:\\n pass\\n print(\\\"\\\\n{} 0 1 2 \\\\n\\\".format(spacer)\\\\\\n +\\\"{}0 {} {} {} {} {} \\\\n\\\".format(spacer,board[0][0],VLINE, board[0][1],VLINE, board[0][2])\\\\\\n +spacer+HLINE\\\\\\n +\\\"{}1 {} {} {} {} {} \\\\n\\\".format(spacer,board[1][0],VLINE, board[1][1],VLINE, board[1][2])\\\\\\n +spacer+HLINE\\\\\\n +\\\"{}2 {} {} {} {} {} \\\\n\\\".format(spacer,board[2][0],VLINE, board[2][1],VLINE, board[2][2]))\\n\\n elif(server_msg[0]==\\\"LIST\\\"):\\n all_users=eval(original[5::])\\n print(\\\"USER\\\\t|\\\\tSTATUS\\\")\\n for i in all_users:\\n print(\\\"{}\\\\t|\\\\t{}\\\".format(i[0], \\\"available\\\" if i[1] == 0 else \\\"unavailable\\\"))\\n print(\\\"\\\\n\\\")\\n\\n elif(server_msg[0]==\\\"SERVER_OFF\\\"):\\n print('\\\\n' + SERVER_OFF + '\\\\n\\\\n')\\n exit_sig()\\n sys.stdout.flush()\",\n \"def addr_entry(key):\\n\\n # getting the input\\n rawline = input(\\n 'please enter ' + key + ': '\\n )\\n\\n # get rid of trailing whitespace\\n line = rawline.strip()\\n\\n # replace tabs\\n line = line.replace('\\\\t', ' ')\\n\\n # remove forbidden strings\\n trans_tab = dict.fromkeys(\\n map(ord, '\\\\:.\\\\\\\"\\\\'!@#$\\\\\\\\\\\\/'), None\\n )\\n\\n line = line.translate(trans_tab)\\n\\n if rawline != line:\\n print(\\n 'Warning: string has been changed: \\\\''\\n + rawline\\n + '\\\\' to \\\\''\\n + line\\n + '\\\\'.'\\n )\\n return line\",\n \"def localizeForHostName(filename): \\n hostname = socket.gethostname()\\n if hostname in filename:\\n updated_filename = filename.replace(hostname, '')\\n return updated_filename.strip('-')\\n return filename\",\n \"def hostname(self, code):\\n return self.langs[code]\",\n \"def input_desc():\\r\\n desc = input(\\\"Entrez ici les remarques générales du directeur du tournoi: \\\")\\r\\n return desc\",\n \"def ask(self):\\n subprocess.run([\\\"say\\\", \\\"-v\\\", \\\"Kyoko\\\", str(self.answer)])\",\n \"def get_address():\\r\\n address = input(\\\"What is the customer's address?: \\\")\\r\\n\\r\\n return address\",\n \"def test_sanitized_hostname(self):\\n value = \\\" ../ ../some/dubious/hostname \\\"\\n response = clean.hostname(value)\\n assert response == \\\"somedubioushostname\\\"\",\n \"def ask_for_interface():\\n return input(\\\"Interface name : \\\")\",\n \"def user(inp):\\n user = inp.text.lower().replace(' ', '-')\\n return 'http://www.wikidot.com/user:info/' + user\",\n \"def convert_diplomaticletters():\\n\\n local('cd import_scripts;../bin/python import_diplomaticletters.py import')\",\n \"def local_task_entry(text=\\\"> \\\"):\\n task_completer = localTaskCompleter()\\n while True:\\n result = prompt(text, completer=task_completer)\\n if result not in task_completer.tasks:\\n print(\\\"Invalid task, try again.\\\")\\n else:\\n return result\",\n \"def via_host_name(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"via_host_name\\\")\",\n \"def via_host_name(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"via_host_name\\\")\",\n \"def inask(question: str) -> str:\\n answer = input(question)\\n return answer\",\n \"def prompt_for_credentials(realm, uri, user, password):\\n\\n try:\\n if realm is None:\\n realm = raw_input('Realm: ')\\n if uri is None:\\n uri = raw_input('URI: ')\\n if user is None:\\n user = raw_input('Username: ')\\n if password is None:\\n password = getpass.getpass()\\n\\n return realm, uri, user, password\\n except KeyboardInterrupt:\\n print\\n return None, None, None, None\",\n \"def getURL():\\n url = input('Paste URL: ')\\n\\n return url\",\n \"def UserHost(user, host):\\n if user:\\n return user + '@' + host\\n else:\\n return host\",\n \"def main():\\n with Scrapli(**MY_DEVICE) as conn:\\n print(conn.get_prompt())\\n print(conn.send_command(\\\"show run | i hostname\\\").result)\",\n \"def requestInput(st):\\n return input(st+\\\": \\\")\",\n \"def CiscoLoopback():\\n cisco = {}\\n print \\\"***Cisco router configuration***\\\"\\n as_number = raw_input(\\\"\\\\tInsert BGP AS number: \\\")\\n while True:\\n cisco_loopback = {}\\n loopback_name = raw_input(\\\"\\\\tInsert loopback name (Press 'q' to quit): \\\")\\n if loopback_name is not 'q':\\n loopback_address = raw_input(\\\"\\\\tLoopback address (eg. x.x.x.x): \\\")\\n loopback_network = raw_input(\\\"\\\\tLoopback network (eg. x.x.x.0): \\\")\\n loopback_mask = raw_input(\\\"\\\\tLoopback mask (eg. 255.255.255.0): \\\")\\n cisco_loopback[\\\"address\\\"] = loopback_address\\n cisco_loopback[\\\"network\\\"] = loopback_network\\n cisco_loopback[\\\"mask\\\"] = loopback_mask\\n cisco[loopback_name] = cisco_loopback\\n elif loopback_name == 'q':\\n break\\n else:\\n print \\\"You just typed something incorrect!\\\"\\n return (cisco, as_number)\",\n \"def ask_user_input(prompt: str) -> str:\\n return input(prompt)\",\n \"def parse_command_line(description):\\n ip = socket.gethostbyname(socket.gethostname())\\n parser = argparse.ArgumentParser(description=description)\\n parser.add_argument('host', metavar='host', nargs='?', help='IP or hostname', default=ip)\\n parser.add_argument('-p', metavar='port', type=int, default=7000,\\n help='TCP port (default 7000)')\\n parser.add_argument('-a', metavar='cafile', default=None,\\n help='Set up a basic encrypted Connection')\\n args = parser.parse_args()\\n address = (args.host, args.p, args.a)\\n return address\",\n \"def register_username(self):\\n while True:\\n message = self.receive()\\n if message == Message.OK:\\n break\\n print(message.decode(\\\"UTF-8\\\"))\\n while True:\\n self.username = input(\\\"Username: \\\")\\n if len(self.username) > 0:\\n self.send(self.username.encode(\\\"UTF-8\\\"))\\n break\\n else:\\n print(\\\"Empty username!\\\")\",\n \"def ssh_connection(hostname, username):\\n\\n #We testing if the username can to connect to the hostname\\n if username == \\\"company1\\\":\\n if hostname in open(\\\"./servers_list_company1.txt\\\", \\\"r\\\").read():\\n pass\\n else:\\n return 1\\n elif username == \\\"company2\\\":\\n if hostname in open(\\\"./servers_list_company2.txt\\\", \\\"r\\\").read():\\n pass\\n else:\\n return 1\\n else:\\n return 1\\n\\n #Connexion au serveur (nb, il faut que l'échange de clé ssh est eu lieu)\\n try:\\n ssh = paramiko.SSHClient()\\n ssh.set_missing_host_key_policy(paramiko.AutoAddPolicy())\\n ssh.connect(hostname, username=\\\"postfix\\\", timeout=4)\\n print \\\"Connected to %s\\\" % hostname\\n except paramiko.AuthenticationException:\\n return 1\\n except:\\n return 1\\n\\n return ssh\",\n \"def click_advanced_and_input_the_computer_account_ou_truenas_servers(driver, ca_ou):\\n if is_element_present(driver, '//button[@ix-auto=\\\"button__ADVANCED OPTIONS\\\"]'):\\n driver.find_element_by_xpath('//button[@ix-auto=\\\"button__ADVANCED OPTIONS\\\"]').click()\\n assert wait_on_element(driver, 7, '//input[@ix-auto=\\\"input__Computer Account OU\\\"]', 'inputable')\\n driver.find_element_by_xpath('//input[@ix-auto=\\\"input__Computer Account OU\\\"]').clear()\\n driver.find_element_by_xpath('//input[@ix-auto=\\\"input__Computer Account OU\\\"]').send_keys(ca_ou)\",\n \"def location_prompt(location, msg='WEAthER'):\\n from x84.bbs import getterminal, echo, getch\\n term = getterminal()\\n echo(u''.join((u'\\\\r\\\\n\\\\r\\\\n',\\n term.yellow(u'diSPlAY %s fOR ' % (msg,)),\\n term.bold('%(city)s, %(state)s' % location),\\n term.yellow(' ? '),\\n term.bold_yellow(u'['),\\n term.underline_yellow(u'yn'),\\n term.bold_yellow(u']'),\\n u': '),))\\n while True:\\n inp = getch()\\n if inp is None or inp in (u'n', u'N', 'q', 'Q', term.KEY_EXIT):\\n return False\\n if inp in (u'y', u'Y', u' ', term.KEY_ENTER):\\n return True\",\n \"def main():\\n # input the dna\\n dna = str(input('Please give me a DNA strand and I\\\\'ll find the complement: '))\\n # check the input format\\n while not is_dna(dna):\\n print('Illegal format')\\n dna = str(input('Please give me a DNA strand (A, T, C, G only) and I\\\\'ll find the complement: '))\\n\\n # find the complement strand\\n ans = build_complement(dna)\\n print('The complement of ' + str(dna.upper()) + ' is ' + str(ans))\",\n \"def get_host_by_name(self, hostname: str) -> Union[str, Literal[False]]:\\n self._read_line()\\n if self._debug:\\n print(\\\"*** Get host by name\\\")\\n if isinstance(hostname, str):\\n hostname = bytes(hostname, \\\"utf-8\\\")\\n\\n self._uart_write(b'AT+CDNSGIP=\\\"' + hostname + b'\\\"\\\\r\\\\n')\\n self._read_line(10000) # Read the +CDNSGIP, takes a while\\n\\n if not self._parse_reply(b\\\"+CDNSGIP: \\\", idx=2):\\n return False\\n return self._buf\",\n \"def set_hostname(dut, host_name):\\n cmd = \\\"sudo hostname {}\\\".format(host_name)\\n st.config(dut, cmd)\\n return\",\n \"def choose_server(self, domain):\\n try:\\n domain = domain.encode('idna').decode('utf-8')\\n except TypeError:\\n domain = domain.decode('utf-8').encode('idna').decode('utf-8')\\n except AttributeError:\\n domain = domain.decode('utf-8').encode('idna').decode('utf-8')\\n if domain.endswith(\\\"-NORID\\\"):\\n return NICClient.NORIDHOST\\n if domain.endswith(\\\"id\\\"):\\n return NICClient.PANDIHOST\\n if domain.endswith(\\\"hr\\\"):\\n return NICClient.HR_HOST\\n\\n domain = domain.split('.')\\n if len(domain) < 2:\\n return None\\n tld = domain[-1]\\n if tld[0].isdigit():\\n return NICClient.ANICHOST\\n elif tld == 'ai':\\n return NICClient.AI_HOST\\n elif tld == 'app':\\n return NICClient.APP_HOST\\n elif tld == 'dev':\\n return NICClient.DEV_HOST\\n elif tld == 'games':\\n return NICClient.GAMES_HOST\\n elif tld == 'page':\\n return NICClient.PAGE_HOST\\n elif tld == 'money':\\n return NICClient.MONEY_HOST\\n elif tld == 'online':\\n return NICClient.ONLINE_HOST\\n elif tld == 'cl':\\n return NICClient.CL_HOST\\n elif tld == 'ar':\\n return NICClient.AR_HOST\\n elif tld == 'by':\\n return NICClient.BY_HOST\\n elif tld == 'cr':\\n return NICClient.CR_HOST\\n elif tld == 'ca':\\n return NICClient.CA_HOST\\n elif tld == 'do':\\n return NICClient.DO_HOST\\n elif tld == 'de':\\n return NICClient.DE_HOST\\n elif tld == 'hk':\\n return NICClient.HK_HOST\\n elif tld == 'hn':\\n return NICClient.HN_HOST\\n elif tld == 'jobs':\\n return NICClient.JOBS_HOST\\n elif tld == 'lat':\\n return NICClient.LAT_HOST\\n elif tld == 'li':\\n return NICClient.LI_HOST\\n elif tld == 'mx':\\n return NICClient.MX_HOST\\n elif tld == 'pe':\\n return NICClient.PE_HOST\\n elif tld == 'ist':\\n return NICClient.IST_HOST\\n elif tld == 'kz':\\n return NICClient.KZ_HOST\\n elif tld == 'abogado':\\n return NICClient.ABOGADO_HOST\\n elif tld == 'accountant':\\n return NICClient.ACCOUNTANT_HOST\\n elif tld == 'aero':\\n return NICClient.AERO_HOST\\n elif tld == 'ag':\\n return NICClient.AG_HOST\\n elif tld == 'ai':\\n return NICClient.AI_HOST\\n elif tld == 'allfinanz':\\n return NICClient.ALLFINANZ_HOST\\n elif tld == 'alsace':\\n return NICClient.ALSACE_HOST\\n elif tld == 'am':\\n return NICClient.AM_HOST\\n elif tld == 'amsterdam':\\n return NICClient.AMSTERDAM_HOST\\n elif tld == 'aquarelle':\\n return NICClient.AQUARELLE_HOST\\n elif tld == 'as':\\n return NICClient.AS_HOST\\n elif tld == 'asia':\\n return NICClient.ASIA_HOST\\n elif tld == 'au':\\n return NICClient.AU_HOST\\n elif tld == 'aw':\\n return NICClient.AW_HOST\\n elif tld == 'ax':\\n return NICClient.AX_HOST\\n elif tld == 'bank':\\n return NICClient.BANK_HOST\\n elif tld == 'bar':\\n return NICClient.BAR_HOST\\n elif tld == 'barclaycard':\\n return NICClient.BARCLAYCARD_HOST\\n elif tld == 'barclays':\\n return NICClient.BARCLAYS_HOST\\n elif tld == 'bayern':\\n return NICClient.BAYERN_HOST\\n elif tld == 'beer':\\n return NICClient.BEER_HOST\\n elif tld == 'berlin':\\n return NICClient.BERLIN_HOST\\n elif tld == 'bi':\\n return NICClient.BI_HOST\\n elif tld == 'bid':\\n return NICClient.BID_HOST\\n elif tld == 'bio':\\n return NICClient.BIO_HOST\\n elif tld == 'bmw':\\n return NICClient.BMW_HOST\\n elif tld == 'biz':\\n return NICClient.BIZ_HOST\\n elif tld == 'bj':\\n return NICClient.BJ_HOST\\n elif tld == 'blog':\\n return NICClient.BLOG_HOST\\n elif tld == 'brussels':\\n return NICClient.BRUSSELS_HOST\\n elif tld == 'budapest':\\n return NICClient.BUDAPEST_HOST\\n elif tld == 'build':\\n return NICClient.BUILD_HOST\\n elif tld == 'buzz':\\n return NICClient.BUZZ_HOST\\n elif tld == 'bw':\\n return NICClient.BW_HOST\\n elif tld == 'by':\\n return NICClient.BY_HOST\\n elif tld == 'bzh':\\n return NICClient.BZH_HOST\\n elif tld == 'ca':\\n return NICClient.CA_HOST\\n elif tld == 'cam':\\n return NICClient.CAM_HOST\\n elif tld == 'cancerresearch':\\n return NICClient.CANCERRESEARCH_HOST\\n elif tld == 'capetown':\\n return NICClient.CAPETOWN_HOST\\n elif tld == 'career':\\n return NICClient.CAREER_HOST\\n elif tld == 'casa':\\n return NICClient.CASA_HOST\\n elif tld == 'cat':\\n return NICClient.CAT_HOST\\n elif tld == 'cc':\\n return NICClient.CC_HOST\\n elif tld == 'ch':\\n return NICClient.CH_HOST\\n elif tld == 'ci':\\n return NICClient.CI_HOST\\n elif tld == 'cl':\\n return NICClient.CL_HOST\\n elif tld == 'cloud':\\n return NICClient.CLOUD_HOST\\n elif tld == 'club':\\n return NICClient.CLUB_HOST\\n elif tld == 'cm':\\n return NICClient.CM_HOST\\n elif tld == 'cologne':\\n return NICClient.COLOGNE_HOST\\n elif tld == 'cooking':\\n return NICClient.COOKING_HOST\\n elif tld == 'coop':\\n return NICClient.COOP_HOST\\n elif tld == 'cricket':\\n return NICClient.CRICKET_HOST\\n elif tld == 'cuisinella':\\n return NICClient.CUISINELLA_HOST\\n elif tld == 'cx':\\n return NICClient.CX_HOST\\n elif tld == 'cymru':\\n return NICClient.CYMRU_HOST\\n elif tld == 'cz':\\n return NICClient.CZ_HOST\\n elif tld == 'date':\\n return NICClient.DATE_HOST\\n elif tld == 'de':\\n return NICClient.DE_HOST\\n elif tld == 'desi':\\n return NICClient.DESI_HOST\\n elif tld == 'dk':\\n return NICClient.DK_HOST\\n elif tld == 'dm':\\n return NICClient.DM_HOST\\n elif tld == 'do':\\n return NICClient.DO_HOST\\n elif tld == 'download':\\n return NICClient.DOWNLOAD_HOST\\n elif tld == 'durban':\\n return NICClient.DURBAN_HOST\\n elif tld == 'dvag':\\n return NICClient.DVAG_HOST\\n elif tld == 'ee':\\n return NICClient.EE_HOST\\n elif tld == 'eu':\\n return NICClient.EU_HOST\\n elif tld == 'eurovision':\\n return NICClient.EUROVISION_HOST\\n elif tld == 'eus':\\n return NICClient.EUS_HOST\\n elif tld == 'faith':\\n return NICClient.FAITH_HOST\\n elif tld == 'fashion':\\n return NICClient.FASHION_HOST\\n elif tld == 'fi':\\n return NICClient.FI_HOST\\n elif tld == 'film':\\n return NICClient.FILM_HOST\\n elif tld == 'firmdale':\\n return NICClient.FIRMDALE_HOST\\n elif tld == 'fishing':\\n return NICClient.FISHING_HOST\\n elif tld == 'fit':\\n return NICClient.FIT_HOST\\n elif tld == 'flsmidth':\\n return NICClient.FLSMIDTH_HOST\\n elif tld == 'frl':\\n return NICClient.FRL_HOST\\n elif tld == 'frogans':\\n return NICClient.FROGANS_HOST\\n elif tld == 'ga':\\n return NICClient.GA_HOST\\n elif tld == 'gal':\\n return NICClient.GAL_HOST\\n elif tld == 'games':\\n return NICClient.GAMES_HOST\\n elif tld == 'garden':\\n return NICClient.GARDEN_HOST\\n elif tld == 'gd':\\n return NICClient.GD_HOST\\n elif tld == 'gdn':\\n return NICClient.GDN_HOST\\n elif tld == 'gent':\\n return NICClient.GENT_HOST\\n elif tld == 'gg':\\n return NICClient.GG_HOST\\n elif tld == 'gl':\\n return NICClient.GL_HOST\\n elif tld == 'global':\\n return NICClient.GLOBAL_HOST\\n elif tld == 'gmx':\\n return NICClient.GMX_HOST\\n elif tld == 'gold':\\n return NICClient.GOLD_HOST\\n elif tld == 'gop':\\n return NICClient.GOP_HOST\\n elif tld == 'gov':\\n return NICClient.GOV_HOST\\n elif tld == 'gq':\\n return NICClient.GQ_HOST\\n elif tld == 'gy':\\n return NICClient.GY_HOST\\n elif tld == 'hamburg':\\n return NICClient.HAMBURG_HOST\\n elif tld == 'hn':\\n return NICClient.HN_HOST\\n elif tld == 'horse':\\n return NICClient.HORSE_HOST\\n elif tld == 'hr':\\n return NICClient.HR_HOST\\n elif tld == 'ht':\\n return NICClient.HT_HOST\\n elif tld == 'hu':\\n return NICClient.HU_HOST\\n elif tld == 'ibm':\\n return NICClient.IBM_HOST\\n elif tld == 'ie':\\n return NICClient.IE_HOST\\n elif tld == 'ifm':\\n return NICClient.IFM_HOST\\n elif tld == 'im':\\n return NICClient.IM_HOST\\n elif tld == 'int':\\n return NICClient.INT_HOST\\n elif tld == 'io':\\n return NICClient.IO_HOST\\n elif tld == 'is':\\n return NICClient.IS_HOST\\n elif tld == 'it':\\n return NICClient.IT_HOST\\n elif tld == 'java':\\n return NICClient.JAVA_HOST\\n elif tld == 'je':\\n return NICClient.JE_HOST\\n elif tld == 'jetzt':\\n return NICClient.JETZT_HOST\\n elif tld == 'jobs':\\n return NICClient.JOBS_HOST\\n elif tld == 'joburg':\\n return NICClient.JOBURG_HOST\\n elif tld == 'ki':\\n return NICClient.KI_HOST\\n elif tld == 'kiwi':\\n return NICClient.KIWI_HOST\\n elif tld == 'koeln':\\n return NICClient.KOELN_HOST\\n elif tld == 'ky':\\n return NICClient.KY_HOST\\n elif tld == 'la':\\n return NICClient.LA_HOST\\n elif tld == 'lacaixa':\\n return NICClient.LACAIXA_HOST\\n elif tld == 'lat':\\n return NICClient.LAT_HOST\\n elif tld == 'latrobe':\\n return NICClient.LATROBE_HOST\\n elif tld == 'leclerc':\\n return NICClient.LECLERC_HOST\\n elif tld == 'li':\\n return NICClient.LI_HOST\\n elif tld == 'live':\\n return NICClient.LIVE_HOST\\n elif tld == 'loan':\\n return NICClient.LOAN_HOST\\n elif tld == 'london':\\n return NICClient.LONDON_HOST\\n elif tld == 'lt':\\n return NICClient.LT_HOST\\n elif tld == 'lu':\\n return NICClient.LU_HOST\\n elif tld == 'luxe':\\n return NICClient.LUXE_HOST\\n elif tld == 'luxury':\\n return NICClient.LUXURY_HOST\\n elif tld == 'ma':\\n return NICClient.MA_HOST\\n elif tld == 'madrid':\\n return NICClient.MADRID_HOST\\n elif tld == 'mango':\\n return NICClient.MANGO_HOST\\n elif tld == 'md':\\n return NICClient.MD_HOST\\n elif tld == 'me':\\n return NICClient.ME_HOST\\n elif tld == 'men':\\n return NICClient.MEN_HOST\\n elif tld == 'menu':\\n return NICClient.MENU_HOST\\n elif tld == 'mg':\\n return NICClient.MG_HOST\\n elif tld == 'miami':\\n return NICClient.MIAMI_HOST\\n elif tld == 'mini':\\n return NICClient.MINI_HOST\\n elif tld == 'ml':\\n return NICClient.ML_HOST\\n elif tld == 'mo':\\n return NICClient.MO_HOST\\n elif tld == 'moe':\\n return NICClient.MOE_HOST\\n elif tld == 'monash':\\n return NICClient.MONASH_HOST\\n elif tld == 'moscow':\\n return NICClient.MOSCOW_HOST\\n elif tld == 'ms':\\n return NICClient.MS_HOST\\n elif tld == 'mu':\\n return NICClient.MU_HOST\\n elif tld == 'museum':\\n return NICClient.MUSEUM_HOST\\n elif tld == 'na':\\n return NICClient.NA_HOST\\n elif tld == 'name':\\n return NICClient.NAME_HOST\\n elif tld == 'nc':\\n return NICClient.NC_HOST\\n elif tld == 'news':\\n return NICClient.NEWS_HOST\\n elif tld == 'nf':\\n return NICClient.NF_HOST\\n elif tld == 'nl':\\n return NICClient.NL_HOST\\n elif tld == 'no':\\n return NICClient.NO_HOST\\n elif tld == 'nrw':\\n return NICClient.NRW_HOST\\n elif tld == 'nu':\\n return NICClient.NU_HOST\\n elif tld == 'nyc':\\n return NICClient.NYC_HOST\\n elif tld == 'one':\\n return NICClient.ONE_HOST\\n elif tld == 'online':\\n return NICClient.ONLINE_HOST\\n elif tld == 'ooo':\\n return NICClient.OOO_HOST\\n elif tld == 'ovh':\\n return NICClient.OVH_HOST\\n elif tld == 'paris':\\n return NICClient.PARIS_HOST\\n elif tld == 'party':\\n return NICClient.PARTY_HOST\\n elif tld == 'pf':\\n return NICClient.PF_HOST\\n elif tld == 'physio':\\n return NICClient.PHYSIO_HOST\\n elif tld == 'plus':\\n return NICClient.PLUS_HOST\\n elif tld == 'pm':\\n return NICClient.PM_HOST\\n elif tld == 'pohl':\\n return NICClient.POHL_HOST\\n elif tld == 'post':\\n return NICClient.POST_HOST\\n elif tld == 'qpon':\\n return NICClient.QPON_HOST\\n elif tld == 'quebec':\\n return NICClient.QUEBEC_HOST\\n elif tld == 'racing':\\n return NICClient.RACING_HOST\\n elif tld == 're':\\n return NICClient.RE_HOST\\n elif tld == 'reise':\\n return NICClient.REISE_HOST\\n elif tld == 'review':\\n return NICClient.REVIEW_HOST\\n elif tld == 'rodeo':\\n return NICClient.RODEO_HOST\\n elif tld == 'ruhr':\\n return NICClient.RUHR_HOST\\n elif tld == 'samsung':\\n return NICClient.SAMSUNG_HOST\\n elif tld == 'saarland':\\n return NICClient.SAARLAND_HOST\\n elif tld == 'sb':\\n return NICClient.SB_HOST\\n elif tld == 'sca':\\n return NICClient.SCA_HOST\\n elif tld == 'scb':\\n return NICClient.SCB_HOST\\n elif tld == 'schmidt':\\n return NICClient.SCHMIDT_HOST\\n elif tld == 'science':\\n return NICClient.SCIENCE_HOST\\n elif tld == 'scot':\\n return NICClient.SCOT_HOST\\n elif tld == 'se':\\n return NICClient.SE_HOST\\n elif tld == 'sh':\\n return NICClient.SH_HOST\\n elif tld == 'si':\\n return NICClient.SI_HOST\\n elif tld == 'sk':\\n return NICClient.SK_HOST\\n elif tld == 'sky':\\n return NICClient.SKY_HOST\\n elif tld == 'sm':\\n return NICClient.SM_HOST\\n elif tld == 'sn':\\n return NICClient.SN_HOST\\n elif tld == 'so':\\n return NICClient.SO_HOST\\n elif tld == 'spiegel':\\n return NICClient.SPIEGEL_HOST\\n elif tld == 'st':\\n return NICClient.ST_HOST\\n elif tld == 'stream':\\n return NICClient.STREAM_HOST\\n elif tld == 'study':\\n return NICClient.STUDY_HOST\\n elif tld == 'sucks':\\n return NICClient.SUCKS_HOST\\n elif tld == 'surf':\\n return NICClient.SURF_HOST\\n elif tld == 'sx':\\n return NICClient.SX_HOST\\n elif tld == 'sydney':\\n return NICClient.SYDNEY_HOST\\n elif tld == 'taipei':\\n return NICClient.TAIPEI_HOST\\n elif tld == 'tatar':\\n return NICClient.TATAR_HOST\\n elif tld == 'tc':\\n return NICClient.TC_HOST\\n elif tld == 'tel':\\n return NICClient.TEL_HOST\\n elif tld == 'tf':\\n return NICClient.TF_HOST\\n elif tld == 'tirol':\\n return NICClient.TIROL_HOST\\n elif tld == 'tk':\\n return NICClient.TK_HOST\\n elif tld == 'tl':\\n return NICClient.TL_HOST\\n elif tld == 'tm':\\n return NICClient.TM_HOST\\n elif tld == 'top':\\n return NICClient.TOP_HOST\\n elif tld == 'tr':\\n return NICClient.TR_HOST\\n elif tld == 'trade':\\n return NICClient.TRADE_HOST\\n elif tld == 'travel':\\n return NICClient.TRAVEL_HOST\\n elif tld == 'trust':\\n return NICClient.TRUST_HOST\\n elif tld == 'tui':\\n return NICClient.TUI_HOST\\n elif tld == 'tv':\\n return NICClient.TV_HOST\\n elif tld == 'co.ua':\\n return NICClient.CO.UA_HOST\\n elif tld == 'uno':\\n return NICClient.UNO_HOST\\n elif tld == 'us':\\n return NICClient.US_HOST\\n elif tld == 'uz':\\n return NICClient.UZ_HOST\\n elif tld == 'versicherung':\\n return NICClient.VERSICHERUNG_HOST\\n elif tld == 'vg':\\n return NICClient.VG_HOST\\n elif tld == 'vip':\\n return NICClient.VIP_HOST\\n elif tld == 'vlaanderen':\\n return NICClient.VLAANDEREN_HOST\\n elif tld == 'vodka':\\n return NICClient.VODKA_HOST\\n elif tld == 'voting':\\n return NICClient.VOTING_HOST\\n elif tld == 'wales':\\n return NICClient.WALES_HOST\\n elif tld == 'webcam':\\n return NICClient.WEBCAM_HOST\\n elif tld == 'wed':\\n return NICClient.WED_HOST\\n elif tld == 'wedding':\\n return NICClient.WEDDING_HOST\\n elif tld == 'wf':\\n return NICClient.WF_HOST\\n elif tld == 'whoswho':\\n return NICClient.WHOSWHO_HOST\\n elif tld == 'wien':\\n return NICClient.WIEN_HOST\\n elif tld == 'win':\\n return NICClient.WIN_HOST\\n elif tld == 'work':\\n return NICClient.WORK_HOST\\n elif tld == 'ws':\\n return NICClient.WS_HOST\\n elif tld == 'wtc':\\n return NICClient.WTC_HOST\\n elif tld == 'xxx':\\n return NICClient.XXX_HOST\\n elif tld == 'yoga':\\n return NICClient.YOGA_HOST\\n elif tld == 'yt':\\n return NICClient.YT_HOST\\n elif tld == 'zm':\\n return NICClient.ZM_HOST\\n else:\\n return tld + NICClient.QNICHOST_TAIL\",\n \"def enter_username(self):\",\n \"def set_real_name(user: User, real_name: str=\\\"\\\") -> Result:\\n current, *rest = user.pw_gecos.split(\\\",\\\")\\n if current == real_name:\\n return Result(State.unchanged)\\n command([\\\"/usr/bin/chfn\\\", \\\"--full-name\\\", real_name, user.pw_name])\\n user.pw_gecos = \\\",\\\".join([real_name, *rest])\\n return Result(State.success)\",\n \"def _get_local_ca_host():\\n beamline_id = os.uname()[1][:4]\\n if not beamline_id.startswith('xf'):\\n raise LocationError('You are not on a registered beamline computer. '\\n 'Unable to guess which channel archiver to use. '\\n 'Please specify the channel archiver you wish to'\\n 'obtain data from.')\\n return 'http://' + beamline_id + '-ca/cgi-bin/ArchiveDataServer.cgi'\",\n \"def get_string(opt=\\\"encode\\\"):\\n text = input(f\\\"Enter string to {opt}: \\\")\\n return text\",\n \"async def chutiya (fuks):\\n if not fuks.text[0].isalpha() and fuks.text[0] not in (\\\"/\\\", \\\"#\\\", \\\"@\\\", \\\"!\\\"):\\n index = random.randint(0, len(memes.CHU_STRINGS) - 1)\\n reply_text = memes.FUK_STRINGS[index]\\n await fuks.edit(reply_text)\",\n \"def test_returnCanonicalName(self):\\n servers = {\\n ('1.1.2.3', 53): {\\n (b'example.com', A): {\\n 'answers': [(b'example.com', Record_CNAME(b'example.net')),\\n (b'example.net', Record_A('10.0.0.7'))],\\n },\\n },\\n }\\n resolver = self._getResolver(servers)\\n d = resolver.lookupAddress(b'example.com')\\n d.addCallback(lambda results: results[0]) # Get the answer section\\n d.addCallback(\\n self.assertEqual,\\n [RRHeader(b'example.com', CNAME, payload=Record_CNAME(b'example.net')),\\n RRHeader(b'example.net', A, payload=Record_A('10.0.0.7'))])\\n return d\",\n \"def changer_rep():\\r\\n\\troot = tkinter.Tk()\\r\\n\\ttext=tkinter.Text(root, wrap = 'none')\\r\\n\\ttext.insert('1.0', \\\"Indiquer le nom du nouveau répertoire : \\\\n\\\")\\r\\n\\ttext.pack()\\r\\n\\tv = tkinter.StringVar()\\r\\n\\treq=tkinter.Entry(root, textvariable=v, validate='all')\\t\\r\\n\\treq.pack()\\t\\r\\n\\tvalidate = tkinter.Button(root, text='valider', command=root.quit)\\r\\n\\tvalidate.pack()\\r\\n\\troot.mainloop()\\r\\n\\texecution(v.get())\",\n \"def prompt_user_passwd(self, host, realm):\\n return None, None\",\n \"async def chutiya(chus):\\n if not chus.text[0].isalpha() and chus.text[0] not in (\\\"/\\\", \\\"#\\\", \\\"@\\\", \\\"!\\\"):\\n index = random.randint(0, len(CHU_STRINGS) - 1)\\n reply_text = CHU_STRINGS[index]\\n await chus.edit(reply_text)\",\n \"def getRequestHostname():\",\n \"def on_the_network_global_configuration_page_change_the_first_nameserver_to_nameserver1(driver, nameserver1):\\n global nameserver_1\\n nameserver_1 = nameserver1\\n assert wait_on_element(driver, 7, '//h4[contains(.,\\\"Hostname and Domain\\\")]')\\n assert wait_on_element(driver, 5, '//input[@ix-auto=\\\"input__Nameserver 1\\\"]', 'inputable')\\n driver.find_element_by_xpath('//input[@ix-auto=\\\"input__Nameserver 1\\\"]').clear()\\n driver.find_element_by_xpath('//input[@ix-auto=\\\"input__Nameserver 1\\\"]').send_keys(nameserver1)\",\n \"def getUsername():\\n return input(\\\"Username\\\")\",\n \"def create_host(self, wwpns, hostname):\\n\\n if not wwpns or len(wwpns) == 0 or not hostname or len(hostname) == 0:\\n ex_args = {'wwpns': wwpns,\\n 'hostname': hostname}\\n raise SVCCreateHostParameterError(**ex_args)\\n\\n ports = ':'.join(wwpns)\\n # get the host shortname.\\n hostname_str = hostname.split('.')[0]\\n LOG.debug(\\\"enter: create_host(): wwpns=%(wwpns)s\\\"\\n \\\" hostname=%(hostname)s\\\"\\n % {'wwpns': ports, 'hostname': hostname_str})\\n\\n rand_id = str(random.randint(0, 99999999)).zfill(8)\\n host_name = '%s-%s' % (self._hostname_prefix(hostname_str), rand_id)\\n\\n cmd = 'mkhost -name %(host_name)s -hbawwpn %(ports)s -force' % locals()\\n\\n output, err_output = self._svc_command(cmd)\\n\\n if err_output:\\n # err_output should be a list type\\n if isinstance(err_output, types.ListType):\\n err_msg = err_output[0]\\n else:\\n err_msg = err_output\\n err_code = err_msg.split()[0]\\n\\n if err_code and err_code == 'CMMVC6035E':\\n # host has been defined on the storage, but we don't see it.\\n # return None and ask caller to run cfgdev to relogin to SAN\\n # and retry get_host_from_wwpns().\\n return None\\n\\n msg = (_(\\\"create_host() failure cmd=%(cmd)s, error:%(err_output)s.\\\"\\n \\\" Make sure host and storage are zoned properly and check\\\"\\n \\\" SAN fabric connectivity\\\") % locals())\\n\\n LOG.exception(msg)\\n ex_args = {'host_name': hostname_str,\\n 'err_output': err_output}\\n raise SVCCreateHostFailed(**ex_args)\\n\\n return host_name\",\n \"def host_name(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"host_name\\\")\",\n \"def get_hostname_helper(self, sock, nameservers, hostname):\\n hostname = hostname.rstrip(\\\".\\\") #framework can't handle \\\"\\\" or anything ending with a dot\\n\\n aliases = []\\n addresses = []\\n\\n #1. See if the answer is in local information, and if so return it to the client.\\n if self.caching:\\n for alias in self.cache.lookup(hostname, Type.CNAME, Class.IN):\\n aliases.append(alias.rdata.data)\\n for address in self.cache.lookup(hostname, Type.A, Class.IN):\\n addresses.append(address.rdata.data)\\n \\n if aliases != []:\\n return hostname, aliases, addresses\\n\\n #3. Send them queries until one returns a response.\\n # Create and send query\\n \\n while True:\\n response = self._get_single_A(sock, nameservers, hostname)\\n \\n if response == None:\\n break\\n \\n for answer in [ans for ans in response.answers + response.additionals if ans.type_ == Type.A and ans.name == hostname]:\\n addresses.append(answer.rdata.data)\\n for additional in [ans for ans in response.additionals if ans.type_ == Type.CNAME and ans.name == hostname]:\\n aliases.append(additional.rdata.data)\\n if addresses != []:\\n break\\n \\n ns2 = nameservers\\n nameservers, remaining = self.extract_ip(response.authorities, response.additionals, hostname)\\n if nameservers == []:\\n nameservers = self.get_ns(sock, ns2, remaining)\\n return hostname, aliases, addresses\",\n \"def setup(self):\\n for k, v in self.data.items():\\n kq = (k[0].upper() + k[1:].replace(\\\"_\\\", \\\" \\\")).replace(\\\"url\\\", \\\"URL\\\")\\n option = input(f\\\"🎤 {kq} [{v}]: \\\").strip() or v.strip()\\n option = self._validate_option(k, option)\\n self.data[k] = option\",\n \"def login(host):\\n\\n\\n \\\"\\\"\\\" change this settings to make use. \\\"\\\"\\\"\\n gateway_user = \\\"lonli\\\"\\n gateway_ip = \\\"127.0.0.1\\\"\\n gateway_port = \\\"22\\\"\\n gateway_key = \\\"/home/lonli/.ssh/id_rsa\\\"\\n\\n \\\"\\\"\\\" change abbove settings to make use. \\\"\\\"\\\"\\n\\n\\n if host:\\n try:\\n subprocess.check_output([\\\"ssh\\\", \\\"-p\\\", gateway_port, \\\"-i\\\", gateway_key,\\n \\\"{0}@{1}\\\".format(gateway_user, gateway_ip), \\\"grep {0} ~/.ssh/config\\\".format(host)])\\n except subprocess.CalledProcessError as e:\\n print(\\\"'{0}' does not exists in the configuratian of the gateway!\\\".format(host), file=sys.stderr)\\n return\\n\\n to_gateway = \\\"ssh -p {0} -i {1} {2}@{3}\\\".format(gateway_port, gateway_key, gateway_user, gateway_ip)\\n ssh = pexpect.spawn(to_gateway)\\n if host:\\n\\n \\n \\\"\\\"\\\" change this settings to make use. \\\"\\\"\\\"\\n exps = [\\n (\\\"lonli@arch\\\", 'echo -n \\\"Enter diretory : \\\" && read && [ -d \\\"${REPLY}\\\" ] && cd ${REPLY}'),\\n (\\\"Enter diretory : \\\", \\\"/tmp\\\"),\\n (\\\"/tmp\\\", \\\"pwd\\\"),\\n ]\\n \\\"\\\"\\\" change abbove session to make use. \\\"\\\"\\\"\\n\\n\\n for p, s in exps:\\n # print(\\\"expect : {0}, then send : {1}\\\".format(p, s))\\n ssh.expect(p)\\n ssh.sendline(s)\\n winch_handler = sigwinch_handler(ssh)\\n signal.signal(signal.SIGWINCH, winch_handler)\\n winch_handler(None, None)\\n ssh.interact()\",\n \"def fix_location(in_text):\\n prompt = ConversionPrompt(\\n 'I', 'O',\\n (\\\"lenox ma\\\", \\\"Lenox, MA\\\"),\\n (\\\"london\\\", \\\"London, U.K.\\\"),\\n (\\\"chicago\\\", \\\"Chicago, IL\\\"),\\n (\\\"dallas, tx\\\", \\\"Dallas, TX\\\"),\\n engine='babbage'\\n )\\n return prompt.convert(in_text)\",\n \"def get_ascii_input(self, varname, deflt=None):\\n try:\\n value = self.request.var(varname, deflt)\\n if value is not None:\\n value.decode(\\\"ascii\\\")\\n return value\\n except UnicodeDecodeError:\\n raise MKUserError(varname, _(\\\"The given text must only contain ASCII characters.\\\"))\",\n \"def url_fix_host(urlparts):\\n # if not urlparts[1]:\\n # urlparts[2] = urllib.unquote(urlparts[2])\\n # return False\\n userpass, netloc = urllib.splituser(urlparts[1])\\n if userpass:\\n userpass = urllib.unquote(userpass)\\n netloc, is_idn = idna_encode(urllib.unquote(netloc).lower())\\n # a leading backslash in path causes urlsplit() to add the\\n # path components up to the first slash to host\\n # try to find this case...\\n i = netloc.find(\\\"\\\\\\\\\\\")\\n if i != -1:\\n # ...and fix it by prepending the misplaced components to the path\\n comps = netloc[i:] # note: still has leading backslash\\n if not urlparts[2] or urlparts[2] == '/':\\n urlparts[2] = comps\\n else:\\n urlparts[2] = \\\"%s%s\\\" % (comps, urllib.unquote(urlparts[2]))\\n netloc = netloc[:i]\\n else:\\n # a leading ? in path causes urlsplit() to add the query to the\\n # host name\\n i = netloc.find(\\\"?\\\")\\n if i != -1:\\n netloc, urlparts[3] = netloc.split('?', 1)\\n # path\\n urlparts[2] = urllib.unquote(urlparts[2])\\n if userpass and userpass != ':':\\n # append AT for easy concatenation\\n userpass += \\\"@\\\"\\n else:\\n userpass = \\\"\\\"\\n\\n if urlparts[0] in default_ports:\\n dport = default_ports[urlparts[0]]\\n host, port = splitport(netloc, port=dport)\\n\\n host = host.rstrip('. ')\\n if port != dport:\\n host = \\\"%s:%d\\\" % (host, port)\\n netloc = host\\n urlparts[1] = userpass + netloc\\n return is_idn\",\n \"def prompt_username():\\n msg = \\\"Enter Username: \\\"\\n username = raw_input(msg)\\n return username\",\n \"def __init__(self, prompt, ip_address, port):\\n self._ip_address = ip_address\\n self._port = int(port)\\n self._sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\\n self._sock.connect((self._ip_address, self._port))\\n self._prompt = prompt\\n while True:\\n reply = self._receive(False)\\n if reply is not None:\\n if to_str(reply) == self._prompt:\\n break\\n else:\\n break\",\n \"def main():\\n username = str(input())\\n print(str.lower(username))\",\n \"def _parse_host(host: str) -> str:\\n urlparse_host = urlsplit(host).hostname\\n if urlparse_host:\\n # In this case, host = https://xx.cloud.databricks.com\\n return urlparse_host\\n else:\\n # In this case, host = xx.cloud.databricks.com\\n return host\",\n \"def test_alt_name_request(self):\\n oim = OIM()\\n hostname = 'test.' + DOMAIN\\n san = 'test-san.' + DOMAIN\\n san2 = 'test-san2.' + DOMAIN\\n rc, _, _, msg = oim.request('--hostname', hostname,\\n '--altname', san,\\n '--altname', san2)\\n self.assertEqual(rc, 0, \\\"Failed to request certificate\\\\n%s\\\" % msg)\\n self.assert_(oim.reqid != '', msg)\",\n \"def letters(string, user_input):\\n\\n string = upper(string) # pass the string to the upper function to capitalize it\\n options = [] # The array where the options given by the user will be stored\\n new_string = '' # Empty string to append to\\n\\n for char in user_input: # Itterate over words in the options passed by the user \\n options.append(char) # Append them to the options list\\n\\n for char in string: # Itterate over each character in the original string\\n if char == 'A' and 'A' in options: # If the character is \\\"A\\\" and given by the user\\n char = '4' # Repalce it with \\\"4\\\"\\n if char == 'E' and 'E' in options: # If the character is \\\"E\\\" and given by the user\\n char = '3' # Repalce it with \\\"3\\\"\\n if char == 'S' and 'S' in options: # If the character is \\\"A\\\" and given by the user\\n char = '5' # Repalce it with \\\"5\\\"\\n if char == 'B' and 'B' in options: # If the character is \\\"B\\\" and given by the user\\n char = '13' # Repalce it with \\\"13\\\"\\n if char == 'O' and 'O' in options: # If the character is \\\"O\\\" and given by the user\\n char = '0' # Repalce it with \\\"0\\\"\\n if char == 'I' and 'I' in options: # If the character is \\\"I\\\" and given by the user\\n char = '1' # Repalce it with \\\"1\\\"\\n if char == 'V' and 'V' in options: # If the character is \\\"V\\\" and given by the user\\n char = '\\\\/' # Repalce it with \\\"\\\\/\\\"\\n if char == 'W' and 'W' in options: # If the character is \\\"W\\\" and given by the user\\n char = '\\\\/\\\\/' # Repalce it with \\\"\\\\/\\\\/\\\"\\n new_string += char # Append all the characters to the empty string\\n\\n return new_string # Return the new string\",\n \"def test_correct_sheme_host_sent_with_request(self):\\n req = self.httpbin.get_my_ip(dry_run=True)\\n self.assertIn(self.httpbin.client['host'], urlparse(req.prepared_request.url).netloc)\\n self.assertIn(self.httpbin.client['scheme'], urlparse(req.prepared_request.url).scheme)\\n self.assertIn(self.httpbin.client['get_my_ip']['path'], urlparse(req.prepared_request.url).path)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.61417115","0.59693193","0.58512926","0.5775678","0.5465449","0.54297626","0.5409156","0.53970236","0.5384489","0.5359659","0.52832544","0.52559316","0.5251943","0.52433914","0.5231732","0.5215157","0.51479506","0.5126778","0.51189417","0.5097943","0.5059022","0.5053837","0.50431156","0.5021858","0.4990966","0.49832574","0.4978507","0.4972717","0.49571148","0.4932861","0.49322683","0.49308506","0.49282566","0.49219686","0.48815027","0.4880856","0.4876059","0.487307","0.487307","0.48722494","0.48712304","0.48700893","0.48519045","0.48501518","0.48436993","0.48283163","0.48229262","0.48162478","0.48084682","0.48051092","0.48045567","0.47773367","0.47607115","0.47398233","0.47373357","0.47370356","0.47370356","0.47331882","0.47324818","0.4732006","0.4727894","0.47224587","0.47218806","0.47177887","0.47093463","0.4707667","0.46990472","0.4692852","0.46847647","0.46820843","0.4678533","0.46751827","0.46704653","0.46695372","0.4663703","0.46614543","0.46561778","0.4651794","0.4646959","0.46446618","0.46403465","0.46375862","0.46289995","0.4621565","0.4615591","0.4613723","0.4613602","0.46089354","0.46058905","0.46025816","0.45937717","0.4590137","0.45866507","0.45819384","0.45764774","0.45756757","0.45725524","0.45693338","0.45657483","0.45651156","0.4561234"],"string":"[\n \"0.61417115\",\n \"0.59693193\",\n \"0.58512926\",\n \"0.5775678\",\n \"0.5465449\",\n \"0.54297626\",\n \"0.5409156\",\n \"0.53970236\",\n \"0.5384489\",\n \"0.5359659\",\n \"0.52832544\",\n \"0.52559316\",\n \"0.5251943\",\n \"0.52433914\",\n \"0.5231732\",\n \"0.5215157\",\n \"0.51479506\",\n \"0.5126778\",\n \"0.51189417\",\n \"0.5097943\",\n \"0.5059022\",\n \"0.5053837\",\n \"0.50431156\",\n \"0.5021858\",\n \"0.4990966\",\n \"0.49832574\",\n \"0.4978507\",\n \"0.4972717\",\n \"0.49571148\",\n \"0.4932861\",\n \"0.49322683\",\n \"0.49308506\",\n \"0.49282566\",\n \"0.49219686\",\n \"0.48815027\",\n \"0.4880856\",\n \"0.4876059\",\n \"0.487307\",\n \"0.487307\",\n \"0.48722494\",\n \"0.48712304\",\n \"0.48700893\",\n \"0.48519045\",\n \"0.48501518\",\n \"0.48436993\",\n \"0.48283163\",\n \"0.48229262\",\n \"0.48162478\",\n \"0.48084682\",\n \"0.48051092\",\n \"0.48045567\",\n \"0.47773367\",\n \"0.47607115\",\n \"0.47398233\",\n \"0.47373357\",\n \"0.47370356\",\n \"0.47370356\",\n \"0.47331882\",\n \"0.47324818\",\n \"0.4732006\",\n \"0.4727894\",\n \"0.47224587\",\n \"0.47218806\",\n \"0.47177887\",\n \"0.47093463\",\n \"0.4707667\",\n \"0.46990472\",\n \"0.4692852\",\n \"0.46847647\",\n \"0.46820843\",\n \"0.4678533\",\n \"0.46751827\",\n \"0.46704653\",\n \"0.46695372\",\n \"0.4663703\",\n \"0.46614543\",\n \"0.46561778\",\n \"0.4651794\",\n \"0.4646959\",\n \"0.46446618\",\n \"0.46403465\",\n \"0.46375862\",\n \"0.46289995\",\n \"0.4621565\",\n \"0.4615591\",\n \"0.4613723\",\n \"0.4613602\",\n \"0.46089354\",\n \"0.46058905\",\n \"0.46025816\",\n \"0.45937717\",\n \"0.4590137\",\n \"0.45866507\",\n \"0.45819384\",\n \"0.45764774\",\n \"0.45756757\",\n \"0.45725524\",\n \"0.45693338\",\n \"0.45657483\",\n \"0.45651156\",\n \"0.4561234\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":92595,"cells":{"query":{"kind":"string","value":"Convert to an emulab style mac"},"document":{"kind":"string","value":"def emulab_mac(mac):\n\n return \"\".join(mac.lower().split(':'))"},"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 macFor(cls, board):\n return cls.MAC_PREFIX + '{:02X}'.format(int(board))","def mac_ntoa(mac):\n return '%.2x:%.2x:%.2x:%.2x:%.2x:%.2x' % tuple(map(ord, list(mac)))","def good_mac(mac):\r\n return mac.upper().replace('-', ':')","def get_mac_string():\n mac_int = getnode()\n mac_str = ':'.join((\"%012x\" % mac_int)[i:i + 2] for i in range(0, 12, 2))\n return mac_str","def macaroon(node_index):\n node = Node.from_index(node_index)\n with open(node.macaroon(), 'rb') as f:\n content = f.read()\n click.echo(binascii.hexlify(content))","def mac_string(self) :\n\t\tif not getattr(self,'pkg02') :\n\t\t\treturn None \n\t\tif self.pkg02[:2] not in ['01','02','04','05' ] :\n\t\t\treturn None\n\t\treturn self.pkg02 + ' ' + self.p_fields.mac_string()","def doMacTweaks(self, actual_ch, ch, mods):\n if not g.isMac:\n return actual_ch, ch, mods\n if ch == 'Backspace':\n # On the Mac, the reported char can be DEL (7F)\n return '\\b', ch, mods\n if len(mods) == 1 and mods[0] == 'Alt':\n # Patch provided by resi147.\n # See the thread: special characters in MacOSX, like '@'.\n mac_d = {\n '/': '\\\\',\n '5': '[',\n '6': ']',\n '7': '|',\n '8': '{',\n '9': '}',\n 'e': '€',\n 'l': '@',\n }\n if ch.lower() in mac_d:\n # Ignore the case.\n actual_ch = ch = g.checkUnicode(mac_d.get(ch.lower()))\n mods = []\n return actual_ch, ch, mods","def enlabel(mi_, ma_):\n\treturn \"Unicode characters from {} to {} codepoints\".format(mi_, ma_)","def get_mac(self) -> str:\n hex_mac = hexlify(self.message)[160:172].decode().upper()\n return (\n hex_mac[0:2]\n + \":\"\n + hex_mac[2:4]\n + \":\"\n + hex_mac[4:6]\n + \":\"\n + hex_mac[6:8]\n + \":\"\n + hex_mac[8:10]\n + \":\"\n + hex_mac[10:12]\n )","def abdul(self):\n\t\tthismsg = \"\\r\\n\"+self.ESC+\"1;33m\"+self.A220+self.A220+self.A220+self.A220+self.ESC+\"0;33m\"+self.A220+self.A220+self.ESC+\"1m\"+self.A220+self.A220+self.ESC+\"0;33m\"+self.A220+self.ESC+\"1m\"+self.A220+self.A220+self.A220+self.ESC+\"0;33m\"+self.A220+self.ESC+\"1m\"+self.A220+self.A220+self.ESC+\"0;33m\"+self.A220+self.ESC+\"1m\"+self.A220+self.ESC+\"0;33m\"+self.A220+self.A220+self.ESC+\"1m\"+self.A220+self.ESC+\"0;33m\"+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.ESC+\"1;30m\"+self.A220+self.ESC+\"C\"+self.ESC+\"0;33m\"+self.A220+self.A220+self.A220+self.A220+\"\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"79C\"+self.A220+self.ESC+\"1;43m\"+self.A219+self.A178+self.ESC+\"0;33m\"+self.A219+self.A219+self.A219+self.A223+self.A219+self.A219+self.A223+self.ESC+\"32m\"+self.A220+self.ESC+\"33m\"+self.A223+self.ESC+\"32m\"+self.A220+self.ESC+\"C\"+self.A254+self.ESC+\"33m\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\"1;30;43m\"+self.A176+self.ESC+\"C\"+self.ESC+\"0;33m\"+self.A219+self.ESC+\"1;43m\"+self.A177+self.A176+self.ESC+\"C\"+self.A176+self.ESC+\"C\"+self.ESC+\"37;40mSaga\"+self.ESC+\"Cof\"+self.ESC+\"Cthe\"+self.ESC+\"CRed\"+self.ESC+\"CDragon\"+self.ESC+\"C-\"+self.ESC+\"C\"+self.ESC+\"33mAbduls\"+self.ESC+\"CArmour \"+self.ESC+\"C\"+self.ESC+\"0;33m\"+self.A223+\"\\r\\n\"\n\t\tthismsg += self.ESC+\"1;43m\"+self.A219+self.ESC+\"0;33m\"+self.A219+self.A219+self.A223+self.ESC+\"32m\"+self.A220+self.ESC+\"1;42m\"+self.A177+self.ESC+\"0;32m\"+self.A220+self.ESC+\"C\"+self.A223+self.ESC+\"1m\"+self.A223+self.ESC+\"2C\"+self.ESC+\"0;33m\"+self.A223+self.A223+self.A223+self.ESC+\"30;43m\"+self.A177+self.A176+self.ESC+\"33;40m\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\"1;30;43m\"+self.A176+self.ESC+\"2C\"+self.ESC+\"0;33m\"+self.A223+self.A219+self.ESC+\"C\"+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+\"\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"79C\"+self.ESC+\"1;30;43m\"+self.A176+self.ESC+\"33m\"+self.A219+self.ESC+\"0;33m\"+self.A219+self.A219+self.A219+self.A220+self.ESC+\"32m\"+self.A223+self.ESC+\"33m\"+self.A220+self.A219+self.A223+self.ESC+\"37m\"+self.A220+self.ESC+\"1;47m\"+self.A176+self.ESC+\"0m\"+self.A219+self.A223+self.ESC+\"1;30;47m\"+self.A176+self.A176+self.ESC+\"40m\"+self.A220+self.A220+self.ESC+\"0;33m\"+self.A223+self.ESC+\"30;43m\"+self.A177+self.A176+self.ESC+\"33;40m\"+self.A219+self.A219+self.A219+self.ESC+\"1;30;43m\"+self.A177+self.ESC+\"C\"+self.ESC+\"0;33m\"+self.A220+self.ESC+\"1;30m\"+self.A223+self.ESC+\"4C\"+self.ESC+\"0;33mBehind\"+self.ESC+\"Cthe\"+self.ESC+\"Cdesk\"+self.ESC+\"Cof\"+self.ESC+\"Cthe\"+self.ESC+\"Carmour\"+self.ESC+\"Cshop\"+self.ESC+\"Cis\"+self.ESC+\"Can\\r\\n\"\n\t\tthismsg += self.ESC+\"1;43m\"+self.A219+self.ESC+\"0;33m\"+self.A219+self.A219+self.A219+self.A219+self.ESC+\"30;43m\"+self.A176+self.A177+self.ESC+\"C\"+self.ESC+\"37;40m\"+self.A223+self.ESC+\"1;47m\"+self.A177+self.A176+self.ESC+\"C\"+self.ESC+\"0m\"+self.A219+self.A219+self.A219+self.ESC+\"1;30;47m\"+self.A176+self.A177+self.A178+self.ESC+\"C\"+self.ESC+\"0;30;43m\"+self.A177+self.A176+self.ESC+\"33;40m\"+self.A219+self.A219+self.ESC+\"1;30;43m\"+self.A178+self.ESC+\"C\"+self.ESC+\"40m\"+self.A223+self.A220+self.ESC+\"4C\"+self.ESC+\"0;33mamazingly\"+self.ESC+\"Cattractive\"+self.ESC+\"Clooking\"+self.ESC+\"Cfemale - she seems\\r\\n\"\n\t\tthismsg += self.ESC+\"1;43m\"+self.A219+self.ESC+\"0;33m\"+self.A219+self.A219+self.ESC+\"30;43m\"+self.A176+self.A177+self.ESC+\"33;40m\"+self.A223+self.ESC+\"1;37m\"+self.A220+self.ESC+\"47m\"+self.A177+self.A176+self.ESC+\"0m\"+self.A220+self.A220+self.A220+self.A220+self.A220+self.ESC+\"1;30m\"+self.A220+self.A220+self.A223+self.ESC+\"47m\"+self.A177+self.A178+self.ESC+\"C\"+self.ESC+\"0;30;43m\"+self.A177+self.A176+self.ESC+\"33;40m\"+self.A219+self.ESC+\"1;30;43m\"+self.A219+self.ESC+\"2C\"+self.ESC+\"0;32m\"+self.A220+self.A254+self.ESC+\"3C\"+self.ESC+\"33mbusy, doing her mails but she\"+self.ESC+\"Casks\"+self.ESC+\"C\\\"\"+self.ESC+\"1mHow\\r\\n\"\n\t\tthismsg += self.ESC+\"43m\"+self.A219+self.ESC+\"0;33m\"+self.A223+self.A219+self.ESC+\"30;43m\"+self.A176+self.A177+self.ESC+\"C\"+self.ESC+\"1;37;47m\"+self.A178+self.ESC+\"40m\"+self.A222+self.A222+self.ESC+\"47m\"+self.A176+self.ESC+\"C\"+self.ESC+\"30m\"+self.A176+self.ESC+\"C\"+self.A177+self.ESC+\"40m\"+self.A220+self.ESC+\"47m\"+self.A178+self.ESC+\"40m\"+self.A223+self.A220+self.ESC+\"47m\"+self.A219+self.ESC+\"C\"+self.ESC+\"0;30;43m\"+self.A177+self.A176+self.ESC+\"33;40m\"+self.A223+self.ESC+\"32m\"+self.A220+self.A178+self.ESC+\"6C\"+self.ESC+\"1;33mmay\"+self.ESC+\"CI\"+self.ESC+\"Cbe\"+self.ESC+\"Cof\"+self.ESC+\"Cservice?\"+self.ESC+\"0;33m\\\"\\r\\n\"\n\t\tthismsg += self.ESC+\"1m\"+self.A220+self.ESC+\"0;33m\"+self.A223+self.ESC+\"C\"+self.A220+self.A220+self.ESC+\"C\"+self.ESC+\"1;37m\"+self.A223+self.ESC+\"47m\"+self.A178+self.ESC+\"0m\"+self.A220+self.ESC+\"1;47m\"+self.A177+self.ESC+\"0m\"+self.A220+self.ESC+\"1;30;47m\"+self.A176+self.ESC+\"0m\"+self.A220+self.ESC+\"1;30m\"+self.A223+self.A223+self.A220+self.ESC+\"47m\"+self.A177+self.A178+self.ESC+\"C\"+self.ESC+\"0;30;43m\"+self.A177+self.ESC+\"33;40m\"+self.A223+self.ESC+\"32m\"+self.A220+self.ESC+\"1;42m\"+self.A176+self.ESC+\"0;32m\"+self.A220+\"\\r\\n\"\n\t\tthismsg += self.ESC+\"1;33;43m\"+self.A219+self.ESC+\"0;33m\"+self.A219+self.A220+self.A223+self.ESC+\"C\"+self.A220+self.ESC+\"1;43m\"+self.A176+self.ESC+\"0;33m\"+self.A220+self.ESC+\"1;37m\"+self.A223+self.ESC+\"0m\"+self.A220+self.ESC+\"2C\"+self.A220+self.A219+self.ESC+\"1;30;47m\"+self.A176+self.A176+self.ESC+\"40m\"+self.A223+self.ESC+\"47m\"+self.A219+self.ESC+\"C\"+self.ESC+\"0;30;43m\"+self.A177+self.A176+self.ESC+\"33;40m\"+self.A220+self.ESC+\"32m\"+self.A223+self.ESC+\"1;30m\"+self.A220+self.ESC+\"7C\"+self.ESC+\"0;33m[\"+self.ESC+\"1mB\"+self.ESC+\"0;33m]\"+self.ESC+\"1muy\"+self.ESC+\"CArmour\\r\\n\"\n\t\tthismsg += self.ESC+\"43m\"+self.A219+self.ESC+\"0;33m\"+self.A219+self.A219+self.A219+self.A220+self.A223+self.ESC+\"1;37m\"+self.A220+self.A220+self.ESC+\"0m\"+self.A223+self.ESC+\"C\"+self.ESC+\"1;47m\"+self.A223+self.ESC+\"0m\"+self.A219+self.A220+self.A220+self.A220+self.A220+self.ESC+\"1;30;47m\"+self.A176+self.A177+self.ESC+\"40m\"+self.A220+self.ESC+\"0;33m\"+self.A223+self.A223+self.ESC+\"30;43m\"+self.A177+self.A176+self.ESC+\"1;40m\"+self.A219+self.ESC+\"7C\"+self.ESC+\"0;33m[\"+self.ESC+\"1mS\"+self.ESC+\"0;33m]\"+self.ESC+\"1mell\"+self.ESC+\"CArmour\\r\\n\"\n\t\tthismsg += self.ESC+\"43m\"+self.A219+self.ESC+\"0;33m\"+self.A219+self.ESC+\"30;43m\"+self.A176+self.A177+self.ESC+\"C\"+self.ESC+\"1;37;47m\"+self.A219+self.A178+self.ESC+\"40m\"+self.A220+self.ESC+\"47m\"+self.A177+self.A176+self.ESC+\"0m\"+self.A220+self.A220+self.A220+self.A220+self.A219+self.A220+self.A223+self.ESC+\"1;30m\"+self.A220+self.A220+self.A219+self.A219+self.A220+self.ESC+\"0;33m\"+self.A223+self.ESC+\"1;30m\"+self.A219+self.ESC+\"7C\"+self.ESC+\"0;33m[\"+self.ESC+\"1mY\"+self.ESC+\"0;33m]\"+self.ESC+\"1mour\"+self.ESC+\"CStats\\r\\n\"\n\t\tthismsg += self.ESC+\"43m\"+self.A178+self.ESC+\"0;33m\"+self.A223+self.A220+self.A223+self.ESC+\"30;43m\"+self.A176+self.A223+self.ESC+\"1;37;40m\"+self.A223+self.A223+self.ESC+\"47m\"+self.A178+self.A177+self.A176+self.A176+self.ESC+\"0m\"+self.A219+self.A219+self.A223+self.ESC+\"1;30m\"+self.A220+self.A176+self.A177+self.A178+self.A223+self.A223+self.ESC+\"0;33m\"+self.A220+self.A219+self.ESC+\"1;30m\"+self.A219+self.ESC+\"7C\"+self.ESC+\"0;33m[\"+self.ESC+\"1mR\"+self.ESC+\"0;33m]\"+self.ESC+\"1meturn\"+self.ESC+\"Cto\"+self.ESC+\"CTown\\r\\n\"\n\t\tthismsg += self.ESC+\"43m\"+self.A177+self.ESC+\"0;33m\"+self.A219+self.A220+self.A219+self.A219+self.A223+self.ESC+\"32m\"+self.A220+self.ESC+\"1;42m\"+self.A176+self.ESC+\"0;32m\"+self.A220+self.ESC+\"C\"+self.ESC+\"33m\"+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.ESC+\"C\"+self.A220+self.A220+self.A219+self.ESC+\"30;43m\"+self.A176+self.ESC+\"33;40m\"+self.A219+self.A219+self.ESC+\"1;30m\"+self.A223+self.ESC+\"0;33m\"+self.A220+self.A254+self.ESC+\"C\"+self.ESC+\"1;30m\"+self.A220+self.A223+\"\\r\\n\"\n\t\tthismsg += self.ESC+\"33;43m\"+self.A176+self.ESC+\"0;33m\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A220+self.ESC+\"32m\"+self.A223+self.ESC+\"C\"+self.A178+self.A254+self.ESC+\"33m\"+self.A219+self.ESC+\"30;43m\"+self.A177+self.A176+self.ESC+\"33;40m\"+self.A223+self.A220+self.ESC+\"1;43m\"+self.A176+self.ESC+\"0;33m\"+self.A220+self.A223+self.A220+self.A223+self.A223+self.A220+self.ESC+\"3C\"+self.ESC+\"1;30m\"+self.A177+self.A220+self.ESC+\"2C\"+self.ESC+\"33m\"+self.A220+self.A220+self.ESC+\"0;33m\"+self.A220+self.A220+self.ESC+\"1m\"+self.A220+self.ESC+\"0;33m\"+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+\"\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"79C\"+self.ESC+\"1;30;43m\"+self.A176+self.ESC+\"33m\"+self.A176+self.ESC+\"0;33m\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A220+self.A220+self.ESC+\"32m\"+self.A254+self.ESC+\"33m\"+self.A223+self.A219+self.A219+self.A220+self.A223+self.A220+self.A220+self.A223+self.A223+self.ESC+\"1;30m\"+self.A220+self.A220+self.A219+self.ESC+\"2C\"+self.A220+self.A178+self.A220+self.ESC+\"C\"+self.ESC+\"33;43m\"+self.A177+self.ESC+\"2C\"+self.ESC+\"0m \"+self.ESC+\"3C\"+self.ESC+\"33m\"+self.A220+\"\\r\\n\"\n\t\tthismsg += self.A223+self.A223+self.A223+self.ESC+\"1;30m\"+self.A223+self.ESC+\"0;33m\"+self.A223+self.A223+self.ESC+\"1;30m\"+self.A223+self.ESC+\"0;33m\"+self.A223+self.ESC+\"1;30m\"+self.A223+self.A223+self.ESC+\"0;33m\"+self.A223+self.ESC+\"1;30m\"+self.A223+self.A223+self.A223+self.ESC+\"0;33m\"+self.A223+self.ESC+\"1;30m\"+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.ESC+\"C\"+self.ESC+\"0;33m\"+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.ESC+\"1;30m\"+self.A223+self.ESC+\"C\"+self.ESC+\"0;33m\"+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+\"\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"79C\"+self.A223+self.ESC+\"0m\\r\\n\"\n\t\treturn thismsg","def mac_pton(s):\n return binascii.unhexlify(s.replace(\":\", \"\"))","def python_to_matlab(code):\n outline=code\n outline=outline.replace(\"**\",\".^\")\n outline=outline.replace(\"*\",\".*\")\n outline=outline.replace(\"dot(b,\",\"b'*(\")\n outline=outline.replace(\"dot(bhat,\",\"bhat'*(\")\n outline=outline.replace(\"dot(Ahat,\",\"Ahat*(\")\n outline=outline.replace(\"dot(A,\",\"(A*\")\n outline=outline.replace(\"( c)\",\"c\")\n outline=outline.replace(\"-0\",\"\")\n #print(outline)\n #print('******************')\n return outline","def pretty_hebrew(val):\n return 'font-size:20px; font-family: Times New Roman; text-align: right; max-width: 500px'","def mac(self):\n mac = hexlify(WLAN().config('mac'), ':').decode()\n return mac.upper() # MAC-address in upper case","def getMacName(self):\r\n return CipherSuite.canonicalMacName(self.cipherSuite)","def label(mi_, ma_):\n\treturn \"caractères Unicode des points de code {} à {}\".format(mi_, ma_)","def _getMac( self, bSeed ):\n\n\t\tbMac = ( bSeed >> 24 ) & 0xFF\n\t\treturn '%02X' % bMac","def to_mac(self):\n ts_type = self.ts_types['mac']\n try:\n dt_obj = duparser.parse(self.timestamp)\n if hasattr(dt_obj.tzinfo, '_offset'):\n dt_tz = dt_obj.tzinfo._offset.total_seconds()\n dt_obj = duparser.parse(self.timestamp, ignoretz=True)\n else:\n dt_tz = 0\n self.out_mac = str(int((dt_obj - self.epoch_2001).total_seconds() - int(dt_tz)))\n ts_output = str(\"{}\\t\\t{}\".format(ts_type, self.out_mac))\n except Exception:\n exc_type, exc_obj, exc_tb = sys.exc_info()\n print(str(exc_type) + \" - \" + str(exc_obj) + \" - line \" + str(exc_tb.tb_lineno))\n self.out_mac = ts_output = False\n return self.out_mac, ts_output","def macOS(self) -> str:\n\n result = subprocess.run([\"sw_vers\", \"-productVersion\"], stdout=subprocess.PIPE)\n\n return result.stdout.decode(\"utf-8\").strip()","def from_mac(self):\n reason = \"[!] Mac Absolute timestamps are 9 digits, commonly followed by a decimal and up to 6 digits for milliseconds\"\n ts_type = self.ts_types['mac']\n try:\n if \".\" not in self.mac or not ((len(mac.split(\".\")[0]) == 9) and (len(self.mac.split(\".\")[1]) in range(0, 7))) or not ''.join(self.mac.split(\".\")).isdigit():\n self.in_mac = indiv_output = combined_output = False\n pass\n else:\n dt_obj = self.epoch_2001 + timedelta(seconds=float(self.mac))\n self.in_mac = dt_obj.strftime('%Y-%m-%d %H:%M:%S.%f')\n indiv_output = str(\"{} {} UTC\".format(ts_type, self.in_mac))\n combined_output = str(\"{}{}\\t\\t{} UTC{}\".format(self.left_color, ts_type, self.in_mac, self.right_color))\n except Exception:\n exc_type, exc_obj, exc_tb = sys.exc_info()\n print(str(exc_type) + \" - \" + str(exc_obj) + \" - line \" + str(exc_tb.tb_lineno))\n self.in_mac = indiv_output = combined_output = False\n return self.in_mac, indiv_output, combined_output, reason","def get_mac(self) -> str:\n self.sendline(\"iw {} info\".format(self.iface_dut))\n # We are looking for MAC definition of STA\n # wdev 0x1\n # addr 96:4e:c9:cc:7a:2c\n # type managed\n self.expect(\"addr (?P<mac>..:..:..:..:..:..)\\r\\n\\t(type|ssid)\")\n return self.match.group('mac')","def getMac(teff, logg):\n # For Dwarfs: Doyle et al. 2014\n # 5200 < teff < 6400\n # 4.0 < logg < 4.6\n if logg > 3.5:\n mac = 3.21 + (2.33 * (teff - 5777.) * (10**(-3))) + (2.00 * ((teff - 5777.)**2) * (10**(-6))) - (2.00 * (logg - 4.44))\n # For subgiants and giants: Hekker & Melendez 2007\n elif 2.9 <= logg <= 3.5: # subgiants\n mac = -8.426 + (0.00241*teff)\n elif 1.5 <= logg < 2.9: # giants\n mac = -3.953 + (0.00195*teff)\n elif logg < 1.5: # bright giants\n mac = -0.214 + (0.00158*teff)\n\n # For negative values, keep a minimum of 0.3 km/s\n if mac < 0.30:\n mac = 0.30\n return round(mac, 2)","def tower(self):\n\t\tthismsg = \"\\r\\n\"+self.ESC+\"255D\"+self.ESC+\"0;1;44m \"+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"58C\"+self.ESC+\"44m \"+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"44m \"+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"68C\"+self.ESC+\"44m \"+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"44m \"+self.ESC+\"30m\"+self.A219+\" \"+self.A219+\" \"+self.A219+\" \"+self.A219+\" \"+self.A219+\" \"+self.ESC+\"0;44m\"+self.A219+\" \"+self.A219+\" \"+self.A219+\" \"+self.ESC+\"1m\"+self.A219+\" \"+self.A219+\" \"+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"52C\"+self.ESC+\"44m \"+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"44m \"+self.ESC+\"30m\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\"43m\"+self.A223+self.A223+self.ESC+\"0;43m\"+self.A223+self.A223+self.A223+self.A223+self.ESC+\"1m\"+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"40C\"+self.ESC+\"43m\"+self.ESC+\"0;34;43m\"+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.ESC+\"30m\"+self.A223+self.A223+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"44m \"+self.ESC+\"1m\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\"0;33;43m\"+self.A219+self.ESC+\"C\"+self.ESC+\"1;36;40mYou reach th\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"42Ce castle and fight your way up the\"+self.ESC+\"C\"+self.ESC+\"0;33;43m\"+self.A219+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"44m \"+self.ESC+\"32;40m\"+self.A178+self.A219+self.ESC+\"44m \"+self.ESC+\"1;30m\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\"0;33;43m\"+self.A219+self.ESC+\"C\"+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"30C\"+self.ESC+\"43m\"+self.ESC+\"1;36;40mtower!\"+self.ESC+\"41C\"+self.ESC+\"0;33;43m\"+self.A219+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"44m \"+self.ESC+\"32;40m\"+self.A176+self.A219+self.ESC+\"44m \"+self.ESC+\"40m\"+self.A176+self.A177+self.A219+self.A219+self.ESC+\"44m \"+self.ESC+\"40m\"+self.A219+self.ESC+\"44m \"+self.ESC+\"1;30m\"+self.A219+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"24C\"+self.ESC+\"44m\"+self.ESC+\"40m\"+self.A221+self.ESC+\"44m\"+self.A219+self.A219+self.A219+self.ESC+\"0;33;43m\"+self.A219+self.ESC+\"48C\"+self.A219+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"32m\"+self.A178+self.A177+self.A177+self.A219+self.ESC+\"44m \"+self.ESC+\"40m\"+self.A176+self.A177+self.A177+self.A177+self.A177+self.ESC+\"44m \"+self.ESC+\"40m\"+self.A176+self.A177+self.A219+self.ESC+\"44m \"+self.ESC+\"1;30m\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"28C\"+self.ESC+\"44m\"+self.ESC+\"0;33;43m\"+self.A219+self.ESC+\"C\"+self.ESC+\"1;36;40mYou blindly slash at any and all who\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"66C stand in \"+self.ESC+\"0;33;43m\"+self.A219+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"32m\"+self.A177+self.A176+self.A177+self.A176+self.A178+self.A219+self.ESC+\"44m \"+self.ESC+\"40m\"+self.A176+self.A177+self.A176+self.A177+self.A177+self.A219+self.A178+self.ESC+\"44m \"+self.ESC+\"1;30m\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\"0;33;43m\"+self.A219+self.ESC+\"C\"+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"30C\"+self.ESC+\"43m\"+self.ESC+\"1;36;40myour path - screaming to find the prisoner \"+self.ESC+\"4C\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"77C\"+self.ESC+\"0;33;43m\"+self.A219+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"32m\"+self.A176+self.A177+self.A177+self.A178+self.A178+self.ESC+\"44m \"+self.ESC+\"40m\"+self.A176+self.A177+self.A177+self.A178+self.A178+self.ESC+\"44m \"+self.ESC+\"40m\"+self.A176+self.A178+self.A178+self.ESC+\"44m \"+self.ESC+\"1;30m\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"28C\"+self.ESC+\"44m\"+self.ESC+\"0;33;43m\"+self.A219+self.ESC+\"C\"+self.ESC+\"1;36;40myou seek - and the jailer of your he\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"66Cart.\"+self.ESC+\"7C\"+self.ESC+\"0;33;43m\"+self.A219+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"44m \"+self.ESC+\"32;40m\"+self.A176+self.A176+self.A177+self.ESC+\"33m\"+self.A176+self.ESC+\"32m\"+self.A177+self.A176+self.A177+self.ESC+\"33m\"+self.A178+self.ESC+\"44m \"+self.ESC+\"32;40m\"+self.A178+self.A176+self.A177+self.A176+self.A178+self.ESC+\"44m \"+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"23C\"+self.ESC+\"44m\"+self.ESC+\"1;30m\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\"0;33;43m\"+self.A219+self.ESC+\"48C\"+self.A219+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"32m\"+self.A176+self.A177+self.A177+self.A177+self.A176+self.A178+self.A177+self.ESC+\"33m\"+self.A176+self.A177+self.A178+self.ESC+\"32m\"+self.A177+self.A177+self.A178+self.A176+self.A178+self.A176+self.A178+self.ESC+\"44m \"+self.ESC+\"1;30m\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\"43m\"+self.A220+self.A220+self.ESC+\"0;43m\"+self.A220+self.A220+self.A220+self.A220+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"34C\"+self.ESC+\"43m\"+self.A220+self.ESC+\"1m\"+self.A220+self.A220+self.A220+self.ESC+\"0;34;43m\"+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"76C\"+self.ESC+\"43m\"+self.ESC+\"30m\"+self.A220+self.A220+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"44m \"+self.ESC+\"32;40m\"+self.A177+self.A176+self.A177+self.A176+self.A176+self.ESC+\"33m\"+self.A176+self.A177+self.A178+self.A177+self.A177+self.ESC+\"32m\"+self.A177+self.A178+self.A177+self.A177+self.ESC+\"44m \"+self.ESC+\"1;30m\"+self.A219+self.A219+self.ESC+\"40m\"+self.A222+self.ESC+\"44m\"+self.A219+self.A219+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"28C\"+self.ESC+\"44m\"+self.A219+self.A219+self.ESC+\"0;44m\"+self.A219+self.A219+self.ESC+\"40m\"+self.A221+self.A219+self.ESC+\"1;44m\"+self.A219+self.ESC+\"40m\"+self.A221+self.ESC+\"44m\"+self.A219+self.A219+\" \"+self.ESC+\"0;33;43m\"+self.A219+self.ESC+\"40m\"+self.A223+self.A223+\"\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"44C\"+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.ESC+\"43m\"+self.A219+self.ESC+\"34m\"+self.A219+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"44m \"+self.ESC+\"33;40m\"+self.A176+self.A177+self.A178+self.ESC+\"44m \"+self.ESC+\"1;30m\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\"0;44m\"+self.A219+self.A219+self.A219+self.ESC+\"1m\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"37C\"+self.ESC+\"44m\"+self.A219+\" \"+self.ESC+\"0;33;43m\"+self.A219+self.ESC+\"2C\"+self.ESC+\"1;37;40mYou make it to the top and \\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"74C\"+self.ESC+\"0;33;43m\"+self.A219+self.ESC+\"44m \"+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"44m \"+self.ESC+\"40m\"+self.A176+self.A177+self.A176+self.A178+self.ESC+\"44m \"+self.ESC+\"1;30m\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\"0;44m\"+self.A219+self.A219+self.ESC+\"1m\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+\" \"+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"40C\"+self.ESC+\"44m \"+self.ESC+\"0;33;43m\"+self.A219+self.ESC+\"4C\"+self.ESC+\"1;37;40mthrow open the door...\"+self.ESC+\"6C\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"74C\"+self.ESC+\"0;33;43m\"+self.A219+self.ESC+\"44m \"+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"44m \"+self.ESC+\"40m\"+self.A176+self.A178+self.A177+self.A178+self.ESC+\"44m \"+self.ESC+\"32;40m\"+self.A177+self.ESC+\"1;42m\"+self.A178+self.A219+self.ESC+\"30;44m\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"31C\"+self.ESC+\"44m\"+self.ESC+\"0;44m\"+self.A219+self.A219+self.ESC+\"1m\"+self.A219+self.A219+self.A219+self.A219+self.A219+\" \"+self.ESC+\"0;33;43m\"+self.A219+self.ESC+\"1;32;42m\"+self.A177+self.A178+self.A178+self.ESC+\"0;33m\"+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+\"\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"53C\"+self.ESC+\"32m\"+self.A178+self.ESC+\"1;42m\"+self.A219+self.A219+self.ESC+\"0;33m\"+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.ESC+\"43m\"+self.A219+self.ESC+\"44m \"+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"44m \"+self.ESC+\"32;40m\"+self.A176+self.ESC+\"1;42m\"+self.A176+self.ESC+\"0;32m\"+self.A177+self.ESC+\"1;42m\"+self.A219+self.A219+self.ESC+\"0;33m\"+self.A176+self.A177+self.A178+self.ESC+\"44m \"+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"20C\"+self.ESC+\"44m\"+self.ESC+\"1;32;42m\"+self.A178+self.A219+self.ESC+\"0;32m\"+self.A176+self.A178+self.ESC+\"1;42m\"+self.A219+self.ESC+\"0;32m\"+self.A178+self.ESC+\"1;30;44m\"+self.A219+self.A219+self.A219+self.ESC+\"0;44m\"+self.A219+self.A219+self.A219+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"32C\"+self.ESC+\"44m\"+self.ESC+\"1m\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+\" \"+self.ESC+\"0;32m\"+self.A177+self.ESC+\"1;42m\"+self.A176+self.ESC+\"0;32m\"+self.A178+self.ESC+\"1;42m\"+self.A178+self.ESC+\"0;32m\"+self.A177+self.ESC+\"1;42m\"+self.A178+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"46C\"+self.ESC+\"42m\"+self.ESC+\"44m \"+self.ESC+\"0;32m\"+self.A177+self.A178+self.A176+self.ESC+\"1;42m\"+self.A178+self.ESC+\"0;32m\"+self.A176+self.A178+self.ESC+\"1;42m\"+self.A219+self.A178+self.A178+self.ESC+\"44m \"+self.ESC+\"42m\"+self.A176+self.A177+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"63C\"+self.ESC+\"42m\"+self.A178+self.ESC+\"44m \"+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"42m \"+self.ESC+\"0;32m\"+self.A177+self.A176+self.ESC+\"1;42m\"+self.A178+self.A176+self.ESC+\"0;32m\"+self.A176+self.ESC+\"1;42m\"+self.A178+self.A219+self.ESC+\"0;32m\"+self.A178+self.A219+self.ESC+\"42m \"+self.ESC+\"40m\"+self.A177+self.A177+self.A176+self.A176+self.A177+\"\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"21C\"+self.ESC+\"1;42m\"+self.A178+self.A176+self.ESC+\"30;40m\"+self.A219+self.ESC+\"0;32m\"+self.A177+self.A177+self.A178+self.ESC+\"1;42m\"+self.A219+self.ESC+\"30;40m\"+self.A219+self.A219+self.ESC+\"0m\"+self.A219+self.A219+self.A219+self.ESC+\"1m\"+self.A219+self.A219+self.A219+\"\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"36C\"+self.ESC+\"0;32m\"+self.A177+self.A178+self.A176+self.ESC+\"1;42m\"+self.A176+self.ESC+\"0;32m\"+self.A176+self.ESC+\"1;42m\"+self.A219+self.A178+self.ESC+\"0;32m\"+self.A176+self.ESC+\"1;42m\"+self.A219+self.A219+\" \"+self.A178+\" \"+self.ESC+\"0;32m\"+self.A177+self.A176+self.A176+\"\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"52C\"+self.ESC+\"1;42m\"+self.A176+self.ESC+\"0;32m\"+self.A177+self.A176+self.ESC+\"1;42m\"+self.A176+self.ESC+\"0;32m\"+self.A177+self.ESC+\"1;42m\"+self.A176+self.A178+self.A176+self.ESC+\"0;32m\"+self.A178+self.ESC+\"1;42m\"+self.A176+self.ESC+\"44m \"+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"63C\"+self.ESC+\"44m \"+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"42m\"+self.A176+self.A176+self.ESC+\"0;32m\"+self.A177+self.A176+self.ESC+\"1;42m\"+self.A176+self.A176+self.A178+self.A219+self.A219+self.A178+self.A178+self.A176+self.A176+\" \"+self.A176+self.A176+self.ESC+\"0;32m\"+self.A177+self.A176+self.A177+self.A178+self.A176+self.A177+self.ESC+\"1;42m\"+self.A176+self.ESC+\"0;32m\"+self.A176+self.ESC+\"1;42m\"+self.A219+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"26C\"+self.ESC+\"42m\"+self.ESC+\"0;32m\"+self.A178+self.ESC+\"1;42m\"+self.A219+self.ESC+\"0;32m\"+self.A177+self.A178+self.ESC+\"1;42m\"+self.A176+self.ESC+\"0;32m\"+self.A177+self.ESC+\"1;42m\"+self.A176+self.A176+self.ESC+\"0;32m\"+self.A177+self.A177+\"\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"36C\"+self.ESC+\"1;42m\"+self.A176+self.ESC+\"0;32m\"+self.A177+self.ESC+\"1;42m\"+self.A176+self.ESC+\"0;32m\"+self.A176+self.A177+self.A177+self.A177+self.ESC+\"1;42m\"+self.A219+self.A176+self.ESC+\"0;32m\"+self.A178+self.A178+self.A177+self.A176+self.A178+self.ESC+\"1;42m\"+self.A176+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"51C\"+self.ESC+\"42m\"+self.ESC+\"0;32m\"+self.A177+self.A176+self.A177+self.A178+self.A178+self.ESC+\"1;42m\"+self.A178+self.ESC+\"0;32m\"+self.A177+self.A178+self.ESC+\"1;42m\"+self.A177+self.A178+self.ESC+\"0;32m\"+self.A177+self.ESC+\"1;42m\"+self.A176+self.ESC+\"44m \"+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"64C\"+self.ESC+\"44m \"+self.ESC+\"37m<MORE> \"+self.ESC+\"40m\\r\\n\"\n\t\treturn thismsg","def get_sonic_mac(host, asicnum, port):\n if host.facts[\"num_asic\"] == 1:\n cmd = \"sudo ip link show {}\".format(port)\n else:\n ns = \"asic\" + str(asicnum)\n cmd = \"sudo ip netns exec {} ip link show {}\".format(ns, port)\n output = host.command(cmd)\n mac = output['stdout_lines'][1].split()[1]\n logger.info(\"host: %s, asic: %d, port: %s, mac: %s\", host.hostname, asicnum, port, mac)\n return mac","def mac_from_vm(vm: libvirt.virDomain = None) -> str:\n doc = minidom.parseString(vm.XMLDesc())\n interfaces = doc.getElementsByTagName('mac')\n return interfaces[0].getAttribute('address')","def generate_mac():\n rand_str = generate_name(choices=\"0123456789abcdef\", length=12)\n return \":\".join(re.findall(\"..\", rand_str))","def generate_mac():\n rand_str = generate_name(choices=\"0123456789abcdef\", length=12)\n return \":\".join(re.findall(\"..\", rand_str))","def get_eos_mac(nbr, nbr_intf):\n if \"port-channel\" in nbr_intf.lower():\n # convert Port-Channel1 to po1\n shell_intf = \"po\" + nbr_intf[-1]\n else:\n # convert Ethernet1 to eth1\n shell_intf = \"eth\" + nbr_intf[-1]\n\n output = nbr['host'].command(\"ip addr show dev %s\" % shell_intf)\n # 8: Ethernet0: <BROADCAST,MULTICAST,UP,LOWER_UP> mtu 9100 ...\n # link/ether a6:69:05:fd:da:5f brd ff:ff:ff:ff:ff:ff\n\n mac = output['stdout_lines'][1].split()[1]\n return {'mac': mac, \"shell_intf\": shell_intf}","def header(self):\n\t\tthismsg = \"\\r\\n\"+self.ESC + \"0m \" +self.A220 + self.A220 + self.A220 + self.A220 + self.A220 +\" \" + self.ESC + \"0m\\r\\n\"\n\t\tthismsg += \" \"+self.A220+self.A220+self.A219+self.A219+self.A219+self.A219+self.ESC+\"1;47m\"+self.A176+self.A177+self.A178+self.ESC+\"40m\"+self.A219+self.A219+self.A220+self.A220+self.ESC+\"0m \"+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"1m \"+self.ESC+\"31mS\"+self.ESC+\"0;31mAGA\"+self.ESC+\"37m \"+self.A219+self.A219+self.ESC+\"30;47mo\"+self.ESC+\"37;40m\"+self.A219+self.ESC+\"1;47m\"+self.A176+self.A176+self.A177+self.A177+self.A178+self.ESC+\"40m\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\"0m \"+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"1m \"+self.ESC+\"31mO\"+self.ESC+\"0;31mF THE\"+self.ESC+\"37m \"+self.A219+self.A219+self.ESC+\"30;47mO\"+self.ESC+\"37;40m\"+self.A219+self.A219+self.ESC+\"1;47m\"+self.A176+self.A177+self.A177+self.A178+self.ESC+\"40m\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\"0m \"+self.A220+self.A220+self.A220+self.ESC+\"1m\"+self.A220+self.A220+self.ESC+\"0m \"+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"1m \"+self.ESC+\"31mR\"+self.ESC+\"0;31mED\"+self.ESC+\"37m \"+self.A219+self.ESC+\"30;47mo\"+self.ESC+\"37;40m\"+self.A219+self.A219+self.ESC+\"1;47m\"+self.A176+self.A176+self.A177+self.A178+self.ESC+\"40m\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\"0m \"+self.A223+self.A219+self.ESC+\"1;47m\"+self.A176+self.A219+self.A219+self.A219+self.ESC+\"40m\"+self.A220+self.A220+\" \"+self.ESC+\"0;31m\"+self.A220+self.ESC+\"37m \"+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"31m \"+self.ESC+\"1mD\"+self.ESC+\"0;31mRAGON 0.9.9\"+self.ESC+\"37m \"+self.A223+self.A219+self.A219+self.ESC+\"1;47m\"+self.A176+self.A177+self.A177+self.A178+self.A219+self.ESC+\"40m\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A223+self.ESC+\"0m \"+self.A219+self.ESC+\"1;47m\"+self.A219+self.A219+self.ESC+\"40m\"+self.A223+self.A223+self.ESC+\"0;31m\"+self.A220+self.ESC+\"1;41m\"+self.A176+\" \"+self.ESC+\"0;31m\"+self.A220+self.ESC+\"37m \"+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"31m concept\"+self.ESC+\"37m \"+self.A223+self.ESC+\"1;47m\"+self.A176+self.A177+self.A177+self.A178+self.A178+self.ESC+\"40m\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A223+self.ESC+\"0m \"+self.ESC+\"1m\"+self.A220+self.ESC+\"0m \"+self.ESC+\"1m\"+self.A220+self.ESC+\"0m \"+self.ESC+\"31m\"+self.A220+self.A220+self.ESC+\"1;41m\"+self.A176+self.A178+\" \"+self.A176+\" \"+self.ESC+\"0;31m\"+self.A220+self.A220+self.ESC+\"37m \"+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"31m Seth Robinson \"+self.ESC+\"37m\"+self.A222+\" \"+self.A223+self.A223+self.ESC+\"1;47m\"+self.A178+self.ESC+\"40m\"+self.A219+self.A219+self.A219+self.A219+self.A223+self.A223+self.ESC+\"0m \"+self.A220+self.ESC+\"1;47m\"+self.A220+self.ESC+\"40m\"+self.A223+\" \"+self.ESC+\"0m\"+self.A220+self.A220+self.ESC+\"1;47m\"+self.A220+self.ESC+\"40m\"+self.A223+self.A223+\" \"+self.ESC+\"0m\"+self.A223+self.A219+self.A220+self.ESC+\"1m\"+self.A220+\" \"+self.ESC+\"0;31m\"+self.A223+self.ESC+\"1;41m\"+self.A176+self.A177+self.A178+\" \"+self.ESC+\"0;31m\"+self.A223+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A176+\" \"+self.ESC+\"0;31m\"+self.A220+self.ESC+\"37m \"+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"31m by\"+self.ESC+\"0m \"+self.A219+\" \"+self.A220+self.ESC+\"1;47m\"+self.A220+self.ESC+\"40m\"+self.A223+\" \"+self.ESC+\"0m\"+self.A220+self.ESC+\"1;47m\"+self.A220+self.A220+self.A219+self.ESC+\"40m\"+self.A223+self.ESC+\"0m \"+self.A223+self.ESC+\"1;47m\"+self.A176+self.A219+self.ESC+\"40m\"+self.A220+\" \"+self.ESC+\"0;31m\"+self.A223+self.ESC+\"1;41m\"+self.A177+self.A178+\" \"+self.ESC+\"0;37;40m \"+self.ESC+\"1;31;41m\"+self.A176+\" \"+self.A176+\" \"+self.A176+self.ESC+\"0;31m\"+self.A220+self.ESC+\"37m \"+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"1;34m J\"+self.ESC+\"0;34m.\"+self.ESC+\"1mT\"+self.ESC+\"0;34m.\"+self.ESC+\"1mS\"+self.ESC+\"0;34mage\"+self.ESC+\"0m \"+self.A219+self.ESC+\"1;30;47m\"+self.A176+self.ESC+\"0;37;40m\"+self.A221+\" \"+self.A220+self.ESC+\"1;47m\"+self.A177+self.A176+self.ESC+\"40m\"+self.A223+\" \"+self.ESC+\"0m\"+self.A220+self.ESC+\"1;47m\"+self.A220+self.A220+self.A219+self.ESC+\"40m\"+self.A223+self.ESC+\"0m \"+self.A223+self.ESC+\"1;47m\"+self.A177+self.A219+self.A219+self.ESC+\"40m\"+self.A220+\" \"+self.ESC+\"0;31m\"+self.A223+self.ESC+\"1;41m\"+self.A177+self.A176+self.A178+\" \"+self.ESC+\"0;31m\"+self.A220+self.A223+self.ESC+\"1;41m\"+self.A176+self.A178+self.A176+self.A176+self.A177+self.A177+self.ESC+\"0;37;40m \"+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += \" \"+self.A219+self.A219+\" \"+self.ESC+\"1;47m\"+self.A176+self.A177+self.A219+self.ESC+\"40m\"+self.A223+\" \"+self.ESC+\"0m\"+self.A223+self.ESC+\"1m\"+self.A223+self.ESC+\"41m\"+self.A223+self.ESC+\"0;31m\"+self.A220+self.A220+self.A220+self.A220+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A178+self.A177+self.A176+\" \"+self.A220+self.A220+self.A220+self.A220+self.A223+self.A220+self.ESC+\"1;41m\"+self.A176+self.ESC+\"0;31m\"+self.A220+self.ESC+\"1;41m\"+self.A177+self.ESC+\"0;31m\"+self.A220+self.ESC+\"41m \"+self.ESC+\"37;40m \"+self.ESC+\"31m\"+self.A223+self.ESC+\"1;41m\"+self.A177+self.A178+\" \"+self.A219+self.ESC+\"0;37;40m \"+self.ESC+\"1;31;41m\"+self.A178+self.A178+self.A176+self.A177+self.A177+self.A219+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"37m \"+self.A219+self.ESC+\"1;30;47m\"+self.A176+self.ESC+\"0;37;40m \"+self.A219+self.A223+self.ESC+\"1m\"+self.A223+self.ESC+\"0;31m\"+self.A220+self.A220+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A223+self.A223+self.A223+\" \"+self.A220+self.A220+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A223+self.A220+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A223+self.A223+self.A223+\" \"+self.A176+self.A176+\" \"+self.ESC+\"1;41m\"+self.A176+\" \"+self.A178+self.A178+self.A219+self.ESC+\"0;31m\"+self.A220+\" \"+self.A223+self.ESC+\"1;41m\"+self.A177+self.A176+\" \"+self.ESC+\"0;37;40m \"+self.ESC+\"1;31;41m\"+self.A176+self.A178+self.A176+self.A177+self.A177+self.A219+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"37m \"+self.A219+self.ESC+\"1;30;47m\"+self.A176+self.ESC+\"0;37;40m \"+self.A219+\" \"+self.ESC+\"31m\"+self.A220+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A223+\" \"+self.A220+self.A220+self.A220+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A178+self.A177+self.A176+self.ESC+\"37m \"+self.ESC+\"31m\"+self.A178+self.A177+self.A177+self.A223+\" \"+self.ESC+\"1;41m\"+self.A176+\" \"+self.A177+self.A178+self.A219+\" \"+self.ESC+\"0;31m\"+self.A220+\" \"+self.A223+self.ESC+\"1;41m\"+self.A177+self.A219+self.ESC+\"0;31m\"+self.A220+self.A223+self.ESC+\"1;41m\"+self.A178+\" \"+self.A177+self.A178+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"37m \"+self.ESC+\"1;30;47m\"+self.A176+self.ESC+\"0;37;40m\"+self.A219+\" \"+self.A219+\" \"+self.ESC+\"31m\"+self.A220+self.ESC+\"41m \"+self.ESC+\"37;40m \"+self.ESC+\"31m\"+self.A220+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A178+self.A177+self.A176+\" \"+self.A220+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A223+self.A223+self.ESC+\"37m \"+self.ESC+\"1;31;41m\"+self.A176+\" \"+self.A178+self.A177+self.A219+self.A219+self.ESC+\"0;37;40m \"+self.ESC+\"1;31;41m\"+self.A176+self.A177+self.A178+self.ESC+\"0;37;40m \"+self.ESC+\"31m\"+self.A220+self.A220+self.A223+self.ESC+\"1;41m\"+self.A178+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"37m \"+self.ESC+\"1;30;47m\"+self.A176+self.ESC+\"0;37;40m\"+self.A219+self.ESC+\"1;30;47m\"+self.A176+self.ESC+\"0;37;40m \"+self.ESC+\"31m\"+self.A220+self.A220+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A220+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A219+self.A178+self.A178+self.A177+self.A176+self.A223+\" \"+self.A220+self.A220+\" \"+self.A223+self.ESC+\"37m \"+self.ESC+\"1;31;41m\"+self.A176+\" \"+self.A177+self.A178+self.A219+self.ESC+\"0;37;40m \"+self.ESC+\"1;31;41m\"+self.A177+self.A178+self.A219+self.ESC+\"0;37;40m \"+self.ESC+\"1;31;41m\"+self.A176+self.A178+self.A178+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"37m \"+self.A219+self.A219+self.A219+\" \"+self.ESC+\"31m\"+self.A220+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A221+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A178+self.A223+self.A223+self.A223+\" \"+self.A220+self.A220+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A223+self.A223+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A220+\" \"+self.A220+self.A220+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A178+self.A178+self.A177+self.A223+\" \"+self.ESC+\"1;41m\"+self.A176+\" \"+self.A177+self.A219+self.A219+self.ESC+\"0;37;40m \"+self.ESC+\"1;31;41m\"+self.A176+self.A177+\" \"+self.ESC+\"0;31m\"+self.A220+self.ESC+\"1;41m\"+self.A176+self.A178+self.A178+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"37m \"+self.A222+self.ESC+\"1;30;47m\"+self.A176+self.A177+self.ESC+\"0;37;40m\"+self.A221+\" \"+self.ESC+\"31m\"+self.A220+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A176+\" \"+self.ESC+\"0;31m\"+self.A223+\" \"+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A220+self.A220+self.A220+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A220+self.A220+\" \"+self.A223+self.A223+self.A220+\" \"+self.A223+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A178+self.A178+self.A178+self.ESC+\"37m \"+self.ESC+\"1;31;41m\"+self.A176+\" \"+self.A178+self.A219+self.ESC+\"0;37;40m \"+self.ESC+\"31m\"+self.A223+self.ESC+\"1;41m\"+self.A176+self.A177+self.A178+self.ESC+\"0;37;40m \"+self.ESC+\"1;31;41m\"+self.A176+self.A178+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"37m \"+self.A222+self.ESC+\"1;30;47m\"+self.A176+self.A177+self.ESC+\"0;37;40m\"+self.A219+\" \"+self.ESC+\"31m\"+self.A220+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A176+self.ESC+\"0;31m\"+self.A223+self.A223+\" \"+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A220+self.A222+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A220+self.A220+\" \"+self.A223+self.A178+self.ESC+\"37m \"+self.ESC+\"1;31;41m\"+self.A176+self.A177+self.A178+self.A219+self.ESC+\"0;37;40m \"+self.ESC+\"1;31;41m\"+self.A176+self.A177+\" \"+self.A178+self.ESC+\"0;37;40m \"+self.ESC+\"1;31;41m\"+self.A176+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += \" \"+self.A219+self.ESC+\"1;30;47m\"+self.A177+self.ESC+\"0;37;40m\"+self.A219+\" \"+self.ESC+\"31m\"+self.A222+self.A219+self.ESC+\"1;41m\"+self.A176+self.A176+self.ESC+\"0;31m\"+self.A221+\" \"+self.ESC+\"1;5;32m\"+self.A220+self.A220+self.A223+\" \"+self.ESC+\"0;31;41m \"+self.ESC+\"40m\"+self.A178+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A223+self.A223+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A220+self.A222+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A220+\" \"+self.A220+self.A220+self.A220+self.A220+self.A223+self.ESC+\"1;41m\"+self.A176+self.A177+self.A178+self.ESC+\"0;31m\"+self.A220+\" \"+self.ESC+\"1;41m\"+self.A176+self.A177+\" \"+self.ESC+\"0;37;40m \"+self.ESC+\"1;31;41m\"+self.A176+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"37m \"+self.A219+self.ESC+\"1;30;47m\"+self.A176+self.ESC+\"0;37;40m\"+self.A219+\" \"+self.ESC+\"31m\"+self.A222+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A176+\" \"+self.ESC+\"0;37;40m \"+self.ESC+\"1;5;32m\"+self.A219+self.A219+self.A223+\" \"+self.ESC+\"0;31m\"+self.A222+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A178+self.A220+self.A222+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A220+self.A222+self.A219+self.ESC+\"41m \"+self.ESC+\"37;40m \"+self.ESC+\"31;41m \"+self.ESC+\"40m\"+self.A178+self.A223+\" \"+self.A223+self.ESC+\"1;41m\"+self.A176+self.A177+self.A219+self.ESC+\"0;37;40m \"+self.ESC+\"1;31;41m\"+self.A176+self.A177+self.A219+self.A178+self.ESC+\"0;37;40m \"+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += \" \"+self.A222+self.ESC+\"1;30;47m\"+self.A176+self.A177+self.ESC+\"0;37;40m\"+self.A219+\" \"+self.ESC+\"31m\"+self.A222+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A178+self.A176+self.ESC+\"0;37;40m \"+self.ESC+\"1;5;32m\"+self.A223+\" \"+self.ESC+\"0;31m\"+self.A220+self.A220+self.A220+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A178+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A223+\" \"+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A220+self.A222+self.A219+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A220+\" \"+self.A223+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A178+self.A223+\" \"+self.A223+self.ESC+\"1;41m\"+self.A176+self.A219+self.ESC+\"0;37;40m \"+self.ESC+\"31;41m \"+self.A177+self.A219+self.A178+self.ESC+\"37;40m \"+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += \" \"+self.A219+self.ESC+\"1;30;47m\"+self.A176+self.ESC+\"0;37;40m\"+self.A221+\" \"+self.ESC+\"31m\"+self.A222+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A177+self.A176+self.ESC+\"0;31m\"+self.A221+self.A220+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A176+self.ESC+\"0;31m\"+self.A223+self.A223+\" \"+self.A222+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A220+self.A222+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A176+self.A176+self.ESC+\"0;31m\"+self.A220+self.A220+self.ESC+\"37m \"+self.ESC+\"1;31;41m\"+self.A176+self.A177+self.ESC+\"0;37;40m \"+self.ESC+\"31;41m \"+self.A176+self.A177+self.A219+self.A178+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"37m \"+self.A219+self.ESC+\"1;30;47m\"+self.A177+self.ESC+\"0;37;40m \"+self.ESC+\"31m\"+self.A220+self.A220+self.A223+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A176+self.A222+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A223+self.ESC+\"37m \"+self.ESC+\"31m\"+self.A220+self.ESC+\"1;41m\"+self.A176+\" \"+self.ESC+\"0;31m\"+self.A220+self.A222+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A177+self.A177+self.A176+\" \"+self.ESC+\"0;37;40m \"+self.ESC+\"31m\"+self.A220+\" \"+self.ESC+\"1;41m\"+self.A176+self.A219+self.ESC+\"0;37;40m \"+self.ESC+\"31;41m \"+self.A177+\" \"+self.A219+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"37m \"+self.A219+self.A221+\" \"+self.ESC+\"31;41m \"+self.ESC+\"37;40m \"+self.ESC+\"31;41m \"+self.ESC+\"40m\"+self.A177+self.A177+self.A176+self.A178+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A176+self.ESC+\"0;37;40m \"+self.ESC+\"31m\"+self.A220+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A177+self.A176+self.A176+\" \"+self.ESC+\"0;31m\"+self.A222+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A178+self.A177+self.A176+self.ESC+\"0;31m\"+self.A220+\" \"+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A178+self.A220+self.A220+\" \"+self.ESC+\"1;41m\"+self.A178+self.ESC+\"0;31m\"+self.A220+\" \"+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A177+self.A219+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"37m \"+self.ESC+\"1m\"+self.A220+\" \"+self.ESC+\"0m\"+self.A223+\" \"+self.ESC+\"31;41m \"+self.ESC+\"40m\"+self.A223+\" \"+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A219+self.A178+self.A178+self.A177+self.A176+self.A176+self.A177+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A176+self.ESC+\"0;37;40m \"+self.ESC+\"31;41m \"+self.A178+self.A177+self.A177+self.A176+\" \"+self.ESC+\"40m\"+self.A220+self.A222+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A178+self.A177+self.A176+self.ESC+\"0;37;40m \"+self.ESC+\"31m\"+self.A223+self.A178+\" \"+self.ESC+\"1;41m\"+self.A177+self.A219+self.ESC+\"0;37;40m \"+self.ESC+\"31;41m \"+self.A176+self.A176+self.A178+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"37m \"+self.A223+self.ESC+\"1m\"+self.A219+self.A220+\" \"+self.ESC+\"0;31;41m \"+self.A177+self.A178+self.A176+self.A176+\" \"+self.ESC+\"40m\"+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A219+self.A176+self.A178+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A176+self.ESC+\"0;37;40m \"+self.ESC+\"31m\"+self.A222+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A219+self.A178+self.A178+self.A177+self.A176+\" \"+self.ESC+\"0;37;40m \"+self.ESC+\"31;41m \"+self.A219+self.A178+self.A177+self.A176+self.ESC+\"37;40m \"+self.ESC+\"31m\"+self.A223+\" \"+self.ESC+\"1;41m\"+self.A176+self.ESC+\"0;31m\"+self.A223+\" \"+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A176+self.A177+self.A219+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += \" \"+self.A223+self.ESC+\"1;47m\"+self.A223+self.ESC+\"40m\"+self.A219+self.A223+self.ESC+\"0;31m\"+self.A220+self.ESC+\"1;41m\"+self.A178+self.A176+self.A176+\" \"+self.ESC+\"0;31m\"+self.A223+\" \"+self.ESC+\"1;37;47m\"+self.A222+self.ESC+\"40m\"+self.A221+self.A223+self.A220+\" \"+self.ESC+\"0;31m\"+self.A177+\" \"+self.A223+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A221+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A176+self.ESC+\"0;31m\"+self.A223+\" \"+self.A220+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A219+self.A178+self.A178+self.A177+self.A176+\" \"+self.ESC+\"0;31m\"+self.A220+self.A223+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A219+self.A178+self.A177+self.A176+\" \"+self.ESC+\"0;31m\"+self.A223+\" \"+self.ESC+\"1;41m\"+self.A176+self.ESC+\"0;37;40m \"+self.ESC+\"31;41m \"+self.A176+\" \"+self.A177+self.A219+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"37m \"+self.A223+self.ESC+\"31m\"+self.A222+self.ESC+\"1;41m\"+self.A177+self.A176+\" \"+self.ESC+\"0;31m\"+self.A223+\" \"+self.ESC+\"1;37;47m\"+self.A222+self.ESC+\"40m\"+self.A221+\" \"+self.ESC+\"47m\"+self.A222+self.ESC+\"40m\"+self.A221+\" \"+self.ESC+\"0;31m\"+self.A178+self.A177+\" \"+self.A223+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A176+self.ESC+\"0;37;40m \"+self.ESC+\"33m\"+self.A220+\" \"+self.ESC+\"31m\"+self.A223+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A219+self.A178+self.A177+self.A176+self.A176+\" \"+self.ESC+\"0;31m\"+self.A220+self.A223+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A178+self.A177+self.A176+\" \"+self.ESC+\"0;37;40m \"+self.ESC+\"31;41m \"+self.ESC+\"40m\"+self.A220+self.A223+\" \"+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A176+self.A176+\" \"+self.A178+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"37m \"+self.ESC+\"31;41m \"+self.A176+self.A176+self.ESC+\"37;40m \"+self.A220+self.ESC+\"1m\"+self.A219+self.ESC+\"0m\"+self.A223+self.ESC+\"1m\"+self.A219+self.A221+\" \"+self.A223+\" \"+self.A223+self.A220+\" \"+self.ESC+\"0;31;41m \"+self.ESC+\"40m\"+self.A223+self.A220+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A223+\" \"+self.ESC+\"33m\"+self.A219+self.A219+\" \"+self.ESC+\"31m\"+self.A223+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A219+self.A178+self.A177+self.A176+\" \"+self.ESC+\"0;37;40m \"+self.ESC+\"31;41m \"+self.A178+self.A177+self.A176+self.ESC+\"40m\"+self.A220+\" \"+self.ESC+\"1;41m\"+self.A176+self.ESC+\"0;37;40m \"+self.ESC+\"31;41m \"+self.A176+self.A177+\" \"+self.A219+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += \" \"+self.A220+self.ESC+\"1m\"+self.A219+\" \"+self.A219+self.A221+\" \"+self.A223+\" \"+self.A220+\" \"+self.A223+self.A220+\" \"+self.ESC+\"0;31m\"+self.A220+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A223+self.A220+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A223+\" \"+self.ESC+\"1;33;43m\"+self.A177+self.A176+self.ESC+\"0;33m\"+self.A219+self.A219+\" \"+self.ESC+\"31m\"+self.A223+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A219+self.A178+self.A177+self.A176+\" \"+self.ESC+\"0;31m\"+self.A219+self.A220+self.A222+self.ESC+\"1;41m\"+self.A219+self.A177+self.A176+self.ESC+\"0;31m\"+self.A220+self.ESC+\"1;41m\"+self.A176+self.ESC+\"0;37;40m \"+self.ESC+\"31m\"+self.A220+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A176+\" \"+self.A178+self.A219+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += \" \"+self.A223+self.ESC+\"1m\"+self.A220+\" \"+self.A223+\" \"+self.A220+self.A223+self.A220+\" \"+self.A223+\" \"+self.A223+self.ESC+\"0;31m\"+self.A220+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A223+self.A223+self.A220+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A223+\" \"+self.ESC+\"1;33m\"+self.A223+self.ESC+\"43m\"+self.A219+self.A219+self.ESC+\"40m\"+self.A223+self.ESC+\"0;33m\"+self.A220+self.A219+\" \"+self.ESC+\"31;41m \"+self.ESC+\"1m\"+self.A219+self.A178+self.A177+self.A176+\" \"+self.ESC+\"0;31m\"+self.A222+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A177+self.A177+self.A176+self.ESC+\"0;31m\"+self.A223+\" \"+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A176+self.A177+self.A178+\" \"+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"37m \"+self.ESC+\"1m\"+self.A220+\" \"+self.ESC+\"0;31m\"+self.A220+self.A220+self.A220+self.A220+self.A220+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A220+self.A220+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A223+self.ESC+\"37m \"+self.ESC+\"1;33;43m\"+self.A177+self.A176+self.ESC+\"0;33m\"+self.A219+self.A219+self.A220+\" \"+self.ESC+\"31;41m \"+self.ESC+\"1m\"+self.A219+self.A178+self.A177+self.A176+\" \"+self.ESC+\"0;31m\"+self.A220+self.A222+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A176+self.A176+self.ESC+\"0;37;40m \"+self.ESC+\"31;41m \"+self.A176+self.A177+self.A178+\" \"+self.A219+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"37m \"+self.ESC+\"30mÙ\"+self.ESC+\"31m\"+self.A222+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A177+self.A177+self.A176+self.A176+\" \"+self.ESC+\"0;31m\"+self.A223+self.A223+self.ESC+\"37m \"+self.ESC+\"1;33m\"+self.A223+self.ESC+\"43m\"+self.A219+self.ESC+\"0;33m\"+self.A223+self.A223+self.A223+self.A220+\" \"+self.ESC+\"31;41m \"+self.ESC+\"1m\"+self.A219+self.A178+self.A177+self.A176+\" \"+self.ESC+\"0;31m\"+self.A220+self.A222+self.A223+\" \"+self.A220+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A177+self.A176+\" \"+self.A219+\" \"+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"37m \"+self.ESC+\"31m\"+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.ESC+\"37m \"+self.ESC+\"1;33m\"+self.A220+self.A220+self.ESC+\"43m\"+self.A176+self.ESC+\"0;33m\"+self.A219+self.A219+\" \"+self.ESC+\"31;41m \"+self.ESC+\"1m\"+self.A219+self.A178+self.A177+self.A176+\" \"+self.ESC+\"0;37;40m \"+self.ESC+\"31m\"+self.A220+self.ESC+\"1;41m\"+self.A176+self.A177+self.A178+self.A176+self.A176+\" \"+self.A219+self.ESC+\"0;37;40m \"+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += \" \"+self.ESC+\"1;33;43m\"+self.A219+self.A178+self.A177+self.A176+self.ESC+\"0;33m\"+self.A219+\" \"+self.ESC+\"31m\"+self.A223+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A219+self.A178+self.A177+self.A176+\" \"+self.ESC+\"0;31m\"+self.A220+\" \"+self.A223+self.ESC+\"1;41m\"+self.A176+self.A177+self.A177+\" \"+self.ESC+\"0;31m\"+self.A223+self.ESC+\"37m \"+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += \" \"+self.ESC+\"1;33;43m\"+self.A219+self.A178+self.ESC+\"40m\"+self.A223+self.ESC+\"0;33m\"+self.A223+self.A223+self.A220+self.A220+\" \"+self.ESC+\"31;41m \"+self.ESC+\"1m\"+self.A219+self.A178+self.A177+self.A177+self.A176+\" \"+self.ESC+\"0;31m\"+self.A220+\" \"+self.A223+self.ESC+\"1;41m\"+self.A177+self.A177+self.ESC+\"0;37;40m \"+self.ESC+\"31;41m \"+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"37m \"+self.ESC+\"1;33m\"+self.A220+self.A220+self.ESC+\"43m\"+self.A219+self.A178+self.A177+self.A176+self.ESC+\"0;33m\"+self.A219+self.A220+\" \"+self.ESC+\"31;41m \"+self.ESC+\"1m\"+self.A219+self.A178+self.A177+self.A177+self.A176+\" \"+self.ESC+\"0;37;40m \"+self.ESC+\"31m\"+self.A223+self.ESC+\"41m \"+self.ESC+\"37;40m \"+self.ESC+\"31;41m \"+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"37m \"+self.ESC+\"1;33m\"+self.A220+self.ESC+\"43m\"+self.A219+self.ESC+\"40m\"+self.A223+self.A223+self.A223+self.ESC+\"0;33m\"+self.A223+self.A220+self.A220+self.A220+\" \"+self.ESC+\"31;41m \"+self.ESC+\"1m\"+self.A178+self.A177+self.A176+self.ESC+\"0;31m\"+self.A223+self.A223+self.A223+self.A223+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A220+\" \"+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A220+\" \"+self.A223+self.ESC+\"41m \"+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"37m \"+self.ESC+\"1;33m\"+self.A220+self.A220+self.ESC+\"43m\"+self.A219+self.A219+self.A178+self.A177+self.A176+self.ESC+\"0;33m\"+self.A219+self.A219+\" \"+self.ESC+\"31m\"+self.A223+self.A223+\" \"+self.A220+self.A220+self.A220+self.A220+self.A220+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A220+\" \"+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A220+\" \"+self.ESC+\"41m \"+self.ESC+\"40m\"+self.A220+self.A220+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"37m \"+self.ESC+\"1;33;43m\"+self.A219+self.A219+self.ESC+\"40m\"+self.A223+self.A223+self.A223+self.ESC+\"0;33m\"+self.A223+self.A223+self.ESC+\"31m\"+self.A220+self.A220+self.ESC+\"41m \"+self.ESC+\"1m\"+self.A176+self.A178+self.A177+\" \"+self.A176+\" \"+self.ESC+\"0;31m\"+self.A220+\" \"+self.ESC+\"41m \"+self.ESC+\"37;40m \"+self.ESC+\"31m\"+self.A223+self.ESC+\"41m \"+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"37m \"+self.ESC+\"1;33m\"+self.A220+self.ESC+\"43m\"+self.A178+self.ESC+\"0;33m\"+self.A220+self.A219+self.A223+\" \"+self.ESC+\"31;41m \"+self.ESC+\"1m\"+self.A176+self.A178+self.A177+self.A177+self.A176+self.A176+self.A176+\" \"+self.ESC+\"0;31m\"+self.A220+\" \"+self.A223+self.ESC+\"41m \"+self.ESC+\"37;40m \"+self.ESC+\"31;41m \"+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"37m \"+self.ESC+\"1;33;43m\"+self.A178+self.A177+self.A176+self.ESC+\"0;33m\"+self.A223+self.A223+\" \"+self.ESC+\"31;41m \"+self.ESC+\"1m\"+self.A176+self.A178+self.A178+\" \"+self.A177+\" \"+self.A176+self.A176+\" \"+self.ESC+\"0;37;40m \"+self.ESC+\"31;41m \"+self.ESC+\"37;40m \"+self.ESC+\"31m\"+self.A223+self.ESC+\"41m \"+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"37m \"+self.ESC+\"1;33m\"+self.A220+self.A220+self.ESC+\"0;33m\"+self.A220+self.A220+self.A223+\" \"+self.ESC+\"31;41m \"+self.ESC+\"1m\"+self.A176+self.A219+self.A178+self.A178+self.A177+self.A177+self.A176+\" \"+self.A176+\" \"+self.A176+self.A176+\" \"+self.ESC+\"0;31m\"+self.A220+\" \"+self.ESC+\"41m \"+self.ESC+\"37;40m \"+self.ESC+\"31;41m \"+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"37m \"+self.ESC+\"0;37m \"+self.ESC+\"1;33m\"+self.A223+self.A223+self.A223+self.ESC+\"0;33m\"+self.A223+\" \"+self.ESC+\"30;41m \"+self.ESC+\"1;31mShatterstar [W/X] \"+self.ESC+\"0;37;40m \"+self.ESC+\"30;41m \"+self.ESC+\"37;40m \"+self.ESC+\"30;41m \"+self.ESC+\"0m\\r\\n\"\n\t\tthismsg += self.ESC+\"37m \"+self.ESC+\"0m\\r\\n\"\n\t\treturn thismsg","def test_asciitable_m_pretty_ansi(self):\n input = '''\n┏━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┓ \n┃\\x1b[1m \\x1b[0m\\x1b[1mReleased \\x1b[0m\\x1b[1m \\x1b[0m┃\\x1b[1m \\x1b[0m\\x1b[1mTitle \\x1b[0m\\x1b[1m \\x1b[0m┃\\x1b[1m \\x1b[0m\\x1b[1m Box Office\\x1b[0m\\x1b[1m \\x1b[0m┃ \n┡━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━┩ \n│\\x1b[36m \\x1b[0m\\x1b[36mDec 20, 2019\\x1b[0m\\x1b[36m \\x1b[0m│\\x1b[35m \\x1b[0m\\x1b[35mStar Wars: The Rise of Skywalker \\x1b[0m\\x1b[35m \\x1b[0m│\\x1b[32m \\x1b[0m\\x1b[32m $952,110,690\\x1b[0m\\x1b[32m \\x1b[0m│ \n│\\x1b[36m \\x1b[0m\\x1b[36mMay 25, 2018\\x1b[0m\\x1b[36m \\x1b[0m│\\x1b[35m \\x1b[0m\\x1b[35mSolo: A Star Wars Story \\x1b[0m\\x1b[35m \\x1b[0m│\\x1b[32m \\x1b[0m\\x1b[32m $393,151,347\\x1b[0m\\x1b[32m \\x1b[0m│ \n│\\x1b[36m \\x1b[0m\\x1b[36mDec 15, 2017\\x1b[0m\\x1b[36m \\x1b[0m│\\x1b[35m \\x1b[0m\\x1b[35mStar Wars Ep. V111: The Last Jedi\\x1b[0m\\x1b[35m \\x1b[0m│\\x1b[32m \\x1b[0m\\x1b[32m$1,332,539,889\\x1b[0m\\x1b[32m \\x1b[0m│ \n│\\x1b[36m \\x1b[0m\\x1b[36mDec 16, 2016\\x1b[0m\\x1b[36m \\x1b[0m│\\x1b[35m \\x1b[0m\\x1b[35mRogue One: A Star Wars Story \\x1b[0m\\x1b[35m \\x1b[0m│\\x1b[32m \\x1b[0m\\x1b[32m$1,332,439,889\\x1b[0m\\x1b[32m \\x1b[0m│ \n└──────────────┴───────────────────────────────────┴────────────────┘ \n'''\n expected = [\n {\n \"released\": \"Dec 20, 2019\\nMay 25, 2018\\nDec 15, 2017\\nDec 16, 2016\",\n \"title\": \"Star Wars: The Rise of Skywalker\\nSolo: A Star Wars Story\\nStar Wars Ep. V111: The Last Jedi\\nRogue One: A Star Wars Story\",\n \"box_office\": \"$952,110,690\\n$393,151,347\\n$1,332,539,889\\n$1,332,439,889\"\n }\n ]\n\n self.assertEqual(jc.parsers.asciitable_m.parse(input, quiet=True), expected)","def sw_mac(self):\n return self.raw.get('sw_mac')","def mac(self) -> str:\n return self.camera_info[\"wifi_mac\"]","def mac(self):\n return self.device.settings[\"device\"][\"mac\"]","def mac(self):\n return self.device.settings[\"device\"][\"mac\"]","def banner_ascii():\n print(\"\")\n print(f\"\\n{RED} Steganography Tool{RESET}\")\n print(f\"{RED} Made By {RESET}\")\n print(f\"{RED} Ehthe Samul Islam Laskar USN:1DS16CS712 {RESET}\")\n print(f\"{RED} B Padma USN:1DS19CS420{RESET}\")\n print(f\"{RED} Nikhil D Kanyal USN:1DS17CS731{RESET}\")\n print(f\"{YELLOW}Type 'help' to see commands{RESET}\")","def macs_to_str(self, reached_max_depth: bool) -> str:\n if self.num_params > 0 and (\n reached_max_depth or not any(self.module.children())\n ):\n return f\"{self.macs:,}\"\n return \"--\"","def accelerator_string():\n if is_mac():\n return \"Command\"\n else:\n return \"Ctrl\"","def repr2col(tups):\n quant = tuple(round(comp * 255) for comp in tups)\n if quant[3] != 255: return \"\".join([\"{0:02x}\".format(m) for m in quant])\n quant = quant[:3]\n for w in shortaliases:\n if quant == shortaliases[w]: return w\n return \"#\" + \"\".join([\"{:02x}\".format(n) if max(n % 17 for n in quant) else \"{:x}\".format(n >> 4) for n in quant])","def cMACW(self):\n return 3.5","def usage():\n\tusagetext = \"\"\"bin2mif (c) 2013 Stephen J. Leary\nUsage: bin2mif [-a] [-w <width>] [-e <endian>] <filename> \nProgram outputs a hex MIF file to stdout.\nDefaults are width = 8 and big endian unless specified.\n\"\"\"\n\tsys.stderr.write(usagetext)","def InputMacAddress(prompt):\n while 1:\n SetPatternColor()\n print prompt,\n SetPatternColor(1)\n val = raw_input().strip()\n SetPatternColor()\n try:\n #if len(val) == 12 and int(val,16):\n if (len(val) == 12 or len(val) == 10): \n return val.upper()\n except ValueError:\n pass\n #SetPattern(0)\n SetPatternColor(0)\n print \"ERROR %s!!\"%prompt","def get_mac(self) :\n\t\ttry :\n\t\t\treturn self.p_fields.f128\n\t\texcept :\n\t\t\treturn None","def _make_sysex_message(self, text_message, display_index):\n if len(text_message) > 28:\n text_message = text_message[:27]\n sysex_message = [240, 0, 0, 102, 23, 18, min(display_index, 3) * 28]\n filled = text_message.ljust(28)\n for c in filled:\n sysex_message.append(ord(c))\n sysex_message.append(247)\n return sysex_message","def test_oldmac(args):\n print('============= Testing for Correctness (Old Mac) =============')\n return _replace_test(args, '\\r')","async def textemote(self, ctx, *, msg):\n await ctx.message.delete()\n if msg != None:\n out = msg.lower()\n text = out.replace(' ', ' ').replace('10', '\\u200B:keycap_ten:')\\\n .replace('ab', '\\u200B🆎').replace('cl', '\\u200B🆑')\\\n .replace('0', '\\u200B:zero:').replace('1', '\\u200B:one:')\\\n .replace('2', '\\u200B:two:').replace('3', '\\u200B:three:')\\\n .replace('4', '\\u200B:four:').replace('5', '\\u200B:five:')\\\n .replace('6', '\\u200B:six:').replace('7', '\\u200B:seven:')\\\n .replace('8', '\\u200B:eight:').replace('9', '\\u200B:nine:')\\\n .replace('!', '\\u200B❗').replace('?', '\\u200B❓')\\\n .replace('vs', '\\u200B🆚').replace('.', '\\u200B🔸')\\\n .replace(',', '🔻').replace('a', '\\u200B🅰')\\\n .replace('b', '\\u200B🅱').replace('c', '\\u200B🇨')\\\n .replace('d', '\\u200B🇩').replace('e', '\\u200B🇪')\\\n .replace('f', '\\u200B🇫').replace('g', '\\u200B🇬')\\\n .replace('h', '\\u200B🇭').replace('i', '\\u200B🇮')\\\n .replace('j', '\\u200B🇯').replace('k', '\\u200B🇰')\\\n .replace('l', '\\u200B🇱').replace('m', '\\u200B🇲')\\\n .replace('n', '\\u200B🇳').replace('ñ', '\\u200B🇳')\\\n .replace('o', '\\u200B🅾').replace('p', '\\u200B🅿')\\\n .replace('q', '\\u200B🇶').replace('r', '\\u200B🇷')\\\n .replace('s', '\\u200B🇸').replace('t', '\\u200B🇹')\\\n .replace('u', '\\u200B🇺').replace('v', '\\u200B🇻')\\\n .replace('w', '\\u200B🇼').replace('x', '\\u200B🇽')\\\n .replace('y', '\\u200B🇾').replace('z', '\\u200B🇿')\n try:\n await ctx.send(text)\n except Exception as e:\n await ctx.send(f'```{e}```')\n else:\n await ctx.send('Args req!', delete_after=3.0)","def to_ascii(self):\n code = self.build()\n for i, line in enumerate(code):\n code[i] = line.replace('1', '|').replace('0', '_')\n return '\\n'.join(code)","def normalize_teh_marbuta_ar(s):\n\n return s.replace(u'\\u0629', u'\\u0647')","def out_mac_control_frames(self) -> str:\n return self._out_mac_control_frames","def to_mac(self):\n try:\n dt_obj = duparser.parse(timestamp)\n self.out_mac = str(int((dt_obj - self.epoch_2001).total_seconds()))\n except Exception as e:\n if not args.log:\n pass\n else:\n logging.error(str(type(e)) + \",\" + str(e))\n self.out_mac = False\n return self.out_mac","def _get_mac(self):\n return self.__mac","def midi_to_ansi_note(midi_note):\n notes = [\"A\", \"A#\", \"B\", \"C\", \"C#\", \"D\", \"D#\", \"E\", \"F\", \"F#\", \"G\", \"G#\"]\n num_notes = 12\n note_name = notes[int((midi_note - 21) % num_notes)]\n note_number = (midi_note - 12) // num_notes\n return f\"{note_name}{note_number}\"","def supercombiner(bot, ev):\n # ported from jenni\n s = 'u'\n for i in iter(range(1, 3000)):\n if unicodedata.category(chr(i)) == \"Mn\":\n s += chr(i)\n if len(s) > 100:\n break\n bot.say(s)","def to_meme(self):\n motif_id = self.id.replace(\" \", \"_\")\n m = \"MOTIF %s\\n\" % motif_id\n m += \"BL MOTIF %s width=0 seqs=0\\n\"% motif_id\n m += \"letter-probability matrix: alength= 4 w= %s nsites= %s E= 0\\n\" % (len(self), np.sum(self.pfm[0]))\n m +=\"\\n\".join([\"\\t\".join([\"%s\" % x for x in row]) for row in self.pwm])\n return m","def basic_char_setup( self ):\n\t\tsmall_bar = 3 # number of points per bar\n\t\twide_bar = round(small_bar * 2.25,0) # 2.25 x small_bar\n\t\tdpl = 50 # dots per line 300dpi/6lpi = 50dpl\n\t\tself._nb = bytes( self.owner.PRINTER_ESC +\n\t\t\t\t\t\t ( '*c%02ia%ib0P' % (small_bar, self.bc_height*dpl) ) + \n\t\t\t\t\t\t self.owner.PRINTER_ESC + \n\t\t\t\t\t\t (\"*p+%02iX\" % small_bar) )\n\t\tself._wb = bytes( self.owner.PRINTER_ESC +\n\t\t\t\t\t\t ('*c%02ia%ib0P' % (wide_bar, self.bc_height*dpl) )+\n\t\t\t\t\t\t self.owner.PRINTER_ESC +\n\t\t\t\t\t\t ('*p+%02iX' % wide_bar ) )\n\t\tself._ns = bytes( self.owner.PRINTER_ESC + ( '*p+%02iX' % small_bar ) )\n\t\tself._ws = bytes( self.owner.PRINTER_ESC + ( '*p+%02iX' % wide_bar ) )\n \n\t\t# DONE nb = bc39_esc+\"*c\"+TRANSFORM(small_bar,'99')+\"a\"+Alltrim(STR(bc39_height*dpl))+\"b0P\"+bc39_esc+\"*p+\"+TRANSFORM(small_bar,'99')+\"X\"\n\t\t# DONE wb = bc39_esc+\"*c\"+TRANSFORM(wide_bar,'99')+\"a\"+Alltrim(STR(bc39_height*dpl))+\"b0P\"+bc39_esc+\"*p+\"+TRANSFORM(wide_bar,'99')+\"X\"\n\t\t# DONE ns = bc39_esc+\"*p+\"+TRANSFORM(small_bar,'99')+\"X\"\n\t\t# DONE ws = bc39_esc+\"*p+\"+TRANSFORM(wide_bar,'99')+\"X\"\n \n\t\t# adjust cusor position to start at top of line and return to bottom of line\n\t\tself._bc_start = bytes( self.owner.PRINTER_ESC + '*p-50Y' )\n\t\tself._bc_end = bytes( self.owner.PRINTER_ESC + '*p+50Y' )\n\t\t# DONE bc39_start = bc39_esc+\"*p-50Y\"\n\t\t# DONE bc39_END = bc39_esc+\"*p+50Y\"\n\n\t\t# setup the structure allowing to print the code codebar section for various LETTERS\n\t\tself._char39 = { u'1' : 'wb+ns+nb+ws+nb+ns+nb+ns+wb' , \n\t\t\t\t\t\t u'2' : 'nb+ns+wb+ws+nb+ns+nb+ns+wb' , \n\t\t\t\t\t\t u'3' : 'wb+ns+wb+ws+nb+ns+nb+ns+nb' , \n\t\t\t\t\t\t u'4' : 'nb+ns+nb+ws+wb+ns+nb+ns+wb' , \n\t\t\t\t\t\t u'5' : 'wb+ns+nb+ws+wb+ns+nb+ns+nb' , \n\t\t\t\t\t\t u'6' : 'nb+ns+wb+ws+wb+ns+nb+ns+nb' , \n\t\t\t\t\t\t u'7' : 'nb+ns+nb+ws+nb+ns+wb+ns+wb' , \n\t\t\t\t\t\t u'8' : 'wb+ns+nb+ws+nb+ns+wb+ns+nb' , \n\t\t\t\t\t\t u'9' : 'nb+ns+wb+ws+nb+ns+wb+ns+nb' , \n\t\t\t\t\t\t u'0' : 'nb+ns+nb+ws+wb+ns+wb+ns+nb' , \n\t\t\t\t\t\t u'A' : 'wb+ns+nb+ns+nb+ws+nb+ns+wb' , \n\t\t\t\t\t\t u'B' : 'nb+ns+wb+ns+nb+ws+nb+ns+wb' , \n\t\t\t\t\t\t u'C' : 'wb+ns+wb+ns+nb+ws+nb+ns+nb' , \n\t\t\t\t\t\t u'D' : 'nb+ns+nb+ns+wb+ws+nb+ns+wb' , \n\t\t\t\t\t\t u'E' : 'wb+ns+nb+ns+wb+ws+nb+ns+nb' , \n\t\t\t\t\t\t u'F' : 'nb+ns+wb+ns+wb+ws+nb+ns+nb' , \n\t\t\t\t\t\t u'G' : 'nb+ns+nb+ns+nb+ws+wb+ns+wb' , \n\t\t\t\t\t\t u'H' : 'wb+ns+nb+ns+nb+ws+wb+ns+nb' , \n\t\t\t\t\t\t u'I' : 'nb+ns+wb+ns+nb+ws+wb+ns+nb' , \n\t\t\t\t\t\t u'J' : 'nb+ns+nb+ns+wb+ws+wb+ns+nb' , \n\t\t\t\t\t\t u'K' : 'wb+ns+nb+ns+nb+ns+nb+ws+wb' , \n\t\t\t\t\t\t u'L' : 'nb+ns+wb+ns+nb+ns+nb+ws+wb' , \n\t\t\t\t\t\t u'M' : 'wb+ns+wb+ns+nb+ns+nb+ws+nb' , \n\t\t\t\t\t\t u'N' : 'nb+ns+nb+ns+wb+ns+nb+ws+wb' , \n\t\t\t\t\t\t u'O' : 'wb+ns+nb+ns+wb+ns+nb+ws+nb' , \n\t\t\t\t\t\t u'P' : 'nb+ns+wb+ns+wb+ns+nb+ws+nb' , \n\t\t\t\t\t\t u'Q' : 'nb+ns+nb+ns+nb+ns+wb+ws+wb' , \n\t\t\t\t\t\t u'R' : 'wb+ns+nb+ns+nb+ns+wb+ws+nb' , \n\t\t\t\t\t\t u'S' : 'nb+ns+wb+ns+nb+ns+wb+ws+nb' , \n\t\t\t\t\t\t u'T' : 'nb+ns+nb+ns+wb+ns+wb+ws+nb' , \n\t\t\t\t\t\t u'U' : 'wb+ws+nb+ns+nb+ns+nb+ns+wb' , \n\t\t\t\t\t\t u'V' : 'nb+ws+wb+ns+nb+ns+nb+ns+wb' , \n\t\t\t\t\t\t u'W' : 'wb+ws+wb+ns+nb+ns+nb+ns+nb' , \n\t\t\t\t\t\t u'X' : 'nb+ws+nb+ns+wb+ns+nb+ns+wb' , \n\t\t\t\t\t\t u'Y' : 'wb+ws+nb+ns+wb+ns+nb+ns+nb' , \n\t\t\t\t\t\t u'Z' : 'nb+ws+wb+ns+wb+ns+nb+ns+nb' , \n\t\t\t\t\t\t u'-' : 'nb+ws+nb+ns+nb+ns+wb+ns+wb' , \n\t\t\t\t\t\t u'.' : 'wb+ws+nb+ns+nb+ns+wb+ns+nb' , \n\t\t\t\t\t\t u' ' : 'nb+ws+wb+ns+nb+ns+wb+ns+nb' , \n\t\t\t\t\t\t u'*' : 'nb+ws+nb+ns+wb+ns+wb+ns+nb' , \n\t\t\t\t\t\t u'$' : 'nb+ws+nb+ws+nb+ws+nb+ns+nb' , \n\t\t\t\t\t\t u'/' : 'nb+ws+nb+ws+nb+ns+nb+ws+nb' , \n\t\t\t\t\t\t u'+' : 'nb+ws+nb+ns+nb+ws+nb+ws+nb' , \n\t\t\t\t\t\t u'%' : 'nb+ns+nb+ws+nb+ws+nb+ws+nb' }","def normalize_teh_marbuta_hsb(s):\n\n return s.replace(u'\\u0127', u'h')","def to_motevo(self):\n m = \"//\\n\"\n m += \"NA {}\\n\".format(self.id)\n m += \"P0\\tA\\tC\\tG\\tT\\n\"\n for i, row in enumerate(self.pfm):\n m += \"{}\\t{}\\n\".format(i, \"\\t\".join([str(int(x)) for x in row]))\n m += \"//\"\n return m","def display():\n screen.addch(head[0],head[1],'x')","def arthur(self):\n\t\tthismsg = \"\\r\\n\"+self.ESC+\"34m\"+self.A220+self.ESC+\"1;44m\"+self.A176+self.ESC+\"0;34m\"+self.A220+self.A223+self.A176+self.ESC+\"C\"+self.A178+self.A176+self.A254+self.ESC+\"2C\"+self.A220+self.A254+self.ESC+\"6C\"+self.A220+self.A178+self.ESC+\"C\"+self.A220+self.A178+self.ESC+\"C\"+self.ESC+\"1;44m\"+self.A177+self.ESC+\"40m\"+self.A223+self.ESC+\"0;34m\"+self.A223+self.ESC+\"1m\"+self.A223+self.A223+self.ESC+\"0;34m\"+self.A223+self.ESC+\"1m\"+self.A223+self.ESC+\"0;34m\"+self.A223+self.A223+self.A223+self.A223+self.ESC+\"1m\"+self.A223+self.ESC+\"0;34m\"+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.ESC+\"C\"+self.A223+\"\\r\\n\"\n\t\tthismsg += self.ESC+\"C\"+self.A223+self.ESC+\"2C\"+self.A176+self.A223+self.A223+self.ESC+\"1;37m\"+self.A220+self.A220+self.A220+self.A220+self.ESC+\"0m\"+self.A220+self.ESC+\"1m\"+self.A223+self.A223+self.ESC+\"0m\"+self.A223+self.ESC+\"1m\"+self.A223+self.A223+self.ESC+\"0m\"+self.A223+self.ESC+\"1m\"+self.A223+self.A220+self.ESC+\"0m\"+self.A220+self.A220+self.A220+self.A220+self.ESC+\"C\"+self.ESC+\"34m\"+self.A223+self.ESC+\"3C\"+self.ESC+\"1;37mSaga\"+self.ESC+\"Cof\"+self.ESC+\"Cthe\"+self.ESC+\"CRed\"+self.ESC+\"CDragon\"+self.ESC+\"C-\"+self.ESC+\"C\"+self.ESC+\"34mKing\"+self.ESC+\"CArthurs\"+self.ESC+\"CWeapons \"+self.ESC+\"C\"+self.ESC+\"0;34m\"+self.A219+\"\\r\\n\"\n\t\tthismsg += self.A223+self.ESC+\"1;37m\"+self.A220+self.A223+self.A223+self.A223+self.A223+self.A223+self.ESC+\"C\"+self.ESC+\"0m\"+self.A220+self.A220+self.A220+self.A220+self.A220+self.A219+self.A219+self.A219+self.ESC+\"30;47m\"+self.A223+self.ESC+\"37;40m\"+self.A219+self.A220+self.A220+self.A220+self.A220+self.A220+self.ESC+\"C\"+self.A223+self.ESC+\"1;30m\"+self.A223+self.ESC+\"0m\"+self.A223+self.ESC+\"1;30m\"+self.A223+self.A223+self.A220+self.ESC+\"4C\"+self.ESC+\"0;34m\"+self.A220+self.A220+self.A220+self.A220+self.ESC+\"C\"+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.ESC+\"C\"+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A178+self.A220+self.A220+self.A220+self.A220+self.ESC+\"C\"+self.A220+self.A220+\"\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"79C\"+self.ESC+\"37m \"+self.ESC+\"1m\"+self.A219+self.ESC+\"C\"+self.A220+self.ESC+\"47m\"+self.A219+self.A219+self.A223+self.A178+self.A177+self.A176+self.ESC+\"0m\"+self.A219+self.A223+self.ESC+\"1m\"+self.A220+self.ESC+\"0m\"+self.A223+self.A219+self.A219+self.ESC+\"30;47m\"+self.A223+self.A176+self.ESC+\"37;40m\"+self.A219+self.A219+self.A223+self.A220+self.A223+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A220+self.ESC+\"C\"+self.ESC+\"1;30;47m\"+self.A178+self.ESC+\"C\"+self.ESC+\"0;34m\"+self.A176+self.ESC+\"3C\"+self.A178+self.ESC+\"20C\"+self.A223+self.ESC+\"17C\"+self.ESC+\"1m\"+self.A220+self.A220+self.A220+self.A220+\"\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"79C\"+self.ESC+\"0m \"+self.ESC+\"1m\"+self.A219+self.ESC+\"C\"+self.ESC+\"47m\"+self.A219+self.A219+self.A178+self.ESC+\"0m\"+self.A219+self.ESC+\"30;47m\"+self.A176+self.ESC+\"37;40m\"+self.A219+self.A219+self.A219+self.ESC+\"C\"+self.ESC+\"1m\"+self.A219+self.ESC+\"47m\"+self.A178+self.ESC+\"40m\"+self.A220+self.ESC+\"0m\"+self.A223+self.A219+self.A219+self.A223+self.A220+self.ESC+\"1;47m\"+self.A176+self.ESC+\"0m\"+self.A219+self.ESC+\"C\"+self.A219+self.A219+self.ESC+\"1;47m\"+self.A176+self.A176+self.A176+self.A176+self.ESC+\"30m\"+self.A176+self.ESC+\"C\"+self.ESC+\"40m\"+self.A219+self.ESC+\"3C\"+self.ESC+\"0;34mYou\"+self.ESC+\"Cwalk\"+self.ESC+\"Cinto\"+self.ESC+\"Cthe\"+self.ESC+\"Cwell\"+self.ESC+\"Cknown\"+self.ESC+\"Cweapons\"+self.ESC+\"6C\"+self.ESC+\"1m\"+self.A219+self.ESC+\"44m\"+self.A178+self.ESC+\"0;34m\"+self.A219+\"\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"79C\"+self.ESC+\"37m \"+self.ESC+\"1m\"+self.A219+self.ESC+\"C\"+self.ESC+\"47m\"+self.A178+self.A178+self.A178+\" \"+self.ESC+\"0;30;47m\"+self.A223+self.A177+self.ESC+\"37;40m\"+self.A219+self.A219+self.A219+self.ESC+\"C\"+self.ESC+\"1m\"+self.A219+self.ESC+\"C\"+self.ESC+\"47m\"+self.A178+self.ESC+\"40m\"+self.A220+self.ESC+\"C\"+self.ESC+\"47m\"+self.A177+self.A176+\" \"+self.ESC+\"C\"+self.ESC+\"0m\"+self.A219+self.ESC+\"1;47m\"+self.A176+self.A176+self.A177+self.A177+self.A177+self.A176+self.ESC+\"30m\"+self.A176+self.ESC+\"C\"+self.ESC+\"40m\"+self.A219+self.ESC+\"3C\"+self.ESC+\"0;34mshop,\"+self.ESC+\"Cyou\"+self.ESC+\"Cpause\"+self.ESC+\"Cto\"+self.ESC+\"Clook\"+self.ESC+\"Caround\"+self.ESC+\"Cat\"+self.ESC+\"Call\"+self.ESC+\"Cof\"+self.ESC+\"2C\"+self.A222+self.ESC+\"1;44m\"+self.A177+self.ESC+\"0;34m\"+self.A219+\"\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"79C\"+self.ESC+\"37m \"+self.ESC+\"1m\"+self.A219+self.ESC+\"C\"+self.A223+self.ESC+\"47m\"+self.A178+self.A177+self.ESC+\"0m\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\"C\"+self.ESC+\"1m\"+self.A219+self.ESC+\"C\"+self.ESC+\"47m\"+self.A178+self.A177+self.ESC+\"0m\"+self.A220+self.A223+self.A220+self.ESC+\"1;47m\"+self.A176+self.A176+self.A177+self.A177+self.A176+self.A176+self.A176+self.ESC+\"30m\"+self.A176+self.ESC+\"0m\"+self.A223+self.ESC+\"C\"+self.ESC+\"1;30m\"+self.A219+self.ESC+\"3C\"+self.ESC+\"0;34mthe\"+self.ESC+\"Cmany\"+self.ESC+\"Cimplements\"+self.ESC+\"Cof\"+self.ESC+\"Cdestruction.\"+self.ESC+\"2CA\"+self.ESC+\"37m \"+self.ESC+\"4C\"+self.ESC+\"1;34;44m\"+self.A176+self.ESC+\"0;34m\"+self.A219+\"\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"79C\"+self.ESC+\"37m \"+self.ESC+\"C \"+self.ESC+\"1m\"+self.A219+self.ESC+\"C\"+self.ESC+\"47m\"+self.A177+self.A176+self.ESC+\"0m\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A223+self.ESC+\"1m\"+self.A220+self.ESC+\"C\"+self.ESC+\"47m\"+self.A178+self.ESC+\"40m\"+self.A220+self.A223+self.ESC+\"47m\"+self.A177+self.A176+self.ESC+\"0m\"+self.A220+self.A223+self.ESC+\"1;47m\"+self.A177+self.A176+self.A176+self.ESC+\"0m\"+self.A219+self.ESC+\"30;47m\"+self.A220+self.A176+self.ESC+\"1m\"+self.A177+self.ESC+\"C\"+self.ESC+\"40m\"+self.A178+self.ESC+\"4C\"+self.ESC+\"0;34mfat\"+self.ESC+\"Cman\"+self.ESC+\"Cwoddles\"+self.ESC+\"Cinto\"+self.ESC+\"Cthe\"+self.ESC+\"Croom,\"+self.ESC+\"Cand\"+self.ESC+\"2C \"+self.ESC+\"37m \"+self.ESC+\"2C\"+self.ESC+\"1;34;44m\"+self.A176+self.ESC+\"0;30;44m\"+self.A176+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"79C\"+self.ESC+\"37m \"+self.ESC+\"C \"+self.ESC+\"1m\"+self.A219+self.ESC+\"C\"+self.ESC+\"47m\"+self.A176+\" \"+self.ESC+\"0m\"+self.A219+self.A219+self.A223+self.ESC+\"33m\"+self.A220+self.ESC+\"37m\"+self.A223+self.ESC+\"1m\"+self.A220+self.ESC+\"47m\"+self.A178+self.A178+self.A177+self.ESC+\"0m\"+self.A220+self.ESC+\"1m\"+self.A223+self.ESC+\"47m\"+self.A177+self.A176+self.ESC+\"0;30;47m\"+self.A176+self.A223+self.ESC+\"1m\"+self.A176+self.ESC+\"0m\"+self.A220+self.A223+self.ESC+\"33m\"+self.A220+self.ESC+\"37m\"+self.A223+self.A219+self.A219+self.ESC+\"1;30;47m\"+self.A178+self.ESC+\"C\"+self.ESC+\"40m\"+self.A178+self.ESC+\"4C\"+self.ESC+\"0;34masks\"+self.ESC+\"C\\\"\"+self.ESC+\"1mWadaya\"+self.ESC+\"Cwant\"+self.ESC+\"Ckid?\"+self.ESC+\"0;34m\\\"\"+self.ESC+\"17C\"+self.ESC+\"37m \"+self.ESC+\"2C\"+self.ESC+\"1;34;44m\"+self.A176+self.ESC+\"0;30;44m\"+self.A176+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"79C\"+self.ESC+\"37m \"+self.ESC+\"C \"+self.ESC+\"1m\"+self.A219+self.ESC+\"C\"+self.ESC+\"47m\"+self.A176+self.ESC+\"0m\"+self.A219+self.ESC+\"30;47m\"+self.A254+self.A176+self.ESC+\"37;40m\"+self.A223+self.ESC+\"1;33m\"+self.A220+self.ESC+\"43m\"+self.A177+self.ESC+\"0;33m\"+self.A220+self.ESC+\"1;37m\"+self.A223+self.ESC+\"47m\"+self.A177+self.A176+self.ESC+\"0m\"+self.A223+self.A220+self.A220+self.A223+self.A223+self.ESC+\"1;47m \"+self.ESC+\"0m\"+self.A223+self.ESC+\"33m\"+self.A220+self.ESC+\"1;43m\"+self.A177+self.ESC+\"C\"+self.ESC+\"0m\"+self.A219+self.A219+self.ESC+\"1;30m\"+self.A219+self.A223+self.ESC+\"C\"+self.A178+self.ESC+\"47C\"+self.ESC+\"0;34m\"+self.A219+self.ESC+\"30;44m\"+self.A177+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"79C\"+self.ESC+\"37m \"+self.ESC+\"34m\"+self.A176+self.ESC+\"C \"+self.ESC+\"1;37;47m\"+self.A178+self.ESC+\"C\"+self.A176+self.ESC+\"0m\"+self.A223+self.ESC+\"1;33m\"+self.A220+self.ESC+\"43m\"+self.A178+self.ESC+\"40m\"+self.A223+self.ESC+\"C\"+self.ESC+\"43m\"+self.A176+self.ESC+\"0;33m\"+self.A223+self.A220+self.ESC+\"C\"+self.ESC+\"1;37;47m\"+self.A177+self.A177+self.A176+self.ESC+\"0m\"+self.A219+self.A223+self.ESC+\"1;33m\"+self.A220+self.ESC+\"43m\"+self.A178+self.ESC+\"C\"+self.ESC+\"0;33m\"+self.A223+self.ESC+\"1;43m\"+self.A176+self.ESC+\"0;33m\"+self.A220+self.ESC+\"37m\"+self.A223+self.ESC+\"1;30m\"+self.A219+self.ESC+\"C\"+self.A178+self.ESC+\"6C\"+self.ESC+\"0;34m[\"+self.ESC+\"1mB\"+self.ESC+\"0;34m]\"+self.ESC+\"1muy\"+self.ESC+\"CWeapon\"+self.ESC+\"30C\"+self.ESC+\"0;34m\"+self.A219+self.ESC+\"30;44m\"+self.A177+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"79C\"+self.ESC+\"37m \"+self.ESC+\"34m\"+self.A178+self.ESC+\"C \"+self.ESC+\"1;37;47m\"+self.A177+self.ESC+\"2C\"+self.ESC+\"40m\"+self.A223+self.ESC+\"33m\"+self.A219+self.A220+self.ESC+\"0;33m\"+self.A178+self.A223+self.ESC+\"37m\"+self.A220+self.A219+self.A220+self.ESC+\"1;47m\"+self.A177+self.A176+self.A176+self.ESC+\"0m\"+self.A219+self.A220+self.ESC+\"1m\"+self.A223+self.ESC+\"0m\"+self.A220+self.A220+self.ESC+\"1;33m\"+self.A223+self.A220+self.A220+self.ESC+\"43m\"+self.A177+self.ESC+\"0;33m\"+self.A223+self.ESC+\"2C\"+self.ESC+\"1;30m\"+self.A219+self.ESC+\"6C\"+self.ESC+\"0;34m[\"+self.ESC+\"1mS\"+self.ESC+\"0;34m]\"+self.ESC+\"1mell\"+self.ESC+\"CWeapon\"+self.ESC+\"29C\"+self.ESC+\"0;34m\"+self.A219+self.ESC+\"30;44m\"+self.A177+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"79C\"+self.ESC+\"37m \"+self.ESC+\"34m\"+self.A176+self.A176+self.ESC+\"C \"+self.ESC+\"1;37;47m\"+self.A176+self.ESC+\"C\"+self.ESC+\"0m\"+self.A223+self.A220+self.A220+self.A220+self.A219+self.ESC+\"1;47m\"+self.A176+self.A176+self.A176+self.A176+self.ESC+\"0;30;47m\"+self.A176+self.ESC+\"37;40m\"+self.A219+self.A219+self.A219+self.A219+self.ESC+\"1;30;47m\"+self.A176+self.ESC+\"0m\"+self.A219+self.A219+self.A220+self.A220+self.ESC+\"1;30m\"+self.A220+self.A223+self.ESC+\"C\"+self.A219+self.ESC+\"7C\"+self.ESC+\"0;34m[\"+self.ESC+\"1mY\"+self.ESC+\"0;34m]\"+self.ESC+\"1mour\"+self.ESC+\"CStats\"+self.ESC+\"30C\"+self.ESC+\"0;34m\"+self.A219+self.ESC+\"30;44m\"+self.A178+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"79C\"+self.ESC+\"37m \"+self.ESC+\"C\"+self.ESC+\"34m\"+self.A176+self.ESC+\"C \"+self.ESC+\"37m\"+self.A223+self.A220+self.ESC+\"C\"+self.A223+self.A223+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\"30;47m\"+self.A220+self.A177+self.ESC+\"37;40m\"+self.A219+self.A219+self.A219+self.ESC+\"1;30;47m\"+self.A220+self.A220+self.A178+self.ESC+\"40m\"+self.A223+self.A223+self.ESC+\"C\"+self.A220+self.A223+self.ESC+\"0;34m\"+self.A176+self.ESC+\"7C[\"+self.ESC+\"1mR\"+self.ESC+\"0;34m]\"+self.ESC+\"1meturn\"+self.ESC+\"Cto\"+self.ESC+\"CTown\"+self.ESC+\"25C\"+self.ESC+\"0;34m\"+self.A222+self.ESC+\"1;44m\"+self.A177+self.ESC+\"0;30;44m\"+self.A178+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"79C\"+self.ESC+\"37m \"+self.ESC+\"34m\"+self.A220+self.ESC+\"1;44m\"+self.A176+self.ESC+\"0;34m\"+self.A220+self.ESC+\"C\"+self.A178+self.A220+self.ESC+\"37m\"+self.A223+self.A223+self.A220+self.A220+self.ESC+\"C\"+self.A223+self.A223+self.A223+self.ESC+\"1;30;47m\"+self.A176+self.A177+self.A219+self.ESC+\"40m\"+self.A223+self.A223+self.A223+self.ESC+\"C\"+self.A220+self.A220+self.A223+self.A223+self.ESC+\"C\"+self.ESC+\"0;34m\"+self.A220+self.A219+self.A176+self.ESC+\"47C\"+self.A219+self.ESC+\"1;44m\"+self.A176+self.ESC+\"0;30;44m\"+self.A178+self.ESC+\"40m\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"79C\"+self.ESC+\"37m \"+self.ESC+\"C\"+self.ESC+\"34m\"+self.A223+self.ESC+\"C\"+self.A220+self.A254+self.A178+self.A219+self.A219+self.A220+self.A220+self.ESC+\"37m\"+self.A223+self.A223+self.A223+self.A220+self.ESC+\"C\"+self.ESC+\"1;30m\"+self.A223+self.ESC+\"C\"+self.A220+self.A223+self.A223+self.A223+self.ESC+\"C\"+self.ESC+\"0;34m\"+self.A176+self.A176+self.A223+self.A178+self.A178+self.A176+self.ESC+\"2C\"+self.ESC+\"1;44m\"+self.A176+self.ESC+\"0;34m\"+self.A223+self.A223+self.A223+self.A178+self.A223+self.A223+self.ESC+\"C\"+self.A223+self.A178+self.A223+self.ESC+\"C\"+self.A223+self.A223+self.ESC+\"C\"+self.A223+self.A176+self.ESC+\"C\"+self.A223+self.ESC+\"C\"+self.A223+self.A223+self.ESC+\"C\"+self.A223+self.A223+self.ESC+\"30;44m\"+self.A177+self.ESC+\"19C\"+self.ESC+\"34;40m\"+self.A223+self.A223+self.A223+self.A223+\"\\r\\n\"\n\t\tthismsg += self.ESC+\"A\"+self.ESC+\"79C\"+self.ESC+\"37m \"+self.ESC+\"34m\"+self.A177+self.A178+self.A219+self.A220+self.ESC+\"C\"+self.A176+self.A223+self.A178+self.A178+self.A178+self.A178+self.A223+self.A176+self.ESC+\"C\"+self.ESC+\"37m\"+self.A223+self.ESC+\"1;30m\"+self.A220+self.A223+self.ESC+\"C\"+self.ESC+\"0;34m\"+self.A223+self.A178+self.A176+self.ESC+\"4C\"+self.A176+self.ESC+\"C\"+self.A220+self.A219+self.ESC+\"30;44m\"+self.A176+\" \"+self.ESC+\"34;40m\"+self.A254+self.ESC+\"37m \"+self.ESC+\"34m\"+self.A220+self.ESC+\"30;44m\"+self.A176+self.A178+self.ESC+\"34;40m\"+self.A219+self.A219+self.A219+self.ESC+\"30;44m\"+self.A176+self.ESC+\"34;40m\"+self.A219+self.A220+self.A219+self.A219+self.A219+self.A219+self.A219+self.A222+self.A219+self.A219+\"\\r\\n\"\n\t\treturn thismsg","def __termcode(num):\r\n return \"\\033[%sm\" % num","def leasemac(macstr):\n maclst = []\n while macstr:\n maclst.append(macstr[:2].lower())\n macstr = macstr[2:]\n newmac = \":\".join(\"%s\" % s for s in maclst)\n return newmac","def _get_output_letter(rgb):\n\n\t\tif rgb == Rgb.pastel_purple():\n\t\t\treturn \"p\"\n\t\telif rgb == Rgb.pastel_yellow():\n\t\t\treturn \"y\"\n\t\telif rgb == Rgb.pastel_green():\n\t\t\treturn \"g\"\n\t\telif rgb == Rgb.pastel_blue():\n\t\t\treturn \"b\"\n\t\telif rgb == Rgb.strong_red():\n\t\t\treturn \" \"\n\n\t\treturn \"?\"","def xterm_to_kitty(filename):\n kitty_lines = []\n lines = get_file_lines(filename)\n\n for line in lines:\n if len(line) >= 1:\n if line[0] == '!':\n # convert to a kitty style comment\n line = '#{}'.format(line[1:])\n print(line)\n kitty_lines.append(line)\n elif line[0] == '*':\n # get rid of *.\n line = line[2:].replace(':', '')\n print(line)\n\n kitty_lines.append(line)\n\n with open('to_kitty_out', 'w') as output:\n output.write('\\n'.join(kitty_lines))","def load_macaroon(macaroon: str) -> str:\n\n # if the macaroon is a file path, load it\n if macaroon.split(\".\")[-1] == \"macaroon\":\n with open(macaroon, \"rb\") as f:\n macaroon_bytes = f.read()\n return macaroon_bytes.hex()\n else:\n # convert the bas64 macaroon to hex\n try:\n macaroon = base64.b64decode(macaroon).hex()\n except:\n pass\n return macaroon","def to_center():\n center_msg = b'\\x02\\x35\\x35\\x03'\n #qpt.write(center_msg)\n #feedback = qpt.readline()\n move_to_position(0,0)\n #return feedback","def setMAC( self, intf, mac ):\n result = self.cmd( 'ifconfig', intf, 'down' )\n result += self.cmd( 'ifconfig', intf, 'hw', 'ether', mac )\n result += self.cmd( 'ifconfig', intf, 'up' )\n return result","def to_morze(text: str) -> str:\n\n\tmorze = {\"A\":\".-\",\"B\":\"-...\",\"C\":\"-.-.\",\"D\":\"-..\",\"E\":\".\",\"F\":\"..-.\",\n\t\"G\":\"--.\",\"H\":\"....\",\"I\":\"..\",\"J\":\"-.-.\",\"K\":\"-.-\",\"L\":\".-..\",\"M\":\"--\",\n\t\"N\":\"-.\",\"O\":\"---\",\"P\":\".--.\",\"Q\":\"--.-\",\"R\":\".-.\",\"S\":\"...\",\"T\":\"-\",\n\t\"U\":\"..-\",\"V\":\"...-\",\"W\":\".--\",\"X\":\".-..\",\"Y\":\"-.--\",\"Z\":\"--..\"}\n\n\ttextMorze = ''.join([morze.get(c.upper(), ' ') for c in text])\n\n\treturn textMorze","def createGlyphs(input):\r\n\r\n command_open = subprocess.Popen([\"start\", \"cmd\",\"/k\", 'echo {}'.format(input)], shell = True)\r\n\r\n time.sleep(2) #time for window to appear\r\n\r\n #print win32gui.FindWindow(None, \"C:\\Windows\\system32\\cmd.exe\")\r\n hwnd = win32gui.FindWindow(None, \"C:\\Windows\\system32\\cmd.exe\")\r\n win32gui.SetForegroundWindow(hwnd)\r\n bbox = win32gui.GetWindowRect(hwnd)\r\n img = ImageGrab.grab(bbox)\r\n #img.show()\r\n\r\n length_of_chars = len(input) * 8 # +2 to compensate for the quotation marks\r\n combined_chars = img.crop((10,34,length_of_chars+9,45))\r\n #combined_chars.show()\r\n\r\n #nonetype error was caused by chaining the .show() wiutth the rest of the stuff\r\n\r\n chars = {x:\"\" for x in input}\r\n\r\n for i, each in enumerate(range(8,combined_chars.size[0]+9,8)): #starts from 1, and +8 to compensate for the -8 below VVV\r\n #if i not in bad_indexes: #this is to avoid the first and last double quotation marks\r\n current_char = input[i]\r\n \r\n indiv_char = combined_chars.crop((each-8,0,each,11))\r\n \r\n w, h = indiv_char.size #should be 8 wide by 9 high\r\n\r\n for i2, pixel in enumerate(indiv_char.getdata()): #tuple values can either be (0, 0, 0) or (192,192,192) for the default terminal colours\r\n if pixel == (192,192,192): \r\n chars[current_char] += u\"\\u2588\"\r\n\r\n else:\r\n chars[current_char] += \" \"\r\n\r\n if i2 % w == w-1: # we want it too look decent so overflow is neeeded onto the next lines\r\n # ^^^ before it was i2 % w == 0, but that lead to a trail behind character, so whats before 0? -1! so w-1!!!\r\n chars[current_char] += \"\\n\"\r\n\r\n chars[current_char] = chars[current_char][:-1] #this is to remove the last \"\\n\"\r\n\r\n return chars","def _generate_mac(topology_id):\n tid = int(topology_id)\n global mac_counter\n global used_macs\n base = '52:54:00:00:00:00'\n ba = base.split(':')\n ba[2] = '%02x' % int(tid / 256)\n ba[3] = '%02x' % int(tid % 256)\n ba[4] = '%02x' % int(len(used_macs[topology_id]) / 256)\n ba[5] = '%02x' % int(mac_counter)\n\n mac_counter += 1\n\n mac_counter = mac_counter % 256\n return ':'.join(ba)","def mac_add():\n brew_installed = shutil.which('brew')\n if not brew_installed:\n print('Installing brew')\n url = 'https://raw.githubusercontent.com/Homebrew/install/master/install'\n command_line = \"/usr/bin/ruby -e \\\"$(curl -fsSL %s)\\\"\" % url\n subprocess.check_call(command_line, shell=True)\n else:\n print('brew present')\n gmsh_installed = shutil.which('gmsh')\n if not gmsh_installed:\n print('Installing gmsh')\n folder_path = os.path.dirname(os.path.abspath(__file__))\n dmginstall_path = os.path.join(folder_path, 'dmginstall.sh')\n url = 'http://gmsh.info/bin/MacOSX/gmsh-3.0.5-MacOSX.dmg'\n command_line = '%s %s' % (dmginstall_path, url)\n print('command_line=%s' % command_line)\n subprocess.check_call(command_line, shell=True)\n gmsh_path = '/Applications/Gmsh.app/Contents/MacOS/gmsh'\n command_line = \"ln -s %s /usr/local/bin/gmsh\" % gmsh_path\n subprocess.check_call(command_line, shell=True)\n else:\n print('gmsh present')\n ccx_installed = shutil.which('ccx')\n if not ccx_installed:\n mac_add_ccx()\n else:\n print('calculix (ccx) present')","def test_cross_language_mac(self):\n alice_private = [\n 0x77, 0x07, 0x6D, 0x0A, 0x73, 0x18, 0xA5, 0x7D,\n 0x3C, 0x16, 0xC1, 0x72, 0x51, 0xB2, 0x66, 0x45,\n 0xDF, 0x4C, 0x2F, 0x87, 0xEB, 0xC0, 0x99, 0x2A,\n 0xB1, 0x77, 0xFB, 0xA5, 0x1D, 0xB9, 0x2C, 0x2A\n ]\n\n bob_key = \"3p7bfXt9wbTTW2HC7OQ1Nz+DQ8hbeGdNrfx+FG+IK08\"\n message = \"Hello world!\"\n extra_info = \"MAC\"\n expected_mac = \"2nSMTXM+TStTU3RUVTNSVVZUTlNWVlpVVGxOV1ZscFY\"\n\n sas_alice = Sas()\n sas_alice._create_sas(bytes(alice_private), 32)\n sas_alice.set_their_pubkey(bob_key)\n\n alice_mac = sas_alice.calculate_mac(message, extra_info)\n\n assert alice_mac == expected_mac","def line(self):\n\t\treturn self.ESC+\"32m-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-\"+self.ESC+\"0m\\r\\n\"","def get_base_mac(self):\n raise NotImplementedError","def get_mac(self, node_id):\n nc = '%02x' % self.node_class\n nr_iface = '%02x' % self.nr_host_interface\n node_id = '%08x' % node_id\n\n return '%s:%s:%s:%s:%s:%s' % (nc, nr_iface, node_id[0:2], node_id[2:4], node_id[4:6], node_id[6:8])","def mv_to_typer(mv):\n\n tab1 = [\n 0.0000000E+00,\n 1.8891380E+02,\n -9.3835290E+01,\n 1.3068619E+02,\n -2.2703580E+02,\n 3.5145659E+02,\n -3.8953900E+02,\n 2.8239471E+02,\n -1.2607281E+02,\n 3.1353611E+01,\n -3.3187769E+00,\n ]\n\n tab2 = [\n 1.334584505E+01,\n 1.472644573E+02,\n -1.844024844E+01,\n 4.031129726E+00,\n -6.249428360E-01,\n 6.468412046E-02,\n -4.458750426E-03,\n 1.994710149E-04,\n -5.313401790E-06,\n 6.481976217E-08,\n 0.000000000E+00,\n ]\n\n tab3 = [\n -8.199599416E+01,\n 1.553962042E+02,\n -8.342197663E+00,\n 4.279433549E-01,\n -1.191577910E-02,\n 1.492290091E-04,\n 0.000000000E+00,\n 0.000000000E+00,\n 0.000000000E+00,\n 0.000000000E+00,\n 0.000000000E+00,\n ]\n\n tab4 = [\n 3.406177836E+04,\n -7.023729171E+03,\n 5.582903813E+02,\n -1.952394635E+01,\n 2.560740231E-01,\n 0.000000000E+00,\n 0.000000000E+00,\n 0.000000000E+00,\n 0.000000000E+00,\n 0.000000000E+00,\n 0.000000000E+00,\n ]\n\n if -0.226 <= mv <= 1.923:\n c = tab1\n elif 1.923 < mv <= 11.361:\n c = tab2\n elif 11.361 < mv <= 19.739:\n c = tab3\n elif 19.739 < mv <= 21.103:\n c = tab4\n else:\n raise ValueError(\"Voltage specified is out of range for Type R thermocouple\")\n\n t = 0.0\n for p in range(0, len(c)):\n t += c[p] * math.pow(mv, p)\n return t","def hAKI(self):\n\n aki = self.get_AKI()\n return \":\".join((\"%02X\" % ord(i) for i in aki)) if aki else \"\"","def bytes_to_mac_str(buff):\n if len(buff) != DataDescription.B_SEQ_MAC_LEN:\n raise TypeError(\"Invalid input\")\n return \"%02X:%02X:%02X:%02X:%02X:%02X\" % buff","def _ufo2glyphs_entry_point():\n args = sys.argv[1:]\n args.insert(0, \"ufo2glyphs\")\n return main(args)","def mac2eui64(mac, prefix=None):\n # http://tools.ietf.org/html/rfc4291#section-2.5.1\n eui64 = re.sub(r\"[.:-]\", \"\", mac).lower()\n eui64 = eui64[0:6] + \"fffe\" + eui64[6:]\n eui64 = hex(int(eui64[0:2], 16) | 2)[2:].zfill(2) + eui64[2:]\n\n if prefix is None:\n return \":\".join(re.findall(r\".{4}\", eui64))\n else:\n try:\n net = ip_network(prefix, strict=False)\n euil = int(\"0x{}\".format(eui64), 16)\n return \"{}/{}\".format(net[euil], net.prefixlen)\n except Exception: # pylint: disable=broad-except\n return","def mac(self, mac):\n\n self._mac = mac","def __repr__(self):\n rep = \"\"\n for host in self.hosts.keys():\n attrs = self.hosts[host]\n rep += f\"machine {host}\\n\\tlogin {attrs[0]}\\n\"\n if attrs[1]:\n rep += f\"\\taccount {attrs[1]}\\n\"\n rep += f\"\\tpassword {attrs[2]}\\n\"\n for macro in self.macros.keys():\n rep += f\"macdef {macro}\\n\"\n for line in self.macros[macro]:\n rep += line\n rep += \"\\n\"\n return rep","def is_mac():\n return sys.platform==\"darwin\"","async def emoji(self, ctx):\n emb = discord.Embed(colour=self.color)\n emb.add_field(name='Usage', value=f'```{self.bot.command_prefix}emoji <emojiname>```')\n await ctx.message.edit(embed=emb)","def matrix_to_text(matrix):\n return ''.join(['{:02x}{:02x}{:02x}{:02x}'.format(\n matrix[0][c], matrix[1][c], matrix[2][c], matrix[3][c]) for c in range(4)])","def mac_addr(address):\n\tprint(':'.join('%02x' % compat_ord(b) for b in address))\n\treturn ':'.join('%s' % format(compat_ord(b), '0>8b') for b in address)","def dpid_to_mac (dpid):\n\n return \"%012x\" % (dpid & 0xffFFffFFffFF,)","def convert_H_kcalmol(en_H):\n return en_H/kcalmol_H","def export_db_macserial(db, path, year):\n\n with open(path, 'w') as fh:\n print('#ifndef GENSERIAL_MODELINFO_AUTOGEN_H', file=fh)\n print('#define GENSERIAL_MODELINFO_AUTOGEN_H\\n', file=fh)\n print('// DO NOT EDIT! This is an autogenerated file.\\n', file=fh)\n print('#include \"macserial.h\"\\n', file=fh)\n\n print('typedef enum {', file=fh)\n\n for info in db:\n print(' {}, // {}'.format(\n info['SystemProductName'].replace(',', '_'),\n info['Specifications']['CPU'][0]\n ), file=fh)\n\n print('} AppleModel;\\n', file=fh)\n print('#define APPLE_MODEL_MAX {}\\n'.format(len(db)), file=fh)\n\n print('static PLATFORMDATA ApplePlatformData[] = {', file=fh)\n for info in db:\n print(' {{ \"{}\", \"{}\" }},'.format(\n info['SystemProductName'],\n info['SystemSerialNumber']\n ), file=fh)\n\n print('};\\n', file=fh)\n\n print('#define APPLE_MODEL_CODE_MAX {}'.format(max(len(info['AppleModelCode']) for info in db)), file=fh)\n print('static const char *AppleModelCode[][APPLE_MODEL_CODE_MAX] = {', file=fh)\n\n for info in db:\n print(' /* {:14} */ {{\"{}\"}},'.format(\n info['SystemProductName'],\n '\", \"'.join(info['AppleModelCode'])\n ), file=fh)\n\n print('};\\n', file=fh)\n\n print('#define APPLE_BOARD_CODE_MAX {}'.format(max(len(info['AppleBoardCode']) for info in db)), file=fh)\n print('static const char *AppleBoardCode[][APPLE_BOARD_CODE_MAX] = {', file=fh)\n\n for info in db:\n print(' /* {:14} */ {{\"{}\"}},'.format(\n info['SystemProductName'],\n '\", \"'.join(info['AppleBoardCode'])\n ), file=fh)\n\n print('};\\n', file=fh)\n\n print('#define APPLE_MODEL_YEAR_MAX {}'.format(max(len(info['AppleModelYear']) for info in db)), file=fh)\n print('static uint32_t AppleModelYear[][APPLE_MODEL_YEAR_MAX] = {', file=fh)\n for info in db:\n print(' /* {:14} */ {{{}}},'.format(\n info['SystemProductName'],\n ', '.join(str(year) for year in info['AppleModelYear'])\n ), file=fh)\n\n print('};\\n', file=fh)\n\n print('static uint32_t ApplePreferredModelYear[] = {', file=fh)\n for info in db:\n print(' /* {:14} */ {},'.format(\n info['SystemProductName'],\n info.get('MacserialModelYear', 0)\n ), file=fh)\n\n print('};\\n', file=fh)\n\n print('#endif // GENSERIAL_MODELINFO_AUTOGEN_H', file=fh)","def getMac(self):\n # Import netifaces here to prevent error importing this module in setup.py\n import netifaces\n interfaces = ['eth0', 'wlan0']\n try:\n interfaces.append(netifaces.gateways()['default'][netifaces.AF_INET][1])\n except:\n pass\n for interface in interfaces:\n try:\n return netifaces.ifaddresses(interface)[netifaces.AF_LINK][0]['addr']\n except ValueError:\n pass\n except:\n exception('Error getting MAC address')\n return None","def writeKitty(b, of):\n with open(of, \"w\") as fh:\n for r in b:\n fh.write(\"%s 0 0 0 %d %d %d %d 0 0 0 0 0 0 0\\n\" % ( r[0], int(r[1]*dw), int(r[2]*dh), int(r[3]*dw), int(r[4]*dh) ) )","def decode_meraki_model(model):\n model_label = \"\"\n\n if \"MX\" in model:\n model_label = \"appliance\"\n if \"MS\" in model:\n model_label = \"switch\"\n if \"MR\" in model:\n model_label = \"wireless\"\n if \"MV\" in model:\n model_label = \"camera\"\n if \"MC\" in model:\n model_label = \"phone\"\n return model_label","def generate_ethmac(peripheral, **kwargs):\n buf = kwargs['buffer']()\n\n result = \"\"\"\nethmac: Network.LiteX_Ethernet @ {{\n sysbus <{}, +0x100>;\n sysbus new Bus.BusMultiRegistration {{ address: {};\n size: {};\n region: \"buffer\" }}\n}}\n\"\"\".format(peripheral['address'], buf['address'], buf['size'])\n\n if 'interrupt' in peripheral['constants']:\n result += ' -> cpu@{}\\n'.format(\n peripheral['constants']['interrupt'])\n\n return result","def mv_to_typen(mv):\n tab1 = [\n 0.0000000E+00,\n 3.8436847E+01,\n 1.1010485E+00,\n 5.2229312E+00,\n 7.2060525E+00,\n 5.8488586E+00,\n 2.7754916E+00,\n 7.7075166E-01,\n 1.1582665E-01,\n 7.3138868E-03,\n ]\n\n tab2 = [\n 0.00000E+00,\n 3.86896E+01,\n -1.08267E+00,\n 4.70205E-02,\n -2.12169E-06,\n -1.17272E-04,\n 5.39280E-06,\n -7.98156E-08,\n 0.00000E+00,\n 0.00000E+00,\n ]\n \n tab3 = [\n 1.972485E+01,\n 3.300943E+01,\n -3.915159E-01,\n 9.855391E-03,\n -1.274371E-04,\n 7.767022E-07,\n 0.000000E+00,\n 0.000000E+00,\n 0.000000E+00,\n 0.000000E+00,\n ]\n\n if -3.99 <= mv <= 0.0:\n c = tab1\n elif 0.0 < mv <= 20.613:\n c = tab2\n elif 20.613 < mv <= 47.513:\n c = tab3\n else:\n raise ValueError(\"Voltage specified is out of range for Type N thermocouple\")\n\n t = 0.0\n for p in range(0, len(c)):\n t += c[p] * math.pow(mv, p)\n return t","def convert(input_filename, output_filename):\n c_file = pkg_resources.resource_filename('ShapelyChipDesigns', 'convert.rb')\n os.system('klayout -z -rd input='+input_filename+' -rd output='+output_filename+' -r '+c_file)","def terminal_diagram(self):\n lines = [[unichr(9632) for x in range(k)]\n for k in self[1]]\n lines += [[unichr(9632) for x in range(k)]\n + [unichr(9675)] for k in self[0]]\n lines.sort(reverse=True, key=len)\n for line in lines:\n print(' '.join(line))\n print('')","def ascii2title(title):\n L = title\n L= np.asarray(L)\n L[L>256] = 32\n y = ''.join(chr(int(i)) for i in L)\n return y","def convert_kcalmol_H(en_kcalmol):\n return en_kcalmol*kcalmol_H","def get_mac_address_table(self):\n\n mac_address_table = []\n command = '/interface bridge host print terse'\n\n output = self._send_command(command)\n\n for host in parse_terse_output(output):\n mac_address_table.append({\n 'mac': cast_mac(host.get('mac-address')),\n 'interface': host.get('interface'),\n 'vlan': -1,\n 'static': True if 'D' not in host.get('_flags') else False,\n 'active': True if 'X' not in host.get('_flags') else False,\n 'moves': -1,\n 'last_move': -1.0\n })\n\n return mac_address_table","def help():\n print(f\"{YELLOW}zwc{RESET} - Hidden messages using Zero Width Characters.\")","def create_terminal() -> str:\n ...","def convert_os2_properties(font_data):\n # should we use the information in the fontdefheader?\n metrics = font_data.pMetrics\n # font encoding (850 indicates PMUGL)\n # > usCodePage - This field is the registered code page for which the font was\n # > designed. Often, this field is 0, which means the font can be used with any of\n # > the OS/2 supported code pages. Hence, if this field is 0, you can specify the\n # > code page you want when you create a logical font. (Logical font creation is\n # > discussed later.) When you create a logical font, the font character to code\n # > point mappings will be made for you. If the font contains special symbols\n # > which have no register code page, a value of 65400 is returned in this field.\n # > In this case, you must use the returned code page value during logical font\n # > creation.\n if metrics.usCodePage in (0, 850):\n encoding = 'ibm-ugl'\n elif metrics.usCodePage == 65400:\n encoding = ''\n else:\n encoding = f'ibm-{metrics.usCodePage}'\n # font selection flags\n decoration = []\n if metrics.fsSelectionFlags.FM_SEL_OUTLINE:\n decoration.append('outline')\n if metrics.fsSelectionFlags.FM_SEL_NEGATIVE:\n decoration.append('negative')\n if metrics.fsSelectionFlags.FM_SEL_UNDERSCORE:\n decoration.append('underline')\n if metrics.fsSelectionFlags.FM_SEL_STRIKEOUT:\n decoration.append('strikethrough')\n properties = Props(\n family=metrics.szFamilyname.decode('latin-1'),\n name=metrics.szFacename.decode('latin-1'),\n font_id=metrics.usRegistryId,\n encoding=encoding,\n ascent=metrics.yLowerCaseAscent,\n descent=metrics.yLowerCaseDescent,\n # total cell depth below baseline\n shift_up=-metrics.yMaxDescender,\n line_height=metrics.yMaxBaselineExt + metrics.yExternalLeading,\n dpi=(metrics.xDeviceRes, metrics.yDeviceRes),\n default_char=metrics.usFirstChar + metrics.usDefaultChar,\n word_boundary=metrics.usFirstChar + metrics.usBreakChar,\n point_size=metrics.usNominalPointSize / 10,\n underline_thickness=metrics.yUnderscoreSize,\n underline_descent=metrics.yUnderscorePosition,\n #ySubscriptXSize='int16',\n subscript_size=metrics.ySubscriptYSize,\n subscript_offset=(metrics.ySubscriptXOffset, metrics.ySubscriptYOffset),\n #ySuperscriptXSize='int16',\n superscript_size=metrics.ySuperscriptYSize,\n superscript_offset=(metrics.ySuperscriptXOffset, metrics.ySuperscriptYOffset),\n #also fsSelectionFlags.FL_SEL_BOLD\n weight=_WEIGHT_MAP.get(metrics.usWeightClass, ''),\n setwidth=_SETWIDTH_MAP.get(metrics.usWidthClass, ''),\n slant='italic' if metrics.fsSelectionFlags.FM_SEL_ITALIC else 'roman',\n decoration=' '.join(decoration)\n # # strikeout stroke thickness\n # yStrikeoutSize='int16',\n # # strikeout height above baseline\n # yStrikeoutPosition='int16',\n )\n return properties"],"string":"[\n \"def macFor(cls, board):\\n return cls.MAC_PREFIX + '{:02X}'.format(int(board))\",\n \"def mac_ntoa(mac):\\n return '%.2x:%.2x:%.2x:%.2x:%.2x:%.2x' % tuple(map(ord, list(mac)))\",\n \"def good_mac(mac):\\r\\n return mac.upper().replace('-', ':')\",\n \"def get_mac_string():\\n mac_int = getnode()\\n mac_str = ':'.join((\\\"%012x\\\" % mac_int)[i:i + 2] for i in range(0, 12, 2))\\n return mac_str\",\n \"def macaroon(node_index):\\n node = Node.from_index(node_index)\\n with open(node.macaroon(), 'rb') as f:\\n content = f.read()\\n click.echo(binascii.hexlify(content))\",\n \"def mac_string(self) :\\n\\t\\tif not getattr(self,'pkg02') :\\n\\t\\t\\treturn None \\n\\t\\tif self.pkg02[:2] not in ['01','02','04','05' ] :\\n\\t\\t\\treturn None\\n\\t\\treturn self.pkg02 + ' ' + self.p_fields.mac_string()\",\n \"def doMacTweaks(self, actual_ch, ch, mods):\\n if not g.isMac:\\n return actual_ch, ch, mods\\n if ch == 'Backspace':\\n # On the Mac, the reported char can be DEL (7F)\\n return '\\\\b', ch, mods\\n if len(mods) == 1 and mods[0] == 'Alt':\\n # Patch provided by resi147.\\n # See the thread: special characters in MacOSX, like '@'.\\n mac_d = {\\n '/': '\\\\\\\\',\\n '5': '[',\\n '6': ']',\\n '7': '|',\\n '8': '{',\\n '9': '}',\\n 'e': '€',\\n 'l': '@',\\n }\\n if ch.lower() in mac_d:\\n # Ignore the case.\\n actual_ch = ch = g.checkUnicode(mac_d.get(ch.lower()))\\n mods = []\\n return actual_ch, ch, mods\",\n \"def enlabel(mi_, ma_):\\n\\treturn \\\"Unicode characters from {} to {} codepoints\\\".format(mi_, ma_)\",\n \"def get_mac(self) -> str:\\n hex_mac = hexlify(self.message)[160:172].decode().upper()\\n return (\\n hex_mac[0:2]\\n + \\\":\\\"\\n + hex_mac[2:4]\\n + \\\":\\\"\\n + hex_mac[4:6]\\n + \\\":\\\"\\n + hex_mac[6:8]\\n + \\\":\\\"\\n + hex_mac[8:10]\\n + \\\":\\\"\\n + hex_mac[10:12]\\n )\",\n \"def abdul(self):\\n\\t\\tthismsg = \\\"\\\\r\\\\n\\\"+self.ESC+\\\"1;33m\\\"+self.A220+self.A220+self.A220+self.A220+self.ESC+\\\"0;33m\\\"+self.A220+self.A220+self.ESC+\\\"1m\\\"+self.A220+self.A220+self.ESC+\\\"0;33m\\\"+self.A220+self.ESC+\\\"1m\\\"+self.A220+self.A220+self.A220+self.ESC+\\\"0;33m\\\"+self.A220+self.ESC+\\\"1m\\\"+self.A220+self.A220+self.ESC+\\\"0;33m\\\"+self.A220+self.ESC+\\\"1m\\\"+self.A220+self.ESC+\\\"0;33m\\\"+self.A220+self.A220+self.ESC+\\\"1m\\\"+self.A220+self.ESC+\\\"0;33m\\\"+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.ESC+\\\"1;30m\\\"+self.A220+self.ESC+\\\"C\\\"+self.ESC+\\\"0;33m\\\"+self.A220+self.A220+self.A220+self.A220+\\\"\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"79C\\\"+self.A220+self.ESC+\\\"1;43m\\\"+self.A219+self.A178+self.ESC+\\\"0;33m\\\"+self.A219+self.A219+self.A219+self.A223+self.A219+self.A219+self.A223+self.ESC+\\\"32m\\\"+self.A220+self.ESC+\\\"33m\\\"+self.A223+self.ESC+\\\"32m\\\"+self.A220+self.ESC+\\\"C\\\"+self.A254+self.ESC+\\\"33m\\\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\\\"1;30;43m\\\"+self.A176+self.ESC+\\\"C\\\"+self.ESC+\\\"0;33m\\\"+self.A219+self.ESC+\\\"1;43m\\\"+self.A177+self.A176+self.ESC+\\\"C\\\"+self.A176+self.ESC+\\\"C\\\"+self.ESC+\\\"37;40mSaga\\\"+self.ESC+\\\"Cof\\\"+self.ESC+\\\"Cthe\\\"+self.ESC+\\\"CRed\\\"+self.ESC+\\\"CDragon\\\"+self.ESC+\\\"C-\\\"+self.ESC+\\\"C\\\"+self.ESC+\\\"33mAbduls\\\"+self.ESC+\\\"CArmour \\\"+self.ESC+\\\"C\\\"+self.ESC+\\\"0;33m\\\"+self.A223+\\\"\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"1;43m\\\"+self.A219+self.ESC+\\\"0;33m\\\"+self.A219+self.A219+self.A223+self.ESC+\\\"32m\\\"+self.A220+self.ESC+\\\"1;42m\\\"+self.A177+self.ESC+\\\"0;32m\\\"+self.A220+self.ESC+\\\"C\\\"+self.A223+self.ESC+\\\"1m\\\"+self.A223+self.ESC+\\\"2C\\\"+self.ESC+\\\"0;33m\\\"+self.A223+self.A223+self.A223+self.ESC+\\\"30;43m\\\"+self.A177+self.A176+self.ESC+\\\"33;40m\\\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\\\"1;30;43m\\\"+self.A176+self.ESC+\\\"2C\\\"+self.ESC+\\\"0;33m\\\"+self.A223+self.A219+self.ESC+\\\"C\\\"+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+\\\"\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"79C\\\"+self.ESC+\\\"1;30;43m\\\"+self.A176+self.ESC+\\\"33m\\\"+self.A219+self.ESC+\\\"0;33m\\\"+self.A219+self.A219+self.A219+self.A220+self.ESC+\\\"32m\\\"+self.A223+self.ESC+\\\"33m\\\"+self.A220+self.A219+self.A223+self.ESC+\\\"37m\\\"+self.A220+self.ESC+\\\"1;47m\\\"+self.A176+self.ESC+\\\"0m\\\"+self.A219+self.A223+self.ESC+\\\"1;30;47m\\\"+self.A176+self.A176+self.ESC+\\\"40m\\\"+self.A220+self.A220+self.ESC+\\\"0;33m\\\"+self.A223+self.ESC+\\\"30;43m\\\"+self.A177+self.A176+self.ESC+\\\"33;40m\\\"+self.A219+self.A219+self.A219+self.ESC+\\\"1;30;43m\\\"+self.A177+self.ESC+\\\"C\\\"+self.ESC+\\\"0;33m\\\"+self.A220+self.ESC+\\\"1;30m\\\"+self.A223+self.ESC+\\\"4C\\\"+self.ESC+\\\"0;33mBehind\\\"+self.ESC+\\\"Cthe\\\"+self.ESC+\\\"Cdesk\\\"+self.ESC+\\\"Cof\\\"+self.ESC+\\\"Cthe\\\"+self.ESC+\\\"Carmour\\\"+self.ESC+\\\"Cshop\\\"+self.ESC+\\\"Cis\\\"+self.ESC+\\\"Can\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"1;43m\\\"+self.A219+self.ESC+\\\"0;33m\\\"+self.A219+self.A219+self.A219+self.A219+self.ESC+\\\"30;43m\\\"+self.A176+self.A177+self.ESC+\\\"C\\\"+self.ESC+\\\"37;40m\\\"+self.A223+self.ESC+\\\"1;47m\\\"+self.A177+self.A176+self.ESC+\\\"C\\\"+self.ESC+\\\"0m\\\"+self.A219+self.A219+self.A219+self.ESC+\\\"1;30;47m\\\"+self.A176+self.A177+self.A178+self.ESC+\\\"C\\\"+self.ESC+\\\"0;30;43m\\\"+self.A177+self.A176+self.ESC+\\\"33;40m\\\"+self.A219+self.A219+self.ESC+\\\"1;30;43m\\\"+self.A178+self.ESC+\\\"C\\\"+self.ESC+\\\"40m\\\"+self.A223+self.A220+self.ESC+\\\"4C\\\"+self.ESC+\\\"0;33mamazingly\\\"+self.ESC+\\\"Cattractive\\\"+self.ESC+\\\"Clooking\\\"+self.ESC+\\\"Cfemale - she seems\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"1;43m\\\"+self.A219+self.ESC+\\\"0;33m\\\"+self.A219+self.A219+self.ESC+\\\"30;43m\\\"+self.A176+self.A177+self.ESC+\\\"33;40m\\\"+self.A223+self.ESC+\\\"1;37m\\\"+self.A220+self.ESC+\\\"47m\\\"+self.A177+self.A176+self.ESC+\\\"0m\\\"+self.A220+self.A220+self.A220+self.A220+self.A220+self.ESC+\\\"1;30m\\\"+self.A220+self.A220+self.A223+self.ESC+\\\"47m\\\"+self.A177+self.A178+self.ESC+\\\"C\\\"+self.ESC+\\\"0;30;43m\\\"+self.A177+self.A176+self.ESC+\\\"33;40m\\\"+self.A219+self.ESC+\\\"1;30;43m\\\"+self.A219+self.ESC+\\\"2C\\\"+self.ESC+\\\"0;32m\\\"+self.A220+self.A254+self.ESC+\\\"3C\\\"+self.ESC+\\\"33mbusy, doing her mails but she\\\"+self.ESC+\\\"Casks\\\"+self.ESC+\\\"C\\\\\\\"\\\"+self.ESC+\\\"1mHow\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"43m\\\"+self.A219+self.ESC+\\\"0;33m\\\"+self.A223+self.A219+self.ESC+\\\"30;43m\\\"+self.A176+self.A177+self.ESC+\\\"C\\\"+self.ESC+\\\"1;37;47m\\\"+self.A178+self.ESC+\\\"40m\\\"+self.A222+self.A222+self.ESC+\\\"47m\\\"+self.A176+self.ESC+\\\"C\\\"+self.ESC+\\\"30m\\\"+self.A176+self.ESC+\\\"C\\\"+self.A177+self.ESC+\\\"40m\\\"+self.A220+self.ESC+\\\"47m\\\"+self.A178+self.ESC+\\\"40m\\\"+self.A223+self.A220+self.ESC+\\\"47m\\\"+self.A219+self.ESC+\\\"C\\\"+self.ESC+\\\"0;30;43m\\\"+self.A177+self.A176+self.ESC+\\\"33;40m\\\"+self.A223+self.ESC+\\\"32m\\\"+self.A220+self.A178+self.ESC+\\\"6C\\\"+self.ESC+\\\"1;33mmay\\\"+self.ESC+\\\"CI\\\"+self.ESC+\\\"Cbe\\\"+self.ESC+\\\"Cof\\\"+self.ESC+\\\"Cservice?\\\"+self.ESC+\\\"0;33m\\\\\\\"\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"1m\\\"+self.A220+self.ESC+\\\"0;33m\\\"+self.A223+self.ESC+\\\"C\\\"+self.A220+self.A220+self.ESC+\\\"C\\\"+self.ESC+\\\"1;37m\\\"+self.A223+self.ESC+\\\"47m\\\"+self.A178+self.ESC+\\\"0m\\\"+self.A220+self.ESC+\\\"1;47m\\\"+self.A177+self.ESC+\\\"0m\\\"+self.A220+self.ESC+\\\"1;30;47m\\\"+self.A176+self.ESC+\\\"0m\\\"+self.A220+self.ESC+\\\"1;30m\\\"+self.A223+self.A223+self.A220+self.ESC+\\\"47m\\\"+self.A177+self.A178+self.ESC+\\\"C\\\"+self.ESC+\\\"0;30;43m\\\"+self.A177+self.ESC+\\\"33;40m\\\"+self.A223+self.ESC+\\\"32m\\\"+self.A220+self.ESC+\\\"1;42m\\\"+self.A176+self.ESC+\\\"0;32m\\\"+self.A220+\\\"\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"1;33;43m\\\"+self.A219+self.ESC+\\\"0;33m\\\"+self.A219+self.A220+self.A223+self.ESC+\\\"C\\\"+self.A220+self.ESC+\\\"1;43m\\\"+self.A176+self.ESC+\\\"0;33m\\\"+self.A220+self.ESC+\\\"1;37m\\\"+self.A223+self.ESC+\\\"0m\\\"+self.A220+self.ESC+\\\"2C\\\"+self.A220+self.A219+self.ESC+\\\"1;30;47m\\\"+self.A176+self.A176+self.ESC+\\\"40m\\\"+self.A223+self.ESC+\\\"47m\\\"+self.A219+self.ESC+\\\"C\\\"+self.ESC+\\\"0;30;43m\\\"+self.A177+self.A176+self.ESC+\\\"33;40m\\\"+self.A220+self.ESC+\\\"32m\\\"+self.A223+self.ESC+\\\"1;30m\\\"+self.A220+self.ESC+\\\"7C\\\"+self.ESC+\\\"0;33m[\\\"+self.ESC+\\\"1mB\\\"+self.ESC+\\\"0;33m]\\\"+self.ESC+\\\"1muy\\\"+self.ESC+\\\"CArmour\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"43m\\\"+self.A219+self.ESC+\\\"0;33m\\\"+self.A219+self.A219+self.A219+self.A220+self.A223+self.ESC+\\\"1;37m\\\"+self.A220+self.A220+self.ESC+\\\"0m\\\"+self.A223+self.ESC+\\\"C\\\"+self.ESC+\\\"1;47m\\\"+self.A223+self.ESC+\\\"0m\\\"+self.A219+self.A220+self.A220+self.A220+self.A220+self.ESC+\\\"1;30;47m\\\"+self.A176+self.A177+self.ESC+\\\"40m\\\"+self.A220+self.ESC+\\\"0;33m\\\"+self.A223+self.A223+self.ESC+\\\"30;43m\\\"+self.A177+self.A176+self.ESC+\\\"1;40m\\\"+self.A219+self.ESC+\\\"7C\\\"+self.ESC+\\\"0;33m[\\\"+self.ESC+\\\"1mS\\\"+self.ESC+\\\"0;33m]\\\"+self.ESC+\\\"1mell\\\"+self.ESC+\\\"CArmour\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"43m\\\"+self.A219+self.ESC+\\\"0;33m\\\"+self.A219+self.ESC+\\\"30;43m\\\"+self.A176+self.A177+self.ESC+\\\"C\\\"+self.ESC+\\\"1;37;47m\\\"+self.A219+self.A178+self.ESC+\\\"40m\\\"+self.A220+self.ESC+\\\"47m\\\"+self.A177+self.A176+self.ESC+\\\"0m\\\"+self.A220+self.A220+self.A220+self.A220+self.A219+self.A220+self.A223+self.ESC+\\\"1;30m\\\"+self.A220+self.A220+self.A219+self.A219+self.A220+self.ESC+\\\"0;33m\\\"+self.A223+self.ESC+\\\"1;30m\\\"+self.A219+self.ESC+\\\"7C\\\"+self.ESC+\\\"0;33m[\\\"+self.ESC+\\\"1mY\\\"+self.ESC+\\\"0;33m]\\\"+self.ESC+\\\"1mour\\\"+self.ESC+\\\"CStats\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"43m\\\"+self.A178+self.ESC+\\\"0;33m\\\"+self.A223+self.A220+self.A223+self.ESC+\\\"30;43m\\\"+self.A176+self.A223+self.ESC+\\\"1;37;40m\\\"+self.A223+self.A223+self.ESC+\\\"47m\\\"+self.A178+self.A177+self.A176+self.A176+self.ESC+\\\"0m\\\"+self.A219+self.A219+self.A223+self.ESC+\\\"1;30m\\\"+self.A220+self.A176+self.A177+self.A178+self.A223+self.A223+self.ESC+\\\"0;33m\\\"+self.A220+self.A219+self.ESC+\\\"1;30m\\\"+self.A219+self.ESC+\\\"7C\\\"+self.ESC+\\\"0;33m[\\\"+self.ESC+\\\"1mR\\\"+self.ESC+\\\"0;33m]\\\"+self.ESC+\\\"1meturn\\\"+self.ESC+\\\"Cto\\\"+self.ESC+\\\"CTown\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"43m\\\"+self.A177+self.ESC+\\\"0;33m\\\"+self.A219+self.A220+self.A219+self.A219+self.A223+self.ESC+\\\"32m\\\"+self.A220+self.ESC+\\\"1;42m\\\"+self.A176+self.ESC+\\\"0;32m\\\"+self.A220+self.ESC+\\\"C\\\"+self.ESC+\\\"33m\\\"+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.ESC+\\\"C\\\"+self.A220+self.A220+self.A219+self.ESC+\\\"30;43m\\\"+self.A176+self.ESC+\\\"33;40m\\\"+self.A219+self.A219+self.ESC+\\\"1;30m\\\"+self.A223+self.ESC+\\\"0;33m\\\"+self.A220+self.A254+self.ESC+\\\"C\\\"+self.ESC+\\\"1;30m\\\"+self.A220+self.A223+\\\"\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"33;43m\\\"+self.A176+self.ESC+\\\"0;33m\\\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A220+self.ESC+\\\"32m\\\"+self.A223+self.ESC+\\\"C\\\"+self.A178+self.A254+self.ESC+\\\"33m\\\"+self.A219+self.ESC+\\\"30;43m\\\"+self.A177+self.A176+self.ESC+\\\"33;40m\\\"+self.A223+self.A220+self.ESC+\\\"1;43m\\\"+self.A176+self.ESC+\\\"0;33m\\\"+self.A220+self.A223+self.A220+self.A223+self.A223+self.A220+self.ESC+\\\"3C\\\"+self.ESC+\\\"1;30m\\\"+self.A177+self.A220+self.ESC+\\\"2C\\\"+self.ESC+\\\"33m\\\"+self.A220+self.A220+self.ESC+\\\"0;33m\\\"+self.A220+self.A220+self.ESC+\\\"1m\\\"+self.A220+self.ESC+\\\"0;33m\\\"+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+\\\"\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"79C\\\"+self.ESC+\\\"1;30;43m\\\"+self.A176+self.ESC+\\\"33m\\\"+self.A176+self.ESC+\\\"0;33m\\\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A220+self.A220+self.ESC+\\\"32m\\\"+self.A254+self.ESC+\\\"33m\\\"+self.A223+self.A219+self.A219+self.A220+self.A223+self.A220+self.A220+self.A223+self.A223+self.ESC+\\\"1;30m\\\"+self.A220+self.A220+self.A219+self.ESC+\\\"2C\\\"+self.A220+self.A178+self.A220+self.ESC+\\\"C\\\"+self.ESC+\\\"33;43m\\\"+self.A177+self.ESC+\\\"2C\\\"+self.ESC+\\\"0m \\\"+self.ESC+\\\"3C\\\"+self.ESC+\\\"33m\\\"+self.A220+\\\"\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.A223+self.A223+self.A223+self.ESC+\\\"1;30m\\\"+self.A223+self.ESC+\\\"0;33m\\\"+self.A223+self.A223+self.ESC+\\\"1;30m\\\"+self.A223+self.ESC+\\\"0;33m\\\"+self.A223+self.ESC+\\\"1;30m\\\"+self.A223+self.A223+self.ESC+\\\"0;33m\\\"+self.A223+self.ESC+\\\"1;30m\\\"+self.A223+self.A223+self.A223+self.ESC+\\\"0;33m\\\"+self.A223+self.ESC+\\\"1;30m\\\"+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.ESC+\\\"C\\\"+self.ESC+\\\"0;33m\\\"+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.ESC+\\\"1;30m\\\"+self.A223+self.ESC+\\\"C\\\"+self.ESC+\\\"0;33m\\\"+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+\\\"\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"79C\\\"+self.A223+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\treturn thismsg\",\n \"def mac_pton(s):\\n return binascii.unhexlify(s.replace(\\\":\\\", \\\"\\\"))\",\n \"def python_to_matlab(code):\\n outline=code\\n outline=outline.replace(\\\"**\\\",\\\".^\\\")\\n outline=outline.replace(\\\"*\\\",\\\".*\\\")\\n outline=outline.replace(\\\"dot(b,\\\",\\\"b'*(\\\")\\n outline=outline.replace(\\\"dot(bhat,\\\",\\\"bhat'*(\\\")\\n outline=outline.replace(\\\"dot(Ahat,\\\",\\\"Ahat*(\\\")\\n outline=outline.replace(\\\"dot(A,\\\",\\\"(A*\\\")\\n outline=outline.replace(\\\"( c)\\\",\\\"c\\\")\\n outline=outline.replace(\\\"-0\\\",\\\"\\\")\\n #print(outline)\\n #print('******************')\\n return outline\",\n \"def pretty_hebrew(val):\\n return 'font-size:20px; font-family: Times New Roman; text-align: right; max-width: 500px'\",\n \"def mac(self):\\n mac = hexlify(WLAN().config('mac'), ':').decode()\\n return mac.upper() # MAC-address in upper case\",\n \"def getMacName(self):\\r\\n return CipherSuite.canonicalMacName(self.cipherSuite)\",\n \"def label(mi_, ma_):\\n\\treturn \\\"caractères Unicode des points de code {} à {}\\\".format(mi_, ma_)\",\n \"def _getMac( self, bSeed ):\\n\\n\\t\\tbMac = ( bSeed >> 24 ) & 0xFF\\n\\t\\treturn '%02X' % bMac\",\n \"def to_mac(self):\\n ts_type = self.ts_types['mac']\\n try:\\n dt_obj = duparser.parse(self.timestamp)\\n if hasattr(dt_obj.tzinfo, '_offset'):\\n dt_tz = dt_obj.tzinfo._offset.total_seconds()\\n dt_obj = duparser.parse(self.timestamp, ignoretz=True)\\n else:\\n dt_tz = 0\\n self.out_mac = str(int((dt_obj - self.epoch_2001).total_seconds() - int(dt_tz)))\\n ts_output = str(\\\"{}\\\\t\\\\t{}\\\".format(ts_type, self.out_mac))\\n except Exception:\\n exc_type, exc_obj, exc_tb = sys.exc_info()\\n print(str(exc_type) + \\\" - \\\" + str(exc_obj) + \\\" - line \\\" + str(exc_tb.tb_lineno))\\n self.out_mac = ts_output = False\\n return self.out_mac, ts_output\",\n \"def macOS(self) -> str:\\n\\n result = subprocess.run([\\\"sw_vers\\\", \\\"-productVersion\\\"], stdout=subprocess.PIPE)\\n\\n return result.stdout.decode(\\\"utf-8\\\").strip()\",\n \"def from_mac(self):\\n reason = \\\"[!] Mac Absolute timestamps are 9 digits, commonly followed by a decimal and up to 6 digits for milliseconds\\\"\\n ts_type = self.ts_types['mac']\\n try:\\n if \\\".\\\" not in self.mac or not ((len(mac.split(\\\".\\\")[0]) == 9) and (len(self.mac.split(\\\".\\\")[1]) in range(0, 7))) or not ''.join(self.mac.split(\\\".\\\")).isdigit():\\n self.in_mac = indiv_output = combined_output = False\\n pass\\n else:\\n dt_obj = self.epoch_2001 + timedelta(seconds=float(self.mac))\\n self.in_mac = dt_obj.strftime('%Y-%m-%d %H:%M:%S.%f')\\n indiv_output = str(\\\"{} {} UTC\\\".format(ts_type, self.in_mac))\\n combined_output = str(\\\"{}{}\\\\t\\\\t{} UTC{}\\\".format(self.left_color, ts_type, self.in_mac, self.right_color))\\n except Exception:\\n exc_type, exc_obj, exc_tb = sys.exc_info()\\n print(str(exc_type) + \\\" - \\\" + str(exc_obj) + \\\" - line \\\" + str(exc_tb.tb_lineno))\\n self.in_mac = indiv_output = combined_output = False\\n return self.in_mac, indiv_output, combined_output, reason\",\n \"def get_mac(self) -> str:\\n self.sendline(\\\"iw {} info\\\".format(self.iface_dut))\\n # We are looking for MAC definition of STA\\n # wdev 0x1\\n # addr 96:4e:c9:cc:7a:2c\\n # type managed\\n self.expect(\\\"addr (?P<mac>..:..:..:..:..:..)\\\\r\\\\n\\\\t(type|ssid)\\\")\\n return self.match.group('mac')\",\n \"def getMac(teff, logg):\\n # For Dwarfs: Doyle et al. 2014\\n # 5200 < teff < 6400\\n # 4.0 < logg < 4.6\\n if logg > 3.5:\\n mac = 3.21 + (2.33 * (teff - 5777.) * (10**(-3))) + (2.00 * ((teff - 5777.)**2) * (10**(-6))) - (2.00 * (logg - 4.44))\\n # For subgiants and giants: Hekker & Melendez 2007\\n elif 2.9 <= logg <= 3.5: # subgiants\\n mac = -8.426 + (0.00241*teff)\\n elif 1.5 <= logg < 2.9: # giants\\n mac = -3.953 + (0.00195*teff)\\n elif logg < 1.5: # bright giants\\n mac = -0.214 + (0.00158*teff)\\n\\n # For negative values, keep a minimum of 0.3 km/s\\n if mac < 0.30:\\n mac = 0.30\\n return round(mac, 2)\",\n \"def tower(self):\\n\\t\\tthismsg = \\\"\\\\r\\\\n\\\"+self.ESC+\\\"255D\\\"+self.ESC+\\\"0;1;44m \\\"+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"58C\\\"+self.ESC+\\\"44m \\\"+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"44m \\\"+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"68C\\\"+self.ESC+\\\"44m \\\"+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"44m \\\"+self.ESC+\\\"30m\\\"+self.A219+\\\" \\\"+self.A219+\\\" \\\"+self.A219+\\\" \\\"+self.A219+\\\" \\\"+self.A219+\\\" \\\"+self.ESC+\\\"0;44m\\\"+self.A219+\\\" \\\"+self.A219+\\\" \\\"+self.A219+\\\" \\\"+self.ESC+\\\"1m\\\"+self.A219+\\\" \\\"+self.A219+\\\" \\\"+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"52C\\\"+self.ESC+\\\"44m \\\"+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"44m \\\"+self.ESC+\\\"30m\\\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\\\"43m\\\"+self.A223+self.A223+self.ESC+\\\"0;43m\\\"+self.A223+self.A223+self.A223+self.A223+self.ESC+\\\"1m\\\"+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"40C\\\"+self.ESC+\\\"43m\\\"+self.ESC+\\\"0;34;43m\\\"+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.ESC+\\\"30m\\\"+self.A223+self.A223+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"44m \\\"+self.ESC+\\\"1m\\\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\\\"0;33;43m\\\"+self.A219+self.ESC+\\\"C\\\"+self.ESC+\\\"1;36;40mYou reach th\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"42Ce castle and fight your way up the\\\"+self.ESC+\\\"C\\\"+self.ESC+\\\"0;33;43m\\\"+self.A219+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"44m \\\"+self.ESC+\\\"32;40m\\\"+self.A178+self.A219+self.ESC+\\\"44m \\\"+self.ESC+\\\"1;30m\\\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\\\"0;33;43m\\\"+self.A219+self.ESC+\\\"C\\\"+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"30C\\\"+self.ESC+\\\"43m\\\"+self.ESC+\\\"1;36;40mtower!\\\"+self.ESC+\\\"41C\\\"+self.ESC+\\\"0;33;43m\\\"+self.A219+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"44m \\\"+self.ESC+\\\"32;40m\\\"+self.A176+self.A219+self.ESC+\\\"44m \\\"+self.ESC+\\\"40m\\\"+self.A176+self.A177+self.A219+self.A219+self.ESC+\\\"44m \\\"+self.ESC+\\\"40m\\\"+self.A219+self.ESC+\\\"44m \\\"+self.ESC+\\\"1;30m\\\"+self.A219+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"24C\\\"+self.ESC+\\\"44m\\\"+self.ESC+\\\"40m\\\"+self.A221+self.ESC+\\\"44m\\\"+self.A219+self.A219+self.A219+self.ESC+\\\"0;33;43m\\\"+self.A219+self.ESC+\\\"48C\\\"+self.A219+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"32m\\\"+self.A178+self.A177+self.A177+self.A219+self.ESC+\\\"44m \\\"+self.ESC+\\\"40m\\\"+self.A176+self.A177+self.A177+self.A177+self.A177+self.ESC+\\\"44m \\\"+self.ESC+\\\"40m\\\"+self.A176+self.A177+self.A219+self.ESC+\\\"44m \\\"+self.ESC+\\\"1;30m\\\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"28C\\\"+self.ESC+\\\"44m\\\"+self.ESC+\\\"0;33;43m\\\"+self.A219+self.ESC+\\\"C\\\"+self.ESC+\\\"1;36;40mYou blindly slash at any and all who\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"66C stand in \\\"+self.ESC+\\\"0;33;43m\\\"+self.A219+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"32m\\\"+self.A177+self.A176+self.A177+self.A176+self.A178+self.A219+self.ESC+\\\"44m \\\"+self.ESC+\\\"40m\\\"+self.A176+self.A177+self.A176+self.A177+self.A177+self.A219+self.A178+self.ESC+\\\"44m \\\"+self.ESC+\\\"1;30m\\\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\\\"0;33;43m\\\"+self.A219+self.ESC+\\\"C\\\"+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"30C\\\"+self.ESC+\\\"43m\\\"+self.ESC+\\\"1;36;40myour path - screaming to find the prisoner \\\"+self.ESC+\\\"4C\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"77C\\\"+self.ESC+\\\"0;33;43m\\\"+self.A219+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"32m\\\"+self.A176+self.A177+self.A177+self.A178+self.A178+self.ESC+\\\"44m \\\"+self.ESC+\\\"40m\\\"+self.A176+self.A177+self.A177+self.A178+self.A178+self.ESC+\\\"44m \\\"+self.ESC+\\\"40m\\\"+self.A176+self.A178+self.A178+self.ESC+\\\"44m \\\"+self.ESC+\\\"1;30m\\\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"28C\\\"+self.ESC+\\\"44m\\\"+self.ESC+\\\"0;33;43m\\\"+self.A219+self.ESC+\\\"C\\\"+self.ESC+\\\"1;36;40myou seek - and the jailer of your he\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"66Cart.\\\"+self.ESC+\\\"7C\\\"+self.ESC+\\\"0;33;43m\\\"+self.A219+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"44m \\\"+self.ESC+\\\"32;40m\\\"+self.A176+self.A176+self.A177+self.ESC+\\\"33m\\\"+self.A176+self.ESC+\\\"32m\\\"+self.A177+self.A176+self.A177+self.ESC+\\\"33m\\\"+self.A178+self.ESC+\\\"44m \\\"+self.ESC+\\\"32;40m\\\"+self.A178+self.A176+self.A177+self.A176+self.A178+self.ESC+\\\"44m \\\"+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"23C\\\"+self.ESC+\\\"44m\\\"+self.ESC+\\\"1;30m\\\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\\\"0;33;43m\\\"+self.A219+self.ESC+\\\"48C\\\"+self.A219+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"32m\\\"+self.A176+self.A177+self.A177+self.A177+self.A176+self.A178+self.A177+self.ESC+\\\"33m\\\"+self.A176+self.A177+self.A178+self.ESC+\\\"32m\\\"+self.A177+self.A177+self.A178+self.A176+self.A178+self.A176+self.A178+self.ESC+\\\"44m \\\"+self.ESC+\\\"1;30m\\\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\\\"43m\\\"+self.A220+self.A220+self.ESC+\\\"0;43m\\\"+self.A220+self.A220+self.A220+self.A220+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"34C\\\"+self.ESC+\\\"43m\\\"+self.A220+self.ESC+\\\"1m\\\"+self.A220+self.A220+self.A220+self.ESC+\\\"0;34;43m\\\"+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"76C\\\"+self.ESC+\\\"43m\\\"+self.ESC+\\\"30m\\\"+self.A220+self.A220+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"44m \\\"+self.ESC+\\\"32;40m\\\"+self.A177+self.A176+self.A177+self.A176+self.A176+self.ESC+\\\"33m\\\"+self.A176+self.A177+self.A178+self.A177+self.A177+self.ESC+\\\"32m\\\"+self.A177+self.A178+self.A177+self.A177+self.ESC+\\\"44m \\\"+self.ESC+\\\"1;30m\\\"+self.A219+self.A219+self.ESC+\\\"40m\\\"+self.A222+self.ESC+\\\"44m\\\"+self.A219+self.A219+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"28C\\\"+self.ESC+\\\"44m\\\"+self.A219+self.A219+self.ESC+\\\"0;44m\\\"+self.A219+self.A219+self.ESC+\\\"40m\\\"+self.A221+self.A219+self.ESC+\\\"1;44m\\\"+self.A219+self.ESC+\\\"40m\\\"+self.A221+self.ESC+\\\"44m\\\"+self.A219+self.A219+\\\" \\\"+self.ESC+\\\"0;33;43m\\\"+self.A219+self.ESC+\\\"40m\\\"+self.A223+self.A223+\\\"\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"44C\\\"+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.ESC+\\\"43m\\\"+self.A219+self.ESC+\\\"34m\\\"+self.A219+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"44m \\\"+self.ESC+\\\"33;40m\\\"+self.A176+self.A177+self.A178+self.ESC+\\\"44m \\\"+self.ESC+\\\"1;30m\\\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\\\"0;44m\\\"+self.A219+self.A219+self.A219+self.ESC+\\\"1m\\\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"37C\\\"+self.ESC+\\\"44m\\\"+self.A219+\\\" \\\"+self.ESC+\\\"0;33;43m\\\"+self.A219+self.ESC+\\\"2C\\\"+self.ESC+\\\"1;37;40mYou make it to the top and \\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"74C\\\"+self.ESC+\\\"0;33;43m\\\"+self.A219+self.ESC+\\\"44m \\\"+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"44m \\\"+self.ESC+\\\"40m\\\"+self.A176+self.A177+self.A176+self.A178+self.ESC+\\\"44m \\\"+self.ESC+\\\"1;30m\\\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\\\"0;44m\\\"+self.A219+self.A219+self.ESC+\\\"1m\\\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+\\\" \\\"+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"40C\\\"+self.ESC+\\\"44m \\\"+self.ESC+\\\"0;33;43m\\\"+self.A219+self.ESC+\\\"4C\\\"+self.ESC+\\\"1;37;40mthrow open the door...\\\"+self.ESC+\\\"6C\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"74C\\\"+self.ESC+\\\"0;33;43m\\\"+self.A219+self.ESC+\\\"44m \\\"+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"44m \\\"+self.ESC+\\\"40m\\\"+self.A176+self.A178+self.A177+self.A178+self.ESC+\\\"44m \\\"+self.ESC+\\\"32;40m\\\"+self.A177+self.ESC+\\\"1;42m\\\"+self.A178+self.A219+self.ESC+\\\"30;44m\\\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"31C\\\"+self.ESC+\\\"44m\\\"+self.ESC+\\\"0;44m\\\"+self.A219+self.A219+self.ESC+\\\"1m\\\"+self.A219+self.A219+self.A219+self.A219+self.A219+\\\" \\\"+self.ESC+\\\"0;33;43m\\\"+self.A219+self.ESC+\\\"1;32;42m\\\"+self.A177+self.A178+self.A178+self.ESC+\\\"0;33m\\\"+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+\\\"\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"53C\\\"+self.ESC+\\\"32m\\\"+self.A178+self.ESC+\\\"1;42m\\\"+self.A219+self.A219+self.ESC+\\\"0;33m\\\"+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.ESC+\\\"43m\\\"+self.A219+self.ESC+\\\"44m \\\"+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"44m \\\"+self.ESC+\\\"32;40m\\\"+self.A176+self.ESC+\\\"1;42m\\\"+self.A176+self.ESC+\\\"0;32m\\\"+self.A177+self.ESC+\\\"1;42m\\\"+self.A219+self.A219+self.ESC+\\\"0;33m\\\"+self.A176+self.A177+self.A178+self.ESC+\\\"44m \\\"+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"20C\\\"+self.ESC+\\\"44m\\\"+self.ESC+\\\"1;32;42m\\\"+self.A178+self.A219+self.ESC+\\\"0;32m\\\"+self.A176+self.A178+self.ESC+\\\"1;42m\\\"+self.A219+self.ESC+\\\"0;32m\\\"+self.A178+self.ESC+\\\"1;30;44m\\\"+self.A219+self.A219+self.A219+self.ESC+\\\"0;44m\\\"+self.A219+self.A219+self.A219+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"32C\\\"+self.ESC+\\\"44m\\\"+self.ESC+\\\"1m\\\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+\\\" \\\"+self.ESC+\\\"0;32m\\\"+self.A177+self.ESC+\\\"1;42m\\\"+self.A176+self.ESC+\\\"0;32m\\\"+self.A178+self.ESC+\\\"1;42m\\\"+self.A178+self.ESC+\\\"0;32m\\\"+self.A177+self.ESC+\\\"1;42m\\\"+self.A178+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"46C\\\"+self.ESC+\\\"42m\\\"+self.ESC+\\\"44m \\\"+self.ESC+\\\"0;32m\\\"+self.A177+self.A178+self.A176+self.ESC+\\\"1;42m\\\"+self.A178+self.ESC+\\\"0;32m\\\"+self.A176+self.A178+self.ESC+\\\"1;42m\\\"+self.A219+self.A178+self.A178+self.ESC+\\\"44m \\\"+self.ESC+\\\"42m\\\"+self.A176+self.A177+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"63C\\\"+self.ESC+\\\"42m\\\"+self.A178+self.ESC+\\\"44m \\\"+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"42m \\\"+self.ESC+\\\"0;32m\\\"+self.A177+self.A176+self.ESC+\\\"1;42m\\\"+self.A178+self.A176+self.ESC+\\\"0;32m\\\"+self.A176+self.ESC+\\\"1;42m\\\"+self.A178+self.A219+self.ESC+\\\"0;32m\\\"+self.A178+self.A219+self.ESC+\\\"42m \\\"+self.ESC+\\\"40m\\\"+self.A177+self.A177+self.A176+self.A176+self.A177+\\\"\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"21C\\\"+self.ESC+\\\"1;42m\\\"+self.A178+self.A176+self.ESC+\\\"30;40m\\\"+self.A219+self.ESC+\\\"0;32m\\\"+self.A177+self.A177+self.A178+self.ESC+\\\"1;42m\\\"+self.A219+self.ESC+\\\"30;40m\\\"+self.A219+self.A219+self.ESC+\\\"0m\\\"+self.A219+self.A219+self.A219+self.ESC+\\\"1m\\\"+self.A219+self.A219+self.A219+\\\"\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"36C\\\"+self.ESC+\\\"0;32m\\\"+self.A177+self.A178+self.A176+self.ESC+\\\"1;42m\\\"+self.A176+self.ESC+\\\"0;32m\\\"+self.A176+self.ESC+\\\"1;42m\\\"+self.A219+self.A178+self.ESC+\\\"0;32m\\\"+self.A176+self.ESC+\\\"1;42m\\\"+self.A219+self.A219+\\\" \\\"+self.A178+\\\" \\\"+self.ESC+\\\"0;32m\\\"+self.A177+self.A176+self.A176+\\\"\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"52C\\\"+self.ESC+\\\"1;42m\\\"+self.A176+self.ESC+\\\"0;32m\\\"+self.A177+self.A176+self.ESC+\\\"1;42m\\\"+self.A176+self.ESC+\\\"0;32m\\\"+self.A177+self.ESC+\\\"1;42m\\\"+self.A176+self.A178+self.A176+self.ESC+\\\"0;32m\\\"+self.A178+self.ESC+\\\"1;42m\\\"+self.A176+self.ESC+\\\"44m \\\"+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"63C\\\"+self.ESC+\\\"44m \\\"+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"42m\\\"+self.A176+self.A176+self.ESC+\\\"0;32m\\\"+self.A177+self.A176+self.ESC+\\\"1;42m\\\"+self.A176+self.A176+self.A178+self.A219+self.A219+self.A178+self.A178+self.A176+self.A176+\\\" \\\"+self.A176+self.A176+self.ESC+\\\"0;32m\\\"+self.A177+self.A176+self.A177+self.A178+self.A176+self.A177+self.ESC+\\\"1;42m\\\"+self.A176+self.ESC+\\\"0;32m\\\"+self.A176+self.ESC+\\\"1;42m\\\"+self.A219+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"26C\\\"+self.ESC+\\\"42m\\\"+self.ESC+\\\"0;32m\\\"+self.A178+self.ESC+\\\"1;42m\\\"+self.A219+self.ESC+\\\"0;32m\\\"+self.A177+self.A178+self.ESC+\\\"1;42m\\\"+self.A176+self.ESC+\\\"0;32m\\\"+self.A177+self.ESC+\\\"1;42m\\\"+self.A176+self.A176+self.ESC+\\\"0;32m\\\"+self.A177+self.A177+\\\"\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"36C\\\"+self.ESC+\\\"1;42m\\\"+self.A176+self.ESC+\\\"0;32m\\\"+self.A177+self.ESC+\\\"1;42m\\\"+self.A176+self.ESC+\\\"0;32m\\\"+self.A176+self.A177+self.A177+self.A177+self.ESC+\\\"1;42m\\\"+self.A219+self.A176+self.ESC+\\\"0;32m\\\"+self.A178+self.A178+self.A177+self.A176+self.A178+self.ESC+\\\"1;42m\\\"+self.A176+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"51C\\\"+self.ESC+\\\"42m\\\"+self.ESC+\\\"0;32m\\\"+self.A177+self.A176+self.A177+self.A178+self.A178+self.ESC+\\\"1;42m\\\"+self.A178+self.ESC+\\\"0;32m\\\"+self.A177+self.A178+self.ESC+\\\"1;42m\\\"+self.A177+self.A178+self.ESC+\\\"0;32m\\\"+self.A177+self.ESC+\\\"1;42m\\\"+self.A176+self.ESC+\\\"44m \\\"+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"64C\\\"+self.ESC+\\\"44m \\\"+self.ESC+\\\"37m<MORE> \\\"+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\treturn thismsg\",\n \"def get_sonic_mac(host, asicnum, port):\\n if host.facts[\\\"num_asic\\\"] == 1:\\n cmd = \\\"sudo ip link show {}\\\".format(port)\\n else:\\n ns = \\\"asic\\\" + str(asicnum)\\n cmd = \\\"sudo ip netns exec {} ip link show {}\\\".format(ns, port)\\n output = host.command(cmd)\\n mac = output['stdout_lines'][1].split()[1]\\n logger.info(\\\"host: %s, asic: %d, port: %s, mac: %s\\\", host.hostname, asicnum, port, mac)\\n return mac\",\n \"def mac_from_vm(vm: libvirt.virDomain = None) -> str:\\n doc = minidom.parseString(vm.XMLDesc())\\n interfaces = doc.getElementsByTagName('mac')\\n return interfaces[0].getAttribute('address')\",\n \"def generate_mac():\\n rand_str = generate_name(choices=\\\"0123456789abcdef\\\", length=12)\\n return \\\":\\\".join(re.findall(\\\"..\\\", rand_str))\",\n \"def generate_mac():\\n rand_str = generate_name(choices=\\\"0123456789abcdef\\\", length=12)\\n return \\\":\\\".join(re.findall(\\\"..\\\", rand_str))\",\n \"def get_eos_mac(nbr, nbr_intf):\\n if \\\"port-channel\\\" in nbr_intf.lower():\\n # convert Port-Channel1 to po1\\n shell_intf = \\\"po\\\" + nbr_intf[-1]\\n else:\\n # convert Ethernet1 to eth1\\n shell_intf = \\\"eth\\\" + nbr_intf[-1]\\n\\n output = nbr['host'].command(\\\"ip addr show dev %s\\\" % shell_intf)\\n # 8: Ethernet0: <BROADCAST,MULTICAST,UP,LOWER_UP> mtu 9100 ...\\n # link/ether a6:69:05:fd:da:5f brd ff:ff:ff:ff:ff:ff\\n\\n mac = output['stdout_lines'][1].split()[1]\\n return {'mac': mac, \\\"shell_intf\\\": shell_intf}\",\n \"def header(self):\\n\\t\\tthismsg = \\\"\\\\r\\\\n\\\"+self.ESC + \\\"0m \\\" +self.A220 + self.A220 + self.A220 + self.A220 + self.A220 +\\\" \\\" + self.ESC + \\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += \\\" \\\"+self.A220+self.A220+self.A219+self.A219+self.A219+self.A219+self.ESC+\\\"1;47m\\\"+self.A176+self.A177+self.A178+self.ESC+\\\"40m\\\"+self.A219+self.A219+self.A220+self.A220+self.ESC+\\\"0m \\\"+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"1m \\\"+self.ESC+\\\"31mS\\\"+self.ESC+\\\"0;31mAGA\\\"+self.ESC+\\\"37m \\\"+self.A219+self.A219+self.ESC+\\\"30;47mo\\\"+self.ESC+\\\"37;40m\\\"+self.A219+self.ESC+\\\"1;47m\\\"+self.A176+self.A176+self.A177+self.A177+self.A178+self.ESC+\\\"40m\\\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\\\"0m \\\"+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"1m \\\"+self.ESC+\\\"31mO\\\"+self.ESC+\\\"0;31mF THE\\\"+self.ESC+\\\"37m \\\"+self.A219+self.A219+self.ESC+\\\"30;47mO\\\"+self.ESC+\\\"37;40m\\\"+self.A219+self.A219+self.ESC+\\\"1;47m\\\"+self.A176+self.A177+self.A177+self.A178+self.ESC+\\\"40m\\\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\\\"0m \\\"+self.A220+self.A220+self.A220+self.ESC+\\\"1m\\\"+self.A220+self.A220+self.ESC+\\\"0m \\\"+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"1m \\\"+self.ESC+\\\"31mR\\\"+self.ESC+\\\"0;31mED\\\"+self.ESC+\\\"37m \\\"+self.A219+self.ESC+\\\"30;47mo\\\"+self.ESC+\\\"37;40m\\\"+self.A219+self.A219+self.ESC+\\\"1;47m\\\"+self.A176+self.A176+self.A177+self.A178+self.ESC+\\\"40m\\\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\\\"0m \\\"+self.A223+self.A219+self.ESC+\\\"1;47m\\\"+self.A176+self.A219+self.A219+self.A219+self.ESC+\\\"40m\\\"+self.A220+self.A220+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A220+self.ESC+\\\"37m \\\"+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"31m \\\"+self.ESC+\\\"1mD\\\"+self.ESC+\\\"0;31mRAGON 0.9.9\\\"+self.ESC+\\\"37m \\\"+self.A223+self.A219+self.A219+self.ESC+\\\"1;47m\\\"+self.A176+self.A177+self.A177+self.A178+self.A219+self.ESC+\\\"40m\\\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A223+self.ESC+\\\"0m \\\"+self.A219+self.ESC+\\\"1;47m\\\"+self.A219+self.A219+self.ESC+\\\"40m\\\"+self.A223+self.A223+self.ESC+\\\"0;31m\\\"+self.A220+self.ESC+\\\"1;41m\\\"+self.A176+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A220+self.ESC+\\\"37m \\\"+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"31m concept\\\"+self.ESC+\\\"37m \\\"+self.A223+self.ESC+\\\"1;47m\\\"+self.A176+self.A177+self.A177+self.A178+self.A178+self.ESC+\\\"40m\\\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A223+self.ESC+\\\"0m \\\"+self.ESC+\\\"1m\\\"+self.A220+self.ESC+\\\"0m \\\"+self.ESC+\\\"1m\\\"+self.A220+self.ESC+\\\"0m \\\"+self.ESC+\\\"31m\\\"+self.A220+self.A220+self.ESC+\\\"1;41m\\\"+self.A176+self.A178+\\\" \\\"+self.A176+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A220+self.A220+self.ESC+\\\"37m \\\"+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"31m Seth Robinson \\\"+self.ESC+\\\"37m\\\"+self.A222+\\\" \\\"+self.A223+self.A223+self.ESC+\\\"1;47m\\\"+self.A178+self.ESC+\\\"40m\\\"+self.A219+self.A219+self.A219+self.A219+self.A223+self.A223+self.ESC+\\\"0m \\\"+self.A220+self.ESC+\\\"1;47m\\\"+self.A220+self.ESC+\\\"40m\\\"+self.A223+\\\" \\\"+self.ESC+\\\"0m\\\"+self.A220+self.A220+self.ESC+\\\"1;47m\\\"+self.A220+self.ESC+\\\"40m\\\"+self.A223+self.A223+\\\" \\\"+self.ESC+\\\"0m\\\"+self.A223+self.A219+self.A220+self.ESC+\\\"1m\\\"+self.A220+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A223+self.ESC+\\\"1;41m\\\"+self.A176+self.A177+self.A178+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A223+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A176+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A220+self.ESC+\\\"37m \\\"+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"31m by\\\"+self.ESC+\\\"0m \\\"+self.A219+\\\" \\\"+self.A220+self.ESC+\\\"1;47m\\\"+self.A220+self.ESC+\\\"40m\\\"+self.A223+\\\" \\\"+self.ESC+\\\"0m\\\"+self.A220+self.ESC+\\\"1;47m\\\"+self.A220+self.A220+self.A219+self.ESC+\\\"40m\\\"+self.A223+self.ESC+\\\"0m \\\"+self.A223+self.ESC+\\\"1;47m\\\"+self.A176+self.A219+self.ESC+\\\"40m\\\"+self.A220+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A223+self.ESC+\\\"1;41m\\\"+self.A177+self.A178+\\\" \\\"+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"1;31;41m\\\"+self.A176+\\\" \\\"+self.A176+\\\" \\\"+self.A176+self.ESC+\\\"0;31m\\\"+self.A220+self.ESC+\\\"37m \\\"+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"1;34m J\\\"+self.ESC+\\\"0;34m.\\\"+self.ESC+\\\"1mT\\\"+self.ESC+\\\"0;34m.\\\"+self.ESC+\\\"1mS\\\"+self.ESC+\\\"0;34mage\\\"+self.ESC+\\\"0m \\\"+self.A219+self.ESC+\\\"1;30;47m\\\"+self.A176+self.ESC+\\\"0;37;40m\\\"+self.A221+\\\" \\\"+self.A220+self.ESC+\\\"1;47m\\\"+self.A177+self.A176+self.ESC+\\\"40m\\\"+self.A223+\\\" \\\"+self.ESC+\\\"0m\\\"+self.A220+self.ESC+\\\"1;47m\\\"+self.A220+self.A220+self.A219+self.ESC+\\\"40m\\\"+self.A223+self.ESC+\\\"0m \\\"+self.A223+self.ESC+\\\"1;47m\\\"+self.A177+self.A219+self.A219+self.ESC+\\\"40m\\\"+self.A220+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A223+self.ESC+\\\"1;41m\\\"+self.A177+self.A176+self.A178+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A220+self.A223+self.ESC+\\\"1;41m\\\"+self.A176+self.A178+self.A176+self.A176+self.A177+self.A177+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += \\\" \\\"+self.A219+self.A219+\\\" \\\"+self.ESC+\\\"1;47m\\\"+self.A176+self.A177+self.A219+self.ESC+\\\"40m\\\"+self.A223+\\\" \\\"+self.ESC+\\\"0m\\\"+self.A223+self.ESC+\\\"1m\\\"+self.A223+self.ESC+\\\"41m\\\"+self.A223+self.ESC+\\\"0;31m\\\"+self.A220+self.A220+self.A220+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A178+self.A177+self.A176+\\\" \\\"+self.A220+self.A220+self.A220+self.A220+self.A223+self.A220+self.ESC+\\\"1;41m\\\"+self.A176+self.ESC+\\\"0;31m\\\"+self.A220+self.ESC+\\\"1;41m\\\"+self.A177+self.ESC+\\\"0;31m\\\"+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"37;40m \\\"+self.ESC+\\\"31m\\\"+self.A223+self.ESC+\\\"1;41m\\\"+self.A177+self.A178+\\\" \\\"+self.A219+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"1;31;41m\\\"+self.A178+self.A178+self.A176+self.A177+self.A177+self.A219+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"37m \\\"+self.A219+self.ESC+\\\"1;30;47m\\\"+self.A176+self.ESC+\\\"0;37;40m \\\"+self.A219+self.A223+self.ESC+\\\"1m\\\"+self.A223+self.ESC+\\\"0;31m\\\"+self.A220+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A223+self.A223+self.A223+\\\" \\\"+self.A220+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A223+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A223+self.A223+self.A223+\\\" \\\"+self.A176+self.A176+\\\" \\\"+self.ESC+\\\"1;41m\\\"+self.A176+\\\" \\\"+self.A178+self.A178+self.A219+self.ESC+\\\"0;31m\\\"+self.A220+\\\" \\\"+self.A223+self.ESC+\\\"1;41m\\\"+self.A177+self.A176+\\\" \\\"+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"1;31;41m\\\"+self.A176+self.A178+self.A176+self.A177+self.A177+self.A219+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"37m \\\"+self.A219+self.ESC+\\\"1;30;47m\\\"+self.A176+self.ESC+\\\"0;37;40m \\\"+self.A219+\\\" \\\"+self.ESC+\\\"31m\\\"+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A223+\\\" \\\"+self.A220+self.A220+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A178+self.A177+self.A176+self.ESC+\\\"37m \\\"+self.ESC+\\\"31m\\\"+self.A178+self.A177+self.A177+self.A223+\\\" \\\"+self.ESC+\\\"1;41m\\\"+self.A176+\\\" \\\"+self.A177+self.A178+self.A219+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A220+\\\" \\\"+self.A223+self.ESC+\\\"1;41m\\\"+self.A177+self.A219+self.ESC+\\\"0;31m\\\"+self.A220+self.A223+self.ESC+\\\"1;41m\\\"+self.A178+\\\" \\\"+self.A177+self.A178+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"37m \\\"+self.ESC+\\\"1;30;47m\\\"+self.A176+self.ESC+\\\"0;37;40m\\\"+self.A219+\\\" \\\"+self.A219+\\\" \\\"+self.ESC+\\\"31m\\\"+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"37;40m \\\"+self.ESC+\\\"31m\\\"+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A178+self.A177+self.A176+\\\" \\\"+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A223+self.A223+self.ESC+\\\"37m \\\"+self.ESC+\\\"1;31;41m\\\"+self.A176+\\\" \\\"+self.A178+self.A177+self.A219+self.A219+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"1;31;41m\\\"+self.A176+self.A177+self.A178+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"31m\\\"+self.A220+self.A220+self.A223+self.ESC+\\\"1;41m\\\"+self.A178+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"37m \\\"+self.ESC+\\\"1;30;47m\\\"+self.A176+self.ESC+\\\"0;37;40m\\\"+self.A219+self.ESC+\\\"1;30;47m\\\"+self.A176+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"31m\\\"+self.A220+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A219+self.A178+self.A178+self.A177+self.A176+self.A223+\\\" \\\"+self.A220+self.A220+\\\" \\\"+self.A223+self.ESC+\\\"37m \\\"+self.ESC+\\\"1;31;41m\\\"+self.A176+\\\" \\\"+self.A177+self.A178+self.A219+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"1;31;41m\\\"+self.A177+self.A178+self.A219+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"1;31;41m\\\"+self.A176+self.A178+self.A178+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"37m \\\"+self.A219+self.A219+self.A219+\\\" \\\"+self.ESC+\\\"31m\\\"+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A221+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A178+self.A223+self.A223+self.A223+\\\" \\\"+self.A220+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A223+self.A223+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A220+\\\" \\\"+self.A220+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A178+self.A178+self.A177+self.A223+\\\" \\\"+self.ESC+\\\"1;41m\\\"+self.A176+\\\" \\\"+self.A177+self.A219+self.A219+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"1;31;41m\\\"+self.A176+self.A177+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A220+self.ESC+\\\"1;41m\\\"+self.A176+self.A178+self.A178+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"37m \\\"+self.A222+self.ESC+\\\"1;30;47m\\\"+self.A176+self.A177+self.ESC+\\\"0;37;40m\\\"+self.A221+\\\" \\\"+self.ESC+\\\"31m\\\"+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A176+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A223+\\\" \\\"+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A220+self.A220+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A220+self.A220+\\\" \\\"+self.A223+self.A223+self.A220+\\\" \\\"+self.A223+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A178+self.A178+self.A178+self.ESC+\\\"37m \\\"+self.ESC+\\\"1;31;41m\\\"+self.A176+\\\" \\\"+self.A178+self.A219+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"31m\\\"+self.A223+self.ESC+\\\"1;41m\\\"+self.A176+self.A177+self.A178+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"1;31;41m\\\"+self.A176+self.A178+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"37m \\\"+self.A222+self.ESC+\\\"1;30;47m\\\"+self.A176+self.A177+self.ESC+\\\"0;37;40m\\\"+self.A219+\\\" \\\"+self.ESC+\\\"31m\\\"+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A176+self.ESC+\\\"0;31m\\\"+self.A223+self.A223+\\\" \\\"+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A220+self.A222+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A220+self.A220+\\\" \\\"+self.A223+self.A178+self.ESC+\\\"37m \\\"+self.ESC+\\\"1;31;41m\\\"+self.A176+self.A177+self.A178+self.A219+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"1;31;41m\\\"+self.A176+self.A177+\\\" \\\"+self.A178+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"1;31;41m\\\"+self.A176+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += \\\" \\\"+self.A219+self.ESC+\\\"1;30;47m\\\"+self.A177+self.ESC+\\\"0;37;40m\\\"+self.A219+\\\" \\\"+self.ESC+\\\"31m\\\"+self.A222+self.A219+self.ESC+\\\"1;41m\\\"+self.A176+self.A176+self.ESC+\\\"0;31m\\\"+self.A221+\\\" \\\"+self.ESC+\\\"1;5;32m\\\"+self.A220+self.A220+self.A223+\\\" \\\"+self.ESC+\\\"0;31;41m \\\"+self.ESC+\\\"40m\\\"+self.A178+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A223+self.A223+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A220+self.A222+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A220+\\\" \\\"+self.A220+self.A220+self.A220+self.A220+self.A223+self.ESC+\\\"1;41m\\\"+self.A176+self.A177+self.A178+self.ESC+\\\"0;31m\\\"+self.A220+\\\" \\\"+self.ESC+\\\"1;41m\\\"+self.A176+self.A177+\\\" \\\"+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"1;31;41m\\\"+self.A176+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"37m \\\"+self.A219+self.ESC+\\\"1;30;47m\\\"+self.A176+self.ESC+\\\"0;37;40m\\\"+self.A219+\\\" \\\"+self.ESC+\\\"31m\\\"+self.A222+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A176+\\\" \\\"+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"1;5;32m\\\"+self.A219+self.A219+self.A223+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A222+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A178+self.A220+self.A222+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A220+self.A222+self.A219+self.ESC+\\\"41m \\\"+self.ESC+\\\"37;40m \\\"+self.ESC+\\\"31;41m \\\"+self.ESC+\\\"40m\\\"+self.A178+self.A223+\\\" \\\"+self.A223+self.ESC+\\\"1;41m\\\"+self.A176+self.A177+self.A219+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"1;31;41m\\\"+self.A176+self.A177+self.A219+self.A178+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += \\\" \\\"+self.A222+self.ESC+\\\"1;30;47m\\\"+self.A176+self.A177+self.ESC+\\\"0;37;40m\\\"+self.A219+\\\" \\\"+self.ESC+\\\"31m\\\"+self.A222+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A178+self.A176+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"1;5;32m\\\"+self.A223+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A220+self.A220+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A178+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A223+\\\" \\\"+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A220+self.A222+self.A219+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A220+\\\" \\\"+self.A223+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A178+self.A223+\\\" \\\"+self.A223+self.ESC+\\\"1;41m\\\"+self.A176+self.A219+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"31;41m \\\"+self.A177+self.A219+self.A178+self.ESC+\\\"37;40m \\\"+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += \\\" \\\"+self.A219+self.ESC+\\\"1;30;47m\\\"+self.A176+self.ESC+\\\"0;37;40m\\\"+self.A221+\\\" \\\"+self.ESC+\\\"31m\\\"+self.A222+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A177+self.A176+self.ESC+\\\"0;31m\\\"+self.A221+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A176+self.ESC+\\\"0;31m\\\"+self.A223+self.A223+\\\" \\\"+self.A222+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A220+self.A222+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A176+self.A176+self.ESC+\\\"0;31m\\\"+self.A220+self.A220+self.ESC+\\\"37m \\\"+self.ESC+\\\"1;31;41m\\\"+self.A176+self.A177+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"31;41m \\\"+self.A176+self.A177+self.A219+self.A178+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"37m \\\"+self.A219+self.ESC+\\\"1;30;47m\\\"+self.A177+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"31m\\\"+self.A220+self.A220+self.A223+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A176+self.A222+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A223+self.ESC+\\\"37m \\\"+self.ESC+\\\"31m\\\"+self.A220+self.ESC+\\\"1;41m\\\"+self.A176+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A220+self.A222+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A177+self.A177+self.A176+\\\" \\\"+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"31m\\\"+self.A220+\\\" \\\"+self.ESC+\\\"1;41m\\\"+self.A176+self.A219+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"31;41m \\\"+self.A177+\\\" \\\"+self.A219+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"37m \\\"+self.A219+self.A221+\\\" \\\"+self.ESC+\\\"31;41m \\\"+self.ESC+\\\"37;40m \\\"+self.ESC+\\\"31;41m \\\"+self.ESC+\\\"40m\\\"+self.A177+self.A177+self.A176+self.A178+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A176+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"31m\\\"+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A177+self.A176+self.A176+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A222+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A178+self.A177+self.A176+self.ESC+\\\"0;31m\\\"+self.A220+\\\" \\\"+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A178+self.A220+self.A220+\\\" \\\"+self.ESC+\\\"1;41m\\\"+self.A178+self.ESC+\\\"0;31m\\\"+self.A220+\\\" \\\"+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A177+self.A219+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"37m \\\"+self.ESC+\\\"1m\\\"+self.A220+\\\" \\\"+self.ESC+\\\"0m\\\"+self.A223+\\\" \\\"+self.ESC+\\\"31;41m \\\"+self.ESC+\\\"40m\\\"+self.A223+\\\" \\\"+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A219+self.A178+self.A178+self.A177+self.A176+self.A176+self.A177+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A176+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"31;41m \\\"+self.A178+self.A177+self.A177+self.A176+\\\" \\\"+self.ESC+\\\"40m\\\"+self.A220+self.A222+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A178+self.A177+self.A176+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"31m\\\"+self.A223+self.A178+\\\" \\\"+self.ESC+\\\"1;41m\\\"+self.A177+self.A219+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"31;41m \\\"+self.A176+self.A176+self.A178+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"37m \\\"+self.A223+self.ESC+\\\"1m\\\"+self.A219+self.A220+\\\" \\\"+self.ESC+\\\"0;31;41m \\\"+self.A177+self.A178+self.A176+self.A176+\\\" \\\"+self.ESC+\\\"40m\\\"+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A219+self.A176+self.A178+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A176+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"31m\\\"+self.A222+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A219+self.A178+self.A178+self.A177+self.A176+\\\" \\\"+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"31;41m \\\"+self.A219+self.A178+self.A177+self.A176+self.ESC+\\\"37;40m \\\"+self.ESC+\\\"31m\\\"+self.A223+\\\" \\\"+self.ESC+\\\"1;41m\\\"+self.A176+self.ESC+\\\"0;31m\\\"+self.A223+\\\" \\\"+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A176+self.A177+self.A219+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += \\\" \\\"+self.A223+self.ESC+\\\"1;47m\\\"+self.A223+self.ESC+\\\"40m\\\"+self.A219+self.A223+self.ESC+\\\"0;31m\\\"+self.A220+self.ESC+\\\"1;41m\\\"+self.A178+self.A176+self.A176+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A223+\\\" \\\"+self.ESC+\\\"1;37;47m\\\"+self.A222+self.ESC+\\\"40m\\\"+self.A221+self.A223+self.A220+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A177+\\\" \\\"+self.A223+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A221+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A176+self.ESC+\\\"0;31m\\\"+self.A223+\\\" \\\"+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A219+self.A178+self.A178+self.A177+self.A176+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A220+self.A223+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A219+self.A178+self.A177+self.A176+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A223+\\\" \\\"+self.ESC+\\\"1;41m\\\"+self.A176+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"31;41m \\\"+self.A176+\\\" \\\"+self.A177+self.A219+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"37m \\\"+self.A223+self.ESC+\\\"31m\\\"+self.A222+self.ESC+\\\"1;41m\\\"+self.A177+self.A176+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A223+\\\" \\\"+self.ESC+\\\"1;37;47m\\\"+self.A222+self.ESC+\\\"40m\\\"+self.A221+\\\" \\\"+self.ESC+\\\"47m\\\"+self.A222+self.ESC+\\\"40m\\\"+self.A221+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A178+self.A177+\\\" \\\"+self.A223+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A176+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"33m\\\"+self.A220+\\\" \\\"+self.ESC+\\\"31m\\\"+self.A223+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A219+self.A178+self.A177+self.A176+self.A176+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A220+self.A223+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A178+self.A177+self.A176+\\\" \\\"+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"31;41m \\\"+self.ESC+\\\"40m\\\"+self.A220+self.A223+\\\" \\\"+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A176+self.A176+\\\" \\\"+self.A178+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"37m \\\"+self.ESC+\\\"31;41m \\\"+self.A176+self.A176+self.ESC+\\\"37;40m \\\"+self.A220+self.ESC+\\\"1m\\\"+self.A219+self.ESC+\\\"0m\\\"+self.A223+self.ESC+\\\"1m\\\"+self.A219+self.A221+\\\" \\\"+self.A223+\\\" \\\"+self.A223+self.A220+\\\" \\\"+self.ESC+\\\"0;31;41m \\\"+self.ESC+\\\"40m\\\"+self.A223+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A223+\\\" \\\"+self.ESC+\\\"33m\\\"+self.A219+self.A219+\\\" \\\"+self.ESC+\\\"31m\\\"+self.A223+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A219+self.A178+self.A177+self.A176+\\\" \\\"+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"31;41m \\\"+self.A178+self.A177+self.A176+self.ESC+\\\"40m\\\"+self.A220+\\\" \\\"+self.ESC+\\\"1;41m\\\"+self.A176+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"31;41m \\\"+self.A176+self.A177+\\\" \\\"+self.A219+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += \\\" \\\"+self.A220+self.ESC+\\\"1m\\\"+self.A219+\\\" \\\"+self.A219+self.A221+\\\" \\\"+self.A223+\\\" \\\"+self.A220+\\\" \\\"+self.A223+self.A220+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A223+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A223+\\\" \\\"+self.ESC+\\\"1;33;43m\\\"+self.A177+self.A176+self.ESC+\\\"0;33m\\\"+self.A219+self.A219+\\\" \\\"+self.ESC+\\\"31m\\\"+self.A223+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A219+self.A178+self.A177+self.A176+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A219+self.A220+self.A222+self.ESC+\\\"1;41m\\\"+self.A219+self.A177+self.A176+self.ESC+\\\"0;31m\\\"+self.A220+self.ESC+\\\"1;41m\\\"+self.A176+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"31m\\\"+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A176+\\\" \\\"+self.A178+self.A219+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += \\\" \\\"+self.A223+self.ESC+\\\"1m\\\"+self.A220+\\\" \\\"+self.A223+\\\" \\\"+self.A220+self.A223+self.A220+\\\" \\\"+self.A223+\\\" \\\"+self.A223+self.ESC+\\\"0;31m\\\"+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A223+self.A223+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A223+\\\" \\\"+self.ESC+\\\"1;33m\\\"+self.A223+self.ESC+\\\"43m\\\"+self.A219+self.A219+self.ESC+\\\"40m\\\"+self.A223+self.ESC+\\\"0;33m\\\"+self.A220+self.A219+\\\" \\\"+self.ESC+\\\"31;41m \\\"+self.ESC+\\\"1m\\\"+self.A219+self.A178+self.A177+self.A176+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A222+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A177+self.A177+self.A176+self.ESC+\\\"0;31m\\\"+self.A223+\\\" \\\"+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A176+self.A177+self.A178+\\\" \\\"+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"37m \\\"+self.ESC+\\\"1m\\\"+self.A220+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A220+self.A220+self.A220+self.A220+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A220+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A223+self.ESC+\\\"37m \\\"+self.ESC+\\\"1;33;43m\\\"+self.A177+self.A176+self.ESC+\\\"0;33m\\\"+self.A219+self.A219+self.A220+\\\" \\\"+self.ESC+\\\"31;41m \\\"+self.ESC+\\\"1m\\\"+self.A219+self.A178+self.A177+self.A176+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A220+self.A222+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A176+self.A176+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"31;41m \\\"+self.A176+self.A177+self.A178+\\\" \\\"+self.A219+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"37m \\\"+self.ESC+\\\"30mÙ\\\"+self.ESC+\\\"31m\\\"+self.A222+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A177+self.A177+self.A176+self.A176+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A223+self.A223+self.ESC+\\\"37m \\\"+self.ESC+\\\"1;33m\\\"+self.A223+self.ESC+\\\"43m\\\"+self.A219+self.ESC+\\\"0;33m\\\"+self.A223+self.A223+self.A223+self.A220+\\\" \\\"+self.ESC+\\\"31;41m \\\"+self.ESC+\\\"1m\\\"+self.A219+self.A178+self.A177+self.A176+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A220+self.A222+self.A223+\\\" \\\"+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A177+self.A176+\\\" \\\"+self.A219+\\\" \\\"+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"37m \\\"+self.ESC+\\\"31m\\\"+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.ESC+\\\"37m \\\"+self.ESC+\\\"1;33m\\\"+self.A220+self.A220+self.ESC+\\\"43m\\\"+self.A176+self.ESC+\\\"0;33m\\\"+self.A219+self.A219+\\\" \\\"+self.ESC+\\\"31;41m \\\"+self.ESC+\\\"1m\\\"+self.A219+self.A178+self.A177+self.A176+\\\" \\\"+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"31m\\\"+self.A220+self.ESC+\\\"1;41m\\\"+self.A176+self.A177+self.A178+self.A176+self.A176+\\\" \\\"+self.A219+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += \\\" \\\"+self.ESC+\\\"1;33;43m\\\"+self.A219+self.A178+self.A177+self.A176+self.ESC+\\\"0;33m\\\"+self.A219+\\\" \\\"+self.ESC+\\\"31m\\\"+self.A223+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A219+self.A178+self.A177+self.A176+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A220+\\\" \\\"+self.A223+self.ESC+\\\"1;41m\\\"+self.A176+self.A177+self.A177+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A223+self.ESC+\\\"37m \\\"+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += \\\" \\\"+self.ESC+\\\"1;33;43m\\\"+self.A219+self.A178+self.ESC+\\\"40m\\\"+self.A223+self.ESC+\\\"0;33m\\\"+self.A223+self.A223+self.A220+self.A220+\\\" \\\"+self.ESC+\\\"31;41m \\\"+self.ESC+\\\"1m\\\"+self.A219+self.A178+self.A177+self.A177+self.A176+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A220+\\\" \\\"+self.A223+self.ESC+\\\"1;41m\\\"+self.A177+self.A177+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"31;41m \\\"+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"37m \\\"+self.ESC+\\\"1;33m\\\"+self.A220+self.A220+self.ESC+\\\"43m\\\"+self.A219+self.A178+self.A177+self.A176+self.ESC+\\\"0;33m\\\"+self.A219+self.A220+\\\" \\\"+self.ESC+\\\"31;41m \\\"+self.ESC+\\\"1m\\\"+self.A219+self.A178+self.A177+self.A177+self.A176+\\\" \\\"+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"31m\\\"+self.A223+self.ESC+\\\"41m \\\"+self.ESC+\\\"37;40m \\\"+self.ESC+\\\"31;41m \\\"+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"37m \\\"+self.ESC+\\\"1;33m\\\"+self.A220+self.ESC+\\\"43m\\\"+self.A219+self.ESC+\\\"40m\\\"+self.A223+self.A223+self.A223+self.ESC+\\\"0;33m\\\"+self.A223+self.A220+self.A220+self.A220+\\\" \\\"+self.ESC+\\\"31;41m \\\"+self.ESC+\\\"1m\\\"+self.A178+self.A177+self.A176+self.ESC+\\\"0;31m\\\"+self.A223+self.A223+self.A223+self.A223+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A220+\\\" \\\"+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A220+\\\" \\\"+self.A223+self.ESC+\\\"41m \\\"+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"37m \\\"+self.ESC+\\\"1;33m\\\"+self.A220+self.A220+self.ESC+\\\"43m\\\"+self.A219+self.A219+self.A178+self.A177+self.A176+self.ESC+\\\"0;33m\\\"+self.A219+self.A219+\\\" \\\"+self.ESC+\\\"31m\\\"+self.A223+self.A223+\\\" \\\"+self.A220+self.A220+self.A220+self.A220+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A220+\\\" \\\"+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A220+\\\" \\\"+self.ESC+\\\"41m \\\"+self.ESC+\\\"40m\\\"+self.A220+self.A220+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"37m \\\"+self.ESC+\\\"1;33;43m\\\"+self.A219+self.A219+self.ESC+\\\"40m\\\"+self.A223+self.A223+self.A223+self.ESC+\\\"0;33m\\\"+self.A223+self.A223+self.ESC+\\\"31m\\\"+self.A220+self.A220+self.ESC+\\\"41m \\\"+self.ESC+\\\"1m\\\"+self.A176+self.A178+self.A177+\\\" \\\"+self.A176+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A220+\\\" \\\"+self.ESC+\\\"41m \\\"+self.ESC+\\\"37;40m \\\"+self.ESC+\\\"31m\\\"+self.A223+self.ESC+\\\"41m \\\"+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"37m \\\"+self.ESC+\\\"1;33m\\\"+self.A220+self.ESC+\\\"43m\\\"+self.A178+self.ESC+\\\"0;33m\\\"+self.A220+self.A219+self.A223+\\\" \\\"+self.ESC+\\\"31;41m \\\"+self.ESC+\\\"1m\\\"+self.A176+self.A178+self.A177+self.A177+self.A176+self.A176+self.A176+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A220+\\\" \\\"+self.A223+self.ESC+\\\"41m \\\"+self.ESC+\\\"37;40m \\\"+self.ESC+\\\"31;41m \\\"+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"37m \\\"+self.ESC+\\\"1;33;43m\\\"+self.A178+self.A177+self.A176+self.ESC+\\\"0;33m\\\"+self.A223+self.A223+\\\" \\\"+self.ESC+\\\"31;41m \\\"+self.ESC+\\\"1m\\\"+self.A176+self.A178+self.A178+\\\" \\\"+self.A177+\\\" \\\"+self.A176+self.A176+\\\" \\\"+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"31;41m \\\"+self.ESC+\\\"37;40m \\\"+self.ESC+\\\"31m\\\"+self.A223+self.ESC+\\\"41m \\\"+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"37m \\\"+self.ESC+\\\"1;33m\\\"+self.A220+self.A220+self.ESC+\\\"0;33m\\\"+self.A220+self.A220+self.A223+\\\" \\\"+self.ESC+\\\"31;41m \\\"+self.ESC+\\\"1m\\\"+self.A176+self.A219+self.A178+self.A178+self.A177+self.A177+self.A176+\\\" \\\"+self.A176+\\\" \\\"+self.A176+self.A176+\\\" \\\"+self.ESC+\\\"0;31m\\\"+self.A220+\\\" \\\"+self.ESC+\\\"41m \\\"+self.ESC+\\\"37;40m \\\"+self.ESC+\\\"31;41m \\\"+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"37m \\\"+self.ESC+\\\"0;37m \\\"+self.ESC+\\\"1;33m\\\"+self.A223+self.A223+self.A223+self.ESC+\\\"0;33m\\\"+self.A223+\\\" \\\"+self.ESC+\\\"30;41m \\\"+self.ESC+\\\"1;31mShatterstar [W/X] \\\"+self.ESC+\\\"0;37;40m \\\"+self.ESC+\\\"30;41m \\\"+self.ESC+\\\"37;40m \\\"+self.ESC+\\\"30;41m \\\"+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"37m \\\"+self.ESC+\\\"0m\\\\r\\\\n\\\"\\n\\t\\treturn thismsg\",\n \"def test_asciitable_m_pretty_ansi(self):\\n input = '''\\n┏━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┓ \\n┃\\\\x1b[1m \\\\x1b[0m\\\\x1b[1mReleased \\\\x1b[0m\\\\x1b[1m \\\\x1b[0m┃\\\\x1b[1m \\\\x1b[0m\\\\x1b[1mTitle \\\\x1b[0m\\\\x1b[1m \\\\x1b[0m┃\\\\x1b[1m \\\\x1b[0m\\\\x1b[1m Box Office\\\\x1b[0m\\\\x1b[1m \\\\x1b[0m┃ \\n┡━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━┩ \\n│\\\\x1b[36m \\\\x1b[0m\\\\x1b[36mDec 20, 2019\\\\x1b[0m\\\\x1b[36m \\\\x1b[0m│\\\\x1b[35m \\\\x1b[0m\\\\x1b[35mStar Wars: The Rise of Skywalker \\\\x1b[0m\\\\x1b[35m \\\\x1b[0m│\\\\x1b[32m \\\\x1b[0m\\\\x1b[32m $952,110,690\\\\x1b[0m\\\\x1b[32m \\\\x1b[0m│ \\n│\\\\x1b[36m \\\\x1b[0m\\\\x1b[36mMay 25, 2018\\\\x1b[0m\\\\x1b[36m \\\\x1b[0m│\\\\x1b[35m \\\\x1b[0m\\\\x1b[35mSolo: A Star Wars Story \\\\x1b[0m\\\\x1b[35m \\\\x1b[0m│\\\\x1b[32m \\\\x1b[0m\\\\x1b[32m $393,151,347\\\\x1b[0m\\\\x1b[32m \\\\x1b[0m│ \\n│\\\\x1b[36m \\\\x1b[0m\\\\x1b[36mDec 15, 2017\\\\x1b[0m\\\\x1b[36m \\\\x1b[0m│\\\\x1b[35m \\\\x1b[0m\\\\x1b[35mStar Wars Ep. V111: The Last Jedi\\\\x1b[0m\\\\x1b[35m \\\\x1b[0m│\\\\x1b[32m \\\\x1b[0m\\\\x1b[32m$1,332,539,889\\\\x1b[0m\\\\x1b[32m \\\\x1b[0m│ \\n│\\\\x1b[36m \\\\x1b[0m\\\\x1b[36mDec 16, 2016\\\\x1b[0m\\\\x1b[36m \\\\x1b[0m│\\\\x1b[35m \\\\x1b[0m\\\\x1b[35mRogue One: A Star Wars Story \\\\x1b[0m\\\\x1b[35m \\\\x1b[0m│\\\\x1b[32m \\\\x1b[0m\\\\x1b[32m$1,332,439,889\\\\x1b[0m\\\\x1b[32m \\\\x1b[0m│ \\n└──────────────┴───────────────────────────────────┴────────────────┘ \\n'''\\n expected = [\\n {\\n \\\"released\\\": \\\"Dec 20, 2019\\\\nMay 25, 2018\\\\nDec 15, 2017\\\\nDec 16, 2016\\\",\\n \\\"title\\\": \\\"Star Wars: The Rise of Skywalker\\\\nSolo: A Star Wars Story\\\\nStar Wars Ep. V111: The Last Jedi\\\\nRogue One: A Star Wars Story\\\",\\n \\\"box_office\\\": \\\"$952,110,690\\\\n$393,151,347\\\\n$1,332,539,889\\\\n$1,332,439,889\\\"\\n }\\n ]\\n\\n self.assertEqual(jc.parsers.asciitable_m.parse(input, quiet=True), expected)\",\n \"def sw_mac(self):\\n return self.raw.get('sw_mac')\",\n \"def mac(self) -> str:\\n return self.camera_info[\\\"wifi_mac\\\"]\",\n \"def mac(self):\\n return self.device.settings[\\\"device\\\"][\\\"mac\\\"]\",\n \"def mac(self):\\n return self.device.settings[\\\"device\\\"][\\\"mac\\\"]\",\n \"def banner_ascii():\\n print(\\\"\\\")\\n print(f\\\"\\\\n{RED} Steganography Tool{RESET}\\\")\\n print(f\\\"{RED} Made By {RESET}\\\")\\n print(f\\\"{RED} Ehthe Samul Islam Laskar USN:1DS16CS712 {RESET}\\\")\\n print(f\\\"{RED} B Padma USN:1DS19CS420{RESET}\\\")\\n print(f\\\"{RED} Nikhil D Kanyal USN:1DS17CS731{RESET}\\\")\\n print(f\\\"{YELLOW}Type 'help' to see commands{RESET}\\\")\",\n \"def macs_to_str(self, reached_max_depth: bool) -> str:\\n if self.num_params > 0 and (\\n reached_max_depth or not any(self.module.children())\\n ):\\n return f\\\"{self.macs:,}\\\"\\n return \\\"--\\\"\",\n \"def accelerator_string():\\n if is_mac():\\n return \\\"Command\\\"\\n else:\\n return \\\"Ctrl\\\"\",\n \"def repr2col(tups):\\n quant = tuple(round(comp * 255) for comp in tups)\\n if quant[3] != 255: return \\\"\\\".join([\\\"{0:02x}\\\".format(m) for m in quant])\\n quant = quant[:3]\\n for w in shortaliases:\\n if quant == shortaliases[w]: return w\\n return \\\"#\\\" + \\\"\\\".join([\\\"{:02x}\\\".format(n) if max(n % 17 for n in quant) else \\\"{:x}\\\".format(n >> 4) for n in quant])\",\n \"def cMACW(self):\\n return 3.5\",\n \"def usage():\\n\\tusagetext = \\\"\\\"\\\"bin2mif (c) 2013 Stephen J. Leary\\nUsage: bin2mif [-a] [-w <width>] [-e <endian>] <filename> \\nProgram outputs a hex MIF file to stdout.\\nDefaults are width = 8 and big endian unless specified.\\n\\\"\\\"\\\"\\n\\tsys.stderr.write(usagetext)\",\n \"def InputMacAddress(prompt):\\n while 1:\\n SetPatternColor()\\n print prompt,\\n SetPatternColor(1)\\n val = raw_input().strip()\\n SetPatternColor()\\n try:\\n #if len(val) == 12 and int(val,16):\\n if (len(val) == 12 or len(val) == 10): \\n return val.upper()\\n except ValueError:\\n pass\\n #SetPattern(0)\\n SetPatternColor(0)\\n print \\\"ERROR %s!!\\\"%prompt\",\n \"def get_mac(self) :\\n\\t\\ttry :\\n\\t\\t\\treturn self.p_fields.f128\\n\\t\\texcept :\\n\\t\\t\\treturn None\",\n \"def _make_sysex_message(self, text_message, display_index):\\n if len(text_message) > 28:\\n text_message = text_message[:27]\\n sysex_message = [240, 0, 0, 102, 23, 18, min(display_index, 3) * 28]\\n filled = text_message.ljust(28)\\n for c in filled:\\n sysex_message.append(ord(c))\\n sysex_message.append(247)\\n return sysex_message\",\n \"def test_oldmac(args):\\n print('============= Testing for Correctness (Old Mac) =============')\\n return _replace_test(args, '\\\\r')\",\n \"async def textemote(self, ctx, *, msg):\\n await ctx.message.delete()\\n if msg != None:\\n out = msg.lower()\\n text = out.replace(' ', ' ').replace('10', '\\\\u200B:keycap_ten:')\\\\\\n .replace('ab', '\\\\u200B🆎').replace('cl', '\\\\u200B🆑')\\\\\\n .replace('0', '\\\\u200B:zero:').replace('1', '\\\\u200B:one:')\\\\\\n .replace('2', '\\\\u200B:two:').replace('3', '\\\\u200B:three:')\\\\\\n .replace('4', '\\\\u200B:four:').replace('5', '\\\\u200B:five:')\\\\\\n .replace('6', '\\\\u200B:six:').replace('7', '\\\\u200B:seven:')\\\\\\n .replace('8', '\\\\u200B:eight:').replace('9', '\\\\u200B:nine:')\\\\\\n .replace('!', '\\\\u200B❗').replace('?', '\\\\u200B❓')\\\\\\n .replace('vs', '\\\\u200B🆚').replace('.', '\\\\u200B🔸')\\\\\\n .replace(',', '🔻').replace('a', '\\\\u200B🅰')\\\\\\n .replace('b', '\\\\u200B🅱').replace('c', '\\\\u200B🇨')\\\\\\n .replace('d', '\\\\u200B🇩').replace('e', '\\\\u200B🇪')\\\\\\n .replace('f', '\\\\u200B🇫').replace('g', '\\\\u200B🇬')\\\\\\n .replace('h', '\\\\u200B🇭').replace('i', '\\\\u200B🇮')\\\\\\n .replace('j', '\\\\u200B🇯').replace('k', '\\\\u200B🇰')\\\\\\n .replace('l', '\\\\u200B🇱').replace('m', '\\\\u200B🇲')\\\\\\n .replace('n', '\\\\u200B🇳').replace('ñ', '\\\\u200B🇳')\\\\\\n .replace('o', '\\\\u200B🅾').replace('p', '\\\\u200B🅿')\\\\\\n .replace('q', '\\\\u200B🇶').replace('r', '\\\\u200B🇷')\\\\\\n .replace('s', '\\\\u200B🇸').replace('t', '\\\\u200B🇹')\\\\\\n .replace('u', '\\\\u200B🇺').replace('v', '\\\\u200B🇻')\\\\\\n .replace('w', '\\\\u200B🇼').replace('x', '\\\\u200B🇽')\\\\\\n .replace('y', '\\\\u200B🇾').replace('z', '\\\\u200B🇿')\\n try:\\n await ctx.send(text)\\n except Exception as e:\\n await ctx.send(f'```{e}```')\\n else:\\n await ctx.send('Args req!', delete_after=3.0)\",\n \"def to_ascii(self):\\n code = self.build()\\n for i, line in enumerate(code):\\n code[i] = line.replace('1', '|').replace('0', '_')\\n return '\\\\n'.join(code)\",\n \"def normalize_teh_marbuta_ar(s):\\n\\n return s.replace(u'\\\\u0629', u'\\\\u0647')\",\n \"def out_mac_control_frames(self) -> str:\\n return self._out_mac_control_frames\",\n \"def to_mac(self):\\n try:\\n dt_obj = duparser.parse(timestamp)\\n self.out_mac = str(int((dt_obj - self.epoch_2001).total_seconds()))\\n except Exception as e:\\n if not args.log:\\n pass\\n else:\\n logging.error(str(type(e)) + \\\",\\\" + str(e))\\n self.out_mac = False\\n return self.out_mac\",\n \"def _get_mac(self):\\n return self.__mac\",\n \"def midi_to_ansi_note(midi_note):\\n notes = [\\\"A\\\", \\\"A#\\\", \\\"B\\\", \\\"C\\\", \\\"C#\\\", \\\"D\\\", \\\"D#\\\", \\\"E\\\", \\\"F\\\", \\\"F#\\\", \\\"G\\\", \\\"G#\\\"]\\n num_notes = 12\\n note_name = notes[int((midi_note - 21) % num_notes)]\\n note_number = (midi_note - 12) // num_notes\\n return f\\\"{note_name}{note_number}\\\"\",\n \"def supercombiner(bot, ev):\\n # ported from jenni\\n s = 'u'\\n for i in iter(range(1, 3000)):\\n if unicodedata.category(chr(i)) == \\\"Mn\\\":\\n s += chr(i)\\n if len(s) > 100:\\n break\\n bot.say(s)\",\n \"def to_meme(self):\\n motif_id = self.id.replace(\\\" \\\", \\\"_\\\")\\n m = \\\"MOTIF %s\\\\n\\\" % motif_id\\n m += \\\"BL MOTIF %s width=0 seqs=0\\\\n\\\"% motif_id\\n m += \\\"letter-probability matrix: alength= 4 w= %s nsites= %s E= 0\\\\n\\\" % (len(self), np.sum(self.pfm[0]))\\n m +=\\\"\\\\n\\\".join([\\\"\\\\t\\\".join([\\\"%s\\\" % x for x in row]) for row in self.pwm])\\n return m\",\n \"def basic_char_setup( self ):\\n\\t\\tsmall_bar = 3 # number of points per bar\\n\\t\\twide_bar = round(small_bar * 2.25,0) # 2.25 x small_bar\\n\\t\\tdpl = 50 # dots per line 300dpi/6lpi = 50dpl\\n\\t\\tself._nb = bytes( self.owner.PRINTER_ESC +\\n\\t\\t\\t\\t\\t\\t ( '*c%02ia%ib0P' % (small_bar, self.bc_height*dpl) ) + \\n\\t\\t\\t\\t\\t\\t self.owner.PRINTER_ESC + \\n\\t\\t\\t\\t\\t\\t (\\\"*p+%02iX\\\" % small_bar) )\\n\\t\\tself._wb = bytes( self.owner.PRINTER_ESC +\\n\\t\\t\\t\\t\\t\\t ('*c%02ia%ib0P' % (wide_bar, self.bc_height*dpl) )+\\n\\t\\t\\t\\t\\t\\t self.owner.PRINTER_ESC +\\n\\t\\t\\t\\t\\t\\t ('*p+%02iX' % wide_bar ) )\\n\\t\\tself._ns = bytes( self.owner.PRINTER_ESC + ( '*p+%02iX' % small_bar ) )\\n\\t\\tself._ws = bytes( self.owner.PRINTER_ESC + ( '*p+%02iX' % wide_bar ) )\\n \\n\\t\\t# DONE nb = bc39_esc+\\\"*c\\\"+TRANSFORM(small_bar,'99')+\\\"a\\\"+Alltrim(STR(bc39_height*dpl))+\\\"b0P\\\"+bc39_esc+\\\"*p+\\\"+TRANSFORM(small_bar,'99')+\\\"X\\\"\\n\\t\\t# DONE wb = bc39_esc+\\\"*c\\\"+TRANSFORM(wide_bar,'99')+\\\"a\\\"+Alltrim(STR(bc39_height*dpl))+\\\"b0P\\\"+bc39_esc+\\\"*p+\\\"+TRANSFORM(wide_bar,'99')+\\\"X\\\"\\n\\t\\t# DONE ns = bc39_esc+\\\"*p+\\\"+TRANSFORM(small_bar,'99')+\\\"X\\\"\\n\\t\\t# DONE ws = bc39_esc+\\\"*p+\\\"+TRANSFORM(wide_bar,'99')+\\\"X\\\"\\n \\n\\t\\t# adjust cusor position to start at top of line and return to bottom of line\\n\\t\\tself._bc_start = bytes( self.owner.PRINTER_ESC + '*p-50Y' )\\n\\t\\tself._bc_end = bytes( self.owner.PRINTER_ESC + '*p+50Y' )\\n\\t\\t# DONE bc39_start = bc39_esc+\\\"*p-50Y\\\"\\n\\t\\t# DONE bc39_END = bc39_esc+\\\"*p+50Y\\\"\\n\\n\\t\\t# setup the structure allowing to print the code codebar section for various LETTERS\\n\\t\\tself._char39 = { u'1' : 'wb+ns+nb+ws+nb+ns+nb+ns+wb' , \\n\\t\\t\\t\\t\\t\\t u'2' : 'nb+ns+wb+ws+nb+ns+nb+ns+wb' , \\n\\t\\t\\t\\t\\t\\t u'3' : 'wb+ns+wb+ws+nb+ns+nb+ns+nb' , \\n\\t\\t\\t\\t\\t\\t u'4' : 'nb+ns+nb+ws+wb+ns+nb+ns+wb' , \\n\\t\\t\\t\\t\\t\\t u'5' : 'wb+ns+nb+ws+wb+ns+nb+ns+nb' , \\n\\t\\t\\t\\t\\t\\t u'6' : 'nb+ns+wb+ws+wb+ns+nb+ns+nb' , \\n\\t\\t\\t\\t\\t\\t u'7' : 'nb+ns+nb+ws+nb+ns+wb+ns+wb' , \\n\\t\\t\\t\\t\\t\\t u'8' : 'wb+ns+nb+ws+nb+ns+wb+ns+nb' , \\n\\t\\t\\t\\t\\t\\t u'9' : 'nb+ns+wb+ws+nb+ns+wb+ns+nb' , \\n\\t\\t\\t\\t\\t\\t u'0' : 'nb+ns+nb+ws+wb+ns+wb+ns+nb' , \\n\\t\\t\\t\\t\\t\\t u'A' : 'wb+ns+nb+ns+nb+ws+nb+ns+wb' , \\n\\t\\t\\t\\t\\t\\t u'B' : 'nb+ns+wb+ns+nb+ws+nb+ns+wb' , \\n\\t\\t\\t\\t\\t\\t u'C' : 'wb+ns+wb+ns+nb+ws+nb+ns+nb' , \\n\\t\\t\\t\\t\\t\\t u'D' : 'nb+ns+nb+ns+wb+ws+nb+ns+wb' , \\n\\t\\t\\t\\t\\t\\t u'E' : 'wb+ns+nb+ns+wb+ws+nb+ns+nb' , \\n\\t\\t\\t\\t\\t\\t u'F' : 'nb+ns+wb+ns+wb+ws+nb+ns+nb' , \\n\\t\\t\\t\\t\\t\\t u'G' : 'nb+ns+nb+ns+nb+ws+wb+ns+wb' , \\n\\t\\t\\t\\t\\t\\t u'H' : 'wb+ns+nb+ns+nb+ws+wb+ns+nb' , \\n\\t\\t\\t\\t\\t\\t u'I' : 'nb+ns+wb+ns+nb+ws+wb+ns+nb' , \\n\\t\\t\\t\\t\\t\\t u'J' : 'nb+ns+nb+ns+wb+ws+wb+ns+nb' , \\n\\t\\t\\t\\t\\t\\t u'K' : 'wb+ns+nb+ns+nb+ns+nb+ws+wb' , \\n\\t\\t\\t\\t\\t\\t u'L' : 'nb+ns+wb+ns+nb+ns+nb+ws+wb' , \\n\\t\\t\\t\\t\\t\\t u'M' : 'wb+ns+wb+ns+nb+ns+nb+ws+nb' , \\n\\t\\t\\t\\t\\t\\t u'N' : 'nb+ns+nb+ns+wb+ns+nb+ws+wb' , \\n\\t\\t\\t\\t\\t\\t u'O' : 'wb+ns+nb+ns+wb+ns+nb+ws+nb' , \\n\\t\\t\\t\\t\\t\\t u'P' : 'nb+ns+wb+ns+wb+ns+nb+ws+nb' , \\n\\t\\t\\t\\t\\t\\t u'Q' : 'nb+ns+nb+ns+nb+ns+wb+ws+wb' , \\n\\t\\t\\t\\t\\t\\t u'R' : 'wb+ns+nb+ns+nb+ns+wb+ws+nb' , \\n\\t\\t\\t\\t\\t\\t u'S' : 'nb+ns+wb+ns+nb+ns+wb+ws+nb' , \\n\\t\\t\\t\\t\\t\\t u'T' : 'nb+ns+nb+ns+wb+ns+wb+ws+nb' , \\n\\t\\t\\t\\t\\t\\t u'U' : 'wb+ws+nb+ns+nb+ns+nb+ns+wb' , \\n\\t\\t\\t\\t\\t\\t u'V' : 'nb+ws+wb+ns+nb+ns+nb+ns+wb' , \\n\\t\\t\\t\\t\\t\\t u'W' : 'wb+ws+wb+ns+nb+ns+nb+ns+nb' , \\n\\t\\t\\t\\t\\t\\t u'X' : 'nb+ws+nb+ns+wb+ns+nb+ns+wb' , \\n\\t\\t\\t\\t\\t\\t u'Y' : 'wb+ws+nb+ns+wb+ns+nb+ns+nb' , \\n\\t\\t\\t\\t\\t\\t u'Z' : 'nb+ws+wb+ns+wb+ns+nb+ns+nb' , \\n\\t\\t\\t\\t\\t\\t u'-' : 'nb+ws+nb+ns+nb+ns+wb+ns+wb' , \\n\\t\\t\\t\\t\\t\\t u'.' : 'wb+ws+nb+ns+nb+ns+wb+ns+nb' , \\n\\t\\t\\t\\t\\t\\t u' ' : 'nb+ws+wb+ns+nb+ns+wb+ns+nb' , \\n\\t\\t\\t\\t\\t\\t u'*' : 'nb+ws+nb+ns+wb+ns+wb+ns+nb' , \\n\\t\\t\\t\\t\\t\\t u'$' : 'nb+ws+nb+ws+nb+ws+nb+ns+nb' , \\n\\t\\t\\t\\t\\t\\t u'/' : 'nb+ws+nb+ws+nb+ns+nb+ws+nb' , \\n\\t\\t\\t\\t\\t\\t u'+' : 'nb+ws+nb+ns+nb+ws+nb+ws+nb' , \\n\\t\\t\\t\\t\\t\\t u'%' : 'nb+ns+nb+ws+nb+ws+nb+ws+nb' }\",\n \"def normalize_teh_marbuta_hsb(s):\\n\\n return s.replace(u'\\\\u0127', u'h')\",\n \"def to_motevo(self):\\n m = \\\"//\\\\n\\\"\\n m += \\\"NA {}\\\\n\\\".format(self.id)\\n m += \\\"P0\\\\tA\\\\tC\\\\tG\\\\tT\\\\n\\\"\\n for i, row in enumerate(self.pfm):\\n m += \\\"{}\\\\t{}\\\\n\\\".format(i, \\\"\\\\t\\\".join([str(int(x)) for x in row]))\\n m += \\\"//\\\"\\n return m\",\n \"def display():\\n screen.addch(head[0],head[1],'x')\",\n \"def arthur(self):\\n\\t\\tthismsg = \\\"\\\\r\\\\n\\\"+self.ESC+\\\"34m\\\"+self.A220+self.ESC+\\\"1;44m\\\"+self.A176+self.ESC+\\\"0;34m\\\"+self.A220+self.A223+self.A176+self.ESC+\\\"C\\\"+self.A178+self.A176+self.A254+self.ESC+\\\"2C\\\"+self.A220+self.A254+self.ESC+\\\"6C\\\"+self.A220+self.A178+self.ESC+\\\"C\\\"+self.A220+self.A178+self.ESC+\\\"C\\\"+self.ESC+\\\"1;44m\\\"+self.A177+self.ESC+\\\"40m\\\"+self.A223+self.ESC+\\\"0;34m\\\"+self.A223+self.ESC+\\\"1m\\\"+self.A223+self.A223+self.ESC+\\\"0;34m\\\"+self.A223+self.ESC+\\\"1m\\\"+self.A223+self.ESC+\\\"0;34m\\\"+self.A223+self.A223+self.A223+self.A223+self.ESC+\\\"1m\\\"+self.A223+self.ESC+\\\"0;34m\\\"+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.A223+self.ESC+\\\"C\\\"+self.A223+\\\"\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"C\\\"+self.A223+self.ESC+\\\"2C\\\"+self.A176+self.A223+self.A223+self.ESC+\\\"1;37m\\\"+self.A220+self.A220+self.A220+self.A220+self.ESC+\\\"0m\\\"+self.A220+self.ESC+\\\"1m\\\"+self.A223+self.A223+self.ESC+\\\"0m\\\"+self.A223+self.ESC+\\\"1m\\\"+self.A223+self.A223+self.ESC+\\\"0m\\\"+self.A223+self.ESC+\\\"1m\\\"+self.A223+self.A220+self.ESC+\\\"0m\\\"+self.A220+self.A220+self.A220+self.A220+self.ESC+\\\"C\\\"+self.ESC+\\\"34m\\\"+self.A223+self.ESC+\\\"3C\\\"+self.ESC+\\\"1;37mSaga\\\"+self.ESC+\\\"Cof\\\"+self.ESC+\\\"Cthe\\\"+self.ESC+\\\"CRed\\\"+self.ESC+\\\"CDragon\\\"+self.ESC+\\\"C-\\\"+self.ESC+\\\"C\\\"+self.ESC+\\\"34mKing\\\"+self.ESC+\\\"CArthurs\\\"+self.ESC+\\\"CWeapons \\\"+self.ESC+\\\"C\\\"+self.ESC+\\\"0;34m\\\"+self.A219+\\\"\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.A223+self.ESC+\\\"1;37m\\\"+self.A220+self.A223+self.A223+self.A223+self.A223+self.A223+self.ESC+\\\"C\\\"+self.ESC+\\\"0m\\\"+self.A220+self.A220+self.A220+self.A220+self.A220+self.A219+self.A219+self.A219+self.ESC+\\\"30;47m\\\"+self.A223+self.ESC+\\\"37;40m\\\"+self.A219+self.A220+self.A220+self.A220+self.A220+self.A220+self.ESC+\\\"C\\\"+self.A223+self.ESC+\\\"1;30m\\\"+self.A223+self.ESC+\\\"0m\\\"+self.A223+self.ESC+\\\"1;30m\\\"+self.A223+self.A223+self.A220+self.ESC+\\\"4C\\\"+self.ESC+\\\"0;34m\\\"+self.A220+self.A220+self.A220+self.A220+self.ESC+\\\"C\\\"+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.ESC+\\\"C\\\"+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A220+self.A178+self.A220+self.A220+self.A220+self.A220+self.ESC+\\\"C\\\"+self.A220+self.A220+\\\"\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"79C\\\"+self.ESC+\\\"37m \\\"+self.ESC+\\\"1m\\\"+self.A219+self.ESC+\\\"C\\\"+self.A220+self.ESC+\\\"47m\\\"+self.A219+self.A219+self.A223+self.A178+self.A177+self.A176+self.ESC+\\\"0m\\\"+self.A219+self.A223+self.ESC+\\\"1m\\\"+self.A220+self.ESC+\\\"0m\\\"+self.A223+self.A219+self.A219+self.ESC+\\\"30;47m\\\"+self.A223+self.A176+self.ESC+\\\"37;40m\\\"+self.A219+self.A219+self.A223+self.A220+self.A223+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A220+self.ESC+\\\"C\\\"+self.ESC+\\\"1;30;47m\\\"+self.A178+self.ESC+\\\"C\\\"+self.ESC+\\\"0;34m\\\"+self.A176+self.ESC+\\\"3C\\\"+self.A178+self.ESC+\\\"20C\\\"+self.A223+self.ESC+\\\"17C\\\"+self.ESC+\\\"1m\\\"+self.A220+self.A220+self.A220+self.A220+\\\"\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"79C\\\"+self.ESC+\\\"0m \\\"+self.ESC+\\\"1m\\\"+self.A219+self.ESC+\\\"C\\\"+self.ESC+\\\"47m\\\"+self.A219+self.A219+self.A178+self.ESC+\\\"0m\\\"+self.A219+self.ESC+\\\"30;47m\\\"+self.A176+self.ESC+\\\"37;40m\\\"+self.A219+self.A219+self.A219+self.ESC+\\\"C\\\"+self.ESC+\\\"1m\\\"+self.A219+self.ESC+\\\"47m\\\"+self.A178+self.ESC+\\\"40m\\\"+self.A220+self.ESC+\\\"0m\\\"+self.A223+self.A219+self.A219+self.A223+self.A220+self.ESC+\\\"1;47m\\\"+self.A176+self.ESC+\\\"0m\\\"+self.A219+self.ESC+\\\"C\\\"+self.A219+self.A219+self.ESC+\\\"1;47m\\\"+self.A176+self.A176+self.A176+self.A176+self.ESC+\\\"30m\\\"+self.A176+self.ESC+\\\"C\\\"+self.ESC+\\\"40m\\\"+self.A219+self.ESC+\\\"3C\\\"+self.ESC+\\\"0;34mYou\\\"+self.ESC+\\\"Cwalk\\\"+self.ESC+\\\"Cinto\\\"+self.ESC+\\\"Cthe\\\"+self.ESC+\\\"Cwell\\\"+self.ESC+\\\"Cknown\\\"+self.ESC+\\\"Cweapons\\\"+self.ESC+\\\"6C\\\"+self.ESC+\\\"1m\\\"+self.A219+self.ESC+\\\"44m\\\"+self.A178+self.ESC+\\\"0;34m\\\"+self.A219+\\\"\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"79C\\\"+self.ESC+\\\"37m \\\"+self.ESC+\\\"1m\\\"+self.A219+self.ESC+\\\"C\\\"+self.ESC+\\\"47m\\\"+self.A178+self.A178+self.A178+\\\" \\\"+self.ESC+\\\"0;30;47m\\\"+self.A223+self.A177+self.ESC+\\\"37;40m\\\"+self.A219+self.A219+self.A219+self.ESC+\\\"C\\\"+self.ESC+\\\"1m\\\"+self.A219+self.ESC+\\\"C\\\"+self.ESC+\\\"47m\\\"+self.A178+self.ESC+\\\"40m\\\"+self.A220+self.ESC+\\\"C\\\"+self.ESC+\\\"47m\\\"+self.A177+self.A176+\\\" \\\"+self.ESC+\\\"C\\\"+self.ESC+\\\"0m\\\"+self.A219+self.ESC+\\\"1;47m\\\"+self.A176+self.A176+self.A177+self.A177+self.A177+self.A176+self.ESC+\\\"30m\\\"+self.A176+self.ESC+\\\"C\\\"+self.ESC+\\\"40m\\\"+self.A219+self.ESC+\\\"3C\\\"+self.ESC+\\\"0;34mshop,\\\"+self.ESC+\\\"Cyou\\\"+self.ESC+\\\"Cpause\\\"+self.ESC+\\\"Cto\\\"+self.ESC+\\\"Clook\\\"+self.ESC+\\\"Caround\\\"+self.ESC+\\\"Cat\\\"+self.ESC+\\\"Call\\\"+self.ESC+\\\"Cof\\\"+self.ESC+\\\"2C\\\"+self.A222+self.ESC+\\\"1;44m\\\"+self.A177+self.ESC+\\\"0;34m\\\"+self.A219+\\\"\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"79C\\\"+self.ESC+\\\"37m \\\"+self.ESC+\\\"1m\\\"+self.A219+self.ESC+\\\"C\\\"+self.A223+self.ESC+\\\"47m\\\"+self.A178+self.A177+self.ESC+\\\"0m\\\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\\\"C\\\"+self.ESC+\\\"1m\\\"+self.A219+self.ESC+\\\"C\\\"+self.ESC+\\\"47m\\\"+self.A178+self.A177+self.ESC+\\\"0m\\\"+self.A220+self.A223+self.A220+self.ESC+\\\"1;47m\\\"+self.A176+self.A176+self.A177+self.A177+self.A176+self.A176+self.A176+self.ESC+\\\"30m\\\"+self.A176+self.ESC+\\\"0m\\\"+self.A223+self.ESC+\\\"C\\\"+self.ESC+\\\"1;30m\\\"+self.A219+self.ESC+\\\"3C\\\"+self.ESC+\\\"0;34mthe\\\"+self.ESC+\\\"Cmany\\\"+self.ESC+\\\"Cimplements\\\"+self.ESC+\\\"Cof\\\"+self.ESC+\\\"Cdestruction.\\\"+self.ESC+\\\"2CA\\\"+self.ESC+\\\"37m \\\"+self.ESC+\\\"4C\\\"+self.ESC+\\\"1;34;44m\\\"+self.A176+self.ESC+\\\"0;34m\\\"+self.A219+\\\"\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"79C\\\"+self.ESC+\\\"37m \\\"+self.ESC+\\\"C \\\"+self.ESC+\\\"1m\\\"+self.A219+self.ESC+\\\"C\\\"+self.ESC+\\\"47m\\\"+self.A177+self.A176+self.ESC+\\\"0m\\\"+self.A219+self.A219+self.A219+self.A219+self.A219+self.A219+self.A223+self.ESC+\\\"1m\\\"+self.A220+self.ESC+\\\"C\\\"+self.ESC+\\\"47m\\\"+self.A178+self.ESC+\\\"40m\\\"+self.A220+self.A223+self.ESC+\\\"47m\\\"+self.A177+self.A176+self.ESC+\\\"0m\\\"+self.A220+self.A223+self.ESC+\\\"1;47m\\\"+self.A177+self.A176+self.A176+self.ESC+\\\"0m\\\"+self.A219+self.ESC+\\\"30;47m\\\"+self.A220+self.A176+self.ESC+\\\"1m\\\"+self.A177+self.ESC+\\\"C\\\"+self.ESC+\\\"40m\\\"+self.A178+self.ESC+\\\"4C\\\"+self.ESC+\\\"0;34mfat\\\"+self.ESC+\\\"Cman\\\"+self.ESC+\\\"Cwoddles\\\"+self.ESC+\\\"Cinto\\\"+self.ESC+\\\"Cthe\\\"+self.ESC+\\\"Croom,\\\"+self.ESC+\\\"Cand\\\"+self.ESC+\\\"2C \\\"+self.ESC+\\\"37m \\\"+self.ESC+\\\"2C\\\"+self.ESC+\\\"1;34;44m\\\"+self.A176+self.ESC+\\\"0;30;44m\\\"+self.A176+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"79C\\\"+self.ESC+\\\"37m \\\"+self.ESC+\\\"C \\\"+self.ESC+\\\"1m\\\"+self.A219+self.ESC+\\\"C\\\"+self.ESC+\\\"47m\\\"+self.A176+\\\" \\\"+self.ESC+\\\"0m\\\"+self.A219+self.A219+self.A223+self.ESC+\\\"33m\\\"+self.A220+self.ESC+\\\"37m\\\"+self.A223+self.ESC+\\\"1m\\\"+self.A220+self.ESC+\\\"47m\\\"+self.A178+self.A178+self.A177+self.ESC+\\\"0m\\\"+self.A220+self.ESC+\\\"1m\\\"+self.A223+self.ESC+\\\"47m\\\"+self.A177+self.A176+self.ESC+\\\"0;30;47m\\\"+self.A176+self.A223+self.ESC+\\\"1m\\\"+self.A176+self.ESC+\\\"0m\\\"+self.A220+self.A223+self.ESC+\\\"33m\\\"+self.A220+self.ESC+\\\"37m\\\"+self.A223+self.A219+self.A219+self.ESC+\\\"1;30;47m\\\"+self.A178+self.ESC+\\\"C\\\"+self.ESC+\\\"40m\\\"+self.A178+self.ESC+\\\"4C\\\"+self.ESC+\\\"0;34masks\\\"+self.ESC+\\\"C\\\\\\\"\\\"+self.ESC+\\\"1mWadaya\\\"+self.ESC+\\\"Cwant\\\"+self.ESC+\\\"Ckid?\\\"+self.ESC+\\\"0;34m\\\\\\\"\\\"+self.ESC+\\\"17C\\\"+self.ESC+\\\"37m \\\"+self.ESC+\\\"2C\\\"+self.ESC+\\\"1;34;44m\\\"+self.A176+self.ESC+\\\"0;30;44m\\\"+self.A176+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"79C\\\"+self.ESC+\\\"37m \\\"+self.ESC+\\\"C \\\"+self.ESC+\\\"1m\\\"+self.A219+self.ESC+\\\"C\\\"+self.ESC+\\\"47m\\\"+self.A176+self.ESC+\\\"0m\\\"+self.A219+self.ESC+\\\"30;47m\\\"+self.A254+self.A176+self.ESC+\\\"37;40m\\\"+self.A223+self.ESC+\\\"1;33m\\\"+self.A220+self.ESC+\\\"43m\\\"+self.A177+self.ESC+\\\"0;33m\\\"+self.A220+self.ESC+\\\"1;37m\\\"+self.A223+self.ESC+\\\"47m\\\"+self.A177+self.A176+self.ESC+\\\"0m\\\"+self.A223+self.A220+self.A220+self.A223+self.A223+self.ESC+\\\"1;47m \\\"+self.ESC+\\\"0m\\\"+self.A223+self.ESC+\\\"33m\\\"+self.A220+self.ESC+\\\"1;43m\\\"+self.A177+self.ESC+\\\"C\\\"+self.ESC+\\\"0m\\\"+self.A219+self.A219+self.ESC+\\\"1;30m\\\"+self.A219+self.A223+self.ESC+\\\"C\\\"+self.A178+self.ESC+\\\"47C\\\"+self.ESC+\\\"0;34m\\\"+self.A219+self.ESC+\\\"30;44m\\\"+self.A177+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"79C\\\"+self.ESC+\\\"37m \\\"+self.ESC+\\\"34m\\\"+self.A176+self.ESC+\\\"C \\\"+self.ESC+\\\"1;37;47m\\\"+self.A178+self.ESC+\\\"C\\\"+self.A176+self.ESC+\\\"0m\\\"+self.A223+self.ESC+\\\"1;33m\\\"+self.A220+self.ESC+\\\"43m\\\"+self.A178+self.ESC+\\\"40m\\\"+self.A223+self.ESC+\\\"C\\\"+self.ESC+\\\"43m\\\"+self.A176+self.ESC+\\\"0;33m\\\"+self.A223+self.A220+self.ESC+\\\"C\\\"+self.ESC+\\\"1;37;47m\\\"+self.A177+self.A177+self.A176+self.ESC+\\\"0m\\\"+self.A219+self.A223+self.ESC+\\\"1;33m\\\"+self.A220+self.ESC+\\\"43m\\\"+self.A178+self.ESC+\\\"C\\\"+self.ESC+\\\"0;33m\\\"+self.A223+self.ESC+\\\"1;43m\\\"+self.A176+self.ESC+\\\"0;33m\\\"+self.A220+self.ESC+\\\"37m\\\"+self.A223+self.ESC+\\\"1;30m\\\"+self.A219+self.ESC+\\\"C\\\"+self.A178+self.ESC+\\\"6C\\\"+self.ESC+\\\"0;34m[\\\"+self.ESC+\\\"1mB\\\"+self.ESC+\\\"0;34m]\\\"+self.ESC+\\\"1muy\\\"+self.ESC+\\\"CWeapon\\\"+self.ESC+\\\"30C\\\"+self.ESC+\\\"0;34m\\\"+self.A219+self.ESC+\\\"30;44m\\\"+self.A177+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"79C\\\"+self.ESC+\\\"37m \\\"+self.ESC+\\\"34m\\\"+self.A178+self.ESC+\\\"C \\\"+self.ESC+\\\"1;37;47m\\\"+self.A177+self.ESC+\\\"2C\\\"+self.ESC+\\\"40m\\\"+self.A223+self.ESC+\\\"33m\\\"+self.A219+self.A220+self.ESC+\\\"0;33m\\\"+self.A178+self.A223+self.ESC+\\\"37m\\\"+self.A220+self.A219+self.A220+self.ESC+\\\"1;47m\\\"+self.A177+self.A176+self.A176+self.ESC+\\\"0m\\\"+self.A219+self.A220+self.ESC+\\\"1m\\\"+self.A223+self.ESC+\\\"0m\\\"+self.A220+self.A220+self.ESC+\\\"1;33m\\\"+self.A223+self.A220+self.A220+self.ESC+\\\"43m\\\"+self.A177+self.ESC+\\\"0;33m\\\"+self.A223+self.ESC+\\\"2C\\\"+self.ESC+\\\"1;30m\\\"+self.A219+self.ESC+\\\"6C\\\"+self.ESC+\\\"0;34m[\\\"+self.ESC+\\\"1mS\\\"+self.ESC+\\\"0;34m]\\\"+self.ESC+\\\"1mell\\\"+self.ESC+\\\"CWeapon\\\"+self.ESC+\\\"29C\\\"+self.ESC+\\\"0;34m\\\"+self.A219+self.ESC+\\\"30;44m\\\"+self.A177+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"79C\\\"+self.ESC+\\\"37m \\\"+self.ESC+\\\"34m\\\"+self.A176+self.A176+self.ESC+\\\"C \\\"+self.ESC+\\\"1;37;47m\\\"+self.A176+self.ESC+\\\"C\\\"+self.ESC+\\\"0m\\\"+self.A223+self.A220+self.A220+self.A220+self.A219+self.ESC+\\\"1;47m\\\"+self.A176+self.A176+self.A176+self.A176+self.ESC+\\\"0;30;47m\\\"+self.A176+self.ESC+\\\"37;40m\\\"+self.A219+self.A219+self.A219+self.A219+self.ESC+\\\"1;30;47m\\\"+self.A176+self.ESC+\\\"0m\\\"+self.A219+self.A219+self.A220+self.A220+self.ESC+\\\"1;30m\\\"+self.A220+self.A223+self.ESC+\\\"C\\\"+self.A219+self.ESC+\\\"7C\\\"+self.ESC+\\\"0;34m[\\\"+self.ESC+\\\"1mY\\\"+self.ESC+\\\"0;34m]\\\"+self.ESC+\\\"1mour\\\"+self.ESC+\\\"CStats\\\"+self.ESC+\\\"30C\\\"+self.ESC+\\\"0;34m\\\"+self.A219+self.ESC+\\\"30;44m\\\"+self.A178+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"79C\\\"+self.ESC+\\\"37m \\\"+self.ESC+\\\"C\\\"+self.ESC+\\\"34m\\\"+self.A176+self.ESC+\\\"C \\\"+self.ESC+\\\"37m\\\"+self.A223+self.A220+self.ESC+\\\"C\\\"+self.A223+self.A223+self.A219+self.A219+self.A219+self.A219+self.A219+self.ESC+\\\"30;47m\\\"+self.A220+self.A177+self.ESC+\\\"37;40m\\\"+self.A219+self.A219+self.A219+self.ESC+\\\"1;30;47m\\\"+self.A220+self.A220+self.A178+self.ESC+\\\"40m\\\"+self.A223+self.A223+self.ESC+\\\"C\\\"+self.A220+self.A223+self.ESC+\\\"0;34m\\\"+self.A176+self.ESC+\\\"7C[\\\"+self.ESC+\\\"1mR\\\"+self.ESC+\\\"0;34m]\\\"+self.ESC+\\\"1meturn\\\"+self.ESC+\\\"Cto\\\"+self.ESC+\\\"CTown\\\"+self.ESC+\\\"25C\\\"+self.ESC+\\\"0;34m\\\"+self.A222+self.ESC+\\\"1;44m\\\"+self.A177+self.ESC+\\\"0;30;44m\\\"+self.A178+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"79C\\\"+self.ESC+\\\"37m \\\"+self.ESC+\\\"34m\\\"+self.A220+self.ESC+\\\"1;44m\\\"+self.A176+self.ESC+\\\"0;34m\\\"+self.A220+self.ESC+\\\"C\\\"+self.A178+self.A220+self.ESC+\\\"37m\\\"+self.A223+self.A223+self.A220+self.A220+self.ESC+\\\"C\\\"+self.A223+self.A223+self.A223+self.ESC+\\\"1;30;47m\\\"+self.A176+self.A177+self.A219+self.ESC+\\\"40m\\\"+self.A223+self.A223+self.A223+self.ESC+\\\"C\\\"+self.A220+self.A220+self.A223+self.A223+self.ESC+\\\"C\\\"+self.ESC+\\\"0;34m\\\"+self.A220+self.A219+self.A176+self.ESC+\\\"47C\\\"+self.A219+self.ESC+\\\"1;44m\\\"+self.A176+self.ESC+\\\"0;30;44m\\\"+self.A178+self.ESC+\\\"40m\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"79C\\\"+self.ESC+\\\"37m \\\"+self.ESC+\\\"C\\\"+self.ESC+\\\"34m\\\"+self.A223+self.ESC+\\\"C\\\"+self.A220+self.A254+self.A178+self.A219+self.A219+self.A220+self.A220+self.ESC+\\\"37m\\\"+self.A223+self.A223+self.A223+self.A220+self.ESC+\\\"C\\\"+self.ESC+\\\"1;30m\\\"+self.A223+self.ESC+\\\"C\\\"+self.A220+self.A223+self.A223+self.A223+self.ESC+\\\"C\\\"+self.ESC+\\\"0;34m\\\"+self.A176+self.A176+self.A223+self.A178+self.A178+self.A176+self.ESC+\\\"2C\\\"+self.ESC+\\\"1;44m\\\"+self.A176+self.ESC+\\\"0;34m\\\"+self.A223+self.A223+self.A223+self.A178+self.A223+self.A223+self.ESC+\\\"C\\\"+self.A223+self.A178+self.A223+self.ESC+\\\"C\\\"+self.A223+self.A223+self.ESC+\\\"C\\\"+self.A223+self.A176+self.ESC+\\\"C\\\"+self.A223+self.ESC+\\\"C\\\"+self.A223+self.A223+self.ESC+\\\"C\\\"+self.A223+self.A223+self.ESC+\\\"30;44m\\\"+self.A177+self.ESC+\\\"19C\\\"+self.ESC+\\\"34;40m\\\"+self.A223+self.A223+self.A223+self.A223+\\\"\\\\r\\\\n\\\"\\n\\t\\tthismsg += self.ESC+\\\"A\\\"+self.ESC+\\\"79C\\\"+self.ESC+\\\"37m \\\"+self.ESC+\\\"34m\\\"+self.A177+self.A178+self.A219+self.A220+self.ESC+\\\"C\\\"+self.A176+self.A223+self.A178+self.A178+self.A178+self.A178+self.A223+self.A176+self.ESC+\\\"C\\\"+self.ESC+\\\"37m\\\"+self.A223+self.ESC+\\\"1;30m\\\"+self.A220+self.A223+self.ESC+\\\"C\\\"+self.ESC+\\\"0;34m\\\"+self.A223+self.A178+self.A176+self.ESC+\\\"4C\\\"+self.A176+self.ESC+\\\"C\\\"+self.A220+self.A219+self.ESC+\\\"30;44m\\\"+self.A176+\\\" \\\"+self.ESC+\\\"34;40m\\\"+self.A254+self.ESC+\\\"37m \\\"+self.ESC+\\\"34m\\\"+self.A220+self.ESC+\\\"30;44m\\\"+self.A176+self.A178+self.ESC+\\\"34;40m\\\"+self.A219+self.A219+self.A219+self.ESC+\\\"30;44m\\\"+self.A176+self.ESC+\\\"34;40m\\\"+self.A219+self.A220+self.A219+self.A219+self.A219+self.A219+self.A219+self.A222+self.A219+self.A219+\\\"\\\\r\\\\n\\\"\\n\\t\\treturn thismsg\",\n \"def __termcode(num):\\r\\n return \\\"\\\\033[%sm\\\" % num\",\n \"def leasemac(macstr):\\n maclst = []\\n while macstr:\\n maclst.append(macstr[:2].lower())\\n macstr = macstr[2:]\\n newmac = \\\":\\\".join(\\\"%s\\\" % s for s in maclst)\\n return newmac\",\n \"def _get_output_letter(rgb):\\n\\n\\t\\tif rgb == Rgb.pastel_purple():\\n\\t\\t\\treturn \\\"p\\\"\\n\\t\\telif rgb == Rgb.pastel_yellow():\\n\\t\\t\\treturn \\\"y\\\"\\n\\t\\telif rgb == Rgb.pastel_green():\\n\\t\\t\\treturn \\\"g\\\"\\n\\t\\telif rgb == Rgb.pastel_blue():\\n\\t\\t\\treturn \\\"b\\\"\\n\\t\\telif rgb == Rgb.strong_red():\\n\\t\\t\\treturn \\\" \\\"\\n\\n\\t\\treturn \\\"?\\\"\",\n \"def xterm_to_kitty(filename):\\n kitty_lines = []\\n lines = get_file_lines(filename)\\n\\n for line in lines:\\n if len(line) >= 1:\\n if line[0] == '!':\\n # convert to a kitty style comment\\n line = '#{}'.format(line[1:])\\n print(line)\\n kitty_lines.append(line)\\n elif line[0] == '*':\\n # get rid of *.\\n line = line[2:].replace(':', '')\\n print(line)\\n\\n kitty_lines.append(line)\\n\\n with open('to_kitty_out', 'w') as output:\\n output.write('\\\\n'.join(kitty_lines))\",\n \"def load_macaroon(macaroon: str) -> str:\\n\\n # if the macaroon is a file path, load it\\n if macaroon.split(\\\".\\\")[-1] == \\\"macaroon\\\":\\n with open(macaroon, \\\"rb\\\") as f:\\n macaroon_bytes = f.read()\\n return macaroon_bytes.hex()\\n else:\\n # convert the bas64 macaroon to hex\\n try:\\n macaroon = base64.b64decode(macaroon).hex()\\n except:\\n pass\\n return macaroon\",\n \"def to_center():\\n center_msg = b'\\\\x02\\\\x35\\\\x35\\\\x03'\\n #qpt.write(center_msg)\\n #feedback = qpt.readline()\\n move_to_position(0,0)\\n #return feedback\",\n \"def setMAC( self, intf, mac ):\\n result = self.cmd( 'ifconfig', intf, 'down' )\\n result += self.cmd( 'ifconfig', intf, 'hw', 'ether', mac )\\n result += self.cmd( 'ifconfig', intf, 'up' )\\n return result\",\n \"def to_morze(text: str) -> str:\\n\\n\\tmorze = {\\\"A\\\":\\\".-\\\",\\\"B\\\":\\\"-...\\\",\\\"C\\\":\\\"-.-.\\\",\\\"D\\\":\\\"-..\\\",\\\"E\\\":\\\".\\\",\\\"F\\\":\\\"..-.\\\",\\n\\t\\\"G\\\":\\\"--.\\\",\\\"H\\\":\\\"....\\\",\\\"I\\\":\\\"..\\\",\\\"J\\\":\\\"-.-.\\\",\\\"K\\\":\\\"-.-\\\",\\\"L\\\":\\\".-..\\\",\\\"M\\\":\\\"--\\\",\\n\\t\\\"N\\\":\\\"-.\\\",\\\"O\\\":\\\"---\\\",\\\"P\\\":\\\".--.\\\",\\\"Q\\\":\\\"--.-\\\",\\\"R\\\":\\\".-.\\\",\\\"S\\\":\\\"...\\\",\\\"T\\\":\\\"-\\\",\\n\\t\\\"U\\\":\\\"..-\\\",\\\"V\\\":\\\"...-\\\",\\\"W\\\":\\\".--\\\",\\\"X\\\":\\\".-..\\\",\\\"Y\\\":\\\"-.--\\\",\\\"Z\\\":\\\"--..\\\"}\\n\\n\\ttextMorze = ''.join([morze.get(c.upper(), ' ') for c in text])\\n\\n\\treturn textMorze\",\n \"def createGlyphs(input):\\r\\n\\r\\n command_open = subprocess.Popen([\\\"start\\\", \\\"cmd\\\",\\\"/k\\\", 'echo {}'.format(input)], shell = True)\\r\\n\\r\\n time.sleep(2) #time for window to appear\\r\\n\\r\\n #print win32gui.FindWindow(None, \\\"C:\\\\Windows\\\\system32\\\\cmd.exe\\\")\\r\\n hwnd = win32gui.FindWindow(None, \\\"C:\\\\Windows\\\\system32\\\\cmd.exe\\\")\\r\\n win32gui.SetForegroundWindow(hwnd)\\r\\n bbox = win32gui.GetWindowRect(hwnd)\\r\\n img = ImageGrab.grab(bbox)\\r\\n #img.show()\\r\\n\\r\\n length_of_chars = len(input) * 8 # +2 to compensate for the quotation marks\\r\\n combined_chars = img.crop((10,34,length_of_chars+9,45))\\r\\n #combined_chars.show()\\r\\n\\r\\n #nonetype error was caused by chaining the .show() wiutth the rest of the stuff\\r\\n\\r\\n chars = {x:\\\"\\\" for x in input}\\r\\n\\r\\n for i, each in enumerate(range(8,combined_chars.size[0]+9,8)): #starts from 1, and +8 to compensate for the -8 below VVV\\r\\n #if i not in bad_indexes: #this is to avoid the first and last double quotation marks\\r\\n current_char = input[i]\\r\\n \\r\\n indiv_char = combined_chars.crop((each-8,0,each,11))\\r\\n \\r\\n w, h = indiv_char.size #should be 8 wide by 9 high\\r\\n\\r\\n for i2, pixel in enumerate(indiv_char.getdata()): #tuple values can either be (0, 0, 0) or (192,192,192) for the default terminal colours\\r\\n if pixel == (192,192,192): \\r\\n chars[current_char] += u\\\"\\\\u2588\\\"\\r\\n\\r\\n else:\\r\\n chars[current_char] += \\\" \\\"\\r\\n\\r\\n if i2 % w == w-1: # we want it too look decent so overflow is neeeded onto the next lines\\r\\n # ^^^ before it was i2 % w == 0, but that lead to a trail behind character, so whats before 0? -1! so w-1!!!\\r\\n chars[current_char] += \\\"\\\\n\\\"\\r\\n\\r\\n chars[current_char] = chars[current_char][:-1] #this is to remove the last \\\"\\\\n\\\"\\r\\n\\r\\n return chars\",\n \"def _generate_mac(topology_id):\\n tid = int(topology_id)\\n global mac_counter\\n global used_macs\\n base = '52:54:00:00:00:00'\\n ba = base.split(':')\\n ba[2] = '%02x' % int(tid / 256)\\n ba[3] = '%02x' % int(tid % 256)\\n ba[4] = '%02x' % int(len(used_macs[topology_id]) / 256)\\n ba[5] = '%02x' % int(mac_counter)\\n\\n mac_counter += 1\\n\\n mac_counter = mac_counter % 256\\n return ':'.join(ba)\",\n \"def mac_add():\\n brew_installed = shutil.which('brew')\\n if not brew_installed:\\n print('Installing brew')\\n url = 'https://raw.githubusercontent.com/Homebrew/install/master/install'\\n command_line = \\\"/usr/bin/ruby -e \\\\\\\"$(curl -fsSL %s)\\\\\\\"\\\" % url\\n subprocess.check_call(command_line, shell=True)\\n else:\\n print('brew present')\\n gmsh_installed = shutil.which('gmsh')\\n if not gmsh_installed:\\n print('Installing gmsh')\\n folder_path = os.path.dirname(os.path.abspath(__file__))\\n dmginstall_path = os.path.join(folder_path, 'dmginstall.sh')\\n url = 'http://gmsh.info/bin/MacOSX/gmsh-3.0.5-MacOSX.dmg'\\n command_line = '%s %s' % (dmginstall_path, url)\\n print('command_line=%s' % command_line)\\n subprocess.check_call(command_line, shell=True)\\n gmsh_path = '/Applications/Gmsh.app/Contents/MacOS/gmsh'\\n command_line = \\\"ln -s %s /usr/local/bin/gmsh\\\" % gmsh_path\\n subprocess.check_call(command_line, shell=True)\\n else:\\n print('gmsh present')\\n ccx_installed = shutil.which('ccx')\\n if not ccx_installed:\\n mac_add_ccx()\\n else:\\n print('calculix (ccx) present')\",\n \"def test_cross_language_mac(self):\\n alice_private = [\\n 0x77, 0x07, 0x6D, 0x0A, 0x73, 0x18, 0xA5, 0x7D,\\n 0x3C, 0x16, 0xC1, 0x72, 0x51, 0xB2, 0x66, 0x45,\\n 0xDF, 0x4C, 0x2F, 0x87, 0xEB, 0xC0, 0x99, 0x2A,\\n 0xB1, 0x77, 0xFB, 0xA5, 0x1D, 0xB9, 0x2C, 0x2A\\n ]\\n\\n bob_key = \\\"3p7bfXt9wbTTW2HC7OQ1Nz+DQ8hbeGdNrfx+FG+IK08\\\"\\n message = \\\"Hello world!\\\"\\n extra_info = \\\"MAC\\\"\\n expected_mac = \\\"2nSMTXM+TStTU3RUVTNSVVZUTlNWVlpVVGxOV1ZscFY\\\"\\n\\n sas_alice = Sas()\\n sas_alice._create_sas(bytes(alice_private), 32)\\n sas_alice.set_their_pubkey(bob_key)\\n\\n alice_mac = sas_alice.calculate_mac(message, extra_info)\\n\\n assert alice_mac == expected_mac\",\n \"def line(self):\\n\\t\\treturn self.ESC+\\\"32m-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-\\\"+self.ESC+\\\"0m\\\\r\\\\n\\\"\",\n \"def get_base_mac(self):\\n raise NotImplementedError\",\n \"def get_mac(self, node_id):\\n nc = '%02x' % self.node_class\\n nr_iface = '%02x' % self.nr_host_interface\\n node_id = '%08x' % node_id\\n\\n return '%s:%s:%s:%s:%s:%s' % (nc, nr_iface, node_id[0:2], node_id[2:4], node_id[4:6], node_id[6:8])\",\n \"def mv_to_typer(mv):\\n\\n tab1 = [\\n 0.0000000E+00,\\n 1.8891380E+02,\\n -9.3835290E+01,\\n 1.3068619E+02,\\n -2.2703580E+02,\\n 3.5145659E+02,\\n -3.8953900E+02,\\n 2.8239471E+02,\\n -1.2607281E+02,\\n 3.1353611E+01,\\n -3.3187769E+00,\\n ]\\n\\n tab2 = [\\n 1.334584505E+01,\\n 1.472644573E+02,\\n -1.844024844E+01,\\n 4.031129726E+00,\\n -6.249428360E-01,\\n 6.468412046E-02,\\n -4.458750426E-03,\\n 1.994710149E-04,\\n -5.313401790E-06,\\n 6.481976217E-08,\\n 0.000000000E+00,\\n ]\\n\\n tab3 = [\\n -8.199599416E+01,\\n 1.553962042E+02,\\n -8.342197663E+00,\\n 4.279433549E-01,\\n -1.191577910E-02,\\n 1.492290091E-04,\\n 0.000000000E+00,\\n 0.000000000E+00,\\n 0.000000000E+00,\\n 0.000000000E+00,\\n 0.000000000E+00,\\n ]\\n\\n tab4 = [\\n 3.406177836E+04,\\n -7.023729171E+03,\\n 5.582903813E+02,\\n -1.952394635E+01,\\n 2.560740231E-01,\\n 0.000000000E+00,\\n 0.000000000E+00,\\n 0.000000000E+00,\\n 0.000000000E+00,\\n 0.000000000E+00,\\n 0.000000000E+00,\\n ]\\n\\n if -0.226 <= mv <= 1.923:\\n c = tab1\\n elif 1.923 < mv <= 11.361:\\n c = tab2\\n elif 11.361 < mv <= 19.739:\\n c = tab3\\n elif 19.739 < mv <= 21.103:\\n c = tab4\\n else:\\n raise ValueError(\\\"Voltage specified is out of range for Type R thermocouple\\\")\\n\\n t = 0.0\\n for p in range(0, len(c)):\\n t += c[p] * math.pow(mv, p)\\n return t\",\n \"def hAKI(self):\\n\\n aki = self.get_AKI()\\n return \\\":\\\".join((\\\"%02X\\\" % ord(i) for i in aki)) if aki else \\\"\\\"\",\n \"def bytes_to_mac_str(buff):\\n if len(buff) != DataDescription.B_SEQ_MAC_LEN:\\n raise TypeError(\\\"Invalid input\\\")\\n return \\\"%02X:%02X:%02X:%02X:%02X:%02X\\\" % buff\",\n \"def _ufo2glyphs_entry_point():\\n args = sys.argv[1:]\\n args.insert(0, \\\"ufo2glyphs\\\")\\n return main(args)\",\n \"def mac2eui64(mac, prefix=None):\\n # http://tools.ietf.org/html/rfc4291#section-2.5.1\\n eui64 = re.sub(r\\\"[.:-]\\\", \\\"\\\", mac).lower()\\n eui64 = eui64[0:6] + \\\"fffe\\\" + eui64[6:]\\n eui64 = hex(int(eui64[0:2], 16) | 2)[2:].zfill(2) + eui64[2:]\\n\\n if prefix is None:\\n return \\\":\\\".join(re.findall(r\\\".{4}\\\", eui64))\\n else:\\n try:\\n net = ip_network(prefix, strict=False)\\n euil = int(\\\"0x{}\\\".format(eui64), 16)\\n return \\\"{}/{}\\\".format(net[euil], net.prefixlen)\\n except Exception: # pylint: disable=broad-except\\n return\",\n \"def mac(self, mac):\\n\\n self._mac = mac\",\n \"def __repr__(self):\\n rep = \\\"\\\"\\n for host in self.hosts.keys():\\n attrs = self.hosts[host]\\n rep += f\\\"machine {host}\\\\n\\\\tlogin {attrs[0]}\\\\n\\\"\\n if attrs[1]:\\n rep += f\\\"\\\\taccount {attrs[1]}\\\\n\\\"\\n rep += f\\\"\\\\tpassword {attrs[2]}\\\\n\\\"\\n for macro in self.macros.keys():\\n rep += f\\\"macdef {macro}\\\\n\\\"\\n for line in self.macros[macro]:\\n rep += line\\n rep += \\\"\\\\n\\\"\\n return rep\",\n \"def is_mac():\\n return sys.platform==\\\"darwin\\\"\",\n \"async def emoji(self, ctx):\\n emb = discord.Embed(colour=self.color)\\n emb.add_field(name='Usage', value=f'```{self.bot.command_prefix}emoji <emojiname>```')\\n await ctx.message.edit(embed=emb)\",\n \"def matrix_to_text(matrix):\\n return ''.join(['{:02x}{:02x}{:02x}{:02x}'.format(\\n matrix[0][c], matrix[1][c], matrix[2][c], matrix[3][c]) for c in range(4)])\",\n \"def mac_addr(address):\\n\\tprint(':'.join('%02x' % compat_ord(b) for b in address))\\n\\treturn ':'.join('%s' % format(compat_ord(b), '0>8b') for b in address)\",\n \"def dpid_to_mac (dpid):\\n\\n return \\\"%012x\\\" % (dpid & 0xffFFffFFffFF,)\",\n \"def convert_H_kcalmol(en_H):\\n return en_H/kcalmol_H\",\n \"def export_db_macserial(db, path, year):\\n\\n with open(path, 'w') as fh:\\n print('#ifndef GENSERIAL_MODELINFO_AUTOGEN_H', file=fh)\\n print('#define GENSERIAL_MODELINFO_AUTOGEN_H\\\\n', file=fh)\\n print('// DO NOT EDIT! This is an autogenerated file.\\\\n', file=fh)\\n print('#include \\\"macserial.h\\\"\\\\n', file=fh)\\n\\n print('typedef enum {', file=fh)\\n\\n for info in db:\\n print(' {}, // {}'.format(\\n info['SystemProductName'].replace(',', '_'),\\n info['Specifications']['CPU'][0]\\n ), file=fh)\\n\\n print('} AppleModel;\\\\n', file=fh)\\n print('#define APPLE_MODEL_MAX {}\\\\n'.format(len(db)), file=fh)\\n\\n print('static PLATFORMDATA ApplePlatformData[] = {', file=fh)\\n for info in db:\\n print(' {{ \\\"{}\\\", \\\"{}\\\" }},'.format(\\n info['SystemProductName'],\\n info['SystemSerialNumber']\\n ), file=fh)\\n\\n print('};\\\\n', file=fh)\\n\\n print('#define APPLE_MODEL_CODE_MAX {}'.format(max(len(info['AppleModelCode']) for info in db)), file=fh)\\n print('static const char *AppleModelCode[][APPLE_MODEL_CODE_MAX] = {', file=fh)\\n\\n for info in db:\\n print(' /* {:14} */ {{\\\"{}\\\"}},'.format(\\n info['SystemProductName'],\\n '\\\", \\\"'.join(info['AppleModelCode'])\\n ), file=fh)\\n\\n print('};\\\\n', file=fh)\\n\\n print('#define APPLE_BOARD_CODE_MAX {}'.format(max(len(info['AppleBoardCode']) for info in db)), file=fh)\\n print('static const char *AppleBoardCode[][APPLE_BOARD_CODE_MAX] = {', file=fh)\\n\\n for info in db:\\n print(' /* {:14} */ {{\\\"{}\\\"}},'.format(\\n info['SystemProductName'],\\n '\\\", \\\"'.join(info['AppleBoardCode'])\\n ), file=fh)\\n\\n print('};\\\\n', file=fh)\\n\\n print('#define APPLE_MODEL_YEAR_MAX {}'.format(max(len(info['AppleModelYear']) for info in db)), file=fh)\\n print('static uint32_t AppleModelYear[][APPLE_MODEL_YEAR_MAX] = {', file=fh)\\n for info in db:\\n print(' /* {:14} */ {{{}}},'.format(\\n info['SystemProductName'],\\n ', '.join(str(year) for year in info['AppleModelYear'])\\n ), file=fh)\\n\\n print('};\\\\n', file=fh)\\n\\n print('static uint32_t ApplePreferredModelYear[] = {', file=fh)\\n for info in db:\\n print(' /* {:14} */ {},'.format(\\n info['SystemProductName'],\\n info.get('MacserialModelYear', 0)\\n ), file=fh)\\n\\n print('};\\\\n', file=fh)\\n\\n print('#endif // GENSERIAL_MODELINFO_AUTOGEN_H', file=fh)\",\n \"def getMac(self):\\n # Import netifaces here to prevent error importing this module in setup.py\\n import netifaces\\n interfaces = ['eth0', 'wlan0']\\n try:\\n interfaces.append(netifaces.gateways()['default'][netifaces.AF_INET][1])\\n except:\\n pass\\n for interface in interfaces:\\n try:\\n return netifaces.ifaddresses(interface)[netifaces.AF_LINK][0]['addr']\\n except ValueError:\\n pass\\n except:\\n exception('Error getting MAC address')\\n return None\",\n \"def writeKitty(b, of):\\n with open(of, \\\"w\\\") as fh:\\n for r in b:\\n fh.write(\\\"%s 0 0 0 %d %d %d %d 0 0 0 0 0 0 0\\\\n\\\" % ( r[0], int(r[1]*dw), int(r[2]*dh), int(r[3]*dw), int(r[4]*dh) ) )\",\n \"def decode_meraki_model(model):\\n model_label = \\\"\\\"\\n\\n if \\\"MX\\\" in model:\\n model_label = \\\"appliance\\\"\\n if \\\"MS\\\" in model:\\n model_label = \\\"switch\\\"\\n if \\\"MR\\\" in model:\\n model_label = \\\"wireless\\\"\\n if \\\"MV\\\" in model:\\n model_label = \\\"camera\\\"\\n if \\\"MC\\\" in model:\\n model_label = \\\"phone\\\"\\n return model_label\",\n \"def generate_ethmac(peripheral, **kwargs):\\n buf = kwargs['buffer']()\\n\\n result = \\\"\\\"\\\"\\nethmac: Network.LiteX_Ethernet @ {{\\n sysbus <{}, +0x100>;\\n sysbus new Bus.BusMultiRegistration {{ address: {};\\n size: {};\\n region: \\\"buffer\\\" }}\\n}}\\n\\\"\\\"\\\".format(peripheral['address'], buf['address'], buf['size'])\\n\\n if 'interrupt' in peripheral['constants']:\\n result += ' -> cpu@{}\\\\n'.format(\\n peripheral['constants']['interrupt'])\\n\\n return result\",\n \"def mv_to_typen(mv):\\n tab1 = [\\n 0.0000000E+00,\\n 3.8436847E+01,\\n 1.1010485E+00,\\n 5.2229312E+00,\\n 7.2060525E+00,\\n 5.8488586E+00,\\n 2.7754916E+00,\\n 7.7075166E-01,\\n 1.1582665E-01,\\n 7.3138868E-03,\\n ]\\n\\n tab2 = [\\n 0.00000E+00,\\n 3.86896E+01,\\n -1.08267E+00,\\n 4.70205E-02,\\n -2.12169E-06,\\n -1.17272E-04,\\n 5.39280E-06,\\n -7.98156E-08,\\n 0.00000E+00,\\n 0.00000E+00,\\n ]\\n \\n tab3 = [\\n 1.972485E+01,\\n 3.300943E+01,\\n -3.915159E-01,\\n 9.855391E-03,\\n -1.274371E-04,\\n 7.767022E-07,\\n 0.000000E+00,\\n 0.000000E+00,\\n 0.000000E+00,\\n 0.000000E+00,\\n ]\\n\\n if -3.99 <= mv <= 0.0:\\n c = tab1\\n elif 0.0 < mv <= 20.613:\\n c = tab2\\n elif 20.613 < mv <= 47.513:\\n c = tab3\\n else:\\n raise ValueError(\\\"Voltage specified is out of range for Type N thermocouple\\\")\\n\\n t = 0.0\\n for p in range(0, len(c)):\\n t += c[p] * math.pow(mv, p)\\n return t\",\n \"def convert(input_filename, output_filename):\\n c_file = pkg_resources.resource_filename('ShapelyChipDesigns', 'convert.rb')\\n os.system('klayout -z -rd input='+input_filename+' -rd output='+output_filename+' -r '+c_file)\",\n \"def terminal_diagram(self):\\n lines = [[unichr(9632) for x in range(k)]\\n for k in self[1]]\\n lines += [[unichr(9632) for x in range(k)]\\n + [unichr(9675)] for k in self[0]]\\n lines.sort(reverse=True, key=len)\\n for line in lines:\\n print(' '.join(line))\\n print('')\",\n \"def ascii2title(title):\\n L = title\\n L= np.asarray(L)\\n L[L>256] = 32\\n y = ''.join(chr(int(i)) for i in L)\\n return y\",\n \"def convert_kcalmol_H(en_kcalmol):\\n return en_kcalmol*kcalmol_H\",\n \"def get_mac_address_table(self):\\n\\n mac_address_table = []\\n command = '/interface bridge host print terse'\\n\\n output = self._send_command(command)\\n\\n for host in parse_terse_output(output):\\n mac_address_table.append({\\n 'mac': cast_mac(host.get('mac-address')),\\n 'interface': host.get('interface'),\\n 'vlan': -1,\\n 'static': True if 'D' not in host.get('_flags') else False,\\n 'active': True if 'X' not in host.get('_flags') else False,\\n 'moves': -1,\\n 'last_move': -1.0\\n })\\n\\n return mac_address_table\",\n \"def help():\\n print(f\\\"{YELLOW}zwc{RESET} - Hidden messages using Zero Width Characters.\\\")\",\n \"def create_terminal() -> str:\\n ...\",\n \"def convert_os2_properties(font_data):\\n # should we use the information in the fontdefheader?\\n metrics = font_data.pMetrics\\n # font encoding (850 indicates PMUGL)\\n # > usCodePage - This field is the registered code page for which the font was\\n # > designed. Often, this field is 0, which means the font can be used with any of\\n # > the OS/2 supported code pages. Hence, if this field is 0, you can specify the\\n # > code page you want when you create a logical font. (Logical font creation is\\n # > discussed later.) When you create a logical font, the font character to code\\n # > point mappings will be made for you. If the font contains special symbols\\n # > which have no register code page, a value of 65400 is returned in this field.\\n # > In this case, you must use the returned code page value during logical font\\n # > creation.\\n if metrics.usCodePage in (0, 850):\\n encoding = 'ibm-ugl'\\n elif metrics.usCodePage == 65400:\\n encoding = ''\\n else:\\n encoding = f'ibm-{metrics.usCodePage}'\\n # font selection flags\\n decoration = []\\n if metrics.fsSelectionFlags.FM_SEL_OUTLINE:\\n decoration.append('outline')\\n if metrics.fsSelectionFlags.FM_SEL_NEGATIVE:\\n decoration.append('negative')\\n if metrics.fsSelectionFlags.FM_SEL_UNDERSCORE:\\n decoration.append('underline')\\n if metrics.fsSelectionFlags.FM_SEL_STRIKEOUT:\\n decoration.append('strikethrough')\\n properties = Props(\\n family=metrics.szFamilyname.decode('latin-1'),\\n name=metrics.szFacename.decode('latin-1'),\\n font_id=metrics.usRegistryId,\\n encoding=encoding,\\n ascent=metrics.yLowerCaseAscent,\\n descent=metrics.yLowerCaseDescent,\\n # total cell depth below baseline\\n shift_up=-metrics.yMaxDescender,\\n line_height=metrics.yMaxBaselineExt + metrics.yExternalLeading,\\n dpi=(metrics.xDeviceRes, metrics.yDeviceRes),\\n default_char=metrics.usFirstChar + metrics.usDefaultChar,\\n word_boundary=metrics.usFirstChar + metrics.usBreakChar,\\n point_size=metrics.usNominalPointSize / 10,\\n underline_thickness=metrics.yUnderscoreSize,\\n underline_descent=metrics.yUnderscorePosition,\\n #ySubscriptXSize='int16',\\n subscript_size=metrics.ySubscriptYSize,\\n subscript_offset=(metrics.ySubscriptXOffset, metrics.ySubscriptYOffset),\\n #ySuperscriptXSize='int16',\\n superscript_size=metrics.ySuperscriptYSize,\\n superscript_offset=(metrics.ySuperscriptXOffset, metrics.ySuperscriptYOffset),\\n #also fsSelectionFlags.FL_SEL_BOLD\\n weight=_WEIGHT_MAP.get(metrics.usWeightClass, ''),\\n setwidth=_SETWIDTH_MAP.get(metrics.usWidthClass, ''),\\n slant='italic' if metrics.fsSelectionFlags.FM_SEL_ITALIC else 'roman',\\n decoration=' '.join(decoration)\\n # # strikeout stroke thickness\\n # yStrikeoutSize='int16',\\n # # strikeout height above baseline\\n # yStrikeoutPosition='int16',\\n )\\n return properties\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6040826","0.59956956","0.5854787","0.5779863","0.5675275","0.56129545","0.5586242","0.552146","0.54683423","0.5457044","0.54549307","0.54475677","0.5410852","0.5382184","0.5353483","0.5331743","0.5249846","0.52239674","0.5209968","0.51962715","0.51827663","0.51617885","0.51070666","0.5103786","0.5084833","0.50759417","0.50759417","0.5061284","0.50549024","0.50225395","0.5011279","0.49739587","0.49640402","0.49640402","0.49612582","0.4938447","0.4929726","0.4898526","0.48964506","0.4866847","0.48554632","0.48280492","0.48251224","0.4813232","0.48078313","0.48053765","0.47997564","0.47974032","0.47949824","0.47869545","0.47844338","0.4782132","0.47820625","0.47818148","0.47808236","0.47398376","0.47337157","0.47285938","0.47209632","0.4712874","0.47029355","0.47014663","0.46907723","0.4674292","0.46717656","0.46716657","0.4655811","0.46485782","0.46421877","0.46282417","0.46237805","0.4623337","0.46114028","0.46064278","0.46058634","0.4605324","0.46050692","0.4600253","0.4577183","0.45544872","0.4551651","0.45479286","0.45464173","0.4546246","0.45344117","0.4519851","0.45171496","0.45134398","0.45115504","0.45114738","0.4501673","0.44993815","0.44980332","0.4497364","0.4496369","0.44956705","0.44944984","0.449415","0.44934878","0.44933778"],"string":"[\n \"0.6040826\",\n \"0.59956956\",\n \"0.5854787\",\n \"0.5779863\",\n \"0.5675275\",\n \"0.56129545\",\n \"0.5586242\",\n \"0.552146\",\n \"0.54683423\",\n \"0.5457044\",\n \"0.54549307\",\n \"0.54475677\",\n \"0.5410852\",\n \"0.5382184\",\n \"0.5353483\",\n \"0.5331743\",\n \"0.5249846\",\n \"0.52239674\",\n \"0.5209968\",\n \"0.51962715\",\n \"0.51827663\",\n \"0.51617885\",\n \"0.51070666\",\n \"0.5103786\",\n \"0.5084833\",\n \"0.50759417\",\n \"0.50759417\",\n \"0.5061284\",\n \"0.50549024\",\n \"0.50225395\",\n \"0.5011279\",\n \"0.49739587\",\n \"0.49640402\",\n \"0.49640402\",\n \"0.49612582\",\n \"0.4938447\",\n \"0.4929726\",\n \"0.4898526\",\n \"0.48964506\",\n \"0.4866847\",\n \"0.48554632\",\n \"0.48280492\",\n \"0.48251224\",\n \"0.4813232\",\n \"0.48078313\",\n \"0.48053765\",\n \"0.47997564\",\n \"0.47974032\",\n \"0.47949824\",\n \"0.47869545\",\n \"0.47844338\",\n \"0.4782132\",\n \"0.47820625\",\n \"0.47818148\",\n \"0.47808236\",\n \"0.47398376\",\n \"0.47337157\",\n \"0.47285938\",\n \"0.47209632\",\n \"0.4712874\",\n \"0.47029355\",\n \"0.47014663\",\n \"0.46907723\",\n \"0.4674292\",\n \"0.46717656\",\n \"0.46716657\",\n \"0.4655811\",\n \"0.46485782\",\n \"0.46421877\",\n \"0.46282417\",\n \"0.46237805\",\n \"0.4623337\",\n \"0.46114028\",\n \"0.46064278\",\n \"0.46058634\",\n \"0.4605324\",\n \"0.46050692\",\n \"0.4600253\",\n \"0.4577183\",\n \"0.45544872\",\n \"0.4551651\",\n \"0.45479286\",\n \"0.45464173\",\n \"0.4546246\",\n \"0.45344117\",\n \"0.4519851\",\n \"0.45171496\",\n \"0.45134398\",\n \"0.45115504\",\n \"0.45114738\",\n \"0.4501673\",\n \"0.44993815\",\n \"0.44980332\",\n \"0.4497364\",\n \"0.4496369\",\n \"0.44956705\",\n \"0.44944984\",\n \"0.449415\",\n \"0.44934878\",\n \"0.44933778\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.69217736"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":92596,"cells":{"query":{"kind":"string","value":"Stitch together the steps"},"document":{"kind":"string","value":"def provision_bmc(hostname):\n\n LOGGER.info(\"Provisioning %s\", hostname)\n\n mac = get_mac_address(hostname)\n\n blink_light(hostname)\n\n index = location_to_index()\n\n generate_sql(index, mac)\n\n blink_light(hostname, on=False)"},"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 do_step(self) -> None:","def _step(self) -> None:","def step_forward(self):","def step(self):\n #1. Time progresses\n self.time_operator.step()\n \n #2. Form and dissolve relationships\"\n self.relationship_operator.step()\n\n #3. HIV transmission\n self.infection_operator.step()","def _insertAllSteps(self): \n self.uMics = self.inputCoordinatesTiltedPairs.get().getUntilted().getMicrographs()\n self.tMics = self.inputCoordinatesTiltedPairs.get().getTilted().getMicrographs()\n\n self.inputMics = self._createSetOfParticles('auxMics')\n self.inputMics.copyInfo(self.uMics)\n self.inputMics.setStore(False)\n \n for micU, micT in izip(self.uMics, self.tMics):\n micU.cleanObjId()\n micT.cleanObjId()\n self.inputMics.append(micU)\n self.inputMics.append(micT)\n\n self.samplingInput = self.uMics.getSamplingRate()\n \n\n if self.downsampleType.get() != OTHER:\n # If 'same as picking' or 'original' get sampling rate from input micrographs\n #TODO: Review this when downsampling before picking is possible\n self.samplingFinal = self.samplingInput\n else:\n # If 'other' multiply the input sampling rate by the factor provided\n self.samplingFinal = self.samplingInput*self.downFactor.get()\n \n # Write pos files for each micrograph\n firstStepId = self._insertFunctionStep('writePosFilesStep')\n \n # For each micrograph insert the steps\n #run in parallel\n \n deps = []\n for mic in self.inputMics:\n localDeps = [firstStepId]\n micrographToExtract = mic.getFileName()\n micName = removeBaseExt(mic.getFileName())\n micId = mic.getObjId()\n\n # If downsample type is 'other' perform a downsample\n if self.downsampleType == OTHER:\n fnDownsampled = self._getTmpPath(micName+\"_downsampled.xmp\")\n downFactor = self.downFactor.get()\n args = \"-i %(micrographToExtract)s -o %(fnDownsampled)s --step %(downFactor)f --method fourier\"\n localDeps=[self._insertRunJobStep(\"xmipp_transform_downsample\", args % locals(),prerequisites=localDeps)]\n micrographToExtract = fnDownsampled\n \n # If remove dust \n if self.doRemoveDust:\n fnNoDust = self._getTmpPath(micName+\"_noDust.xmp\")\n \n thresholdDust = self.thresholdDust.get() #TODO: remove this extra variable\n args=\" -i %(micrographToExtract)s -o %(fnNoDust)s --bad_pixels outliers %(thresholdDust)f\"\n localDeps=[self._insertRunJobStep(\"xmipp_transform_filter\", args % locals(),prerequisites=localDeps)]\n micrographToExtract = fnNoDust\n \n #self._insertFunctionStep('getCTF', micId, micName, micrographToExtract)\n micName = removeBaseExt(mic.getFileName())\n \n # Actually extract\n deps.append(self._insertFunctionStep('extractParticlesStep', micId, micName, \n None, micrographToExtract, prerequisites=localDeps))\n # TODO: Delete temporary files\n \n # Insert step to create output objects \n self._insertFunctionStep('createOutputStep', prerequisites=deps)","def getSteps():","def _step(self):\n pass","def step(self, state):","def _step(self, whence):\n pass","def rollout(env, acts):\n total_rew = 0\n env.reset()\n steps = 0\n for act in acts:\n _obs, rew, done, _info = env.step(act)\n steps += 1\n total_rew += rew\n if done:\n break\n\n return steps, total_rew","def step(self):\n\n pass","def merge_steps(self, steps):\n raise NotImplementedError","def Advance():\n warp.step()","def step(self):\n while self.state != STATE_TERMINAL:\n self.step_strategies[self.state]()","def step(self, move):","def transduce(self,inputs):\n self.start()\n return [self.step(inp) for inp in inputs]","def step(self, memories):\n return","def step(self, action):","def run(commands):\n\n # Work out total steps to take\n max_steps = sum([c.steps for c in commands])\n\n count = 0\n while count != max_steps:\n for command in commands:\n # we want to interleave the commands\n if command.steps > 0:\n command.stepper.step(1, command.direction)\n command.steps -= 1\n count += 1","def _step(self, a):\n state, rew, done, info = super()._step(a)\n render = self.get_render_obs()\n return render, sum(self.rewards), bool(done), {}","def _step(self, a):\n obs, rew, done, info = super()._step(a)\n # if self.robot.body_xyz[0] > self.threshold:\n # rew = 1.0\n # self.threshold += 1\n # else:\n # rew = 0.0\n # self.steps += 1\n # if self.steps > self.max_episode_steps:\n # done = True\n return obs, rew, done, info","def step(self):\n self.latent.step()","def _step_snell(self) -> None:\n self.snell.step()","def perform_step(self) -> None:\n pass","def beam_step(self, paths, extra):\n h_i, dec_out, context, src_mask = extra\n last = paths[:, :, -1].view(1, -1)\n dec_out, h_i = self.decode_rnn(context, h_i, dec_out, last, src_mask)\n probs = self.output(dec_out)\n dec_out = dec_out.squeeze(0)\n return probs, (h_i, dec_out, context, src_mask)","def _insertAllSteps(self):\n \n # Get pointer to input micrographs \n self.particlePickingRun = self.xmippParticlePicking.get()\n \n copyId = self._insertFunctionStep('copyInputFilesStep')\n # Get micrographs to pick\n #self.inputMicrographs.set(self.getInputMicrographs())\n \n deps = []\n for mic in self.getInputMicrographs():\n stepId = self._insertFunctionStep('autopickMicrographStep', mic.getFileName(), prerequisites=[copyId])\n deps.append(stepId)\n \n self._insertFunctionStep('_createOutput',self._getExtraPath(), prerequisites=deps)","def step(self, amt=1):\n \n # For checking if all the animations have their framse looked at\n #activewormind = [i for i, x in enumerate(self._idlelist) if x == False]\n #print \"Worm {} at {:5g}\".format(activewormind, 1000*(time.time() - starttime))\n # save times activated for each worm \n [self.timedata[i].append(1000*(time.time() - starttime)) for i, x in enumerate(self._idlelist) if x == False]\n \n #self._led.buffer = [0] * 480\n self._led.pixheights = [-100] * self._led.numLEDs\n #print type(self._led.buffer)\n for ledcopy in self._ledcopies:\n # self._led.buffer = map(ixor, self._led.buffer, ledcopy.buffer)\n # use pixheights but assume all buffers same size\n # print ledcopy.driver[0].pixheights\n for pix in range(self._led.numLEDs):\n #for ledcopy in self._ledcopies:\n if self._led.pixheights[pix] == ledcopy.driver[0].pixheights[pix]:\n for i in range(3):\n self._led.buffer[3*pix + i] ^= ledcopy.buffer[3*pix + i]\n elif self._led.pixheights[pix] < ledcopy.driver[0].pixheights[pix]:\n for i in range(3):\n self._led.buffer[3*pix + i] = ledcopy.buffer[3*pix + i]\n self._led.pixheights[pix] = ledcopy.driver[0].pixheights[pix] \n self._step += 1","def replay_steps(s):\n if s is None:\n print \"nothing to do\"\n return\n for (addr,action,arg) in s:\n action(arg)","def step(self, action):\n reward_all = 0\n pose_all = []\n raw_states = []\n for _ in range(self.action_repeat):\n state, reward, done, _ = self.env.step(action) # run up the mountain\n\n processed_state = self.preprocess_state(state)\n raw_states.append(state)\n\n if done and self.i < 200:\n self.success = True\n\n reward_all += reward\n pose_all.append(processed_state)\n\n self.i += 1\n\n if done:\n missing = self.action_repeat - len(pose_all)\n pose_all.extend([pose_all[-1]] * missing)\n break\n\n next_state = np.concatenate(pose_all)\n return next_state, reward_all, done, raw_states","def __step(self, p):\n action = self.__action(p)\n temp_state = self.state\n\n if self.state == 0:\n if action == 1:\n self.state += 1\n elif self.state == 1:\n if action == 1:\n self.state -= 1\n else:\n self.state += 1\n else:\n if action == 1:\n self.state += 1\n else:\n self.state -= 1\n \n self.trajectory.append([temp_state, action, self.__reward(self.state)])","def step(self, action) -> (list, float, bool):\n if len(self.curr_stim) == 0:\n self.curr_stim += [action[0]] * action[1] + [-action[0]] * action[1]\n self.system_step()\n self.frame += 1 / self.config[\"Fs\"]\n self.history.append(self.x2-self.x1)\n return self.get_state(), 0, False","def steps(self,num_steps):\n if self.last_sensation == TERMINAL_STATE:\n self.start_episode()\n for step in range(num_steps):\n next_sensation,reward = self.env(self.next_action)\n self.collect_data(self.last_sensation, self.next_action, reward, next_sensation)\n self.next_action = self.agent(next_sensation,reward)\n self.last_sensation = next_sensation\n if self.last_sensation == TERMINAL_STATE:\n self.start_episode()","def get_next_steps(self, steps):\n for step in range(steps):\n # Actual calulation: Runge-Kutta 2\n\n # Step 1\n k1 = [\n self.vel * self.dt,\n self.get_next_acc() * self.dt\n ]\n\n # Step 2\n next_pos = self.pos + k1[0] * 0.5\n next_vel = self.vel + k1[1] * 0.5\n self.disps, self.dists = self.get_relative_distances(positions=next_pos)\n k2 = [\n next_vel * self.dt,\n self.get_next_acc(save=False) * self.dt\n ]\n\n # Step 3\n next_pos = self.pos + k2[0] * 0.5\n next_vel = self.vel + k2[1] * 0.5\n self.disps, self.dists = self.get_relative_distances(positions=next_pos)\n k3 = [\n next_vel * self.dt,\n self.get_next_acc(save=False) * self.dt\n ]\n\n # Step 4\n next_pos = self.pos + k3[0]\n next_vel = self.vel + k3[1]\n self.disps, self.dists = self.get_relative_distances(positions=next_pos)\n k4 = [\n next_vel * self.dt,\n self.get_next_acc(save=False) * self.dt\n ]\n\n # Move forward\n self.pos = self.pos + 1/6 * (k1[0] + 2*k2[0] + 2*k3[0] + k4[0])\n self.vel = self.vel + 1/6 * (k1[1] + 2*k2[1] + 2*k3[1] + k4[1])\n\n # Saving of statistics\n self.save_system_information(self.pos, self.vel)","def step(self, **kwargs):\n pass","def step(self, action):\n self.game.play_single_turn([action])\n next_obs = self.get_state()\n reward = self.game.player_1.score - self.game.turn_count\n done = self.game.check_for_end_of_game() or self.game.turn_count > 25\n\n if done:\n if self.game.player_1.score > self.game.player_2.score:\n reward += 25\n elif self.game.player_2.score > self.game.player_1.score:\n reward -= 25\n\n if self.game.save_images_suffix:\n image_suffix = f'{self.game.save_images_suffix}_{self.game.turn_count+1}'\n self.game.game_board.graphical_output(save=True, display=False, image_suffix=image_suffix)\n\n return next_obs, reward/100, done","def next_step(self):\n self.proceed()\n self.execute_current()","def step(self, actions):\n observations = []\n rewards = []\n dones = []\n self.perform_action(self.car1, actions[0])\n self.perform_action(self.car2, actions[1])\n p.stepSimulation()\n observations = self._get_obs()\n for i in range(2):\n rewards.append(self._get_rewards(i, observations[i]))\n self.step_counter+=1\n self.reduce = self.reduce * 0.9993\n for i in range(2):\n done, cause = self._check_done(observations[i])\n dones.append(done)\n if(done == 1):\n if(cause==1):\n rewards[1] += 72000\n rewards[0] -= 72000\n elif(cause==0):\n rewards[0] += 72000\n rewards[1] -= 72000\n elif(cause==2):\n rewards[i] -= 72000\n return observations, rewards, dones, None","def _worker(self):\n player = self.first_player # first player\n q = [None, self.q1, self.q2]\n states = [None, 'reset', 'reset']\n not_ret = None\n while True:\n op, a, ret = q[player].get()\n # print(f'worker: player: {player} op: {op}')\n try:\n assert op == states[\n player], f'state {op} expects {states[player]}'\n\n if op == 'reset':\n if player == self.first_player:\n # first time case\n s = self.env.reset()\n # set trace\n self.trace.set_init(self.env)\n legal_moves = self.env.legal_moves()\n ret.set((s, None, None, None, legal_moves))\n states[player] = 'step'\n else:\n states[player] = 'step'\n\n # switch player\n if player == 1:\n player = 2\n else:\n player = 1\n not_ret = ret\n elif op == 'step':\n # handle error\n s, r, done, info = self.env.step(a)\n # keep trace\n self.trace.step(a)\n\n if done:\n ret.set((s, r, done, info, []))\n not_ret.set((s, -r, done, info, []))\n # end\n self.done = True\n break\n else:\n legal_moves = self.env.legal_moves()\n not_ret.set((s, -r, done, info, legal_moves))\n\n # switch player\n if player == 1:\n player = 2\n else:\n player = 1\n not_ret = ret\n else:\n raise NotImplementedError()\n except Exception as e:\n # exception\n # return as exception\n ret.set(e)\n if not_ret is not None:\n not_ret.set(\n Exception('your opponent did something unexpected'))\n self._exception(\n Exception('something else terminated the environment'))\n break","def step(self):\n for layer in self.layers:\n layer.step()","def step(self):\r\n self.datacollector.collect(self)\r\n self.datacollector2.collect(self)\r\n self.datacollector3.collect(self)\r\n self.datacollector4.collect(self)\r\n self.datacollector5.collect(self)\r\n self.datacollector6.collect(self)\r\n self.datacollector7.collect(self)\r\n self.datacollector8.collect(self)\r\n self.datacollector9.collect(self)\r\n self.datacollector10.collect(self)\r\n self.datacollector11.collect(self)\r\n self.datacollector12.collect(self)\r\n self.datacollector13.collect(self)\r\n\r\n self.datacollector14.collect(self)\r\n self.datacollector15.collect(self)\r\n self.datacollector16.collect(self)\r\n self.datacollector17.collect(self)\r\n self.datacollector18.collect(self)\r\n self.datacollector19.collect(self)\r\n self.datacollector20.collect(self)\r\n self.datacollector21.collect(self)\r\n self.datacollector22.collect(self)\r\n self.datacollector23.collect(self)\r\n self.datacollector24.collect(self)\r\n self.datacollector25.collect(self)\r\n self.datacollector26.collect(self)\r\n self.schedule.step()","def side_step(r, num_repeats=1):\n for i in range(num_repeats):\n r.go(-10, -150)\n time.sleep(2)\n r.go(-20)\n time.sleep(1)\n r.go(-10, 150)\n time.sleep(2)\n r.stop()\n time.sleep(.15)\n for i in range(num_repeats):\n r.go(-10, 150)\n time.sleep(2)\n r.go(-20)\n time.sleep(1)\n r.go(-10, -150)\n time.sleep(2)\n r.stop()\n time.sleep(.15)\n for i in range(num_repeats):\n r.go(10, 150)\n time.sleep(2)\n r.go(20)\n time.sleep(1)\n r.go(10, -150)\n time.sleep(2)\n r.stop()\n time.sleep(.15)\n for i in range(num_repeats):\n r.go(10, -150)\n time.sleep(2)\n r.go(20)\n time.sleep(1)\n r.go(10, 150)\n time.sleep(2)\n r.stop()\n time.sleep(.15)","def step(self):\r\n raise NotImplementedError","def step(self, action):\n actions = action.reshape(2, 2)\n action = {'GoalieBrain': actions[0], 'StrikerBrain': actions[1]}\n #action = {'GoalieBrain': [5, 5], 'StrikerBrain': [2, 2]}\n #print(action)\n info = self.env.step(action)\n\n state0 = info[self.brain_names[0]].vector_observations\n state1 = info[self.brain_names[1]].vector_observations\n state = np.vstack((state0, state1))\n reward0 = info[self.brain_names[0]].rewards\n reward1 = info[self.brain_names[1]].rewards\n reward = reward0 + reward1\n done0 = info[self.brain_names[0]].local_done\n done1 = info[self.brain_names[1]].local_done\n done = done0 + done1\n return state, reward, done","def _step(self, a):\n obs, rew, done, info = super()._step(a)\n # rew = +1 if past int threshold for first time in episode\n # if self.robot.body_xyz[0] > self.threshold:\n # self.threshold += 1\n # rew = 1.0\n # else:\n # rew = 0.0\n # self.steps += 1\n # if self.steps > self.max_episode_steps:\n # done = True\n return obs, rew, done, info","def step(self):\n #_increment timers\n for agent in self.agents:\n agent.tick()\n\n # choose agent pair\n agentA, agentB = self.choose()\n\n # interact\n agentA.step(agentB)\n agentB.step(agentA)\n\n # log results\n self.logger.log(agentA, agentB)\n\n # increment counters\n self.steps += 1\n self.time += 1","def step(self):\n raise NotImplementedError","def step(self):\n\t\tnumpy.random.shuffle(self.agents_list)\n\t\tfor agent in self.agents_list:\n\t\t\tagent.produce()\n\t\tfor agent in self.agents_list:\n\t\t\tagent.charge()\n\t\tfor agent in self.agents_list:\n\t\t\tif agent.strategy == 0: \n\t\t\t\tagent.retribute()\n\t\tfor agent in self.agents_list:\n\t\t\tif agent.strategy == 0: \n\t\t\t\tagent.give()\n\t\tfor agent in self.agents_list:\n\t\t\tagent.consume()\n\t\tfor agent in self.agents_list:\n\t\t\tagent.solve_consumption_deficit()\n\t\tfor site in self.sites:\n\t\t\tsite.recovery()\n\t\tfor agent in self.agents_list:\n\t\t\tagent.sprout()","def step(self, s, a):\n raise NotImplementedError","def step(self, actions):\n assert len(actions) == len(self.workers)\n for worker, action in zip(self.workers, actions):\n worker.action_queue.put(action)\n\n results = []\n for worker in self.workers:\n worker.action_queue.join()\n results.append(worker.result_queue.get())\n\n observations, rewards, dones = zip(*results)\n\n for i in range(self.num_envs):\n self.curr_episode_reward[i] += rewards[i]\n if dones[i]:\n self.episode_rewards[i].append(self.curr_episode_reward[i])\n self.curr_episode_reward[i] = 0\n\n return observations, rewards, dones","def step(self, action):\n if np.abs(action[0][0]-action[1][0])==2:\n self.board[(action[0][0]+action[1][0])//2,(action[0][1]+action[1][1])//2]=0\n self.board[action[1]] = self.board[action[0]] \n if action[1][0]==0 or action[1][0]==7:\n self.board[action[1]] = 2*np.sign(self.board[action[0]])\n self.board[action[0]] = 0\n self.turn = (self.turn + 1)%2\n self.actions=[]\n for i in range(8):\n for j in range(8):\n if np.sign(self.board[i,j])==(-1)**self.turn:\n moves=(self.bdiag(i,j),self.badiag(i,j),self.fdiag(i,j),self.fadiag(i,j))\n for r in range(4):\n if moves[r] is not None:\n self.actions.append(moves[r])\n winner = self.winner(action)\n if winner is not None:\n rewards = np.array([winner,(-1)*winner])\n else:\n rewards = np.array([0,0])\n self.done = winner is not None\n return self.board.copy(), rewards, self.done, self.turn","def step(self, action):\n reward = 0\n pose_all = []\n self.rotor_speeds = np.array([action]*4)\n for _ in range(self.action_repeat):\n done = self.sim.next_timestep(self.rotor_speeds) # update the sim pose and velocities\n reward += self.get_reward()\n pose_all += [self.sim.pose]\n if self.sim.crashed:\n reward = -5\n done = True\n #if (np.square(self.sim.pose[:3] - self.target_pos)).sum() < 1: # Close enough!\n #done = True\n next_state = np.concatenate(pose_all)\n return next_state, reward, done","def step(self, action: list) -> None:\n self._input = np.array(\n [self._thrust_surge(action[0]), self._moment_steer(action[1])]\n )\n w, q = odesolver45(\n self._state_dot, self._state, self.config.simulation.t_step_size\n )\n\n self._state = q\n self._state[2] = geom.princip(self._state[2])\n\n self._prev_states = np.vstack([self._prev_states, self._state])\n self._prev_inputs = np.vstack([self._prev_inputs, self._input])\n\n self._step_counter += 1","def run_one_step(self):\n pass","def step(self):\n self.world.slosh_oceans()\n self.world.transfer_energy_vertically()\n self.world.transfer_energy_horizontally()\n self.world.absorb_energy_from_core()\n self.world.absorb_energy_from_sun(self.sun)","def step(self):\n self.log.info(\"Stepping %r\", self)\n # get enabled transitions before reseting inputs\n scope_transitions = self.get_enabled_transitions_by_scope()\n\n # reset inputs/outputs at the start of a big step\n if self.enabled_inputs:\n self.log.info(\"Reseting inputs and outputs at start of big step...\")\n self.enabled_inputs = set()\n self.outputs = set()\n self.locals = set()\n\n # execute transitions\n # - note that each scope is non-overlapping by definition\n updates = dict()\n for scope, transitions in scope_transitions.items():\n if len(transitions) > 1: # non-determinism\n pass\n transition = transitions[0]\n self._execute_transition(transition, updates)\n\n # update variables\n self.variables.update(updates)\n self.log.info(\"Variables now %r\", self.variables)","def step(self, actions):\n assert (len(actions) == self.num_actions)\n actions = np.around(actions)\n actions = np.clip(actions, 0, 1)\n self.done = self.network.perform_actions(actions)\n self.cur_pos = self._get_current_pos_in_1d()\n self.reward = self.network.get_reward()\n\n return self.cur_pos, self.reward, self.done, {}","def step_async(self, actions):","def stepo_cmd(cmd, cnt, args):\n #log(\"stepo\"+str(args[1:]))\n cpu.set_break(cpu.pc+4)\n cpu.list_breaks()\n go_cmd(cmd, cnt, args)\n cpu.clear_break(cpu.pc+4)\n cpu.list_breaks()","def step(self, steps):\n self._simulate(endStep=self.currentStep+steps)","def step(self, action):\n self._last_base_position = self.rex.GetBasePosition()\n self._last_base_orientation = self.rex.GetBaseOrientation()\n if self._is_render:\n # Sleep, otherwise the computation takes less time than real time,\n # which will make the visualization like a fast-forward video.\n time_spent = time.time() - self._last_frame_time\n self._last_frame_time = time.time()\n time_to_sleep = self.control_time_step - time_spent\n if time_to_sleep > 0:\n time.sleep(time_to_sleep)\n base_pos = self.rex.GetBasePosition()\n # Keep the previous orientation of the camera set by the user.\n [yaw, pitch, dist] = self._pybullet_client.getDebugVisualizerCamera()[8:11]\n self._pybullet_client.resetDebugVisualizerCamera(dist, yaw, pitch, base_pos)\n\n for env_randomizer in self._env_randomizers:\n env_randomizer.randomize_step(self)\n\n # change up swing and stance ratio and desired speeds randomly for robustness\n if np.random.randint(300) == 0:\n self.ratio = np.random.uniform(self.min_swing_ratio, self.max_swing_ratio)\n\n if np.random.randint(300) == 0:\n self.speed = np.random.uniform(self.min_speed, self.max_speed)\n self.speed_des[0] = self.speed\n\n if np.random.randint(300) == 0:\n self.side_speed = np.random.uniform(self.min_side_speed, self.max_side_speed)\n self.speed_des[1] = self.side_speed\n\n self.base_vel_curr_trans, self.base_vel_curr_rot = self.get_base_velocity()\n action = self._transform_action_to_motor_command(action)\n self.rex.Step(action)\n self.base_vel_next_trans, self.base_vel_next_rot = self.get_base_velocity()\n \n self._env_step_counter += 1\n self.phase += self._action_repeat # the cycle length is CYCLE_TIME/time_step so can add \n # how many times an action was repeated\n\n if self.phase > self.cycle_len:\n self.phase = self.phase % self.cycle_len \n self.cycle_complete += 1\n\n reward = self._reward()\n done = self._termination()\n\n if done:\n self.rex.Terminate()\n\n return np.array(self._get_observation_np()), reward, done, {'action': action}","def step(self, action):\n \n success = False\n self.curr_step += 1\n self._take_action(action)\n self._take_action(action)\n self._take_action(action)\n\n # initialize reward and get state \n reward = 0.0\n ob = self._get_state()\n\n # give dense rewards \n if not self.sparse_reward:\n reward = self._get_reward()\n\n # bad terminal conditions\n if self.curr_step >= self.max_steps \\\n or self.target_distance >= self.max_distance \\\n or self.mean_radius_sheep >= self.max_radius:\n self.finish = True\n if self.sparse_reward:\n reward = -1.0\n\n # good terminal conditions\n if self.target_distance <= 1.0:\n success = True\n self.finish = True\n if self.sparse_reward:\n reward = 1.0\n\n # update rl parameters\n self.episode_length += 1\n self.episode_reward += reward\n\n # generate info return parameter\n if self.info_mode == 1 and self.finish:\n info = {'r':self.episode_reward, 'l':self.episode_length, \n 's': success}\n else:\n info = {'n':self.num_sheep, 's': success}\n\n return ob, reward, self.finish, info","def compute_step(self):\n self._apply_controls(step=self.step, post=True)\n self._step += 1\n for gate_layer in self._tebd_propagator.gate_layers:\n self._t_mps.apply_nn_gate_layer(gate_layer)\n self._t_mps.apply_process_tensors(self.step,\n self._process_tensors)\n for gate_layer in self._tebd_propagator.gate_layers:\n self._t_mps.apply_nn_gate_layer(gate_layer)\n self._apply_controls(step=self.step, post=False)\n self._append_results()","def twist(self):\n self.right()\n time.sleep(1)\n self.stop()\n self.left()\n time.sleep(1)\n self.stop()","def train_loop_pre(self, current_step):\r\n pass","def step(self):\n try:\n self.agents.sort(key=lambda x: x.dist)\n except Exception as e:\n print(e)\n\n for agent in self.agents:\n try:\n agent.step()\n except Exception as e:\n print(e)\n\n\n # Removes agents if they reach exit\n for exit in self.model.exits:\n x, y = exit.pos[0] * 6 + 1, exit.pos[1] * 6 + 1\n if agent.node == (x, y):\n try:\n agent.saved()\n except Exception as e:\n print(e)","def step(self, _action):\n action = np.hstack((np.zeros(6), _action/10.))\n self.ref_skel.set_positions(self.ref_state.angles)\n for i in range(self.step_per_frame):\n # self.skel.set_forces(self.skel.get_spd(self.ref_skel.q + action, self.world.time_step(), self.Kp, self.Kd))\n self.skel.set_forces(self.skel.get_spd(self.ref_state.angles + action, self.world.time_step(), self.Kp, self.Kd))\n self.world.step()\n\n self.ref_state_time += self.step_per_frame * self.world.time_step()\n if self.ref_state_time >= self.ref_state.dt:\n self.ref_state_time -= self.ref_state.dt\n self.ref_state = self.ref_state.get_next()\n\n return tuple([self.state(), self.reward(), self.is_done(), dict()])","def step(self, action):\n pass","def step(self, action):\n pass","def step(self, action):\n\n # ==\n # Transition, reward and termination\n done = False\n reward = self.get_current_reward(self.state)\n\n # Leaf and absorbing nodes\n if self.state <= 1:\n done = True\n if self.state == 1:\n self.state = 0 # go to absorbing\n else:\n self.state = int(self.state // 2)\n\n # ==\n # Features\n phi = self.state_2_features(self.state)\n\n return phi, reward, done, {}","def step(self, action):\n\n action[1] = 0 if action[1] < 0 else 1\n\n if not self.moving:\n self.agent_host.sendCommand(\"move 0.5\")\n time.sleep(.2)\n self.moving = True\n\n # Get Action\n command = \"strafe \" + str(action[0])\n if ((action[0] < 0 and self.allow_left) or (action[0] > 0 and self.allow_right)):\n self.agent_host.sendCommand(command)\n time.sleep(.2)\n self.agent_host.sendCommand(\"strafe 0\")\n time.sleep(.1)\n\n if action[1]:\n if self.checkCommand:\n self.jumpsOverDitches += 1\n self.checkCommand = False\n self.agent_host.sendCommand(\"jump 1\")\n time.sleep(.2)\n self.agent_host.sendCommand(\"jump 0\")\n\n # if (command == \"crouch 1\"):\n # self.agent_host.sendCommand(command)\n # time.sleep(.3)\n # self.agent_host.sendCommand(\"crouch 0\")\n # time.sleep(.2)\n\n self.episode_step += 1\n\n # Get Observation\n world_state = self.agent_host.getWorldState()\n for error in world_state.errors:\n print(\"Error:\", error.text)\n self.obs, self.allow_left, self.allow_right, curZPos, curXPos = self.get_observation(world_state)\n if curZPos:\n self.curZPos = curZPos\n if curXPos:\n if self.obs[3 + int(curXPos)]:\n self.checkCommand = True\n self.numDitchesEncountered += 1\n\n # Get Done\n done = not world_state.is_mission_running \n\n # Get Reward\n reward = 0\n for r in world_state.rewards:\n reward += r.getValue()\n self.episode_return += reward\n\n return self.obs, reward, done, dict()","def _step(self, rl_actions):\n self.timer += 1\n # pdb.set_trace()\n # perform acceleration and (optionally) lane change actions for\n # traci-controlled human-driven vehicles\n if len(self.controlled_ids) > 0:\n accel = []\n for veh_id in self.controlled_ids:\n # acceleration action\n action = self.vehicles.get_acc_controller(veh_id).get_action(\n self)\n accel.append(action)\n\n # lane changing action\n lane_flag = 0\n if type(self.scenario.lanes) is dict:\n if any(v > 1 for v in self.scenario.lanes.values()):\n lane_flag = 1\n else:\n if self.scenario.lanes > 1:\n lane_flag = 1\n\n if self.vehicles.get_lane_changing_controller(veh_id) \\\n is not None and lane_flag:\n new_lane = \\\n self.vehicles.get_lane_changing_controller(\n veh_id).get_action(self)\n self.apply_lane_change([veh_id], target_lane=[new_lane])\n\n self.apply_acceleration(self.controlled_ids, acc=accel)\n\n # perform (optionally) lane change actions for sumo-controlled\n # human-driven vehicles\n if len(self.sumo_ids) > 0:\n for veh_id in self.sumo_ids:\n # lane changing action\n if self.scenario.lanes > 1:\n lc_contr = self.vehicles.get_lane_changing_controller(\n veh_id)\n if lc_contr is not None:\n new_lane = lc_contr.get_action(self)\n self.apply_lane_change([veh_id], target_lane=[new_lane])\n\n # perform (optionally) routing actions for all vehicle in the network,\n # including rl vehicles\n routing_ids = []\n routing_actions = []\n for veh_id in self.ids:\n if self.vehicles.get_routing_controller(veh_id) is not None:\n routing_ids.append(veh_id)\n route_contr = self.vehicles.get_routing_controller(veh_id)\n routing_actions.append(route_contr.choose_route(self))\n\n self.choose_routes(veh_ids=routing_ids, route_choices=routing_actions)\n\n self.apply_rl_actions(rl_actions)\n\n self.additional_command()\n # pdb.set_trace()\n self.traci_connection.simulationStep()\n #Error being thrown from sumo/src/utils/foxtools/MFXImageHelper.cpp\n # self.traci_connection.gui.screenshot(\"View #0\", \"/home/mkoren/Pictures/a\"+str(self.timer)+\".png\")\n # pdb.set_trace()\n # store new observations in the network after traci simulation step\n network_observations = \\\n self.traci_connection.vehicle.getSubscriptionResults()\n crash = False\n for veh_id in self.ids:\n prev_pos = self.get_x_by_id(veh_id)\n prev_lane = self.vehicles.get_lane(veh_id)\n try:\n self.vehicles.set_position(\n veh_id, network_observations[veh_id][tc.VAR_LANEPOSITION])\n except KeyError:\n continue\n self.vehicles.set_edge(\n veh_id, network_observations[veh_id][tc.VAR_ROAD_ID])\n self.vehicles.set_lane(\n veh_id, network_observations[veh_id][tc.VAR_LANE_INDEX])\n if network_observations[veh_id][tc.VAR_LANE_INDEX] != prev_lane \\\n and veh_id in self.rl_ids:\n self.vehicles.set_state(veh_id, \"last_lc\", self.timer)\n self.vehicles.set_speed(\n veh_id, network_observations[veh_id][tc.VAR_SPEED])\n\n try:\n change = self.get_x_by_id(veh_id) - prev_pos\n if change < 0:\n change += self.scenario.length\n new_abs_pos = self.vehicles.get_absolute_position(\n veh_id) + change\n self.vehicles.set_absolute_position(veh_id, new_abs_pos)\n except ValueError or TypeError:\n self.vehicles.set_absolute_position(veh_id, -1001)\n\n if self.vehicles.get_position(veh_id) < 0 or \\\n self.vehicles.get_speed(veh_id) < 0:\n crash = True\n\n # collect headway, leader id, and follower id data\n headway_data = \\\n self.get_headway_dict(network_observations=network_observations)\n\n for veh_id in self.ids:\n try:\n self.vehicles.set_headway(\n veh_id, headway_data[veh_id][\"headway\"])\n self.vehicles.set_leader(\n veh_id, headway_data[veh_id][\"leader\"])\n self.vehicles.set_follower(\n veh_id, headway_data[veh_id][\"follower\"])\n except KeyError:\n # occurs in the case of crashes, so headway is assumed to\n # be very small\n self.vehicles.set_headway(veh_id, 1e-3)\n self.vehicles.set_leader(veh_id, None)\n self.vehicles.set_follower(veh_id, None)\n\n # collect list of sorted vehicle ids\n self.sorted_ids, self.sorted_extra_data = self.sort_by_position()\n\n # collect information of the state of the network based on the\n # environment class used\n if self.vehicles.num_rl_vehicles > 0:\n self.state = self.get_state()\n # rllab requires non-multi agent to have state shape as\n # num-states x num_vehicles\n if not self.multi_agent:\n self.state = self.state.T\n else:\n self.state = []\n # collect observation new state associated with action\n\n next_observation = list(self.state)\n\n # crash encodes whether sumo experienced a crash\n crash = \\\n crash \\\n or self.traci_connection.simulation.getEndingTeleportNumber() != 0 \\\n or self.traci_connection.simulation.getStartingTeleportNumber() != 0\n\n # compute the reward\n if self.vehicles.num_rl_vehicles > 0:\n reward = self.compute_reward(self.state, rl_actions, fail=crash)\n else:\n reward = 0\n\n # Are we in a multi-agent scenario? If so, the action space is a list.\n if self.multi_agent:\n done_n = self.vehicles.num_rl_vehicles * [0]\n info_n = {'n': []}\n\n if self.shared_reward:\n info_n['reward_n'] = [reward] * len(self.action_space)\n else:\n info_n['reward_n'] = reward\n\n if crash:\n done_n = self.vehicles.num_rl_vehicles * [1]\n if self.fail_safe:\n logging.error(\"Crash has occurred! Check your failsafes\")\n\n info_n['done_n'] = done_n\n info_n['state'] = self.state\n done = np.all(done_n)\n return self.state, sum(reward), done, info_n\n\n else:\n if crash:\n logging.info(\"Crash has occurred! Check failsafes!\")\n return Step(observation=next_observation, reward=reward,\n done=True)\n else:\n return Step(observation=next_observation, reward=reward,\n done=False)","def train_loop_post(self, current_step):\r\n pass","def step(self, step=None):\n pass","def step(self, actions, agent_id=0):\n self._last_state = self._current_state\n\n # TODO\n # action = actions.discrete_actions[0]-1\n action = actions.argmax()\n\n done = 0\n if self._stage == 0: # is fixation\n if action == 0:\n reward = 0.\n else:\n reward = -1.\n self._current_state = 1\n self._stage = 1\n elif self._stage == 1: # is first stage, use prob_transition\n if action == 1 or action == 2:\n if np.random.random() < self._prob_transition[0][action-1]:\n self._current_state = 2\n else:\n self._current_state = 3\n reward = 0.\n else: # pick a next state at random\n reward = -1.\n self._current_state = np.random.random() < 0.5 and 2 or 3\n self._stage = 2\n else: # is second stage, use prob_reward\n # Given an action (arm pulled), sample reward, return\n if action == 1 or action == 2:\n current_prob_rewards = self._prob_reward[self._current_state-2]\n self._best_reward = self._max_reward*np.max(current_prob_rewards)\n thisProb = current_prob_rewards[action-1]\n if np.random.random() < thisProb:\n # print(\"give reward\")\n reward = self._max_reward\n else:\n reward = 0.0\n else:\n reward = -1.\n\n self._total_reward += reward\n self._best_total_reward += self._best_reward\n self._stage = 0\n self._current_state = 0\n self._trial += 1\n self._since_flipped += 1\n # if more than self._min_stable trials since flipping, certain chance of flipping prob rews\n if (self._since_flipped >= self._min_stable) and (np.random.random() <= self._flip_prob):\n self._randomize()\n self._since_flipped = 0\n\n\n self._last_action = np.zeros(self._num_arms)\n self._last_action[action] = 1\n # conditions to end episode\n if self._step >= self._steps_per_ep-1:\n self._state = READY_TO_END_EPISODE\n done = 1\n\n self._step += 1\n self._prev_reward = reward\n\n obs = self._current_state\n reset = done == 1. or self._step == MAX_FRAMES\n\n # print(np.array([[obs]]).shape)\n\n # print(reward, self._stage)\n return np.array([obs]), reward, done, reset","def step(self, p1_action, p2_action):\r\n p1_r = self.paddle_1.update(p1_action)\r\n p2_r = self.paddle_2.update(p2_action)\r\n \r\n p1_state, p2_state, p1_reward, p2_reward, done = self.ball.update(self.paddle_1, self.paddle_2)\r\n\r\n return p1_state, p2_state, (p1_reward+p1_r), (p2_reward+p2_r), done","def try_step_multi (self, actions):\n legal_action_indices = []\n legal_action_features = []\n\n for i, action in enumerate(actions):\n object_index, new_location, action_means, action_stds = action\n \n position = new_location[:2]\n rotation = new_location[2]\n\n prev_transform = self.e.objects[object_index].transform\n\n if self.e.act(object_index, Command(position, rotation)):\n inputs = self.get_feature_only()\n\n legal_action_indices.append(i)\n legal_action_features.append(inputs)\n\n # Back transform\n self.e.act(object_index, \n self.command_from_transform(prev_transform), check_condition = False )\n\n\n if len(legal_action_indices) == 0:\n return ([], [])\n\n legal_action_feats = np.stack(legal_action_features)\n\n action_size = legal_action_feats.shape[0]\n num_steps = legal_action_feats.shape[1]\n feature_size = legal_action_feats.shape[2]\n epoch_size = int(np.ceil(action_size / self.config.batch_size))\n\n zero_filled = np.zeros( ( epoch_size * self.config.batch_size, num_steps, feature_size ))\n\n zero_filled[:legal_action_feats.shape[0]] = legal_action_feats\n\n zero_filled = zero_filled.reshape((epoch_size, self.config.batch_size, num_steps, feature_size))\n\n all_progress = []\n for i in range(epoch_size):\n progress = self.progress_estimator.predict(zero_filled[i], sess = self.session)\n all_progress.append(progress)\n\n all_progress = np.concatenate(all_progress)\n \n\n return (legal_action_indices, all_progress[:action_size])","def _step(self, a):\n # potential_old = self.potential\n obs, rew, done, info = super()._step(a)\n # state = self.robot.calc_state()\n # alive = float(self.robot.alive_bonus(state[0]+self.robot.initial_z, self.robot.body_rpy[1]))\n # alive *= 0.05\n # cost = 0.01 * -np.square(a).sum()\n # progress = float(self.potential - potential_old)\n # # print (\"Rewarsd\", alive, progress)\n # rew = alive + progress + cost\n # # if self.robot.body_xyz[0] > 5:\n # # rew = 1.0\n # # else:\n # # rew = 0.0\n # # print (\"ROBOT: \", self.robot.body_xyz[2] < 0.3)\n # # if done:\n # # print (\"DONE\")\n return obs, rew, done, info","def step(self, action):\n # TODO: code here\n y, x = self.state\n dy, dx = self.moves[action]\n next_x, next_y = x+dx, y+dy\n\n next_x = np.clip(next_x, 0, self.width-1) # clip the values to the world\n next_y = np.clip(next_y, 0, self.height-1) # clip the values to the world\n\n if next_y == 1:\n rand = np.random.uniform()\n if rand < 0.2:\n next_x += 1\n elif rand < 0.7:\n next_x += 2\n else:\n next_x += 3\n\n next_x = np.clip(next_x, 0, self.width - 1)\n\n if next_x == 4 and next_y == 1:\n reward = -1\n done = True\n elif next_x == 4 and next_y == 2:\n reward = 1\n done = True\n else:\n reward = 0\n done = False\n\n next_state = (next_y, next_x)\n self.state = next_state\n\n return next_state, reward, done, {}","def WarpStep(iters=5):\n MSG(\"WarpStep\")\n for j in range(iters):\n warp.step()\n return","def build_step(self):\n\n pass","def step(self, a):\n if self.mirror and self.phase >= self.max_phase / 2:\n a = self.reflect_action(a)\n self.time += 1\n\n self.posbefore = self.robot_skeleton.q[0]\n\n self.do_dart_clocks(a)\n self.set_phase(self.phase + 1)\n\n self.posafter = self.robot_skeleton.q[0]\n\n # ref_pos, ref_vel = self.get_kin_state()\n # self.set_state(ref_pos, ref_vel)\n\n # common behavior for returning step() results\n done = self.is_done()\n ob = self._get_obs()\n reward = self.compute_reward()\n self.reward_buffer.append(reward)\n self.total_reward += reward\n\n self.energy += np.square(a).sum()\n return ob, reward, done, {}","def foward_shimmey(self):\n for x in range(6):\n self.right(primary=60, counter=30)\n time.sleep(.5)\n self.left(primary=70, counter=30)\n time.sleep(.5)\n self.back()\n time.sleep(2) \n self.stop()","def build_step(self):\n pass","def build_step(self):\n pass","def step(self):\n self.function()","def _step(self, board, elapsedTime):\n\t\tpass","def run_one_timestep(self):\n\n print(\"=\"*50)\n print()\n\n forks = []\n\n # Update sensors\n for sensor in self.sensors:\n if isinstance(sensor, Camera):\n thread = threading.Thread(target=sensor.update)\n forks.append(thread)\n thread.start()\n else:\n sensor.update()\n\n for fork in forks:\n fork.join()\n\n forks = []\n # Update sensobs\n for sensob in self.sensobs:\n thread = threading.Thread(target=sensob.update)\n forks.append(thread)\n thread.start()\n\n for fork in forks:\n fork.join()\n\n # Update behaviours\n for behav in self.active_behavs:\n behav.update()\n\n # Invoke arbitrator\n motor_rec = self.arbit.choose_action() # Returns a tuple(list(motor_recommendations), halt)\n print(\"Arbitrator chose: \"+str(motor_rec))\n\n if motor_rec[1]: # Check halt recommendation\n return False # Halt and exit program\n\n\n # Update motobs\n print(self.motobs)\n i = 0\n for motob in self.motobs: # Updates each motob with it's respective motor recommendation\n print(\"Bbcon: Updating motob \" + str(i))\n\n motob.update(motor_rec[0][i])\n i += 1\n\n # Wait\n time.sleep(0.5) #waits half a second\n\n # Reset sensors\n for sensor in self.sensors:\n sensor.reset()\n\n return True","def _step(self, action):\n \n obs, reward, done, info = self.env.step(action)\n\n \n advice=self.generateAdvice()[1]\n\n obs = {\n \"image\": obs,\n \"advice\": advice\n }\n\n\n \n\n\n\n return obs, reward, done, info","def debuggerstep(self, action):\n\n\n\n #Calculate actual Next State you are supposed to reach via action\n if action == 0:\n nxtState = (self.state[0] - 1, self.state[1])\n elif action == 1:\n nxtState = (self.state[0], self.state[1] - 1)\n elif action == 2:\n nxtState = (self.state[0], self.state[1] + 1) \n elif action == 3: \n nxtState = (self.state[0] + 1, self.state[1])\n\n \n #BUT YOU CAN ONLY REACH THERE WITH 80% PROBABIBILITY\n #Stocasticity Implementation\n correctMove = random.random() \n \n #Check if nextState to reach is valid, Redundant Check (Might have to remove in future iterations)\n if self.isValid(nxtState): \n #If you have a valid next state, you reach there with 80% probability\n if correctMove <= 0.8: \n \n print(\"Ended up in correct state taking action \", end = \"\")\n if (action == 0): \n print(\"Up\")\n elif (action == 1): \n print(\"Left\")\n elif (action == 2): \n print(\"Right\")\n elif(action == 3): \n print(\"Down\")\n self.state = nxtState\n self.isEnd()\n return nxtState, self.giveReward(), self.isTerminal\n \n #Else you didn't end up in right place\n else: \n \n print(\"Ended up in wrong state. Had to go \", end = \"\")\n if (action == 0): \n print(\"Up \", end = \"\")\n elif (action == 1): \n print(\"Left \", end = \"\")\n elif (action == 2): \n print(\"Right \", end = \"\")\n elif(action == 3): \n print(\"Down \", end = \"\")\n \n print(\"And end up in \", end = \"\")\n print(nxtState)\n \n print(\"But ended up in: \", end = \"\")\n \n \n #Find remaining states that can be possibly reached: \n altActions =[]\n \n if action == 0:\n altActions.append(1)\n altActions.append(2)\n elif action == 1: \n altActions.append(0)\n altActions.append(3)\n elif action == 2: \n altActions.append(0)\n altActions.append(3)\n else: \n altActions.append(1)\n altActions.append(2)\n \n \n #Pick one random of all possible next states\n altAction = random.choice(altActions)\n #Check if alternate possibility is valid \n if altAction == 0:\n nxtState1 = (self.state[0] - 1, self.state[1])\n elif altAction == 1:\n nxtState1 = (self.state[0], self.state[1] - 1)\n elif altAction == 2:\n nxtState1 = (self.state[0], self.state[1] + 1) \n elif altAction == 3: \n nxtState1 = (self.state[0] + 1, self.state[1])\n \n \n \n #If alternate possibility is valid, update values\n if self.isValid(nxtState1): \n print(nxtState1)\n \n #Update Values \n self.state = nxtState1\n self.isEnd()\n return nxtState1, self.giveReward(), self.isTerminal\n \n #If alternate possibility is not valid, then you stay in place\n else: \n #Stay in place \n print(self.state)\n print(\"Stayed in Place!\")\n self.isEnd()\n return self.state, self.giveReward(), self.isTerminal\n else: \n #Stay in place \n print(self.state)\n print(\"Invalid action picked, Stayed in Place!\")\n self.isEnd()\n return self.state, self.giveReward(), self.isTerminal","def step_env(self):\n raise NotImplementedError\n # Not needed for this homework","def step(self):\n raise NotImplementedError()","def step(self):\n raise NotImplementedError()","def step(self):\n raise NotImplementedError()","def shifter(self):\n #self.BA_shift = self.timeshift_latitude(self.latB, self.latA)\n #self.BC_shift = self.timeshift_latitude(self.latB, self.latC)\n\n\n self.shifted = True #changing boolean to True when function is called.\n\n secondsA = self.secondsA\n secondsB = self.secondsB\n secondsC = self.secondsC\n\n NeA = self.holefill(self.NeA, secondsA)\n NeB = self.holefill(self.NeB, secondsB)\n NeC = self.holefill(self.NeC, secondsC)\n\n start = 0\n stop = len(NeA) - np.max(np.array([self.BA_shift, self.BC_shift]))\n\n startA = start + self.BA_shift\n stopA = stop + self.BA_shift\n\n startC = start + self.BC_shift\n stopC = stop + self.BC_shift\n\n NeA = NeA[startA:stopA]\n NeB = NeB[start:stop]\n NeC = NeC[startC:stopC]\n\n longA = self.holefill(self.longA, secondsA)\n longB = self.holefill(self.longB, secondsB)\n longC = self.holefill(self.longC, secondsC)\n longA = longA[startA:stopA]\n longB = longB[start:stop]\n longC = longC[startC:stopC]\n\n latA = self.holefill(self.latA, secondsA)\n latB = self.holefill(self.latB, secondsB)\n latC = self.holefill(self.latC, secondsC)\n latA = latA[startA:stopA]\n latB = latB[start:stop]\n latC = latC[startC:stopC]\n\n radA = self.holefill(self.radA, secondsA)\n radB = self.holefill(self.radB, secondsB)\n radC = self.holefill(self.radC, secondsC)\n radA = radA[startA:stopA]\n radB = radB[start:stop]\n radC = radC[startC:stopC]\n\n velA = self.holefill(self.velA, secondsA)\n velB = self.holefill(self.velB, secondsB)\n velC = self.holefill(self.velC, secondsC)\n velA = velA[startA:stopA]\n velB = velB[start:stop]\n velC = velC[start:stop]\n\n altA = self.holefill(self.altA, secondsA)\n altB = self.holefill(self.altB, secondsB)\n altC = self.holefill(self.altC, secondsC)\n altA = altA[startA:stopA]\n altB = altB[start:stop]\n altC = altC[startC:stopC]\n\n\n mlatA = self.holefill(self.mlatA, secondsA)\n mlatB = self.holefill(self.mlatB, secondsB)\n mlatC = self.holefill(self.mlatC, secondsC)\n mlatA = mlatA[startA:stopA]\n mlatB = mlatB[start:stop]\n mlatC = mlatC[startC:stopC]\n\n mlongA = self.holefill(self.mlongA, secondsA)\n mlongB = self.holefill(self.mlongB, secondsB)\n mlongC = self.holefill(self.mlongC, secondsC)\n mlongA = mlongA[startA:stopA]\n mlongB = mlongB[start:stop]\n mlongC = mlongC[startC:stopC]\n\n mltA = self.holefill(self.mltA, secondsA)\n mltB = self.holefill(self.mltB, secondsB)\n mltC = self.holefill(self.mltC, secondsC)\n mltA = mltA[startA:stopA]\n mltB = mltB[start:stop]\n mltC = mltC[startC:stopC]\n\n secondsA = self.holefill(secondsA, secondsA)\n secondsB = self.holefill(secondsB, secondsB)\n secondsC = self.holefill(secondsC, secondsC)\n secondsA = secondsA[startA:stopA]\n secondsB = secondsB[start:stop]\n secondsC = secondsC[startC:stopC]\n\n indsA = np.nonzero(secondsA)[0]\n indsB = np.nonzero(secondsB)[0]\n indsC = np.nonzero(secondsC)[0]\n\n inds = np.intersect1d(indsA, indsB)\n inds = np.intersect1d(inds, indsC)\n\n self.NeA = NeA[inds]\n self.NeB = NeB[inds]\n self.NeC = NeC[inds]\n\n self.longA = longA[inds]\n self.longB = longB[inds]\n self.longC = longC[inds]\n\n self.latA = latA[inds]\n self.latB = latB[inds]\n self.latC = latC[inds]\n\n self.radA = radA[inds]\n self.radB = radB[inds]\n self.radC = radC[inds]\n\n self.velA = velA[inds]\n self.velB = velB[inds]\n self.velC = velC[inds]\n\n self.altA = altA[inds]\n self.altB = altB[inds]\n self.altC = altC[inds]\n\n self.mlatA = mlatA[inds]\n self.mlatB = mlatB[inds]\n self.mlatC = mlatC[inds]\n\n self.mlongA = mlongA[inds]\n self.mlongB = mlongB[inds]\n self.mlongC = mlongC[inds]\n\n self.mltA = mltA[inds]\n self.mltB = mltB[inds]\n self.mltC = mltC[inds]\n\n self.secondsA = secondsA[inds]\n self.secondsB = secondsB[inds]\n self.secondsC = secondsC[inds]","def step(self, actions):\n self.step_async(actions)\n return self.step_wait()","def step(self, actions):\n self.step_async(actions)\n return self.step_wait()","def step(self, action_history, observations):\n return self.call(action_history, observations)","def step(self, action, state):\r\n done = False\r\n next_state = json.loads(state)\r\n state_shift = self.action_map[action]\r\n\r\n next_state = np.array(next_state) + np.array(state_shift)\r\n next_state = next_state.tolist()\r\n\r\n reward = self.reward[next_state[0], next_state[1]]\r\n self.reward[next_state[0], next_state[1]] = 0\r\n\r\n next_state = json.dumps(next_state)\r\n\r\n if reward < 0 or reward == 4:\r\n done = True\r\n\r\n return next_state, reward, done","def _step(self):\n title()\n self.runCount = 1\n self.experiment.pause = False\n self._runExperiment()\n self.pause = True","def step(self, action):\n self.steps += 1\n self.robots[0].setAction(action)\n for i in range(self.num_agents):\n if i != 0 and self.policies[i:i+1]: # self.policies[0] is dummy\n self.robots[i].setAction(self.policies[i](self.robots[i].getObservation()))\n # rewards = [ -1.0 * self.num_foods / self.max_steps for _ in range(self.num_agents) ] # so agent needs to eat foods quickly\n rewards = [ 0.0 for _ in range(self.num_agents) ]\n for i in range(self.BULLET_STEPS):\n p.stepSimulation()\n rewards = [ rewards[i]+self._getReward(self.robots[i]) for i in range(self.num_agents) ]\n self.episode_rewards = [ self.episode_rewards[i]+rewards[i] for i in range(self.num_agents) ]\n obs = self.robots[0].getObservation()\n done = self._isDone()\n info = { 'steps': self.steps }\n if done:\n # TODO\n info['episode'] = { 'r': self.episode_rewards[0], 'l': self.steps, 'r_all': self.episode_rewards }\n # print(self.episode_rewards, self.steps)\n return obs, rewards[0], done, info","def step(self, action):\r\n s = self.get_state()\r\n\r\n elements = np.arange(self.S)\r\n # weights = np.squeeze(self.nextStateProbability[s,action])\r\n weights = self.nextStateProbability[s, action]\r\n nexts = choices(elements, weights, k=1)[0]\r\n\r\n # p = self.nextStateProbability[s,action]\r\n # reward = self.rewardsTable[s,action, nexts][0]\r\n reward = self.rewardsTable[s, action, nexts]\r\n\r\n # fully observable MDP: observation is the actual state\r\n self.currentObservation = nexts\r\n\r\n gameOver = False\r\n if self.currentIteration > np.Inf:\r\n ob = self.reset()\r\n gameOver = True # game ends\r\n else:\r\n ob = self.get_state()\r\n\r\n history = {\"time\": self.currentIteration, \"state_t\": s, \"action_t\": action,\r\n \"reward_tp1\": reward, \"state_tp1\": nexts}\r\n # history version with actions and states, not their indices\r\n # history = {\"time\": self.currentIteration, \"action_t\": self.actionListGivenIndex[action],\r\n # \"reward_tp1\": reward, \"observation_tp1\": self.stateListGivenIndex[self.get_state()]}\r\n self.currentIteration += 1\r\n return ob, reward, gameOver, history"],"string":"[\n \"def do_step(self) -> None:\",\n \"def _step(self) -> None:\",\n \"def step_forward(self):\",\n \"def step(self):\\n #1. Time progresses\\n self.time_operator.step()\\n \\n #2. Form and dissolve relationships\\\"\\n self.relationship_operator.step()\\n\\n #3. HIV transmission\\n self.infection_operator.step()\",\n \"def _insertAllSteps(self): \\n self.uMics = self.inputCoordinatesTiltedPairs.get().getUntilted().getMicrographs()\\n self.tMics = self.inputCoordinatesTiltedPairs.get().getTilted().getMicrographs()\\n\\n self.inputMics = self._createSetOfParticles('auxMics')\\n self.inputMics.copyInfo(self.uMics)\\n self.inputMics.setStore(False)\\n \\n for micU, micT in izip(self.uMics, self.tMics):\\n micU.cleanObjId()\\n micT.cleanObjId()\\n self.inputMics.append(micU)\\n self.inputMics.append(micT)\\n\\n self.samplingInput = self.uMics.getSamplingRate()\\n \\n\\n if self.downsampleType.get() != OTHER:\\n # If 'same as picking' or 'original' get sampling rate from input micrographs\\n #TODO: Review this when downsampling before picking is possible\\n self.samplingFinal = self.samplingInput\\n else:\\n # If 'other' multiply the input sampling rate by the factor provided\\n self.samplingFinal = self.samplingInput*self.downFactor.get()\\n \\n # Write pos files for each micrograph\\n firstStepId = self._insertFunctionStep('writePosFilesStep')\\n \\n # For each micrograph insert the steps\\n #run in parallel\\n \\n deps = []\\n for mic in self.inputMics:\\n localDeps = [firstStepId]\\n micrographToExtract = mic.getFileName()\\n micName = removeBaseExt(mic.getFileName())\\n micId = mic.getObjId()\\n\\n # If downsample type is 'other' perform a downsample\\n if self.downsampleType == OTHER:\\n fnDownsampled = self._getTmpPath(micName+\\\"_downsampled.xmp\\\")\\n downFactor = self.downFactor.get()\\n args = \\\"-i %(micrographToExtract)s -o %(fnDownsampled)s --step %(downFactor)f --method fourier\\\"\\n localDeps=[self._insertRunJobStep(\\\"xmipp_transform_downsample\\\", args % locals(),prerequisites=localDeps)]\\n micrographToExtract = fnDownsampled\\n \\n # If remove dust \\n if self.doRemoveDust:\\n fnNoDust = self._getTmpPath(micName+\\\"_noDust.xmp\\\")\\n \\n thresholdDust = self.thresholdDust.get() #TODO: remove this extra variable\\n args=\\\" -i %(micrographToExtract)s -o %(fnNoDust)s --bad_pixels outliers %(thresholdDust)f\\\"\\n localDeps=[self._insertRunJobStep(\\\"xmipp_transform_filter\\\", args % locals(),prerequisites=localDeps)]\\n micrographToExtract = fnNoDust\\n \\n #self._insertFunctionStep('getCTF', micId, micName, micrographToExtract)\\n micName = removeBaseExt(mic.getFileName())\\n \\n # Actually extract\\n deps.append(self._insertFunctionStep('extractParticlesStep', micId, micName, \\n None, micrographToExtract, prerequisites=localDeps))\\n # TODO: Delete temporary files\\n \\n # Insert step to create output objects \\n self._insertFunctionStep('createOutputStep', prerequisites=deps)\",\n \"def getSteps():\",\n \"def _step(self):\\n pass\",\n \"def step(self, state):\",\n \"def _step(self, whence):\\n pass\",\n \"def rollout(env, acts):\\n total_rew = 0\\n env.reset()\\n steps = 0\\n for act in acts:\\n _obs, rew, done, _info = env.step(act)\\n steps += 1\\n total_rew += rew\\n if done:\\n break\\n\\n return steps, total_rew\",\n \"def step(self):\\n\\n pass\",\n \"def merge_steps(self, steps):\\n raise NotImplementedError\",\n \"def Advance():\\n warp.step()\",\n \"def step(self):\\n while self.state != STATE_TERMINAL:\\n self.step_strategies[self.state]()\",\n \"def step(self, move):\",\n \"def transduce(self,inputs):\\n self.start()\\n return [self.step(inp) for inp in inputs]\",\n \"def step(self, memories):\\n return\",\n \"def step(self, action):\",\n \"def run(commands):\\n\\n # Work out total steps to take\\n max_steps = sum([c.steps for c in commands])\\n\\n count = 0\\n while count != max_steps:\\n for command in commands:\\n # we want to interleave the commands\\n if command.steps > 0:\\n command.stepper.step(1, command.direction)\\n command.steps -= 1\\n count += 1\",\n \"def _step(self, a):\\n state, rew, done, info = super()._step(a)\\n render = self.get_render_obs()\\n return render, sum(self.rewards), bool(done), {}\",\n \"def _step(self, a):\\n obs, rew, done, info = super()._step(a)\\n # if self.robot.body_xyz[0] > self.threshold:\\n # rew = 1.0\\n # self.threshold += 1\\n # else:\\n # rew = 0.0\\n # self.steps += 1\\n # if self.steps > self.max_episode_steps:\\n # done = True\\n return obs, rew, done, info\",\n \"def step(self):\\n self.latent.step()\",\n \"def _step_snell(self) -> None:\\n self.snell.step()\",\n \"def perform_step(self) -> None:\\n pass\",\n \"def beam_step(self, paths, extra):\\n h_i, dec_out, context, src_mask = extra\\n last = paths[:, :, -1].view(1, -1)\\n dec_out, h_i = self.decode_rnn(context, h_i, dec_out, last, src_mask)\\n probs = self.output(dec_out)\\n dec_out = dec_out.squeeze(0)\\n return probs, (h_i, dec_out, context, src_mask)\",\n \"def _insertAllSteps(self):\\n \\n # Get pointer to input micrographs \\n self.particlePickingRun = self.xmippParticlePicking.get()\\n \\n copyId = self._insertFunctionStep('copyInputFilesStep')\\n # Get micrographs to pick\\n #self.inputMicrographs.set(self.getInputMicrographs())\\n \\n deps = []\\n for mic in self.getInputMicrographs():\\n stepId = self._insertFunctionStep('autopickMicrographStep', mic.getFileName(), prerequisites=[copyId])\\n deps.append(stepId)\\n \\n self._insertFunctionStep('_createOutput',self._getExtraPath(), prerequisites=deps)\",\n \"def step(self, amt=1):\\n \\n # For checking if all the animations have their framse looked at\\n #activewormind = [i for i, x in enumerate(self._idlelist) if x == False]\\n #print \\\"Worm {} at {:5g}\\\".format(activewormind, 1000*(time.time() - starttime))\\n # save times activated for each worm \\n [self.timedata[i].append(1000*(time.time() - starttime)) for i, x in enumerate(self._idlelist) if x == False]\\n \\n #self._led.buffer = [0] * 480\\n self._led.pixheights = [-100] * self._led.numLEDs\\n #print type(self._led.buffer)\\n for ledcopy in self._ledcopies:\\n # self._led.buffer = map(ixor, self._led.buffer, ledcopy.buffer)\\n # use pixheights but assume all buffers same size\\n # print ledcopy.driver[0].pixheights\\n for pix in range(self._led.numLEDs):\\n #for ledcopy in self._ledcopies:\\n if self._led.pixheights[pix] == ledcopy.driver[0].pixheights[pix]:\\n for i in range(3):\\n self._led.buffer[3*pix + i] ^= ledcopy.buffer[3*pix + i]\\n elif self._led.pixheights[pix] < ledcopy.driver[0].pixheights[pix]:\\n for i in range(3):\\n self._led.buffer[3*pix + i] = ledcopy.buffer[3*pix + i]\\n self._led.pixheights[pix] = ledcopy.driver[0].pixheights[pix] \\n self._step += 1\",\n \"def replay_steps(s):\\n if s is None:\\n print \\\"nothing to do\\\"\\n return\\n for (addr,action,arg) in s:\\n action(arg)\",\n \"def step(self, action):\\n reward_all = 0\\n pose_all = []\\n raw_states = []\\n for _ in range(self.action_repeat):\\n state, reward, done, _ = self.env.step(action) # run up the mountain\\n\\n processed_state = self.preprocess_state(state)\\n raw_states.append(state)\\n\\n if done and self.i < 200:\\n self.success = True\\n\\n reward_all += reward\\n pose_all.append(processed_state)\\n\\n self.i += 1\\n\\n if done:\\n missing = self.action_repeat - len(pose_all)\\n pose_all.extend([pose_all[-1]] * missing)\\n break\\n\\n next_state = np.concatenate(pose_all)\\n return next_state, reward_all, done, raw_states\",\n \"def __step(self, p):\\n action = self.__action(p)\\n temp_state = self.state\\n\\n if self.state == 0:\\n if action == 1:\\n self.state += 1\\n elif self.state == 1:\\n if action == 1:\\n self.state -= 1\\n else:\\n self.state += 1\\n else:\\n if action == 1:\\n self.state += 1\\n else:\\n self.state -= 1\\n \\n self.trajectory.append([temp_state, action, self.__reward(self.state)])\",\n \"def step(self, action) -> (list, float, bool):\\n if len(self.curr_stim) == 0:\\n self.curr_stim += [action[0]] * action[1] + [-action[0]] * action[1]\\n self.system_step()\\n self.frame += 1 / self.config[\\\"Fs\\\"]\\n self.history.append(self.x2-self.x1)\\n return self.get_state(), 0, False\",\n \"def steps(self,num_steps):\\n if self.last_sensation == TERMINAL_STATE:\\n self.start_episode()\\n for step in range(num_steps):\\n next_sensation,reward = self.env(self.next_action)\\n self.collect_data(self.last_sensation, self.next_action, reward, next_sensation)\\n self.next_action = self.agent(next_sensation,reward)\\n self.last_sensation = next_sensation\\n if self.last_sensation == TERMINAL_STATE:\\n self.start_episode()\",\n \"def get_next_steps(self, steps):\\n for step in range(steps):\\n # Actual calulation: Runge-Kutta 2\\n\\n # Step 1\\n k1 = [\\n self.vel * self.dt,\\n self.get_next_acc() * self.dt\\n ]\\n\\n # Step 2\\n next_pos = self.pos + k1[0] * 0.5\\n next_vel = self.vel + k1[1] * 0.5\\n self.disps, self.dists = self.get_relative_distances(positions=next_pos)\\n k2 = [\\n next_vel * self.dt,\\n self.get_next_acc(save=False) * self.dt\\n ]\\n\\n # Step 3\\n next_pos = self.pos + k2[0] * 0.5\\n next_vel = self.vel + k2[1] * 0.5\\n self.disps, self.dists = self.get_relative_distances(positions=next_pos)\\n k3 = [\\n next_vel * self.dt,\\n self.get_next_acc(save=False) * self.dt\\n ]\\n\\n # Step 4\\n next_pos = self.pos + k3[0]\\n next_vel = self.vel + k3[1]\\n self.disps, self.dists = self.get_relative_distances(positions=next_pos)\\n k4 = [\\n next_vel * self.dt,\\n self.get_next_acc(save=False) * self.dt\\n ]\\n\\n # Move forward\\n self.pos = self.pos + 1/6 * (k1[0] + 2*k2[0] + 2*k3[0] + k4[0])\\n self.vel = self.vel + 1/6 * (k1[1] + 2*k2[1] + 2*k3[1] + k4[1])\\n\\n # Saving of statistics\\n self.save_system_information(self.pos, self.vel)\",\n \"def step(self, **kwargs):\\n pass\",\n \"def step(self, action):\\n self.game.play_single_turn([action])\\n next_obs = self.get_state()\\n reward = self.game.player_1.score - self.game.turn_count\\n done = self.game.check_for_end_of_game() or self.game.turn_count > 25\\n\\n if done:\\n if self.game.player_1.score > self.game.player_2.score:\\n reward += 25\\n elif self.game.player_2.score > self.game.player_1.score:\\n reward -= 25\\n\\n if self.game.save_images_suffix:\\n image_suffix = f'{self.game.save_images_suffix}_{self.game.turn_count+1}'\\n self.game.game_board.graphical_output(save=True, display=False, image_suffix=image_suffix)\\n\\n return next_obs, reward/100, done\",\n \"def next_step(self):\\n self.proceed()\\n self.execute_current()\",\n \"def step(self, actions):\\n observations = []\\n rewards = []\\n dones = []\\n self.perform_action(self.car1, actions[0])\\n self.perform_action(self.car2, actions[1])\\n p.stepSimulation()\\n observations = self._get_obs()\\n for i in range(2):\\n rewards.append(self._get_rewards(i, observations[i]))\\n self.step_counter+=1\\n self.reduce = self.reduce * 0.9993\\n for i in range(2):\\n done, cause = self._check_done(observations[i])\\n dones.append(done)\\n if(done == 1):\\n if(cause==1):\\n rewards[1] += 72000\\n rewards[0] -= 72000\\n elif(cause==0):\\n rewards[0] += 72000\\n rewards[1] -= 72000\\n elif(cause==2):\\n rewards[i] -= 72000\\n return observations, rewards, dones, None\",\n \"def _worker(self):\\n player = self.first_player # first player\\n q = [None, self.q1, self.q2]\\n states = [None, 'reset', 'reset']\\n not_ret = None\\n while True:\\n op, a, ret = q[player].get()\\n # print(f'worker: player: {player} op: {op}')\\n try:\\n assert op == states[\\n player], f'state {op} expects {states[player]}'\\n\\n if op == 'reset':\\n if player == self.first_player:\\n # first time case\\n s = self.env.reset()\\n # set trace\\n self.trace.set_init(self.env)\\n legal_moves = self.env.legal_moves()\\n ret.set((s, None, None, None, legal_moves))\\n states[player] = 'step'\\n else:\\n states[player] = 'step'\\n\\n # switch player\\n if player == 1:\\n player = 2\\n else:\\n player = 1\\n not_ret = ret\\n elif op == 'step':\\n # handle error\\n s, r, done, info = self.env.step(a)\\n # keep trace\\n self.trace.step(a)\\n\\n if done:\\n ret.set((s, r, done, info, []))\\n not_ret.set((s, -r, done, info, []))\\n # end\\n self.done = True\\n break\\n else:\\n legal_moves = self.env.legal_moves()\\n not_ret.set((s, -r, done, info, legal_moves))\\n\\n # switch player\\n if player == 1:\\n player = 2\\n else:\\n player = 1\\n not_ret = ret\\n else:\\n raise NotImplementedError()\\n except Exception as e:\\n # exception\\n # return as exception\\n ret.set(e)\\n if not_ret is not None:\\n not_ret.set(\\n Exception('your opponent did something unexpected'))\\n self._exception(\\n Exception('something else terminated the environment'))\\n break\",\n \"def step(self):\\n for layer in self.layers:\\n layer.step()\",\n \"def step(self):\\r\\n self.datacollector.collect(self)\\r\\n self.datacollector2.collect(self)\\r\\n self.datacollector3.collect(self)\\r\\n self.datacollector4.collect(self)\\r\\n self.datacollector5.collect(self)\\r\\n self.datacollector6.collect(self)\\r\\n self.datacollector7.collect(self)\\r\\n self.datacollector8.collect(self)\\r\\n self.datacollector9.collect(self)\\r\\n self.datacollector10.collect(self)\\r\\n self.datacollector11.collect(self)\\r\\n self.datacollector12.collect(self)\\r\\n self.datacollector13.collect(self)\\r\\n\\r\\n self.datacollector14.collect(self)\\r\\n self.datacollector15.collect(self)\\r\\n self.datacollector16.collect(self)\\r\\n self.datacollector17.collect(self)\\r\\n self.datacollector18.collect(self)\\r\\n self.datacollector19.collect(self)\\r\\n self.datacollector20.collect(self)\\r\\n self.datacollector21.collect(self)\\r\\n self.datacollector22.collect(self)\\r\\n self.datacollector23.collect(self)\\r\\n self.datacollector24.collect(self)\\r\\n self.datacollector25.collect(self)\\r\\n self.datacollector26.collect(self)\\r\\n self.schedule.step()\",\n \"def side_step(r, num_repeats=1):\\n for i in range(num_repeats):\\n r.go(-10, -150)\\n time.sleep(2)\\n r.go(-20)\\n time.sleep(1)\\n r.go(-10, 150)\\n time.sleep(2)\\n r.stop()\\n time.sleep(.15)\\n for i in range(num_repeats):\\n r.go(-10, 150)\\n time.sleep(2)\\n r.go(-20)\\n time.sleep(1)\\n r.go(-10, -150)\\n time.sleep(2)\\n r.stop()\\n time.sleep(.15)\\n for i in range(num_repeats):\\n r.go(10, 150)\\n time.sleep(2)\\n r.go(20)\\n time.sleep(1)\\n r.go(10, -150)\\n time.sleep(2)\\n r.stop()\\n time.sleep(.15)\\n for i in range(num_repeats):\\n r.go(10, -150)\\n time.sleep(2)\\n r.go(20)\\n time.sleep(1)\\n r.go(10, 150)\\n time.sleep(2)\\n r.stop()\\n time.sleep(.15)\",\n \"def step(self):\\r\\n raise NotImplementedError\",\n \"def step(self, action):\\n actions = action.reshape(2, 2)\\n action = {'GoalieBrain': actions[0], 'StrikerBrain': actions[1]}\\n #action = {'GoalieBrain': [5, 5], 'StrikerBrain': [2, 2]}\\n #print(action)\\n info = self.env.step(action)\\n\\n state0 = info[self.brain_names[0]].vector_observations\\n state1 = info[self.brain_names[1]].vector_observations\\n state = np.vstack((state0, state1))\\n reward0 = info[self.brain_names[0]].rewards\\n reward1 = info[self.brain_names[1]].rewards\\n reward = reward0 + reward1\\n done0 = info[self.brain_names[0]].local_done\\n done1 = info[self.brain_names[1]].local_done\\n done = done0 + done1\\n return state, reward, done\",\n \"def _step(self, a):\\n obs, rew, done, info = super()._step(a)\\n # rew = +1 if past int threshold for first time in episode\\n # if self.robot.body_xyz[0] > self.threshold:\\n # self.threshold += 1\\n # rew = 1.0\\n # else:\\n # rew = 0.0\\n # self.steps += 1\\n # if self.steps > self.max_episode_steps:\\n # done = True\\n return obs, rew, done, info\",\n \"def step(self):\\n #_increment timers\\n for agent in self.agents:\\n agent.tick()\\n\\n # choose agent pair\\n agentA, agentB = self.choose()\\n\\n # interact\\n agentA.step(agentB)\\n agentB.step(agentA)\\n\\n # log results\\n self.logger.log(agentA, agentB)\\n\\n # increment counters\\n self.steps += 1\\n self.time += 1\",\n \"def step(self):\\n raise NotImplementedError\",\n \"def step(self):\\n\\t\\tnumpy.random.shuffle(self.agents_list)\\n\\t\\tfor agent in self.agents_list:\\n\\t\\t\\tagent.produce()\\n\\t\\tfor agent in self.agents_list:\\n\\t\\t\\tagent.charge()\\n\\t\\tfor agent in self.agents_list:\\n\\t\\t\\tif agent.strategy == 0: \\n\\t\\t\\t\\tagent.retribute()\\n\\t\\tfor agent in self.agents_list:\\n\\t\\t\\tif agent.strategy == 0: \\n\\t\\t\\t\\tagent.give()\\n\\t\\tfor agent in self.agents_list:\\n\\t\\t\\tagent.consume()\\n\\t\\tfor agent in self.agents_list:\\n\\t\\t\\tagent.solve_consumption_deficit()\\n\\t\\tfor site in self.sites:\\n\\t\\t\\tsite.recovery()\\n\\t\\tfor agent in self.agents_list:\\n\\t\\t\\tagent.sprout()\",\n \"def step(self, s, a):\\n raise NotImplementedError\",\n \"def step(self, actions):\\n assert len(actions) == len(self.workers)\\n for worker, action in zip(self.workers, actions):\\n worker.action_queue.put(action)\\n\\n results = []\\n for worker in self.workers:\\n worker.action_queue.join()\\n results.append(worker.result_queue.get())\\n\\n observations, rewards, dones = zip(*results)\\n\\n for i in range(self.num_envs):\\n self.curr_episode_reward[i] += rewards[i]\\n if dones[i]:\\n self.episode_rewards[i].append(self.curr_episode_reward[i])\\n self.curr_episode_reward[i] = 0\\n\\n return observations, rewards, dones\",\n \"def step(self, action):\\n if np.abs(action[0][0]-action[1][0])==2:\\n self.board[(action[0][0]+action[1][0])//2,(action[0][1]+action[1][1])//2]=0\\n self.board[action[1]] = self.board[action[0]] \\n if action[1][0]==0 or action[1][0]==7:\\n self.board[action[1]] = 2*np.sign(self.board[action[0]])\\n self.board[action[0]] = 0\\n self.turn = (self.turn + 1)%2\\n self.actions=[]\\n for i in range(8):\\n for j in range(8):\\n if np.sign(self.board[i,j])==(-1)**self.turn:\\n moves=(self.bdiag(i,j),self.badiag(i,j),self.fdiag(i,j),self.fadiag(i,j))\\n for r in range(4):\\n if moves[r] is not None:\\n self.actions.append(moves[r])\\n winner = self.winner(action)\\n if winner is not None:\\n rewards = np.array([winner,(-1)*winner])\\n else:\\n rewards = np.array([0,0])\\n self.done = winner is not None\\n return self.board.copy(), rewards, self.done, self.turn\",\n \"def step(self, action):\\n reward = 0\\n pose_all = []\\n self.rotor_speeds = np.array([action]*4)\\n for _ in range(self.action_repeat):\\n done = self.sim.next_timestep(self.rotor_speeds) # update the sim pose and velocities\\n reward += self.get_reward()\\n pose_all += [self.sim.pose]\\n if self.sim.crashed:\\n reward = -5\\n done = True\\n #if (np.square(self.sim.pose[:3] - self.target_pos)).sum() < 1: # Close enough!\\n #done = True\\n next_state = np.concatenate(pose_all)\\n return next_state, reward, done\",\n \"def step(self, action: list) -> None:\\n self._input = np.array(\\n [self._thrust_surge(action[0]), self._moment_steer(action[1])]\\n )\\n w, q = odesolver45(\\n self._state_dot, self._state, self.config.simulation.t_step_size\\n )\\n\\n self._state = q\\n self._state[2] = geom.princip(self._state[2])\\n\\n self._prev_states = np.vstack([self._prev_states, self._state])\\n self._prev_inputs = np.vstack([self._prev_inputs, self._input])\\n\\n self._step_counter += 1\",\n \"def run_one_step(self):\\n pass\",\n \"def step(self):\\n self.world.slosh_oceans()\\n self.world.transfer_energy_vertically()\\n self.world.transfer_energy_horizontally()\\n self.world.absorb_energy_from_core()\\n self.world.absorb_energy_from_sun(self.sun)\",\n \"def step(self):\\n self.log.info(\\\"Stepping %r\\\", self)\\n # get enabled transitions before reseting inputs\\n scope_transitions = self.get_enabled_transitions_by_scope()\\n\\n # reset inputs/outputs at the start of a big step\\n if self.enabled_inputs:\\n self.log.info(\\\"Reseting inputs and outputs at start of big step...\\\")\\n self.enabled_inputs = set()\\n self.outputs = set()\\n self.locals = set()\\n\\n # execute transitions\\n # - note that each scope is non-overlapping by definition\\n updates = dict()\\n for scope, transitions in scope_transitions.items():\\n if len(transitions) > 1: # non-determinism\\n pass\\n transition = transitions[0]\\n self._execute_transition(transition, updates)\\n\\n # update variables\\n self.variables.update(updates)\\n self.log.info(\\\"Variables now %r\\\", self.variables)\",\n \"def step(self, actions):\\n assert (len(actions) == self.num_actions)\\n actions = np.around(actions)\\n actions = np.clip(actions, 0, 1)\\n self.done = self.network.perform_actions(actions)\\n self.cur_pos = self._get_current_pos_in_1d()\\n self.reward = self.network.get_reward()\\n\\n return self.cur_pos, self.reward, self.done, {}\",\n \"def step_async(self, actions):\",\n \"def stepo_cmd(cmd, cnt, args):\\n #log(\\\"stepo\\\"+str(args[1:]))\\n cpu.set_break(cpu.pc+4)\\n cpu.list_breaks()\\n go_cmd(cmd, cnt, args)\\n cpu.clear_break(cpu.pc+4)\\n cpu.list_breaks()\",\n \"def step(self, steps):\\n self._simulate(endStep=self.currentStep+steps)\",\n \"def step(self, action):\\n self._last_base_position = self.rex.GetBasePosition()\\n self._last_base_orientation = self.rex.GetBaseOrientation()\\n if self._is_render:\\n # Sleep, otherwise the computation takes less time than real time,\\n # which will make the visualization like a fast-forward video.\\n time_spent = time.time() - self._last_frame_time\\n self._last_frame_time = time.time()\\n time_to_sleep = self.control_time_step - time_spent\\n if time_to_sleep > 0:\\n time.sleep(time_to_sleep)\\n base_pos = self.rex.GetBasePosition()\\n # Keep the previous orientation of the camera set by the user.\\n [yaw, pitch, dist] = self._pybullet_client.getDebugVisualizerCamera()[8:11]\\n self._pybullet_client.resetDebugVisualizerCamera(dist, yaw, pitch, base_pos)\\n\\n for env_randomizer in self._env_randomizers:\\n env_randomizer.randomize_step(self)\\n\\n # change up swing and stance ratio and desired speeds randomly for robustness\\n if np.random.randint(300) == 0:\\n self.ratio = np.random.uniform(self.min_swing_ratio, self.max_swing_ratio)\\n\\n if np.random.randint(300) == 0:\\n self.speed = np.random.uniform(self.min_speed, self.max_speed)\\n self.speed_des[0] = self.speed\\n\\n if np.random.randint(300) == 0:\\n self.side_speed = np.random.uniform(self.min_side_speed, self.max_side_speed)\\n self.speed_des[1] = self.side_speed\\n\\n self.base_vel_curr_trans, self.base_vel_curr_rot = self.get_base_velocity()\\n action = self._transform_action_to_motor_command(action)\\n self.rex.Step(action)\\n self.base_vel_next_trans, self.base_vel_next_rot = self.get_base_velocity()\\n \\n self._env_step_counter += 1\\n self.phase += self._action_repeat # the cycle length is CYCLE_TIME/time_step so can add \\n # how many times an action was repeated\\n\\n if self.phase > self.cycle_len:\\n self.phase = self.phase % self.cycle_len \\n self.cycle_complete += 1\\n\\n reward = self._reward()\\n done = self._termination()\\n\\n if done:\\n self.rex.Terminate()\\n\\n return np.array(self._get_observation_np()), reward, done, {'action': action}\",\n \"def step(self, action):\\n \\n success = False\\n self.curr_step += 1\\n self._take_action(action)\\n self._take_action(action)\\n self._take_action(action)\\n\\n # initialize reward and get state \\n reward = 0.0\\n ob = self._get_state()\\n\\n # give dense rewards \\n if not self.sparse_reward:\\n reward = self._get_reward()\\n\\n # bad terminal conditions\\n if self.curr_step >= self.max_steps \\\\\\n or self.target_distance >= self.max_distance \\\\\\n or self.mean_radius_sheep >= self.max_radius:\\n self.finish = True\\n if self.sparse_reward:\\n reward = -1.0\\n\\n # good terminal conditions\\n if self.target_distance <= 1.0:\\n success = True\\n self.finish = True\\n if self.sparse_reward:\\n reward = 1.0\\n\\n # update rl parameters\\n self.episode_length += 1\\n self.episode_reward += reward\\n\\n # generate info return parameter\\n if self.info_mode == 1 and self.finish:\\n info = {'r':self.episode_reward, 'l':self.episode_length, \\n 's': success}\\n else:\\n info = {'n':self.num_sheep, 's': success}\\n\\n return ob, reward, self.finish, info\",\n \"def compute_step(self):\\n self._apply_controls(step=self.step, post=True)\\n self._step += 1\\n for gate_layer in self._tebd_propagator.gate_layers:\\n self._t_mps.apply_nn_gate_layer(gate_layer)\\n self._t_mps.apply_process_tensors(self.step,\\n self._process_tensors)\\n for gate_layer in self._tebd_propagator.gate_layers:\\n self._t_mps.apply_nn_gate_layer(gate_layer)\\n self._apply_controls(step=self.step, post=False)\\n self._append_results()\",\n \"def twist(self):\\n self.right()\\n time.sleep(1)\\n self.stop()\\n self.left()\\n time.sleep(1)\\n self.stop()\",\n \"def train_loop_pre(self, current_step):\\r\\n pass\",\n \"def step(self):\\n try:\\n self.agents.sort(key=lambda x: x.dist)\\n except Exception as e:\\n print(e)\\n\\n for agent in self.agents:\\n try:\\n agent.step()\\n except Exception as e:\\n print(e)\\n\\n\\n # Removes agents if they reach exit\\n for exit in self.model.exits:\\n x, y = exit.pos[0] * 6 + 1, exit.pos[1] * 6 + 1\\n if agent.node == (x, y):\\n try:\\n agent.saved()\\n except Exception as e:\\n print(e)\",\n \"def step(self, _action):\\n action = np.hstack((np.zeros(6), _action/10.))\\n self.ref_skel.set_positions(self.ref_state.angles)\\n for i in range(self.step_per_frame):\\n # self.skel.set_forces(self.skel.get_spd(self.ref_skel.q + action, self.world.time_step(), self.Kp, self.Kd))\\n self.skel.set_forces(self.skel.get_spd(self.ref_state.angles + action, self.world.time_step(), self.Kp, self.Kd))\\n self.world.step()\\n\\n self.ref_state_time += self.step_per_frame * self.world.time_step()\\n if self.ref_state_time >= self.ref_state.dt:\\n self.ref_state_time -= self.ref_state.dt\\n self.ref_state = self.ref_state.get_next()\\n\\n return tuple([self.state(), self.reward(), self.is_done(), dict()])\",\n \"def step(self, action):\\n pass\",\n \"def step(self, action):\\n pass\",\n \"def step(self, action):\\n\\n # ==\\n # Transition, reward and termination\\n done = False\\n reward = self.get_current_reward(self.state)\\n\\n # Leaf and absorbing nodes\\n if self.state <= 1:\\n done = True\\n if self.state == 1:\\n self.state = 0 # go to absorbing\\n else:\\n self.state = int(self.state // 2)\\n\\n # ==\\n # Features\\n phi = self.state_2_features(self.state)\\n\\n return phi, reward, done, {}\",\n \"def step(self, action):\\n\\n action[1] = 0 if action[1] < 0 else 1\\n\\n if not self.moving:\\n self.agent_host.sendCommand(\\\"move 0.5\\\")\\n time.sleep(.2)\\n self.moving = True\\n\\n # Get Action\\n command = \\\"strafe \\\" + str(action[0])\\n if ((action[0] < 0 and self.allow_left) or (action[0] > 0 and self.allow_right)):\\n self.agent_host.sendCommand(command)\\n time.sleep(.2)\\n self.agent_host.sendCommand(\\\"strafe 0\\\")\\n time.sleep(.1)\\n\\n if action[1]:\\n if self.checkCommand:\\n self.jumpsOverDitches += 1\\n self.checkCommand = False\\n self.agent_host.sendCommand(\\\"jump 1\\\")\\n time.sleep(.2)\\n self.agent_host.sendCommand(\\\"jump 0\\\")\\n\\n # if (command == \\\"crouch 1\\\"):\\n # self.agent_host.sendCommand(command)\\n # time.sleep(.3)\\n # self.agent_host.sendCommand(\\\"crouch 0\\\")\\n # time.sleep(.2)\\n\\n self.episode_step += 1\\n\\n # Get Observation\\n world_state = self.agent_host.getWorldState()\\n for error in world_state.errors:\\n print(\\\"Error:\\\", error.text)\\n self.obs, self.allow_left, self.allow_right, curZPos, curXPos = self.get_observation(world_state)\\n if curZPos:\\n self.curZPos = curZPos\\n if curXPos:\\n if self.obs[3 + int(curXPos)]:\\n self.checkCommand = True\\n self.numDitchesEncountered += 1\\n\\n # Get Done\\n done = not world_state.is_mission_running \\n\\n # Get Reward\\n reward = 0\\n for r in world_state.rewards:\\n reward += r.getValue()\\n self.episode_return += reward\\n\\n return self.obs, reward, done, dict()\",\n \"def _step(self, rl_actions):\\n self.timer += 1\\n # pdb.set_trace()\\n # perform acceleration and (optionally) lane change actions for\\n # traci-controlled human-driven vehicles\\n if len(self.controlled_ids) > 0:\\n accel = []\\n for veh_id in self.controlled_ids:\\n # acceleration action\\n action = self.vehicles.get_acc_controller(veh_id).get_action(\\n self)\\n accel.append(action)\\n\\n # lane changing action\\n lane_flag = 0\\n if type(self.scenario.lanes) is dict:\\n if any(v > 1 for v in self.scenario.lanes.values()):\\n lane_flag = 1\\n else:\\n if self.scenario.lanes > 1:\\n lane_flag = 1\\n\\n if self.vehicles.get_lane_changing_controller(veh_id) \\\\\\n is not None and lane_flag:\\n new_lane = \\\\\\n self.vehicles.get_lane_changing_controller(\\n veh_id).get_action(self)\\n self.apply_lane_change([veh_id], target_lane=[new_lane])\\n\\n self.apply_acceleration(self.controlled_ids, acc=accel)\\n\\n # perform (optionally) lane change actions for sumo-controlled\\n # human-driven vehicles\\n if len(self.sumo_ids) > 0:\\n for veh_id in self.sumo_ids:\\n # lane changing action\\n if self.scenario.lanes > 1:\\n lc_contr = self.vehicles.get_lane_changing_controller(\\n veh_id)\\n if lc_contr is not None:\\n new_lane = lc_contr.get_action(self)\\n self.apply_lane_change([veh_id], target_lane=[new_lane])\\n\\n # perform (optionally) routing actions for all vehicle in the network,\\n # including rl vehicles\\n routing_ids = []\\n routing_actions = []\\n for veh_id in self.ids:\\n if self.vehicles.get_routing_controller(veh_id) is not None:\\n routing_ids.append(veh_id)\\n route_contr = self.vehicles.get_routing_controller(veh_id)\\n routing_actions.append(route_contr.choose_route(self))\\n\\n self.choose_routes(veh_ids=routing_ids, route_choices=routing_actions)\\n\\n self.apply_rl_actions(rl_actions)\\n\\n self.additional_command()\\n # pdb.set_trace()\\n self.traci_connection.simulationStep()\\n #Error being thrown from sumo/src/utils/foxtools/MFXImageHelper.cpp\\n # self.traci_connection.gui.screenshot(\\\"View #0\\\", \\\"/home/mkoren/Pictures/a\\\"+str(self.timer)+\\\".png\\\")\\n # pdb.set_trace()\\n # store new observations in the network after traci simulation step\\n network_observations = \\\\\\n self.traci_connection.vehicle.getSubscriptionResults()\\n crash = False\\n for veh_id in self.ids:\\n prev_pos = self.get_x_by_id(veh_id)\\n prev_lane = self.vehicles.get_lane(veh_id)\\n try:\\n self.vehicles.set_position(\\n veh_id, network_observations[veh_id][tc.VAR_LANEPOSITION])\\n except KeyError:\\n continue\\n self.vehicles.set_edge(\\n veh_id, network_observations[veh_id][tc.VAR_ROAD_ID])\\n self.vehicles.set_lane(\\n veh_id, network_observations[veh_id][tc.VAR_LANE_INDEX])\\n if network_observations[veh_id][tc.VAR_LANE_INDEX] != prev_lane \\\\\\n and veh_id in self.rl_ids:\\n self.vehicles.set_state(veh_id, \\\"last_lc\\\", self.timer)\\n self.vehicles.set_speed(\\n veh_id, network_observations[veh_id][tc.VAR_SPEED])\\n\\n try:\\n change = self.get_x_by_id(veh_id) - prev_pos\\n if change < 0:\\n change += self.scenario.length\\n new_abs_pos = self.vehicles.get_absolute_position(\\n veh_id) + change\\n self.vehicles.set_absolute_position(veh_id, new_abs_pos)\\n except ValueError or TypeError:\\n self.vehicles.set_absolute_position(veh_id, -1001)\\n\\n if self.vehicles.get_position(veh_id) < 0 or \\\\\\n self.vehicles.get_speed(veh_id) < 0:\\n crash = True\\n\\n # collect headway, leader id, and follower id data\\n headway_data = \\\\\\n self.get_headway_dict(network_observations=network_observations)\\n\\n for veh_id in self.ids:\\n try:\\n self.vehicles.set_headway(\\n veh_id, headway_data[veh_id][\\\"headway\\\"])\\n self.vehicles.set_leader(\\n veh_id, headway_data[veh_id][\\\"leader\\\"])\\n self.vehicles.set_follower(\\n veh_id, headway_data[veh_id][\\\"follower\\\"])\\n except KeyError:\\n # occurs in the case of crashes, so headway is assumed to\\n # be very small\\n self.vehicles.set_headway(veh_id, 1e-3)\\n self.vehicles.set_leader(veh_id, None)\\n self.vehicles.set_follower(veh_id, None)\\n\\n # collect list of sorted vehicle ids\\n self.sorted_ids, self.sorted_extra_data = self.sort_by_position()\\n\\n # collect information of the state of the network based on the\\n # environment class used\\n if self.vehicles.num_rl_vehicles > 0:\\n self.state = self.get_state()\\n # rllab requires non-multi agent to have state shape as\\n # num-states x num_vehicles\\n if not self.multi_agent:\\n self.state = self.state.T\\n else:\\n self.state = []\\n # collect observation new state associated with action\\n\\n next_observation = list(self.state)\\n\\n # crash encodes whether sumo experienced a crash\\n crash = \\\\\\n crash \\\\\\n or self.traci_connection.simulation.getEndingTeleportNumber() != 0 \\\\\\n or self.traci_connection.simulation.getStartingTeleportNumber() != 0\\n\\n # compute the reward\\n if self.vehicles.num_rl_vehicles > 0:\\n reward = self.compute_reward(self.state, rl_actions, fail=crash)\\n else:\\n reward = 0\\n\\n # Are we in a multi-agent scenario? If so, the action space is a list.\\n if self.multi_agent:\\n done_n = self.vehicles.num_rl_vehicles * [0]\\n info_n = {'n': []}\\n\\n if self.shared_reward:\\n info_n['reward_n'] = [reward] * len(self.action_space)\\n else:\\n info_n['reward_n'] = reward\\n\\n if crash:\\n done_n = self.vehicles.num_rl_vehicles * [1]\\n if self.fail_safe:\\n logging.error(\\\"Crash has occurred! Check your failsafes\\\")\\n\\n info_n['done_n'] = done_n\\n info_n['state'] = self.state\\n done = np.all(done_n)\\n return self.state, sum(reward), done, info_n\\n\\n else:\\n if crash:\\n logging.info(\\\"Crash has occurred! Check failsafes!\\\")\\n return Step(observation=next_observation, reward=reward,\\n done=True)\\n else:\\n return Step(observation=next_observation, reward=reward,\\n done=False)\",\n \"def train_loop_post(self, current_step):\\r\\n pass\",\n \"def step(self, step=None):\\n pass\",\n \"def step(self, actions, agent_id=0):\\n self._last_state = self._current_state\\n\\n # TODO\\n # action = actions.discrete_actions[0]-1\\n action = actions.argmax()\\n\\n done = 0\\n if self._stage == 0: # is fixation\\n if action == 0:\\n reward = 0.\\n else:\\n reward = -1.\\n self._current_state = 1\\n self._stage = 1\\n elif self._stage == 1: # is first stage, use prob_transition\\n if action == 1 or action == 2:\\n if np.random.random() < self._prob_transition[0][action-1]:\\n self._current_state = 2\\n else:\\n self._current_state = 3\\n reward = 0.\\n else: # pick a next state at random\\n reward = -1.\\n self._current_state = np.random.random() < 0.5 and 2 or 3\\n self._stage = 2\\n else: # is second stage, use prob_reward\\n # Given an action (arm pulled), sample reward, return\\n if action == 1 or action == 2:\\n current_prob_rewards = self._prob_reward[self._current_state-2]\\n self._best_reward = self._max_reward*np.max(current_prob_rewards)\\n thisProb = current_prob_rewards[action-1]\\n if np.random.random() < thisProb:\\n # print(\\\"give reward\\\")\\n reward = self._max_reward\\n else:\\n reward = 0.0\\n else:\\n reward = -1.\\n\\n self._total_reward += reward\\n self._best_total_reward += self._best_reward\\n self._stage = 0\\n self._current_state = 0\\n self._trial += 1\\n self._since_flipped += 1\\n # if more than self._min_stable trials since flipping, certain chance of flipping prob rews\\n if (self._since_flipped >= self._min_stable) and (np.random.random() <= self._flip_prob):\\n self._randomize()\\n self._since_flipped = 0\\n\\n\\n self._last_action = np.zeros(self._num_arms)\\n self._last_action[action] = 1\\n # conditions to end episode\\n if self._step >= self._steps_per_ep-1:\\n self._state = READY_TO_END_EPISODE\\n done = 1\\n\\n self._step += 1\\n self._prev_reward = reward\\n\\n obs = self._current_state\\n reset = done == 1. or self._step == MAX_FRAMES\\n\\n # print(np.array([[obs]]).shape)\\n\\n # print(reward, self._stage)\\n return np.array([obs]), reward, done, reset\",\n \"def step(self, p1_action, p2_action):\\r\\n p1_r = self.paddle_1.update(p1_action)\\r\\n p2_r = self.paddle_2.update(p2_action)\\r\\n \\r\\n p1_state, p2_state, p1_reward, p2_reward, done = self.ball.update(self.paddle_1, self.paddle_2)\\r\\n\\r\\n return p1_state, p2_state, (p1_reward+p1_r), (p2_reward+p2_r), done\",\n \"def try_step_multi (self, actions):\\n legal_action_indices = []\\n legal_action_features = []\\n\\n for i, action in enumerate(actions):\\n object_index, new_location, action_means, action_stds = action\\n \\n position = new_location[:2]\\n rotation = new_location[2]\\n\\n prev_transform = self.e.objects[object_index].transform\\n\\n if self.e.act(object_index, Command(position, rotation)):\\n inputs = self.get_feature_only()\\n\\n legal_action_indices.append(i)\\n legal_action_features.append(inputs)\\n\\n # Back transform\\n self.e.act(object_index, \\n self.command_from_transform(prev_transform), check_condition = False )\\n\\n\\n if len(legal_action_indices) == 0:\\n return ([], [])\\n\\n legal_action_feats = np.stack(legal_action_features)\\n\\n action_size = legal_action_feats.shape[0]\\n num_steps = legal_action_feats.shape[1]\\n feature_size = legal_action_feats.shape[2]\\n epoch_size = int(np.ceil(action_size / self.config.batch_size))\\n\\n zero_filled = np.zeros( ( epoch_size * self.config.batch_size, num_steps, feature_size ))\\n\\n zero_filled[:legal_action_feats.shape[0]] = legal_action_feats\\n\\n zero_filled = zero_filled.reshape((epoch_size, self.config.batch_size, num_steps, feature_size))\\n\\n all_progress = []\\n for i in range(epoch_size):\\n progress = self.progress_estimator.predict(zero_filled[i], sess = self.session)\\n all_progress.append(progress)\\n\\n all_progress = np.concatenate(all_progress)\\n \\n\\n return (legal_action_indices, all_progress[:action_size])\",\n \"def _step(self, a):\\n # potential_old = self.potential\\n obs, rew, done, info = super()._step(a)\\n # state = self.robot.calc_state()\\n # alive = float(self.robot.alive_bonus(state[0]+self.robot.initial_z, self.robot.body_rpy[1]))\\n # alive *= 0.05\\n # cost = 0.01 * -np.square(a).sum()\\n # progress = float(self.potential - potential_old)\\n # # print (\\\"Rewarsd\\\", alive, progress)\\n # rew = alive + progress + cost\\n # # if self.robot.body_xyz[0] > 5:\\n # # rew = 1.0\\n # # else:\\n # # rew = 0.0\\n # # print (\\\"ROBOT: \\\", self.robot.body_xyz[2] < 0.3)\\n # # if done:\\n # # print (\\\"DONE\\\")\\n return obs, rew, done, info\",\n \"def step(self, action):\\n # TODO: code here\\n y, x = self.state\\n dy, dx = self.moves[action]\\n next_x, next_y = x+dx, y+dy\\n\\n next_x = np.clip(next_x, 0, self.width-1) # clip the values to the world\\n next_y = np.clip(next_y, 0, self.height-1) # clip the values to the world\\n\\n if next_y == 1:\\n rand = np.random.uniform()\\n if rand < 0.2:\\n next_x += 1\\n elif rand < 0.7:\\n next_x += 2\\n else:\\n next_x += 3\\n\\n next_x = np.clip(next_x, 0, self.width - 1)\\n\\n if next_x == 4 and next_y == 1:\\n reward = -1\\n done = True\\n elif next_x == 4 and next_y == 2:\\n reward = 1\\n done = True\\n else:\\n reward = 0\\n done = False\\n\\n next_state = (next_y, next_x)\\n self.state = next_state\\n\\n return next_state, reward, done, {}\",\n \"def WarpStep(iters=5):\\n MSG(\\\"WarpStep\\\")\\n for j in range(iters):\\n warp.step()\\n return\",\n \"def build_step(self):\\n\\n pass\",\n \"def step(self, a):\\n if self.mirror and self.phase >= self.max_phase / 2:\\n a = self.reflect_action(a)\\n self.time += 1\\n\\n self.posbefore = self.robot_skeleton.q[0]\\n\\n self.do_dart_clocks(a)\\n self.set_phase(self.phase + 1)\\n\\n self.posafter = self.robot_skeleton.q[0]\\n\\n # ref_pos, ref_vel = self.get_kin_state()\\n # self.set_state(ref_pos, ref_vel)\\n\\n # common behavior for returning step() results\\n done = self.is_done()\\n ob = self._get_obs()\\n reward = self.compute_reward()\\n self.reward_buffer.append(reward)\\n self.total_reward += reward\\n\\n self.energy += np.square(a).sum()\\n return ob, reward, done, {}\",\n \"def foward_shimmey(self):\\n for x in range(6):\\n self.right(primary=60, counter=30)\\n time.sleep(.5)\\n self.left(primary=70, counter=30)\\n time.sleep(.5)\\n self.back()\\n time.sleep(2) \\n self.stop()\",\n \"def build_step(self):\\n pass\",\n \"def build_step(self):\\n pass\",\n \"def step(self):\\n self.function()\",\n \"def _step(self, board, elapsedTime):\\n\\t\\tpass\",\n \"def run_one_timestep(self):\\n\\n print(\\\"=\\\"*50)\\n print()\\n\\n forks = []\\n\\n # Update sensors\\n for sensor in self.sensors:\\n if isinstance(sensor, Camera):\\n thread = threading.Thread(target=sensor.update)\\n forks.append(thread)\\n thread.start()\\n else:\\n sensor.update()\\n\\n for fork in forks:\\n fork.join()\\n\\n forks = []\\n # Update sensobs\\n for sensob in self.sensobs:\\n thread = threading.Thread(target=sensob.update)\\n forks.append(thread)\\n thread.start()\\n\\n for fork in forks:\\n fork.join()\\n\\n # Update behaviours\\n for behav in self.active_behavs:\\n behav.update()\\n\\n # Invoke arbitrator\\n motor_rec = self.arbit.choose_action() # Returns a tuple(list(motor_recommendations), halt)\\n print(\\\"Arbitrator chose: \\\"+str(motor_rec))\\n\\n if motor_rec[1]: # Check halt recommendation\\n return False # Halt and exit program\\n\\n\\n # Update motobs\\n print(self.motobs)\\n i = 0\\n for motob in self.motobs: # Updates each motob with it's respective motor recommendation\\n print(\\\"Bbcon: Updating motob \\\" + str(i))\\n\\n motob.update(motor_rec[0][i])\\n i += 1\\n\\n # Wait\\n time.sleep(0.5) #waits half a second\\n\\n # Reset sensors\\n for sensor in self.sensors:\\n sensor.reset()\\n\\n return True\",\n \"def _step(self, action):\\n \\n obs, reward, done, info = self.env.step(action)\\n\\n \\n advice=self.generateAdvice()[1]\\n\\n obs = {\\n \\\"image\\\": obs,\\n \\\"advice\\\": advice\\n }\\n\\n\\n \\n\\n\\n\\n return obs, reward, done, info\",\n \"def debuggerstep(self, action):\\n\\n\\n\\n #Calculate actual Next State you are supposed to reach via action\\n if action == 0:\\n nxtState = (self.state[0] - 1, self.state[1])\\n elif action == 1:\\n nxtState = (self.state[0], self.state[1] - 1)\\n elif action == 2:\\n nxtState = (self.state[0], self.state[1] + 1) \\n elif action == 3: \\n nxtState = (self.state[0] + 1, self.state[1])\\n\\n \\n #BUT YOU CAN ONLY REACH THERE WITH 80% PROBABIBILITY\\n #Stocasticity Implementation\\n correctMove = random.random() \\n \\n #Check if nextState to reach is valid, Redundant Check (Might have to remove in future iterations)\\n if self.isValid(nxtState): \\n #If you have a valid next state, you reach there with 80% probability\\n if correctMove <= 0.8: \\n \\n print(\\\"Ended up in correct state taking action \\\", end = \\\"\\\")\\n if (action == 0): \\n print(\\\"Up\\\")\\n elif (action == 1): \\n print(\\\"Left\\\")\\n elif (action == 2): \\n print(\\\"Right\\\")\\n elif(action == 3): \\n print(\\\"Down\\\")\\n self.state = nxtState\\n self.isEnd()\\n return nxtState, self.giveReward(), self.isTerminal\\n \\n #Else you didn't end up in right place\\n else: \\n \\n print(\\\"Ended up in wrong state. Had to go \\\", end = \\\"\\\")\\n if (action == 0): \\n print(\\\"Up \\\", end = \\\"\\\")\\n elif (action == 1): \\n print(\\\"Left \\\", end = \\\"\\\")\\n elif (action == 2): \\n print(\\\"Right \\\", end = \\\"\\\")\\n elif(action == 3): \\n print(\\\"Down \\\", end = \\\"\\\")\\n \\n print(\\\"And end up in \\\", end = \\\"\\\")\\n print(nxtState)\\n \\n print(\\\"But ended up in: \\\", end = \\\"\\\")\\n \\n \\n #Find remaining states that can be possibly reached: \\n altActions =[]\\n \\n if action == 0:\\n altActions.append(1)\\n altActions.append(2)\\n elif action == 1: \\n altActions.append(0)\\n altActions.append(3)\\n elif action == 2: \\n altActions.append(0)\\n altActions.append(3)\\n else: \\n altActions.append(1)\\n altActions.append(2)\\n \\n \\n #Pick one random of all possible next states\\n altAction = random.choice(altActions)\\n #Check if alternate possibility is valid \\n if altAction == 0:\\n nxtState1 = (self.state[0] - 1, self.state[1])\\n elif altAction == 1:\\n nxtState1 = (self.state[0], self.state[1] - 1)\\n elif altAction == 2:\\n nxtState1 = (self.state[0], self.state[1] + 1) \\n elif altAction == 3: \\n nxtState1 = (self.state[0] + 1, self.state[1])\\n \\n \\n \\n #If alternate possibility is valid, update values\\n if self.isValid(nxtState1): \\n print(nxtState1)\\n \\n #Update Values \\n self.state = nxtState1\\n self.isEnd()\\n return nxtState1, self.giveReward(), self.isTerminal\\n \\n #If alternate possibility is not valid, then you stay in place\\n else: \\n #Stay in place \\n print(self.state)\\n print(\\\"Stayed in Place!\\\")\\n self.isEnd()\\n return self.state, self.giveReward(), self.isTerminal\\n else: \\n #Stay in place \\n print(self.state)\\n print(\\\"Invalid action picked, Stayed in Place!\\\")\\n self.isEnd()\\n return self.state, self.giveReward(), self.isTerminal\",\n \"def step_env(self):\\n raise NotImplementedError\\n # Not needed for this homework\",\n \"def step(self):\\n raise NotImplementedError()\",\n \"def step(self):\\n raise NotImplementedError()\",\n \"def step(self):\\n raise NotImplementedError()\",\n \"def shifter(self):\\n #self.BA_shift = self.timeshift_latitude(self.latB, self.latA)\\n #self.BC_shift = self.timeshift_latitude(self.latB, self.latC)\\n\\n\\n self.shifted = True #changing boolean to True when function is called.\\n\\n secondsA = self.secondsA\\n secondsB = self.secondsB\\n secondsC = self.secondsC\\n\\n NeA = self.holefill(self.NeA, secondsA)\\n NeB = self.holefill(self.NeB, secondsB)\\n NeC = self.holefill(self.NeC, secondsC)\\n\\n start = 0\\n stop = len(NeA) - np.max(np.array([self.BA_shift, self.BC_shift]))\\n\\n startA = start + self.BA_shift\\n stopA = stop + self.BA_shift\\n\\n startC = start + self.BC_shift\\n stopC = stop + self.BC_shift\\n\\n NeA = NeA[startA:stopA]\\n NeB = NeB[start:stop]\\n NeC = NeC[startC:stopC]\\n\\n longA = self.holefill(self.longA, secondsA)\\n longB = self.holefill(self.longB, secondsB)\\n longC = self.holefill(self.longC, secondsC)\\n longA = longA[startA:stopA]\\n longB = longB[start:stop]\\n longC = longC[startC:stopC]\\n\\n latA = self.holefill(self.latA, secondsA)\\n latB = self.holefill(self.latB, secondsB)\\n latC = self.holefill(self.latC, secondsC)\\n latA = latA[startA:stopA]\\n latB = latB[start:stop]\\n latC = latC[startC:stopC]\\n\\n radA = self.holefill(self.radA, secondsA)\\n radB = self.holefill(self.radB, secondsB)\\n radC = self.holefill(self.radC, secondsC)\\n radA = radA[startA:stopA]\\n radB = radB[start:stop]\\n radC = radC[startC:stopC]\\n\\n velA = self.holefill(self.velA, secondsA)\\n velB = self.holefill(self.velB, secondsB)\\n velC = self.holefill(self.velC, secondsC)\\n velA = velA[startA:stopA]\\n velB = velB[start:stop]\\n velC = velC[start:stop]\\n\\n altA = self.holefill(self.altA, secondsA)\\n altB = self.holefill(self.altB, secondsB)\\n altC = self.holefill(self.altC, secondsC)\\n altA = altA[startA:stopA]\\n altB = altB[start:stop]\\n altC = altC[startC:stopC]\\n\\n\\n mlatA = self.holefill(self.mlatA, secondsA)\\n mlatB = self.holefill(self.mlatB, secondsB)\\n mlatC = self.holefill(self.mlatC, secondsC)\\n mlatA = mlatA[startA:stopA]\\n mlatB = mlatB[start:stop]\\n mlatC = mlatC[startC:stopC]\\n\\n mlongA = self.holefill(self.mlongA, secondsA)\\n mlongB = self.holefill(self.mlongB, secondsB)\\n mlongC = self.holefill(self.mlongC, secondsC)\\n mlongA = mlongA[startA:stopA]\\n mlongB = mlongB[start:stop]\\n mlongC = mlongC[startC:stopC]\\n\\n mltA = self.holefill(self.mltA, secondsA)\\n mltB = self.holefill(self.mltB, secondsB)\\n mltC = self.holefill(self.mltC, secondsC)\\n mltA = mltA[startA:stopA]\\n mltB = mltB[start:stop]\\n mltC = mltC[startC:stopC]\\n\\n secondsA = self.holefill(secondsA, secondsA)\\n secondsB = self.holefill(secondsB, secondsB)\\n secondsC = self.holefill(secondsC, secondsC)\\n secondsA = secondsA[startA:stopA]\\n secondsB = secondsB[start:stop]\\n secondsC = secondsC[startC:stopC]\\n\\n indsA = np.nonzero(secondsA)[0]\\n indsB = np.nonzero(secondsB)[0]\\n indsC = np.nonzero(secondsC)[0]\\n\\n inds = np.intersect1d(indsA, indsB)\\n inds = np.intersect1d(inds, indsC)\\n\\n self.NeA = NeA[inds]\\n self.NeB = NeB[inds]\\n self.NeC = NeC[inds]\\n\\n self.longA = longA[inds]\\n self.longB = longB[inds]\\n self.longC = longC[inds]\\n\\n self.latA = latA[inds]\\n self.latB = latB[inds]\\n self.latC = latC[inds]\\n\\n self.radA = radA[inds]\\n self.radB = radB[inds]\\n self.radC = radC[inds]\\n\\n self.velA = velA[inds]\\n self.velB = velB[inds]\\n self.velC = velC[inds]\\n\\n self.altA = altA[inds]\\n self.altB = altB[inds]\\n self.altC = altC[inds]\\n\\n self.mlatA = mlatA[inds]\\n self.mlatB = mlatB[inds]\\n self.mlatC = mlatC[inds]\\n\\n self.mlongA = mlongA[inds]\\n self.mlongB = mlongB[inds]\\n self.mlongC = mlongC[inds]\\n\\n self.mltA = mltA[inds]\\n self.mltB = mltB[inds]\\n self.mltC = mltC[inds]\\n\\n self.secondsA = secondsA[inds]\\n self.secondsB = secondsB[inds]\\n self.secondsC = secondsC[inds]\",\n \"def step(self, actions):\\n self.step_async(actions)\\n return self.step_wait()\",\n \"def step(self, actions):\\n self.step_async(actions)\\n return self.step_wait()\",\n \"def step(self, action_history, observations):\\n return self.call(action_history, observations)\",\n \"def step(self, action, state):\\r\\n done = False\\r\\n next_state = json.loads(state)\\r\\n state_shift = self.action_map[action]\\r\\n\\r\\n next_state = np.array(next_state) + np.array(state_shift)\\r\\n next_state = next_state.tolist()\\r\\n\\r\\n reward = self.reward[next_state[0], next_state[1]]\\r\\n self.reward[next_state[0], next_state[1]] = 0\\r\\n\\r\\n next_state = json.dumps(next_state)\\r\\n\\r\\n if reward < 0 or reward == 4:\\r\\n done = True\\r\\n\\r\\n return next_state, reward, done\",\n \"def _step(self):\\n title()\\n self.runCount = 1\\n self.experiment.pause = False\\n self._runExperiment()\\n self.pause = True\",\n \"def step(self, action):\\n self.steps += 1\\n self.robots[0].setAction(action)\\n for i in range(self.num_agents):\\n if i != 0 and self.policies[i:i+1]: # self.policies[0] is dummy\\n self.robots[i].setAction(self.policies[i](self.robots[i].getObservation()))\\n # rewards = [ -1.0 * self.num_foods / self.max_steps for _ in range(self.num_agents) ] # so agent needs to eat foods quickly\\n rewards = [ 0.0 for _ in range(self.num_agents) ]\\n for i in range(self.BULLET_STEPS):\\n p.stepSimulation()\\n rewards = [ rewards[i]+self._getReward(self.robots[i]) for i in range(self.num_agents) ]\\n self.episode_rewards = [ self.episode_rewards[i]+rewards[i] for i in range(self.num_agents) ]\\n obs = self.robots[0].getObservation()\\n done = self._isDone()\\n info = { 'steps': self.steps }\\n if done:\\n # TODO\\n info['episode'] = { 'r': self.episode_rewards[0], 'l': self.steps, 'r_all': self.episode_rewards }\\n # print(self.episode_rewards, self.steps)\\n return obs, rewards[0], done, info\",\n \"def step(self, action):\\r\\n s = self.get_state()\\r\\n\\r\\n elements = np.arange(self.S)\\r\\n # weights = np.squeeze(self.nextStateProbability[s,action])\\r\\n weights = self.nextStateProbability[s, action]\\r\\n nexts = choices(elements, weights, k=1)[0]\\r\\n\\r\\n # p = self.nextStateProbability[s,action]\\r\\n # reward = self.rewardsTable[s,action, nexts][0]\\r\\n reward = self.rewardsTable[s, action, nexts]\\r\\n\\r\\n # fully observable MDP: observation is the actual state\\r\\n self.currentObservation = nexts\\r\\n\\r\\n gameOver = False\\r\\n if self.currentIteration > np.Inf:\\r\\n ob = self.reset()\\r\\n gameOver = True # game ends\\r\\n else:\\r\\n ob = self.get_state()\\r\\n\\r\\n history = {\\\"time\\\": self.currentIteration, \\\"state_t\\\": s, \\\"action_t\\\": action,\\r\\n \\\"reward_tp1\\\": reward, \\\"state_tp1\\\": nexts}\\r\\n # history version with actions and states, not their indices\\r\\n # history = {\\\"time\\\": self.currentIteration, \\\"action_t\\\": self.actionListGivenIndex[action],\\r\\n # \\\"reward_tp1\\\": reward, \\\"observation_tp1\\\": self.stateListGivenIndex[self.get_state()]}\\r\\n self.currentIteration += 1\\r\\n return ob, reward, gameOver, history\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7023969","0.68842584","0.6786682","0.6676937","0.66504234","0.66142386","0.6586369","0.6399443","0.63699806","0.6316583","0.6300726","0.6257871","0.62492967","0.6244361","0.6241148","0.6221081","0.62170863","0.6203809","0.61842567","0.609894","0.60633224","0.6030294","0.6027132","0.6013036","0.5997533","0.59927773","0.59781766","0.5975023","0.59666723","0.595736","0.5946955","0.59350383","0.59132236","0.59124553","0.5883799","0.58802","0.5880014","0.58701164","0.58631366","0.58619994","0.5858798","0.5852991","0.58401585","0.5837425","0.58311296","0.5824893","0.58158726","0.5796747","0.5788174","0.57868373","0.57654417","0.5763441","0.57597375","0.5757498","0.57574826","0.5757104","0.57450426","0.5741315","0.5737747","0.5736147","0.57217777","0.56957203","0.5686131","0.5683849","0.5680842","0.56768954","0.5674372","0.5674372","0.5670874","0.5668457","0.56567436","0.5654488","0.56535864","0.5649533","0.56489795","0.56461555","0.5637036","0.5635394","0.5629772","0.5624793","0.56202775","0.56195736","0.56168103","0.56168103","0.56087565","0.5604516","0.56044513","0.55924946","0.55919206","0.55889696","0.5588819","0.5588819","0.5588819","0.5587241","0.55822295","0.55822295","0.55743253","0.5569521","0.5569393","0.556537","0.5556575"],"string":"[\n \"0.7023969\",\n \"0.68842584\",\n \"0.6786682\",\n \"0.6676937\",\n \"0.66504234\",\n \"0.66142386\",\n \"0.6586369\",\n \"0.6399443\",\n \"0.63699806\",\n \"0.6316583\",\n \"0.6300726\",\n \"0.6257871\",\n \"0.62492967\",\n \"0.6244361\",\n \"0.6241148\",\n \"0.6221081\",\n \"0.62170863\",\n \"0.6203809\",\n \"0.61842567\",\n \"0.609894\",\n \"0.60633224\",\n \"0.6030294\",\n \"0.6027132\",\n \"0.6013036\",\n \"0.5997533\",\n \"0.59927773\",\n \"0.59781766\",\n \"0.5975023\",\n \"0.59666723\",\n \"0.595736\",\n \"0.5946955\",\n \"0.59350383\",\n \"0.59132236\",\n \"0.59124553\",\n \"0.5883799\",\n \"0.58802\",\n \"0.5880014\",\n \"0.58701164\",\n \"0.58631366\",\n \"0.58619994\",\n \"0.5858798\",\n \"0.5852991\",\n \"0.58401585\",\n \"0.5837425\",\n \"0.58311296\",\n \"0.5824893\",\n \"0.58158726\",\n \"0.5796747\",\n \"0.5788174\",\n \"0.57868373\",\n \"0.57654417\",\n \"0.5763441\",\n \"0.57597375\",\n \"0.5757498\",\n \"0.57574826\",\n \"0.5757104\",\n \"0.57450426\",\n \"0.5741315\",\n \"0.5737747\",\n \"0.5736147\",\n \"0.57217777\",\n \"0.56957203\",\n \"0.5686131\",\n \"0.5683849\",\n \"0.5680842\",\n \"0.56768954\",\n \"0.5674372\",\n \"0.5674372\",\n \"0.5670874\",\n \"0.5668457\",\n \"0.56567436\",\n \"0.5654488\",\n \"0.56535864\",\n \"0.5649533\",\n \"0.56489795\",\n \"0.56461555\",\n \"0.5637036\",\n \"0.5635394\",\n \"0.5629772\",\n \"0.5624793\",\n \"0.56202775\",\n \"0.56195736\",\n \"0.56168103\",\n \"0.56168103\",\n \"0.56087565\",\n \"0.5604516\",\n \"0.56044513\",\n \"0.55924946\",\n \"0.55919206\",\n \"0.55889696\",\n \"0.5588819\",\n \"0.5588819\",\n \"0.5588819\",\n \"0.5587241\",\n \"0.55822295\",\n \"0.55822295\",\n \"0.55743253\",\n \"0.5569521\",\n \"0.5569393\",\n \"0.556537\",\n \"0.5556575\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":92597,"cells":{"query":{"kind":"string","value":"See if an IP is up"},"document":{"kind":"string","value":"def check_alive(hostname):\n\n LOGGER.info(\"Seeing if %s is alive\", hostname)\n\n try:\n subprocess.check_call(\"ping -o -c1 -W1 {}\".format(hostname), shell=True)\n except subprocess.CalledProcessError as exc:\n LOGGER.info(\"Host %s appears down\", hostname)\n return False\n else:\n return True"},"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 checkIP(self, ip = None):\n\t\treturn os.system(\"ping -c 1 -w2 \" + ip + \" > /dev/null 2>&1\") == 0","def is_alive(self):\n ret = subprocess.call(\n shlex.split(\"ping -c 1 -W 2 %s\" % self.ip_address),\n stdout=open('/dev/null', 'w'),\n stderr=subprocess.STDOUT,\n )\n \n if ret == 0:\n return True\n else:\n return False","def containsip(url):\r\n try:\r\n if ip.ip_address(url):\r\n return 1\r\n except:\r\n return 0","def isReachable(self):\n cmd = \"ping -c 1 %s\" % self.ip\n ping_output = commands.getoutput(cmd)\n logger.debug(cmd)\n logger.debug(ping_output)\n return re.search(\"1[\\s\\w]+received\", ping_output) is not None","def icmp_probe(self, ip):\n\n\t\tcmd = 'ping %s -n 10' % ip\n\t\tp = Popen(cmd, shell=True, stdin=PIPE, stderr=PIPE, stdout=PIPE)\n\t\tres = p.stdout.read()\n\n\t\tres = res.decode()\n\t\tif len(p.stderr.read()) == 0:\n\t\t\tif 'Destination host unreachable' in res:\n\t\t\t\treturn False\n\t\t\treturn True\n\t\telse:\n\t\t\treturn False","def IsLinkup(nic,timeout):\n nic = nic.strip()\n current = time.time()\n timeout += current\n while current < timeout:\n data = os.popen(\"ipconfig\").read().split(\"Ethernet adapter\")\n for item in data:\n if item.count(nic) and item.count(\"isconnected\") == 0: #Connected\n return 1\n time.sleep(0.5)\n current = time.time()\n return 0","def include_up_hosts(nmap_host):\n if nmap_host.status == 'up':\n return True\n return False","def is_black_ip(ip):\n cur = conn.cursor()\n cur.execute('SELECT COUNT(ip) FROM black_ips WHERE ip = ?', (ip,))\n\n row = cur.fetchone()\n return row[0] > 0","def is_valid_ip(ip):\n ...","def is_if_up(ifname):\n with open('/sys/class/net/' + ifname + '/carrier', 'r') as f:\n status = f.readline()\n return (status == '1')","def health_check(self):\n headers = {\"NDS-Proxy-Ping\": \"NPP\"}\n url = \"http://{host}:{port}/upm\".format(host=self.upm_host, port=self.upm_port)\n is_available, http_code = http_utilities.get(url, headers=headers)\n\n if http_code == 200:\n self.log.info(\"The UPM is available\")\n return True\n else:\n self.log.error(\"The UPM is not available\")\n return False","def internet_on():\n try:\n urllib.request.urlopen('http://216.58.192.142', timeout=1)\n return True\n except urllib.error.URLError: \n return False","def local_is_up(self, target):\n try:\n check_address(target)\n except ValueError:\n self.logger.warning('Target must be a tuple (IP, port), where IP '\n 'is a string (i.e. \"192.168.0.1\") and port is '\n 'an integer (i.e. 40000). Alternatively '\n 'target can be a valid UNIX domain socket.')\n return False\n\n self.check_tunnels()\n return self.tunnel_is_up.get(target, True)","def online_check():\n try_first_ips = [\n \"216.58.213.238\", # google\n \"8.8.8.8\", # google\n \"8.8.4.4\", # google\n \"46.228.47.115\", # yahoo\n ]\n last_resort_ips = [ # dns root servers\n \"198.41.0.4\",\n \"192.228.79.201\",\n \"192.33.4.12\",\n \"128.8.10.90\",\n \"192.203.230.10\",\n \"192.5.5.241\",\n \"192.112.36.4\",\n \"128.63.2.53\",\n \"192.36.148.17\",\n \"192.58.128.30\",\n \"193.0.14.129\",\n \"198.32.64.12\",\n \"202.12.27.33\"\n ]\n\n iplists = []\n iplists.append(try_first_ips)\n iplists.append(rand_ips(max_num=50))\n iplists.append(last_resort_ips)\n\n return any(can_ping_host(ip) for ip in chain(*iplists))","def _IsReady(self):\n if self.ip_address is None:\n self._GetIpAddress()\n if self.ip_address is not None:\n url = 'http://%s' % (self.ip_address)\n r = requests.get(url)\n if r.status_code == 200:\n return True\n return False","def is_website_online(host):\n try:\n socket.gethostbyname(host)\n except socket.gaierror:\n return False\n else:\n return True","def isIP(ipToTest):\n \n try:\n socket.inet_aton(ipToTest)\n return True\n except socket.error:\n return False","def validate_nic_down(self): \n\n pool = WorkerPool()\n\n try: \n for nic, hostname in self.nic_to_address_map:\n address = self.nic_to_address_map[(nic, hostname)]\n cmd = Ping('ping validation', address, ctxt=REMOTE, remoteHost='localhost')\n pool.addCommand(cmd)\n pool.join()\n\n for cmd in pool.getCompletedItems():\n results = cmd.get_results()\n if results.rc == 0:\n return False\n finally:\n pool.haltWork()\n pool.joinWorkers()\n pool.join()\n\n tinctest.logger.info(\"Successfully brought down nics ...\") \n return True","def ping_ip(ip_address, count):\n reply = subprocess.run(\n f\"ping -c {count} -n {ip_address}\",\n shell=True,\n stdout=subprocess.PIPE,\n stderr=subprocess.PIPE,\n encoding=\"utf-8\",\n )\n if reply.returncode == 0:\n return True\n else:\n return False","def IsWirelessUp(self):\n return self.wifi.IsUp()","def check_up(addr, p):\n sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n result = sock.connect_ex((addr, p))\n sock.close()\n if result == 0:\n ans = True\n else:\n ans = False\n return ans","def test_ping(self):\n status, output = commands.getstatusoutput('ping -c 5 %s' % self.known_ip)\n assert status == 0","def check_status(self):\n try:\n self.server.ping()\n return True\n except Exception as e:\n return False","def is_reserved(ip):\n if ip_between(ip, \"0.0.0.0\", \"0.255.255.255\"):\n return True\n elif ip_between(ip, \"10.0.0.0\", \"10.255.255.255\"):\n return True\n elif ip_between(ip, \"100.64.0.0\", \"100.127.255.255\"):\n return True\n elif ip_between(ip, \"127.0.0.0\", \"127.255.255.255\"):\n return True\n elif ip_between(ip, \"169.254.0.0\", \"169.254.255.255\"):\n return True\n elif ip_between(ip, \"172.16.0.0\", \"172.31.255.255\"):\n return True\n elif ip_between(ip, \"192.0.0.0\", \"192.0.0.255\"):\n return True\n elif ip_between(ip, \"192.0.2.0\", \"192.0.2.255\"):\n return True\n elif ip_between(ip, \"192.88.99.0\", \"192.88.99.255\"):\n return True\n elif ip_between(ip, \"192.168.0.0\", \"192.168.255.255\"):\n return True\n elif ip_between(ip, \"198.18.0.0\", \"198.19.255.255\"):\n return True\n elif ip_between(ip, \"198.51.100.0\", \"198.51.100.255\"):\n return True\n elif ip_between(ip, \"203.0.113.0\", \"203.0.113.255\"):\n return True\n elif ip_between(ip, \"224.0.0.0\", \"255.255.255.255\"):\n return True\n else:\n return False","def testIP(IP):\n \"\"\" will feed this an array with mp.Pool.map() \"\"\"\n IPup = []\n IPdown = []\n with open(os.devnull, \"w\") as fnull:\n if subprocess.call(['ping','-c 1 -W 1',IP,],stdout=fnull, stderr=fnull)==0:\n print 'host %s is UP' % IP\n return IP\n else:\n print 'host %s is DOWN' % IP\n return None","def is_alive(addr, user):\n return _ssh_master_cmd(addr, user, 'check') == 0","def checkIPValid(self, ipAddr):\n try:\n socket.inet_aton(ipAddr)\n return True\n except socket.error:\n return False","def isonline():\n\n conn = httplib.HTTPConnection(\"www.google.com\", timeout=5)\n try:\n conn.request(\"HEAD\", \"/\")\n conn.close()\n return True\n except:\n conn.close()\n return False","def is_connected():\r\n ipconfig_output = terminal('ipconfig | findstr /i gateway')\r\n if ipconfig_output != None:\r\n return any(i for i in ipconfig_output if i.isdigit())\r\n \r\n # Alternative way if ipconfig has error in some systems\r\n ## Slower than ipconfig workaround\r\n try:\r\n socket().connect(('8.8.8.8', 53))\r\n return True\r\n except:\r\n return False","def is_ip(self) -> bool:\n return self.typ == ETH_P_IP","def is_connected():\n import socket\n try:\n host = socket.gethostbyname(\"www.gov.uk\")\n socket.create_connection((host, 80), 2)\n return True\n except:\n pass\n return False","def checkIP(self):\n\t\tself.get(\"https://ifconfig.me/\")\n\t\treturn self.findId(\"ip_address\").text","def is_ready(self, addr: int, /) -> bool:","def check_vpn_interface():\n return validate_vpn_interface(call_command('netstat -i')[0].split('\\n'))","def check_proxy_status(proxy_ip):\n try:\n status = subprocess.check_output([\"ping\", \"-c\",\"1\", proxy_ip]).decode('utf-8')\n if status.find(\"1 received\") > -1:\n return True\n except subprocess.CalledProcessError as e:\n return False\n\n return False","async def ping(self, ctx, ip):\n\n # Check for valid IP else do DNS lookup\n valid_ip = re.compile(\"[0-9]{,3}\\.[0-9]{,3}\\.[0-9]{,3}\")\n valid_hostname = re.compile(\".*\\.[a-zA-Z]{2,}\")\n valid = False\n\n if valid_ip.match(ip):\n valid = True\n elif valid_hostname.match(ip):\n valid = True\n try:\n await self.bot.say('Doing DNS lookup...')\n ip = socket.gethostbyname(ip)\n\n if valid == True:\n start = time.time()\n response = os.system(\"sudo ping -c 1 -w3 \" + ip)\n duration = time.time() - start\n duration = round(duration * 1000, 0)\n if response == 0:\n await self.bot.say(ip + ' is up and responding in ' +\n str(duration) + 'ms.')\n else:\n await self.bot.say(ip + ' is not reachable.')\n else:\n await self.bot.say(ip + ' is not a valid IP or Domain.')\n\n except socket.gaierror:\n await self.bot.say('Whoops! That Address cant be resolved!')","def IsLinkdown(nic,timeout):\n nic = nic.strip()\n current = time.time()\n timeout += current\n while current < timeout:\n data = os.popen(\"ipconfig\").read().split(\"Ethernet adapter\")\n for item in data:\n if item.count(nic) and item.count(\"isconnected\"): #Disconnected\n return 1\n time.sleep(0.5)\n current = time.time()\n return 0","def validIPAddress(ip):\n try:\n socket.inet_aton(ip)\n return True\n except socket.error:\n return False","def _checknet():\n exit_code = os.system('ping -c 1 www.baidu.com 1>/dev/null 2>&1')\n return exit_code","def ping(self):\n return True","def ping(self):\n return True","def isup(self):\n if self.cloudserver:\n # print self.cloudserver.status\n if self.cloudserver.status in (\"ACTIVE\",):\n return True\n \n return False","def is_actual_ip(self, ip_addr):\n try:\n socket.inet_aton(ip_addr)\n return True\n except socket.error:\n return False","def check_availability(self):\n\t\tif not self.connection_is_usable:\n\t\t\treturn False\n\t\twith self.client_lock:\n\t\t\tif self.stream is None:\n\t\t\t\treturn False\n\t\t\tif self.last_ping is None or self.last_ping.age() >= self.ping_max_age:\n\t\t\t\tself.last_ping = SendPing(self, self.ping_timeout)\n\t\t\tlast_ping = self.last_ping\n\t\treturn last_ping.answered(self.ping_timeout)","def checkAlive(self, timeout = 1500 , port = 3389):\n time_retry = 90\n # ugly c-style loop \n while 1:\n try:\n ip = self.getIp()\n if not ip:\n logging.warning(\"!Failed to obtain ip address\")\n else:\n logging.info(\"Probing \" + str(ip) + \":\" + str(port) + \" for connectivity\")\n sock = socket.create_connection((ip,port) , timeout)\n sock.close()\n logging.info(\"Server \" + str(ip) + \":\" + str(port) + \" successfully responded\")\n return True\n except Exception as e:\n logging.error(\"!: Failed to probe the remote server for a connection!\")\n logging.error(\"!:\" + str(e))\n logging.error(traceback.format_exc())\n timeout = timeout - time_retry\n if timeout > 0:\n logging.info(\"--- Waiting more \" + str(timeout) + \" for it to respond\");\n time.sleep(time_retry)\n else:\n break\n\n return False","def _LeasedIP(self):\n check_command = 'ip addr show {interface} | grep \"inet \"'.format(\n interface=self.interface)\n try:\n # grep exit with return code 0 when we have retrieved an IP.\n out = self._device.CheckOutput(check_command)\n except device_types.CalledProcessError:\n return False\n # ex: inet 192.168.159.78/20 brd 192.168.159.255 scope global wlan0\n return out.split()[1].split('/')[0]","async def check_pause(self, ip: str) -> bool:\n miner = self.miners[ip]\n return not miner.running.is_set()","def is_alive(self, site):\n try:\n return requests.get(site).status_code == 200\n except Exception:\n pass","def _wait_until_up(self, address, port, timeout):\n\n def check_up(addr, p):\n \"\"\"\n Find out if a port at an address is occupied\n \"\"\"\n sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n result = sock.connect_ex((addr, p))\n sock.close()\n if result == 0:\n ans = True\n else:\n ans = False\n return ans\n\n max_time = time() + timeout\n up = False\n while not up and time() < max_time:\n self.log.debug(\"Checking if Galaxy is up...\")\n up = check_up(address, port)\n\n # If we query Galaxy immediately it may reset the connection:\n sleep(10)\n\n if not up:\n raise Exception('There was no response at {} on port {} for {} seconds'\n .format(address, port, timeout))","def _wait_for_ip(name, session):\n start_time = datetime.now()\n status = None\n while status is None:\n status = get_vm_ip(name, session)\n if status is not None:\n # ignore APIPA address\n if status.startswith(\"169\"):\n status = None\n check_time = datetime.now()\n delta = check_time - start_time\n log.debug(\n \"Waited %s seconds for %s to report ip address...\", delta.seconds, name\n )\n if delta.seconds > 180:\n log.warning(\"Timeout getting IP address\")\n break\n time.sleep(5)","def checkForIsUpAlert(self, website, availability):\n checkTime = time.time()\n # Verify that the system has been running for longer than 2 minutes\n if (checkTime - self.originalTime >= 120):\n # Verify that the system has been alerted by the site\n if website.name in self.alertsDic:\n # Verify that the UP alert wasn't already sent\n if self.alertsDic[website.name]:\n if availability > 0.8 :\n alert = {'website': website.name, 'time': time.time(), 'availability': availability,\n 'status': 'UP'}\n self.alertsDic[website.name] = None\n self.mailer.sendAlert(alert)\n self.queueAlerts.put(alert)\n return","def ip_status(ip, rnge):\n\tactive = []\n\tdown = []\n\ttimed_out = []\n\t\n\t\"\"\" Get the first 3 fields of IP \"\"\"\n\taddres = ip.split(\".\")[:3]\n\tne_ip = \".\".join(addres)\n\t\n\t\"\"\" Get the last field of IP \"\"\"\n\tlst_field = ip.split(\".\")[3]\n\t\n\tif rnge == \"0\" or rnge == \"\":\n\t\trnge = 1\n\t\n\tif 0 <= int(rnge) <= 255 and ip_pattern.match(ip):\n\t\t\"\"\" Calculate the actual range \"\"\"\n\t\tactual_rnge = int(lst_field) + int(rnge)\n\n\t\t\"\"\" Range of IP address to be pinged \"\"\"\n\t\t\"\"\" Right now it starts from 0 through 10 but can be changed in the below line \"\"\"\n\t\n\t\tfor i in range(int(lst_field), actual_rnge):\t\t\n\t\t\n\t\t\tip_fin = ne_ip + \".\" + str(i)\n\t\t\tstatus = sp.check_output([\"ping \", ip_fin])\n\t\t\n\t\t\tif pattern1.search(status) :\n\t\t\t\tdown.append(ip_fin)\n\t\t\telif pattern2.search(status):\n\t\t\t\ttimed_out.append(ip_fin)\n\t\t\telse:\n\t\t\t\tactive.append(ip_fin)\n\t\n\t\tresult = \"\\nActive IP addresses are: \" + str(active) + \"\\n\\n\" + \"Unreachable IP: \" + str(down) + \"\\n\\n\" + \"Timed out IP's: \" + str(timed_out)\n\t\treturn result\n\t\n\telse:\n\t\treturn \"1) Range should be in between 0 and 255 inclusive. \\n2) Check the IP format.\"","def in_remaining_local_ips(self, ip: str) -> bool:\n return ip in self.remaining_local_ips","def _ip_is_usable(self, current_ip):\n # Consider IP addresses only.\n try:\n ipaddress.ip_address(current_ip)\n except ValueError:\n return False\n\n # Never use real IP.\n if current_ip == self.real_ip:\n return False\n\n # Do dot allow IP reuse.\n if not self._ip_is_safe(current_ip):\n return False\n\n return True","def check_ups(self, ups): #pylint: disable=no-self-use\n try:\n args = [\n MONITOR_COMMAND,\n f\"{ups}@localhost\"\n ]\n #pylint: disable=unused-variable\n results = subprocess.run(args, check=True, capture_output=True)\n return True\n except subprocess.CalledProcessError:\n return False","def match(self, _ip):\n try:\n return bool(ip_address(_ip) in self.network)\n except ValueError:\n return False","def check_public_ipv6(node):\n\n ok = False\n for ii in node.ifaces:\n# try:\n for ip in ii.addrs:\n ip = ip.split('/')[0]\n if ipaddress.ip_address(ip).version == 6:\n if ip[0] not in ['f',\":\"]:\n ok = True\n\n# except:\n# print(ok)\n\n return ok","def internet_on(): \n try:\n urlopen('http://www.google.com', timeout=2)\n return True\n except urlopen.URLError as err: \n return False","def ping(conn: psycopg2.connect) -> bool:\n\n is_alive = False\n with conn.cursor() as cur:\n cur.execute(\"SELECT 1\")\n if cur.description is not None:\n fetched = cur.fetchall()\n try:\n is_alive = fetched[0][0] == 1\n except IndexError:\n pass\n return is_alive","def wait(self, timeout=None):\n if timeout is None:\n timeout = self.timeout\n started = time.time()\n while True:\n if self.get_ip():\n self.state = State.RUNNING\n return True\n else:\n time.sleep(1)\n if timeout != 0:\n if time.time() - started > timeout:\n return False","def ping(self):\n self.last_seen_at = time.time()\n last_online = self.online\n self.online = True\n return last_online != self.online","def checklan(ipaddr, network):\n return True","def nat_waitforconn_alive():\r\n return NAT_STATE_DATA[\"mux\"] != None and NAT_STATE_DATA[\"mux\"].isAlive()","async def ping_http(self) -> bool:\n # pause logic\n if not self.running.is_set():\n self.add_to_output(\"Paused...\")\n await self.running.wait()\n\n # ping port 80 (HTTP)\n if await self.ping(80):\n # ping returned true, HTTP is up\n return True\n else:\n # ping returned false, HTTP is down\n return False","def isup(sourcename) :\n return s.isUp(sourcename)","def check_no_network():\n try:\n socket.gethostbyname(\"www.google.com\")\n return False\n except:\n return True","def ping():\n api_online = bool(check_url(\"https://rest.ensembl.org/info/ping?\"))\n vertebrate_url_online = bool(check_url(\"http://ftp.ensembl.org\"))\n other_url_online = bool(check_url(\"http://ftp.ensemblgenomes.org\"))\n return api_online and vertebrate_url_online and other_url_online","def ip_is_local(ip):\n return localinterfaces.is_public_ip(ip) or localinterfaces.is_local_ip(ip)","def validateIP():\n try:\n s = socket.inet_aton(args.target)\n except socket.error:\n print(\"\")\n print(f\"{bad_cmd} Bad IP address\")\n print(\"\")\n sys.exit()","def is_ip(address):\n try:\n socket.inet_pton(socket.AF_INET, address)\n except socket.error:\n try:\n socket.inet_pton(socket.AF_INET6, address)\n except socket.error:\n return False\n return True","def pass_sanity_checks():\n if not check_database_connection():\n return False\n\n # The IP address might take some time to arrive\n have_public_ip = False\n for i in range(10):\n if public_ip():\n have_public_ip = True\n break\n else:\n time.sleep(5)\n\n if not have_public_ip:\n return False\n\n if not access_s3():\n return False\n\n # If we get here we're good\n return True","def is_reachable(self, userid):\n return self._smtclient.get_guest_connection_status(userid)","async def ping_ssh(self) -> bool:\n # pause logic\n if not self.running.is_set():\n self.add_to_output(\"Paused...\")\n await self.running.wait()\n\n # ping port 22 (SSH)\n if await self.ping(22):\n # ping returned true, SSH is up\n return True\n else:\n # ping returned false, SSH is down\n return False","def checkWifi():\n try:\n subprocess.check_output(\"iwgetid\")\n return True\n except subprocess.CalledProcessError: # if not connected\n return False","def alive(p):\n return p.is_alive()","def is_safe_ip(ip):\n # should define global\n # white_ip_list = ('127.0.0.1', '192.168.1.1')\n try:\n socket.inet_pton(socket.AF_INET, ip)\n except AttributeError:\n try:\n socket.inet_aton(ip)\n except socket.error:\n return False\n except socket.error:\n return False\n\n if ip.count('.') == 3 and ip in current_app.config['API_WHITE_IP_LIST']:\n return True\n else:\n return False","def _check_connection() -> bool:\n return bool(subprocess.check_output([\"hostname\", \"-I\"]))","def is_alive(self):\n if self.status == 1:\n return True\n else:\n return False","def public_ip():\n found_public_ip = False\n try:\n metadata = get_instance_metadata()\n for key, value in metadata.items():\n LOG.info(\"{0}: {1}\".format(key, value))\n\n if metadata['public-ipv4'] is not None:\n try:\n socket.inet_aton(metadata['public-ipv4'])\n found_public_ip = True\n except socket.error:\n found_public_ip = False\n\n except Exception:\n LOG.exception('check_database_connection')\n return False\n return found_public_ip","def is_host_available(self, context, hostname):\n try:\n host = self._get_plugin().get_host_by_name(context, hostname)\n return host['availability'] == constants.HOST_UP\n except ext_host.HostNotFoundByName:\n # Does not exist yet\n return False","def _check_tunnel(self, _srv):\n if self.skip_tunnel_checkup:\n self.tunnel_is_up[_srv.local_address] = True\n return\n self.logger.info('Checking tunnel to: {0}'.format(_srv.remote_address))\n if isinstance(_srv.local_address, string_types): # UNIX stream\n s = socket.socket(socket.AF_UNIX, socket.SOCK_STREAM)\n else:\n s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n s.settimeout(TUNNEL_TIMEOUT)\n try:\n # Windows raises WinError 10049 if trying to connect to 0.0.0.0\n connect_to = ('127.0.0.1', _srv.local_port) \\\n if _srv.local_host == '0.0.0.0' else _srv.local_address\n s.connect(connect_to)\n self.tunnel_is_up[_srv.local_address] = _srv.tunnel_ok.get(\n timeout=TUNNEL_TIMEOUT * 1.1\n )\n self.logger.debug(\n 'Tunnel to {0} is DOWN'.format(_srv.remote_address)\n )\n except socket.error:\n self.logger.debug(\n 'Tunnel to {0} is DOWN'.format(_srv.remote_address)\n )\n self.tunnel_is_up[_srv.local_address] = False\n\n except queue.Empty:\n self.logger.debug(\n 'Tunnel to {0} is UP'.format(_srv.remote_address)\n )\n self.tunnel_is_up[_srv.local_address] = True\n finally:\n s.close()","def verify_ip_address(ip):\n try:\n ipaddress.ip_address(ip)\n return True\n except ValueError:\n return False","def check_up(self):\n\n s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n s.settimeout(BusController.PULSE_DELAY * 0.8)\n output = True\n start_time = time.time()\n try:\n s.connect((self.__address[0], BusController.HEART_BEAT_PORT))\n data = \"Check\".encode()\n s.send(data)\n data = s.recv(1024).decode()\n if int(data) != int(self.__id):\n print(f\"bus had the wrong ID\\nwas supposed to be {self.__id}, but received {data}\")\n output = False\n # listen for an answer\n except Exception as e:\n print(f\"exception in check_up (heart_beat) look:{e}\")\n print(f\"something went wrong, couldn't establish connection with {self.__address}\")\n output = False\n s.close()\n return output","def __check_ping(self):\n if not self.communications.ping():\n self.communications.ping(True)","def canDo_url(self, url):\n hostname = urlparse.urlsplit(url)[1]\n for hostEnd in self.highwireHosts:\n if hostname.endswith(hostEnd):\n logging.log(5, 'url hostname %s ends with %s -> highwire' % (hostname, hostEnd))\n return True\n\n if hostname in self.hostCache:\n ipAddr = self.hostCache[hostname]\n else:\n logging.debug('Looking up IP for %s' % hostname)\n try:\n ipAddr = socket.gethostbyname(hostname)\n self.hostCache[hostname] = ipAddr\n except socket.gaierror:\n raise pubGetError('Illegal hostname %s in link' % hostname, 'invalidHostname', hostname)\n\n ipParts = ipAddr.split('.')\n ipParts = [ int(x) for x in ipParts ]\n result = ipParts[0] == 171 and ipParts[1] in range(64, 68)\n if result == True:\n logging.log(5, 'hostname %s is highwire host' % hostname)\n return result","def reachable(self):\n service = build('gmail', 'v1', http=Http(timeout=1.0))\n url = urlparse.urlparse(service._baseUrl)\n host = url.hostname\n port = url.port\n try:\n socket.getaddrinfo(host, port, proto=socket.IPPROTO_TCP)\n except (socket.herror, socket.gaierror, URLError, OSError):\n return False\n return True","def get_vpnssl_status(iface):\n if iface in netifaces.interfaces():\n addr = netifaces.ifaddresses(iface)\n if len(addr) > 0: # vpn0 remains in the array even when gone, for whatever reason. So check if there is anything in there.\n return True\n\n return False","def check_input(data):\n if data.has_key('fqdn') and data.has_key('ip'):\n\n try:\n socket.inet_aton(data['ip'])\n return True\n except socket.error:\n return False","def is_ipv4(ip):\n try:\n socket.inet_aton(ip)\n except socket.error:\n return False\n return True","def _is_valid_ip(ip):\n return _is_valid_ipv4(ip) or _is_valid_ipv6(ip)","def is_available():","def ipinfo_ip_check(ip):\n if not is_IPv4Address(ip):\n return None\n\n response = requests.get('http://ipinfo.io/%s/json' % ip)\n return response.json()","def is_available(cls):\n\n try:\n proc = subprocess.Popen(\n ['systemctl', 'status', 'NetworkManager'],\n stdout=subprocess.PIPE,\n stderr=subprocess.PIPE,\n )\n proc.communicate()\n return proc.returncode == 0\n except OSError:\n return False","def check_port_availability(self, hostname, port):\n sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n self.logger.debug(\"Attempting to connect to {}:{}\\\n \".format(hostname, port))\n num_retries = 1000\n retry_count = 0\n port_up = False\n while(retry_count < num_retries):\n if(sock.connect_ex((hostname, int(port)))):\n self.logger.debug(\"{} port is up on {}\".format(port, hostname))\n port_up = True\n break\n retry_count += 1\n time.sleep(0.1)\n return port_up","def is_ip(self,inputs):\n format = '((?:(?:25[0-5]|2[0-4]\\\\d|[01]?\\\\d?\\\\d)\\\\.){3}(?:25[0-5]|2[0-4]\\\\d|[01]?\\\\d?\\\\d))'\n pattern = re.match(format, inputs)\n if pattern is not None:\n return True\n else:\n return False","def is_alive():\n\n ## ---------------------------------------------------------------\n \n cmd = dict()\n cmd[\"type_\"] = \"is_alive\"\n cmd[\"name_\"] = \"\"\n\n s = socket.socket(\n socket.AF_INET,\n socket.SOCK_STREAM\n )\n try:\n s.connect((getml.host, getml.port))\n except ConnectionRefusedError:\n return False\n\n comm.send_string(s, json.dumps(cmd))\n\n s.close()\n\n return True","def valid_ip(ip):\n return valid_ipv4(ip) or valid_ipv6(ip)","def check_address():\n\n for i in range(1, 97):\n hostname = \"sm{}-ipmi\".format(i)\n\n if not check_alive(hostname):\n LOGGER.error(\"%s is dead\", hostname)\n else:\n blink_light(hostname)\n raw_input(\"Is host {} on? \".format(hostname))\n blink_light(hostname, on=False)","def check_status():\n js = _get_jetstream_conn()\n i = js.compute.instances.get(session.attributes.get('instance_id'))\n if not i:\n return question(\"There was a problem. Please retry your command.\")\n\n status = i.state\n if session.attributes['status'] != status:\n msg = \"New instance status is {0}.\".format(status)\n if not session.attributes['public_ip'] and status == 'running':\n # Attach a floating IP to the instance\n fip = None\n fips = js.network.floating_ips()\n for ip in fips:\n if not ip.in_use():\n fip = ip\n if fip:\n i.add_floating_ip(fip.public_ip)\n session.attributes['public_ip'] = fip.public_ip\n else:\n msg = \"Instance status is {0}\".format(status)\n\n session.attributes['status'] = status\n\n if session.attributes['status'] != 'running':\n q = \"Would you like to check the status again?\"\n return question(msg + q).reprompt(q)\n else:\n card_content = 'Access your instance at http://{0}'.format(\n session.attributes.get('public_ip'))\n return statement(msg).simple_card(\n title=\"Instance {0} was launched.\".format(i.name),\n content=msg + card_content)","def usercheck(userip):\n if userip in session:\n return userstate.check(session[userip])\n else:\n return False"],"string":"[\n \"def checkIP(self, ip = None):\\n\\t\\treturn os.system(\\\"ping -c 1 -w2 \\\" + ip + \\\" > /dev/null 2>&1\\\") == 0\",\n \"def is_alive(self):\\n ret = subprocess.call(\\n shlex.split(\\\"ping -c 1 -W 2 %s\\\" % self.ip_address),\\n stdout=open('/dev/null', 'w'),\\n stderr=subprocess.STDOUT,\\n )\\n \\n if ret == 0:\\n return True\\n else:\\n return False\",\n \"def containsip(url):\\r\\n try:\\r\\n if ip.ip_address(url):\\r\\n return 1\\r\\n except:\\r\\n return 0\",\n \"def isReachable(self):\\n cmd = \\\"ping -c 1 %s\\\" % self.ip\\n ping_output = commands.getoutput(cmd)\\n logger.debug(cmd)\\n logger.debug(ping_output)\\n return re.search(\\\"1[\\\\s\\\\w]+received\\\", ping_output) is not None\",\n \"def icmp_probe(self, ip):\\n\\n\\t\\tcmd = 'ping %s -n 10' % ip\\n\\t\\tp = Popen(cmd, shell=True, stdin=PIPE, stderr=PIPE, stdout=PIPE)\\n\\t\\tres = p.stdout.read()\\n\\n\\t\\tres = res.decode()\\n\\t\\tif len(p.stderr.read()) == 0:\\n\\t\\t\\tif 'Destination host unreachable' in res:\\n\\t\\t\\t\\treturn False\\n\\t\\t\\treturn True\\n\\t\\telse:\\n\\t\\t\\treturn False\",\n \"def IsLinkup(nic,timeout):\\n nic = nic.strip()\\n current = time.time()\\n timeout += current\\n while current < timeout:\\n data = os.popen(\\\"ipconfig\\\").read().split(\\\"Ethernet adapter\\\")\\n for item in data:\\n if item.count(nic) and item.count(\\\"isconnected\\\") == 0: #Connected\\n return 1\\n time.sleep(0.5)\\n current = time.time()\\n return 0\",\n \"def include_up_hosts(nmap_host):\\n if nmap_host.status == 'up':\\n return True\\n return False\",\n \"def is_black_ip(ip):\\n cur = conn.cursor()\\n cur.execute('SELECT COUNT(ip) FROM black_ips WHERE ip = ?', (ip,))\\n\\n row = cur.fetchone()\\n return row[0] > 0\",\n \"def is_valid_ip(ip):\\n ...\",\n \"def is_if_up(ifname):\\n with open('/sys/class/net/' + ifname + '/carrier', 'r') as f:\\n status = f.readline()\\n return (status == '1')\",\n \"def health_check(self):\\n headers = {\\\"NDS-Proxy-Ping\\\": \\\"NPP\\\"}\\n url = \\\"http://{host}:{port}/upm\\\".format(host=self.upm_host, port=self.upm_port)\\n is_available, http_code = http_utilities.get(url, headers=headers)\\n\\n if http_code == 200:\\n self.log.info(\\\"The UPM is available\\\")\\n return True\\n else:\\n self.log.error(\\\"The UPM is not available\\\")\\n return False\",\n \"def internet_on():\\n try:\\n urllib.request.urlopen('http://216.58.192.142', timeout=1)\\n return True\\n except urllib.error.URLError: \\n return False\",\n \"def local_is_up(self, target):\\n try:\\n check_address(target)\\n except ValueError:\\n self.logger.warning('Target must be a tuple (IP, port), where IP '\\n 'is a string (i.e. \\\"192.168.0.1\\\") and port is '\\n 'an integer (i.e. 40000). Alternatively '\\n 'target can be a valid UNIX domain socket.')\\n return False\\n\\n self.check_tunnels()\\n return self.tunnel_is_up.get(target, True)\",\n \"def online_check():\\n try_first_ips = [\\n \\\"216.58.213.238\\\", # google\\n \\\"8.8.8.8\\\", # google\\n \\\"8.8.4.4\\\", # google\\n \\\"46.228.47.115\\\", # yahoo\\n ]\\n last_resort_ips = [ # dns root servers\\n \\\"198.41.0.4\\\",\\n \\\"192.228.79.201\\\",\\n \\\"192.33.4.12\\\",\\n \\\"128.8.10.90\\\",\\n \\\"192.203.230.10\\\",\\n \\\"192.5.5.241\\\",\\n \\\"192.112.36.4\\\",\\n \\\"128.63.2.53\\\",\\n \\\"192.36.148.17\\\",\\n \\\"192.58.128.30\\\",\\n \\\"193.0.14.129\\\",\\n \\\"198.32.64.12\\\",\\n \\\"202.12.27.33\\\"\\n ]\\n\\n iplists = []\\n iplists.append(try_first_ips)\\n iplists.append(rand_ips(max_num=50))\\n iplists.append(last_resort_ips)\\n\\n return any(can_ping_host(ip) for ip in chain(*iplists))\",\n \"def _IsReady(self):\\n if self.ip_address is None:\\n self._GetIpAddress()\\n if self.ip_address is not None:\\n url = 'http://%s' % (self.ip_address)\\n r = requests.get(url)\\n if r.status_code == 200:\\n return True\\n return False\",\n \"def is_website_online(host):\\n try:\\n socket.gethostbyname(host)\\n except socket.gaierror:\\n return False\\n else:\\n return True\",\n \"def isIP(ipToTest):\\n \\n try:\\n socket.inet_aton(ipToTest)\\n return True\\n except socket.error:\\n return False\",\n \"def validate_nic_down(self): \\n\\n pool = WorkerPool()\\n\\n try: \\n for nic, hostname in self.nic_to_address_map:\\n address = self.nic_to_address_map[(nic, hostname)]\\n cmd = Ping('ping validation', address, ctxt=REMOTE, remoteHost='localhost')\\n pool.addCommand(cmd)\\n pool.join()\\n\\n for cmd in pool.getCompletedItems():\\n results = cmd.get_results()\\n if results.rc == 0:\\n return False\\n finally:\\n pool.haltWork()\\n pool.joinWorkers()\\n pool.join()\\n\\n tinctest.logger.info(\\\"Successfully brought down nics ...\\\") \\n return True\",\n \"def ping_ip(ip_address, count):\\n reply = subprocess.run(\\n f\\\"ping -c {count} -n {ip_address}\\\",\\n shell=True,\\n stdout=subprocess.PIPE,\\n stderr=subprocess.PIPE,\\n encoding=\\\"utf-8\\\",\\n )\\n if reply.returncode == 0:\\n return True\\n else:\\n return False\",\n \"def IsWirelessUp(self):\\n return self.wifi.IsUp()\",\n \"def check_up(addr, p):\\n sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\\n result = sock.connect_ex((addr, p))\\n sock.close()\\n if result == 0:\\n ans = True\\n else:\\n ans = False\\n return ans\",\n \"def test_ping(self):\\n status, output = commands.getstatusoutput('ping -c 5 %s' % self.known_ip)\\n assert status == 0\",\n \"def check_status(self):\\n try:\\n self.server.ping()\\n return True\\n except Exception as e:\\n return False\",\n \"def is_reserved(ip):\\n if ip_between(ip, \\\"0.0.0.0\\\", \\\"0.255.255.255\\\"):\\n return True\\n elif ip_between(ip, \\\"10.0.0.0\\\", \\\"10.255.255.255\\\"):\\n return True\\n elif ip_between(ip, \\\"100.64.0.0\\\", \\\"100.127.255.255\\\"):\\n return True\\n elif ip_between(ip, \\\"127.0.0.0\\\", \\\"127.255.255.255\\\"):\\n return True\\n elif ip_between(ip, \\\"169.254.0.0\\\", \\\"169.254.255.255\\\"):\\n return True\\n elif ip_between(ip, \\\"172.16.0.0\\\", \\\"172.31.255.255\\\"):\\n return True\\n elif ip_between(ip, \\\"192.0.0.0\\\", \\\"192.0.0.255\\\"):\\n return True\\n elif ip_between(ip, \\\"192.0.2.0\\\", \\\"192.0.2.255\\\"):\\n return True\\n elif ip_between(ip, \\\"192.88.99.0\\\", \\\"192.88.99.255\\\"):\\n return True\\n elif ip_between(ip, \\\"192.168.0.0\\\", \\\"192.168.255.255\\\"):\\n return True\\n elif ip_between(ip, \\\"198.18.0.0\\\", \\\"198.19.255.255\\\"):\\n return True\\n elif ip_between(ip, \\\"198.51.100.0\\\", \\\"198.51.100.255\\\"):\\n return True\\n elif ip_between(ip, \\\"203.0.113.0\\\", \\\"203.0.113.255\\\"):\\n return True\\n elif ip_between(ip, \\\"224.0.0.0\\\", \\\"255.255.255.255\\\"):\\n return True\\n else:\\n return False\",\n \"def testIP(IP):\\n \\\"\\\"\\\" will feed this an array with mp.Pool.map() \\\"\\\"\\\"\\n IPup = []\\n IPdown = []\\n with open(os.devnull, \\\"w\\\") as fnull:\\n if subprocess.call(['ping','-c 1 -W 1',IP,],stdout=fnull, stderr=fnull)==0:\\n print 'host %s is UP' % IP\\n return IP\\n else:\\n print 'host %s is DOWN' % IP\\n return None\",\n \"def is_alive(addr, user):\\n return _ssh_master_cmd(addr, user, 'check') == 0\",\n \"def checkIPValid(self, ipAddr):\\n try:\\n socket.inet_aton(ipAddr)\\n return True\\n except socket.error:\\n return False\",\n \"def isonline():\\n\\n conn = httplib.HTTPConnection(\\\"www.google.com\\\", timeout=5)\\n try:\\n conn.request(\\\"HEAD\\\", \\\"/\\\")\\n conn.close()\\n return True\\n except:\\n conn.close()\\n return False\",\n \"def is_connected():\\r\\n ipconfig_output = terminal('ipconfig | findstr /i gateway')\\r\\n if ipconfig_output != None:\\r\\n return any(i for i in ipconfig_output if i.isdigit())\\r\\n \\r\\n # Alternative way if ipconfig has error in some systems\\r\\n ## Slower than ipconfig workaround\\r\\n try:\\r\\n socket().connect(('8.8.8.8', 53))\\r\\n return True\\r\\n except:\\r\\n return False\",\n \"def is_ip(self) -> bool:\\n return self.typ == ETH_P_IP\",\n \"def is_connected():\\n import socket\\n try:\\n host = socket.gethostbyname(\\\"www.gov.uk\\\")\\n socket.create_connection((host, 80), 2)\\n return True\\n except:\\n pass\\n return False\",\n \"def checkIP(self):\\n\\t\\tself.get(\\\"https://ifconfig.me/\\\")\\n\\t\\treturn self.findId(\\\"ip_address\\\").text\",\n \"def is_ready(self, addr: int, /) -> bool:\",\n \"def check_vpn_interface():\\n return validate_vpn_interface(call_command('netstat -i')[0].split('\\\\n'))\",\n \"def check_proxy_status(proxy_ip):\\n try:\\n status = subprocess.check_output([\\\"ping\\\", \\\"-c\\\",\\\"1\\\", proxy_ip]).decode('utf-8')\\n if status.find(\\\"1 received\\\") > -1:\\n return True\\n except subprocess.CalledProcessError as e:\\n return False\\n\\n return False\",\n \"async def ping(self, ctx, ip):\\n\\n # Check for valid IP else do DNS lookup\\n valid_ip = re.compile(\\\"[0-9]{,3}\\\\.[0-9]{,3}\\\\.[0-9]{,3}\\\")\\n valid_hostname = re.compile(\\\".*\\\\.[a-zA-Z]{2,}\\\")\\n valid = False\\n\\n if valid_ip.match(ip):\\n valid = True\\n elif valid_hostname.match(ip):\\n valid = True\\n try:\\n await self.bot.say('Doing DNS lookup...')\\n ip = socket.gethostbyname(ip)\\n\\n if valid == True:\\n start = time.time()\\n response = os.system(\\\"sudo ping -c 1 -w3 \\\" + ip)\\n duration = time.time() - start\\n duration = round(duration * 1000, 0)\\n if response == 0:\\n await self.bot.say(ip + ' is up and responding in ' +\\n str(duration) + 'ms.')\\n else:\\n await self.bot.say(ip + ' is not reachable.')\\n else:\\n await self.bot.say(ip + ' is not a valid IP or Domain.')\\n\\n except socket.gaierror:\\n await self.bot.say('Whoops! That Address cant be resolved!')\",\n \"def IsLinkdown(nic,timeout):\\n nic = nic.strip()\\n current = time.time()\\n timeout += current\\n while current < timeout:\\n data = os.popen(\\\"ipconfig\\\").read().split(\\\"Ethernet adapter\\\")\\n for item in data:\\n if item.count(nic) and item.count(\\\"isconnected\\\"): #Disconnected\\n return 1\\n time.sleep(0.5)\\n current = time.time()\\n return 0\",\n \"def validIPAddress(ip):\\n try:\\n socket.inet_aton(ip)\\n return True\\n except socket.error:\\n return False\",\n \"def _checknet():\\n exit_code = os.system('ping -c 1 www.baidu.com 1>/dev/null 2>&1')\\n return exit_code\",\n \"def ping(self):\\n return True\",\n \"def ping(self):\\n return True\",\n \"def isup(self):\\n if self.cloudserver:\\n # print self.cloudserver.status\\n if self.cloudserver.status in (\\\"ACTIVE\\\",):\\n return True\\n \\n return False\",\n \"def is_actual_ip(self, ip_addr):\\n try:\\n socket.inet_aton(ip_addr)\\n return True\\n except socket.error:\\n return False\",\n \"def check_availability(self):\\n\\t\\tif not self.connection_is_usable:\\n\\t\\t\\treturn False\\n\\t\\twith self.client_lock:\\n\\t\\t\\tif self.stream is None:\\n\\t\\t\\t\\treturn False\\n\\t\\t\\tif self.last_ping is None or self.last_ping.age() >= self.ping_max_age:\\n\\t\\t\\t\\tself.last_ping = SendPing(self, self.ping_timeout)\\n\\t\\t\\tlast_ping = self.last_ping\\n\\t\\treturn last_ping.answered(self.ping_timeout)\",\n \"def checkAlive(self, timeout = 1500 , port = 3389):\\n time_retry = 90\\n # ugly c-style loop \\n while 1:\\n try:\\n ip = self.getIp()\\n if not ip:\\n logging.warning(\\\"!Failed to obtain ip address\\\")\\n else:\\n logging.info(\\\"Probing \\\" + str(ip) + \\\":\\\" + str(port) + \\\" for connectivity\\\")\\n sock = socket.create_connection((ip,port) , timeout)\\n sock.close()\\n logging.info(\\\"Server \\\" + str(ip) + \\\":\\\" + str(port) + \\\" successfully responded\\\")\\n return True\\n except Exception as e:\\n logging.error(\\\"!: Failed to probe the remote server for a connection!\\\")\\n logging.error(\\\"!:\\\" + str(e))\\n logging.error(traceback.format_exc())\\n timeout = timeout - time_retry\\n if timeout > 0:\\n logging.info(\\\"--- Waiting more \\\" + str(timeout) + \\\" for it to respond\\\");\\n time.sleep(time_retry)\\n else:\\n break\\n\\n return False\",\n \"def _LeasedIP(self):\\n check_command = 'ip addr show {interface} | grep \\\"inet \\\"'.format(\\n interface=self.interface)\\n try:\\n # grep exit with return code 0 when we have retrieved an IP.\\n out = self._device.CheckOutput(check_command)\\n except device_types.CalledProcessError:\\n return False\\n # ex: inet 192.168.159.78/20 brd 192.168.159.255 scope global wlan0\\n return out.split()[1].split('/')[0]\",\n \"async def check_pause(self, ip: str) -> bool:\\n miner = self.miners[ip]\\n return not miner.running.is_set()\",\n \"def is_alive(self, site):\\n try:\\n return requests.get(site).status_code == 200\\n except Exception:\\n pass\",\n \"def _wait_until_up(self, address, port, timeout):\\n\\n def check_up(addr, p):\\n \\\"\\\"\\\"\\n Find out if a port at an address is occupied\\n \\\"\\\"\\\"\\n sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\\n result = sock.connect_ex((addr, p))\\n sock.close()\\n if result == 0:\\n ans = True\\n else:\\n ans = False\\n return ans\\n\\n max_time = time() + timeout\\n up = False\\n while not up and time() < max_time:\\n self.log.debug(\\\"Checking if Galaxy is up...\\\")\\n up = check_up(address, port)\\n\\n # If we query Galaxy immediately it may reset the connection:\\n sleep(10)\\n\\n if not up:\\n raise Exception('There was no response at {} on port {} for {} seconds'\\n .format(address, port, timeout))\",\n \"def _wait_for_ip(name, session):\\n start_time = datetime.now()\\n status = None\\n while status is None:\\n status = get_vm_ip(name, session)\\n if status is not None:\\n # ignore APIPA address\\n if status.startswith(\\\"169\\\"):\\n status = None\\n check_time = datetime.now()\\n delta = check_time - start_time\\n log.debug(\\n \\\"Waited %s seconds for %s to report ip address...\\\", delta.seconds, name\\n )\\n if delta.seconds > 180:\\n log.warning(\\\"Timeout getting IP address\\\")\\n break\\n time.sleep(5)\",\n \"def checkForIsUpAlert(self, website, availability):\\n checkTime = time.time()\\n # Verify that the system has been running for longer than 2 minutes\\n if (checkTime - self.originalTime >= 120):\\n # Verify that the system has been alerted by the site\\n if website.name in self.alertsDic:\\n # Verify that the UP alert wasn't already sent\\n if self.alertsDic[website.name]:\\n if availability > 0.8 :\\n alert = {'website': website.name, 'time': time.time(), 'availability': availability,\\n 'status': 'UP'}\\n self.alertsDic[website.name] = None\\n self.mailer.sendAlert(alert)\\n self.queueAlerts.put(alert)\\n return\",\n \"def ip_status(ip, rnge):\\n\\tactive = []\\n\\tdown = []\\n\\ttimed_out = []\\n\\t\\n\\t\\\"\\\"\\\" Get the first 3 fields of IP \\\"\\\"\\\"\\n\\taddres = ip.split(\\\".\\\")[:3]\\n\\tne_ip = \\\".\\\".join(addres)\\n\\t\\n\\t\\\"\\\"\\\" Get the last field of IP \\\"\\\"\\\"\\n\\tlst_field = ip.split(\\\".\\\")[3]\\n\\t\\n\\tif rnge == \\\"0\\\" or rnge == \\\"\\\":\\n\\t\\trnge = 1\\n\\t\\n\\tif 0 <= int(rnge) <= 255 and ip_pattern.match(ip):\\n\\t\\t\\\"\\\"\\\" Calculate the actual range \\\"\\\"\\\"\\n\\t\\tactual_rnge = int(lst_field) + int(rnge)\\n\\n\\t\\t\\\"\\\"\\\" Range of IP address to be pinged \\\"\\\"\\\"\\n\\t\\t\\\"\\\"\\\" Right now it starts from 0 through 10 but can be changed in the below line \\\"\\\"\\\"\\n\\t\\n\\t\\tfor i in range(int(lst_field), actual_rnge):\\t\\t\\n\\t\\t\\n\\t\\t\\tip_fin = ne_ip + \\\".\\\" + str(i)\\n\\t\\t\\tstatus = sp.check_output([\\\"ping \\\", ip_fin])\\n\\t\\t\\n\\t\\t\\tif pattern1.search(status) :\\n\\t\\t\\t\\tdown.append(ip_fin)\\n\\t\\t\\telif pattern2.search(status):\\n\\t\\t\\t\\ttimed_out.append(ip_fin)\\n\\t\\t\\telse:\\n\\t\\t\\t\\tactive.append(ip_fin)\\n\\t\\n\\t\\tresult = \\\"\\\\nActive IP addresses are: \\\" + str(active) + \\\"\\\\n\\\\n\\\" + \\\"Unreachable IP: \\\" + str(down) + \\\"\\\\n\\\\n\\\" + \\\"Timed out IP's: \\\" + str(timed_out)\\n\\t\\treturn result\\n\\t\\n\\telse:\\n\\t\\treturn \\\"1) Range should be in between 0 and 255 inclusive. \\\\n2) Check the IP format.\\\"\",\n \"def in_remaining_local_ips(self, ip: str) -> bool:\\n return ip in self.remaining_local_ips\",\n \"def _ip_is_usable(self, current_ip):\\n # Consider IP addresses only.\\n try:\\n ipaddress.ip_address(current_ip)\\n except ValueError:\\n return False\\n\\n # Never use real IP.\\n if current_ip == self.real_ip:\\n return False\\n\\n # Do dot allow IP reuse.\\n if not self._ip_is_safe(current_ip):\\n return False\\n\\n return True\",\n \"def check_ups(self, ups): #pylint: disable=no-self-use\\n try:\\n args = [\\n MONITOR_COMMAND,\\n f\\\"{ups}@localhost\\\"\\n ]\\n #pylint: disable=unused-variable\\n results = subprocess.run(args, check=True, capture_output=True)\\n return True\\n except subprocess.CalledProcessError:\\n return False\",\n \"def match(self, _ip):\\n try:\\n return bool(ip_address(_ip) in self.network)\\n except ValueError:\\n return False\",\n \"def check_public_ipv6(node):\\n\\n ok = False\\n for ii in node.ifaces:\\n# try:\\n for ip in ii.addrs:\\n ip = ip.split('/')[0]\\n if ipaddress.ip_address(ip).version == 6:\\n if ip[0] not in ['f',\\\":\\\"]:\\n ok = True\\n\\n# except:\\n# print(ok)\\n\\n return ok\",\n \"def internet_on(): \\n try:\\n urlopen('http://www.google.com', timeout=2)\\n return True\\n except urlopen.URLError as err: \\n return False\",\n \"def ping(conn: psycopg2.connect) -> bool:\\n\\n is_alive = False\\n with conn.cursor() as cur:\\n cur.execute(\\\"SELECT 1\\\")\\n if cur.description is not None:\\n fetched = cur.fetchall()\\n try:\\n is_alive = fetched[0][0] == 1\\n except IndexError:\\n pass\\n return is_alive\",\n \"def wait(self, timeout=None):\\n if timeout is None:\\n timeout = self.timeout\\n started = time.time()\\n while True:\\n if self.get_ip():\\n self.state = State.RUNNING\\n return True\\n else:\\n time.sleep(1)\\n if timeout != 0:\\n if time.time() - started > timeout:\\n return False\",\n \"def ping(self):\\n self.last_seen_at = time.time()\\n last_online = self.online\\n self.online = True\\n return last_online != self.online\",\n \"def checklan(ipaddr, network):\\n return True\",\n \"def nat_waitforconn_alive():\\r\\n return NAT_STATE_DATA[\\\"mux\\\"] != None and NAT_STATE_DATA[\\\"mux\\\"].isAlive()\",\n \"async def ping_http(self) -> bool:\\n # pause logic\\n if not self.running.is_set():\\n self.add_to_output(\\\"Paused...\\\")\\n await self.running.wait()\\n\\n # ping port 80 (HTTP)\\n if await self.ping(80):\\n # ping returned true, HTTP is up\\n return True\\n else:\\n # ping returned false, HTTP is down\\n return False\",\n \"def isup(sourcename) :\\n return s.isUp(sourcename)\",\n \"def check_no_network():\\n try:\\n socket.gethostbyname(\\\"www.google.com\\\")\\n return False\\n except:\\n return True\",\n \"def ping():\\n api_online = bool(check_url(\\\"https://rest.ensembl.org/info/ping?\\\"))\\n vertebrate_url_online = bool(check_url(\\\"http://ftp.ensembl.org\\\"))\\n other_url_online = bool(check_url(\\\"http://ftp.ensemblgenomes.org\\\"))\\n return api_online and vertebrate_url_online and other_url_online\",\n \"def ip_is_local(ip):\\n return localinterfaces.is_public_ip(ip) or localinterfaces.is_local_ip(ip)\",\n \"def validateIP():\\n try:\\n s = socket.inet_aton(args.target)\\n except socket.error:\\n print(\\\"\\\")\\n print(f\\\"{bad_cmd} Bad IP address\\\")\\n print(\\\"\\\")\\n sys.exit()\",\n \"def is_ip(address):\\n try:\\n socket.inet_pton(socket.AF_INET, address)\\n except socket.error:\\n try:\\n socket.inet_pton(socket.AF_INET6, address)\\n except socket.error:\\n return False\\n return True\",\n \"def pass_sanity_checks():\\n if not check_database_connection():\\n return False\\n\\n # The IP address might take some time to arrive\\n have_public_ip = False\\n for i in range(10):\\n if public_ip():\\n have_public_ip = True\\n break\\n else:\\n time.sleep(5)\\n\\n if not have_public_ip:\\n return False\\n\\n if not access_s3():\\n return False\\n\\n # If we get here we're good\\n return True\",\n \"def is_reachable(self, userid):\\n return self._smtclient.get_guest_connection_status(userid)\",\n \"async def ping_ssh(self) -> bool:\\n # pause logic\\n if not self.running.is_set():\\n self.add_to_output(\\\"Paused...\\\")\\n await self.running.wait()\\n\\n # ping port 22 (SSH)\\n if await self.ping(22):\\n # ping returned true, SSH is up\\n return True\\n else:\\n # ping returned false, SSH is down\\n return False\",\n \"def checkWifi():\\n try:\\n subprocess.check_output(\\\"iwgetid\\\")\\n return True\\n except subprocess.CalledProcessError: # if not connected\\n return False\",\n \"def alive(p):\\n return p.is_alive()\",\n \"def is_safe_ip(ip):\\n # should define global\\n # white_ip_list = ('127.0.0.1', '192.168.1.1')\\n try:\\n socket.inet_pton(socket.AF_INET, ip)\\n except AttributeError:\\n try:\\n socket.inet_aton(ip)\\n except socket.error:\\n return False\\n except socket.error:\\n return False\\n\\n if ip.count('.') == 3 and ip in current_app.config['API_WHITE_IP_LIST']:\\n return True\\n else:\\n return False\",\n \"def _check_connection() -> bool:\\n return bool(subprocess.check_output([\\\"hostname\\\", \\\"-I\\\"]))\",\n \"def is_alive(self):\\n if self.status == 1:\\n return True\\n else:\\n return False\",\n \"def public_ip():\\n found_public_ip = False\\n try:\\n metadata = get_instance_metadata()\\n for key, value in metadata.items():\\n LOG.info(\\\"{0}: {1}\\\".format(key, value))\\n\\n if metadata['public-ipv4'] is not None:\\n try:\\n socket.inet_aton(metadata['public-ipv4'])\\n found_public_ip = True\\n except socket.error:\\n found_public_ip = False\\n\\n except Exception:\\n LOG.exception('check_database_connection')\\n return False\\n return found_public_ip\",\n \"def is_host_available(self, context, hostname):\\n try:\\n host = self._get_plugin().get_host_by_name(context, hostname)\\n return host['availability'] == constants.HOST_UP\\n except ext_host.HostNotFoundByName:\\n # Does not exist yet\\n return False\",\n \"def _check_tunnel(self, _srv):\\n if self.skip_tunnel_checkup:\\n self.tunnel_is_up[_srv.local_address] = True\\n return\\n self.logger.info('Checking tunnel to: {0}'.format(_srv.remote_address))\\n if isinstance(_srv.local_address, string_types): # UNIX stream\\n s = socket.socket(socket.AF_UNIX, socket.SOCK_STREAM)\\n else:\\n s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\\n s.settimeout(TUNNEL_TIMEOUT)\\n try:\\n # Windows raises WinError 10049 if trying to connect to 0.0.0.0\\n connect_to = ('127.0.0.1', _srv.local_port) \\\\\\n if _srv.local_host == '0.0.0.0' else _srv.local_address\\n s.connect(connect_to)\\n self.tunnel_is_up[_srv.local_address] = _srv.tunnel_ok.get(\\n timeout=TUNNEL_TIMEOUT * 1.1\\n )\\n self.logger.debug(\\n 'Tunnel to {0} is DOWN'.format(_srv.remote_address)\\n )\\n except socket.error:\\n self.logger.debug(\\n 'Tunnel to {0} is DOWN'.format(_srv.remote_address)\\n )\\n self.tunnel_is_up[_srv.local_address] = False\\n\\n except queue.Empty:\\n self.logger.debug(\\n 'Tunnel to {0} is UP'.format(_srv.remote_address)\\n )\\n self.tunnel_is_up[_srv.local_address] = True\\n finally:\\n s.close()\",\n \"def verify_ip_address(ip):\\n try:\\n ipaddress.ip_address(ip)\\n return True\\n except ValueError:\\n return False\",\n \"def check_up(self):\\n\\n s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\\n s.settimeout(BusController.PULSE_DELAY * 0.8)\\n output = True\\n start_time = time.time()\\n try:\\n s.connect((self.__address[0], BusController.HEART_BEAT_PORT))\\n data = \\\"Check\\\".encode()\\n s.send(data)\\n data = s.recv(1024).decode()\\n if int(data) != int(self.__id):\\n print(f\\\"bus had the wrong ID\\\\nwas supposed to be {self.__id}, but received {data}\\\")\\n output = False\\n # listen for an answer\\n except Exception as e:\\n print(f\\\"exception in check_up (heart_beat) look:{e}\\\")\\n print(f\\\"something went wrong, couldn't establish connection with {self.__address}\\\")\\n output = False\\n s.close()\\n return output\",\n \"def __check_ping(self):\\n if not self.communications.ping():\\n self.communications.ping(True)\",\n \"def canDo_url(self, url):\\n hostname = urlparse.urlsplit(url)[1]\\n for hostEnd in self.highwireHosts:\\n if hostname.endswith(hostEnd):\\n logging.log(5, 'url hostname %s ends with %s -> highwire' % (hostname, hostEnd))\\n return True\\n\\n if hostname in self.hostCache:\\n ipAddr = self.hostCache[hostname]\\n else:\\n logging.debug('Looking up IP for %s' % hostname)\\n try:\\n ipAddr = socket.gethostbyname(hostname)\\n self.hostCache[hostname] = ipAddr\\n except socket.gaierror:\\n raise pubGetError('Illegal hostname %s in link' % hostname, 'invalidHostname', hostname)\\n\\n ipParts = ipAddr.split('.')\\n ipParts = [ int(x) for x in ipParts ]\\n result = ipParts[0] == 171 and ipParts[1] in range(64, 68)\\n if result == True:\\n logging.log(5, 'hostname %s is highwire host' % hostname)\\n return result\",\n \"def reachable(self):\\n service = build('gmail', 'v1', http=Http(timeout=1.0))\\n url = urlparse.urlparse(service._baseUrl)\\n host = url.hostname\\n port = url.port\\n try:\\n socket.getaddrinfo(host, port, proto=socket.IPPROTO_TCP)\\n except (socket.herror, socket.gaierror, URLError, OSError):\\n return False\\n return True\",\n \"def get_vpnssl_status(iface):\\n if iface in netifaces.interfaces():\\n addr = netifaces.ifaddresses(iface)\\n if len(addr) > 0: # vpn0 remains in the array even when gone, for whatever reason. So check if there is anything in there.\\n return True\\n\\n return False\",\n \"def check_input(data):\\n if data.has_key('fqdn') and data.has_key('ip'):\\n\\n try:\\n socket.inet_aton(data['ip'])\\n return True\\n except socket.error:\\n return False\",\n \"def is_ipv4(ip):\\n try:\\n socket.inet_aton(ip)\\n except socket.error:\\n return False\\n return True\",\n \"def _is_valid_ip(ip):\\n return _is_valid_ipv4(ip) or _is_valid_ipv6(ip)\",\n \"def is_available():\",\n \"def ipinfo_ip_check(ip):\\n if not is_IPv4Address(ip):\\n return None\\n\\n response = requests.get('http://ipinfo.io/%s/json' % ip)\\n return response.json()\",\n \"def is_available(cls):\\n\\n try:\\n proc = subprocess.Popen(\\n ['systemctl', 'status', 'NetworkManager'],\\n stdout=subprocess.PIPE,\\n stderr=subprocess.PIPE,\\n )\\n proc.communicate()\\n return proc.returncode == 0\\n except OSError:\\n return False\",\n \"def check_port_availability(self, hostname, port):\\n sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\\n self.logger.debug(\\\"Attempting to connect to {}:{}\\\\\\n \\\".format(hostname, port))\\n num_retries = 1000\\n retry_count = 0\\n port_up = False\\n while(retry_count < num_retries):\\n if(sock.connect_ex((hostname, int(port)))):\\n self.logger.debug(\\\"{} port is up on {}\\\".format(port, hostname))\\n port_up = True\\n break\\n retry_count += 1\\n time.sleep(0.1)\\n return port_up\",\n \"def is_ip(self,inputs):\\n format = '((?:(?:25[0-5]|2[0-4]\\\\\\\\d|[01]?\\\\\\\\d?\\\\\\\\d)\\\\\\\\.){3}(?:25[0-5]|2[0-4]\\\\\\\\d|[01]?\\\\\\\\d?\\\\\\\\d))'\\n pattern = re.match(format, inputs)\\n if pattern is not None:\\n return True\\n else:\\n return False\",\n \"def is_alive():\\n\\n ## ---------------------------------------------------------------\\n \\n cmd = dict()\\n cmd[\\\"type_\\\"] = \\\"is_alive\\\"\\n cmd[\\\"name_\\\"] = \\\"\\\"\\n\\n s = socket.socket(\\n socket.AF_INET,\\n socket.SOCK_STREAM\\n )\\n try:\\n s.connect((getml.host, getml.port))\\n except ConnectionRefusedError:\\n return False\\n\\n comm.send_string(s, json.dumps(cmd))\\n\\n s.close()\\n\\n return True\",\n \"def valid_ip(ip):\\n return valid_ipv4(ip) or valid_ipv6(ip)\",\n \"def check_address():\\n\\n for i in range(1, 97):\\n hostname = \\\"sm{}-ipmi\\\".format(i)\\n\\n if not check_alive(hostname):\\n LOGGER.error(\\\"%s is dead\\\", hostname)\\n else:\\n blink_light(hostname)\\n raw_input(\\\"Is host {} on? \\\".format(hostname))\\n blink_light(hostname, on=False)\",\n \"def check_status():\\n js = _get_jetstream_conn()\\n i = js.compute.instances.get(session.attributes.get('instance_id'))\\n if not i:\\n return question(\\\"There was a problem. Please retry your command.\\\")\\n\\n status = i.state\\n if session.attributes['status'] != status:\\n msg = \\\"New instance status is {0}.\\\".format(status)\\n if not session.attributes['public_ip'] and status == 'running':\\n # Attach a floating IP to the instance\\n fip = None\\n fips = js.network.floating_ips()\\n for ip in fips:\\n if not ip.in_use():\\n fip = ip\\n if fip:\\n i.add_floating_ip(fip.public_ip)\\n session.attributes['public_ip'] = fip.public_ip\\n else:\\n msg = \\\"Instance status is {0}\\\".format(status)\\n\\n session.attributes['status'] = status\\n\\n if session.attributes['status'] != 'running':\\n q = \\\"Would you like to check the status again?\\\"\\n return question(msg + q).reprompt(q)\\n else:\\n card_content = 'Access your instance at http://{0}'.format(\\n session.attributes.get('public_ip'))\\n return statement(msg).simple_card(\\n title=\\\"Instance {0} was launched.\\\".format(i.name),\\n content=msg + card_content)\",\n \"def usercheck(userip):\\n if userip in session:\\n return userstate.check(session[userip])\\n else:\\n return False\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.73951226","0.7028806","0.6933253","0.6876365","0.67340374","0.67282856","0.67181677","0.66473746","0.6614493","0.66109097","0.6580546","0.658013","0.65744126","0.6572488","0.65436584","0.65303814","0.651624","0.6508601","0.64948905","0.6459352","0.6427603","0.63940024","0.6379851","0.6349297","0.63463014","0.63370657","0.63337815","0.63084346","0.62929505","0.62880546","0.6285135","0.6278585","0.62633","0.62519604","0.62149215","0.62103415","0.62007767","0.61900955","0.61477965","0.61372817","0.61372817","0.61271626","0.61261684","0.6121658","0.6101853","0.6097375","0.60955054","0.609204","0.60687345","0.60190856","0.6016775","0.6013109","0.6011593","0.5996923","0.5984635","0.59841216","0.5980766","0.5970057","0.5961317","0.5961149","0.59454733","0.59432214","0.5928582","0.59234774","0.5919512","0.59184206","0.5908503","0.5903347","0.5899097","0.58913183","0.5886754","0.5881422","0.58807063","0.587863","0.587241","0.58709276","0.5870226","0.5854386","0.5837677","0.5835884","0.58347607","0.5828381","0.5827797","0.5826706","0.58266187","0.5825914","0.58229375","0.58201784","0.5820053","0.58117914","0.5804939","0.5802097","0.57937205","0.57889193","0.57877165","0.57813776","0.57785696","0.5778255","0.5777415","0.5767348"],"string":"[\n \"0.73951226\",\n \"0.7028806\",\n \"0.6933253\",\n \"0.6876365\",\n \"0.67340374\",\n \"0.67282856\",\n \"0.67181677\",\n \"0.66473746\",\n \"0.6614493\",\n \"0.66109097\",\n \"0.6580546\",\n \"0.658013\",\n \"0.65744126\",\n \"0.6572488\",\n \"0.65436584\",\n \"0.65303814\",\n \"0.651624\",\n \"0.6508601\",\n \"0.64948905\",\n \"0.6459352\",\n \"0.6427603\",\n \"0.63940024\",\n \"0.6379851\",\n \"0.6349297\",\n \"0.63463014\",\n \"0.63370657\",\n \"0.63337815\",\n \"0.63084346\",\n \"0.62929505\",\n \"0.62880546\",\n \"0.6285135\",\n \"0.6278585\",\n \"0.62633\",\n \"0.62519604\",\n \"0.62149215\",\n \"0.62103415\",\n \"0.62007767\",\n \"0.61900955\",\n \"0.61477965\",\n \"0.61372817\",\n \"0.61372817\",\n \"0.61271626\",\n \"0.61261684\",\n \"0.6121658\",\n \"0.6101853\",\n \"0.6097375\",\n \"0.60955054\",\n \"0.609204\",\n \"0.60687345\",\n \"0.60190856\",\n \"0.6016775\",\n \"0.6013109\",\n \"0.6011593\",\n \"0.5996923\",\n \"0.5984635\",\n \"0.59841216\",\n \"0.5980766\",\n \"0.5970057\",\n \"0.5961317\",\n \"0.5961149\",\n \"0.59454733\",\n \"0.59432214\",\n \"0.5928582\",\n \"0.59234774\",\n \"0.5919512\",\n \"0.59184206\",\n \"0.5908503\",\n \"0.5903347\",\n \"0.5899097\",\n \"0.58913183\",\n \"0.5886754\",\n \"0.5881422\",\n \"0.58807063\",\n \"0.587863\",\n \"0.587241\",\n \"0.58709276\",\n \"0.5870226\",\n \"0.5854386\",\n \"0.5837677\",\n \"0.5835884\",\n \"0.58347607\",\n \"0.5828381\",\n \"0.5827797\",\n \"0.5826706\",\n \"0.58266187\",\n \"0.5825914\",\n \"0.58229375\",\n \"0.58201784\",\n \"0.5820053\",\n \"0.58117914\",\n \"0.5804939\",\n \"0.5802097\",\n \"0.57937205\",\n \"0.57889193\",\n \"0.57877165\",\n \"0.57813776\",\n \"0.57785696\",\n \"0.5778255\",\n \"0.5777415\",\n \"0.5767348\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.58888644"},"document_rank":{"kind":"string","value":"70"}}},{"rowIdx":92598,"cells":{"query":{"kind":"string","value":"Verify that the right node blinks"},"document":{"kind":"string","value":"def check_address():\n\n for i in range(1, 97):\n hostname = \"sm{}-ipmi\".format(i)\n\n if not check_alive(hostname):\n LOGGER.error(\"%s is dead\", hostname)\n else:\n blink_light(hostname)\n raw_input(\"Is host {} on? \".format(hostname))\n blink_light(hostname, on=False)"},"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 check_balance(self):\n\t\tif math.abs(self.get_height(treenode=self.left) - self.get_height(treenode=self.right)) > 1:\n\t\t\tself.rebalance()","def test_update_node_state_smartfail(self):\n pass","def test_breeding_failed_carn(self):\n nt.assert_equal(self.carn.breeding(1), None)","async def test_early_exit(self):\n n = Node()\n run_task = asyncio.create_task(n.run_node())\n await asyncio.sleep(0)\n self.assertFalse(n.check_alive())\n n.exit_node()\n await n.wait_running()\n await n.wait_stopped()\n await run_task\n await self._check_exited_node(n)","def BlockheightCheck(self):\n if self.CurrentBlockheight == BC.Default().Height:\n if len(self.Peers) > 0:\n logger.debug(\"Blockheight is not advancing ...\")\n next_hash = BC.Default().GetHeaderHash(self.CurrentBlockheight + 1)\n culprit_found = False\n for peer in self.Peers:\n if next_hash in peer.myblockrequests:\n culprit_found = True\n peer.Disconnect()\n break\n\n # this happens when we're connecting to other nodes that are stuck themselves\n if not culprit_found:\n for peer in self.Peers:\n peer.Disconnect()\n else:\n self.CurrentBlockheight = BC.Default().Height","def verify_stack_up(self, prop=1.0, timeout=25):\n for _ in range(timeout):\n links = 0\n links_up = 0\n for i, dpid in enumerate(self.dpids):\n dp_name = self.dp_name(i)\n for link in self.non_host_links(dpid):\n status = self.stack_port_status(dpid, dp_name, link.port)\n links += 1\n if status == 3: # up\n links_up += 1\n prop_up = links_up / links\n if prop_up >= prop:\n return\n time.sleep(1)\n self.fail('not enough links up: %f / %f' % (links_up, links))","def test_goto_field_boss_hartmann_blue_fails(self):\n\n sopTester.updateModel('mcp', TestHelper.mcpState['all_off'])\n sopTester.updateModel('hartmann', TestHelper.hartmannState['blue_fails'])\n\n cmdState = self.actorState.gotoField\n cmdState.reinitialize(self.cmd)\n\n self._goto_field_boss(12, 37, 0, 0, cmdState, didFail=True, finish=True)","def test_blink(self):\n display = get_display(1)\n display.register_state(main.Fade)\n prev = display.strand[0]\n for i in range(1000):\n display.tick()\n assert(all(0 <= display.strand[0][i] <= 255 for i in range(3)))\n assert display.strand[0] != prev\n prev = display.strand[0]","def test_good_fork_higher(self):\n bvh = self.BlockValidationHandler()\n\n root = self.btm.chain_head\n\n # create a new valid chain 5 long from the current root\n new_head = self.btm.generate_chain(root, 5,\n {'add_to_store': True})\n self.btm.set_chain_head(new_head[-1])\n # generate candidate chain 8 long from the same root\n new_block = self.btm.generate_chain(root, 8,\n {'add_to_cache': True})\n bv = self.create_block_validator(new_block[-1], bvh.on_block_validated)\n bv.run()\n\n self.assertTrue(bvh.has_result())\n self.assertTrue(new_block[-1].status == BlockStatus.Valid)\n self.assertTrue(bvh.result[\"commit_new_block\"])","def node_check():\n while True:\n if LAST_RECEIVED:\n for loc in dict(LAST_RECEIVED):\n if (datetime.datetime.utcnow() - LAST_RECEIVED[loc]).total_seconds() >= 60:\n ROLLING_TEMPS.pop(loc)\n ROLLING_HUMS.pop(loc)\n LAST_RECEIVED.pop(loc)\n print(\"connection to\", loc, \"has been lost\")\n else:\n print(\"No nodes found. Please check that nodes are running.\")\n\n time.sleep(6)","def test_accept_depth(self, nodeOneId, nodeTwoId):\n logging.info(\">>> Entered : test_accept_depth \\n\")\n try:\n self.nodes[nodeTwoId].setminingmaxblock(1000)\n self.nodes[nodeTwoId].setexcessiveblock(1010, 4)\n\n # Mine an excessive block. Node One should not accept it\n addr = self.nodes[nodeTwoId].getnewaddress()\n for i in range(0,10):\n self.nodes[nodeOneId].sendtoaddress(addr, 1.0)\n self.nodes[nodeOneId].generate(1)\n time.sleep(2) #give blocks a chance to fully propagate\n counts = [ x.getblockcount() for x in self.nodes[0:2] ]\n\n logging.info(\"Counts: Node1 = %d and Node2 = %d \" %(counts[0], counts[1]))\n assert_equal(counts[0]-counts[1], 1)\n # Mine a block on top. Node 1 should still not accept it\n self.nodes[nodeOneId].generate(1)\n time.sleep(2) #give blocks a chance to fully propagate\n counts = [ x.getblockcount() for x in self.nodes[0:2] ]\n logging.info(\"Counts: Node1 = %d and Node2 = %d \" %(counts[0], counts[1]))\n assert_equal(counts[0]-counts[1], 2)\n\n # Change node 1 to AD=2. The assertion will fail if it doesn't accept the chain now \n self.nodes[nodeTwoId].setexcessiveblock(1010, 2)\n self.nodes[nodeOneId].generate(1)\n time.sleep(2) #give blocks a chance to fully propagate !!!!\n\n counts = [ x.getblockcount() for x in self.nodes[0:2] ]\n logging.info(\"Counts: Node1 = %d and Node2 = %d \" %(counts[0], counts[1]))\n assert_equal(counts[0]-counts[1], 0)\n except (Exception, JSONRPCException) as e1:\n logging.info(e1)\n exc_type, exc_obj, exc_tb = sys.exc_info()\n fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)[1]\n raise TestAssertionError({\"file_name\": fname, \"line_num\": exc_tb.tb_lineno, \\\n \"error_type\": exc_type.__name__, \"error_msg\": str( e1 ), \\\n \"n1\" : self.bins[nodeOneId], \"n2\" : self.bins[nodeTwoId], \"amount\" : \"N/A\", \"numsig\" : \"N/A\"})","def test_get_node_state_smartfail(self):\n pass","def event_m20_11_x84(z44=20111330):\n \"\"\"State 0,1: Wait for branch destruction\"\"\"\n IsObjBroken(0, z44, 1)\n assert ConditionGroup(0)\n \"\"\"State 2: End state\"\"\"\n return 0","def test_balance_right2(our_bsts):\n assert our_bsts[0].depth() == our_bsts[1]","def test_block_bad_state(self):\n pass","def test_balance_old(self):\n successes = 0\n failures = 0\n iterations = NUM_CALLS\n\n for _ in range(iterations):\n\n handler = self.new_handler()\n ret = check_balance(handler.root)\n if ret:\n successes += 1\n else:\n failures += 1\n handler.debug_wrapper()\n\n if failures != 0:\n print('\\n=================================================\\n')\n\n self.assertEqual(failures, 0,\n msg=f'{BColors.FAIL}\\n\\t[-]\\tFrom state: Failed to always generate balanced trees! ' +\n f'{failures}/{iterations} failures! {BColors.ENDC}')\n print(f\"{BColors.OKGREEN}\\t[+]\\tFrom state: Validated that all trees all balanced upon generation in {successes} trees.{BColors.ENDC}\")","def test_nodeCircuitBreaker(self):\n disco = create_disco()\n node = create_node(\"somewhere\")\n disco.onMessage(None, NodeActive(node))\n resolved_node = resolve(disco, \"myservice\", \"1.0\")\n\n avail1 = resolved_node.available()\n # Default threshold in CircuitBreaker is three failures:\n resolved_node.failure()\n resolved_node.failure()\n resolved_node.failure()\n avail2 = resolved_node.available()\n resolved_node.success()\n avail3 = resolved_node.available()\n self.assertEqual((avail1, avail2, avail3), (True, False, True))","def test_get_node_sled(self):\n pass","def _WaitForLinkerTestStatus(adb, timeout):","def wait_full_state(node,data):\n\tif len(node.roots) >= node.id:\n\t\treactor.stop()\n\telse:\n\t\treactor.callLater(0, lambda:wait_full_state(node, data))","def test_kth_from_end_exists():\n assert LinkedList.kth_from_end","def test_balance_right_tree(bst_right_balance):\n assert bst_right_balance.balance() == 0","def test_tree_2_nodes_left_unbalanced(one_t):\n one_t.insert(9)\n assert one_t.balance() == 1","def delay_checks(self):\n return False","def wait_for_bond_event(self, expected_bond_event):\n pass","def check_boss_edges(ai_settings, boss):\n\tif boss.check_edges():\n\t\tai_settings.boss_direction *= -1","def is_node_down():\n try:\n hostname = check_command_output(\"hostname\").strip()\n host_fqdn = socket.getfqdn(hostname)\n nodes = get_compute_nodes_info(hostname_filter=hostname)\n if not any(host in nodes for host in [hostname, host_fqdn]):\n log.warning(\"Node is not attached to scheduler. Reporting as down\")\n return True\n\n node = nodes.get(host_fqdn, nodes.get(hostname))\n log.info(\"Node is in state: '{0}'\".format(node.state))\n if all(error_state not in node.state for error_state in SGE_ERROR_STATES):\n return False\n except Exception as e:\n log.error(\"Failed when checking if node is down with exception %s. Reporting node as down.\", e)\n\n return True","def test_tree_2_nodes_right_unbalanced(one_t):\n one_t.insert(11)\n assert one_t.balance() == -1","def is_final_node_reached(self):\n if self.actual_node == self.final_node:\n self.final_node_reached = True","def lookLeftRight(self):\n self.lookLeft()\n time.sleep(0.5)\n self.lookRight()","def test_balanced_removal(self):\n successes = 0\n failures = 0\n iterations = NUM_CALLS\n\n for _ in range(iterations):\n\n failure_callback = False\n handler = self.new_handler(balance=True)\n new_ids = [randint(0, handler.uid) for _ in range(randint(HEIGHT[0], handler.expected_height))]\n new_ids = list(set(new_ids)) # make sure there are no duplicates\n try:\n new_ids.remove(handler.golden_id) # remove golden id from removal if it was randomly selected\n except ValueError:\n pass\n\n for val in new_ids:\n handler.delNodeByID(val)\n if handler.balanced is False:\n failures += 1\n failure_callback = True\n break\n\n if failure_callback:\n break\n state = handler.get_gamestate()\n for val in new_ids:\n if 'node' + str(val) in state['node_points']:\n failures += 1\n break\n\n successes += 1\n\n self.assertEqual(failures, 0,\n msg=f'{BColors.FAIL}\\n\\t[-]\\tModification: Failed to correctly remove nodes (balancing addition) ' +\n f'{failures}/{iterations} failures! {BColors.ENDC}')\n print(f\"{BColors.OKGREEN}\\t[+]\\tModification: Validated removing nodes in balancing mode in {successes} trees.{BColors.ENDC}\")","def check_ball_on_target():\n\n pass","def require_linux_bond_up(self, host_id):\n synced_state_list = self.get_expected_synced_states(host_id)\n host = self.host_information[host_id]['host']\n bond_name = self.host_information[host_id]['bond']\n for _ in range(self.LACP_TIMEOUT*2):\n result = host.cmd('cat /proc/net/bonding/%s|sed \"s/[ \\t]*$//g\"' % bond_name)\n result = '\\n'.join([line.rstrip() for line in result.splitlines()])\n with open(os.path.join(self.tmpdir, 'bonding-state.txt'), 'w') as state_file:\n state_file.write(result)\n matched_all = True\n for state_txt in synced_state_list:\n if not re.search(state_txt, result):\n matched_all = False\n break\n if matched_all:\n return\n time.sleep(1)\n synced_state_txt = r\"\"\"\"\"\"\n for state_txt in synced_state_list:\n synced_state_txt += state_txt + \"\\n\\n\"\n synced_state_txt.strip()\n self.assertFalse(\n re.search(synced_state_txt, result),\n msg='LACP did not synchronize: %s\\n\\nexpected:\\n\\n%s' % (result, synced_state_txt))","def isOkToBoost(self, node):\n para = \"p\"\n stepsAway = 0\n minimumStopWordCount = 5\n maxStepsAwayFromNode = 3\n \n nodes = self.walkSiblings(node)\n for currentNode in nodes:\n # p\n if currentNode.tag == para:\n if stepsAway >= maxStepsAwayFromNode:\n return False\n paraText = Parser.getText(currentNode)\n wordStats = StopWords().getStopWordCount(paraText)\n if wordStats.getStopWordCount > minimumStopWordCount:\n return True\n stepsAway += 1\n return False","def test_breeding_parent_weight_carn(self):\n start_weight = 50\n self.carn.weight = 50\n self.carn.breeding(10)\n nt.assert_less(self.carn.weight, start_weight)","def test_left(self) -> None:\n\n self._perform_background_initial_update()\n\n u1 = self.register_user(\"u1\", \"pass\")\n u1token = self.login(\"u1\", \"pass\")\n r1 = self.helper.create_room_as(u1, tok=u1token)\n\n u2 = self.register_user(\"u2\", \"pass\")\n u2token = self.login(\"u2\", \"pass\")\n\n self.helper.join(r1, u2, tok=u2token)\n\n r1stats_ante = self._get_current_stats(\"room\", r1)\n assert r1stats_ante is not None\n\n self.helper.leave(r1, u2, tok=u2token)\n\n r1stats_post = self._get_current_stats(\"room\", r1)\n assert r1stats_post is not None\n\n self.assertEqual(\n r1stats_post[\"current_state_events\"] - r1stats_ante[\"current_state_events\"],\n 0,\n )\n self.assertEqual(\n r1stats_post[\"left_members\"] - r1stats_ante[\"left_members\"], +1\n )\n self.assertEqual(\n r1stats_post[\"joined_members\"] - r1stats_ante[\"joined_members\"], -1\n )","def test_balance_left_tree(bst_all_to_left):\n assert bst_all_to_left.balance() == -1","def changes_while_node_down_test(self):\n debug(\"changes_while_node_down_test()\")\n cluster = self.cluster\n cluster.populate(2).start()\n node1, node2 = cluster.nodelist()\n wait(2)\n session = self.patient_cql_connection(node2)\n\n self.prepare_for_changes(session, namespace='ns2')\n node1.stop()\n wait(2)\n self.make_schema_changes(session, namespace='ns2')\n wait(2)\n node2.stop()\n wait(2)\n node1.start()\n node2.start()\n wait(20)\n self.validate_schema_consistent(node1)","def test_node_way_ref_after_delete_2():\n data = cache_query(nodes=[20001, 20002], deps=True)\n assert 'ways' not in data['nodes']['20001']\n assert data['nodes']['20002'] == None\n assert query_row(db_conf, 'osm_roads', 20001) == None\n assert query_row(db_conf, 'osm_barrierpoints', 20001)['type'] == 'block'","def test_state_after_failure(self):\n pass","def check( self ):\n\n if ( self.alive is not None ) \\\n and ( time.time() > ( self.alive + self.timeout ) ):\n return False\n return True","def test_get_node_status_batterystatus(self):\n pass","def poll(cls, context):\r\n return hasattr(bpy.types.Object, \"BlenderNEURON_node\") and \\\r\n bpy.types.Object.BlenderNEURON_node is not None and \\\r\n bpy.types.Object.BlenderNEURON_node.client is not None","def poll(cls, context):\r\n return hasattr(bpy.types.Object, \"BlenderNEURON_node\") and \\\r\n bpy.types.Object.BlenderNEURON_node is not None and \\\r\n bpy.types.Object.BlenderNEURON_node.client is not None","def test_rebalance(self):\n successes = 0\n failures = 0\n iterations = NUM_CALLS\n\n for _ in range(iterations):\n\n handler = self.new_handler()\n state = handler.get_gamestate()\n rebal_handler = AVLHandler.from_graph(state)\n ret = check_balance(rebal_handler.root)\n if ret and rebal_handler.balanced:\n successes += 1\n else:\n failures += 1\n\n self.assertEqual(failures, 0,\n msg=f'{BColors.FAIL}\\n\\t[-]\\tModification: Failed to correctly rebalance deserialized tree! '\n f'{failures}/{iterations} failures! {BColors.ENDC}')\n print(f\"{BColors.OKGREEN}\\t[+]\\tModification: Validated deserialization rebalancing in {successes} trees.{BColors.ENDC}\")","def test_balanced_addition(self):\n successes = 0\n failures = 0\n iterations = NUM_CALLS\n\n for _ in range(iterations):\n\n failure_callback = False\n handler = self.new_handler(balance=True)\n new_vals = [randint(1, POINT_CAP) for _ in range(randint(HEIGHT[0], HEIGHT[1]))]\n for val in new_vals:\n handler.addNewNode(val, b=True)\n if handler.balanced is False:\n handler.debug_wrapper()\n failures += 1\n failure_callback = True\n break\n\n if failure_callback:\n break\n state = handler.get_gamestate()\n for val in new_vals:\n if val not in state['node_points'].values():\n failures += 1\n break\n\n successes += 1\n\n self.assertEqual(failures, 0,\n msg=f'{BColors.FAIL}\\n\\t[-]\\tModification: Failed to correctly add new nodes (balancing addition) ' +\n f'{failures}/{iterations} failures! {BColors.ENDC}')\n print(\n f\"{BColors.OKGREEN}\\t[+]\\tModification: Validated adding nodes in balancing mode in {successes} trees.{BColors.ENDC}\")","def test_cycle_present_true(test_linkedlist):\n test_linkedlist.insert_end('A')\n test_linkedlist.insert_end('B')\n test_linkedlist.insert_end('C')\n # create a cycle - connection from 3rd to 2nd element\n test_linkedlist.head.next.next.next = test_linkedlist.head.next\n # assert cycle_present() function returns True\n assert test_linkedlist.cycle_present()","async def test_dead_peer(self):\n async with Node() as n1:\n async with Node() as n2:\n await n2.join_network(n1.nid())\n peer = next(iter(n1._act_set))\n writer = peer._writer\n peer._writer = None\n await n1.send_broadcast('hello')\n writer.close()\n await writer.wait_closed()","def test_get_node_state(self):\n pass","def __changeroot(self):\n if self.edges[self.best_edge].get_status() == EdgeStatus.branch:\n self.__edge_stub(self.best_edge, Message.changeroot)\n else:\n # changeroot received by father node\n self.__edge_stub(self.best_edge, Message.connect, [self.level])\n self.__change_edge_status(self.best_edge, EdgeStatus.branch)","def test_imbalanced_addition(self):\n successes = 0\n failures = 0\n iterations = NUM_CALLS\n\n for _ in range(iterations):\n\n failure_callback = False\n handler = self.new_handler()\n new_vals = [randint(1, POINT_CAP) for _ in range(randint(HEIGHT[0], HEIGHT[1]))]\n for val in new_vals:\n handler.addNewNode(val, b=False)\n true_bal = check_balance(handler.root)\n if handler.balanced is not true_bal:\n failures += 1\n failure_callback = True\n break\n\n if failure_callback:\n break\n state = handler.get_gamestate()\n for val in new_vals:\n if val not in state['node_points'].values():\n failures += 1\n break\n\n successes += 1\n\n self.assertEqual(failures, 0,\n msg=f'{BColors.FAIL}\\n\\t[-]\\tModification: Failed to correctly add new nodes (non-balancing addition) ' +\n f'{failures}/{iterations} failures! {BColors.ENDC}')\n print(f\"{BColors.OKGREEN}\\t[+]\\tModification: Validated adding nodes in non-balancing mode in {successes} trees.{BColors.ENDC}\")","async def test_node_init(self):\n n = Node()\n await self._check_exited_node(n)\n self.assertIsNone(n.__exit__(None, None, None))","def check_time(self):\n while True:\n for name in self.neighbors:\n if not self.neighbors[name].is_killed:\n if not self.neighbors[name].update_ready and time.time() - self.neighbors[name].send_timer > self.timeout:\n self.neighbors[name].update_ready = True\n if time.time() - self.neighbors[name].kill_timer > 3 * self.timeout:\n self.neighbors[name].is_killed = True","async def test_node_many(self):\n node_list = [Node() for i in range(100)]\n\n # check uids are unique\n uid_list = [n.uid() for n in node_list]\n self.assertEqual(len(uid_list), len(set(uid_list)))\n\n running_list = []\n try:\n for n in node_list:\n # create node and join upon random node in network\n await n.run_node()\n if len(running_list) > 0:\n ct_n, _ = random.choice(running_list)\n await n.join_network(ct_n.nid())\n\n msg = Network_Message()\n n.attach_broadcast_callback(msg.msg_callback)\n n.attach_direct_message_callback(msg.msg_callback)\n running_list.append((n, msg))\n\n for i in range(0, 3):\n await asyncio.sleep(constants.SHU_TIME + 5)\n\n while len(running_list) > 1:\n # broadcast and check all nodes received message\n await running_list[0][0].send_broadcast('hello world')\n for node, msg in running_list[1:]:\n msg_suc = await msg.wait_msg(15)\n self.assertTrue(msg_suc)\n self.assertEqual(msg.nid, running_list[0][0].nid())\n self.assertEqual(msg.msg, 'hello world')\n\n # kill half of the nodes\n kill_list = running_list[:len(running_list) // 2]\n for n, _ in kill_list:\n n.exit_node()\n await asyncio.gather(*[n.wait_stopped() for n, _ in kill_list])\n\n new_list = running_list[len(running_list) // 2:]\n for _, msg in new_list:\n msg.reset()\n\n running_list = new_list\n await asyncio.sleep(constants.SHU_TIME + 5)\n finally:\n for n in node_list:\n n.exit_node()\n await asyncio.gather(*[n.wait_stopped() for n in node_list])","def test_node_value():\n from linked_list import Node\n node2 = Node(\"Something\")\n assert node2.value == \"Something\" and node2.nxt is None","def step_tree(self):\n if random.random() < self.world.f or self.any_neighbor_burning():\n self.set_state(\"orange\")","def test_set_healthy(self):\n\n notifier = mock.Mock()\n\n node = Node({'healthchecks': []}, {}, mock.Mock())\n node.set_notifier(notifier)\n\n # set as unhealthy\n node.set_healthy(False)\n\n # set as healthy and expect a notification\n node.set_healthy(True)\n notifier.is_healthy_again.assert_called_once()","def fsm_testDeadlock(machine):\n # We return these two in the end\n property_exists = False\n latest_state = machine.state\n\n # Go through each state and get triggers\n # If no non-default triggers are available\n # return with False and latest_state\n # (That means there's no actions available == deadlock)\n\n for state in machine.states:\n latest_state = state\n print \"Current state: \", state\n for trigger in machine.machine.get_triggers(state):\n # If no: a non-default trigger exists, no deadlock, move on to next state\n # filter out triggers with names \"to_...\" (since automatically generated ones are to_<state>)\n if trigger[:3] != \"to_\":\n print \"Trigger: \", trigger\n property_exists = True\n break\n # If state has no non-default triggers, deadlock exists.\n # return false and latest_state\n # no need to iterate the rest of the states\n if not property_exists:\n return property_exists, latest_state\n return property_exists, latest_state","def test_imbalanced_removal(self):\n successes = 0\n failures = 0\n iterations = NUM_CALLS\n\n for _ in range(iterations):\n\n failure_callback = False\n handler = self.new_handler()\n new_ids = [randint(0, handler.uid) for _ in range(randint(HEIGHT[0], handler.expected_height))]\n new_ids = list(set(new_ids)) # make sure there are no duplicates\n try:\n new_ids.remove(handler.golden_id) # remove golden id from removal if it was randomly selected\n except ValueError:\n pass\n\n for val in new_ids:\n handler.delNodeByID(val)\n true_bal = check_balance(handler.root)\n if handler.balanced is not true_bal:\n failures += 1\n failure_callback = True\n break\n\n if failure_callback:\n break\n state = handler.get_gamestate()\n for val in new_ids:\n if 'node' + str(val) in state['node_points']:\n failures += 1\n break\n\n successes += 1\n\n self.assertEqual(failures, 0,\n msg=f'{BColors.FAIL}\\n\\t[-]\\tModification: Failed to correctly remove nodes (non-balancing addition) ' +\n f'{failures}/{iterations} failures! {BColors.ENDC}')\n print(f\"{BColors.OKGREEN}\\t[+]\\tModification: Validated removing nodes in non-balancing mode in {successes} trees.{BColors.ENDC}\")","def test_balance_known_tree_2(known_bst):\n known_bst[0].insert_non_balance(12)\n assert known_bst[0].balance() == known_bst[2] - 1","def test_DeadPersonTrue (self) :\n\t\t\n\t\tself.person1.updateHealth ()\n\t\tself.assertEqual(self.person1.getHealth(), 0)\n\t\tself.assertTrue(self.person1.DeadPerson())","def check_if_alive():\n global SOCKET1\n global PRINT_LOCK\n global DATA\n start = current_time()\n msg = pickle.dumps(\"is_alive\")\n while True:\n\n if (current_time() - start) < 10:\n continue\n\n for every_one in DATA[\"neighbor\"]:\n remote = (\"127.0.0.1\", every_one[2])\n # out of the present list\n # decrease waiting time\n SOCKET1.settimeout(2)\n SOCKET1.sendto(msg, remote)\n try:\n recv_msg = pickle.loads(SOCKET1.recvfrom(512)[0])\n if recv_msg is \"yes\":\n with PRINT_LOCK:\n print(\"{} is alive\".format(every_one[0]))\n except (OSError, socket.timeout) as e_ra:\n # reset waiting time\n SOCKET1.settimeout(socket.getdefaulttimeout())\n with PRINT_LOCK:\n print(\"{} is dead : {}\".format(every_one[0], e_ra))\n index = DATA[\"neighbor\"].index(every_one)\n DATA[\"neighbor\"].pop(index)\n index = DATA[\"distance_vec\"].index(\n [every_one[0], every_one[1]])\n DATA[\"distance_vec\"].pop(index)\n bellman_ford(DATA[\"router_id\"], DATA[\"distance_vec\"])\n # ensuring the time diff is always round about 10\n start = current_time()","def test_DeadPersonFalse (self) :\n\t\t\n\t\tself.person1.updateHealth ()\n\t\tself.assertFalse(self.person2.DeadPerson())","def fsm_test(machine):\n print \"############################################\"\n print \"###### Testing machine for deadlocks: ######\"\n print \"############################################\"\n property_exists, latest_state = fsm_testDeadlock(machine)\n if property_exists:\n print property_exists, \", property holds. No deadlocks\"\n else:\n print property_exists, \", property does not hold. Latest state tested: \", latest_state\n\n print \"###########################################\"\n print \"###### Testing machine for feedback: ######\"\n print \"###########################################\"\n property_exists, latest_state = fsm_testFeedback(machine)\n if property_exists:\n print property_exists, \", property holds. Every transition gives feedback\"\n else:\n print property_exists, \", property does not hold. Latest state tested: \", latest_state","def decide_reward(prev_node, cur_node):\n # Constant decay\n reward = constants.REWARD_DECAY\n\n # First check if any players died\n prev_agent_ids = prev_node.get_alive_agents()\n cur_agent_ids = cur_node.get_alive_agents()\n missing_agent_ids = [agent for agent in prev_agent_ids if agent not in cur_agent_ids]\n this_agent = cur_node.get_agent_obj()\n\n # Agent died\n if this_agent.agent_id in missing_agent_ids:\n reward += constants.REWARD_DIED\n\n # Check if any enemies died (regardless of dying from whom)\n for enemy in this_agent.enemies:\n if enemy.name == 'AgentDummy':\n continue\n enemy_agent_id = int(enemy.name[-1])\n if enemy_agent_id in missing_agent_ids:\n reward += constants.REWARD_ENEMY_DIED\n\n\n # Then, check if picked up powerups\n cur_pos = this_agent.position\n item_enum = constants.Item\n powerups = [item_enum.Kick, item_enum.IncrRange, item_enum.ExtraBomb]\n\n if prev_node.game_env._board[cur_pos] in [p.value for p in powerups]:\n reward += constants.REWARD_POWERUP\n\n\n # Lastly, check if agent destroyed any wood boxes\n prev_laid_bombs = prev_node.get_agent_laid_bombs()\n\n def reward_bomb_for_direction(bomb, prev_board, cur_board, direction):\n for off in range(1, bomb.blast_strength):\n pos = (bomb.position[0] + off * direction[0], bomb.position[1] + off * direction[1])\n exit = False\n # If the explosion is blocked, exit early\n if not utility.position_on_board(prev_board, pos):\n break\n if utility.position_is_wall(prev_board, pos):\n exit = True\n # If a wood box is to be flamed in previous state and\n # is actually flamed in current state, then give reward\n if utility.position_is_wood(prev_board, pos) and utility.position_is_flames(cur_board, pos):\n return constants.REWARD_WOOD\n if exit:\n break\n return 0\n\n\n for bomb in prev_laid_bombs:\n # About to explode\n if bomb.life == 1:\n # NOTE:\n # For now, as long as any wood boxes are destroyed within the bomb range\n # of this exploding bomb it's counted towards this agent.\n # If bomb life > 1 but it's triggered by other bombs then we don't care\n prev_board, cur_board = prev_node.game_env._board, cur_node.game_env._board\n reward += reward_bomb_for_direction(bomb, prev_board, cur_board, direction=(1, 0))\n reward += reward_bomb_for_direction(bomb, prev_board, cur_board, direction=(0, 1))\n reward += reward_bomb_for_direction(bomb, prev_board, cur_board, direction=(-1, 0))\n reward += reward_bomb_for_direction(bomb, prev_board, cur_board, direction=(0, -1))\n\n return reward","def test_breeding_parent_weight(self):\n Herbivore.set_parameters({\"gamma\": 1})\n start_weight = 50\n self.herb.weight = start_weight\n self.herb.breeding(10)\n nt.assert_less(self.herb.weight, start_weight)","def test_good_fork_lower(self):\n bvh = self.BlockValidationHandler()\n\n root = self.btm.chain_head\n\n # create a new valid chain 5 long from the current root\n new_head = self.btm.generate_chain(root, 5,\n {'add_to_store': True})\n self.btm.set_chain_head(new_head[-1])\n # generate candidate chain 3 long from the same root\n new_block = self.btm.generate_chain(root, 3,\n {'add_to_cache': True})\n\n bv = self.create_block_validator(new_block[-1], bvh.on_block_validated)\n bv.run()\n\n self.assertTrue(bvh.has_result())\n self.assertTrue(new_block[-1].status == BlockStatus.Valid)\n self.assertFalse(bvh.result[\"commit_new_block\"])","def test_banned(self) -> None:\n\n self._perform_background_initial_update()\n\n u1 = self.register_user(\"u1\", \"pass\")\n u1token = self.login(\"u1\", \"pass\")\n r1 = self.helper.create_room_as(u1, tok=u1token)\n\n u2 = self.register_user(\"u2\", \"pass\")\n u2token = self.login(\"u2\", \"pass\")\n\n self.helper.join(r1, u2, tok=u2token)\n\n r1stats_ante = self._get_current_stats(\"room\", r1)\n assert r1stats_ante is not None\n\n self.helper.change_membership(r1, u1, u2, \"ban\", tok=u1token)\n\n r1stats_post = self._get_current_stats(\"room\", r1)\n assert r1stats_post is not None\n\n self.assertEqual(\n r1stats_post[\"current_state_events\"] - r1stats_ante[\"current_state_events\"],\n 0,\n )\n self.assertEqual(\n r1stats_post[\"banned_members\"] - r1stats_ante[\"banned_members\"], +1\n )\n self.assertEqual(\n r1stats_post[\"joined_members\"] - r1stats_ante[\"joined_members\"], -1\n )","def isdone(node_dict):\n # compute heading difference\n hdiff = heading_diff(r_target, node_dict['pn'].heading)\n # return if we are we close enough\n return abs(hdiff) < abs(tol)","def _ensure_is_alive(self):\n if self._hit_points == 0:\n raise UnitIsDead('Unit is dead!')","def update_blinker_from_stalk(self, blinker_time: int, left_blinker_stalk: bool, right_blinker_stalk: bool):\n\n if left_blinker_stalk:\n self.right_blinker_cnt = 0\n if not self.left_blinker_prev:\n self.left_blinker_cnt = blinker_time\n\n if right_blinker_stalk:\n self.left_blinker_cnt = 0\n if not self.right_blinker_prev:\n self.right_blinker_cnt = blinker_time\n\n self.left_blinker_cnt = max(self.left_blinker_cnt - 1, 0)\n self.right_blinker_cnt = max(self.right_blinker_cnt - 1, 0)\n\n self.left_blinker_prev = left_blinker_stalk\n self.right_blinker_prev = right_blinker_stalk\n\n return bool(left_blinker_stalk or self.left_blinker_cnt > 0), bool(right_blinker_stalk or self.right_blinker_cnt > 0)","def test_blow_up(self):\n self.assertTrue(self.g.__class__)","def IsAfter(self, node, start):\n return node.start > start;","def test_BLINK_FAST_TRACK_PROCESS(self):\n self.verify_references_to_prerequisites(processes.BLINK_FAST_TRACK_PROCESS)","def wait_on_elb_instance ( elb_conn, elb_name, elb_instance_id, elb_state = 'InService', sleep_time = 10, max_wait = 300 ) :\n instance_states = elb_conn.describe_instance_health( load_balancer_name = elb_name,\n instances = [ elb_instance_id ] )\n instance_state = instance_states[ 0 ].state\n while instance_state != elb_state :\n if max_wait <= 0 :\n break\n time.sleep( sleep_time )\n max_wait -= sleep_time\n instance_states = elb_conn.describe_instance_health( load_balancer_name = elb_name,\n instances = [ elb_instance_id ] )\n instance_state = instance_states[ 0 ].state\n\n return instance_state == elb_state","def test_create_node_shutdown_item(self):\n pass","def twist(self):\n self.right()\n time.sleep(1)\n self.stop()\n self.left()\n time.sleep(1)\n self.stop()","def test_timeoutReset(self):\n for i in range(3):\n self.circuit_breaker.failure()\n self.time.advance(29.0)\n available29sec = self.circuit_breaker.available()\n self.time.advance(1.1)\n available30sec = self.circuit_breaker.available()\n self.assertEqual((available29sec, available30sec),\n (False, True))","def test_liveness_bird_down(self):\n with DockerHost('host1',\n additional_docker_options=CLUSTER_STORE_DOCKER_OPTIONS) as host1:\n retry_until_success(host1.assert_is_ready, retries=30)\n host1.execute(\"docker exec -it calico-node sv stop /etc/service/enabled/bird\")\n\n # Check that the readiness script is reporting 'not ready'\n self.assertRaisesRegexp(CalledProcessError, \"calico/node is not ready: bird/confd is not live: Service bird is not running.\",\n host1.execute, \"docker exec calico-node /bin/calico-node -bird-live\")","def test_enquque_enqueue_check_last_node_val():\n queue = Queue()\n anode = queue.enqueue('a')\n bnode = queue.enqueue('b')\n assert queue._queue.last_node is bnode","def test_bird_readiness(self):\n with DockerHost('host1',\n additional_docker_options=CLUSTER_STORE_DOCKER_OPTIONS) as host1, \\\n DockerHost('host2',\n additional_docker_options=CLUSTER_STORE_DOCKER_OPTIONS) as host2:\n retry_until_success(host1.assert_is_ready, retries=30)\n retry_until_success(host2.assert_is_ready, retries=30)\n\n # Create a network and a couple of workloads on each host.\n network1 = host1.create_network(\"subnet1\")\n workload_host1 = host1.create_workload(\"workload1\", network=network1)\n workload_host2 = host2.create_workload(\"workload2\", network=network1)\n\n # Allow network to converge\n self.assert_true(workload_host1.check_can_ping(workload_host2.ip, retries=10))\n\n # Check connectivity in both directions\n self.assert_ip_connectivity(workload_list=[workload_host1,\n workload_host2],\n ip_pass_list=[workload_host1.ip,\n workload_host2.ip])\n\n # Block bgp connectivity between hosts\n host1.execute(\"iptables -t raw -I PREROUTING -p tcp -m multiport --dport 179 -j DROP\")\n host2.execute(\"iptables -t raw -I PREROUTING -p tcp -m multiport --dport 179 -j DROP\")\n host1.execute(\"docker exec -it calico-node sv kill bird\")\n host2.execute(\"docker exec -it calico-node sv kill bird\")\n\n # Check that the readiness script is reporting 'not ready'\n self.assertRaisesRegexp(CalledProcessError, \"calico/node is not ready: BIRD is not ready: BGP not established with\",\n host1.execute, \"docker exec calico-node /bin/calico-node -bird-ready -felix-ready\")\n self.assertRaisesRegexp(CalledProcessError, \"calico/node is not ready: BIRD is not ready: BGP not established with\",\n host1.execute, \"docker exec calico-node /bin/calico-node -bird-ready -felix-ready\")\n\n # Restore connectivity\n host1.execute(\"iptables -t raw -D PREROUTING -p tcp -m multiport --dports 179 -j DROP\")\n host2.execute(\"iptables -t raw -D PREROUTING -p tcp -m multiport --dports 179 -j DROP\")\n\n _log.debug('check connected and retry until \"Established\"')\n retry_until_success(host1.assert_is_ready, retries=30)\n retry_until_success(host2.assert_is_ready, retries=30)\n check_bird_status(host1, [(\"node-to-node mesh\", host2.ip, \"Established\")])\n check_bird_status(host2, [(\"node-to-node mesh\", host1.ip, \"Established\")])","def testIsBiconnected(self):\n self.assertEqual(is_biconnected(self.G1), True)\n self.assertEqual(is_biconnected(self.G2), False)","def test_verify_state_of_a_device_when_disconnected_from_the_device():","def test_randomly_select_node_2(self):\n a, b, c, d = (n() for _ in range(4))\n\n g = BELGraph()\n g.add_edge(a, b)\n g.add_edge(b, c)\n g.add_edge(b, d)\n\n self.assertEqual(1, g.degree(a))\n self.assertEqual(3, g.degree(b))\n self.assertEqual(1, g.degree(c))\n self.assertEqual(1, g.degree(d))\n\n no_grow = {c}\n\n node_counter = Counter(\n randomly_select_node(g, no_grow, self.random_state)\n for _ in range(self.trials)\n )\n\n self.assertIn(a, node_counter)\n self.assertAlmostEqual((1 / 5), node_counter[a] / self.trials, places=2)\n\n self.assertIn(b, node_counter)\n self.assertAlmostEqual((3 / 5), node_counter[b] / self.trials, places=2)\n\n self.assertNotIn(c, node_counter)\n\n self.assertIn(d, node_counter)\n self.assertAlmostEqual((1 / 5), node_counter[d] / self.trials, places=2)","def test_bias(self):\n # Folder must be root to load in make_net properly\n if os.getcwd().split('\\\\')[-1] == 'tests': os.chdir('..')\n \n # Create parents\n cfg = Config()\n cfg.genome.bias_min_value = -2\n cfg.genome.bias_max_value = 2\n gene1, gene2 = get_simple_node_gene(0, cfg.genome)\n \n # Ratio of 0.5, so possible to cross to both parents\n p1 = False\n p2 = False\n for _ in range(100):\n gene3 = gene1.crossover(other=gene2, cfg=cfg.genome, ratio=0.5)\n if gene3.bias == gene1.bias:\n p1 = True\n elif gene3.bias == gene2.bias:\n p2 = True\n else:\n raise self.failureException(\"Must be mutated to one of parent's values\")\n if p1 and p2: break\n self.assertTrue(p1 and p2)\n \n # Ratio of 1, so always inherits from first parent\n for _ in range(100):\n gene3 = gene1.crossover(other=gene2, cfg=cfg.genome, ratio=1)\n self.assertEqual(gene3.bias, gene1.bias)\n \n # Ratio of 0, so always inherits from second parent\n for _ in range(100):\n gene3 = gene1.crossover(other=gene2, cfg=cfg.genome, ratio=0)\n self.assertEqual(gene3.bias, gene2.bias)","def jumped_on(self):\r\n pass","def observe_b_t(left_reward, right_reward):\n\n random_variable = random.random()\n\n if random_variable >= (left_reward - right_reward + 1.0)/2:\n return 1\n else:\n return 0","def check_deadlock(self):\n \"\"\" Returns True or False \"\"\"\n \"\"\" A returned value of True mens the robot is in a deadlock and the system is trying to resolve it by turning. \"\"\"\n try:\n odo = self.m.get_last_observation('odometry')[1]\n except Exception as e:\n print e\n return False\n self.deadlock_list.insert(0,odo)\n if len(self.deadlock_list) > self.max_deadlock_time:\n old_odo = self.deadlock_list.pop()\n \n x_dif = abs(odo['x']-old_odo['x'])\n y_dif = abs(odo['y']-old_odo['y'])\n a_dif = abs(odo['angle']-old_odo['angle'])\n \n# print 'x_dif {0}'.format(x_dif)\n# print 'y_dif {0}'.format(y_dif)\n# print 'a_dif {0}'.format(a_dif)\n # check difference between current and old odometry \n if a_dif < 10 and (x_dif + y_dif) < 20:\n # deadlock\n if not self.turning:\n self.pioneer.turn(random.randint(self.minturn, self.maxturn))\n print 'INFO: deadlock, turning!'\n self.turning = True\n self.deadlock_time = time.time()\n return True\n return False","def wait_for_blink_end(self):\n\n\t\t\n\t\t# # # # #\n\t\t# EyeTribe method\n\n\t\tif self.eventdetection == 'native':\n\t\t\t\n\t\t\t# print warning, since EyeTribe does not have a blink detection\n\t\t\t# built into their API\n\t\t\t\n\t\t\tprint(\"WARNING! 'native' event detection has been selected, \\\n\t\t\t\tbut EyeTribe does not offer blink detection; PyGaze algorithm \\\n\t\t\t\twill be used\")\n\n\t\t# # # # #\n\t\t# PyGaze method\n\t\t\n\t\tblinking = True\n\t\t\n\t\t# loop while there is a blink\n\t\twhile blinking:\n\t\t\t# get newest sample\n\t\t\tgazepos = self.sample()\n\t\t\t# check if it's valid\n\t\t\tif self.is_valid_sample(gazepos):\n\t\t\t\t# if it is a valid sample, blinking has stopped\n\t\t\t\tblinking = False\n\t\t\n\t\t# return timestamp of blink end\n\t\treturn clock.get_time()","def blink(self):\n self.n_blinks += 1\n if self.n_blinks > self.max_blinks:\n self.active = False","def done(self):\n return self.left + 1 == self.right","def updateLives(self):\n if self._ball.ballBottom() == True:\n self._tries = self._tries - 1\n return True","async def expected_ball_received(self):\n # We do nothing in that case","def test_blink_exception(self):\n test_exception = BlinkException([1, \"No good\"])\n self.assertEqual(test_exception.errid, 1)\n self.assertEqual(test_exception.message, \"No good\")","def _check_for_completion(self, node):\n dis=0\n for i in range(node.state.size):\n dis+=(node.state[i]-self.goal.state[i])**2\n\n dis=np.sqrt(dis)\n if(dis<=self.step_size):\n return True\n else: return False","def goal_check(current_node, target_node):\n if current_node.id == target_node.id:\n return True\n else:\n return False","def is_ringing(self) -> bool:","def changes_while_node_toggle_test(self):\n debug(\"changes_while_node_toggle_test()\")\n cluster = self.cluster\n cluster.populate(2).start()\n node1, node2 = cluster.nodelist()\n wait(2)\n session = self.patient_cql_connection(node2)\n\n self.prepare_for_changes(session, namespace='ns2')\n node1.stop()\n wait(2)\n self.make_schema_changes(session, namespace='ns2')\n wait(2)\n node2.stop()\n wait(2)\n node1.start()\n node2.start()\n wait(20)\n self.validate_schema_consistent(node1)","def test_modifyNodes(self):\n\n # wait until our lb is ACTIVE before trying to update it\n lbaas_utils.wait_for_active_status(self)\n # modify/disable a node on our loadbalancer\n self.logging.info(\"Testing node disable...\")\n nodes = self.driver.list_lb_nodes(self.lb_id)\n mod_node = nodes['nodes'][0]\n mod_node_id = mod_node['id']\n mod_node_addr = mod_node['address']\n mod_node_data = {'condition':'DISABLED'}\n self.actual_status = self.driver.modify_node(self.lb_id, mod_node_id, mod_node_data)\n lbaas_utils.validate_loadBalancer(self, [mod_node_addr])\n\n # re-enable the node\n self.logging.info(\"Testing re-enable of node...\")\n mod_node_data = {'condition':'ENABLED'}\n self.actual_status = self.driver.modify_node(self.lb_id, mod_node_id, mod_node_data)\n lbaas_utils.validate_loadBalancer(self)\n\n # modify ip / address\n self.logging.info(\"Testing update of node ip...\")\n mod_node_data = {'address': '127.0.0.1'}\n expected_status = '400'\n self.actual_status = self.driver.modify_node(self.lb_id, mod_node_id, mod_node_data)\n self.assertEqual(str(self.actual_status), expected_status, msg = \"ERROR: Attempt to update node ip address succeeded with status: %s. Expected status: %s\" %(self.actual_status, expected_status))\n \n # modify port\n self.logging.info(\"Testing update of node port...\")\n mod_node_data = {'port': '443'}\n expected_status = '400'\n self.actual_status = self.driver.modify_node(self.lb_id, mod_node_id, mod_node_data)\n self.assertEqual(str(self.actual_status), expected_status, msg = \"ERROR: Attempt to update node port succeeded with status: %s. Expected status: %s\" %(self.actual_status, expected_status))\n\n # update of non-existent node\n self.logging.info(\"Testing update of non-existent node...\")\n mod_node_data = {'condition':\"DISABLED\"}\n expected_status = '404'\n self.actual_status = self.driver.modify_node(self.lb_id, '0', mod_node_data)\n self.assertEqual(str(self.actual_status), expected_status, msg = \"ERROR: Attempt to update node port succeeded with status: %s. Expected status: %s\" %(self.actual_status, expected_status))\n\n # lower-case condition\n #self.logging.info(\"Testing lowercase condition...\")\n #mod_node_data = {'condition':'disabled'}\n #self.actual_status = self.driver.modify_node(self.lb_id, mod_node_id, mod_node_data)\n #lbaas_utils.validate_loadBalancer(self, [mod_node_addr])\n #self.actual_status = self.driver.modify_node(self.lb_id, mod_node_id, mod_node_data)\n #lbaas_utils.validate_loadBalancer(self, [mod_node_addr])\n\n # lower-case condition\n self.logging.info(\"Testing bad condition...\")\n mod_node_data = {'condition':'TASERED_BADLY'}\n expected_statuses = ['400','512']\n self.actual_status = self.driver.modify_node(self.lb_id, mod_node_id, mod_node_data)\n self.assertTrue(str(self.actual_status) in expected_statuses, msg = \"ERROR: Attempt to update to bad condition succeeded with status: %s. Expected status: %s\" %(self.actual_status, expected_status))","def test_check_failed_highstate(self):\n self.assertEqual(self.checkredis.check_failed_highstate(\"aw1-php70-qa\", \"01\"), False)","def test_node_exists():\n assert Node"],"string":"[\n \"def check_balance(self):\\n\\t\\tif math.abs(self.get_height(treenode=self.left) - self.get_height(treenode=self.right)) > 1:\\n\\t\\t\\tself.rebalance()\",\n \"def test_update_node_state_smartfail(self):\\n pass\",\n \"def test_breeding_failed_carn(self):\\n nt.assert_equal(self.carn.breeding(1), None)\",\n \"async def test_early_exit(self):\\n n = Node()\\n run_task = asyncio.create_task(n.run_node())\\n await asyncio.sleep(0)\\n self.assertFalse(n.check_alive())\\n n.exit_node()\\n await n.wait_running()\\n await n.wait_stopped()\\n await run_task\\n await self._check_exited_node(n)\",\n \"def BlockheightCheck(self):\\n if self.CurrentBlockheight == BC.Default().Height:\\n if len(self.Peers) > 0:\\n logger.debug(\\\"Blockheight is not advancing ...\\\")\\n next_hash = BC.Default().GetHeaderHash(self.CurrentBlockheight + 1)\\n culprit_found = False\\n for peer in self.Peers:\\n if next_hash in peer.myblockrequests:\\n culprit_found = True\\n peer.Disconnect()\\n break\\n\\n # this happens when we're connecting to other nodes that are stuck themselves\\n if not culprit_found:\\n for peer in self.Peers:\\n peer.Disconnect()\\n else:\\n self.CurrentBlockheight = BC.Default().Height\",\n \"def verify_stack_up(self, prop=1.0, timeout=25):\\n for _ in range(timeout):\\n links = 0\\n links_up = 0\\n for i, dpid in enumerate(self.dpids):\\n dp_name = self.dp_name(i)\\n for link in self.non_host_links(dpid):\\n status = self.stack_port_status(dpid, dp_name, link.port)\\n links += 1\\n if status == 3: # up\\n links_up += 1\\n prop_up = links_up / links\\n if prop_up >= prop:\\n return\\n time.sleep(1)\\n self.fail('not enough links up: %f / %f' % (links_up, links))\",\n \"def test_goto_field_boss_hartmann_blue_fails(self):\\n\\n sopTester.updateModel('mcp', TestHelper.mcpState['all_off'])\\n sopTester.updateModel('hartmann', TestHelper.hartmannState['blue_fails'])\\n\\n cmdState = self.actorState.gotoField\\n cmdState.reinitialize(self.cmd)\\n\\n self._goto_field_boss(12, 37, 0, 0, cmdState, didFail=True, finish=True)\",\n \"def test_blink(self):\\n display = get_display(1)\\n display.register_state(main.Fade)\\n prev = display.strand[0]\\n for i in range(1000):\\n display.tick()\\n assert(all(0 <= display.strand[0][i] <= 255 for i in range(3)))\\n assert display.strand[0] != prev\\n prev = display.strand[0]\",\n \"def test_good_fork_higher(self):\\n bvh = self.BlockValidationHandler()\\n\\n root = self.btm.chain_head\\n\\n # create a new valid chain 5 long from the current root\\n new_head = self.btm.generate_chain(root, 5,\\n {'add_to_store': True})\\n self.btm.set_chain_head(new_head[-1])\\n # generate candidate chain 8 long from the same root\\n new_block = self.btm.generate_chain(root, 8,\\n {'add_to_cache': True})\\n bv = self.create_block_validator(new_block[-1], bvh.on_block_validated)\\n bv.run()\\n\\n self.assertTrue(bvh.has_result())\\n self.assertTrue(new_block[-1].status == BlockStatus.Valid)\\n self.assertTrue(bvh.result[\\\"commit_new_block\\\"])\",\n \"def node_check():\\n while True:\\n if LAST_RECEIVED:\\n for loc in dict(LAST_RECEIVED):\\n if (datetime.datetime.utcnow() - LAST_RECEIVED[loc]).total_seconds() >= 60:\\n ROLLING_TEMPS.pop(loc)\\n ROLLING_HUMS.pop(loc)\\n LAST_RECEIVED.pop(loc)\\n print(\\\"connection to\\\", loc, \\\"has been lost\\\")\\n else:\\n print(\\\"No nodes found. Please check that nodes are running.\\\")\\n\\n time.sleep(6)\",\n \"def test_accept_depth(self, nodeOneId, nodeTwoId):\\n logging.info(\\\">>> Entered : test_accept_depth \\\\n\\\")\\n try:\\n self.nodes[nodeTwoId].setminingmaxblock(1000)\\n self.nodes[nodeTwoId].setexcessiveblock(1010, 4)\\n\\n # Mine an excessive block. Node One should not accept it\\n addr = self.nodes[nodeTwoId].getnewaddress()\\n for i in range(0,10):\\n self.nodes[nodeOneId].sendtoaddress(addr, 1.0)\\n self.nodes[nodeOneId].generate(1)\\n time.sleep(2) #give blocks a chance to fully propagate\\n counts = [ x.getblockcount() for x in self.nodes[0:2] ]\\n\\n logging.info(\\\"Counts: Node1 = %d and Node2 = %d \\\" %(counts[0], counts[1]))\\n assert_equal(counts[0]-counts[1], 1)\\n # Mine a block on top. Node 1 should still not accept it\\n self.nodes[nodeOneId].generate(1)\\n time.sleep(2) #give blocks a chance to fully propagate\\n counts = [ x.getblockcount() for x in self.nodes[0:2] ]\\n logging.info(\\\"Counts: Node1 = %d and Node2 = %d \\\" %(counts[0], counts[1]))\\n assert_equal(counts[0]-counts[1], 2)\\n\\n # Change node 1 to AD=2. The assertion will fail if it doesn't accept the chain now \\n self.nodes[nodeTwoId].setexcessiveblock(1010, 2)\\n self.nodes[nodeOneId].generate(1)\\n time.sleep(2) #give blocks a chance to fully propagate !!!!\\n\\n counts = [ x.getblockcount() for x in self.nodes[0:2] ]\\n logging.info(\\\"Counts: Node1 = %d and Node2 = %d \\\" %(counts[0], counts[1]))\\n assert_equal(counts[0]-counts[1], 0)\\n except (Exception, JSONRPCException) as e1:\\n logging.info(e1)\\n exc_type, exc_obj, exc_tb = sys.exc_info()\\n fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)[1]\\n raise TestAssertionError({\\\"file_name\\\": fname, \\\"line_num\\\": exc_tb.tb_lineno, \\\\\\n \\\"error_type\\\": exc_type.__name__, \\\"error_msg\\\": str( e1 ), \\\\\\n \\\"n1\\\" : self.bins[nodeOneId], \\\"n2\\\" : self.bins[nodeTwoId], \\\"amount\\\" : \\\"N/A\\\", \\\"numsig\\\" : \\\"N/A\\\"})\",\n \"def test_get_node_state_smartfail(self):\\n pass\",\n \"def event_m20_11_x84(z44=20111330):\\n \\\"\\\"\\\"State 0,1: Wait for branch destruction\\\"\\\"\\\"\\n IsObjBroken(0, z44, 1)\\n assert ConditionGroup(0)\\n \\\"\\\"\\\"State 2: End state\\\"\\\"\\\"\\n return 0\",\n \"def test_balance_right2(our_bsts):\\n assert our_bsts[0].depth() == our_bsts[1]\",\n \"def test_block_bad_state(self):\\n pass\",\n \"def test_balance_old(self):\\n successes = 0\\n failures = 0\\n iterations = NUM_CALLS\\n\\n for _ in range(iterations):\\n\\n handler = self.new_handler()\\n ret = check_balance(handler.root)\\n if ret:\\n successes += 1\\n else:\\n failures += 1\\n handler.debug_wrapper()\\n\\n if failures != 0:\\n print('\\\\n=================================================\\\\n')\\n\\n self.assertEqual(failures, 0,\\n msg=f'{BColors.FAIL}\\\\n\\\\t[-]\\\\tFrom state: Failed to always generate balanced trees! ' +\\n f'{failures}/{iterations} failures! {BColors.ENDC}')\\n print(f\\\"{BColors.OKGREEN}\\\\t[+]\\\\tFrom state: Validated that all trees all balanced upon generation in {successes} trees.{BColors.ENDC}\\\")\",\n \"def test_nodeCircuitBreaker(self):\\n disco = create_disco()\\n node = create_node(\\\"somewhere\\\")\\n disco.onMessage(None, NodeActive(node))\\n resolved_node = resolve(disco, \\\"myservice\\\", \\\"1.0\\\")\\n\\n avail1 = resolved_node.available()\\n # Default threshold in CircuitBreaker is three failures:\\n resolved_node.failure()\\n resolved_node.failure()\\n resolved_node.failure()\\n avail2 = resolved_node.available()\\n resolved_node.success()\\n avail3 = resolved_node.available()\\n self.assertEqual((avail1, avail2, avail3), (True, False, True))\",\n \"def test_get_node_sled(self):\\n pass\",\n \"def _WaitForLinkerTestStatus(adb, timeout):\",\n \"def wait_full_state(node,data):\\n\\tif len(node.roots) >= node.id:\\n\\t\\treactor.stop()\\n\\telse:\\n\\t\\treactor.callLater(0, lambda:wait_full_state(node, data))\",\n \"def test_kth_from_end_exists():\\n assert LinkedList.kth_from_end\",\n \"def test_balance_right_tree(bst_right_balance):\\n assert bst_right_balance.balance() == 0\",\n \"def test_tree_2_nodes_left_unbalanced(one_t):\\n one_t.insert(9)\\n assert one_t.balance() == 1\",\n \"def delay_checks(self):\\n return False\",\n \"def wait_for_bond_event(self, expected_bond_event):\\n pass\",\n \"def check_boss_edges(ai_settings, boss):\\n\\tif boss.check_edges():\\n\\t\\tai_settings.boss_direction *= -1\",\n \"def is_node_down():\\n try:\\n hostname = check_command_output(\\\"hostname\\\").strip()\\n host_fqdn = socket.getfqdn(hostname)\\n nodes = get_compute_nodes_info(hostname_filter=hostname)\\n if not any(host in nodes for host in [hostname, host_fqdn]):\\n log.warning(\\\"Node is not attached to scheduler. Reporting as down\\\")\\n return True\\n\\n node = nodes.get(host_fqdn, nodes.get(hostname))\\n log.info(\\\"Node is in state: '{0}'\\\".format(node.state))\\n if all(error_state not in node.state for error_state in SGE_ERROR_STATES):\\n return False\\n except Exception as e:\\n log.error(\\\"Failed when checking if node is down with exception %s. Reporting node as down.\\\", e)\\n\\n return True\",\n \"def test_tree_2_nodes_right_unbalanced(one_t):\\n one_t.insert(11)\\n assert one_t.balance() == -1\",\n \"def is_final_node_reached(self):\\n if self.actual_node == self.final_node:\\n self.final_node_reached = True\",\n \"def lookLeftRight(self):\\n self.lookLeft()\\n time.sleep(0.5)\\n self.lookRight()\",\n \"def test_balanced_removal(self):\\n successes = 0\\n failures = 0\\n iterations = NUM_CALLS\\n\\n for _ in range(iterations):\\n\\n failure_callback = False\\n handler = self.new_handler(balance=True)\\n new_ids = [randint(0, handler.uid) for _ in range(randint(HEIGHT[0], handler.expected_height))]\\n new_ids = list(set(new_ids)) # make sure there are no duplicates\\n try:\\n new_ids.remove(handler.golden_id) # remove golden id from removal if it was randomly selected\\n except ValueError:\\n pass\\n\\n for val in new_ids:\\n handler.delNodeByID(val)\\n if handler.balanced is False:\\n failures += 1\\n failure_callback = True\\n break\\n\\n if failure_callback:\\n break\\n state = handler.get_gamestate()\\n for val in new_ids:\\n if 'node' + str(val) in state['node_points']:\\n failures += 1\\n break\\n\\n successes += 1\\n\\n self.assertEqual(failures, 0,\\n msg=f'{BColors.FAIL}\\\\n\\\\t[-]\\\\tModification: Failed to correctly remove nodes (balancing addition) ' +\\n f'{failures}/{iterations} failures! {BColors.ENDC}')\\n print(f\\\"{BColors.OKGREEN}\\\\t[+]\\\\tModification: Validated removing nodes in balancing mode in {successes} trees.{BColors.ENDC}\\\")\",\n \"def check_ball_on_target():\\n\\n pass\",\n \"def require_linux_bond_up(self, host_id):\\n synced_state_list = self.get_expected_synced_states(host_id)\\n host = self.host_information[host_id]['host']\\n bond_name = self.host_information[host_id]['bond']\\n for _ in range(self.LACP_TIMEOUT*2):\\n result = host.cmd('cat /proc/net/bonding/%s|sed \\\"s/[ \\\\t]*$//g\\\"' % bond_name)\\n result = '\\\\n'.join([line.rstrip() for line in result.splitlines()])\\n with open(os.path.join(self.tmpdir, 'bonding-state.txt'), 'w') as state_file:\\n state_file.write(result)\\n matched_all = True\\n for state_txt in synced_state_list:\\n if not re.search(state_txt, result):\\n matched_all = False\\n break\\n if matched_all:\\n return\\n time.sleep(1)\\n synced_state_txt = r\\\"\\\"\\\"\\\"\\\"\\\"\\n for state_txt in synced_state_list:\\n synced_state_txt += state_txt + \\\"\\\\n\\\\n\\\"\\n synced_state_txt.strip()\\n self.assertFalse(\\n re.search(synced_state_txt, result),\\n msg='LACP did not synchronize: %s\\\\n\\\\nexpected:\\\\n\\\\n%s' % (result, synced_state_txt))\",\n \"def isOkToBoost(self, node):\\n para = \\\"p\\\"\\n stepsAway = 0\\n minimumStopWordCount = 5\\n maxStepsAwayFromNode = 3\\n \\n nodes = self.walkSiblings(node)\\n for currentNode in nodes:\\n # p\\n if currentNode.tag == para:\\n if stepsAway >= maxStepsAwayFromNode:\\n return False\\n paraText = Parser.getText(currentNode)\\n wordStats = StopWords().getStopWordCount(paraText)\\n if wordStats.getStopWordCount > minimumStopWordCount:\\n return True\\n stepsAway += 1\\n return False\",\n \"def test_breeding_parent_weight_carn(self):\\n start_weight = 50\\n self.carn.weight = 50\\n self.carn.breeding(10)\\n nt.assert_less(self.carn.weight, start_weight)\",\n \"def test_left(self) -> None:\\n\\n self._perform_background_initial_update()\\n\\n u1 = self.register_user(\\\"u1\\\", \\\"pass\\\")\\n u1token = self.login(\\\"u1\\\", \\\"pass\\\")\\n r1 = self.helper.create_room_as(u1, tok=u1token)\\n\\n u2 = self.register_user(\\\"u2\\\", \\\"pass\\\")\\n u2token = self.login(\\\"u2\\\", \\\"pass\\\")\\n\\n self.helper.join(r1, u2, tok=u2token)\\n\\n r1stats_ante = self._get_current_stats(\\\"room\\\", r1)\\n assert r1stats_ante is not None\\n\\n self.helper.leave(r1, u2, tok=u2token)\\n\\n r1stats_post = self._get_current_stats(\\\"room\\\", r1)\\n assert r1stats_post is not None\\n\\n self.assertEqual(\\n r1stats_post[\\\"current_state_events\\\"] - r1stats_ante[\\\"current_state_events\\\"],\\n 0,\\n )\\n self.assertEqual(\\n r1stats_post[\\\"left_members\\\"] - r1stats_ante[\\\"left_members\\\"], +1\\n )\\n self.assertEqual(\\n r1stats_post[\\\"joined_members\\\"] - r1stats_ante[\\\"joined_members\\\"], -1\\n )\",\n \"def test_balance_left_tree(bst_all_to_left):\\n assert bst_all_to_left.balance() == -1\",\n \"def changes_while_node_down_test(self):\\n debug(\\\"changes_while_node_down_test()\\\")\\n cluster = self.cluster\\n cluster.populate(2).start()\\n node1, node2 = cluster.nodelist()\\n wait(2)\\n session = self.patient_cql_connection(node2)\\n\\n self.prepare_for_changes(session, namespace='ns2')\\n node1.stop()\\n wait(2)\\n self.make_schema_changes(session, namespace='ns2')\\n wait(2)\\n node2.stop()\\n wait(2)\\n node1.start()\\n node2.start()\\n wait(20)\\n self.validate_schema_consistent(node1)\",\n \"def test_node_way_ref_after_delete_2():\\n data = cache_query(nodes=[20001, 20002], deps=True)\\n assert 'ways' not in data['nodes']['20001']\\n assert data['nodes']['20002'] == None\\n assert query_row(db_conf, 'osm_roads', 20001) == None\\n assert query_row(db_conf, 'osm_barrierpoints', 20001)['type'] == 'block'\",\n \"def test_state_after_failure(self):\\n pass\",\n \"def check( self ):\\n\\n if ( self.alive is not None ) \\\\\\n and ( time.time() > ( self.alive + self.timeout ) ):\\n return False\\n return True\",\n \"def test_get_node_status_batterystatus(self):\\n pass\",\n \"def poll(cls, context):\\r\\n return hasattr(bpy.types.Object, \\\"BlenderNEURON_node\\\") and \\\\\\r\\n bpy.types.Object.BlenderNEURON_node is not None and \\\\\\r\\n bpy.types.Object.BlenderNEURON_node.client is not None\",\n \"def poll(cls, context):\\r\\n return hasattr(bpy.types.Object, \\\"BlenderNEURON_node\\\") and \\\\\\r\\n bpy.types.Object.BlenderNEURON_node is not None and \\\\\\r\\n bpy.types.Object.BlenderNEURON_node.client is not None\",\n \"def test_rebalance(self):\\n successes = 0\\n failures = 0\\n iterations = NUM_CALLS\\n\\n for _ in range(iterations):\\n\\n handler = self.new_handler()\\n state = handler.get_gamestate()\\n rebal_handler = AVLHandler.from_graph(state)\\n ret = check_balance(rebal_handler.root)\\n if ret and rebal_handler.balanced:\\n successes += 1\\n else:\\n failures += 1\\n\\n self.assertEqual(failures, 0,\\n msg=f'{BColors.FAIL}\\\\n\\\\t[-]\\\\tModification: Failed to correctly rebalance deserialized tree! '\\n f'{failures}/{iterations} failures! {BColors.ENDC}')\\n print(f\\\"{BColors.OKGREEN}\\\\t[+]\\\\tModification: Validated deserialization rebalancing in {successes} trees.{BColors.ENDC}\\\")\",\n \"def test_balanced_addition(self):\\n successes = 0\\n failures = 0\\n iterations = NUM_CALLS\\n\\n for _ in range(iterations):\\n\\n failure_callback = False\\n handler = self.new_handler(balance=True)\\n new_vals = [randint(1, POINT_CAP) for _ in range(randint(HEIGHT[0], HEIGHT[1]))]\\n for val in new_vals:\\n handler.addNewNode(val, b=True)\\n if handler.balanced is False:\\n handler.debug_wrapper()\\n failures += 1\\n failure_callback = True\\n break\\n\\n if failure_callback:\\n break\\n state = handler.get_gamestate()\\n for val in new_vals:\\n if val not in state['node_points'].values():\\n failures += 1\\n break\\n\\n successes += 1\\n\\n self.assertEqual(failures, 0,\\n msg=f'{BColors.FAIL}\\\\n\\\\t[-]\\\\tModification: Failed to correctly add new nodes (balancing addition) ' +\\n f'{failures}/{iterations} failures! {BColors.ENDC}')\\n print(\\n f\\\"{BColors.OKGREEN}\\\\t[+]\\\\tModification: Validated adding nodes in balancing mode in {successes} trees.{BColors.ENDC}\\\")\",\n \"def test_cycle_present_true(test_linkedlist):\\n test_linkedlist.insert_end('A')\\n test_linkedlist.insert_end('B')\\n test_linkedlist.insert_end('C')\\n # create a cycle - connection from 3rd to 2nd element\\n test_linkedlist.head.next.next.next = test_linkedlist.head.next\\n # assert cycle_present() function returns True\\n assert test_linkedlist.cycle_present()\",\n \"async def test_dead_peer(self):\\n async with Node() as n1:\\n async with Node() as n2:\\n await n2.join_network(n1.nid())\\n peer = next(iter(n1._act_set))\\n writer = peer._writer\\n peer._writer = None\\n await n1.send_broadcast('hello')\\n writer.close()\\n await writer.wait_closed()\",\n \"def test_get_node_state(self):\\n pass\",\n \"def __changeroot(self):\\n if self.edges[self.best_edge].get_status() == EdgeStatus.branch:\\n self.__edge_stub(self.best_edge, Message.changeroot)\\n else:\\n # changeroot received by father node\\n self.__edge_stub(self.best_edge, Message.connect, [self.level])\\n self.__change_edge_status(self.best_edge, EdgeStatus.branch)\",\n \"def test_imbalanced_addition(self):\\n successes = 0\\n failures = 0\\n iterations = NUM_CALLS\\n\\n for _ in range(iterations):\\n\\n failure_callback = False\\n handler = self.new_handler()\\n new_vals = [randint(1, POINT_CAP) for _ in range(randint(HEIGHT[0], HEIGHT[1]))]\\n for val in new_vals:\\n handler.addNewNode(val, b=False)\\n true_bal = check_balance(handler.root)\\n if handler.balanced is not true_bal:\\n failures += 1\\n failure_callback = True\\n break\\n\\n if failure_callback:\\n break\\n state = handler.get_gamestate()\\n for val in new_vals:\\n if val not in state['node_points'].values():\\n failures += 1\\n break\\n\\n successes += 1\\n\\n self.assertEqual(failures, 0,\\n msg=f'{BColors.FAIL}\\\\n\\\\t[-]\\\\tModification: Failed to correctly add new nodes (non-balancing addition) ' +\\n f'{failures}/{iterations} failures! {BColors.ENDC}')\\n print(f\\\"{BColors.OKGREEN}\\\\t[+]\\\\tModification: Validated adding nodes in non-balancing mode in {successes} trees.{BColors.ENDC}\\\")\",\n \"async def test_node_init(self):\\n n = Node()\\n await self._check_exited_node(n)\\n self.assertIsNone(n.__exit__(None, None, None))\",\n \"def check_time(self):\\n while True:\\n for name in self.neighbors:\\n if not self.neighbors[name].is_killed:\\n if not self.neighbors[name].update_ready and time.time() - self.neighbors[name].send_timer > self.timeout:\\n self.neighbors[name].update_ready = True\\n if time.time() - self.neighbors[name].kill_timer > 3 * self.timeout:\\n self.neighbors[name].is_killed = True\",\n \"async def test_node_many(self):\\n node_list = [Node() for i in range(100)]\\n\\n # check uids are unique\\n uid_list = [n.uid() for n in node_list]\\n self.assertEqual(len(uid_list), len(set(uid_list)))\\n\\n running_list = []\\n try:\\n for n in node_list:\\n # create node and join upon random node in network\\n await n.run_node()\\n if len(running_list) > 0:\\n ct_n, _ = random.choice(running_list)\\n await n.join_network(ct_n.nid())\\n\\n msg = Network_Message()\\n n.attach_broadcast_callback(msg.msg_callback)\\n n.attach_direct_message_callback(msg.msg_callback)\\n running_list.append((n, msg))\\n\\n for i in range(0, 3):\\n await asyncio.sleep(constants.SHU_TIME + 5)\\n\\n while len(running_list) > 1:\\n # broadcast and check all nodes received message\\n await running_list[0][0].send_broadcast('hello world')\\n for node, msg in running_list[1:]:\\n msg_suc = await msg.wait_msg(15)\\n self.assertTrue(msg_suc)\\n self.assertEqual(msg.nid, running_list[0][0].nid())\\n self.assertEqual(msg.msg, 'hello world')\\n\\n # kill half of the nodes\\n kill_list = running_list[:len(running_list) // 2]\\n for n, _ in kill_list:\\n n.exit_node()\\n await asyncio.gather(*[n.wait_stopped() for n, _ in kill_list])\\n\\n new_list = running_list[len(running_list) // 2:]\\n for _, msg in new_list:\\n msg.reset()\\n\\n running_list = new_list\\n await asyncio.sleep(constants.SHU_TIME + 5)\\n finally:\\n for n in node_list:\\n n.exit_node()\\n await asyncio.gather(*[n.wait_stopped() for n in node_list])\",\n \"def test_node_value():\\n from linked_list import Node\\n node2 = Node(\\\"Something\\\")\\n assert node2.value == \\\"Something\\\" and node2.nxt is None\",\n \"def step_tree(self):\\n if random.random() < self.world.f or self.any_neighbor_burning():\\n self.set_state(\\\"orange\\\")\",\n \"def test_set_healthy(self):\\n\\n notifier = mock.Mock()\\n\\n node = Node({'healthchecks': []}, {}, mock.Mock())\\n node.set_notifier(notifier)\\n\\n # set as unhealthy\\n node.set_healthy(False)\\n\\n # set as healthy and expect a notification\\n node.set_healthy(True)\\n notifier.is_healthy_again.assert_called_once()\",\n \"def fsm_testDeadlock(machine):\\n # We return these two in the end\\n property_exists = False\\n latest_state = machine.state\\n\\n # Go through each state and get triggers\\n # If no non-default triggers are available\\n # return with False and latest_state\\n # (That means there's no actions available == deadlock)\\n\\n for state in machine.states:\\n latest_state = state\\n print \\\"Current state: \\\", state\\n for trigger in machine.machine.get_triggers(state):\\n # If no: a non-default trigger exists, no deadlock, move on to next state\\n # filter out triggers with names \\\"to_...\\\" (since automatically generated ones are to_<state>)\\n if trigger[:3] != \\\"to_\\\":\\n print \\\"Trigger: \\\", trigger\\n property_exists = True\\n break\\n # If state has no non-default triggers, deadlock exists.\\n # return false and latest_state\\n # no need to iterate the rest of the states\\n if not property_exists:\\n return property_exists, latest_state\\n return property_exists, latest_state\",\n \"def test_imbalanced_removal(self):\\n successes = 0\\n failures = 0\\n iterations = NUM_CALLS\\n\\n for _ in range(iterations):\\n\\n failure_callback = False\\n handler = self.new_handler()\\n new_ids = [randint(0, handler.uid) for _ in range(randint(HEIGHT[0], handler.expected_height))]\\n new_ids = list(set(new_ids)) # make sure there are no duplicates\\n try:\\n new_ids.remove(handler.golden_id) # remove golden id from removal if it was randomly selected\\n except ValueError:\\n pass\\n\\n for val in new_ids:\\n handler.delNodeByID(val)\\n true_bal = check_balance(handler.root)\\n if handler.balanced is not true_bal:\\n failures += 1\\n failure_callback = True\\n break\\n\\n if failure_callback:\\n break\\n state = handler.get_gamestate()\\n for val in new_ids:\\n if 'node' + str(val) in state['node_points']:\\n failures += 1\\n break\\n\\n successes += 1\\n\\n self.assertEqual(failures, 0,\\n msg=f'{BColors.FAIL}\\\\n\\\\t[-]\\\\tModification: Failed to correctly remove nodes (non-balancing addition) ' +\\n f'{failures}/{iterations} failures! {BColors.ENDC}')\\n print(f\\\"{BColors.OKGREEN}\\\\t[+]\\\\tModification: Validated removing nodes in non-balancing mode in {successes} trees.{BColors.ENDC}\\\")\",\n \"def test_balance_known_tree_2(known_bst):\\n known_bst[0].insert_non_balance(12)\\n assert known_bst[0].balance() == known_bst[2] - 1\",\n \"def test_DeadPersonTrue (self) :\\n\\t\\t\\n\\t\\tself.person1.updateHealth ()\\n\\t\\tself.assertEqual(self.person1.getHealth(), 0)\\n\\t\\tself.assertTrue(self.person1.DeadPerson())\",\n \"def check_if_alive():\\n global SOCKET1\\n global PRINT_LOCK\\n global DATA\\n start = current_time()\\n msg = pickle.dumps(\\\"is_alive\\\")\\n while True:\\n\\n if (current_time() - start) < 10:\\n continue\\n\\n for every_one in DATA[\\\"neighbor\\\"]:\\n remote = (\\\"127.0.0.1\\\", every_one[2])\\n # out of the present list\\n # decrease waiting time\\n SOCKET1.settimeout(2)\\n SOCKET1.sendto(msg, remote)\\n try:\\n recv_msg = pickle.loads(SOCKET1.recvfrom(512)[0])\\n if recv_msg is \\\"yes\\\":\\n with PRINT_LOCK:\\n print(\\\"{} is alive\\\".format(every_one[0]))\\n except (OSError, socket.timeout) as e_ra:\\n # reset waiting time\\n SOCKET1.settimeout(socket.getdefaulttimeout())\\n with PRINT_LOCK:\\n print(\\\"{} is dead : {}\\\".format(every_one[0], e_ra))\\n index = DATA[\\\"neighbor\\\"].index(every_one)\\n DATA[\\\"neighbor\\\"].pop(index)\\n index = DATA[\\\"distance_vec\\\"].index(\\n [every_one[0], every_one[1]])\\n DATA[\\\"distance_vec\\\"].pop(index)\\n bellman_ford(DATA[\\\"router_id\\\"], DATA[\\\"distance_vec\\\"])\\n # ensuring the time diff is always round about 10\\n start = current_time()\",\n \"def test_DeadPersonFalse (self) :\\n\\t\\t\\n\\t\\tself.person1.updateHealth ()\\n\\t\\tself.assertFalse(self.person2.DeadPerson())\",\n \"def fsm_test(machine):\\n print \\\"############################################\\\"\\n print \\\"###### Testing machine for deadlocks: ######\\\"\\n print \\\"############################################\\\"\\n property_exists, latest_state = fsm_testDeadlock(machine)\\n if property_exists:\\n print property_exists, \\\", property holds. No deadlocks\\\"\\n else:\\n print property_exists, \\\", property does not hold. Latest state tested: \\\", latest_state\\n\\n print \\\"###########################################\\\"\\n print \\\"###### Testing machine for feedback: ######\\\"\\n print \\\"###########################################\\\"\\n property_exists, latest_state = fsm_testFeedback(machine)\\n if property_exists:\\n print property_exists, \\\", property holds. Every transition gives feedback\\\"\\n else:\\n print property_exists, \\\", property does not hold. Latest state tested: \\\", latest_state\",\n \"def decide_reward(prev_node, cur_node):\\n # Constant decay\\n reward = constants.REWARD_DECAY\\n\\n # First check if any players died\\n prev_agent_ids = prev_node.get_alive_agents()\\n cur_agent_ids = cur_node.get_alive_agents()\\n missing_agent_ids = [agent for agent in prev_agent_ids if agent not in cur_agent_ids]\\n this_agent = cur_node.get_agent_obj()\\n\\n # Agent died\\n if this_agent.agent_id in missing_agent_ids:\\n reward += constants.REWARD_DIED\\n\\n # Check if any enemies died (regardless of dying from whom)\\n for enemy in this_agent.enemies:\\n if enemy.name == 'AgentDummy':\\n continue\\n enemy_agent_id = int(enemy.name[-1])\\n if enemy_agent_id in missing_agent_ids:\\n reward += constants.REWARD_ENEMY_DIED\\n\\n\\n # Then, check if picked up powerups\\n cur_pos = this_agent.position\\n item_enum = constants.Item\\n powerups = [item_enum.Kick, item_enum.IncrRange, item_enum.ExtraBomb]\\n\\n if prev_node.game_env._board[cur_pos] in [p.value for p in powerups]:\\n reward += constants.REWARD_POWERUP\\n\\n\\n # Lastly, check if agent destroyed any wood boxes\\n prev_laid_bombs = prev_node.get_agent_laid_bombs()\\n\\n def reward_bomb_for_direction(bomb, prev_board, cur_board, direction):\\n for off in range(1, bomb.blast_strength):\\n pos = (bomb.position[0] + off * direction[0], bomb.position[1] + off * direction[1])\\n exit = False\\n # If the explosion is blocked, exit early\\n if not utility.position_on_board(prev_board, pos):\\n break\\n if utility.position_is_wall(prev_board, pos):\\n exit = True\\n # If a wood box is to be flamed in previous state and\\n # is actually flamed in current state, then give reward\\n if utility.position_is_wood(prev_board, pos) and utility.position_is_flames(cur_board, pos):\\n return constants.REWARD_WOOD\\n if exit:\\n break\\n return 0\\n\\n\\n for bomb in prev_laid_bombs:\\n # About to explode\\n if bomb.life == 1:\\n # NOTE:\\n # For now, as long as any wood boxes are destroyed within the bomb range\\n # of this exploding bomb it's counted towards this agent.\\n # If bomb life > 1 but it's triggered by other bombs then we don't care\\n prev_board, cur_board = prev_node.game_env._board, cur_node.game_env._board\\n reward += reward_bomb_for_direction(bomb, prev_board, cur_board, direction=(1, 0))\\n reward += reward_bomb_for_direction(bomb, prev_board, cur_board, direction=(0, 1))\\n reward += reward_bomb_for_direction(bomb, prev_board, cur_board, direction=(-1, 0))\\n reward += reward_bomb_for_direction(bomb, prev_board, cur_board, direction=(0, -1))\\n\\n return reward\",\n \"def test_breeding_parent_weight(self):\\n Herbivore.set_parameters({\\\"gamma\\\": 1})\\n start_weight = 50\\n self.herb.weight = start_weight\\n self.herb.breeding(10)\\n nt.assert_less(self.herb.weight, start_weight)\",\n \"def test_good_fork_lower(self):\\n bvh = self.BlockValidationHandler()\\n\\n root = self.btm.chain_head\\n\\n # create a new valid chain 5 long from the current root\\n new_head = self.btm.generate_chain(root, 5,\\n {'add_to_store': True})\\n self.btm.set_chain_head(new_head[-1])\\n # generate candidate chain 3 long from the same root\\n new_block = self.btm.generate_chain(root, 3,\\n {'add_to_cache': True})\\n\\n bv = self.create_block_validator(new_block[-1], bvh.on_block_validated)\\n bv.run()\\n\\n self.assertTrue(bvh.has_result())\\n self.assertTrue(new_block[-1].status == BlockStatus.Valid)\\n self.assertFalse(bvh.result[\\\"commit_new_block\\\"])\",\n \"def test_banned(self) -> None:\\n\\n self._perform_background_initial_update()\\n\\n u1 = self.register_user(\\\"u1\\\", \\\"pass\\\")\\n u1token = self.login(\\\"u1\\\", \\\"pass\\\")\\n r1 = self.helper.create_room_as(u1, tok=u1token)\\n\\n u2 = self.register_user(\\\"u2\\\", \\\"pass\\\")\\n u2token = self.login(\\\"u2\\\", \\\"pass\\\")\\n\\n self.helper.join(r1, u2, tok=u2token)\\n\\n r1stats_ante = self._get_current_stats(\\\"room\\\", r1)\\n assert r1stats_ante is not None\\n\\n self.helper.change_membership(r1, u1, u2, \\\"ban\\\", tok=u1token)\\n\\n r1stats_post = self._get_current_stats(\\\"room\\\", r1)\\n assert r1stats_post is not None\\n\\n self.assertEqual(\\n r1stats_post[\\\"current_state_events\\\"] - r1stats_ante[\\\"current_state_events\\\"],\\n 0,\\n )\\n self.assertEqual(\\n r1stats_post[\\\"banned_members\\\"] - r1stats_ante[\\\"banned_members\\\"], +1\\n )\\n self.assertEqual(\\n r1stats_post[\\\"joined_members\\\"] - r1stats_ante[\\\"joined_members\\\"], -1\\n )\",\n \"def isdone(node_dict):\\n # compute heading difference\\n hdiff = heading_diff(r_target, node_dict['pn'].heading)\\n # return if we are we close enough\\n return abs(hdiff) < abs(tol)\",\n \"def _ensure_is_alive(self):\\n if self._hit_points == 0:\\n raise UnitIsDead('Unit is dead!')\",\n \"def update_blinker_from_stalk(self, blinker_time: int, left_blinker_stalk: bool, right_blinker_stalk: bool):\\n\\n if left_blinker_stalk:\\n self.right_blinker_cnt = 0\\n if not self.left_blinker_prev:\\n self.left_blinker_cnt = blinker_time\\n\\n if right_blinker_stalk:\\n self.left_blinker_cnt = 0\\n if not self.right_blinker_prev:\\n self.right_blinker_cnt = blinker_time\\n\\n self.left_blinker_cnt = max(self.left_blinker_cnt - 1, 0)\\n self.right_blinker_cnt = max(self.right_blinker_cnt - 1, 0)\\n\\n self.left_blinker_prev = left_blinker_stalk\\n self.right_blinker_prev = right_blinker_stalk\\n\\n return bool(left_blinker_stalk or self.left_blinker_cnt > 0), bool(right_blinker_stalk or self.right_blinker_cnt > 0)\",\n \"def test_blow_up(self):\\n self.assertTrue(self.g.__class__)\",\n \"def IsAfter(self, node, start):\\n return node.start > start;\",\n \"def test_BLINK_FAST_TRACK_PROCESS(self):\\n self.verify_references_to_prerequisites(processes.BLINK_FAST_TRACK_PROCESS)\",\n \"def wait_on_elb_instance ( elb_conn, elb_name, elb_instance_id, elb_state = 'InService', sleep_time = 10, max_wait = 300 ) :\\n instance_states = elb_conn.describe_instance_health( load_balancer_name = elb_name,\\n instances = [ elb_instance_id ] )\\n instance_state = instance_states[ 0 ].state\\n while instance_state != elb_state :\\n if max_wait <= 0 :\\n break\\n time.sleep( sleep_time )\\n max_wait -= sleep_time\\n instance_states = elb_conn.describe_instance_health( load_balancer_name = elb_name,\\n instances = [ elb_instance_id ] )\\n instance_state = instance_states[ 0 ].state\\n\\n return instance_state == elb_state\",\n \"def test_create_node_shutdown_item(self):\\n pass\",\n \"def twist(self):\\n self.right()\\n time.sleep(1)\\n self.stop()\\n self.left()\\n time.sleep(1)\\n self.stop()\",\n \"def test_timeoutReset(self):\\n for i in range(3):\\n self.circuit_breaker.failure()\\n self.time.advance(29.0)\\n available29sec = self.circuit_breaker.available()\\n self.time.advance(1.1)\\n available30sec = self.circuit_breaker.available()\\n self.assertEqual((available29sec, available30sec),\\n (False, True))\",\n \"def test_liveness_bird_down(self):\\n with DockerHost('host1',\\n additional_docker_options=CLUSTER_STORE_DOCKER_OPTIONS) as host1:\\n retry_until_success(host1.assert_is_ready, retries=30)\\n host1.execute(\\\"docker exec -it calico-node sv stop /etc/service/enabled/bird\\\")\\n\\n # Check that the readiness script is reporting 'not ready'\\n self.assertRaisesRegexp(CalledProcessError, \\\"calico/node is not ready: bird/confd is not live: Service bird is not running.\\\",\\n host1.execute, \\\"docker exec calico-node /bin/calico-node -bird-live\\\")\",\n \"def test_enquque_enqueue_check_last_node_val():\\n queue = Queue()\\n anode = queue.enqueue('a')\\n bnode = queue.enqueue('b')\\n assert queue._queue.last_node is bnode\",\n \"def test_bird_readiness(self):\\n with DockerHost('host1',\\n additional_docker_options=CLUSTER_STORE_DOCKER_OPTIONS) as host1, \\\\\\n DockerHost('host2',\\n additional_docker_options=CLUSTER_STORE_DOCKER_OPTIONS) as host2:\\n retry_until_success(host1.assert_is_ready, retries=30)\\n retry_until_success(host2.assert_is_ready, retries=30)\\n\\n # Create a network and a couple of workloads on each host.\\n network1 = host1.create_network(\\\"subnet1\\\")\\n workload_host1 = host1.create_workload(\\\"workload1\\\", network=network1)\\n workload_host2 = host2.create_workload(\\\"workload2\\\", network=network1)\\n\\n # Allow network to converge\\n self.assert_true(workload_host1.check_can_ping(workload_host2.ip, retries=10))\\n\\n # Check connectivity in both directions\\n self.assert_ip_connectivity(workload_list=[workload_host1,\\n workload_host2],\\n ip_pass_list=[workload_host1.ip,\\n workload_host2.ip])\\n\\n # Block bgp connectivity between hosts\\n host1.execute(\\\"iptables -t raw -I PREROUTING -p tcp -m multiport --dport 179 -j DROP\\\")\\n host2.execute(\\\"iptables -t raw -I PREROUTING -p tcp -m multiport --dport 179 -j DROP\\\")\\n host1.execute(\\\"docker exec -it calico-node sv kill bird\\\")\\n host2.execute(\\\"docker exec -it calico-node sv kill bird\\\")\\n\\n # Check that the readiness script is reporting 'not ready'\\n self.assertRaisesRegexp(CalledProcessError, \\\"calico/node is not ready: BIRD is not ready: BGP not established with\\\",\\n host1.execute, \\\"docker exec calico-node /bin/calico-node -bird-ready -felix-ready\\\")\\n self.assertRaisesRegexp(CalledProcessError, \\\"calico/node is not ready: BIRD is not ready: BGP not established with\\\",\\n host1.execute, \\\"docker exec calico-node /bin/calico-node -bird-ready -felix-ready\\\")\\n\\n # Restore connectivity\\n host1.execute(\\\"iptables -t raw -D PREROUTING -p tcp -m multiport --dports 179 -j DROP\\\")\\n host2.execute(\\\"iptables -t raw -D PREROUTING -p tcp -m multiport --dports 179 -j DROP\\\")\\n\\n _log.debug('check connected and retry until \\\"Established\\\"')\\n retry_until_success(host1.assert_is_ready, retries=30)\\n retry_until_success(host2.assert_is_ready, retries=30)\\n check_bird_status(host1, [(\\\"node-to-node mesh\\\", host2.ip, \\\"Established\\\")])\\n check_bird_status(host2, [(\\\"node-to-node mesh\\\", host1.ip, \\\"Established\\\")])\",\n \"def testIsBiconnected(self):\\n self.assertEqual(is_biconnected(self.G1), True)\\n self.assertEqual(is_biconnected(self.G2), False)\",\n \"def test_verify_state_of_a_device_when_disconnected_from_the_device():\",\n \"def test_randomly_select_node_2(self):\\n a, b, c, d = (n() for _ in range(4))\\n\\n g = BELGraph()\\n g.add_edge(a, b)\\n g.add_edge(b, c)\\n g.add_edge(b, d)\\n\\n self.assertEqual(1, g.degree(a))\\n self.assertEqual(3, g.degree(b))\\n self.assertEqual(1, g.degree(c))\\n self.assertEqual(1, g.degree(d))\\n\\n no_grow = {c}\\n\\n node_counter = Counter(\\n randomly_select_node(g, no_grow, self.random_state)\\n for _ in range(self.trials)\\n )\\n\\n self.assertIn(a, node_counter)\\n self.assertAlmostEqual((1 / 5), node_counter[a] / self.trials, places=2)\\n\\n self.assertIn(b, node_counter)\\n self.assertAlmostEqual((3 / 5), node_counter[b] / self.trials, places=2)\\n\\n self.assertNotIn(c, node_counter)\\n\\n self.assertIn(d, node_counter)\\n self.assertAlmostEqual((1 / 5), node_counter[d] / self.trials, places=2)\",\n \"def test_bias(self):\\n # Folder must be root to load in make_net properly\\n if os.getcwd().split('\\\\\\\\')[-1] == 'tests': os.chdir('..')\\n \\n # Create parents\\n cfg = Config()\\n cfg.genome.bias_min_value = -2\\n cfg.genome.bias_max_value = 2\\n gene1, gene2 = get_simple_node_gene(0, cfg.genome)\\n \\n # Ratio of 0.5, so possible to cross to both parents\\n p1 = False\\n p2 = False\\n for _ in range(100):\\n gene3 = gene1.crossover(other=gene2, cfg=cfg.genome, ratio=0.5)\\n if gene3.bias == gene1.bias:\\n p1 = True\\n elif gene3.bias == gene2.bias:\\n p2 = True\\n else:\\n raise self.failureException(\\\"Must be mutated to one of parent's values\\\")\\n if p1 and p2: break\\n self.assertTrue(p1 and p2)\\n \\n # Ratio of 1, so always inherits from first parent\\n for _ in range(100):\\n gene3 = gene1.crossover(other=gene2, cfg=cfg.genome, ratio=1)\\n self.assertEqual(gene3.bias, gene1.bias)\\n \\n # Ratio of 0, so always inherits from second parent\\n for _ in range(100):\\n gene3 = gene1.crossover(other=gene2, cfg=cfg.genome, ratio=0)\\n self.assertEqual(gene3.bias, gene2.bias)\",\n \"def jumped_on(self):\\r\\n pass\",\n \"def observe_b_t(left_reward, right_reward):\\n\\n random_variable = random.random()\\n\\n if random_variable >= (left_reward - right_reward + 1.0)/2:\\n return 1\\n else:\\n return 0\",\n \"def check_deadlock(self):\\n \\\"\\\"\\\" Returns True or False \\\"\\\"\\\"\\n \\\"\\\"\\\" A returned value of True mens the robot is in a deadlock and the system is trying to resolve it by turning. \\\"\\\"\\\"\\n try:\\n odo = self.m.get_last_observation('odometry')[1]\\n except Exception as e:\\n print e\\n return False\\n self.deadlock_list.insert(0,odo)\\n if len(self.deadlock_list) > self.max_deadlock_time:\\n old_odo = self.deadlock_list.pop()\\n \\n x_dif = abs(odo['x']-old_odo['x'])\\n y_dif = abs(odo['y']-old_odo['y'])\\n a_dif = abs(odo['angle']-old_odo['angle'])\\n \\n# print 'x_dif {0}'.format(x_dif)\\n# print 'y_dif {0}'.format(y_dif)\\n# print 'a_dif {0}'.format(a_dif)\\n # check difference between current and old odometry \\n if a_dif < 10 and (x_dif + y_dif) < 20:\\n # deadlock\\n if not self.turning:\\n self.pioneer.turn(random.randint(self.minturn, self.maxturn))\\n print 'INFO: deadlock, turning!'\\n self.turning = True\\n self.deadlock_time = time.time()\\n return True\\n return False\",\n \"def wait_for_blink_end(self):\\n\\n\\t\\t\\n\\t\\t# # # # #\\n\\t\\t# EyeTribe method\\n\\n\\t\\tif self.eventdetection == 'native':\\n\\t\\t\\t\\n\\t\\t\\t# print warning, since EyeTribe does not have a blink detection\\n\\t\\t\\t# built into their API\\n\\t\\t\\t\\n\\t\\t\\tprint(\\\"WARNING! 'native' event detection has been selected, \\\\\\n\\t\\t\\t\\tbut EyeTribe does not offer blink detection; PyGaze algorithm \\\\\\n\\t\\t\\t\\twill be used\\\")\\n\\n\\t\\t# # # # #\\n\\t\\t# PyGaze method\\n\\t\\t\\n\\t\\tblinking = True\\n\\t\\t\\n\\t\\t# loop while there is a blink\\n\\t\\twhile blinking:\\n\\t\\t\\t# get newest sample\\n\\t\\t\\tgazepos = self.sample()\\n\\t\\t\\t# check if it's valid\\n\\t\\t\\tif self.is_valid_sample(gazepos):\\n\\t\\t\\t\\t# if it is a valid sample, blinking has stopped\\n\\t\\t\\t\\tblinking = False\\n\\t\\t\\n\\t\\t# return timestamp of blink end\\n\\t\\treturn clock.get_time()\",\n \"def blink(self):\\n self.n_blinks += 1\\n if self.n_blinks > self.max_blinks:\\n self.active = False\",\n \"def done(self):\\n return self.left + 1 == self.right\",\n \"def updateLives(self):\\n if self._ball.ballBottom() == True:\\n self._tries = self._tries - 1\\n return True\",\n \"async def expected_ball_received(self):\\n # We do nothing in that case\",\n \"def test_blink_exception(self):\\n test_exception = BlinkException([1, \\\"No good\\\"])\\n self.assertEqual(test_exception.errid, 1)\\n self.assertEqual(test_exception.message, \\\"No good\\\")\",\n \"def _check_for_completion(self, node):\\n dis=0\\n for i in range(node.state.size):\\n dis+=(node.state[i]-self.goal.state[i])**2\\n\\n dis=np.sqrt(dis)\\n if(dis<=self.step_size):\\n return True\\n else: return False\",\n \"def goal_check(current_node, target_node):\\n if current_node.id == target_node.id:\\n return True\\n else:\\n return False\",\n \"def is_ringing(self) -> bool:\",\n \"def changes_while_node_toggle_test(self):\\n debug(\\\"changes_while_node_toggle_test()\\\")\\n cluster = self.cluster\\n cluster.populate(2).start()\\n node1, node2 = cluster.nodelist()\\n wait(2)\\n session = self.patient_cql_connection(node2)\\n\\n self.prepare_for_changes(session, namespace='ns2')\\n node1.stop()\\n wait(2)\\n self.make_schema_changes(session, namespace='ns2')\\n wait(2)\\n node2.stop()\\n wait(2)\\n node1.start()\\n node2.start()\\n wait(20)\\n self.validate_schema_consistent(node1)\",\n \"def test_modifyNodes(self):\\n\\n # wait until our lb is ACTIVE before trying to update it\\n lbaas_utils.wait_for_active_status(self)\\n # modify/disable a node on our loadbalancer\\n self.logging.info(\\\"Testing node disable...\\\")\\n nodes = self.driver.list_lb_nodes(self.lb_id)\\n mod_node = nodes['nodes'][0]\\n mod_node_id = mod_node['id']\\n mod_node_addr = mod_node['address']\\n mod_node_data = {'condition':'DISABLED'}\\n self.actual_status = self.driver.modify_node(self.lb_id, mod_node_id, mod_node_data)\\n lbaas_utils.validate_loadBalancer(self, [mod_node_addr])\\n\\n # re-enable the node\\n self.logging.info(\\\"Testing re-enable of node...\\\")\\n mod_node_data = {'condition':'ENABLED'}\\n self.actual_status = self.driver.modify_node(self.lb_id, mod_node_id, mod_node_data)\\n lbaas_utils.validate_loadBalancer(self)\\n\\n # modify ip / address\\n self.logging.info(\\\"Testing update of node ip...\\\")\\n mod_node_data = {'address': '127.0.0.1'}\\n expected_status = '400'\\n self.actual_status = self.driver.modify_node(self.lb_id, mod_node_id, mod_node_data)\\n self.assertEqual(str(self.actual_status), expected_status, msg = \\\"ERROR: Attempt to update node ip address succeeded with status: %s. Expected status: %s\\\" %(self.actual_status, expected_status))\\n \\n # modify port\\n self.logging.info(\\\"Testing update of node port...\\\")\\n mod_node_data = {'port': '443'}\\n expected_status = '400'\\n self.actual_status = self.driver.modify_node(self.lb_id, mod_node_id, mod_node_data)\\n self.assertEqual(str(self.actual_status), expected_status, msg = \\\"ERROR: Attempt to update node port succeeded with status: %s. Expected status: %s\\\" %(self.actual_status, expected_status))\\n\\n # update of non-existent node\\n self.logging.info(\\\"Testing update of non-existent node...\\\")\\n mod_node_data = {'condition':\\\"DISABLED\\\"}\\n expected_status = '404'\\n self.actual_status = self.driver.modify_node(self.lb_id, '0', mod_node_data)\\n self.assertEqual(str(self.actual_status), expected_status, msg = \\\"ERROR: Attempt to update node port succeeded with status: %s. Expected status: %s\\\" %(self.actual_status, expected_status))\\n\\n # lower-case condition\\n #self.logging.info(\\\"Testing lowercase condition...\\\")\\n #mod_node_data = {'condition':'disabled'}\\n #self.actual_status = self.driver.modify_node(self.lb_id, mod_node_id, mod_node_data)\\n #lbaas_utils.validate_loadBalancer(self, [mod_node_addr])\\n #self.actual_status = self.driver.modify_node(self.lb_id, mod_node_id, mod_node_data)\\n #lbaas_utils.validate_loadBalancer(self, [mod_node_addr])\\n\\n # lower-case condition\\n self.logging.info(\\\"Testing bad condition...\\\")\\n mod_node_data = {'condition':'TASERED_BADLY'}\\n expected_statuses = ['400','512']\\n self.actual_status = self.driver.modify_node(self.lb_id, mod_node_id, mod_node_data)\\n self.assertTrue(str(self.actual_status) in expected_statuses, msg = \\\"ERROR: Attempt to update to bad condition succeeded with status: %s. Expected status: %s\\\" %(self.actual_status, expected_status))\",\n \"def test_check_failed_highstate(self):\\n self.assertEqual(self.checkredis.check_failed_highstate(\\\"aw1-php70-qa\\\", \\\"01\\\"), False)\",\n \"def test_node_exists():\\n assert Node\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6044601","0.5912378","0.58205146","0.5657362","0.5654809","0.56453246","0.5585713","0.5581643","0.5547597","0.5540926","0.55249363","0.55155265","0.5514777","0.54998475","0.5494642","0.549018","0.5473279","0.5464496","0.5460745","0.5455696","0.5443095","0.54380023","0.543707","0.5433832","0.5428811","0.542654","0.54203373","0.54034084","0.53659046","0.5351508","0.5327442","0.53248715","0.5320687","0.53136444","0.5307901","0.53055334","0.53051066","0.53013426","0.52962685","0.5293898","0.52935225","0.528954","0.5287577","0.5287577","0.52766913","0.5274439","0.5269613","0.52583116","0.5248096","0.524701","0.5244224","0.52391744","0.5237838","0.5232734","0.52317935","0.52259535","0.5225806","0.5217765","0.52121335","0.5211032","0.5201957","0.5192144","0.5189032","0.5183711","0.51820844","0.51798385","0.51721346","0.5171553","0.51694673","0.51683307","0.51674545","0.5165976","0.51638174","0.516243","0.5142372","0.5140117","0.51388866","0.51378274","0.5134103","0.5133812","0.51289237","0.51271594","0.5124428","0.5121806","0.5121761","0.51214087","0.5121176","0.51173013","0.51110715","0.51110333","0.5109335","0.51092327","0.51081914","0.50999165","0.50974095","0.5096845","0.50948715","0.5090715","0.5090375","0.50863427","0.5080921"],"string":"[\n \"0.6044601\",\n \"0.5912378\",\n \"0.58205146\",\n \"0.5657362\",\n \"0.5654809\",\n \"0.56453246\",\n \"0.5585713\",\n \"0.5581643\",\n \"0.5547597\",\n \"0.5540926\",\n \"0.55249363\",\n \"0.55155265\",\n \"0.5514777\",\n \"0.54998475\",\n \"0.5494642\",\n \"0.549018\",\n \"0.5473279\",\n \"0.5464496\",\n \"0.5460745\",\n \"0.5455696\",\n \"0.5443095\",\n \"0.54380023\",\n \"0.543707\",\n \"0.5433832\",\n \"0.5428811\",\n \"0.542654\",\n \"0.54203373\",\n \"0.54034084\",\n \"0.53659046\",\n \"0.5351508\",\n \"0.5327442\",\n \"0.53248715\",\n \"0.5320687\",\n \"0.53136444\",\n \"0.5307901\",\n \"0.53055334\",\n \"0.53051066\",\n \"0.53013426\",\n \"0.52962685\",\n \"0.5293898\",\n \"0.52935225\",\n \"0.528954\",\n \"0.5287577\",\n \"0.5287577\",\n \"0.52766913\",\n \"0.5274439\",\n \"0.5269613\",\n \"0.52583116\",\n \"0.5248096\",\n \"0.524701\",\n \"0.5244224\",\n \"0.52391744\",\n \"0.5237838\",\n \"0.5232734\",\n \"0.52317935\",\n \"0.52259535\",\n \"0.5225806\",\n \"0.5217765\",\n \"0.52121335\",\n \"0.5211032\",\n \"0.5201957\",\n \"0.5192144\",\n \"0.5189032\",\n \"0.5183711\",\n \"0.51820844\",\n \"0.51798385\",\n \"0.51721346\",\n \"0.5171553\",\n \"0.51694673\",\n \"0.51683307\",\n \"0.51674545\",\n \"0.5165976\",\n \"0.51638174\",\n \"0.516243\",\n \"0.5142372\",\n \"0.5140117\",\n \"0.51388866\",\n \"0.51378274\",\n \"0.5134103\",\n \"0.5133812\",\n \"0.51289237\",\n \"0.51271594\",\n \"0.5124428\",\n \"0.5121806\",\n \"0.5121761\",\n \"0.51214087\",\n \"0.5121176\",\n \"0.51173013\",\n \"0.51110715\",\n \"0.51110333\",\n \"0.5109335\",\n \"0.51092327\",\n \"0.51081914\",\n \"0.50999165\",\n \"0.50974095\",\n \"0.5096845\",\n \"0.50948715\",\n \"0.5090715\",\n \"0.5090375\",\n \"0.50863427\",\n \"0.5080921\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":92599,"cells":{"query":{"kind":"string","value":"Gather the MAC addresses of eth0 from all SM hosts"},"document":{"kind":"string","value":"def get_eth0_macs():\n\n\n mac_dict = {\n 'mac_to_host': {},\n 'host_to_mac': {}\n }\n\n # Host Range\n for i in range(1, 95):\n\n if i == 47: # 47 is broken\n continue\n\n hostname = \"sm{}-ipmi\".format(i)\n realname = \"sm{}\".format(i)\n mac = get_eth0_mac_address(hostname).lower()\n mac = emulab_mac(mac)\n\n mac_dict['host_to_mac'][realname] = mac\n mac_dict['mac_to_host'][mac] = realname\n\n with open(\"macs.json\", \"w\") as mac_json:\n\n json.dump(mac_dict, mac_json, indent=4)"},"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_mac_address_table(self):\n\n mac_address_table = []\n command = '/interface bridge host print terse'\n\n output = self._send_command(command)\n\n for host in parse_terse_output(output):\n mac_address_table.append({\n 'mac': cast_mac(host.get('mac-address')),\n 'interface': host.get('interface'),\n 'vlan': -1,\n 'static': True if 'D' not in host.get('_flags') else False,\n 'active': True if 'X' not in host.get('_flags') else False,\n 'moves': -1,\n 'last_move': -1.0\n })\n\n return mac_address_table","def getmacaddrs():\n # Unpack just for the sake of being meaningful.\n ifaddrs, sockaddr_dl, sockaddr = PLATFORM_LOOKUP[PLATFORM]\n ptr = c_void_p(None)\n result = LIBC.getifaddrs(pointer(ptr))\n if result != 0:\n return {}\n ifa = ifaddrs.from_address(ptr.value)\n result = {}\n\n while True:\n name = ifa.ifa_name\n if name not in result:\n result[name] = []\n # Some interface (such as a TUN virtual network) doesn't give us\n # ifa_addr at all and we can usually skip them because they're hardly\n # relevant for our usage case.\n if ifa.ifa_addr:\n sa = sockaddr.from_address(ifa.ifa_addr)\n if sa.sa_family == AF_LINK:\n si = sockaddr_dl.from_address(ifa.ifa_addr)\n addr = \"%s\" % si\n if addr:\n result[name].append(addr)\n if ifa.ifa_next:\n ifa = ifaddrs.from_address(ifa.ifa_next)\n else:\n break\n\n LIBC.freeifaddrs(ptr)\n return result","def get_eth0_mac_address(hostname):\n\n command = \"ipmitool -H {} -U ADMIN -P ADMIN raw 0x30 0x21\".format(hostname)\n\n try:\n result = subprocess.check_output(command, shell=True)\n except subprocess.CalledProcessError as exc:\n LOGGER.exception(\"Failed to get eth0 mac for %s\", hostname)\n\n # Remove space and newline\n result = result.strip()\n mac = \":\".join(result.split()[4:]) # No verification :-(\n\n return mac","def _get_mac_addresses_for_domain(self, domain):\n xml = domain.XMLDesc()\n etree = ET.XML(xml)\n elems = etree.findall(\"devices/interface[@type='network']/mac\")\n\n result = []\n for elem in elems:\n mac_address = elem.get(\"address\")\n result.append(mac_address)\n\n return result","def get_mac_address(self, result, host):\n if \"mac\" in result['scan'][host][\"addresses\"]:\n return result['scan'][host][\"addresses\"][\"mac\"]\n else:\n return \"\"","def get_mac_address():\n eth0_interface = 'eth0'\n addresses = netifaces.ifaddresses(eth0_interface)[netifaces.AF_LINK][0]\n mac_address = addresses['addr']\n return mac_address","def getMac(self):\n # Import netifaces here to prevent error importing this module in setup.py\n import netifaces\n interfaces = ['eth0', 'wlan0']\n try:\n interfaces.append(netifaces.gateways()['default'][netifaces.AF_INET][1])\n except:\n pass\n for interface in interfaces:\n try:\n return netifaces.ifaddresses(interface)[netifaces.AF_LINK][0]['addr']\n except ValueError:\n pass\n except:\n exception('Error getting MAC address')\n return None","def scan_devices(self):\n self._update_info()\n return [client[\"mac\"] for client in self.last_results]","def arp_scan(subnet):\n\n answered = scapy.arping(subnet)[0]\n\n machines = []\n for i in answered:\n ip, mac = i[1].psrc, i[1].hwsrc\n try:\n host = socket.gethostbyaddr(i[1].psrc)[0]\n except Exception:\n host = \"??\"\n machines.append({\"ip\": ip, \"mac\": mac, \"host\": host})\n\n return machines","def get_mac_address(hostname):\n\n url = \"https://{}/redfish/v1/Managers/1/EthernetInterfaces/1/\".format(hostname)\n eth_dict = requests.get(url, auth=(USER,PASS),verify=False).json()\n mac_address = eth_dict['MACAddress']\n\n LOGGER.info(\"IPMI BMC %s reports MAC address as %s\", hostnameh, mac_address)\n\n return mac_address","def scan_devices(self):\n self._update_info()\n\n return [client['mac'] for client in self.last_results]","def scan(self):\n walk_result = utils.snmp_walk(self.host, self.credential,\n \"BRIDGE-MIB::dot1dTpFdbPort\")\n if not walk_result:\n return None\n\n mac_list = []\n for result in walk_result:\n if not result or result['value'] == str(0):\n continue\n temp = {}\n mac_numbers = result['iid'].split('.')\n temp['mac'] = self._get_mac_address(mac_numbers)\n temp['port'] = self._get_port(result['value'])\n temp['vlan'] = self._get_vlan_id(temp['port'])\n mac_list.append(temp)\n\n return mac_list","def get_mac():\n\n interface = [x for x in netifaces.interfaces() if 'wlan' in x or 'wlp' in x][0]\n return netifaces.ifaddresses(interface)[netifaces.AF_LINK][0]['addr']","def possible_mac_addresses(interface):\n\n mac_addrs = []\n\n # In case of VLANs, just grab the parent interface\n if interface.interface_type == 'vlan':\n interface = interface.parent\n\n # Bonding/bridge: append the MACs of the physical interfaces\n # TODO: drop the public/bootable check once we decide how to send the extra\n # information to clients\n for slave in interface.all_slaves():\n if slave.mac and (slave.interface_type != \"public\" or slave.bootable):\n mac_addrs.append(slave.mac)\n\n # Handle physical interfaces, and bonding with a dedicated MAC\n # TODO: drop the public/bootable check once we decide how to send the extra\n # information to clients\n if interface.mac and (interface.interface_type != \"public\" or interface.bootable):\n mac_addrs.append(interface.mac)\n\n return mac_addrs","def mac_from_vm(vm: libvirt.virDomain = None) -> str:\n doc = minidom.parseString(vm.XMLDesc())\n interfaces = doc.getElementsByTagName('mac')\n return interfaces[0].getAttribute('address')","def get_my_mac_set(iface_filter=None):\n\n out_set = set()\n if sys.platform.startswith(\"win\"):\n from scapy.arch.windows import NetworkInterface\n if type(iface_filter) == NetworkInterface:\n out_set.add(iface_filter.mac)\n\n for iface in sc.get_if_list():\n if iface_filter is not None and iface != iface_filter:\n continue\n try:\n mac = sc.get_if_hwaddr(iface)\n except Exception as e:\n continue\n else:\n out_set.add(mac)\n\n return out_set","def get_mac(ip):\n p = Ether(dst=\"ff:ff:ff:ff:ff:ff\") / ARP(op=1, pdst=ip)\n result = srp(p, verbose=False, iface=IFACE)[0]\n return result[0][1].hwsrc if result else None","def _parse_mac_addr_table(self, cmd_output, mac_regex):\n lines = ensure_string(cmd_output).split(\"\\n\")\n\n arp_table = defaultdict(list)\n for line in lines:\n match = mac_regex.match(line)\n\n if not match:\n continue\n\n groups = match.groups()\n ip_address = groups[0]\n mac_address = groups[1]\n arp_table[mac_address].append(ip_address)\n\n return arp_table","def _get_management_macs_for_topology(topology_id):\n existing_macs = list()\n\n domains = libvirtUtils.get_domains_for_topology(topology_id)\n for d in domains:\n m = libvirtUtils.get_management_interface_mac_for_domain(d['name'])\n logger.debug('adding existing mac %s to existing_macs list' % m)\n existing_macs.append(m)\n\n return existing_macs","def get_arp_list(self):\n arp_list = []\n for ip, value in self.ip_to_mac.items():\n arp_list.append({'ip': str(ip),\n 'hw_addr': str(value[0]),\n 'last_update': datetime.datetime.fromtimestamp(value[1]).strftime('%Y-%m-%d %H:%M:%S')})\n\n return arp_list","def _get_mac_address(self, mac_numbers):\n\n mac = \"\"\n for num in mac_numbers:\n num = self._convert_to_hex(num)\n mac = ':'.join((mac, num))\n mac = mac[1:]\n return mac","def ipaddrs( host ):\n return socket.gethostbyaddr(host)[2][0]","def scan(backend, timeout=10):\n result = []\n for (mac, name) in backend.scan_for_devices(timeout):\n print(mac + \" \" + name)\n return result","def _get_ipv6_addresses(self, host: str) -> Dict[str, List[IPv6Address]]:\n if host == \"self\":\n command = \"show ipv6 interface\"\n elif host == \"peer\":\n command = \"failover exec mate show ipv6 interface\"\n\n show_ipv6_interface = self.show(command)\n show_ipv6_interface_lines: List[str] = show_ipv6_interface.strip().splitlines()\n first_line = show_ipv6_interface_lines.pop(0)\n interface: str = first_line.split()[0]\n ipv6_addresses: List[IPv6Interface] = []\n results: Dict[str, List] = {}\n for line in show_ipv6_interface_lines:\n # match IPv6 addresses under interface line\n if line[0].isspace():\n match = RE_IPV6_INTERFACE_MATCH.match(line)\n if match:\n ipv6_addresses.append(IPv6Interface(f\"{match.group(1)}{match.group(2)}\"))\n # update results mapping interface to matched IPv6 addresses and generate the next interface name\n else:\n if ipv6_addresses:\n results[interface] = ipv6_addresses\n ipv6_addresses = []\n interface = line.split()[0]\n\n # Add final interface in iteration if it has IPv6 addresses\n if ipv6_addresses:\n results[interface] = ipv6_addresses\n\n log.debug(\"Host %s: ip interfaces %s\", self.host, results)\n return results","def get_mac(self, node_id):\n nc = '%02x' % self.node_class\n nr_iface = '%02x' % self.nr_host_interface\n node_id = '%08x' % node_id\n\n return '%s:%s:%s:%s:%s:%s' % (nc, nr_iface, node_id[0:2], node_id[2:4], node_id[4:6], node_id[6:8])","def _get_mac_address():\n if not sys.platform.startswith('linux'):\n raise RuntimeError(\n 'Cannot get the MAC address on non-Linux platforms'\n )\n ifname = get_default_iface_name_linux()\n s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\n info = fcntl.ioctl(s.fileno(), 0x8927,\n struct.pack('256s', bytes(ifname, 'utf-8')[:15]))\n return ''.join('%02x' % b for b in info[18:24])","def find_my_IP_and_MAC():\n mac = ':'.join(re.findall('..', '%012x' % getnode()))\n # I write IP and not domain cause i want to save time.\n p = sr1(IP(dst=\"google.com\", ttl=0) / ICMP() / \"XXXXXXXXXXX\",verbose=0,timeout=5) #verbose = withuot output\n return mac,p.dst","def topo_conf():\n for k in switches.keys():\n switches_ip[k] = IPAddr((192<<24)+int(k))\n switches_mac[k] = EthAddr(\"aa\"+ \"%010d\"%(k))","def get_mac(ip):\n arp_request = scapy.ARP(pdst=ip)\n broadcast = scapy.Ether(dst=\"ff:ff:ff:ff:ff:ff\")\n arp_request_broadcast = broadcast / arp_request\n answered_list = scapy.srp(\n arp_request_broadcast,\n timeout=5,\n verbose=False)[0]\n return answered_list[0][1].hwsrc","def get_mac_addresses(self, task):\n raise exception.UnsupportedDriverExtension(\n driver=task.node.driver, extension='get_mac_addresses')","def get_sonic_mac(host, asicnum, port):\n if host.facts[\"num_asic\"] == 1:\n cmd = \"sudo ip link show {}\".format(port)\n else:\n ns = \"asic\" + str(asicnum)\n cmd = \"sudo ip netns exec {} ip link show {}\".format(ns, port)\n output = host.command(cmd)\n mac = output['stdout_lines'][1].split()[1]\n logger.info(\"host: %s, asic: %d, port: %s, mac: %s\", host.hostname, asicnum, port, mac)\n return mac","def get_server_ip_mac(self, server_id):\n port_list = self.list_port()\n if not isinstance(port_list, list):\n return\n interface_list = []\n for port in port_list:\n if port[\"device_id\"] == server_id:\n port_info = {}\n port_info['mac'] = port['mac_address']\n port_info['ip_address'] = port['fixed_ips'][0]['ip_address']\n interface_list.append(port_info)\n\n LOG_OBJ.info(\"VM Interface Info : %s \" % interface_list)\n return interface_list","def getMacAddress(self, wlanInterface):\n self.wlanInterface = wlanInterface\n self.storeMacAddress=[]\n s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\n info = fcntl.ioctl(s.fileno(), 0x8927, struct.pack('256s', '%s'[:15]) % str(self.wlanInterface))\n self.storeMacAddress.append(''.join(['%02x:' % ord(char) for char in info[18:24]])[:-1])\n return self.storeMacAddress","def get_mac(ip):\n ans, _ = srp(Ether(dst='ff:ff:ff:ff:ff:ff')/ARP(pdst=ip), timeout=3, verbose=0)\n if ans:\n return ans[0][1].src\n else:\n return None","def get_network_device_mac(devices, user, passwd, base_api_url):\n network_device_mac_address = ''\n\n if devices:\n selected_network_device = generate_network_devices_menu(devices, purpose='DHCP')\n network_device_mac_address = get_mac_address(selected_network_device, base_api_url, user, passwd)\n\n if network_device_mac_address:\n logging.info('device {} mac address is {}'.format(selected_network_device, network_device_mac_address))\n \n return network_device_mac_address","def _build_nodes(self):\n res = super(MacFix, self)._build_nodes()\n\n observed_addresses = set(inner for outer in [host.mac_addresses for host in self._hosts.values()] for inner in outer)\n unsatisfied = self._macfix__mac_addresses.difference(observed_addresses)\n unexpected = observed_addresses.difference(self._macfix__mac_addresses)\n if len(unsatisfied) > 1:\n raise AssertionError(\"unsatisfied: %s -- 2 or more host MAC addresses could not be found. Are you using the correct topology file?\" % unsatisfied)\n assert len(unexpected) == len(unsatisfied)\n if len(unsatisfied) == 1:\n self._macfix__result = {list(unsatisfied)[0]: list(unexpected)[0]}\n else:\n self._macfix__result = {}\n\n return res","def mon_hosts(self, mon_ips):\n hosts = []\n for ceph_addrs in mon_ips:\n # NOTE(jamespage): This looks odd but deals with\n # use with ceph-proxy which\n # presents all monitors in\n # a single space delimited field.\n for addr in ceph_addrs.split(' '):\n hosts.append(ch_ip.format_ipv6_addr(addr) or addr)\n hosts.sort()\n return hosts","def _new_recovery_broadcast_arps(victim_ips: Iterable[str]) -> List[ARP]:\n victim_macs = _request_macs(victim_ips)\n return [ARP(psrc=v_ip, pdst=v_ip, hwsrc=v_mac)\n for v_ip, v_mac in victim_macs.items()]","def get_bt_smarthub_data(self):\n import btsmarthub_devicelist\n\n data = btsmarthub_devicelist.get_devicelist(router_ip=self.host, only_active_devices=True)\n devices = {}\n for device in data:\n try:\n devices[device['UserHostName']] = {\n 'ip': device['IPAddress'],\n 'mac': device['PhysAddress'],\n 'host': device['UserHostName'],\n 'status': device['Active']\n }\n except (KeyError, 'no'):\n pass\n return devices","def machine_lookup_all(session, hostname, public_ip = True):\n client = session.client('ec2')\n response = client.describe_instances(Filters=[{\"Name\":\"tag:Name\", \"Values\":[hostname]},\n {\"Name\":\"instance-state-name\", \"Values\":[\"running\"]}])\n\n addresses = []\n items = response['Reservations']\n if len(items) > 0:\n for i in items:\n item = i['Instances'][0]\n if 'PublicIpAddress' in item and public_ip:\n addresses.append(item['PublicIpAddress'])\n elif 'PrivateIpAddress' in item and not public_ip:\n addresses.append(item['PrivateIpAddress'])\n return addresses","def scan(ip):\n arp_request = scapy.ARP(pdst=ip)\n broadcast = scapy.Ether(dst=\"ff:ff:ff:ff:ff:ff\")\n arp_request_broadcast = broadcast/arp_request\n answered_list = scapy.srp(arp_request_broadcast , timeout = 1, verbose=False)[0]\n target_list=[]\n for element in answered_list:\n target_dict = {\"ip\":element[1].psrc, \"mac\":element[1].hwsrc}\n target_list.append(target_dict)\n return target_list","def get_rand_mac(self):\n random_mac = []\n\n # add manufacturer\n random_mac.append(self.get_rand_manufact())\n\n # generate the last 24 bits of the random hex\n for i in range(0, 3):\n rand_digit1 = self.get_rand_hex_digit()\n rand_digit2 = self.get_rand_hex_digit()\n random_mac.append(rand_digit1 + rand_digit2)\n\n return ':'.join(random_mac)","def _listing(self, mac_addresses):\n # Initialize key variables\n preliminary_listing = []\n listing = []\n\n # Cycle through mac addresses, get the manufacturer\n for mac_address in mac_addresses:\n # Get manufacturer\n manufacturer = self._manufacturer(mac_address)\n data_dict = {}\n data_dict['mac_address'] = mac_address\n data_dict['manufacturer'] = manufacturer\n preliminary_listing.append(data_dict)\n\n # Get IP address and hostname for each mac address\n for item in preliminary_listing:\n mac_address = item['mac_address']\n manufacturer = item['manufacturer']\n\n if mac_address in self.rarp_table:\n # MAC address has related IP\n if bool(self.rarp_table[mac_address]) is True:\n for ip_address in self.rarp_table[mac_address]:\n data_dict = {}\n data_dict['mac_address'] = mac_address\n data_dict['manufacturer'] = manufacturer\n data_dict['ip_address'] = ip_address\n data_dict['hostname'] = ''\n\n if ip_address in self.arp_table:\n if 'hostname' in self.arp_table[ip_address]:\n hostname = self.arp_table[\n ip_address]['hostname']\n data_dict['hostname'] = hostname\n\n listing.append(data_dict)\n else:\n # MAC address has no related IP\n data_dict = {}\n data_dict['mac_address'] = mac_address\n data_dict['manufacturer'] = manufacturer\n data_dict['ip_address'] = ''\n data_dict['hostname'] = ''\n listing.append(data_dict)\n\n # Return\n return listing","def get_devices(ip, mask):\n\n addr = ip + \"/\" + mask\n network = ipaddress.ip_network(addr, strict=False)\n values = (\"sysDescr\", \"sysUpTime\", \"sysLocation\", \"sysName\")\n devices = {}\n\n for ip in network:\n device_info = {}\n for value in values:\n iterator = getCmd(SnmpEngine(),\n CommunityData('public'),\n UdpTransportTarget((str(ip), 161)),\n ContextData(),\n ObjectType(ObjectIdentity('SNMPv2-MIB', str(value), 0)))\n\n error_indication, error_status, error_index, var_binds = next(iterator)\n\n if error_indication: # SNMP engine errors\n print(str(ip) + \"> \" + str(error_indication))\n else:\n if error_status: # SNMP agent errors\n print(\n '%s at %s' % (\n error_status.prettyPrint(), var_binds[int(error_index) - 1] if error_index else '?'))\n else:\n for varBind in var_binds: # SNMP response contents\n device_info[value] = str(varBind).split(\"=\")[1].replace(\" \", \"\")\n\n if device_info == {}:\n continue\n device_info[\"ip\"] = str(ip)\n devices[str(ip)] = device_info\n\n return devices","def connectentryfanoutmac(localpop,\n hostmac,\n hostvlan,\n forwards,\n meter,\n mac):\n hwswitch = Container.fromAnchor(localpop.properties['HwSwitch'])\n hwswitchname = hwswitch.resourceName\n swswitch = Container.fromAnchor(localpop.properties['SwSwitch'])\n swswitchname = swswitch.resourceName\n topology = Container.getContainer(localpop.properties['SwSwitch']['containerName'])\n\n # print \"connectentryfanout localpop\", localpop, \"host\", host, \"hostvlan\", hostvlan, \"mac\", mac\n\n # Find the port on the software switch connected to the hardware switch\n links = getlinks2(topology, swswitchname, hwswitchname)\n if links == None or len(links) == 0:\n print \"No links from\", swswitchname, \"to\", hwswitchname\n return None\n hwswitchlink = None\n swport_tohw = None\n for link in links:\n (node, port) = linkednode2(link, hwswitchname)\n if port != None:\n # Found it!\n hwswitchlink = link\n swport_tohw = port\n break\n if swport_tohw == None:\n print \"No output port on\", swswitchname, \"facing\", hwswitchname\n return None\n\n # The fanout flow is \"interesting\" in that the input plus the multiple outputs\n # all are on the same port (but different VLANs). Fill in the outputs.\n for f in forwards:\n f.outPort = str(swport_tohw)\n # print \"FORW: outport\", f.outPort, \"vlan\", f.vlan, \"dstMac\", f.dstMac\n\n # Convert the list of forwarding destinations to a Java array.\n fwdsarr = jarray.array(forwards, SdnControllerClientL2Forward)\n\n # print \"dpid\", swswitch.props['dpid']\n # This flow being installed is unusual in that it does a source MAC address\n # filter as well\n fh = SCC.SdnInstallForward(javaByteArray2(swswitch.properties['DPID']),\n 1,\n BigInteger.ZERO,\n str(swport_tohw),\n int(hostvlan),\n hostmac,\n mac,\n fwdsarr,\n 0,\n 0,\n meter)\n\n return fh","def all_client_assoc_ap(ap_mac):\n\n url = CMX_URL + '/api/location/v2/clients'\n header = {'content-type': 'application/json', 'accept': 'application/json'}\n response = requests.get(url, headers=header, auth=CMX_AUTH, verify=False)\n clients_json = response.json()\n clients_mac_info = []\n for client in clients_json:\n if client['apMacAddress'] == ap_mac:\n clients_mac_info.append(client['macAddress'])\n return clients_mac_info","def get_all_interfaces():\n global all_interfaces\n if all_interfaces:\n return all_interfaces\n\n f = open('/proc/net/dev','r')\n ifacelist = f.read().split('\\n')\n f.close()\n\n # remove 2 lines header\n ifacelist.pop(0)\n ifacelist.pop(0)\n\n all_interfaces = {}\n # loop to check each line\n for line in ifacelist:\n\n ifacedata = line.replace(' ','').split(':')\n\n # check the data have 2 elements\n if len(ifacedata) == 2:\n all_interfaces[ifacedata[0]] = get_interface_ip(ifacedata[0])\n\n return all_interfaces","def get_eos_mac(nbr, nbr_intf):\n if \"port-channel\" in nbr_intf.lower():\n # convert Port-Channel1 to po1\n shell_intf = \"po\" + nbr_intf[-1]\n else:\n # convert Ethernet1 to eth1\n shell_intf = \"eth\" + nbr_intf[-1]\n\n output = nbr['host'].command(\"ip addr show dev %s\" % shell_intf)\n # 8: Ethernet0: <BROADCAST,MULTICAST,UP,LOWER_UP> mtu 9100 ...\n # link/ether a6:69:05:fd:da:5f brd ff:ff:ff:ff:ff:ff\n\n mac = output['stdout_lines'][1].split()[1]\n return {'mac': mac, \"shell_intf\": shell_intf}","def get_eap_mab(self):\n for m in self.get_tag(self.mac):\n v = m[1]\n if not isinstance(v, int):\n v = self._get_vlan(v)\n yield [m[0], v]","def connected_devices_arp(self, run_test):\n\n\n if not run_test:\n return\n\n res = {}\n\n ts = int(time.time())\n\n route_cmd = \"ip r | grep /24 | awk '{print $1;}'\"\n subnet = Popen(route_cmd, shell=True,\n stdout=PIPE).stdout.read().decode('utf-8')\n\n nmap_cmd = f'nmap -sn {subnet}'\n Popen(nmap_cmd, shell=True, stdout=PIPE)\n\n arp_cmd = (\"/usr/sbin/arp -i eth0 -n | grep : |\"\n \"grep -v '_gateway' | tr -s ' ' | \"\n \"cut -f3 -d' ' | sort | uniq\")\n\n arp_res = Popen(arp_cmd, shell=True,\n stdout=PIPE).stdout.read().decode('utf-8')\n res['arp'] = arp_res\n\n devices = set(arp_res.strip().split(\"\\n\"))\n active_devices = [[dev, ts, 1] for dev in devices]\n\n for device in active_devices:\n if self.dev_db.contains(where('mac_addr') == device[0]):\n self.dev_db.update(increment(\"n\"),\n where('mac_addr') == device[0])\n self.dev_db.update(tdb_set('last_seen', device[1]),\n where('mac_addr') == device[0])\n else:\n self.dev_db.insert({'mac_addr': device[0],\n 'last_seen': device[1],\n 'n': device[2]})\n\n print(self.dev_db.all())\n ndev_past_day = len(self.dev_db.search(\n where('last_seen') > (ts - 86400)))\n ndev_past_week = len(self.dev_db.search(\n where('last_seen') > (ts - 86400*7)))\n\n print(ndev_past_day)\n self.results[\"devices_active\"] = len(active_devices)\n self.results[\"devices_total\"] = self.dev_db.count(where('n') >= 1)\n self.results[\"devices_1day\"] = ndev_past_day\n self.results[\"devices_1week\"] = ndev_past_week\n\n if not self.quiet:\n print('\\n --- Number of Devices ---')\n print(f'Number of active devices: '\n f'{self.results[\"devices_active\"]}')\n print(f'Number of total devices: '\n f'{self.results[\"devices_total\"]}')\n print(f'Number of devices in last 1 day:'\n f' {self.results[\"devices_1day\"]}')\n print(f'Number of devices in last week:'\n f' {self.results[\"devices_1week\"]}')\n return res","def get_my_mac():\n\n mac_set = get_my_mac_set(iface_filter=get_default_route()[1])\n return mac_set.pop()","def _get_ipv4_addresses(self, host: str) -> Dict[str, List[IPv4Address]]:\n if host == \"self\":\n command = \"show ip address\"\n elif host == \"peer\":\n command = \"failover exec mate show ip address\"\n\n show_ip_address = self.show(command)\n re_ip_addresses = RE_SHOW_IP_ADDRESS.findall(show_ip_address)\n\n results = {\n interface: [IPv4Interface(f\"{address}/{netmask}\")] for interface, address, netmask in re_ip_addresses\n }\n log.debug(\"Host %s: ip interfaces %s\", self.host)\n return results","def get_host_interfaces(self, context, host_uuid):\n result = {}\n interfaces = self._get_cgtsclient().iinterface.list(host_uuid)\n for interface in interfaces:\n if interface.networktype != \"data\":\n continue\n providernets = interface.providernetworks\n result[interface.uuid] = {'uuid': interface.uuid,\n 'mtu': interface.imtu,\n 'vlans': '',\n 'network_type': interface.networktype,\n 'providernets': providernets}\n return result","def ipv6_addresses(self) -> Dict[str, List[IPv6Address]]:\n log.debug(\"Host %s: ipv6 addresses of the devices interfaces %s.\", self.host, self._get_ipv6_addresses(\"self\"))\n return self._get_ipv6_addresses(\"self\")","def get_mac_address(ifname):\n try:\n return open('/sys/class/net/' + ifname + '/address') \\\n .readline().strip()\n except:\n SysTools.logger.error(\"Failed to get mac-address of %s\", ifname)\n return \"00:00:00:00:00:00\"","def get_allhosts():\n connection, tablename = HomeNetwork.get_connection_info()\n query = 'SELECT hostname from {}'.format(tablename)\n output = pandas.read_sql_query(query, connection).to_json(orient='records')\n\n for host in json.loads(output):\n yield host[\"hostname\"]","def dump_macs(self, context):\n nmeta = self._nmeta\n dpid = self.dpid\n macs = []\n db_cursor = nmeta.dbidmac.find({'dpid': dpid, 'context': context})\n if db_cursor:\n for document in db_cursor:\n mac = document['mac']\n if mac:\n macs.append(mac)\n return macs","def list_hosts():\n task_run(\"/bin/hostname -f\",RING_1_dev__allnodes)","def get_all_host(self, conf, tenant_id, network_id):\n\t\tpass","def search_mac_in_arp(args):\n arp_table = get_arp_table()\n \n mac = convert_mac(args.mac, 2)\n columns = ['IP', 'AGE(min)', 'MAC', 'INTERFACE', 'VENDOR']\n result = []\n for row in arp_table.split('\\n'):\n if mac in row:\n lists = []\n _,ip,age,r_mac,_,interface = row.strip().split()\n r_mac = convert_mac(mac, 1)\n vendor = convert_mac(r_mac, 3)\n vendor = find_in_database(vendor)\n r_mac = blue + r_mac + endblue\n lists = [ip, age, r_mac, interface, vendor]\n result.append(lists)\n else:\n pass\n print(tabulate(result, headers=columns))","async def get_cells_connected():\n global CELLS\n # Perfrom modem lan query\n try:\n async with asyncssh.connect(IP_MODEM,username=USER_MODEM,password=PASS_MODEM,known_hosts=None) as conn:\n result = await conn.run('status lan')\n connected_devices = re.findall(pattern = r'connected_device {.*?}', string = result.stdout, flags=re.DOTALL)\n for mac in connected_devices:\n mac_addr = re.search(pattern=r'mac = (00:02:95:03:.*?)\\n',string=mac)\n if mac_addr:\n mac_addr = mac_addr.group(1)\n if mac_addr not in CELLS:\n ip = re.search(pattern='ip = (192.168.0.\\d+)\\n', string=mac).group(1)\n type = await get_cell_type(ip)\n CELLS[mac_addr] = {\n 'ip' : ip,\n 'type' : type,\n 'fault': None,\n 'old_faults' : set()\n }\n LOGGER.info(f\"Femto-cell S60Z {type} with IP {ip} is connected\")\n fault = await get_cell_fault(mac_addr,CELLS[mac_addr]['ip'],CELLS[mac_addr]['type'])\n if fault != CELLS[mac_addr]['fault']:\n CELLS[mac_addr]['fault'] = fault\n if CELLS[mac_addr]['fault'] == 0:\n LOGGER.info(f\"Number of alarms in femto {CELLS[mac_addr]['ip']} {CELLS[mac_addr]['type']} is {CELLS[mac_addr]['fault']}\")\n else:\n LOGGER.warning(f\"Number of alarms in femto {CELLS[mac_addr]['ip']} {CELLS[mac_addr]['type']} changed to {CELLS[mac_addr]['fault']}\") \n except:\n LOGGER.warning('unable to get lan status from modem')","def _find_devices_mac(self):\n self.keyboards.append(Keyboard(self))\n self.mice.append(MightyMouse(self))\n self.mice.append(Mouse(self))","def get_arp_table():\n IP = ''\n login = ''\n password = ''\n telnet = pexpect.spawn('telnet {}'.format(IP), timeout=30)\n telnet.expect('Username:')\n telnet.sendline(login)\n telnet.expect('Password:')\n telnet.sendline(password)\n telnet.expect('#')\n telnet.sendline('terminal length 0')\n telnet.expect('#')\n telnet.sendline('show arp')\n telnet.expect('#')\n arp_table = telnet.before.decode('utf-8')\n telnet.close()\n return arp_table","def get_adapter_mac(cls, client_object):\n parsed_data = DefaultCRUDImpl.get_adapter_info(\n client_object)\n for record in parsed_data['table']:\n if record['dev'] == client_object.name:\n return record['mac']\n pylogger.warning('Did not find a MAC address for adapter %r on %r' %\n (client_object.name, client_object.ip))","def mac_address(self):\n mac = [\n self.random.randint(0x00, 0xff),\n self.random.randint(0x00, 0xff),\n self.random.randint(0x00, 0xff),\n self.random.randint(0x00, 0xff),\n self.random.randint(0x00, 0xff),\n self.random.randint(0x00, 0xff)\n ]\n return ':'.join(map(lambda x: f\"{x:02X}\", mac))","def _parse_ip_table_arp(self, arp_output):\n arp_regex = re.compile(r\".*?\\((.*?)\\) at (.*?)\\s+\")\n return self._parse_mac_addr_table(arp_output, arp_regex)","def get_mac(self) -> str:\n self.sendline(\"iw {} info\".format(self.iface_dut))\n # We are looking for MAC definition of STA\n # wdev 0x1\n # addr 96:4e:c9:cc:7a:2c\n # type managed\n self.expect(\"addr (?P<mac>..:..:..:..:..:..)\\r\\n\\t(type|ssid)\")\n return self.match.group('mac')","def configure_host_ips(h3, h4, ip_address_hs):\n\n h3.libs.ip.flush_ip('eth1')\n h3.libs.ip.interface('eth1', up=False)\n\n h4.libs.ip.flush_ip('eth1')\n h4.libs.ip.interface('eth1', up=False)\n\n h3.libs.ip.interface(portlbl='eth1', addr=\"{}/{}\".format(\n ip_address_hs[0], MASK), up=True)\n h4.libs.ip.interface(portlbl='eth1', addr=\"{}/{}\".format(\n ip_address_hs[1], MASK), up=True)","def fetch():\n\t\n\t_interfaces = [Interface(iface) for iface in netifaces.interfaces()]\n\t\n\tfor iface in _interfaces: \n\t\tif (iface.id in BLACK_ID) or (iface.mac in BLACK_MAC) or (len(iface.mac) < 5):\n\t\t\t_interfaces.remove(iface)\n\t\t\t\n\treturn _interfaces","def extract_client_macs(self):\n\t\tmacs_clients = []\n\n\t\t# data: client -> AP or client <- AP\n\t\tif self.type == DATA_TYPE:\n\t\t\tif self.from_ds == 1 and self.to_ds == 0:\n\t\t\t\tmacs_clients.append(self.upper_layer.dst)\n\t\t\telif self.from_ds == 0 and self.to_ds == 1:\n\t\t\t\tmacs_clients.append(self.upper_layer.src)\n\n\t\treturn [addr for addr in macs_clients if not utils.is_special_mac(addr)]","def get_hosts(network):\n\n nm = nmap.PortScanner()\n nm.scan(network, arguments=\"-sn\")\n\n for host in nm.all_hosts():\n details = nm[host]\n\n if \"ipv4\" not in details[\"addresses\"]:\n continue\n\n vendor = details.get(\"vendor\")\n vendor_name = None\n\n if vendor and len(vendor) > 0:\n vendor_name = list(vendor.values())[0]\n\n yield {\n \"hostname\": details.get(\"hostname\"),\n \"ip\": details[\"addresses\"][\"ipv4\"],\n \"vendor\": vendor_name\n }","def all_machines():\n return sorted(MACHINES, key=str)","def getAllMachines():\n\n validHosts = (subprocess.getoutput(\"qconf -shgrpl\").split())\n machineList = []\n processedHGList = []\n readNodes = False\n for host in validHosts:\n hostMachineList = ((subprocess.getoutput(\"qconf -shgrp_tree \" + str(host))).split())\n for element in hostMachineList:\n if '@' not in element: # If it is not a HG name\n if element not in machineList:\n machineList.append(element) # Searching whole list for this node\n machineList.sort()\n return machineList","def parse_nmap_host(host, hostmap):\n\n # Get the timestamp.\n timestamp = host.get(\"endtime\")\n if timestamp:\n timestamp = long(timestamp)\n if not timestamp:\n timestamp = host.get(\"starttime\")\n if timestamp:\n timestamp = long(timestamp)\n\n # Get all the IP addresses. Skip the MAC addresses.\n ip_addresses = []\n for node in host.findall(\".//address\"):\n if node.get(\"addrtype\", \"\") not in (\"ipv4, ipv6\"):\n continue\n address = node.get(\"addr\")\n if not address:\n continue\n if address not in hostmap:\n hostmap[address] = IP(address)\n ip_addresses.append( hostmap[address] )\n\n # Link all the IP addresses to each other.\n ips_visited = set()\n for ip_1 in ip_addresses:\n if ip_1.address not in ips_visited:\n ips_visited.add(ip_1.address)\n for ip_2 in ip_addresses:\n if ip_2.address not in ips_visited:\n ips_visited.add(ip_2.address)\n ip_1.add_resource(ip_2)\n ips_visited.clear()\n\n # Get all the hostnames.\n domain_names = []\n for node in host.findall(\".//hostname\"):\n hostname = node.get(\"name\")\n if not hostname:\n continue\n if hostname not in hostmap:\n hostmap[hostname] = Domain(hostname)\n domain_names.append( hostmap[hostname] )\n\n # Link all domain names to all IP addresses.\n for name in domain_names:\n for ip in ip_addresses:\n name.add_resource(ip)\n\n # Abort if no resources were found.\n if not ip_addresses and not domain_names:\n return []\n\n # Get the portscan results.\n ports = set()\n for node in host.findall(\".//port\"):\n try:\n portid = node.get(\"portid\")\n protocol = node.get(\"protocol\")\n if protocol not in (\"tcp\", \"udp\"):\n continue\n try:\n port = int(portid)\n except Exception:\n port = getservbyname(portid)\n state = node.find(\"state\").get(\"state\")\n if state not in (\"open\", \"closed\", \"filtered\"):\n continue\n ports.add( (state, protocol, port) )\n except Exception:\n warn(\"Error parsing portscan results: %s\" % format_exc(),\n RuntimeWarning)\n\n # Get the traceroute results.\n traces = []\n for node in host.findall(\".//trace\"):\n try:\n port = int( node.get(\"port\") )\n proto = node.get(\"proto\")\n hops = {}\n broken = False\n for node in node.findall(\".//hop\"):\n try:\n ttl = int( node.get(\"ttl\") )\n address = node.get(\"ipaddr\")\n rtt = float( node.get(\"rtt\") )\n hostname = node.get(\"host\", None)\n hops[ttl] = Hop(address, rtt, hostname)\n except Exception:\n warn(\"Error parsing traceroute results: %s\" %\n format_exc(), RuntimeWarning)\n broken = True\n break\n if not broken:\n if hops:\n ttl = hops.keys()\n sane_hops = tuple(\n hops.get(i, None)\n for i in xrange(min(*ttl), max(*ttl) + 1)\n )\n else:\n sane_hops = ()\n traces.append( (port, proto, sane_hops) )\n except Exception:\n warn(\"Error parsing traceroute results: %s\" %\n format_exc(), RuntimeWarning)\n\n # This is where we'll gather all the results.\n results = ip_addresses + domain_names\n\n # Link the portscan results to the IP addresses.\n for ip in ip_addresses:\n portscan = Portscan(ip, ports, timestamp)\n results.append(portscan)\n\n # Link the traceroute results to the IP addresses.\n for ip in ip_addresses:\n if ip.version == 4:\n for trace in traces:\n traceroute = Traceroute(ip, *trace)\n results.append(traceroute)\n\n # Return the results.\n return results","def get_devices(mac=None):\n wemo_devices = discover_wemo()\n\n if mac:\n dev = get_device(mac, wemo_devices)\n if not dev:\n return []\n return [dev]\n\n return wemo_devices","def all_hosts(self):\n ...","def network_interfaces():\n try:\n command = which('ipadm')\n args = ('show-addr', '-p', '-o', 'STATE,ADDR')\n pattern = r'ok:(\\d+\\.\\d+\\.\\d+\\.\\d+)'\n except CommandMissing:\n # Fall back to old command on old solaris releases.\n command = which('/usr/sbin/ifconfig')\n args = ('-a')\n pattern = r'inet (\\d+\\.\\d+\\.\\d+\\.\\d+)'\n addrs = []\n output = sh(command, *args)\n for line in output:\n match = re.match(pattern, line)\n if match:\n addr = match.group(1)\n if not addr.startswith(\"127.\"):\n addrs.append(addr)\n return addrs","def get_host_mapping(cohesity_client):\n hosts = cohesity_client.network.list_hosts() or []\n for host in hosts:\n exported_res_dict[\"Hosts Mapping\"].append(host.ip)\n return hosts","def get_info_hosts():\n print(\"\\nMapeando...\")\n host_ip = socket.gethostbyname(socket.gethostname()).split('.')\n base_ip = \".\".join(host_ip[0:3]) + '.'\n host_validos = []\n return_codes = dict()\n for i in range(1, 255):\n return_codes[base_ip + str(i)] = retorna_codigo_ping(base_ip + str(i))\n if i %20 == 0:\n print(\".\", end = \"\")\n if return_codes[base_ip + str(i)] == 0:\n host_validos.append(base_ip + str(i))\n print(\"\\nMapeamento completo, informações sobre portas enviadas...\")\n \n return host_validos","def __get_mac_address(self):\n str_hex_mac = uuid.UUID(int=uuid.getnode()).hex[-12:]\n return str_hex_mac","def enumerate_network(arg):\n\n network = ip_network(arg, strict=False)\n data = list(map(str, network.hosts()))\n data.insert(0, str(network.network_address))\n if network.prefixlen != network.max_prefixlen:\n data.append(str(network.broadcast_address))\n return data","def get_os_service_scan_details(self):\n self._scanned = True\n result = self._scanner.scan(self._ips, self._ports, arguments='-A')\n hosts = []\n if self._scanned:\n # for each host scanned\n for host in self._scanner.all_hosts():\n print(\"-----------------\")\n print(result['scan'][host])\n print(\"-----------------\")\n state = result['scan'][host][\"status\"][\"state\"]\n mac = self.get_mac_address(result, host)\n vendor = self.get_vendor(result, host, mac)\n val_arr = self.get_os_details(result, host)\n name = val_arr[0]\n os_gen = val_arr[1]\n os_family = val_arr[2]\n hosts.append(Host(host, state, name, os_family, os_gen, vendor, mac))\n\n return hosts","def get_ip_address(host):\n for h in host:\n ip = h.address['addr']\n return ip","def select_wireless_endpoint_addresses(self):\n device_info = self.wireless_endpoint.DeviceInfoGet()\n network_info = device_info.NetworkInfoGet()\n\n for network_interface in network_info.InterfaceGet():\n interface_name = network_interface.NameGet()\n if interface_name == self.wireless_endpoint_traffic_interface:\n return [address.split('/')[0]\n for address in network_interface.IPv6GlobalGet()]\n\n return []","def host_ip(host):\n return host.cmd('ip addr show {}-eth1 | awk \\'/inet / {{ print $2 }}\\' | cut -d\\'/\\' -f1'.format(host.name, host.name), stdout=sp.PIPE).strip()","def arp_scan(interface: str, ip_range: str) -> List[str]:\n ans, unans = srp(Ether(dst='ff:ff:ff:ff:ff:ff') / ARP(pdst=ip_range), iface=interface, timeout=2, verbose=False)\n\n ip_addresses = []\n for req, res in ans:\n ip_addresses.append(req[ARP].pdst)\n\n return ip_addresses","def sx_router_neigh_get_mac(handle, rif, addr): \n try:\n neigh_entry_cnt_p = new_uint32_t_p()\n neigh_entry_list_p = new_sx_neigh_get_entry_t_arr(1)\n\n filter_p = new_sx_neigh_filter_t_p()\n neigh_filter = sx_neigh_filter_t()\n neigh_filter.filter_by_rif = SX_KEY_FILTER_FIELD_NOT_VALID\n neigh_filter.rif = 0\n sx_neigh_filter_t_p_assign(filter_p, neigh_filter)\n \n rc = sx_api_router_neigh_get(handle, SX_ACCESS_CMD_GET, rif, addr, filter_p, neigh_entry_list_p, neigh_entry_cnt_p)\n if rc == SX_STATUS_ENTRY_NOT_FOUND:\n return None \n sx_check_rc(rc)\n\n neighbor_entry = sx_neigh_get_entry_t_arr_getitem(neigh_entry_list_p, 0)\n \n return neighbor_entry.neigh_data.mac_addr.to_str()\n \n finally:\n delete_sx_neigh_filter_t_p(filter_p)\n delete_sx_neigh_get_entry_t_arr(neigh_entry_list_p) \n delete_uint32_t_p(neigh_entry_cnt_p)","def all_interfaces():\n max_possible = 128 # arbitrary. raise if needed.\n number_of_bytes = max_possible * 32\n s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\n names = array.array('B', '\\0' * number_of_bytes)\n outbytes = struct.unpack('iL', fcntl.ioctl(\n s.fileno(),\n 0x8912, # SIOCGIFCONF\n struct.pack('iL', number_of_bytes, names.buffer_info()[0])\n ))[0]\n namestr = names.tostring()\n interfaces = {}\n\n for i in range(0, outbytes, 40):\n name = namestr[i:i+16].split('\\0', 1)[0]\n ip = namestr[i+20:i+24]\n interfaces[name] = format_ip(ip)\n return interfaces","def _get_server_hardware_mac_from_ilo(server_hardware):\n try:\n client = get_ilorest_client(server_hardware)\n ilo_path = \"/rest/v1/systems/1\"\n hardware = jsonutils.loads(client.get(ilo_path).text)\n hardware_mac = hardware['HostCorrelation']['HostMACAddress'][0]\n except redfish.JsonDecodingError as exc:\n LOG.error(\"Failed in JSON object getting path: %s\", ilo_path)\n raise exception.OneViewError(error=exc)\n except (ValueError, TypeError, IndexError) as exc:\n LOG.error(\n \"Failed to get mac from server hardware %(server_hardware)s \"\n \"via iLO. Error: %(message)s\", {\n \"server_hardware\": server_hardware.get(\"uri\"),\n \"message\": exc\n }\n )\n raise exception.OneViewError(error=exc)\n\n return hardware_mac","def fact():\n result = []\n\n interfaces = [SCNetworkInterfaceGetBSDName(i) for i in SCNetworkInterfaceCopyAll()]\n\n for i in interfaces:\n try:\n active = subprocess.check_output(\n [\"/usr/sbin/ipconfig\", \"getifaddr\", i]\n ).strip()\n if active:\n result.append(i)\n except subprocess.CalledProcessError:\n continue\n\n return {factoid: result}","def intGet(): \n macchanger, ip, iwconfig = pathGet()\n interfaces = []\n a = str(subprocess.check_output(\"{} link show\".format(ip), shell=True))\n ints = a.split(': ')\n for i in range(len(ints)):\n if len(ints[i].split()) == 1:\n if ints[i] not in [\"1\", \"lo\", \"b'1\"]:\n interface = {'name':str(ints[i])}\n interfaces.append(interface)\n # Get interface properties\n for interface in interfaces:\n name = interface['name']\n macs = subprocess.check_output(\"{} -s {}\".format(macchanger, name), shell=True).decode(\"utf-8\")\n interface['cMac'] = macs.split()[2]\n interface['cVend'] = macs.split(\"(\")[1].split(\")\")[0]\n interface['pMac'] = macs.split(\"\\n\")[1].split()[2]\n interface['pVend'] = macs.split(\"\\n\")[1].split(\"(\")[1].split(\")\")[0]\n try:\n mon = subprocess.check_output(\"{} {} 2> /dev/null\".format(iwconfig, name), shell=True).split()\n mon1 = mon[3].decode(\"utf-8\").split(':')[1]\n if mon1 == 'off/any':\n mon1 = mon[4].decode(\"utf-8\").split(':')[1]\n interface['mon'] = mon1\n except:\n interface['mon'] = 'Wired'\n return(interfaces)","def hosts_cmd(args):\n r = requete(\"Hosts.Host:get\")\n if not r:\n return\n if len(args) > 0:\n for i in range(0, len(args)):\n for _, host in r['status'].items():\n if (host['MACAddress'].lower() == args[i].lower()\n or host['HostName'].lower() == args[i].lower()\n or host['IPAddress'] == args[i]):\n # pprint.pprint(host)\n json.dump(host, sys.stdout, indent=4)\n else:\n #pprint.pprint(r['status'])\n for _, host in r['status'].items():\n actif = \" \" if host['Active'] else \"*\"\n if mac_parser is None:\n s = \"%-18s %-15s %c %-35s %s\" % (host['MACAddress'], host['InterfaceType'], actif, host['HostName'], host['IPAddress'])\n else:\n s = \"%-18s %-12s %-15s %c %-35s %s\" % (host['MACAddress'], mac_parser.get_manuf(host['MACAddress']), host.get('InterfaceType', \"\"), actif, host['HostName'], host['IPAddress'])\n print(s)","def get_broadcast_addresses():\n addr_list = []\n if HAS_NETIFACES:\n for iface in netifaces.interfaces():\n addresses = netifaces.ifaddresses(iface).get(netifaces.AF_INET)\n if addresses is None:\n continue\n for address in addresses:\n broadcast_addr = address.get(\"broadcast\")\n if broadcast_addr is None:\n continue\n addr_list.append(broadcast_addr)\n return [\"127.0.0.1\", \"255.255.255.255\", \"<broadcast>\"] + addr_list","def get_mac_address(self):\n\t\treturn call_sdk_function('PrlSrvCfgNet_GetMacAddress', self.handle)","def mac_address(self):\n if self._mac_address is None:\n expression = expressions.WPA_MAC\n name = expressions.MAC_ADDRESS_NAME\n command = self.status_command\n self._mac_address = self._match(expression,\n name,\n command)\n return self._mac_address","def net_if_addrs():\n ret = []\n for items in cext.net_if_addrs():\n items = list(items)\n items[0] = py2_strencode(items[0])\n ret.append(items)\n return ret","def hh_assign_ports():\n print('******* Assigning ports')\n for device in ci_addrs.switches_hh1:\n print ('******* Connecting to ', device.get('ip'))\n net_connect = ConnectHandler(**device)\n output_vlan_cp = net_connect.send_config_set(cp_hh1_config_commands)\n print (output_vlan_cp)\n output_vlan_cloud = net_connect.send_config_set(cloud_hh1_config_commands)\n print (output_vlan_cloud)\n output_vlan_f5 = net_connect.send_config_set(f5_hh_config_commands)\n print (output_vlan_f5)\n net_connect.send_config_set('wr')\n\n for device in ci_addrs.switches_hh2:\n print ('******* Connecting to ', device.get('ip'))\n net_connect = ConnectHandler(**device)\n output_vlan_cp = net_connect.send_config_set(cp_hh2_config_commands)\n print (output_vlan_cp)\n output_vlan_cloud = net_connect.send_config_set(cloud_hh2_config_commands)\n print (output_vlan_cloud)\n output_vlan_f5 = net_connect.send_config_set(f5_hh_config_commands)\n print (output_vlan_f5)\n net_connect.send_config_set('wr')","def _detect_ap_dynamic_addresses(self, ap_mac_list):\n lease_data = self.components['LinuxServer'].get_dhcp_leases()\n map = {}\n ip = mac = start_time = None\n for line in lease_data:\n l = line.split()\n if l[0] == \"lease\":\n ip = l[1]\n\n elif l[0] == \"hardware\":\n mac = l[2].strip(\";\")\n\n elif l[0] == \"starts\":\n start_time_str = \" \".join(l[2:]).strip(\";\")\n start_time = time.mktime(time.strptime(start_time_str, \"%Y/%m/%d %H:%M:%S\"))\n\n if ip and mac and start_time:\n if map.has_key(mac):\n if map[mac][0] < start_time:\n map[mac] = (start_time, ip)\n\n else:\n map[mac] = (start_time, ip)\n\n ip = mac = start_time = None\n\n for ap_mac in ap_mac_list:\n if map.has_key(ap_mac.lower()):\n self.mac_to_ip[ap_mac.lower()] = map[ap_mac.lower()][1]\n\n else:\n raise Exception(\"IP entry of the MAC %s was not found in the \\\n dhcpd.leases file\" % ap_mac.lower())","def _generate_mac(topology_id):\n tid = int(topology_id)\n global mac_counter\n global used_macs\n base = '52:54:00:00:00:00'\n ba = base.split(':')\n ba[2] = '%02x' % int(tid / 256)\n ba[3] = '%02x' % int(tid % 256)\n ba[4] = '%02x' % int(len(used_macs[topology_id]) / 256)\n ba[5] = '%02x' % int(mac_counter)\n\n mac_counter += 1\n\n mac_counter = mac_counter % 256\n return ':'.join(ba)","def totalPacketIpMAC(host_id):\n try:\n global sqlalche_obj\n sqlalche_obj.sql_alchemy_db_connection_open()\n odu100_mac_result = sqlalche_obj.session.query(Odu100MacFilterTable.filterMacAddress).filter(\n and_(Odu100MacFilterTable.host_id == host_id, Odu100MacFilterTable.filterMacAddress != \"\")).all()\n odu100_ip_result = sqlalche_obj.session.query(Odu100IpFilterTable.ipFilterIpAddress).filter(\n and_(Odu100IpFilterTable.host_id == host_id, Odu100IpFilterTable.ipFilterIpAddress != \"\")).all()\n return {\"success\": 0, \"ip_result\": odu100_ip_result, \"mac_result\": odu100_mac_result}\n\n except ProgrammingError as e:\n return {\"success\": 1, \"result\": \"Some Programming Error Occurs\", \"detail\": \"\"}\n except AttributeError as e:\n return {\"success\": 1, \"result\": \"Some Attribute Error Occurs\", \"detail\": \"\"}\n except OperationalError as e:\n return {\"success\": 1, \"result\": \"Some Operational Error Occurs\", \"detail\": \"\"}\n except TimeoutError as e:\n return {\"success\": 1, \"result\": \"Timeout Error Occurs\", \"detail\": \"\"}\n except NameError as e:\n return {\"success\": 1, \"result\": \"Some Name Error Occurs\", \"detail\": \"\"}\n except UnboundExecutionError as e:\n return {\"success\": 1, \"result\": \"Unbound Execution Error Occurs\", \"detail\": \"\"}\n except DatabaseError as e:\n return {\"success\": 1, \"result\": \"Database Error Occurs,Contact Your Administrator\", \"detail\": \"\"}\n except DisconnectionError as e:\n return {\"success\": 1, \"result\": \"Database Disconnected\", \"detail\": \"\"}\n except NoResultFound as e:\n return {\"success\": 1, \"result\": \"No result Found For this opeartion\", \"detail\": \"\"}\n except UnmappedInstanceError as e:\n return {\"success\": 1, \"result\": \"Some Unmapped instance error\", \"detail\": \"\"}\n except NoReferenceError as e:\n return {\"success\": 1, \"result\": \"No reference Exists\", \"detail\": \"\"}\n except SAWarning as e:\n return {\"success\": 1, \"result\": \"Warning Occurs\", \"detail\": \"\"}\n except Exception as e:\n return {\"success\": 1, \"result\": \"Operation Failed,contact Administrator\", \"detail\": \"\"}\n finally:\n sqlalche_obj.sql_alchemy_db_connection_close()"],"string":"[\n \"def get_mac_address_table(self):\\n\\n mac_address_table = []\\n command = '/interface bridge host print terse'\\n\\n output = self._send_command(command)\\n\\n for host in parse_terse_output(output):\\n mac_address_table.append({\\n 'mac': cast_mac(host.get('mac-address')),\\n 'interface': host.get('interface'),\\n 'vlan': -1,\\n 'static': True if 'D' not in host.get('_flags') else False,\\n 'active': True if 'X' not in host.get('_flags') else False,\\n 'moves': -1,\\n 'last_move': -1.0\\n })\\n\\n return mac_address_table\",\n \"def getmacaddrs():\\n # Unpack just for the sake of being meaningful.\\n ifaddrs, sockaddr_dl, sockaddr = PLATFORM_LOOKUP[PLATFORM]\\n ptr = c_void_p(None)\\n result = LIBC.getifaddrs(pointer(ptr))\\n if result != 0:\\n return {}\\n ifa = ifaddrs.from_address(ptr.value)\\n result = {}\\n\\n while True:\\n name = ifa.ifa_name\\n if name not in result:\\n result[name] = []\\n # Some interface (such as a TUN virtual network) doesn't give us\\n # ifa_addr at all and we can usually skip them because they're hardly\\n # relevant for our usage case.\\n if ifa.ifa_addr:\\n sa = sockaddr.from_address(ifa.ifa_addr)\\n if sa.sa_family == AF_LINK:\\n si = sockaddr_dl.from_address(ifa.ifa_addr)\\n addr = \\\"%s\\\" % si\\n if addr:\\n result[name].append(addr)\\n if ifa.ifa_next:\\n ifa = ifaddrs.from_address(ifa.ifa_next)\\n else:\\n break\\n\\n LIBC.freeifaddrs(ptr)\\n return result\",\n \"def get_eth0_mac_address(hostname):\\n\\n command = \\\"ipmitool -H {} -U ADMIN -P ADMIN raw 0x30 0x21\\\".format(hostname)\\n\\n try:\\n result = subprocess.check_output(command, shell=True)\\n except subprocess.CalledProcessError as exc:\\n LOGGER.exception(\\\"Failed to get eth0 mac for %s\\\", hostname)\\n\\n # Remove space and newline\\n result = result.strip()\\n mac = \\\":\\\".join(result.split()[4:]) # No verification :-(\\n\\n return mac\",\n \"def _get_mac_addresses_for_domain(self, domain):\\n xml = domain.XMLDesc()\\n etree = ET.XML(xml)\\n elems = etree.findall(\\\"devices/interface[@type='network']/mac\\\")\\n\\n result = []\\n for elem in elems:\\n mac_address = elem.get(\\\"address\\\")\\n result.append(mac_address)\\n\\n return result\",\n \"def get_mac_address(self, result, host):\\n if \\\"mac\\\" in result['scan'][host][\\\"addresses\\\"]:\\n return result['scan'][host][\\\"addresses\\\"][\\\"mac\\\"]\\n else:\\n return \\\"\\\"\",\n \"def get_mac_address():\\n eth0_interface = 'eth0'\\n addresses = netifaces.ifaddresses(eth0_interface)[netifaces.AF_LINK][0]\\n mac_address = addresses['addr']\\n return mac_address\",\n \"def getMac(self):\\n # Import netifaces here to prevent error importing this module in setup.py\\n import netifaces\\n interfaces = ['eth0', 'wlan0']\\n try:\\n interfaces.append(netifaces.gateways()['default'][netifaces.AF_INET][1])\\n except:\\n pass\\n for interface in interfaces:\\n try:\\n return netifaces.ifaddresses(interface)[netifaces.AF_LINK][0]['addr']\\n except ValueError:\\n pass\\n except:\\n exception('Error getting MAC address')\\n return None\",\n \"def scan_devices(self):\\n self._update_info()\\n return [client[\\\"mac\\\"] for client in self.last_results]\",\n \"def arp_scan(subnet):\\n\\n answered = scapy.arping(subnet)[0]\\n\\n machines = []\\n for i in answered:\\n ip, mac = i[1].psrc, i[1].hwsrc\\n try:\\n host = socket.gethostbyaddr(i[1].psrc)[0]\\n except Exception:\\n host = \\\"??\\\"\\n machines.append({\\\"ip\\\": ip, \\\"mac\\\": mac, \\\"host\\\": host})\\n\\n return machines\",\n \"def get_mac_address(hostname):\\n\\n url = \\\"https://{}/redfish/v1/Managers/1/EthernetInterfaces/1/\\\".format(hostname)\\n eth_dict = requests.get(url, auth=(USER,PASS),verify=False).json()\\n mac_address = eth_dict['MACAddress']\\n\\n LOGGER.info(\\\"IPMI BMC %s reports MAC address as %s\\\", hostnameh, mac_address)\\n\\n return mac_address\",\n \"def scan_devices(self):\\n self._update_info()\\n\\n return [client['mac'] for client in self.last_results]\",\n \"def scan(self):\\n walk_result = utils.snmp_walk(self.host, self.credential,\\n \\\"BRIDGE-MIB::dot1dTpFdbPort\\\")\\n if not walk_result:\\n return None\\n\\n mac_list = []\\n for result in walk_result:\\n if not result or result['value'] == str(0):\\n continue\\n temp = {}\\n mac_numbers = result['iid'].split('.')\\n temp['mac'] = self._get_mac_address(mac_numbers)\\n temp['port'] = self._get_port(result['value'])\\n temp['vlan'] = self._get_vlan_id(temp['port'])\\n mac_list.append(temp)\\n\\n return mac_list\",\n \"def get_mac():\\n\\n interface = [x for x in netifaces.interfaces() if 'wlan' in x or 'wlp' in x][0]\\n return netifaces.ifaddresses(interface)[netifaces.AF_LINK][0]['addr']\",\n \"def possible_mac_addresses(interface):\\n\\n mac_addrs = []\\n\\n # In case of VLANs, just grab the parent interface\\n if interface.interface_type == 'vlan':\\n interface = interface.parent\\n\\n # Bonding/bridge: append the MACs of the physical interfaces\\n # TODO: drop the public/bootable check once we decide how to send the extra\\n # information to clients\\n for slave in interface.all_slaves():\\n if slave.mac and (slave.interface_type != \\\"public\\\" or slave.bootable):\\n mac_addrs.append(slave.mac)\\n\\n # Handle physical interfaces, and bonding with a dedicated MAC\\n # TODO: drop the public/bootable check once we decide how to send the extra\\n # information to clients\\n if interface.mac and (interface.interface_type != \\\"public\\\" or interface.bootable):\\n mac_addrs.append(interface.mac)\\n\\n return mac_addrs\",\n \"def mac_from_vm(vm: libvirt.virDomain = None) -> str:\\n doc = minidom.parseString(vm.XMLDesc())\\n interfaces = doc.getElementsByTagName('mac')\\n return interfaces[0].getAttribute('address')\",\n \"def get_my_mac_set(iface_filter=None):\\n\\n out_set = set()\\n if sys.platform.startswith(\\\"win\\\"):\\n from scapy.arch.windows import NetworkInterface\\n if type(iface_filter) == NetworkInterface:\\n out_set.add(iface_filter.mac)\\n\\n for iface in sc.get_if_list():\\n if iface_filter is not None and iface != iface_filter:\\n continue\\n try:\\n mac = sc.get_if_hwaddr(iface)\\n except Exception as e:\\n continue\\n else:\\n out_set.add(mac)\\n\\n return out_set\",\n \"def get_mac(ip):\\n p = Ether(dst=\\\"ff:ff:ff:ff:ff:ff\\\") / ARP(op=1, pdst=ip)\\n result = srp(p, verbose=False, iface=IFACE)[0]\\n return result[0][1].hwsrc if result else None\",\n \"def _parse_mac_addr_table(self, cmd_output, mac_regex):\\n lines = ensure_string(cmd_output).split(\\\"\\\\n\\\")\\n\\n arp_table = defaultdict(list)\\n for line in lines:\\n match = mac_regex.match(line)\\n\\n if not match:\\n continue\\n\\n groups = match.groups()\\n ip_address = groups[0]\\n mac_address = groups[1]\\n arp_table[mac_address].append(ip_address)\\n\\n return arp_table\",\n \"def _get_management_macs_for_topology(topology_id):\\n existing_macs = list()\\n\\n domains = libvirtUtils.get_domains_for_topology(topology_id)\\n for d in domains:\\n m = libvirtUtils.get_management_interface_mac_for_domain(d['name'])\\n logger.debug('adding existing mac %s to existing_macs list' % m)\\n existing_macs.append(m)\\n\\n return existing_macs\",\n \"def get_arp_list(self):\\n arp_list = []\\n for ip, value in self.ip_to_mac.items():\\n arp_list.append({'ip': str(ip),\\n 'hw_addr': str(value[0]),\\n 'last_update': datetime.datetime.fromtimestamp(value[1]).strftime('%Y-%m-%d %H:%M:%S')})\\n\\n return arp_list\",\n \"def _get_mac_address(self, mac_numbers):\\n\\n mac = \\\"\\\"\\n for num in mac_numbers:\\n num = self._convert_to_hex(num)\\n mac = ':'.join((mac, num))\\n mac = mac[1:]\\n return mac\",\n \"def ipaddrs( host ):\\n return socket.gethostbyaddr(host)[2][0]\",\n \"def scan(backend, timeout=10):\\n result = []\\n for (mac, name) in backend.scan_for_devices(timeout):\\n print(mac + \\\" \\\" + name)\\n return result\",\n \"def _get_ipv6_addresses(self, host: str) -> Dict[str, List[IPv6Address]]:\\n if host == \\\"self\\\":\\n command = \\\"show ipv6 interface\\\"\\n elif host == \\\"peer\\\":\\n command = \\\"failover exec mate show ipv6 interface\\\"\\n\\n show_ipv6_interface = self.show(command)\\n show_ipv6_interface_lines: List[str] = show_ipv6_interface.strip().splitlines()\\n first_line = show_ipv6_interface_lines.pop(0)\\n interface: str = first_line.split()[0]\\n ipv6_addresses: List[IPv6Interface] = []\\n results: Dict[str, List] = {}\\n for line in show_ipv6_interface_lines:\\n # match IPv6 addresses under interface line\\n if line[0].isspace():\\n match = RE_IPV6_INTERFACE_MATCH.match(line)\\n if match:\\n ipv6_addresses.append(IPv6Interface(f\\\"{match.group(1)}{match.group(2)}\\\"))\\n # update results mapping interface to matched IPv6 addresses and generate the next interface name\\n else:\\n if ipv6_addresses:\\n results[interface] = ipv6_addresses\\n ipv6_addresses = []\\n interface = line.split()[0]\\n\\n # Add final interface in iteration if it has IPv6 addresses\\n if ipv6_addresses:\\n results[interface] = ipv6_addresses\\n\\n log.debug(\\\"Host %s: ip interfaces %s\\\", self.host, results)\\n return results\",\n \"def get_mac(self, node_id):\\n nc = '%02x' % self.node_class\\n nr_iface = '%02x' % self.nr_host_interface\\n node_id = '%08x' % node_id\\n\\n return '%s:%s:%s:%s:%s:%s' % (nc, nr_iface, node_id[0:2], node_id[2:4], node_id[4:6], node_id[6:8])\",\n \"def _get_mac_address():\\n if not sys.platform.startswith('linux'):\\n raise RuntimeError(\\n 'Cannot get the MAC address on non-Linux platforms'\\n )\\n ifname = get_default_iface_name_linux()\\n s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\\n info = fcntl.ioctl(s.fileno(), 0x8927,\\n struct.pack('256s', bytes(ifname, 'utf-8')[:15]))\\n return ''.join('%02x' % b for b in info[18:24])\",\n \"def find_my_IP_and_MAC():\\n mac = ':'.join(re.findall('..', '%012x' % getnode()))\\n # I write IP and not domain cause i want to save time.\\n p = sr1(IP(dst=\\\"google.com\\\", ttl=0) / ICMP() / \\\"XXXXXXXXXXX\\\",verbose=0,timeout=5) #verbose = withuot output\\n return mac,p.dst\",\n \"def topo_conf():\\n for k in switches.keys():\\n switches_ip[k] = IPAddr((192<<24)+int(k))\\n switches_mac[k] = EthAddr(\\\"aa\\\"+ \\\"%010d\\\"%(k))\",\n \"def get_mac(ip):\\n arp_request = scapy.ARP(pdst=ip)\\n broadcast = scapy.Ether(dst=\\\"ff:ff:ff:ff:ff:ff\\\")\\n arp_request_broadcast = broadcast / arp_request\\n answered_list = scapy.srp(\\n arp_request_broadcast,\\n timeout=5,\\n verbose=False)[0]\\n return answered_list[0][1].hwsrc\",\n \"def get_mac_addresses(self, task):\\n raise exception.UnsupportedDriverExtension(\\n driver=task.node.driver, extension='get_mac_addresses')\",\n \"def get_sonic_mac(host, asicnum, port):\\n if host.facts[\\\"num_asic\\\"] == 1:\\n cmd = \\\"sudo ip link show {}\\\".format(port)\\n else:\\n ns = \\\"asic\\\" + str(asicnum)\\n cmd = \\\"sudo ip netns exec {} ip link show {}\\\".format(ns, port)\\n output = host.command(cmd)\\n mac = output['stdout_lines'][1].split()[1]\\n logger.info(\\\"host: %s, asic: %d, port: %s, mac: %s\\\", host.hostname, asicnum, port, mac)\\n return mac\",\n \"def get_server_ip_mac(self, server_id):\\n port_list = self.list_port()\\n if not isinstance(port_list, list):\\n return\\n interface_list = []\\n for port in port_list:\\n if port[\\\"device_id\\\"] == server_id:\\n port_info = {}\\n port_info['mac'] = port['mac_address']\\n port_info['ip_address'] = port['fixed_ips'][0]['ip_address']\\n interface_list.append(port_info)\\n\\n LOG_OBJ.info(\\\"VM Interface Info : %s \\\" % interface_list)\\n return interface_list\",\n \"def getMacAddress(self, wlanInterface):\\n self.wlanInterface = wlanInterface\\n self.storeMacAddress=[]\\n s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\\n info = fcntl.ioctl(s.fileno(), 0x8927, struct.pack('256s', '%s'[:15]) % str(self.wlanInterface))\\n self.storeMacAddress.append(''.join(['%02x:' % ord(char) for char in info[18:24]])[:-1])\\n return self.storeMacAddress\",\n \"def get_mac(ip):\\n ans, _ = srp(Ether(dst='ff:ff:ff:ff:ff:ff')/ARP(pdst=ip), timeout=3, verbose=0)\\n if ans:\\n return ans[0][1].src\\n else:\\n return None\",\n \"def get_network_device_mac(devices, user, passwd, base_api_url):\\n network_device_mac_address = ''\\n\\n if devices:\\n selected_network_device = generate_network_devices_menu(devices, purpose='DHCP')\\n network_device_mac_address = get_mac_address(selected_network_device, base_api_url, user, passwd)\\n\\n if network_device_mac_address:\\n logging.info('device {} mac address is {}'.format(selected_network_device, network_device_mac_address))\\n \\n return network_device_mac_address\",\n \"def _build_nodes(self):\\n res = super(MacFix, self)._build_nodes()\\n\\n observed_addresses = set(inner for outer in [host.mac_addresses for host in self._hosts.values()] for inner in outer)\\n unsatisfied = self._macfix__mac_addresses.difference(observed_addresses)\\n unexpected = observed_addresses.difference(self._macfix__mac_addresses)\\n if len(unsatisfied) > 1:\\n raise AssertionError(\\\"unsatisfied: %s -- 2 or more host MAC addresses could not be found. Are you using the correct topology file?\\\" % unsatisfied)\\n assert len(unexpected) == len(unsatisfied)\\n if len(unsatisfied) == 1:\\n self._macfix__result = {list(unsatisfied)[0]: list(unexpected)[0]}\\n else:\\n self._macfix__result = {}\\n\\n return res\",\n \"def mon_hosts(self, mon_ips):\\n hosts = []\\n for ceph_addrs in mon_ips:\\n # NOTE(jamespage): This looks odd but deals with\\n # use with ceph-proxy which\\n # presents all monitors in\\n # a single space delimited field.\\n for addr in ceph_addrs.split(' '):\\n hosts.append(ch_ip.format_ipv6_addr(addr) or addr)\\n hosts.sort()\\n return hosts\",\n \"def _new_recovery_broadcast_arps(victim_ips: Iterable[str]) -> List[ARP]:\\n victim_macs = _request_macs(victim_ips)\\n return [ARP(psrc=v_ip, pdst=v_ip, hwsrc=v_mac)\\n for v_ip, v_mac in victim_macs.items()]\",\n \"def get_bt_smarthub_data(self):\\n import btsmarthub_devicelist\\n\\n data = btsmarthub_devicelist.get_devicelist(router_ip=self.host, only_active_devices=True)\\n devices = {}\\n for device in data:\\n try:\\n devices[device['UserHostName']] = {\\n 'ip': device['IPAddress'],\\n 'mac': device['PhysAddress'],\\n 'host': device['UserHostName'],\\n 'status': device['Active']\\n }\\n except (KeyError, 'no'):\\n pass\\n return devices\",\n \"def machine_lookup_all(session, hostname, public_ip = True):\\n client = session.client('ec2')\\n response = client.describe_instances(Filters=[{\\\"Name\\\":\\\"tag:Name\\\", \\\"Values\\\":[hostname]},\\n {\\\"Name\\\":\\\"instance-state-name\\\", \\\"Values\\\":[\\\"running\\\"]}])\\n\\n addresses = []\\n items = response['Reservations']\\n if len(items) > 0:\\n for i in items:\\n item = i['Instances'][0]\\n if 'PublicIpAddress' in item and public_ip:\\n addresses.append(item['PublicIpAddress'])\\n elif 'PrivateIpAddress' in item and not public_ip:\\n addresses.append(item['PrivateIpAddress'])\\n return addresses\",\n \"def scan(ip):\\n arp_request = scapy.ARP(pdst=ip)\\n broadcast = scapy.Ether(dst=\\\"ff:ff:ff:ff:ff:ff\\\")\\n arp_request_broadcast = broadcast/arp_request\\n answered_list = scapy.srp(arp_request_broadcast , timeout = 1, verbose=False)[0]\\n target_list=[]\\n for element in answered_list:\\n target_dict = {\\\"ip\\\":element[1].psrc, \\\"mac\\\":element[1].hwsrc}\\n target_list.append(target_dict)\\n return target_list\",\n \"def get_rand_mac(self):\\n random_mac = []\\n\\n # add manufacturer\\n random_mac.append(self.get_rand_manufact())\\n\\n # generate the last 24 bits of the random hex\\n for i in range(0, 3):\\n rand_digit1 = self.get_rand_hex_digit()\\n rand_digit2 = self.get_rand_hex_digit()\\n random_mac.append(rand_digit1 + rand_digit2)\\n\\n return ':'.join(random_mac)\",\n \"def _listing(self, mac_addresses):\\n # Initialize key variables\\n preliminary_listing = []\\n listing = []\\n\\n # Cycle through mac addresses, get the manufacturer\\n for mac_address in mac_addresses:\\n # Get manufacturer\\n manufacturer = self._manufacturer(mac_address)\\n data_dict = {}\\n data_dict['mac_address'] = mac_address\\n data_dict['manufacturer'] = manufacturer\\n preliminary_listing.append(data_dict)\\n\\n # Get IP address and hostname for each mac address\\n for item in preliminary_listing:\\n mac_address = item['mac_address']\\n manufacturer = item['manufacturer']\\n\\n if mac_address in self.rarp_table:\\n # MAC address has related IP\\n if bool(self.rarp_table[mac_address]) is True:\\n for ip_address in self.rarp_table[mac_address]:\\n data_dict = {}\\n data_dict['mac_address'] = mac_address\\n data_dict['manufacturer'] = manufacturer\\n data_dict['ip_address'] = ip_address\\n data_dict['hostname'] = ''\\n\\n if ip_address in self.arp_table:\\n if 'hostname' in self.arp_table[ip_address]:\\n hostname = self.arp_table[\\n ip_address]['hostname']\\n data_dict['hostname'] = hostname\\n\\n listing.append(data_dict)\\n else:\\n # MAC address has no related IP\\n data_dict = {}\\n data_dict['mac_address'] = mac_address\\n data_dict['manufacturer'] = manufacturer\\n data_dict['ip_address'] = ''\\n data_dict['hostname'] = ''\\n listing.append(data_dict)\\n\\n # Return\\n return listing\",\n \"def get_devices(ip, mask):\\n\\n addr = ip + \\\"/\\\" + mask\\n network = ipaddress.ip_network(addr, strict=False)\\n values = (\\\"sysDescr\\\", \\\"sysUpTime\\\", \\\"sysLocation\\\", \\\"sysName\\\")\\n devices = {}\\n\\n for ip in network:\\n device_info = {}\\n for value in values:\\n iterator = getCmd(SnmpEngine(),\\n CommunityData('public'),\\n UdpTransportTarget((str(ip), 161)),\\n ContextData(),\\n ObjectType(ObjectIdentity('SNMPv2-MIB', str(value), 0)))\\n\\n error_indication, error_status, error_index, var_binds = next(iterator)\\n\\n if error_indication: # SNMP engine errors\\n print(str(ip) + \\\"> \\\" + str(error_indication))\\n else:\\n if error_status: # SNMP agent errors\\n print(\\n '%s at %s' % (\\n error_status.prettyPrint(), var_binds[int(error_index) - 1] if error_index else '?'))\\n else:\\n for varBind in var_binds: # SNMP response contents\\n device_info[value] = str(varBind).split(\\\"=\\\")[1].replace(\\\" \\\", \\\"\\\")\\n\\n if device_info == {}:\\n continue\\n device_info[\\\"ip\\\"] = str(ip)\\n devices[str(ip)] = device_info\\n\\n return devices\",\n \"def connectentryfanoutmac(localpop,\\n hostmac,\\n hostvlan,\\n forwards,\\n meter,\\n mac):\\n hwswitch = Container.fromAnchor(localpop.properties['HwSwitch'])\\n hwswitchname = hwswitch.resourceName\\n swswitch = Container.fromAnchor(localpop.properties['SwSwitch'])\\n swswitchname = swswitch.resourceName\\n topology = Container.getContainer(localpop.properties['SwSwitch']['containerName'])\\n\\n # print \\\"connectentryfanout localpop\\\", localpop, \\\"host\\\", host, \\\"hostvlan\\\", hostvlan, \\\"mac\\\", mac\\n\\n # Find the port on the software switch connected to the hardware switch\\n links = getlinks2(topology, swswitchname, hwswitchname)\\n if links == None or len(links) == 0:\\n print \\\"No links from\\\", swswitchname, \\\"to\\\", hwswitchname\\n return None\\n hwswitchlink = None\\n swport_tohw = None\\n for link in links:\\n (node, port) = linkednode2(link, hwswitchname)\\n if port != None:\\n # Found it!\\n hwswitchlink = link\\n swport_tohw = port\\n break\\n if swport_tohw == None:\\n print \\\"No output port on\\\", swswitchname, \\\"facing\\\", hwswitchname\\n return None\\n\\n # The fanout flow is \\\"interesting\\\" in that the input plus the multiple outputs\\n # all are on the same port (but different VLANs). Fill in the outputs.\\n for f in forwards:\\n f.outPort = str(swport_tohw)\\n # print \\\"FORW: outport\\\", f.outPort, \\\"vlan\\\", f.vlan, \\\"dstMac\\\", f.dstMac\\n\\n # Convert the list of forwarding destinations to a Java array.\\n fwdsarr = jarray.array(forwards, SdnControllerClientL2Forward)\\n\\n # print \\\"dpid\\\", swswitch.props['dpid']\\n # This flow being installed is unusual in that it does a source MAC address\\n # filter as well\\n fh = SCC.SdnInstallForward(javaByteArray2(swswitch.properties['DPID']),\\n 1,\\n BigInteger.ZERO,\\n str(swport_tohw),\\n int(hostvlan),\\n hostmac,\\n mac,\\n fwdsarr,\\n 0,\\n 0,\\n meter)\\n\\n return fh\",\n \"def all_client_assoc_ap(ap_mac):\\n\\n url = CMX_URL + '/api/location/v2/clients'\\n header = {'content-type': 'application/json', 'accept': 'application/json'}\\n response = requests.get(url, headers=header, auth=CMX_AUTH, verify=False)\\n clients_json = response.json()\\n clients_mac_info = []\\n for client in clients_json:\\n if client['apMacAddress'] == ap_mac:\\n clients_mac_info.append(client['macAddress'])\\n return clients_mac_info\",\n \"def get_all_interfaces():\\n global all_interfaces\\n if all_interfaces:\\n return all_interfaces\\n\\n f = open('/proc/net/dev','r')\\n ifacelist = f.read().split('\\\\n')\\n f.close()\\n\\n # remove 2 lines header\\n ifacelist.pop(0)\\n ifacelist.pop(0)\\n\\n all_interfaces = {}\\n # loop to check each line\\n for line in ifacelist:\\n\\n ifacedata = line.replace(' ','').split(':')\\n\\n # check the data have 2 elements\\n if len(ifacedata) == 2:\\n all_interfaces[ifacedata[0]] = get_interface_ip(ifacedata[0])\\n\\n return all_interfaces\",\n \"def get_eos_mac(nbr, nbr_intf):\\n if \\\"port-channel\\\" in nbr_intf.lower():\\n # convert Port-Channel1 to po1\\n shell_intf = \\\"po\\\" + nbr_intf[-1]\\n else:\\n # convert Ethernet1 to eth1\\n shell_intf = \\\"eth\\\" + nbr_intf[-1]\\n\\n output = nbr['host'].command(\\\"ip addr show dev %s\\\" % shell_intf)\\n # 8: Ethernet0: <BROADCAST,MULTICAST,UP,LOWER_UP> mtu 9100 ...\\n # link/ether a6:69:05:fd:da:5f brd ff:ff:ff:ff:ff:ff\\n\\n mac = output['stdout_lines'][1].split()[1]\\n return {'mac': mac, \\\"shell_intf\\\": shell_intf}\",\n \"def get_eap_mab(self):\\n for m in self.get_tag(self.mac):\\n v = m[1]\\n if not isinstance(v, int):\\n v = self._get_vlan(v)\\n yield [m[0], v]\",\n \"def connected_devices_arp(self, run_test):\\n\\n\\n if not run_test:\\n return\\n\\n res = {}\\n\\n ts = int(time.time())\\n\\n route_cmd = \\\"ip r | grep /24 | awk '{print $1;}'\\\"\\n subnet = Popen(route_cmd, shell=True,\\n stdout=PIPE).stdout.read().decode('utf-8')\\n\\n nmap_cmd = f'nmap -sn {subnet}'\\n Popen(nmap_cmd, shell=True, stdout=PIPE)\\n\\n arp_cmd = (\\\"/usr/sbin/arp -i eth0 -n | grep : |\\\"\\n \\\"grep -v '_gateway' | tr -s ' ' | \\\"\\n \\\"cut -f3 -d' ' | sort | uniq\\\")\\n\\n arp_res = Popen(arp_cmd, shell=True,\\n stdout=PIPE).stdout.read().decode('utf-8')\\n res['arp'] = arp_res\\n\\n devices = set(arp_res.strip().split(\\\"\\\\n\\\"))\\n active_devices = [[dev, ts, 1] for dev in devices]\\n\\n for device in active_devices:\\n if self.dev_db.contains(where('mac_addr') == device[0]):\\n self.dev_db.update(increment(\\\"n\\\"),\\n where('mac_addr') == device[0])\\n self.dev_db.update(tdb_set('last_seen', device[1]),\\n where('mac_addr') == device[0])\\n else:\\n self.dev_db.insert({'mac_addr': device[0],\\n 'last_seen': device[1],\\n 'n': device[2]})\\n\\n print(self.dev_db.all())\\n ndev_past_day = len(self.dev_db.search(\\n where('last_seen') > (ts - 86400)))\\n ndev_past_week = len(self.dev_db.search(\\n where('last_seen') > (ts - 86400*7)))\\n\\n print(ndev_past_day)\\n self.results[\\\"devices_active\\\"] = len(active_devices)\\n self.results[\\\"devices_total\\\"] = self.dev_db.count(where('n') >= 1)\\n self.results[\\\"devices_1day\\\"] = ndev_past_day\\n self.results[\\\"devices_1week\\\"] = ndev_past_week\\n\\n if not self.quiet:\\n print('\\\\n --- Number of Devices ---')\\n print(f'Number of active devices: '\\n f'{self.results[\\\"devices_active\\\"]}')\\n print(f'Number of total devices: '\\n f'{self.results[\\\"devices_total\\\"]}')\\n print(f'Number of devices in last 1 day:'\\n f' {self.results[\\\"devices_1day\\\"]}')\\n print(f'Number of devices in last week:'\\n f' {self.results[\\\"devices_1week\\\"]}')\\n return res\",\n \"def get_my_mac():\\n\\n mac_set = get_my_mac_set(iface_filter=get_default_route()[1])\\n return mac_set.pop()\",\n \"def _get_ipv4_addresses(self, host: str) -> Dict[str, List[IPv4Address]]:\\n if host == \\\"self\\\":\\n command = \\\"show ip address\\\"\\n elif host == \\\"peer\\\":\\n command = \\\"failover exec mate show ip address\\\"\\n\\n show_ip_address = self.show(command)\\n re_ip_addresses = RE_SHOW_IP_ADDRESS.findall(show_ip_address)\\n\\n results = {\\n interface: [IPv4Interface(f\\\"{address}/{netmask}\\\")] for interface, address, netmask in re_ip_addresses\\n }\\n log.debug(\\\"Host %s: ip interfaces %s\\\", self.host)\\n return results\",\n \"def get_host_interfaces(self, context, host_uuid):\\n result = {}\\n interfaces = self._get_cgtsclient().iinterface.list(host_uuid)\\n for interface in interfaces:\\n if interface.networktype != \\\"data\\\":\\n continue\\n providernets = interface.providernetworks\\n result[interface.uuid] = {'uuid': interface.uuid,\\n 'mtu': interface.imtu,\\n 'vlans': '',\\n 'network_type': interface.networktype,\\n 'providernets': providernets}\\n return result\",\n \"def ipv6_addresses(self) -> Dict[str, List[IPv6Address]]:\\n log.debug(\\\"Host %s: ipv6 addresses of the devices interfaces %s.\\\", self.host, self._get_ipv6_addresses(\\\"self\\\"))\\n return self._get_ipv6_addresses(\\\"self\\\")\",\n \"def get_mac_address(ifname):\\n try:\\n return open('/sys/class/net/' + ifname + '/address') \\\\\\n .readline().strip()\\n except:\\n SysTools.logger.error(\\\"Failed to get mac-address of %s\\\", ifname)\\n return \\\"00:00:00:00:00:00\\\"\",\n \"def get_allhosts():\\n connection, tablename = HomeNetwork.get_connection_info()\\n query = 'SELECT hostname from {}'.format(tablename)\\n output = pandas.read_sql_query(query, connection).to_json(orient='records')\\n\\n for host in json.loads(output):\\n yield host[\\\"hostname\\\"]\",\n \"def dump_macs(self, context):\\n nmeta = self._nmeta\\n dpid = self.dpid\\n macs = []\\n db_cursor = nmeta.dbidmac.find({'dpid': dpid, 'context': context})\\n if db_cursor:\\n for document in db_cursor:\\n mac = document['mac']\\n if mac:\\n macs.append(mac)\\n return macs\",\n \"def list_hosts():\\n task_run(\\\"/bin/hostname -f\\\",RING_1_dev__allnodes)\",\n \"def get_all_host(self, conf, tenant_id, network_id):\\n\\t\\tpass\",\n \"def search_mac_in_arp(args):\\n arp_table = get_arp_table()\\n \\n mac = convert_mac(args.mac, 2)\\n columns = ['IP', 'AGE(min)', 'MAC', 'INTERFACE', 'VENDOR']\\n result = []\\n for row in arp_table.split('\\\\n'):\\n if mac in row:\\n lists = []\\n _,ip,age,r_mac,_,interface = row.strip().split()\\n r_mac = convert_mac(mac, 1)\\n vendor = convert_mac(r_mac, 3)\\n vendor = find_in_database(vendor)\\n r_mac = blue + r_mac + endblue\\n lists = [ip, age, r_mac, interface, vendor]\\n result.append(lists)\\n else:\\n pass\\n print(tabulate(result, headers=columns))\",\n \"async def get_cells_connected():\\n global CELLS\\n # Perfrom modem lan query\\n try:\\n async with asyncssh.connect(IP_MODEM,username=USER_MODEM,password=PASS_MODEM,known_hosts=None) as conn:\\n result = await conn.run('status lan')\\n connected_devices = re.findall(pattern = r'connected_device {.*?}', string = result.stdout, flags=re.DOTALL)\\n for mac in connected_devices:\\n mac_addr = re.search(pattern=r'mac = (00:02:95:03:.*?)\\\\n',string=mac)\\n if mac_addr:\\n mac_addr = mac_addr.group(1)\\n if mac_addr not in CELLS:\\n ip = re.search(pattern='ip = (192.168.0.\\\\d+)\\\\n', string=mac).group(1)\\n type = await get_cell_type(ip)\\n CELLS[mac_addr] = {\\n 'ip' : ip,\\n 'type' : type,\\n 'fault': None,\\n 'old_faults' : set()\\n }\\n LOGGER.info(f\\\"Femto-cell S60Z {type} with IP {ip} is connected\\\")\\n fault = await get_cell_fault(mac_addr,CELLS[mac_addr]['ip'],CELLS[mac_addr]['type'])\\n if fault != CELLS[mac_addr]['fault']:\\n CELLS[mac_addr]['fault'] = fault\\n if CELLS[mac_addr]['fault'] == 0:\\n LOGGER.info(f\\\"Number of alarms in femto {CELLS[mac_addr]['ip']} {CELLS[mac_addr]['type']} is {CELLS[mac_addr]['fault']}\\\")\\n else:\\n LOGGER.warning(f\\\"Number of alarms in femto {CELLS[mac_addr]['ip']} {CELLS[mac_addr]['type']} changed to {CELLS[mac_addr]['fault']}\\\") \\n except:\\n LOGGER.warning('unable to get lan status from modem')\",\n \"def _find_devices_mac(self):\\n self.keyboards.append(Keyboard(self))\\n self.mice.append(MightyMouse(self))\\n self.mice.append(Mouse(self))\",\n \"def get_arp_table():\\n IP = ''\\n login = ''\\n password = ''\\n telnet = pexpect.spawn('telnet {}'.format(IP), timeout=30)\\n telnet.expect('Username:')\\n telnet.sendline(login)\\n telnet.expect('Password:')\\n telnet.sendline(password)\\n telnet.expect('#')\\n telnet.sendline('terminal length 0')\\n telnet.expect('#')\\n telnet.sendline('show arp')\\n telnet.expect('#')\\n arp_table = telnet.before.decode('utf-8')\\n telnet.close()\\n return arp_table\",\n \"def get_adapter_mac(cls, client_object):\\n parsed_data = DefaultCRUDImpl.get_adapter_info(\\n client_object)\\n for record in parsed_data['table']:\\n if record['dev'] == client_object.name:\\n return record['mac']\\n pylogger.warning('Did not find a MAC address for adapter %r on %r' %\\n (client_object.name, client_object.ip))\",\n \"def mac_address(self):\\n mac = [\\n self.random.randint(0x00, 0xff),\\n self.random.randint(0x00, 0xff),\\n self.random.randint(0x00, 0xff),\\n self.random.randint(0x00, 0xff),\\n self.random.randint(0x00, 0xff),\\n self.random.randint(0x00, 0xff)\\n ]\\n return ':'.join(map(lambda x: f\\\"{x:02X}\\\", mac))\",\n \"def _parse_ip_table_arp(self, arp_output):\\n arp_regex = re.compile(r\\\".*?\\\\((.*?)\\\\) at (.*?)\\\\s+\\\")\\n return self._parse_mac_addr_table(arp_output, arp_regex)\",\n \"def get_mac(self) -> str:\\n self.sendline(\\\"iw {} info\\\".format(self.iface_dut))\\n # We are looking for MAC definition of STA\\n # wdev 0x1\\n # addr 96:4e:c9:cc:7a:2c\\n # type managed\\n self.expect(\\\"addr (?P<mac>..:..:..:..:..:..)\\\\r\\\\n\\\\t(type|ssid)\\\")\\n return self.match.group('mac')\",\n \"def configure_host_ips(h3, h4, ip_address_hs):\\n\\n h3.libs.ip.flush_ip('eth1')\\n h3.libs.ip.interface('eth1', up=False)\\n\\n h4.libs.ip.flush_ip('eth1')\\n h4.libs.ip.interface('eth1', up=False)\\n\\n h3.libs.ip.interface(portlbl='eth1', addr=\\\"{}/{}\\\".format(\\n ip_address_hs[0], MASK), up=True)\\n h4.libs.ip.interface(portlbl='eth1', addr=\\\"{}/{}\\\".format(\\n ip_address_hs[1], MASK), up=True)\",\n \"def fetch():\\n\\t\\n\\t_interfaces = [Interface(iface) for iface in netifaces.interfaces()]\\n\\t\\n\\tfor iface in _interfaces: \\n\\t\\tif (iface.id in BLACK_ID) or (iface.mac in BLACK_MAC) or (len(iface.mac) < 5):\\n\\t\\t\\t_interfaces.remove(iface)\\n\\t\\t\\t\\n\\treturn _interfaces\",\n \"def extract_client_macs(self):\\n\\t\\tmacs_clients = []\\n\\n\\t\\t# data: client -> AP or client <- AP\\n\\t\\tif self.type == DATA_TYPE:\\n\\t\\t\\tif self.from_ds == 1 and self.to_ds == 0:\\n\\t\\t\\t\\tmacs_clients.append(self.upper_layer.dst)\\n\\t\\t\\telif self.from_ds == 0 and self.to_ds == 1:\\n\\t\\t\\t\\tmacs_clients.append(self.upper_layer.src)\\n\\n\\t\\treturn [addr for addr in macs_clients if not utils.is_special_mac(addr)]\",\n \"def get_hosts(network):\\n\\n nm = nmap.PortScanner()\\n nm.scan(network, arguments=\\\"-sn\\\")\\n\\n for host in nm.all_hosts():\\n details = nm[host]\\n\\n if \\\"ipv4\\\" not in details[\\\"addresses\\\"]:\\n continue\\n\\n vendor = details.get(\\\"vendor\\\")\\n vendor_name = None\\n\\n if vendor and len(vendor) > 0:\\n vendor_name = list(vendor.values())[0]\\n\\n yield {\\n \\\"hostname\\\": details.get(\\\"hostname\\\"),\\n \\\"ip\\\": details[\\\"addresses\\\"][\\\"ipv4\\\"],\\n \\\"vendor\\\": vendor_name\\n }\",\n \"def all_machines():\\n return sorted(MACHINES, key=str)\",\n \"def getAllMachines():\\n\\n validHosts = (subprocess.getoutput(\\\"qconf -shgrpl\\\").split())\\n machineList = []\\n processedHGList = []\\n readNodes = False\\n for host in validHosts:\\n hostMachineList = ((subprocess.getoutput(\\\"qconf -shgrp_tree \\\" + str(host))).split())\\n for element in hostMachineList:\\n if '@' not in element: # If it is not a HG name\\n if element not in machineList:\\n machineList.append(element) # Searching whole list for this node\\n machineList.sort()\\n return machineList\",\n \"def parse_nmap_host(host, hostmap):\\n\\n # Get the timestamp.\\n timestamp = host.get(\\\"endtime\\\")\\n if timestamp:\\n timestamp = long(timestamp)\\n if not timestamp:\\n timestamp = host.get(\\\"starttime\\\")\\n if timestamp:\\n timestamp = long(timestamp)\\n\\n # Get all the IP addresses. Skip the MAC addresses.\\n ip_addresses = []\\n for node in host.findall(\\\".//address\\\"):\\n if node.get(\\\"addrtype\\\", \\\"\\\") not in (\\\"ipv4, ipv6\\\"):\\n continue\\n address = node.get(\\\"addr\\\")\\n if not address:\\n continue\\n if address not in hostmap:\\n hostmap[address] = IP(address)\\n ip_addresses.append( hostmap[address] )\\n\\n # Link all the IP addresses to each other.\\n ips_visited = set()\\n for ip_1 in ip_addresses:\\n if ip_1.address not in ips_visited:\\n ips_visited.add(ip_1.address)\\n for ip_2 in ip_addresses:\\n if ip_2.address not in ips_visited:\\n ips_visited.add(ip_2.address)\\n ip_1.add_resource(ip_2)\\n ips_visited.clear()\\n\\n # Get all the hostnames.\\n domain_names = []\\n for node in host.findall(\\\".//hostname\\\"):\\n hostname = node.get(\\\"name\\\")\\n if not hostname:\\n continue\\n if hostname not in hostmap:\\n hostmap[hostname] = Domain(hostname)\\n domain_names.append( hostmap[hostname] )\\n\\n # Link all domain names to all IP addresses.\\n for name in domain_names:\\n for ip in ip_addresses:\\n name.add_resource(ip)\\n\\n # Abort if no resources were found.\\n if not ip_addresses and not domain_names:\\n return []\\n\\n # Get the portscan results.\\n ports = set()\\n for node in host.findall(\\\".//port\\\"):\\n try:\\n portid = node.get(\\\"portid\\\")\\n protocol = node.get(\\\"protocol\\\")\\n if protocol not in (\\\"tcp\\\", \\\"udp\\\"):\\n continue\\n try:\\n port = int(portid)\\n except Exception:\\n port = getservbyname(portid)\\n state = node.find(\\\"state\\\").get(\\\"state\\\")\\n if state not in (\\\"open\\\", \\\"closed\\\", \\\"filtered\\\"):\\n continue\\n ports.add( (state, protocol, port) )\\n except Exception:\\n warn(\\\"Error parsing portscan results: %s\\\" % format_exc(),\\n RuntimeWarning)\\n\\n # Get the traceroute results.\\n traces = []\\n for node in host.findall(\\\".//trace\\\"):\\n try:\\n port = int( node.get(\\\"port\\\") )\\n proto = node.get(\\\"proto\\\")\\n hops = {}\\n broken = False\\n for node in node.findall(\\\".//hop\\\"):\\n try:\\n ttl = int( node.get(\\\"ttl\\\") )\\n address = node.get(\\\"ipaddr\\\")\\n rtt = float( node.get(\\\"rtt\\\") )\\n hostname = node.get(\\\"host\\\", None)\\n hops[ttl] = Hop(address, rtt, hostname)\\n except Exception:\\n warn(\\\"Error parsing traceroute results: %s\\\" %\\n format_exc(), RuntimeWarning)\\n broken = True\\n break\\n if not broken:\\n if hops:\\n ttl = hops.keys()\\n sane_hops = tuple(\\n hops.get(i, None)\\n for i in xrange(min(*ttl), max(*ttl) + 1)\\n )\\n else:\\n sane_hops = ()\\n traces.append( (port, proto, sane_hops) )\\n except Exception:\\n warn(\\\"Error parsing traceroute results: %s\\\" %\\n format_exc(), RuntimeWarning)\\n\\n # This is where we'll gather all the results.\\n results = ip_addresses + domain_names\\n\\n # Link the portscan results to the IP addresses.\\n for ip in ip_addresses:\\n portscan = Portscan(ip, ports, timestamp)\\n results.append(portscan)\\n\\n # Link the traceroute results to the IP addresses.\\n for ip in ip_addresses:\\n if ip.version == 4:\\n for trace in traces:\\n traceroute = Traceroute(ip, *trace)\\n results.append(traceroute)\\n\\n # Return the results.\\n return results\",\n \"def get_devices(mac=None):\\n wemo_devices = discover_wemo()\\n\\n if mac:\\n dev = get_device(mac, wemo_devices)\\n if not dev:\\n return []\\n return [dev]\\n\\n return wemo_devices\",\n \"def all_hosts(self):\\n ...\",\n \"def network_interfaces():\\n try:\\n command = which('ipadm')\\n args = ('show-addr', '-p', '-o', 'STATE,ADDR')\\n pattern = r'ok:(\\\\d+\\\\.\\\\d+\\\\.\\\\d+\\\\.\\\\d+)'\\n except CommandMissing:\\n # Fall back to old command on old solaris releases.\\n command = which('/usr/sbin/ifconfig')\\n args = ('-a')\\n pattern = r'inet (\\\\d+\\\\.\\\\d+\\\\.\\\\d+\\\\.\\\\d+)'\\n addrs = []\\n output = sh(command, *args)\\n for line in output:\\n match = re.match(pattern, line)\\n if match:\\n addr = match.group(1)\\n if not addr.startswith(\\\"127.\\\"):\\n addrs.append(addr)\\n return addrs\",\n \"def get_host_mapping(cohesity_client):\\n hosts = cohesity_client.network.list_hosts() or []\\n for host in hosts:\\n exported_res_dict[\\\"Hosts Mapping\\\"].append(host.ip)\\n return hosts\",\n \"def get_info_hosts():\\n print(\\\"\\\\nMapeando...\\\")\\n host_ip = socket.gethostbyname(socket.gethostname()).split('.')\\n base_ip = \\\".\\\".join(host_ip[0:3]) + '.'\\n host_validos = []\\n return_codes = dict()\\n for i in range(1, 255):\\n return_codes[base_ip + str(i)] = retorna_codigo_ping(base_ip + str(i))\\n if i %20 == 0:\\n print(\\\".\\\", end = \\\"\\\")\\n if return_codes[base_ip + str(i)] == 0:\\n host_validos.append(base_ip + str(i))\\n print(\\\"\\\\nMapeamento completo, informações sobre portas enviadas...\\\")\\n \\n return host_validos\",\n \"def __get_mac_address(self):\\n str_hex_mac = uuid.UUID(int=uuid.getnode()).hex[-12:]\\n return str_hex_mac\",\n \"def enumerate_network(arg):\\n\\n network = ip_network(arg, strict=False)\\n data = list(map(str, network.hosts()))\\n data.insert(0, str(network.network_address))\\n if network.prefixlen != network.max_prefixlen:\\n data.append(str(network.broadcast_address))\\n return data\",\n \"def get_os_service_scan_details(self):\\n self._scanned = True\\n result = self._scanner.scan(self._ips, self._ports, arguments='-A')\\n hosts = []\\n if self._scanned:\\n # for each host scanned\\n for host in self._scanner.all_hosts():\\n print(\\\"-----------------\\\")\\n print(result['scan'][host])\\n print(\\\"-----------------\\\")\\n state = result['scan'][host][\\\"status\\\"][\\\"state\\\"]\\n mac = self.get_mac_address(result, host)\\n vendor = self.get_vendor(result, host, mac)\\n val_arr = self.get_os_details(result, host)\\n name = val_arr[0]\\n os_gen = val_arr[1]\\n os_family = val_arr[2]\\n hosts.append(Host(host, state, name, os_family, os_gen, vendor, mac))\\n\\n return hosts\",\n \"def get_ip_address(host):\\n for h in host:\\n ip = h.address['addr']\\n return ip\",\n \"def select_wireless_endpoint_addresses(self):\\n device_info = self.wireless_endpoint.DeviceInfoGet()\\n network_info = device_info.NetworkInfoGet()\\n\\n for network_interface in network_info.InterfaceGet():\\n interface_name = network_interface.NameGet()\\n if interface_name == self.wireless_endpoint_traffic_interface:\\n return [address.split('/')[0]\\n for address in network_interface.IPv6GlobalGet()]\\n\\n return []\",\n \"def host_ip(host):\\n return host.cmd('ip addr show {}-eth1 | awk \\\\'/inet / {{ print $2 }}\\\\' | cut -d\\\\'/\\\\' -f1'.format(host.name, host.name), stdout=sp.PIPE).strip()\",\n \"def arp_scan(interface: str, ip_range: str) -> List[str]:\\n ans, unans = srp(Ether(dst='ff:ff:ff:ff:ff:ff') / ARP(pdst=ip_range), iface=interface, timeout=2, verbose=False)\\n\\n ip_addresses = []\\n for req, res in ans:\\n ip_addresses.append(req[ARP].pdst)\\n\\n return ip_addresses\",\n \"def sx_router_neigh_get_mac(handle, rif, addr): \\n try:\\n neigh_entry_cnt_p = new_uint32_t_p()\\n neigh_entry_list_p = new_sx_neigh_get_entry_t_arr(1)\\n\\n filter_p = new_sx_neigh_filter_t_p()\\n neigh_filter = sx_neigh_filter_t()\\n neigh_filter.filter_by_rif = SX_KEY_FILTER_FIELD_NOT_VALID\\n neigh_filter.rif = 0\\n sx_neigh_filter_t_p_assign(filter_p, neigh_filter)\\n \\n rc = sx_api_router_neigh_get(handle, SX_ACCESS_CMD_GET, rif, addr, filter_p, neigh_entry_list_p, neigh_entry_cnt_p)\\n if rc == SX_STATUS_ENTRY_NOT_FOUND:\\n return None \\n sx_check_rc(rc)\\n\\n neighbor_entry = sx_neigh_get_entry_t_arr_getitem(neigh_entry_list_p, 0)\\n \\n return neighbor_entry.neigh_data.mac_addr.to_str()\\n \\n finally:\\n delete_sx_neigh_filter_t_p(filter_p)\\n delete_sx_neigh_get_entry_t_arr(neigh_entry_list_p) \\n delete_uint32_t_p(neigh_entry_cnt_p)\",\n \"def all_interfaces():\\n max_possible = 128 # arbitrary. raise if needed.\\n number_of_bytes = max_possible * 32\\n s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\\n names = array.array('B', '\\\\0' * number_of_bytes)\\n outbytes = struct.unpack('iL', fcntl.ioctl(\\n s.fileno(),\\n 0x8912, # SIOCGIFCONF\\n struct.pack('iL', number_of_bytes, names.buffer_info()[0])\\n ))[0]\\n namestr = names.tostring()\\n interfaces = {}\\n\\n for i in range(0, outbytes, 40):\\n name = namestr[i:i+16].split('\\\\0', 1)[0]\\n ip = namestr[i+20:i+24]\\n interfaces[name] = format_ip(ip)\\n return interfaces\",\n \"def _get_server_hardware_mac_from_ilo(server_hardware):\\n try:\\n client = get_ilorest_client(server_hardware)\\n ilo_path = \\\"/rest/v1/systems/1\\\"\\n hardware = jsonutils.loads(client.get(ilo_path).text)\\n hardware_mac = hardware['HostCorrelation']['HostMACAddress'][0]\\n except redfish.JsonDecodingError as exc:\\n LOG.error(\\\"Failed in JSON object getting path: %s\\\", ilo_path)\\n raise exception.OneViewError(error=exc)\\n except (ValueError, TypeError, IndexError) as exc:\\n LOG.error(\\n \\\"Failed to get mac from server hardware %(server_hardware)s \\\"\\n \\\"via iLO. Error: %(message)s\\\", {\\n \\\"server_hardware\\\": server_hardware.get(\\\"uri\\\"),\\n \\\"message\\\": exc\\n }\\n )\\n raise exception.OneViewError(error=exc)\\n\\n return hardware_mac\",\n \"def fact():\\n result = []\\n\\n interfaces = [SCNetworkInterfaceGetBSDName(i) for i in SCNetworkInterfaceCopyAll()]\\n\\n for i in interfaces:\\n try:\\n active = subprocess.check_output(\\n [\\\"/usr/sbin/ipconfig\\\", \\\"getifaddr\\\", i]\\n ).strip()\\n if active:\\n result.append(i)\\n except subprocess.CalledProcessError:\\n continue\\n\\n return {factoid: result}\",\n \"def intGet(): \\n macchanger, ip, iwconfig = pathGet()\\n interfaces = []\\n a = str(subprocess.check_output(\\\"{} link show\\\".format(ip), shell=True))\\n ints = a.split(': ')\\n for i in range(len(ints)):\\n if len(ints[i].split()) == 1:\\n if ints[i] not in [\\\"1\\\", \\\"lo\\\", \\\"b'1\\\"]:\\n interface = {'name':str(ints[i])}\\n interfaces.append(interface)\\n # Get interface properties\\n for interface in interfaces:\\n name = interface['name']\\n macs = subprocess.check_output(\\\"{} -s {}\\\".format(macchanger, name), shell=True).decode(\\\"utf-8\\\")\\n interface['cMac'] = macs.split()[2]\\n interface['cVend'] = macs.split(\\\"(\\\")[1].split(\\\")\\\")[0]\\n interface['pMac'] = macs.split(\\\"\\\\n\\\")[1].split()[2]\\n interface['pVend'] = macs.split(\\\"\\\\n\\\")[1].split(\\\"(\\\")[1].split(\\\")\\\")[0]\\n try:\\n mon = subprocess.check_output(\\\"{} {} 2> /dev/null\\\".format(iwconfig, name), shell=True).split()\\n mon1 = mon[3].decode(\\\"utf-8\\\").split(':')[1]\\n if mon1 == 'off/any':\\n mon1 = mon[4].decode(\\\"utf-8\\\").split(':')[1]\\n interface['mon'] = mon1\\n except:\\n interface['mon'] = 'Wired'\\n return(interfaces)\",\n \"def hosts_cmd(args):\\n r = requete(\\\"Hosts.Host:get\\\")\\n if not r:\\n return\\n if len(args) > 0:\\n for i in range(0, len(args)):\\n for _, host in r['status'].items():\\n if (host['MACAddress'].lower() == args[i].lower()\\n or host['HostName'].lower() == args[i].lower()\\n or host['IPAddress'] == args[i]):\\n # pprint.pprint(host)\\n json.dump(host, sys.stdout, indent=4)\\n else:\\n #pprint.pprint(r['status'])\\n for _, host in r['status'].items():\\n actif = \\\" \\\" if host['Active'] else \\\"*\\\"\\n if mac_parser is None:\\n s = \\\"%-18s %-15s %c %-35s %s\\\" % (host['MACAddress'], host['InterfaceType'], actif, host['HostName'], host['IPAddress'])\\n else:\\n s = \\\"%-18s %-12s %-15s %c %-35s %s\\\" % (host['MACAddress'], mac_parser.get_manuf(host['MACAddress']), host.get('InterfaceType', \\\"\\\"), actif, host['HostName'], host['IPAddress'])\\n print(s)\",\n \"def get_broadcast_addresses():\\n addr_list = []\\n if HAS_NETIFACES:\\n for iface in netifaces.interfaces():\\n addresses = netifaces.ifaddresses(iface).get(netifaces.AF_INET)\\n if addresses is None:\\n continue\\n for address in addresses:\\n broadcast_addr = address.get(\\\"broadcast\\\")\\n if broadcast_addr is None:\\n continue\\n addr_list.append(broadcast_addr)\\n return [\\\"127.0.0.1\\\", \\\"255.255.255.255\\\", \\\"<broadcast>\\\"] + addr_list\",\n \"def get_mac_address(self):\\n\\t\\treturn call_sdk_function('PrlSrvCfgNet_GetMacAddress', self.handle)\",\n \"def mac_address(self):\\n if self._mac_address is None:\\n expression = expressions.WPA_MAC\\n name = expressions.MAC_ADDRESS_NAME\\n command = self.status_command\\n self._mac_address = self._match(expression,\\n name,\\n command)\\n return self._mac_address\",\n \"def net_if_addrs():\\n ret = []\\n for items in cext.net_if_addrs():\\n items = list(items)\\n items[0] = py2_strencode(items[0])\\n ret.append(items)\\n return ret\",\n \"def hh_assign_ports():\\n print('******* Assigning ports')\\n for device in ci_addrs.switches_hh1:\\n print ('******* Connecting to ', device.get('ip'))\\n net_connect = ConnectHandler(**device)\\n output_vlan_cp = net_connect.send_config_set(cp_hh1_config_commands)\\n print (output_vlan_cp)\\n output_vlan_cloud = net_connect.send_config_set(cloud_hh1_config_commands)\\n print (output_vlan_cloud)\\n output_vlan_f5 = net_connect.send_config_set(f5_hh_config_commands)\\n print (output_vlan_f5)\\n net_connect.send_config_set('wr')\\n\\n for device in ci_addrs.switches_hh2:\\n print ('******* Connecting to ', device.get('ip'))\\n net_connect = ConnectHandler(**device)\\n output_vlan_cp = net_connect.send_config_set(cp_hh2_config_commands)\\n print (output_vlan_cp)\\n output_vlan_cloud = net_connect.send_config_set(cloud_hh2_config_commands)\\n print (output_vlan_cloud)\\n output_vlan_f5 = net_connect.send_config_set(f5_hh_config_commands)\\n print (output_vlan_f5)\\n net_connect.send_config_set('wr')\",\n \"def _detect_ap_dynamic_addresses(self, ap_mac_list):\\n lease_data = self.components['LinuxServer'].get_dhcp_leases()\\n map = {}\\n ip = mac = start_time = None\\n for line in lease_data:\\n l = line.split()\\n if l[0] == \\\"lease\\\":\\n ip = l[1]\\n\\n elif l[0] == \\\"hardware\\\":\\n mac = l[2].strip(\\\";\\\")\\n\\n elif l[0] == \\\"starts\\\":\\n start_time_str = \\\" \\\".join(l[2:]).strip(\\\";\\\")\\n start_time = time.mktime(time.strptime(start_time_str, \\\"%Y/%m/%d %H:%M:%S\\\"))\\n\\n if ip and mac and start_time:\\n if map.has_key(mac):\\n if map[mac][0] < start_time:\\n map[mac] = (start_time, ip)\\n\\n else:\\n map[mac] = (start_time, ip)\\n\\n ip = mac = start_time = None\\n\\n for ap_mac in ap_mac_list:\\n if map.has_key(ap_mac.lower()):\\n self.mac_to_ip[ap_mac.lower()] = map[ap_mac.lower()][1]\\n\\n else:\\n raise Exception(\\\"IP entry of the MAC %s was not found in the \\\\\\n dhcpd.leases file\\\" % ap_mac.lower())\",\n \"def _generate_mac(topology_id):\\n tid = int(topology_id)\\n global mac_counter\\n global used_macs\\n base = '52:54:00:00:00:00'\\n ba = base.split(':')\\n ba[2] = '%02x' % int(tid / 256)\\n ba[3] = '%02x' % int(tid % 256)\\n ba[4] = '%02x' % int(len(used_macs[topology_id]) / 256)\\n ba[5] = '%02x' % int(mac_counter)\\n\\n mac_counter += 1\\n\\n mac_counter = mac_counter % 256\\n return ':'.join(ba)\",\n \"def totalPacketIpMAC(host_id):\\n try:\\n global sqlalche_obj\\n sqlalche_obj.sql_alchemy_db_connection_open()\\n odu100_mac_result = sqlalche_obj.session.query(Odu100MacFilterTable.filterMacAddress).filter(\\n and_(Odu100MacFilterTable.host_id == host_id, Odu100MacFilterTable.filterMacAddress != \\\"\\\")).all()\\n odu100_ip_result = sqlalche_obj.session.query(Odu100IpFilterTable.ipFilterIpAddress).filter(\\n and_(Odu100IpFilterTable.host_id == host_id, Odu100IpFilterTable.ipFilterIpAddress != \\\"\\\")).all()\\n return {\\\"success\\\": 0, \\\"ip_result\\\": odu100_ip_result, \\\"mac_result\\\": odu100_mac_result}\\n\\n except ProgrammingError as e:\\n return {\\\"success\\\": 1, \\\"result\\\": \\\"Some Programming Error Occurs\\\", \\\"detail\\\": \\\"\\\"}\\n except AttributeError as e:\\n return {\\\"success\\\": 1, \\\"result\\\": \\\"Some Attribute Error Occurs\\\", \\\"detail\\\": \\\"\\\"}\\n except OperationalError as e:\\n return {\\\"success\\\": 1, \\\"result\\\": \\\"Some Operational Error Occurs\\\", \\\"detail\\\": \\\"\\\"}\\n except TimeoutError as e:\\n return {\\\"success\\\": 1, \\\"result\\\": \\\"Timeout Error Occurs\\\", \\\"detail\\\": \\\"\\\"}\\n except NameError as e:\\n return {\\\"success\\\": 1, \\\"result\\\": \\\"Some Name Error Occurs\\\", \\\"detail\\\": \\\"\\\"}\\n except UnboundExecutionError as e:\\n return {\\\"success\\\": 1, \\\"result\\\": \\\"Unbound Execution Error Occurs\\\", \\\"detail\\\": \\\"\\\"}\\n except DatabaseError as e:\\n return {\\\"success\\\": 1, \\\"result\\\": \\\"Database Error Occurs,Contact Your Administrator\\\", \\\"detail\\\": \\\"\\\"}\\n except DisconnectionError as e:\\n return {\\\"success\\\": 1, \\\"result\\\": \\\"Database Disconnected\\\", \\\"detail\\\": \\\"\\\"}\\n except NoResultFound as e:\\n return {\\\"success\\\": 1, \\\"result\\\": \\\"No result Found For this opeartion\\\", \\\"detail\\\": \\\"\\\"}\\n except UnmappedInstanceError as e:\\n return {\\\"success\\\": 1, \\\"result\\\": \\\"Some Unmapped instance error\\\", \\\"detail\\\": \\\"\\\"}\\n except NoReferenceError as e:\\n return {\\\"success\\\": 1, \\\"result\\\": \\\"No reference Exists\\\", \\\"detail\\\": \\\"\\\"}\\n except SAWarning as e:\\n return {\\\"success\\\": 1, \\\"result\\\": \\\"Warning Occurs\\\", \\\"detail\\\": \\\"\\\"}\\n except Exception as e:\\n return {\\\"success\\\": 1, \\\"result\\\": \\\"Operation Failed,contact Administrator\\\", \\\"detail\\\": \\\"\\\"}\\n finally:\\n sqlalche_obj.sql_alchemy_db_connection_close()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7146105","0.67204034","0.6417322","0.6113413","0.60379297","0.6028078","0.6010826","0.5976122","0.5964501","0.5955474","0.5925722","0.5905432","0.5898975","0.58903706","0.58572847","0.5804898","0.5784774","0.5769922","0.57560873","0.575395","0.57504755","0.5730582","0.56988835","0.56964844","0.56533927","0.56429094","0.5636537","0.5615029","0.5610227","0.55961734","0.5587016","0.55870074","0.55649555","0.553885","0.5491608","0.5448351","0.54388094","0.54347354","0.54311347","0.54307723","0.5417551","0.54122853","0.53917813","0.5389334","0.5388286","0.5387401","0.5378831","0.5376945","0.5346988","0.5339672","0.5339547","0.5337496","0.53310406","0.53306997","0.5308635","0.5288402","0.5282697","0.52787167","0.5277865","0.52768064","0.5263912","0.52533484","0.5245852","0.524247","0.5240517","0.5231619","0.520669","0.5201663","0.52003795","0.5187751","0.5187136","0.5163295","0.5162993","0.5156177","0.51522523","0.5151847","0.5139687","0.5137212","0.51292086","0.51205057","0.512045","0.51172537","0.5111664","0.5094178","0.50781","0.50766486","0.5074448","0.5070426","0.50665987","0.5062745","0.5061905","0.505631","0.50380695","0.5033698","0.5030978","0.50300187","0.5029329","0.50291026","0.5026912","0.502566"],"string":"[\n \"0.7146105\",\n \"0.67204034\",\n \"0.6417322\",\n \"0.6113413\",\n \"0.60379297\",\n \"0.6028078\",\n \"0.6010826\",\n \"0.5976122\",\n \"0.5964501\",\n \"0.5955474\",\n \"0.5925722\",\n \"0.5905432\",\n \"0.5898975\",\n \"0.58903706\",\n \"0.58572847\",\n \"0.5804898\",\n \"0.5784774\",\n \"0.5769922\",\n \"0.57560873\",\n \"0.575395\",\n \"0.57504755\",\n \"0.5730582\",\n \"0.56988835\",\n \"0.56964844\",\n \"0.56533927\",\n \"0.56429094\",\n \"0.5636537\",\n \"0.5615029\",\n \"0.5610227\",\n \"0.55961734\",\n \"0.5587016\",\n \"0.55870074\",\n \"0.55649555\",\n \"0.553885\",\n \"0.5491608\",\n \"0.5448351\",\n \"0.54388094\",\n \"0.54347354\",\n \"0.54311347\",\n \"0.54307723\",\n \"0.5417551\",\n \"0.54122853\",\n \"0.53917813\",\n \"0.5389334\",\n \"0.5388286\",\n \"0.5387401\",\n \"0.5378831\",\n \"0.5376945\",\n \"0.5346988\",\n \"0.5339672\",\n \"0.5339547\",\n \"0.5337496\",\n \"0.53310406\",\n \"0.53306997\",\n \"0.5308635\",\n \"0.5288402\",\n \"0.5282697\",\n \"0.52787167\",\n \"0.5277865\",\n \"0.52768064\",\n \"0.5263912\",\n \"0.52533484\",\n \"0.5245852\",\n \"0.524247\",\n \"0.5240517\",\n \"0.5231619\",\n \"0.520669\",\n \"0.5201663\",\n \"0.52003795\",\n \"0.5187751\",\n \"0.5187136\",\n \"0.5163295\",\n \"0.5162993\",\n \"0.5156177\",\n \"0.51522523\",\n \"0.5151847\",\n \"0.5139687\",\n \"0.5137212\",\n \"0.51292086\",\n \"0.51205057\",\n \"0.512045\",\n \"0.51172537\",\n \"0.5111664\",\n \"0.5094178\",\n \"0.50781\",\n \"0.50766486\",\n \"0.5074448\",\n \"0.5070426\",\n \"0.50665987\",\n \"0.5062745\",\n \"0.5061905\",\n \"0.505631\",\n \"0.50380695\",\n \"0.5033698\",\n \"0.5030978\",\n \"0.50300187\",\n \"0.5029329\",\n \"0.50291026\",\n \"0.5026912\",\n \"0.502566\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.70925903"},"document_rank":{"kind":"string","value":"1"}}}],"truncated":false,"partial":true},"paginationData":{"pageIndex":925,"numItemsPerPage":100,"numTotalItems":95772,"offset":92500,"length":100}},"jwt":"eyJhbGciOiJFZERTQSIsImtpZCI6IjVHZDBvd0g5MTM2eDZjc1FvbE1zcktNWUZoRVFoUm5PVVVybEpjOUhaUEEifQ.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc4NTU3NTY2MiwianRpIjoiZDI3N2E1ZmYtOTAzMS00YmYxLWE5ZjAtOWQ2N2M2OTBhOTkyIiwic3ViIjoiL2RhdGFzZXRzL25vbWljLWFpL2Nvcm5zdGFjay1weXRob24tdjEiLCJleHAiOjE3ODU1NzkyNjIsImlzcyI6Imh0dHBzOi8vaHVnZ2luZ2ZhY2UuY28ifQ.iG0ckr1A0piEYpzXba3pRmAvlmFvAJCMtcGlJhhWDEx9omRkhkq-Ov0-hNzL1TJrscvTW3mA99pBXs1Q1ndiAw","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 |
|---|---|---|---|---|---|---|
return a list of news sources that are close to the provided (lat, long) values | def suggestSourcesAtCoordinate(self, latitude: Union[int, float], longitude: Union[int, float], radiusKm: Union[int, float], count: int = 20, **kwargs):
assert isinstance(latitude, (int, float)), "The 'latitude' should be a number"
assert isinstance(longitude, (int, float)), "The 'longitude' should be a... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def closest_stations(lat: float, lon: float, limit: int = 1) -> List[Dict]:\n dist_sorted = sorted(\n STATIONS, key=lambda s: distance((lat, lon), (s[\"lat\"], s[\"lon\"]))\n )\n return dist_sorted[:limit]",
"def findStations(self, coords=[], offset=[], minThreshold=10, maxThreshold=100):\n\n ... | [
"0.67547834",
"0.63278764",
"0.6324769",
"0.61340594",
"0.6128573",
"0.60959363",
"0.6066078",
"0.6008798",
"0.5947017",
"0.5842012",
"0.58200216",
"0.5802729",
"0.5781583",
"0.57792777",
"0.573028",
"0.5714511",
"0.5701796",
"0.5698442",
"0.5684616",
"0.5659369",
"0.5651931"... | 0.5848714 | 9 |
return a list of news sources that are close to the provided (lat, long) values | def suggestSourcesAtPlace(self, conceptUri: str, dataType: Union[str, List[str]] = "news", page = 1, count = 20, **kwargs):
params = {"action": "getSourcesAtPlace", "conceptUri": conceptUri, "page": page, "count": count, "dataType": dataType}
params.update(kwargs)
return self.jsonRequest("/api/v... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def closest_stations(lat: float, lon: float, limit: int = 1) -> List[Dict]:\n dist_sorted = sorted(\n STATIONS, key=lambda s: distance((lat, lon), (s[\"lat\"], s[\"lon\"]))\n )\n return dist_sorted[:limit]",
"def findStations(self, coords=[], offset=[], minThreshold=10, maxThreshold=100):\n\n ... | [
"0.67547834",
"0.63278764",
"0.6324769",
"0.61340594",
"0.6128573",
"0.60959363",
"0.6066078",
"0.6008798",
"0.5947017",
"0.5848714",
"0.5842012",
"0.58200216",
"0.5802729",
"0.5781583",
"0.57792777",
"0.573028",
"0.5714511",
"0.5701796",
"0.5698442",
"0.5684616",
"0.5659369"... | 0.0 | -1 |
return a list of news sources that match the prefix | def suggestAuthors(self, prefix: str, page: int = 1, count: int = 20, **kwargs):
assert page > 0, "page parameter should be above 0"
params = {"prefix": prefix, "page": page, "count": count}
params.update(kwargs)
return self.jsonRequest("/api/v1/suggestAuthorsFast", params) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def suggestNewsSources(self, prefix: str, dataType: Union[str, list] = [\"news\", \"pr\", \"blog\"], page: int = 1, count: int = 20, **kwargs):\n assert page > 0, \"page parameter should be above 0\"\n params = {\"prefix\": prefix, \"dataType\": dataType, \"page\": page, \"count\": count}\n pa... | [
"0.6308919",
"0.62707055",
"0.6084322",
"0.6078376",
"0.6037707",
"0.6009768",
"0.5960838",
"0.59466416",
"0.594522",
"0.59372747",
"0.5871036",
"0.579802",
"0.5768899",
"0.5733959",
"0.5702365",
"0.56964064",
"0.5685058",
"0.568116",
"0.5660605",
"0.56461525",
"0.55997753",
... | 0.0 | -1 |
return a list of event types that match the prefix | def suggestEventTypes(self, prefix: str, page: int = 1, count: int = 20, **kwargs):
assert page > 0, "page parameter should be above 0"
params = {"prefix": prefix, "page": page, "count": count}
params.update(kwargs)
return self.jsonRequest("/api/v1/eventType/suggestEventTypes", params) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_all(self):\r\n return list(pecan.request.storage_conn.get_event_types())",
"def get_triggers_by_prefix(uuid: UUID, prefix: str | Tuple) -> Optional[List['Trigger']]:\n scenario = store.get_scenario(uuid)\n if scenario:\n return [trigger for trigger in scenario.trigger_manager.triggers... | [
"0.63858974",
"0.6202084",
"0.5924098",
"0.5862218",
"0.5741972",
"0.566967",
"0.562749",
"0.5544773",
"0.5511223",
"0.5464088",
"0.54471403",
"0.54358304",
"0.54006886",
"0.53771496",
"0.537508",
"0.537111",
"0.5363539",
"0.53595304",
"0.5346293",
"0.5343913",
"0.53282255",
... | 0.64759165 | 0 |
return a list of concept classes that match the given prefix | def suggestConceptClasses(self, prefix: str, lang: str = "eng", conceptLang: str = "eng", source: Union[str, List[str]] = ["dbpedia", "custom"], page: int = 1, count: int = 20, returnInfo: ReturnInfo = ReturnInfo(), **kwargs):
assert page > 0, "page parameter should be above 0"
params = { "prefix": pref... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def words_start_with_prefix(self, prefix: str) -> List[str]:\n if not self.starts_with(prefix):\n return []\n if self.search(prefix):\n return [prefix]\n\n curr = self.root\n for ch in prefix:\n curr = curr.children.get(ch)\n ... | [
"0.63769776",
"0.6048061",
"0.60433567",
"0.587656",
"0.583887",
"0.57681125",
"0.57679045",
"0.5767587",
"0.5692277",
"0.5622705",
"0.5620777",
"0.55592585",
"0.5542315",
"0.5525467",
"0.5497741",
"0.5459923",
"0.5453932",
"0.54335916",
"0.53916717",
"0.53893685",
"0.5383738... | 0.5906344 | 3 |
return a concept uri that is the best match for the given concept label if there are multiple matches for the given conceptLabel, they are sorted based on their frequency of occurence in news (most to least frequent) | def getConceptUri(self, conceptLabel: str, lang: str = "eng", sources: Union[str, List[str]] = ["concepts"]):
matches = self.suggestConcepts(conceptLabel, lang = lang, sources = sources)
if matches != None and isinstance(matches, list) and len(matches) > 0 and "uri" in matches[0]:
return mat... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getCategoryUri(self, categoryLabel: str):\n matches = self.suggestCategories(categoryLabel)\n if matches != None and isinstance(matches, list) and len(matches) > 0 and \"uri\" in matches[0]:\n return matches[0][\"uri\"]\n return None",
"def uri_sort_key(uri):\n if uri.start... | [
"0.5373891",
"0.5326923",
"0.5324317",
"0.5230062",
"0.5156094",
"0.51187223",
"0.49526542",
"0.49000037",
"0.4895589",
"0.48791897",
"0.48177102",
"0.46961698",
"0.4668031",
"0.46295252",
"0.46246976",
"0.46214887",
"0.46188977",
"0.46140313",
"0.4609746",
"0.45985618",
"0.4... | 0.6000219 | 0 |
return a location uri that is the best match for the given location label | def getLocationUri(self, locationLabel: str, lang: str = "eng", sources: Union[str, List[str]] = ["place", "country"], countryUri: Union[str, None] = None, sortByDistanceTo: Union[List, Tuple, None] = None):
matches = self.suggestLocations(locationLabel, sources = sources, lang = lang, countryUri = countryUri, ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_family_location(family_label):\n\n search_dirs = [\"/hps/nobackup/production/xfam/rfam/RELEASES/14.3/miRNA_relabelled/batch1_chunk1_searches\",\n \"/hps/nobackup/production/xfam/rfam/RELEASES/14.3/miRNA_relabelled/batch1_chunk2_searches\",\n \"/hps/nobackup/production... | [
"0.6586528",
"0.62700546",
"0.60183",
"0.5761023",
"0.5684978",
"0.56574464",
"0.5643387",
"0.5624193",
"0.5612762",
"0.5518236",
"0.5503416",
"0.5490034",
"0.54579645",
"0.5414388",
"0.54064244",
"0.53824854",
"0.534134",
"0.53146106",
"0.5304863",
"0.5296975",
"0.5288527",
... | 0.6828578 | 0 |
return a category uri that is the best match for the given label | def getCategoryUri(self, categoryLabel: str):
matches = self.suggestCategories(categoryLabel)
if matches != None and isinstance(matches, list) and len(matches) > 0 and "uri" in matches[0]:
return matches[0]["uri"]
return None | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def parse_category_label(label: str) -> str:\n return number_first_regex.sub(\n '_',\n space_regex.sub(\n '_',\n label.strip().lower().replace('*', '').replace('(', '').replace(\n ')', '').replace('.', '')))",
"def get_label_name(label):\n\tindex = np.argmax(... | [
"0.5820102",
"0.57796377",
"0.57189244",
"0.568857",
"0.56706095",
"0.5670265",
"0.5614227",
"0.55840105",
"0.547457",
"0.544891",
"0.5384078",
"0.5384078",
"0.537984",
"0.537984",
"0.5355162",
"0.53501034",
"0.534399",
"0.5342672",
"0.53198165",
"0.5298704",
"0.5283161",
"... | 0.7599166 | 0 |
return the news source that best matches the source name | def getNewsSourceUri(self, sourceName: str, dataType: Union[str, List[str]] = ["news", "pr", "blog"]):
matches = self.suggestNewsSources(sourceName, dataType = dataType)
if matches != None and isinstance(matches, list) and len(matches) > 0 and "uri" in matches[0]:
return matches[0]["uri"]
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def find_source(self, name):\n t = filter( lambda x: x.name==name, self.point_sources+self.extended_sources)\n return t[0] if len(t)==1 else None",
"def get_source(source_name):\n if source_name == \"SCHOLAR_CENSUS\":\n from mec_data.source.scholar import ScholarSource\n\n return S... | [
"0.69307625",
"0.66296846",
"0.61865705",
"0.6125315",
"0.61133474",
"0.6100435",
"0.60152495",
"0.5923847",
"0.58923537",
"0.5828837",
"0.58286136",
"0.58227843",
"0.5820799",
"0.58204955",
"0.5797983",
"0.5777223",
"0.56680614",
"0.56497854",
"0.564131",
"0.5613789",
"0.552... | 0.6504152 | 2 |
alternative (shorter) name for the method getNewsSourceUri() | def getSourceUri(self, sourceName: str, dataType: Union[str, List[str]] = ["news", "pr", "blog"]):
return self.getNewsSourceUri(sourceName, dataType) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getNewsSourceUri(self, sourceName: str, dataType: Union[str, List[str]] = [\"news\", \"pr\", \"blog\"]):\n matches = self.suggestNewsSources(sourceName, dataType = dataType)\n if matches != None and isinstance(matches, list) and len(matches) > 0 and \"uri\" in matches[0]:\n return matc... | [
"0.76972795",
"0.68226814",
"0.6523007",
"0.6433535",
"0.64236754",
"0.63689995",
"0.6365982",
"0.63083386",
"0.63083386",
"0.6300353",
"0.628872",
"0.6269325",
"0.626673",
"0.6256632",
"0.6249758",
"0.6189944",
"0.61506224",
"0.61473215",
"0.61441106",
"0.606726",
"0.6057817... | 0.7801554 | 0 |
return the URI of the source group that best matches the name | def getSourceGroupUri(self, sourceGroupName: str):
matches = self.suggestSourceGroups(sourceGroupName)
if matches is not None and isinstance(matches, list) and len(matches) > 0 and "uri" in matches[0]:
return matches[0]["uri"]
return None | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getURI(self, *args):\n return _libsbml.GroupsExtension_getURI(self, *args)",
"def _source(self, namespace):\n if not namespace:\n source = self._default_source\n else:\n source = self._sources.get(namespace)\n if not source:\n raise GroupResolverSo... | [
"0.62811434",
"0.6188032",
"0.6106267",
"0.5954231",
"0.58817136",
"0.58752364",
"0.5858591",
"0.580521",
"0.57376444",
"0.56671643",
"0.56523913",
"0.56210285",
"0.55978084",
"0.55902463",
"0.55665797",
"0.5522302",
"0.55100167",
"0.54542893",
"0.54542893",
"0.54542893",
"0.... | 0.7802163 | 0 |
return a uri of the concept class that is the best match for the given label | def getConceptClassUri(self, classLabel: str, lang: str = "eng"):
matches = self.suggestConceptClasses(classLabel, lang = lang)
if matches is not None and isinstance(matches, list) and len(matches) > 0 and "uri" in matches[0]:
return matches[0]["uri"]
return None | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getConceptUri(self, conceptLabel: str, lang: str = \"eng\", sources: Union[str, List[str]] = [\"concepts\"]):\n matches = self.suggestConcepts(conceptLabel, lang = lang, sources = sources)\n if matches != None and isinstance(matches, list) and len(matches) > 0 and \"uri\" in matches[0]:\n ... | [
"0.6339133",
"0.60691535",
"0.6041036",
"0.59156215",
"0.5846136",
"0.575784",
"0.56709063",
"0.5510237",
"0.54722863",
"0.5397338",
"0.5382017",
"0.5376677",
"0.53355074",
"0.53352785",
"0.53339684",
"0.5312528",
"0.5297552",
"0.5296992",
"0.52870166",
"0.5281717",
"0.527981... | 0.74872255 | 0 |
return detailed information about a particular concept | def getConceptInfo(self, conceptUri: str,
returnInfo: ReturnInfo = ReturnInfo(conceptInfo = ConceptInfoFlags(synonyms = True, image = True, description = True))):
params = returnInfo.getParams()
params.update({"uri": conceptUri })
return self.jsonRequest("/api/v1/concept/g... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def concept_details(\n self,\n concept_id: str,\n params: Optional[Dict] = None,\n headers: Optional[Dict] = None,\n ) -> ConceptDetails:\n method = self._get_method(\"concept_details\")\n method = method.format(**{\"id\": concept_id})\n\n return self.call_api_ge... | [
"0.7382096",
"0.6283711",
"0.62482786",
"0.61796254",
"0.5913981",
"0.58909845",
"0.58331335",
"0.5800988",
"0.5798764",
"0.571371",
"0.56614435",
"0.5658186",
"0.5639102",
"0.56273085",
"0.56273085",
"0.5625537",
"0.56052315",
"0.55974036",
"0.5594931",
"0.55901265",
"0.5580... | 0.6814808 | 1 |
return author uri that is the best match for the given author name (and potentially source url) if there are multiple matches for the given author name, they are sorted based on the number of articles they have written (from most to least frequent) | def getAuthorUri(self, authorName: str):
matches = self.suggestAuthors(authorName)
if matches is not None and isinstance(matches, list) and len(matches) > 0 and "uri" in matches[0]:
return matches[0]["uri"]
return None | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_author_name_urls(dept_name, dept_url):\n\t# Change to \"School of Humanities\" to match the name used in Enlighten\n\t# Done because the string obtained from http://www.gla.ac.uk/schools/ contains the Gaelic name as well\n\tif \"Humanities\" in dept_name:\n\t\tdept_name = \"School of Humanities\"\n\n\t# ge... | [
"0.65008885",
"0.6420538",
"0.6307418",
"0.6182964",
"0.5921297",
"0.5848237",
"0.57895356",
"0.57528603",
"0.56621397",
"0.5610987",
"0.556793",
"0.5543805",
"0.55181277",
"0.55117804",
"0.5501808",
"0.5497998",
"0.547425",
"0.54675233",
"0.5461011",
"0.5434933",
"0.5433778"... | 0.6498803 | 1 |
return event type uri that is the best match for the given label | def getEventTypeUri(self, eventTypeLabel: str):
matches = self.suggestEventTypes(eventTypeLabel)
if matches is not None and isinstance(matches, list) and len(matches) > 0 and "uri" in matches[0]:
return matches[0]["uri"]
return None | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getCategoryUri(self, categoryLabel: str):\n matches = self.suggestCategories(categoryLabel)\n if matches != None and isinstance(matches, list) and len(matches) > 0 and \"uri\" in matches[0]:\n return matches[0][\"uri\"]\n return None",
"def get_type_label(type_url):\n retur... | [
"0.5890905",
"0.5888158",
"0.5736647",
"0.57230157",
"0.57189935",
"0.56664103",
"0.5650868",
"0.5550143",
"0.5519739",
"0.54728514",
"0.5453214",
"0.5382518",
"0.53772074",
"0.533073",
"0.5309743",
"0.52296954",
"0.51811314",
"0.5180448",
"0.5166474",
"0.51578933",
"0.514757... | 0.7921001 | 0 |
if you have article urls and you want to query them in ER you first have to obtain their uris in the ER. articleUrls a single article url or a list of article urls returns dict where key is article url and value is either None if no match found or a string with article URI. | def getArticleUris(self, articleUrls: Union[str, List[str]]):
assert isinstance(articleUrls, (six.string_types, list)), "Expected a single article url or a list of urls"
return self.jsonRequest("/api/v1/articleMapper", { "articleUrl": articleUrls }) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_article_dictionary(url):\n articles = {\"title\": get_article_title(url), \"content\": get_article_body(url)}\n return articles",
"async def get_article_links(self):\n urls = []\n for page in range(self._start, self._end+1):\n urls.append(self._searchURL + str(page))\n ... | [
"0.6657474",
"0.6604622",
"0.65535355",
"0.65198207",
"0.6463539",
"0.6353567",
"0.6336571",
"0.61973166",
"0.61275584",
"0.6114767",
"0.6050255",
"0.60251725",
"0.5979369",
"0.58659524",
"0.5813537",
"0.5808789",
"0.57911414",
"0.5790488",
"0.57822967",
"0.5779605",
"0.56730... | 0.68681526 | 0 |
return the list of URIs of all known source groups | def getSourceGroups(self):
ret = self.jsonRequest("/api/v1/sourceGroup/getSourceGroups", {})
return ret | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def sources(self):\n srcs = GroupResolver.sources(self)\n if srcs:\n srcs.remove(self.default_sourcename)\n srcs.insert(0, self.default_sourcename)\n return srcs",
"def source_list(self):\n return list(self._client.group.streams_by_name().keys())",
"def source_... | [
"0.7338798",
"0.6820434",
"0.6810196",
"0.64609927",
"0.6334199",
"0.62872773",
"0.62260437",
"0.6215513",
"0.61615586",
"0.6148272",
"0.614812",
"0.6053038",
"0.6036904",
"0.6008529",
"0.6004802",
"0.59989154",
"0.5949554",
"0.5941072",
"0.5938796",
"0.58946294",
"0.5885352"... | 0.70465386 | 1 |
return info about the source group | def getSourceGroup(self, sourceGroupUri: str):
ret = self.jsonRequest("/api/v1/sourceGroup/getSourceGroupInfo", { "uri": sourceGroupUri })
return ret | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def source_group_id(self):\n return self._source_group_id",
"def getSourceGroups(self):\n ret = self.jsonRequest(\"/api/v1/sourceGroup/getSourceGroups\", {})\n return ret",
"def getGroup(self):\n\t\treturn self.Group",
"def _source(self, namespace):\n if not namespace:\n ... | [
"0.6965876",
"0.63914514",
"0.63097787",
"0.6098065",
"0.6034737",
"0.6034737",
"0.6034737",
"0.6034737",
"0.6034737",
"0.6034737",
"0.5995682",
"0.5991559",
"0.59797585",
"0.5949926",
"0.594139",
"0.59092706",
"0.58857083",
"0.5862467",
"0.5850348",
"0.5844907",
"0.58210206"... | 0.65173215 | 1 |
ensure that queries are not made too fast | def _sleepIfNecessary(self):
t = time.time()
if t - self._lastQueryTime < self._minDelayBetweenRequests:
time.sleep(self._minDelayBetweenRequests - (t - self._lastQueryTime))
self._lastQueryTime = t | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _run_query(self):",
"def query3() :",
"def query(self, query):",
"def _optimise(self):\n pass",
"def make_query(self):",
"def query(self):",
"def test_optimize(self):\n # Same id, data and user_id\n id = data = user_id = get_rand_string()\n self.conn.add(id=id, user_id=u... | [
"0.67880195",
"0.6588136",
"0.6182041",
"0.6181996",
"0.6142477",
"0.6004911",
"0.5807992",
"0.57814604",
"0.5721506",
"0.5706068",
"0.56834257",
"0.565829",
"0.56531525",
"0.5645818",
"0.56294364",
"0.56176543",
"0.5567455",
"0.55609345",
"0.55547845",
"0.5550071",
"0.553724... | 0.0 | -1 |
create instance of article mapper it will map from article urls to article uris the mappings can be remembered so it will not repeat requests for the same article urls | def __init__(self, er: EventRegistry, rememberMappings: bool = True):
self._er = er
self._articleUrlToUri = {}
self._rememberMappings = rememberMappings | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getArticleUris(self, articleUrls: Union[str, List[str]]):\n assert isinstance(articleUrls, (six.string_types, list)), \"Expected a single article url or a list of urls\"\n return self.jsonRequest(\"/api/v1/articleMapper\", { \"articleUrl\": articleUrls })",
"def __init__(self, URL):\n\n ... | [
"0.61131257",
"0.6091058",
"0.6061315",
"0.58105254",
"0.57481873",
"0.5724342",
"0.5658919",
"0.561547",
"0.55640405",
"0.55405253",
"0.54538",
"0.5376726",
"0.53651744",
"0.5354169",
"0.5334251",
"0.53232306",
"0.5276144",
"0.52740747",
"0.52676415",
"0.5246305",
"0.5234518... | 0.5719332 | 6 |
given the article url, return an array with 0, 1 or more article uris. Not all returned article uris are necessarily valid anymore. For news sources of lower importance we remove the duplicated articles and just keep the latest content | def getArticleUri(self, articleUrl: str):
if articleUrl in self._articleUrlToUri:
return self._articleUrlToUri[articleUrl]
res = self._er.getArticleUris(articleUrl)
if res and articleUrl in res:
val = res[articleUrl]
if self._rememberMappings:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_article_URLs(sourceURL):\n sourceURL = sourceURL.encode().decode()\n articleList = []\n soup = bs.BeautifulSoup(urllib.request.urlopen(sourceURL),'lxml')\n #remove any tables\n for table in soup.find_all(\"table\"):\n table.extract()\n #remove any special wiki pages\n for citati... | [
"0.682737",
"0.6749216",
"0.6643437",
"0.6284453",
"0.6279683",
"0.6254851",
"0.624617",
"0.6225951",
"0.62110454",
"0.61618197",
"0.61546147",
"0.6124345",
"0.60510105",
"0.59439665",
"0.5877986",
"0.5868573",
"0.58341867",
"0.5820606",
"0.5815287",
"0.57822466",
"0.57494754... | 0.5514409 | 40 |
Given a 10x BAM entry as a pysam.AlignedSegment, extract the barcode, UMI, and sequence. | def extract_10x(alignment):
barcode = alignment.get_tag('CR')
umi = alignment.get_tag('UR')
# The quality scores for the sequence is returned as an array.array of
# unsigned chars.
sequence = alignment.query_sequence
return ([barcode], [umi], sequence) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def parse_bam():\n global sample_name, header, segmentID, bam\n sys.stderr.write(time.strftime(\"%c\") + \" Busy with parsing bam file...\\n\")\n bam = pysam.AlignmentFile(NanoSV.opts_bam, 'rb')\n if not bam.has_index():\n sys.exit('The bam has no index file')\n header = bam.header\n if 'H... | [
"0.6318754",
"0.5959911",
"0.57797533",
"0.56292635",
"0.5563726",
"0.5562032",
"0.54856086",
"0.53108937",
"0.52643204",
"0.5243333",
"0.5230461",
"0.52059263",
"0.5188892",
"0.51638234",
"0.51475555",
"0.51249605",
"0.5097929",
"0.5083921",
"0.50224",
"0.500398",
"0.4997373... | 0.61430377 | 1 |
Detect what technology was used to generate this BAM. | def detect_technology(self):
logger.warning('Only 10x Genomics BAM files can be detected.')
with pysam.AlignmentFile(self.path, 'rb') as f:
# Check first read of file to see the headers.
for item in f.fetch(until_eof=True):
# This is a 10x BAM
if a... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def filter_technology(self, what):\n return self.form.set_value(\"technology\", what)",
"def supported_features(self):\n return SUPPORT_LGSMARTTV",
"def is_supported(self) -> bool:\n if self.builders and self.app.builder.name not in self.builders:\n return False\n if self... | [
"0.53937185",
"0.53510743",
"0.5293819",
"0.52716935",
"0.5255712",
"0.5241225",
"0.5211665",
"0.5197686",
"0.5197202",
"0.5197202",
"0.518907",
"0.51142985",
"0.5114245",
"0.51077163",
"0.5107098",
"0.5107098",
"0.5100026",
"0.509623",
"0.50713795",
"0.5060234",
"0.50579786"... | 0.6483788 | 0 |
Split the BAM into FASTQs. | def to_fastq(self, prefix='', threads=1):
# Write to uncompressed FASTQ for speed
fastqs = [
f'{prefix}_{i+1}.fastq.gz' if prefix else f'{i+1}.fastq.gz'
for i in range(self.technology.n_files)
]
logger.info(f'Splitting BAM file into FASTQs {", ".join(fastqs)}')
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def split(self, reads_in_first_split=None):\n split_index = 0\n self.out_fns = []\n writer = open(self._out_fn(split_index), 'w')\n self.out_fns.append(self._out_fn(split_index))\n if reads_in_first_split is None:\n reads_in_first_split = self.reads_per_split\n\n ... | [
"0.6038243",
"0.57537574",
"0.5666764",
"0.56272155",
"0.56049055",
"0.55941576",
"0.5548215",
"0.5543489",
"0.5456373",
"0.5375242",
"0.5366417",
"0.53647095",
"0.5272073",
"0.52577937",
"0.518868",
"0.5184102",
"0.5157686",
"0.5124461",
"0.5069689",
"0.5058081",
"0.5036406"... | 0.56357974 | 3 |
Evaluate current holding stocks at Today and update cur stock attribute | def evaluate_cur_stocks(self):
today = datetime.today()
close_val = PRICE_DF.iloc[PRICE_DF.index.get_loc(today, method="ffill")]
close_val = close_val[self.cur_stocks.index]
close_val = pd.DataFrame({"PRICE_CURRENT" : close_val.values}, index=self.cur_stocks.index)
evaluated_stoc... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def update(stock):\n fetch_data_yahoo(stock) \n if stock['Cap'] != None:\n calc_class(stock)",
"async def daily(self, ctx):\r\n # TODO: Asssess whether this can be cleaned up. \r\n # As it stands, very similar to inv()\r\n author = ctx.author\r\n with DB() as db:\r\n ... | [
"0.7198389",
"0.67817557",
"0.6673082",
"0.6595928",
"0.6560851",
"0.6535039",
"0.6521472",
"0.6423094",
"0.6384765",
"0.63523483",
"0.6341521",
"0.63283294",
"0.62257564",
"0.6195676",
"0.60492074",
"0.6039613",
"0.6026212",
"0.6016211",
"0.59771603",
"0.59657294",
"0.591991... | 0.7214952 | 0 |
Make historical change of price for pocket | def evaluate_history(self, price_info):
historic_stocks = util.historic_stocks(self)
historic_df = pd.DataFrame(columns=historic_stocks)
for record in self.history:
update_row = util.update_row(historic_df, record)
historic_df.loc[record.date] = update_row
p... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def new_get_historical_price(base, target, date):\n return {base: {target: 10}}",
"async def on_symbol_price_updated(self, price: MetatraderSymbolPrice):\n self._pricesBySymbol[price['symbol']] = price\n positions = list(filter(lambda p: p['symbol'] == price['symbol'], self._positions))\n ... | [
"0.6546065",
"0.6513961",
"0.6417625",
"0.6332728",
"0.6320226",
"0.6215916",
"0.6133184",
"0.61270934",
"0.60963976",
"0.60816777",
"0.6043165",
"0.60069615",
"0.59561163",
"0.5949848",
"0.59252536",
"0.5910207",
"0.58720535",
"0.5871314",
"0.5849163",
"0.5824082",
"0.581505... | 0.57770026 | 22 |
Make Pocket cur holding stock to empty | def flush(self):
self.cur_stocks = self.cur_stocks.drop(self.cur_stocks.index)
# add history that flushed whole stocks | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def take(self):\n print(\"You fill the kettle with water.\")\n inventory.remove('kettle')\n collect('filled kettle')",
"def reset(self):\n for key in self.portfolio.keys():\n self.portfolio[key] = {'holdings': 0}\n self.buys[key] = 0\n self.portfolio['bala... | [
"0.64346164",
"0.63459474",
"0.61906755",
"0.6181591",
"0.6101768",
"0.60579175",
"0.6056559",
"0.6015891",
"0.5999291",
"0.59982437",
"0.59696317",
"0.59237593",
"0.5851878",
"0.58299017",
"0.5800235",
"0.5799133",
"0.5781456",
"0.5750289",
"0.57455724",
"0.567645",
"0.56746... | 0.60396004 | 7 |
Analyzing the performance of manager's pocket. | def analyze(self, start, end):
return | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def measure(self):\n pass",
"def advancedStats():",
"def stats(self):",
"def measure(self, recommender):",
"def monitor(self):\n procdata = self.collect_userprocs_info()\n now = int(time.time())\n #-------------------\n proclist = []\n for name in procdata:\n ... | [
"0.67233044",
"0.6397932",
"0.6334954",
"0.626483",
"0.61850554",
"0.61839694",
"0.615876",
"0.60849315",
"0.6067302",
"0.5993176",
"0.5979811",
"0.58584976",
"0.5830627",
"0.5822538",
"0.5812257",
"0.5808406",
"0.57874054",
"0.5766286",
"0.57624614",
"0.574542",
"0.57443357"... | 0.0 | -1 |
Get audio frames and make an audio deque | def audio_thread(audio_que, audio_ent):
vad = VADAudio(
aggressiveness=2,
input_rate=16000
)
audio_que.put(AudioFrame(vad.vad_collector(), vad.ssl_read()))
audio_ent.set()
if len(audio_que.queue) > 500:
audio_que.get() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def audio_stream() -> typing.Iterable[bytes]:\n frames = frame_queue.get()\n while frames:\n yield frames\n frames = frame_queue.get()",
"def get_audio():\n\tbuf = None\n\tnum_new_bytes = BUFFER_SIZE // REFRESH_BUFFER_FACTOR\n\twith open(INFILE) as fifo:\n\t\twhile True:\n\t\t... | [
"0.75554425",
"0.6910365",
"0.66622657",
"0.6431581",
"0.62361145",
"0.62292373",
"0.6207139",
"0.6162148",
"0.6150026",
"0.61357945",
"0.60847557",
"0.606302",
"0.5995642",
"0.5961134",
"0.593233",
"0.59278435",
"0.59212625",
"0.591338",
"0.5859772",
"0.58524656",
"0.5821281... | 0.58684295 | 18 |
Returns the byte size of a file at ``file_path``. | def file_size(file_path):
stat = os.stat(file_path)
assert stat_fn.S_ISDIR(stat.st_mode) == False
return stat.st_size | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_file_size_in_bytes(file_path):\n size = os.path.getsize(file_path)\n return size",
"def file_size(file_path):\n if os.path.isfile(file_path):\n file_info = os.stat(file_path)\n return convert_bytes(file_info.st_size)",
"def file_size(file_path):\n if os.path.isfile(file_path):\n... | [
"0.8997737",
"0.897519",
"0.897519",
"0.897519",
"0.8965862",
"0.8730011",
"0.8686579",
"0.8589061",
"0.855717",
"0.8339082",
"0.8333081",
"0.8267659",
"0.8118673",
"0.80132294",
"0.8005676",
"0.8005676",
"0.7886345",
"0.7868195",
"0.78022426",
"0.77924824",
"0.77924824",
"... | 0.818919 | 12 |
Returns a string Hex md5 digest for the file at ``file_path``. | def file_md5(file_path):
log.debug("Calculating md5 for %s", file_path)
start_ms = time.time() * 10**3
fp = open(file_path, 'rb')
try:
# returns tuple (md5_hex, md5_base64, size)
md5, _, _ = boto.utils.compute_md5(fp)
finally:
fp.close()
duration_ms = (time.time() * 10**3... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def md5_hash(file_path):\n with open(file_path, 'rb') as fp:\n return md5(fp.read()).hexdigest()",
"def GetFileMd5(file_path):\n return binascii.hexlify(GetFileHashes(file_path, do_md5=True)['md5'])",
"def get_file_checksum(file_path):\n with open(file_path) as f:\n content = f.read()\n ... | [
"0.82983875",
"0.79927385",
"0.7966914",
"0.7918441",
"0.7798802",
"0.77385974",
"0.77119756",
"0.77119756",
"0.76662797",
"0.75967014",
"0.75758934",
"0.75569177",
"0.7496125",
"0.74683166",
"0.7464667",
"0.7431425",
"0.7404594",
"0.7364096",
"0.734743",
"0.73444986",
"0.726... | 0.76608855 | 9 |
Ensure the directories for ``path`` exist. | def ensure_dir(path):
try:
os.makedirs(path)
except (EnvironmentError) as e:
if not(e.errno == errno.EEXIST and
e.filename == path):
raise
return | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def ensure_dirs_exist(path):\n os.makedirs(path, exist_ok=True)",
"def assure_path_exists(self, path):\n\n dir = os.path.dirname(path)\n if not os.path.exists(dir):\n os.makedirs(dir)",
"def ensure_dir_exists(path: Union[str,Path]) -> None:\n# path = str(path)\n assert not os... | [
"0.82048607",
"0.8184131",
"0.7938871",
"0.7728657",
"0.77272743",
"0.7708402",
"0.76807654",
"0.7665548",
"0.76503545",
"0.76297635",
"0.75548595",
"0.7539441",
"0.74489266",
"0.74481565",
"0.7425621",
"0.74024767",
"0.74016887",
"0.7400134",
"0.73874825",
"0.7316488",
"0.72... | 0.75274724 | 12 |
Format the ``bytes`` as a human readable value. | def human_disk_bytes(bytes):
patterns = [
(1024.0 ** 3, "G"),
(1024.0 ** 2, "M"),
(1024.0, "K")
]
for scale, label in patterns:
if bytes >= scale:
return "{i:.1f}{label}".format(i=(bytes/scale), label=label)
return "%sB" % (bytes,) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def humanize_bytes(value: bytes) -> str:\n if len(value) < INLINE_LENGTH:\n payload = value.hex()\n else:\n hex = value.hex()\n head = hex[:DISPLAY_CHARS]\n tail = hex[-1 * DISPLAY_CHARS :]\n payload = f\"{head}..{tail}\"\n\n return f\"bytes{len(value)}({payload})\"",
... | [
"0.76535106",
"0.6859878",
"0.6772026",
"0.6762991",
"0.66572475",
"0.66351366",
"0.6503492",
"0.634785",
"0.6321941",
"0.6208951",
"0.6163069",
"0.6109563",
"0.6077164",
"0.60303795",
"0.6028045",
"0.6020948",
"0.6005915",
"0.6003533",
"0.6002519",
"0.59668106",
"0.5863311",... | 0.64928573 | 7 |
Creates a temporary hard link for the file at ``source_path``. The link is created when the context is entered and destroyed when it is left. You can also call the ``link`` and ``close`` functions to achieve the same result. The full path of the ``source_path`` is recreated under a temporary directory so that we can pa... | def __init__(self, source_path):
self._source_path = source_path
self._stable_dir = None
self.stable_path = None
self.ignore_next_exit = False | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def link(self):\n\n if self.path_source is not None:\n full_source_path = os.path.join(\n os.path.expandvars(self.path_source), self.name\n )\n full_destination_path = os.path.join(\n os.path.expandvars(self.path_destination), self.name\n ... | [
"0.6136548",
"0.6093798",
"0.5965859",
"0.585038",
"0.5806252",
"0.5596196",
"0.55958414",
"0.5442566",
"0.54166704",
"0.54117393",
"0.53947926",
"0.53625023",
"0.5336499",
"0.5300534",
"0.52561206",
"0.5227541",
"0.5223866",
"0.51948196",
"0.51883584",
"0.5175947",
"0.516497... | 0.4557406 | 73 |
Generates the stable link and returns it. If the link could not be generated because the source file was not found ``None`` is returned. Call ``close`` to delete the link. | def link(self):
self.__enter__()
return self.stable_path | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __enter__(self):\n if self.stable_path:\n return self.stable_path\n \n _, file_name = os.path.split(self._source_path)\n stable_dir = tempfile.mkdtemp(prefix=\"%s-\" % file_name)\n assert self._source_path.startswith(\"/\")\n stable_path = os.path.join(stabl... | [
"0.5937122",
"0.58447796",
"0.5743967",
"0.56529963",
"0.5629153",
"0.5612357",
"0.55422425",
"0.5530858",
"0.54267585",
"0.53902197",
"0.52353996",
"0.5191223",
"0.51137",
"0.5081782",
"0.5077057",
"0.5065889",
"0.50577205",
"0.5030819",
"0.5026374",
"0.50132394",
"0.5006848... | 0.61671346 | 0 |
Deletes the stable link. | def close(self):
self.__exit__(None, None, None)
return | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def unlink(self, link_id):",
"def remove_link():",
"def delete_secret_link(link_id):\n\n Secret_Link.objects.filter(link_id=link_id).delete()",
"def delete_version(self):\n pass",
"def deletelink(self, link_index=1, child=None):\n child = self.getnodenamed(child) # Verify pointer.\n\n ... | [
"0.7092518",
"0.6725158",
"0.6629403",
"0.66181463",
"0.65411353",
"0.653509",
"0.6453885",
"0.6434074",
"0.64093846",
"0.6395287",
"0.63938314",
"0.6329432",
"0.6159133",
"0.6091675",
"0.6088176",
"0.6084725",
"0.6080175",
"0.6080175",
"0.60257584",
"0.60257584",
"0.60257584... | 0.0 | -1 |
Enters the context and returns self if the link could be created. If the link could not be created because the source path did not exist ``None`` is returned. | def __enter__(self):
if self.stable_path:
return self.stable_path
_, file_name = os.path.split(self._source_path)
stable_dir = tempfile.mkdtemp(prefix="%s-" % file_name)
assert self._source_path.startswith("/")
stable_path = os.path.join(stable_dir, self._sou... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def link(self):\n\n if self.path_source is not None:\n full_source_path = os.path.join(\n os.path.expandvars(self.path_source), self.name\n )\n full_destination_path = os.path.join(\n os.path.expandvars(self.path_destination), self.name\n ... | [
"0.57441884",
"0.5403153",
"0.52979624",
"0.5242806",
"0.5223527",
"0.5191228",
"0.5150666",
"0.51408464",
"0.51333934",
"0.51022995",
"0.50961906",
"0.5043649",
"0.5019827",
"0.5007971",
"0.4994224",
"0.4989699",
"0.49889046",
"0.49748132",
"0.4961593",
"0.49501863",
"0.4932... | 0.6574273 | 0 |
Return a list of all pypet trajectory names in a a given hdf5 file. | def getTrajectorynamesInFile(filename):
assert pathlib.Path(filename).exists(), f"{filename} does not exist!"
hdf = h5py.File(filename, "r")
all_traj_names = list(hdf.keys())
hdf.close()
return all_traj_names | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def labels(self):\n with self._h5file('r') as h5file:\n return list(h5file.keys())",
"def list_h5(walk_dir):\n\n file_list = []\n for root, subdirs, files in os.walk(walk_dir):\n\n for filename in files:\n file_path = os.path.join(root, filename)\n if file_pat... | [
"0.5963088",
"0.59348214",
"0.5826333",
"0.58148545",
"0.5610662",
"0.56103677",
"0.55904347",
"0.5585358",
"0.5580575",
"0.5579591",
"0.5578491",
"0.5534216",
"0.54983604",
"0.5476016",
"0.54249066",
"0.5373145",
"0.5356421",
"0.53518355",
"0.53175646",
"0.5283302",
"0.52732... | 0.8125475 | 0 |
Read HDF file with simulation results and return the chosen trajectory. | def loadPypetTrajectory(filename, trajectoryName):
assert pathlib.Path(filename).exists(), f"{filename} does not exist!"
logging.info(f"Loading results from {filename}")
# if trajectoryName is not specified, load the most recent trajectory
if trajectoryName == None:
trajectoryName = getTrajecto... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def read_simulation_results(self,\n fname_sims, \n fname_pareto = None, \n fname_cull = None):\n\n \n self.fname_sims = fname_sims\n self.__read_file(fname_sims, 'sim_results')\n \n if fname_pareto is no... | [
"0.64999044",
"0.6371696",
"0.6073903",
"0.5837715",
"0.57869893",
"0.57349133",
"0.5704984",
"0.5631741",
"0.5587649",
"0.5532272",
"0.55024004",
"0.5448979",
"0.5440913",
"0.54274464",
"0.5425254",
"0.54140824",
"0.54065776",
"0.539318",
"0.53873175",
"0.53624594",
"0.53507... | 0.0 | -1 |
Load the simulated data of a run and its parameters from a pypetTrajectory. | def getRun(runId, pypetTrajectory, pypetShortNames=True):
exploredParameters = pypetTrajectory.f_get_explored_parameters()
niceParKeys = [p.split(".")[-1] for p in exploredParameters.keys()]
pypetTrajectory.results[runId].f_load()
result = pypetTrajectory.results[runId].f_to_dict(fast_access=True, shor... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def loadData(self, **kwargs):\n \n Simulation.loadData(self, **kwargs)\n if crp_flag:\n self.data['T'] = self.data.D*crp.kpc/crp.c_light/3600/24/365.25/1e6\n else: \n self.data['T'] = self.data.D*siu.kpc/siu.c_light/3600/24/365.25/1e6",
"def load(self) -> None:\n... | [
"0.60243386",
"0.5950635",
"0.5801095",
"0.57834077",
"0.57368153",
"0.56159323",
"0.5614498",
"0.5600852",
"0.55473953",
"0.5526737",
"0.54745704",
"0.54700917",
"0.54387504",
"0.5425601",
"0.54211926",
"0.54067695",
"0.53979605",
"0.5389131",
"0.5389131",
"0.5385771",
"0.53... | 0.54232967 | 14 |
Method calculates and returns terms covered by Term object | def get_covered_terms(self):
base = 0
x_index = [index for index, item in enumerate(self.bin_str[::-1]) if item == '-']
for index, item in enumerate(self.bin_str[::-1]):
if item == '1':
base += 2 ** index
yield base
for i in range(len(x_index)):
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _get_terms(self):\n return self.__terms",
"def _get_terms(self):\n return self.__terms",
"def _get_terms(self):\n return self.__terms",
"def _get_terms(self):\n return self.__terms",
"def _get_terms(self):\n return self.__terms",
"def _get_terms(self):\n return self.__terms",
"def... | [
"0.66107965",
"0.66107965",
"0.66107965",
"0.66107965",
"0.66107965",
"0.66107965",
"0.66107965",
"0.66107965",
"0.66107965",
"0.61759037",
"0.6123893",
"0.6059991",
"0.6038169",
"0.6015828",
"0.6014338",
"0.60119563",
"0.5838138",
"0.579935",
"0.57963955",
"0.56884027",
"0.5... | 0.5419943 | 41 |
Function returns true if the binary strings differ by only one bit, else false | def differ_by_one(nums):
num1, num2 = nums
difference = 0
for index, bit in enumerate(num1):
if bit != num2[index]:
difference += 1
if difference > 1:
return False
if difference == 1:
num1.covered = num2.covered = True
return True | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def strings_differ(string1, string2):\n if len(string1) != len(string2):\n return True\n invalid_bits = 0\n for a, b in zip(string1, string2):\n invalid_bits += a != b\n return invalid_bits != 0",
"def any_equal_block(b: bytes) -> bool:\n b = [b[i:i + 16] for i in range(0, len(b), 16... | [
"0.7363422",
"0.7006106",
"0.6955231",
"0.6935423",
"0.69168013",
"0.68464726",
"0.6827639",
"0.68151927",
"0.67339444",
"0.6722084",
"0.6646812",
"0.6597543",
"0.6540759",
"0.64366025",
"0.641615",
"0.63223135",
"0.62831414",
"0.6280919",
"0.62527806",
"0.6245337",
"0.623591... | 0.5974088 | 38 |
Function returns a reduced Term object | def reduce_bits(nums):
num1, num2 = nums
result = ""
for index, bit in enumerate(num1):
result += bit if bit == num2[index] else '-'
return Term(result) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __call__(self, *args, **kw):\n return self.transform(Term.__call__(self, *args, **kw))",
"def __call__(self, *args: TermLike) -> 'Term':\n return Term(self, args)",
"def _get_term(self):\n return self.__term",
"def _get_term(self):\n return self.__term",
"def _get_term(self):\n r... | [
"0.6299694",
"0.6096665",
"0.5878431",
"0.5878431",
"0.5878431",
"0.5878431",
"0.5878431",
"0.5878431",
"0.5878431",
"0.5878431",
"0.5878431",
"0.5758068",
"0.56476766",
"0.55836403",
"0.5566976",
"0.5552307",
"0.55443543",
"0.553262",
"0.5525708",
"0.55073017",
"0.5463503",
... | 0.0 | -1 |
Function uses itertools.combinations to generate possible permutations of minterms and xterms | def get_pairs(terms):
return itertools.combinations(terms, 2) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def permutations(xs):\n if not xs:\n yield []\n else:\n for x, xs in selections(xs):\n for ys in permutations(xs):\n yield [x] + ys",
"def get_combinations(self):\n all_steps = self.do_steps()\n self.option = [k for k, v in all_steps.items()]\n r... | [
"0.63869417",
"0.62922204",
"0.6280528",
"0.60896415",
"0.60720867",
"0.6060668",
"0.6060022",
"0.6006552",
"0.5943192",
"0.59218216",
"0.59115046",
"0.5862979",
"0.586069",
"0.58377963",
"0.5827487",
"0.5826101",
"0.5818908",
"0.57880336",
"0.57866234",
"0.576338",
"0.575172... | 0.6309878 | 1 |
Function takes an input of possible pairs and returns a set of reduced pairs | def reduce_pairs(pairs):
return set(map(reduce_bits, filter(differ_by_one, pairs))) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def pair_combos(iterable):\n pairs = set()\n for a in iterable:\n for b in iterable:\n pairs.add(a + b)\n return list(pairs)",
"def all_pairs(elements):\n if len(elements) < 2:\n return []\n elif len(elements) == 2:\n return [(elements[0], elements[1])]\n else:\n... | [
"0.6682539",
"0.6649223",
"0.658668",
"0.6501677",
"0.6372383",
"0.63125974",
"0.62352455",
"0.615719",
"0.6116768",
"0.6116768",
"0.61058265",
"0.6084855",
"0.6050953",
"0.60320103",
"0.6000207",
"0.59864324",
"0.5941454",
"0.59207493",
"0.59112364",
"0.58852816",
"0.5879278... | 0.8235042 | 0 |
Function formats reduced expression | def format_minimized_expression(prime_implicants):
if not prime_implicants: # If no prime implicants, return 0
return '0'
elif prime_implicants[0] == '-' * len(prime_implicants[0]): # If all dashes, return 1
return '1'
result = ''
for iteration, prime_implicant in enumerate(prime_impl... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def reformat(ctx):\n pass",
"def expression(self):\n\n result = u\"{}({}\".format(self.function.lower(),\n self.metric_name)\n\n if self.dimensions_str:\n result += u\"{{{}}}\".format(self.dimensions_str)\n\n if self.deterministic:\n r... | [
"0.619423",
"0.61758894",
"0.6155742",
"0.5957292",
"0.5936892",
"0.5857234",
"0.5797273",
"0.5764045",
"0.5746475",
"0.56976485",
"0.5655259",
"0.5650681",
"0.5552232",
"0.5550531",
"0.55274117",
"0.55160505",
"0.5513322",
"0.5504952",
"0.54818195",
"0.54667133",
"0.54610825... | 0.5509869 | 17 |
Function returns list of essential prime implicants. If any term in m_terms is covered by only one primeimplicant, that primeimplicant is considered to be essential. | def terms_covered_once(prime_implicants, m_terms):
covered = []
for prime_implicant in prime_implicants:
for covered_term in prime_implicant.get_covered_terms():
covered.append(covered_term)
return [i for i in covered if covered.count(i) == 1 and i in m_terms] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_term_max_coverage(prime_implicants, m_terms):\n term_max_coverage = max(prime_implicants, key=lambda prime_implicant: len(\n [i for i in prime_implicant.get_covered_terms() if i in m_terms]))\n\n return term_max_coverage",
"def getInterim(self):\n return [x for x in self.xeps if x.int... | [
"0.51042086",
"0.50913125",
"0.50038147",
"0.49761307",
"0.4944465",
"0.49364462",
"0.4881296",
"0.48746046",
"0.48463184",
"0.48211226",
"0.48175967",
"0.48172054",
"0.4810883",
"0.48001704",
"0.4791628",
"0.47870997",
"0.47426683",
"0.46842042",
"0.46507025",
"0.46479034",
... | 0.700567 | 0 |
Function returns the prime implicant that covers the most m_terms | def get_term_max_coverage(prime_implicants, m_terms):
term_max_coverage = max(prime_implicants, key=lambda prime_implicant: len(
[i for i in prime_implicant.get_covered_terms() if i in m_terms]))
return term_max_coverage | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def problem41():\n for i in range(len(PANDIGITAL), 1, -1):\n cur_max = 0\n for p in itertools.permutations(PANDIGITAL[:i]):\n n = int(\"\".join(p))\n if pelib.is_prime(n) and n > cur_max:\n cur_max = n\n\n if cur_max > 0:\n return cur_max",
... | [
"0.6628919",
"0.6173766",
"0.6173766",
"0.6153234",
"0.60713315",
"0.5992777",
"0.59897196",
"0.5982625",
"0.59818715",
"0.59119636",
"0.58534485",
"0.58469534",
"0.5822166",
"0.58060354",
"0.57984334",
"0.57881075",
"0.57745135",
"0.5755864",
"0.5747243",
"0.57214177",
"0.56... | 0.7448263 | 0 |
Function minimizes a sumofminterms equation, and returns a list of essential prime implicants and prime implicants | def minimize(n_bits, m_terms, x_terms):
# Error checking
if max(m_terms, default=0) > 2 ** n_bits or max(x_terms, default=0) > 2 ** n_bits:
raise ValueError("integer overflow")
# Minimizing
minterms = [Term(format(i, '0{}b'.format(n_bits))) for i in m_terms]
dcterms = [Term(format(i, '0{}b'... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def summation_of_primes():\n \n k=1\n acc=2\n for x in range(2000000):\n if x!=0 and x%2!=0 and x%4!=0 and x%6!=0 and x%8!=0 and x%10!=0:\n k=1\n for m in range(x):\n if x!=1 and m!=0 and m!=1 and x%m==0 and x!=m:\n k=2\n if k==1... | [
"0.6033164",
"0.5887534",
"0.5837873",
"0.5818655",
"0.5659827",
"0.5645945",
"0.5618449",
"0.5573604",
"0.5546379",
"0.54956293",
"0.5495497",
"0.5453119",
"0.54498684",
"0.5446226",
"0.5422607",
"0.5383173",
"0.5375919",
"0.5374314",
"0.5360579",
"0.53528404",
"0.53416526",... | 0.5769164 | 4 |
Creates the job client. Can also be used for recreating the job client (e.g. in the case of communication failure). Multiple job requests can be done in parallel if needed, by creating an instance of the class for each job. Note that one class instance should only be used for one job, as each instance stores variables ... | def create_client(self) -> None:
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_client(self) -> None:\n self._client = gapic.JobServiceClient(\n client_options=dict(api_endpoint=self._region + _UCAIP_ENDPOINT_SUFFIX))",
"def jobserver_job():\n return _MakeJob()",
"def make_instance(self, include_optional):\n # model = kloudio.models.new_job.NewJob() # noqa:... | [
"0.77097774",
"0.61034995",
"0.60916775",
"0.6037993",
"0.5971743",
"0.5929555",
"0.590849",
"0.5902025",
"0.58814514",
"0.58814514",
"0.5853616",
"0.5816075",
"0.58144796",
"0.5787646",
"0.5761592",
"0.5736792",
"0.5715204",
"0.57091993",
"0.56645805",
"0.5615225",
"0.560661... | 0.5961302 | 5 |
Get training args for runner._launch_aip_training. The training args contain the inputs/outputs/exec_properties to the tfx.scripts.run_executor module. | def create_training_args(self, input_dict, output_dict, exec_properties,
executor_class_path, training_inputs,
job_id) -> Dict[Text, Any]:
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_training_args(self, input_dict: Dict[Text, List[types.Artifact]],\n output_dict: Dict[Text, List[types.Artifact]],\n exec_properties: Dict[Text, Any],\n executor_class_path: Text,\n training_inputs: D... | [
"0.70724356",
"0.6887883",
"0.68650603",
"0.6819622",
"0.6720383",
"0.6570948",
"0.6565429",
"0.65353024",
"0.6483057",
"0.6375926",
"0.6367454",
"0.63338363",
"0.6326136",
"0.6322333",
"0.6315006",
"0.629442",
"0.62928957",
"0.6290497",
"0.6287055",
"0.62419564",
"0.61994785... | 0.7656704 | 0 |
Creates the job spec. | def _create_job_spec(
self,
job_id: Text,
training_input: Dict[Text, Any],
job_labels: Optional[Dict[Text, Text]] = None) -> Dict[Text, Any]:
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _create_job_spec(\n self,\n job_id: Text,\n training_input: Dict[Text, Any],\n job_labels: Optional[Dict[Text, Text]] = None) -> Dict[Text, Any]:\n\n job_spec = {\n 'display_name': job_id,\n 'job_spec': training_input,\n 'labels': job_labels,\n }\n return job_s... | [
"0.72192025",
"0.71575874",
"0.652794",
"0.6339889",
"0.6263073",
"0.6225149",
"0.62125117",
"0.6160883",
"0.61028594",
"0.6097879",
"0.6095217",
"0.60905993",
"0.60423464",
"0.59173054",
"0.58997864",
"0.58075607",
"0.5807247",
"0.58008564",
"0.5778806",
"0.57773787",
"0.575... | 0.7605234 | 0 |
Launches a longrunning job. | def launch_job(self,
job_id: Text,
parent: Text,
training_input: Dict[Text, Any],
job_labels: Optional[Dict[Text, Text]] = None) -> None:
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _launch_job(self, job):\n details = self.sm.get_job_details(job.jobId)\n handler = self.handlers[details[0]['method']]\n type = details[0]['type']\n resultId = details[0]['resultid']\n job.set_phase('EXECUTING')\n job.set_start_time(datetime.utcnow().isoformat())\n job.add_result(resultId,... | [
"0.7621034",
"0.6952546",
"0.6635061",
"0.65210956",
"0.643192",
"0.6390821",
"0.62709486",
"0.62590075",
"0.62201256",
"0.6138722",
"0.6126732",
"0.60962737",
"0.6070916",
"0.6067779",
"0.60474116",
"0.6016898",
"0.5952437",
"0.5939165",
"0.5926706",
"0.59081113",
"0.5884174... | 0.6375229 | 6 |
Gets the the longrunning job. | def get_job(self) -> Union[Dict[Text, Text], CustomJob]:
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_job(self) -> CustomJob:\n return self._client.get_custom_job(name=self._job_name)",
"def job(self):\n return self.batch[self.job_id]",
"def get_job(self) -> Job:\n return self.jobs_list[self.sel_idx]",
"def get_job(self) -> Dict[Text, Text]:\n request = self._client.projects().job... | [
"0.7959508",
"0.7827083",
"0.7651665",
"0.7608159",
"0.746725",
"0.7420048",
"0.73030406",
"0.7280481",
"0.72452736",
"0.7163087",
"0.7111556",
"0.71088976",
"0.7100397",
"0.7080314",
"0.70724916",
"0.6938223",
"0.6938223",
"0.69369984",
"0.69169027",
"0.6887253",
"0.6841543"... | 0.6627546 | 25 |
Gets the state of the longrunning job. | def get_job_state(
self,
response: Union[Dict[Text, Text], CustomJob]) -> Union[Text, JobState]:
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"async def get_job_state(jobId: int) -> State: \n return mngr.getStateJob(str(jobId))",
"def get_state(self):\n\t\treturn Job(SDK.PrlVm_GetState(self.handle)[0])",
"def get_job_state(self, job_origin_id):",
"def get_job_state(self, response) -> JobState:\n return response.state",
"def status(self):... | [
"0.7776277",
"0.76635754",
"0.76310503",
"0.75910527",
"0.7253256",
"0.7204234",
"0.7049449",
"0.6893693",
"0.6854992",
"0.67731065",
"0.67512316",
"0.67479825",
"0.6723624",
"0.66703707",
"0.6613418",
"0.6607828",
"0.6564291",
"0.65610725",
"0.6466642",
"0.6450617",
"0.64280... | 0.69745475 | 7 |
Gets the job name. | def get_job_name(self) -> Text:
return self._job_name | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def job_name(self) -> str:\n return pulumi.get(self, \"job_name\")",
"def job_name(self):\n return self._stub.List(self._message).job_name",
"def job_name(self) -> str:\n return self._step_execution_context.job_name",
"def job_name(self) -> pulumi.Input[str]:\n return pulumi.get(s... | [
"0.9124221",
"0.8854782",
"0.86039746",
"0.8555109",
"0.8552845",
"0.82115984",
"0.7826915",
"0.7780019",
"0.7780019",
"0.7755838",
"0.7453526",
"0.74189264",
"0.7279247",
"0.7250569",
"0.72497904",
"0.72245",
"0.7056386",
"0.705631",
"0.6979349",
"0.6973385",
"0.69360894",
... | 0.8991103 | 1 |
Creates the discovery job client. Can also be used for recreating the job client (e.g. in the case of communication failure). Multiple job requests can be done in parallel if needed, by creating an instance of the class for each job. Note that one class instance should only be used for one job, as each instance stores ... | def create_client(self) -> None:
self._client = discovery.build('ml', 'v1') | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_client(self) -> None:\n self._client = gapic.JobServiceClient(\n client_options=dict(api_endpoint=self._region + _UCAIP_ENDPOINT_SUFFIX))",
"def __init__(self,\n job_uids=None,\n cluster_id=None,\n cluster_match_string=None,\n e... | [
"0.6929064",
"0.57790893",
"0.55540615",
"0.5551953",
"0.54512167",
"0.54504716",
"0.5449162",
"0.5445811",
"0.53970695",
"0.5364705",
"0.5301948",
"0.5293237",
"0.5282406",
"0.52800995",
"0.52337587",
"0.5223323",
"0.5223323",
"0.5222134",
"0.5202863",
"0.51972383",
"0.51919... | 0.6168927 | 1 |
Get training args for runner._launch_aip_training. The training args contain the inputs/outputs/exec_properties to the tfx.scripts.run_executor module. | def create_training_args(self, input_dict: Dict[Text, List[types.Artifact]],
output_dict: Dict[Text, List[types.Artifact]],
exec_properties: Dict[Text, Any],
executor_class_path: Text,
training_inputs: Dict[Text,... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_training_args(self, input_dict, output_dict, exec_properties,\n executor_class_path, training_inputs,\n job_id) -> Dict[Text, Any]:\n pass",
"def get_train_args():\n d_train = cfg.get_train_args_schema().fields\n \n if num_local_gpus ... | [
"0.7657387",
"0.6888748",
"0.68656194",
"0.682167",
"0.67204",
"0.6571265",
"0.6566474",
"0.6535822",
"0.6482168",
"0.63752997",
"0.6367284",
"0.6333624",
"0.6325504",
"0.6321786",
"0.63143045",
"0.62942094",
"0.62941766",
"0.6291347",
"0.6287213",
"0.6240908",
"0.61983275",
... | 0.70728606 | 1 |
Creates the job spec. | def _create_job_spec(
self,
job_id: Text,
training_input: Dict[Text, Any],
job_labels: Optional[Dict[Text, Text]] = None) -> Dict[Text, Any]:
job_spec = {
'jobId': job_id,
'trainingInput': training_input,
'labels': job_labels,
}
return job_spec | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _create_job_spec(\n self,\n job_id: Text,\n training_input: Dict[Text, Any],\n job_labels: Optional[Dict[Text, Text]] = None) -> Dict[Text, Any]:\n pass",
"def _create_job_spec(\n self,\n job_id: Text,\n training_input: Dict[Text, Any],\n job_labels: Optional[Dict[T... | [
"0.7605234",
"0.72192025",
"0.652794",
"0.6339889",
"0.6263073",
"0.6225149",
"0.62125117",
"0.6160883",
"0.61028594",
"0.6097879",
"0.6095217",
"0.60905993",
"0.60423464",
"0.59173054",
"0.58997864",
"0.58075607",
"0.5807247",
"0.58008564",
"0.5778806",
"0.57773787",
"0.5755... | 0.71575874 | 2 |
Launches a longrunning job. | def launch_job(self,
job_id: Text,
project: Text,
training_input: Dict[Text, Any],
job_labels: Optional[Dict[Text, Text]] = None) -> None:
parent = 'projects/{}'.format(project)
job_spec = self._create_job_spec(job_id, training_input, job_labe... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _launch_job(self, job):\n details = self.sm.get_job_details(job.jobId)\n handler = self.handlers[details[0]['method']]\n type = details[0]['type']\n resultId = details[0]['resultid']\n job.set_phase('EXECUTING')\n job.set_start_time(datetime.utcnow().isoformat())\n job.add_result(resultId,... | [
"0.76209676",
"0.69519645",
"0.6634889",
"0.652025",
"0.643076",
"0.63896775",
"0.6375683",
"0.6270224",
"0.62586224",
"0.6220635",
"0.6138094",
"0.61267763",
"0.60981447",
"0.6071404",
"0.60678756",
"0.6048099",
"0.6015662",
"0.5951581",
"0.59388965",
"0.59266406",
"0.590749... | 0.5742829 | 34 |
Gets the longrunning job. | def get_job(self) -> Dict[Text, Text]:
request = self._client.projects().jobs().get(name=self._job_name)
return request.execute() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_job(self) -> CustomJob:\n return self._client.get_custom_job(name=self._job_name)",
"def job(self):\n return self.batch[self.job_id]",
"def get_job(self) -> Job:\n return self.jobs_list[self.sel_idx]",
"def current_job(self):\n assert(ExecutorThread.executor_object is not None... | [
"0.78749716",
"0.755368",
"0.7488666",
"0.7287057",
"0.7284264",
"0.7177822",
"0.71693903",
"0.7139543",
"0.7112392",
"0.698487",
"0.6967169",
"0.6921034",
"0.6912143",
"0.6863278",
"0.6839613",
"0.68319654",
"0.6814457",
"0.677886",
"0.6773958",
"0.67043626",
"0.6698025",
... | 0.7425105 | 3 |
Gets the state of the longrunning job. | def get_job_state(self, response) -> Text:
return response['state'] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"async def get_job_state(jobId: int) -> State: \n return mngr.getStateJob(str(jobId))",
"def get_state(self):\n\t\treturn Job(SDK.PrlVm_GetState(self.handle)[0])",
"def get_job_state(self, job_origin_id):",
"def get_job_state(self, response) -> JobState:\n return response.state",
"def status(self):... | [
"0.7776277",
"0.76635754",
"0.76310503",
"0.75910527",
"0.7253256",
"0.7204234",
"0.7049449",
"0.69745475",
"0.6854992",
"0.67731065",
"0.67512316",
"0.67479825",
"0.6723624",
"0.66703707",
"0.6613418",
"0.6607828",
"0.6564291",
"0.65610725",
"0.6466642",
"0.6450617",
"0.6428... | 0.6893693 | 8 |
Creates the Gapic job client. Can also be used for recreating the job client (e.g. in the case of communication failure). Multiple job requests can be done in parallel if needed, by creating an instance of the class for each job. Note that one class instance should only be used for one job, as each instance stores vari... | def create_client(self) -> None:
self._client = gapic.JobServiceClient(
client_options=dict(api_endpoint=self._region + _UCAIP_ENDPOINT_SUFFIX)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __new__(cls, *_args, **_kwargs):\n if cls._instance is None:\n cls._instance = super(TestJob, cls).__new__(cls)\n return cls._instance",
"def make_instance(self, include_optional):\n # model = kloudio.models.new_job.NewJob() # noqa: E501\n if include_optional :\n ... | [
"0.61560833",
"0.6033133",
"0.59234405",
"0.57237214",
"0.5716353",
"0.5714552",
"0.57126325",
"0.5688973",
"0.56439745",
"0.5621558",
"0.56208074",
"0.56127506",
"0.5611762",
"0.55921733",
"0.5577413",
"0.5556657",
"0.5548118",
"0.5516411",
"0.5515737",
"0.5490988",
"0.54838... | 0.80078906 | 0 |
Get training args for runner._launch_aip_training. The training args contain the inputs/outputs/exec_properties to the tfx.scripts.run_executor module. | def create_training_args(self, input_dict: Dict[Text, List[types.Artifact]],
output_dict: Dict[Text, List[types.Artifact]],
exec_properties: Dict[Text, Any],
executor_class_path: Text,
training_inputs: Dict[Text,... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_training_args(self, input_dict, output_dict, exec_properties,\n executor_class_path, training_inputs,\n job_id) -> Dict[Text, Any]:\n pass",
"def create_training_args(self, input_dict: Dict[Text, List[types.Artifact]],\n ... | [
"0.7656112",
"0.7071737",
"0.68881834",
"0.6817965",
"0.6719776",
"0.65706426",
"0.6564103",
"0.65336037",
"0.6483007",
"0.6375374",
"0.6366846",
"0.6333173",
"0.6325439",
"0.6321799",
"0.63139856",
"0.62937886",
"0.62911975",
"0.62895596",
"0.6287303",
"0.62421274",
"0.61994... | 0.6864444 | 3 |
Creates the job spec. | def _create_job_spec(
self,
job_id: Text,
training_input: Dict[Text, Any],
job_labels: Optional[Dict[Text, Text]] = None) -> Dict[Text, Any]:
job_spec = {
'display_name': job_id,
'job_spec': training_input,
'labels': job_labels,
}
return job_spec | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _create_job_spec(\n self,\n job_id: Text,\n training_input: Dict[Text, Any],\n job_labels: Optional[Dict[Text, Text]] = None) -> Dict[Text, Any]:\n pass",
"def _create_job_spec(\n self,\n job_id: Text,\n training_input: Dict[Text, Any],\n job_labels: Optional[Dict[T... | [
"0.7605234",
"0.71575874",
"0.652794",
"0.6339889",
"0.6263073",
"0.6225149",
"0.62125117",
"0.6160883",
"0.61028594",
"0.6097879",
"0.6095217",
"0.60905993",
"0.60423464",
"0.59173054",
"0.58997864",
"0.58075607",
"0.5807247",
"0.58008564",
"0.5778806",
"0.57773787",
"0.5755... | 0.72192025 | 1 |
Launches a longrunning job. | def launch_job(self,
job_id: Text,
project: Text,
training_input: Dict[Text, Any],
job_labels: Optional[Dict[Text, Text]] = None) -> None:
parent = 'projects/{project}/locations/{location}'.format(
project=project, location=self._region)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _launch_job(self, job):\n details = self.sm.get_job_details(job.jobId)\n handler = self.handlers[details[0]['method']]\n type = details[0]['type']\n resultId = details[0]['resultid']\n job.set_phase('EXECUTING')\n job.set_start_time(datetime.utcnow().isoformat())\n job.add_result(resultId,... | [
"0.7621034",
"0.6952546",
"0.6635061",
"0.65210956",
"0.643192",
"0.6390821",
"0.6375229",
"0.62709486",
"0.62590075",
"0.62201256",
"0.6138722",
"0.6126732",
"0.60962737",
"0.6070916",
"0.6067779",
"0.60474116",
"0.6016898",
"0.5952437",
"0.5939165",
"0.5926706",
"0.59081113... | 0.5639164 | 43 |
Gets the longrunning job. | def get_job(self) -> CustomJob:
return self._client.get_custom_job(name=self._job_name) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def job(self):\n return self.batch[self.job_id]",
"def get_job(self) -> Job:\n return self.jobs_list[self.sel_idx]",
"def get_job(self) -> Dict[Text, Text]:\n request = self._client.projects().jobs().get(name=self._job_name)\n return request.execute()",
"def current_job(self):\n as... | [
"0.755368",
"0.7488666",
"0.7425105",
"0.7287057",
"0.7284264",
"0.7177822",
"0.71693903",
"0.7139543",
"0.7112392",
"0.698487",
"0.6967169",
"0.6921034",
"0.6912143",
"0.6863278",
"0.6839613",
"0.68319654",
"0.6814457",
"0.677886",
"0.6773958",
"0.67043626",
"0.6698025",
"... | 0.78749716 | 0 |
Gets the state of the longrunning job. | def get_job_state(self, response) -> JobState:
return response.state | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"async def get_job_state(jobId: int) -> State: \n return mngr.getStateJob(str(jobId))",
"def get_state(self):\n\t\treturn Job(SDK.PrlVm_GetState(self.handle)[0])",
"def get_job_state(self, job_origin_id):",
"def status(self):\n return self.job_proto.status",
"def get_status(self):\n\t\treturn c... | [
"0.7774955",
"0.7662598",
"0.76315635",
"0.7255315",
"0.72048146",
"0.7050449",
"0.69742876",
"0.6893676",
"0.68554026",
"0.67728364",
"0.67517",
"0.6748638",
"0.672395",
"0.66709757",
"0.661294",
"0.6608844",
"0.65649486",
"0.6563904",
"0.6467926",
"0.6450994",
"0.6427336",
... | 0.75909746 | 3 |
Gets the job client. | def get_job_client(
enable_ucaip: Optional[bool] = False,
ucaip_region: Optional[Text] = None
) -> Union[CAIPJobClient, UCAIPJobClient]:
if enable_ucaip:
return UCAIPJobClient(ucaip_region)
return CAIPJobClient() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_client(self):\n return self.client",
"def client(self):\n return self._thread._client",
"def client(self):\n\n if self._client is None:\n self._client = self._get_client()\n return self._client",
"def get_job(self) -> CustomJob:\n return self._client.get_cust... | [
"0.7423273",
"0.72262716",
"0.71783745",
"0.70032036",
"0.69956785",
"0.6968737",
"0.69335467",
"0.6916196",
"0.66505975",
"0.654899",
"0.6530459",
"0.65132034",
"0.647153",
"0.647153",
"0.64516896",
"0.64152753",
"0.6394034",
"0.6392622",
"0.6389914",
"0.63815117",
"0.637198... | 0.6007895 | 37 |
Construct a toplevel widget with the parent MASTER. | def __init__(self, master=None, cnf={}, **kw):
if kw:
cnf = _cnfmerge((cnf, kw))
extra = ()
for wmkey in ['screen', 'class_', 'class', 'visual',
'colormap']:
if wmkey in cnf:
val = cnf[wmkey]
# TBD: a hack needed because s... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self, master, _type=REGULAR, **kw):\r\n Frame.__init__(self, master, **kw)\r\n self.main_frame = Frame(self, bd=1)\r\n self.main_frame.pack()",
"def __init__(self, master=None):\n\n # Use super() to inherit Frame method from superclass tk\n super().__init__(master)... | [
"0.69735175",
"0.67381746",
"0.669472",
"0.668831",
"0.6686077",
"0.6609715",
"0.6572637",
"0.6540365",
"0.6484257",
"0.64818954",
"0.6438975",
"0.64249706",
"0.6423365",
"0.64200574",
"0.6408428",
"0.63591474",
"0.6356483",
"0.63561195",
"0.63275886",
"0.62708426",
"0.626065... | 0.6029286 | 36 |
User re.search or re.match to capture the from, to, subject | def get_email_details(header: str) -> dict:
try:
m = re.match(
r"""
([\w\W]* # remove lines
(
^Date: \s*(?P<date>[\w\W]{25}) # obtain date ("date")
|^From: \s*(?P<from>[\w\W]*?$) ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def recipient(self):\n\t\trecipient = re.search(r\"([Tt]\\s*o )(.*)(from.*)\",self.raw_text()[:250])\n\t\t\n\t\tif recipient: \t\n\t\t\trecipient = recipient.group(2) \t\n\t\t\trecipient = re.sub(r\"(\\w+\\s*\\w+),.*\",r\"\\1\",recipient) #attempting to clear out titles and such\n\t\t\t# recipient = re.sub(r\"([sS... | [
"0.5718387",
"0.5634224",
"0.56214267",
"0.54608035",
"0.54419184",
"0.5416016",
"0.5409387",
"0.5352327",
"0.5340618",
"0.53319365",
"0.5329645",
"0.5308167",
"0.52676654",
"0.52523005",
"0.5220662",
"0.52080166",
"0.5198048",
"0.51962763",
"0.51787484",
"0.51620984",
"0.515... | 0.5106384 | 28 |
A pserve script aware of static resources. | def pserve():
import pyramid.scripts.pserve
import pyramid_fanstatic
import os
dirname = os.path.dirname(__file__)
dirname = os.path.join(dirname, 'resources')
pyramid.scripts.pserve.add_file_callback(
pyramid_fanstatic.file_callback(dirname))
pyramid.scripts.pserve.main() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def server_static (filename):\n return static_file(filename, root=\"./static\")",
"def server_static(filename):\n return static_file(filename, root='static/stats')",
"def statics(file, type='img'):\n return bottle.static_file(file, root=HOME+STATIC_PATH+'/'+type)",
"def static(filename):\n return... | [
"0.7150718",
"0.69992626",
"0.67572206",
"0.6541767",
"0.6521592",
"0.6472664",
"0.64559674",
"0.64321667",
"0.6429534",
"0.63892156",
"0.6383383",
"0.63797045",
"0.6370705",
"0.636868",
"0.6363368",
"0.63551867",
"0.6335698",
"0.630819",
"0.6294129",
"0.6259669",
"0.6254209"... | 0.76249796 | 0 |
approved condo payment creates an egreso | def test_approved__condo_payment_creates_egreso(self):
pago = Pagos_Condominio.objects.get(id = 1)
try:
self.assertIsNotNone(pago.egreso)
except:
print 'WARNING: _loaded_values in model is creating problem'
#self.assertEqual(hasattr(pago, 'egreso'), True) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def payment(self, **post):\n cr, uid, context = request.cr, request.uid, request.context\n payment_obj = request.registry.get('payment.acquirer')\n sale_order_obj = request.registry.get('sale.order')\n\n order = request.website.sale_get_order(context=context)\n order.write({'user... | [
"0.68200374",
"0.66911495",
"0.6539901",
"0.64811856",
"0.6442946",
"0.6428527",
"0.6295282",
"0.62489337",
"0.61958116",
"0.6165144",
"0.6128512",
"0.6098366",
"0.60955703",
"0.60880065",
"0.6079592",
"0.6073143",
"0.6071113",
"0.60629845",
"0.60420525",
"0.6017494",
"0.5990... | 0.68466234 | 0 |
approved condo payment creates an egreso | def test_egreso_updates_account_balance(self):
banco = Bancos.objects.get(id=1)
previous_balance = banco.balance
condominio = Condominio.objects.get(rif='J6750435')
factura_condominio = Factura_Condominio.objects.get(id=1)
pais = Paises.objects.get(pk="Venezuela")
tipo_egreso, created =Tipos_Egresos.object... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_approved__condo_payment_creates_egreso(self):\n\t\tpago = Pagos_Condominio.objects.get(id = 1)\n\t\ttry:\n\t\t\tself.assertIsNotNone(pago.egreso)\n\t\texcept:\n\t\t\tprint 'WARNING: _loaded_values in model is creating problem'\n\t\t#self.assertEqual(hasattr(pago, 'egreso'), True)",
"def payment(self, **... | [
"0.68466234",
"0.68200374",
"0.66911495",
"0.6539901",
"0.64811856",
"0.6442946",
"0.6428527",
"0.6295282",
"0.62489337",
"0.61958116",
"0.6165144",
"0.6128512",
"0.6098366",
"0.60955703",
"0.60880065",
"0.6079592",
"0.6073143",
"0.6071113",
"0.60629845",
"0.60420525",
"0.601... | 0.0 | -1 |
extra_col being saved updates account balance | def test_extra_col_updates_account_balance(self):
banco = Bancos.objects.get(id=1)
previous_balance = banco.balance
condominio = Condominio.objects.get(rif='J6750435')
factura_condominio = Factura_Condominio.objects.get(id=1)
data ={
'id':1,
'factura': factura_condominio,
'titulo': 'sample_extra_col'... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def update_db_account_balances(self, quote:str, **kwargs):\n\n\t\t# real_balance \t\t\t\t= kwargs.get('real_balance', None)\n\t\t# real_locked \t\t\t\t= kwargs.get('real_locked', None)\n\t\t# internal_balance \t\t\t= kwargs.get('internal_balance', None)\n\t\t# internal_locked \t\t\t= kwargs.get('internal_locked', ... | [
"0.64701575",
"0.644509",
"0.6379475",
"0.6347287",
"0.6296751",
"0.61106837",
"0.61038214",
"0.6090905",
"0.6013311",
"0.5932772",
"0.5912174",
"0.5902762",
"0.5897663",
"0.5892612",
"0.5846604",
"0.5769641",
"0.5735082",
"0.57056206",
"0.56956476",
"0.56887454",
"0.5668993"... | 0.7536452 | 0 |
Creates the Real NVP or NICE bijector. | def __init__(self,
num_masked=None,
fraction_masked=None,
shift_and_log_scale_fn=None,
bijector_fn=None,
is_constant_jacobian=False,
validate_args=False,
name=None):
name = name or 'real_nvp'
with tf.name_sc... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create(self):\n\t\treturn handle_to_object(call_sdk_function('PrlVirtNet_Create'))",
"def create_entity(self):\n \n if self.ORION_CB.get_entity(self.params['name']) is None:\n \n print('[INFO]: Create new PID entity')\n \n entity_dict = {\"id\":self.p... | [
"0.5961635",
"0.57741874",
"0.5597742",
"0.52777296",
"0.5205733",
"0.51504385",
"0.5143571",
"0.5095149",
"0.50811416",
"0.50780755",
"0.5067421",
"0.50485516",
"0.5039967",
"0.49959785",
"0.49959785",
"0.49661916",
"0.4958066",
"0.4939919",
"0.493104",
"0.49255878",
"0.4914... | 0.0 | -1 |
Construct a new RationalQuadraticSpline bijector. For each argument, the innermost axis indexes bins/knots and batch axes index axes of `x`/`y` spaces. A `RationalQuadraticSpline` with a separate transform for each of three dimensions might have `bin_widths` shaped `[3, 32]`. To use the same spline for each of `x`'s th... | def __init__(self,
bin_widths,
bin_heights,
knot_slopes,
range_min=-1.,
validate_args=False,
name=None):
with tf.name_scope(name or 'RationalQuadraticSpline') as name:
dtype = dtype_util.common_dtype(
[bin_widt... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def interpolateSpline(x, y) :\n n = len(x)\n\n dim = 4 * (n - 1)\n b = np.zeros((dim, 1))\n A = np.zeros((dim, dim))\n\n for i in range(n-1):\n x1 = x[i]\n x2 = x[i+1]\n y1 = y[i]\n y2 = y[i+1]\n b[i*4:(i+1)*4, 0] = [y1, y2, 0, 0]\n\n A[i*4, i*4:(i+1)*4] = [... | [
"0.5183332",
"0.51626414",
"0.5119118",
"0.5079179",
"0.4961845",
"0.49498278",
"0.49099728",
"0.49079925",
"0.48892424",
"0.48506638",
"0.48246297",
"0.478306",
"0.47814262",
"0.4725182",
"0.4706628",
"0.46775335",
"0.46716303",
"0.46659052",
"0.46585312",
"0.46565458",
"0.4... | 0.6718378 | 0 |
Captures shared computations across forward/inverse/logdet. Only one of `x` or `y` should be specified. | def _compute_shared(self, x=None, y=None):
with tf.name_scope("comput_shared") as name:
assert (x is None) != (y is None)
is_x = x is not None
with tf.name_scope("piece0"):
range_min = tf.convert_to_tensor(self.range_min, name='range_min')
kx = _knot_positions(self.bin_widths, ran... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def forward(self, x, y):\n\n masked = self.mask * x\n\n s = self.s(masked, y)\n t = self.t(masked, y)\n z = masked + (1 - self.mask) * (x * s.exp() + t)\n\n log_det = (s * (1 - self.mask)).sum(1)\n\n return z, log_det",
"def test_log():\n x, y = fwd.Variable(), fwd.Va... | [
"0.6799488",
"0.5780545",
"0.561331",
"0.54602194",
"0.5458519",
"0.5442325",
"0.5433614",
"0.5368498",
"0.5366357",
"0.53153783",
"0.53071904",
"0.5299364",
"0.52870744",
"0.5253826",
"0.5222706",
"0.5199964",
"0.5198029",
"0.5174347",
"0.5129113",
"0.5123612",
"0.51117826",... | 0.0 | -1 |
Compute the forward transformation (Appendix A.1). | def _forward(self, x):
d = self._compute_shared(x=x)
relx = (x - d.x_k) / d.w_k
spline_val = (
d.y_k + ((d.h_k * (d.s_k * relx**2 + d.d_k * relx * (1 - relx))) /
(d.s_k + (d.d_kp1 + d.d_k - 2 * d.s_k) * relx * (1 - relx))))
y_val = spline_val #tf.where(d.out_of_bounds, x, spline... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def forward_transform(self):\n\n if self._pipeline:\n #return functools.reduce(lambda x, y: x | y, [step[1] for step in self._pipeline[: -1]])\n return functools.reduce(lambda x, y: x | y, [step.transform for step in self._pipeline[:-1]])\n else:\n return None",
"de... | [
"0.7412555",
"0.6997475",
"0.6997431",
"0.6997431",
"0.68757826",
"0.6822799",
"0.6821863",
"0.67669487",
"0.67491704",
"0.6735819",
"0.67256963",
"0.67136383",
"0.67097676",
"0.6668364",
"0.6662344",
"0.6639448",
"0.6637169",
"0.6623411",
"0.661096",
"0.660531",
"0.66014105"... | 0.0 | -1 |
Compute the inverse transformation (Appendix A.3). | def _inverse(self, y):
d = self._compute_shared(y=y)
rely = y - d.y_k # tf.where(d.out_of_bounds, tf.zeros_like(y), y - d.y_k)
term2 = rely * (d.d_kp1 + d.d_k - 2 * d.s_k)
# These terms are the a, b, c terms of the quadratic formula.
a = d.h_k * (d.s_k - d.d_k) + term2
b = d.h_k * d.d_k - term2
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def inverse_transform(self, X):\n ...",
"def inverse_transform(self, X):\n ...",
"def inverse_transform(self, X):\n ...",
"def inverse_transform(self, X):\n ...",
"def inverse_transform(self, X):\n ...",
"def inverse_transform(self, X, copy=...):\n ...",
"def i... | [
"0.7994033",
"0.7994033",
"0.7994033",
"0.7994033",
"0.7994033",
"0.7689036",
"0.76750916",
"0.76586753",
"0.7580127",
"0.75664145",
"0.7531505",
"0.74678403",
"0.7457205",
"0.7444826",
"0.7433826",
"0.74283147",
"0.7378608",
"0.73695505",
"0.7344377",
"0.733746",
"0.7327305"... | 0.6896039 | 77 |
Compute the forward derivative (Appendix A.2). | def _forward_log_det_jacobian(self, x):
d = self._compute_shared(x=x)
relx = (x - d.x_k) / d.w_k
relx = relx # tf.where(d.out_of_bounds, 0.5*tf.ones_like(x), relx)
grad = (
2 * tf.math.log(d.s_k) +
tf.math.log(d.d_kp1 * relx**2 + 2 * d.s_k * relx * (1 - relx) + # newln
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def derivative(func: Callable, x: float, delta: float) -> float:\n return (func(x + delta) - func(x - delta)) / (2.0 * delta)",
"def derivative ( self ):\n return self.__derivative",
"def deriv(func: Callable[[ndarrray], ndarray],\n input_: ndarray,\n delta: float = 0.00... | [
"0.7290176",
"0.7042805",
"0.69695157",
"0.6969174",
"0.68731177",
"0.6854815",
"0.6830507",
"0.6792242",
"0.67888516",
"0.67416143",
"0.6734721",
"0.6734721",
"0.6696811",
"0.6691667",
"0.6688757",
"0.666151",
"0.6658981",
"0.6657034",
"0.6640104",
"0.6640104",
"0.66360956",... | 0.0 | -1 |
Flattens and normalizes the plastic strain time derivative tensor. Normalize plastic strain time derivative by dividing by the strain time derivative magnitude | def calc_output(self, plastic_strain_dot, strain_dot):
# Calculate Frobenius norm of elastic strain time derivative tensor
strain_dot_mag = np.sqrt(np.mean(np.mean(np.square(strain_dot), axis=2), axis=1))
# To avoid singularities, if strain rate magnitude is zero, set it equal to one
s... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def normalize_transition(self):\n self._t /= self._t.sum(1)[:, np.newaxis]",
"def _partial_flatten_and_normalize(x):\n x = np.reshape(x, (x.shape[0], -1))\n return (x - np.mean(x)) / np.std(x)",
"def unnormalize(tensor, mean, std):\n for t, m, s in zip(tensor, mean, std):\n t.mul_(s).add... | [
"0.62458545",
"0.5984606",
"0.59279716",
"0.5894949",
"0.58369017",
"0.57565486",
"0.5707113",
"0.5672379",
"0.5637169",
"0.56276435",
"0.5559953",
"0.55526054",
"0.5547735",
"0.55388963",
"0.55312216",
"0.5528915",
"0.5524366",
"0.551839",
"0.5514702",
"0.54963565",
"0.54954... | 0.630154 | 0 |
Perform beta forecast for a set of scenarios on a regression. | def forecast(run_metadata,
specification,
output_root, mark_best, production_tag,
preprocess_only,
verbose, with_debugger,
**input_versions):
cli_tools.configure_logging_to_terminal(verbose)
do_forecast(
run_metadata=run_metadata,
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def linear_regression_forecasting(x_train,y_train,x_valid,y_valid,x_test,y_test):\n y_train = y_train.reshape(TRAINING_BATCH_SIZE,N_PREDICTIONS*N_OUTPUT_FEATURES)\n y_valid = y_valid.reshape(VALIDATION_BATCH_SIZE,N_PREDICTIONS*N_OUTPUT_FEATURES)\n ... | [
"0.6112371",
"0.59843546",
"0.5966853",
"0.59554493",
"0.5932704",
"0.5894218",
"0.5783442",
"0.564491",
"0.56345904",
"0.56212074",
"0.56020945",
"0.558852",
"0.5585161",
"0.55805236",
"0.55623627",
"0.5552391",
"0.5545439",
"0.55443627",
"0.5535678",
"0.55313015",
"0.551574... | 0.53226084 | 38 |
Gets the area cords for the dancefloor | def get_dancefloor_area(self):
cords = []
x1 = self.coordinates[0]
y1 = self.coordinates[1]
x2 = self.coordinates[0] + self.width
y2 = self.coordinates[1] + self.height
if self.width <= 0:
x1, x2 = x2, x1
if self.height <= 0:
y1, y2 = y2, ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_referenced_floor_area() -> np.ndarray:\n\n return envelope.get_referenced_floor_area()",
"def area(self):\n return _cantera.wall_area(self.__wall_id)",
"def get_area(self):\n ### Original\n from pyresample.spherical_geometry import get_polygon_area\n\n return get_polygon_... | [
"0.66182476",
"0.65240717",
"0.63324845",
"0.6230986",
"0.6223651",
"0.6190598",
"0.6087059",
"0.60808396",
"0.6071041",
"0.60708934",
"0.60700375",
"0.6048479",
"0.60449976",
"0.5992652",
"0.59492236",
"0.59431803",
"0.5929464",
"0.5906095",
"0.58992434",
"0.5882558",
"0.586... | 0.8184448 | 0 |
This function sends a SUCCESS/FAILED response to the CloudFormation stack that invoked the Lambda function. | def send_cf_response(event, context, success=True, reason='Unknown'):
data = {
'Status': 'SUCCESS' if success else 'FAILED',
'PhysicalResourceId': context.log_stream_name,
'StackId': event['StackId'],
'RequestId': event['RequestId'],
'LogicalResourceId': event['LogicalResourc... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def lambda_handler(event, context):\n\n return {\n \"statusCode\": 200,\n \"body\": json.dumps({\n 'message': 'API deployed',\n })\n }",
"def lambda_handler(event, context):\n return {\n 'statusCode': 200,\n 'body': say_hello()\n }",
"def lambda_respons... | [
"0.7016714",
"0.6934398",
"0.65957916",
"0.6580948",
"0.65773124",
"0.6350327",
"0.62960196",
"0.6292117",
"0.62879604",
"0.626153",
"0.62377375",
"0.6202347",
"0.6150959",
"0.61024934",
"0.60883546",
"0.59804237",
"0.5948244",
"0.5916419",
"0.5903106",
"0.58938974",
"0.58765... | 0.7171703 | 0 |
Save the model to a json file. | def save_model(self, model_path):
MODEL_FILE = 'simple_model.dat'
model = {
'm': self.m.tolist(),
'b': self.b,
'FEAST_CORE_URL': FEAST_CORE_URL,
'FEAST_SERVING_URL': FEAST_SERVING_URL,
'ENTITY_ID': ENTITY_ID,
}
logging.info('Saving model to %s', model_path)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def save_model(model):\n json_string = model.to_json()\n with open(MODEL_SAVE_JSON, 'w') as fp:\n fp.write(json_string)",
"def save_model(model, model_file):\n json_string = model.to_json()\n with open(model_file, 'w') as f:\n json.dump(json_string, f)",
"def save_model(self, model_fi... | [
"0.8499312",
"0.8433259",
"0.8345494",
"0.797389",
"0.7874161",
"0.787403",
"0.7827332",
"0.77711433",
"0.7760659",
"0.76583546",
"0.75438595",
"0.7513002",
"0.7510078",
"0.75076234",
"0.7502369",
"0.74815285",
"0.74815285",
"0.74815285",
"0.746824",
"0.7452417",
"0.74390745"... | 0.77844816 | 7 |
testuser1 has a 'testing' subnamespace, testuser2 tries to see its permissions but fails. | def testRandomUserCannotGETRandomUserSubNamespace(self):
headers = {
'accept': 'application/json',
}
self.addBasicAuthHeader(headers, 'testuser2', 'secret')
path = defaults.sep.join([defaults.namespaceCategoryName,
'testuser1', 'testing'])
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _check_namespace_access(self, namespace, user):\n if not namespace.owners.filter(id=user.id).count():\n raise exceptions.PermissionDenied(\n 'The namespace listed on your filename must match one of '\n 'the namespaces you have access to.'\n )",
"def ... | [
"0.6429766",
"0.6371066",
"0.6243876",
"0.6156751",
"0.6154213",
"0.61048347",
"0.6101933",
"0.60836434",
"0.60561347",
"0.6050891",
"0.6036248",
"0.60299695",
"0.6001029",
"0.5991596",
"0.59487164",
"0.5943904",
"0.5931206",
"0.5913553",
"0.5899936",
"0.58955866",
"0.5859986... | 0.66301256 | 0 |
Convert all dicts to HashableDicts in nested structure. | def parse(cls, el):
if isinstance(el, list):
for i, x in enumerate(el):
el[i] = HashableDict.parse(x)
elif isinstance(el, dict):
d = HashableDict()
for k, v in el.iteritems():
d[k] = HashableDict.parse(v)
return d
re... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def flatten(self):\n flat = {}\n for d in self.dicts:\n flat.update(d)\n return flat",
"def _deep_asdict(self):\n return {\n \"collections\": {k: p._deep_asdict() for (k, p) in self.collections.items()},\n }",
"def get_hashable_entries(nested:dict):\n ... | [
"0.65635574",
"0.64972985",
"0.63339704",
"0.633059",
"0.62674254",
"0.6173345",
"0.6043641",
"0.6025789",
"0.60111827",
"0.59066117",
"0.58889693",
"0.5853961",
"0.5845323",
"0.58159864",
"0.57850933",
"0.57364714",
"0.57173663",
"0.56847185",
"0.5674789",
"0.5667077",
"0.56... | 0.0 | -1 |
Checks if two intervals are overlapping. Function requires intervals to be proper (end >= start). Works with any types of comparable by mathematical operators objects. | def ranges_overlap(start1, end1, start2, end2):
return start1 <= end2 and end1 >= start2 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def dates_intervals_are_overlapped(start_1, end_1, start_2, end_2):\n return end_1 >= start_2 and end_2 >= start_1",
"def overlap(start1, end1, start2, end2):\n return not (end1 < start2 or end2 < start1)",
"def overlap(t1start, t1end, t2start, t2end):\n\n return (t1start <= t2start <= t1end) or (t2st... | [
"0.8221766",
"0.8196568",
"0.80125463",
"0.7714692",
"0.76665205",
"0.7543957",
"0.75031185",
"0.74704397",
"0.7351677",
"0.73447514",
"0.72555596",
"0.72339934",
"0.72237253",
"0.71955794",
"0.7165406",
"0.71499693",
"0.70835686",
"0.7058538",
"0.70213556",
"0.69799066",
"0.... | 0.82069135 | 1 |
Returns (list of) sum of intervals. Intervals could be tuple of any orderable values (ex. integers, dates). If sets are overlapping, they are "merged" together and resulting interval is (min(start1, start2), max(end1, end2)). >>> sum_of_intervals([(1, 5), (4, 10), (7, 13), (15, 20), (21, 30)]) [(1, 13), (15, 20), (21, ... | def sum_of_intervals(intervals):
intervals_set = set()
for v_start, v_end in intervals:
intervals_set.add((v_start, True))
intervals_set.add((v_end, False))
intervals = list(intervals_set)
intervals.sort(key=lambda a: a[0])
current_start = None
started = 0
result = []
fo... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def intervals(start, end, delta, intervals_right_closed=False):\r\n result = []\r\n interval_start, interval_end = None, None\r\n while True:\r\n interval_start = start if interval_end is None else interval_end\r\n interval_end = min(end, interval_start + delta)\r\n result.append(thal... | [
"0.59077203",
"0.563408",
"0.5407047",
"0.5392709",
"0.52991736",
"0.52946335",
"0.52101135",
"0.5203491",
"0.5171669",
"0.51691556",
"0.5138005",
"0.5121623",
"0.51164556",
"0.506967",
"0.506666",
"0.5054887",
"0.5036717",
"0.50176084",
"0.5004771",
"0.49338767",
"0.49328962... | 0.7316026 | 0 |
Normalize decimal without scientific notation (remove exponent and trailing zeros). | def normalize_decimal(d):
return d.quantize(Decimal(1)) if d == d.to_integral() else d.normalize() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def remove_exponent(val):\n context = decimal.Context(prec=self.max_digits)\n return val.quantize(decimal.Decimal(1), context=context) if val == val.to_integral() else val.normalize(context)",
"def normalize(amount, min=2, max=4):\n if not amount:\n return amount\n\n # To Decim... | [
"0.71156347",
"0.6817118",
"0.6672521",
"0.6446351",
"0.62960124",
"0.61859643",
"0.6139326",
"0.5954451",
"0.5883746",
"0.5880353",
"0.58401924",
"0.5834147",
"0.57599866",
"0.5726349",
"0.57119143",
"0.5709601",
"0.5705417",
"0.5702603",
"0.5699816",
"0.56875485",
"0.568076... | 0.7325013 | 0 |
Use IPMITOOL to blink a chassis LED | def blink_light(hostname, on=True):
if on:
opt = "force"
else:
opt = "0"
LOGGER.info("Blinking chassis LED for %s", hostname)
command = "ipmitool -U {} -P {} -H {} chassis identify {}".format(
USER, PASS, hostname, opt)
subprocess.check_call(command, shell=True) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"async def blink(my_board, pin):\n\n # set the pin mode\n await my_board.set_pin_mode_digital_output(pin)\n\n # toggle the pin 4 times and exit\n for x in range(4):\n print('ON')\n await my_board.digital_write(pin, 1)\n await asyncio.sleep(1)\n print('OFF')\n await my_... | [
"0.73258066",
"0.69174576",
"0.6745456",
"0.6721252",
"0.66942364",
"0.66491276",
"0.6608069",
"0.65577483",
"0.64461106",
"0.63938886",
"0.63897306",
"0.6379322",
"0.637831",
"0.63509107",
"0.63327783",
"0.626326",
"0.62223494",
"0.61845493",
"0.6181832",
"0.6151551",
"0.612... | 0.7913171 | 0 |
Get the MAC for eth0 using IPMI | def get_eth0_mac_address(hostname):
command = "ipmitool -H {} -U ADMIN -P ADMIN raw 0x30 0x21".format(hostname)
try:
result = subprocess.check_output(command, shell=True)
except subprocess.CalledProcessError as exc:
LOGGER.exception("Failed to get eth0 mac for %s", hostname)
# Remove ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_mac_address():\n eth0_interface = 'eth0'\n addresses = netifaces.ifaddresses(eth0_interface)[netifaces.AF_LINK][0]\n mac_address = addresses['addr']\n return mac_address",
"def getMac(self):\n # Import netifaces here to prevent error importing this module in setup.py\n import ne... | [
"0.78554046",
"0.78055936",
"0.7448746",
"0.7251684",
"0.72502637",
"0.72043616",
"0.7181221",
"0.71635437",
"0.70859075",
"0.69643205",
"0.69631994",
"0.6941659",
"0.69308925",
"0.6928495",
"0.68998766",
"0.6882689",
"0.6879579",
"0.68495977",
"0.68219835",
"0.67996645",
"0.... | 0.8025036 | 0 |
Get the BMC Mac from RedFish | def get_mac_address(hostname):
url = "https://{}/redfish/v1/Managers/1/EthernetInterfaces/1/".format(hostname)
eth_dict = requests.get(url, auth=(USER,PASS),verify=False).json()
mac_address = eth_dict['MACAddress']
LOGGER.info("IPMI BMC %s reports MAC address as %s", hostnameh, mac_address)
retur... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _get_mac(self):\n return self.__mac",
"def get_sonic_mac(host, asicnum, port):\n if host.facts[\"num_asic\"] == 1:\n cmd = \"sudo ip link show {}\".format(port)\n else:\n ns = \"asic\" + str(asicnum)\n cmd = \"sudo ip netns exec {} ip link show {}\".format(ns, port)\n output ... | [
"0.6588843",
"0.6586241",
"0.6426141",
"0.63516474",
"0.62712574",
"0.61390996",
"0.61328363",
"0.6111466",
"0.6111466",
"0.6086921",
"0.6073458",
"0.6061298",
"0.6035936",
"0.6028136",
"0.60262287",
"0.59856164",
"0.59211636",
"0.59168446",
"0.59006864",
"0.58995175",
"0.589... | 0.5884323 | 21 |
Prompt and convert cab and ru to a hostname | def location_to_index():
while True:
cabinet = int(raw_input("Cabinet: "))
ru = int(raw_input("Rack U: "))
index = ((cabinet - 1) * 32) + ru
correct = raw_input("Does index {} sound right (y/n)? ".format(index))
if 'y' in correct:
break
LOGGER.info("Mapp... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_host_name():\r\n\tglobal HOST_NAME\r\n\r\n\twhile True:\r\n\t\tname_of_host = input(\"What is your name (As it appears in zoom without the '(Host, me)' part)? \")\r\n\t\tHOST_NAME = name_of_host + \" (Host, me)\"\r\n\t\tcorrect = input(f\"Host name set as {HOST_NAME}, is this correct? [Y/N]: \")\r\n\t\tif ... | [
"0.61417115",
"0.59693193",
"0.58512926",
"0.5775678",
"0.5465449",
"0.54297626",
"0.5409156",
"0.53970236",
"0.5384489",
"0.5359659",
"0.52832544",
"0.52559316",
"0.5251943",
"0.52433914",
"0.5231732",
"0.5215157",
"0.51479506",
"0.5126778",
"0.51189417",
"0.5097943",
"0.505... | 0.0 | -1 |
Convert to an emulab style mac | def emulab_mac(mac):
return "".join(mac.lower().split(':')) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def macFor(cls, board):\n return cls.MAC_PREFIX + '{:02X}'.format(int(board))",
"def mac_ntoa(mac):\n return '%.2x:%.2x:%.2x:%.2x:%.2x:%.2x' % tuple(map(ord, list(mac)))",
"def good_mac(mac):\r\n return mac.upper().replace('-', ':')",
"def get_mac_string():\n mac_int = getnode()\n mac_str =... | [
"0.6040826",
"0.59956956",
"0.5854787",
"0.5779863",
"0.5675275",
"0.56129545",
"0.5586242",
"0.552146",
"0.54683423",
"0.5457044",
"0.54549307",
"0.54475677",
"0.5410852",
"0.5382184",
"0.5353483",
"0.5331743",
"0.5249846",
"0.52239674",
"0.5209968",
"0.51962715",
"0.5182766... | 0.69217736 | 0 |
Stitch together the steps | def provision_bmc(hostname):
LOGGER.info("Provisioning %s", hostname)
mac = get_mac_address(hostname)
blink_light(hostname)
index = location_to_index()
generate_sql(index, mac)
blink_light(hostname, on=False) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def do_step(self) -> None:",
"def _step(self) -> None:",
"def step_forward(self):",
"def step(self):\n #1. Time progresses\n self.time_operator.step()\n \n #2. Form and dissolve relationships\"\n self.relationship_operator.step()\n\n #3. HIV transmission\n sel... | [
"0.7023969",
"0.68842584",
"0.6786682",
"0.6676937",
"0.66504234",
"0.66142386",
"0.6586369",
"0.6399443",
"0.63699806",
"0.6316583",
"0.6300726",
"0.6257871",
"0.62492967",
"0.6244361",
"0.6241148",
"0.6221081",
"0.62170863",
"0.6203809",
"0.61842567",
"0.609894",
"0.6063322... | 0.0 | -1 |
See if an IP is up | def check_alive(hostname):
LOGGER.info("Seeing if %s is alive", hostname)
try:
subprocess.check_call("ping -o -c1 -W1 {}".format(hostname), shell=True)
except subprocess.CalledProcessError as exc:
LOGGER.info("Host %s appears down", hostname)
return False
else:
return T... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def checkIP(self, ip = None):\n\t\treturn os.system(\"ping -c 1 -w2 \" + ip + \" > /dev/null 2>&1\") == 0",
"def is_alive(self):\n ret = subprocess.call(\n shlex.split(\"ping -c 1 -W 2 %s\" % self.ip_address),\n stdout=open('/dev/null', 'w'),\n stderr=subprocess.STDOUT,\n ... | [
"0.73951226",
"0.7028806",
"0.6933253",
"0.6876365",
"0.67340374",
"0.67282856",
"0.67181677",
"0.66473746",
"0.6614493",
"0.66109097",
"0.6580546",
"0.658013",
"0.65744126",
"0.6572488",
"0.65436584",
"0.65303814",
"0.651624",
"0.6508601",
"0.64948905",
"0.6459352",
"0.64276... | 0.58888644 | 70 |
Verify that the right node blinks | def check_address():
for i in range(1, 97):
hostname = "sm{}-ipmi".format(i)
if not check_alive(hostname):
LOGGER.error("%s is dead", hostname)
else:
blink_light(hostname)
raw_input("Is host {} on? ".format(hostname))
blink_light(hostname, on... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def check_balance(self):\n\t\tif math.abs(self.get_height(treenode=self.left) - self.get_height(treenode=self.right)) > 1:\n\t\t\tself.rebalance()",
"def test_update_node_state_smartfail(self):\n pass",
"def test_breeding_failed_carn(self):\n nt.assert_equal(self.carn.breeding(1), None)",
"asyn... | [
"0.6044601",
"0.5912378",
"0.58205146",
"0.5657362",
"0.5654809",
"0.56453246",
"0.5585713",
"0.5581643",
"0.5547597",
"0.5540926",
"0.55249363",
"0.55155265",
"0.5514777",
"0.54998475",
"0.5494642",
"0.549018",
"0.5473279",
"0.5464496",
"0.5460745",
"0.5455696",
"0.5443095",... | 0.0 | -1 |
Gather the MAC addresses of eth0 from all SM hosts | def get_eth0_macs():
mac_dict = {
'mac_to_host': {},
'host_to_mac': {}
}
# Host Range
for i in range(1, 95):
if i == 47: # 47 is broken
continue
hostname = "sm{}-ipmi".format(i)
realname = "sm{}".format(i)
mac = get_eth0_mac_ad... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_mac_address_table(self):\n\n mac_address_table = []\n command = '/interface bridge host print terse'\n\n output = self._send_command(command)\n\n for host in parse_terse_output(output):\n mac_address_table.append({\n 'mac': cast_mac(host.get('mac-addres... | [
"0.7146105",
"0.67204034",
"0.6417322",
"0.6113413",
"0.60379297",
"0.6028078",
"0.6010826",
"0.5976122",
"0.5964501",
"0.5955474",
"0.5925722",
"0.5905432",
"0.5898975",
"0.58903706",
"0.58572847",
"0.5804898",
"0.5784774",
"0.5769922",
"0.57560873",
"0.575395",
"0.57504755"... | 0.70925903 | 1 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.