None:\\n t = generate_graph_resources(5)\\n field(t, (\\\"dr_1\\\", \\\"ds_1\\\", \\\"f1\\\")).references.append(\\n (FieldAddress(\\\"dr_2\\\", \\\"ds_2\\\", \\\"f1\\\"), \\\"to\\\")\\n )\\n field(t, (\\\"dr_1\\\", \\\"ds_1\\\", \\\"f1\\\")).references.append(\\n (FieldAddress(\\\"dr_3\\\", \\\"ds_3\\\", \\\"f1\\\"), \\\"to\\\")\\n )\\n field(t, (\\\"dr_1\\\", \\\"ds_1\\\", \\\"f1\\\")).references.append(\\n (FieldAddress(\\\"dr_4\\\", \\\"ds_4\\\", \\\"f1\\\"), \\\"to\\\")\\n )\\n field(t, (\\\"dr_1\\\", \\\"ds_1\\\", \\\"f1\\\")).references.append(\\n (FieldAddress(\\\"dr_5\\\", \\\"ds_5\\\", \\\"f1\\\"), \\\"to\\\")\\n )\\n field(t, (\\\"dr_1\\\", \\\"ds_1\\\", \\\"f1\\\")).identity = \\\"email\\\"\\n traversal = Traversal(DatasetGraph(*t), {\\\"email\\\": \\\"X\\\"})\\n\\n assert incoming_edges(traversal, CollectionAddress(\\\"dr_1\\\", \\\"ds_1\\\")) == {\\n Edge(\\n FieldAddress(\\\"__ROOT__\\\", \\\"__ROOT__\\\", \\\"email\\\"),\\n FieldAddress(\\\"dr_1\\\", \\\"ds_1\\\", \\\"f1\\\"),\\n )\\n }\\n assert outgoing_edges(traversal, CollectionAddress(\\\"dr_1\\\", \\\"ds_1\\\")) == {\\n Edge(FieldAddress(\\\"dr_1\\\", \\\"ds_1\\\", \\\"f1\\\"), FieldAddress(\\\"dr_3\\\", \\\"ds_3\\\", \\\"f1\\\")),\\n Edge(FieldAddress(\\\"dr_1\\\", \\\"ds_1\\\", \\\"f1\\\"), FieldAddress(\\\"dr_4\\\", \\\"ds_4\\\", \\\"f1\\\")),\\n Edge(FieldAddress(\\\"dr_1\\\", \\\"ds_1\\\", \\\"f1\\\"), FieldAddress(\\\"dr_5\\\", \\\"ds_5\\\", \\\"f1\\\")),\\n Edge(FieldAddress(\\\"dr_1\\\", \\\"ds_1\\\", \\\"f1\\\"), FieldAddress(\\\"dr_2\\\", \\\"ds_2\\\", \\\"f1\\\")),\\n }\\n\\n assert outgoing_edges(traversal, CollectionAddress(\\\"dr_5\\\", \\\"ds_5\\\")) == set()\\n assert incoming_edges(traversal, CollectionAddress(\\\"dr_2\\\", \\\"ds_2\\\")) == {\\n Edge(FieldAddress(\\\"dr_1\\\", \\\"ds_1\\\", \\\"f1\\\"), FieldAddress(\\\"dr_2\\\", \\\"ds_2\\\", \\\"f1\\\"))\\n }\\n assert incoming_edges(traversal, CollectionAddress(\\\"dr_3\\\", \\\"ds_3\\\")) == {\\n Edge(FieldAddress(\\\"dr_1\\\", \\\"ds_1\\\", \\\"f1\\\"), FieldAddress(\\\"dr_3\\\", \\\"ds_3\\\", \\\"f1\\\"))\\n }\\n assert incoming_edges(traversal, CollectionAddress(\\\"dr_4\\\", \\\"ds_4\\\")) == {\\n Edge(FieldAddress(\\\"dr_1\\\", \\\"ds_1\\\", \\\"f1\\\"), FieldAddress(\\\"dr_4\\\", \\\"ds_4\\\", \\\"f1\\\"))\\n }\\n assert incoming_edges(traversal, CollectionAddress(\\\"dr_5\\\", \\\"ds_5\\\")) == {\\n Edge(FieldAddress(\\\"dr_1\\\", \\\"ds_1\\\", \\\"f1\\\"), FieldAddress(\\\"dr_5\\\", \\\"ds_5\\\", \\\"f1\\\"))\\n }\\n traversal_map, terminators = traversal.traversal_map()\\n assert traversal_map == {\\n \\\"__ROOT__:__ROOT__\\\": {\\\"from\\\": {}, \\\"to\\\": {\\\"dr_1:ds_1\\\": {\\\"email -> f1\\\"}}},\\n \\\"dr_1:ds_1\\\": {\\n \\\"from\\\": {\\\"__ROOT__:__ROOT__\\\": {\\\"email -> f1\\\"}},\\n \\\"to\\\": {\\n \\\"dr_2:ds_2\\\": {\\\"f1 -> f1\\\"},\\n \\\"dr_3:ds_3\\\": {\\\"f1 -> f1\\\"},\\n \\\"dr_4:ds_4\\\": {\\\"f1 -> f1\\\"},\\n \\\"dr_5:ds_5\\\": {\\\"f1 -> f1\\\"},\\n },\\n },\\n \\\"dr_2:ds_2\\\": {\\\"from\\\": {\\\"dr_1:ds_1\\\": {\\\"f1 -> f1\\\"}}, \\\"to\\\": {}},\\n \\\"dr_3:ds_3\\\": {\\\"from\\\": {\\\"dr_1:ds_1\\\": {\\\"f1 -> f1\\\"}}, \\\"to\\\": {}},\\n \\\"dr_4:ds_4\\\": {\\\"from\\\": {\\\"dr_1:ds_1\\\": {\\\"f1 -> f1\\\"}}, \\\"to\\\": {}},\\n \\\"dr_5:ds_5\\\": {\\\"from\\\": {\\\"dr_1:ds_1\\\": {\\\"f1 -> f1\\\"}}, \\\"to\\\": {}},\\n }\\n\\n assert set(terminators) == {\\n CollectionAddress(\\\"dr_2\\\", \\\"ds_2\\\"),\\n CollectionAddress(\\\"dr_3\\\", \\\"ds_3\\\"),\\n CollectionAddress(\\\"dr_4\\\", \\\"ds_4\\\"),\\n CollectionAddress(\\\"dr_5\\\", \\\"ds_5\\\"),\\n }\",\n \"def sample_from_subpop(instance, params, subpop):\\n y = subpop\\n x = np.random.choice([-1,+1], size=params['d'])\\n x[instance['indices'][subpop]] = instance['values'][subpop]\\n return x, y, subpop\",\n \"def test_plot_graphs(self):\\n\\n # Graphs who are not embedded, i.e., have no coordinates.\\n COORDS_NO = {\\n 'Graph',\\n 'BarabasiAlbert',\\n 'ErdosRenyi',\\n 'FullConnected',\\n 'RandomRegular',\\n 'StochasticBlockModel',\\n }\\n\\n # Coordinates are not in 2D or 3D.\\n COORDS_WRONG_DIM = {'ImgPatches'}\\n\\n Gs = []\\n for classname in set(graphs.__all__) - COORDS_NO - COORDS_WRONG_DIM:\\n Graph = getattr(graphs, classname)\\n\\n # Classes who require parameters.\\n if classname == 'NNGraph':\\n Xin = np.arange(90).reshape(30, 3)\\n Gs.append(Graph(Xin))\\n elif classname in ['ImgPatches', 'Grid2dImgPatches']:\\n Gs.append(Graph(img=self._img, patch_shape=(3, 3)))\\n elif classname == 'LineGraph':\\n Gs.append(Graph(graphs.Sensor(20, seed=42)))\\n else:\\n Gs.append(Graph())\\n\\n # Add more test cases.\\n if classname == 'TwoMoons':\\n Gs.append(Graph(moontype='standard'))\\n Gs.append(Graph(moontype='synthesized'))\\n elif classname == 'Cube':\\n Gs.append(Graph(nb_dim=2))\\n Gs.append(Graph(nb_dim=3))\\n elif classname == 'DavidSensorNet':\\n Gs.append(Graph(N=64))\\n Gs.append(Graph(N=500))\\n Gs.append(Graph(N=128))\\n\\n for G in Gs:\\n self.assertTrue(hasattr(G, 'coords'))\\n self.assertEqual(G.N, G.coords.shape[0])\\n\\n signal = np.arange(G.N) + 0.3\\n\\n G.plot(backend='pyqtgraph')\\n G.plot(backend='matplotlib')\\n G.plot(signal, backend='pyqtgraph')\\n G.plot(signal, backend='matplotlib')\\n plotting.close_all()\",\n \"def run_generations(init_len):\\n num_graphs = 0\\n current_gen = [nx.path_graph(init_len)]\\n complete_graph_list = current_gen.copy()\\n while len(current_gen) and current_gen[0].size() < (3*init_len - 7):\\n current_gen = generation_next(current_gen)\\n num_graphs += show_graph_list(current_gen)\\n complete_graph_list.extend(filter_bridge_case(current_gen))\\n print(num_graphs)\\n return complete_graph_list\",\n \"def test_Tree():\",\n \"def test07_add_type_triples(self):\\n r = LDPRS('http://ex.org/abc')\\n g = Graph()\\n r.add_type_triples(g)\\n self.assertEqual(len(g), 2)\",\n \"def main():\\n GRAPH = lambda_graph()\\n GRAPH.save_graph(\\\"pylon\\\")\\n meshName = \\\"pylon.mesh\\\"\\n cmd = \\\"./population/linuxShow \\\"+meshName\\n process = subprocess.Popen(cmd, shell=True, stdout=subprocess.PIPE, stdin=subprocess.PIPE)\\n process.communicate()\\n print \\\"nodes:\\\", GRAPH.number_of_nodes()\\n print \\\"edges\\\", GRAPH.number_of_edges()\",\n \"def test_generator6(self):\\n xpb = XPathBuilder()\\n xp1 = xp2 = None\\n base_xp = xpb.base.foo.bar\\n base_gen = None\\n with base_xp as b:\\n base_gen = b\\n xp1 = xpb.a.b.c.join(b())\\n xp2 = xpb.test.join(b())\\n xp1_exp = '/a/b/c/base/foo/bar'\\n xp2_exp = '/test/base/foo/bar'\\n base_exp = '/base/foo/bar'\\n # check tree structure\\n self.assertTrue(base_xp._parent is None)\\n self.assertTrue(len(base_xp._children[0]._children[0]._children) == 0)\\n self.assertTrue(base_gen._parent is None)\\n self.assertTrue(len(base_gen._children) == 0)\\n # check xpath\\n self.assertEqual(xp1.tostring(), xp1_exp)\\n self.assertEqual(xp2.tostring(), xp2_exp)\\n self.assertEqual(base_xp.tostring(), base_exp)\\n self.assertEqual(base_gen.tostring(), base_exp)\",\n \"def test_createSubLinkographWithoutCommands(self):\\n self.performTestForParams()\",\n \"def crossover(self, pop):\\n intDiscID = self.iID + self.dID\\n varID = self.cID\\n goldenRatio = (1.0 + sqrt(5)) / 2.0\\n dx = np.zeros_like(pop[0])\\n children = []\\n used = []\\n for i in range(0, int(self.fracElite * len(pop)), 1):\\n r = int(rand() * self.population)\\n while r in used or r == i:\\n r = int(rand() * self.population)\\n\\n used.append(i)\\n children.append(cp.deepcopy(pop[r]))\\n dx = abs(pop[i] - children[i]) / goldenRatio\\n children[i] = children[i] + dx * varID + np.round(dx * intDiscID)\\n children[i] = simple_bounds(children[i], self.lb, self.ub)\\n\\n return (\\n children, used)\",\n \"def get_subgraphs(graph):\\n nodes_powerset = get_nodes_combinations(graph)\\n #print(\\\"Doing\\\")\\n #draw_graph(graph)\\n subgraphs = []\\n for nodes in nodes_powerset:\\n subg = graph.subgraph(nodes)\\n nodes = subg.nodes(data=True)\\n if nx.is_weakly_connected(subg):\\n subgraphs.append(subg)\\n return subgraphs\",\n \"def create_data_sample(self) -> DataNode:\\n root = DataNode(\\\"Grandpa\\\")\\n\\n node1 = DataNode(\\\"Node1\\\", parent=root)\\n node2 = DataNode(\\\"Node2\\\", parent=root)\\n node3 = DataNode(\\\"Node3\\\", parent=root)\\n\\n child1_1 = DataNode(\\\"Child1_1\\\", parent=node1)\\n child1_2 = DataNode(\\\"Child1_2\\\", parent=node1)\\n\\n child2_1 = DataNode(\\\"Child2_1\\\", parent=node2)\\n child2_2 = DataNode(\\\"Child2_2\\\", parent=node2)\\n child2_3 = DataNode(\\\"Child2_3\\\", parent=node2)\\n\\n child3_1 = DataNode(\\\"Child3_1\\\", parent=node3)\\n\\n grandchild1_1_1 = DataNode(\\\"Grandchild1_1_1\\\", parent=child1_1)\\n grandchild1_1_2 = DataNode(\\\"Grandchild1_1_2\\\", parent=child1_1)\\n\\n grandchild1_2_1 = DataNode(\\\"Grandchild1_2_1\\\", parent=child1_2)\\n\\n grandchild2_1_1 = DataNode(\\\"Grandchild2_1_1\\\", parent=child2_1)\\n grandchild2_1_2 = DataNode(\\\"Grandchild2_1_2\\\", parent=child2_1)\\n grandchild2_1_3 = DataNode(\\\"Grandchild2_1_3\\\", parent=child2_1)\\n\\n grandchild2_2_1 = DataNode(\\\"Grandchild2_2_1\\\", parent=child2_2)\\n grandchild2_2_2 = DataNode(\\\"Grandchild2_2_2\\\", parent=child2_2)\\n\\n grandchild2_3_1 = DataNode(\\\"Grandchild2_3_1\\\", parent=child2_3)\\n grandchild2_3_2 = DataNode(\\\"Grandchild2_3_2\\\", parent=child2_3)\\n grandchild2_3_3 = DataNode(\\\"Grandchild2_3_3\\\", parent=child2_3)\\n grandchild2_3_4 = DataNode(\\\"Grandchild2_3_4\\\", parent=child2_3)\\n\\n grandchild3_1_1 = DataNode(\\\"Grandchild3_1_1\\\", parent=child3_1)\\n grandchild3_1_2 = DataNode(\\\"Grandchild3_1_2\\\", parent=child3_1)\\n grandchild3_1_3 = DataNode(\\\"Grandchild3_1_3\\\", parent=child3_1)\\n grandchild3_1_4 = DataNode(\\\"Grandchild3_1_4\\\", parent=child3_1)\\n grandchild3_1_5 = DataNode(\\\"Grandchild3_1_5\\\", parent=child3_1)\\n\\n return root\",\n \"def test_ExplorePath_Simple( self ):\\n links = []\\n n1 = graph.Node( 10, 50 )\\n n2 = graph.Node( 10, 50 )\\n n3 = graph.Node( 10, 50 )\\n n7 = graph.Node( 10, 50 )\\n\\n links.append( graph.Link( n1, n2 ) )\\n links.append( graph.Link( n2, n3 ) )\\n links.append( graph.Link( n3, n7 ) )\\n roots = [n1]\\n actual = nodes.explorePath( links, roots, n1 )\\n expected = [ n1, n2, n3, n7 ]\\n self.assertEqual( expected, actual )\",\n \"def _build_graph(self):\\n pass\",\n \"def testGraphExtract(self):\\n graph = Graph2()\\n graph.parseFile(TESTFILE)\",\n \"def test_multiple_task_groups_dag(\\n self, test_multiple_taskgroups_dag, multiple_taskgroups_dag_expected_edges\\n ):\\n (\\n dag,\\n group1,\\n group2,\\n group3,\\n (\\n group1_emp1,\\n group1_emp2,\\n group1_emp3,\\n group2_emp1,\\n group2_emp2,\\n group2_emp3,\\n group2_op1,\\n group2_op2,\\n group3_emp1,\\n group3_emp2,\\n group3_emp3,\\n emp_in1,\\n emp_in2,\\n emp_in3,\\n emp_in4,\\n emp_out1,\\n emp_out2,\\n emp_out3,\\n emp_out4,\\n op_in1,\\n op_out1,\\n ),\\n ) = test_multiple_taskgroups_dag\\n\\n group1_emp1 >> Label(\\\"label group1.group1_emp1 <=> group1.group1_emp2\\\") >> group1_emp3\\n\\n emp_in1 >> group1\\n emp_in2 >> Label(\\\"label emp_in2 <=> group1\\\") >> group1\\n [emp_in3, emp_in4] >> Label(\\\"label emp_in3/emp_in4 <=> group1\\\") >> group1\\n XComArg(op_in1, \\\"test_key\\\") >> Label(\\\"label op_in1 <=> group1\\\") >> group1\\n\\n (\\n [group2_emp1, group2_emp2]\\n >> Label(\\\"label group2.group2_emp1/group2.group2_emp2 <=> group2.group2_emp3\\\")\\n >> group2_emp3\\n )\\n (\\n group2_emp1\\n >> Label(\\\"label group2.group2_emp1 <=> group2.group2_emp2/group2.group2_emp3\\\")\\n >> [group2_emp2, group2_emp3]\\n )\\n group2_emp3 >> Label(\\\"label group2.group2_emp3 <=> group3\\\") >> group3\\n\\n (\\n XComArg(group2_op1, \\\"test_key\\\")\\n >> Label(\\\"label group2.group2_op1 <=> group2.group2_op2\\\")\\n >> XComArg(group2_op2, \\\"test_key\\\")\\n )\\n XComArg(group2_op2, \\\"test_key\\\") >> Label(\\\"label group2.group2_op2 <=> group3\\\") >> group3\\n\\n group3 >> emp_out1\\n group3 >> Label(\\\"label group3 <=> emp_out2\\\") >> emp_out2\\n group3 >> Label(\\\"label group3 <=> emp_out3/emp_out4\\\") >> [emp_out3, emp_out4]\\n group3 >> Label(\\\"label group3 <=> op_out1\\\") >> XComArg(op_out1, \\\"test_key\\\")\\n\\n group1 >> Label(\\\"label group1 <=> group2\\\") >> group2\\n\\n compare_dag_edges(dag_edges(dag), multiple_taskgroups_dag_expected_edges)\",\n \"def test_generator7(self):\\n xpb = XPathBuilder()\\n xp1 = xp2 = None\\n base_xp = xpb.foo.bar & xpb.x.y\\n base_gen = None\\n with base_xp as b:\\n base_gen = b\\n xp1 = b() | xpb.c\\n xp2 = b() | xpb.d\\n xp1_exp = '/foo/bar and /x/y or /c'\\n xp2_exp = '/foo/bar and /x/y or /d'\\n base_exp = '/foo/bar and /x/y'\\n # check tree structure\\n self.assertTrue(base_xp._parent is None)\\n self.assertTrue(base_gen._parent is None)\\n # check xpath\\n self.assertEqual(xp1.tostring(), xp1_exp)\\n self.assertEqual(xp2.tostring(), xp2_exp)\\n self.assertEqual(base_xp.tostring(), base_exp)\\n self.assertEqual(base_gen.tostring(), base_exp)\",\n \"def explore(self, *args):\",\n \"def iter_func(root_name, root, set_traverse, list_funcs, G, strings,\\n plot_nodes, cur_pos, xgrain, min_weight, max_weight):\\n set_traverse.append(root)\\n nbs = G.neighbors(root)\\n nbs = G[root]\\n\\n plot_nodes.append(cur_pos)\\n xgrain = xgrain/2.0\\n\\n flag_pn = -1\\n for nb in nbs.keys():\\n if nb in set_traverse:\\n continue\\n\\n next_pos = [0, 0, 0]\\n if root.name == root_name:\\n next_pos[0] = cur_pos[0]\\n else:\\n next_pos[0] = cur_pos[0] + xgrain*flag_pn*( 0.8+0.2*(nbs[nb][\\\"weight\\\"]-1.0/max_weight)/(1.0/min_weight-1.0/max_weight) ) #* (nbs[nb][\\\"weight\\\"]-1.0/max_weight)/(1.0/min_weight-1.0/max_weight)\\n next_pos[1] = cur_pos[1] + 3.0*(nbs[nb][\\\"weight\\\"]-1.0/max_weight)/(1.0/min_weight-1.0/max_weight)\\n next_pos[2] = nb.name\\n\\n flag_pn = flag_pn*(-1)\\n\\n strings.append([root, nb])\\n set_traverse, strings, plot_nodes = iter_func(root_name, nb, set_traverse, list_funcs, G, strings, plot_nodes, next_pos, xgrain, min_weight, max_weight)\\n\\n return set_traverse, strings, plot_nodes\",\n \"def test_treewidth_complete_graphs():\\n\\n def test_kn(size):\\n \\\"\\\"\\\"Test on complete graphs.\\\"\\\"\\\"\\n graph = Graph()\\n for one in range(size):\\n for two in range(one + 1, size):\\n graph.add_edge(one, two)\\n eq_(size-1, graph.approx_treewidth())\\n for size in range(2, 6):\\n test_kn(size)\",\n \"def __init__(self, prefix, downstream, upstream, root):\\n super(SubGraph, self).__init__(prefix, downstream, upstream, root)\",\n \"def test_multiple_triples(self):\\n self.graph.add((artis, RDF.type, zoo))\\n self.graph.add((artis, RDF.type, org))\\n self.graph.add((berlin_zoo, RDF.type, zoo))\\n self.assertEquals(len(list(self.graph.triples((None, None, None)))), 3)\\n\\n self.assertEquals(len(list(self.graph.triples((artis, None, None)))), 2)\\n self.assertEquals(len(list(self.graph.triples((None, RDF.type, None)))), 3)\\n self.assertEquals(len(list(self.graph.triples((None, None, zoo)))), 2)\\n self.assertEquals(len(list(self.graph.triples((None, None, org)))), 1)\",\n \"def testFlatSeries(self):\\n self.AddToChart(self.chart, [5, 5, 5])\\n self.assertEqual(self.Param('chd'), 's:AAA')\\n self.chart.left.min = 0\\n self.chart.left.max = 5\\n self.assertEqual(self.Param('chd'), 's:999')\\n self.chart.left.min = 5\\n self.chart.left.max = 15\\n self.assertEqual(self.Param('chd'), 's:AAA')\",\n \"def main():\\n filenames = sys.argv[1]\\n fdir = sys.argv[2]\\n filenames = filenames.split(',')\\n\\n # print (filenames)\\n graph = PGraph(fdir, filenames, \\\"Multi-Source Foraging\\\")\\n # graph = PGraph(fdir, filenames, \\\"Cooperative Transport\\\")\\n # graph = PGraph(fdir, filenames, \\\"Nest Maintenance\\\")\\n # graph = PGraph(\\n # fdir, filenames, \\\"Nest Maintenance \\\\n with \\\\n Handcoded behaviors\\\")\\n graph.gen_plot()\\n\\n # box = BoxGraph(fdir, filenames, \\\"Single-Source Foraging\\\")\\n # box = BoxGraph(fdir, filenames, False, (-1, 100), \\\"Multi-Source Foraging\\\")\\n box = BoxGraph(fdir, filenames, False, (-1, 120), \\\"Nest Maintenance with Handcoded behaviors\\\")\\n # box = BoxGraph(\\n # fdir, filenames, \\\"Nest Maintenance \\\\n with \\\\n Handcoded behaviors\\\")\\n box.gen_plot()\",\n \"def _setup_graph(self):\\n pass\",\n \"def _setup_graph(self):\\n pass\",\n \"def testBasic1(self):\\n nodes = self.G.nodes()\\n assert len(nodes) == len( set(nodes) )\",\n \"def test_restore_multiple_in_subgraph(self):\\n subgraph = self._subgraph()\\n subgraph['id'] = 15\\n task1 = self._remote_task()\\n task1['id'] = 1\\n task2 = self._remote_task()\\n task2['id'] = 2\\n task1['parameters']['containing_subgraph'] = 15\\n task2['parameters']['containing_subgraph'] = 15\\n\\n graph = self._restore_graph([subgraph, task1, task2])\\n assert len(graph.tasks) == 3\\n subgraphs = [op for op in graph.tasks if op.is_subgraph]\\n remote_tasks = [op for op in graph.tasks if not op.is_subgraph]\\n\\n # those are all references to the same subgraph, the subgraph was\\n # NOT restored multiple times\\n assert remote_tasks[0].containing_subgraph \\\\\\n is remote_tasks[1].containing_subgraph \\\\\\n is subgraphs[0]\\n\\n assert len(subgraphs[0].tasks) == 2\",\n \"def test_wp_association_bp(self):\\n test_graph = wikipathways_to_bel(WP2359, self.hgnc_manager)\\n\\n self.assertEqual(type(test_graph), BELGraph, msg='Error with graph type')\\n\\n self.assertEqual(test_graph.summary_dict()['Number of Nodes'], 2)\\n self.assertEqual(test_graph.summary_dict()['Number of Edges'], 1)\\n self.assertEqual(count_relations(test_graph)['regulates'], 1)\",\n \"def generate_graph(data, x=\\\"Red blood Cells\\\", y=\\\"Paletes\\\", z=\\\"Leukocytes\\\", graph_type='scatter', selected_points=[], *args, **kwargs):\\n\\n # Return an empty figure if the input is empty\\n if x == [] and y==[]:\\n return go.Figure()\\n \\n # Transform the data into wide format\\n melted = pd.melt(data, value_vars=data.columns)\\n\\n if graph_type==\\\"histogram\\\":\\n if len(x) > 1:\\n return generate_histogram(melted, x, y, \\\"wide\\\", **kwargs)\\n elif len(x) == 0:\\n return go.Figure()\\n return generate_histogram(data, x, y, \\\"long\\\", **kwargs)\\n\\n elif graph_type==\\\"scatter\\\":\\n if len(x) == 1 and len(y) == 1:\\n return generate_scatter(data, x[0], y[0], \\\"long\\\", selected_points=selected_points, **kwargs)\\n\\n elif len(x) > 1 or len(y) > 1:\\n return generate_scatter_matrix(data, x, y, data_format=\\\"long\\\", **kwargs)\\n\\n return generate_scatter(melted, x, y, \\\"wide\\\")\\n\\n elif graph_type==\\\"box\\\":\\n return generate_box_plot(data, x, \\\"long\\\", **kwargs)\\n\\n elif graph_type==\\\"heatmap\\\":\\n if len(x) > 0 and len(y) > 0:\\n return generate_heatmap(data, x, y, \\\"long\\\", **kwargs)\\n return go.Figure()\\n\\n elif graph_type==\\\"par_coords\\\":\\n return generate_parallel_coords(data, x, y, \\\"long\\\", **kwargs)\\n\\n elif graph_type==\\\"strip\\\":\\n if len(x) > 0 and len(y) > 0:\\n return generate_strip(data, x, y, \\\"long\\\", **kwargs)\\n return go.Figure()\\n\\n elif graph_type==\\\"ternary\\\":\\n if len(x) > 0 and len(y) > 0 and len(z) > 0:\\n return generate_ternary(data, x, y, z, \\\"long\\\", **kwargs)\\n return go.Figure()\",\n \"def project_pop(self):\\n M = self.N[0:2]\\n for x in range(10):\\n M.append(self.run_step(M))\\n split_N = split_list(M)\\n \\n fig = self.make_figure(split_N)\\n fig.update_layout(title='Projected Fish Population')\\n\\n return fig\",\n \"def generate_test_graph(sameDomain = False):\\n num = 100\\n\\n urls = []\\n emails = []\\n nodes={}\\n if sameDomain:\\n domain = generate_domainname()\\n else:\\n domain = None\\n for i in range(num):\\n urls.append(generate_url(domain))\\n emails.append(generate_email())\\n \\n used_urls = set()\\n used_emails = set()\\n for u in urls:\\n l = random.choices(urls, k = floor(num/4))\\n #l = [u for u in urls]\\n e = random.choices(emails, k = floor(num/10))\\n #e = [e for e in emails]\\n used_urls.update(l)\\n used_emails.update(e)\\n nodes[u] = testNode(u, l, e)\\n nodes[u].generate_page()\\n \\n return nodes, urls, emails\",\n \"def setUp(self):\\n self.complete = nx.Graph()\\n self.complete.add_edge(1, 2)\\n self.complete.add_edge(2, 3)\\n self.complete.add_edge(1, 3)\\n\\n self.small_tree = nx.Graph()\\n self.small_tree.add_edge(1, 3)\\n self.small_tree.add_edge(4, 3)\\n self.small_tree.add_edge(2, 3)\\n self.small_tree.add_edge(3, 5)\\n self.small_tree.add_edge(5, 6)\\n self.small_tree.add_edge(5, 7)\\n self.small_tree.add_edge(6, 7)\\n\\n self.deterministic_graph = nx.Graph()\\n self.deterministic_graph.add_edge(0, 1) # deg(0) = 1\\n\\n self.deterministic_graph.add_edge(1, 2) # deg(1) = 2\\n\\n self.deterministic_graph.add_edge(2, 3)\\n self.deterministic_graph.add_edge(2, 4) # deg(2) = 3\\n\\n self.deterministic_graph.add_edge(3, 4)\\n self.deterministic_graph.add_edge(3, 5)\\n self.deterministic_graph.add_edge(3, 6) # deg(3) = 4\\n\\n self.deterministic_graph.add_edge(4, 5)\\n self.deterministic_graph.add_edge(4, 6)\\n self.deterministic_graph.add_edge(4, 7) # deg(4) = 5\\n\\n self.deterministic_graph.add_edge(5, 6)\\n self.deterministic_graph.add_edge(5, 7)\\n self.deterministic_graph.add_edge(5, 8)\\n self.deterministic_graph.add_edge(5, 9) # deg(5) = 6\\n\\n self.deterministic_graph.add_edge(6, 7)\\n self.deterministic_graph.add_edge(6, 8)\\n self.deterministic_graph.add_edge(6, 9) # deg(6) = 6\\n\\n self.deterministic_graph.add_edge(7, 8)\\n self.deterministic_graph.add_edge(7, 9) # deg(7) = 5\\n\\n self.deterministic_graph.add_edge(8, 9) # deg(8) = 4\",\n \"def main():\\n\\n bGraph = DiGraph()\\n bGraph + Vertex(\\\"alpha\\\", 20, deltaInherent=5)\\n bGraph[\\\"beta\\\"] = Vertex(\\\"beta\\\", 13)\\n bGraph.add_edge(bGraph[\\\"alpha\\\"], bGraph[\\\"beta\\\"], \\\"aa_lin\\\", [1])\\n print(bGraph[\\\"alpha\\\"])\\n print(bGraph)\\n bGraph + Vertex(\\\"gamma\\\", 4, randomFlag=True, randomInfo=(0, 10))\\n bGraph.apply_floating_deltas()\\n for vert in bGraph:\\n print(vert)\",\n \"def makeQuadSubplots(df_rad_obs, \\n df_dir_obs, \\n df_rad_sen, \\n df_dir_sen, \\n suptitle='Big title',\\n eps=3, \\n min_samples=50):\\n fig, axs = plt.subplots(2, 2, \\n figsize=(10,10)\\n )\\n\\n fig.suptitle('Clustering Output', fontsize=20)\\n\\n populateSubPlot(df=df_rad_obs,\\n eps=eps, \\n min_samples=min_samples,\\n fig=fig, \\n axs=axs, \\n row=0, \\n col=0, title='Obsever Wards Radiant')\\n\\n\\n populateSubPlot(df=df_dir_obs, \\n eps=eps, \\n min_samples=min_samples,\\n fig=fig, \\n axs=axs, \\n row=0, \\n col=1, title='Obsever Wards Dire')\\n\\n\\n populateSubPlot(df=df_rad_sen, \\n eps=eps, \\n min_samples=min_samples,\\n fig=fig, \\n axs=axs, \\n row=1, \\n col=0, title='Sentry Wards Radiant')\\n\\n populateSubPlot(df=df_dir_sen, \\n eps=eps, \\n min_samples=min_samples,\\n fig=fig, \\n axs=axs, \\n row=1, \\n col=1, title='Sentry Wards Dire')\\n \\n \\n return fig, axs\",\n \"def di_subgraph(self, nbunch):\\n\\t\\t\\n\\t\\t# create new subgraph.\\n\\t\\tg = nx.DiGraph()\\n\\t\\t\\n\\t\\t# add edges and nodes.\\n\\t\\tg.add_edges_from( self.edges(nbunch=nbunch) )\\n\\t\\t\\n\\t\\treturn g\",\n \"def create_hierarchy(self):\\n\\t\\tpass\",\n \"def test_xcomarg_set(self, test_dag):\\n # Unpack the fixture\\n dag, (op1, op2, op3, op4) = test_dag\\n # Arrange the operators with a Label in the middle\\n op1_arg = XComArg(op1, \\\"test_key\\\")\\n op1_arg.set_downstream(op2, Label(\\\"Label 1\\\"))\\n op1.set_downstream([op3, op4])\\n # Check that the DAG has the right edge info\\n assert dag.get_edge_info(op1.task_id, op2.task_id) == {\\\"label\\\": \\\"Label 1\\\"}\\n assert dag.get_edge_info(op1.task_id, op4.task_id) == {}\",\n \"def rectangle_graph():\\n scaled = scale((200, 200, 200), 2)\\n print scaled\\n pylon_graph = graph.graph()\\n base = rectangle(ORIGIN, WIDTH, LENGTH)\\n base_ids = pylon_graph.add_nodes(base, \\\"base\\\")\\n pylon_graph.connect_neighbours(base_ids, LENGTH)\\n pylon_graph.connect_neighbours(base_ids, WIDTH)\\n all_ids = []\\n for i in range(LEVELS):\\n level = offset(base, LENGTH * i, \\\"z\\\")\\n level_ids = pylon_graph.add_nodes(level, \\\"level\\\" + str(i))\\n all_ids.extend(level_ids)\\n pylon_graph.connect_neighbours(all_ids, LENGTH)\\n pylon_graph.connect_neighbours(all_ids, WIDTH)\\n return pylon_graph\",\n \"def subplot_2(self, Graph, n_tabs):\\n # The code below walks does a pre-order traversal of the tree\\n # For exact details about the structure of self.Graph refer description in init function.\\n\\n attr_name = list(Graph.keys())[0]\\n print(\\\"\\\\t\\\"*(n_tabs),\\\"feature name :\\\",attr_name)\\n for val in list(Graph[attr_name].keys()):\\n if (val[1]==1):\\n des = \\\"greater\\\"\\n else:\\n des = \\\"lower\\\"\\n print(\\\"\\\\t\\\"*(n_tabs+1),\\\"feature threashold :\\\", val[0],\\\" \\\",des)\\n sub_graph = Graph[attr_name][val]\\n if (type(sub_graph)==dict):\\n self.subplot_2(sub_graph, n_tabs+2)\\n else:\\n print(\\\"\\\\t\\\"*(n_tabs+2), \\\"prediction :\\\",sub_graph)\",\n \"def recreate_subgraphs_name():\\n global SUBGRAPHS\\n for (name, subgraph) in SUBGRAPHS.items():\\n subgraph.set_name(\\\"\\\\\\\"cluster_\\\" + subgraph.get_name() + \\\"\\\\\\\"\\\")\",\n \"def show_custom_graph(self):\\n pass\",\n \"def test_dot(self):\\n graph = graphviz.Graph(comment='The Round Table')\\n graph.node('A', 'King Arthur')\\n graph.node('B', 'Sir Bedevere the Wise')\\n graph.edges(['AB'])\\n\\n st.graphviz_chart(graph)\\n\\n c = self.get_delta_from_queue().new_element.graphviz_chart\\n self.assertEqual(hasattr(c, 'spec'), True)\",\n \"def test_sizergrid():\\n regular_grid(8, 3)\\n mpl.show()\",\n \"def test_get_grid_edge_nodes(flopy_dis_mf6):\\n mf6 = flopy_dis_mf6[1]\\n mf6.initialize()\\n\\n with pytest.raises(NotImplementedError):\\n mf6.get_grid_edge_nodes(1, np.zeros((1, 1)))\",\n \"def create_subgrid(self)->list:\\n return [subgrid.Subgrid(i) for i in range(0, 9)]\",\n \"def test_get_related_nodes(self):\\n pass\",\n \"def test_graphid_construction():\\n _ = _ir.GraphId(\\\"g\\\")\",\n \"def discover_structure_from_pops(self, pops, data):\\n\\n def create_maximal_pgm(pops):\\n pgm = nx.DiGraph()\\n pgm.add_nodes_from(pops) # create nodes\\n for node in pops:\\n edges = [(parent, node) for parent in pops.get(node) if\\n node.rsplit('_', 1)[0] != parent.rsplit('_', 1)[0]]\\n pgm.add_edges_from(edges) # add edges\\n return pgm\\n\\n def markov_blanket(graph, parent_node, node):\\n mb = set(pa for pa in graph.predecessors(node)) # add parent nodes\\n mb |= set(ch for ch in graph.successors(node)) # add child nodes\\n for child in graph.successors(node): # add parents of children\\n mb |= set(pa for pa in graph.predecessors(child))\\n if node in mb: # remove node\\n mb.remove(node)\\n if parent_node in mb: # remove parent_node\\n mb.remove(parent_node)\\n return mb\\n\\n max_pgm = create_maximal_pgm(pops)\\n\\n if self.draw:\\n plt.title('Maximal PGM (only intra-edges)')\\n signal_pos_map = {}\\n pos = {}\\n for node in max_pgm.nodes:\\n if node.rsplit('_', 1)[0] not in signal_pos_map:\\n signal_pos_map.update({node.rsplit('_', 1)[0]: len(signal_pos_map)})\\n x_coordinate = int(node[-1:])\\n y_coordinate = signal_pos_map.get(node.rsplit('_', 1)[0])\\n pos.update({node: [x_coordinate, y_coordinate]})\\n nx.draw(max_pgm, pos=pos, with_labels=True)\\n plt.show()\\n pass\\n\\n if isinstance(data, ADTree):\\n cb_estimator = GSquareEstimator(adtree=data)\\n else:\\n cb_estimator = BaseEstimator(data=data, complete_samples_only=False)\\n # procedure similar to PC algorithm\\n pgm = max_pgm.copy()\\n condition_set_size = 0\\n L().log.debug('---------------------------------------------------')\\n L().log.debug('---- Conditional Independence Tests ---------------')\\n L().log.debug('---------------------------------------------------')\\n\\n #\\n if self.optimization_chi_square:\\n import scipy.stats as scs\\n def chi_square_of_df_cols(df, col1, col2):\\n df_col1, df_col2 = df[col1], df[col2]\\n categories_2 = list(df_col2.unique())\\n categories_1 = list(df_col1.unique())\\n result = [[sum((df_col1 == cat1) & (df_col2 == cat2))\\n for cat2 in categories_2]\\n for cat1 in categories_1]\\n\\n chi = scs.chi2_contingency(result)\\n\\n\\n return chi\\n\\n remove_edges = []\\n for (source, target) in pgm.edges():\\n # check how correlated those two edges are / independent of MB and all the other stuff\\n dat = chi_square_of_df_cols(self.data, source, target) # 1 = more corr. 0 = less corr.\\n chi2, p, sufficient_data = dat[0], dat[1], dat[2]\\n #print(\\\"%s Chi = %s, p=%s\\\" % (str([source, target]), str(chi2), str(p)))\\n\\n if chi2 < self.chi_square_thresh and pgm.has_edge(source, target):\\n L().log.debug('remove edge ' + str((source, target)))\\n remove_edges.append((source, target))\\n pgm.remove_edges_from(remove_edges)\\n #import sys\\n #sys.exit(0)\\n\\n\\n # additionally remove edges which are conditionally independent\\n # e.g. given a-> b c->b and given a, c is independent of b, then I can remove c!!!\\n remove_edges = []\\n for (source, target) in pgm.edges():\\n condition_set = [a for a in pgm.predecessors(target) if a != source]\\n if not condition_set:continue\\n _, p_val, _ = cb_estimator.test_conditional_independence(source, target, list(condition_set))\\n if p_val > self.alpha:\\n if pgm.has_edge(source, target):\\n L().log.debug('remove edge ' + str((source, target)))\\n remove_edges.append((source, target))\\n pgm.remove_edges_from(remove_edges)\\n\\n else:\\n while True:\\n cont = False\\n remove_edges = []\\n for (source, target) in pgm.edges():\\n mb = markov_blanket(pgm, target, source)\\n if len(mb) >= condition_set_size:\\n L().log.debug('testing ' + source + ' --> ' + target)\\n L().log.debug('markov blanket of ' + source + ' is ' + str(mb))\\n for condition_set in combinations(mb, condition_set_size):\\n L().log.debug(\\n 'independence test of ' + source + ' and ' + target + ' with subset ' + str(condition_set))\\n _, p_val, _ = cb_estimator.test_conditional_independence(source, target, list(condition_set))\\n #if isnan(p_val): # pgmpy CI test returns NaN instead of 1\\n # p_val = 1\\n L().log.debug('p_val = ' + str(p_val))\\n if p_val > self.alpha:\\n if pgm.has_edge(source, target):\\n L().log.debug('remove edge ' + str((source, target)))\\n remove_edges.append((source, target))\\n break\\n pass\\n cont = True\\n pass\\n condition_set_size += 1\\n pgm.remove_edges_from(remove_edges)\\n if cont is False:\\n break\\n if condition_set_size > self.max_reach:\\n break\\n\\n if self.draw:\\n plt.title('PGM after CI tests (only inter-edges)')\\n signal_pos_map = {}\\n pos = {}\\n for node in pgm.nodes:\\n if node.rsplit('_', 1)[0] not in signal_pos_map:\\n signal_pos_map.update({node.rsplit('_', 1)[0]: len(signal_pos_map)})\\n x_coordinate = int(node[-1:])\\n y_coordinate = signal_pos_map.get(node.rsplit('_', 1)[0])\\n pos.update({node: [x_coordinate, y_coordinate]})\\n nx.draw(pgm, pos=pos, with_labels=True)\\n plt.show()\\n pass\\n\\n nodes = list(pops.keys())\\n edges = [list(edge) for edge in pgm.edges]\\n return nodes, edges\",\n \"def test_ExplorePath( self ):\\n links = []\\n n1 = graph.Node( 10, 10 )\\n n2 = graph.Node( 10, 20 )\\n n3 = graph.Node( 10, 30 )\\n n4a = graph.Node( 5, 40 )\\n n4b = graph.Node( 15, 40 )\\n n5a = graph.Node( 5, 50 )\\n n5b = graph.Node( 15, 50 )\\n n6a = graph.Node( 5, 60 )\\n n6b = graph.Node( 15, 60 )\\n n7 = graph.Node( 10, 70 )\\n\\n links.append( graph.Link( n1, n2 ) )\\n links.append( graph.Link( n2, n3 ) )\\n links.append( graph.Link( n3, n4a ) )\\n links.append( graph.Link( n3, n4b ) )\\n links.append( graph.Link( n4a, n5a ) )\\n links.append( graph.Link( n4b, n5b ) )\\n links.append( graph.Link( n5a, n6a ) )\\n links.append( graph.Link( n6a, n7 ) )\\n links.append( graph.Link( n5b, n7 ) )\\n roots = [n1]\\n actual = nodes.explorePath( links, n1, n1 )\\n expected = [ n1, n2, n3, n4b, n5b, n7 ]\\n self.assertEqual( expected, actual )\",\n \"def create_graph_domain():\\n \\n \\\"\\\"\\\"\\n Fetch data\\n \\\"\\\"\\\"\\n \\n from input.read_input import read_item_data\\n df = read_item_data()\\n df['item_id'] = df.index\\n dct_title = df['title'].to_dict()\\n dct_domain = df['domain_id'].to_dict()\\n dct_cat= df['category_id'].to_dict()\\n \\n dct_price = df['price'].to_dict()\\n \\n \\\"\\\"\\\" Ratio stuff \\\"\\\"\\\" \\n from input.create_ratio import get_ratio\\n dct_ratio_dom = get_ratio(which='domain_id')\\n \\n ratio_df = get_ratio(which='item_id',full=True)\\n ratio_df['popularity'] = 100.0*ratio_df['bought'] + ratio_df['searched']\\n dct_ratio_item_b = ratio_df['popularity'].to_dict()\\n \\n \\n \\n \\\"\\\"\\\"\\n JSON\\n \\n \\\"\\\"\\\"\\n check = lambda x: x <= np.round(413163*0.8).astype(np.int32)\\n \\n DATA_PATH = path.join(DATA_DIR,'train_dataset.jl')\\n line_i = 0\\n \\n \\n\\n \\\"\\\"\\\"\\n Create graph vertices\\n \\\"\\\"\\\"\\n g = ig.Graph() \\n from input.read_input import get_mappings\\n counter, f_map_func, r_map_func = get_mappings()\\n \\n num_items = df.shape[0]\\n for k in dct_title.keys():\\n g.add_vertex(value=k,deg=dct_ratio_item_b[k],domain_id=dct_domain[k],price=dct_price[k],cat='item_id')\\n\\n \\\"\\\"\\\" ['item_id','domain_id','category_id','product_id'] \\\"\\\"\\\"\\n \\n for k in pd.unique(df['domain_id']):\\n g.add_vertex(value=k,cat='domain_id')\\n\\n\\n for k in pd.unique(df['category_id']):\\n g.add_vertex(value=k,cat='category_id')\\n\\n\\n for k in pd.unique(df['product_id']):\\n g.add_vertex(value=k,cat='product_id')\\n\\n \\n \\n \\\"\\\"\\\"\\n Create edges\\n \\\"\\\"\\\"\\n E1 = []\\n E2 = []\\n \\n with jsonlines.open(DATA_PATH) as reader:\\n for line_i, obj in enumerate(reader):\\n if check(line_i):\\n print(line_i)\\n L = []\\n for h in obj['user_history']:\\n if h['event_type'] == 'view':\\n #print(\\\"Viewed {}\\\".format(dct[h['event_info']]))\\n L.append(h['event_info'])\\n elif h['event_type'] == 'search':\\n #print(\\\"Searched {}\\\".format(h['event_info']))\\n pass\\n L_domain = [dct_domain[k] for k in L]\\n L_domain = pd.unique(L_domain)\\n L_cat = [dct_cat[k] for k in L]\\n L_cat = pd.unique(L_cat)\\n \\n for i in range(len(L)):\\n E1.append(dct_domain[L[i]])\\n E2.append(dct_domain[obj['item_bought']] )\\n\\n \\n \\n E1 = f_map_func['domain_id'](E1)\\n E2 = f_map_func['domain_id'](E2)\\n \\n \\n E = pd.Series(list(zip(E1,E2))).value_counts()\\n g.add_edges(E.index)\\n g.es[\\\"weight\\\"] = E.values\\n \\n \\n g.write_pickle(fname=path.join(DATA_DIR,'graph_domain_to_domain.pkl'))\",\n \"def test_graph2():\\n mol_graph1 = DGLGraph([(0, 1), (0, 2), (1, 2)])\\n mol_graph2 = DGLGraph([(0, 1), (1, 2), (1, 3), (1, 4)])\\n batch_mol_graph = dgl.batch([mol_graph1, mol_graph2])\\n node_feats = torch.arange(batch_mol_graph.number_of_nodes()).float().reshape(-1, 1)\\n edge_feats = torch.arange(2 * batch_mol_graph.number_of_edges()).float().reshape(-1, 2)\\n\\n complete_graph1 = get_complete_graph(mol_graph1.number_of_nodes())\\n complete_graph2 = get_complete_graph(mol_graph2.number_of_nodes())\\n batch_complete_graph = dgl.batch([complete_graph1, complete_graph2])\\n atom_pair_feats = torch.arange(batch_complete_graph.number_of_edges()).float().reshape(-1, 1)\\n\\n return batch_mol_graph, node_feats, edge_feats, batch_complete_graph, atom_pair_feats\",\n \"def build_drop_fullgraphs(self, do_subgraph=False, graph_lib='pygraphviz'):\\n if 'pygraphviz' == graph_lib:\\n G = pgv.AGraph(strict=True, directed=True)\\n else:\\n G = nx.Graph()\\n do_subgraph = False\\n subgraph_dict = defaultdict(list) # k - node-ip, v - a list of graph nodes\\n oid_gnid_dict = dict()\\n\\n for i, oid in enumerate(self.pg_spec.keys()):\\n oid_gnid_dict[oid] = str(i)\\n logger.info(\\\"oid to gid mapping done\\\")\\n\\n for dropspec in self.pg_spec.itervalues():\\n gid = oid_gnid_dict[dropspec['oid']]\\n ip = dropspec['node']\\n subgraph_dict[ip].append(gid)\\n if (dropspec['type'] == 'app'):\\n G.add_node(gid, shape='rect', label='')#, fixedsize=True, hight=.05, width=.05)\\n elif (dropspec['type'] == 'plain'): #parallelogram\\n G.add_node(gid, shape='circle', label='')#, fixedsize=True, hight=.05, width=.05)\\n logger.info(\\\"Graph nodes added\\\")\\n\\n for dropspec in self.pg_spec.itervalues():\\n gid = oid_gnid_dict[dropspec['oid']]\\n if (dropspec['type'] == 'app'):\\n ds_kw = 'outputs' #down stream key word\\n elif (dropspec['type'] == 'plain'):\\n ds_kw = 'consumers'\\n else:\\n ds_kw = 'None'\\n if (ds_kw in dropspec):\\n for doid in dropspec[ds_kw]:\\n G.add_edge(gid, oid_gnid_dict[doid])\\n logger.info(\\\"Graph edges added\\\")\\n\\n if (do_subgraph):\\n for i, subgraph_nodes in enumerate(subgraph_dict.values()):\\n # we don't care about the subgraph label or rank\\n subgraph = G.add_subgraph(subgraph_nodes, label='%d' % i, name=\\\"cluster_%d\\\" % i, rank=\\\"same\\\")\\n subgraph.graph_attr['rank']='same'\\n logger.info(\\\"Subgraph added\\\")\\n\\n return G\",\n \"def slice_graph_fwd( startea, reg ): \\r\\n\\tgraph = vcg_Graph.vcgGraph({\\\"title\\\":'\\\"Slice for %s\\\"' % reg, \\\\\\r\\n\\t\\t\\\"manhattan_edges\\\":\\\"no\\\", \\\"layoutalgorithm\\\":\\\"maxdepth\\\"})\\r\\n\\t#\\r\\n\\t# Retrieve the name of the current basic block\\r\\n\\t# \\r\\n\\tworklist = []\\r\\n\\tdata_bib = {}\\r\\n\\tstartnode = slice_node( startea, 0, reg )\\r\\n\\trootnode = graph.Add_Node( startnode.to_name() )\\r\\n\\tdata_bib[ startnode.to_name() ] = startnode\\r\\n\\tworklist.insert( 0, rootnode )\\r\\n\\twhile len( worklist ) > 0:\\r\\n\\t\\tcurrnode = worklist.pop()\\r\\n\\t\\tcurrslice = data_bib[ currnode.get_name() ]\\r\\n\\t\\ttgt_reg = currslice.get_target_reg()\\r\\n\\t\\tif tgt_reg == \\\"END\\\":\\r\\n\\t\\t# Do not process this node any further\\r\\n\\t\\t\\tpass\\r\\n\\t\\telif tgt_reg == \\\"\\\" or (( len( currslice.get_lines()) > 0) and \\\\\\r\\n\\t\\t\\tcurrslice.endea != currslice.get_lines()[-1][0]):\\r\\n\\t\\t\\t# Nothing much happening here, just proceed to parent bocks\\r\\n\\t\\t\\tif ua_mnem( currslice.endea ) == \\\"call\\\":\\r\\n\\t\\t\\t\\txrefs = get_short_crefs_from( currslice.endea )\\r\\n\\t\\t\\telse:\\r\\n\\t\\t\\t\\txrefs = get_crefs_from( currslice.endea )\\r\\n\\t\\t\\tfor ref in xrefs:\\r\\n\\t\\t\\t\\tnewslice = slice_node( ref, 0, currslice.reg )\\r\\n\\t\\t\\t\\tif graph.Get_Node( newslice.to_name() ) == 0:\\r\\n\\t\\t\\t\\t\\tnewnode = graph.Add_Node( newslice.to_name() )\\r\\n\\t\\t\\t\\t\\tworklist.insert( 0, newnode )\\r\\n\\t\\t\\t\\t\\tdata_bib[ newslice.to_name() ] = newslice\\r\\n\\t\\t\\t\\tgraph.Add_Link( currnode.get_name(), newslice.to_name())\\r\\n\\t\\telse:\\r\\n\\t\\t\\t# Register was modified, use new register\\r\\n\\t\\t\\txrefs = get_crefs_from( currslice.endea )\\r\\n\\t\\t\\tfor ref in xrefs:\\r\\n\\t\\t\\t\\tnewslice = slice_node( ref, 0, tgt_reg )\\r\\n\\t\\t\\t\\tif graph.Get_Node( newslice.to_name() ) == 0:\\r\\n\\t\\t\\t\\t\\tnewnode = graph.Add_Node( newslice.to_name() )\\r\\n\\t\\t\\t\\t\\tworklist.insert( 0, newnode )\\r\\n\\t\\t\\t\\t\\tdata_bib[ newslice.to_name() ] = newslice\\r\\n\\t\\t\\t\\tgraph.Add_Link( currnode.get_name(), newslice.to_name())\\r\\n\\t\\t\\txrefs = get_crefs_from( currslice.endea )\\r\\n\\t\\t\\tfor ref in xrefs:\\r\\n\\t\\t\\t\\tnewslice = slice_node( ref, 0, currslice.reg )\\r\\n\\t\\t\\t\\tif graph.Get_Node( newslice.to_name() ) == 0:\\r\\n\\t\\t\\t\\t\\tnewnode = graph.Add_Node( newslice.to_name() )\\r\\n\\t\\t\\t\\t\\tworklist.insert( 0, newnode )\\r\\n\\t\\t\\t\\t\\tdata_bib[ newslice.to_name() ] = newslice\\r\\n\\t\\t\\t\\tgraph.Add_Link( currnode.get_name(), newslice.to_name())\\r\\n\\treturn [ graph, data_bib ]\",\n \"def SimpleReferenceGrid(min_x,min_y,max_x,max_y,x_divisions,y_divisions,\\n color=(0.5,1.0,0.5,1.0),xoff=-0.15,yoff=-0.04,\\n label_type=None,shapes_name=\\\"Grid\\\"):\\n\\n shps=gview.GvShapes(name=shapes_name)\\n gview.undo_register( shps )\\n shps.add_field('position','string',20)\\n\\n if os.name == 'nt':\\n font=\\\"-adobe-helvetica-medium-r-*-*-12-*-*-*-*-*-*-*\\\"\\n else:\\n #font=\\\"-adobe-helvetica-medium-r-*-*-12-*-*-*-*-*-*-*\\\"\\n #font=\\\"-urw-helvetica-medium-r-normal-*-9-*-*-*-p-*-iso8859-2\\\"\\n font=\\\"-adobe-helvetica-medium-r-normal-*-8-*-*-*-p-*-iso10646-1\\\"\\n #font=\\\"-misc-fixed-medium-r-*-*-9-*-*-*-*-*-*-*\\\"\\n\\n\\n lxoff=(max_x-min_x)*xoff # horizontal label placement\\n lyoff=(max_y-min_y)*yoff # vertical label placement\\n\\n hspc=(max_x-min_x)/x_divisions\\n vspc=(max_y-min_y)/y_divisions\\n\\n for hval in numpy.arange(min_x,max_x+hspc/100.0,hspc):\\n nshp=gview.GvShape(type=gview.GVSHAPE_LINE)\\n nshp.set_node(hval,max_y,0,0)\\n nshp.set_node(hval,min_y,0,1)\\n shps.append(nshp)\\n pshp=gview.GvShape(type=gview.GVSHAPE_POINT)\\n pshp.set_node(hval,min_y+lyoff)\\n pshp.set_property('position',\\\"%.1f\\\" % hval)\\n shps.append(pshp)\\n\\n for vval in numpy.arange(min_y,max_y+vspc/100.0,vspc):\\n nshp=gview.GvShape(type=gview.GVSHAPE_LINE)\\n nshp.set_node(min_x,vval,0,0)\\n nshp.set_node(max_x,vval,0,1)\\n shps.append(nshp)\\n pshp=gview.GvShape(type=gview.GVSHAPE_POINT)\\n pshp.set_node(min_x+lxoff,vval)\\n pshp.set_property('position',\\\"%.1f\\\" % vval)\\n shps.append(pshp)\\n\\n cstr=gvogrfs.gv_to_ogr_color(color)\\n if len(cstr) < 9:\\n cstr=cstr+\\\"FF\\\"\\n clstr=str(color[0])+' '+str(color[1])+' '+str(color[2])+' '+str(color[3])\\n\\n layer=gview.GvShapesLayer(shps)\\n layer.set_property('_line_color',clstr)\\n layer.set_property('_point_color',clstr)\\n # Set antialias property so that lines look nice\\n # when rotated.\\n layer.set_property('_gl_antialias','1')\\n layer.set_property('_gv_ogrfs_point',\\n 'LABEL(t:{position},f:\\\"'+font+'\\\",c:'+cstr+')')\\n layer.set_read_only(True) \\n\\n return layer\",\n \"def __test(graph): \\n \\n if not isinstance(graph, basegraph):\\n raise TypeError(\\\"Expected type was Graph.\\\")\\n \\n print \\\"### iPATH TEST DATA STRUCTURE\\\"\\n print \\\"### Data Type: Graph ({})\\\".format(str(graph.__class__.__bases__[0].__name__))\\n print \\\"### Implementation: {}\\\".format(str(graph.__class__.__name__))\\n \\n print \\\"\\\\n*** ADD NODE ***\\\\n\\\" \\n for i in range(10):\\n print \\\"add_node({})\\\".format(str(i)) \\n graph.add_node(i) \\n \\n print \\\"\\\\n*** ADD ARC ***\\\\n\\\" \\n for i in range(10):\\n print \\\"add_arc({}, {}, {})\\\".format(str(i), str(i + 1), str(2 * (i + 1)))\\n graph.add_arc(i, i + 1, 2 * (i + 1))\\n print \\\"add_arc({}, {}, {})\\\".format(str(i), str(i + 2), str(2 * (i + 2)))\\n graph.add_arc(i, i + 2, 2 * (i + 2))\\n \\n print \\\"\\\\n*** GRAPH ***\\\\n\\\" \\n print \\\"\\\\n{}\\\\n\\\".format(str(graph))\\n \\n print \\\"\\\\n*** REMOVE NODE ***\\\\n\\\" \\n print \\\"remove_node(5)\\\"\\n graph.remove_node(5)\\n \\n print \\\"\\\\n*** GRAPH ***\\\\n\\\" \\n print \\\"\\\\n{}\\\\n\\\".format(str(graph))\\n \\n print \\\"\\\\n*** REMOVE ARC ***\\\\n\\\" \\n print \\\"remove_arc(7, 8)\\\" \\n graph.remove_arc(7, 8)\\n \\n print \\\"\\\\n*** GRAPH ***\\\\n\\\" \\n print \\\"\\\\n{}\\\\n\\\".format(str(graph))\\n \\n print \\\"\\\\n*** INCIDENT ARCS ***\\\\n\\\" \\n for node in graph.get_nodes():\\n print \\\"Incident Arcs of {}\\\\t{}\\\\n\\\".format(str(node), str(graph.get_incident_arcs(node._id)))\\n \\n print \\\"\\\\n*** ADJACENCY ***\\\\n\\\" \\n for i in range(10):\\n for j in range(10):\\n if graph.are_adjacent(i, j) == True:\\n print \\\"Adjacency Between ({}, {}): True\\\\n\\\".format(str(i), str(j))\\n \\n print \\\"\\\\n*** NODES ***\\\\n\\\" \\n print \\\"numNodes: {}\\\\n\\\".format(str(graph.get_num_nodes())) \\n print \\\"Nodes: {}\\\\n\\\".format(str(graph.get_nodes())) \\n \\n print \\\"\\\\n*** ARCS ***\\\\n\\\" \\n print \\\"numArcs: {}\\\\n\\\".format(str(graph.get_num_arcs())) \\n print \\\"Arcs: {}\\\\n\\\".format(str(graph.get_arcs())) \\n \\n print \\\"\\\\n*** SEARCH BFS ***\\\\n\\\" \\n for i in range(10): \\n print \\\"bfs({})\\\".format(str(i))\\n Lbfs = graph.bfs(i)\\n for n in Lbfs:\\n print \\\"{}\\\\n\\\".format(str(n))\\n print \\\"\\\\n\\\"\\n \\n print \\\"\\\\n*** SEARCH DFS ***\\\\n\\\" \\n for i in range(9):\\n print \\\"dfs({})\\\".format(str(i))\\n Ldfs = graph.dfs(i)\\n for n in Ldfs:\\n print \\\"{}\\\\n\\\".format(str(n))\\n print \\\"\\\\n\\\"\\n \\n print \\\"\\\\n### END OF TEST ###\\\\n\\\"\",\n \"def do_printgraph(self, args):\\n self.currentGraph.printGraph()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.79100144","0.65580076","0.64052117","0.63540345","0.6348667","0.61608034","0.6133355","0.5854858","0.5819954","0.5817296","0.5729944","0.57231635","0.5651493","0.5646747","0.5616531","0.5610424","0.56057763","0.5573422","0.55470526","0.5529936","0.55223936","0.548946","0.54719025","0.5463685","0.542794","0.54104775","0.5367909","0.5331161","0.53173673","0.531653","0.53138065","0.5299587","0.529403","0.5286369","0.5277277","0.5274089","0.5259896","0.52557755","0.5255154","0.5250453","0.52455384","0.5243805","0.52431357","0.5232331","0.5225946","0.52239835","0.522327","0.5223267","0.5208987","0.520408","0.52030396","0.5198093","0.5191159","0.5181923","0.5181079","0.51773447","0.5159624","0.51575315","0.5149096","0.51418734","0.5138336","0.51333183","0.5131424","0.5124564","0.51216453","0.5116607","0.51041955","0.5098653","0.5098653","0.5097246","0.5088556","0.5087094","0.5084827","0.508432","0.50815696","0.5080257","0.5074737","0.5064817","0.5062849","0.50582373","0.50573325","0.50557715","0.50545186","0.5053927","0.5051967","0.5051801","0.50475746","0.5039426","0.5038892","0.5036758","0.50346255","0.5032037","0.5031954","0.502397","0.5022857","0.5020285","0.50197035","0.5015002","0.50122994","0.500538"],"string":"[\n \"0.79100144\",\n \"0.65580076\",\n \"0.64052117\",\n \"0.63540345\",\n \"0.6348667\",\n \"0.61608034\",\n \"0.6133355\",\n \"0.5854858\",\n \"0.5819954\",\n \"0.5817296\",\n \"0.5729944\",\n \"0.57231635\",\n \"0.5651493\",\n \"0.5646747\",\n \"0.5616531\",\n \"0.5610424\",\n \"0.56057763\",\n \"0.5573422\",\n \"0.55470526\",\n \"0.5529936\",\n \"0.55223936\",\n \"0.548946\",\n \"0.54719025\",\n \"0.5463685\",\n \"0.542794\",\n \"0.54104775\",\n \"0.5367909\",\n \"0.5331161\",\n \"0.53173673\",\n \"0.531653\",\n \"0.53138065\",\n \"0.5299587\",\n \"0.529403\",\n \"0.5286369\",\n \"0.5277277\",\n \"0.5274089\",\n \"0.5259896\",\n \"0.52557755\",\n \"0.5255154\",\n \"0.5250453\",\n \"0.52455384\",\n \"0.5243805\",\n \"0.52431357\",\n \"0.5232331\",\n \"0.5225946\",\n \"0.52239835\",\n \"0.522327\",\n \"0.5223267\",\n \"0.5208987\",\n \"0.520408\",\n \"0.52030396\",\n \"0.5198093\",\n \"0.5191159\",\n \"0.5181923\",\n \"0.5181079\",\n \"0.51773447\",\n \"0.5159624\",\n \"0.51575315\",\n \"0.5149096\",\n \"0.51418734\",\n \"0.5138336\",\n \"0.51333183\",\n \"0.5131424\",\n \"0.5124564\",\n \"0.51216453\",\n \"0.5116607\",\n \"0.51041955\",\n \"0.5098653\",\n \"0.5098653\",\n \"0.5097246\",\n \"0.5088556\",\n \"0.5087094\",\n \"0.5084827\",\n \"0.508432\",\n \"0.50815696\",\n \"0.5080257\",\n \"0.5074737\",\n \"0.5064817\",\n \"0.5062849\",\n \"0.50582373\",\n \"0.50573325\",\n \"0.50557715\",\n \"0.50545186\",\n \"0.5053927\",\n \"0.5051967\",\n \"0.5051801\",\n \"0.50475746\",\n \"0.5039426\",\n \"0.5038892\",\n \"0.5036758\",\n \"0.50346255\",\n \"0.5032037\",\n \"0.5031954\",\n \"0.502397\",\n \"0.5022857\",\n \"0.5020285\",\n \"0.50197035\",\n \"0.5015002\",\n \"0.50122994\",\n \"0.500538\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.8254092"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":6584,"cells":{"query":{"kind":"string","value":"Test the popxl create multiple callsites for a subgraph input example"},"document":{"kind":"string","value":"def test_documentation_popxl_multi_callsites_graph_input(self):\n filename = \"multi_call_graph_input.py\"\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)"},"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 test_documentation_popxl_create_multi_subgraph(self):\n filename = \"create_multi_graphs_from_same_func.py\"\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)","def test_documentation_popxl_basic_subgraph(self):\n filename = \"basic_graph.py\"\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)","def test_documentation_popxl_repeat_2(self):\n filename = \"repeat_graph_2.py\"\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)","def test_documentation_popxl_repeat_1(self):\n filename = \"repeat_graph_1.py\"\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)","def test_documentation_popxl_repeat_0(self):\n filename = \"repeat_graph_0.py\"\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)","def _test_multiple(self, fin_graphzip, fin_insts, T, n=None):\n for t in range(T+1)[1:]:\n self._test_graphzip_subgen(fin_graphzip, fin_insts, n)","def sub_graph_merging(self):","def test_simple(self):\n exp = [{'type': NewickEvents.OPEN_SUBTREE, 'comments': []},\n {'type': NewickEvents.OPEN_SUBTREE, 'comments': []},\n {'edge_info': None, 'type': NewickEvents.TIP, 'comments': [], 'label': 'h'},\n {'edge_info': None, 'type': NewickEvents.TIP, 'comments': [], 'label': 'p'},\n {'edge_info': '1', 'type': NewickEvents.CLOSE_SUBTREE,\n 'comments': [], 'label': 'hp'},\n {'edge_info': None, 'type': NewickEvents.TIP, 'comments': [], 'label': 'g'},\n {'edge_info': None, 'type': NewickEvents.CLOSE_SUBTREE,\n 'comments': [], 'label': 'hpg'}\n ]\n content = '((h,p)hp:1,g)hpg;'\n self._do_test(content, exp)\n content = '((h,[pretest]p[test][posttest])hp,g)hpg;'\n exp = [{'type': NewickEvents.OPEN_SUBTREE, 'comments': []},\n {'type': NewickEvents.OPEN_SUBTREE, 'comments': []},\n {'edge_info': None, 'type': NewickEvents.TIP, 'comments': [], 'label': 'h'},\n {'edge_info': None, 'type': NewickEvents.TIP,\n 'comments': ['pretest', 'test', 'posttest'], 'label': 'p'},\n {'edge_info': None, 'type': NewickEvents.CLOSE_SUBTREE,\n 'comments': [], 'label': 'hp'},\n {'edge_info': None, 'type': NewickEvents.TIP, 'comments': [], 'label': 'g'},\n {'edge_info': None, 'type': NewickEvents.CLOSE_SUBTREE,\n 'comments': [], 'label': 'hpg'}\n ]\n self._do_test(content, exp)","def sample_from_subpop(instance, params, subpop):\n y = subpop\n x = np.random.choice([-1,+1], size=params['d'])\n x[instance['indices'][subpop]] = instance['values'][subpop]\n return x, y, subpop","def test_generator8(self):\n xpb = XPathBuilder()\n xp1 = xp2 = None\n base_xp = (xpb.foo.bar | xpb.x.y).parenthesize()\n base_gen = None\n with base_xp as b:\n base_gen = b\n xp1 = b() & xpb.c\n xp2 = b() & xpb.d\n xp1_exp = '(/foo/bar or /x/y) and /c'\n xp2_exp = '(/foo/bar or /x/y) and /d'\n base_exp = '(/foo/bar or /x/y)'\n # check tree structure\n self.assertTrue(base_xp._parent is None)\n self.assertTrue(base_gen._parent is None)\n # check xpath\n self.assertEqual(xp1.tostring(), xp1_exp)\n self.assertEqual(xp2.tostring(), xp2_exp)\n self.assertEqual(base_xp.tostring(), base_exp)\n self.assertEqual(base_gen.tostring(), base_exp)","def test_generator7(self):\n xpb = XPathBuilder()\n xp1 = xp2 = None\n base_xp = xpb.foo.bar & xpb.x.y\n base_gen = None\n with base_xp as b:\n base_gen = b\n xp1 = b() | xpb.c\n xp2 = b() | xpb.d\n xp1_exp = '/foo/bar and /x/y or /c'\n xp2_exp = '/foo/bar and /x/y or /d'\n base_exp = '/foo/bar and /x/y'\n # check tree structure\n self.assertTrue(base_xp._parent is None)\n self.assertTrue(base_gen._parent is None)\n # check xpath\n self.assertEqual(xp1.tostring(), xp1_exp)\n self.assertEqual(xp2.tostring(), xp2_exp)\n self.assertEqual(base_xp.tostring(), base_exp)\n self.assertEqual(base_gen.tostring(), base_exp)","def test_dummy3(self):\n xpb = XPathBuilder()\n xp = xpb.dummy()\n self.assertTrue(xp.parenthesize() is xp)","def test_xcomarg_set(self, test_dag):\n # Unpack the fixture\n dag, (op1, op2, op3, op4) = test_dag\n # Arrange the operators with a Label in the middle\n op1_arg = XComArg(op1, \"test_key\")\n op1_arg.set_downstream(op2, Label(\"Label 1\"))\n op1.set_downstream([op3, op4])\n # Check that the DAG has the right edge info\n assert dag.get_edge_info(op1.task_id, op2.task_id) == {\"label\": \"Label 1\"}\n assert dag.get_edge_info(op1.task_id, op4.task_id) == {}","def test_createSubLinkographWithoutCommands(self):\n self.performTestForParams()","def test_generator6(self):\n xpb = XPathBuilder()\n xp1 = xp2 = None\n base_xp = xpb.base.foo.bar\n base_gen = None\n with base_xp as b:\n base_gen = b\n xp1 = xpb.a.b.c.join(b())\n xp2 = xpb.test.join(b())\n xp1_exp = '/a/b/c/base/foo/bar'\n xp2_exp = '/test/base/foo/bar'\n base_exp = '/base/foo/bar'\n # check tree structure\n self.assertTrue(base_xp._parent is None)\n self.assertTrue(len(base_xp._children[0]._children[0]._children) == 0)\n self.assertTrue(base_gen._parent is None)\n self.assertTrue(len(base_gen._children) == 0)\n # check xpath\n self.assertEqual(xp1.tostring(), xp1_exp)\n self.assertEqual(xp2.tostring(), xp2_exp)\n self.assertEqual(base_xp.tostring(), base_exp)\n self.assertEqual(base_gen.tostring(), base_exp)","def populate_graph(self):","def test_multiple_task_groups_dag(\n self, test_multiple_taskgroups_dag, multiple_taskgroups_dag_expected_edges\n ):\n (\n dag,\n group1,\n group2,\n group3,\n (\n group1_emp1,\n group1_emp2,\n group1_emp3,\n group2_emp1,\n group2_emp2,\n group2_emp3,\n group2_op1,\n group2_op2,\n group3_emp1,\n group3_emp2,\n group3_emp3,\n emp_in1,\n emp_in2,\n emp_in3,\n emp_in4,\n emp_out1,\n emp_out2,\n emp_out3,\n emp_out4,\n op_in1,\n op_out1,\n ),\n ) = test_multiple_taskgroups_dag\n\n group1_emp1 >> Label(\"label group1.group1_emp1 <=> group1.group1_emp2\") >> group1_emp3\n\n emp_in1 >> group1\n emp_in2 >> Label(\"label emp_in2 <=> group1\") >> group1\n [emp_in3, emp_in4] >> Label(\"label emp_in3/emp_in4 <=> group1\") >> group1\n XComArg(op_in1, \"test_key\") >> Label(\"label op_in1 <=> group1\") >> group1\n\n (\n [group2_emp1, group2_emp2]\n >> Label(\"label group2.group2_emp1/group2.group2_emp2 <=> group2.group2_emp3\")\n >> group2_emp3\n )\n (\n group2_emp1\n >> Label(\"label group2.group2_emp1 <=> group2.group2_emp2/group2.group2_emp3\")\n >> [group2_emp2, group2_emp3]\n )\n group2_emp3 >> Label(\"label group2.group2_emp3 <=> group3\") >> group3\n\n (\n XComArg(group2_op1, \"test_key\")\n >> Label(\"label group2.group2_op1 <=> group2.group2_op2\")\n >> XComArg(group2_op2, \"test_key\")\n )\n XComArg(group2_op2, \"test_key\") >> Label(\"label group2.group2_op2 <=> group3\") >> group3\n\n group3 >> emp_out1\n group3 >> Label(\"label group3 <=> emp_out2\") >> emp_out2\n group3 >> Label(\"label group3 <=> emp_out3/emp_out4\") >> [emp_out3, emp_out4]\n group3 >> Label(\"label group3 <=> op_out1\") >> XComArg(op_out1, \"test_key\")\n\n group1 >> Label(\"label group1 <=> group2\") >> group2\n\n compare_dag_edges(dag_edges(dag), multiple_taskgroups_dag_expected_edges)","def test_path7():\n path = [(0, 0, 1)]\n path += [\n [('A', 3, 0)],\n (0, 1, 1),\n [('A', 2, 0)],\n (np.pi/2, 1, 1),\n [('B',3,0)],\n (0, 1, 1),\n [('B',2,0)],\n (np.pi/2, 1, 1),\n [('C',3,0)],\n (0, 1, 1),\n [('C',2,0)],\n (np.pi/2, 1, 1),\n [('D', 3, 0)],\n (0, 1, 1),\n [('D', 2,0)],\n (np.pi/2, 1, 1),\n ] * 4\n execute_path(path,True)","def explore(self, *args):","def create_subbasin_graph():\n subbasin_to_downstream = pd.read_csv(module_dir + '/../data/simulations_shervan/test.rvh', sep='\\s+', skiprows=7, nrows=724, names=['subbasin', 'downstream_subbasin'], usecols=[1,2])\n subbasin_to_downstream['subbasin'] = subbasin_to_downstream['subbasin']\n subbasin_to_downstream['downstream_subbasin'] = 'sub' + subbasin_to_downstream['downstream_subbasin'].astype(str)\n subbasin_to_downstream['edge'] = 1\n\n for subbasin in subbasin_to_downstream['subbasin'].unique():\n is_sink = 1 if len(subbasin_to_downstream[(subbasin_to_downstream['subbasin'] == subbasin) & subbasin_to_downstream['edge'] == 1]) == 0 else 0\n subbasin_to_downstream = subbasin_to_downstream.append({'subbasin': subbasin, 'downstream_subbasin': subbasin, 'edge': is_sink}, ignore_index=True)\n subbasin_to_downstream = subbasin_to_downstream.append({'subbasin': 'sub-1', 'downstream_subbasin': 'sub-1', 'edge': 1}, ignore_index=True)\n \n adj = subbasin_to_downstream.pivot(index='subbasin', columns='downstream_subbasin', values='edge').fillna(0) \n adj = adj.sort_index(axis=0).sort_index(axis=1)\n \n G = nx.from_numpy_matrix(adj.values, parallel_edges=False, create_using=nx.DiGraph())\n label_mapping = dict(zip(range(len(adj.values)), adj.index))\n G = nx.relabel_nodes(G, label_mapping)\n \n return G","def setUp(self):\n\n singleLabels = linkoCreate.Linkograph(\n [({'A'}, set(), {1,2,3}),\n ({'D'}, {0}, {3,4}),\n ({'A'}, {0}, {4}),\n ({'C'}, {0,1}, {4}),\n ({'A'}, {1,2,3}, set())],\n ['A', 'B', 'C', 'D'])\n\n singleSubLinko0_2 = linkoCreate.Linkograph(\n [({'A'}, set(), {1,2}),\n ({'D'}, {0}, set()),\n ({'A'}, {0}, set())],\n ['A', 'B', 'C', 'D'])\n\n singleSubLinko0_1 = linkoCreate.Linkograph(\n [({'A'}, set(), {1}),\n ({'D'}, {0}, set())],\n ['A', 'B', 'C', 'D'])\n\n singleSubLinko0_0 = linkoCreate.Linkograph(\n [({'A'}, set(), set())],\n ['A', 'B', 'C', 'D'])\n\n singleSubLinko1_2 = linkoCreate.Linkograph(\n [({'D'}, set(), set()),\n ({'A'}, set(), set())],\n ['A', 'B', 'C', 'D'])\n\n singleSubLinko1_1 = linkoCreate.Linkograph(\n [({'D'}, set(), set())],\n ['A', 'B', 'C', 'D'])\n\n trivialLinkograph = linkoCreate.Linkograph(\n [], ['A', 'B', 'C', 'D'])\n\n\n singleSubLinko1_4 = linkoCreate.Linkograph(\n [({'D'}, set(), {2,3}),\n ({'A'}, set(), {3}),\n ({'C'}, {0}, {3}),\n ({'A'}, {0,1,2}, set())],\n ['A', 'B', 'C', 'D'])\n\n singleSubLinko2_4 = linkoCreate.Linkograph(\n [({'A'}, set(), {2}),\n ({'C'}, set(), {2}),\n ({'A'}, {0,1}, set())],\n ['A', 'B', 'C', 'D'])\n\n singleSubLinko3_4 = linkoCreate.Linkograph(\n [({'C'}, set(), {1}),\n ({'A'}, {0}, set())],\n ['A', 'B', 'C', 'D'])\n\n singleSubLinko4_4 = linkoCreate.Linkograph(\n [({'A'}, set(), set())],\n ['A', 'B', 'C', 'D'])\n\n simpleLinko = linkoCreate.Linkograph(\n [({'A', 'B', 'C'}, set(), {1,2,3}),\n ({'D'}, {0}, {3,4}),\n ({'A'}, {0}, {4}),\n ({'B', 'C'}, {0,1}, {4}),\n ({'A'}, {1,2,3}, set())],\n ['A', 'B', 'C', 'D'])\n\n if self.id().split('.')[-1] == 'test_createSubLinkographWithoutCommands':\n self.testParams = [\n {'linko': singleLabels,\n 'lowerBound': None,\n 'upperBound': None,\n 'ExpectedLinkograph': singleLabels},\n\n {'linko': singleLabels,\n 'lowerBound': 0,\n 'upperBound': 4,\n 'ExpectedLinkograph': singleLabels},\n\n {'linko': singleLabels,\n 'lowerBound': 0,\n 'upperBound': 5,\n 'ExpectedLinkograph': singleLabels},\n\n {'linko': singleLabels,\n 'lowerBound': 0,\n 'upperBound': 2,\n 'ExpectedLinkograph': singleSubLinko0_2},\n\n {'linko': singleLabels,\n 'lowerBound': -1,\n 'upperBound': 2,\n 'ExpectedLinkograph': singleSubLinko0_2},\n\n {'linko': singleLabels,\n 'lowerBound': None,\n 'upperBound': 2,\n 'ExpectedLinkograph': singleSubLinko0_2},\n\n {'linko': singleLabels,\n 'lowerBound': 0,\n 'upperBound': 1,\n 'ExpectedLinkograph': singleSubLinko0_1},\n\n {'linko': singleLabels,\n 'lowerBound': 0,\n 'upperBound': 0,\n 'ExpectedLinkograph': singleSubLinko0_0},\n\n {'linko': singleLabels,\n 'lowerBound': 0,\n 'upperBound': -1,\n 'ExpectedLinkograph': trivialLinkograph},\n\n {'linko': singleLabels,\n 'lowerBound': 1,\n 'upperBound': 2,\n 'ExpectedLinkograph': singleSubLinko1_2},\n\n {'linko': singleLabels,\n 'lowerBound': 1,\n 'upperBound': 1,\n 'ExpectedLinkograph': singleSubLinko1_1},\n\n {'linko': singleLabels,\n 'lowerBound': 1,\n 'upperBound': 0,\n 'ExpectedLinkograph': trivialLinkograph},\n\n {'linko': singleLabels,\n 'lowerBound': -1,\n 'upperBound': -1,\n 'ExpectedLinkograph': trivialLinkograph},\n\n {'linko': singleLabels,\n 'lowerBound': 1,\n 'upperBound': 4,\n 'ExpectedLinkograph': singleSubLinko1_4},\n\n {'linko': singleLabels,\n 'lowerBound': 2,\n 'upperBound': 4,\n 'ExpectedLinkograph': singleSubLinko2_4},\n\n {'linko': singleLabels,\n 'lowerBound': 3,\n 'upperBound': 4,\n 'ExpectedLinkograph': singleSubLinko3_4},\n\n {'linko': singleLabels,\n 'lowerBound': 4,\n 'upperBound': 4,\n 'ExpectedLinkograph': singleSubLinko4_4},\n\n ]","def test_build_graph(self):\n insert_good_data()\n dataframe = get_dataframe()\n results = processing.build_graph(dataframe, figure_path, False)\n # 1\n self.assertEqual(results, \"Updated html File and Opened it\")","def generate_subgraph(format):\n\n # get business information\n directorypath = genpath+directory\n if os.path.isfile(directorypath):\n \n bizdata = pd.read_csv( directorypath, escapechar='\\\\')\n\n #create a directory of page-id and object-ids\n tempdf = bizdata.set_index('pageid')\n tempdf = tempdf['objectid']\n dictionary = tempdf.to_dict()\n\n uncgraph = pd.read_csv(inpath+graphfile, escapechar='\\\\')\n uncgraph = uncgraph.dropna()\n uncgraph['likee_object_id'] = uncgraph.apply(lambda x: dictionary.get(x['likee_page_id']), axis=1)\n cgraph = uncgraph.dropna()\n cgraph = cgraph[['liker_page_id', 'likee_page_id']]\n cgraph.columns = ['Source', 'Target']\n\n \n print_stats(cgraph)\n if format == 'networkx' :\n print \"[Generating a networkX graph...]\" \n cgraph.to_csv(genpath+subgraph+'.ntx', index=False, header=False, sep= ' ')\n else:\n print \"[Generating a csv graph...]\" \n cgraph.to_csv(genpath+subgraph+'.csv', index=False)\n\n\n else:\n print \"Either file is missing or is not readable\"","def test_calls(self):\n ex = self.ex\n m = self.m\n n = self.n\n\n nreps = random.randint(1, 10)\n ex.nreps = nreps\n ex.vary[\"X\"][\"with\"].add(\"rep\")\n ex.infer_lds()\n\n cmds = ex.generate_cmds()\n\n idx = random.randint(0, nreps - 1)\n self.assertIn([\"name\", m, n, \"X_%d\" % idx, m, \"Y\", m, \"Z\", n], cmds)","def show_subgraph(dfs_codes, nsupport, mapper):\n\tglobal __subgraph_count\n\n\tg = build_graph(dfs_codes)\n\tg.id = __subgraph_count\n\t__subgraph_count += 1\n\tg.gprint(nsupport, mapper)","def main(\n num_sampled=[3, 3],\n max_depth=2,\n num_iters=1000,\n do_graph=False,\n # These are for checking stats on smaller data\n subsample=False,\n plot=False,\n # Generates a random matrix for comparison\n random=False,\n # Visualise the connection matrix\n vis_connect=False,\n subsample_vis=False,\n # Generate final graphs\n final=False,\n # Analyse\n analyse=False,\n only_exp=False,\n # Which regions are considered here\n # A_name, B_name = \"MOp\", \"SSP-ll\"\n A_name=\"VISp\",\n B_name=\"VISl\",\n desired_depth=1,\n desired_samples=79,\n):\n np.random.seed(42)\n\n if random:\n AB, BA, AA, BB = gen_random_matrix(150, 50, 0, 0.04, 0, 0.0)\n matrix_vis(AB, BA, AA, BB, 10, name=\"test_vis.png\")\n\n os.makedirs(os.path.dirname(pickle_loc), exist_ok=True)\n convert_mouse_data(A_name, B_name)\n to_use = [True, True, True, True]\n mc, args_dict = load_matrix_data(to_use, A_name, B_name)\n print(\"{} - {}, {} - {}\".format(A_name, B_name, mc.num_a, mc.num_b))\n\n result = {}\n result[\"matrix_stats\"] = print_args_dict(args_dict, out=False)\n\n if only_exp:\n mpf_res = mpf_connectome(mc, num_sampled, max_depth, args_dict)\n mpf_val = [\n mpf_res[\"expected\"],\n mpf_res[\"expected\"] / num_sampled[1],\n \"{}_{}\".format(A_name, B_name),\n \"Statistical estimation\",\n ]\n if do_graph:\n print(\"Converting matrix\")\n gc.collect()\n mc.create_connections()\n print(\"Finished conversion\")\n graph = mc.graph\n to_write = [mc.num_a, mc.num_b]\n del mc\n gc.collect()\n reverse_graph = reverse(graph)\n graph_res = graph_connectome(\n num_sampled,\n max_depth,\n graph=graph,\n reverse_graph=reverse_graph,\n to_write=to_write,\n num_iters=num_iters,\n )\n to_add = np.mean(graph_res[\"full_results\"][\"Connections\"].values)\n graph_val = [\n to_add,\n to_add / num_sampled[1],\n \"{}_{}\".format(A_name, B_name),\n \"Statistical estimation\",\n ]\n return mpf_val, graph_val\n return mpf_val, None\n\n # Convert to a pickle\n # if not os.path.isfile(pickle_loc):\n # print(\"Converting matrix\")\n # gc.collect()\n # mc.create_connections()\n # print(\"Finished conversion\")\n # graph = mc.graph\n # to_write = [mc.num_a, mc.num_b]\n # del mc\n # gc.collect()\n\n # handle_pickle(graph, \"graph.pickle\", \"w\")\n # handle_pickle(reverse(graph), \"r_graph.pickle\", \"w\")\n # handle_pickle(to_write, \"graph_size.pickle\", \"w\")\n\n if vis_connect:\n if subsample_vis:\n print(\"Plotting subsampled matrix vis\")\n new_mc = mc.subsample(int(mc.num_a / 10), int(mc.num_b / 10))\n matrix_vis(\n new_mc.ab,\n new_mc.ba,\n new_mc.aa,\n new_mc.bb,\n 15,\n name=\"mc_mat_vis_sub10.pdf\",\n )\n else:\n o_name = \"mc_mat_vis_{}_to_{}.pdf\".format(A_name, B_name)\n print(\"Plotting full matrix vis\")\n matrix_vis(mc.ab, mc.ba, mc.aa, mc.bb, 150, name=o_name)\n print(\"done vis\")\n\n print(mc, print_args_dict(args_dict, out=False))\n\n result = None\n if subsample:\n result = check_stats(mc, 1000, 1, 20000, 1, plot)\n if final:\n result = {}\n\n # For different depths and number of samples\n for depth in range(1, 4):\n for ns in range(1, num_sampled[0] + 1):\n ns_2 = [ns] * 2\n mpf_res = mpf_connectome(mc, ns_2, depth, args_dict)\n result[\"mpf_{}_{}\".format(depth, ns)] = mpf_res\n\n # Save this for plotting\n cols = [\"Number of samples\", \"Proportion of connections\", \"Max distance\"]\n depth_name = [None, \"Direct synapse\", \"Two synapses\", \"Three synapses\"]\n vals = []\n for depth in range(1, 4):\n for ns in range(1, num_sampled[0] + 1):\n this = result[\"mpf_{}_{}\".format(depth, ns)]\n val = [ns, this[\"expected\"] / ns, depth_name[depth]]\n vals.append(val)\n df = pd.DataFrame(vals, columns=cols)\n os.makedirs(os.path.join(here, \"..\", \"results\"), exist_ok=True)\n df.to_csv(\n os.path.join(\n here, \"..\", \"results\", \"{}_to_{}_depth.csv\".format(A_name, B_name)\n ),\n index=False,\n )\n\n cols = [\"Number of sampled connected neurons\", \"Probability\"]\n total_pmf = result[\"mpf_{}_{}\".format(desired_depth, desired_samples)][\"total\"]\n vals = []\n for k, v in total_pmf.items():\n vals.append([k, float(v)])\n df = pd.DataFrame(vals, columns=cols)\n df.to_csv(\n os.path.join(\n here,\n \"..\",\n \"results\",\n \"{}_to_{}_pmf_{}_{}.csv\".format(\n A_name, B_name, desired_depth, desired_samples\n ),\n ),\n index=False,\n )\n if analyse:\n result = {}\n result[\"matrix_stats\"] = args_dict\n\n mpf_res = mpf_connectome(\n mc,\n num_sampled,\n max_depth,\n args_dict,\n clt_start=30,\n sr=None,\n mean_estimate=True,\n )\n result[\"mean\"] = mpf_res\n\n vals = []\n cols = [\"Number of connected neurons\", \"Probability\", \"Calculation\"]\n for k, v in mpf_res[\"total\"].items():\n vals.append([k, float(v), \"Mean estimation\"])\n\n mpf_res = mpf_connectome(mc, num_sampled, max_depth, args_dict, clt_start=30)\n result[\"mpf\"] = mpf_res\n\n for k, v in mpf_res[\"total\"].items():\n vals.append([k, float(v), \"Statistical estimation\"])\n\n if do_graph:\n print(\"Converting matrix\")\n gc.collect()\n mc.create_connections()\n print(\"Finished conversion\")\n graph = mc.graph\n to_write = [mc.num_a, mc.num_b]\n del mc\n gc.collect()\n reverse_graph = reverse(graph)\n\n graph_res = graph_connectome(\n num_sampled,\n max_depth,\n graph=graph,\n reverse_graph=reverse_graph,\n to_write=to_write,\n num_iters=num_iters,\n )\n\n result[\"difference\"] = (\n dist_difference(mpf_res[\"total\"], graph_res[\"dist\"]),\n )\n result[\"graph\"] = graph_res\n\n for k, v in graph_res[\"dist\"].items():\n vals.append([k, float(v), \"Monte Carlo simulation\"])\n\n df = pd.DataFrame(vals, columns=cols)\n df.to_csv(\n os.path.join(\n here,\n \"..\",\n \"results\",\n \"{}_to_{}_pmf_final_{}_{}.csv\".format(\n A_name, B_name, max_depth, num_sampled[0]\n ),\n ),\n index=False,\n )\n\n if result is not None:\n with open(os.path.join(here, \"..\", \"results\", \"mouse.txt\"), \"w\") as f:\n pprint(result, width=120, stream=f)\n\n return result","def examples():\r\n\r\n # get some data for a single name\r\n x = blp.bdp('BDEV LN Equity', 'px_last')\r\n print(x)\r\n print('the type of x', type(x))\r\n print('the value of x:', x.iloc[0]['px_last'])\r\n\r\n\r\n # get multiple data for a single name\r\n y = blp.bdp('BDEV LN Equity', flds=['px_bid', 'px_ask'])\r\n print(y)\r\n\r\n\r\n # get multiple data for multiple names\r\n z = blp.bdp(tickers=['BDEV LN Equity', 'BARC LN Equity'], flds=['px_bid', 'px_ask'])\r\n print(z)\r\n print('here is the bdev ask >>>', z.loc['BDEV LN Equity','px_ask'])","def test_generator3(self):\n xpb = XPathBuilder()\n xp1 = xp2 = None\n base_xp = xpb.base.foo[xpb.attr('abc') == 'x']\n with base_xp as b:\n xp1 = b().bar.text() == 'foo'\n xp2 = b().x.y.z[42]\n base_exp = '/base/foo[@abc = \"x\"]'\n xp1_exp = '/base/foo[@abc = \"x\"]/bar/text() = \"foo\"'\n xp2_exp = '/base/foo[@abc = \"x\"]/x/y/z[42]'\n self.assertEqual(base_xp.tostring(), base_exp)\n self.assertEqual(xp1.tostring(), xp1_exp)\n self.assertEqual(xp2.tostring(), xp2_exp)","def test_extract_graph(default_plugin_resolver):\n dpr = default_plugin_resolver\n nx_graph = nx.Graph()\n nx_graph.add_weighted_edges_from(\n [(1, 0, 2), (1, 4, 3), (2, 5, 5), (2, 7, 6), (3, 1, 7), (5, 6, 10), (6, 2, 11),]\n )\n desired_nodes = {2, 5, 6}\n nx_extracted_graph = nx.Graph()\n nx_extracted_graph.add_weighted_edges_from([(2, 5, 5), (5, 6, 10), (6, 2, 11)])\n graph = dpr.wrappers.Graph.NetworkXGraph(nx_graph)\n desired_nodes_wrapped = dpr.wrappers.NodeSet.PythonNodeSet(desired_nodes)\n extracted_graph = dpr.wrappers.Graph.NetworkXGraph(nx_extracted_graph)\n MultiVerify(\n dpr, \"subgraph.extract_subgraph\", graph, desired_nodes_wrapped\n ).assert_equals(extracted_graph)","def test_generator4(self):\n xpb = XPathBuilder()\n xp1 = xp2 = None\n base_xp = xpb.base.foo.where(xpb.attr('abc').equals('x'))\n with base_xp as b:\n xp1 = b().bar.text().equals('foo')\n xp2 = b().x.y.z.where(42)\n base_exp = '/base/foo[@abc = \"x\"]'\n xp1_exp = '/base/foo[@abc = \"x\"]/bar/text() = \"foo\"'\n xp2_exp = '/base/foo[@abc = \"x\"]/x/y/z[42]'\n self.assertEqual(base_xp.tostring(), base_exp)\n self.assertEqual(xp1.tostring(), xp1_exp)\n self.assertEqual(xp2.tostring(), xp2_exp)","def test_dummy6(self):\n xpb = XPathBuilder()\n xp = xpb.dummy()\n xp = xpb.bar | xp\n exp = '/bar'\n self.assertEqual(xp.tostring(), exp)","def run_tests(g: Graph) -> None:\n print( g.nodes() , \"->\" , ', '.join([f\"{l}\" for l in g.scc()]) , f\"({g.cyclic()})\" )\n for n in g.nodes():\n for m in [m for m in g.nodes() if m != n]:\n p = g.path(n,m)\n if p is not None:\n assert p[0] == n\n assert p[-1] == m\n for i in range(1,len(p)):\n assert g.is_edge(p[i-1], p[i])\n print(\" \", n, \"->\", m, \":\", ' -> '.join([f\"{v}\" for v in p]))","def test_createData():\n\n sys = LVsystem.Ecosystem()\n\n sys.addSpecies('rabbit')\n sys.setInteraction('rabbit', 'hen', 0)\n sys.setInteraction('rabbit', 'fox', -1)\n sys.setInitialCond('rabbit', 30)\n sys.setGrowthRate('rabbit', 0.09)\n sys.setCarrCap('rabbit', 10000)\n sys.setChangeRate('rabbit', 400)\n\n sys.addSpecies('hen')\n sys.setInteraction('hen', 'rabbit', 0)\n sys.setInteraction('hen', 'fox', -1)\n sys.setInitialCond('hen', 10)\n sys.setGrowthRate('hen', 0.07)\n sys.setCarrCap('hen', 10000)\n sys.setChangeRate('hen', 500)\n\n sys.addSpecies('fox')\n sys.setInteraction('fox', 'rabbit', 1)\n sys.setInteraction('fox', 'hen', 1)\n sys.setInitialCond('fox', 20)\n sys.setGrowthRate('fox', -0.06)\n sys.setCarrCap('fox', 1)\n sys.setChangeRate('fox', 250)\n\n \n data = sys.create_data()\n \n assert data[0] == 3\n assert data[1] == ['rabbit', 'hen', 'fox']\n assert data[2] == [30,10,20]\n assert data[3] == [0.09,0.07,-0.06] \n assert data[4] == [10000,10000,1]\n assert data[5] == [400,500,250]\n assert data[6][1][2] == -data[6][2][1]\n assert data[6][2][2] == 0\n \n sys.removeSpecies('rabbit')\n sys.removeSpecies('fox')\n sys.removeSpecies('hen')","def test_restore_multiple_in_subgraph(self):\n subgraph = self._subgraph()\n subgraph['id'] = 15\n task1 = self._remote_task()\n task1['id'] = 1\n task2 = self._remote_task()\n task2['id'] = 2\n task1['parameters']['containing_subgraph'] = 15\n task2['parameters']['containing_subgraph'] = 15\n\n graph = self._restore_graph([subgraph, task1, task2])\n assert len(graph.tasks) == 3\n subgraphs = [op for op in graph.tasks if op.is_subgraph]\n remote_tasks = [op for op in graph.tasks if not op.is_subgraph]\n\n # those are all references to the same subgraph, the subgraph was\n # NOT restored multiple times\n assert remote_tasks[0].containing_subgraph \\\n is remote_tasks[1].containing_subgraph \\\n is subgraphs[0]\n\n assert len(subgraphs[0].tasks) == 2","def test_example(self):\n\n solution = Solution()\n\n nums = [1, 2, 3]\n\n expected_output = [\n (3,),\n (1,),\n (2,),\n (1, 2, 3),\n (1, 3),\n (2, 3),\n (1, 2),\n ()\n ]\n actual_output = solution.subsets(nums)\n\n for ss in expected_output:\n self.assertIn(ss, actual_output)","def test_generator5(self):\n xpb = XPathBuilder()\n xp = None\n base_xp = xpb.base.foo.bar\n base_gen = None\n with base_xp as b:\n base_gen = b\n xp = b().join(xpb.a.b.c[3])\n exp = '/base/foo/bar/a/b/c[3]'\n base_exp = '/base/foo/bar'\n # check tree structure\n self.assertTrue(base_xp._parent is None)\n self.assertTrue(len(base_xp._children[0]._children[0]._children) == 0)\n self.assertTrue(base_gen._parent is None)\n self.assertTrue(len(base_gen._children) == 0)\n # check xpath\n self.assertEqual(xp.tostring(), exp)\n self.assertEqual(base_xp.tostring(), base_exp)\n self.assertEqual(base_gen.tostring(), base_exp)","def test_multi_sink(self):\n with self.assertRaises(ValidationError):\n with Graph('g') as graph:\n pike.glob('a', '*')\n pike.glob('b', '*')","def test__graph_structure():\n assert PES_GRAPH == (\n ('CH2CH2+OH', 'CH2CH+H2O', 'C2H4OH', 'C2H5O', 'CH3CHO+H'),\n (frozenset({0, 1}), frozenset({0, 2}), frozenset({2, 3}),\n frozenset({3, 4}), frozenset({1, 2})))\n assert pgraph.species(PES_GRAPH) == (\n ('CH2CH2+OH', 'CH2CH+H2O', 'C2H4OH', 'C2H5O', 'CH3CHO+H'))\n assert pgraph.channels(PES_GRAPH) == (\n (frozenset({0, 1}), frozenset({0, 2}), frozenset({2, 3}),\n frozenset({3, 4}), frozenset({1, 2})))\n print('\\npes graph')\n print(PES_GRAPH)","def test_case1(self):\n\n graph = BipartiteGraph()\n\n graph.addEdge(\"supervisor1\",\"student1\")\n\n val1 = graph.getStudents(\"supervisor1\")\n val2 = graph.getSupervisors(\"student1\")\n\n expected1 = [\"student1\"]\n expected2 = [\"supervisor1\"]\n\n self.assertEqual((val1,val2),(expected1,expected2))","def test_parent_with_iterables(self):\n def makeCubesAndGrp():\n cmds.file(new=1, f=1)\n cubes = []\n for x in range(10):\n cubes.append(pm.polyCube()[0])\n group = pm.group(empty=True)\n return cubes, group\n\n cubes, group = makeCubesAndGrp()\n res1 = pm.parent(cubes[:4], group)\n self.assertEqual(cubes[:4], res1)\n res2 = pm.parent(cubes, group)\n self.assertEqual(cubes[4:], res2)\n\n cubes, group = makeCubesAndGrp()\n res1 = pm.parent(cubes[:4] + [group])\n self.assertEqual(cubes[:4], res1)\n res2 = pm.parent(cubes, group)\n self.assertEqual(cubes[4:], res2)\n\n cubes, group = makeCubesAndGrp()\n res1 = pm.parent(cubes[0], cubes[1], cubes[2], cubes[3], group)\n self.assertEqual(cubes[:4], res1)\n res2 = pm.parent(cubes, group)\n self.assertEqual(cubes[4:], res2)\n\n cubes, group = makeCubesAndGrp()\n res1 = pm.parent(cubes[0], cubes[1], [cubes[2], cubes[3], group])\n self.assertEqual(cubes[:4], res1)\n res2 = pm.parent(cubes, group)\n self.assertEqual(cubes[4:], res2)\n\n cubes, group = makeCubesAndGrp()\n res1 = pm.parent([cubes[0], cubes[1]], cubes[2], [cubes[3], group])\n self.assertEqual(cubes[:4], res1)\n res2 = pm.parent(cubes, group)\n self.assertEqual(cubes[4:], res2)","def test_programs():\n yield 4, 4, 1\n yield 16, 12, 2","def tenpar_subset_test():\n model_d = \"ies_10par_xsec\"\n test_d = os.path.join(model_d, \"master_subset_test\")\n template_d = os.path.join(model_d, \"test_template\")\n if not os.path.exists(template_d):\n raise Exception(\"template_d {0} not found\".format(template_d))\n if os.path.exists(test_d):\n shutil.rmtree(test_d)\n # shutil.copytree(base_d,test_d)\n pst = pyemu.Pst(os.path.join(template_d, \"pest.pst\"))\n pst.control_data.noptmax = 3\n\n # first without subset\n pst.pestpp_options = {}\n pst.pestpp_options[\"ies_num_reals\"] = 50\n pst.pestpp_options[\"ies_lambda_mults\"] = \"1.0\"\n pst.pestpp_options[\"ies_accept_phi_fac\"] = 100.0\n pst.write(os.path.join(template_d, \"pest.pst\"))\n pyemu.helpers.start_slaves(template_d, exe_path, \"pest.pst\", num_slaves=10,\n slave_root=model_d, master_dir=test_d)\n df_base = pd.read_csv(os.path.join(test_d, \"pest.phi.meas.csv\"),index_col=0)\n\n pst.pestpp_options = {}\n pst.pestpp_options[\"ies_num_reals\"] = 50\n pst.pestpp_options[\"ies_lambda_mults\"] = \"1.0\"\n pst.pestpp_options[\"ies_subset_size\"] = 15\n pst.pestpp_options[\"ies_accept_phi_fac\"] = 100.0\n\n pst.write(os.path.join(template_d, \"pest.pst\"))\n pyemu.helpers.start_slaves(template_d, exe_path, \"pest.pst\", num_slaves=10,\n slave_root=model_d, master_dir=test_d)\n df_sub = pd.read_csv(os.path.join(test_d, \"pest.phi.meas.csv\"),index_col=0)\n diff = (df_sub - df_base).apply(np.abs)\n print(diff.max())\n print(df_sub.iloc[-1,:])\n print(df_base.iloc[-1,:])\n assert diff.max().max() == 0.0","def test_path1():\n path = [(0,0,1)]\n path.append([('A',4,0)])\n path.append((0,1,1))\n path.append([('A',3,0)])\n path.append((0,1,1))\n path.append([('A',2,0)])\n path.append((0,1,1))\n path.append([('A',1,0)])\n\n execute_path(path, True)","def test_multiple_triples(self):\n self.graph.add((artis, RDF.type, zoo))\n self.graph.add((artis, RDF.type, org))\n self.graph.add((berlin_zoo, RDF.type, zoo))\n self.assertEquals(len(list(self.graph.triples((None, None, None)))), 3)\n\n self.assertEquals(len(list(self.graph.triples((artis, None, None)))), 2)\n self.assertEquals(len(list(self.graph.triples((None, RDF.type, None)))), 3)\n self.assertEquals(len(list(self.graph.triples((None, None, zoo)))), 2)\n self.assertEquals(len(list(self.graph.triples((None, None, org)))), 1)","def test_EnumerateTestSet():\n protos = list(random_networkx_generator.EnumerateTestSet())\n assert len(protos) == 100","def iter_func(root_name, root, set_traverse, list_funcs, G, strings,\n plot_nodes, cur_pos, xgrain, min_weight, max_weight):\n set_traverse.append(root)\n nbs = G.neighbors(root)\n nbs = G[root]\n\n plot_nodes.append(cur_pos)\n xgrain = xgrain/2.0\n\n flag_pn = -1\n for nb in nbs.keys():\n if nb in set_traverse:\n continue\n\n next_pos = [0, 0, 0]\n if root.name == root_name:\n next_pos[0] = cur_pos[0]\n else:\n next_pos[0] = cur_pos[0] + xgrain*flag_pn*( 0.8+0.2*(nbs[nb][\"weight\"]-1.0/max_weight)/(1.0/min_weight-1.0/max_weight) ) #* (nbs[nb][\"weight\"]-1.0/max_weight)/(1.0/min_weight-1.0/max_weight)\n next_pos[1] = cur_pos[1] + 3.0*(nbs[nb][\"weight\"]-1.0/max_weight)/(1.0/min_weight-1.0/max_weight)\n next_pos[2] = nb.name\n\n flag_pn = flag_pn*(-1)\n\n strings.append([root, nb])\n set_traverse, strings, plot_nodes = iter_func(root_name, nb, set_traverse, list_funcs, G, strings, plot_nodes, next_pos, xgrain, min_weight, max_weight)\n\n return set_traverse, strings, plot_nodes","def test_excel(test_data,tmp_path):\n\n for d in test_data:\n\n gpm = GenotypePhenotypeMap(genotype=d[\"genotype\"],\n wildtype=d[\"wildtype\"],\n phenotype=d[\"phenotype\"],\n uncertainty=d[\"uncertainty\"])\n\n # Write excel file\n excel_file = os.path.join(tmp_path,\"tmp.xlsx\")\n gpm.to_excel(filename=excel_file)\n assert os.path.isfile(excel_file)\n\n # Read in and make sure it worked.\n new_gpm = gpmap.read_excel(filename=excel_file,wildtype=d[\"wildtype\"])\n conftest.compare_gpmap(gpm,new_gpm)\n\n # Do not give wildtype. Should still work because the wildtype was\n # inferred.\n gpm_read = gpmap.read_excel(filename=excel_file)\n conftest.compare_gpmap(gpm,gpm_read)\n\n # Check ability to read labels back in\n site_labels = [f\"{x}\" for x in range(10,10+len(d[\"wildtype\"]),1)]\n gpm = GenotypePhenotypeMap(genotype=d[\"genotype\"],\n wildtype=d[\"wildtype\"],\n site_labels=site_labels)\n out_file = os.path.join(tmp_path,\"tmp.xlsx\")\n gpm.to_excel(out_file)\n\n gpm_read = gpmap.read_excel(out_file)\n\n for i in range(len(gpm_read.site_labels)):\n\n # Skip virtual site_labels added for invariant sites\n if len(d[\"mutations\"][i]) == 1:\n continue\n\n assert gpm_read.site_labels[i] == gpm.site_labels[i]\n\n # Read in with bad wildtype. Should throw warning and then have\n # sequential site labels.\n with pytest.warns(UserWarning):\n gpm_read = gpmap.read_excel(out_file,wildtype=d[\"mutant\"])\n\n assert np.array_equal(gpm_read.site_labels,range(len(d[\"wildtype\"])))","def main(dot_file):\n global SUBGRAPHS, PARENTS\n graph = graph_from_dot(dot_file)\n SUBGRAPHS = {}\n PARENTS = {}\n extract_subgraphs([graph])\n \n for (name, subgraph) in SUBGRAPHS.items():\n nodes = extract_nodes(subgraph)\n for node in nodes:\n (name_function, result, function_call_line) = analyse_label_function_calls(node)\n if name_function is not None:\n (label_node1, label_node2, bb) = create_labels(node, result, function_call_line)\n node.set_label(label_node1)\n nodes_to_update = get_nodes_to_update(subgraph, graph.get_name())\n update_nodes(nodes_to_update, bb)\n nodes.append(create_new_node(subgraph, node, label_node2, bb))\n update_edges(subgraph, graph.get_name(), bb)\n create_new_edge(graph, node.get_name(), SUBGRAPHS[name_function])\n recreate_subgraphs_name()\n export_graph(graph, \"main_output\", \"png\")\n export_graph(graph, \"main_output\", \"dot\")\n return graph","def run_generations(init_len):\n num_graphs = 0\n current_gen = [nx.path_graph(init_len)]\n complete_graph_list = current_gen.copy()\n while len(current_gen) and current_gen[0].size() < (3*init_len - 7):\n current_gen = generation_next(current_gen)\n num_graphs += show_graph_list(current_gen)\n complete_graph_list.extend(filter_bridge_case(current_gen))\n print(num_graphs)\n return complete_graph_list","def test_general_subset_level():\n pass","def test_clips_deftemplate_parse_smoke(self):\n G = CreateTestCarpetingGraph()\n \n nlpGraphProcessor = NLPResultGraphParser()\n res = BLNlpClipsRuleBase()\n seen = []\n nlpGraphProcessor.ParseObject(G, Strings.ndStep, seen, res)\n nlpGraphProcessor.ParseObject(G, \"CarpetingPattern\", seen, res)\n nlpGraphProcessor.ParseObject(G, \"ProcessSubStep\", seen, res)\n\n # Deftemplate with part relation and extra fields\n template = next(t for t in res.Deftemplates if t.TemplateName == Strings.ndStep)\n self.assertTrue(template != None)\n self.assertEqual(len(template.Slots), 4)\n self.assertTrue(next(s for s in template.Slots if s.Name == \"Id\") != None)\n self.assertTrue(next(s for s in template.Slots if s.Name == \"ProcessId\") != None)\n self.assertTrue(next(s for s in template.Slots if s.Name == Strings.after) != None)\n\n # Deftemplate with several has relations\n template = next(t for t in res.Deftemplates if t.TemplateName == \"CarpetingPattern\")\n self.assertTrue(template != None)\n self.assertEqual(len(template.Slots), 4)\n self.assertTrue(next(s for s in template.Slots if s.Name == \"Id\") != None)\n self.assertTrue(next(s for s in template.Slots if s.Name == \"RotationAngle\") != None)\n self.assertTrue(next(s for s in template.Slots if s.Name == \"ShiftDirection\") != None)\n self.assertTrue(next(s for s in template.Slots if s.Name == \"ShiftRatio\") != None)\n\n # Deftemplate with in relation\n template = next(t for t in res.Deftemplates if t.TemplateName == \"ProcessSubStep\")\n self.assertTrue(template != None)\n self.assertEqual(len(template.Slots), 7)\n self.assertTrue(next(s for s in template.Slots if s.Name == \"Id\") != None)\n self.assertTrue(next(s for s in template.Slots if s.Name == \"Instruction\") != None)\n self.assertTrue(next(s for s in template.Slots if s.Name == \"ProcessStepId\") != None)\n self.assertTrue(next(s for s in template.Slots if s.Name == \"StepNumber\") != None)","def test07_add_type_triples(self):\n r = LDPRS('http://ex.org/abc')\n g = Graph()\n r.add_type_triples(g)\n self.assertEqual(len(g), 2)","def test_multiple_task_groups_reversed_dag(\n self, test_multiple_taskgroups_dag, multiple_taskgroups_dag_expected_edges\n ):\n (\n dag,\n group1,\n group2,\n group3,\n (\n group1_emp1,\n group1_emp2,\n group1_emp3,\n group2_emp1,\n group2_emp2,\n group2_emp3,\n group2_op1,\n group2_op2,\n group3_emp1,\n group3_emp2,\n group3_emp3,\n emp_in1,\n emp_in2,\n emp_in3,\n emp_in4,\n emp_out1,\n emp_out2,\n emp_out3,\n emp_out4,\n op_in1,\n op_out1,\n ),\n ) = test_multiple_taskgroups_dag\n\n group1_emp3 << Label(\"label group1.group1_emp1 <=> group1.group1_emp2\") << group1_emp1\n\n group1 << emp_in1\n group1 << Label(\"label emp_in2 <=> group1\") << emp_in2\n group1 << Label(\"label emp_in3/emp_in4 <=> group1\") << [emp_in3, emp_in4]\n group1 << Label(\"label op_in1 <=> group1\") << XComArg(op_in1, \"test_key\")\n\n (\n group2_emp3\n << Label(\"label group2.group2_emp1/group2.group2_emp2 <=> group2.group2_emp3\")\n << [group2_emp1, group2_emp2]\n )\n (\n [group2_emp2, group2_emp3]\n << Label(\"label group2.group2_emp1 <=> group2.group2_emp2/group2.group2_emp3\")\n << group2_emp1\n )\n group3 << Label(\"label group2.group2_emp3 <=> group3\") << group2_emp3\n\n (\n XComArg(group2_op2, \"test_key\")\n << Label(\"label group2.group2_op1 <=> group2.group2_op2\")\n << XComArg(group2_op1, \"test_key\")\n )\n group3 << Label(\"label group2.group2_op2 <=> group3\") << XComArg(group2_op2, \"test_key\")\n\n emp_out1 << group3\n emp_out2 << Label(\"label group3 <=> emp_out2\") << group3\n [emp_out3, emp_out4] << Label(\"label group3 <=> emp_out3/emp_out4\") << group3\n XComArg(op_out1, \"test_key\") << Label(\"label group3 <=> op_out1\") << group3\n\n group2 << Label(\"label group1 <=> group2\") << group1\n\n compare_dag_edges(dag_edges(dag), multiple_taskgroups_dag_expected_edges)","def test_ExplorePath_Simple( self ):\n links = []\n n1 = graph.Node( 10, 50 )\n n2 = graph.Node( 10, 50 )\n n3 = graph.Node( 10, 50 )\n n7 = graph.Node( 10, 50 )\n\n links.append( graph.Link( n1, n2 ) )\n links.append( graph.Link( n2, n3 ) )\n links.append( graph.Link( n3, n7 ) )\n roots = [n1]\n actual = nodes.explorePath( links, roots, n1 )\n expected = [ n1, n2, n3, n7 ]\n self.assertEqual( expected, actual )","def gexf_graph():\n # you must replace these lines and supply your own graph\n \n \n \n my_gexf = Gexf(\"JiajiaXie\", \"My awesome graph\")\n graph=my_gexf.addGraph(\"undirected\", \"static\", \"My awesome networks\")\n \n atr1=graph.addNodeAttribute('Type',type='string')\n\n\n for set in data_specific:\n if graph.nodeExists(set['set_num']) ==0:\n tm1=graph.addNode(set['set_num'], set['name'], r='0', g='0', b='0')\n tm1.addAttribute(atr1,\"set\")\n\n\n\n counter_test=1\n for set, part in data_parts.items():\n for key, part_list in part.items():\n interme =part_list['color']\n red=interme[0]+interme[1]\n green=interme[2]+interme[3]\n blue=interme[4]+interme[5]\n\n red_de=str(int(red,16))\n green_de=str(int(green,16))\n blue_de=str(int(blue,16))\n if graph.nodeExists(part_list['id'])==0:\n tm2=graph.addNode(part_list['id'], part_list['part_name'],r=red_de, g=green_de, b = blue_de)\n tm2.addAttribute(atr1,\"part\")\n\n\n counter_test+=1\n graph.addEdge(\"_\"+str(counter_test), set, part_list['id'], part_list['quantity'])\n\n\n\n f=open('bricks_graph.gexf','wb')\n my_gexf.write(f)\n\n\n return my_gexf.graphs[0]","def test_xcomarg_shift(self, test_dag):\n # Unpack the fixture\n dag, (op1, op2, op3, op4) = test_dag\n # Arrange the operators with a Label in the middle\n op1_arg = XComArg(op1, \"test_key\")\n op1_arg >> Label(\"Label 1\") >> [op2, op3]\n op1_arg >> op4\n # Check that the DAG has the right edge info\n assert dag.get_edge_info(op1.task_id, op2.task_id) == {\"label\": \"Label 1\"}\n assert dag.get_edge_info(op1.task_id, op4.task_id) == {}","def generate_test():\n o = []\n pos = [384, 288]\n note_group_size = GAN_PARAMS[\"note_group_size\"]\n generate_set(begin=3 * note_group_size, start_pos=pos,\n length_multiplier=dist_multiplier, group_id=3, plot_map=True)","def test_build_poset_lattice():\n lattice = build_poset_lattice(all_games_gen(2))\n assert len(lattice.edges()) == 36","def create_nodes(self):","def __test(graph): \n \n if not isinstance(graph, basegraph):\n raise TypeError(\"Expected type was Graph.\")\n \n print \"### iPATH TEST DATA STRUCTURE\"\n print \"### Data Type: Graph ({})\".format(str(graph.__class__.__bases__[0].__name__))\n print \"### Implementation: {}\".format(str(graph.__class__.__name__))\n \n print \"\\n*** ADD NODE ***\\n\" \n for i in range(10):\n print \"add_node({})\".format(str(i)) \n graph.add_node(i) \n \n print \"\\n*** ADD ARC ***\\n\" \n for i in range(10):\n print \"add_arc({}, {}, {})\".format(str(i), str(i + 1), str(2 * (i + 1)))\n graph.add_arc(i, i + 1, 2 * (i + 1))\n print \"add_arc({}, {}, {})\".format(str(i), str(i + 2), str(2 * (i + 2)))\n graph.add_arc(i, i + 2, 2 * (i + 2))\n \n print \"\\n*** GRAPH ***\\n\" \n print \"\\n{}\\n\".format(str(graph))\n \n print \"\\n*** REMOVE NODE ***\\n\" \n print \"remove_node(5)\"\n graph.remove_node(5)\n \n print \"\\n*** GRAPH ***\\n\" \n print \"\\n{}\\n\".format(str(graph))\n \n print \"\\n*** REMOVE ARC ***\\n\" \n print \"remove_arc(7, 8)\" \n graph.remove_arc(7, 8)\n \n print \"\\n*** GRAPH ***\\n\" \n print \"\\n{}\\n\".format(str(graph))\n \n print \"\\n*** INCIDENT ARCS ***\\n\" \n for node in graph.get_nodes():\n print \"Incident Arcs of {}\\t{}\\n\".format(str(node), str(graph.get_incident_arcs(node._id)))\n \n print \"\\n*** ADJACENCY ***\\n\" \n for i in range(10):\n for j in range(10):\n if graph.are_adjacent(i, j) == True:\n print \"Adjacency Between ({}, {}): True\\n\".format(str(i), str(j))\n \n print \"\\n*** NODES ***\\n\" \n print \"numNodes: {}\\n\".format(str(graph.get_num_nodes())) \n print \"Nodes: {}\\n\".format(str(graph.get_nodes())) \n \n print \"\\n*** ARCS ***\\n\" \n print \"numArcs: {}\\n\".format(str(graph.get_num_arcs())) \n print \"Arcs: {}\\n\".format(str(graph.get_arcs())) \n \n print \"\\n*** SEARCH BFS ***\\n\" \n for i in range(10): \n print \"bfs({})\".format(str(i))\n Lbfs = graph.bfs(i)\n for n in Lbfs:\n print \"{}\\n\".format(str(n))\n print \"\\n\"\n \n print \"\\n*** SEARCH DFS ***\\n\" \n for i in range(9):\n print \"dfs({})\".format(str(i))\n Ldfs = graph.dfs(i)\n for n in Ldfs:\n print \"{}\\n\".format(str(n))\n print \"\\n\"\n \n print \"\\n### END OF TEST ###\\n\"","def gen_graph(self):","def test_pytree(self):\n\n # Arguments are of the form [([x00, x01], [x10]), dict(a=ya, b=yb)]\n def add_all_jax(x_pair_of_list, y_dict):\n x_list_0, x_list_1 = x_pair_of_list\n return functools.reduce(operator.add,\n x_list_0 + x_list_1 + [y_dict[\"a\"], y_dict[\"b\"]])\n\n self.CheckShapePolymorphism(\n add_all_jax,\n input_signature=[([tf.TensorSpec([None]), tf.TensorSpec([None])],\n [tf.TensorSpec([None])]),\n dict(a=tf.TensorSpec([None]), b=tf.TensorSpec([None]))],\n in_shapes=[([\"(v,)\", \"(v,)\"], [(\"v,\")]),\n dict(a=\"(v,)\", b=\"(v,)\")],\n expected_output_signature=tf.TensorSpec([None]))\n\n # Now partial in_shapes; the parts of the in_shapes that are not specified\n # must have full input_signatures.\n self.CheckShapePolymorphism(\n add_all_jax,\n input_signature=[([tf.TensorSpec([4]), tf.TensorSpec([4])],\n [tf.TensorSpec([4])]),\n dict(a=tf.TensorSpec([4]), b=tf.TensorSpec([4]))],\n in_shapes=[([\"(4,)\", \"(_,)\"], [(\"4,\")]),\n dict(a=\"(_,)\", b=\"(4,)\")],\n expected_output_signature=tf.TensorSpec([4]))","def test_generator1(self):\n xpb = XPathBuilder()\n xp = xpb.foo\n xp = xp.bar\n xp = xp.baz[xpb.attr('x') == 'y']\n xp = xp[1]\n exp = '/foo/bar/baz[@x = \"y\"][1]'\n self.assertEqual(xp.tostring(), exp)","def create_four_subplots():\n pass","def test_hierarchical_register_and_contain(self):\n space = Space()\n\n categories = {\"asdfa\": 0.1, 2: 0.2, 3: 0.3, 4: 0.4}\n dim = Categorical(\"yolo.nested\", categories, shape=2)\n space.register(dim)\n dim = Integer(\"yolo2.nested\", \"uniform\", -3, 6)\n space.register(dim)\n dim = Real(\"yolo3\", \"norm\", 0.9)\n space.register(dim)\n\n trial = Trial(\n params=[\n {\"name\": \"yolo.nested\", \"value\": [\"asdfa\", 2], \"type\": \"categorical\"},\n {\"name\": \"yolo2.nested\", \"value\": 1, \"type\": \"integer\"},\n {\"name\": \"yolo3\", \"value\": 0.5, \"type\": \"real\"},\n ]\n )\n\n assert \"yolo\" in trial.params\n assert \"nested\" in trial.params[\"yolo\"]\n assert \"yolo2\" in trial.params\n assert \"nested\" in trial.params[\"yolo2\"]\n assert \"yolo3\" in trial.params\n\n assert trial in space","def test_make_pop(self, pop_size, cell_number, microcell_number):\n for i in [0, 1]:\n pe.Parameters.instance().use_ages = i\n # Population is initialised with no households\n pop_params = {\"population_size\": pop_size,\n \"cell_number\": cell_number,\n \"microcell_number\": microcell_number}\n test_pop = ToyPopulationFactory.make_pop(pop_params)\n\n total_people = 0\n count_non_empty_cells = 0\n for cell in test_pop.cells:\n for microcell in cell.microcells:\n total_people += len(microcell.persons)\n if len(cell.persons) > 0:\n count_non_empty_cells += 1\n # Test there is at least one non-empty cell\n self.assertTrue(count_non_empty_cells >= 1)\n # Test that everyone in the population has been assigned a\n # microcell\n self.assertEqual(total_people, pop_size)\n\n # Test a population class object is returned\n self.assertIsInstance(test_pop, pe.Population)","def test_path4():\n path = [\n (0, 0, 1),\n [('A', 3, 0)],\n (0, 1, 1),\n [('A', 2, 0)],\n (np.pi/2, 1, 1),\n (0, 1, 1),\n (np.pi/2, 1, 1),\n (0, 1, 1),\n (np.pi/2, 1, 1),\n (0, 1, 1),\n (np.pi/2, 1, 1),\n ] * 4\n execute_path(path,True)","def crossover(self, pop):\n intDiscID = self.iID + self.dID\n varID = self.cID\n goldenRatio = (1.0 + sqrt(5)) / 2.0\n dx = np.zeros_like(pop[0])\n children = []\n used = []\n for i in range(0, int(self.fracElite * len(pop)), 1):\n r = int(rand() * self.population)\n while r in used or r == i:\n r = int(rand() * self.population)\n\n used.append(i)\n children.append(cp.deepcopy(pop[r]))\n dx = abs(pop[i] - children[i]) / goldenRatio\n children[i] = children[i] + dx * varID + np.round(dx * intDiscID)\n children[i] = simple_bounds(children[i], self.lb, self.ub)\n\n return (\n children, used)","def main(showSamples=True, showConfusion=True):\n ndigit = 10\n elambda = [0.4, 0.6, 0.8]\n for i in elambda:\n test(ndigit, i, showSamples, showConfusion)\n if showSamples:\n pltmulti('graphs.pdf')","def gexf_graph():\n # you must replace these lines and supply your own graph\n gexf = Gexf(\"author\", \"title\")\n mygraph = gexf.addGraph(\"undirected\", \"static\", \"A web network\")\n atr_type = mygraph.addNodeAttribute('Type', type='string')\n atr_id = mygraph.addNodeAttribute('id', type='string')\n atr_label = mygraph.addNodeAttribute('label', type='string')\n atr_color_r = mygraph.addNodeAttribute('color_r', type='string', defaultValue='0')\n atr_color_g = mygraph.addNodeAttribute('color_g', type='string', defaultValue='0')\n atr_color_b = mygraph.addNodeAttribute('color_b', type='string', defaultValue='0')\n k = 0\n for i in range(min_parts()):\n tmp = mygraph.addNode(set_num[i], name[i], r=\"0\", g=\"0\", b=\"0\")\n tmp.addAttribute(atr_type, \"set\")\n tmp.addAttribute(atr_id, set_num[i])\n tmp.addAttribute(atr_label, name[i])\n for j in range(len(Parts[i][\"Parts\"])):\n if mygraph.nodeExists(Parts[i][\"Parts\"][j][\"number\"]+\"_\"+Parts[i][\"Parts\"][j][\"color\"][\"rgb\"])==0:\n temp = mygraph.addNode((Parts[i][\"Parts\"][j][\"number\"]+\"_\"+Parts[i][\"Parts\"][j][\"color\"][\"rgb\"]), Parts[i][\"Parts\"][j][\"name\"], r=str(int(Parts[i][\"Parts\"][j][\"color\"][\"rgb\"][0:2], 16)), g=str(int(Parts[i][\"Parts\"][j][\"color\"][\"rgb\"][2:4], 16)), b=str(int(Parts[i][\"Parts\"][j][\"color\"][\"rgb\"][4:6], 16)))\n temp.addAttribute(atr_type, \"part\")\n temp.addAttribute(atr_id, (Parts[i][\"Parts\"][j][\"number\"]+\"_\"+Parts[i][\"Parts\"][j][\"color\"][\"rgb\"]))\n temp.addAttribute(atr_label, Parts[i][\"Parts\"][j][\"name\"])\n temp.addAttribute(atr_color_r, Parts[i][\"Parts\"][j][\"color\"][\"rgb\"][0:2])\n temp.addAttribute(atr_color_g, Parts[i][\"Parts\"][j][\"color\"][\"rgb\"][2:4])\n temp.addAttribute(atr_color_b, Parts[i][\"Parts\"][j][\"color\"][\"rgb\"][4:6])\n mygraph.addEdge(str(k), set_num[i], (Parts[i][\"Parts\"][j][\"number\"]+\"_\"+Parts[i][\"Parts\"][j][\"color\"][\"rgb\"]), weight=Parts[i][\"Parts\"][j][\"quantity\"])\n k = k+1\n output_file = open(\"bricks_graph.gexf\", \"wb\")\n gexf.write(output_file)\n return -1","def test_get_grid_edge_nodes(flopy_dis_mf6):\n mf6 = flopy_dis_mf6[1]\n mf6.initialize()\n\n with pytest.raises(NotImplementedError):\n mf6.get_grid_edge_nodes(1, np.zeros((1, 1)))","def generate_graph(data, x=\"Red blood Cells\", y=\"Paletes\", z=\"Leukocytes\", graph_type='scatter', selected_points=[], *args, **kwargs):\n\n # Return an empty figure if the input is empty\n if x == [] and y==[]:\n return go.Figure()\n \n # Transform the data into wide format\n melted = pd.melt(data, value_vars=data.columns)\n\n if graph_type==\"histogram\":\n if len(x) > 1:\n return generate_histogram(melted, x, y, \"wide\", **kwargs)\n elif len(x) == 0:\n return go.Figure()\n return generate_histogram(data, x, y, \"long\", **kwargs)\n\n elif graph_type==\"scatter\":\n if len(x) == 1 and len(y) == 1:\n return generate_scatter(data, x[0], y[0], \"long\", selected_points=selected_points, **kwargs)\n\n elif len(x) > 1 or len(y) > 1:\n return generate_scatter_matrix(data, x, y, data_format=\"long\", **kwargs)\n\n return generate_scatter(melted, x, y, \"wide\")\n\n elif graph_type==\"box\":\n return generate_box_plot(data, x, \"long\", **kwargs)\n\n elif graph_type==\"heatmap\":\n if len(x) > 0 and len(y) > 0:\n return generate_heatmap(data, x, y, \"long\", **kwargs)\n return go.Figure()\n\n elif graph_type==\"par_coords\":\n return generate_parallel_coords(data, x, y, \"long\", **kwargs)\n\n elif graph_type==\"strip\":\n if len(x) > 0 and len(y) > 0:\n return generate_strip(data, x, y, \"long\", **kwargs)\n return go.Figure()\n\n elif graph_type==\"ternary\":\n if len(x) > 0 and len(y) > 0 and len(z) > 0:\n return generate_ternary(data, x, y, z, \"long\", **kwargs)\n return go.Figure()","def test_subkey(man):\n errors = []\n\n G = man.writeTest()\n\n G.addVertex(\"Work\", \"Thing\", {})\n G.addVertex(\"Workflow\", \"Thing\", {})\n G.addVertex(\"Other\", \"Thing\", {})\n G.addVertex(\"OtherGuy\", \"Thing\", {})\n\n G.addEdge(\"Work\", \"Other\", \"edge\")\n G.addEdge(\"Workflow\", \"OtherGuy\", \"edge\")\n\n count = 0\n for i in G.query().V(\"Work\").out():\n count += 1\n if count != 1:\n errors.append(\"Incorrect outgoing vertex count %d != %d\" % (count, 1))\n\n count = 0\n for i in G.query().V(\"Work\").outE():\n count += 1\n if count != 1:\n errors.append(\"Incorrect outgoing edge count %d != %d\" % (count, 1))\n\n count = 0\n for i in G.query().V(\"Other\").inE():\n count += 1\n if count != 1:\n errors.append(\"Incorrect incoming edge count %d != %d\" % (count, 1))\n\n return errors","def bclone():\n node = nuke.selectedNodes()\n if len(node)==1:\n clone1 = nuke.createNode(\"NoOp\", inpanel = False)\n clone1.setName(\"Bclone\")\n clone1['label'].setValue(node[0].name()+\"\\nClone_Parent\")\n clone1['tile_color'].setValue(2521651711)\n clone1['note_font_color'].setValue(1583243007)\n clone1xpos = clone1['xpos'].getValue()\n clone1ypos = clone1['ypos'].getValue()\n \n clone2 = nuke.createNode(\"NoOp\", inpanel = False)\n clone2.setName(\"Bclone\")\n clone2['label'].setValue(node[0].name()+\"\\nClone\")\n clone2['hide_input'].setValue(True)\n clone2['tile_color'].setValue(2521651711)\n clone2['note_font_color'].setValue(1583243007)\n clone2['xpos'].setValue(clone1xpos)\n clone2['ypos'].setValue(clone1ypos)\n\n if len(node)==0:\n clone1 = nuke.createNode(\"NoOp\", inpanel = False)\n clone1.setName(\"Bclone\")\n clone1['label'].setValue(\"Clone_Parent\")\n clone1['tile_color'].setValue(2521651711)\n clone1['note_font_color'].setValue(1583243007)\n clone1xpos = clone1['xpos'].getValue()\n clone1ypos = clone1['ypos'].getValue()\n \n clone2 = nuke.createNode(\"NoOp\", inpanel = False)\n clone2.setName(\"Bclone\")\n clone2['label'].setValue(\"Clone\")\n clone2['hide_input'].setValue(True)\n clone2['tile_color'].setValue(2521651711)\n clone2['note_font_color'].setValue(1583243007)\n clone2['xpos'].setValue(clone1xpos)\n clone2['ypos'].setValue(clone1ypos)\n if len(node)!=0 and len(node)!=1:\n nuke.message('Just select one node to clone !')","def test_dummy4(self):\n xpb = XPathBuilder()\n xp = xpb.dummy()\n self.assertTrue(xp.log_not() is xp)","def test_duplicate(self):\n test_file = os.path.join(INPUT_HYPM_PATH, 'unit_364-2013-225-1-0.mdd')\n\n mdd.procall([test_file])\n \n self.compare_node58()","def test_general_subset_all():\n pass","def main():\n dims = params['dims']\n\n for d in dims:\n print('**** Running test for d={0:d} ****'.format(d))\n run_test(d)","def sub_graph_merging(self):\n raise NotImplementedError()","def test_get_dependencies_subgraph_by_dfs(\n self, source_node, expected_nodes_in, expected_nodes_out\n ):\n graph = nx.DiGraph()\n graph.add_node(\"pack1\")\n graph.add_node(\"pack2\")\n graph.add_node(\"pack3\")\n graph.add_node(\"pack4\")\n graph.add_edge(\"pack1\", \"pack2\")\n graph.add_edge(\"pack2\", \"pack3\")\n dfs_graph = PackDependencies.get_dependencies_subgraph_by_dfs(\n graph, source_node\n )\n for i in expected_nodes_in:\n assert i in dfs_graph.nodes()\n for i in expected_nodes_out:\n assert i not in dfs_graph.nodes()","def test_zip_batch(self):\n assert self.design.layout.layers[0].name == 'top'","def test_Tree():","def dump_subgraph_for_debug(self):\n\n import pypipegraph2 as ppg\n\n nodes = []\n seen = set()\n edges = []\n counter = [0]\n node_to_counters = {}\n\n def descend(node):\n if node in seen:\n return\n seen.add(node)\n j = self.runner.jobs[node]\n if isinstance(j, ppg.FileInvariant):\n nodes.append(f\"Path('{counter[0]}').write_text('A')\")\n nodes.append(f\"job_{counter[0]} = ppg.FileInvariant('{counter[0]}')\")\n elif isinstance(j, ppg.ParameterInvariant):\n nodes.append(\n f\"job_{counter[0]} = ppg.ParameterInvariant('{counter[0]}', 55)\"\n )\n elif isinstance(j, ppg.FunctionInvariant):\n nodes.append(\n f\"job_{counter[0]} = ppg.FunctionInvariant('{counter[0]}', lambda: 55)\"\n )\n elif isinstance(j, ppg.SharedMultiFileGeneratingJob):\n nodes.append(\n f\"job_{counter[0]} = ppg.SharedMultiFileGeneratingJob('{counter[0]}', {[x.name for x in j.files]!r}, dummy_smfg, depend_on_function=False)\"\n )\n elif isinstance(j, ppg.TempFileGeneratingJob):\n nodes.append(\n f\"job_{counter[0]} = ppg.TempFileGeneratingJob('{counter[0]}', dummy_fg, depend_on_function=False)\"\n )\n elif isinstance(j, ppg.FileGeneratingJob):\n nodes.append(\n f\"job_{counter[0]} = ppg.FileGeneratingJob('{counter[0]}', dummy_fg, depend_on_function=False)\"\n )\n elif isinstance(j, ppg.MultiTempFileGeneratingJob):\n files = [counter[0] + \"/\" + x.name for x in j.files]\n nodes.append(\n f\"job_{counter[0]} = ppg.MultiTempFileGeneratingJob({files!r}, dummy_mfg, depend_on_function=False)\"\n )\n elif isinstance(j, ppg.MultiFileGeneratingJob):\n files = [str(counter[0]) + \"/\" + x.name for x in j.files]\n nodes.append(\n f\"job_{counter[0]} = ppg.MultiFileGeneratingJob({files!r}, dummy_mfg, depend_on_function=False)\"\n )\n elif isinstance(j, ppg.DataLoadingJob):\n nodes.append(\n f\"job_{counter[0]} = ppg.DataLoadingJob('{counter[0]}', lambda: None, depend_on_function=False)\"\n )\n elif isinstance(j, ppg.AttributeLoadingJob):\n nodes.append(\n f\"job_{counter[0]} = ppg.AttributeLoadingJob('{counter[0]}', DummyObject(), 'attr_{counter[0]}', lambda: None, depend_on_function=False)\"\n )\n else:\n raise ValueError(j)\n node_to_counters[node] = counter[0]\n counter[0] += 1\n for parent in self.runner.dag.predecessors(node):\n descend(parent)\n\n def build_edges(node):\n for parent in self.runner.dag.predecessors(node):\n edges.append(\n f\"edges.append(('{node_to_counters[node]}', '{node_to_counters[parent]}'))\"\n )\n build_edges(parent)\n\n descend(self.job_id)\n edges.append(\"edges = []\")\n build_edges(self.job_id)\n edges.extend(\n [\n \"for (a,b) in edges:\",\n \" if a in ppg.global_pipegraph.jobs and b in ppg.global_pipegraph.jobs:\",\n \" ppg.global_pipegraph.jobs[a].depends_on(ppg.global_pipegraph.jobs[b])\",\n ]\n )\n with open(\"subgraph_debug.py\", \"w\") as op:\n lines = \"\"\"\nclass DummyObject:\n pass\n\ndef dummy_smfg(files, prefix):\n Path(prefix).mkdir(exist_ok=True, parents=True)\n for f in files:\n f.write_text(\"hello\")\n\n\ndef dummy_mfg(files):\n for f in files:\n f.parent.mkdir(exist_ok=True, parents=True)\n f.write_text(\"hello\")\n\ndef dummy_fg(of):\n of.parent.mkdir(exist_ok=True, parents=True)\n of.write_text(\"fg\")\n\n\"\"\".split(\n \"\\n\"\n )\n lines += nodes\n lines += edges\n lines += [\"\", \"ppg.run()\", \"ppg.run\"]\n\n op.write(\"\\n\".join(\" \" + l for l in lines))","def setUp(self):\n\n\n # InverseLabeling\n invLabeling0 = {'L0': [0, 1, 2]}\n\n invLabeling1 = {'L0' : [0, 2],\n 'L1' : [1]}\n\n invLabeling2 = {\n 'L0' : [0],\n 'L1' : [1],\n 'L2' : [2]\n }\n\n invLabeling3 = {\n 'L1' : [0, 1],\n 'L2' : [2]\n }\n\n invLabeling4 = {\n 'L0' : [0,1],\n 'L1' : [0],\n 'L2' : [2]\n }\n\n invLabeling5 = {\n 'L0': [0, 1, 2],\n 'L1': []\n }\n \n # Create some ontologies\n ontology0 = {'L0': ['L0']}\n\n ontology1 = {}\n\n ontology2 = {'L0': ['L1']}\n\n ontology3 = {'L0': ['L1', 'L2'],\n 'L1': ['L2'],\n 'L2': ['L0']}\n\n if self.id().split('.')[-1] == 'test_createLinkograph':\n self.testParams = [\n {'inverseLabeling': invLabeling0,\n 'ontology': ontology0,\n 'ExpectedLinkograph':\n linkoCreate.Linkograph(\n [({'L0'}, set(), {1, 2}),\n ({'L0'}, {0}, {2}),\n ({'L0'}, {0,1}, set())] \n )},\n\n {'inverseLabeling': invLabeling0,\n 'ontology': ontology1,\n 'ExpectedLinkograph':\n linkoCreate.Linkograph(\n [({'L0'}, set(), set()),\n ({'L0'}, set(), set()),\n ({'L0'}, set(), set())]\n )},\n\n {'inverseLabeling': invLabeling0,\n 'ontology': ontology2,\n 'ExpectedLinkograph':\n linkoCreate.Linkograph(\n [({'L0'}, set(), set()),\n ({'L0'}, set(), set()),\n ({'L0'}, set(), set())]\n )},\n\n\n {'inverseLabeling': invLabeling1,\n 'ontology': ontology0,\n 'ExpectedLinkograph':\n linkoCreate.Linkograph(\n [({'L0'}, set(), {2}),\n ({'L1'}, set(), set()),\n ({'L0'}, {0}, set())]\n )},\n\n {'inverseLabeling': invLabeling1,\n 'ontology': ontology1,\n 'ExpectedLinkograph':\n linkoCreate.Linkograph(\n [({'L0'}, set(), set()),\n ({'L1'}, set(), set()),\n ({'L0'}, set(), set())]\n )},\n\n {'inverseLabeling': invLabeling1,\n 'ontology': ontology2,\n 'ExpectedLinkograph':\n linkoCreate.Linkograph(\n [({'L0'}, set(), {1}),\n ({'L1'}, {0}, set()),\n ({'L0'}, set(), set())]\n )},\n\n {'inverseLabeling': invLabeling0,\n 'ontology': ontology3,\n 'ExpectedLinkograph':\n linkoCreate.Linkograph(\n [({'L0'}, set(), set()),\n ({'L0'}, set(), set()),\n ({'L0'}, set(), set())]\n )},\n\n {'inverseLabeling': invLabeling1,\n 'ontology': ontology3,\n 'ExpectedLinkograph':\n linkoCreate.Linkograph(\n [({'L0'}, set(), {1}),\n ({'L1'}, {0}, set()),\n ({'L0'}, set(), set())]\n )},\n\n {'inverseLabeling': invLabeling2,\n 'ontology': ontology3,\n 'ExpectedLinkograph':\n linkoCreate.Linkograph(\n [({'L0'}, set(), {1,2}),\n ({'L1'}, {0}, {2}),\n ({'L2'}, {0, 1}, set())]\n )},\n\n {'inverseLabeling': invLabeling3,\n 'ontology': ontology3,\n 'ExpectedLinkograph':\n linkoCreate.Linkograph(\n [({'L1'}, set(), {2}),\n ({'L1'}, set(), {2}),\n ({'L2'}, {0, 1}, set())]\n )},\n\n {'inverseLabeling': invLabeling4,\n 'ontology': ontology3,\n 'ExpectedLinkograph':\n linkoCreate.Linkograph(\n [({'L0', 'L1'}, set(), {2}),\n ({'L0'}, set(), {2}),\n ({'L2'}, {0, 1}, set())]\n )},\n\n {'inverseLabeling': invLabeling5,\n 'ontology': ontology3,\n 'ExpectedLinkograph':\n linkoCreate.Linkograph(\n [({'L0'}, set(), set()),\n ({'L0'}, set(), set()),\n ({'L0'}, set(), set())]\n )},\n\n ]","def test_input_valid_subgraph(self, dim):\r\n with pytest.raises(ValueError, match=\"Input is not a valid subgraph\"):\r\n clique.swap([0, dim], nx.empty_graph(dim))","def test_dummy5(self):\n xpb = XPathBuilder()\n xp = xpb.dummy()\n xp = xpb.foo & xp\n exp = '/foo'\n self.assertEqual(xp.tostring(), exp)","def test6():\n setLogLevel(\"info\")\n info(\"Configuracion Unidad experimental\")\n \"\"\" 1 -> Definicion de la topologia \"\"\"\n t1 = Topologia1()\n ue1 = UnidadExperimental()\n ue1.setTopo(t1)\n ue1.definirNodosClaves(A = 'h1', C='h2', V='h3') # Caso solo para trafico normal\n ue1.setController('ryu', 'simple_switch_13.py,ofctl_rest.py')\n info(\"Configuracion del experimento\")\n \"\"\" 3. Confiracion del experimento \"\"\"\n exp1 = Experimento()\n exp1.configureParams(ue1)\n exp1.configurarTrafico('ataque')\n \"\"\" 4. Inicio del experimento \"\"\"\n exp1.startTest()\n \"\"\" 5. Aplicacion de pruebas \"\"\"\n exp1.trafico.iperfMeasure()\n exp1.trafico.iperfMeasure(filename='iperf_ataque_test.log')\n \"\"\" 6. Fin del experimento \"\"\"\n exp1.endTest()\n info(\"Removiendo la topologia\\n\")\n exp1.killTest()\n info(\"Removiendo el controlador\\n\")\n exp1.killController() # Si no se pone no se finaliza el controlador","def test_ExplorePath( self ):\n links = []\n n1 = graph.Node( 10, 10 )\n n2 = graph.Node( 10, 20 )\n n3 = graph.Node( 10, 30 )\n n4a = graph.Node( 5, 40 )\n n4b = graph.Node( 15, 40 )\n n5a = graph.Node( 5, 50 )\n n5b = graph.Node( 15, 50 )\n n6a = graph.Node( 5, 60 )\n n6b = graph.Node( 15, 60 )\n n7 = graph.Node( 10, 70 )\n\n links.append( graph.Link( n1, n2 ) )\n links.append( graph.Link( n2, n3 ) )\n links.append( graph.Link( n3, n4a ) )\n links.append( graph.Link( n3, n4b ) )\n links.append( graph.Link( n4a, n5a ) )\n links.append( graph.Link( n4b, n5b ) )\n links.append( graph.Link( n5a, n6a ) )\n links.append( graph.Link( n6a, n7 ) )\n links.append( graph.Link( n5b, n7 ) )\n roots = [n1]\n actual = nodes.explorePath( links, n1, n1 )\n expected = [ n1, n2, n3, n4b, n5b, n7 ]\n self.assertEqual( expected, actual )","def par_test_8(self):\n\n for i in range(4):\n self.XY_par_factor.setMaxDepth(i)\n self.XY_par_factor.setMaxDepth(i)\n\n res = self.XY_factor.mult(self.XY_factor)\n par_res = self.XY_par_factor.mult(self.XY_par_factor)\n assert res.rand_vars == par_res.rand_vars and res.values == par_res.values","def test_construct_subcircuit_layers(self):\r\n dev = qml.device(\"default.qubit\", wires=3)\r\n\r\n def circuit(params):\r\n # section 1\r\n qml.RX(params[0], wires=0)\r\n # section 2\r\n qml.RY(params[1], wires=0)\r\n qml.CNOT(wires=[0, 1])\r\n qml.CNOT(wires=[1, 2])\r\n # section 3\r\n qml.RX(params[2], wires=0)\r\n qml.RY(params[3], wires=1)\r\n qml.RZ(params[4], wires=2)\r\n qml.CNOT(wires=[0, 1])\r\n qml.CNOT(wires=[1, 2])\r\n # section 4\r\n qml.RX(params[5], wires=0)\r\n qml.RY(params[6], wires=1)\r\n qml.RZ(params[7], wires=2)\r\n qml.CNOT(wires=[0, 1])\r\n qml.CNOT(wires=[1, 2])\r\n return qml.expval(qml.PauliX(0)), qml.expval(qml.PauliX(1)), qml.expval(qml.PauliX(2))\r\n\r\n circuit = qml.QNode(circuit, dev)\r\n\r\n params = np.ones([8])\r\n tapes = circuit.metric_tensor(params, only_construct=True)\r\n\r\n # this circuit should split into 4 independent\r\n # sections or layers when constructing subcircuits\r\n assert len(tapes) == 4\r\n\r\n # first layer subcircuit\r\n assert len(tapes[0].operations) == 1\r\n assert isinstance(tapes[0].operations[0], qml.Hadamard) # PauliX decomp\r\n\r\n # second layer subcircuit\r\n assert len(tapes[1].operations) == 4\r\n assert isinstance(tapes[1].operations[0], qml.RX)\r\n # PauliY decomp\r\n assert isinstance(tapes[1].operations[1], qml.PauliZ)\r\n assert isinstance(tapes[1].operations[2], qml.S)\r\n assert isinstance(tapes[1].operations[3], qml.Hadamard)\r\n\r\n # # third layer subcircuit\r\n assert len(tapes[2].operations) == 8\r\n assert isinstance(tapes[2].operations[0], qml.RX)\r\n assert isinstance(tapes[2].operations[1], qml.RY)\r\n assert isinstance(tapes[2].operations[2], qml.CNOT)\r\n assert isinstance(tapes[2].operations[3], qml.CNOT)\r\n # PauliX decomp\r\n assert isinstance(tapes[2].operations[4], qml.Hadamard)\r\n # PauliY decomp\r\n assert isinstance(tapes[2].operations[5], qml.PauliZ)\r\n assert isinstance(tapes[2].operations[6], qml.S)\r\n assert isinstance(tapes[2].operations[7], qml.Hadamard)\r\n\r\n # # fourth layer subcircuit\r\n assert len(tapes[3].operations) == 13\r\n assert isinstance(tapes[3].operations[0], qml.RX)\r\n assert isinstance(tapes[3].operations[1], qml.RY)\r\n assert isinstance(tapes[3].operations[2], qml.CNOT)\r\n assert isinstance(tapes[3].operations[3], qml.CNOT)\r\n assert isinstance(tapes[3].operations[4], qml.RX)\r\n assert isinstance(tapes[3].operations[5], qml.RY)\r\n assert isinstance(tapes[3].operations[6], qml.RZ)\r\n assert isinstance(tapes[3].operations[7], qml.CNOT)\r\n assert isinstance(tapes[3].operations[8], qml.CNOT)\r\n # PauliX decomp\r\n assert isinstance(tapes[3].operations[9], qml.Hadamard)\r\n # PauliY decomp\r\n assert isinstance(tapes[3].operations[10], qml.PauliZ)\r\n assert isinstance(tapes[3].operations[11], qml.S)\r\n assert isinstance(tapes[3].operations[12], qml.Hadamard)","def test_get_child():\n \n root_ts = TrackSegment(flow_dict=flow_dict) \n \n # ROOT MODULE\n start = root_ts.get_child(\"start\")\n # check depth\n assert(start.depth == root_ts.depth+1)\n # check parent\n assert(start.parent is root_ts)\n # check module_id\n assert(start.module_id == 'start')\n \n # CHILD (1,2,3)\n root_ts_get_child_result = root_ts.get_child((1,2,3))\n # check depth\n assert(root_ts_get_child_result.depth == root_ts.depth+1)\n # check parent\n assert(root_ts_get_child_result.parent is root_ts)\n \n # CHILD (2,3,4)\n root_ts_get_child_result2 = root_ts_get_child_result.get_child((2,3,4))\n # check depth\n assert(root_ts_get_child_result2.depth == root_ts.depth+2)\n # check parent \n assert(root_ts_get_child_result2.parent is root_ts_get_child_result)\n \n print(\"TEST GET_CHILD: success!\")","def test_get_subvertices_from_vertex(self):\n subvertices = list()\n subvertices.append(PartitionedVertex(None, \"\"))\n subvertices.append(PartitionedVertex(None, \"\"))\n subvert1 = PartitionedVertex(None, \"\")\n subvert2 = PartitionedVertex(None, \"\")\n\n subedges = list()\n subedges.append(MultiCastPartitionedEdge(subvertices[0],\n subvertices[1]))\n subedges.append(MultiCastPartitionedEdge(subvertices[1],\n subvertices[1]))\n\n graph_mapper = GraphMapper()\n vert = TestVertex(4, \"Some testing vertex\")\n\n vertex_slice = Slice(0, 1)\n graph_mapper.add_subvertex(subvert1, vertex_slice, vert)\n vertex_slice = Slice(2, 3)\n graph_mapper.add_subvertex(subvert2, vertex_slice, vert)\n\n returned_subverts = graph_mapper.get_subvertices_from_vertex(vert)\n\n self.assertIn(subvert1, returned_subverts)\n self.assertIn(subvert2, returned_subverts)\n for sub in subvertices:\n self.assertNotIn(sub, returned_subverts)","def setUp(self):\n self.complete = nx.Graph()\n self.complete.add_edge(1, 2)\n self.complete.add_edge(2, 3)\n self.complete.add_edge(1, 3)\n\n self.small_tree = nx.Graph()\n self.small_tree.add_edge(1, 2)\n self.small_tree.add_edge(2, 3)\n self.small_tree.add_edge(3, 4)\n self.small_tree.add_edge(1, 4)\n self.small_tree.add_edge(2, 4)\n self.small_tree.add_edge(4, 5)\n self.small_tree.add_edge(5, 6)\n self.small_tree.add_edge(5, 7)\n self.small_tree.add_edge(6, 7)\n\n self.deterministic_graph = nx.Graph()\n self.deterministic_graph.add_edge(1, 2)\n self.deterministic_graph.add_edge(1, 3)\n self.deterministic_graph.add_edge(3, 4)\n self.deterministic_graph.add_edge(2, 4)\n self.deterministic_graph.add_edge(3, 5)\n self.deterministic_graph.add_edge(4, 5)\n self.deterministic_graph.add_edge(3, 6)\n self.deterministic_graph.add_edge(5, 6)","def __init__(self, firstParent, secondParent):\n CrossOver.__init__(self, \"Group Point CrossOver\", firstParent, secondParent)","def testFlatSeries(self):\n self.AddToChart(self.chart, [5, 5, 5])\n self.assertEqual(self.Param('chd'), 's:AAA')\n self.chart.left.min = 0\n self.chart.left.max = 5\n self.assertEqual(self.Param('chd'), 's:999')\n self.chart.left.min = 5\n self.chart.left.max = 15\n self.assertEqual(self.Param('chd'), 's:AAA')","def generate_test_graph(sameDomain = False):\n num = 100\n\n urls = []\n emails = []\n nodes={}\n if sameDomain:\n domain = generate_domainname()\n else:\n domain = None\n for i in range(num):\n urls.append(generate_url(domain))\n emails.append(generate_email())\n \n used_urls = set()\n used_emails = set()\n for u in urls:\n l = random.choices(urls, k = floor(num/4))\n #l = [u for u in urls]\n e = random.choices(emails, k = floor(num/10))\n #e = [e for e in emails]\n used_urls.update(l)\n used_emails.update(e)\n nodes[u] = testNode(u, l, e)\n nodes[u].generate_page()\n \n return nodes, urls, emails","def test_propene(self):\n def draw(image: ShapeImage):\n image.add_line((400, 400), (500, 400))\n image.add_line((400, 410), (500, 410))\n image.add_line((500, 400), (587, 350))\n\n self._test_shape(\n image_size=(1000, 1000),\n expected_corners=np.array([\n [[400, 400]],\n [[500, 400]],\n [[587, 350]]\n ]),\n drawer=draw,\n expected_edges=np.array([\n [[400, 400, 500, 400]],\n [[400, 410, 500, 410]],\n [[500, 400, 587, 350]]\n ])\n )","def genNextPop(prevPop, masterList, popSize):\n parSize = int(popSize / 10) #top 10%\n parentPop = [Team(prevPop[i].roster) for i in range(parSize)]\n parentPop = getStats(parentPop, masterList) # inefficent\n rosterSize = len(parentPop[0].roster)\n newPop = []\n #parentsList = [] #debug\n for i in range(popSize):\n chromosome = doCrossover(parentPop, parSize, rosterSize)\n newPop.append(Team(chromosome))\n getStats(newPop, masterList)\n #showStats(newPop, masterList, \"all\")\n #debug code\n #for playerInd in chromosome:\n # print(masterList[playerInd].pos)\n\n return newPop","def test_population_movements_with_compilation(self):\n self._pystepx = PySTEPXIsland(nb_islands=4, init_script=init_script)\n print self._pystepx._rc[0]['gp_engine']\n self._pystepx._rc[0].execute('elems = gp_engine.get_evolver().select_and_remove_individuals(0.01)',\n block=True)\n print self._pystepx._rc[0]['elems']","def project_pop(self):\n M = self.N[0:2]\n for x in range(10):\n M.append(self.run_step(M))\n split_N = split_list(M)\n \n fig = self.make_figure(split_N)\n fig.update_layout(title='Projected Fish Population')\n\n return fig"],"string":"[\n \"def test_documentation_popxl_create_multi_subgraph(self):\\n filename = \\\"create_multi_graphs_from_same_func.py\\\"\\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)\",\n \"def test_documentation_popxl_basic_subgraph(self):\\n filename = \\\"basic_graph.py\\\"\\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)\",\n \"def test_documentation_popxl_repeat_2(self):\\n filename = \\\"repeat_graph_2.py\\\"\\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)\",\n \"def test_documentation_popxl_repeat_1(self):\\n filename = \\\"repeat_graph_1.py\\\"\\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)\",\n \"def test_documentation_popxl_repeat_0(self):\\n filename = \\\"repeat_graph_0.py\\\"\\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)\",\n \"def _test_multiple(self, fin_graphzip, fin_insts, T, n=None):\\n for t in range(T+1)[1:]:\\n self._test_graphzip_subgen(fin_graphzip, fin_insts, n)\",\n \"def sub_graph_merging(self):\",\n \"def test_simple(self):\\n exp = [{'type': NewickEvents.OPEN_SUBTREE, 'comments': []},\\n {'type': NewickEvents.OPEN_SUBTREE, 'comments': []},\\n {'edge_info': None, 'type': NewickEvents.TIP, 'comments': [], 'label': 'h'},\\n {'edge_info': None, 'type': NewickEvents.TIP, 'comments': [], 'label': 'p'},\\n {'edge_info': '1', 'type': NewickEvents.CLOSE_SUBTREE,\\n 'comments': [], 'label': 'hp'},\\n {'edge_info': None, 'type': NewickEvents.TIP, 'comments': [], 'label': 'g'},\\n {'edge_info': None, 'type': NewickEvents.CLOSE_SUBTREE,\\n 'comments': [], 'label': 'hpg'}\\n ]\\n content = '((h,p)hp:1,g)hpg;'\\n self._do_test(content, exp)\\n content = '((h,[pretest]p[test][posttest])hp,g)hpg;'\\n exp = [{'type': NewickEvents.OPEN_SUBTREE, 'comments': []},\\n {'type': NewickEvents.OPEN_SUBTREE, 'comments': []},\\n {'edge_info': None, 'type': NewickEvents.TIP, 'comments': [], 'label': 'h'},\\n {'edge_info': None, 'type': NewickEvents.TIP,\\n 'comments': ['pretest', 'test', 'posttest'], 'label': 'p'},\\n {'edge_info': None, 'type': NewickEvents.CLOSE_SUBTREE,\\n 'comments': [], 'label': 'hp'},\\n {'edge_info': None, 'type': NewickEvents.TIP, 'comments': [], 'label': 'g'},\\n {'edge_info': None, 'type': NewickEvents.CLOSE_SUBTREE,\\n 'comments': [], 'label': 'hpg'}\\n ]\\n self._do_test(content, exp)\",\n \"def sample_from_subpop(instance, params, subpop):\\n y = subpop\\n x = np.random.choice([-1,+1], size=params['d'])\\n x[instance['indices'][subpop]] = instance['values'][subpop]\\n return x, y, subpop\",\n \"def test_generator8(self):\\n xpb = XPathBuilder()\\n xp1 = xp2 = None\\n base_xp = (xpb.foo.bar | xpb.x.y).parenthesize()\\n base_gen = None\\n with base_xp as b:\\n base_gen = b\\n xp1 = b() & xpb.c\\n xp2 = b() & xpb.d\\n xp1_exp = '(/foo/bar or /x/y) and /c'\\n xp2_exp = '(/foo/bar or /x/y) and /d'\\n base_exp = '(/foo/bar or /x/y)'\\n # check tree structure\\n self.assertTrue(base_xp._parent is None)\\n self.assertTrue(base_gen._parent is None)\\n # check xpath\\n self.assertEqual(xp1.tostring(), xp1_exp)\\n self.assertEqual(xp2.tostring(), xp2_exp)\\n self.assertEqual(base_xp.tostring(), base_exp)\\n self.assertEqual(base_gen.tostring(), base_exp)\",\n \"def test_generator7(self):\\n xpb = XPathBuilder()\\n xp1 = xp2 = None\\n base_xp = xpb.foo.bar & xpb.x.y\\n base_gen = None\\n with base_xp as b:\\n base_gen = b\\n xp1 = b() | xpb.c\\n xp2 = b() | xpb.d\\n xp1_exp = '/foo/bar and /x/y or /c'\\n xp2_exp = '/foo/bar and /x/y or /d'\\n base_exp = '/foo/bar and /x/y'\\n # check tree structure\\n self.assertTrue(base_xp._parent is None)\\n self.assertTrue(base_gen._parent is None)\\n # check xpath\\n self.assertEqual(xp1.tostring(), xp1_exp)\\n self.assertEqual(xp2.tostring(), xp2_exp)\\n self.assertEqual(base_xp.tostring(), base_exp)\\n self.assertEqual(base_gen.tostring(), base_exp)\",\n \"def test_dummy3(self):\\n xpb = XPathBuilder()\\n xp = xpb.dummy()\\n self.assertTrue(xp.parenthesize() is xp)\",\n \"def test_xcomarg_set(self, test_dag):\\n # Unpack the fixture\\n dag, (op1, op2, op3, op4) = test_dag\\n # Arrange the operators with a Label in the middle\\n op1_arg = XComArg(op1, \\\"test_key\\\")\\n op1_arg.set_downstream(op2, Label(\\\"Label 1\\\"))\\n op1.set_downstream([op3, op4])\\n # Check that the DAG has the right edge info\\n assert dag.get_edge_info(op1.task_id, op2.task_id) == {\\\"label\\\": \\\"Label 1\\\"}\\n assert dag.get_edge_info(op1.task_id, op4.task_id) == {}\",\n \"def test_createSubLinkographWithoutCommands(self):\\n self.performTestForParams()\",\n \"def test_generator6(self):\\n xpb = XPathBuilder()\\n xp1 = xp2 = None\\n base_xp = xpb.base.foo.bar\\n base_gen = None\\n with base_xp as b:\\n base_gen = b\\n xp1 = xpb.a.b.c.join(b())\\n xp2 = xpb.test.join(b())\\n xp1_exp = '/a/b/c/base/foo/bar'\\n xp2_exp = '/test/base/foo/bar'\\n base_exp = '/base/foo/bar'\\n # check tree structure\\n self.assertTrue(base_xp._parent is None)\\n self.assertTrue(len(base_xp._children[0]._children[0]._children) == 0)\\n self.assertTrue(base_gen._parent is None)\\n self.assertTrue(len(base_gen._children) == 0)\\n # check xpath\\n self.assertEqual(xp1.tostring(), xp1_exp)\\n self.assertEqual(xp2.tostring(), xp2_exp)\\n self.assertEqual(base_xp.tostring(), base_exp)\\n self.assertEqual(base_gen.tostring(), base_exp)\",\n \"def populate_graph(self):\",\n \"def test_multiple_task_groups_dag(\\n self, test_multiple_taskgroups_dag, multiple_taskgroups_dag_expected_edges\\n ):\\n (\\n dag,\\n group1,\\n group2,\\n group3,\\n (\\n group1_emp1,\\n group1_emp2,\\n group1_emp3,\\n group2_emp1,\\n group2_emp2,\\n group2_emp3,\\n group2_op1,\\n group2_op2,\\n group3_emp1,\\n group3_emp2,\\n group3_emp3,\\n emp_in1,\\n emp_in2,\\n emp_in3,\\n emp_in4,\\n emp_out1,\\n emp_out2,\\n emp_out3,\\n emp_out4,\\n op_in1,\\n op_out1,\\n ),\\n ) = test_multiple_taskgroups_dag\\n\\n group1_emp1 >> Label(\\\"label group1.group1_emp1 <=> group1.group1_emp2\\\") >> group1_emp3\\n\\n emp_in1 >> group1\\n emp_in2 >> Label(\\\"label emp_in2 <=> group1\\\") >> group1\\n [emp_in3, emp_in4] >> Label(\\\"label emp_in3/emp_in4 <=> group1\\\") >> group1\\n XComArg(op_in1, \\\"test_key\\\") >> Label(\\\"label op_in1 <=> group1\\\") >> group1\\n\\n (\\n [group2_emp1, group2_emp2]\\n >> Label(\\\"label group2.group2_emp1/group2.group2_emp2 <=> group2.group2_emp3\\\")\\n >> group2_emp3\\n )\\n (\\n group2_emp1\\n >> Label(\\\"label group2.group2_emp1 <=> group2.group2_emp2/group2.group2_emp3\\\")\\n >> [group2_emp2, group2_emp3]\\n )\\n group2_emp3 >> Label(\\\"label group2.group2_emp3 <=> group3\\\") >> group3\\n\\n (\\n XComArg(group2_op1, \\\"test_key\\\")\\n >> Label(\\\"label group2.group2_op1 <=> group2.group2_op2\\\")\\n >> XComArg(group2_op2, \\\"test_key\\\")\\n )\\n XComArg(group2_op2, \\\"test_key\\\") >> Label(\\\"label group2.group2_op2 <=> group3\\\") >> group3\\n\\n group3 >> emp_out1\\n group3 >> Label(\\\"label group3 <=> emp_out2\\\") >> emp_out2\\n group3 >> Label(\\\"label group3 <=> emp_out3/emp_out4\\\") >> [emp_out3, emp_out4]\\n group3 >> Label(\\\"label group3 <=> op_out1\\\") >> XComArg(op_out1, \\\"test_key\\\")\\n\\n group1 >> Label(\\\"label group1 <=> group2\\\") >> group2\\n\\n compare_dag_edges(dag_edges(dag), multiple_taskgroups_dag_expected_edges)\",\n \"def test_path7():\\n path = [(0, 0, 1)]\\n path += [\\n [('A', 3, 0)],\\n (0, 1, 1),\\n [('A', 2, 0)],\\n (np.pi/2, 1, 1),\\n [('B',3,0)],\\n (0, 1, 1),\\n [('B',2,0)],\\n (np.pi/2, 1, 1),\\n [('C',3,0)],\\n (0, 1, 1),\\n [('C',2,0)],\\n (np.pi/2, 1, 1),\\n [('D', 3, 0)],\\n (0, 1, 1),\\n [('D', 2,0)],\\n (np.pi/2, 1, 1),\\n ] * 4\\n execute_path(path,True)\",\n \"def explore(self, *args):\",\n \"def create_subbasin_graph():\\n subbasin_to_downstream = pd.read_csv(module_dir + '/../data/simulations_shervan/test.rvh', sep='\\\\s+', skiprows=7, nrows=724, names=['subbasin', 'downstream_subbasin'], usecols=[1,2])\\n subbasin_to_downstream['subbasin'] = subbasin_to_downstream['subbasin']\\n subbasin_to_downstream['downstream_subbasin'] = 'sub' + subbasin_to_downstream['downstream_subbasin'].astype(str)\\n subbasin_to_downstream['edge'] = 1\\n\\n for subbasin in subbasin_to_downstream['subbasin'].unique():\\n is_sink = 1 if len(subbasin_to_downstream[(subbasin_to_downstream['subbasin'] == subbasin) & subbasin_to_downstream['edge'] == 1]) == 0 else 0\\n subbasin_to_downstream = subbasin_to_downstream.append({'subbasin': subbasin, 'downstream_subbasin': subbasin, 'edge': is_sink}, ignore_index=True)\\n subbasin_to_downstream = subbasin_to_downstream.append({'subbasin': 'sub-1', 'downstream_subbasin': 'sub-1', 'edge': 1}, ignore_index=True)\\n \\n adj = subbasin_to_downstream.pivot(index='subbasin', columns='downstream_subbasin', values='edge').fillna(0) \\n adj = adj.sort_index(axis=0).sort_index(axis=1)\\n \\n G = nx.from_numpy_matrix(adj.values, parallel_edges=False, create_using=nx.DiGraph())\\n label_mapping = dict(zip(range(len(adj.values)), adj.index))\\n G = nx.relabel_nodes(G, label_mapping)\\n \\n return G\",\n \"def setUp(self):\\n\\n singleLabels = linkoCreate.Linkograph(\\n [({'A'}, set(), {1,2,3}),\\n ({'D'}, {0}, {3,4}),\\n ({'A'}, {0}, {4}),\\n ({'C'}, {0,1}, {4}),\\n ({'A'}, {1,2,3}, set())],\\n ['A', 'B', 'C', 'D'])\\n\\n singleSubLinko0_2 = linkoCreate.Linkograph(\\n [({'A'}, set(), {1,2}),\\n ({'D'}, {0}, set()),\\n ({'A'}, {0}, set())],\\n ['A', 'B', 'C', 'D'])\\n\\n singleSubLinko0_1 = linkoCreate.Linkograph(\\n [({'A'}, set(), {1}),\\n ({'D'}, {0}, set())],\\n ['A', 'B', 'C', 'D'])\\n\\n singleSubLinko0_0 = linkoCreate.Linkograph(\\n [({'A'}, set(), set())],\\n ['A', 'B', 'C', 'D'])\\n\\n singleSubLinko1_2 = linkoCreate.Linkograph(\\n [({'D'}, set(), set()),\\n ({'A'}, set(), set())],\\n ['A', 'B', 'C', 'D'])\\n\\n singleSubLinko1_1 = linkoCreate.Linkograph(\\n [({'D'}, set(), set())],\\n ['A', 'B', 'C', 'D'])\\n\\n trivialLinkograph = linkoCreate.Linkograph(\\n [], ['A', 'B', 'C', 'D'])\\n\\n\\n singleSubLinko1_4 = linkoCreate.Linkograph(\\n [({'D'}, set(), {2,3}),\\n ({'A'}, set(), {3}),\\n ({'C'}, {0}, {3}),\\n ({'A'}, {0,1,2}, set())],\\n ['A', 'B', 'C', 'D'])\\n\\n singleSubLinko2_4 = linkoCreate.Linkograph(\\n [({'A'}, set(), {2}),\\n ({'C'}, set(), {2}),\\n ({'A'}, {0,1}, set())],\\n ['A', 'B', 'C', 'D'])\\n\\n singleSubLinko3_4 = linkoCreate.Linkograph(\\n [({'C'}, set(), {1}),\\n ({'A'}, {0}, set())],\\n ['A', 'B', 'C', 'D'])\\n\\n singleSubLinko4_4 = linkoCreate.Linkograph(\\n [({'A'}, set(), set())],\\n ['A', 'B', 'C', 'D'])\\n\\n simpleLinko = linkoCreate.Linkograph(\\n [({'A', 'B', 'C'}, set(), {1,2,3}),\\n ({'D'}, {0}, {3,4}),\\n ({'A'}, {0}, {4}),\\n ({'B', 'C'}, {0,1}, {4}),\\n ({'A'}, {1,2,3}, set())],\\n ['A', 'B', 'C', 'D'])\\n\\n if self.id().split('.')[-1] == 'test_createSubLinkographWithoutCommands':\\n self.testParams = [\\n {'linko': singleLabels,\\n 'lowerBound': None,\\n 'upperBound': None,\\n 'ExpectedLinkograph': singleLabels},\\n\\n {'linko': singleLabels,\\n 'lowerBound': 0,\\n 'upperBound': 4,\\n 'ExpectedLinkograph': singleLabels},\\n\\n {'linko': singleLabels,\\n 'lowerBound': 0,\\n 'upperBound': 5,\\n 'ExpectedLinkograph': singleLabels},\\n\\n {'linko': singleLabels,\\n 'lowerBound': 0,\\n 'upperBound': 2,\\n 'ExpectedLinkograph': singleSubLinko0_2},\\n\\n {'linko': singleLabels,\\n 'lowerBound': -1,\\n 'upperBound': 2,\\n 'ExpectedLinkograph': singleSubLinko0_2},\\n\\n {'linko': singleLabels,\\n 'lowerBound': None,\\n 'upperBound': 2,\\n 'ExpectedLinkograph': singleSubLinko0_2},\\n\\n {'linko': singleLabels,\\n 'lowerBound': 0,\\n 'upperBound': 1,\\n 'ExpectedLinkograph': singleSubLinko0_1},\\n\\n {'linko': singleLabels,\\n 'lowerBound': 0,\\n 'upperBound': 0,\\n 'ExpectedLinkograph': singleSubLinko0_0},\\n\\n {'linko': singleLabels,\\n 'lowerBound': 0,\\n 'upperBound': -1,\\n 'ExpectedLinkograph': trivialLinkograph},\\n\\n {'linko': singleLabels,\\n 'lowerBound': 1,\\n 'upperBound': 2,\\n 'ExpectedLinkograph': singleSubLinko1_2},\\n\\n {'linko': singleLabels,\\n 'lowerBound': 1,\\n 'upperBound': 1,\\n 'ExpectedLinkograph': singleSubLinko1_1},\\n\\n {'linko': singleLabels,\\n 'lowerBound': 1,\\n 'upperBound': 0,\\n 'ExpectedLinkograph': trivialLinkograph},\\n\\n {'linko': singleLabels,\\n 'lowerBound': -1,\\n 'upperBound': -1,\\n 'ExpectedLinkograph': trivialLinkograph},\\n\\n {'linko': singleLabels,\\n 'lowerBound': 1,\\n 'upperBound': 4,\\n 'ExpectedLinkograph': singleSubLinko1_4},\\n\\n {'linko': singleLabels,\\n 'lowerBound': 2,\\n 'upperBound': 4,\\n 'ExpectedLinkograph': singleSubLinko2_4},\\n\\n {'linko': singleLabels,\\n 'lowerBound': 3,\\n 'upperBound': 4,\\n 'ExpectedLinkograph': singleSubLinko3_4},\\n\\n {'linko': singleLabels,\\n 'lowerBound': 4,\\n 'upperBound': 4,\\n 'ExpectedLinkograph': singleSubLinko4_4},\\n\\n ]\",\n \"def test_build_graph(self):\\n insert_good_data()\\n dataframe = get_dataframe()\\n results = processing.build_graph(dataframe, figure_path, False)\\n # 1\\n self.assertEqual(results, \\\"Updated html File and Opened it\\\")\",\n \"def generate_subgraph(format):\\n\\n # get business information\\n directorypath = genpath+directory\\n if os.path.isfile(directorypath):\\n \\n bizdata = pd.read_csv( directorypath, escapechar='\\\\\\\\')\\n\\n #create a directory of page-id and object-ids\\n tempdf = bizdata.set_index('pageid')\\n tempdf = tempdf['objectid']\\n dictionary = tempdf.to_dict()\\n\\n uncgraph = pd.read_csv(inpath+graphfile, escapechar='\\\\\\\\')\\n uncgraph = uncgraph.dropna()\\n uncgraph['likee_object_id'] = uncgraph.apply(lambda x: dictionary.get(x['likee_page_id']), axis=1)\\n cgraph = uncgraph.dropna()\\n cgraph = cgraph[['liker_page_id', 'likee_page_id']]\\n cgraph.columns = ['Source', 'Target']\\n\\n \\n print_stats(cgraph)\\n if format == 'networkx' :\\n print \\\"[Generating a networkX graph...]\\\" \\n cgraph.to_csv(genpath+subgraph+'.ntx', index=False, header=False, sep= ' ')\\n else:\\n print \\\"[Generating a csv graph...]\\\" \\n cgraph.to_csv(genpath+subgraph+'.csv', index=False)\\n\\n\\n else:\\n print \\\"Either file is missing or is not readable\\\"\",\n \"def test_calls(self):\\n ex = self.ex\\n m = self.m\\n n = self.n\\n\\n nreps = random.randint(1, 10)\\n ex.nreps = nreps\\n ex.vary[\\\"X\\\"][\\\"with\\\"].add(\\\"rep\\\")\\n ex.infer_lds()\\n\\n cmds = ex.generate_cmds()\\n\\n idx = random.randint(0, nreps - 1)\\n self.assertIn([\\\"name\\\", m, n, \\\"X_%d\\\" % idx, m, \\\"Y\\\", m, \\\"Z\\\", n], cmds)\",\n \"def show_subgraph(dfs_codes, nsupport, mapper):\\n\\tglobal __subgraph_count\\n\\n\\tg = build_graph(dfs_codes)\\n\\tg.id = __subgraph_count\\n\\t__subgraph_count += 1\\n\\tg.gprint(nsupport, mapper)\",\n \"def main(\\n num_sampled=[3, 3],\\n max_depth=2,\\n num_iters=1000,\\n do_graph=False,\\n # These are for checking stats on smaller data\\n subsample=False,\\n plot=False,\\n # Generates a random matrix for comparison\\n random=False,\\n # Visualise the connection matrix\\n vis_connect=False,\\n subsample_vis=False,\\n # Generate final graphs\\n final=False,\\n # Analyse\\n analyse=False,\\n only_exp=False,\\n # Which regions are considered here\\n # A_name, B_name = \\\"MOp\\\", \\\"SSP-ll\\\"\\n A_name=\\\"VISp\\\",\\n B_name=\\\"VISl\\\",\\n desired_depth=1,\\n desired_samples=79,\\n):\\n np.random.seed(42)\\n\\n if random:\\n AB, BA, AA, BB = gen_random_matrix(150, 50, 0, 0.04, 0, 0.0)\\n matrix_vis(AB, BA, AA, BB, 10, name=\\\"test_vis.png\\\")\\n\\n os.makedirs(os.path.dirname(pickle_loc), exist_ok=True)\\n convert_mouse_data(A_name, B_name)\\n to_use = [True, True, True, True]\\n mc, args_dict = load_matrix_data(to_use, A_name, B_name)\\n print(\\\"{} - {}, {} - {}\\\".format(A_name, B_name, mc.num_a, mc.num_b))\\n\\n result = {}\\n result[\\\"matrix_stats\\\"] = print_args_dict(args_dict, out=False)\\n\\n if only_exp:\\n mpf_res = mpf_connectome(mc, num_sampled, max_depth, args_dict)\\n mpf_val = [\\n mpf_res[\\\"expected\\\"],\\n mpf_res[\\\"expected\\\"] / num_sampled[1],\\n \\\"{}_{}\\\".format(A_name, B_name),\\n \\\"Statistical estimation\\\",\\n ]\\n if do_graph:\\n print(\\\"Converting matrix\\\")\\n gc.collect()\\n mc.create_connections()\\n print(\\\"Finished conversion\\\")\\n graph = mc.graph\\n to_write = [mc.num_a, mc.num_b]\\n del mc\\n gc.collect()\\n reverse_graph = reverse(graph)\\n graph_res = graph_connectome(\\n num_sampled,\\n max_depth,\\n graph=graph,\\n reverse_graph=reverse_graph,\\n to_write=to_write,\\n num_iters=num_iters,\\n )\\n to_add = np.mean(graph_res[\\\"full_results\\\"][\\\"Connections\\\"].values)\\n graph_val = [\\n to_add,\\n to_add / num_sampled[1],\\n \\\"{}_{}\\\".format(A_name, B_name),\\n \\\"Statistical estimation\\\",\\n ]\\n return mpf_val, graph_val\\n return mpf_val, None\\n\\n # Convert to a pickle\\n # if not os.path.isfile(pickle_loc):\\n # print(\\\"Converting matrix\\\")\\n # gc.collect()\\n # mc.create_connections()\\n # print(\\\"Finished conversion\\\")\\n # graph = mc.graph\\n # to_write = [mc.num_a, mc.num_b]\\n # del mc\\n # gc.collect()\\n\\n # handle_pickle(graph, \\\"graph.pickle\\\", \\\"w\\\")\\n # handle_pickle(reverse(graph), \\\"r_graph.pickle\\\", \\\"w\\\")\\n # handle_pickle(to_write, \\\"graph_size.pickle\\\", \\\"w\\\")\\n\\n if vis_connect:\\n if subsample_vis:\\n print(\\\"Plotting subsampled matrix vis\\\")\\n new_mc = mc.subsample(int(mc.num_a / 10), int(mc.num_b / 10))\\n matrix_vis(\\n new_mc.ab,\\n new_mc.ba,\\n new_mc.aa,\\n new_mc.bb,\\n 15,\\n name=\\\"mc_mat_vis_sub10.pdf\\\",\\n )\\n else:\\n o_name = \\\"mc_mat_vis_{}_to_{}.pdf\\\".format(A_name, B_name)\\n print(\\\"Plotting full matrix vis\\\")\\n matrix_vis(mc.ab, mc.ba, mc.aa, mc.bb, 150, name=o_name)\\n print(\\\"done vis\\\")\\n\\n print(mc, print_args_dict(args_dict, out=False))\\n\\n result = None\\n if subsample:\\n result = check_stats(mc, 1000, 1, 20000, 1, plot)\\n if final:\\n result = {}\\n\\n # For different depths and number of samples\\n for depth in range(1, 4):\\n for ns in range(1, num_sampled[0] + 1):\\n ns_2 = [ns] * 2\\n mpf_res = mpf_connectome(mc, ns_2, depth, args_dict)\\n result[\\\"mpf_{}_{}\\\".format(depth, ns)] = mpf_res\\n\\n # Save this for plotting\\n cols = [\\\"Number of samples\\\", \\\"Proportion of connections\\\", \\\"Max distance\\\"]\\n depth_name = [None, \\\"Direct synapse\\\", \\\"Two synapses\\\", \\\"Three synapses\\\"]\\n vals = []\\n for depth in range(1, 4):\\n for ns in range(1, num_sampled[0] + 1):\\n this = result[\\\"mpf_{}_{}\\\".format(depth, ns)]\\n val = [ns, this[\\\"expected\\\"] / ns, depth_name[depth]]\\n vals.append(val)\\n df = pd.DataFrame(vals, columns=cols)\\n os.makedirs(os.path.join(here, \\\"..\\\", \\\"results\\\"), exist_ok=True)\\n df.to_csv(\\n os.path.join(\\n here, \\\"..\\\", \\\"results\\\", \\\"{}_to_{}_depth.csv\\\".format(A_name, B_name)\\n ),\\n index=False,\\n )\\n\\n cols = [\\\"Number of sampled connected neurons\\\", \\\"Probability\\\"]\\n total_pmf = result[\\\"mpf_{}_{}\\\".format(desired_depth, desired_samples)][\\\"total\\\"]\\n vals = []\\n for k, v in total_pmf.items():\\n vals.append([k, float(v)])\\n df = pd.DataFrame(vals, columns=cols)\\n df.to_csv(\\n os.path.join(\\n here,\\n \\\"..\\\",\\n \\\"results\\\",\\n \\\"{}_to_{}_pmf_{}_{}.csv\\\".format(\\n A_name, B_name, desired_depth, desired_samples\\n ),\\n ),\\n index=False,\\n )\\n if analyse:\\n result = {}\\n result[\\\"matrix_stats\\\"] = args_dict\\n\\n mpf_res = mpf_connectome(\\n mc,\\n num_sampled,\\n max_depth,\\n args_dict,\\n clt_start=30,\\n sr=None,\\n mean_estimate=True,\\n )\\n result[\\\"mean\\\"] = mpf_res\\n\\n vals = []\\n cols = [\\\"Number of connected neurons\\\", \\\"Probability\\\", \\\"Calculation\\\"]\\n for k, v in mpf_res[\\\"total\\\"].items():\\n vals.append([k, float(v), \\\"Mean estimation\\\"])\\n\\n mpf_res = mpf_connectome(mc, num_sampled, max_depth, args_dict, clt_start=30)\\n result[\\\"mpf\\\"] = mpf_res\\n\\n for k, v in mpf_res[\\\"total\\\"].items():\\n vals.append([k, float(v), \\\"Statistical estimation\\\"])\\n\\n if do_graph:\\n print(\\\"Converting matrix\\\")\\n gc.collect()\\n mc.create_connections()\\n print(\\\"Finished conversion\\\")\\n graph = mc.graph\\n to_write = [mc.num_a, mc.num_b]\\n del mc\\n gc.collect()\\n reverse_graph = reverse(graph)\\n\\n graph_res = graph_connectome(\\n num_sampled,\\n max_depth,\\n graph=graph,\\n reverse_graph=reverse_graph,\\n to_write=to_write,\\n num_iters=num_iters,\\n )\\n\\n result[\\\"difference\\\"] = (\\n dist_difference(mpf_res[\\\"total\\\"], graph_res[\\\"dist\\\"]),\\n )\\n result[\\\"graph\\\"] = graph_res\\n\\n for k, v in graph_res[\\\"dist\\\"].items():\\n vals.append([k, float(v), \\\"Monte Carlo simulation\\\"])\\n\\n df = pd.DataFrame(vals, columns=cols)\\n df.to_csv(\\n os.path.join(\\n here,\\n \\\"..\\\",\\n \\\"results\\\",\\n \\\"{}_to_{}_pmf_final_{}_{}.csv\\\".format(\\n A_name, B_name, max_depth, num_sampled[0]\\n ),\\n ),\\n index=False,\\n )\\n\\n if result is not None:\\n with open(os.path.join(here, \\\"..\\\", \\\"results\\\", \\\"mouse.txt\\\"), \\\"w\\\") as f:\\n pprint(result, width=120, stream=f)\\n\\n return result\",\n \"def examples():\\r\\n\\r\\n # get some data for a single name\\r\\n x = blp.bdp('BDEV LN Equity', 'px_last')\\r\\n print(x)\\r\\n print('the type of x', type(x))\\r\\n print('the value of x:', x.iloc[0]['px_last'])\\r\\n\\r\\n\\r\\n # get multiple data for a single name\\r\\n y = blp.bdp('BDEV LN Equity', flds=['px_bid', 'px_ask'])\\r\\n print(y)\\r\\n\\r\\n\\r\\n # get multiple data for multiple names\\r\\n z = blp.bdp(tickers=['BDEV LN Equity', 'BARC LN Equity'], flds=['px_bid', 'px_ask'])\\r\\n print(z)\\r\\n print('here is the bdev ask >>>', z.loc['BDEV LN Equity','px_ask'])\",\n \"def test_generator3(self):\\n xpb = XPathBuilder()\\n xp1 = xp2 = None\\n base_xp = xpb.base.foo[xpb.attr('abc') == 'x']\\n with base_xp as b:\\n xp1 = b().bar.text() == 'foo'\\n xp2 = b().x.y.z[42]\\n base_exp = '/base/foo[@abc = \\\"x\\\"]'\\n xp1_exp = '/base/foo[@abc = \\\"x\\\"]/bar/text() = \\\"foo\\\"'\\n xp2_exp = '/base/foo[@abc = \\\"x\\\"]/x/y/z[42]'\\n self.assertEqual(base_xp.tostring(), base_exp)\\n self.assertEqual(xp1.tostring(), xp1_exp)\\n self.assertEqual(xp2.tostring(), xp2_exp)\",\n \"def test_extract_graph(default_plugin_resolver):\\n dpr = default_plugin_resolver\\n nx_graph = nx.Graph()\\n nx_graph.add_weighted_edges_from(\\n [(1, 0, 2), (1, 4, 3), (2, 5, 5), (2, 7, 6), (3, 1, 7), (5, 6, 10), (6, 2, 11),]\\n )\\n desired_nodes = {2, 5, 6}\\n nx_extracted_graph = nx.Graph()\\n nx_extracted_graph.add_weighted_edges_from([(2, 5, 5), (5, 6, 10), (6, 2, 11)])\\n graph = dpr.wrappers.Graph.NetworkXGraph(nx_graph)\\n desired_nodes_wrapped = dpr.wrappers.NodeSet.PythonNodeSet(desired_nodes)\\n extracted_graph = dpr.wrappers.Graph.NetworkXGraph(nx_extracted_graph)\\n MultiVerify(\\n dpr, \\\"subgraph.extract_subgraph\\\", graph, desired_nodes_wrapped\\n ).assert_equals(extracted_graph)\",\n \"def test_generator4(self):\\n xpb = XPathBuilder()\\n xp1 = xp2 = None\\n base_xp = xpb.base.foo.where(xpb.attr('abc').equals('x'))\\n with base_xp as b:\\n xp1 = b().bar.text().equals('foo')\\n xp2 = b().x.y.z.where(42)\\n base_exp = '/base/foo[@abc = \\\"x\\\"]'\\n xp1_exp = '/base/foo[@abc = \\\"x\\\"]/bar/text() = \\\"foo\\\"'\\n xp2_exp = '/base/foo[@abc = \\\"x\\\"]/x/y/z[42]'\\n self.assertEqual(base_xp.tostring(), base_exp)\\n self.assertEqual(xp1.tostring(), xp1_exp)\\n self.assertEqual(xp2.tostring(), xp2_exp)\",\n \"def test_dummy6(self):\\n xpb = XPathBuilder()\\n xp = xpb.dummy()\\n xp = xpb.bar | xp\\n exp = '/bar'\\n self.assertEqual(xp.tostring(), exp)\",\n \"def run_tests(g: Graph) -> None:\\n print( g.nodes() , \\\"->\\\" , ', '.join([f\\\"{l}\\\" for l in g.scc()]) , f\\\"({g.cyclic()})\\\" )\\n for n in g.nodes():\\n for m in [m for m in g.nodes() if m != n]:\\n p = g.path(n,m)\\n if p is not None:\\n assert p[0] == n\\n assert p[-1] == m\\n for i in range(1,len(p)):\\n assert g.is_edge(p[i-1], p[i])\\n print(\\\" \\\", n, \\\"->\\\", m, \\\":\\\", ' -> '.join([f\\\"{v}\\\" for v in p]))\",\n \"def test_createData():\\n\\n sys = LVsystem.Ecosystem()\\n\\n sys.addSpecies('rabbit')\\n sys.setInteraction('rabbit', 'hen', 0)\\n sys.setInteraction('rabbit', 'fox', -1)\\n sys.setInitialCond('rabbit', 30)\\n sys.setGrowthRate('rabbit', 0.09)\\n sys.setCarrCap('rabbit', 10000)\\n sys.setChangeRate('rabbit', 400)\\n\\n sys.addSpecies('hen')\\n sys.setInteraction('hen', 'rabbit', 0)\\n sys.setInteraction('hen', 'fox', -1)\\n sys.setInitialCond('hen', 10)\\n sys.setGrowthRate('hen', 0.07)\\n sys.setCarrCap('hen', 10000)\\n sys.setChangeRate('hen', 500)\\n\\n sys.addSpecies('fox')\\n sys.setInteraction('fox', 'rabbit', 1)\\n sys.setInteraction('fox', 'hen', 1)\\n sys.setInitialCond('fox', 20)\\n sys.setGrowthRate('fox', -0.06)\\n sys.setCarrCap('fox', 1)\\n sys.setChangeRate('fox', 250)\\n\\n \\n data = sys.create_data()\\n \\n assert data[0] == 3\\n assert data[1] == ['rabbit', 'hen', 'fox']\\n assert data[2] == [30,10,20]\\n assert data[3] == [0.09,0.07,-0.06] \\n assert data[4] == [10000,10000,1]\\n assert data[5] == [400,500,250]\\n assert data[6][1][2] == -data[6][2][1]\\n assert data[6][2][2] == 0\\n \\n sys.removeSpecies('rabbit')\\n sys.removeSpecies('fox')\\n sys.removeSpecies('hen')\",\n \"def test_restore_multiple_in_subgraph(self):\\n subgraph = self._subgraph()\\n subgraph['id'] = 15\\n task1 = self._remote_task()\\n task1['id'] = 1\\n task2 = self._remote_task()\\n task2['id'] = 2\\n task1['parameters']['containing_subgraph'] = 15\\n task2['parameters']['containing_subgraph'] = 15\\n\\n graph = self._restore_graph([subgraph, task1, task2])\\n assert len(graph.tasks) == 3\\n subgraphs = [op for op in graph.tasks if op.is_subgraph]\\n remote_tasks = [op for op in graph.tasks if not op.is_subgraph]\\n\\n # those are all references to the same subgraph, the subgraph was\\n # NOT restored multiple times\\n assert remote_tasks[0].containing_subgraph \\\\\\n is remote_tasks[1].containing_subgraph \\\\\\n is subgraphs[0]\\n\\n assert len(subgraphs[0].tasks) == 2\",\n \"def test_example(self):\\n\\n solution = Solution()\\n\\n nums = [1, 2, 3]\\n\\n expected_output = [\\n (3,),\\n (1,),\\n (2,),\\n (1, 2, 3),\\n (1, 3),\\n (2, 3),\\n (1, 2),\\n ()\\n ]\\n actual_output = solution.subsets(nums)\\n\\n for ss in expected_output:\\n self.assertIn(ss, actual_output)\",\n \"def test_generator5(self):\\n xpb = XPathBuilder()\\n xp = None\\n base_xp = xpb.base.foo.bar\\n base_gen = None\\n with base_xp as b:\\n base_gen = b\\n xp = b().join(xpb.a.b.c[3])\\n exp = '/base/foo/bar/a/b/c[3]'\\n base_exp = '/base/foo/bar'\\n # check tree structure\\n self.assertTrue(base_xp._parent is None)\\n self.assertTrue(len(base_xp._children[0]._children[0]._children) == 0)\\n self.assertTrue(base_gen._parent is None)\\n self.assertTrue(len(base_gen._children) == 0)\\n # check xpath\\n self.assertEqual(xp.tostring(), exp)\\n self.assertEqual(base_xp.tostring(), base_exp)\\n self.assertEqual(base_gen.tostring(), base_exp)\",\n \"def test_multi_sink(self):\\n with self.assertRaises(ValidationError):\\n with Graph('g') as graph:\\n pike.glob('a', '*')\\n pike.glob('b', '*')\",\n \"def test__graph_structure():\\n assert PES_GRAPH == (\\n ('CH2CH2+OH', 'CH2CH+H2O', 'C2H4OH', 'C2H5O', 'CH3CHO+H'),\\n (frozenset({0, 1}), frozenset({0, 2}), frozenset({2, 3}),\\n frozenset({3, 4}), frozenset({1, 2})))\\n assert pgraph.species(PES_GRAPH) == (\\n ('CH2CH2+OH', 'CH2CH+H2O', 'C2H4OH', 'C2H5O', 'CH3CHO+H'))\\n assert pgraph.channels(PES_GRAPH) == (\\n (frozenset({0, 1}), frozenset({0, 2}), frozenset({2, 3}),\\n frozenset({3, 4}), frozenset({1, 2})))\\n print('\\\\npes graph')\\n print(PES_GRAPH)\",\n \"def test_case1(self):\\n\\n graph = BipartiteGraph()\\n\\n graph.addEdge(\\\"supervisor1\\\",\\\"student1\\\")\\n\\n val1 = graph.getStudents(\\\"supervisor1\\\")\\n val2 = graph.getSupervisors(\\\"student1\\\")\\n\\n expected1 = [\\\"student1\\\"]\\n expected2 = [\\\"supervisor1\\\"]\\n\\n self.assertEqual((val1,val2),(expected1,expected2))\",\n \"def test_parent_with_iterables(self):\\n def makeCubesAndGrp():\\n cmds.file(new=1, f=1)\\n cubes = []\\n for x in range(10):\\n cubes.append(pm.polyCube()[0])\\n group = pm.group(empty=True)\\n return cubes, group\\n\\n cubes, group = makeCubesAndGrp()\\n res1 = pm.parent(cubes[:4], group)\\n self.assertEqual(cubes[:4], res1)\\n res2 = pm.parent(cubes, group)\\n self.assertEqual(cubes[4:], res2)\\n\\n cubes, group = makeCubesAndGrp()\\n res1 = pm.parent(cubes[:4] + [group])\\n self.assertEqual(cubes[:4], res1)\\n res2 = pm.parent(cubes, group)\\n self.assertEqual(cubes[4:], res2)\\n\\n cubes, group = makeCubesAndGrp()\\n res1 = pm.parent(cubes[0], cubes[1], cubes[2], cubes[3], group)\\n self.assertEqual(cubes[:4], res1)\\n res2 = pm.parent(cubes, group)\\n self.assertEqual(cubes[4:], res2)\\n\\n cubes, group = makeCubesAndGrp()\\n res1 = pm.parent(cubes[0], cubes[1], [cubes[2], cubes[3], group])\\n self.assertEqual(cubes[:4], res1)\\n res2 = pm.parent(cubes, group)\\n self.assertEqual(cubes[4:], res2)\\n\\n cubes, group = makeCubesAndGrp()\\n res1 = pm.parent([cubes[0], cubes[1]], cubes[2], [cubes[3], group])\\n self.assertEqual(cubes[:4], res1)\\n res2 = pm.parent(cubes, group)\\n self.assertEqual(cubes[4:], res2)\",\n \"def test_programs():\\n yield 4, 4, 1\\n yield 16, 12, 2\",\n \"def tenpar_subset_test():\\n model_d = \\\"ies_10par_xsec\\\"\\n test_d = os.path.join(model_d, \\\"master_subset_test\\\")\\n template_d = os.path.join(model_d, \\\"test_template\\\")\\n if not os.path.exists(template_d):\\n raise Exception(\\\"template_d {0} not found\\\".format(template_d))\\n if os.path.exists(test_d):\\n shutil.rmtree(test_d)\\n # shutil.copytree(base_d,test_d)\\n pst = pyemu.Pst(os.path.join(template_d, \\\"pest.pst\\\"))\\n pst.control_data.noptmax = 3\\n\\n # first without subset\\n pst.pestpp_options = {}\\n pst.pestpp_options[\\\"ies_num_reals\\\"] = 50\\n pst.pestpp_options[\\\"ies_lambda_mults\\\"] = \\\"1.0\\\"\\n pst.pestpp_options[\\\"ies_accept_phi_fac\\\"] = 100.0\\n pst.write(os.path.join(template_d, \\\"pest.pst\\\"))\\n pyemu.helpers.start_slaves(template_d, exe_path, \\\"pest.pst\\\", num_slaves=10,\\n slave_root=model_d, master_dir=test_d)\\n df_base = pd.read_csv(os.path.join(test_d, \\\"pest.phi.meas.csv\\\"),index_col=0)\\n\\n pst.pestpp_options = {}\\n pst.pestpp_options[\\\"ies_num_reals\\\"] = 50\\n pst.pestpp_options[\\\"ies_lambda_mults\\\"] = \\\"1.0\\\"\\n pst.pestpp_options[\\\"ies_subset_size\\\"] = 15\\n pst.pestpp_options[\\\"ies_accept_phi_fac\\\"] = 100.0\\n\\n pst.write(os.path.join(template_d, \\\"pest.pst\\\"))\\n pyemu.helpers.start_slaves(template_d, exe_path, \\\"pest.pst\\\", num_slaves=10,\\n slave_root=model_d, master_dir=test_d)\\n df_sub = pd.read_csv(os.path.join(test_d, \\\"pest.phi.meas.csv\\\"),index_col=0)\\n diff = (df_sub - df_base).apply(np.abs)\\n print(diff.max())\\n print(df_sub.iloc[-1,:])\\n print(df_base.iloc[-1,:])\\n assert diff.max().max() == 0.0\",\n \"def test_path1():\\n path = [(0,0,1)]\\n path.append([('A',4,0)])\\n path.append((0,1,1))\\n path.append([('A',3,0)])\\n path.append((0,1,1))\\n path.append([('A',2,0)])\\n path.append((0,1,1))\\n path.append([('A',1,0)])\\n\\n execute_path(path, True)\",\n \"def test_multiple_triples(self):\\n self.graph.add((artis, RDF.type, zoo))\\n self.graph.add((artis, RDF.type, org))\\n self.graph.add((berlin_zoo, RDF.type, zoo))\\n self.assertEquals(len(list(self.graph.triples((None, None, None)))), 3)\\n\\n self.assertEquals(len(list(self.graph.triples((artis, None, None)))), 2)\\n self.assertEquals(len(list(self.graph.triples((None, RDF.type, None)))), 3)\\n self.assertEquals(len(list(self.graph.triples((None, None, zoo)))), 2)\\n self.assertEquals(len(list(self.graph.triples((None, None, org)))), 1)\",\n \"def test_EnumerateTestSet():\\n protos = list(random_networkx_generator.EnumerateTestSet())\\n assert len(protos) == 100\",\n \"def iter_func(root_name, root, set_traverse, list_funcs, G, strings,\\n plot_nodes, cur_pos, xgrain, min_weight, max_weight):\\n set_traverse.append(root)\\n nbs = G.neighbors(root)\\n nbs = G[root]\\n\\n plot_nodes.append(cur_pos)\\n xgrain = xgrain/2.0\\n\\n flag_pn = -1\\n for nb in nbs.keys():\\n if nb in set_traverse:\\n continue\\n\\n next_pos = [0, 0, 0]\\n if root.name == root_name:\\n next_pos[0] = cur_pos[0]\\n else:\\n next_pos[0] = cur_pos[0] + xgrain*flag_pn*( 0.8+0.2*(nbs[nb][\\\"weight\\\"]-1.0/max_weight)/(1.0/min_weight-1.0/max_weight) ) #* (nbs[nb][\\\"weight\\\"]-1.0/max_weight)/(1.0/min_weight-1.0/max_weight)\\n next_pos[1] = cur_pos[1] + 3.0*(nbs[nb][\\\"weight\\\"]-1.0/max_weight)/(1.0/min_weight-1.0/max_weight)\\n next_pos[2] = nb.name\\n\\n flag_pn = flag_pn*(-1)\\n\\n strings.append([root, nb])\\n set_traverse, strings, plot_nodes = iter_func(root_name, nb, set_traverse, list_funcs, G, strings, plot_nodes, next_pos, xgrain, min_weight, max_weight)\\n\\n return set_traverse, strings, plot_nodes\",\n \"def test_excel(test_data,tmp_path):\\n\\n for d in test_data:\\n\\n gpm = GenotypePhenotypeMap(genotype=d[\\\"genotype\\\"],\\n wildtype=d[\\\"wildtype\\\"],\\n phenotype=d[\\\"phenotype\\\"],\\n uncertainty=d[\\\"uncertainty\\\"])\\n\\n # Write excel file\\n excel_file = os.path.join(tmp_path,\\\"tmp.xlsx\\\")\\n gpm.to_excel(filename=excel_file)\\n assert os.path.isfile(excel_file)\\n\\n # Read in and make sure it worked.\\n new_gpm = gpmap.read_excel(filename=excel_file,wildtype=d[\\\"wildtype\\\"])\\n conftest.compare_gpmap(gpm,new_gpm)\\n\\n # Do not give wildtype. Should still work because the wildtype was\\n # inferred.\\n gpm_read = gpmap.read_excel(filename=excel_file)\\n conftest.compare_gpmap(gpm,gpm_read)\\n\\n # Check ability to read labels back in\\n site_labels = [f\\\"{x}\\\" for x in range(10,10+len(d[\\\"wildtype\\\"]),1)]\\n gpm = GenotypePhenotypeMap(genotype=d[\\\"genotype\\\"],\\n wildtype=d[\\\"wildtype\\\"],\\n site_labels=site_labels)\\n out_file = os.path.join(tmp_path,\\\"tmp.xlsx\\\")\\n gpm.to_excel(out_file)\\n\\n gpm_read = gpmap.read_excel(out_file)\\n\\n for i in range(len(gpm_read.site_labels)):\\n\\n # Skip virtual site_labels added for invariant sites\\n if len(d[\\\"mutations\\\"][i]) == 1:\\n continue\\n\\n assert gpm_read.site_labels[i] == gpm.site_labels[i]\\n\\n # Read in with bad wildtype. Should throw warning and then have\\n # sequential site labels.\\n with pytest.warns(UserWarning):\\n gpm_read = gpmap.read_excel(out_file,wildtype=d[\\\"mutant\\\"])\\n\\n assert np.array_equal(gpm_read.site_labels,range(len(d[\\\"wildtype\\\"])))\",\n \"def main(dot_file):\\n global SUBGRAPHS, PARENTS\\n graph = graph_from_dot(dot_file)\\n SUBGRAPHS = {}\\n PARENTS = {}\\n extract_subgraphs([graph])\\n \\n for (name, subgraph) in SUBGRAPHS.items():\\n nodes = extract_nodes(subgraph)\\n for node in nodes:\\n (name_function, result, function_call_line) = analyse_label_function_calls(node)\\n if name_function is not None:\\n (label_node1, label_node2, bb) = create_labels(node, result, function_call_line)\\n node.set_label(label_node1)\\n nodes_to_update = get_nodes_to_update(subgraph, graph.get_name())\\n update_nodes(nodes_to_update, bb)\\n nodes.append(create_new_node(subgraph, node, label_node2, bb))\\n update_edges(subgraph, graph.get_name(), bb)\\n create_new_edge(graph, node.get_name(), SUBGRAPHS[name_function])\\n recreate_subgraphs_name()\\n export_graph(graph, \\\"main_output\\\", \\\"png\\\")\\n export_graph(graph, \\\"main_output\\\", \\\"dot\\\")\\n return graph\",\n \"def run_generations(init_len):\\n num_graphs = 0\\n current_gen = [nx.path_graph(init_len)]\\n complete_graph_list = current_gen.copy()\\n while len(current_gen) and current_gen[0].size() < (3*init_len - 7):\\n current_gen = generation_next(current_gen)\\n num_graphs += show_graph_list(current_gen)\\n complete_graph_list.extend(filter_bridge_case(current_gen))\\n print(num_graphs)\\n return complete_graph_list\",\n \"def test_general_subset_level():\\n pass\",\n \"def test_clips_deftemplate_parse_smoke(self):\\n G = CreateTestCarpetingGraph()\\n \\n nlpGraphProcessor = NLPResultGraphParser()\\n res = BLNlpClipsRuleBase()\\n seen = []\\n nlpGraphProcessor.ParseObject(G, Strings.ndStep, seen, res)\\n nlpGraphProcessor.ParseObject(G, \\\"CarpetingPattern\\\", seen, res)\\n nlpGraphProcessor.ParseObject(G, \\\"ProcessSubStep\\\", seen, res)\\n\\n # Deftemplate with part relation and extra fields\\n template = next(t for t in res.Deftemplates if t.TemplateName == Strings.ndStep)\\n self.assertTrue(template != None)\\n self.assertEqual(len(template.Slots), 4)\\n self.assertTrue(next(s for s in template.Slots if s.Name == \\\"Id\\\") != None)\\n self.assertTrue(next(s for s in template.Slots if s.Name == \\\"ProcessId\\\") != None)\\n self.assertTrue(next(s for s in template.Slots if s.Name == Strings.after) != None)\\n\\n # Deftemplate with several has relations\\n template = next(t for t in res.Deftemplates if t.TemplateName == \\\"CarpetingPattern\\\")\\n self.assertTrue(template != None)\\n self.assertEqual(len(template.Slots), 4)\\n self.assertTrue(next(s for s in template.Slots if s.Name == \\\"Id\\\") != None)\\n self.assertTrue(next(s for s in template.Slots if s.Name == \\\"RotationAngle\\\") != None)\\n self.assertTrue(next(s for s in template.Slots if s.Name == \\\"ShiftDirection\\\") != None)\\n self.assertTrue(next(s for s in template.Slots if s.Name == \\\"ShiftRatio\\\") != None)\\n\\n # Deftemplate with in relation\\n template = next(t for t in res.Deftemplates if t.TemplateName == \\\"ProcessSubStep\\\")\\n self.assertTrue(template != None)\\n self.assertEqual(len(template.Slots), 7)\\n self.assertTrue(next(s for s in template.Slots if s.Name == \\\"Id\\\") != None)\\n self.assertTrue(next(s for s in template.Slots if s.Name == \\\"Instruction\\\") != None)\\n self.assertTrue(next(s for s in template.Slots if s.Name == \\\"ProcessStepId\\\") != None)\\n self.assertTrue(next(s for s in template.Slots if s.Name == \\\"StepNumber\\\") != None)\",\n \"def test07_add_type_triples(self):\\n r = LDPRS('http://ex.org/abc')\\n g = Graph()\\n r.add_type_triples(g)\\n self.assertEqual(len(g), 2)\",\n \"def test_multiple_task_groups_reversed_dag(\\n self, test_multiple_taskgroups_dag, multiple_taskgroups_dag_expected_edges\\n ):\\n (\\n dag,\\n group1,\\n group2,\\n group3,\\n (\\n group1_emp1,\\n group1_emp2,\\n group1_emp3,\\n group2_emp1,\\n group2_emp2,\\n group2_emp3,\\n group2_op1,\\n group2_op2,\\n group3_emp1,\\n group3_emp2,\\n group3_emp3,\\n emp_in1,\\n emp_in2,\\n emp_in3,\\n emp_in4,\\n emp_out1,\\n emp_out2,\\n emp_out3,\\n emp_out4,\\n op_in1,\\n op_out1,\\n ),\\n ) = test_multiple_taskgroups_dag\\n\\n group1_emp3 << Label(\\\"label group1.group1_emp1 <=> group1.group1_emp2\\\") << group1_emp1\\n\\n group1 << emp_in1\\n group1 << Label(\\\"label emp_in2 <=> group1\\\") << emp_in2\\n group1 << Label(\\\"label emp_in3/emp_in4 <=> group1\\\") << [emp_in3, emp_in4]\\n group1 << Label(\\\"label op_in1 <=> group1\\\") << XComArg(op_in1, \\\"test_key\\\")\\n\\n (\\n group2_emp3\\n << Label(\\\"label group2.group2_emp1/group2.group2_emp2 <=> group2.group2_emp3\\\")\\n << [group2_emp1, group2_emp2]\\n )\\n (\\n [group2_emp2, group2_emp3]\\n << Label(\\\"label group2.group2_emp1 <=> group2.group2_emp2/group2.group2_emp3\\\")\\n << group2_emp1\\n )\\n group3 << Label(\\\"label group2.group2_emp3 <=> group3\\\") << group2_emp3\\n\\n (\\n XComArg(group2_op2, \\\"test_key\\\")\\n << Label(\\\"label group2.group2_op1 <=> group2.group2_op2\\\")\\n << XComArg(group2_op1, \\\"test_key\\\")\\n )\\n group3 << Label(\\\"label group2.group2_op2 <=> group3\\\") << XComArg(group2_op2, \\\"test_key\\\")\\n\\n emp_out1 << group3\\n emp_out2 << Label(\\\"label group3 <=> emp_out2\\\") << group3\\n [emp_out3, emp_out4] << Label(\\\"label group3 <=> emp_out3/emp_out4\\\") << group3\\n XComArg(op_out1, \\\"test_key\\\") << Label(\\\"label group3 <=> op_out1\\\") << group3\\n\\n group2 << Label(\\\"label group1 <=> group2\\\") << group1\\n\\n compare_dag_edges(dag_edges(dag), multiple_taskgroups_dag_expected_edges)\",\n \"def test_ExplorePath_Simple( self ):\\n links = []\\n n1 = graph.Node( 10, 50 )\\n n2 = graph.Node( 10, 50 )\\n n3 = graph.Node( 10, 50 )\\n n7 = graph.Node( 10, 50 )\\n\\n links.append( graph.Link( n1, n2 ) )\\n links.append( graph.Link( n2, n3 ) )\\n links.append( graph.Link( n3, n7 ) )\\n roots = [n1]\\n actual = nodes.explorePath( links, roots, n1 )\\n expected = [ n1, n2, n3, n7 ]\\n self.assertEqual( expected, actual )\",\n \"def gexf_graph():\\n # you must replace these lines and supply your own graph\\n \\n \\n \\n my_gexf = Gexf(\\\"JiajiaXie\\\", \\\"My awesome graph\\\")\\n graph=my_gexf.addGraph(\\\"undirected\\\", \\\"static\\\", \\\"My awesome networks\\\")\\n \\n atr1=graph.addNodeAttribute('Type',type='string')\\n\\n\\n for set in data_specific:\\n if graph.nodeExists(set['set_num']) ==0:\\n tm1=graph.addNode(set['set_num'], set['name'], r='0', g='0', b='0')\\n tm1.addAttribute(atr1,\\\"set\\\")\\n\\n\\n\\n counter_test=1\\n for set, part in data_parts.items():\\n for key, part_list in part.items():\\n interme =part_list['color']\\n red=interme[0]+interme[1]\\n green=interme[2]+interme[3]\\n blue=interme[4]+interme[5]\\n\\n red_de=str(int(red,16))\\n green_de=str(int(green,16))\\n blue_de=str(int(blue,16))\\n if graph.nodeExists(part_list['id'])==0:\\n tm2=graph.addNode(part_list['id'], part_list['part_name'],r=red_de, g=green_de, b = blue_de)\\n tm2.addAttribute(atr1,\\\"part\\\")\\n\\n\\n counter_test+=1\\n graph.addEdge(\\\"_\\\"+str(counter_test), set, part_list['id'], part_list['quantity'])\\n\\n\\n\\n f=open('bricks_graph.gexf','wb')\\n my_gexf.write(f)\\n\\n\\n return my_gexf.graphs[0]\",\n \"def test_xcomarg_shift(self, test_dag):\\n # Unpack the fixture\\n dag, (op1, op2, op3, op4) = test_dag\\n # Arrange the operators with a Label in the middle\\n op1_arg = XComArg(op1, \\\"test_key\\\")\\n op1_arg >> Label(\\\"Label 1\\\") >> [op2, op3]\\n op1_arg >> op4\\n # Check that the DAG has the right edge info\\n assert dag.get_edge_info(op1.task_id, op2.task_id) == {\\\"label\\\": \\\"Label 1\\\"}\\n assert dag.get_edge_info(op1.task_id, op4.task_id) == {}\",\n \"def generate_test():\\n o = []\\n pos = [384, 288]\\n note_group_size = GAN_PARAMS[\\\"note_group_size\\\"]\\n generate_set(begin=3 * note_group_size, start_pos=pos,\\n length_multiplier=dist_multiplier, group_id=3, plot_map=True)\",\n \"def test_build_poset_lattice():\\n lattice = build_poset_lattice(all_games_gen(2))\\n assert len(lattice.edges()) == 36\",\n \"def create_nodes(self):\",\n \"def __test(graph): \\n \\n if not isinstance(graph, basegraph):\\n raise TypeError(\\\"Expected type was Graph.\\\")\\n \\n print \\\"### iPATH TEST DATA STRUCTURE\\\"\\n print \\\"### Data Type: Graph ({})\\\".format(str(graph.__class__.__bases__[0].__name__))\\n print \\\"### Implementation: {}\\\".format(str(graph.__class__.__name__))\\n \\n print \\\"\\\\n*** ADD NODE ***\\\\n\\\" \\n for i in range(10):\\n print \\\"add_node({})\\\".format(str(i)) \\n graph.add_node(i) \\n \\n print \\\"\\\\n*** ADD ARC ***\\\\n\\\" \\n for i in range(10):\\n print \\\"add_arc({}, {}, {})\\\".format(str(i), str(i + 1), str(2 * (i + 1)))\\n graph.add_arc(i, i + 1, 2 * (i + 1))\\n print \\\"add_arc({}, {}, {})\\\".format(str(i), str(i + 2), str(2 * (i + 2)))\\n graph.add_arc(i, i + 2, 2 * (i + 2))\\n \\n print \\\"\\\\n*** GRAPH ***\\\\n\\\" \\n print \\\"\\\\n{}\\\\n\\\".format(str(graph))\\n \\n print \\\"\\\\n*** REMOVE NODE ***\\\\n\\\" \\n print \\\"remove_node(5)\\\"\\n graph.remove_node(5)\\n \\n print \\\"\\\\n*** GRAPH ***\\\\n\\\" \\n print \\\"\\\\n{}\\\\n\\\".format(str(graph))\\n \\n print \\\"\\\\n*** REMOVE ARC ***\\\\n\\\" \\n print \\\"remove_arc(7, 8)\\\" \\n graph.remove_arc(7, 8)\\n \\n print \\\"\\\\n*** GRAPH ***\\\\n\\\" \\n print \\\"\\\\n{}\\\\n\\\".format(str(graph))\\n \\n print \\\"\\\\n*** INCIDENT ARCS ***\\\\n\\\" \\n for node in graph.get_nodes():\\n print \\\"Incident Arcs of {}\\\\t{}\\\\n\\\".format(str(node), str(graph.get_incident_arcs(node._id)))\\n \\n print \\\"\\\\n*** ADJACENCY ***\\\\n\\\" \\n for i in range(10):\\n for j in range(10):\\n if graph.are_adjacent(i, j) == True:\\n print \\\"Adjacency Between ({}, {}): True\\\\n\\\".format(str(i), str(j))\\n \\n print \\\"\\\\n*** NODES ***\\\\n\\\" \\n print \\\"numNodes: {}\\\\n\\\".format(str(graph.get_num_nodes())) \\n print \\\"Nodes: {}\\\\n\\\".format(str(graph.get_nodes())) \\n \\n print \\\"\\\\n*** ARCS ***\\\\n\\\" \\n print \\\"numArcs: {}\\\\n\\\".format(str(graph.get_num_arcs())) \\n print \\\"Arcs: {}\\\\n\\\".format(str(graph.get_arcs())) \\n \\n print \\\"\\\\n*** SEARCH BFS ***\\\\n\\\" \\n for i in range(10): \\n print \\\"bfs({})\\\".format(str(i))\\n Lbfs = graph.bfs(i)\\n for n in Lbfs:\\n print \\\"{}\\\\n\\\".format(str(n))\\n print \\\"\\\\n\\\"\\n \\n print \\\"\\\\n*** SEARCH DFS ***\\\\n\\\" \\n for i in range(9):\\n print \\\"dfs({})\\\".format(str(i))\\n Ldfs = graph.dfs(i)\\n for n in Ldfs:\\n print \\\"{}\\\\n\\\".format(str(n))\\n print \\\"\\\\n\\\"\\n \\n print \\\"\\\\n### END OF TEST ###\\\\n\\\"\",\n \"def gen_graph(self):\",\n \"def test_pytree(self):\\n\\n # Arguments are of the form [([x00, x01], [x10]), dict(a=ya, b=yb)]\\n def add_all_jax(x_pair_of_list, y_dict):\\n x_list_0, x_list_1 = x_pair_of_list\\n return functools.reduce(operator.add,\\n x_list_0 + x_list_1 + [y_dict[\\\"a\\\"], y_dict[\\\"b\\\"]])\\n\\n self.CheckShapePolymorphism(\\n add_all_jax,\\n input_signature=[([tf.TensorSpec([None]), tf.TensorSpec([None])],\\n [tf.TensorSpec([None])]),\\n dict(a=tf.TensorSpec([None]), b=tf.TensorSpec([None]))],\\n in_shapes=[([\\\"(v,)\\\", \\\"(v,)\\\"], [(\\\"v,\\\")]),\\n dict(a=\\\"(v,)\\\", b=\\\"(v,)\\\")],\\n expected_output_signature=tf.TensorSpec([None]))\\n\\n # Now partial in_shapes; the parts of the in_shapes that are not specified\\n # must have full input_signatures.\\n self.CheckShapePolymorphism(\\n add_all_jax,\\n input_signature=[([tf.TensorSpec([4]), tf.TensorSpec([4])],\\n [tf.TensorSpec([4])]),\\n dict(a=tf.TensorSpec([4]), b=tf.TensorSpec([4]))],\\n in_shapes=[([\\\"(4,)\\\", \\\"(_,)\\\"], [(\\\"4,\\\")]),\\n dict(a=\\\"(_,)\\\", b=\\\"(4,)\\\")],\\n expected_output_signature=tf.TensorSpec([4]))\",\n \"def test_generator1(self):\\n xpb = XPathBuilder()\\n xp = xpb.foo\\n xp = xp.bar\\n xp = xp.baz[xpb.attr('x') == 'y']\\n xp = xp[1]\\n exp = '/foo/bar/baz[@x = \\\"y\\\"][1]'\\n self.assertEqual(xp.tostring(), exp)\",\n \"def create_four_subplots():\\n pass\",\n \"def test_hierarchical_register_and_contain(self):\\n space = Space()\\n\\n categories = {\\\"asdfa\\\": 0.1, 2: 0.2, 3: 0.3, 4: 0.4}\\n dim = Categorical(\\\"yolo.nested\\\", categories, shape=2)\\n space.register(dim)\\n dim = Integer(\\\"yolo2.nested\\\", \\\"uniform\\\", -3, 6)\\n space.register(dim)\\n dim = Real(\\\"yolo3\\\", \\\"norm\\\", 0.9)\\n space.register(dim)\\n\\n trial = Trial(\\n params=[\\n {\\\"name\\\": \\\"yolo.nested\\\", \\\"value\\\": [\\\"asdfa\\\", 2], \\\"type\\\": \\\"categorical\\\"},\\n {\\\"name\\\": \\\"yolo2.nested\\\", \\\"value\\\": 1, \\\"type\\\": \\\"integer\\\"},\\n {\\\"name\\\": \\\"yolo3\\\", \\\"value\\\": 0.5, \\\"type\\\": \\\"real\\\"},\\n ]\\n )\\n\\n assert \\\"yolo\\\" in trial.params\\n assert \\\"nested\\\" in trial.params[\\\"yolo\\\"]\\n assert \\\"yolo2\\\" in trial.params\\n assert \\\"nested\\\" in trial.params[\\\"yolo2\\\"]\\n assert \\\"yolo3\\\" in trial.params\\n\\n assert trial in space\",\n \"def test_make_pop(self, pop_size, cell_number, microcell_number):\\n for i in [0, 1]:\\n pe.Parameters.instance().use_ages = i\\n # Population is initialised with no households\\n pop_params = {\\\"population_size\\\": pop_size,\\n \\\"cell_number\\\": cell_number,\\n \\\"microcell_number\\\": microcell_number}\\n test_pop = ToyPopulationFactory.make_pop(pop_params)\\n\\n total_people = 0\\n count_non_empty_cells = 0\\n for cell in test_pop.cells:\\n for microcell in cell.microcells:\\n total_people += len(microcell.persons)\\n if len(cell.persons) > 0:\\n count_non_empty_cells += 1\\n # Test there is at least one non-empty cell\\n self.assertTrue(count_non_empty_cells >= 1)\\n # Test that everyone in the population has been assigned a\\n # microcell\\n self.assertEqual(total_people, pop_size)\\n\\n # Test a population class object is returned\\n self.assertIsInstance(test_pop, pe.Population)\",\n \"def test_path4():\\n path = [\\n (0, 0, 1),\\n [('A', 3, 0)],\\n (0, 1, 1),\\n [('A', 2, 0)],\\n (np.pi/2, 1, 1),\\n (0, 1, 1),\\n (np.pi/2, 1, 1),\\n (0, 1, 1),\\n (np.pi/2, 1, 1),\\n (0, 1, 1),\\n (np.pi/2, 1, 1),\\n ] * 4\\n execute_path(path,True)\",\n \"def crossover(self, pop):\\n intDiscID = self.iID + self.dID\\n varID = self.cID\\n goldenRatio = (1.0 + sqrt(5)) / 2.0\\n dx = np.zeros_like(pop[0])\\n children = []\\n used = []\\n for i in range(0, int(self.fracElite * len(pop)), 1):\\n r = int(rand() * self.population)\\n while r in used or r == i:\\n r = int(rand() * self.population)\\n\\n used.append(i)\\n children.append(cp.deepcopy(pop[r]))\\n dx = abs(pop[i] - children[i]) / goldenRatio\\n children[i] = children[i] + dx * varID + np.round(dx * intDiscID)\\n children[i] = simple_bounds(children[i], self.lb, self.ub)\\n\\n return (\\n children, used)\",\n \"def main(showSamples=True, showConfusion=True):\\n ndigit = 10\\n elambda = [0.4, 0.6, 0.8]\\n for i in elambda:\\n test(ndigit, i, showSamples, showConfusion)\\n if showSamples:\\n pltmulti('graphs.pdf')\",\n \"def gexf_graph():\\n # you must replace these lines and supply your own graph\\n gexf = Gexf(\\\"author\\\", \\\"title\\\")\\n mygraph = gexf.addGraph(\\\"undirected\\\", \\\"static\\\", \\\"A web network\\\")\\n atr_type = mygraph.addNodeAttribute('Type', type='string')\\n atr_id = mygraph.addNodeAttribute('id', type='string')\\n atr_label = mygraph.addNodeAttribute('label', type='string')\\n atr_color_r = mygraph.addNodeAttribute('color_r', type='string', defaultValue='0')\\n atr_color_g = mygraph.addNodeAttribute('color_g', type='string', defaultValue='0')\\n atr_color_b = mygraph.addNodeAttribute('color_b', type='string', defaultValue='0')\\n k = 0\\n for i in range(min_parts()):\\n tmp = mygraph.addNode(set_num[i], name[i], r=\\\"0\\\", g=\\\"0\\\", b=\\\"0\\\")\\n tmp.addAttribute(atr_type, \\\"set\\\")\\n tmp.addAttribute(atr_id, set_num[i])\\n tmp.addAttribute(atr_label, name[i])\\n for j in range(len(Parts[i][\\\"Parts\\\"])):\\n if mygraph.nodeExists(Parts[i][\\\"Parts\\\"][j][\\\"number\\\"]+\\\"_\\\"+Parts[i][\\\"Parts\\\"][j][\\\"color\\\"][\\\"rgb\\\"])==0:\\n temp = mygraph.addNode((Parts[i][\\\"Parts\\\"][j][\\\"number\\\"]+\\\"_\\\"+Parts[i][\\\"Parts\\\"][j][\\\"color\\\"][\\\"rgb\\\"]), Parts[i][\\\"Parts\\\"][j][\\\"name\\\"], r=str(int(Parts[i][\\\"Parts\\\"][j][\\\"color\\\"][\\\"rgb\\\"][0:2], 16)), g=str(int(Parts[i][\\\"Parts\\\"][j][\\\"color\\\"][\\\"rgb\\\"][2:4], 16)), b=str(int(Parts[i][\\\"Parts\\\"][j][\\\"color\\\"][\\\"rgb\\\"][4:6], 16)))\\n temp.addAttribute(atr_type, \\\"part\\\")\\n temp.addAttribute(atr_id, (Parts[i][\\\"Parts\\\"][j][\\\"number\\\"]+\\\"_\\\"+Parts[i][\\\"Parts\\\"][j][\\\"color\\\"][\\\"rgb\\\"]))\\n temp.addAttribute(atr_label, Parts[i][\\\"Parts\\\"][j][\\\"name\\\"])\\n temp.addAttribute(atr_color_r, Parts[i][\\\"Parts\\\"][j][\\\"color\\\"][\\\"rgb\\\"][0:2])\\n temp.addAttribute(atr_color_g, Parts[i][\\\"Parts\\\"][j][\\\"color\\\"][\\\"rgb\\\"][2:4])\\n temp.addAttribute(atr_color_b, Parts[i][\\\"Parts\\\"][j][\\\"color\\\"][\\\"rgb\\\"][4:6])\\n mygraph.addEdge(str(k), set_num[i], (Parts[i][\\\"Parts\\\"][j][\\\"number\\\"]+\\\"_\\\"+Parts[i][\\\"Parts\\\"][j][\\\"color\\\"][\\\"rgb\\\"]), weight=Parts[i][\\\"Parts\\\"][j][\\\"quantity\\\"])\\n k = k+1\\n output_file = open(\\\"bricks_graph.gexf\\\", \\\"wb\\\")\\n gexf.write(output_file)\\n return -1\",\n \"def test_get_grid_edge_nodes(flopy_dis_mf6):\\n mf6 = flopy_dis_mf6[1]\\n mf6.initialize()\\n\\n with pytest.raises(NotImplementedError):\\n mf6.get_grid_edge_nodes(1, np.zeros((1, 1)))\",\n \"def generate_graph(data, x=\\\"Red blood Cells\\\", y=\\\"Paletes\\\", z=\\\"Leukocytes\\\", graph_type='scatter', selected_points=[], *args, **kwargs):\\n\\n # Return an empty figure if the input is empty\\n if x == [] and y==[]:\\n return go.Figure()\\n \\n # Transform the data into wide format\\n melted = pd.melt(data, value_vars=data.columns)\\n\\n if graph_type==\\\"histogram\\\":\\n if len(x) > 1:\\n return generate_histogram(melted, x, y, \\\"wide\\\", **kwargs)\\n elif len(x) == 0:\\n return go.Figure()\\n return generate_histogram(data, x, y, \\\"long\\\", **kwargs)\\n\\n elif graph_type==\\\"scatter\\\":\\n if len(x) == 1 and len(y) == 1:\\n return generate_scatter(data, x[0], y[0], \\\"long\\\", selected_points=selected_points, **kwargs)\\n\\n elif len(x) > 1 or len(y) > 1:\\n return generate_scatter_matrix(data, x, y, data_format=\\\"long\\\", **kwargs)\\n\\n return generate_scatter(melted, x, y, \\\"wide\\\")\\n\\n elif graph_type==\\\"box\\\":\\n return generate_box_plot(data, x, \\\"long\\\", **kwargs)\\n\\n elif graph_type==\\\"heatmap\\\":\\n if len(x) > 0 and len(y) > 0:\\n return generate_heatmap(data, x, y, \\\"long\\\", **kwargs)\\n return go.Figure()\\n\\n elif graph_type==\\\"par_coords\\\":\\n return generate_parallel_coords(data, x, y, \\\"long\\\", **kwargs)\\n\\n elif graph_type==\\\"strip\\\":\\n if len(x) > 0 and len(y) > 0:\\n return generate_strip(data, x, y, \\\"long\\\", **kwargs)\\n return go.Figure()\\n\\n elif graph_type==\\\"ternary\\\":\\n if len(x) > 0 and len(y) > 0 and len(z) > 0:\\n return generate_ternary(data, x, y, z, \\\"long\\\", **kwargs)\\n return go.Figure()\",\n \"def test_subkey(man):\\n errors = []\\n\\n G = man.writeTest()\\n\\n G.addVertex(\\\"Work\\\", \\\"Thing\\\", {})\\n G.addVertex(\\\"Workflow\\\", \\\"Thing\\\", {})\\n G.addVertex(\\\"Other\\\", \\\"Thing\\\", {})\\n G.addVertex(\\\"OtherGuy\\\", \\\"Thing\\\", {})\\n\\n G.addEdge(\\\"Work\\\", \\\"Other\\\", \\\"edge\\\")\\n G.addEdge(\\\"Workflow\\\", \\\"OtherGuy\\\", \\\"edge\\\")\\n\\n count = 0\\n for i in G.query().V(\\\"Work\\\").out():\\n count += 1\\n if count != 1:\\n errors.append(\\\"Incorrect outgoing vertex count %d != %d\\\" % (count, 1))\\n\\n count = 0\\n for i in G.query().V(\\\"Work\\\").outE():\\n count += 1\\n if count != 1:\\n errors.append(\\\"Incorrect outgoing edge count %d != %d\\\" % (count, 1))\\n\\n count = 0\\n for i in G.query().V(\\\"Other\\\").inE():\\n count += 1\\n if count != 1:\\n errors.append(\\\"Incorrect incoming edge count %d != %d\\\" % (count, 1))\\n\\n return errors\",\n \"def bclone():\\n node = nuke.selectedNodes()\\n if len(node)==1:\\n clone1 = nuke.createNode(\\\"NoOp\\\", inpanel = False)\\n clone1.setName(\\\"Bclone\\\")\\n clone1['label'].setValue(node[0].name()+\\\"\\\\nClone_Parent\\\")\\n clone1['tile_color'].setValue(2521651711)\\n clone1['note_font_color'].setValue(1583243007)\\n clone1xpos = clone1['xpos'].getValue()\\n clone1ypos = clone1['ypos'].getValue()\\n \\n clone2 = nuke.createNode(\\\"NoOp\\\", inpanel = False)\\n clone2.setName(\\\"Bclone\\\")\\n clone2['label'].setValue(node[0].name()+\\\"\\\\nClone\\\")\\n clone2['hide_input'].setValue(True)\\n clone2['tile_color'].setValue(2521651711)\\n clone2['note_font_color'].setValue(1583243007)\\n clone2['xpos'].setValue(clone1xpos)\\n clone2['ypos'].setValue(clone1ypos)\\n\\n if len(node)==0:\\n clone1 = nuke.createNode(\\\"NoOp\\\", inpanel = False)\\n clone1.setName(\\\"Bclone\\\")\\n clone1['label'].setValue(\\\"Clone_Parent\\\")\\n clone1['tile_color'].setValue(2521651711)\\n clone1['note_font_color'].setValue(1583243007)\\n clone1xpos = clone1['xpos'].getValue()\\n clone1ypos = clone1['ypos'].getValue()\\n \\n clone2 = nuke.createNode(\\\"NoOp\\\", inpanel = False)\\n clone2.setName(\\\"Bclone\\\")\\n clone2['label'].setValue(\\\"Clone\\\")\\n clone2['hide_input'].setValue(True)\\n clone2['tile_color'].setValue(2521651711)\\n clone2['note_font_color'].setValue(1583243007)\\n clone2['xpos'].setValue(clone1xpos)\\n clone2['ypos'].setValue(clone1ypos)\\n if len(node)!=0 and len(node)!=1:\\n nuke.message('Just select one node to clone !')\",\n \"def test_dummy4(self):\\n xpb = XPathBuilder()\\n xp = xpb.dummy()\\n self.assertTrue(xp.log_not() is xp)\",\n \"def test_duplicate(self):\\n test_file = os.path.join(INPUT_HYPM_PATH, 'unit_364-2013-225-1-0.mdd')\\n\\n mdd.procall([test_file])\\n \\n self.compare_node58()\",\n \"def test_general_subset_all():\\n pass\",\n \"def main():\\n dims = params['dims']\\n\\n for d in dims:\\n print('**** Running test for d={0:d} ****'.format(d))\\n run_test(d)\",\n \"def sub_graph_merging(self):\\n raise NotImplementedError()\",\n \"def test_get_dependencies_subgraph_by_dfs(\\n self, source_node, expected_nodes_in, expected_nodes_out\\n ):\\n graph = nx.DiGraph()\\n graph.add_node(\\\"pack1\\\")\\n graph.add_node(\\\"pack2\\\")\\n graph.add_node(\\\"pack3\\\")\\n graph.add_node(\\\"pack4\\\")\\n graph.add_edge(\\\"pack1\\\", \\\"pack2\\\")\\n graph.add_edge(\\\"pack2\\\", \\\"pack3\\\")\\n dfs_graph = PackDependencies.get_dependencies_subgraph_by_dfs(\\n graph, source_node\\n )\\n for i in expected_nodes_in:\\n assert i in dfs_graph.nodes()\\n for i in expected_nodes_out:\\n assert i not in dfs_graph.nodes()\",\n \"def test_zip_batch(self):\\n assert self.design.layout.layers[0].name == 'top'\",\n \"def test_Tree():\",\n \"def dump_subgraph_for_debug(self):\\n\\n import pypipegraph2 as ppg\\n\\n nodes = []\\n seen = set()\\n edges = []\\n counter = [0]\\n node_to_counters = {}\\n\\n def descend(node):\\n if node in seen:\\n return\\n seen.add(node)\\n j = self.runner.jobs[node]\\n if isinstance(j, ppg.FileInvariant):\\n nodes.append(f\\\"Path('{counter[0]}').write_text('A')\\\")\\n nodes.append(f\\\"job_{counter[0]} = ppg.FileInvariant('{counter[0]}')\\\")\\n elif isinstance(j, ppg.ParameterInvariant):\\n nodes.append(\\n f\\\"job_{counter[0]} = ppg.ParameterInvariant('{counter[0]}', 55)\\\"\\n )\\n elif isinstance(j, ppg.FunctionInvariant):\\n nodes.append(\\n f\\\"job_{counter[0]} = ppg.FunctionInvariant('{counter[0]}', lambda: 55)\\\"\\n )\\n elif isinstance(j, ppg.SharedMultiFileGeneratingJob):\\n nodes.append(\\n f\\\"job_{counter[0]} = ppg.SharedMultiFileGeneratingJob('{counter[0]}', {[x.name for x in j.files]!r}, dummy_smfg, depend_on_function=False)\\\"\\n )\\n elif isinstance(j, ppg.TempFileGeneratingJob):\\n nodes.append(\\n f\\\"job_{counter[0]} = ppg.TempFileGeneratingJob('{counter[0]}', dummy_fg, depend_on_function=False)\\\"\\n )\\n elif isinstance(j, ppg.FileGeneratingJob):\\n nodes.append(\\n f\\\"job_{counter[0]} = ppg.FileGeneratingJob('{counter[0]}', dummy_fg, depend_on_function=False)\\\"\\n )\\n elif isinstance(j, ppg.MultiTempFileGeneratingJob):\\n files = [counter[0] + \\\"/\\\" + x.name for x in j.files]\\n nodes.append(\\n f\\\"job_{counter[0]} = ppg.MultiTempFileGeneratingJob({files!r}, dummy_mfg, depend_on_function=False)\\\"\\n )\\n elif isinstance(j, ppg.MultiFileGeneratingJob):\\n files = [str(counter[0]) + \\\"/\\\" + x.name for x in j.files]\\n nodes.append(\\n f\\\"job_{counter[0]} = ppg.MultiFileGeneratingJob({files!r}, dummy_mfg, depend_on_function=False)\\\"\\n )\\n elif isinstance(j, ppg.DataLoadingJob):\\n nodes.append(\\n f\\\"job_{counter[0]} = ppg.DataLoadingJob('{counter[0]}', lambda: None, depend_on_function=False)\\\"\\n )\\n elif isinstance(j, ppg.AttributeLoadingJob):\\n nodes.append(\\n f\\\"job_{counter[0]} = ppg.AttributeLoadingJob('{counter[0]}', DummyObject(), 'attr_{counter[0]}', lambda: None, depend_on_function=False)\\\"\\n )\\n else:\\n raise ValueError(j)\\n node_to_counters[node] = counter[0]\\n counter[0] += 1\\n for parent in self.runner.dag.predecessors(node):\\n descend(parent)\\n\\n def build_edges(node):\\n for parent in self.runner.dag.predecessors(node):\\n edges.append(\\n f\\\"edges.append(('{node_to_counters[node]}', '{node_to_counters[parent]}'))\\\"\\n )\\n build_edges(parent)\\n\\n descend(self.job_id)\\n edges.append(\\\"edges = []\\\")\\n build_edges(self.job_id)\\n edges.extend(\\n [\\n \\\"for (a,b) in edges:\\\",\\n \\\" if a in ppg.global_pipegraph.jobs and b in ppg.global_pipegraph.jobs:\\\",\\n \\\" ppg.global_pipegraph.jobs[a].depends_on(ppg.global_pipegraph.jobs[b])\\\",\\n ]\\n )\\n with open(\\\"subgraph_debug.py\\\", \\\"w\\\") as op:\\n lines = \\\"\\\"\\\"\\nclass DummyObject:\\n pass\\n\\ndef dummy_smfg(files, prefix):\\n Path(prefix).mkdir(exist_ok=True, parents=True)\\n for f in files:\\n f.write_text(\\\"hello\\\")\\n\\n\\ndef dummy_mfg(files):\\n for f in files:\\n f.parent.mkdir(exist_ok=True, parents=True)\\n f.write_text(\\\"hello\\\")\\n\\ndef dummy_fg(of):\\n of.parent.mkdir(exist_ok=True, parents=True)\\n of.write_text(\\\"fg\\\")\\n\\n\\\"\\\"\\\".split(\\n \\\"\\\\n\\\"\\n )\\n lines += nodes\\n lines += edges\\n lines += [\\\"\\\", \\\"ppg.run()\\\", \\\"ppg.run\\\"]\\n\\n op.write(\\\"\\\\n\\\".join(\\\" \\\" + l for l in lines))\",\n \"def setUp(self):\\n\\n\\n # InverseLabeling\\n invLabeling0 = {'L0': [0, 1, 2]}\\n\\n invLabeling1 = {'L0' : [0, 2],\\n 'L1' : [1]}\\n\\n invLabeling2 = {\\n 'L0' : [0],\\n 'L1' : [1],\\n 'L2' : [2]\\n }\\n\\n invLabeling3 = {\\n 'L1' : [0, 1],\\n 'L2' : [2]\\n }\\n\\n invLabeling4 = {\\n 'L0' : [0,1],\\n 'L1' : [0],\\n 'L2' : [2]\\n }\\n\\n invLabeling5 = {\\n 'L0': [0, 1, 2],\\n 'L1': []\\n }\\n \\n # Create some ontologies\\n ontology0 = {'L0': ['L0']}\\n\\n ontology1 = {}\\n\\n ontology2 = {'L0': ['L1']}\\n\\n ontology3 = {'L0': ['L1', 'L2'],\\n 'L1': ['L2'],\\n 'L2': ['L0']}\\n\\n if self.id().split('.')[-1] == 'test_createLinkograph':\\n self.testParams = [\\n {'inverseLabeling': invLabeling0,\\n 'ontology': ontology0,\\n 'ExpectedLinkograph':\\n linkoCreate.Linkograph(\\n [({'L0'}, set(), {1, 2}),\\n ({'L0'}, {0}, {2}),\\n ({'L0'}, {0,1}, set())] \\n )},\\n\\n {'inverseLabeling': invLabeling0,\\n 'ontology': ontology1,\\n 'ExpectedLinkograph':\\n linkoCreate.Linkograph(\\n [({'L0'}, set(), set()),\\n ({'L0'}, set(), set()),\\n ({'L0'}, set(), set())]\\n )},\\n\\n {'inverseLabeling': invLabeling0,\\n 'ontology': ontology2,\\n 'ExpectedLinkograph':\\n linkoCreate.Linkograph(\\n [({'L0'}, set(), set()),\\n ({'L0'}, set(), set()),\\n ({'L0'}, set(), set())]\\n )},\\n\\n\\n {'inverseLabeling': invLabeling1,\\n 'ontology': ontology0,\\n 'ExpectedLinkograph':\\n linkoCreate.Linkograph(\\n [({'L0'}, set(), {2}),\\n ({'L1'}, set(), set()),\\n ({'L0'}, {0}, set())]\\n )},\\n\\n {'inverseLabeling': invLabeling1,\\n 'ontology': ontology1,\\n 'ExpectedLinkograph':\\n linkoCreate.Linkograph(\\n [({'L0'}, set(), set()),\\n ({'L1'}, set(), set()),\\n ({'L0'}, set(), set())]\\n )},\\n\\n {'inverseLabeling': invLabeling1,\\n 'ontology': ontology2,\\n 'ExpectedLinkograph':\\n linkoCreate.Linkograph(\\n [({'L0'}, set(), {1}),\\n ({'L1'}, {0}, set()),\\n ({'L0'}, set(), set())]\\n )},\\n\\n {'inverseLabeling': invLabeling0,\\n 'ontology': ontology3,\\n 'ExpectedLinkograph':\\n linkoCreate.Linkograph(\\n [({'L0'}, set(), set()),\\n ({'L0'}, set(), set()),\\n ({'L0'}, set(), set())]\\n )},\\n\\n {'inverseLabeling': invLabeling1,\\n 'ontology': ontology3,\\n 'ExpectedLinkograph':\\n linkoCreate.Linkograph(\\n [({'L0'}, set(), {1}),\\n ({'L1'}, {0}, set()),\\n ({'L0'}, set(), set())]\\n )},\\n\\n {'inverseLabeling': invLabeling2,\\n 'ontology': ontology3,\\n 'ExpectedLinkograph':\\n linkoCreate.Linkograph(\\n [({'L0'}, set(), {1,2}),\\n ({'L1'}, {0}, {2}),\\n ({'L2'}, {0, 1}, set())]\\n )},\\n\\n {'inverseLabeling': invLabeling3,\\n 'ontology': ontology3,\\n 'ExpectedLinkograph':\\n linkoCreate.Linkograph(\\n [({'L1'}, set(), {2}),\\n ({'L1'}, set(), {2}),\\n ({'L2'}, {0, 1}, set())]\\n )},\\n\\n {'inverseLabeling': invLabeling4,\\n 'ontology': ontology3,\\n 'ExpectedLinkograph':\\n linkoCreate.Linkograph(\\n [({'L0', 'L1'}, set(), {2}),\\n ({'L0'}, set(), {2}),\\n ({'L2'}, {0, 1}, set())]\\n )},\\n\\n {'inverseLabeling': invLabeling5,\\n 'ontology': ontology3,\\n 'ExpectedLinkograph':\\n linkoCreate.Linkograph(\\n [({'L0'}, set(), set()),\\n ({'L0'}, set(), set()),\\n ({'L0'}, set(), set())]\\n )},\\n\\n ]\",\n \"def test_input_valid_subgraph(self, dim):\\r\\n with pytest.raises(ValueError, match=\\\"Input is not a valid subgraph\\\"):\\r\\n clique.swap([0, dim], nx.empty_graph(dim))\",\n \"def test_dummy5(self):\\n xpb = XPathBuilder()\\n xp = xpb.dummy()\\n xp = xpb.foo & xp\\n exp = '/foo'\\n self.assertEqual(xp.tostring(), exp)\",\n \"def test6():\\n setLogLevel(\\\"info\\\")\\n info(\\\"Configuracion Unidad experimental\\\")\\n \\\"\\\"\\\" 1 -> Definicion de la topologia \\\"\\\"\\\"\\n t1 = Topologia1()\\n ue1 = UnidadExperimental()\\n ue1.setTopo(t1)\\n ue1.definirNodosClaves(A = 'h1', C='h2', V='h3') # Caso solo para trafico normal\\n ue1.setController('ryu', 'simple_switch_13.py,ofctl_rest.py')\\n info(\\\"Configuracion del experimento\\\")\\n \\\"\\\"\\\" 3. Confiracion del experimento \\\"\\\"\\\"\\n exp1 = Experimento()\\n exp1.configureParams(ue1)\\n exp1.configurarTrafico('ataque')\\n \\\"\\\"\\\" 4. Inicio del experimento \\\"\\\"\\\"\\n exp1.startTest()\\n \\\"\\\"\\\" 5. Aplicacion de pruebas \\\"\\\"\\\"\\n exp1.trafico.iperfMeasure()\\n exp1.trafico.iperfMeasure(filename='iperf_ataque_test.log')\\n \\\"\\\"\\\" 6. Fin del experimento \\\"\\\"\\\"\\n exp1.endTest()\\n info(\\\"Removiendo la topologia\\\\n\\\")\\n exp1.killTest()\\n info(\\\"Removiendo el controlador\\\\n\\\")\\n exp1.killController() # Si no se pone no se finaliza el controlador\",\n \"def test_ExplorePath( self ):\\n links = []\\n n1 = graph.Node( 10, 10 )\\n n2 = graph.Node( 10, 20 )\\n n3 = graph.Node( 10, 30 )\\n n4a = graph.Node( 5, 40 )\\n n4b = graph.Node( 15, 40 )\\n n5a = graph.Node( 5, 50 )\\n n5b = graph.Node( 15, 50 )\\n n6a = graph.Node( 5, 60 )\\n n6b = graph.Node( 15, 60 )\\n n7 = graph.Node( 10, 70 )\\n\\n links.append( graph.Link( n1, n2 ) )\\n links.append( graph.Link( n2, n3 ) )\\n links.append( graph.Link( n3, n4a ) )\\n links.append( graph.Link( n3, n4b ) )\\n links.append( graph.Link( n4a, n5a ) )\\n links.append( graph.Link( n4b, n5b ) )\\n links.append( graph.Link( n5a, n6a ) )\\n links.append( graph.Link( n6a, n7 ) )\\n links.append( graph.Link( n5b, n7 ) )\\n roots = [n1]\\n actual = nodes.explorePath( links, n1, n1 )\\n expected = [ n1, n2, n3, n4b, n5b, n7 ]\\n self.assertEqual( expected, actual )\",\n \"def par_test_8(self):\\n\\n for i in range(4):\\n self.XY_par_factor.setMaxDepth(i)\\n self.XY_par_factor.setMaxDepth(i)\\n\\n res = self.XY_factor.mult(self.XY_factor)\\n par_res = self.XY_par_factor.mult(self.XY_par_factor)\\n assert res.rand_vars == par_res.rand_vars and res.values == par_res.values\",\n \"def test_construct_subcircuit_layers(self):\\r\\n dev = qml.device(\\\"default.qubit\\\", wires=3)\\r\\n\\r\\n def circuit(params):\\r\\n # section 1\\r\\n qml.RX(params[0], wires=0)\\r\\n # section 2\\r\\n qml.RY(params[1], wires=0)\\r\\n qml.CNOT(wires=[0, 1])\\r\\n qml.CNOT(wires=[1, 2])\\r\\n # section 3\\r\\n qml.RX(params[2], wires=0)\\r\\n qml.RY(params[3], wires=1)\\r\\n qml.RZ(params[4], wires=2)\\r\\n qml.CNOT(wires=[0, 1])\\r\\n qml.CNOT(wires=[1, 2])\\r\\n # section 4\\r\\n qml.RX(params[5], wires=0)\\r\\n qml.RY(params[6], wires=1)\\r\\n qml.RZ(params[7], wires=2)\\r\\n qml.CNOT(wires=[0, 1])\\r\\n qml.CNOT(wires=[1, 2])\\r\\n return qml.expval(qml.PauliX(0)), qml.expval(qml.PauliX(1)), qml.expval(qml.PauliX(2))\\r\\n\\r\\n circuit = qml.QNode(circuit, dev)\\r\\n\\r\\n params = np.ones([8])\\r\\n tapes = circuit.metric_tensor(params, only_construct=True)\\r\\n\\r\\n # this circuit should split into 4 independent\\r\\n # sections or layers when constructing subcircuits\\r\\n assert len(tapes) == 4\\r\\n\\r\\n # first layer subcircuit\\r\\n assert len(tapes[0].operations) == 1\\r\\n assert isinstance(tapes[0].operations[0], qml.Hadamard) # PauliX decomp\\r\\n\\r\\n # second layer subcircuit\\r\\n assert len(tapes[1].operations) == 4\\r\\n assert isinstance(tapes[1].operations[0], qml.RX)\\r\\n # PauliY decomp\\r\\n assert isinstance(tapes[1].operations[1], qml.PauliZ)\\r\\n assert isinstance(tapes[1].operations[2], qml.S)\\r\\n assert isinstance(tapes[1].operations[3], qml.Hadamard)\\r\\n\\r\\n # # third layer subcircuit\\r\\n assert len(tapes[2].operations) == 8\\r\\n assert isinstance(tapes[2].operations[0], qml.RX)\\r\\n assert isinstance(tapes[2].operations[1], qml.RY)\\r\\n assert isinstance(tapes[2].operations[2], qml.CNOT)\\r\\n assert isinstance(tapes[2].operations[3], qml.CNOT)\\r\\n # PauliX decomp\\r\\n assert isinstance(tapes[2].operations[4], qml.Hadamard)\\r\\n # PauliY decomp\\r\\n assert isinstance(tapes[2].operations[5], qml.PauliZ)\\r\\n assert isinstance(tapes[2].operations[6], qml.S)\\r\\n assert isinstance(tapes[2].operations[7], qml.Hadamard)\\r\\n\\r\\n # # fourth layer subcircuit\\r\\n assert len(tapes[3].operations) == 13\\r\\n assert isinstance(tapes[3].operations[0], qml.RX)\\r\\n assert isinstance(tapes[3].operations[1], qml.RY)\\r\\n assert isinstance(tapes[3].operations[2], qml.CNOT)\\r\\n assert isinstance(tapes[3].operations[3], qml.CNOT)\\r\\n assert isinstance(tapes[3].operations[4], qml.RX)\\r\\n assert isinstance(tapes[3].operations[5], qml.RY)\\r\\n assert isinstance(tapes[3].operations[6], qml.RZ)\\r\\n assert isinstance(tapes[3].operations[7], qml.CNOT)\\r\\n assert isinstance(tapes[3].operations[8], qml.CNOT)\\r\\n # PauliX decomp\\r\\n assert isinstance(tapes[3].operations[9], qml.Hadamard)\\r\\n # PauliY decomp\\r\\n assert isinstance(tapes[3].operations[10], qml.PauliZ)\\r\\n assert isinstance(tapes[3].operations[11], qml.S)\\r\\n assert isinstance(tapes[3].operations[12], qml.Hadamard)\",\n \"def test_get_child():\\n \\n root_ts = TrackSegment(flow_dict=flow_dict) \\n \\n # ROOT MODULE\\n start = root_ts.get_child(\\\"start\\\")\\n # check depth\\n assert(start.depth == root_ts.depth+1)\\n # check parent\\n assert(start.parent is root_ts)\\n # check module_id\\n assert(start.module_id == 'start')\\n \\n # CHILD (1,2,3)\\n root_ts_get_child_result = root_ts.get_child((1,2,3))\\n # check depth\\n assert(root_ts_get_child_result.depth == root_ts.depth+1)\\n # check parent\\n assert(root_ts_get_child_result.parent is root_ts)\\n \\n # CHILD (2,3,4)\\n root_ts_get_child_result2 = root_ts_get_child_result.get_child((2,3,4))\\n # check depth\\n assert(root_ts_get_child_result2.depth == root_ts.depth+2)\\n # check parent \\n assert(root_ts_get_child_result2.parent is root_ts_get_child_result)\\n \\n print(\\\"TEST GET_CHILD: success!\\\")\",\n \"def test_get_subvertices_from_vertex(self):\\n subvertices = list()\\n subvertices.append(PartitionedVertex(None, \\\"\\\"))\\n subvertices.append(PartitionedVertex(None, \\\"\\\"))\\n subvert1 = PartitionedVertex(None, \\\"\\\")\\n subvert2 = PartitionedVertex(None, \\\"\\\")\\n\\n subedges = list()\\n subedges.append(MultiCastPartitionedEdge(subvertices[0],\\n subvertices[1]))\\n subedges.append(MultiCastPartitionedEdge(subvertices[1],\\n subvertices[1]))\\n\\n graph_mapper = GraphMapper()\\n vert = TestVertex(4, \\\"Some testing vertex\\\")\\n\\n vertex_slice = Slice(0, 1)\\n graph_mapper.add_subvertex(subvert1, vertex_slice, vert)\\n vertex_slice = Slice(2, 3)\\n graph_mapper.add_subvertex(subvert2, vertex_slice, vert)\\n\\n returned_subverts = graph_mapper.get_subvertices_from_vertex(vert)\\n\\n self.assertIn(subvert1, returned_subverts)\\n self.assertIn(subvert2, returned_subverts)\\n for sub in subvertices:\\n self.assertNotIn(sub, returned_subverts)\",\n \"def setUp(self):\\n self.complete = nx.Graph()\\n self.complete.add_edge(1, 2)\\n self.complete.add_edge(2, 3)\\n self.complete.add_edge(1, 3)\\n\\n self.small_tree = nx.Graph()\\n self.small_tree.add_edge(1, 2)\\n self.small_tree.add_edge(2, 3)\\n self.small_tree.add_edge(3, 4)\\n self.small_tree.add_edge(1, 4)\\n self.small_tree.add_edge(2, 4)\\n self.small_tree.add_edge(4, 5)\\n self.small_tree.add_edge(5, 6)\\n self.small_tree.add_edge(5, 7)\\n self.small_tree.add_edge(6, 7)\\n\\n self.deterministic_graph = nx.Graph()\\n self.deterministic_graph.add_edge(1, 2)\\n self.deterministic_graph.add_edge(1, 3)\\n self.deterministic_graph.add_edge(3, 4)\\n self.deterministic_graph.add_edge(2, 4)\\n self.deterministic_graph.add_edge(3, 5)\\n self.deterministic_graph.add_edge(4, 5)\\n self.deterministic_graph.add_edge(3, 6)\\n self.deterministic_graph.add_edge(5, 6)\",\n \"def __init__(self, firstParent, secondParent):\\n CrossOver.__init__(self, \\\"Group Point CrossOver\\\", firstParent, secondParent)\",\n \"def testFlatSeries(self):\\n self.AddToChart(self.chart, [5, 5, 5])\\n self.assertEqual(self.Param('chd'), 's:AAA')\\n self.chart.left.min = 0\\n self.chart.left.max = 5\\n self.assertEqual(self.Param('chd'), 's:999')\\n self.chart.left.min = 5\\n self.chart.left.max = 15\\n self.assertEqual(self.Param('chd'), 's:AAA')\",\n \"def generate_test_graph(sameDomain = False):\\n num = 100\\n\\n urls = []\\n emails = []\\n nodes={}\\n if sameDomain:\\n domain = generate_domainname()\\n else:\\n domain = None\\n for i in range(num):\\n urls.append(generate_url(domain))\\n emails.append(generate_email())\\n \\n used_urls = set()\\n used_emails = set()\\n for u in urls:\\n l = random.choices(urls, k = floor(num/4))\\n #l = [u for u in urls]\\n e = random.choices(emails, k = floor(num/10))\\n #e = [e for e in emails]\\n used_urls.update(l)\\n used_emails.update(e)\\n nodes[u] = testNode(u, l, e)\\n nodes[u].generate_page()\\n \\n return nodes, urls, emails\",\n \"def test_propene(self):\\n def draw(image: ShapeImage):\\n image.add_line((400, 400), (500, 400))\\n image.add_line((400, 410), (500, 410))\\n image.add_line((500, 400), (587, 350))\\n\\n self._test_shape(\\n image_size=(1000, 1000),\\n expected_corners=np.array([\\n [[400, 400]],\\n [[500, 400]],\\n [[587, 350]]\\n ]),\\n drawer=draw,\\n expected_edges=np.array([\\n [[400, 400, 500, 400]],\\n [[400, 410, 500, 410]],\\n [[500, 400, 587, 350]]\\n ])\\n )\",\n \"def genNextPop(prevPop, masterList, popSize):\\n parSize = int(popSize / 10) #top 10%\\n parentPop = [Team(prevPop[i].roster) for i in range(parSize)]\\n parentPop = getStats(parentPop, masterList) # inefficent\\n rosterSize = len(parentPop[0].roster)\\n newPop = []\\n #parentsList = [] #debug\\n for i in range(popSize):\\n chromosome = doCrossover(parentPop, parSize, rosterSize)\\n newPop.append(Team(chromosome))\\n getStats(newPop, masterList)\\n #showStats(newPop, masterList, \\\"all\\\")\\n #debug code\\n #for playerInd in chromosome:\\n # print(masterList[playerInd].pos)\\n\\n return newPop\",\n \"def test_population_movements_with_compilation(self):\\n self._pystepx = PySTEPXIsland(nb_islands=4, init_script=init_script)\\n print self._pystepx._rc[0]['gp_engine']\\n self._pystepx._rc[0].execute('elems = gp_engine.get_evolver().select_and_remove_individuals(0.01)',\\n block=True)\\n print self._pystepx._rc[0]['elems']\",\n \"def project_pop(self):\\n M = self.N[0:2]\\n for x in range(10):\\n M.append(self.run_step(M))\\n split_N = split_list(M)\\n \\n fig = self.make_figure(split_N)\\n fig.update_layout(title='Projected Fish Population')\\n\\n return fig\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.81625026","0.75757235","0.63863367","0.63754267","0.61888385","0.5910345","0.5906325","0.568147","0.56611234","0.5634181","0.5622761","0.5596353","0.5533244","0.5507174","0.54756355","0.5458438","0.5447056","0.53142405","0.5302135","0.5267153","0.5253093","0.5245071","0.5240114","0.5235194","0.5217268","0.5196521","0.51842874","0.51788044","0.51737154","0.5170908","0.5161676","0.514867","0.514599","0.5143633","0.5143085","0.513967","0.51278925","0.5127106","0.51228756","0.51074904","0.5107393","0.5107199","0.50886023","0.50847566","0.508385","0.50830364","0.5071429","0.50665736","0.5062188","0.50593823","0.5051964","0.5051814","0.5043247","0.5042272","0.50368017","0.5021848","0.5012966","0.5008378","0.4992401","0.4990757","0.49878663","0.49834293","0.49813193","0.49793318","0.49724782","0.4965399","0.496524","0.49537122","0.49523303","0.49405652","0.49405012","0.4940013","0.49369767","0.49287248","0.4925167","0.49217826","0.49160716","0.4912953","0.491277","0.49070102","0.49031508","0.49011728","0.49007982","0.48968747","0.48965114","0.48953682","0.4891352","0.48869544","0.48806882","0.48784497","0.48766583","0.4876114","0.48691735","0.48690647","0.4867235","0.48659053","0.48625904","0.48558822","0.4853836","0.48494703"],"string":"[\n \"0.81625026\",\n \"0.75757235\",\n \"0.63863367\",\n \"0.63754267\",\n \"0.61888385\",\n \"0.5910345\",\n \"0.5906325\",\n \"0.568147\",\n \"0.56611234\",\n \"0.5634181\",\n \"0.5622761\",\n \"0.5596353\",\n \"0.5533244\",\n \"0.5507174\",\n \"0.54756355\",\n \"0.5458438\",\n \"0.5447056\",\n \"0.53142405\",\n \"0.5302135\",\n \"0.5267153\",\n \"0.5253093\",\n \"0.5245071\",\n \"0.5240114\",\n \"0.5235194\",\n \"0.5217268\",\n \"0.5196521\",\n \"0.51842874\",\n \"0.51788044\",\n \"0.51737154\",\n \"0.5170908\",\n \"0.5161676\",\n \"0.514867\",\n \"0.514599\",\n \"0.5143633\",\n \"0.5143085\",\n \"0.513967\",\n \"0.51278925\",\n \"0.5127106\",\n \"0.51228756\",\n \"0.51074904\",\n \"0.5107393\",\n \"0.5107199\",\n \"0.50886023\",\n \"0.50847566\",\n \"0.508385\",\n \"0.50830364\",\n \"0.5071429\",\n \"0.50665736\",\n \"0.5062188\",\n \"0.50593823\",\n \"0.5051964\",\n \"0.5051814\",\n \"0.5043247\",\n \"0.5042272\",\n \"0.50368017\",\n \"0.5021848\",\n \"0.5012966\",\n \"0.5008378\",\n \"0.4992401\",\n \"0.4990757\",\n \"0.49878663\",\n \"0.49834293\",\n \"0.49813193\",\n \"0.49793318\",\n \"0.49724782\",\n \"0.4965399\",\n \"0.496524\",\n \"0.49537122\",\n \"0.49523303\",\n \"0.49405652\",\n \"0.49405012\",\n \"0.4940013\",\n \"0.49369767\",\n \"0.49287248\",\n \"0.4925167\",\n \"0.49217826\",\n \"0.49160716\",\n \"0.4912953\",\n \"0.491277\",\n \"0.49070102\",\n \"0.49031508\",\n \"0.49011728\",\n \"0.49007982\",\n \"0.48968747\",\n \"0.48965114\",\n \"0.48953682\",\n \"0.4891352\",\n \"0.48869544\",\n \"0.48806882\",\n \"0.48784497\",\n \"0.48766583\",\n \"0.4876114\",\n \"0.48691735\",\n \"0.48690647\",\n \"0.4867235\",\n \"0.48659053\",\n \"0.48625904\",\n \"0.48558822\",\n \"0.4853836\",\n \"0.48494703\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.69611263"},"document_rank":{"kind":"string","value":"2"}}},{"rowIdx":6585,"cells":{"query":{"kind":"string","value":"Test the code loading example"},"document":{"kind":"string","value":"def test_documentation_popxl_code_loading(self):\n filename = \"code_loading.py\"\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)"},"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 test_example(decorated_example):\n import visual_coding_2p_analysis","def test_examples():\n import airconics\n # pytest runs test files in ./__pycache__: need to go up two levels\n example_dir = os.path.abspath(\n os.path.join(__file__, '..', '..', 'examples', 'core'))\n example_scripts = os.listdir(example_dir)\n for script in example_scripts:\n if script.endswith('.py'):\n fname = os.path.join(example_dir, script)\n try:\n subprocess.check_call(['python', fname])\n except subprocess.CalledProcessError:\n raise AssertionError('Example {} failed'.format(fname))","def test_load_simple_module():\n loader = Loader()\n main_fname = loader.load(\"https://gist.githubusercontent.com/miohtama/80391980c2e73b285cfe/raw/dd89a55497ba33a6014453d9bb7432ab424c01cf/kivyhello.py#main\")\n mod = path_to_mod_name(main_fname)\n result = loader.run(mod, \"hello\")\n assert result == \"Hello there\"\n loader.close()","def main():\n example()","def main():\n example()","def main():\n example()","def main():\n example()","def test_script(self) -> None:\n main()","def test_module(self):\n pass","def test_load_quality_codes():\n assert len(code_reader.load_quality_codes()) > 0","def _test():\n import doctest","def test_Demo(self):\n self._run(self._example_scenarios, \"Demo\")","def test_example_runs(self):\n run_example(\n verbose=False,\n testapp=self.testapp,\n )","def test_documentation_popxl_nested_code_loading(self):\n filename = \"code_loading_nested.py\"\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)","def test_load_testcase(self):\n tests = self.loader.load(\"tests.sampletest.hellotest.HelloTest\")\n self.assertEqual(len(tests), 1)\n from tests.sampletest.hellotest import HelloTest\n\n self.assertEqual(type(tests[0]), HelloTest)","def test():\n import doctest\n from . import locate\n return doctest.testmod(locate)","def test_main():\n # Setup\n # Exercise\n # Verify","def __main() :\n launchTests()","def runtest(self):","def test_lint(self):\n l = self.l\n l.loadTestsFromTestCase\n l.loadTestsFromModule\n l.loadTestsFromName\n l.loadTestsFromNames","def test():\n pass","def test_pep8_conformance_example(self):\n\n print(\"\\r\\n\")\n\n # Get the path to current directory\n path = os.path.dirname(os.path.realpath(__file__))\n path += \"/../docs/examples/\"\n\n # Find all the examples files\n file_paths = []\n for root, dirnames, filenames in os.walk(path):\n for file_path in fnmatch.filter(filenames, '*.py'):\n file_paths.append(os.path.join(root, file_path))\n\n for path in file_paths:\n self.run_check(path)","def test(self):\n pass","def tests():","def test():\n loader = unittest.TestLoader()\n suite = loader.discover(os.path.dirname(__file__))\n runner = unittest.TextTestRunner()\n runner.run(suite)","def test():\n import unittest\n\n tests = unittest.TestLoader().discover(\"tests\")\n unittest.TextTestRunner(verbosity=2).run(tests)","def test_functional(self):\n with sphinx_build('pyexample'):\n with open('_build/text/docfx_yaml/example.example.yml') as yml_file:\n data = yaml.safe_load(yml_file)\n self.assertEqual(\n data['items'][0]['fullName'],\n 'example.example'\n )","def setUp(self):\n self.example = Example()","def test():\n import unittest\n tests = unittest.TestLoader().discover('tests')\n unittest.TextTestRunner(verbosity=2).run(tests)","def test_examples(fname):\n app = use_app()\n app.start_timer(0, app.quit)\n if \"OLD\" in fname:\n with pytest.warns(FutureWarning):\n runpy.run_path(fname)\n else:\n try:\n runpy.run_path(fname)\n except ImportError as e:\n if \"Numpy required to use images\" in str(e):\n pytest.skip(\"numpy unavailable: skipping image example\")","def test_sample():\n print(os.getcwd())\n run_validator(SAMPLE_FILE_PATH)","def test_examples():\n argv = [\"py.test\", \"-examples\"]\n assert get_sargs(argv) is None","def main():\n print(\"It works!!! ;-)\")\n ###TODO### do something with the various methods/functions of this file","def main():\n doctest.testmod()\n game()","def runTest(self):\r\n self.setUp()\r\n self.test_sceneImport24281()","def test_CL04_import(): \n\tdef test(): \n\t\ttry: \n\t\t\tfrom .. import CL04 \n\t\texcept: \n\t\t\treturn False \n\t\treturn True \n\treturn [\"vice.yields.ccsne.CL04\", test]","def test():\n import unittest\n tests = unittest.TestLoader().discover(tests)\n unittest.TextTestRunner(verbosity=2).run(tests)","def test_example(self):\n self.assertEqual(self.example.get_example(), True)","def runTest(self):\n self.setUp()\n self.test_JupyterNotebooks1()","def test():\r\n import unittest\r\n tests=unittest.TestLoader().discover('tests')\r\n unittest.TextTestRunner(verbosity=2).run(tests)","def test_examples_for_real(platforms, path):\n if 'TRAVIS' in os.environ:\n return #: Doesn't work on travis\n\n #: Pretty hackish but whatever\n prepare_new_app(config)\n\n #: Load the code\n dir_path = os.path.abspath(os.path.split(os.path.dirname(__file__))[0])\n enaml_file = os.path.join(dir_path, 'examples', os.path.normpath(path))\n\n with open(enaml_file, 'rb') as f:\n source = f.read()\n\n #: Trigger a reload\n r = requests.post(\"http://localhost:8888/\", json={\n \"type\": \"reload\",\n \"files\": {'view.enaml': source},\n }).json()\n assert r['ok'], \"Failed to reload {}!\".format(enaml_file)\n\n #: TODO need a way to know when everything is done...\n #: should read the log unil it stops\n time.sleep(5)\n #: Flush logcat\n\n #: Save it\n stats = parse_stats(sh.adb('logcat', '-d'))\n config['stats'][enaml_file] = stats\n\n #: Save it\n data = json.dumps(config,indent=2)\n with open('tmp/stats.json', 'w') as f:\n f.write(data)\n\n #: TODO: Now compare it to the baseline","def test():\n tests = unittest.TestLoader().discover('tests')\n unittest.TextTestRunner(verbosity=2).run(tests)","def test():\n tests = unittest.TestLoader().discover('tests')\n unittest.TextTestRunner(verbosity=2).run(tests)","def test():\n tests = unittest.TestLoader().discover('tests')\n unittest.TextTestRunner(verbosity=2).run(tests)","def test():\r\n import unittest\r\n tests = unittest.TestLoader().discover('tests')\r\n unittest.TextTestRunner(verbosity=2).run(tests)","def test():\r\n import unittest\r\n tests = unittest.TestLoader().discover('tests')\r\n unittest.TextTestRunner(verbosity=2).run(tests)","def Cpp_test():\n pass","def test():\n import unittest\n\n tests = unittest.TestLoader().discover('tests')\n unittest.TextTestRunner(verbosity=2).run(tests)","def test():\n import unittest\n tests = unittest.TestLoader().discover('tests')\n unittest.TextTestRunner(verbosity=2).run(tests)","def test():\n import unittest\n tests = unittest.TestLoader().discover('tests')\n unittest.TextTestRunner(verbosity=2).run(tests)","def test():\n import unittest\n tests = unittest.TestLoader().discover('tests')\n unittest.TextTestRunner(verbosity=2).run(tests)","def test():\n import unittest\n tests = unittest.TestLoader().discover('tests')\n unittest.TextTestRunner(verbosity=2).run(tests)","def test():\n import unittest\n tests = unittest.TestLoader().discover('tests')\n unittest.TextTestRunner(verbosity=2).run(tests)","def test():\n import unittest\n tests = unittest.TestLoader().discover('tests')\n unittest.TextTestRunner(verbosity=2).run(tests)","def test():\n import unittest\n tests = unittest.TestLoader().discover('tests')\n unittest.TextTestRunner(verbosity=2).run(tests)","def test():\n import unittest\n tests = unittest.TestLoader().discover('tests')\n unittest.TextTestRunner(verbosity=2).run(tests)","def test():\n import unittest\n tests = unittest.TestLoader().discover('tests')\n unittest.TextTestRunner(verbosity=2).run(tests)","def test():\n import unittest\n tests = unittest.TestLoader().discover('tests')\n unittest.TextTestRunner(verbosity=2).run(tests)","def test():\n import unittest\n tests = unittest.TestLoader().discover('tests')\n unittest.TextTestRunner(verbosity=2).run(tests)","def test():\n import unittest\n tests = unittest.TestLoader().discover('tests')\n unittest.TextTestRunner(verbosity=2).run(tests)","def test():\n import unittest\n tests = unittest.TestLoader().discover('tests')\n unittest.TextTestRunner(verbosity=2).run(tests)","def test():\n import unittest\n tests = unittest.TestLoader().discover('tests')\n unittest.TextTestRunner(verbosity=2).run(tests)","def test():\n import unittest\n\n tests = unittest.TestLoader().discover(\"tests\")\n unittest.TextTestRunner(verbosity=2).run(tests)","def _test():\n import doctest\n doctest.testmod(verbose=1)","def test_CL13_import(): \n\tdef test(): \n\t\ttry: \n\t\t\tfrom .. import CL13 \n\t\texcept: \n\t\t\treturn False \n\t\treturn True \n\treturn [\"vice.yields.ccsne.CL13\", test]","def _test():\n import doctest\n doctest.testmod()","def _test():\n import doctest\n doctest.testmod()","def test(): \n\treturn [\"vice.yields.ccsne.import\", \n\t\t[ \n\t\t\ttest_LC18_import(), \n\t\t\ttest_CL13_import(), \n\t\t\ttest_CL04_import(), \n\t\t\ttest_WW95_import(), \n\t\t\ttest_NKT13_import(), \n\t\t\ttest_S16_import() \n\t\t] \n\t]","def main():\n run_test_all()","def _test(): # pragma: no cover\r\n print('Starting doctest')\r\n doctest.testmod()\r\n print('Completed doctest')","def unitary_test():","def test():","def test():","def test_LC18_import(): \n\tdef test(): \n\t\ttry: \n\t\t\tfrom .. import LC18 \n\t\texcept: \n\t\t\treturn False \n\t\treturn True \n\treturn [\"vice.yields.ccsne.LC18\", test]","def test():\n import unittest\n tests = unittest \n tests = unittest.TestLoader().discover('tests')\n unittest.TextTestRunner(verbosity=2).run(tests)","def test_load(self):\n command = constituencies.Command()\n command.handle('load', silent=True)","def test_loading_document(self):","def test_module_doc():\r\n\r\n for fname in os.listdir('.'):\r\n if fname.endswith('.py'):\r\n f = fname.split('.')[0]\r\n print 'Executing ', fname\r\n execfile(fname, locals())","def test_load_testcase_in_module(self):\n tests = self.loader.load(\"tests.sampletest.InitTest\")\n self.assertEqual(len(tests), 1)\n from tests.sampletest import InitTest\n\n self.assertEqual(type(tests[0]), InitTest)","def fixture_example_data():\n import_example_data()","def main(source):\n pass","def test_execute_code_sample():\n hello = Hello()\n assert hello.out() == 'Hello, world!'","def test_get_run(self):\n pass","def test_library_and_test_code(self):\n\n def do_check(path):\n \"\"\"The contents of the .iml file should certain sourceFolder entries:\n\n <sourceFolder url=\".../testprojects/tests/java/org/pantsbuild/testproject/ideatestsandlib\" isTestSource=\"true\" />\n \"\"\"\n found = set()\n iml_file = os.path.join(path, 'project.iml')\n self.assertTrue(os.path.exists(iml_file))\n dom = minidom.parse(iml_file)\n for sourceFolder in self._get_sourceFolders(dom):\n url = sourceFolder.getAttribute('url')\n is_test_source = sourceFolder.getAttribute('isTestSource')\n type_attr = sourceFolder.getAttribute('type')\n url = re.sub(r'^.*/testprojects/', 'testprojects/', url)\n found.add(url)\n if url == 'testprojects/tests/java/org/pantsbuild/testproject/ideatestsandlib':\n self.assertEquals('', type_attr)\n self.assertEquals('True', is_test_source)\n\n self.assertEquals(set([\n 'testprojects/tests/java/org/pantsbuild/testproject/ideatestsandlib',\n ]), found)\n\n self._idea_test([\n 'testprojects/tests/java/org/pantsbuild/testproject/ideatestsandlib::'\n ], check_func=do_check)","def lua_test():\n pass","def test_NKT13_import(): \n\tdef test(): \n\t\ttry: \n\t\t\tfrom .. import NKT13 \n\t\texcept: \n\t\t\treturn False \n\t\treturn True \n\treturn [\"vice.yields.ccsne.NKT13\", test]","def test_begin(self):","def test_basic_execution(self):","def runTests(self):\n \n pass","def __test__():\n#-------------------------------------------------------------------------------\n import pylib.tester as tester\n return 0","def _test():\n import doctest\n return doctest.testmod(verbose=True)","def _test():\n import doctest\n return doctest.testmod(verbose=True)","def test():#Test functions\n python = ProgrammingLanguage(\"Java\", \"Dynamic\", True, 1987)\n print(python)","def run_playground():\n from .playground import playground\n playground()","def test_compute_glycemic_load(self):\n pass","def main():\n\n print(\"=\" * 80)\n print(\"DATA STRUCTURE TESTS\")\n test_module(structs.tests)\n test_module(structs.regularization)\n\n print(\"=\" * 80)\n print(\"END-TO-END TESTS\")\n test_module(globals())","def startTestRun(self):","def test_py_compile_basic(self):\n self._test_py_compile('basic')","def test(self):","def test(self):"],"string":"[\n \"def test_example(decorated_example):\\n import visual_coding_2p_analysis\",\n \"def test_examples():\\n import airconics\\n # pytest runs test files in ./__pycache__: need to go up two levels\\n example_dir = os.path.abspath(\\n os.path.join(__file__, '..', '..', 'examples', 'core'))\\n example_scripts = os.listdir(example_dir)\\n for script in example_scripts:\\n if script.endswith('.py'):\\n fname = os.path.join(example_dir, script)\\n try:\\n subprocess.check_call(['python', fname])\\n except subprocess.CalledProcessError:\\n raise AssertionError('Example {} failed'.format(fname))\",\n \"def test_load_simple_module():\\n loader = Loader()\\n main_fname = loader.load(\\\"https://gist.githubusercontent.com/miohtama/80391980c2e73b285cfe/raw/dd89a55497ba33a6014453d9bb7432ab424c01cf/kivyhello.py#main\\\")\\n mod = path_to_mod_name(main_fname)\\n result = loader.run(mod, \\\"hello\\\")\\n assert result == \\\"Hello there\\\"\\n loader.close()\",\n \"def main():\\n example()\",\n \"def main():\\n example()\",\n \"def main():\\n example()\",\n \"def main():\\n example()\",\n \"def test_script(self) -> None:\\n main()\",\n \"def test_module(self):\\n pass\",\n \"def test_load_quality_codes():\\n assert len(code_reader.load_quality_codes()) > 0\",\n \"def _test():\\n import doctest\",\n \"def test_Demo(self):\\n self._run(self._example_scenarios, \\\"Demo\\\")\",\n \"def test_example_runs(self):\\n run_example(\\n verbose=False,\\n testapp=self.testapp,\\n )\",\n \"def test_documentation_popxl_nested_code_loading(self):\\n filename = \\\"code_loading_nested.py\\\"\\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)\",\n \"def test_load_testcase(self):\\n tests = self.loader.load(\\\"tests.sampletest.hellotest.HelloTest\\\")\\n self.assertEqual(len(tests), 1)\\n from tests.sampletest.hellotest import HelloTest\\n\\n self.assertEqual(type(tests[0]), HelloTest)\",\n \"def test():\\n import doctest\\n from . import locate\\n return doctest.testmod(locate)\",\n \"def test_main():\\n # Setup\\n # Exercise\\n # Verify\",\n \"def __main() :\\n launchTests()\",\n \"def runtest(self):\",\n \"def test_lint(self):\\n l = self.l\\n l.loadTestsFromTestCase\\n l.loadTestsFromModule\\n l.loadTestsFromName\\n l.loadTestsFromNames\",\n \"def test():\\n pass\",\n \"def test_pep8_conformance_example(self):\\n\\n print(\\\"\\\\r\\\\n\\\")\\n\\n # Get the path to current directory\\n path = os.path.dirname(os.path.realpath(__file__))\\n path += \\\"/../docs/examples/\\\"\\n\\n # Find all the examples files\\n file_paths = []\\n for root, dirnames, filenames in os.walk(path):\\n for file_path in fnmatch.filter(filenames, '*.py'):\\n file_paths.append(os.path.join(root, file_path))\\n\\n for path in file_paths:\\n self.run_check(path)\",\n \"def test(self):\\n pass\",\n \"def tests():\",\n \"def test():\\n loader = unittest.TestLoader()\\n suite = loader.discover(os.path.dirname(__file__))\\n runner = unittest.TextTestRunner()\\n runner.run(suite)\",\n \"def test():\\n import unittest\\n\\n tests = unittest.TestLoader().discover(\\\"tests\\\")\\n unittest.TextTestRunner(verbosity=2).run(tests)\",\n \"def test_functional(self):\\n with sphinx_build('pyexample'):\\n with open('_build/text/docfx_yaml/example.example.yml') as yml_file:\\n data = yaml.safe_load(yml_file)\\n self.assertEqual(\\n data['items'][0]['fullName'],\\n 'example.example'\\n )\",\n \"def setUp(self):\\n self.example = Example()\",\n \"def test():\\n import unittest\\n tests = unittest.TestLoader().discover('tests')\\n unittest.TextTestRunner(verbosity=2).run(tests)\",\n \"def test_examples(fname):\\n app = use_app()\\n app.start_timer(0, app.quit)\\n if \\\"OLD\\\" in fname:\\n with pytest.warns(FutureWarning):\\n runpy.run_path(fname)\\n else:\\n try:\\n runpy.run_path(fname)\\n except ImportError as e:\\n if \\\"Numpy required to use images\\\" in str(e):\\n pytest.skip(\\\"numpy unavailable: skipping image example\\\")\",\n \"def test_sample():\\n print(os.getcwd())\\n run_validator(SAMPLE_FILE_PATH)\",\n \"def test_examples():\\n argv = [\\\"py.test\\\", \\\"-examples\\\"]\\n assert get_sargs(argv) is None\",\n \"def main():\\n print(\\\"It works!!! ;-)\\\")\\n ###TODO### do something with the various methods/functions of this file\",\n \"def main():\\n doctest.testmod()\\n game()\",\n \"def runTest(self):\\r\\n self.setUp()\\r\\n self.test_sceneImport24281()\",\n \"def test_CL04_import(): \\n\\tdef test(): \\n\\t\\ttry: \\n\\t\\t\\tfrom .. import CL04 \\n\\t\\texcept: \\n\\t\\t\\treturn False \\n\\t\\treturn True \\n\\treturn [\\\"vice.yields.ccsne.CL04\\\", test]\",\n \"def test():\\n import unittest\\n tests = unittest.TestLoader().discover(tests)\\n unittest.TextTestRunner(verbosity=2).run(tests)\",\n \"def test_example(self):\\n self.assertEqual(self.example.get_example(), True)\",\n \"def runTest(self):\\n self.setUp()\\n self.test_JupyterNotebooks1()\",\n \"def test():\\r\\n import unittest\\r\\n tests=unittest.TestLoader().discover('tests')\\r\\n unittest.TextTestRunner(verbosity=2).run(tests)\",\n \"def test_examples_for_real(platforms, path):\\n if 'TRAVIS' in os.environ:\\n return #: Doesn't work on travis\\n\\n #: Pretty hackish but whatever\\n prepare_new_app(config)\\n\\n #: Load the code\\n dir_path = os.path.abspath(os.path.split(os.path.dirname(__file__))[0])\\n enaml_file = os.path.join(dir_path, 'examples', os.path.normpath(path))\\n\\n with open(enaml_file, 'rb') as f:\\n source = f.read()\\n\\n #: Trigger a reload\\n r = requests.post(\\\"http://localhost:8888/\\\", json={\\n \\\"type\\\": \\\"reload\\\",\\n \\\"files\\\": {'view.enaml': source},\\n }).json()\\n assert r['ok'], \\\"Failed to reload {}!\\\".format(enaml_file)\\n\\n #: TODO need a way to know when everything is done...\\n #: should read the log unil it stops\\n time.sleep(5)\\n #: Flush logcat\\n\\n #: Save it\\n stats = parse_stats(sh.adb('logcat', '-d'))\\n config['stats'][enaml_file] = stats\\n\\n #: Save it\\n data = json.dumps(config,indent=2)\\n with open('tmp/stats.json', 'w') as f:\\n f.write(data)\\n\\n #: TODO: Now compare it to the baseline\",\n \"def test():\\n tests = unittest.TestLoader().discover('tests')\\n unittest.TextTestRunner(verbosity=2).run(tests)\",\n \"def test():\\n tests = unittest.TestLoader().discover('tests')\\n unittest.TextTestRunner(verbosity=2).run(tests)\",\n \"def test():\\n tests = unittest.TestLoader().discover('tests')\\n unittest.TextTestRunner(verbosity=2).run(tests)\",\n \"def test():\\r\\n import unittest\\r\\n tests = unittest.TestLoader().discover('tests')\\r\\n unittest.TextTestRunner(verbosity=2).run(tests)\",\n \"def test():\\r\\n import unittest\\r\\n tests = unittest.TestLoader().discover('tests')\\r\\n unittest.TextTestRunner(verbosity=2).run(tests)\",\n \"def Cpp_test():\\n pass\",\n \"def test():\\n import unittest\\n\\n tests = unittest.TestLoader().discover('tests')\\n unittest.TextTestRunner(verbosity=2).run(tests)\",\n \"def test():\\n import unittest\\n tests = unittest.TestLoader().discover('tests')\\n unittest.TextTestRunner(verbosity=2).run(tests)\",\n \"def test():\\n import unittest\\n tests = unittest.TestLoader().discover('tests')\\n unittest.TextTestRunner(verbosity=2).run(tests)\",\n \"def test():\\n import unittest\\n tests = unittest.TestLoader().discover('tests')\\n unittest.TextTestRunner(verbosity=2).run(tests)\",\n \"def test():\\n import unittest\\n tests = unittest.TestLoader().discover('tests')\\n unittest.TextTestRunner(verbosity=2).run(tests)\",\n \"def test():\\n import unittest\\n tests = unittest.TestLoader().discover('tests')\\n unittest.TextTestRunner(verbosity=2).run(tests)\",\n \"def test():\\n import unittest\\n tests = unittest.TestLoader().discover('tests')\\n unittest.TextTestRunner(verbosity=2).run(tests)\",\n \"def test():\\n import unittest\\n tests = unittest.TestLoader().discover('tests')\\n unittest.TextTestRunner(verbosity=2).run(tests)\",\n \"def test():\\n import unittest\\n tests = unittest.TestLoader().discover('tests')\\n unittest.TextTestRunner(verbosity=2).run(tests)\",\n \"def test():\\n import unittest\\n tests = unittest.TestLoader().discover('tests')\\n unittest.TextTestRunner(verbosity=2).run(tests)\",\n \"def test():\\n import unittest\\n tests = unittest.TestLoader().discover('tests')\\n unittest.TextTestRunner(verbosity=2).run(tests)\",\n \"def test():\\n import unittest\\n tests = unittest.TestLoader().discover('tests')\\n unittest.TextTestRunner(verbosity=2).run(tests)\",\n \"def test():\\n import unittest\\n tests = unittest.TestLoader().discover('tests')\\n unittest.TextTestRunner(verbosity=2).run(tests)\",\n \"def test():\\n import unittest\\n tests = unittest.TestLoader().discover('tests')\\n unittest.TextTestRunner(verbosity=2).run(tests)\",\n \"def test():\\n import unittest\\n tests = unittest.TestLoader().discover('tests')\\n unittest.TextTestRunner(verbosity=2).run(tests)\",\n \"def test():\\n import unittest\\n\\n tests = unittest.TestLoader().discover(\\\"tests\\\")\\n unittest.TextTestRunner(verbosity=2).run(tests)\",\n \"def _test():\\n import doctest\\n doctest.testmod(verbose=1)\",\n \"def test_CL13_import(): \\n\\tdef test(): \\n\\t\\ttry: \\n\\t\\t\\tfrom .. import CL13 \\n\\t\\texcept: \\n\\t\\t\\treturn False \\n\\t\\treturn True \\n\\treturn [\\\"vice.yields.ccsne.CL13\\\", test]\",\n \"def _test():\\n import doctest\\n doctest.testmod()\",\n \"def _test():\\n import doctest\\n doctest.testmod()\",\n \"def test(): \\n\\treturn [\\\"vice.yields.ccsne.import\\\", \\n\\t\\t[ \\n\\t\\t\\ttest_LC18_import(), \\n\\t\\t\\ttest_CL13_import(), \\n\\t\\t\\ttest_CL04_import(), \\n\\t\\t\\ttest_WW95_import(), \\n\\t\\t\\ttest_NKT13_import(), \\n\\t\\t\\ttest_S16_import() \\n\\t\\t] \\n\\t]\",\n \"def main():\\n run_test_all()\",\n \"def _test(): # pragma: no cover\\r\\n print('Starting doctest')\\r\\n doctest.testmod()\\r\\n print('Completed doctest')\",\n \"def unitary_test():\",\n \"def test():\",\n \"def test():\",\n \"def test_LC18_import(): \\n\\tdef test(): \\n\\t\\ttry: \\n\\t\\t\\tfrom .. import LC18 \\n\\t\\texcept: \\n\\t\\t\\treturn False \\n\\t\\treturn True \\n\\treturn [\\\"vice.yields.ccsne.LC18\\\", test]\",\n \"def test():\\n import unittest\\n tests = unittest \\n tests = unittest.TestLoader().discover('tests')\\n unittest.TextTestRunner(verbosity=2).run(tests)\",\n \"def test_load(self):\\n command = constituencies.Command()\\n command.handle('load', silent=True)\",\n \"def test_loading_document(self):\",\n \"def test_module_doc():\\r\\n\\r\\n for fname in os.listdir('.'):\\r\\n if fname.endswith('.py'):\\r\\n f = fname.split('.')[0]\\r\\n print 'Executing ', fname\\r\\n execfile(fname, locals())\",\n \"def test_load_testcase_in_module(self):\\n tests = self.loader.load(\\\"tests.sampletest.InitTest\\\")\\n self.assertEqual(len(tests), 1)\\n from tests.sampletest import InitTest\\n\\n self.assertEqual(type(tests[0]), InitTest)\",\n \"def fixture_example_data():\\n import_example_data()\",\n \"def main(source):\\n pass\",\n \"def test_execute_code_sample():\\n hello = Hello()\\n assert hello.out() == 'Hello, world!'\",\n \"def test_get_run(self):\\n pass\",\n \"def test_library_and_test_code(self):\\n\\n def do_check(path):\\n \\\"\\\"\\\"The contents of the .iml file should certain sourceFolder entries:\\n\\n <sourceFolder url=\\\".../testprojects/tests/java/org/pantsbuild/testproject/ideatestsandlib\\\" isTestSource=\\\"true\\\" />\\n \\\"\\\"\\\"\\n found = set()\\n iml_file = os.path.join(path, 'project.iml')\\n self.assertTrue(os.path.exists(iml_file))\\n dom = minidom.parse(iml_file)\\n for sourceFolder in self._get_sourceFolders(dom):\\n url = sourceFolder.getAttribute('url')\\n is_test_source = sourceFolder.getAttribute('isTestSource')\\n type_attr = sourceFolder.getAttribute('type')\\n url = re.sub(r'^.*/testprojects/', 'testprojects/', url)\\n found.add(url)\\n if url == 'testprojects/tests/java/org/pantsbuild/testproject/ideatestsandlib':\\n self.assertEquals('', type_attr)\\n self.assertEquals('True', is_test_source)\\n\\n self.assertEquals(set([\\n 'testprojects/tests/java/org/pantsbuild/testproject/ideatestsandlib',\\n ]), found)\\n\\n self._idea_test([\\n 'testprojects/tests/java/org/pantsbuild/testproject/ideatestsandlib::'\\n ], check_func=do_check)\",\n \"def lua_test():\\n pass\",\n \"def test_NKT13_import(): \\n\\tdef test(): \\n\\t\\ttry: \\n\\t\\t\\tfrom .. import NKT13 \\n\\t\\texcept: \\n\\t\\t\\treturn False \\n\\t\\treturn True \\n\\treturn [\\\"vice.yields.ccsne.NKT13\\\", test]\",\n \"def test_begin(self):\",\n \"def test_basic_execution(self):\",\n \"def runTests(self):\\n \\n pass\",\n \"def __test__():\\n#-------------------------------------------------------------------------------\\n import pylib.tester as tester\\n return 0\",\n \"def _test():\\n import doctest\\n return doctest.testmod(verbose=True)\",\n \"def _test():\\n import doctest\\n return doctest.testmod(verbose=True)\",\n \"def test():#Test functions\\n python = ProgrammingLanguage(\\\"Java\\\", \\\"Dynamic\\\", True, 1987)\\n print(python)\",\n \"def run_playground():\\n from .playground import playground\\n playground()\",\n \"def test_compute_glycemic_load(self):\\n pass\",\n \"def main():\\n\\n print(\\\"=\\\" * 80)\\n print(\\\"DATA STRUCTURE TESTS\\\")\\n test_module(structs.tests)\\n test_module(structs.regularization)\\n\\n print(\\\"=\\\" * 80)\\n print(\\\"END-TO-END TESTS\\\")\\n test_module(globals())\",\n \"def startTestRun(self):\",\n \"def test_py_compile_basic(self):\\n self._test_py_compile('basic')\",\n \"def test(self):\",\n \"def test(self):\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7195194","0.7059802","0.6890149","0.6869557","0.6869557","0.6869557","0.6869557","0.67875314","0.6767249","0.6749942","0.67483723","0.67235684","0.6716485","0.6689319","0.66765344","0.66539055","0.66304076","0.6627514","0.65707266","0.6563701","0.6548684","0.65423787","0.6523631","0.6505159","0.6482269","0.6477886","0.64554393","0.6444179","0.64405406","0.6438618","0.64318645","0.6426611","0.6425933","0.6419005","0.63982403","0.637443","0.6365926","0.6361171","0.63493687","0.63487035","0.63462895","0.6342258","0.6342258","0.6342258","0.6337438","0.6337438","0.6331677","0.6327746","0.63256466","0.63256466","0.63256466","0.63256466","0.63256466","0.63256466","0.63256466","0.63256466","0.63256466","0.63256466","0.63256466","0.63256466","0.63256466","0.63256466","0.6324298","0.6318961","0.63129354","0.62995124","0.62995124","0.62937677","0.62843126","0.62792987","0.626754","0.62627316","0.62627316","0.625056","0.6241495","0.62375814","0.6233974","0.6232294","0.62254655","0.6212669","0.62099594","0.62049687","0.62000775","0.6196725","0.6179676","0.6176563","0.61540693","0.6145558","0.6138003","0.61320925","0.61221206","0.61221206","0.6121171","0.61206573","0.6116493","0.61063963","0.6105194","0.6101723","0.60957116","0.60957116"],"string":"[\n \"0.7195194\",\n \"0.7059802\",\n \"0.6890149\",\n \"0.6869557\",\n \"0.6869557\",\n \"0.6869557\",\n \"0.6869557\",\n \"0.67875314\",\n \"0.6767249\",\n \"0.6749942\",\n \"0.67483723\",\n \"0.67235684\",\n \"0.6716485\",\n \"0.6689319\",\n \"0.66765344\",\n \"0.66539055\",\n \"0.66304076\",\n \"0.6627514\",\n \"0.65707266\",\n \"0.6563701\",\n \"0.6548684\",\n \"0.65423787\",\n \"0.6523631\",\n \"0.6505159\",\n \"0.6482269\",\n \"0.6477886\",\n \"0.64554393\",\n \"0.6444179\",\n \"0.64405406\",\n \"0.6438618\",\n \"0.64318645\",\n \"0.6426611\",\n \"0.6425933\",\n \"0.6419005\",\n \"0.63982403\",\n \"0.637443\",\n \"0.6365926\",\n \"0.6361171\",\n \"0.63493687\",\n \"0.63487035\",\n \"0.63462895\",\n \"0.6342258\",\n \"0.6342258\",\n \"0.6342258\",\n \"0.6337438\",\n \"0.6337438\",\n \"0.6331677\",\n \"0.6327746\",\n \"0.63256466\",\n \"0.63256466\",\n \"0.63256466\",\n \"0.63256466\",\n \"0.63256466\",\n \"0.63256466\",\n \"0.63256466\",\n \"0.63256466\",\n \"0.63256466\",\n \"0.63256466\",\n \"0.63256466\",\n \"0.63256466\",\n \"0.63256466\",\n \"0.63256466\",\n \"0.6324298\",\n \"0.6318961\",\n \"0.63129354\",\n \"0.62995124\",\n \"0.62995124\",\n \"0.62937677\",\n \"0.62843126\",\n \"0.62792987\",\n \"0.626754\",\n \"0.62627316\",\n \"0.62627316\",\n \"0.625056\",\n \"0.6241495\",\n \"0.62375814\",\n \"0.6233974\",\n \"0.6232294\",\n \"0.62254655\",\n \"0.6212669\",\n \"0.62099594\",\n \"0.62049687\",\n \"0.62000775\",\n \"0.6196725\",\n \"0.6179676\",\n \"0.6176563\",\n \"0.61540693\",\n \"0.6145558\",\n \"0.6138003\",\n \"0.61320925\",\n \"0.61221206\",\n \"0.61221206\",\n \"0.6121171\",\n \"0.61206573\",\n \"0.6116493\",\n \"0.61063963\",\n \"0.6105194\",\n \"0.6101723\",\n \"0.60957116\",\n \"0.60957116\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.74833703"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":6586,"cells":{"query":{"kind":"string","value":"Test the nested code loading example"},"document":{"kind":"string","value":"def test_documentation_popxl_nested_code_loading(self):\n filename = \"code_loading_nested.py\"\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)"},"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 test_example(decorated_example):\n import visual_coding_2p_analysis","def inner_test():\n pass","def inner_test():\n pass","def test_documentation_popxl_code_loading(self):\n filename = \"code_loading.py\"\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)","def test_lint(self):\n l = self.l\n l.loadTestsFromTestCase\n l.loadTestsFromModule\n l.loadTestsFromName\n l.loadTestsFromNames","def test_CL04_import(): \n\tdef test(): \n\t\ttry: \n\t\t\tfrom .. import CL04 \n\t\texcept: \n\t\t\treturn False \n\t\treturn True \n\treturn [\"vice.yields.ccsne.CL04\", test]","def test_load_testcase(self):\n tests = self.loader.load(\"tests.sampletest.hellotest.HelloTest\")\n self.assertEqual(len(tests), 1)\n from tests.sampletest.hellotest import HelloTest\n\n self.assertEqual(type(tests[0]), HelloTest)","def test_loading_document(self):","def test_NKT13_import(): \n\tdef test(): \n\t\ttry: \n\t\t\tfrom .. import NKT13 \n\t\texcept: \n\t\t\treturn False \n\t\treturn True \n\treturn [\"vice.yields.ccsne.NKT13\", test]","def test_simple(self):\n bento_info = \"\"\"\\\nName: foo\n\nLibrary:\n Packages: foo, foo.bar\n Modules: fubar\n\"\"\"\n self._test_run(bento_info)","def test_module(self):\n pass","def test_CL13_import(): \n\tdef test(): \n\t\ttry: \n\t\t\tfrom .. import CL13 \n\t\texcept: \n\t\t\treturn False \n\t\treturn True \n\treturn [\"vice.yields.ccsne.CL13\", test]","def _test():\n import doctest","def test_001(settings, inspector):\n sourcepath = os.path.join(settings.sample_path, 'main_basic.scss')\n\n inspector.inspect(sourcepath)\n\n inspector.reset()\n\n assert inspector._CHILDREN_MAP == {}\n assert inspector._PARENTS_MAP == {}\n assert inspector.children(sourcepath) == set([])\n assert inspector.parents(sourcepath) == set([])","def test_LC18_import(): \n\tdef test(): \n\t\ttry: \n\t\t\tfrom .. import LC18 \n\t\texcept: \n\t\t\treturn False \n\t\treturn True \n\treturn [\"vice.yields.ccsne.LC18\", test]","def main():\n\n print(\"=\" * 80)\n print(\"DATA STRUCTURE TESTS\")\n test_module(structs.tests)\n test_module(structs.regularization)\n\n print(\"=\" * 80)\n print(\"END-TO-END TESTS\")\n test_module(globals())","def test_module(self):\n data = (\n os.path.join(\n _CURRENT_DIRECTORY,\n \"fake_project\",\n \"_modules\",\n \"fake_project\",\n \"nested_folder\",\n \"another.html\",\n ),\n \"\",\n )\n\n content = self._get_fake_project_nested_module()\n\n expected = textwrap.dedent(\n '''\\\n #!/usr/bin/env python\n # -*- coding: utf-8 -*-\n\n \"\"\"A module that shows every type of documentable class / method / function.\n\n Attributes:\n ATTRIBUTE_VALUE (float):\n Some number.\n\n \"\"\"\n\n\n ATTRIBUTE_VALUE = 14.3\n\n\n class MyKlass(object):\n \"\"\"A class that does something.\n\n Multi-line information here.\n\n Attributes:\n attribute_value (str):\n Some string.\n\n \"\"\"\n\n attribute_value = \"asdfasdf\"\n\n def __init__(self, value):\n \"\"\"Create this instance.\"\"\"\n # A comment that should show up in the unittest's results\n super(MyKlass, self).__init__()\n\n @staticmethod\n def get_staticmethod():\n \"\"\"int: Get some value.\"\"\"\n return 8\n\n @classmethod\n def get_classmethod(cls):\n \"\"\"int: Get some value.\"\"\"\n return 8\n\n def get_method(self):\n \"\"\"int: Get some value.\"\"\"\n return 8\n\n\n class ParentClass(object):\n \"\"\"The outter class.\n\n Attributes:\n attribute_value (str):\n Some string.\n\n \"\"\"\n\n attribute_value = \"tttt\"\n\n class NestedClass(object):\n \"\"\"A class within a class.\n\n Attributes:\n attribute_value (str):\n Some string.\n\n \"\"\"\n\n attribute_value = \"zzzzzzzzzzzzz\"\n\n @staticmethod\n def get_staticmethod():\n \"\"\"int: Get some value.\"\"\"\n return 5\n\n @classmethod\n def get_classmethod(cls):\n \"\"\"int: Get some value.\"\"\"\n return 5\n\n def get_method(self):\n \"\"\"int: Get some value.\"\"\"\n return 5\n\n @staticmethod\n def get_staticmethod():\n \"\"\"int: Get some value.\"\"\"\n return 6\n\n @classmethod\n def get_classmethod(cls):\n \"\"\"int: Get some value.\"\"\"\n return 6\n\n def get_method(self):\n \"\"\"int: Get some value.\"\"\"\n return 6\n\n\n def _set_private_function_thing(value, another):\n \"\"\"Do something here.\"\"\"\n # Do something with these values\n # and more comment text, here.\n #\n if value:\n return 2\n\n # Another comment\n return 1\n\n\n def set_function_thing(value, another):\n \"\"\"Do something here.\"\"\"\n # Do something with these values\n # and more comment text, here.\n #\n if value:\n return 2\n\n # Another comment\n return 1'''\n )\n\n self._test(data, content, expected) # pylint: disable=no-value-for-parameter","def test_documentation_popxl_nested_session_contexts(self):\n filename = \"nested_session_contexts.py\"\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)","def test(self):\n pass","def test_load_testcase_in_module(self):\n tests = self.loader.load(\"tests.sampletest.InitTest\")\n self.assertEqual(len(tests), 1)\n from tests.sampletest import InitTest\n\n self.assertEqual(type(tests[0]), InitTest)","def test_documentation_popxl_basic_subgraph(self):\n filename = \"basic_graph.py\"\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)","def test_load_simple_module():\n loader = Loader()\n main_fname = loader.load(\"https://gist.githubusercontent.com/miohtama/80391980c2e73b285cfe/raw/dd89a55497ba33a6014453d9bb7432ab424c01cf/kivyhello.py#main\")\n mod = path_to_mod_name(main_fname)\n result = loader.run(mod, \"hello\")\n assert result == \"Hello there\"\n loader.close()","def test_main():\n # Setup\n # Exercise\n # Verify","def fixture_example_data():\n import_example_data()","def test_001_basic(settings, inspector):\n sources = [\n os.path.join(settings.sample_path, 'main_basic.scss'),\n os.path.join(settings.sample_path, 'main_depth_import-3.scss'),\n os.path.join(settings.sample_path, 'main_with_subimports.scss'),\n os.path.join(settings.sample_path, 'main_using_libs.scss'),\n ]\n sourcepath = os.path.join(settings.sample_path, 'main_basic.scss')\n\n inspector.inspect(*sources, library_paths=settings.libraries_fixture_paths)\n\n parents = inspector.parents(sourcepath)\n assert parents == set([\n os.path.join(settings.sample_path, 'main_depth_import-1.scss'),\n os.path.join(settings.sample_path, 'main_depth_import-2.scss'),\n os.path.join(settings.sample_path, 'main_depth_import-3.scss'),\n os.path.join(settings.sample_path, 'main_with_subimports.scss'),\n os.path.join(settings.sample_path, 'main_using_libs.scss'),\n ])","def test(self):\n # -- Test --\n\n # (1)\n\n # (2)\n\n # (3)\n\n # (4)\n # -- Test --","def runtest(self):","def test_compute_glycemic_load(self):\n pass","def test_functional(self):\n with sphinx_build('pyexample'):\n with open('_build/text/docfx_yaml/example.example.yml') as yml_file:\n data = yaml.safe_load(yml_file)\n self.assertEqual(\n data['items'][0]['fullName'],\n 'example.example'\n )","def test_pep8_conformance_example(self):\n\n print(\"\\r\\n\")\n\n # Get the path to current directory\n path = os.path.dirname(os.path.realpath(__file__))\n path += \"/../docs/examples/\"\n\n # Find all the examples files\n file_paths = []\n for root, dirnames, filenames in os.walk(path):\n for file_path in fnmatch.filter(filenames, '*.py'):\n file_paths.append(os.path.join(root, file_path))\n\n for path in file_paths:\n self.run_check(path)","def test_include():\n from bst import BST","def _test(self):","def _test(self):","def _test(self):","def _test(self):","def _test(self):","def test_load_case_objects(cli_args_fixture, tmp_path):\n # cli_args_fixture fixture is called but not used simply in order to mock it in render_json.dump()\n tmp_file = os.path.join(tmp_path, 'stub-load.json')\n # - protocol\n proto_ref, proto_name, proto_blocking, proto_is_database = ('foo', 'bar', True, False)\n # - crawl strategy\n cs_description, cs_name, cs_providers, cs_provider_args, cs_child_provider, cs_filter, cs_rewrites = \\\n ('foo', 'bar', ['baz'], {'buzz': 'buzzbuzz'}, {'qux': True}, {'quux': True}, {'quz': True})\n # - node\n node_ref, node_prov, node_mux, node_hint, node_address, node_service_name, node_children, node_warn, node_err = \\\n ('a_ref', 'a_prov', 'a_mux', True, 'an_add', 'a_name', {'foo': 'child'}, {'bar': True}, {'baz': True})\n stub_json = f\"\"\"\n{{\n \"args\": {{\n \"max_depth\": 0,\n \"skip_nonblocking_grandchildren\": false\n }},\n \"tree\": {{\n \"{node_ref}\": {{\n \"__type__\": \"Node\",\n \"provider\": \"{node_prov}\",\n \"protocol_mux\": \"{node_mux}\",\n \"from_hint\": {str(node_hint).lower()},\n \"address\": \"{node_address}\",\n \"service_name\": \"{node_service_name}\",\n \"children\": {json.dumps(node_children)},\n \"warnings\": {json.dumps(node_warn)},\n \"errors\": {json.dumps(node_err)},\n \"crawl_strategy\": {{\n \"__type__\": \"CrawlStrategy\",\n \"description\": \"{cs_description}\",\n \"name\": \"{cs_name}\",\n \"providers\": {json.dumps(cs_providers)},\n \"provider_args\": {json.dumps(cs_provider_args)},\n \"child_provider\": {json.dumps(cs_child_provider)},\n \"service_name_filter\": {json.dumps(cs_filter)},\n \"service_name_rewrites\": {json.dumps(cs_rewrites)},\n \"protocol\": {{\n \"__type__\": \"Protocol\",\n \"ref\": \"{proto_ref}\",\n \"name\": \"{proto_name}\",\n \"blocking\": {str(proto_blocking).lower()},\n \"is_database\": {str(proto_is_database).lower()}\n }}\n }},\n \"protocol\": {{\n \"__type__\": \"Protocol\",\n \"ref\": \"{proto_ref}\",\n \"name\": \"{proto_name}\",\n \"blocking\": {str(proto_blocking).lower()},\n \"is_database\": {str(proto_is_database).lower()}\n }}\n }}\n }}\n}}\n\"\"\"\n with open(tmp_file, 'w') as f:\n f.write(stub_json)\n\n # act\n tree = render_json.load(tmp_file)\n\n # assert\n # - Node()\n assert isinstance(tree[node_ref], node.Node)\n loaded_node = tree[node_ref]\n assert loaded_node.provider == node_prov\n assert loaded_node.protocol_mux == node_mux\n assert loaded_node.from_hint == node_hint\n assert loaded_node.address == node_address\n assert loaded_node.service_name == node_service_name\n assert loaded_node.children == node_children\n assert loaded_node.warnings == node_warn\n assert loaded_node.errors == node_err\n # - CrawlStrategy()\n assert isinstance(loaded_node.crawl_strategy, charlotte.CrawlStrategy)\n loaded_cs = loaded_node.crawl_strategy\n assert loaded_cs.description == cs_description\n assert loaded_cs.name == cs_name\n assert loaded_cs.providers == cs_providers\n assert loaded_cs.provider_args == cs_provider_args\n assert loaded_cs.child_provider == cs_child_provider\n assert loaded_cs.service_name_filter == cs_filter\n assert loaded_cs.service_name_rewrites == cs_rewrites\n # - Protocol\n assert isinstance(loaded_cs.protocol, charlotte_web.Protocol)\n loaded_protocol = loaded_cs.protocol\n assert loaded_protocol.ref == proto_ref\n assert loaded_protocol.name == proto_name\n assert loaded_protocol.blocking == proto_blocking\n assert loaded_protocol.is_database == proto_is_database\n assert isinstance(loaded_node.protocol, charlotte_web.Protocol)","def runTest(self):\r\n self.setUp()\r\n self.test_sceneImport24281()","def test():\n pass","def test_examples(self):\n with sphinx_build('pyexample'):\n with open('_build/text/docfx_yaml/example.nap.Base.yml') as yml_file:\n data = yaml.safe_load(yml_file)\n for item in data['items']:\n if item['uid'] == 'example.nap.Base.ref':\n self.assertEqual(\n item['example'].split('\\n')[2],\n \"\"\">>> print('docblock 1')\"\"\"\n )\n self.assertEqual(\n item['example'].split('\\n')[7],\n \"\"\">>> print('docblock 2')\"\"\"\n )","def test_loaders():\n\n tempdir = tempfile.mkdtemp()\n\n loader = \"\"\"\nfrom mindbender import api\n\nclass DemoLoader(api.Loader):\n def process(self, asset, subset, version, representation):\n pass\n\n\"\"\"\n\n with open(os.path.join(tempdir, \"my_loader.py\"), \"w\") as f:\n f.write(loader)\n\n try:\n pipeline.register_loaders_path(tempdir)\n loaders = pipeline.discover_loaders()\n\n assert \"DemoLoader\" in list(\n L.__name__ for L in loaders\n ), \"Loader not found in %s\" % \", \".join(\n l.__name__ for l in loaders)\n\n finally:\n shutil.rmtree(tempdir)","def unitary_test():","def test_get_run(self):\n pass","def test_component_resolution_different_file():\n\n assert snippet_eval(ComponentSnippet(modulea.ComponentResolutionViaModule())) == \"hi from module b\\n\"","def main():\n example()","def main():\n example()","def main():\n example()","def main():\n example()","def test_Tree():","def tests():","def test_basic_execution(self):","def test_module_doc():\r\n\r\n for fname in os.listdir('.'):\r\n if fname.endswith('.py'):\r\n f = fname.split('.')[0]\r\n print 'Executing ', fname\r\n execfile(fname, locals())","def test(self, parent, block):\r\n pass","def test_2():","def _test(self):\n pass","def _test(self):\n pass","def _test(self):\n pass","def test_3():","def test_003_library(settings, inspector):\n sources = [\n os.path.join(settings.sample_path, 'main_syntax.scss'),\n os.path.join(settings.sample_path, 'main_commented.scss'),\n os.path.join(settings.sample_path, 'main_basic.scss'),\n os.path.join(settings.sample_path, 'main_depth_import-1.scss'),\n os.path.join(settings.sample_path, 'main_depth_import-2.scss'),\n os.path.join(settings.sample_path, 'main_depth_import-3.scss'),\n os.path.join(settings.sample_path, 'main_with_subimports.scss'),\n os.path.join(settings.sample_path, 'main_using_libs.scss'),\n os.path.join(settings.sample_path, 'main_circular_0.scss'),\n os.path.join(settings.sample_path, 'main_circular_1.scss'),\n os.path.join(settings.sample_path, 'main_circular_2.scss'),\n os.path.join(settings.sample_path, 'main_circular_3.scss'),\n os.path.join(settings.sample_path, 'main_circular_4.scss'),\n os.path.join(settings.sample_path, 'main_circular_bridge.scss'),\n os.path.join(settings.sample_path, 'main_circular_5.scss'),\n ]\n sourcepath = os.path.join(settings.lib1_path, 'components/_panels.scss')\n\n inspector.inspect(*sources, library_paths=settings.libraries_fixture_paths)\n\n parents = inspector.parents(sourcepath)\n assert parents == set([\n os.path.join(settings.lib1_path, 'library_1_fullstack.scss'),\n os.path.join(settings.sample_path, 'main_using_libs.scss'),\n ])","def test():\n import doctest\n from . import locate\n return doctest.testmod(locate)","def test(self):","def test(self):","def test_simple_extension(self):\n bento_info = \"\"\"\\\nName: foo\n\nLibrary:\n Packages: foo, foo.bar\n Modules: fubar\n Extension: foo\n Sources: src/foo.c\n\"\"\"\n self._test_run(bento_info)","def test_example(self):\n self.assertEqual(self.example.get_example(), True)","def test_4():","def test():","def test():","def test_partial_twice_dependent_object_import(self):\n pass","def test_subsystems(self):\n pass","def test_examples():\n import airconics\n # pytest runs test files in ./__pycache__: need to go up two levels\n example_dir = os.path.abspath(\n os.path.join(__file__, '..', '..', 'examples', 'core'))\n example_scripts = os.listdir(example_dir)\n for script in example_scripts:\n if script.endswith('.py'):\n fname = os.path.join(example_dir, script)\n try:\n subprocess.check_call(['python', fname])\n except subprocess.CalledProcessError:\n raise AssertionError('Example {} failed'.format(fname))","def test_nested_template_source_generation(self):\n sources = [source for source in self.loader.get_template_sources('component.child.html')]\n self.assertEqual(len(sources), 2)\n self.assertEqual(sources[0], 'MOCK_BASE_DIR/component/child/child.html')\n self.assertEqual(sources[1], 'MOCK_BASE_DIR_2/component/child/child.html')\n\n sources = [source for source in self.loader.get_template_sources('deeply.nested.component.and.child.html')]\n self.assertEqual(len(sources), 2)\n self.assertEqual(sources[0], 'MOCK_BASE_DIR/deeply/nested/component/and/child/child.html')\n self.assertEqual(sources[1], 'MOCK_BASE_DIR_2/deeply/nested/component/and/child/child.html')\n\n sources = [source for source in self.loader.get_template_sources('component.child/another.html')]\n self.assertEqual(len(sources), 2)\n self.assertEqual(sources[0], 'MOCK_BASE_DIR/component/child/another.html')\n self.assertEqual(sources[1], 'MOCK_BASE_DIR_2/component/child/another.html')","def test_load_model_data(self):\n add_components_and_load_data(\n prereq_modules=IMPORTED_PREREQ_MODULES,\n module_to_test=MODULE_BEING_TESTED,\n test_data_dir=TEST_DATA_DIRECTORY,\n subproblem=\"\",\n stage=\"\",\n )","def test_load_model_data(self):\n add_components_and_load_data(\n prereq_modules=IMPORTED_PREREQ_MODULES,\n module_to_test=MODULE_BEING_TESTED,\n test_data_dir=TEST_DATA_DIRECTORY,\n subproblem=\"\",\n stage=\"\",\n )","def test_load(self):\n command = constituencies.Command()\n command.handle('load', silent=True)","def test_T01():","def main():\n doctest.testmod()\n game()","def test_bed(self):\n #TODO write bed tests","def test_1():","def less_nested_example_vanilla():\n return","def test_S16_import(): \n\tdef test(): \n\t\ttry: \n\t\t\tfrom .. import S16 \n\t\texcept: \n\t\t\treturn False \n\t\treturn True \n\treturn [\"vice.yields.ccsne.S16\", test]","def test_get_imports(self):\n pass","def test_example_runs(self):\n run_example(\n verbose=False,\n testapp=self.testapp,\n )","def test_twice_dependent_object_import(self):\n pass","def test_definition_loading(self):\r\n\r\n modulestore = XMLModuleStore(DATA_DIR, course_dirs=['toy', 'two_toys'])\r\n\r\n location = Location(\"edX\", \"toy\", \"2012_Fall\", \"video\", \"Welcome\", None)\r\n toy_video = modulestore.get_item(location)\r\n location_two = Location(\"edX\", \"toy\", \"TT_2012_Fall\", \"video\", \"Welcome\", None)\r\n two_toy_video = modulestore.get_item(location_two)\r\n self.assertEqual(toy_video.youtube_id_1_0, \"p2Q6BrNhdh8\")\r\n self.assertEqual(two_toy_video.youtube_id_1_0, \"p2Q6BrNhdh9\")","def test_read_namespaced_build(self):\n pass","def test_load(self):\n (spec, check) = bundylogging.load()\n # It returns the checking function\n self.assertEqual(check, bundylogging.check)\n # The plugin stores it's spec\n self.assertEqual(spec, bundylogging.spec)","def test_load_file_contents():\n\n file_name = 'test_fooof_all'\n loaded_data = load_json(file_name, TEST_DATA_PATH)\n\n # Check settings\n for setting in OBJ_DESC['settings']:\n assert setting in loaded_data.keys()\n\n # Check results\n for result in OBJ_DESC['results']:\n assert result in loaded_data.keys()\n\n # Check results\n for datum in OBJ_DESC['data']:\n assert datum in loaded_data.keys()","def inner_test(param: dict):\n pass","def test_data_dump_and_load():\n raw_data = {\"A\": 1, \"B\": 2}\n\n # Dump json\n json_file = os.path.join(tempfile.gettempdir(), \"jade-unit-test-file.json\")\n dump_data(raw_data, json_file)\n assert os.path.exists(json_file)\n\n # Load json\n json_data = load_data(json_file)\n assert json_data == raw_data\n\n if os.path.exists(json_file):\n os.remove(json_file)\n\n # Dump toml\n toml_file = os.path.join(tempfile.gettempdir(), \"jade-unit-test-file.toml\")\n dump_data(raw_data, toml_file)\n assert os.path.exists(toml_file)\n\n # Load toml\n toml_data = load_data(toml_file)\n assert toml_data == raw_data\n\n if os.path.exists(toml_file):\n os.remove(toml_file)\n\n # Re-enable if we add support again.\n # Dump yaml\n # yaml_file = os.path.join(tempfile.gettempdir(), \"jade-unit-test-file.yaml\")\n # dump_data(raw_data, yaml_file)\n # assert os.path.exists(yaml_file)\n\n ## Load yaml\n # yaml_data = load_data(yaml_file)\n # assert yaml_data == raw_data\n\n # if os.path.exists(yaml_file):\n # os.remove(yaml_file)","def test_cms_imported_course_walkthrough(self):\r\n def test_get_html(handler):\r\n # Helper function for getting HTML for a page in Studio and\r\n # checking that it does not error.\r\n resp = self.client.get_html(\r\n get_url(handler, course_key, 'course_key_string')\r\n )\r\n self.assertEqual(resp.status_code, 200)\r\n _test_no_locations(self, resp)\r\n\r\n _, course_items = import_from_xml(modulestore('direct'), 'common/test/data/', ['simple'])\r\n course_key = course_items[0].id\r\n\r\n resp = self._show_course_overview(course_key)\r\n self.assertEqual(resp.status_code, 200)\r\n self.assertContains(resp, 'Chapter 2')\r\n\r\n # go to various pages\r\n test_get_html('import_handler')\r\n test_get_html('export_handler')\r\n test_get_html('course_team_handler')\r\n test_get_html('course_info_handler')\r\n test_get_html('checklists_handler')\r\n test_get_html('assets_handler')\r\n test_get_html('tabs_handler')\r\n test_get_html('settings_handler')\r\n test_get_html('grading_handler')\r\n test_get_html('advanced_settings_handler')\r\n test_get_html('textbooks_list_handler')\r\n\r\n # go look at a subsection page\r\n subsection_key = course_key.make_usage_key('sequential', 'test_sequence')\r\n resp = self.client.get_html(get_url('subsection_handler', subsection_key))\r\n self.assertEqual(resp.status_code, 200)\r\n _test_no_locations(self, resp)\r\n\r\n # go look at the Edit page\r\n unit_key = course_key.make_usage_key('vertical', 'test_vertical')\r\n resp = self.client.get_html(get_url('unit_handler', unit_key))\r\n self.assertEqual(resp.status_code, 200)\r\n _test_no_locations(self, resp)\r\n\r\n def delete_item(category, name):\r\n \"\"\" Helper method for testing the deletion of an xblock item. \"\"\"\r\n item_key = course_key.make_usage_key(category, name)\r\n resp = self.client.delete(get_url('xblock_handler', item_key))\r\n self.assertEqual(resp.status_code, 204)\r\n _test_no_locations(self, resp, status_code=204, html=False)\r\n\r\n # delete a component\r\n delete_item(category='html', name='test_html')\r\n\r\n # delete a unit\r\n delete_item(category='vertical', name='test_vertical')\r\n\r\n # delete a unit\r\n delete_item(category='sequential', name='test_sequence')\r\n\r\n # delete a chapter\r\n delete_item(category='chapter', name='chapter_2')","def test_syntax(self):\n\t\ttheResult = False\n\t\ttry:\n\t\t\tfrom .context import code\n\t\t\tfrom code import restart_service\n\t\t\ttheResult = True\n\t\texcept Exception:\n\t\t\ttheResult = False\n\t\tassert theResult","def _test():\n import doctest\n doctest.testmod(verbose=1)","def runTest(self):\n self.setUp()\n self.test_JupyterNotebooks1()","def test_modul1(self):\n\n self.delayDisplay(\"Starting the test\")\n #\n # first, get some data\n #\n import urllib\n downloads = (\n ('http://slicer.kitware.com/midas3/download?items=5767', 'FA.nrrd', slicer.util.loadVolume),\n )\n\n for url,name,loader in downloads:\n filePath = slicer.app.temporaryPath + '/' + name\n if not os.path.exists(filePath) or os.stat(filePath).st_size == 0:\n logging.info('Requesting download %s from %s...\\n' % (name, url))\n urllib.urlretrieve(url, filePath)\n if loader:\n logging.info('Loading %s...' % (name,))\n loader(filePath)\n self.delayDisplay('Finished with download and loading')\n\n volumeNode = slicer.util.getNode(pattern=\"FA\")\n logic = modulLogic()\n self.assertTrue( logic.hasImageData(volumeNode) )\n self.delayDisplay('Test passed!')","def __main() :\n launchTests()","def runTest(self):\n pagename = self._tester.create_wiki_page(content=\"\"\"\n{{{\n#!rst\n.. code-block:: python\n\n print \"123\"\n}}}\n\"\"\")\n self._tester.go_to_wiki(pagename)\n tc.notfind(\"code-block\")\n tc.find('print')\n tc.find('\"123\"')","def test_basic_workflow(self):\n\n test_data_dir = os.path.join(os.path.dirname(__file__), 'testdata')\n sys.path.append(test_data_dir)\n import basic\n tmpdir = tempfile.mkdtemp()\n package_path = os.path.join(tmpdir, 'workflow.tar.gz')\n try:\n compiler.Compiler().compile(basic.save_most_frequent_word, package_path)\n with open(os.path.join(test_data_dir, 'basic.yaml'), 'r') as f:\n golden = yaml.load(f)\n compiled = self._get_yaml_from_tar(package_path)\n\n self.maxDiff = None\n # Comment next line for generating golden yaml.\n self.assertEqual(golden, compiled)\n finally:\n # Replace next line with commented line for gathering golden yaml.\n shutil.rmtree(tmpdir)\n # print(tmpdir)","def test_script(self) -> None:\n main()","def test_quick_build(self):\n pass","def test_dag_load(self):\n\n with ObservatoryEnvironment().create():\n dag_file = os.path.join(module_file_path(\"academic_observatory_workflows.dags\"), \"openalex_telescope.py\")\n self.assert_dag_load(\"openalex\", dag_file)"],"string":"[\n \"def test_example(decorated_example):\\n import visual_coding_2p_analysis\",\n \"def inner_test():\\n pass\",\n \"def inner_test():\\n pass\",\n \"def test_documentation_popxl_code_loading(self):\\n filename = \\\"code_loading.py\\\"\\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)\",\n \"def test_lint(self):\\n l = self.l\\n l.loadTestsFromTestCase\\n l.loadTestsFromModule\\n l.loadTestsFromName\\n l.loadTestsFromNames\",\n \"def test_CL04_import(): \\n\\tdef test(): \\n\\t\\ttry: \\n\\t\\t\\tfrom .. import CL04 \\n\\t\\texcept: \\n\\t\\t\\treturn False \\n\\t\\treturn True \\n\\treturn [\\\"vice.yields.ccsne.CL04\\\", test]\",\n \"def test_load_testcase(self):\\n tests = self.loader.load(\\\"tests.sampletest.hellotest.HelloTest\\\")\\n self.assertEqual(len(tests), 1)\\n from tests.sampletest.hellotest import HelloTest\\n\\n self.assertEqual(type(tests[0]), HelloTest)\",\n \"def test_loading_document(self):\",\n \"def test_NKT13_import(): \\n\\tdef test(): \\n\\t\\ttry: \\n\\t\\t\\tfrom .. import NKT13 \\n\\t\\texcept: \\n\\t\\t\\treturn False \\n\\t\\treturn True \\n\\treturn [\\\"vice.yields.ccsne.NKT13\\\", test]\",\n \"def test_simple(self):\\n bento_info = \\\"\\\"\\\"\\\\\\nName: foo\\n\\nLibrary:\\n Packages: foo, foo.bar\\n Modules: fubar\\n\\\"\\\"\\\"\\n self._test_run(bento_info)\",\n \"def test_module(self):\\n pass\",\n \"def test_CL13_import(): \\n\\tdef test(): \\n\\t\\ttry: \\n\\t\\t\\tfrom .. import CL13 \\n\\t\\texcept: \\n\\t\\t\\treturn False \\n\\t\\treturn True \\n\\treturn [\\\"vice.yields.ccsne.CL13\\\", test]\",\n \"def _test():\\n import doctest\",\n \"def test_001(settings, inspector):\\n sourcepath = os.path.join(settings.sample_path, 'main_basic.scss')\\n\\n inspector.inspect(sourcepath)\\n\\n inspector.reset()\\n\\n assert inspector._CHILDREN_MAP == {}\\n assert inspector._PARENTS_MAP == {}\\n assert inspector.children(sourcepath) == set([])\\n assert inspector.parents(sourcepath) == set([])\",\n \"def test_LC18_import(): \\n\\tdef test(): \\n\\t\\ttry: \\n\\t\\t\\tfrom .. import LC18 \\n\\t\\texcept: \\n\\t\\t\\treturn False \\n\\t\\treturn True \\n\\treturn [\\\"vice.yields.ccsne.LC18\\\", test]\",\n \"def main():\\n\\n print(\\\"=\\\" * 80)\\n print(\\\"DATA STRUCTURE TESTS\\\")\\n test_module(structs.tests)\\n test_module(structs.regularization)\\n\\n print(\\\"=\\\" * 80)\\n print(\\\"END-TO-END TESTS\\\")\\n test_module(globals())\",\n \"def test_module(self):\\n data = (\\n os.path.join(\\n _CURRENT_DIRECTORY,\\n \\\"fake_project\\\",\\n \\\"_modules\\\",\\n \\\"fake_project\\\",\\n \\\"nested_folder\\\",\\n \\\"another.html\\\",\\n ),\\n \\\"\\\",\\n )\\n\\n content = self._get_fake_project_nested_module()\\n\\n expected = textwrap.dedent(\\n '''\\\\\\n #!/usr/bin/env python\\n # -*- coding: utf-8 -*-\\n\\n \\\"\\\"\\\"A module that shows every type of documentable class / method / function.\\n\\n Attributes:\\n ATTRIBUTE_VALUE (float):\\n Some number.\\n\\n \\\"\\\"\\\"\\n\\n\\n ATTRIBUTE_VALUE = 14.3\\n\\n\\n class MyKlass(object):\\n \\\"\\\"\\\"A class that does something.\\n\\n Multi-line information here.\\n\\n Attributes:\\n attribute_value (str):\\n Some string.\\n\\n \\\"\\\"\\\"\\n\\n attribute_value = \\\"asdfasdf\\\"\\n\\n def __init__(self, value):\\n \\\"\\\"\\\"Create this instance.\\\"\\\"\\\"\\n # A comment that should show up in the unittest's results\\n super(MyKlass, self).__init__()\\n\\n @staticmethod\\n def get_staticmethod():\\n \\\"\\\"\\\"int: Get some value.\\\"\\\"\\\"\\n return 8\\n\\n @classmethod\\n def get_classmethod(cls):\\n \\\"\\\"\\\"int: Get some value.\\\"\\\"\\\"\\n return 8\\n\\n def get_method(self):\\n \\\"\\\"\\\"int: Get some value.\\\"\\\"\\\"\\n return 8\\n\\n\\n class ParentClass(object):\\n \\\"\\\"\\\"The outter class.\\n\\n Attributes:\\n attribute_value (str):\\n Some string.\\n\\n \\\"\\\"\\\"\\n\\n attribute_value = \\\"tttt\\\"\\n\\n class NestedClass(object):\\n \\\"\\\"\\\"A class within a class.\\n\\n Attributes:\\n attribute_value (str):\\n Some string.\\n\\n \\\"\\\"\\\"\\n\\n attribute_value = \\\"zzzzzzzzzzzzz\\\"\\n\\n @staticmethod\\n def get_staticmethod():\\n \\\"\\\"\\\"int: Get some value.\\\"\\\"\\\"\\n return 5\\n\\n @classmethod\\n def get_classmethod(cls):\\n \\\"\\\"\\\"int: Get some value.\\\"\\\"\\\"\\n return 5\\n\\n def get_method(self):\\n \\\"\\\"\\\"int: Get some value.\\\"\\\"\\\"\\n return 5\\n\\n @staticmethod\\n def get_staticmethod():\\n \\\"\\\"\\\"int: Get some value.\\\"\\\"\\\"\\n return 6\\n\\n @classmethod\\n def get_classmethod(cls):\\n \\\"\\\"\\\"int: Get some value.\\\"\\\"\\\"\\n return 6\\n\\n def get_method(self):\\n \\\"\\\"\\\"int: Get some value.\\\"\\\"\\\"\\n return 6\\n\\n\\n def _set_private_function_thing(value, another):\\n \\\"\\\"\\\"Do something here.\\\"\\\"\\\"\\n # Do something with these values\\n # and more comment text, here.\\n #\\n if value:\\n return 2\\n\\n # Another comment\\n return 1\\n\\n\\n def set_function_thing(value, another):\\n \\\"\\\"\\\"Do something here.\\\"\\\"\\\"\\n # Do something with these values\\n # and more comment text, here.\\n #\\n if value:\\n return 2\\n\\n # Another comment\\n return 1'''\\n )\\n\\n self._test(data, content, expected) # pylint: disable=no-value-for-parameter\",\n \"def test_documentation_popxl_nested_session_contexts(self):\\n filename = \\\"nested_session_contexts.py\\\"\\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)\",\n \"def test(self):\\n pass\",\n \"def test_load_testcase_in_module(self):\\n tests = self.loader.load(\\\"tests.sampletest.InitTest\\\")\\n self.assertEqual(len(tests), 1)\\n from tests.sampletest import InitTest\\n\\n self.assertEqual(type(tests[0]), InitTest)\",\n \"def test_documentation_popxl_basic_subgraph(self):\\n filename = \\\"basic_graph.py\\\"\\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)\",\n \"def test_load_simple_module():\\n loader = Loader()\\n main_fname = loader.load(\\\"https://gist.githubusercontent.com/miohtama/80391980c2e73b285cfe/raw/dd89a55497ba33a6014453d9bb7432ab424c01cf/kivyhello.py#main\\\")\\n mod = path_to_mod_name(main_fname)\\n result = loader.run(mod, \\\"hello\\\")\\n assert result == \\\"Hello there\\\"\\n loader.close()\",\n \"def test_main():\\n # Setup\\n # Exercise\\n # Verify\",\n \"def fixture_example_data():\\n import_example_data()\",\n \"def test_001_basic(settings, inspector):\\n sources = [\\n os.path.join(settings.sample_path, 'main_basic.scss'),\\n os.path.join(settings.sample_path, 'main_depth_import-3.scss'),\\n os.path.join(settings.sample_path, 'main_with_subimports.scss'),\\n os.path.join(settings.sample_path, 'main_using_libs.scss'),\\n ]\\n sourcepath = os.path.join(settings.sample_path, 'main_basic.scss')\\n\\n inspector.inspect(*sources, library_paths=settings.libraries_fixture_paths)\\n\\n parents = inspector.parents(sourcepath)\\n assert parents == set([\\n os.path.join(settings.sample_path, 'main_depth_import-1.scss'),\\n os.path.join(settings.sample_path, 'main_depth_import-2.scss'),\\n os.path.join(settings.sample_path, 'main_depth_import-3.scss'),\\n os.path.join(settings.sample_path, 'main_with_subimports.scss'),\\n os.path.join(settings.sample_path, 'main_using_libs.scss'),\\n ])\",\n \"def test(self):\\n # -- Test --\\n\\n # (1)\\n\\n # (2)\\n\\n # (3)\\n\\n # (4)\\n # -- Test --\",\n \"def runtest(self):\",\n \"def test_compute_glycemic_load(self):\\n pass\",\n \"def test_functional(self):\\n with sphinx_build('pyexample'):\\n with open('_build/text/docfx_yaml/example.example.yml') as yml_file:\\n data = yaml.safe_load(yml_file)\\n self.assertEqual(\\n data['items'][0]['fullName'],\\n 'example.example'\\n )\",\n \"def test_pep8_conformance_example(self):\\n\\n print(\\\"\\\\r\\\\n\\\")\\n\\n # Get the path to current directory\\n path = os.path.dirname(os.path.realpath(__file__))\\n path += \\\"/../docs/examples/\\\"\\n\\n # Find all the examples files\\n file_paths = []\\n for root, dirnames, filenames in os.walk(path):\\n for file_path in fnmatch.filter(filenames, '*.py'):\\n file_paths.append(os.path.join(root, file_path))\\n\\n for path in file_paths:\\n self.run_check(path)\",\n \"def test_include():\\n from bst import BST\",\n \"def _test(self):\",\n \"def _test(self):\",\n \"def _test(self):\",\n \"def _test(self):\",\n \"def _test(self):\",\n \"def test_load_case_objects(cli_args_fixture, tmp_path):\\n # cli_args_fixture fixture is called but not used simply in order to mock it in render_json.dump()\\n tmp_file = os.path.join(tmp_path, 'stub-load.json')\\n # - protocol\\n proto_ref, proto_name, proto_blocking, proto_is_database = ('foo', 'bar', True, False)\\n # - crawl strategy\\n cs_description, cs_name, cs_providers, cs_provider_args, cs_child_provider, cs_filter, cs_rewrites = \\\\\\n ('foo', 'bar', ['baz'], {'buzz': 'buzzbuzz'}, {'qux': True}, {'quux': True}, {'quz': True})\\n # - node\\n node_ref, node_prov, node_mux, node_hint, node_address, node_service_name, node_children, node_warn, node_err = \\\\\\n ('a_ref', 'a_prov', 'a_mux', True, 'an_add', 'a_name', {'foo': 'child'}, {'bar': True}, {'baz': True})\\n stub_json = f\\\"\\\"\\\"\\n{{\\n \\\"args\\\": {{\\n \\\"max_depth\\\": 0,\\n \\\"skip_nonblocking_grandchildren\\\": false\\n }},\\n \\\"tree\\\": {{\\n \\\"{node_ref}\\\": {{\\n \\\"__type__\\\": \\\"Node\\\",\\n \\\"provider\\\": \\\"{node_prov}\\\",\\n \\\"protocol_mux\\\": \\\"{node_mux}\\\",\\n \\\"from_hint\\\": {str(node_hint).lower()},\\n \\\"address\\\": \\\"{node_address}\\\",\\n \\\"service_name\\\": \\\"{node_service_name}\\\",\\n \\\"children\\\": {json.dumps(node_children)},\\n \\\"warnings\\\": {json.dumps(node_warn)},\\n \\\"errors\\\": {json.dumps(node_err)},\\n \\\"crawl_strategy\\\": {{\\n \\\"__type__\\\": \\\"CrawlStrategy\\\",\\n \\\"description\\\": \\\"{cs_description}\\\",\\n \\\"name\\\": \\\"{cs_name}\\\",\\n \\\"providers\\\": {json.dumps(cs_providers)},\\n \\\"provider_args\\\": {json.dumps(cs_provider_args)},\\n \\\"child_provider\\\": {json.dumps(cs_child_provider)},\\n \\\"service_name_filter\\\": {json.dumps(cs_filter)},\\n \\\"service_name_rewrites\\\": {json.dumps(cs_rewrites)},\\n \\\"protocol\\\": {{\\n \\\"__type__\\\": \\\"Protocol\\\",\\n \\\"ref\\\": \\\"{proto_ref}\\\",\\n \\\"name\\\": \\\"{proto_name}\\\",\\n \\\"blocking\\\": {str(proto_blocking).lower()},\\n \\\"is_database\\\": {str(proto_is_database).lower()}\\n }}\\n }},\\n \\\"protocol\\\": {{\\n \\\"__type__\\\": \\\"Protocol\\\",\\n \\\"ref\\\": \\\"{proto_ref}\\\",\\n \\\"name\\\": \\\"{proto_name}\\\",\\n \\\"blocking\\\": {str(proto_blocking).lower()},\\n \\\"is_database\\\": {str(proto_is_database).lower()}\\n }}\\n }}\\n }}\\n}}\\n\\\"\\\"\\\"\\n with open(tmp_file, 'w') as f:\\n f.write(stub_json)\\n\\n # act\\n tree = render_json.load(tmp_file)\\n\\n # assert\\n # - Node()\\n assert isinstance(tree[node_ref], node.Node)\\n loaded_node = tree[node_ref]\\n assert loaded_node.provider == node_prov\\n assert loaded_node.protocol_mux == node_mux\\n assert loaded_node.from_hint == node_hint\\n assert loaded_node.address == node_address\\n assert loaded_node.service_name == node_service_name\\n assert loaded_node.children == node_children\\n assert loaded_node.warnings == node_warn\\n assert loaded_node.errors == node_err\\n # - CrawlStrategy()\\n assert isinstance(loaded_node.crawl_strategy, charlotte.CrawlStrategy)\\n loaded_cs = loaded_node.crawl_strategy\\n assert loaded_cs.description == cs_description\\n assert loaded_cs.name == cs_name\\n assert loaded_cs.providers == cs_providers\\n assert loaded_cs.provider_args == cs_provider_args\\n assert loaded_cs.child_provider == cs_child_provider\\n assert loaded_cs.service_name_filter == cs_filter\\n assert loaded_cs.service_name_rewrites == cs_rewrites\\n # - Protocol\\n assert isinstance(loaded_cs.protocol, charlotte_web.Protocol)\\n loaded_protocol = loaded_cs.protocol\\n assert loaded_protocol.ref == proto_ref\\n assert loaded_protocol.name == proto_name\\n assert loaded_protocol.blocking == proto_blocking\\n assert loaded_protocol.is_database == proto_is_database\\n assert isinstance(loaded_node.protocol, charlotte_web.Protocol)\",\n \"def runTest(self):\\r\\n self.setUp()\\r\\n self.test_sceneImport24281()\",\n \"def test():\\n pass\",\n \"def test_examples(self):\\n with sphinx_build('pyexample'):\\n with open('_build/text/docfx_yaml/example.nap.Base.yml') as yml_file:\\n data = yaml.safe_load(yml_file)\\n for item in data['items']:\\n if item['uid'] == 'example.nap.Base.ref':\\n self.assertEqual(\\n item['example'].split('\\\\n')[2],\\n \\\"\\\"\\\">>> print('docblock 1')\\\"\\\"\\\"\\n )\\n self.assertEqual(\\n item['example'].split('\\\\n')[7],\\n \\\"\\\"\\\">>> print('docblock 2')\\\"\\\"\\\"\\n )\",\n \"def test_loaders():\\n\\n tempdir = tempfile.mkdtemp()\\n\\n loader = \\\"\\\"\\\"\\nfrom mindbender import api\\n\\nclass DemoLoader(api.Loader):\\n def process(self, asset, subset, version, representation):\\n pass\\n\\n\\\"\\\"\\\"\\n\\n with open(os.path.join(tempdir, \\\"my_loader.py\\\"), \\\"w\\\") as f:\\n f.write(loader)\\n\\n try:\\n pipeline.register_loaders_path(tempdir)\\n loaders = pipeline.discover_loaders()\\n\\n assert \\\"DemoLoader\\\" in list(\\n L.__name__ for L in loaders\\n ), \\\"Loader not found in %s\\\" % \\\", \\\".join(\\n l.__name__ for l in loaders)\\n\\n finally:\\n shutil.rmtree(tempdir)\",\n \"def unitary_test():\",\n \"def test_get_run(self):\\n pass\",\n \"def test_component_resolution_different_file():\\n\\n assert snippet_eval(ComponentSnippet(modulea.ComponentResolutionViaModule())) == \\\"hi from module b\\\\n\\\"\",\n \"def main():\\n example()\",\n \"def main():\\n example()\",\n \"def main():\\n example()\",\n \"def main():\\n example()\",\n \"def test_Tree():\",\n \"def tests():\",\n \"def test_basic_execution(self):\",\n \"def test_module_doc():\\r\\n\\r\\n for fname in os.listdir('.'):\\r\\n if fname.endswith('.py'):\\r\\n f = fname.split('.')[0]\\r\\n print 'Executing ', fname\\r\\n execfile(fname, locals())\",\n \"def test(self, parent, block):\\r\\n pass\",\n \"def test_2():\",\n \"def _test(self):\\n pass\",\n \"def _test(self):\\n pass\",\n \"def _test(self):\\n pass\",\n \"def test_3():\",\n \"def test_003_library(settings, inspector):\\n sources = [\\n os.path.join(settings.sample_path, 'main_syntax.scss'),\\n os.path.join(settings.sample_path, 'main_commented.scss'),\\n os.path.join(settings.sample_path, 'main_basic.scss'),\\n os.path.join(settings.sample_path, 'main_depth_import-1.scss'),\\n os.path.join(settings.sample_path, 'main_depth_import-2.scss'),\\n os.path.join(settings.sample_path, 'main_depth_import-3.scss'),\\n os.path.join(settings.sample_path, 'main_with_subimports.scss'),\\n os.path.join(settings.sample_path, 'main_using_libs.scss'),\\n os.path.join(settings.sample_path, 'main_circular_0.scss'),\\n os.path.join(settings.sample_path, 'main_circular_1.scss'),\\n os.path.join(settings.sample_path, 'main_circular_2.scss'),\\n os.path.join(settings.sample_path, 'main_circular_3.scss'),\\n os.path.join(settings.sample_path, 'main_circular_4.scss'),\\n os.path.join(settings.sample_path, 'main_circular_bridge.scss'),\\n os.path.join(settings.sample_path, 'main_circular_5.scss'),\\n ]\\n sourcepath = os.path.join(settings.lib1_path, 'components/_panels.scss')\\n\\n inspector.inspect(*sources, library_paths=settings.libraries_fixture_paths)\\n\\n parents = inspector.parents(sourcepath)\\n assert parents == set([\\n os.path.join(settings.lib1_path, 'library_1_fullstack.scss'),\\n os.path.join(settings.sample_path, 'main_using_libs.scss'),\\n ])\",\n \"def test():\\n import doctest\\n from . import locate\\n return doctest.testmod(locate)\",\n \"def test(self):\",\n \"def test(self):\",\n \"def test_simple_extension(self):\\n bento_info = \\\"\\\"\\\"\\\\\\nName: foo\\n\\nLibrary:\\n Packages: foo, foo.bar\\n Modules: fubar\\n Extension: foo\\n Sources: src/foo.c\\n\\\"\\\"\\\"\\n self._test_run(bento_info)\",\n \"def test_example(self):\\n self.assertEqual(self.example.get_example(), True)\",\n \"def test_4():\",\n \"def test():\",\n \"def test():\",\n \"def test_partial_twice_dependent_object_import(self):\\n pass\",\n \"def test_subsystems(self):\\n pass\",\n \"def test_examples():\\n import airconics\\n # pytest runs test files in ./__pycache__: need to go up two levels\\n example_dir = os.path.abspath(\\n os.path.join(__file__, '..', '..', 'examples', 'core'))\\n example_scripts = os.listdir(example_dir)\\n for script in example_scripts:\\n if script.endswith('.py'):\\n fname = os.path.join(example_dir, script)\\n try:\\n subprocess.check_call(['python', fname])\\n except subprocess.CalledProcessError:\\n raise AssertionError('Example {} failed'.format(fname))\",\n \"def test_nested_template_source_generation(self):\\n sources = [source for source in self.loader.get_template_sources('component.child.html')]\\n self.assertEqual(len(sources), 2)\\n self.assertEqual(sources[0], 'MOCK_BASE_DIR/component/child/child.html')\\n self.assertEqual(sources[1], 'MOCK_BASE_DIR_2/component/child/child.html')\\n\\n sources = [source for source in self.loader.get_template_sources('deeply.nested.component.and.child.html')]\\n self.assertEqual(len(sources), 2)\\n self.assertEqual(sources[0], 'MOCK_BASE_DIR/deeply/nested/component/and/child/child.html')\\n self.assertEqual(sources[1], 'MOCK_BASE_DIR_2/deeply/nested/component/and/child/child.html')\\n\\n sources = [source for source in self.loader.get_template_sources('component.child/another.html')]\\n self.assertEqual(len(sources), 2)\\n self.assertEqual(sources[0], 'MOCK_BASE_DIR/component/child/another.html')\\n self.assertEqual(sources[1], 'MOCK_BASE_DIR_2/component/child/another.html')\",\n \"def test_load_model_data(self):\\n add_components_and_load_data(\\n prereq_modules=IMPORTED_PREREQ_MODULES,\\n module_to_test=MODULE_BEING_TESTED,\\n test_data_dir=TEST_DATA_DIRECTORY,\\n subproblem=\\\"\\\",\\n stage=\\\"\\\",\\n )\",\n \"def test_load_model_data(self):\\n add_components_and_load_data(\\n prereq_modules=IMPORTED_PREREQ_MODULES,\\n module_to_test=MODULE_BEING_TESTED,\\n test_data_dir=TEST_DATA_DIRECTORY,\\n subproblem=\\\"\\\",\\n stage=\\\"\\\",\\n )\",\n \"def test_load(self):\\n command = constituencies.Command()\\n command.handle('load', silent=True)\",\n \"def test_T01():\",\n \"def main():\\n doctest.testmod()\\n game()\",\n \"def test_bed(self):\\n #TODO write bed tests\",\n \"def test_1():\",\n \"def less_nested_example_vanilla():\\n return\",\n \"def test_S16_import(): \\n\\tdef test(): \\n\\t\\ttry: \\n\\t\\t\\tfrom .. import S16 \\n\\t\\texcept: \\n\\t\\t\\treturn False \\n\\t\\treturn True \\n\\treturn [\\\"vice.yields.ccsne.S16\\\", test]\",\n \"def test_get_imports(self):\\n pass\",\n \"def test_example_runs(self):\\n run_example(\\n verbose=False,\\n testapp=self.testapp,\\n )\",\n \"def test_twice_dependent_object_import(self):\\n pass\",\n \"def test_definition_loading(self):\\r\\n\\r\\n modulestore = XMLModuleStore(DATA_DIR, course_dirs=['toy', 'two_toys'])\\r\\n\\r\\n location = Location(\\\"edX\\\", \\\"toy\\\", \\\"2012_Fall\\\", \\\"video\\\", \\\"Welcome\\\", None)\\r\\n toy_video = modulestore.get_item(location)\\r\\n location_two = Location(\\\"edX\\\", \\\"toy\\\", \\\"TT_2012_Fall\\\", \\\"video\\\", \\\"Welcome\\\", None)\\r\\n two_toy_video = modulestore.get_item(location_two)\\r\\n self.assertEqual(toy_video.youtube_id_1_0, \\\"p2Q6BrNhdh8\\\")\\r\\n self.assertEqual(two_toy_video.youtube_id_1_0, \\\"p2Q6BrNhdh9\\\")\",\n \"def test_read_namespaced_build(self):\\n pass\",\n \"def test_load(self):\\n (spec, check) = bundylogging.load()\\n # It returns the checking function\\n self.assertEqual(check, bundylogging.check)\\n # The plugin stores it's spec\\n self.assertEqual(spec, bundylogging.spec)\",\n \"def test_load_file_contents():\\n\\n file_name = 'test_fooof_all'\\n loaded_data = load_json(file_name, TEST_DATA_PATH)\\n\\n # Check settings\\n for setting in OBJ_DESC['settings']:\\n assert setting in loaded_data.keys()\\n\\n # Check results\\n for result in OBJ_DESC['results']:\\n assert result in loaded_data.keys()\\n\\n # Check results\\n for datum in OBJ_DESC['data']:\\n assert datum in loaded_data.keys()\",\n \"def inner_test(param: dict):\\n pass\",\n \"def test_data_dump_and_load():\\n raw_data = {\\\"A\\\": 1, \\\"B\\\": 2}\\n\\n # Dump json\\n json_file = os.path.join(tempfile.gettempdir(), \\\"jade-unit-test-file.json\\\")\\n dump_data(raw_data, json_file)\\n assert os.path.exists(json_file)\\n\\n # Load json\\n json_data = load_data(json_file)\\n assert json_data == raw_data\\n\\n if os.path.exists(json_file):\\n os.remove(json_file)\\n\\n # Dump toml\\n toml_file = os.path.join(tempfile.gettempdir(), \\\"jade-unit-test-file.toml\\\")\\n dump_data(raw_data, toml_file)\\n assert os.path.exists(toml_file)\\n\\n # Load toml\\n toml_data = load_data(toml_file)\\n assert toml_data == raw_data\\n\\n if os.path.exists(toml_file):\\n os.remove(toml_file)\\n\\n # Re-enable if we add support again.\\n # Dump yaml\\n # yaml_file = os.path.join(tempfile.gettempdir(), \\\"jade-unit-test-file.yaml\\\")\\n # dump_data(raw_data, yaml_file)\\n # assert os.path.exists(yaml_file)\\n\\n ## Load yaml\\n # yaml_data = load_data(yaml_file)\\n # assert yaml_data == raw_data\\n\\n # if os.path.exists(yaml_file):\\n # os.remove(yaml_file)\",\n \"def test_cms_imported_course_walkthrough(self):\\r\\n def test_get_html(handler):\\r\\n # Helper function for getting HTML for a page in Studio and\\r\\n # checking that it does not error.\\r\\n resp = self.client.get_html(\\r\\n get_url(handler, course_key, 'course_key_string')\\r\\n )\\r\\n self.assertEqual(resp.status_code, 200)\\r\\n _test_no_locations(self, resp)\\r\\n\\r\\n _, course_items = import_from_xml(modulestore('direct'), 'common/test/data/', ['simple'])\\r\\n course_key = course_items[0].id\\r\\n\\r\\n resp = self._show_course_overview(course_key)\\r\\n self.assertEqual(resp.status_code, 200)\\r\\n self.assertContains(resp, 'Chapter 2')\\r\\n\\r\\n # go to various pages\\r\\n test_get_html('import_handler')\\r\\n test_get_html('export_handler')\\r\\n test_get_html('course_team_handler')\\r\\n test_get_html('course_info_handler')\\r\\n test_get_html('checklists_handler')\\r\\n test_get_html('assets_handler')\\r\\n test_get_html('tabs_handler')\\r\\n test_get_html('settings_handler')\\r\\n test_get_html('grading_handler')\\r\\n test_get_html('advanced_settings_handler')\\r\\n test_get_html('textbooks_list_handler')\\r\\n\\r\\n # go look at a subsection page\\r\\n subsection_key = course_key.make_usage_key('sequential', 'test_sequence')\\r\\n resp = self.client.get_html(get_url('subsection_handler', subsection_key))\\r\\n self.assertEqual(resp.status_code, 200)\\r\\n _test_no_locations(self, resp)\\r\\n\\r\\n # go look at the Edit page\\r\\n unit_key = course_key.make_usage_key('vertical', 'test_vertical')\\r\\n resp = self.client.get_html(get_url('unit_handler', unit_key))\\r\\n self.assertEqual(resp.status_code, 200)\\r\\n _test_no_locations(self, resp)\\r\\n\\r\\n def delete_item(category, name):\\r\\n \\\"\\\"\\\" Helper method for testing the deletion of an xblock item. \\\"\\\"\\\"\\r\\n item_key = course_key.make_usage_key(category, name)\\r\\n resp = self.client.delete(get_url('xblock_handler', item_key))\\r\\n self.assertEqual(resp.status_code, 204)\\r\\n _test_no_locations(self, resp, status_code=204, html=False)\\r\\n\\r\\n # delete a component\\r\\n delete_item(category='html', name='test_html')\\r\\n\\r\\n # delete a unit\\r\\n delete_item(category='vertical', name='test_vertical')\\r\\n\\r\\n # delete a unit\\r\\n delete_item(category='sequential', name='test_sequence')\\r\\n\\r\\n # delete a chapter\\r\\n delete_item(category='chapter', name='chapter_2')\",\n \"def test_syntax(self):\\n\\t\\ttheResult = False\\n\\t\\ttry:\\n\\t\\t\\tfrom .context import code\\n\\t\\t\\tfrom code import restart_service\\n\\t\\t\\ttheResult = True\\n\\t\\texcept Exception:\\n\\t\\t\\ttheResult = False\\n\\t\\tassert theResult\",\n \"def _test():\\n import doctest\\n doctest.testmod(verbose=1)\",\n \"def runTest(self):\\n self.setUp()\\n self.test_JupyterNotebooks1()\",\n \"def test_modul1(self):\\n\\n self.delayDisplay(\\\"Starting the test\\\")\\n #\\n # first, get some data\\n #\\n import urllib\\n downloads = (\\n ('http://slicer.kitware.com/midas3/download?items=5767', 'FA.nrrd', slicer.util.loadVolume),\\n )\\n\\n for url,name,loader in downloads:\\n filePath = slicer.app.temporaryPath + '/' + name\\n if not os.path.exists(filePath) or os.stat(filePath).st_size == 0:\\n logging.info('Requesting download %s from %s...\\\\n' % (name, url))\\n urllib.urlretrieve(url, filePath)\\n if loader:\\n logging.info('Loading %s...' % (name,))\\n loader(filePath)\\n self.delayDisplay('Finished with download and loading')\\n\\n volumeNode = slicer.util.getNode(pattern=\\\"FA\\\")\\n logic = modulLogic()\\n self.assertTrue( logic.hasImageData(volumeNode) )\\n self.delayDisplay('Test passed!')\",\n \"def __main() :\\n launchTests()\",\n \"def runTest(self):\\n pagename = self._tester.create_wiki_page(content=\\\"\\\"\\\"\\n{{{\\n#!rst\\n.. code-block:: python\\n\\n print \\\"123\\\"\\n}}}\\n\\\"\\\"\\\")\\n self._tester.go_to_wiki(pagename)\\n tc.notfind(\\\"code-block\\\")\\n tc.find('print')\\n tc.find('\\\"123\\\"')\",\n \"def test_basic_workflow(self):\\n\\n test_data_dir = os.path.join(os.path.dirname(__file__), 'testdata')\\n sys.path.append(test_data_dir)\\n import basic\\n tmpdir = tempfile.mkdtemp()\\n package_path = os.path.join(tmpdir, 'workflow.tar.gz')\\n try:\\n compiler.Compiler().compile(basic.save_most_frequent_word, package_path)\\n with open(os.path.join(test_data_dir, 'basic.yaml'), 'r') as f:\\n golden = yaml.load(f)\\n compiled = self._get_yaml_from_tar(package_path)\\n\\n self.maxDiff = None\\n # Comment next line for generating golden yaml.\\n self.assertEqual(golden, compiled)\\n finally:\\n # Replace next line with commented line for gathering golden yaml.\\n shutil.rmtree(tmpdir)\\n # print(tmpdir)\",\n \"def test_script(self) -> None:\\n main()\",\n \"def test_quick_build(self):\\n pass\",\n \"def test_dag_load(self):\\n\\n with ObservatoryEnvironment().create():\\n dag_file = os.path.join(module_file_path(\\\"academic_observatory_workflows.dags\\\"), \\\"openalex_telescope.py\\\")\\n self.assert_dag_load(\\\"openalex\\\", dag_file)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6656562","0.6570933","0.6570933","0.6489614","0.6259519","0.6246904","0.61843383","0.61584216","0.61110884","0.61107814","0.6097114","0.6087612","0.60835487","0.60752654","0.60712975","0.6059714","0.60356414","0.60127175","0.60122466","0.601197","0.5992272","0.5991382","0.59841245","0.59821856","0.59772164","0.5974786","0.5959991","0.5928582","0.59175533","0.58958703","0.5859042","0.5843219","0.5843219","0.5843219","0.5843219","0.5843219","0.58368576","0.5821035","0.581782","0.5816487","0.5814812","0.5812459","0.58077496","0.5795895","0.5795067","0.5795067","0.5795067","0.5795067","0.5789442","0.5777511","0.57676333","0.57657003","0.575902","0.57486725","0.57480097","0.57480097","0.57480097","0.5747713","0.574615","0.57360345","0.5731538","0.5731538","0.571834","0.57139224","0.5712714","0.57122034","0.57122034","0.57086134","0.57066816","0.5705689","0.5696143","0.5695092","0.5695092","0.5692772","0.5689019","0.56836814","0.5677645","0.5670156","0.56617427","0.56610835","0.5644757","0.5643017","0.56429493","0.56378627","0.56332856","0.56295615","0.56271815","0.5623159","0.5620006","0.561445","0.5614354","0.56114227","0.5608165","0.5606964","0.56052727","0.5603394","0.5603033","0.560051","0.5599641","0.5585677"],"string":"[\n \"0.6656562\",\n \"0.6570933\",\n \"0.6570933\",\n \"0.6489614\",\n \"0.6259519\",\n \"0.6246904\",\n \"0.61843383\",\n \"0.61584216\",\n \"0.61110884\",\n \"0.61107814\",\n \"0.6097114\",\n \"0.6087612\",\n \"0.60835487\",\n \"0.60752654\",\n \"0.60712975\",\n \"0.6059714\",\n \"0.60356414\",\n \"0.60127175\",\n \"0.60122466\",\n \"0.601197\",\n \"0.5992272\",\n \"0.5991382\",\n \"0.59841245\",\n \"0.59821856\",\n \"0.59772164\",\n \"0.5974786\",\n \"0.5959991\",\n \"0.5928582\",\n \"0.59175533\",\n \"0.58958703\",\n \"0.5859042\",\n \"0.5843219\",\n \"0.5843219\",\n \"0.5843219\",\n \"0.5843219\",\n \"0.5843219\",\n \"0.58368576\",\n \"0.5821035\",\n \"0.581782\",\n \"0.5816487\",\n \"0.5814812\",\n \"0.5812459\",\n \"0.58077496\",\n \"0.5795895\",\n \"0.5795067\",\n \"0.5795067\",\n \"0.5795067\",\n \"0.5795067\",\n \"0.5789442\",\n \"0.5777511\",\n \"0.57676333\",\n \"0.57657003\",\n \"0.575902\",\n \"0.57486725\",\n \"0.57480097\",\n \"0.57480097\",\n \"0.57480097\",\n \"0.5747713\",\n \"0.574615\",\n \"0.57360345\",\n \"0.5731538\",\n \"0.5731538\",\n \"0.571834\",\n \"0.57139224\",\n \"0.5712714\",\n \"0.57122034\",\n \"0.57122034\",\n \"0.57086134\",\n \"0.57066816\",\n \"0.5705689\",\n \"0.5696143\",\n \"0.5695092\",\n \"0.5695092\",\n \"0.5692772\",\n \"0.5689019\",\n \"0.56836814\",\n \"0.5677645\",\n \"0.5670156\",\n \"0.56617427\",\n \"0.56610835\",\n \"0.5644757\",\n \"0.5643017\",\n \"0.56429493\",\n \"0.56378627\",\n \"0.56332856\",\n \"0.56295615\",\n \"0.56271815\",\n \"0.5623159\",\n \"0.5620006\",\n \"0.561445\",\n \"0.5614354\",\n \"0.56114227\",\n \"0.5608165\",\n \"0.5606964\",\n \"0.56052727\",\n \"0.5603394\",\n \"0.5603033\",\n \"0.560051\",\n \"0.5599641\",\n \"0.5585677\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.83801514"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":6587,"cells":{"query":{"kind":"string","value":"Test the nested Session contexts example"},"document":{"kind":"string","value":"def test_documentation_popxl_nested_session_contexts(self):\n filename = \"nested_session_contexts.py\"\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)"},"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 test_set_session():","def test_resource(data_manager):\n sessions = set([])\n with data_manager.dal():\n context1 = current_context._get_current_object()\n session = context1.sqlalchemy\n assert isinstance(session, orm.Session)\n sessions.add(session)\n\n with data_manager.dal():\n context2 = current_context._get_current_object()\n assert context2 != context1\n session = context2.sqlalchemy\n assert isinstance(session, orm.Session)\n sessions.add(session)\n\n # Make sure we have two unique sessions\n assert len(sessions) == 2","def test_existing_session_cookie(self):\n\n with self.app_sess1 as c:\n ret1 = c.get('/')\n ret2 = c.get('/')\n self.assertEqual(ret1.data, ret2.data)","def test_session_promotion(self):\r\n cursor = self.db.cursor()\r\n cursor.execute(\"INSERT INTO session VALUES ('123456', 0, 0)\")\r\n incookie = Cookie()\r\n incookie['trac_session'] = '123456'\r\n outcookie = Cookie()\r\n req = Mock(authname='john', base_path='/', incookie=incookie,\r\n outcookie=outcookie)\r\n session = Session(self.env, req)\r\n self.assertEqual('john', session.sid)\r\n session.save()\r\n\r\n cursor.execute(\"SELECT sid,authenticated FROM session\")\r\n self.assertEqual(('john', 1), cursor.fetchone())\r\n self.assertEqual(None, cursor.fetchone())","def session(db):\n db.session.begin_nested()\n\n yield db.session\n\n db.session.rollback()","def test_new_session_promotion(self):\r\n cursor = self.db.cursor()\r\n incookie = Cookie()\r\n incookie['trac_session'] = '123456'\r\n outcookie = Cookie()\r\n req = Mock(authname='john', base_path='/', incookie=incookie,\r\n outcookie=outcookie)\r\n session = Session(self.env, req)\r\n self.assertEqual('john', session.sid)\r\n session.save()\r\n\r\n cursor.execute(\"SELECT sid,authenticated FROM session\")\r\n self.assertEqual(('john', 1), cursor.fetchone())\r\n self.assertEqual(None, cursor.fetchone())","def test_newSession(self):\n session = self.mdk.session()\n session2 = self.mdk.session()\n self.assertSessionHas(session, session._context.traceId, [0])\n self.assertSessionHas(session2, session2._context.traceId, [0])\n self.assertNotEqual(session._context.traceId,\n session2._context.traceId)","def test_childSession(self):\n session = self.mdk.session()\n session.setProperty(\"other\", 123)\n session._context.tick()\n session._context.tick()\n session._context.tick()\n session.setTimeout(13.0)\n session2 = self.mdk.derive(session.externalize())\n self.assertNotEqual(session._context.traceId,\n session2._context.traceId)\n self.assertEqual(session2.getRemainingTime(), None)\n self.assertSessionHas(session2, session2._context.traceId, [1],\n other=123)","def db_subsession(session):\n try:\n with session.begin_nested():\n yield\n except:\n logger.exception(\"Problem with DB sub-session, rolling back.\")","def test_do_login(self):\r\n\r\n with app.test_request_context():\r\n u1 = User.query.filter_by(username='testuser').one()\r\n\r\n self.assertNotIn(CURR_USER_KEY, session)\r\n do_login(u1)\r\n self.assertEqual(session[CURR_USER_KEY], u1.id)","def session(self):","def test_distinct_sessions_cookie(self):\n\n sess1 = None\n sess2 = None\n with self.app_sess1 as c:\n sess1 = c.get('/').data\n\n with self.app_sess2 as c:\n sess2 = c.get('/').data\n\n self.assertNotEqual(sess1, sess2)","def test_client_custom_session():\n c_session = requests.Session()\n client = ConfigureClients(custom_session=c_session)\n assert client.session == c_session","def test_joinSession(self):\n session = self.mdk.session()\n session.setProperty(\"key\", 456)\n session.setProperty(\"key2\", [456, {\"zoo\": \"foo\"}])\n session2 = self.mdk.join(session.externalize())\n self.assertSessionHas(session2, session._context.traceId, [1, 0],\n key=456, key2=[456, {\"zoo\": \"foo\"}])","def session(request):\n session = get_test_db_session()\n request.cls.session = session\n return session","def test_session_auth_token(self):\n\n sess1 = None\n sess2 = None\n test_header = {'X-Auth-Token': 'pretend_token'}\n\n with self.app_sess1 as c:\n ret = c.get('/', headers=test_header)\n sess1 = ret.data\n\n with self.app_sess2 as c:\n ret = c.get('/', headers=test_header)\n sess2 = ret.data\n\n self.assertEqual(sess1, sess2)","def test_find_where_multiple_infos(server, session):\n\n for session in server.sessions:\n session_id = session.get('session_id')\n session_name = session.get('session_name')\n find_where = server.find_where(\n {'session_id': session_id, 'session_name': session_name}\n )\n\n assert find_where == session\n assert isinstance(find_where, Session)\n\n # session.find_where\n for window in session.windows:\n window_id = window.get('window_id')\n window_index = window.get('window_index')\n\n find_where = session.find_where(\n {'window_id': window_id, 'window_index': window_index}\n )\n\n assert find_where == window\n assert isinstance(find_where, Window)\n\n # window.find_where\n for pane in window.panes:\n pane_id = pane.get('pane_id')\n pane_tty = pane.get('pane_tty')\n\n find_where = window.find_where(\n {'pane_id': pane_id, 'pane_tty': pane_tty}\n )\n\n assert find_where == pane\n assert isinstance(find_where, Pane)","def test_get_session(self):\n with self.settings(SESSION_ENGINE='django.contrib.sessions.backends.db'):\n self._run_expired_session_test_for_engine()\n with self.settings(SESSION_ENGINE='django.contrib.sessions.backends.cache'):\n self._run_expired_session_test_for_engine()\n with self.settings(SESSION_ENGINE='django.contrib.sessions.backends.file'):\n self._run_expired_session_test_for_engine()\n with self.settings(SESSION_ENGINE='django.contrib.sessions.backends.cached_db'):\n self._run_expired_session_test_for_engine()","def session(self, context: InjectionContext = None) -> \"ProfileSession\":","def test_modify_detached_session(self):\r\n cursor = self.db.cursor()\r\n cursor.execute(\"INSERT INTO session VALUES ('john', 1, 0)\")\r\n cursor.execute(\"INSERT INTO session_attribute VALUES \"\r\n \"('john', 1, 'foo', 'bar')\")\r\n\r\n session = DetachedSession(self.env, 'john')\r\n self.assertEqual('bar', session['foo'])\r\n session['foo'] = 'baz'\r\n session.save()\r\n cursor.execute(\"SELECT value FROM session_attribute \"\r\n \"WHERE sid='john' AND name='foo'\")\r\n self.assertEqual('baz', cursor.fetchone()[0])","def test_sessions():\n CHECKS = (check_correct_usage, check_expiration, check_bad_cookie, check_various_session_sizes)\n for no_datastore in (False, True):\n if no_datastore:\n test_db = 'without'\n else:\n test_db = 'with'\n for cot in (0, 10*1024, 2**30):\n if cot == 0:\n test_cookie = 'no data stored in cookies'\n elif cot == 2**30:\n test_cookie = 'data only stored in cookies'\n else:\n test_cookie = 'store data in cookies when its encoded size<=%dB' % cot\n for check in CHECKS:\n logger.debug('\\n\\n' + '*'*50)\n logger.debug('Running %s %s datastore and %s' % (check.__name__, test_db, test_cookie))\n yield check, no_datastore, cot","def test_set_session_id(self, context):\n context.set_session_id(b\"abc\")","def test_server_get_session(self):\n server, client = loopback()\n session = server.get_session()\n assert isinstance(session, Session)","def session_context(pytestconfig, request, tmp_env):\n ctx = Context.only()\n\n # Temporary, empty local directory for local data\n session_tmp_dir = Path(request.config._tmp_path_factory.mktemp(\"data\"))\n\n # Set the cache path according to whether pytest --local-cache was given. If True,\n # pick up the existing setting from the user environment. If False, use a pytest-\n # managed cache directory that persists across test sessions.\n ctx.cache_path = (\n ctx.local_data.joinpath(\"cache\")\n if request.config.option.local_cache\n # TODO use pytestconfig.cache.mkdir() when pytest >= 6.3 is available\n else Path(pytestconfig.cache.makedir(\"cache\"))\n )\n\n # Other local data in the temporary directory for this session only\n ctx.local_data = session_tmp_dir\n\n platform_name = \"message-ix-models\"\n\n # Add a platform connected to an in-memory database\n # NB cannot call Config.add_platform() here because it does not support supplying a\n # URL for a HyperSQL database.\n # TODO add that feature upstream.\n ixmp_config.values[\"platform\"][platform_name] = {\n \"class\": \"jdbc\",\n \"driver\": \"hsqldb\",\n \"url\": f\"jdbc:hsqldb:mem://{platform_name}\",\n }\n\n # Launch Platform and connect to testdb (reconnect if closed)\n mp = Platform(name=platform_name)\n mp.open_db()\n\n ctx.platform_info[\"name\"] = platform_name\n\n yield ctx\n\n ctx.close_db()\n ixmp_config.remove_platform(platform_name)","def load_session(session):\n def inner():\n web.ctx.session = session\n return inner","def test_client_get_session(self):\n server, client = loopback()\n session = client.get_session()\n assert isinstance(session, Session)","def test_get_activities_from_recursive_contexts(self):\n from .mockers import context_query\n from .mockers import create_context\n from .mockers import subscribe_contextA, create_contextA, user_status_contextA\n from .mockers import subscribe_contextB, create_contextB, user_status_contextB\n username = 'messi'\n username_not_me = 'xavi'\n self.create_user(username)\n self.create_user(username_not_me)\n self.create_context(create_context, permissions=dict(read='public', write='restricted', subscribe='restricted', invite='restricted'))\n self.create_context(create_contextA, permissions=dict(read='subscribed', write='subscribed', subscribe='restricted', invite='restricted'))\n self.create_context(create_contextB, permissions=dict(read='subscribed', write='subscribed', subscribe='restricted', invite='restricted'))\n self.admin_subscribe_user_to_context(username, subscribe_contextA)\n self.admin_subscribe_user_to_context(username_not_me, subscribe_contextA)\n self.admin_subscribe_user_to_context(username_not_me, subscribe_contextB)\n self.create_activity(username, user_status_contextA)\n self.create_activity(username_not_me, user_status_contextA)\n self.create_activity(username_not_me, user_status_contextB)\n\n res = self.testapp.get('/contexts/%s/activities' % (context_query['context']), '', oauth2Header(username), status=200)\n result = json.loads(res.text)\n self.assertEqual(len(result), 2)\n self.assertEqual(result[0].get('actor', None).get('username'), 'xavi')\n self.assertEqual(result[0].get('object', None).get('objectType', None), 'note')\n self.assertEqual(result[0].get('contexts', None)[0]['url'], subscribe_contextA['object']['url'])\n self.assertEqual(result[1].get('actor', None).get('username'), 'messi')\n self.assertEqual(result[1].get('object', None).get('objectType', None), 'note')\n self.assertEqual(result[1].get('contexts', None)[0]['url'], subscribe_contextA['object']['url'])\n\n res = self.testapp.get('/contexts/%s/activities' % (context_query['context']), '', oauth2Header(username_not_me), status=200)\n result = json.loads(res.text)\n self.assertEqual(len(result), 3)\n self.assertEqual(result[0].get('actor', None).get('username'), 'xavi')\n self.assertEqual(result[0].get('object', None).get('objectType', None), 'note')\n self.assertEqual(result[0].get('contexts', None)[0]['url'], subscribe_contextB['object']['url'])\n self.assertEqual(result[1].get('actor', None).get('username'), 'xavi')\n self.assertEqual(result[1].get('object', None).get('objectType', None), 'note')\n self.assertEqual(result[1].get('contexts', None)[0]['url'], subscribe_contextA['object']['url'])\n self.assertEqual(result[2].get('actor', None).get('username'), 'messi')\n self.assertEqual(result[2].get('object', None).get('objectType', None), 'note')\n self.assertEqual(result[2].get('contexts', None)[0]['url'], subscribe_contextA['object']['url'])","def test_create_session(self):\n study_id = self.storage.create_study(sample_study_spec())\n\n session = sample_session(study_id=study_id)\n self.storage.create_session(session)\n\n self.assertEqual(self.storage.get_session(study_id, session.id), session)","def test_scoring_logic():\n app = create_ctfd()\n with app.app_context():\n admin = login_as_user(app, name=\"admin\", password=\"password\")\n\n register_user(app, name=\"user1\", email=\"user1@ctfd.io\", password=\"password\")\n client1 = login_as_user(app, name=\"user1\", password=\"password\")\n register_user(app, name=\"user2\", email=\"user2@ctfd.io\", password=\"password\")\n client2 = login_as_user(app, name=\"user2\", password=\"password\")\n\n chal1 = gen_challenge(app.db)\n flag1 = gen_flag(app.db, chal=chal1.id, flag='flag')\n chal1_id = chal1.id\n\n chal2 = gen_challenge(app.db)\n flag2 = gen_flag(app.db, chal=chal2.id, flag='flag')\n chal2_id = chal2.id\n\n # user1 solves chal1\n with freeze_time(\"2017-10-3 03:21:34\"):\n with client1.session_transaction() as sess:\n data = {\n \"key\": 'flag',\n \"nonce\": sess.get('nonce')\n }\n r = client1.post('/chal/{}'.format(chal1_id), data=data)\n\n # user1 is now on top\n scores = get_scores(admin)\n assert scores[0]['team'] == 'user1'\n\n # user2 solves chal1 and chal2\n with freeze_time(\"2017-10-4 03:30:34\"):\n with client2.session_transaction() as sess:\n # solve chal1\n data = {\n \"key\": 'flag',\n \"nonce\": sess.get('nonce')\n }\n r = client2.post('/chal/{}'.format(chal1_id), data=data)\n # solve chal2\n data = {\n \"key\": 'flag',\n \"nonce\": sess.get('nonce')\n }\n r = client2.post('/chal/{}'.format(chal2_id), data=data)\n\n # user2 is now on top\n scores = get_scores(admin)\n assert scores[0]['team'] == 'user2'\n\n # user1 solves chal2\n with freeze_time(\"2017-10-5 03:50:34\"):\n with client1.session_transaction() as sess:\n data = {\n \"key\": 'flag',\n \"nonce\": sess.get('nonce')\n }\n r = client1.post('/chal/{}'.format(chal2_id), data=data)\n\n # user2 should still be on top because they solved chal2 first\n scores = get_scores(admin)\n assert scores[0]['team'] == 'user2'\n destroy_ctfd(app)","def test_find_where(server, session):\n # server.find_where\n for session in server.sessions:\n session_id = session.get('session_id')\n\n assert server.find_where({'session_id': session_id}) == session\n assert isinstance(server.find_where({'session_id': session_id}), Session)\n\n # session.find_where\n for window in session.windows:\n window_id = window.get('window_id')\n\n assert session.find_where({'window_id': window_id}) == window\n assert isinstance(session.find_where({'window_id': window_id}), Window)\n\n # window.find_where\n for pane in window.panes:\n pane_id = pane.get('pane_id')\n\n assert window.find_where({'pane_id': pane_id}) == pane\n assert isinstance(window.find_where({'pane_id': pane_id}), Pane)","def test_get_db_session(initialized_db_url):\n with utils.get_db_session(initialized_db_url) as db:\n assert isinstance(db, Session)","def test_get_context():\n\n application_services.add_context('context4', 'value4')\n assert application_services.get_context('context4') == 'value4'","def check_correct_usage(no_datastore, cookie_only_threshold):\n def minitest_divider(test):\n logger.debug('\\n\\n' + '-'*50)\n logger.debug(test + ' (nd=%s cot=%s)' % (no_datastore, cookie_only_threshold))\n\n st = SessionTester(no_datastore=no_datastore, cookie_only_threshold=cookie_only_threshold)\n expected_num_sessions_in_db_if_db_used = lambda a,b=0 : generic_expected_num_sessions_in_db_if_db_used(st, no_datastore, cookie_only_threshold, a, b)\n st.verify_active_sessions_in_db(0)\n\n minitest_divider('try doing nothing (no session should be started)')\n st.noop()\n st.verify_active_sessions_in_db(0)\n\n minitest_divider('start a session with a single write')\n st.start_request()\n str(st)\n assert st.get_expiration()==0, \"no session yet => no expiration yet\"\n assert st.is_active() is False\n st['x'] = 7\n assert st.is_active() is True\n st.finish_request_and_check()\n expected_num_sessions_in_db_if_db_used(1)\n\n minitest_divider('start another session')\n st2 = SessionTester(st=st)\n st2.start_request()\n assert not st2.is_active()\n assert st2.get('x') is None, \"shouldn't get other session's data\"\n assert not st2.is_active(), \"still shouldn't be active - nothing set yet\"\n st2['x'] = 'st2x'\n assert st2.is_active()\n st2.finish_request_and_check()\n expected_num_sessions_in_db_if_db_used(2)\n\n minitest_divider('each session should get a unique sid')\n assert st2.ss.sid != st.ss.sid\n\n minitest_divider('we should still have the values we set earlier')\n st.start_request()\n str(st)\n assert_equal(st['x'], 7)\n st.finish_request_and_check()\n st2.start_request()\n assert_equal(st2['x'], 'st2x')\n st2.finish_request_and_check()\n\n minitest_divider(\"check get session by sid, save(True), and terminate()\")\n if cookie_only_threshold == 0:\n data1 = st.ss.data\n data2 = st2.ss.data\n else:\n # data is being stored in cookie-only form => won't be in the db\n data1 = data2 = {}\n resp = st.get_url('/get_by_sid?sid=%s' % st.ss.sid)\n assert_equal(pickle.loads(b64decode(resp.body)), data1)\n resp = st2.get_url('/get_by_sid?sid=%s' % st2.ss.sid)\n assert_equal(pickle.loads(b64decode(resp.body)), data2)\n expected_num_sessions_in_db_if_db_used(2)\n st.start_request()\n st['y'] = 9 # make the session dirty\n st.save(True) # force it to persist to the db even though it normally wouldn't\n st.finish_request_and_check()\n\n # now the data should be in the db\n resp = st.get_url('/get_by_sid?sid=%s' % st.ss.sid)\n assert_equal(pickle.loads(b64decode(resp.body)), st.ss.data)\n expected_num_sessions_in_db_if_db_used(2, 1)\n st.start_request()\n st.terminate() # remove it from the db\n st.finish_request_and_check()\n expected_num_sessions_in_db_if_db_used(1)\n\n minitest_divider(\"should be able to terminate() and then start a new session all in one request\")\n st.start_request()\n st['y'] = 'yy'\n assert_equal(st.get('y'), 'yy')\n st.terminate()\n assert_raises(KeyError, st.__getitem__, 'y')\n st['x'] = 7\n st.finish_request_and_check()\n expected_num_sessions_in_db_if_db_used(2)\n\n minitest_divider(\"regenerating SID test\")\n initial_sid = st.ss.sid\n st.start_request()\n initial_expir = st.get_expiration()\n st.regenerate_id()\n assert_equal(st['x'], 7, \"data should not be affected\")\n st.finish_request_and_check()\n assert_not_equal(initial_sid, st.ss.sid, \"regenerated sid should be different\")\n assert_equal(initial_expir, st._get_expiration(), \"expiration should not change\")\n st.start_request()\n assert_equal(st['x'], 7, \"data should not be affected\")\n st.finish_request_and_check()\n expected_num_sessions_in_db_if_db_used(2)\n\n minitest_divider(\"regenerating SID test w/new expiration time\")\n initial_sid = st.ss.sid\n st.start_request()\n initial_expir = st.get_expiration()\n new_expir = initial_expir + 120 # something new\n st.regenerate_id(expiration_ts=new_expir)\n assert_equal(st['x'], 7, \"data should not be affected\")\n st.finish_request_and_check()\n assert_not_equal(initial_sid, st.ss.sid, \"regenerated sid should be different\")\n assert_equal(new_expir, st._get_expiration(), \"expiration should be what we asked for\")\n st.start_request()\n assert_equal(st['x'], 7, \"data should not be affected\")\n st.finish_request_and_check()\n expected_num_sessions_in_db_if_db_used(2)\n\n minitest_divider(\"check basic dictionary operations\")\n st.start_request()\n st['s'] = 'aaa'\n st['i'] = 99\n st['f'] = 4.37\n assert_equal(st.pop('s'), 'aaa')\n assert_equal(st.pop('s'), None)\n assert_equal(st.pop('s', 'nil'), 'nil')\n assert st.has_key('i')\n assert not st.has_key('s')\n assert_equal(st.get('i'), 99)\n assert_equal(st.get('ii'), None)\n assert_equal(st.get('iii', 3), 3)\n assert_equal(st.get('f'), st['f'])\n del st['f']\n assert_raises(KeyError, st.__getitem__, 'f')\n assert 'f' not in st\n assert 'i' in st\n assert_equal(st.get('x'), 7)\n st.clear()\n assert 'i' not in st\n assert 'x' not in st\n st.finish_request_and_check()\n\n minitest_divider(\"add complex data (models and objects) to the session\")\n st.start_request()\n st['model'] = make_entity(0)\n st['dict'] = dict(a='alpha', c='charlie', e='echo')\n st['list'] = ['b', 'd', 'f']\n st['set'] = set([2, 3, 5, 7, 11, 13, 17, 19])\n st['tuple'] = (7, 7, 1985)\n st.finish_request_and_check()\n st.start_request()\n st.clear()\n st.finish_request_and_check()\n\n minitest_divider(\"test quick methods: basic usage\")\n st.start_request()\n st.set_quick('msg', 'mc only!')\n assert_equal('mc only!', st['msg'])\n st.finish_request_and_check()\n st.start_request()\n assert_equal('mc only!', st.pop_quick('msg'))\n assert_raises(KeyError, st.__getitem__, 'msg')\n st.finish_request_and_check()\n\n minitest_divider(\"test quick methods: flush memcache (value will be lost if not using cookies)\")\n st.start_request()\n st.set_quick('a', 1)\n st.set_quick('b', 2)\n st.finish_request_and_check()\n st.flush_memcache()\n st.start_request()\n if cookie_only_threshold > 0:\n assert_equal(st['a'], 1)\n assert_equal(st['b'], 2)\n else:\n assert_raises(KeyError, st.__getitem__, 'a')\n assert_raises(KeyError, st.__getitem__, 'b')\n st.finish_request_and_check()\n\n minitest_divider(\"test quick methods: flush memcache should have no impact if another mutator is also used (and this ISNT memcache-only)\")\n st.start_request()\n st['x'] = 24\n st.set_quick('a', 1)\n st.finish_request_and_check()\n st.flush_memcache()\n st.start_request()\n if no_datastore and cookie_only_threshold == 0:\n assert_raises(KeyError, st.__getitem__, 'a')\n assert_raises(KeyError, st.__getitem__, 'x')\n else:\n assert_equal(st['a'], 1)\n assert_equal(st['x'], 24)\n st.set_quick('msg', 'hello')\n st['z'] = 99\n st.finish_request_and_check()","def _setup_app_context_for_test():\n ctx = application.app_context()\n ctx.push()\n yield # tests will run here\n ctx.pop()","def test_index_in_context(self):\n path = '/'\n with self.app.test_client() as client:\n client.get(path)\n self.assertContext('current_page', self.app.get_page(path))","def test_context_creation_and_retrieval(self):\n tracer_id = 'd551573a-01dc-41b2-b197-ea8afb7fbac1'.replace('-', '')\n\n with new_context(tracer_id=tracer_id):\n context = get_context()\n nose.tools.eq_(tracer_id, str(context.tracer_id))","def setUp(self):\n\n app.config['TESTING'] = True\n self.client = app.test_client()\n\n with self.client as c:\n with c.session_transaction() as sess:\n sess['user_id'] = 1","def test_connection_session_switch(self):\n\n connection.set_session(self.session1)\n sync_table(TestConnectModel)\n TCM1 = TestConnectModel.create(id=1, keyspace=self.keyspace1)\n connection.set_session(self.session2)\n sync_table(TestConnectModel)\n TCM2 = TestConnectModel.create(id=1, keyspace=self.keyspace2)\n connection.set_session(self.session1)\n self.assertEqual(1, TestConnectModel.objects.count())\n self.assertEqual(TestConnectModel.objects.first(), TCM1)\n connection.set_session(self.session2)\n self.assertEqual(1, TestConnectModel.objects.count())\n self.assertEqual(TestConnectModel.objects.first(), TCM2)","def setUp(self):\n\n app.config[\"TESTING\"] = True\n app.config[\"SECRET_KEY\"] = \"ABC\"\n self.client = app.test_client()\n\n # Connect to test database\n connect_to_db(app)\n db.drop_all()\n db.create_all()\n load_test()\n\n # Put user1 into session.\n with self.client as c:\n with c.session_transaction() as sess:\n sess[\"current_user\"] = 1","def test_create_Context(self):\n client = IPythonClient()\n dac = Context(client)\n self.assertIs(dac.client, client)\n dac.close()\n client.close()","def test_write_load(self, req_session):\n session = PoorSession(SECRET_KEY)\n session.load(req_session.cookies)\n assert session.data == {'test': True}","def test_add_authenticated_session_var(self):\r\n req = Mock(authname='john', base_path='/', incookie=Cookie())\r\n session = Session(self.env, req)\r\n session['foo'] = 'bar'\r\n session.save()\r\n cursor = self.db.cursor()\r\n cursor.execute(\"SELECT value FROM session_attribute WHERE sid='john'\"\r\n \"AND name='foo'\") \r\n self.assertEqual('bar', cursor.fetchone()[0])","def test_status_persisted(self):\n storage = SessionStorage()\n session1 = storage['key']\n session1['value'] = 'example'\n\n session2 = storage['key']\n self.assertEquals('example', session2['value'])","def test_modify_authenticated_session_var(self):\r\n cursor = self.db.cursor()\r\n cursor.execute(\"INSERT INTO session VALUES ('john', 1, 0)\")\r\n cursor.execute(\"INSERT INTO session_attribute VALUES \"\r\n \"('john', 1, 'foo', 'bar')\")\r\n\r\n req = Mock(authname='john', base_path='/', incookie=Cookie())\r\n session = Session(self.env, req)\r\n self.assertEqual('bar', session['foo'])\r\n session['foo'] = 'baz'\r\n session.save()\r\n cursor.execute(\"SELECT value FROM session_attribute \"\r\n \"WHERE sid='john' AND name='foo'\") \r\n self.assertEqual('baz', cursor.fetchone()[0])","def setUp(self):\n\n # Get the Flask test client.\n self.client = app.test_client()\n app.config[\"TESTING\"] = True\n app.config[\"SECRET_KEY\"] = \"key\"\n app.config[\"SQLALCHEMY_TRACK_MODIFICATIONS\"] = False\n\n with self.client as c:\n with c.session_transaction() as sess:\n sess[\"dietitian_id\"] = 2","def test_im_chat_sessions(self):\n pass","def pytest_sessionstart(session):\n\n db = Database()\n for a in range(1, 4):\n data = {\n 'author': f'test-author-{a}',\n 'text': f'test-text-{a}'\n }\n phrase = PhraseInput(**data)\n db.add(phrase)\n print('created:')\n print(list(db.items.keys()))","def test_authenticated_session(self):\r\n incookie = Cookie()\r\n incookie['trac_session'] = '123456'\r\n outcookie = Cookie()\r\n req = Mock(authname='john', base_path='/', incookie=incookie,\r\n outcookie=outcookie)\r\n session = Session(self.env, req)\r\n self.assertEqual('john', session.sid)\r\n session['foo'] = 'bar'\r\n session.save()\r\n self.assertEquals(0, outcookie['trac_session']['expires'])","def test_client_set_session(self):\n key = load_privatekey(FILETYPE_PEM, server_key_pem)\n cert = load_certificate(FILETYPE_PEM, server_cert_pem)\n ctx = Context(TLSv1_2_METHOD)\n ctx.use_privatekey(key)\n ctx.use_certificate(cert)\n ctx.set_session_id(b\"unity-test\")\n\n def makeServer(socket):\n server = Connection(ctx, socket)\n server.set_accept_state()\n return server\n\n originalServer, originalClient = loopback(server_factory=makeServer)\n originalSession = originalClient.get_session()\n\n def makeClient(socket):\n client = loopback_client_factory(socket)\n client.set_session(originalSession)\n return client\n\n resumedServer, resumedClient = loopback(\n server_factory=makeServer, client_factory=makeClient\n )\n\n # This is a proxy: in general, we have no access to any unique\n # identifier for the session (new enough versions of OpenSSL expose\n # a hash which could be usable, but \"new enough\" is very, very new).\n # Instead, exploit the fact that the master key is re-used if the\n # session is re-used. As long as the master key for the two\n # connections is the same, the session was re-used!\n assert originalServer.master_key() == resumedServer.master_key()","def Session(self):\n return self.context","def test_existing_session_auth_token(self):\n\n test_header = {'X-Auth-Token': 'pretend_token'}\n\n with self.app_sess1 as c:\n ret1 = c.get('/', headers=test_header)\n ret2 = c.get('/', headers=test_header)\n self.assertEqual(ret1.data, ret2.data)","def test_session_is_accessed(self):\n response = self.client.get(\"/auth_processor_attr_access/\")\n self.assertContains(response, \"Session accessed\")","def test_mongo_context_prodmode(self):\n if not helpers.can_connect_to_mongo(helpers.TEST_CONFIG):\n pytest.xfail('no mongo credentials')\n\n mongo_context = schema_utils.MongoContextManager(\n helpers.TEST_CONFIG,\n )\n\n with mongo_context as mongo:\n mongo['test_collection'].insert(self.demo_data)\n\n with mongo_context as _:\n data = mongo['test_collection'].find_one({'butts': True})\n\n assert data['many'] == 10","def test_session( uri ):\n\n # basic setup to handle cookies (all in-memory)\n cj = cookielib.CookieJar()\n opener = urllib2.build_opener( urllib2.HTTPCookieProcessor( cj ) )\n urllib2.install_opener( opener )\n\n # request to get the initial session state\n request = urllib2.Request( uri + '?fetch=true' )\n handle = urllib2.urlopen( request )\n initial_id = get_cookie_value( cj, 'session_test' )\n initial = json.load( handle )\n\n # request to change the session state\n request = urllib2.Request( uri + '?ckey=tkey&cval=tval' )\n handle = urllib2.urlopen( request )\n setup_id = get_cookie_value( cj, 'session_test' )\n setup = json.load( handle )\n\n # request to get the final session state\n request = urllib2.Request( uri + '?fetch=true' )\n handle = urllib2.urlopen( request )\n final_id = get_cookie_value( cj, 'session_test' )\n final = json.load( handle )\n\n # tests consistency of reported session ID\n if initial_id != final_id:\n print 'FAILED: session ID mismatch: %s != %s' % (\n initial_id, final_id\n )\n\n # verifies the host resource isn't cheating\n elif 'tkey' in initial[ 'session' ]:\n print 'FAILED: \"tkey\" present in initial session'\n\n # verifies the updated state has a valid entry\n elif 'tkey' not in final[ 'session' ]:\n print 'FAILED: \"tkey\" not present in final session'\n\n # verifies the updated entry is correct\n elif final[ 'session' ][ 'tkey' ] != 'tval':\n print 'FAILED: \"tkey\" incorrect value \"tval\" != \"%s\"' % (\n final[ 'session' ][ 'tkey' ],\n )\n\n # all checks verified\n else:\n print 'PASSED: \"tkey\" set to \"%s\" in session %s' % (\n final[ 'session' ][ 'tkey' ],\n get_cookie_value( cj, 'session_test' )\n )","def setUp(self):\n\n app.config['TESTING'] = True\n app.config['SECRET_KEY'] = 'key'\n self.client = app.test_client()\n\n with self.client as c:\n with c.session_transaction() as sess:\n sess['user_id'] = 1","def test_new_session(self):\r\n cookie = Cookie()\r\n req = Mock(incookie=Cookie(), outcookie=cookie, authname='anonymous',\r\n base_path='/')\r\n session = Session(self.env, req)\r\n self.assertEqual(session.sid, cookie['trac_session'].value)\r\n cursor = self.db.cursor()\r\n cursor.execute(\"SELECT COUNT(*) FROM session\")\r\n self.assertEqual(0, cursor.fetchone()[0])","def test_execute_with_context(self):\n pass","def test_sessionProperties(self):\n session = self.mdk.session()\n value = [\"123\", {\"12\": 123}]\n session.setProperty(\"key\", value)\n session.setProperty(\"key2\", \"hello\")\n self.assertEqual((session.getProperty(\"key\"), session.getProperty(\"key2\"),\n session.hasProperty(\"key\"), session.hasProperty(\"key2\"),\n session.hasProperty(\"nope\")),\n (value, \"hello\", True, True, False))","def test_add_context():\n\n application_services.add_context('context1', 'value1')\n assert application_services.get_context('context1') == 'value1'","def test_fixture_simple_patch_with_session(testdir):\n\n # create a temporary pytest test module\n testdir.makepyfile(\n \"\"\"\n import requests\n from requests.sessions import Session\n\n def test_simple_with_session(requests_mock):\n with requests_mock.patch('/api/test') as patch:\n patch.returns = requests_mock.good('hello')\n with Session() as s:\n response = s.get('https://test.api/api/test')\n assert response.text == 'hello'\n \"\"\"\n )\n\n # run pytest with the following cmd args\n result = testdir.runpytest(\"-v\")\n\n # fnmatch_lines does an assertion internally\n result.stdout.fnmatch_lines([\"*::test_simple_with_session PASSED*\"])\n\n # make sure that that we get a '0' exit code for the testsuite\n assert result.ret == 0","def test_context_data(self):\n mixin = mixins.SiteMixin(site=Site(domain='test', name='test'))\n ret = mixin.get_context_data()\n # make sure data is just updated, not replaced\n assert ret['builder'] is mixin\n # check if 'site' is in context and if it's valid\n assert ret['site'].domain == 'test' and ret['site'].name == 'test'","def setUp(self):\n\n self.client = server.app.test_client()\n server.app.config['TESTING'] = True\n server.app.config['SECRET_KEY'] = \"123\"\n\n # Connect to test database\n model.connect_to_db(server.app, \"postgresql:///testdb\")\n\n # Create tables and add sample data\n model.db.create_all()\n # example_data()\n\n with self.client as c:\n with c.session_transaction() as session:\n session['user_id'] = 1\n session['username'] = 'j'\n session['name'] = 'l'\n session['cal_id'] = 1","def test_context_data(self):\n response = self.client.get(self.get_url(self.trait.pk))\n context = response.context\n self.assertTrue('form' in context)\n self.assertTrue('trait' in context)\n self.assertEqual(context['trait'], self.trait)","def test_context_data(self):\n response = self.client.get(self.get_url(self.trait.pk))\n context = response.context\n self.assertTrue('form' in context)\n self.assertTrue('trait' in context)\n self.assertEqual(context['trait'], self.trait)","def test_admin_survey_session(self):\n\n # =======================\n # CREATE SURVEY TEST DATA\n # =======================\n\n test_start_time = fields.Datetime.now()\n\n survey_session = self.env['survey.survey'].create({\n 'title': 'User Session Survey',\n 'access_token': 'b137640d-14d4-4748-9ef6-344caaaaafe',\n 'state': 'open',\n 'access_mode': 'public',\n 'users_can_go_back': False,\n 'questions_layout': 'page_per_question',\n 'scoring_type': 'scoring_without_answers'\n })\n\n nickname_question = self.env['survey.question'].create({\n 'survey_id': survey_session.id,\n 'title': 'Nickname',\n 'save_as_nickname': True,\n 'sequence': 1,\n 'question_type': 'char_box',\n })\n text_question = self.env['survey.question'].create({\n 'survey_id': survey_session.id,\n 'title': 'Text Question',\n 'sequence': 2,\n 'question_type': 'char_box',\n })\n date_question = self.env['survey.question'].create({\n 'survey_id': survey_session.id,\n 'title': 'Date Question',\n 'sequence': 3,\n 'question_type': 'date',\n })\n datetime_question = self.env['survey.question'].create({\n 'survey_id': survey_session.id,\n 'title': 'Datetime Question',\n 'sequence': 4,\n 'question_type': 'datetime',\n })\n simple_choice_answer_1 = self.env['survey.question.answer'].create({\n 'value': 'First'\n })\n simple_choice_answer_2 = self.env['survey.question.answer'].create({\n 'value': 'Second'\n })\n simple_choice_answer_3 = self.env['survey.question.answer'].create({\n 'value': 'Third'\n })\n simple_choice_question = self.env['survey.question'].create({\n 'survey_id': survey_session.id,\n 'title': 'Regular Simple Choice',\n 'sequence': 5,\n 'question_type': 'simple_choice',\n 'suggested_answer_ids': [\n (4, simple_choice_answer_1.id),\n (4, simple_choice_answer_2.id),\n (4, simple_choice_answer_3.id)],\n })\n scored_choice_answer_1 = self.env['survey.question.answer'].create({\n 'value': 'Correct',\n 'is_correct': True,\n 'answer_score': 30\n })\n scored_choice_answer_2 = self.env['survey.question.answer'].create({\n 'value': 'Incorrect 1'\n })\n scored_choice_answer_3 = self.env['survey.question.answer'].create({\n 'value': 'Incorrect 2'\n })\n scored_choice_answer_4 = self.env['survey.question.answer'].create({\n 'value': 'Incorrect 3'\n })\n scored_choice_question = self.env['survey.question'].create({\n 'survey_id': survey_session.id,\n 'title': 'Scored Simple Choice',\n 'sequence': 6,\n 'question_type': 'simple_choice',\n 'suggested_answer_ids': [\n (4, scored_choice_answer_1.id),\n (4, scored_choice_answer_2.id),\n (4, scored_choice_answer_3.id),\n (4, scored_choice_answer_4.id)],\n })\n timed_scored_choice_answer_1 = self.env['survey.question.answer'].create({\n 'value': 'Correct',\n 'is_correct': True,\n 'answer_score': 30\n })\n timed_scored_choice_answer_2 = self.env['survey.question.answer'].create({\n 'value': 'Also correct but less points',\n 'is_correct': True,\n 'answer_score': 10\n })\n timed_scored_choice_answer_3 = self.env['survey.question.answer'].create({\n 'value': 'Incorrect',\n 'answer_score': -40\n })\n timed_scored_choice_question = self.env['survey.question'].create({\n 'survey_id': survey_session.id,\n 'title': 'Timed Scored Multiple Choice',\n 'sequence': 6,\n 'question_type': 'multiple_choice',\n 'is_time_limited': True,\n 'time_limit': 1,\n 'suggested_answer_ids': [\n (4, timed_scored_choice_answer_1.id),\n (4, timed_scored_choice_answer_2.id),\n (4, timed_scored_choice_answer_3.id)],\n })\n\n # =======================\n # PART 1 : CREATE SESSION\n # =======================\n\n self.start_tour('/web', 'test_survey_session_create_tour', login='admin')\n\n # tricky part: we only take into account answers created after the session_start_time\n # the create_date of the answers we just saved is set to the beginning of the test.\n # but the session_start_time is set after that.\n # So we cheat on the session start date to be able to count answers properly.\n survey_session.write({'session_start_time': test_start_time - relativedelta(minutes=10)})\n\n attendee_1 = survey_session._create_answer()\n attendee_2 = survey_session._create_answer()\n attendee_3 = survey_session._create_answer()\n all_attendees = [attendee_1, attendee_2, attendee_3]\n\n self.assertEqual('ready', survey_session.session_state)\n self.assertTrue(all(attendee.is_session_answer for attendee in all_attendees),\n \"Created answers should be within the session.\")\n self.assertTrue(all(attendee.state == 'new' for attendee in all_attendees),\n \"Created answers should be in the 'new' state.\")\n\n # =========================================\n # PART 2 : OPEN SESSION AND CHECK ATTENDEES\n # =========================================\n\n self.start_tour('/web', 'test_survey_session_start_tour', login='admin')\n\n self.assertEqual('in_progress', survey_session.session_state)\n self.assertTrue(bool(survey_session.session_start_time))\n\n # ========================================\n # PART 3 : CREATE ANSWERS & MANAGE SESSION\n # ========================================\n\n # create a few answers beforehand to avoid having to back and forth too\n # many times between the tours and the python test\n\n attendee_1.save_lines(nickname_question, 'xxxTheBestxxx')\n attendee_2.save_lines(nickname_question, 'azerty')\n attendee_3.save_lines(nickname_question, 'nicktalope')\n self.assertEqual('xxxTheBestxxx', attendee_1.nickname)\n self.assertEqual('azerty', attendee_2.nickname)\n self.assertEqual('nicktalope', attendee_3.nickname)\n\n attendee_1.save_lines(text_question, 'Attendee 1 is the best')\n attendee_2.save_lines(text_question, 'Attendee 2 rulez')\n attendee_3.save_lines(text_question, 'Attendee 3 will crush you')\n attendee_1.save_lines(date_question, '2010-10-10')\n attendee_2.save_lines(date_question, '2011-11-11')\n attendee_2.save_lines(datetime_question, '2010-10-10 10:00:00')\n attendee_3.save_lines(datetime_question, '2011-11-11 15:55:55')\n attendee_1.save_lines(simple_choice_question, simple_choice_answer_1.id)\n attendee_2.save_lines(simple_choice_question, simple_choice_answer_1.id)\n attendee_3.save_lines(simple_choice_question, simple_choice_answer_2.id)\n attendee_1.save_lines(scored_choice_question, scored_choice_answer_1.id)\n attendee_2.save_lines(scored_choice_question, scored_choice_answer_2.id)\n attendee_3.save_lines(scored_choice_question, scored_choice_answer_3.id)\n attendee_1.save_lines(timed_scored_choice_question,\n [timed_scored_choice_answer_1.id, timed_scored_choice_answer_3.id])\n attendee_2.save_lines(timed_scored_choice_question,\n [timed_scored_choice_answer_1.id, timed_scored_choice_answer_2.id])\n attendee_3.save_lines(timed_scored_choice_question,\n [timed_scored_choice_answer_2.id])\n\n self.start_tour('/web', 'test_survey_session_manage_tour', login='admin')\n\n self.assertFalse(bool(survey_session.session_state))\n self.assertTrue(all(answer.state == 'done' for answer in all_attendees))","def test_session_creation(self):\n response = self.client.get(reverse('index'))\n self.assertEqual(self.client.session['liked_user_ids'], [])","def test_session_state_for_used_flash(self):\n self.response = self.client.get(reverse(views.set_flash_var))\n self.response = self.client.get(reverse(views.render_template))\n self.assertTrue(_SESSION_KEY in self.client.session)\n\n # Flash scope should be removed from the session\n self.response = self.client.get(reverse(views.render_template))\n self.assertFalse(_SESSION_KEY in self.client.session)","def test_do_logout(self):\r\n with app.test_request_context():\r\n u1 = User.query.filter_by(username='testuser').one()\r\n\r\n do_login(u1)\r\n self.assertIn(CURR_USER_KEY, session)\r\n do_logout()\r\n self.assertNotIn(CURR_USER_KEY, session)","def test_run_container2(client):\n with client.application.app_context():\n pytest.skip(\"Not implemented\")","def session(get_session):\n return get_session()","def test_database_sessions(web_app):\n db = DB(connect_url=TEST_URL, web_app=web_app, echo=web_app)\n db.init()\n with db.session() as session:\n archive = Archive(archive_id='0000', name='test')\n session.add(archive)\n with pytest.raises(IntegrityError):\n with db.session() as session:\n archive = Archive(archive_id='0000', name='test')\n session.add(archive)\n with pytest.raises(ValueError):\n with db.session() as session:\n archive = Archive(archive_id='0001', name='test')\n session.add(archive)\n raise ValueError('test')\n db.close()","def setUp(self):\n\n self.client = server.app.test_client()\n server.app.config['TESTING'] = True\n server.app.config['SECRET_KEY'] = \"123\"\n\n # Connect to test database\n model.connect_to_db(server.app, \"postgresql:///testdb\")\n\n # Create tables and add sample data\n model.db.create_all()\n # example_data()\n\n with self.client as c:\n with c.session_transaction() as session:\n session['user_id'] = 33\n session['username'] = 'balloonicorn'\n session['name'] = 'balloonicorn'","def __init__(self, session):\n self._session = session","def test_init_with_request():\n serializers.SessionCreationSerializer(context={\"request\": \"foo\"})","def session_context(func):\r\n def wrapper(*args, **kwargs):\r\n self = args[0]\r\n with self._create_db_session() as db:\r\n self.db = db\r\n return func(*args, **kwargs)\r\n return wrapper","def test_some_page_in_context(self):\n path = '/about/history'\n with self.app.test_client() as client:\n client.get(path)\n self.assertContext('current_page', self.app.get_page(path))","def test_session(self, k8sconfig):\n # Basic.\n config = k8sconfig._replace(token=None)\n sess = k8s.session(config)\n assert \"authorization\" not in sess.headers\n\n # With access token.\n config = k8sconfig._replace(token=\"token\")\n sess = k8s.session(config)\n assert sess.headers[\"authorization\"] == \"Bearer token\"\n\n # With access token and client certificate.\n ccert = k8s.K8sClientCert(crt=\"foo\", key=\"bar\")\n config = k8sconfig._replace(token=\"token\", client_cert=ccert)\n sess = k8s.session(config)\n assert sess.headers[\"authorization\"] == \"Bearer token\"\n assert sess.cert == (\"foo\", \"bar\")","def test_user_get_topteams():\n app = create_ctfd()\n with app.app_context():\n register_user(app)\n client = login_as_user(app)\n r = client.get('/top/10')\n assert r.status_code == 200\n destroy_ctfd(app)","def test_api_livesession_read_token_lti_admin_instruct_record_course_diff(self):\n other_consumer_site = ConsumerSiteFactory()\n # livesession with consumer_site\n livesession = LiveSessionFactory(\n consumer_site=other_consumer_site,\n email=\"admin@openfun.fr\", # explicit to be found in response\n lti_user_id=\"56255f3807599c377bf0e5bf072359fd\", # explicit to be found in response\n lti_id=\"Maths\", # explicit to be found in response\n )\n\n # token with context_id leading to another consumer site\n jwt_token = InstructorOrAdminLtiTokenFactory(\n playlist=livesession.video.playlist,\n context_id=f\"{livesession.video.playlist.lti_id}_diff\",\n # consumer_site is not other_consumer_site\n consumer_site=str(livesession.video.playlist.consumer_site.id),\n user__email=livesession.email,\n user__id=livesession.lti_user_id,\n user__username=livesession.username,\n )\n\n response = self.client.get(\n self._get_url(livesession.video, livesession),\n HTTP_AUTHORIZATION=f\"Bearer {jwt_token}\",\n )\n self.assertEqual(response.status_code, 200)\n self.assertEqual(\n response.json(),\n {\n \"anonymous_id\": None,\n \"consumer_site\": str(other_consumer_site.id),\n \"display_name\": None,\n \"email\": \"admin@openfun.fr\",\n \"id\": str(livesession.id),\n \"is_registered\": False,\n \"language\": \"en\",\n \"live_attendance\": None,\n \"lti_user_id\": \"56255f3807599c377bf0e5bf072359fd\",\n \"lti_id\": \"Maths\",\n \"should_send_reminders\": True,\n \"username\": None,\n \"video\": str(livesession.video.id),\n },\n )","def test_distinct_sessions_auth_token(self):\n\n sess1 = None\n sess2 = None\n\n with self.app_sess1 as c:\n ret = c.get('/', headers={'X-Auth-Token': 'pretend_token'})\n sess1 = ret.data\n\n with self.app_sess2 as c:\n ret = c.get('/', headers={'X-Auth-Token': 'another_pretend_token'})\n sess2 = ret.data\n\n self.assertNotEqual(sess1, sess2)","def test_where(server, session):\n\n window = session.attached_window\n window.split_window() # create second pane\n\n for session in server.sessions:\n session_id = session.get('session_id')\n session_name = session.get('session_name')\n where = server.where({'session_id': session_id, 'session_name': session_name})\n\n assert len(where) == 1\n assert isinstance(where, list)\n assert where[0] == session\n assert isinstance(where[0], Session)\n\n # session.where\n for window in session.windows:\n window_id = window.get('window_id')\n window_index = window.get('window_index')\n\n where = session.where(\n {'window_id': window_id, 'window_index': window_index}\n )\n\n assert len(where) == 1\n assert isinstance(where, list)\n assert where[0] == window\n assert isinstance(where[0], Window)\n\n # window.where\n for pane in window.panes:\n pane_id = pane.get('pane_id')\n pane_tty = pane.get('pane_tty')\n\n where = window.where({'pane_id': pane_id, 'pane_tty': pane_tty})\n\n assert len(where) == 1\n assert isinstance(where, list)\n assert where[0] == pane\n assert isinstance(where[0], Pane)","async def session(self,ctx):\n if ctx.invoked_subcommand is None:\n await ctx.send(\"The current main session is \" + \"```\" + await self.config.sessions.main() + \"```\")","def test_get_by_id(server, session):\n\n window = session.attached_window\n\n window.split_window() # create second pane\n\n for session in server.sessions:\n session_id = session.get('session_id')\n get_by_id = server.get_by_id(session_id)\n\n assert get_by_id == session\n assert isinstance(get_by_id, Session)\n assert server.get_by_id('$' + next(namer)) is None\n\n # session.get_by_id\n for window in session.windows:\n window_id = window.get('window_id')\n\n get_by_id = session.get_by_id(window_id)\n\n assert get_by_id == window\n assert isinstance(get_by_id, Window)\n\n assert session.get_by_id('@' + next(namer)) is None\n\n # window.get_by_id\n for pane in window.panes:\n pane_id = pane.get('pane_id')\n\n get_by_id = window.get_by_id(pane_id)\n\n assert get_by_id == pane\n assert isinstance(get_by_id, Pane)\n assert window.get_by_id('%' + next(namer)) is None","def session_context(self):\n session = self.Session()\n try:\n yield session\n session.commit()\n except: # noqa E722\n session.rollback()\n raise\n finally:\n session.close()","def test_inner_live_setup(self):\n mocktable = self.classes.MockTable\n session = Session(bind=self.bind)\n session.add(mocktable(test=5))\n session.commit()\n session.close()\n\n res = session.query(mocktable).all()\n session.close()\n expected = (res[0].test, len(res))\n\n assert expected == (5, 1)","def setUp(self):\n\n # Get the Flask test client\n self.client = app.test_client()\n\n # Show Flask errors that happen during tests\n app.config['TESTING'] = True\n\n #To test sessions we need to set Secret key \n app.config['SECRET_KEY'] = 'key'\n\n # Connect to test database\n connect_to_db(app, \"postgresql:///testdb\")\n\n # Create tables and add sample data\n db.create_all()\n users()\n reviews()\n\n with self.client as c:\n with c.session_transaction() as sess:\n sess['user_id'] = 1","def test_update_session(self):\r\n now = time.time()\r\n\r\n # Make sure the session has data so that it doesn't get dropped\r\n cursor = self.db.cursor()\r\n cursor.execute(\"INSERT INTO session VALUES ('123456', 0, 1)\")\r\n cursor.execute(\"INSERT INTO session_attribute VALUES \"\r\n \"('123456', 0, 'foo', 'bar')\")\r\n\r\n incookie = Cookie()\r\n incookie['trac_session'] = '123456'\r\n outcookie = Cookie()\r\n req = Mock(authname='anonymous', base_path='/', incookie=incookie,\r\n outcookie=outcookie)\r\n session = Session(self.env, req)\r\n session.save() # updating should not require modifications\r\n\r\n self.assertEqual(PURGE_AGE, outcookie['trac_session']['expires'])\r\n\r\n cursor.execute(\"SELECT last_visit FROM session WHERE sid='123456' AND \"\r\n \"authenticated=0\")\r\n self.assertAlmostEqual(now, int(cursor.fetchone()[0]), -1)","def test_api_livesession_read_token_lti_admin_instruct_record_consumer_diff(self):\n other_consumer_site = ConsumerSiteFactory()\n # livesession with consumer_site\n livesession = LiveSessionFactory(\n consumer_site=other_consumer_site,\n is_from_lti_connection=True,\n email=\"admin@openfun.fr\", # explicit to be found in response\n lti_user_id=\"56255f3807599c377bf0e5bf072359fd\", # explicit to be found in response\n )\n\n # token with context_id leading to another consumer site\n jwt_token = InstructorOrAdminLtiTokenFactory(\n playlist=livesession.video.playlist,\n context_id=str(livesession.video.playlist.lti_id),\n # consumer_site is not other_consumer_site\n consumer_site=str(livesession.video.playlist.consumer_site.id),\n )\n\n response = self.client.get(\n self._get_url(livesession.video, livesession),\n HTTP_AUTHORIZATION=f\"Bearer {jwt_token}\",\n )\n self.assertEqual(response.status_code, 200)\n self.assertEqual(\n response.json(),\n {\n \"anonymous_id\": None,\n \"consumer_site\": str(other_consumer_site.id),\n \"display_name\": None,\n \"email\": \"admin@openfun.fr\",\n \"id\": str(livesession.id),\n \"is_registered\": False,\n \"language\": \"en\",\n \"live_attendance\": None,\n \"lti_user_id\": \"56255f3807599c377bf0e5bf072359fd\",\n \"lti_id\": str(livesession.video.playlist.lti_id),\n \"should_send_reminders\": True,\n \"username\": None,\n \"video\": str(livesession.video.id),\n },\n )","def setUp(self):\n self.hello_url = \"http://localhost:7000\"\n self.store_url = self.hello_url + \"/store\"\n self.session = requests.session()","def iter_sessions():\n return iter(_session_stack)","def test_multiple_requests(self):\n s = self.api.session()\n s.request(\"1.2.3.4\", \"mozilla\", \"/foo/bar\").end()\n s.request(\"1.2.3.4\", \"mozilla\", \"/foo/blah\").end()\n s.end()\n data = self.connector.transcription()\n assert len(data) == 2\n assert data[0].get('action') == \"session_start\"\n assert data[1].get('action') == \"session_end\"","def getSession():\n return call(\"getSession\")","def test_homepage_logged_in(self):\r\n u1 = User.query.filter_by(username='testuser').one()\r\n\r\n with self.client as c:\r\n with c.session_transaction() as sess:\r\n sess[CURR_USER_KEY] = u1.id\r\n response = c.get('/')\r\n self.assertEqual(response.status_code, 200)\r\n self.assertIn(b'Virginia News', response.data)","def test_new_session_create_with_auth_json(self):\n\n with self.app_sess1 as c:\n data = {\n \"token\": \"pretend_token\"\n }\n ret1 = c.post('/', data=json.dumps(data), headers={'Content-Type': 'application/json'})\n ret2 = c.get('/', headers={'X-Auth-Token': 'pretend_token'})\n\n self.assertEqual(ret1.data, ret2.data)","def client(app):\n conn = engine.connect()\n transaction = conn.begin()\n app.db_session = create_session(conn)\n yield app.test_client()\n # No need to retain the junk we\n # put in the DB for testing.\n transaction.rollback()\n conn.close()\n app.db_session.remove()","def establish_session(func, db):\n def inner(*args, **kwargs):\n Session = sessionmaker(bind=db)\n session = Session()\n kwargs['session'] = session\n res = func(*args, **kwargs)\n return res\n\n return inner","def test_context_data(self):\n tagged_traits = TaggedTraitFactory.create_batch(2, trait=self.trait)\n response = self.client.get(self.get_url(self.trait.pk))\n context = response.context\n self.assertIn('source_trait', context)\n self.assertEqual(context['source_trait'], self.trait)\n self.assertIn('tagged_traits_with_xs', context)\n self.assertEqual([el[0] for el in context['tagged_traits_with_xs']],\n list(self.trait.all_taggedtraits.non_archived()))\n self.assertIn('user_is_study_tagger', context)\n self.assertFalse(context['user_is_study_tagger'])\n self.assertIn('is_deprecated', context)\n self.assertIn('show_removed_text', context)\n self.assertIn('new_version_link', context)","def loop_context():\n loop = setup_test_loop()\n yield loop\n # teardown_test_loop(loop)","def test_mongo_context_testmode(self, tmpdir):\n mongo_context = schema_utils.MongoContextManager(\n helpers.TEST_CONFIG,\n _testmode=True,\n _testmode_filepath=tmpdir,\n )\n\n with mongo_context as t_mongo:\n t_mongo['test_collection'].insert(self.demo_data)\n\n with mongo_context as t_mongo:\n data = t_mongo['test_collection'].find_one({'butts': True})\n\n assert data['many'] == 10","def _context():\n global _trident_context\n if _trident_context is None:\n _trident_context = _Context()\n return _trident_context"],"string":"[\n \"def test_set_session():\",\n \"def test_resource(data_manager):\\n sessions = set([])\\n with data_manager.dal():\\n context1 = current_context._get_current_object()\\n session = context1.sqlalchemy\\n assert isinstance(session, orm.Session)\\n sessions.add(session)\\n\\n with data_manager.dal():\\n context2 = current_context._get_current_object()\\n assert context2 != context1\\n session = context2.sqlalchemy\\n assert isinstance(session, orm.Session)\\n sessions.add(session)\\n\\n # Make sure we have two unique sessions\\n assert len(sessions) == 2\",\n \"def test_existing_session_cookie(self):\\n\\n with self.app_sess1 as c:\\n ret1 = c.get('/')\\n ret2 = c.get('/')\\n self.assertEqual(ret1.data, ret2.data)\",\n \"def test_session_promotion(self):\\r\\n cursor = self.db.cursor()\\r\\n cursor.execute(\\\"INSERT INTO session VALUES ('123456', 0, 0)\\\")\\r\\n incookie = Cookie()\\r\\n incookie['trac_session'] = '123456'\\r\\n outcookie = Cookie()\\r\\n req = Mock(authname='john', base_path='/', incookie=incookie,\\r\\n outcookie=outcookie)\\r\\n session = Session(self.env, req)\\r\\n self.assertEqual('john', session.sid)\\r\\n session.save()\\r\\n\\r\\n cursor.execute(\\\"SELECT sid,authenticated FROM session\\\")\\r\\n self.assertEqual(('john', 1), cursor.fetchone())\\r\\n self.assertEqual(None, cursor.fetchone())\",\n \"def session(db):\\n db.session.begin_nested()\\n\\n yield db.session\\n\\n db.session.rollback()\",\n \"def test_new_session_promotion(self):\\r\\n cursor = self.db.cursor()\\r\\n incookie = Cookie()\\r\\n incookie['trac_session'] = '123456'\\r\\n outcookie = Cookie()\\r\\n req = Mock(authname='john', base_path='/', incookie=incookie,\\r\\n outcookie=outcookie)\\r\\n session = Session(self.env, req)\\r\\n self.assertEqual('john', session.sid)\\r\\n session.save()\\r\\n\\r\\n cursor.execute(\\\"SELECT sid,authenticated FROM session\\\")\\r\\n self.assertEqual(('john', 1), cursor.fetchone())\\r\\n self.assertEqual(None, cursor.fetchone())\",\n \"def test_newSession(self):\\n session = self.mdk.session()\\n session2 = self.mdk.session()\\n self.assertSessionHas(session, session._context.traceId, [0])\\n self.assertSessionHas(session2, session2._context.traceId, [0])\\n self.assertNotEqual(session._context.traceId,\\n session2._context.traceId)\",\n \"def test_childSession(self):\\n session = self.mdk.session()\\n session.setProperty(\\\"other\\\", 123)\\n session._context.tick()\\n session._context.tick()\\n session._context.tick()\\n session.setTimeout(13.0)\\n session2 = self.mdk.derive(session.externalize())\\n self.assertNotEqual(session._context.traceId,\\n session2._context.traceId)\\n self.assertEqual(session2.getRemainingTime(), None)\\n self.assertSessionHas(session2, session2._context.traceId, [1],\\n other=123)\",\n \"def db_subsession(session):\\n try:\\n with session.begin_nested():\\n yield\\n except:\\n logger.exception(\\\"Problem with DB sub-session, rolling back.\\\")\",\n \"def test_do_login(self):\\r\\n\\r\\n with app.test_request_context():\\r\\n u1 = User.query.filter_by(username='testuser').one()\\r\\n\\r\\n self.assertNotIn(CURR_USER_KEY, session)\\r\\n do_login(u1)\\r\\n self.assertEqual(session[CURR_USER_KEY], u1.id)\",\n \"def session(self):\",\n \"def test_distinct_sessions_cookie(self):\\n\\n sess1 = None\\n sess2 = None\\n with self.app_sess1 as c:\\n sess1 = c.get('/').data\\n\\n with self.app_sess2 as c:\\n sess2 = c.get('/').data\\n\\n self.assertNotEqual(sess1, sess2)\",\n \"def test_client_custom_session():\\n c_session = requests.Session()\\n client = ConfigureClients(custom_session=c_session)\\n assert client.session == c_session\",\n \"def test_joinSession(self):\\n session = self.mdk.session()\\n session.setProperty(\\\"key\\\", 456)\\n session.setProperty(\\\"key2\\\", [456, {\\\"zoo\\\": \\\"foo\\\"}])\\n session2 = self.mdk.join(session.externalize())\\n self.assertSessionHas(session2, session._context.traceId, [1, 0],\\n key=456, key2=[456, {\\\"zoo\\\": \\\"foo\\\"}])\",\n \"def session(request):\\n session = get_test_db_session()\\n request.cls.session = session\\n return session\",\n \"def test_session_auth_token(self):\\n\\n sess1 = None\\n sess2 = None\\n test_header = {'X-Auth-Token': 'pretend_token'}\\n\\n with self.app_sess1 as c:\\n ret = c.get('/', headers=test_header)\\n sess1 = ret.data\\n\\n with self.app_sess2 as c:\\n ret = c.get('/', headers=test_header)\\n sess2 = ret.data\\n\\n self.assertEqual(sess1, sess2)\",\n \"def test_find_where_multiple_infos(server, session):\\n\\n for session in server.sessions:\\n session_id = session.get('session_id')\\n session_name = session.get('session_name')\\n find_where = server.find_where(\\n {'session_id': session_id, 'session_name': session_name}\\n )\\n\\n assert find_where == session\\n assert isinstance(find_where, Session)\\n\\n # session.find_where\\n for window in session.windows:\\n window_id = window.get('window_id')\\n window_index = window.get('window_index')\\n\\n find_where = session.find_where(\\n {'window_id': window_id, 'window_index': window_index}\\n )\\n\\n assert find_where == window\\n assert isinstance(find_where, Window)\\n\\n # window.find_where\\n for pane in window.panes:\\n pane_id = pane.get('pane_id')\\n pane_tty = pane.get('pane_tty')\\n\\n find_where = window.find_where(\\n {'pane_id': pane_id, 'pane_tty': pane_tty}\\n )\\n\\n assert find_where == pane\\n assert isinstance(find_where, Pane)\",\n \"def test_get_session(self):\\n with self.settings(SESSION_ENGINE='django.contrib.sessions.backends.db'):\\n self._run_expired_session_test_for_engine()\\n with self.settings(SESSION_ENGINE='django.contrib.sessions.backends.cache'):\\n self._run_expired_session_test_for_engine()\\n with self.settings(SESSION_ENGINE='django.contrib.sessions.backends.file'):\\n self._run_expired_session_test_for_engine()\\n with self.settings(SESSION_ENGINE='django.contrib.sessions.backends.cached_db'):\\n self._run_expired_session_test_for_engine()\",\n \"def session(self, context: InjectionContext = None) -> \\\"ProfileSession\\\":\",\n \"def test_modify_detached_session(self):\\r\\n cursor = self.db.cursor()\\r\\n cursor.execute(\\\"INSERT INTO session VALUES ('john', 1, 0)\\\")\\r\\n cursor.execute(\\\"INSERT INTO session_attribute VALUES \\\"\\r\\n \\\"('john', 1, 'foo', 'bar')\\\")\\r\\n\\r\\n session = DetachedSession(self.env, 'john')\\r\\n self.assertEqual('bar', session['foo'])\\r\\n session['foo'] = 'baz'\\r\\n session.save()\\r\\n cursor.execute(\\\"SELECT value FROM session_attribute \\\"\\r\\n \\\"WHERE sid='john' AND name='foo'\\\")\\r\\n self.assertEqual('baz', cursor.fetchone()[0])\",\n \"def test_sessions():\\n CHECKS = (check_correct_usage, check_expiration, check_bad_cookie, check_various_session_sizes)\\n for no_datastore in (False, True):\\n if no_datastore:\\n test_db = 'without'\\n else:\\n test_db = 'with'\\n for cot in (0, 10*1024, 2**30):\\n if cot == 0:\\n test_cookie = 'no data stored in cookies'\\n elif cot == 2**30:\\n test_cookie = 'data only stored in cookies'\\n else:\\n test_cookie = 'store data in cookies when its encoded size<=%dB' % cot\\n for check in CHECKS:\\n logger.debug('\\\\n\\\\n' + '*'*50)\\n logger.debug('Running %s %s datastore and %s' % (check.__name__, test_db, test_cookie))\\n yield check, no_datastore, cot\",\n \"def test_set_session_id(self, context):\\n context.set_session_id(b\\\"abc\\\")\",\n \"def test_server_get_session(self):\\n server, client = loopback()\\n session = server.get_session()\\n assert isinstance(session, Session)\",\n \"def session_context(pytestconfig, request, tmp_env):\\n ctx = Context.only()\\n\\n # Temporary, empty local directory for local data\\n session_tmp_dir = Path(request.config._tmp_path_factory.mktemp(\\\"data\\\"))\\n\\n # Set the cache path according to whether pytest --local-cache was given. If True,\\n # pick up the existing setting from the user environment. If False, use a pytest-\\n # managed cache directory that persists across test sessions.\\n ctx.cache_path = (\\n ctx.local_data.joinpath(\\\"cache\\\")\\n if request.config.option.local_cache\\n # TODO use pytestconfig.cache.mkdir() when pytest >= 6.3 is available\\n else Path(pytestconfig.cache.makedir(\\\"cache\\\"))\\n )\\n\\n # Other local data in the temporary directory for this session only\\n ctx.local_data = session_tmp_dir\\n\\n platform_name = \\\"message-ix-models\\\"\\n\\n # Add a platform connected to an in-memory database\\n # NB cannot call Config.add_platform() here because it does not support supplying a\\n # URL for a HyperSQL database.\\n # TODO add that feature upstream.\\n ixmp_config.values[\\\"platform\\\"][platform_name] = {\\n \\\"class\\\": \\\"jdbc\\\",\\n \\\"driver\\\": \\\"hsqldb\\\",\\n \\\"url\\\": f\\\"jdbc:hsqldb:mem://{platform_name}\\\",\\n }\\n\\n # Launch Platform and connect to testdb (reconnect if closed)\\n mp = Platform(name=platform_name)\\n mp.open_db()\\n\\n ctx.platform_info[\\\"name\\\"] = platform_name\\n\\n yield ctx\\n\\n ctx.close_db()\\n ixmp_config.remove_platform(platform_name)\",\n \"def load_session(session):\\n def inner():\\n web.ctx.session = session\\n return inner\",\n \"def test_client_get_session(self):\\n server, client = loopback()\\n session = client.get_session()\\n assert isinstance(session, Session)\",\n \"def test_get_activities_from_recursive_contexts(self):\\n from .mockers import context_query\\n from .mockers import create_context\\n from .mockers import subscribe_contextA, create_contextA, user_status_contextA\\n from .mockers import subscribe_contextB, create_contextB, user_status_contextB\\n username = 'messi'\\n username_not_me = 'xavi'\\n self.create_user(username)\\n self.create_user(username_not_me)\\n self.create_context(create_context, permissions=dict(read='public', write='restricted', subscribe='restricted', invite='restricted'))\\n self.create_context(create_contextA, permissions=dict(read='subscribed', write='subscribed', subscribe='restricted', invite='restricted'))\\n self.create_context(create_contextB, permissions=dict(read='subscribed', write='subscribed', subscribe='restricted', invite='restricted'))\\n self.admin_subscribe_user_to_context(username, subscribe_contextA)\\n self.admin_subscribe_user_to_context(username_not_me, subscribe_contextA)\\n self.admin_subscribe_user_to_context(username_not_me, subscribe_contextB)\\n self.create_activity(username, user_status_contextA)\\n self.create_activity(username_not_me, user_status_contextA)\\n self.create_activity(username_not_me, user_status_contextB)\\n\\n res = self.testapp.get('/contexts/%s/activities' % (context_query['context']), '', oauth2Header(username), status=200)\\n result = json.loads(res.text)\\n self.assertEqual(len(result), 2)\\n self.assertEqual(result[0].get('actor', None).get('username'), 'xavi')\\n self.assertEqual(result[0].get('object', None).get('objectType', None), 'note')\\n self.assertEqual(result[0].get('contexts', None)[0]['url'], subscribe_contextA['object']['url'])\\n self.assertEqual(result[1].get('actor', None).get('username'), 'messi')\\n self.assertEqual(result[1].get('object', None).get('objectType', None), 'note')\\n self.assertEqual(result[1].get('contexts', None)[0]['url'], subscribe_contextA['object']['url'])\\n\\n res = self.testapp.get('/contexts/%s/activities' % (context_query['context']), '', oauth2Header(username_not_me), status=200)\\n result = json.loads(res.text)\\n self.assertEqual(len(result), 3)\\n self.assertEqual(result[0].get('actor', None).get('username'), 'xavi')\\n self.assertEqual(result[0].get('object', None).get('objectType', None), 'note')\\n self.assertEqual(result[0].get('contexts', None)[0]['url'], subscribe_contextB['object']['url'])\\n self.assertEqual(result[1].get('actor', None).get('username'), 'xavi')\\n self.assertEqual(result[1].get('object', None).get('objectType', None), 'note')\\n self.assertEqual(result[1].get('contexts', None)[0]['url'], subscribe_contextA['object']['url'])\\n self.assertEqual(result[2].get('actor', None).get('username'), 'messi')\\n self.assertEqual(result[2].get('object', None).get('objectType', None), 'note')\\n self.assertEqual(result[2].get('contexts', None)[0]['url'], subscribe_contextA['object']['url'])\",\n \"def test_create_session(self):\\n study_id = self.storage.create_study(sample_study_spec())\\n\\n session = sample_session(study_id=study_id)\\n self.storage.create_session(session)\\n\\n self.assertEqual(self.storage.get_session(study_id, session.id), session)\",\n \"def test_scoring_logic():\\n app = create_ctfd()\\n with app.app_context():\\n admin = login_as_user(app, name=\\\"admin\\\", password=\\\"password\\\")\\n\\n register_user(app, name=\\\"user1\\\", email=\\\"user1@ctfd.io\\\", password=\\\"password\\\")\\n client1 = login_as_user(app, name=\\\"user1\\\", password=\\\"password\\\")\\n register_user(app, name=\\\"user2\\\", email=\\\"user2@ctfd.io\\\", password=\\\"password\\\")\\n client2 = login_as_user(app, name=\\\"user2\\\", password=\\\"password\\\")\\n\\n chal1 = gen_challenge(app.db)\\n flag1 = gen_flag(app.db, chal=chal1.id, flag='flag')\\n chal1_id = chal1.id\\n\\n chal2 = gen_challenge(app.db)\\n flag2 = gen_flag(app.db, chal=chal2.id, flag='flag')\\n chal2_id = chal2.id\\n\\n # user1 solves chal1\\n with freeze_time(\\\"2017-10-3 03:21:34\\\"):\\n with client1.session_transaction() as sess:\\n data = {\\n \\\"key\\\": 'flag',\\n \\\"nonce\\\": sess.get('nonce')\\n }\\n r = client1.post('/chal/{}'.format(chal1_id), data=data)\\n\\n # user1 is now on top\\n scores = get_scores(admin)\\n assert scores[0]['team'] == 'user1'\\n\\n # user2 solves chal1 and chal2\\n with freeze_time(\\\"2017-10-4 03:30:34\\\"):\\n with client2.session_transaction() as sess:\\n # solve chal1\\n data = {\\n \\\"key\\\": 'flag',\\n \\\"nonce\\\": sess.get('nonce')\\n }\\n r = client2.post('/chal/{}'.format(chal1_id), data=data)\\n # solve chal2\\n data = {\\n \\\"key\\\": 'flag',\\n \\\"nonce\\\": sess.get('nonce')\\n }\\n r = client2.post('/chal/{}'.format(chal2_id), data=data)\\n\\n # user2 is now on top\\n scores = get_scores(admin)\\n assert scores[0]['team'] == 'user2'\\n\\n # user1 solves chal2\\n with freeze_time(\\\"2017-10-5 03:50:34\\\"):\\n with client1.session_transaction() as sess:\\n data = {\\n \\\"key\\\": 'flag',\\n \\\"nonce\\\": sess.get('nonce')\\n }\\n r = client1.post('/chal/{}'.format(chal2_id), data=data)\\n\\n # user2 should still be on top because they solved chal2 first\\n scores = get_scores(admin)\\n assert scores[0]['team'] == 'user2'\\n destroy_ctfd(app)\",\n \"def test_find_where(server, session):\\n # server.find_where\\n for session in server.sessions:\\n session_id = session.get('session_id')\\n\\n assert server.find_where({'session_id': session_id}) == session\\n assert isinstance(server.find_where({'session_id': session_id}), Session)\\n\\n # session.find_where\\n for window in session.windows:\\n window_id = window.get('window_id')\\n\\n assert session.find_where({'window_id': window_id}) == window\\n assert isinstance(session.find_where({'window_id': window_id}), Window)\\n\\n # window.find_where\\n for pane in window.panes:\\n pane_id = pane.get('pane_id')\\n\\n assert window.find_where({'pane_id': pane_id}) == pane\\n assert isinstance(window.find_where({'pane_id': pane_id}), Pane)\",\n \"def test_get_db_session(initialized_db_url):\\n with utils.get_db_session(initialized_db_url) as db:\\n assert isinstance(db, Session)\",\n \"def test_get_context():\\n\\n application_services.add_context('context4', 'value4')\\n assert application_services.get_context('context4') == 'value4'\",\n \"def check_correct_usage(no_datastore, cookie_only_threshold):\\n def minitest_divider(test):\\n logger.debug('\\\\n\\\\n' + '-'*50)\\n logger.debug(test + ' (nd=%s cot=%s)' % (no_datastore, cookie_only_threshold))\\n\\n st = SessionTester(no_datastore=no_datastore, cookie_only_threshold=cookie_only_threshold)\\n expected_num_sessions_in_db_if_db_used = lambda a,b=0 : generic_expected_num_sessions_in_db_if_db_used(st, no_datastore, cookie_only_threshold, a, b)\\n st.verify_active_sessions_in_db(0)\\n\\n minitest_divider('try doing nothing (no session should be started)')\\n st.noop()\\n st.verify_active_sessions_in_db(0)\\n\\n minitest_divider('start a session with a single write')\\n st.start_request()\\n str(st)\\n assert st.get_expiration()==0, \\\"no session yet => no expiration yet\\\"\\n assert st.is_active() is False\\n st['x'] = 7\\n assert st.is_active() is True\\n st.finish_request_and_check()\\n expected_num_sessions_in_db_if_db_used(1)\\n\\n minitest_divider('start another session')\\n st2 = SessionTester(st=st)\\n st2.start_request()\\n assert not st2.is_active()\\n assert st2.get('x') is None, \\\"shouldn't get other session's data\\\"\\n assert not st2.is_active(), \\\"still shouldn't be active - nothing set yet\\\"\\n st2['x'] = 'st2x'\\n assert st2.is_active()\\n st2.finish_request_and_check()\\n expected_num_sessions_in_db_if_db_used(2)\\n\\n minitest_divider('each session should get a unique sid')\\n assert st2.ss.sid != st.ss.sid\\n\\n minitest_divider('we should still have the values we set earlier')\\n st.start_request()\\n str(st)\\n assert_equal(st['x'], 7)\\n st.finish_request_and_check()\\n st2.start_request()\\n assert_equal(st2['x'], 'st2x')\\n st2.finish_request_and_check()\\n\\n minitest_divider(\\\"check get session by sid, save(True), and terminate()\\\")\\n if cookie_only_threshold == 0:\\n data1 = st.ss.data\\n data2 = st2.ss.data\\n else:\\n # data is being stored in cookie-only form => won't be in the db\\n data1 = data2 = {}\\n resp = st.get_url('/get_by_sid?sid=%s' % st.ss.sid)\\n assert_equal(pickle.loads(b64decode(resp.body)), data1)\\n resp = st2.get_url('/get_by_sid?sid=%s' % st2.ss.sid)\\n assert_equal(pickle.loads(b64decode(resp.body)), data2)\\n expected_num_sessions_in_db_if_db_used(2)\\n st.start_request()\\n st['y'] = 9 # make the session dirty\\n st.save(True) # force it to persist to the db even though it normally wouldn't\\n st.finish_request_and_check()\\n\\n # now the data should be in the db\\n resp = st.get_url('/get_by_sid?sid=%s' % st.ss.sid)\\n assert_equal(pickle.loads(b64decode(resp.body)), st.ss.data)\\n expected_num_sessions_in_db_if_db_used(2, 1)\\n st.start_request()\\n st.terminate() # remove it from the db\\n st.finish_request_and_check()\\n expected_num_sessions_in_db_if_db_used(1)\\n\\n minitest_divider(\\\"should be able to terminate() and then start a new session all in one request\\\")\\n st.start_request()\\n st['y'] = 'yy'\\n assert_equal(st.get('y'), 'yy')\\n st.terminate()\\n assert_raises(KeyError, st.__getitem__, 'y')\\n st['x'] = 7\\n st.finish_request_and_check()\\n expected_num_sessions_in_db_if_db_used(2)\\n\\n minitest_divider(\\\"regenerating SID test\\\")\\n initial_sid = st.ss.sid\\n st.start_request()\\n initial_expir = st.get_expiration()\\n st.regenerate_id()\\n assert_equal(st['x'], 7, \\\"data should not be affected\\\")\\n st.finish_request_and_check()\\n assert_not_equal(initial_sid, st.ss.sid, \\\"regenerated sid should be different\\\")\\n assert_equal(initial_expir, st._get_expiration(), \\\"expiration should not change\\\")\\n st.start_request()\\n assert_equal(st['x'], 7, \\\"data should not be affected\\\")\\n st.finish_request_and_check()\\n expected_num_sessions_in_db_if_db_used(2)\\n\\n minitest_divider(\\\"regenerating SID test w/new expiration time\\\")\\n initial_sid = st.ss.sid\\n st.start_request()\\n initial_expir = st.get_expiration()\\n new_expir = initial_expir + 120 # something new\\n st.regenerate_id(expiration_ts=new_expir)\\n assert_equal(st['x'], 7, \\\"data should not be affected\\\")\\n st.finish_request_and_check()\\n assert_not_equal(initial_sid, st.ss.sid, \\\"regenerated sid should be different\\\")\\n assert_equal(new_expir, st._get_expiration(), \\\"expiration should be what we asked for\\\")\\n st.start_request()\\n assert_equal(st['x'], 7, \\\"data should not be affected\\\")\\n st.finish_request_and_check()\\n expected_num_sessions_in_db_if_db_used(2)\\n\\n minitest_divider(\\\"check basic dictionary operations\\\")\\n st.start_request()\\n st['s'] = 'aaa'\\n st['i'] = 99\\n st['f'] = 4.37\\n assert_equal(st.pop('s'), 'aaa')\\n assert_equal(st.pop('s'), None)\\n assert_equal(st.pop('s', 'nil'), 'nil')\\n assert st.has_key('i')\\n assert not st.has_key('s')\\n assert_equal(st.get('i'), 99)\\n assert_equal(st.get('ii'), None)\\n assert_equal(st.get('iii', 3), 3)\\n assert_equal(st.get('f'), st['f'])\\n del st['f']\\n assert_raises(KeyError, st.__getitem__, 'f')\\n assert 'f' not in st\\n assert 'i' in st\\n assert_equal(st.get('x'), 7)\\n st.clear()\\n assert 'i' not in st\\n assert 'x' not in st\\n st.finish_request_and_check()\\n\\n minitest_divider(\\\"add complex data (models and objects) to the session\\\")\\n st.start_request()\\n st['model'] = make_entity(0)\\n st['dict'] = dict(a='alpha', c='charlie', e='echo')\\n st['list'] = ['b', 'd', 'f']\\n st['set'] = set([2, 3, 5, 7, 11, 13, 17, 19])\\n st['tuple'] = (7, 7, 1985)\\n st.finish_request_and_check()\\n st.start_request()\\n st.clear()\\n st.finish_request_and_check()\\n\\n minitest_divider(\\\"test quick methods: basic usage\\\")\\n st.start_request()\\n st.set_quick('msg', 'mc only!')\\n assert_equal('mc only!', st['msg'])\\n st.finish_request_and_check()\\n st.start_request()\\n assert_equal('mc only!', st.pop_quick('msg'))\\n assert_raises(KeyError, st.__getitem__, 'msg')\\n st.finish_request_and_check()\\n\\n minitest_divider(\\\"test quick methods: flush memcache (value will be lost if not using cookies)\\\")\\n st.start_request()\\n st.set_quick('a', 1)\\n st.set_quick('b', 2)\\n st.finish_request_and_check()\\n st.flush_memcache()\\n st.start_request()\\n if cookie_only_threshold > 0:\\n assert_equal(st['a'], 1)\\n assert_equal(st['b'], 2)\\n else:\\n assert_raises(KeyError, st.__getitem__, 'a')\\n assert_raises(KeyError, st.__getitem__, 'b')\\n st.finish_request_and_check()\\n\\n minitest_divider(\\\"test quick methods: flush memcache should have no impact if another mutator is also used (and this ISNT memcache-only)\\\")\\n st.start_request()\\n st['x'] = 24\\n st.set_quick('a', 1)\\n st.finish_request_and_check()\\n st.flush_memcache()\\n st.start_request()\\n if no_datastore and cookie_only_threshold == 0:\\n assert_raises(KeyError, st.__getitem__, 'a')\\n assert_raises(KeyError, st.__getitem__, 'x')\\n else:\\n assert_equal(st['a'], 1)\\n assert_equal(st['x'], 24)\\n st.set_quick('msg', 'hello')\\n st['z'] = 99\\n st.finish_request_and_check()\",\n \"def _setup_app_context_for_test():\\n ctx = application.app_context()\\n ctx.push()\\n yield # tests will run here\\n ctx.pop()\",\n \"def test_index_in_context(self):\\n path = '/'\\n with self.app.test_client() as client:\\n client.get(path)\\n self.assertContext('current_page', self.app.get_page(path))\",\n \"def test_context_creation_and_retrieval(self):\\n tracer_id = 'd551573a-01dc-41b2-b197-ea8afb7fbac1'.replace('-', '')\\n\\n with new_context(tracer_id=tracer_id):\\n context = get_context()\\n nose.tools.eq_(tracer_id, str(context.tracer_id))\",\n \"def setUp(self):\\n\\n app.config['TESTING'] = True\\n self.client = app.test_client()\\n\\n with self.client as c:\\n with c.session_transaction() as sess:\\n sess['user_id'] = 1\",\n \"def test_connection_session_switch(self):\\n\\n connection.set_session(self.session1)\\n sync_table(TestConnectModel)\\n TCM1 = TestConnectModel.create(id=1, keyspace=self.keyspace1)\\n connection.set_session(self.session2)\\n sync_table(TestConnectModel)\\n TCM2 = TestConnectModel.create(id=1, keyspace=self.keyspace2)\\n connection.set_session(self.session1)\\n self.assertEqual(1, TestConnectModel.objects.count())\\n self.assertEqual(TestConnectModel.objects.first(), TCM1)\\n connection.set_session(self.session2)\\n self.assertEqual(1, TestConnectModel.objects.count())\\n self.assertEqual(TestConnectModel.objects.first(), TCM2)\",\n \"def setUp(self):\\n\\n app.config[\\\"TESTING\\\"] = True\\n app.config[\\\"SECRET_KEY\\\"] = \\\"ABC\\\"\\n self.client = app.test_client()\\n\\n # Connect to test database\\n connect_to_db(app)\\n db.drop_all()\\n db.create_all()\\n load_test()\\n\\n # Put user1 into session.\\n with self.client as c:\\n with c.session_transaction() as sess:\\n sess[\\\"current_user\\\"] = 1\",\n \"def test_create_Context(self):\\n client = IPythonClient()\\n dac = Context(client)\\n self.assertIs(dac.client, client)\\n dac.close()\\n client.close()\",\n \"def test_write_load(self, req_session):\\n session = PoorSession(SECRET_KEY)\\n session.load(req_session.cookies)\\n assert session.data == {'test': True}\",\n \"def test_add_authenticated_session_var(self):\\r\\n req = Mock(authname='john', base_path='/', incookie=Cookie())\\r\\n session = Session(self.env, req)\\r\\n session['foo'] = 'bar'\\r\\n session.save()\\r\\n cursor = self.db.cursor()\\r\\n cursor.execute(\\\"SELECT value FROM session_attribute WHERE sid='john'\\\"\\r\\n \\\"AND name='foo'\\\") \\r\\n self.assertEqual('bar', cursor.fetchone()[0])\",\n \"def test_status_persisted(self):\\n storage = SessionStorage()\\n session1 = storage['key']\\n session1['value'] = 'example'\\n\\n session2 = storage['key']\\n self.assertEquals('example', session2['value'])\",\n \"def test_modify_authenticated_session_var(self):\\r\\n cursor = self.db.cursor()\\r\\n cursor.execute(\\\"INSERT INTO session VALUES ('john', 1, 0)\\\")\\r\\n cursor.execute(\\\"INSERT INTO session_attribute VALUES \\\"\\r\\n \\\"('john', 1, 'foo', 'bar')\\\")\\r\\n\\r\\n req = Mock(authname='john', base_path='/', incookie=Cookie())\\r\\n session = Session(self.env, req)\\r\\n self.assertEqual('bar', session['foo'])\\r\\n session['foo'] = 'baz'\\r\\n session.save()\\r\\n cursor.execute(\\\"SELECT value FROM session_attribute \\\"\\r\\n \\\"WHERE sid='john' AND name='foo'\\\") \\r\\n self.assertEqual('baz', cursor.fetchone()[0])\",\n \"def setUp(self):\\n\\n # Get the Flask test client.\\n self.client = app.test_client()\\n app.config[\\\"TESTING\\\"] = True\\n app.config[\\\"SECRET_KEY\\\"] = \\\"key\\\"\\n app.config[\\\"SQLALCHEMY_TRACK_MODIFICATIONS\\\"] = False\\n\\n with self.client as c:\\n with c.session_transaction() as sess:\\n sess[\\\"dietitian_id\\\"] = 2\",\n \"def test_im_chat_sessions(self):\\n pass\",\n \"def pytest_sessionstart(session):\\n\\n db = Database()\\n for a in range(1, 4):\\n data = {\\n 'author': f'test-author-{a}',\\n 'text': f'test-text-{a}'\\n }\\n phrase = PhraseInput(**data)\\n db.add(phrase)\\n print('created:')\\n print(list(db.items.keys()))\",\n \"def test_authenticated_session(self):\\r\\n incookie = Cookie()\\r\\n incookie['trac_session'] = '123456'\\r\\n outcookie = Cookie()\\r\\n req = Mock(authname='john', base_path='/', incookie=incookie,\\r\\n outcookie=outcookie)\\r\\n session = Session(self.env, req)\\r\\n self.assertEqual('john', session.sid)\\r\\n session['foo'] = 'bar'\\r\\n session.save()\\r\\n self.assertEquals(0, outcookie['trac_session']['expires'])\",\n \"def test_client_set_session(self):\\n key = load_privatekey(FILETYPE_PEM, server_key_pem)\\n cert = load_certificate(FILETYPE_PEM, server_cert_pem)\\n ctx = Context(TLSv1_2_METHOD)\\n ctx.use_privatekey(key)\\n ctx.use_certificate(cert)\\n ctx.set_session_id(b\\\"unity-test\\\")\\n\\n def makeServer(socket):\\n server = Connection(ctx, socket)\\n server.set_accept_state()\\n return server\\n\\n originalServer, originalClient = loopback(server_factory=makeServer)\\n originalSession = originalClient.get_session()\\n\\n def makeClient(socket):\\n client = loopback_client_factory(socket)\\n client.set_session(originalSession)\\n return client\\n\\n resumedServer, resumedClient = loopback(\\n server_factory=makeServer, client_factory=makeClient\\n )\\n\\n # This is a proxy: in general, we have no access to any unique\\n # identifier for the session (new enough versions of OpenSSL expose\\n # a hash which could be usable, but \\\"new enough\\\" is very, very new).\\n # Instead, exploit the fact that the master key is re-used if the\\n # session is re-used. As long as the master key for the two\\n # connections is the same, the session was re-used!\\n assert originalServer.master_key() == resumedServer.master_key()\",\n \"def Session(self):\\n return self.context\",\n \"def test_existing_session_auth_token(self):\\n\\n test_header = {'X-Auth-Token': 'pretend_token'}\\n\\n with self.app_sess1 as c:\\n ret1 = c.get('/', headers=test_header)\\n ret2 = c.get('/', headers=test_header)\\n self.assertEqual(ret1.data, ret2.data)\",\n \"def test_session_is_accessed(self):\\n response = self.client.get(\\\"/auth_processor_attr_access/\\\")\\n self.assertContains(response, \\\"Session accessed\\\")\",\n \"def test_mongo_context_prodmode(self):\\n if not helpers.can_connect_to_mongo(helpers.TEST_CONFIG):\\n pytest.xfail('no mongo credentials')\\n\\n mongo_context = schema_utils.MongoContextManager(\\n helpers.TEST_CONFIG,\\n )\\n\\n with mongo_context as mongo:\\n mongo['test_collection'].insert(self.demo_data)\\n\\n with mongo_context as _:\\n data = mongo['test_collection'].find_one({'butts': True})\\n\\n assert data['many'] == 10\",\n \"def test_session( uri ):\\n\\n # basic setup to handle cookies (all in-memory)\\n cj = cookielib.CookieJar()\\n opener = urllib2.build_opener( urllib2.HTTPCookieProcessor( cj ) )\\n urllib2.install_opener( opener )\\n\\n # request to get the initial session state\\n request = urllib2.Request( uri + '?fetch=true' )\\n handle = urllib2.urlopen( request )\\n initial_id = get_cookie_value( cj, 'session_test' )\\n initial = json.load( handle )\\n\\n # request to change the session state\\n request = urllib2.Request( uri + '?ckey=tkey&cval=tval' )\\n handle = urllib2.urlopen( request )\\n setup_id = get_cookie_value( cj, 'session_test' )\\n setup = json.load( handle )\\n\\n # request to get the final session state\\n request = urllib2.Request( uri + '?fetch=true' )\\n handle = urllib2.urlopen( request )\\n final_id = get_cookie_value( cj, 'session_test' )\\n final = json.load( handle )\\n\\n # tests consistency of reported session ID\\n if initial_id != final_id:\\n print 'FAILED: session ID mismatch: %s != %s' % (\\n initial_id, final_id\\n )\\n\\n # verifies the host resource isn't cheating\\n elif 'tkey' in initial[ 'session' ]:\\n print 'FAILED: \\\"tkey\\\" present in initial session'\\n\\n # verifies the updated state has a valid entry\\n elif 'tkey' not in final[ 'session' ]:\\n print 'FAILED: \\\"tkey\\\" not present in final session'\\n\\n # verifies the updated entry is correct\\n elif final[ 'session' ][ 'tkey' ] != 'tval':\\n print 'FAILED: \\\"tkey\\\" incorrect value \\\"tval\\\" != \\\"%s\\\"' % (\\n final[ 'session' ][ 'tkey' ],\\n )\\n\\n # all checks verified\\n else:\\n print 'PASSED: \\\"tkey\\\" set to \\\"%s\\\" in session %s' % (\\n final[ 'session' ][ 'tkey' ],\\n get_cookie_value( cj, 'session_test' )\\n )\",\n \"def setUp(self):\\n\\n app.config['TESTING'] = True\\n app.config['SECRET_KEY'] = 'key'\\n self.client = app.test_client()\\n\\n with self.client as c:\\n with c.session_transaction() as sess:\\n sess['user_id'] = 1\",\n \"def test_new_session(self):\\r\\n cookie = Cookie()\\r\\n req = Mock(incookie=Cookie(), outcookie=cookie, authname='anonymous',\\r\\n base_path='/')\\r\\n session = Session(self.env, req)\\r\\n self.assertEqual(session.sid, cookie['trac_session'].value)\\r\\n cursor = self.db.cursor()\\r\\n cursor.execute(\\\"SELECT COUNT(*) FROM session\\\")\\r\\n self.assertEqual(0, cursor.fetchone()[0])\",\n \"def test_execute_with_context(self):\\n pass\",\n \"def test_sessionProperties(self):\\n session = self.mdk.session()\\n value = [\\\"123\\\", {\\\"12\\\": 123}]\\n session.setProperty(\\\"key\\\", value)\\n session.setProperty(\\\"key2\\\", \\\"hello\\\")\\n self.assertEqual((session.getProperty(\\\"key\\\"), session.getProperty(\\\"key2\\\"),\\n session.hasProperty(\\\"key\\\"), session.hasProperty(\\\"key2\\\"),\\n session.hasProperty(\\\"nope\\\")),\\n (value, \\\"hello\\\", True, True, False))\",\n \"def test_add_context():\\n\\n application_services.add_context('context1', 'value1')\\n assert application_services.get_context('context1') == 'value1'\",\n \"def test_fixture_simple_patch_with_session(testdir):\\n\\n # create a temporary pytest test module\\n testdir.makepyfile(\\n \\\"\\\"\\\"\\n import requests\\n from requests.sessions import Session\\n\\n def test_simple_with_session(requests_mock):\\n with requests_mock.patch('/api/test') as patch:\\n patch.returns = requests_mock.good('hello')\\n with Session() as s:\\n response = s.get('https://test.api/api/test')\\n assert response.text == 'hello'\\n \\\"\\\"\\\"\\n )\\n\\n # run pytest with the following cmd args\\n result = testdir.runpytest(\\\"-v\\\")\\n\\n # fnmatch_lines does an assertion internally\\n result.stdout.fnmatch_lines([\\\"*::test_simple_with_session PASSED*\\\"])\\n\\n # make sure that that we get a '0' exit code for the testsuite\\n assert result.ret == 0\",\n \"def test_context_data(self):\\n mixin = mixins.SiteMixin(site=Site(domain='test', name='test'))\\n ret = mixin.get_context_data()\\n # make sure data is just updated, not replaced\\n assert ret['builder'] is mixin\\n # check if 'site' is in context and if it's valid\\n assert ret['site'].domain == 'test' and ret['site'].name == 'test'\",\n \"def setUp(self):\\n\\n self.client = server.app.test_client()\\n server.app.config['TESTING'] = True\\n server.app.config['SECRET_KEY'] = \\\"123\\\"\\n\\n # Connect to test database\\n model.connect_to_db(server.app, \\\"postgresql:///testdb\\\")\\n\\n # Create tables and add sample data\\n model.db.create_all()\\n # example_data()\\n\\n with self.client as c:\\n with c.session_transaction() as session:\\n session['user_id'] = 1\\n session['username'] = 'j'\\n session['name'] = 'l'\\n session['cal_id'] = 1\",\n \"def test_context_data(self):\\n response = self.client.get(self.get_url(self.trait.pk))\\n context = response.context\\n self.assertTrue('form' in context)\\n self.assertTrue('trait' in context)\\n self.assertEqual(context['trait'], self.trait)\",\n \"def test_context_data(self):\\n response = self.client.get(self.get_url(self.trait.pk))\\n context = response.context\\n self.assertTrue('form' in context)\\n self.assertTrue('trait' in context)\\n self.assertEqual(context['trait'], self.trait)\",\n \"def test_admin_survey_session(self):\\n\\n # =======================\\n # CREATE SURVEY TEST DATA\\n # =======================\\n\\n test_start_time = fields.Datetime.now()\\n\\n survey_session = self.env['survey.survey'].create({\\n 'title': 'User Session Survey',\\n 'access_token': 'b137640d-14d4-4748-9ef6-344caaaaafe',\\n 'state': 'open',\\n 'access_mode': 'public',\\n 'users_can_go_back': False,\\n 'questions_layout': 'page_per_question',\\n 'scoring_type': 'scoring_without_answers'\\n })\\n\\n nickname_question = self.env['survey.question'].create({\\n 'survey_id': survey_session.id,\\n 'title': 'Nickname',\\n 'save_as_nickname': True,\\n 'sequence': 1,\\n 'question_type': 'char_box',\\n })\\n text_question = self.env['survey.question'].create({\\n 'survey_id': survey_session.id,\\n 'title': 'Text Question',\\n 'sequence': 2,\\n 'question_type': 'char_box',\\n })\\n date_question = self.env['survey.question'].create({\\n 'survey_id': survey_session.id,\\n 'title': 'Date Question',\\n 'sequence': 3,\\n 'question_type': 'date',\\n })\\n datetime_question = self.env['survey.question'].create({\\n 'survey_id': survey_session.id,\\n 'title': 'Datetime Question',\\n 'sequence': 4,\\n 'question_type': 'datetime',\\n })\\n simple_choice_answer_1 = self.env['survey.question.answer'].create({\\n 'value': 'First'\\n })\\n simple_choice_answer_2 = self.env['survey.question.answer'].create({\\n 'value': 'Second'\\n })\\n simple_choice_answer_3 = self.env['survey.question.answer'].create({\\n 'value': 'Third'\\n })\\n simple_choice_question = self.env['survey.question'].create({\\n 'survey_id': survey_session.id,\\n 'title': 'Regular Simple Choice',\\n 'sequence': 5,\\n 'question_type': 'simple_choice',\\n 'suggested_answer_ids': [\\n (4, simple_choice_answer_1.id),\\n (4, simple_choice_answer_2.id),\\n (4, simple_choice_answer_3.id)],\\n })\\n scored_choice_answer_1 = self.env['survey.question.answer'].create({\\n 'value': 'Correct',\\n 'is_correct': True,\\n 'answer_score': 30\\n })\\n scored_choice_answer_2 = self.env['survey.question.answer'].create({\\n 'value': 'Incorrect 1'\\n })\\n scored_choice_answer_3 = self.env['survey.question.answer'].create({\\n 'value': 'Incorrect 2'\\n })\\n scored_choice_answer_4 = self.env['survey.question.answer'].create({\\n 'value': 'Incorrect 3'\\n })\\n scored_choice_question = self.env['survey.question'].create({\\n 'survey_id': survey_session.id,\\n 'title': 'Scored Simple Choice',\\n 'sequence': 6,\\n 'question_type': 'simple_choice',\\n 'suggested_answer_ids': [\\n (4, scored_choice_answer_1.id),\\n (4, scored_choice_answer_2.id),\\n (4, scored_choice_answer_3.id),\\n (4, scored_choice_answer_4.id)],\\n })\\n timed_scored_choice_answer_1 = self.env['survey.question.answer'].create({\\n 'value': 'Correct',\\n 'is_correct': True,\\n 'answer_score': 30\\n })\\n timed_scored_choice_answer_2 = self.env['survey.question.answer'].create({\\n 'value': 'Also correct but less points',\\n 'is_correct': True,\\n 'answer_score': 10\\n })\\n timed_scored_choice_answer_3 = self.env['survey.question.answer'].create({\\n 'value': 'Incorrect',\\n 'answer_score': -40\\n })\\n timed_scored_choice_question = self.env['survey.question'].create({\\n 'survey_id': survey_session.id,\\n 'title': 'Timed Scored Multiple Choice',\\n 'sequence': 6,\\n 'question_type': 'multiple_choice',\\n 'is_time_limited': True,\\n 'time_limit': 1,\\n 'suggested_answer_ids': [\\n (4, timed_scored_choice_answer_1.id),\\n (4, timed_scored_choice_answer_2.id),\\n (4, timed_scored_choice_answer_3.id)],\\n })\\n\\n # =======================\\n # PART 1 : CREATE SESSION\\n # =======================\\n\\n self.start_tour('/web', 'test_survey_session_create_tour', login='admin')\\n\\n # tricky part: we only take into account answers created after the session_start_time\\n # the create_date of the answers we just saved is set to the beginning of the test.\\n # but the session_start_time is set after that.\\n # So we cheat on the session start date to be able to count answers properly.\\n survey_session.write({'session_start_time': test_start_time - relativedelta(minutes=10)})\\n\\n attendee_1 = survey_session._create_answer()\\n attendee_2 = survey_session._create_answer()\\n attendee_3 = survey_session._create_answer()\\n all_attendees = [attendee_1, attendee_2, attendee_3]\\n\\n self.assertEqual('ready', survey_session.session_state)\\n self.assertTrue(all(attendee.is_session_answer for attendee in all_attendees),\\n \\\"Created answers should be within the session.\\\")\\n self.assertTrue(all(attendee.state == 'new' for attendee in all_attendees),\\n \\\"Created answers should be in the 'new' state.\\\")\\n\\n # =========================================\\n # PART 2 : OPEN SESSION AND CHECK ATTENDEES\\n # =========================================\\n\\n self.start_tour('/web', 'test_survey_session_start_tour', login='admin')\\n\\n self.assertEqual('in_progress', survey_session.session_state)\\n self.assertTrue(bool(survey_session.session_start_time))\\n\\n # ========================================\\n # PART 3 : CREATE ANSWERS & MANAGE SESSION\\n # ========================================\\n\\n # create a few answers beforehand to avoid having to back and forth too\\n # many times between the tours and the python test\\n\\n attendee_1.save_lines(nickname_question, 'xxxTheBestxxx')\\n attendee_2.save_lines(nickname_question, 'azerty')\\n attendee_3.save_lines(nickname_question, 'nicktalope')\\n self.assertEqual('xxxTheBestxxx', attendee_1.nickname)\\n self.assertEqual('azerty', attendee_2.nickname)\\n self.assertEqual('nicktalope', attendee_3.nickname)\\n\\n attendee_1.save_lines(text_question, 'Attendee 1 is the best')\\n attendee_2.save_lines(text_question, 'Attendee 2 rulez')\\n attendee_3.save_lines(text_question, 'Attendee 3 will crush you')\\n attendee_1.save_lines(date_question, '2010-10-10')\\n attendee_2.save_lines(date_question, '2011-11-11')\\n attendee_2.save_lines(datetime_question, '2010-10-10 10:00:00')\\n attendee_3.save_lines(datetime_question, '2011-11-11 15:55:55')\\n attendee_1.save_lines(simple_choice_question, simple_choice_answer_1.id)\\n attendee_2.save_lines(simple_choice_question, simple_choice_answer_1.id)\\n attendee_3.save_lines(simple_choice_question, simple_choice_answer_2.id)\\n attendee_1.save_lines(scored_choice_question, scored_choice_answer_1.id)\\n attendee_2.save_lines(scored_choice_question, scored_choice_answer_2.id)\\n attendee_3.save_lines(scored_choice_question, scored_choice_answer_3.id)\\n attendee_1.save_lines(timed_scored_choice_question,\\n [timed_scored_choice_answer_1.id, timed_scored_choice_answer_3.id])\\n attendee_2.save_lines(timed_scored_choice_question,\\n [timed_scored_choice_answer_1.id, timed_scored_choice_answer_2.id])\\n attendee_3.save_lines(timed_scored_choice_question,\\n [timed_scored_choice_answer_2.id])\\n\\n self.start_tour('/web', 'test_survey_session_manage_tour', login='admin')\\n\\n self.assertFalse(bool(survey_session.session_state))\\n self.assertTrue(all(answer.state == 'done' for answer in all_attendees))\",\n \"def test_session_creation(self):\\n response = self.client.get(reverse('index'))\\n self.assertEqual(self.client.session['liked_user_ids'], [])\",\n \"def test_session_state_for_used_flash(self):\\n self.response = self.client.get(reverse(views.set_flash_var))\\n self.response = self.client.get(reverse(views.render_template))\\n self.assertTrue(_SESSION_KEY in self.client.session)\\n\\n # Flash scope should be removed from the session\\n self.response = self.client.get(reverse(views.render_template))\\n self.assertFalse(_SESSION_KEY in self.client.session)\",\n \"def test_do_logout(self):\\r\\n with app.test_request_context():\\r\\n u1 = User.query.filter_by(username='testuser').one()\\r\\n\\r\\n do_login(u1)\\r\\n self.assertIn(CURR_USER_KEY, session)\\r\\n do_logout()\\r\\n self.assertNotIn(CURR_USER_KEY, session)\",\n \"def test_run_container2(client):\\n with client.application.app_context():\\n pytest.skip(\\\"Not implemented\\\")\",\n \"def session(get_session):\\n return get_session()\",\n \"def test_database_sessions(web_app):\\n db = DB(connect_url=TEST_URL, web_app=web_app, echo=web_app)\\n db.init()\\n with db.session() as session:\\n archive = Archive(archive_id='0000', name='test')\\n session.add(archive)\\n with pytest.raises(IntegrityError):\\n with db.session() as session:\\n archive = Archive(archive_id='0000', name='test')\\n session.add(archive)\\n with pytest.raises(ValueError):\\n with db.session() as session:\\n archive = Archive(archive_id='0001', name='test')\\n session.add(archive)\\n raise ValueError('test')\\n db.close()\",\n \"def setUp(self):\\n\\n self.client = server.app.test_client()\\n server.app.config['TESTING'] = True\\n server.app.config['SECRET_KEY'] = \\\"123\\\"\\n\\n # Connect to test database\\n model.connect_to_db(server.app, \\\"postgresql:///testdb\\\")\\n\\n # Create tables and add sample data\\n model.db.create_all()\\n # example_data()\\n\\n with self.client as c:\\n with c.session_transaction() as session:\\n session['user_id'] = 33\\n session['username'] = 'balloonicorn'\\n session['name'] = 'balloonicorn'\",\n \"def __init__(self, session):\\n self._session = session\",\n \"def test_init_with_request():\\n serializers.SessionCreationSerializer(context={\\\"request\\\": \\\"foo\\\"})\",\n \"def session_context(func):\\r\\n def wrapper(*args, **kwargs):\\r\\n self = args[0]\\r\\n with self._create_db_session() as db:\\r\\n self.db = db\\r\\n return func(*args, **kwargs)\\r\\n return wrapper\",\n \"def test_some_page_in_context(self):\\n path = '/about/history'\\n with self.app.test_client() as client:\\n client.get(path)\\n self.assertContext('current_page', self.app.get_page(path))\",\n \"def test_session(self, k8sconfig):\\n # Basic.\\n config = k8sconfig._replace(token=None)\\n sess = k8s.session(config)\\n assert \\\"authorization\\\" not in sess.headers\\n\\n # With access token.\\n config = k8sconfig._replace(token=\\\"token\\\")\\n sess = k8s.session(config)\\n assert sess.headers[\\\"authorization\\\"] == \\\"Bearer token\\\"\\n\\n # With access token and client certificate.\\n ccert = k8s.K8sClientCert(crt=\\\"foo\\\", key=\\\"bar\\\")\\n config = k8sconfig._replace(token=\\\"token\\\", client_cert=ccert)\\n sess = k8s.session(config)\\n assert sess.headers[\\\"authorization\\\"] == \\\"Bearer token\\\"\\n assert sess.cert == (\\\"foo\\\", \\\"bar\\\")\",\n \"def test_user_get_topteams():\\n app = create_ctfd()\\n with app.app_context():\\n register_user(app)\\n client = login_as_user(app)\\n r = client.get('/top/10')\\n assert r.status_code == 200\\n destroy_ctfd(app)\",\n \"def test_api_livesession_read_token_lti_admin_instruct_record_course_diff(self):\\n other_consumer_site = ConsumerSiteFactory()\\n # livesession with consumer_site\\n livesession = LiveSessionFactory(\\n consumer_site=other_consumer_site,\\n email=\\\"admin@openfun.fr\\\", # explicit to be found in response\\n lti_user_id=\\\"56255f3807599c377bf0e5bf072359fd\\\", # explicit to be found in response\\n lti_id=\\\"Maths\\\", # explicit to be found in response\\n )\\n\\n # token with context_id leading to another consumer site\\n jwt_token = InstructorOrAdminLtiTokenFactory(\\n playlist=livesession.video.playlist,\\n context_id=f\\\"{livesession.video.playlist.lti_id}_diff\\\",\\n # consumer_site is not other_consumer_site\\n consumer_site=str(livesession.video.playlist.consumer_site.id),\\n user__email=livesession.email,\\n user__id=livesession.lti_user_id,\\n user__username=livesession.username,\\n )\\n\\n response = self.client.get(\\n self._get_url(livesession.video, livesession),\\n HTTP_AUTHORIZATION=f\\\"Bearer {jwt_token}\\\",\\n )\\n self.assertEqual(response.status_code, 200)\\n self.assertEqual(\\n response.json(),\\n {\\n \\\"anonymous_id\\\": None,\\n \\\"consumer_site\\\": str(other_consumer_site.id),\\n \\\"display_name\\\": None,\\n \\\"email\\\": \\\"admin@openfun.fr\\\",\\n \\\"id\\\": str(livesession.id),\\n \\\"is_registered\\\": False,\\n \\\"language\\\": \\\"en\\\",\\n \\\"live_attendance\\\": None,\\n \\\"lti_user_id\\\": \\\"56255f3807599c377bf0e5bf072359fd\\\",\\n \\\"lti_id\\\": \\\"Maths\\\",\\n \\\"should_send_reminders\\\": True,\\n \\\"username\\\": None,\\n \\\"video\\\": str(livesession.video.id),\\n },\\n )\",\n \"def test_distinct_sessions_auth_token(self):\\n\\n sess1 = None\\n sess2 = None\\n\\n with self.app_sess1 as c:\\n ret = c.get('/', headers={'X-Auth-Token': 'pretend_token'})\\n sess1 = ret.data\\n\\n with self.app_sess2 as c:\\n ret = c.get('/', headers={'X-Auth-Token': 'another_pretend_token'})\\n sess2 = ret.data\\n\\n self.assertNotEqual(sess1, sess2)\",\n \"def test_where(server, session):\\n\\n window = session.attached_window\\n window.split_window() # create second pane\\n\\n for session in server.sessions:\\n session_id = session.get('session_id')\\n session_name = session.get('session_name')\\n where = server.where({'session_id': session_id, 'session_name': session_name})\\n\\n assert len(where) == 1\\n assert isinstance(where, list)\\n assert where[0] == session\\n assert isinstance(where[0], Session)\\n\\n # session.where\\n for window in session.windows:\\n window_id = window.get('window_id')\\n window_index = window.get('window_index')\\n\\n where = session.where(\\n {'window_id': window_id, 'window_index': window_index}\\n )\\n\\n assert len(where) == 1\\n assert isinstance(where, list)\\n assert where[0] == window\\n assert isinstance(where[0], Window)\\n\\n # window.where\\n for pane in window.panes:\\n pane_id = pane.get('pane_id')\\n pane_tty = pane.get('pane_tty')\\n\\n where = window.where({'pane_id': pane_id, 'pane_tty': pane_tty})\\n\\n assert len(where) == 1\\n assert isinstance(where, list)\\n assert where[0] == pane\\n assert isinstance(where[0], Pane)\",\n \"async def session(self,ctx):\\n if ctx.invoked_subcommand is None:\\n await ctx.send(\\\"The current main session is \\\" + \\\"```\\\" + await self.config.sessions.main() + \\\"```\\\")\",\n \"def test_get_by_id(server, session):\\n\\n window = session.attached_window\\n\\n window.split_window() # create second pane\\n\\n for session in server.sessions:\\n session_id = session.get('session_id')\\n get_by_id = server.get_by_id(session_id)\\n\\n assert get_by_id == session\\n assert isinstance(get_by_id, Session)\\n assert server.get_by_id('$' + next(namer)) is None\\n\\n # session.get_by_id\\n for window in session.windows:\\n window_id = window.get('window_id')\\n\\n get_by_id = session.get_by_id(window_id)\\n\\n assert get_by_id == window\\n assert isinstance(get_by_id, Window)\\n\\n assert session.get_by_id('@' + next(namer)) is None\\n\\n # window.get_by_id\\n for pane in window.panes:\\n pane_id = pane.get('pane_id')\\n\\n get_by_id = window.get_by_id(pane_id)\\n\\n assert get_by_id == pane\\n assert isinstance(get_by_id, Pane)\\n assert window.get_by_id('%' + next(namer)) is None\",\n \"def session_context(self):\\n session = self.Session()\\n try:\\n yield session\\n session.commit()\\n except: # noqa E722\\n session.rollback()\\n raise\\n finally:\\n session.close()\",\n \"def test_inner_live_setup(self):\\n mocktable = self.classes.MockTable\\n session = Session(bind=self.bind)\\n session.add(mocktable(test=5))\\n session.commit()\\n session.close()\\n\\n res = session.query(mocktable).all()\\n session.close()\\n expected = (res[0].test, len(res))\\n\\n assert expected == (5, 1)\",\n \"def setUp(self):\\n\\n # Get the Flask test client\\n self.client = app.test_client()\\n\\n # Show Flask errors that happen during tests\\n app.config['TESTING'] = True\\n\\n #To test sessions we need to set Secret key \\n app.config['SECRET_KEY'] = 'key'\\n\\n # Connect to test database\\n connect_to_db(app, \\\"postgresql:///testdb\\\")\\n\\n # Create tables and add sample data\\n db.create_all()\\n users()\\n reviews()\\n\\n with self.client as c:\\n with c.session_transaction() as sess:\\n sess['user_id'] = 1\",\n \"def test_update_session(self):\\r\\n now = time.time()\\r\\n\\r\\n # Make sure the session has data so that it doesn't get dropped\\r\\n cursor = self.db.cursor()\\r\\n cursor.execute(\\\"INSERT INTO session VALUES ('123456', 0, 1)\\\")\\r\\n cursor.execute(\\\"INSERT INTO session_attribute VALUES \\\"\\r\\n \\\"('123456', 0, 'foo', 'bar')\\\")\\r\\n\\r\\n incookie = Cookie()\\r\\n incookie['trac_session'] = '123456'\\r\\n outcookie = Cookie()\\r\\n req = Mock(authname='anonymous', base_path='/', incookie=incookie,\\r\\n outcookie=outcookie)\\r\\n session = Session(self.env, req)\\r\\n session.save() # updating should not require modifications\\r\\n\\r\\n self.assertEqual(PURGE_AGE, outcookie['trac_session']['expires'])\\r\\n\\r\\n cursor.execute(\\\"SELECT last_visit FROM session WHERE sid='123456' AND \\\"\\r\\n \\\"authenticated=0\\\")\\r\\n self.assertAlmostEqual(now, int(cursor.fetchone()[0]), -1)\",\n \"def test_api_livesession_read_token_lti_admin_instruct_record_consumer_diff(self):\\n other_consumer_site = ConsumerSiteFactory()\\n # livesession with consumer_site\\n livesession = LiveSessionFactory(\\n consumer_site=other_consumer_site,\\n is_from_lti_connection=True,\\n email=\\\"admin@openfun.fr\\\", # explicit to be found in response\\n lti_user_id=\\\"56255f3807599c377bf0e5bf072359fd\\\", # explicit to be found in response\\n )\\n\\n # token with context_id leading to another consumer site\\n jwt_token = InstructorOrAdminLtiTokenFactory(\\n playlist=livesession.video.playlist,\\n context_id=str(livesession.video.playlist.lti_id),\\n # consumer_site is not other_consumer_site\\n consumer_site=str(livesession.video.playlist.consumer_site.id),\\n )\\n\\n response = self.client.get(\\n self._get_url(livesession.video, livesession),\\n HTTP_AUTHORIZATION=f\\\"Bearer {jwt_token}\\\",\\n )\\n self.assertEqual(response.status_code, 200)\\n self.assertEqual(\\n response.json(),\\n {\\n \\\"anonymous_id\\\": None,\\n \\\"consumer_site\\\": str(other_consumer_site.id),\\n \\\"display_name\\\": None,\\n \\\"email\\\": \\\"admin@openfun.fr\\\",\\n \\\"id\\\": str(livesession.id),\\n \\\"is_registered\\\": False,\\n \\\"language\\\": \\\"en\\\",\\n \\\"live_attendance\\\": None,\\n \\\"lti_user_id\\\": \\\"56255f3807599c377bf0e5bf072359fd\\\",\\n \\\"lti_id\\\": str(livesession.video.playlist.lti_id),\\n \\\"should_send_reminders\\\": True,\\n \\\"username\\\": None,\\n \\\"video\\\": str(livesession.video.id),\\n },\\n )\",\n \"def setUp(self):\\n self.hello_url = \\\"http://localhost:7000\\\"\\n self.store_url = self.hello_url + \\\"/store\\\"\\n self.session = requests.session()\",\n \"def iter_sessions():\\n return iter(_session_stack)\",\n \"def test_multiple_requests(self):\\n s = self.api.session()\\n s.request(\\\"1.2.3.4\\\", \\\"mozilla\\\", \\\"/foo/bar\\\").end()\\n s.request(\\\"1.2.3.4\\\", \\\"mozilla\\\", \\\"/foo/blah\\\").end()\\n s.end()\\n data = self.connector.transcription()\\n assert len(data) == 2\\n assert data[0].get('action') == \\\"session_start\\\"\\n assert data[1].get('action') == \\\"session_end\\\"\",\n \"def getSession():\\n return call(\\\"getSession\\\")\",\n \"def test_homepage_logged_in(self):\\r\\n u1 = User.query.filter_by(username='testuser').one()\\r\\n\\r\\n with self.client as c:\\r\\n with c.session_transaction() as sess:\\r\\n sess[CURR_USER_KEY] = u1.id\\r\\n response = c.get('/')\\r\\n self.assertEqual(response.status_code, 200)\\r\\n self.assertIn(b'Virginia News', response.data)\",\n \"def test_new_session_create_with_auth_json(self):\\n\\n with self.app_sess1 as c:\\n data = {\\n \\\"token\\\": \\\"pretend_token\\\"\\n }\\n ret1 = c.post('/', data=json.dumps(data), headers={'Content-Type': 'application/json'})\\n ret2 = c.get('/', headers={'X-Auth-Token': 'pretend_token'})\\n\\n self.assertEqual(ret1.data, ret2.data)\",\n \"def client(app):\\n conn = engine.connect()\\n transaction = conn.begin()\\n app.db_session = create_session(conn)\\n yield app.test_client()\\n # No need to retain the junk we\\n # put in the DB for testing.\\n transaction.rollback()\\n conn.close()\\n app.db_session.remove()\",\n \"def establish_session(func, db):\\n def inner(*args, **kwargs):\\n Session = sessionmaker(bind=db)\\n session = Session()\\n kwargs['session'] = session\\n res = func(*args, **kwargs)\\n return res\\n\\n return inner\",\n \"def test_context_data(self):\\n tagged_traits = TaggedTraitFactory.create_batch(2, trait=self.trait)\\n response = self.client.get(self.get_url(self.trait.pk))\\n context = response.context\\n self.assertIn('source_trait', context)\\n self.assertEqual(context['source_trait'], self.trait)\\n self.assertIn('tagged_traits_with_xs', context)\\n self.assertEqual([el[0] for el in context['tagged_traits_with_xs']],\\n list(self.trait.all_taggedtraits.non_archived()))\\n self.assertIn('user_is_study_tagger', context)\\n self.assertFalse(context['user_is_study_tagger'])\\n self.assertIn('is_deprecated', context)\\n self.assertIn('show_removed_text', context)\\n self.assertIn('new_version_link', context)\",\n \"def loop_context():\\n loop = setup_test_loop()\\n yield loop\\n # teardown_test_loop(loop)\",\n \"def test_mongo_context_testmode(self, tmpdir):\\n mongo_context = schema_utils.MongoContextManager(\\n helpers.TEST_CONFIG,\\n _testmode=True,\\n _testmode_filepath=tmpdir,\\n )\\n\\n with mongo_context as t_mongo:\\n t_mongo['test_collection'].insert(self.demo_data)\\n\\n with mongo_context as t_mongo:\\n data = t_mongo['test_collection'].find_one({'butts': True})\\n\\n assert data['many'] == 10\",\n \"def _context():\\n global _trident_context\\n if _trident_context is None:\\n _trident_context = _Context()\\n return _trident_context\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.67018175","0.6668321","0.6427891","0.63258743","0.62819177","0.6264992","0.6244206","0.6197762","0.6128129","0.60963386","0.59784377","0.59195083","0.5882602","0.58783644","0.5873574","0.58591706","0.5849194","0.584282","0.5837098","0.58088905","0.58034945","0.5788038","0.5782187","0.5770325","0.5761921","0.57494193","0.5720195","0.5711418","0.5710981","0.56825006","0.56716037","0.56655604","0.56559104","0.5636023","0.5634938","0.5630474","0.56059533","0.5595552","0.55857","0.5585573","0.5581559","0.55664426","0.5557496","0.5534353","0.5529654","0.55266625","0.5508452","0.5500517","0.54963416","0.54885745","0.54804724","0.5476506","0.54731405","0.54723406","0.5462534","0.54206276","0.541661","0.54102975","0.5402018","0.54007375","0.53992075","0.53959405","0.5386174","0.5386174","0.5378541","0.53755707","0.53733563","0.5362562","0.53600085","0.5356902","0.5354855","0.53510857","0.5349936","0.5342898","0.5333078","0.531519","0.53082657","0.5297952","0.52938706","0.5278837","0.52777827","0.52648354","0.5263927","0.52626866","0.52568936","0.52558583","0.5254524","0.52526283","0.5251947","0.524848","0.52481824","0.524413","0.5243311","0.52384037","0.52333266","0.5224298","0.52218133","0.52213496","0.52186054","0.521327"],"string":"[\n \"0.67018175\",\n \"0.6668321\",\n \"0.6427891\",\n \"0.63258743\",\n \"0.62819177\",\n \"0.6264992\",\n \"0.6244206\",\n \"0.6197762\",\n \"0.6128129\",\n \"0.60963386\",\n \"0.59784377\",\n \"0.59195083\",\n \"0.5882602\",\n \"0.58783644\",\n \"0.5873574\",\n \"0.58591706\",\n \"0.5849194\",\n \"0.584282\",\n \"0.5837098\",\n \"0.58088905\",\n \"0.58034945\",\n \"0.5788038\",\n \"0.5782187\",\n \"0.5770325\",\n \"0.5761921\",\n \"0.57494193\",\n \"0.5720195\",\n \"0.5711418\",\n \"0.5710981\",\n \"0.56825006\",\n \"0.56716037\",\n \"0.56655604\",\n \"0.56559104\",\n \"0.5636023\",\n \"0.5634938\",\n \"0.5630474\",\n \"0.56059533\",\n \"0.5595552\",\n \"0.55857\",\n \"0.5585573\",\n \"0.5581559\",\n \"0.55664426\",\n \"0.5557496\",\n \"0.5534353\",\n \"0.5529654\",\n \"0.55266625\",\n \"0.5508452\",\n \"0.5500517\",\n \"0.54963416\",\n \"0.54885745\",\n \"0.54804724\",\n \"0.5476506\",\n \"0.54731405\",\n \"0.54723406\",\n \"0.5462534\",\n \"0.54206276\",\n \"0.541661\",\n \"0.54102975\",\n \"0.5402018\",\n \"0.54007375\",\n \"0.53992075\",\n \"0.53959405\",\n \"0.5386174\",\n \"0.5386174\",\n \"0.5378541\",\n \"0.53755707\",\n \"0.53733563\",\n \"0.5362562\",\n \"0.53600085\",\n \"0.5356902\",\n \"0.5354855\",\n \"0.53510857\",\n \"0.5349936\",\n \"0.5342898\",\n \"0.5333078\",\n \"0.531519\",\n \"0.53082657\",\n \"0.5297952\",\n \"0.52938706\",\n \"0.5278837\",\n \"0.52777827\",\n \"0.52648354\",\n \"0.5263927\",\n \"0.52626866\",\n \"0.52568936\",\n \"0.52558583\",\n \"0.5254524\",\n \"0.52526283\",\n \"0.5251947\",\n \"0.524848\",\n \"0.52481824\",\n \"0.524413\",\n \"0.5243311\",\n \"0.52384037\",\n \"0.52333266\",\n \"0.5224298\",\n \"0.52218133\",\n \"0.52213496\",\n \"0.52186054\",\n \"0.521327\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.8149533"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":6588,"cells":{"query":{"kind":"string","value":"Test the popxl call_with_info example"},"document":{"kind":"string","value":"def test_documentation_popxl_call_with_info(self):\n filename = \"call_with_info.py\"\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)"},"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 hxlinfo():\n run_script(hxlinfo_main)","def test_get_info(self):\n pass","def test_application_info(self, mocked_serial, mocked_check):\n from supvisors.rpcinterface import RPCInterface\n # create RPC instance\n rpc = RPCInterface(self.supervisor)\n # test RPC call\n self.assertEqual({'name': 'appli'}, rpc.get_application_info('dummy'))\n self.assertEqual([call()], mocked_check.call_args_list)\n self.assertEqual([call('dummy')], mocked_serial.call_args_list)","def verify_call(obj):\n\tassert obj.tag == 'OMOBJ'\n\tattr = obj[0]\n\t\n\tassert attr.tag == 'OMATTR'\n\tpairs, application = attr\n\t\n\tassert application.tag == 'OMA'\n\tsymbol, args = application\n\t\n\tassert symbol.tag == 'OMS'\n\tassert symbol.get('cd') == \"scscp1\"\n\tassert symbol.get('name') == \"procedure_call\"\n\t\n\tassert args.tag == 'OMA'\n\tassert len(args) > 0\n\tname_symbol = args[0]\n\t\n\tassert name_symbol.tag == 'OMS'\n\tcd = name_symbol.get('cd')\n\tproc_name = name_symbol.get('name')\n\t\n\t#2. Now handle the extra information\n\tassert pairs.tag == 'OMATP'\n\tassert len(pairs) % 2 == 0\n\t\n\textras = {}\n\tcall_id = None\n\treturn_type = None\n\t\n\tfor i in range(0, len(pairs), 2):\n\t\tsymbol = pairs[i]\n\t\tassert symbol.tag == 'OMS'\n\t\tassert symbol.get('cd') == \"scscp1\"\n\t\tname = symbol.get('name')\n\t\textras[name] = pairs[i+1]\n\t\t\n\t\tif name == 'call_id':\n\t\t\tassert call_id is None\n\t\t\tcall_id = pairs[i+1].text\n\t\t\tprint(call_id)\n\t\telif name.startswith('option_return_'):\n\t\t\tassert return_type is None\n\t\t\treturn_type = ReturnTypes[name[14:]]\n\t\n\t#Some information is mandatory\n\tassert call_id is not None\n\tassert return_type is not None\n\t\n\treturn cd, proc_name, call_id, return_type, args[1:], extras","def info(self, *args, **kwargs):","def test_process_info(self, mocked_get, mocked_check):\n from supvisors.rpcinterface import RPCInterface\n # create RPC instance\n rpc = RPCInterface(self.supervisor)\n # test first RPC call with process namespec\n self.assertEqual([{'name': 'proc'}], rpc.get_process_info('appli:proc'))\n self.assertEqual([call()], mocked_check.call_args_list)\n self.assertEqual([call('appli:proc')], mocked_get.call_args_list)\n # reset patches\n mocked_check.reset_mock()\n mocked_get.reset_mock()\n # test second RPC call with group namespec\n self.assertEqual([{'name': 'proc_1'}, {'name': 'proc_2'}],\n rpc.get_process_info('appli:*'))\n self.assertEqual([call()], mocked_check.call_args_list)\n self.assertEqual([call('appli:*')], mocked_get.call_args_list)","def get_info(self, info):\r\n pass","def setInfo(*args):","def getInfo():","def info() -> None:","def test_get_info_function() -> None:\n current_directory = Path.cwd()\n with zipfile.ZipFile(\n current_directory / 'app' / 'tests' / 'files' / 'oneFile.zip') as zip_object:\n res = get_info_about_file(zip_object, 'dotnetfx.exe')\n assert res == {'path': 'dotnetfx.exe', 'size': 21823560}","def test_documentation_popxl_addition(self):\n filename = \"simple_addition.py\"\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)","def hxlinfo_main(args, stdin=STDIN, stdout=sys.stdout, stderr=sys.stderr):\n parser = make_args(\n 'Display JSON-formatted metadata for a data source (does not have to be HXLated).',\n hxl_output=False\n )\n\n args = parser.parse_args(args)\n\n do_common_args(args)\n\n json.dump(hxl.input.info(args.infile or stdin, make_input_options(args)), stdout, indent=2, ensure_ascii=False)\n\n return EXIT_OK","def test_print_info(clarisse):\n info = \"test print info\"\n assert bool(clarisse.print_info(info)) is False","def test_get_cell_info(self):\n expected = { 'Experiment': \"20220101_EGS1_12345AA\",\n 'Cell': '12345AA0018/20220101_1234_1-A1-A1_AAA66666_deadbeef',\n 'Pool': '12345AA0018',\n 'Date': '20220101',\n 'Number': '1234',\n 'Slot': '1-A1-A1',\n 'CellID': 'AAA66666',\n 'Checksum': 'deadbeef',\n 'Project': '12345',\n 'Base': '12345AA0018/20220101_1234_1-A1-A1_AAA66666_deadbeef/'\n '20220101_EGS1_12345AA_12345AA0018_AAA66666_deadbeef',\n 'Files in pass': 'unknown',\n 'Files in fail': 1,\n 'Files in fast5 fail': 1,\n '_counts': [\n {'_barcode': '.', '_label': 'All passed reads', '_part': 'pass', 'total_reads': 200},\n {'_barcode': '.', '_label': 'Passed and lambda-filtered reads', '_part': 'nolambda'},\n {'_barcode': '.', '_label': 'All failed reads', '_part': 'fail'} ],\n '_blobs': ['../../__blob__'],\n '_duplex' : [ ['Duplex pairs', 1],\n ['from total passing reads', 200],\n ['% of passing reads', '1.00%'] ],\n '_filter_type': 'none',\n '_final_summary': {'is_rna': False},\n '_nanoplot': '../../__nanoplot__',\n }\n\n\n got = get_cell_info( experiment = \"20220101_EGS1_12345AA\",\n cell = \"12345AA0018/20220101_1234_1-A1-A1_AAA66666_deadbeef\",\n cell_content = { '.': dict( fast5_pass = ['x.fast5'],\n fastq_fail = ['y.fastq'],\n fast5_fail = ['y.fast5'] ) },\n counts = { ('.','pass'): dict(total_reads = 200),\n ('.','fail'): dict(),\n ('.','nolambda'): dict() },\n fin_summary = dict(is_rna = False),\n blobs = ['__blob__'],\n nanoplot = '__nanoplot__',\n duplex = 1,\n fast5_meta = dict() )\n\n if VERBOSE:\n pprint(got)\n\n self.assertEqual( type(got), OrderedDict )\n self.assertEqual( dict(got), expected )","def test_all_process_info(self, mocked_check):\n from supvisors.rpcinterface import RPCInterface\n # prepare context\n self.supervisor.supvisors.context.processes = {\n 'proc_1': Mock(**{'serial.return_value': {'name': 'proc_1'}}),\n 'proc_2': Mock(**{'serial.return_value': {'name': 'proc_2'}})}\n # create RPC instance\n rpc = RPCInterface(self.supervisor)\n # test RPC call\n self.assertItemsEqual([{'name': 'proc_1'}, {'name': 'proc_2'}],\n rpc.get_all_process_info())\n self.assertEqual([call()], mocked_check.call_args_list)","def _handle_info_response(self, resp, info, prev_info):\r\n if info.line_num != prev_info.line_num:\r\n return\r\n\r\n if resp['calltip']:\r\n info.editor.show_calltip('Arguments', resp['calltip'],\r\n signature=True,\r\n at_position=prev_info.position)\r\n\r\n if resp['name']:\r\n self.send_to_inspector.emit(\r\n resp['name'], resp['argspec'],\r\n resp['note'], resp['docstring'],\r\n not prev_info.auto)","def test_get_patch_info_returns(self):\n # This test assumes IIQ isn't installed, thus the pile of errors that'll\n # occur shouldn't prevent us from getting a PatchInfo object\n fake_log = MagicMock()\n patch_info = versions.get_patch_info('bogus-patch.tgz', fake_log)\n\n self.assertTrue(isinstance(patch_info, versions._PatchInfo))\n self.assertEqual(patch_info.iiq_dir, '')","def rpc_info():","def test_all_applications_info(self, mocked_get, mocked_check):\n from supvisors.rpcinterface import RPCInterface\n # prepare context\n self.supervisor.supvisors.context.applications = {\n 'dummy_1': None, 'dummy_2': None}\n # create RPC instance\n rpc = RPCInterface(self.supervisor)\n # test RPC call\n self.assertItemsEqual([{'name': 'appli_1'}, {'name': 'appli_2'}],\n rpc.get_all_applications_info())\n self.assertEqual([call()], mocked_check.call_args_list)\n self.assertItemsEqual([call('dummy_1'), call('dummy_2')],\n mocked_get.call_args_list)","def svn_info_invoke_receiver(svn_info_receiver_t__obj, void_baton, char_path, svn_info_t_info, apr_pool_t_pool): # real signature unknown; restored from __doc__\n pass","def after_call_used(self, function_info, subscript, call_code, return_value, code_reference):\n # pylint: disable=too-many-arguments\n if function_info == ('pandas.io.parsers', 'read_csv'):\n operator_context = OperatorContext(OperatorType.DATA_SOURCE, function_info)\n return_value = self.execute_inspection_visits_no_parents(operator_context, code_reference,\n return_value, function_info)\n if function_info == ('pandas.core.groupby.generic', 'agg'):\n operator_context = OperatorContext(OperatorType.GROUP_BY_AGG, function_info)\n return_value = self.execute_inspection_visits_no_parents(operator_context, code_reference,\n return_value.reset_index(), function_info)\n elif function_info == ('pandas.core.frame', 'dropna'):\n operator_context = OperatorContext(OperatorType.SELECTION, function_info)\n return_value = execute_inspection_visits_unary_operator_df(self, operator_context, code_reference,\n self.input_data[-1],\n self.input_data[-1].annotations,\n return_value)\n elif function_info == ('pandas.core.frame', '__getitem__'):\n # TODO: Can this also be a select\n if self.select:\n self.select = False\n # Gets converted to Selection later?\n operator_context = OperatorContext(OperatorType.SELECTION, function_info)\n return_value = execute_inspection_visits_unary_operator_df(self, operator_context, code_reference,\n self.input_data[-1],\n self.input_data[-1].annotations,\n return_value)\n elif isinstance(return_value, MlinspectDataFrame):\n operator_context = OperatorContext(OperatorType.PROJECTION, function_info)\n return_value['mlinspect_index'] = range(1, len(return_value) + 1)\n return_value = execute_inspection_visits_unary_operator_df(self, operator_context, code_reference,\n self.input_data[-1],\n self.input_data[-1].annotations,\n return_value)\n elif isinstance(return_value, MlinspectSeries):\n operator_context = OperatorContext(OperatorType.PROJECTION, function_info)\n return_value = self.execute_inspection_visits_unary_operator_series(operator_context, code_reference,\n return_value,\n function_info)\n elif function_info == ('pandas.core.frame', 'groupby'):\n description = self.code_reference_to_description[code_reference]\n return_value.name = description # TODO: Do not use name here but something else to transport the value\n if function_info == ('pandas.core.frame', 'merge'):\n operator_context = OperatorContext(OperatorType.JOIN, function_info)\n return_value = self.execute_inspection_visits_join_operator_df(operator_context, code_reference,\n return_value,\n function_info)\n\n self.input_data.pop()\n\n return return_value","def test_address_info(self):\n from supvisors.rpcinterface import RPCInterface\n # prepare context\n self.supervisor.supvisors.context.addresses = {\n '10.0.0.1': Mock(**{'serial.return_value': 'address_info'})}\n # create RPC instance\n rpc = RPCInterface(self.supervisor)\n # test with known address\n self.assertEqual('address_info', rpc.get_address_info('10.0.0.1'))\n # test with unknown address\n with self.assertRaises(RPCError) as exc:\n rpc.get_address_info('10.0.0.0')\n self.assertEqual(Faults.BAD_ADDRESS, exc.exception.code)\n self.assertEqual('BAD_ADDRESS: address 10.0.0.0 unknown in Supvisors',\n exc.exception.text)","def test_info_get(self):\n response = self.client.open(\n '/info',\n method='GET')\n self.assert200(response,\n 'Response body is : ' + response.data.decode('utf-8'))","def info(): # noqa: E501\n return 'do some magic!'","def print_info(*args):\n print(CGREEN2 + str(*args) + CEND)","def test_pop_returns_value(new_dll):\n assert new_dll.pop() == 3","def get_system_info():\n query = {\"type\": \"op\", \"cmd\": \"<show><system><info></info></system></show>\"}\n\n return __proxy__[\"panos.call\"](query)","def test_ctcpQuery_USERINFO(self):\n self.client.userinfo = \"info\"\n self.client.ctcpQuery_USERINFO(self.user, self.channel, \"data\")\n self.assertEqual(\n self.client.methods, [(\"ctcpMakeReply\", (\"Wolf\", [(\"USERINFO\", \"info\")]))]\n )","def test_rpcCall(self):\n pass","def info():\n print __doc__\n sys.exit(1)","def process_info(self, info):\n return info","def test_info(manager):\n manager.test_window(\"one\")\n manager.c.sync()\n info = manager.c.window.info()\n assert info[\"name\"] == \"one\"\n assert info[\"group\"] == \"a\"\n assert info[\"wm_class\"][0] == \"TestWindow\"\n assert \"x\" in info\n assert \"y\" in info\n assert \"width\" in info\n assert \"height\" in info\n assert \"id\" in info","def test_get_orderbook_info(mocker, expected_response, expected_data, client) -> None:\n\n mocker.patch(\"requests.Session.request\", return_value=expected_response)\n actual_data = client.get_orderbook_info(pair_id=1)\n\n assert actual_data == expected_data","def test_call_command(self):\n p = call('whoami', shell=False, executable=None, path=None)\n self.assertIsInstance(p, Response)\n self.assertIsInstance(p.stdout, str)\n self.assertIsInstance(p.stderr, str)","def getCallTip(self, command='', *args, **kwds):\n return ('', '', '')","def return_info(self):\n\t\treturn self.info","def get_info():\r\n\r\n path = \"data.xlsx\" # change path depending on the name and location of the file\r\n xl_book = xlrd.open_workbook(path)\r\n xl_sheet = xl_book.sheet_by_index(0) # selects the first sheet in the spreadsheet\r\n emails = xl_sheet.col_values(1, 1) # emails are in second column\r\n names = xl_sheet.col_values(0, 1) # client names are in first column\r\n return emails, names","def test_get_pair_info(mocker, expected_response, expected_data, client) -> None:\n\n mocker.patch(\"requests.Session.request\", return_value=expected_response)\n actual_data = client.get_pair_info(pair_id=1)\n\n assert actual_data == expected_data","def testGetInfoRemote(self):\n self.stream_start(mode='client',\n plugins=['xep_0030'])\n\n events = set()\n\n def handle_disco_info(iq):\n events.add('disco_info')\n\n\n self.xmpp.add_event_handler('disco_info', handle_disco_info)\n\n\n self.xmpp.wrap(self.xmpp['xep_0030'].get_info('user@localhost', 'foo'))\n self.wait_()\n\n self.send(\"\"\"\n <iq type=\"get\" to=\"user@localhost\" id=\"1\">\n <query xmlns=\"http://jabber.org/protocol/disco#info\"\n node=\"foo\" />\n </iq>\n \"\"\")\n\n self.recv(\"\"\"\n <iq type=\"result\" to=\"tester@localhost\" id=\"1\">\n <query xmlns=\"http://jabber.org/protocol/disco#info\"\n node=\"foo\">\n <identity category=\"client\" type=\"bot\" />\n <feature var=\"urn:xmpp:ping\" />\n </query>\n </iq>\n \"\"\")\n\n self.assertEqual(events, {'disco_info'},\n \"Disco info event was not triggered: %s\" % events)","def call(self, callee: \"SIPPhoneTemplate\") -> None:","def getInfoFromExcel(excel_path):\n book = xlrd.open_workbook(excel_path)\n field = book.sheets()[0].row_values(0)\n insid_index = field.index('id')\n tester_index = field.index('Responsible Tester')\n #tester_index = field.index('Tester')\n insid = book.sheets()[0].col_values(insid_index)\n tester = book.sheets()[0].col_values(tester_index)\n insid.pop(0) #delete 'id'\n tester.pop(0) #delete 'tester'\n #print insid\n #print tester\n info = {}\n for i in range(len(insid)):\n info[int(insid[i])] = str(tester[i])\n \n return info","def test_info():\n mock_getgrall = [grp.struct_group((\"foo\", \"*\", 20, [\"test\"]))]\n with patch(\"grp.getgrall\", MagicMock(return_value=mock_getgrall)):\n ret = {\"passwd\": \"*\", \"gid\": 20, \"name\": \"foo\", \"members\": [\"test\"]}\n assert mac_group.info(\"foo\") == ret","def __xcall__(self, cmd):\n\n return call_pkgconfig(cmd + [self.name], self.paths)","def printinfo(name, age=35): # create function printinfo with arguments name, and age(defualt to 35 if not specified)\n print(\"Name: \", name) # print name\n print(\"Age \", age) # print age, will print 35 unless specified in the call statement\n return # exit the function printinfo()","def getInfo(notification):","def test_info(config):\n conventional_commits = ConventionalCommitsCz(config)\n info = conventional_commits.info()\n assert isinstance(info, str)","def get_xcom(**context):\n ti = context['ti']\n data = ti.xcom_pull(task_ids='xcom_from_bash', key='return_value')\n logging.info(data)","def test_add_batch_info(self):\n resp = self.query_with_token(\n self.access_token, batch_info_query.format(**self.batch_data))\n self.assertIn('data', resp)\n self.assertEqual(\n resp['data']['createBatchInfo']['batchInfo']['supplier']['name'],\n self.supplier.name)","def chain2Info(self, action, args=(), kwargs={}, actionReturnsExitCode=False ):\r\n if not isinstance( action, str):\r\n objToCall=action\r\n else:\r\n objToCall = getattr( self, action )\r\n result = objToCall(*args,**args)\r\n resultInfo = ReturnInfo( value = result, exitCode=None ) \r\n return reultInfo, self","def test_pop_methods(self):\n\n batch = Batch(Mock())\n\n # mock BatchRequests\n mock_obj = Mock()\n mock_ref = Mock()\n batch._objects_batch = mock_obj\n batch._reference_batch = mock_ref\n\n mock_obj.pop.assert_not_called()\n mock_ref.pop.assert_not_called()\n\n # pop object default value\n batch.pop_object()\n mock_obj.pop.assert_called_with(-1)\n mock_ref.pop.assert_not_called()\n # reset mock objects\n mock_obj.reset_mock()\n mock_ref.reset_mock()\n\n # pop object at index\n batch.pop_object(10)\n mock_obj.pop.assert_called_with(10)\n mock_ref.pop.assert_not_called()\n # reset mock objects\n mock_obj.reset_mock()\n mock_ref.reset_mock()\n\n # pop reference default value\n batch.pop_reference()\n mock_obj.pop.assert_not_called()\n mock_ref.pop.assert_called_with(-1)\n # reset mock objects\n mock_obj.reset_mock()\n mock_ref.reset_mock()\n\n # pop reference at index\n batch.pop_reference(9)\n mock_obj.pop.assert_not_called()\n mock_ref.pop.assert_called_with(9)","def test_name_and_value_arguments(self):\n mock_call = Call(\"some_name\", 1024, 3.1415, \"hello\")\n assert_that(str(mock_call), equal_to(\"Call(some_name, 1024, 3.1415, hello)\"))","def test_documentation_popxl_replication(self):\n filename = \"replication.py\"\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)","def info(self):","def info(self):","def test_basiccall(self):\n ex = self.ex\n i = self.i\n\n sig = (\"name\", \"char*\", \"int*\", \"double*\", \"double*\")\n ex.call = BasicCall(sig, \"N\", 100, 1.5, [10000])\n cmds = ex.generate_cmds()\n self.assertIn([\"name\", \"N\", 100, 1.5, [10000]], cmds)\n\n # now with a range\n lenrange = random.randint(1, 10)\n ex.range = [i, range(lenrange)]\n ex.call = BasicCall(sig, \"N\", i - 1, 1.5, [i * i])\n cmds = ex.generate_cmds()\n idx = random.randint(0, lenrange - 1)\n self.assertIn([\"name\", \"N\", idx - 1, 1.5, [idx * idx]], cmds)","def info_cmd(args):\n livebox_info()","def request_info(self):\r\n if self.use_http():\r\n self.enqueue_http_request(\"money/info\", {}, \"info\")\r\n else:\r\n self.send_signed_call(\"private/info\", {}, \"info\")","def test_documentation_popxl_in_sequence(self):\n filename = \"in_sequence.py\"\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)","def get_pex_info(entry_point):\r\n from . import pex_info\r\n\r\n pex_info_content = read_pex_info_content(entry_point)\r\n if pex_info_content:\r\n return pex_info.PexInfo.from_json(pex_info_content)\r\n raise ValueError('Invalid entry_point: %s' % entry_point)","def prep_info_callset(df, info):\n df = df.copy()\n df = df[df.ID.isin(info.ID_original.tolist())]\n return df","def test_user_info(self):\n fake_fetchall = MagicMock()\n fake_fetchall.return_value = [(1234, 5678)]\n db = database.Database()\n db._cursor.fetchall = fake_fetchall\n\n info = db.user_info('sally')\n expected = (1234, 5678)\n\n self.assertEqual(info, expected)","def test_get_cell(workbook):\n assert workbook.get_cell(3,1) == '507906000030242007'","def batch_info():\n return BatchInfo(\"Applitools Demo Visual Tests\")","def get_info(self):\n pass","def get_info(self):\n pass","def on_info_click(self, event):\n def on_close(event, wind):\n wind.Close()\n wind.Destroy()\n event.Skip()\n wind = wx.PopupTransientWindow(self, wx.RAISED_BORDER)\n if self.auto_save.GetValue():\n info = \"'auto-save' is currently selected. Temperature bounds will be saved when you click 'next' or 'back'.\"\n else:\n info = \"'auto-save' is not selected. Temperature bounds will only be saved when you click 'save'.\"\n text = wx.StaticText(wind, -1, info)\n box = wx.StaticBox(wind, -1, 'Info:')\n boxSizer = wx.StaticBoxSizer(box, wx.VERTICAL)\n boxSizer.Add(text, 5, wx.ALL | wx.CENTER)\n exit_btn = wx.Button(wind, wx.ID_EXIT, 'Close')\n wind.Bind(wx.EVT_BUTTON, lambda evt: on_close(evt, wind), exit_btn)\n boxSizer.Add(exit_btn, 5, wx.ALL | wx.CENTER)\n wind.SetSizer(boxSizer)\n wind.Layout()\n wind.Popup()","def info(self) -> int:","def test_1_variantcall(install_test_files, data_dir):\n with make_workdir() as workdir:\n cl = [\"bcbio_nextgen.py\",\n get_post_process_yaml(data_dir, workdir),\n os.path.join(data_dir, os.pardir, \"100326_FC6107FAAXX\"),\n os.path.join(data_dir, \"run_info-variantcall.yaml\")]\n subprocess.check_call(cl)","def info(self, msg, *args, **kwargs):\n pass","def test_get_info(self):\n self.addCleanup(self.sdkapi.guest_delete, self.userid)\n\n self.sdkapi.guest_create(self.userid, 1, 1024, disk_list=self.disks)\n self.sdkapi.guest_deploy(self.userid, self.image_name)\n\n # get info in shutdown state\n info_off = self.sdkapi.guest_get_info(self.userid)\n self.assertEquals(info_off['power_state'], 'off')\n self.assertEquals(info_off['mem_kb'], 0)\n self.assertEquals(info_off['cpu_time_us'], 0)\n\n # get info in active state\n self.sdkapi.guest_start(self.userid)\n self.assertTrue(self.sdkutils.wait_until_guest_in_power_state(\n self.userid, 'on'))\n time.sleep(1)\n info_on = self.sdkapi.guest_get_info(self.userid)\n self.assertEquals(info_on['power_state'], 'on')\n self.assertNotEqual(info_on['cpu_time_us'], 0)\n self.assertNotEqual(info_on['mem_kb'], 0)\n\n # get info in paused state\n self.sdkapi.guest_pause(self.userid)\n info_on = self.sdkapi.guest_get_info(self.userid)\n self.assertEquals(info_on['power_state'], 'on')\n self.assertNotEqual(info_on['cpu_time_us'], 0)\n self.assertNotEqual(info_on['mem_kb'], 0)","def _call(*, call, method, hub_token_header, data=None, **kwargs):\n response = None\n headers = {}\n if 'headers' in kwargs: # pragma: no cover\n raise ValueError('Headers already defined: {}'.format(kwargs['headers']))\n if hub_token_header:\n if 'hub_token' not in kwargs:\n raise AttributeError('Asked to do a call to the hub but no hub_token provided.')\n headers['Authorization'] = kwargs['hub_token']\n if data is not None:\n data = json.dumps(data)\n headers['content-type'] = 'application/json'\n\n if 'remote' in kwargs and kwargs['remote']: # remote call\n if 'cloud_token' not in kwargs:\n raise AttributeError('Asked to do remote call but no cloud_token provided.')\n headers['Authorization'] = kwargs['cloud_token']\n headers['X-Hub-Key'] = kwargs['hub_token']\n response = cloud_api.remote(apicall=call, data=data, headers=headers)\n else: # local call\n if 'host' not in kwargs or not kwargs['host']:\n raise AttributeError(\n 'Local call but no hostname was provided. Either set keyword remote or host.')\n try:\n response = method(_getBase(**kwargs) + call, headers=headers, data=data, timeout=5)\n except requests.exceptions.RequestException as e: # pragma: no cover\n raise ConnectionError(str(e)) from None\n\n # evaluate response, wether it was remote or local\n if response.status_code == 200:\n return response.json()\n elif response.status_code == 410: # pragma: no cover\n raise APIError(\n response.status_code, 'API version outdated. Update python-cozify. %s - %s - %s' %\n (response.reason, response.url, response.text))\n else: # pragma: no cover\n raise APIError(response.status_code,\n '%s - %s - %s' % (response.reason, response.url, response.text))","def test_get_details7(self):\n pass","def before_call_used_args(self, function_info, subscript, call_code, args_code, code_reference, store, args_values):\n # pylint: disable=too-many-arguments\n if function_info == ('pandas.core.frame', 'merge'):\n assert isinstance(args_values[0], MlinspectDataFrame)\n args_values[0]['mlinspect_index_y'] = range(1, len(args_values[0]) + 1)\n self.df_arg = args_values[0]\n elif function_info == ('pandas.core.frame', '__getitem__') and isinstance(args_values, MlinspectSeries):\n self.select = True\n self.before_call_used_args_add_description(args_values, code_reference, function_info, args_code)","def call(self, procedure: str, *args: aiowamp.WAMPType,\n kwargs: aiowamp.WAMPDict = None,\n receive_progress: bool = None,\n call_timeout: float = None,\n cancel_mode: aiowamp.CancelMode = None,\n disclose_me: bool = None,\n resource_key: str = None,\n options: aiowamp.WAMPDict = None) -> aiowamp.CallABC:\n ...","def get_info(self) -> types.NestedArray:\n raise NotImplementedError('No support of get_info for this environment.')","def got_info(self, cloud_obj):","def get_software_info():\n query = {\n \"type\": \"op\",\n \"cmd\": \"<request><system><software><info></info></software></system></request>\",\n }\n\n return __proxy__[\"panos.call\"](query)","def get_info_cli(self, no):\n #\n os.system(\"toilet -f smblock --filter border:metal -w 50 'Indo Phone Number Checker - ( IPNC )'\")\n print()\n os.system(\"toilet -f smblock ' by : @danrfq' --filter gay\")\n print()\n\n for data in self.get_info(no):\n if \"message\" in data:\n print(c.fg.red+\"[ ERROR ]\\nFormat Nomor Yang Anda Masukkan Salah!\"+c.end+\"\\n\"+c.fg.lightgreen+\"Contoh Nomor : +6281291718019\"+c.end)\n else:\n print(c.fg.yellow+\"\"\"╔ [ {} Information ]\n╠\n╠ International : {}\n╠ National : {}\n╠ Provider : {}\n╠ Type : {}\n╠ Location : {}\n╠ Timezones : {}\n╠\n╚ [ Finish ]\"\"\".\n format(no,\n data[\"international\"],\n data[\"national\"],\n data['provider'],\n data[\"type\"].replace(\"_\",\" \").title(),\n data[\"location\"],\n \", \".join(data[\"timezone\"]))+c.end\n )","def InfoCall(connection, functionno, rc):\n\n ssl_logging = logging.getLogger('SSL_InfoCall')\n\n ssl_logging.debug('In InfoCall')\n ssl_logging.debug('State : %s' % connection.state_string())\n ssl_logging.debug('Fuction Number: %s' % functionno)\n ssl_logging.debug('Return Code : %s' % rc)\n return 0","def pex_info_name(entry_point):\r\n return os.path.join(entry_point, 'PEX-INFO')","def test_ws_getItemInfosWithExtraInfosRequest(self):\n self.changeUser('pmCreator1')\n self.failUnless(len(self.portal.portal_catalog(portal_type='MeetingItemPga')) == 0)\n # prepare data for a default item\n req = self._prepareCreationData()\n # add one annex\n data = {'title': 'My annex 1', 'filename': 'smallTestFile.pdf', 'file': 'smallTestFile.pdf'}\n req._creationData._annexes = [self._prepareAnnexInfo(**data)]\n # create the item\n newItem, reponse = self._createItem(req)\n newItemUID = newItem.UID()\n # get informations about the item, by default 'showExtraInfos' is False\n resp = self._getItemInfos(newItemUID, showExtraInfos=True)\n extraInfosFields = SOAPView(self.portal, req)._getExtraInfosFields(newItem)\n # check that every field considered as extra informations is returned in the response\n for extraInfosField in extraInfosFields:\n self.failUnless(extraInfosField.getName() in resp)","def call_method(method, meta):\n try:\n return method()\n except (OSError, NotImplementedError) as e:\n ometa = meta.copy()\n ometa[MK_PAYLOAD] = False\n ometa[MK_ERROR] = type(e).__name__\n return ometa.data, None","def test_ctcpQuery_CLIENTINFO(self):\n self.client.ctcpQuery_CLIENTINFO(self.user, self.channel, \"\")\n self.client.ctcpQuery_CLIENTINFO(self.user, self.channel, \"PING PONG\")\n info = (\n \"ACTION CLIENTINFO DCC ERRMSG FINGER PING SOURCE TIME \" \"USERINFO VERSION\"\n )\n self.assertEqual(\n self.client.methods,\n [\n (\"ctcpMakeReply\", (\"Wolf\", [(\"CLIENTINFO\", info)])),\n (\"ctcpMakeReply\", (\"Wolf\", [(\"CLIENTINFO\", None)])),\n ],\n )","def main():\n outfile = 'result.txt'\n\n if os.path.exists(outfile):\n os.remove(outfile)\n\n for arg in sys.argv[1:]:\n get_info(arg, outfile)","def svn_info_dup(svn_info_t_info, apr_pool_t_pool): # real signature unknown; restored from __doc__\n pass","def df_info(df: pd.DataFrame, return_info=False, shape=True, cols=True, info_prefix=''):\n info = info_prefix\n if shape:\n info = f'{info}Shape = {df.shape}'\n if cols:\n info = f'{info} , Cols = {df.columns.tolist()}'\n print(info)\n if return_info:\n return info","def testOverrideGlobalInfoHandler(self):\n self.stream_start(mode='component',\n jid='tester.localhost',\n plugins=['xep_0030'])\n\n def dynamic_global(jid, node, ifrom, iq):\n result = self.xmpp['xep_0030'].stanza.DiscoInfo()\n result['node'] = node\n result.add_identity('component', 'generic', name='Dynamic Info')\n return result\n\n self.xmpp['xep_0030'].set_node_handler('get_info',\n handler=dynamic_global)\n\n self.xmpp['xep_0030'].restore_defaults(jid='user@tester.localhost',\n node='testing')\n\n self.xmpp['xep_0030'].add_feature(jid='user@tester.localhost',\n node='testing',\n feature='urn:xmpp:ping')\n\n self.recv(\"\"\"\n <iq type=\"get\" id=\"test\"\n to=\"user@tester.localhost\"\n from=\"tester@localhost\">\n <query xmlns=\"http://jabber.org/protocol/disco#info\"\n node=\"testing\" />\n </iq>\n \"\"\")\n\n self.send(\"\"\"\n <iq type=\"result\" id=\"test\"\n to=\"tester@localhost\"\n from=\"user@tester.localhost\">\n <query xmlns=\"http://jabber.org/protocol/disco#info\"\n node=\"testing\">\n <feature var=\"urn:xmpp:ping\" />\n </query>\n </iq>\n \"\"\")","def test_fax_inbound_automation_get(self):\n pass","def info(msg, *args):\n if args:\n msg %= args\n click.echo(msg, file=sys.stdout)","def process_info(self, info):\n info['result'] = info['result'].value\n return info","def test_info(self):\n self.run_function(\"group.add\", [self._group], gid=self._gid)\n self.run_function(\"user.add\", [self._user])\n self.run_function(\"group.adduser\", [self._group, self._user])\n group_info = self.run_function(\"group.info\", [self._group])\n\n self.assertEqual(group_info[\"name\"], self._group)\n self.assertEqual(group_info[\"gid\"], self._gid)\n self.assertIn(self._user, str(group_info[\"members\"]))","def printinfo(arg1, *vartuple):\n print('This is the output:')\n print(arg1)\n\n for var in vartuple:\n print(var)\n\n return","def test_endpointPOP3(self):\n self._endpointTest(\"pop3\")","def handle_info(self, api, command):\n return self.handle_log(api, command, level=logging.INFO)","def test_get_invoice_info(mocker, expected_response, expected_data, client) -> None:\n\n mocker.patch(\"requests.Session.request\", return_value=expected_response)\n actual_data = client.get_invoice_info(invoice_id=1)\n\n assert actual_data == expected_data","def test_cell_list_detail_success(self, mock_list):\n self.shell('cell-list -r 1 --detail')\n mock_list.assert_called_once_with(detail=True)","def test_patch_info(self):\n patch_info = versions.PatchInfo(iiq_dir='/some/path',\n patches_dir='/another/path',\n is_installed=False,\n specific_patch='this_patch',\n readme='data from readme',\n all_patches=('patch1', 'patch2'))\n self.assertTrue(isinstance(patch_info, versions._PatchInfo))","def meta_info(environ, start_response, logger, handle):\n pass","def show_info(title, message):\n\n pass"],"string":"[\n \"def hxlinfo():\\n run_script(hxlinfo_main)\",\n \"def test_get_info(self):\\n pass\",\n \"def test_application_info(self, mocked_serial, mocked_check):\\n from supvisors.rpcinterface import RPCInterface\\n # create RPC instance\\n rpc = RPCInterface(self.supervisor)\\n # test RPC call\\n self.assertEqual({'name': 'appli'}, rpc.get_application_info('dummy'))\\n self.assertEqual([call()], mocked_check.call_args_list)\\n self.assertEqual([call('dummy')], mocked_serial.call_args_list)\",\n \"def verify_call(obj):\\n\\tassert obj.tag == 'OMOBJ'\\n\\tattr = obj[0]\\n\\t\\n\\tassert attr.tag == 'OMATTR'\\n\\tpairs, application = attr\\n\\t\\n\\tassert application.tag == 'OMA'\\n\\tsymbol, args = application\\n\\t\\n\\tassert symbol.tag == 'OMS'\\n\\tassert symbol.get('cd') == \\\"scscp1\\\"\\n\\tassert symbol.get('name') == \\\"procedure_call\\\"\\n\\t\\n\\tassert args.tag == 'OMA'\\n\\tassert len(args) > 0\\n\\tname_symbol = args[0]\\n\\t\\n\\tassert name_symbol.tag == 'OMS'\\n\\tcd = name_symbol.get('cd')\\n\\tproc_name = name_symbol.get('name')\\n\\t\\n\\t#2. Now handle the extra information\\n\\tassert pairs.tag == 'OMATP'\\n\\tassert len(pairs) % 2 == 0\\n\\t\\n\\textras = {}\\n\\tcall_id = None\\n\\treturn_type = None\\n\\t\\n\\tfor i in range(0, len(pairs), 2):\\n\\t\\tsymbol = pairs[i]\\n\\t\\tassert symbol.tag == 'OMS'\\n\\t\\tassert symbol.get('cd') == \\\"scscp1\\\"\\n\\t\\tname = symbol.get('name')\\n\\t\\textras[name] = pairs[i+1]\\n\\t\\t\\n\\t\\tif name == 'call_id':\\n\\t\\t\\tassert call_id is None\\n\\t\\t\\tcall_id = pairs[i+1].text\\n\\t\\t\\tprint(call_id)\\n\\t\\telif name.startswith('option_return_'):\\n\\t\\t\\tassert return_type is None\\n\\t\\t\\treturn_type = ReturnTypes[name[14:]]\\n\\t\\n\\t#Some information is mandatory\\n\\tassert call_id is not None\\n\\tassert return_type is not None\\n\\t\\n\\treturn cd, proc_name, call_id, return_type, args[1:], extras\",\n \"def info(self, *args, **kwargs):\",\n \"def test_process_info(self, mocked_get, mocked_check):\\n from supvisors.rpcinterface import RPCInterface\\n # create RPC instance\\n rpc = RPCInterface(self.supervisor)\\n # test first RPC call with process namespec\\n self.assertEqual([{'name': 'proc'}], rpc.get_process_info('appli:proc'))\\n self.assertEqual([call()], mocked_check.call_args_list)\\n self.assertEqual([call('appli:proc')], mocked_get.call_args_list)\\n # reset patches\\n mocked_check.reset_mock()\\n mocked_get.reset_mock()\\n # test second RPC call with group namespec\\n self.assertEqual([{'name': 'proc_1'}, {'name': 'proc_2'}],\\n rpc.get_process_info('appli:*'))\\n self.assertEqual([call()], mocked_check.call_args_list)\\n self.assertEqual([call('appli:*')], mocked_get.call_args_list)\",\n \"def get_info(self, info):\\r\\n pass\",\n \"def setInfo(*args):\",\n \"def getInfo():\",\n \"def info() -> None:\",\n \"def test_get_info_function() -> None:\\n current_directory = Path.cwd()\\n with zipfile.ZipFile(\\n current_directory / 'app' / 'tests' / 'files' / 'oneFile.zip') as zip_object:\\n res = get_info_about_file(zip_object, 'dotnetfx.exe')\\n assert res == {'path': 'dotnetfx.exe', 'size': 21823560}\",\n \"def test_documentation_popxl_addition(self):\\n filename = \\\"simple_addition.py\\\"\\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)\",\n \"def hxlinfo_main(args, stdin=STDIN, stdout=sys.stdout, stderr=sys.stderr):\\n parser = make_args(\\n 'Display JSON-formatted metadata for a data source (does not have to be HXLated).',\\n hxl_output=False\\n )\\n\\n args = parser.parse_args(args)\\n\\n do_common_args(args)\\n\\n json.dump(hxl.input.info(args.infile or stdin, make_input_options(args)), stdout, indent=2, ensure_ascii=False)\\n\\n return EXIT_OK\",\n \"def test_print_info(clarisse):\\n info = \\\"test print info\\\"\\n assert bool(clarisse.print_info(info)) is False\",\n \"def test_get_cell_info(self):\\n expected = { 'Experiment': \\\"20220101_EGS1_12345AA\\\",\\n 'Cell': '12345AA0018/20220101_1234_1-A1-A1_AAA66666_deadbeef',\\n 'Pool': '12345AA0018',\\n 'Date': '20220101',\\n 'Number': '1234',\\n 'Slot': '1-A1-A1',\\n 'CellID': 'AAA66666',\\n 'Checksum': 'deadbeef',\\n 'Project': '12345',\\n 'Base': '12345AA0018/20220101_1234_1-A1-A1_AAA66666_deadbeef/'\\n '20220101_EGS1_12345AA_12345AA0018_AAA66666_deadbeef',\\n 'Files in pass': 'unknown',\\n 'Files in fail': 1,\\n 'Files in fast5 fail': 1,\\n '_counts': [\\n {'_barcode': '.', '_label': 'All passed reads', '_part': 'pass', 'total_reads': 200},\\n {'_barcode': '.', '_label': 'Passed and lambda-filtered reads', '_part': 'nolambda'},\\n {'_barcode': '.', '_label': 'All failed reads', '_part': 'fail'} ],\\n '_blobs': ['../../__blob__'],\\n '_duplex' : [ ['Duplex pairs', 1],\\n ['from total passing reads', 200],\\n ['% of passing reads', '1.00%'] ],\\n '_filter_type': 'none',\\n '_final_summary': {'is_rna': False},\\n '_nanoplot': '../../__nanoplot__',\\n }\\n\\n\\n got = get_cell_info( experiment = \\\"20220101_EGS1_12345AA\\\",\\n cell = \\\"12345AA0018/20220101_1234_1-A1-A1_AAA66666_deadbeef\\\",\\n cell_content = { '.': dict( fast5_pass = ['x.fast5'],\\n fastq_fail = ['y.fastq'],\\n fast5_fail = ['y.fast5'] ) },\\n counts = { ('.','pass'): dict(total_reads = 200),\\n ('.','fail'): dict(),\\n ('.','nolambda'): dict() },\\n fin_summary = dict(is_rna = False),\\n blobs = ['__blob__'],\\n nanoplot = '__nanoplot__',\\n duplex = 1,\\n fast5_meta = dict() )\\n\\n if VERBOSE:\\n pprint(got)\\n\\n self.assertEqual( type(got), OrderedDict )\\n self.assertEqual( dict(got), expected )\",\n \"def test_all_process_info(self, mocked_check):\\n from supvisors.rpcinterface import RPCInterface\\n # prepare context\\n self.supervisor.supvisors.context.processes = {\\n 'proc_1': Mock(**{'serial.return_value': {'name': 'proc_1'}}),\\n 'proc_2': Mock(**{'serial.return_value': {'name': 'proc_2'}})}\\n # create RPC instance\\n rpc = RPCInterface(self.supervisor)\\n # test RPC call\\n self.assertItemsEqual([{'name': 'proc_1'}, {'name': 'proc_2'}],\\n rpc.get_all_process_info())\\n self.assertEqual([call()], mocked_check.call_args_list)\",\n \"def _handle_info_response(self, resp, info, prev_info):\\r\\n if info.line_num != prev_info.line_num:\\r\\n return\\r\\n\\r\\n if resp['calltip']:\\r\\n info.editor.show_calltip('Arguments', resp['calltip'],\\r\\n signature=True,\\r\\n at_position=prev_info.position)\\r\\n\\r\\n if resp['name']:\\r\\n self.send_to_inspector.emit(\\r\\n resp['name'], resp['argspec'],\\r\\n resp['note'], resp['docstring'],\\r\\n not prev_info.auto)\",\n \"def test_get_patch_info_returns(self):\\n # This test assumes IIQ isn't installed, thus the pile of errors that'll\\n # occur shouldn't prevent us from getting a PatchInfo object\\n fake_log = MagicMock()\\n patch_info = versions.get_patch_info('bogus-patch.tgz', fake_log)\\n\\n self.assertTrue(isinstance(patch_info, versions._PatchInfo))\\n self.assertEqual(patch_info.iiq_dir, '')\",\n \"def rpc_info():\",\n \"def test_all_applications_info(self, mocked_get, mocked_check):\\n from supvisors.rpcinterface import RPCInterface\\n # prepare context\\n self.supervisor.supvisors.context.applications = {\\n 'dummy_1': None, 'dummy_2': None}\\n # create RPC instance\\n rpc = RPCInterface(self.supervisor)\\n # test RPC call\\n self.assertItemsEqual([{'name': 'appli_1'}, {'name': 'appli_2'}],\\n rpc.get_all_applications_info())\\n self.assertEqual([call()], mocked_check.call_args_list)\\n self.assertItemsEqual([call('dummy_1'), call('dummy_2')],\\n mocked_get.call_args_list)\",\n \"def svn_info_invoke_receiver(svn_info_receiver_t__obj, void_baton, char_path, svn_info_t_info, apr_pool_t_pool): # real signature unknown; restored from __doc__\\n pass\",\n \"def after_call_used(self, function_info, subscript, call_code, return_value, code_reference):\\n # pylint: disable=too-many-arguments\\n if function_info == ('pandas.io.parsers', 'read_csv'):\\n operator_context = OperatorContext(OperatorType.DATA_SOURCE, function_info)\\n return_value = self.execute_inspection_visits_no_parents(operator_context, code_reference,\\n return_value, function_info)\\n if function_info == ('pandas.core.groupby.generic', 'agg'):\\n operator_context = OperatorContext(OperatorType.GROUP_BY_AGG, function_info)\\n return_value = self.execute_inspection_visits_no_parents(operator_context, code_reference,\\n return_value.reset_index(), function_info)\\n elif function_info == ('pandas.core.frame', 'dropna'):\\n operator_context = OperatorContext(OperatorType.SELECTION, function_info)\\n return_value = execute_inspection_visits_unary_operator_df(self, operator_context, code_reference,\\n self.input_data[-1],\\n self.input_data[-1].annotations,\\n return_value)\\n elif function_info == ('pandas.core.frame', '__getitem__'):\\n # TODO: Can this also be a select\\n if self.select:\\n self.select = False\\n # Gets converted to Selection later?\\n operator_context = OperatorContext(OperatorType.SELECTION, function_info)\\n return_value = execute_inspection_visits_unary_operator_df(self, operator_context, code_reference,\\n self.input_data[-1],\\n self.input_data[-1].annotations,\\n return_value)\\n elif isinstance(return_value, MlinspectDataFrame):\\n operator_context = OperatorContext(OperatorType.PROJECTION, function_info)\\n return_value['mlinspect_index'] = range(1, len(return_value) + 1)\\n return_value = execute_inspection_visits_unary_operator_df(self, operator_context, code_reference,\\n self.input_data[-1],\\n self.input_data[-1].annotations,\\n return_value)\\n elif isinstance(return_value, MlinspectSeries):\\n operator_context = OperatorContext(OperatorType.PROJECTION, function_info)\\n return_value = self.execute_inspection_visits_unary_operator_series(operator_context, code_reference,\\n return_value,\\n function_info)\\n elif function_info == ('pandas.core.frame', 'groupby'):\\n description = self.code_reference_to_description[code_reference]\\n return_value.name = description # TODO: Do not use name here but something else to transport the value\\n if function_info == ('pandas.core.frame', 'merge'):\\n operator_context = OperatorContext(OperatorType.JOIN, function_info)\\n return_value = self.execute_inspection_visits_join_operator_df(operator_context, code_reference,\\n return_value,\\n function_info)\\n\\n self.input_data.pop()\\n\\n return return_value\",\n \"def test_address_info(self):\\n from supvisors.rpcinterface import RPCInterface\\n # prepare context\\n self.supervisor.supvisors.context.addresses = {\\n '10.0.0.1': Mock(**{'serial.return_value': 'address_info'})}\\n # create RPC instance\\n rpc = RPCInterface(self.supervisor)\\n # test with known address\\n self.assertEqual('address_info', rpc.get_address_info('10.0.0.1'))\\n # test with unknown address\\n with self.assertRaises(RPCError) as exc:\\n rpc.get_address_info('10.0.0.0')\\n self.assertEqual(Faults.BAD_ADDRESS, exc.exception.code)\\n self.assertEqual('BAD_ADDRESS: address 10.0.0.0 unknown in Supvisors',\\n exc.exception.text)\",\n \"def test_info_get(self):\\n response = self.client.open(\\n '/info',\\n method='GET')\\n self.assert200(response,\\n 'Response body is : ' + response.data.decode('utf-8'))\",\n \"def info(): # noqa: E501\\n return 'do some magic!'\",\n \"def print_info(*args):\\n print(CGREEN2 + str(*args) + CEND)\",\n \"def test_pop_returns_value(new_dll):\\n assert new_dll.pop() == 3\",\n \"def get_system_info():\\n query = {\\\"type\\\": \\\"op\\\", \\\"cmd\\\": \\\"<show><system><info></info></system></show>\\\"}\\n\\n return __proxy__[\\\"panos.call\\\"](query)\",\n \"def test_ctcpQuery_USERINFO(self):\\n self.client.userinfo = \\\"info\\\"\\n self.client.ctcpQuery_USERINFO(self.user, self.channel, \\\"data\\\")\\n self.assertEqual(\\n self.client.methods, [(\\\"ctcpMakeReply\\\", (\\\"Wolf\\\", [(\\\"USERINFO\\\", \\\"info\\\")]))]\\n )\",\n \"def test_rpcCall(self):\\n pass\",\n \"def info():\\n print __doc__\\n sys.exit(1)\",\n \"def process_info(self, info):\\n return info\",\n \"def test_info(manager):\\n manager.test_window(\\\"one\\\")\\n manager.c.sync()\\n info = manager.c.window.info()\\n assert info[\\\"name\\\"] == \\\"one\\\"\\n assert info[\\\"group\\\"] == \\\"a\\\"\\n assert info[\\\"wm_class\\\"][0] == \\\"TestWindow\\\"\\n assert \\\"x\\\" in info\\n assert \\\"y\\\" in info\\n assert \\\"width\\\" in info\\n assert \\\"height\\\" in info\\n assert \\\"id\\\" in info\",\n \"def test_get_orderbook_info(mocker, expected_response, expected_data, client) -> None:\\n\\n mocker.patch(\\\"requests.Session.request\\\", return_value=expected_response)\\n actual_data = client.get_orderbook_info(pair_id=1)\\n\\n assert actual_data == expected_data\",\n \"def test_call_command(self):\\n p = call('whoami', shell=False, executable=None, path=None)\\n self.assertIsInstance(p, Response)\\n self.assertIsInstance(p.stdout, str)\\n self.assertIsInstance(p.stderr, str)\",\n \"def getCallTip(self, command='', *args, **kwds):\\n return ('', '', '')\",\n \"def return_info(self):\\n\\t\\treturn self.info\",\n \"def get_info():\\r\\n\\r\\n path = \\\"data.xlsx\\\" # change path depending on the name and location of the file\\r\\n xl_book = xlrd.open_workbook(path)\\r\\n xl_sheet = xl_book.sheet_by_index(0) # selects the first sheet in the spreadsheet\\r\\n emails = xl_sheet.col_values(1, 1) # emails are in second column\\r\\n names = xl_sheet.col_values(0, 1) # client names are in first column\\r\\n return emails, names\",\n \"def test_get_pair_info(mocker, expected_response, expected_data, client) -> None:\\n\\n mocker.patch(\\\"requests.Session.request\\\", return_value=expected_response)\\n actual_data = client.get_pair_info(pair_id=1)\\n\\n assert actual_data == expected_data\",\n \"def testGetInfoRemote(self):\\n self.stream_start(mode='client',\\n plugins=['xep_0030'])\\n\\n events = set()\\n\\n def handle_disco_info(iq):\\n events.add('disco_info')\\n\\n\\n self.xmpp.add_event_handler('disco_info', handle_disco_info)\\n\\n\\n self.xmpp.wrap(self.xmpp['xep_0030'].get_info('user@localhost', 'foo'))\\n self.wait_()\\n\\n self.send(\\\"\\\"\\\"\\n <iq type=\\\"get\\\" to=\\\"user@localhost\\\" id=\\\"1\\\">\\n <query xmlns=\\\"http://jabber.org/protocol/disco#info\\\"\\n node=\\\"foo\\\" />\\n </iq>\\n \\\"\\\"\\\")\\n\\n self.recv(\\\"\\\"\\\"\\n <iq type=\\\"result\\\" to=\\\"tester@localhost\\\" id=\\\"1\\\">\\n <query xmlns=\\\"http://jabber.org/protocol/disco#info\\\"\\n node=\\\"foo\\\">\\n <identity category=\\\"client\\\" type=\\\"bot\\\" />\\n <feature var=\\\"urn:xmpp:ping\\\" />\\n </query>\\n </iq>\\n \\\"\\\"\\\")\\n\\n self.assertEqual(events, {'disco_info'},\\n \\\"Disco info event was not triggered: %s\\\" % events)\",\n \"def call(self, callee: \\\"SIPPhoneTemplate\\\") -> None:\",\n \"def getInfoFromExcel(excel_path):\\n book = xlrd.open_workbook(excel_path)\\n field = book.sheets()[0].row_values(0)\\n insid_index = field.index('id')\\n tester_index = field.index('Responsible Tester')\\n #tester_index = field.index('Tester')\\n insid = book.sheets()[0].col_values(insid_index)\\n tester = book.sheets()[0].col_values(tester_index)\\n insid.pop(0) #delete 'id'\\n tester.pop(0) #delete 'tester'\\n #print insid\\n #print tester\\n info = {}\\n for i in range(len(insid)):\\n info[int(insid[i])] = str(tester[i])\\n \\n return info\",\n \"def test_info():\\n mock_getgrall = [grp.struct_group((\\\"foo\\\", \\\"*\\\", 20, [\\\"test\\\"]))]\\n with patch(\\\"grp.getgrall\\\", MagicMock(return_value=mock_getgrall)):\\n ret = {\\\"passwd\\\": \\\"*\\\", \\\"gid\\\": 20, \\\"name\\\": \\\"foo\\\", \\\"members\\\": [\\\"test\\\"]}\\n assert mac_group.info(\\\"foo\\\") == ret\",\n \"def __xcall__(self, cmd):\\n\\n return call_pkgconfig(cmd + [self.name], self.paths)\",\n \"def printinfo(name, age=35): # create function printinfo with arguments name, and age(defualt to 35 if not specified)\\n print(\\\"Name: \\\", name) # print name\\n print(\\\"Age \\\", age) # print age, will print 35 unless specified in the call statement\\n return # exit the function printinfo()\",\n \"def getInfo(notification):\",\n \"def test_info(config):\\n conventional_commits = ConventionalCommitsCz(config)\\n info = conventional_commits.info()\\n assert isinstance(info, str)\",\n \"def get_xcom(**context):\\n ti = context['ti']\\n data = ti.xcom_pull(task_ids='xcom_from_bash', key='return_value')\\n logging.info(data)\",\n \"def test_add_batch_info(self):\\n resp = self.query_with_token(\\n self.access_token, batch_info_query.format(**self.batch_data))\\n self.assertIn('data', resp)\\n self.assertEqual(\\n resp['data']['createBatchInfo']['batchInfo']['supplier']['name'],\\n self.supplier.name)\",\n \"def chain2Info(self, action, args=(), kwargs={}, actionReturnsExitCode=False ):\\r\\n if not isinstance( action, str):\\r\\n objToCall=action\\r\\n else:\\r\\n objToCall = getattr( self, action )\\r\\n result = objToCall(*args,**args)\\r\\n resultInfo = ReturnInfo( value = result, exitCode=None ) \\r\\n return reultInfo, self\",\n \"def test_pop_methods(self):\\n\\n batch = Batch(Mock())\\n\\n # mock BatchRequests\\n mock_obj = Mock()\\n mock_ref = Mock()\\n batch._objects_batch = mock_obj\\n batch._reference_batch = mock_ref\\n\\n mock_obj.pop.assert_not_called()\\n mock_ref.pop.assert_not_called()\\n\\n # pop object default value\\n batch.pop_object()\\n mock_obj.pop.assert_called_with(-1)\\n mock_ref.pop.assert_not_called()\\n # reset mock objects\\n mock_obj.reset_mock()\\n mock_ref.reset_mock()\\n\\n # pop object at index\\n batch.pop_object(10)\\n mock_obj.pop.assert_called_with(10)\\n mock_ref.pop.assert_not_called()\\n # reset mock objects\\n mock_obj.reset_mock()\\n mock_ref.reset_mock()\\n\\n # pop reference default value\\n batch.pop_reference()\\n mock_obj.pop.assert_not_called()\\n mock_ref.pop.assert_called_with(-1)\\n # reset mock objects\\n mock_obj.reset_mock()\\n mock_ref.reset_mock()\\n\\n # pop reference at index\\n batch.pop_reference(9)\\n mock_obj.pop.assert_not_called()\\n mock_ref.pop.assert_called_with(9)\",\n \"def test_name_and_value_arguments(self):\\n mock_call = Call(\\\"some_name\\\", 1024, 3.1415, \\\"hello\\\")\\n assert_that(str(mock_call), equal_to(\\\"Call(some_name, 1024, 3.1415, hello)\\\"))\",\n \"def test_documentation_popxl_replication(self):\\n filename = \\\"replication.py\\\"\\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)\",\n \"def info(self):\",\n \"def info(self):\",\n \"def test_basiccall(self):\\n ex = self.ex\\n i = self.i\\n\\n sig = (\\\"name\\\", \\\"char*\\\", \\\"int*\\\", \\\"double*\\\", \\\"double*\\\")\\n ex.call = BasicCall(sig, \\\"N\\\", 100, 1.5, [10000])\\n cmds = ex.generate_cmds()\\n self.assertIn([\\\"name\\\", \\\"N\\\", 100, 1.5, [10000]], cmds)\\n\\n # now with a range\\n lenrange = random.randint(1, 10)\\n ex.range = [i, range(lenrange)]\\n ex.call = BasicCall(sig, \\\"N\\\", i - 1, 1.5, [i * i])\\n cmds = ex.generate_cmds()\\n idx = random.randint(0, lenrange - 1)\\n self.assertIn([\\\"name\\\", \\\"N\\\", idx - 1, 1.5, [idx * idx]], cmds)\",\n \"def info_cmd(args):\\n livebox_info()\",\n \"def request_info(self):\\r\\n if self.use_http():\\r\\n self.enqueue_http_request(\\\"money/info\\\", {}, \\\"info\\\")\\r\\n else:\\r\\n self.send_signed_call(\\\"private/info\\\", {}, \\\"info\\\")\",\n \"def test_documentation_popxl_in_sequence(self):\\n filename = \\\"in_sequence.py\\\"\\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)\",\n \"def get_pex_info(entry_point):\\r\\n from . import pex_info\\r\\n\\r\\n pex_info_content = read_pex_info_content(entry_point)\\r\\n if pex_info_content:\\r\\n return pex_info.PexInfo.from_json(pex_info_content)\\r\\n raise ValueError('Invalid entry_point: %s' % entry_point)\",\n \"def prep_info_callset(df, info):\\n df = df.copy()\\n df = df[df.ID.isin(info.ID_original.tolist())]\\n return df\",\n \"def test_user_info(self):\\n fake_fetchall = MagicMock()\\n fake_fetchall.return_value = [(1234, 5678)]\\n db = database.Database()\\n db._cursor.fetchall = fake_fetchall\\n\\n info = db.user_info('sally')\\n expected = (1234, 5678)\\n\\n self.assertEqual(info, expected)\",\n \"def test_get_cell(workbook):\\n assert workbook.get_cell(3,1) == '507906000030242007'\",\n \"def batch_info():\\n return BatchInfo(\\\"Applitools Demo Visual Tests\\\")\",\n \"def get_info(self):\\n pass\",\n \"def get_info(self):\\n pass\",\n \"def on_info_click(self, event):\\n def on_close(event, wind):\\n wind.Close()\\n wind.Destroy()\\n event.Skip()\\n wind = wx.PopupTransientWindow(self, wx.RAISED_BORDER)\\n if self.auto_save.GetValue():\\n info = \\\"'auto-save' is currently selected. Temperature bounds will be saved when you click 'next' or 'back'.\\\"\\n else:\\n info = \\\"'auto-save' is not selected. Temperature bounds will only be saved when you click 'save'.\\\"\\n text = wx.StaticText(wind, -1, info)\\n box = wx.StaticBox(wind, -1, 'Info:')\\n boxSizer = wx.StaticBoxSizer(box, wx.VERTICAL)\\n boxSizer.Add(text, 5, wx.ALL | wx.CENTER)\\n exit_btn = wx.Button(wind, wx.ID_EXIT, 'Close')\\n wind.Bind(wx.EVT_BUTTON, lambda evt: on_close(evt, wind), exit_btn)\\n boxSizer.Add(exit_btn, 5, wx.ALL | wx.CENTER)\\n wind.SetSizer(boxSizer)\\n wind.Layout()\\n wind.Popup()\",\n \"def info(self) -> int:\",\n \"def test_1_variantcall(install_test_files, data_dir):\\n with make_workdir() as workdir:\\n cl = [\\\"bcbio_nextgen.py\\\",\\n get_post_process_yaml(data_dir, workdir),\\n os.path.join(data_dir, os.pardir, \\\"100326_FC6107FAAXX\\\"),\\n os.path.join(data_dir, \\\"run_info-variantcall.yaml\\\")]\\n subprocess.check_call(cl)\",\n \"def info(self, msg, *args, **kwargs):\\n pass\",\n \"def test_get_info(self):\\n self.addCleanup(self.sdkapi.guest_delete, self.userid)\\n\\n self.sdkapi.guest_create(self.userid, 1, 1024, disk_list=self.disks)\\n self.sdkapi.guest_deploy(self.userid, self.image_name)\\n\\n # get info in shutdown state\\n info_off = self.sdkapi.guest_get_info(self.userid)\\n self.assertEquals(info_off['power_state'], 'off')\\n self.assertEquals(info_off['mem_kb'], 0)\\n self.assertEquals(info_off['cpu_time_us'], 0)\\n\\n # get info in active state\\n self.sdkapi.guest_start(self.userid)\\n self.assertTrue(self.sdkutils.wait_until_guest_in_power_state(\\n self.userid, 'on'))\\n time.sleep(1)\\n info_on = self.sdkapi.guest_get_info(self.userid)\\n self.assertEquals(info_on['power_state'], 'on')\\n self.assertNotEqual(info_on['cpu_time_us'], 0)\\n self.assertNotEqual(info_on['mem_kb'], 0)\\n\\n # get info in paused state\\n self.sdkapi.guest_pause(self.userid)\\n info_on = self.sdkapi.guest_get_info(self.userid)\\n self.assertEquals(info_on['power_state'], 'on')\\n self.assertNotEqual(info_on['cpu_time_us'], 0)\\n self.assertNotEqual(info_on['mem_kb'], 0)\",\n \"def _call(*, call, method, hub_token_header, data=None, **kwargs):\\n response = None\\n headers = {}\\n if 'headers' in kwargs: # pragma: no cover\\n raise ValueError('Headers already defined: {}'.format(kwargs['headers']))\\n if hub_token_header:\\n if 'hub_token' not in kwargs:\\n raise AttributeError('Asked to do a call to the hub but no hub_token provided.')\\n headers['Authorization'] = kwargs['hub_token']\\n if data is not None:\\n data = json.dumps(data)\\n headers['content-type'] = 'application/json'\\n\\n if 'remote' in kwargs and kwargs['remote']: # remote call\\n if 'cloud_token' not in kwargs:\\n raise AttributeError('Asked to do remote call but no cloud_token provided.')\\n headers['Authorization'] = kwargs['cloud_token']\\n headers['X-Hub-Key'] = kwargs['hub_token']\\n response = cloud_api.remote(apicall=call, data=data, headers=headers)\\n else: # local call\\n if 'host' not in kwargs or not kwargs['host']:\\n raise AttributeError(\\n 'Local call but no hostname was provided. Either set keyword remote or host.')\\n try:\\n response = method(_getBase(**kwargs) + call, headers=headers, data=data, timeout=5)\\n except requests.exceptions.RequestException as e: # pragma: no cover\\n raise ConnectionError(str(e)) from None\\n\\n # evaluate response, wether it was remote or local\\n if response.status_code == 200:\\n return response.json()\\n elif response.status_code == 410: # pragma: no cover\\n raise APIError(\\n response.status_code, 'API version outdated. Update python-cozify. %s - %s - %s' %\\n (response.reason, response.url, response.text))\\n else: # pragma: no cover\\n raise APIError(response.status_code,\\n '%s - %s - %s' % (response.reason, response.url, response.text))\",\n \"def test_get_details7(self):\\n pass\",\n \"def before_call_used_args(self, function_info, subscript, call_code, args_code, code_reference, store, args_values):\\n # pylint: disable=too-many-arguments\\n if function_info == ('pandas.core.frame', 'merge'):\\n assert isinstance(args_values[0], MlinspectDataFrame)\\n args_values[0]['mlinspect_index_y'] = range(1, len(args_values[0]) + 1)\\n self.df_arg = args_values[0]\\n elif function_info == ('pandas.core.frame', '__getitem__') and isinstance(args_values, MlinspectSeries):\\n self.select = True\\n self.before_call_used_args_add_description(args_values, code_reference, function_info, args_code)\",\n \"def call(self, procedure: str, *args: aiowamp.WAMPType,\\n kwargs: aiowamp.WAMPDict = None,\\n receive_progress: bool = None,\\n call_timeout: float = None,\\n cancel_mode: aiowamp.CancelMode = None,\\n disclose_me: bool = None,\\n resource_key: str = None,\\n options: aiowamp.WAMPDict = None) -> aiowamp.CallABC:\\n ...\",\n \"def get_info(self) -> types.NestedArray:\\n raise NotImplementedError('No support of get_info for this environment.')\",\n \"def got_info(self, cloud_obj):\",\n \"def get_software_info():\\n query = {\\n \\\"type\\\": \\\"op\\\",\\n \\\"cmd\\\": \\\"<request><system><software><info></info></software></system></request>\\\",\\n }\\n\\n return __proxy__[\\\"panos.call\\\"](query)\",\n \"def get_info_cli(self, no):\\n #\\n os.system(\\\"toilet -f smblock --filter border:metal -w 50 'Indo Phone Number Checker - ( IPNC )'\\\")\\n print()\\n os.system(\\\"toilet -f smblock ' by : @danrfq' --filter gay\\\")\\n print()\\n\\n for data in self.get_info(no):\\n if \\\"message\\\" in data:\\n print(c.fg.red+\\\"[ ERROR ]\\\\nFormat Nomor Yang Anda Masukkan Salah!\\\"+c.end+\\\"\\\\n\\\"+c.fg.lightgreen+\\\"Contoh Nomor : +6281291718019\\\"+c.end)\\n else:\\n print(c.fg.yellow+\\\"\\\"\\\"╔ [ {} Information ]\\n╠\\n╠ International : {}\\n╠ National : {}\\n╠ Provider : {}\\n╠ Type : {}\\n╠ Location : {}\\n╠ Timezones : {}\\n╠\\n╚ [ Finish ]\\\"\\\"\\\".\\n format(no,\\n data[\\\"international\\\"],\\n data[\\\"national\\\"],\\n data['provider'],\\n data[\\\"type\\\"].replace(\\\"_\\\",\\\" \\\").title(),\\n data[\\\"location\\\"],\\n \\\", \\\".join(data[\\\"timezone\\\"]))+c.end\\n )\",\n \"def InfoCall(connection, functionno, rc):\\n\\n ssl_logging = logging.getLogger('SSL_InfoCall')\\n\\n ssl_logging.debug('In InfoCall')\\n ssl_logging.debug('State : %s' % connection.state_string())\\n ssl_logging.debug('Fuction Number: %s' % functionno)\\n ssl_logging.debug('Return Code : %s' % rc)\\n return 0\",\n \"def pex_info_name(entry_point):\\r\\n return os.path.join(entry_point, 'PEX-INFO')\",\n \"def test_ws_getItemInfosWithExtraInfosRequest(self):\\n self.changeUser('pmCreator1')\\n self.failUnless(len(self.portal.portal_catalog(portal_type='MeetingItemPga')) == 0)\\n # prepare data for a default item\\n req = self._prepareCreationData()\\n # add one annex\\n data = {'title': 'My annex 1', 'filename': 'smallTestFile.pdf', 'file': 'smallTestFile.pdf'}\\n req._creationData._annexes = [self._prepareAnnexInfo(**data)]\\n # create the item\\n newItem, reponse = self._createItem(req)\\n newItemUID = newItem.UID()\\n # get informations about the item, by default 'showExtraInfos' is False\\n resp = self._getItemInfos(newItemUID, showExtraInfos=True)\\n extraInfosFields = SOAPView(self.portal, req)._getExtraInfosFields(newItem)\\n # check that every field considered as extra informations is returned in the response\\n for extraInfosField in extraInfosFields:\\n self.failUnless(extraInfosField.getName() in resp)\",\n \"def call_method(method, meta):\\n try:\\n return method()\\n except (OSError, NotImplementedError) as e:\\n ometa = meta.copy()\\n ometa[MK_PAYLOAD] = False\\n ometa[MK_ERROR] = type(e).__name__\\n return ometa.data, None\",\n \"def test_ctcpQuery_CLIENTINFO(self):\\n self.client.ctcpQuery_CLIENTINFO(self.user, self.channel, \\\"\\\")\\n self.client.ctcpQuery_CLIENTINFO(self.user, self.channel, \\\"PING PONG\\\")\\n info = (\\n \\\"ACTION CLIENTINFO DCC ERRMSG FINGER PING SOURCE TIME \\\" \\\"USERINFO VERSION\\\"\\n )\\n self.assertEqual(\\n self.client.methods,\\n [\\n (\\\"ctcpMakeReply\\\", (\\\"Wolf\\\", [(\\\"CLIENTINFO\\\", info)])),\\n (\\\"ctcpMakeReply\\\", (\\\"Wolf\\\", [(\\\"CLIENTINFO\\\", None)])),\\n ],\\n )\",\n \"def main():\\n outfile = 'result.txt'\\n\\n if os.path.exists(outfile):\\n os.remove(outfile)\\n\\n for arg in sys.argv[1:]:\\n get_info(arg, outfile)\",\n \"def svn_info_dup(svn_info_t_info, apr_pool_t_pool): # real signature unknown; restored from __doc__\\n pass\",\n \"def df_info(df: pd.DataFrame, return_info=False, shape=True, cols=True, info_prefix=''):\\n info = info_prefix\\n if shape:\\n info = f'{info}Shape = {df.shape}'\\n if cols:\\n info = f'{info} , Cols = {df.columns.tolist()}'\\n print(info)\\n if return_info:\\n return info\",\n \"def testOverrideGlobalInfoHandler(self):\\n self.stream_start(mode='component',\\n jid='tester.localhost',\\n plugins=['xep_0030'])\\n\\n def dynamic_global(jid, node, ifrom, iq):\\n result = self.xmpp['xep_0030'].stanza.DiscoInfo()\\n result['node'] = node\\n result.add_identity('component', 'generic', name='Dynamic Info')\\n return result\\n\\n self.xmpp['xep_0030'].set_node_handler('get_info',\\n handler=dynamic_global)\\n\\n self.xmpp['xep_0030'].restore_defaults(jid='user@tester.localhost',\\n node='testing')\\n\\n self.xmpp['xep_0030'].add_feature(jid='user@tester.localhost',\\n node='testing',\\n feature='urn:xmpp:ping')\\n\\n self.recv(\\\"\\\"\\\"\\n <iq type=\\\"get\\\" id=\\\"test\\\"\\n to=\\\"user@tester.localhost\\\"\\n from=\\\"tester@localhost\\\">\\n <query xmlns=\\\"http://jabber.org/protocol/disco#info\\\"\\n node=\\\"testing\\\" />\\n </iq>\\n \\\"\\\"\\\")\\n\\n self.send(\\\"\\\"\\\"\\n <iq type=\\\"result\\\" id=\\\"test\\\"\\n to=\\\"tester@localhost\\\"\\n from=\\\"user@tester.localhost\\\">\\n <query xmlns=\\\"http://jabber.org/protocol/disco#info\\\"\\n node=\\\"testing\\\">\\n <feature var=\\\"urn:xmpp:ping\\\" />\\n </query>\\n </iq>\\n \\\"\\\"\\\")\",\n \"def test_fax_inbound_automation_get(self):\\n pass\",\n \"def info(msg, *args):\\n if args:\\n msg %= args\\n click.echo(msg, file=sys.stdout)\",\n \"def process_info(self, info):\\n info['result'] = info['result'].value\\n return info\",\n \"def test_info(self):\\n self.run_function(\\\"group.add\\\", [self._group], gid=self._gid)\\n self.run_function(\\\"user.add\\\", [self._user])\\n self.run_function(\\\"group.adduser\\\", [self._group, self._user])\\n group_info = self.run_function(\\\"group.info\\\", [self._group])\\n\\n self.assertEqual(group_info[\\\"name\\\"], self._group)\\n self.assertEqual(group_info[\\\"gid\\\"], self._gid)\\n self.assertIn(self._user, str(group_info[\\\"members\\\"]))\",\n \"def printinfo(arg1, *vartuple):\\n print('This is the output:')\\n print(arg1)\\n\\n for var in vartuple:\\n print(var)\\n\\n return\",\n \"def test_endpointPOP3(self):\\n self._endpointTest(\\\"pop3\\\")\",\n \"def handle_info(self, api, command):\\n return self.handle_log(api, command, level=logging.INFO)\",\n \"def test_get_invoice_info(mocker, expected_response, expected_data, client) -> None:\\n\\n mocker.patch(\\\"requests.Session.request\\\", return_value=expected_response)\\n actual_data = client.get_invoice_info(invoice_id=1)\\n\\n assert actual_data == expected_data\",\n \"def test_cell_list_detail_success(self, mock_list):\\n self.shell('cell-list -r 1 --detail')\\n mock_list.assert_called_once_with(detail=True)\",\n \"def test_patch_info(self):\\n patch_info = versions.PatchInfo(iiq_dir='/some/path',\\n patches_dir='/another/path',\\n is_installed=False,\\n specific_patch='this_patch',\\n readme='data from readme',\\n all_patches=('patch1', 'patch2'))\\n self.assertTrue(isinstance(patch_info, versions._PatchInfo))\",\n \"def meta_info(environ, start_response, logger, handle):\\n pass\",\n \"def show_info(title, message):\\n\\n pass\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6383911","0.5714342","0.55663514","0.5375583","0.532167","0.52700174","0.5258182","0.5240253","0.51907164","0.5187251","0.51472026","0.50939924","0.5084021","0.50806963","0.507654","0.5028689","0.50142485","0.49954024","0.4964053","0.49308","0.49162632","0.4889757","0.48548278","0.48462722","0.4829606","0.48205596","0.48201934","0.48180994","0.47867846","0.4786442","0.47680753","0.47460273","0.47422758","0.4729599","0.47082764","0.47072533","0.47055408","0.4705246","0.46863875","0.46861896","0.4680918","0.4650679","0.464461","0.46310905","0.46120685","0.46037155","0.4588301","0.45876884","0.4587433","0.45860124","0.45793778","0.45778674","0.45753038","0.45551494","0.45551494","0.45543382","0.4554075","0.45531243","0.4545149","0.45408547","0.45366028","0.45054495","0.45013094","0.44972938","0.44949204","0.44949204","0.44941145","0.44751275","0.4473639","0.4472719","0.44713157","0.44683638","0.44672975","0.44637358","0.4456193","0.4455615","0.44464648","0.44366753","0.44359055","0.44345355","0.44344303","0.44277602","0.44249442","0.44221655","0.4418834","0.44068232","0.44046128","0.4400749","0.4400578","0.4398876","0.43983194","0.4390595","0.4386194","0.43792915","0.43773672","0.43747872","0.43745047","0.4364967","0.43648437","0.43604663"],"string":"[\n \"0.6383911\",\n \"0.5714342\",\n \"0.55663514\",\n \"0.5375583\",\n \"0.532167\",\n \"0.52700174\",\n \"0.5258182\",\n \"0.5240253\",\n \"0.51907164\",\n \"0.5187251\",\n \"0.51472026\",\n \"0.50939924\",\n \"0.5084021\",\n \"0.50806963\",\n \"0.507654\",\n \"0.5028689\",\n \"0.50142485\",\n \"0.49954024\",\n \"0.4964053\",\n \"0.49308\",\n \"0.49162632\",\n \"0.4889757\",\n \"0.48548278\",\n \"0.48462722\",\n \"0.4829606\",\n \"0.48205596\",\n \"0.48201934\",\n \"0.48180994\",\n \"0.47867846\",\n \"0.4786442\",\n \"0.47680753\",\n \"0.47460273\",\n \"0.47422758\",\n \"0.4729599\",\n \"0.47082764\",\n \"0.47072533\",\n \"0.47055408\",\n \"0.4705246\",\n \"0.46863875\",\n \"0.46861896\",\n \"0.4680918\",\n \"0.4650679\",\n \"0.464461\",\n \"0.46310905\",\n \"0.46120685\",\n \"0.46037155\",\n \"0.4588301\",\n \"0.45876884\",\n \"0.4587433\",\n \"0.45860124\",\n \"0.45793778\",\n \"0.45778674\",\n \"0.45753038\",\n \"0.45551494\",\n \"0.45551494\",\n \"0.45543382\",\n \"0.4554075\",\n \"0.45531243\",\n \"0.4545149\",\n \"0.45408547\",\n \"0.45366028\",\n \"0.45054495\",\n \"0.45013094\",\n \"0.44972938\",\n \"0.44949204\",\n \"0.44949204\",\n \"0.44941145\",\n \"0.44751275\",\n \"0.4473639\",\n \"0.4472719\",\n \"0.44713157\",\n \"0.44683638\",\n \"0.44672975\",\n \"0.44637358\",\n \"0.4456193\",\n \"0.4455615\",\n \"0.44464648\",\n \"0.44366753\",\n \"0.44359055\",\n \"0.44345355\",\n \"0.44344303\",\n \"0.44277602\",\n \"0.44249442\",\n \"0.44221655\",\n \"0.4418834\",\n \"0.44068232\",\n \"0.44046128\",\n \"0.4400749\",\n \"0.4400578\",\n \"0.4398876\",\n \"0.43983194\",\n \"0.4390595\",\n \"0.4386194\",\n \"0.43792915\",\n \"0.43773672\",\n \"0.43747872\",\n \"0.43745047\",\n \"0.4364967\",\n \"0.43648437\",\n \"0.43604663\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.82054126"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":6589,"cells":{"query":{"kind":"string","value":"Test the popxl basic repeat example"},"document":{"kind":"string","value":"def test_documentation_popxl_repeat_0(self):\n filename = \"repeat_graph_0.py\"\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)"},"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 test_documentation_popxl_repeat_1(self):\n filename = \"repeat_graph_1.py\"\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)","def test_documentation_popxl_repeat_2(self):\n filename = \"repeat_graph_2.py\"\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)","def test_documentation_popxl_in_sequence(self):\n filename = \"in_sequence.py\"\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)","def repeat(x, repeats, axis=None):\r\n return RepeatOp(axis=axis)(x, repeats)","async def repeat(ctx, times : int, content='repeating...'):\n for i in range(times):\n await bot.say(content)","def populate(self, pop_size):\n for _ in range(pop_size):\n sample = next(self._exp_source)\n self._add(sample)","def test_documentation_popxl_replication(self):\n filename = \"replication.py\"\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)","def repeat(self):\n return self._repeat","async def repeat(self,ctx, times: int, content='repeating...'):\n for i in range(times):\n await ctx.send(content)","def repeat(self, count):\n return self.Sequence((self,) * count)","def generator():\n mygenerator = (x for x in range(3))\n for element in mygenerator:\n print 'poprve = ', element\n\n for element in mygenerator:\n print 'podruhe = ', element","def test_repeat_seq():\n\n out_file = \"out.txt\"\n try:\n if os.path.isfile(out_file):\n os.remove(out_file)\n\n rv, out = getstatusoutput(f'{prg} -f {repeat}')\n assert rv == 0\n expected = (' 1: amigo_repeat.txt\\n'\n 'Wrote 5 gene IDs from 1 file to file \"out.txt\"')\n assert out == expected\n assert os.path.isfile(out_file)\n exp_repeat = '\\n'.join(\n sorted(\"\"\"\n AT4G14690 AT5G41340 AT5G03720 AT5G12020 AT2G22360\n \"\"\".split()))\n assert open(out_file).read().strip() == exp_repeat.strip()\n\n finally:\n if os.path.isfile(out_file):\n os.remove(out_file)","def test_repeat_seq():\n\n out_file = \"out.txt\"\n try:\n if os.path.isfile(out_file):\n os.remove(out_file)\n\n rv, out = getstatusoutput(f'{prg} -f {repeat}')\n assert rv == 0\n expected = (' 1: amigo_repeat.txt\\n'\n 'Wrote 5 gene IDs from 1 file to file \"out.txt\"')\n assert out == expected\n assert os.path.isfile(out_file)\n exp_repeat = '\\n'.join(\n sorted(\"\"\"\n AT4G14690 AT5G41340 AT5G03720 AT5G12020 AT2G22360\n \"\"\".split()))\n assert open(out_file).read().strip() == exp_repeat.strip()\n\n finally:\n if os.path.isfile(out_file):\n os.remove(out_file)","def foxGrowth():\r\n # you need these lines for modifying global variables\r\n global CURRENTRABBITPOP\r\n global CURRENTFOXPOP\r\n\r\n # TO DO\r\n #pass\r\n for i in range(CURRENTFOXPOP):\r\n if CURRENTRABBITPOP > 10:\r\n if random.random() <= (CURRENTRABBITPOP/MAXRABBITPOP):\r\n CURRENTRABBITPOP -= 1\r\n # fox reproducing\r\n if random.random() <= (1/3):\r\n CURRENTFOXPOP += 1\r\n else:\r\n # fox dying\r\n if random.random() <= 0.1:\r\n CURRENTFOXPOP -= 1","def test_x_repeating(name, ipset_x_repeating):\n with pytest.raises(ValueError):\n interpolation.interpolate(*ipset_x_repeating, kind=name, **IPARGS.get(name, {}))","def repeat_nd(x, reps):\n return RepeatND(reps)(x)","def foxGrowth():\r\n # you need these lines for modifying global variables\r\n global CURRENTRABBITPOP\r\n global CURRENTFOXPOP\r\n\r\n # TO DO\r\n #pass\r\n for i in range(CURRENTFOXPOP):\r\n if CURRENTRABBITPOP > 10:\r\n if random.random() <= (CURRENTRABBITPOP/MAXRABBITPOP):\r\n CURRENTRABBITPOP -= 1\r\n # fox reproducing\r\n if random.random() <= (1/3):\r\n CURRENTFOXPOP += 1\r\n else:\r\n # fox dying\r\n if random.random() <= 0.9:\r\n CURRENTFOXPOP -= 1","async def repeat(times : int, content='repeating...'):\n for i in range(times):\n await bot.say(content)","def rabbitGrowth():\r\n # you need this line for modifying global variables\r\n global CURRENTRABBITPOP\r\n\r\n # TO DO\r\n #pass\r\n for i in range(CURRENTRABBITPOP):\r\n if random.random() <= (1 - (CURRENTRABBITPOP/MAXRABBITPOP)):\r\n CURRENTRABBITPOP += 1","def rabbitGrowth():\r\n # you need this line for modifying global variables\r\n global CURRENTRABBITPOP\r\n\r\n # TO DO\r\n #pass\r\n for i in range(CURRENTRABBITPOP):\r\n if random.random() <= (1 - (CURRENTRABBITPOP/MAXRABBITPOP)):\r\n CURRENTRABBITPOP += 1","def repeat(self, repeats):\n return SeriesDefault.register(pandas.Series.repeat)(self, repeats=repeats)","def test_op_repeat(self) -> None:\n op_base = OpIncrForTest()\n kwargs_per_step_to_add = [\n dict(key_in=\"data.val.a\", key_out=\"data.val.b\"),\n dict(key_in=\"data.val.b\", key_out=\"data.val.c\"),\n dict(key_in=\"data.val.b\", key_out=\"data.val.d\"),\n dict(key_in=\"data.val.d\", key_out=\"data.val.d\"),\n ]\n op_repeat = OpRepeat(op_base, kwargs_per_step_to_add)\n sample_dict = NDict({})\n sample_dict[\"data.val.a\"] = 5\n sample_dict = op_repeat(sample_dict, \"_.test_repeat\", incr_value=3)\n self.assertEqual(sample_dict[\"data.val.a\"], 5)\n self.assertEqual(sample_dict[\"data.val.b\"], 8)\n self.assertEqual(sample_dict[\"data.val.c\"], 11)\n self.assertEqual(sample_dict[\"data.val.d\"], 14)\n\n op_repeat.reverse(\n sample_dict,\n key_to_follow=\"data.val.d\",\n key_to_reverse=\"data.val.d\",\n op_id=\"_.test_repeat\",\n )\n self.assertEqual(sample_dict[\"data.val.a\"], 5)\n self.assertEqual(sample_dict[\"data.val.b\"], 8)\n self.assertEqual(sample_dict[\"data.val.c\"], 11)\n self.assertEqual(sample_dict[\"data.val.d\"], 8)\n\n sample_dict[\"data.val.e\"] = 48\n op_repeat.reverse(\n sample_dict,\n key_to_follow=\"data.val.d\",\n key_to_reverse=\"data.val.e\",\n op_id=\"_.test_repeat\",\n )\n self.assertEqual(sample_dict[\"data.val.a\"], 5)\n self.assertEqual(sample_dict[\"data.val.b\"], 8)\n self.assertEqual(sample_dict[\"data.val.c\"], 11)\n self.assertEqual(sample_dict[\"data.val.d\"], 8)\n self.assertEqual(sample_dict[\"data.val.e\"], 42)","def repeat(self, repeat: bool=None):\n self._select_interface(self._rc_repeat, self._http_repeat, repeat)","def test_make_pop(self, pop_size, cell_number, microcell_number):\n for i in [0, 1]:\n pe.Parameters.instance().use_ages = i\n # Population is initialised with no households\n pop_params = {\"population_size\": pop_size,\n \"cell_number\": cell_number,\n \"microcell_number\": microcell_number}\n test_pop = ToyPopulationFactory.make_pop(pop_params)\n\n total_people = 0\n count_non_empty_cells = 0\n for cell in test_pop.cells:\n for microcell in cell.microcells:\n total_people += len(microcell.persons)\n if len(cell.persons) > 0:\n count_non_empty_cells += 1\n # Test there is at least one non-empty cell\n self.assertTrue(count_non_empty_cells >= 1)\n # Test that everyone in the population has been assigned a\n # microcell\n self.assertEqual(total_people, pop_size)\n\n # Test a population class object is returned\n self.assertIsInstance(test_pop, pe.Population)","async def repeat(ctx, times: int, content='repeating...'):\n for i in range(times):\n await ctx.send(content)","def MakeRepeat1(self,content):\n return self.register(Repeat1(content,reg=self))","def test_pop(self):\n sched = Schedule()\n inst_map = InstructionScheduleMap()\n\n inst_map.add(\"tmp\", 100, sched)\n self.assertEqual(inst_map.pop(\"tmp\", 100), sched)\n self.assertFalse(inst_map.has(\"tmp\", 100))\n\n self.assertEqual(inst_map.qubit_instructions(100), [])\n self.assertEqual(inst_map.qubits_with_instruction(\"tmp\"), [])\n with self.assertRaises(PulseError):\n inst_map.pop(\"not_there\", (0,))","def runSimulation(numSteps):\n\n rabbit_pop = []\n fox_pop = [] \n \n for steps in range(numSteps):\n rabbitGrowth()\n foxGrowth()\n rabbit_pop.append(CURRENTRABBITPOP)\n fox_pop.append(CURRENTFOXPOP)\n \n return (rabbit_pop, fox_pop)","def test_documentation_popxl_addition(self):\n filename = \"simple_addition.py\"\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)","def repeat(self, number_of_repeats):\n return \"G\" + str(number_of_repeats)","def test_pop_methods(self):\n\n batch = Batch(Mock())\n\n # mock BatchRequests\n mock_obj = Mock()\n mock_ref = Mock()\n batch._objects_batch = mock_obj\n batch._reference_batch = mock_ref\n\n mock_obj.pop.assert_not_called()\n mock_ref.pop.assert_not_called()\n\n # pop object default value\n batch.pop_object()\n mock_obj.pop.assert_called_with(-1)\n mock_ref.pop.assert_not_called()\n # reset mock objects\n mock_obj.reset_mock()\n mock_ref.reset_mock()\n\n # pop object at index\n batch.pop_object(10)\n mock_obj.pop.assert_called_with(10)\n mock_ref.pop.assert_not_called()\n # reset mock objects\n mock_obj.reset_mock()\n mock_ref.reset_mock()\n\n # pop reference default value\n batch.pop_reference()\n mock_obj.pop.assert_not_called()\n mock_ref.pop.assert_called_with(-1)\n # reset mock objects\n mock_obj.reset_mock()\n mock_ref.reset_mock()\n\n # pop reference at index\n batch.pop_reference(9)\n mock_obj.pop.assert_not_called()\n mock_ref.pop.assert_called_with(9)","def test_pop_gate(self):\n sched = Schedule()\n inst_map = InstructionScheduleMap()\n\n inst_map.add(XGate(), 100, sched)\n self.assertEqual(inst_map.pop(XGate(), 100), sched)\n self.assertFalse(inst_map.has(XGate(), 100))\n\n self.assertEqual(inst_map.qubit_instructions(100), [])\n self.assertEqual(inst_map.qubits_with_instruction(XGate()), [])\n with self.assertRaises(PulseError):\n inst_map.pop(\"not_there\", (0,))","def generate_RC_codebook(cube_dim, reps, permutate=1, item_num=0):\r\n\r\n\timport numpy as np\r\n\t\r\n\tcodebook = []\r\n\tcodeindex = []\r\n\t\r\n\tcube_size = np.prod(cube_dim)\r\n\tif item_num == 0 or item_num > cube_size:\r\n\t\tntargets = cube_size\r\n\telse:\r\n\t\tntargets = item_num\r\n\t\r\n\tindices = np.arange(cube_size)\r\n\tcube = indices.reshape(cube_dim)\r\n\t\r\n\tlen_codebook = sum(cube_dim)#6col + 6row =12\r\n\t\r\n\tfor rep in range(reps):\r\n\t\t# random permute the codebook for one rep\r\n\t\tind_code = np.random.permutation(len_codebook)\r\n\t\t\r\n\t\t# avoid consecutive appearance between reps\r\n\t\tif rep > 0:\r\n\t\t\twhile ind_code[0]==last_ind_code:\r\n\t\t\t\tind_code = np.random.permutation(len_codebook)\r\n\t\tlast_ind_code = ind_code[-1]\r\n\t\t\r\n\t\tfor i in ind_code:\r\n\t\t\tif i < cube_dim[0]:\r\n\t\t\t\tcode_slice = cube[i,:]\r\n\t\t\t\tcode_slice = code_slice[np.nonzero(code_slice<ntargets)]\r\n\t\t\t\tcodebook.append(code_slice.tolist())\r\n\t\t\telse:\r\n\t\t\t\tcode_slice = cube[:,i-cube_dim[0]]\r\n\t\t\t\tcode_slice = code_slice[np.nonzero(code_slice<ntargets)]\r\n\t\t\t\tcodebook.append(code_slice.tolist())\r\n\t\tcodeindex.extend(ind_code)\t\r\n\r\n\treturn cube, codebook, codeindex","def foxGrowth():\r\n # you need these lines for modifying global variables\r\n global CURRENTRABBITPOP\r\n global CURRENTFOXPOP\r\n\r\n \r\n \r\n for fox in range(CURRENTFOXPOP):\r\n if CURRENTRABBITPOP > 10:\r\n prob_fox_eats_rabbit = float(CURRENTRABBITPOP)/MAXRABBITPOP\r\n if prob_fox_eats_rabbit > random.random():\r\n CURRENTRABBITPOP -= 1\r\n if 1.0/3.0 > random.random():\r\n CURRENTFOXPOP += 1\r\n elif CURRENTFOXPOP > 10:\r\n if 0.9 > random.random():\r\n CURRENTFOXPOP -= 1","def poprve():\n mylist = [1, 2, 3]\n for element in mylist:\n print element","def repeat(self):\n return self._get('repeat')","def test_calls(self):\n ex = self.ex\n m = self.m\n n = self.n\n\n nreps = random.randint(1, 10)\n ex.nreps = nreps\n ex.vary[\"X\"][\"with\"].add(\"rep\")\n ex.infer_lds()\n\n cmds = ex.generate_cmds()\n\n idx = random.randint(0, nreps - 1)\n self.assertIn([\"name\", m, n, \"X_%d\" % idx, m, \"Y\", m, \"Z\", n], cmds)","def test_data_norange(self):\n ex = self.ex\n m = self.m\n n = self.n\n\n nreps = random.randint(1, 10)\n lensumrange = random.randint(1, 10)\n\n ex.nreps = nreps\n ex.sumrange = [\"j\", range(lensumrange)]\n ex.vary[\"X\"][\"with\"].add(\"rep\")\n ex.vary[\"Y\"][\"with\"].add(\"j\")\n ex.vary[\"Y\"][\"along\"] = 0\n ex.vary[\"Z\"][\"with\"].update([\"rep\", \"j\"])\n ex.infer_lds()\n\n cmds = ex.generate_cmds()\n\n self.assertIn([\"smalloc\", \"X\", nreps * m * n], cmds)\n idx = random.randint(0, nreps - 1)\n self.assertIn([\"soffset\", \"X\", idx * m * n, \"X_%d\" % idx], cmds)\n\n self.assertIn([\n \"dmalloc\", \"Y\", lensumrange * m * m + (lensumrange - 1) * m\n ], cmds)\n idx = random.randint(0, lensumrange - 1)\n self.assertIn([\"doffset\", \"Y\", idx * m, \"Y_%d\" % idx], cmds)\n\n self.assertIn([\"cmalloc\", \"Z\", nreps * lensumrange * n * n], cmds)\n idxrep = random.randint(0, nreps - 1)\n idxrange = random.randint(0, lensumrange - 1)\n self.assertIn([\"coffset\", \"Z\",\n (idxrep * lensumrange + idxrange) * n * n,\n \"Z_%d_%d\" % (idxrep, idxrange)], cmds)","def repeat(self, count):\n x = HSeq()\n for i in range(count):\n x = x.concatenate(self)\n return x","def generate_pops(target_reg, exclude_regs=[], count=1, allow_dups=True):\n\n random_regs = []\n\n for _ in range(0, count-1):\n random_reg = get_random_register(exclude_regs=exclude_regs)\n\n random_regs.append(random_reg)\n\n pops = ''\n\n for reg in random_regs:\n pops += f'pop {reg}; '\n\n pops += f'pop {target_reg}; '\n\n return pops","def test_7_replay_4(self):\n self._execute_replay_nr(4)\n\n self.grid.add_pawn(5, 'H')\n self.grid.add_pawn(3, 'B')\n self.grid.add_pawn(2, 'H')\n self.grid.add_pawn(1, 'B')\n self.grid.add_pawn(1, 'H')\n\n # self.grid.print_grid()\n # print(self.minmaxBot_7.choose_move(self.grid))","def project_pop(self):\n M = self.N[0:2]\n for x in range(10):\n M.append(self.run_step(M))\n split_N = split_list(M)\n \n fig = self.make_figure(split_N)\n fig.update_layout(title='Projected Fish Population')\n\n return fig","def test_raises(self):\n no_replicates = 25\n try:\n replicate(experiment3, no_replicates)\n except RuntimeError as err:\n self.assertEqual(err, FAKE_ERROR)\n else:\n assert False","def run_memory_exp(nb_repeat, name, cmd):\n # Create the required folders\n if not os.path.exists(\"../logs\"):\n os.mkdir(\"../logs\")\n if not os.path.exists(f\"../logs/{name}\"):\n os.mkdir(f\"../logs/{name}\")\n\n # Log the configuration\n with open(f\"../logs/{name}/cmd_template.txt\", \"w\") as cmd_log:\n cmd_log.write(cmd)\n\n # Repeat the simulation\n for i in range(nb_repeat):\n print(\"[Memory experience] Progression: {}%\".format(i * 100 // nb_repeat))\n itr_cmd = cmd + \" --output {}/itr_{}\".format(name, i)\n args = get_args(itr_cmd.split(\" \"))\n sim = Sim()\n sim.from_args(args)\n print(\"[Memory experience] Done !\")","def _generate_pileups(self):\n pass","def test_INVOKE_repeat(self, propose):\n self.rep.proposals[1] = PROPOSAL1\n self.failIf(propose.called)","def foxGrowth():\n # you need these lines for modifying global variables\n global CURRENTRABBITPOP\n global CURRENTFOXPOP\n \n for fox in range(CURRENTFOXPOP):\n fox_eat_prob = float(CURRENTRABBITPOP) / MAXRABBITPOP\n if random.random() < fox_eat_prob and CURRENTRABBITPOP > 10:\n CURRENTRABBITPOP -= 1\n if random.random() < (1.0 / 3.0):\n CURRENTFOXPOP += 1\n else: \n if random.random() < (1.0 / 10.0) and CURRENTFOXPOP > 10:\n CURRENTFOXPOP -= 1","def Repeat(dataset, count=None):\n return dataset.repeat(count=count)","def repeated_iteration(self) -> global___Statement.Iteration.RepeatedIteration:","def test_repeatable(self):\n\n def run(seed, ModelClass=Model):\n \"\"\"Return the history of a run\"\"\"\n model = ModelClass(random_seed=seed)\n return model.one_trial(1, 10)\n\n self.assertEqual(run(0, ModelClass=Model).data, run(0, ModelClass=Model).data)\n self.assertEqual(run(0, ModelClass=ReplicatedModel).data, run(0, ModelClass=ReplicatedModel).data)","def repeat(self, min=0, max=None):\n raise NotImplemented()","def rabbitGrowth():\n # you need this line for modifying global variables\n global CURRENTRABBITPOP\n\n for rabbit in range(CURRENTRABBITPOP):\n rabbit_reproduction_prob = 1.0 - (float(CURRENTRABBITPOP) / MAXRABBITPOP)\n if random.random() < rabbit_reproduction_prob and CURRENTRABBITPOP < 1000:\n CURRENTRABBITPOP += 1","def population(params, n=100):\r\n pops = []\r\n for i in range(n):\r\n pop = []\r\n for param in params:\r\n pop.append(np.random.choice(param))\r\n\r\n individuale = Individuale(pop)\r\n pops.append(individuale)\r\n # print(\"No.{} : {} : {}\".format(i, individuale, individuale.x))\r\n return pops","def runSimulation(numSteps):\r\n rabbit_populations = []\r\n fox_populations = []\r\n for step in range(numSteps):\r\n rabbitGrowth()\r\n foxGrowth()\r\n rabbit_populations.append(CURRENTRABBITPOP)\r\n fox_populations.append(CURRENTFOXPOP)\r\n return (rabbit_populations, fox_populations)","def run_example1():\r\n #outcomes = set([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])\r\n #outcomes = set(['Heads','Tails'])\r\n #outcomes = set([\"Red\", \"Green\", \"Blue\"])\r\n outcomes = set([\"Sunday\", \"Mondy\", \"Tuesday\", \"Wednesday\", \"Thursday\", \"Friday\", \"Saturday\"])\r\n \r\n length = 7\r\n seq_outcomes = gen_permutations(outcomes,length)\r\n print \"Computed\", len(seq_outcomes), \"sequences of\", str(length), \"outcomes\"\r\n #print \"Sequences were\", seq_outcomes\r","def podruhe():\n mylist = [x for x in range(3)]\n for element in mylist:\n print element","def repeat(fn):\n def repeated():\n i = 0\n while i < random_test_iterations:\n fn()\n i += 1\n # nosetest runs functions that start with 'test_'\n repeated.__name__ = fn.__name__\n return repeated","def repeat(word, repetitions):\n return word * repetitions","def repeat(num_times):\n\n def decorator_repeat(func):\n \"\"\"\n defines wrapper_repeat(*args, **kwargs)\n\n :returns: wrapper_repeat\n \"\"\"\n\n @functools.wraps(func)\n def wrapper_repeat(*args, **kwargs):\n \"\"\"\n func(*args, **kwargs) num_times\n\n :return: last return value\n \"\"\"\n for _ in range(num_times):\n value = func(*args, **kwargs)\n return value\n\n return wrapper_repeat\n\n return decorator_repeat","def repeat(s):\r\n\r\n return s","def testspec(arr: list[int]) -> None:\n\n print(50*'-')\n print(arr)\n print_rem(arr)\n rev_dupes(arr)\n print(arr)","def repeat(a, repeats, axis=None):\n return afnumpy.asarray(a).repeat(repeats, axis=axis)","def test_pop_no_args(self):\r\n msg_list = messages.MessageList()\r\n # Adds 5 Message objects to the list.\r\n msg_list.push(messages.StringMessage(\"a\"))\r\n msg_list.push(messages.StringMessage(\"b\"))\r\n msg_list.push(messages.StringMessage(\"c\"))\r\n msg_list.push(messages.StringMessage(\"d\"))\r\n msg_list.push(messages.StringMessage(\"e\"))\r\n\r\n self.assertEqual(msg_list.length(), 5)\r\n popped = msg_list.pop()\r\n self.assertEqual(msg_list.length(), 4)\r\n self.assertEqual(popped.msg, \"e\")\r\n msg_list.pop()\r\n msg_list.pop()\r\n msg_list.pop()\r\n msg_list.pop()\r\n self.assertRaises(IndexError, msg_list.pop)","def generatesack(nmbrofitems):\n while nmbrofitems != 0:\n newitem = Sackitem(random.randint(1,10),random.randint(1,20))\n itemlist.append(newitem)\n nmbrofitems -= 1\n return itemlist","def initialPop(popSize,rangeMin,rangeMax,genLength):\n\t\n\tpop=[]\n\n\tfor i in range(popSize):\n\t\tgenome=[]\n\t\tfor j in range(genLength):\n\t\t\tparam=random.uniform(rangeMin,rangeMax)\n\t\t\tgenome.append(param)\n\t\tpop.append(Gen(genome)) #add each random genome to the pop\n\t\t\t\t\n\treturn pop","def fillLoop():\n print('Now looping spotfill (press enter to quit).')\n x = 1\n while x != 0:\n x = spotfill()\n print('Ending spotfill.')\n return","def test_generate_sample_sheet(self):\n pass","def alert_pet(self, reps=3):\n for x in range(0,reps):\n time.sleep(1)\n GPIO.output(self.alert_pin, 0)\n time.sleep(1)\n GPIO.output(self.alert_pin, 1)\n return","def test_reset(sim):\n repeats = 3\n dt = 1\n sim.setup(timestep=dt, min_delay=dt)\n p = sim.Population(1, sim.IF_curr_exp(i_offset=0.1))\n p.record('v')\n\n for i in range(repeats):\n sim.run(10.0)\n sim.reset()\n data = p.get_data(clear=False)\n sim.end()\n\n assert len(data.segments) == repeats\n for segment in data.segments[1:]:\n assert_array_almost_equal(segment.analogsignals[0],\n data.segments[0].analogsignals[0], 10)","def test_remainder(self):\n alp = list(range(5))\n targets = generate_targets(alp, 12)\n\n counts = Counter(targets)\n for item in alp:\n self.assertGreaterEqual(counts[item], 2)\n self.assertLessEqual(counts[item], 3)","def simulate_fish_population(fish, days):\n for _ in range(days):\n new_fish = []\n for fish in fish:\n if fish == 0:\n new_fish.append(6)\n new_fish.append(8)\n else:\n new_fish.append(fish - 1)\n fish = new_fish\n return fish","def inversion_crossover(self, pop):\n children, tmpNonComb, used = ([] for i in range(3))\n for i in range(0, int(len(pop) * self.fracElite), 1):\n r = int(rand() * len(pop))\n while r == i:\n r = int(rand() * len(pop))\n\n if sum(self.cID + self.dID + self.iID) != 0:\n nonComb1 = pop[i][:np.where(self.cID + self.dID + self.iID == 1)[0][(-1)] + 1]\n nonComb2 = pop[r][:np.where(self.cID + self.dID + self.iID == 1)[0][(-1)] + 1]\n if sum(self.xID) != 0:\n comb1 = pop[i][:np.where(self.xID == 1)[0][(-1)] + 1]\n comb2 = pop[r][:np.where(self.xID == 1)[0][(-1)] + 1]\n if sum(self.cID + self.dID + self.iID) != 0:\n c = int(rand() * len(nonComb1))\n if rand() > 0.5:\n tmpNonComb.append(np.array(nonComb1[0:c + 1].tolist() + nonComb2[c + 1:].tolist()))\n else:\n tmpNonComb.append(np.array(nonComb2[0:c + 1].tolist() + nonComb1[c + 1:].tolist()))\n used.append(i)\n if sum(self.xID) != 0:\n c = int(rand() * len(comb1))\n for c1 in range(c, len(comb1), 1):\n d2 = (contains_sublist(comb2, comb1[c1]) + 1) % len(comb1)\n d1 = contains_sublist(comb1, comb2[d2])\n c2 = contains_sublist(comb2, comb1[((d1 + 1) % len(comb1))]) % len(comb1)\n tmp1 = cp.copy(comb1)\n if c1 < d1:\n tmp1[(c1 + 1):(d1 + 1)] = list(reversed(tmp1[c1 + 1:d1 + 1]))\n else:\n tmp1[d1:c1] = list(reversed(tmp1[d1:c1]))\n tmp2 = cp.copy(comb2)\n if c2 < d2:\n tmp2[c2:d2] = list(reversed(tmp2[c2:d2]))\n else:\n tmp2[(d2 + 1):(c2 + 1)] = list(reversed(tmp2[d2 + 1:c2 + 1]))\n if sum(self.cID + self.dID + self.iID) == 0 and sum(self.xID) != 0:\n children.append(tmp1)\n children.append(tmp2)\n elif sum(self.cID + self.dID + self.iID) != 0 and sum(self.xID) != 0:\n children.append(np.concatenate(tmpNonComb[(-1)], tmp1))\n children.append(np.concatenate(tmpNonComb[(-1)], tmp2))\n used.append(i)\n used.append(r)\n\n if sum(self.cID + self.dID + self.iID) != 0 and sum(self.xID) == 0:\n children = tmpNonComb\n return (\n children, used)","def test_push_pop(values):\n test_stack = stack.Stack()\n\n for value in values:\n test_stack.push(value)\n\n for expected_value in reversed(values):\n value = test_stack.pop()\n assert value == expected_value\n\n with pytest.raises(stack.StackEmptyError):\n test_stack.pop()","def run_simulation(env, pop, nr):\n pop_f = []\n pop_pl = []\n pop_el = []\n\n\n for individual in pop:\n fitness, player_life, enemy_life, time = env.play(pcont=individual)\n pop_f.append(fitness)\n pop_pl.append(player_life)\n pop_el.append(enemy_life)\n\n return pop_f, pop_pl, pop_el","def reproduce(population:list):\n new_gen = []\n probs = []\n for p in population:\n probs.append(p[3])\n while len(new_gen) != len(probs):\n parents = selection(probs)\n son,eval_son,daughter,eval_daughter = xo(population[parents[0]][0],population[parents[0]][1], population[parents[1]][0],population[parents[1]][1],2)\n new_gen.append([son,eval_son])\n new_gen.append([daughter,eval_daughter])\n # mutation\n # lets say 5% of the population gets mutated\n how_many_to_mutate = int(NUM_OF_CHROMOZOMS * (1/100))\n t = [i for i in range(NUM_OF_CHROMOZOMS)]\n # choose percent of the population randomly, uniformly\n indices_to_mutate = choice(t, how_many_to_mutate, replace=False)\n for i in range(len(indices_to_mutate)):\n mutate(new_gen[indices_to_mutate[i]])\n\n evaluateAll(new_gen)\n return new_gen","def crossover(population, kw=None, **kwargs):\n future_population = []\n while len(future_population) < len(population):\n p1, p2 = random.choice(population)['notes'], random.choice(population)['notes']\n split = random.randint(1, len(p1) - 1)\n map(future_population.append, [p1[:split] + p2[split:], p2[:split] + p1[split:]])\n return future_population","def genPopulation(self):\r\n self.population_list = []\r\n for i in xrange(0, self.pop_size):\r\n individual = bitarray(self.indv_size)\r\n # Loop for randomizing the 'individual' string.\r\n for j in xrange(0, self.board_size):\r\n vert_pos = random.randint(0, self.board_size-1)\r\n vert_pos_bitnum = toBitArray(vert_pos, self.pos_bits_size)\r\n # print \"\\t\\t\", j, vert_pos_bitnum, vert_pos\r\n for k in range(0, self.pos_bits_size):\r\n individual[j * self.pos_bits_size + k] = vert_pos_bitnum[k]\r\n self.population_list.append(individual)\r\n # print \"\\t\", i, individual\r","def repeat_expt(epsilon, gamma,\n result_nonprivate, \n repetitions=10,\n outfile_singles=None, outfile_aggregates=None, \n data_blocker=1, windsorized=False):\n \n \n blocker = gupt.GuptRunTime.get_data_blockers()[data_blocker-1]\n # 1 NaiveDataBlocker\n # 2 ResamplingDataBlockerConstantSize \n # 3 ResamplingDataBlockerConstantBlocks\n\n if not windsorized:\n DP_mode=\"standard_DP\"\n else:\n DP_mode=\"windsorized_DP\"\n\n logger.info(\"Running %d repetitions with data_blocker=%s\" % (repetitions, blocker))\n logger.info(\"epsilon=%s gamma=%s, in mode %s\" % (epsilon, gamma, DP_mode))\n \n results, starttime = [], time.clock()\n \n # results = pickle.load( open( \"res.pickle\", \"rb\" ))\n \n \n for i in range(repetitions):\n\n # TODO: Perhaps they DO or DO NOT have to be recreated in each run?\n blocker = gupt.GuptRunTime.get_data_blockers()[data_blocker-1]\n reader = censusdatadriver.get_reader()\n runtime = gupt.GuptRunTime(MeanComputer, reader, epsilon, \n blocker_name=blocker, blocker_args=gamma)\n # end TODO\n\n if not windsorized:\n res=runtime.start()\n else:\n res=runtime.start_windsorized()\n \n # artificial 2nd dimension, just for testing these routines:\n # res = res + res\n \n print report_results(res, result_nonprivate, DP_mode, blocker, \n epsilon, gamma, outfile_singles)\n sleep_short()\n \n results.append(res)\n\n # pickle.dump(results, open( \"res.pickle\", \"wb\" ) )\n \n \n duration = time.clock() - starttime\n logger.info(\"%d repetitions took %.2f seconds\" % (repetitions, duration))\n \n mean, std = analyze_results(results) # , result_nonprivate)\n \n print report_results_repeated(mean, std, DP_mode, blocker,\n epsilon, gamma, repetitions,\n outfile=outfile_aggregates)","def repeat(self, count):\n x = _OSeq()\n for i in range(count):\n x = x.concatenate(self)\n return x","def pop():","def run_expt_many_times(result_nonprivate, repetitions=10,\n data_blocker=1, windsorized=False,\n outfile_singles=None, outfile_aggregates=None):\n for epsilon in numpy.arange(0.1, 2.2, 0.2):#(0.2, 10, 0.2):\n for gamma in range(1, 10, 1): # (1, 7, 1):\n \n repeat_expt(epsilon, gamma,\n data_blocker=data_blocker, windsorized=windsorized,\n result_nonprivate=result_nonprivate,\n repetitions=repetitions,\n outfile_singles=outfile_singles, \n outfile_aggregates=outfile_aggregates)","def reset_next_population(self):\n self.next_population = []","def repeat_value(value: Any = None, repeat_count: int = None) -> ObservableBase:\n from ..operators.observable.repeat import repeat_value\n return repeat_value(value, repeat_count)","def init_pop(self):\n genes = np.random.randn( self.population_size * self.individual.gene_count )\n self.population = genes.reshape((self.population_size, -1))\n #print(self.population)","def genNextPop(prevPop, masterList, popSize):\n parSize = int(popSize / 10) #top 10%\n parentPop = [Team(prevPop[i].roster) for i in range(parSize)]\n parentPop = getStats(parentPop, masterList) # inefficent\n rosterSize = len(parentPop[0].roster)\n newPop = []\n #parentsList = [] #debug\n for i in range(popSize):\n chromosome = doCrossover(parentPop, parSize, rosterSize)\n newPop.append(Team(chromosome))\n getStats(newPop, masterList)\n #showStats(newPop, masterList, \"all\")\n #debug code\n #for playerInd in chromosome:\n # print(masterList[playerInd].pos)\n\n return newPop","def test_pop(self):\n test_list = DoubleLinkedList()\n test_list.push(15)\n test_list.push(150)\n test_list.push(13)\n test_list.push(155)\n test_list.push(1)\n test_list.pop()\n self.assertEqual(test_list.last().get_elem(), 155)","def rand_pop(l: list):\n i = randrange(len(l)) \n l[i], l[-1] = l[-1], l[i] \n return l.pop()","def rep_pops(self):\n return _PopFlag(self.rep_pop_flag)","def repeated(self, *args, **kwargs):\n return self.rep.RepeatBorders(self._trg, *args, **kwargs)","def test_make_pop_exception(self, patch_log, patch_random):\n patch_random.side_effect = ValueError\n # Population is initialised with no households\n pop_params = {\"population_size\": 10, \"cell_number\": 1,\n \"microcell_number\": 1}\n ToyPopulationFactory.make_pop(pop_params)\n patch_log.assert_called_once_with(\"ValueError in ToyPopulation\"\n + \"Factory.make_pop()\")","def get_random_population():\r\n return [ get_random_individual() for _ in range(POPULATION_COUNT) ]","def test_shift(doctest):","def test_solo_cell():\n cell = c6.Cell(loc=[1, 1])\n for i in range(10):\n cell.step()","def runSimulation(numSteps):\r\n\r\n # TO DO\r\n #pass\r\n rabbits = []\r\n foxes = []\r\n for i in range(numSteps):\r\n rabbitGrowth()\r\n foxGrowth()\r\n rabbits.append(CURRENTRABBITPOP)\r\n foxes.append(CURRENTFOXPOP)\r\n return rabbits, foxes","def runSimulation(numSteps):\r\n\r\n # TO DO\r\n #pass\r\n rabbits = []\r\n foxes = []\r\n for i in range(numSteps):\r\n rabbitGrowth()\r\n foxGrowth()\r\n rabbits.append(CURRENTRABBITPOP)\r\n foxes.append(CURRENTFOXPOP)\r\n return rabbits, foxes","def popmany(self, num=1):\n return [self.next() for i in range(num)]","def ipset_x_repeating():\n x = np.linspace(0, 10, 11)\n x[5] = x[4]\n return IPSet(x=x, y=np.linspace(-1, 1, 11), x_new=np.linspace(2, 5, 7))","def test_reset(self):\r\n self.p += 8\r\n self.p.reset()\r\n self.assertEqual(str(self.p), '0% [....................]')","def play():\n decks = make_decks()\n\n deck_pulls = {deck: [] for deck in decks}\n for i in range(100):\n deck = random.choice(decks)\n deck_pulls[deck].append(deck.pull())\n\n return decks, deck_pulls","def test_endpointPOP3(self):\n self._endpointTest(\"pop3\")"],"string":"[\n \"def test_documentation_popxl_repeat_1(self):\\n filename = \\\"repeat_graph_1.py\\\"\\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)\",\n \"def test_documentation_popxl_repeat_2(self):\\n filename = \\\"repeat_graph_2.py\\\"\\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)\",\n \"def test_documentation_popxl_in_sequence(self):\\n filename = \\\"in_sequence.py\\\"\\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)\",\n \"def repeat(x, repeats, axis=None):\\r\\n return RepeatOp(axis=axis)(x, repeats)\",\n \"async def repeat(ctx, times : int, content='repeating...'):\\n for i in range(times):\\n await bot.say(content)\",\n \"def populate(self, pop_size):\\n for _ in range(pop_size):\\n sample = next(self._exp_source)\\n self._add(sample)\",\n \"def test_documentation_popxl_replication(self):\\n filename = \\\"replication.py\\\"\\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)\",\n \"def repeat(self):\\n return self._repeat\",\n \"async def repeat(self,ctx, times: int, content='repeating...'):\\n for i in range(times):\\n await ctx.send(content)\",\n \"def repeat(self, count):\\n return self.Sequence((self,) * count)\",\n \"def generator():\\n mygenerator = (x for x in range(3))\\n for element in mygenerator:\\n print 'poprve = ', element\\n\\n for element in mygenerator:\\n print 'podruhe = ', element\",\n \"def test_repeat_seq():\\n\\n out_file = \\\"out.txt\\\"\\n try:\\n if os.path.isfile(out_file):\\n os.remove(out_file)\\n\\n rv, out = getstatusoutput(f'{prg} -f {repeat}')\\n assert rv == 0\\n expected = (' 1: amigo_repeat.txt\\\\n'\\n 'Wrote 5 gene IDs from 1 file to file \\\"out.txt\\\"')\\n assert out == expected\\n assert os.path.isfile(out_file)\\n exp_repeat = '\\\\n'.join(\\n sorted(\\\"\\\"\\\"\\n AT4G14690 AT5G41340 AT5G03720 AT5G12020 AT2G22360\\n \\\"\\\"\\\".split()))\\n assert open(out_file).read().strip() == exp_repeat.strip()\\n\\n finally:\\n if os.path.isfile(out_file):\\n os.remove(out_file)\",\n \"def test_repeat_seq():\\n\\n out_file = \\\"out.txt\\\"\\n try:\\n if os.path.isfile(out_file):\\n os.remove(out_file)\\n\\n rv, out = getstatusoutput(f'{prg} -f {repeat}')\\n assert rv == 0\\n expected = (' 1: amigo_repeat.txt\\\\n'\\n 'Wrote 5 gene IDs from 1 file to file \\\"out.txt\\\"')\\n assert out == expected\\n assert os.path.isfile(out_file)\\n exp_repeat = '\\\\n'.join(\\n sorted(\\\"\\\"\\\"\\n AT4G14690 AT5G41340 AT5G03720 AT5G12020 AT2G22360\\n \\\"\\\"\\\".split()))\\n assert open(out_file).read().strip() == exp_repeat.strip()\\n\\n finally:\\n if os.path.isfile(out_file):\\n os.remove(out_file)\",\n \"def foxGrowth():\\r\\n # you need these lines for modifying global variables\\r\\n global CURRENTRABBITPOP\\r\\n global CURRENTFOXPOP\\r\\n\\r\\n # TO DO\\r\\n #pass\\r\\n for i in range(CURRENTFOXPOP):\\r\\n if CURRENTRABBITPOP > 10:\\r\\n if random.random() <= (CURRENTRABBITPOP/MAXRABBITPOP):\\r\\n CURRENTRABBITPOP -= 1\\r\\n # fox reproducing\\r\\n if random.random() <= (1/3):\\r\\n CURRENTFOXPOP += 1\\r\\n else:\\r\\n # fox dying\\r\\n if random.random() <= 0.1:\\r\\n CURRENTFOXPOP -= 1\",\n \"def test_x_repeating(name, ipset_x_repeating):\\n with pytest.raises(ValueError):\\n interpolation.interpolate(*ipset_x_repeating, kind=name, **IPARGS.get(name, {}))\",\n \"def repeat_nd(x, reps):\\n return RepeatND(reps)(x)\",\n \"def foxGrowth():\\r\\n # you need these lines for modifying global variables\\r\\n global CURRENTRABBITPOP\\r\\n global CURRENTFOXPOP\\r\\n\\r\\n # TO DO\\r\\n #pass\\r\\n for i in range(CURRENTFOXPOP):\\r\\n if CURRENTRABBITPOP > 10:\\r\\n if random.random() <= (CURRENTRABBITPOP/MAXRABBITPOP):\\r\\n CURRENTRABBITPOP -= 1\\r\\n # fox reproducing\\r\\n if random.random() <= (1/3):\\r\\n CURRENTFOXPOP += 1\\r\\n else:\\r\\n # fox dying\\r\\n if random.random() <= 0.9:\\r\\n CURRENTFOXPOP -= 1\",\n \"async def repeat(times : int, content='repeating...'):\\n for i in range(times):\\n await bot.say(content)\",\n \"def rabbitGrowth():\\r\\n # you need this line for modifying global variables\\r\\n global CURRENTRABBITPOP\\r\\n\\r\\n # TO DO\\r\\n #pass\\r\\n for i in range(CURRENTRABBITPOP):\\r\\n if random.random() <= (1 - (CURRENTRABBITPOP/MAXRABBITPOP)):\\r\\n CURRENTRABBITPOP += 1\",\n \"def rabbitGrowth():\\r\\n # you need this line for modifying global variables\\r\\n global CURRENTRABBITPOP\\r\\n\\r\\n # TO DO\\r\\n #pass\\r\\n for i in range(CURRENTRABBITPOP):\\r\\n if random.random() <= (1 - (CURRENTRABBITPOP/MAXRABBITPOP)):\\r\\n CURRENTRABBITPOP += 1\",\n \"def repeat(self, repeats):\\n return SeriesDefault.register(pandas.Series.repeat)(self, repeats=repeats)\",\n \"def test_op_repeat(self) -> None:\\n op_base = OpIncrForTest()\\n kwargs_per_step_to_add = [\\n dict(key_in=\\\"data.val.a\\\", key_out=\\\"data.val.b\\\"),\\n dict(key_in=\\\"data.val.b\\\", key_out=\\\"data.val.c\\\"),\\n dict(key_in=\\\"data.val.b\\\", key_out=\\\"data.val.d\\\"),\\n dict(key_in=\\\"data.val.d\\\", key_out=\\\"data.val.d\\\"),\\n ]\\n op_repeat = OpRepeat(op_base, kwargs_per_step_to_add)\\n sample_dict = NDict({})\\n sample_dict[\\\"data.val.a\\\"] = 5\\n sample_dict = op_repeat(sample_dict, \\\"_.test_repeat\\\", incr_value=3)\\n self.assertEqual(sample_dict[\\\"data.val.a\\\"], 5)\\n self.assertEqual(sample_dict[\\\"data.val.b\\\"], 8)\\n self.assertEqual(sample_dict[\\\"data.val.c\\\"], 11)\\n self.assertEqual(sample_dict[\\\"data.val.d\\\"], 14)\\n\\n op_repeat.reverse(\\n sample_dict,\\n key_to_follow=\\\"data.val.d\\\",\\n key_to_reverse=\\\"data.val.d\\\",\\n op_id=\\\"_.test_repeat\\\",\\n )\\n self.assertEqual(sample_dict[\\\"data.val.a\\\"], 5)\\n self.assertEqual(sample_dict[\\\"data.val.b\\\"], 8)\\n self.assertEqual(sample_dict[\\\"data.val.c\\\"], 11)\\n self.assertEqual(sample_dict[\\\"data.val.d\\\"], 8)\\n\\n sample_dict[\\\"data.val.e\\\"] = 48\\n op_repeat.reverse(\\n sample_dict,\\n key_to_follow=\\\"data.val.d\\\",\\n key_to_reverse=\\\"data.val.e\\\",\\n op_id=\\\"_.test_repeat\\\",\\n )\\n self.assertEqual(sample_dict[\\\"data.val.a\\\"], 5)\\n self.assertEqual(sample_dict[\\\"data.val.b\\\"], 8)\\n self.assertEqual(sample_dict[\\\"data.val.c\\\"], 11)\\n self.assertEqual(sample_dict[\\\"data.val.d\\\"], 8)\\n self.assertEqual(sample_dict[\\\"data.val.e\\\"], 42)\",\n \"def repeat(self, repeat: bool=None):\\n self._select_interface(self._rc_repeat, self._http_repeat, repeat)\",\n \"def test_make_pop(self, pop_size, cell_number, microcell_number):\\n for i in [0, 1]:\\n pe.Parameters.instance().use_ages = i\\n # Population is initialised with no households\\n pop_params = {\\\"population_size\\\": pop_size,\\n \\\"cell_number\\\": cell_number,\\n \\\"microcell_number\\\": microcell_number}\\n test_pop = ToyPopulationFactory.make_pop(pop_params)\\n\\n total_people = 0\\n count_non_empty_cells = 0\\n for cell in test_pop.cells:\\n for microcell in cell.microcells:\\n total_people += len(microcell.persons)\\n if len(cell.persons) > 0:\\n count_non_empty_cells += 1\\n # Test there is at least one non-empty cell\\n self.assertTrue(count_non_empty_cells >= 1)\\n # Test that everyone in the population has been assigned a\\n # microcell\\n self.assertEqual(total_people, pop_size)\\n\\n # Test a population class object is returned\\n self.assertIsInstance(test_pop, pe.Population)\",\n \"async def repeat(ctx, times: int, content='repeating...'):\\n for i in range(times):\\n await ctx.send(content)\",\n \"def MakeRepeat1(self,content):\\n return self.register(Repeat1(content,reg=self))\",\n \"def test_pop(self):\\n sched = Schedule()\\n inst_map = InstructionScheduleMap()\\n\\n inst_map.add(\\\"tmp\\\", 100, sched)\\n self.assertEqual(inst_map.pop(\\\"tmp\\\", 100), sched)\\n self.assertFalse(inst_map.has(\\\"tmp\\\", 100))\\n\\n self.assertEqual(inst_map.qubit_instructions(100), [])\\n self.assertEqual(inst_map.qubits_with_instruction(\\\"tmp\\\"), [])\\n with self.assertRaises(PulseError):\\n inst_map.pop(\\\"not_there\\\", (0,))\",\n \"def runSimulation(numSteps):\\n\\n rabbit_pop = []\\n fox_pop = [] \\n \\n for steps in range(numSteps):\\n rabbitGrowth()\\n foxGrowth()\\n rabbit_pop.append(CURRENTRABBITPOP)\\n fox_pop.append(CURRENTFOXPOP)\\n \\n return (rabbit_pop, fox_pop)\",\n \"def test_documentation_popxl_addition(self):\\n filename = \\\"simple_addition.py\\\"\\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)\",\n \"def repeat(self, number_of_repeats):\\n return \\\"G\\\" + str(number_of_repeats)\",\n \"def test_pop_methods(self):\\n\\n batch = Batch(Mock())\\n\\n # mock BatchRequests\\n mock_obj = Mock()\\n mock_ref = Mock()\\n batch._objects_batch = mock_obj\\n batch._reference_batch = mock_ref\\n\\n mock_obj.pop.assert_not_called()\\n mock_ref.pop.assert_not_called()\\n\\n # pop object default value\\n batch.pop_object()\\n mock_obj.pop.assert_called_with(-1)\\n mock_ref.pop.assert_not_called()\\n # reset mock objects\\n mock_obj.reset_mock()\\n mock_ref.reset_mock()\\n\\n # pop object at index\\n batch.pop_object(10)\\n mock_obj.pop.assert_called_with(10)\\n mock_ref.pop.assert_not_called()\\n # reset mock objects\\n mock_obj.reset_mock()\\n mock_ref.reset_mock()\\n\\n # pop reference default value\\n batch.pop_reference()\\n mock_obj.pop.assert_not_called()\\n mock_ref.pop.assert_called_with(-1)\\n # reset mock objects\\n mock_obj.reset_mock()\\n mock_ref.reset_mock()\\n\\n # pop reference at index\\n batch.pop_reference(9)\\n mock_obj.pop.assert_not_called()\\n mock_ref.pop.assert_called_with(9)\",\n \"def test_pop_gate(self):\\n sched = Schedule()\\n inst_map = InstructionScheduleMap()\\n\\n inst_map.add(XGate(), 100, sched)\\n self.assertEqual(inst_map.pop(XGate(), 100), sched)\\n self.assertFalse(inst_map.has(XGate(), 100))\\n\\n self.assertEqual(inst_map.qubit_instructions(100), [])\\n self.assertEqual(inst_map.qubits_with_instruction(XGate()), [])\\n with self.assertRaises(PulseError):\\n inst_map.pop(\\\"not_there\\\", (0,))\",\n \"def generate_RC_codebook(cube_dim, reps, permutate=1, item_num=0):\\r\\n\\r\\n\\timport numpy as np\\r\\n\\t\\r\\n\\tcodebook = []\\r\\n\\tcodeindex = []\\r\\n\\t\\r\\n\\tcube_size = np.prod(cube_dim)\\r\\n\\tif item_num == 0 or item_num > cube_size:\\r\\n\\t\\tntargets = cube_size\\r\\n\\telse:\\r\\n\\t\\tntargets = item_num\\r\\n\\t\\r\\n\\tindices = np.arange(cube_size)\\r\\n\\tcube = indices.reshape(cube_dim)\\r\\n\\t\\r\\n\\tlen_codebook = sum(cube_dim)#6col + 6row =12\\r\\n\\t\\r\\n\\tfor rep in range(reps):\\r\\n\\t\\t# random permute the codebook for one rep\\r\\n\\t\\tind_code = np.random.permutation(len_codebook)\\r\\n\\t\\t\\r\\n\\t\\t# avoid consecutive appearance between reps\\r\\n\\t\\tif rep > 0:\\r\\n\\t\\t\\twhile ind_code[0]==last_ind_code:\\r\\n\\t\\t\\t\\tind_code = np.random.permutation(len_codebook)\\r\\n\\t\\tlast_ind_code = ind_code[-1]\\r\\n\\t\\t\\r\\n\\t\\tfor i in ind_code:\\r\\n\\t\\t\\tif i < cube_dim[0]:\\r\\n\\t\\t\\t\\tcode_slice = cube[i,:]\\r\\n\\t\\t\\t\\tcode_slice = code_slice[np.nonzero(code_slice<ntargets)]\\r\\n\\t\\t\\t\\tcodebook.append(code_slice.tolist())\\r\\n\\t\\t\\telse:\\r\\n\\t\\t\\t\\tcode_slice = cube[:,i-cube_dim[0]]\\r\\n\\t\\t\\t\\tcode_slice = code_slice[np.nonzero(code_slice<ntargets)]\\r\\n\\t\\t\\t\\tcodebook.append(code_slice.tolist())\\r\\n\\t\\tcodeindex.extend(ind_code)\\t\\r\\n\\r\\n\\treturn cube, codebook, codeindex\",\n \"def foxGrowth():\\r\\n # you need these lines for modifying global variables\\r\\n global CURRENTRABBITPOP\\r\\n global CURRENTFOXPOP\\r\\n\\r\\n \\r\\n \\r\\n for fox in range(CURRENTFOXPOP):\\r\\n if CURRENTRABBITPOP > 10:\\r\\n prob_fox_eats_rabbit = float(CURRENTRABBITPOP)/MAXRABBITPOP\\r\\n if prob_fox_eats_rabbit > random.random():\\r\\n CURRENTRABBITPOP -= 1\\r\\n if 1.0/3.0 > random.random():\\r\\n CURRENTFOXPOP += 1\\r\\n elif CURRENTFOXPOP > 10:\\r\\n if 0.9 > random.random():\\r\\n CURRENTFOXPOP -= 1\",\n \"def poprve():\\n mylist = [1, 2, 3]\\n for element in mylist:\\n print element\",\n \"def repeat(self):\\n return self._get('repeat')\",\n \"def test_calls(self):\\n ex = self.ex\\n m = self.m\\n n = self.n\\n\\n nreps = random.randint(1, 10)\\n ex.nreps = nreps\\n ex.vary[\\\"X\\\"][\\\"with\\\"].add(\\\"rep\\\")\\n ex.infer_lds()\\n\\n cmds = ex.generate_cmds()\\n\\n idx = random.randint(0, nreps - 1)\\n self.assertIn([\\\"name\\\", m, n, \\\"X_%d\\\" % idx, m, \\\"Y\\\", m, \\\"Z\\\", n], cmds)\",\n \"def test_data_norange(self):\\n ex = self.ex\\n m = self.m\\n n = self.n\\n\\n nreps = random.randint(1, 10)\\n lensumrange = random.randint(1, 10)\\n\\n ex.nreps = nreps\\n ex.sumrange = [\\\"j\\\", range(lensumrange)]\\n ex.vary[\\\"X\\\"][\\\"with\\\"].add(\\\"rep\\\")\\n ex.vary[\\\"Y\\\"][\\\"with\\\"].add(\\\"j\\\")\\n ex.vary[\\\"Y\\\"][\\\"along\\\"] = 0\\n ex.vary[\\\"Z\\\"][\\\"with\\\"].update([\\\"rep\\\", \\\"j\\\"])\\n ex.infer_lds()\\n\\n cmds = ex.generate_cmds()\\n\\n self.assertIn([\\\"smalloc\\\", \\\"X\\\", nreps * m * n], cmds)\\n idx = random.randint(0, nreps - 1)\\n self.assertIn([\\\"soffset\\\", \\\"X\\\", idx * m * n, \\\"X_%d\\\" % idx], cmds)\\n\\n self.assertIn([\\n \\\"dmalloc\\\", \\\"Y\\\", lensumrange * m * m + (lensumrange - 1) * m\\n ], cmds)\\n idx = random.randint(0, lensumrange - 1)\\n self.assertIn([\\\"doffset\\\", \\\"Y\\\", idx * m, \\\"Y_%d\\\" % idx], cmds)\\n\\n self.assertIn([\\\"cmalloc\\\", \\\"Z\\\", nreps * lensumrange * n * n], cmds)\\n idxrep = random.randint(0, nreps - 1)\\n idxrange = random.randint(0, lensumrange - 1)\\n self.assertIn([\\\"coffset\\\", \\\"Z\\\",\\n (idxrep * lensumrange + idxrange) * n * n,\\n \\\"Z_%d_%d\\\" % (idxrep, idxrange)], cmds)\",\n \"def repeat(self, count):\\n x = HSeq()\\n for i in range(count):\\n x = x.concatenate(self)\\n return x\",\n \"def generate_pops(target_reg, exclude_regs=[], count=1, allow_dups=True):\\n\\n random_regs = []\\n\\n for _ in range(0, count-1):\\n random_reg = get_random_register(exclude_regs=exclude_regs)\\n\\n random_regs.append(random_reg)\\n\\n pops = ''\\n\\n for reg in random_regs:\\n pops += f'pop {reg}; '\\n\\n pops += f'pop {target_reg}; '\\n\\n return pops\",\n \"def test_7_replay_4(self):\\n self._execute_replay_nr(4)\\n\\n self.grid.add_pawn(5, 'H')\\n self.grid.add_pawn(3, 'B')\\n self.grid.add_pawn(2, 'H')\\n self.grid.add_pawn(1, 'B')\\n self.grid.add_pawn(1, 'H')\\n\\n # self.grid.print_grid()\\n # print(self.minmaxBot_7.choose_move(self.grid))\",\n \"def project_pop(self):\\n M = self.N[0:2]\\n for x in range(10):\\n M.append(self.run_step(M))\\n split_N = split_list(M)\\n \\n fig = self.make_figure(split_N)\\n fig.update_layout(title='Projected Fish Population')\\n\\n return fig\",\n \"def test_raises(self):\\n no_replicates = 25\\n try:\\n replicate(experiment3, no_replicates)\\n except RuntimeError as err:\\n self.assertEqual(err, FAKE_ERROR)\\n else:\\n assert False\",\n \"def run_memory_exp(nb_repeat, name, cmd):\\n # Create the required folders\\n if not os.path.exists(\\\"../logs\\\"):\\n os.mkdir(\\\"../logs\\\")\\n if not os.path.exists(f\\\"../logs/{name}\\\"):\\n os.mkdir(f\\\"../logs/{name}\\\")\\n\\n # Log the configuration\\n with open(f\\\"../logs/{name}/cmd_template.txt\\\", \\\"w\\\") as cmd_log:\\n cmd_log.write(cmd)\\n\\n # Repeat the simulation\\n for i in range(nb_repeat):\\n print(\\\"[Memory experience] Progression: {}%\\\".format(i * 100 // nb_repeat))\\n itr_cmd = cmd + \\\" --output {}/itr_{}\\\".format(name, i)\\n args = get_args(itr_cmd.split(\\\" \\\"))\\n sim = Sim()\\n sim.from_args(args)\\n print(\\\"[Memory experience] Done !\\\")\",\n \"def _generate_pileups(self):\\n pass\",\n \"def test_INVOKE_repeat(self, propose):\\n self.rep.proposals[1] = PROPOSAL1\\n self.failIf(propose.called)\",\n \"def foxGrowth():\\n # you need these lines for modifying global variables\\n global CURRENTRABBITPOP\\n global CURRENTFOXPOP\\n \\n for fox in range(CURRENTFOXPOP):\\n fox_eat_prob = float(CURRENTRABBITPOP) / MAXRABBITPOP\\n if random.random() < fox_eat_prob and CURRENTRABBITPOP > 10:\\n CURRENTRABBITPOP -= 1\\n if random.random() < (1.0 / 3.0):\\n CURRENTFOXPOP += 1\\n else: \\n if random.random() < (1.0 / 10.0) and CURRENTFOXPOP > 10:\\n CURRENTFOXPOP -= 1\",\n \"def Repeat(dataset, count=None):\\n return dataset.repeat(count=count)\",\n \"def repeated_iteration(self) -> global___Statement.Iteration.RepeatedIteration:\",\n \"def test_repeatable(self):\\n\\n def run(seed, ModelClass=Model):\\n \\\"\\\"\\\"Return the history of a run\\\"\\\"\\\"\\n model = ModelClass(random_seed=seed)\\n return model.one_trial(1, 10)\\n\\n self.assertEqual(run(0, ModelClass=Model).data, run(0, ModelClass=Model).data)\\n self.assertEqual(run(0, ModelClass=ReplicatedModel).data, run(0, ModelClass=ReplicatedModel).data)\",\n \"def repeat(self, min=0, max=None):\\n raise NotImplemented()\",\n \"def rabbitGrowth():\\n # you need this line for modifying global variables\\n global CURRENTRABBITPOP\\n\\n for rabbit in range(CURRENTRABBITPOP):\\n rabbit_reproduction_prob = 1.0 - (float(CURRENTRABBITPOP) / MAXRABBITPOP)\\n if random.random() < rabbit_reproduction_prob and CURRENTRABBITPOP < 1000:\\n CURRENTRABBITPOP += 1\",\n \"def population(params, n=100):\\r\\n pops = []\\r\\n for i in range(n):\\r\\n pop = []\\r\\n for param in params:\\r\\n pop.append(np.random.choice(param))\\r\\n\\r\\n individuale = Individuale(pop)\\r\\n pops.append(individuale)\\r\\n # print(\\\"No.{} : {} : {}\\\".format(i, individuale, individuale.x))\\r\\n return pops\",\n \"def runSimulation(numSteps):\\r\\n rabbit_populations = []\\r\\n fox_populations = []\\r\\n for step in range(numSteps):\\r\\n rabbitGrowth()\\r\\n foxGrowth()\\r\\n rabbit_populations.append(CURRENTRABBITPOP)\\r\\n fox_populations.append(CURRENTFOXPOP)\\r\\n return (rabbit_populations, fox_populations)\",\n \"def run_example1():\\r\\n #outcomes = set([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])\\r\\n #outcomes = set(['Heads','Tails'])\\r\\n #outcomes = set([\\\"Red\\\", \\\"Green\\\", \\\"Blue\\\"])\\r\\n outcomes = set([\\\"Sunday\\\", \\\"Mondy\\\", \\\"Tuesday\\\", \\\"Wednesday\\\", \\\"Thursday\\\", \\\"Friday\\\", \\\"Saturday\\\"])\\r\\n \\r\\n length = 7\\r\\n seq_outcomes = gen_permutations(outcomes,length)\\r\\n print \\\"Computed\\\", len(seq_outcomes), \\\"sequences of\\\", str(length), \\\"outcomes\\\"\\r\\n #print \\\"Sequences were\\\", seq_outcomes\\r\",\n \"def podruhe():\\n mylist = [x for x in range(3)]\\n for element in mylist:\\n print element\",\n \"def repeat(fn):\\n def repeated():\\n i = 0\\n while i < random_test_iterations:\\n fn()\\n i += 1\\n # nosetest runs functions that start with 'test_'\\n repeated.__name__ = fn.__name__\\n return repeated\",\n \"def repeat(word, repetitions):\\n return word * repetitions\",\n \"def repeat(num_times):\\n\\n def decorator_repeat(func):\\n \\\"\\\"\\\"\\n defines wrapper_repeat(*args, **kwargs)\\n\\n :returns: wrapper_repeat\\n \\\"\\\"\\\"\\n\\n @functools.wraps(func)\\n def wrapper_repeat(*args, **kwargs):\\n \\\"\\\"\\\"\\n func(*args, **kwargs) num_times\\n\\n :return: last return value\\n \\\"\\\"\\\"\\n for _ in range(num_times):\\n value = func(*args, **kwargs)\\n return value\\n\\n return wrapper_repeat\\n\\n return decorator_repeat\",\n \"def repeat(s):\\r\\n\\r\\n return s\",\n \"def testspec(arr: list[int]) -> None:\\n\\n print(50*'-')\\n print(arr)\\n print_rem(arr)\\n rev_dupes(arr)\\n print(arr)\",\n \"def repeat(a, repeats, axis=None):\\n return afnumpy.asarray(a).repeat(repeats, axis=axis)\",\n \"def test_pop_no_args(self):\\r\\n msg_list = messages.MessageList()\\r\\n # Adds 5 Message objects to the list.\\r\\n msg_list.push(messages.StringMessage(\\\"a\\\"))\\r\\n msg_list.push(messages.StringMessage(\\\"b\\\"))\\r\\n msg_list.push(messages.StringMessage(\\\"c\\\"))\\r\\n msg_list.push(messages.StringMessage(\\\"d\\\"))\\r\\n msg_list.push(messages.StringMessage(\\\"e\\\"))\\r\\n\\r\\n self.assertEqual(msg_list.length(), 5)\\r\\n popped = msg_list.pop()\\r\\n self.assertEqual(msg_list.length(), 4)\\r\\n self.assertEqual(popped.msg, \\\"e\\\")\\r\\n msg_list.pop()\\r\\n msg_list.pop()\\r\\n msg_list.pop()\\r\\n msg_list.pop()\\r\\n self.assertRaises(IndexError, msg_list.pop)\",\n \"def generatesack(nmbrofitems):\\n while nmbrofitems != 0:\\n newitem = Sackitem(random.randint(1,10),random.randint(1,20))\\n itemlist.append(newitem)\\n nmbrofitems -= 1\\n return itemlist\",\n \"def initialPop(popSize,rangeMin,rangeMax,genLength):\\n\\t\\n\\tpop=[]\\n\\n\\tfor i in range(popSize):\\n\\t\\tgenome=[]\\n\\t\\tfor j in range(genLength):\\n\\t\\t\\tparam=random.uniform(rangeMin,rangeMax)\\n\\t\\t\\tgenome.append(param)\\n\\t\\tpop.append(Gen(genome)) #add each random genome to the pop\\n\\t\\t\\t\\t\\n\\treturn pop\",\n \"def fillLoop():\\n print('Now looping spotfill (press enter to quit).')\\n x = 1\\n while x != 0:\\n x = spotfill()\\n print('Ending spotfill.')\\n return\",\n \"def test_generate_sample_sheet(self):\\n pass\",\n \"def alert_pet(self, reps=3):\\n for x in range(0,reps):\\n time.sleep(1)\\n GPIO.output(self.alert_pin, 0)\\n time.sleep(1)\\n GPIO.output(self.alert_pin, 1)\\n return\",\n \"def test_reset(sim):\\n repeats = 3\\n dt = 1\\n sim.setup(timestep=dt, min_delay=dt)\\n p = sim.Population(1, sim.IF_curr_exp(i_offset=0.1))\\n p.record('v')\\n\\n for i in range(repeats):\\n sim.run(10.0)\\n sim.reset()\\n data = p.get_data(clear=False)\\n sim.end()\\n\\n assert len(data.segments) == repeats\\n for segment in data.segments[1:]:\\n assert_array_almost_equal(segment.analogsignals[0],\\n data.segments[0].analogsignals[0], 10)\",\n \"def test_remainder(self):\\n alp = list(range(5))\\n targets = generate_targets(alp, 12)\\n\\n counts = Counter(targets)\\n for item in alp:\\n self.assertGreaterEqual(counts[item], 2)\\n self.assertLessEqual(counts[item], 3)\",\n \"def simulate_fish_population(fish, days):\\n for _ in range(days):\\n new_fish = []\\n for fish in fish:\\n if fish == 0:\\n new_fish.append(6)\\n new_fish.append(8)\\n else:\\n new_fish.append(fish - 1)\\n fish = new_fish\\n return fish\",\n \"def inversion_crossover(self, pop):\\n children, tmpNonComb, used = ([] for i in range(3))\\n for i in range(0, int(len(pop) * self.fracElite), 1):\\n r = int(rand() * len(pop))\\n while r == i:\\n r = int(rand() * len(pop))\\n\\n if sum(self.cID + self.dID + self.iID) != 0:\\n nonComb1 = pop[i][:np.where(self.cID + self.dID + self.iID == 1)[0][(-1)] + 1]\\n nonComb2 = pop[r][:np.where(self.cID + self.dID + self.iID == 1)[0][(-1)] + 1]\\n if sum(self.xID) != 0:\\n comb1 = pop[i][:np.where(self.xID == 1)[0][(-1)] + 1]\\n comb2 = pop[r][:np.where(self.xID == 1)[0][(-1)] + 1]\\n if sum(self.cID + self.dID + self.iID) != 0:\\n c = int(rand() * len(nonComb1))\\n if rand() > 0.5:\\n tmpNonComb.append(np.array(nonComb1[0:c + 1].tolist() + nonComb2[c + 1:].tolist()))\\n else:\\n tmpNonComb.append(np.array(nonComb2[0:c + 1].tolist() + nonComb1[c + 1:].tolist()))\\n used.append(i)\\n if sum(self.xID) != 0:\\n c = int(rand() * len(comb1))\\n for c1 in range(c, len(comb1), 1):\\n d2 = (contains_sublist(comb2, comb1[c1]) + 1) % len(comb1)\\n d1 = contains_sublist(comb1, comb2[d2])\\n c2 = contains_sublist(comb2, comb1[((d1 + 1) % len(comb1))]) % len(comb1)\\n tmp1 = cp.copy(comb1)\\n if c1 < d1:\\n tmp1[(c1 + 1):(d1 + 1)] = list(reversed(tmp1[c1 + 1:d1 + 1]))\\n else:\\n tmp1[d1:c1] = list(reversed(tmp1[d1:c1]))\\n tmp2 = cp.copy(comb2)\\n if c2 < d2:\\n tmp2[c2:d2] = list(reversed(tmp2[c2:d2]))\\n else:\\n tmp2[(d2 + 1):(c2 + 1)] = list(reversed(tmp2[d2 + 1:c2 + 1]))\\n if sum(self.cID + self.dID + self.iID) == 0 and sum(self.xID) != 0:\\n children.append(tmp1)\\n children.append(tmp2)\\n elif sum(self.cID + self.dID + self.iID) != 0 and sum(self.xID) != 0:\\n children.append(np.concatenate(tmpNonComb[(-1)], tmp1))\\n children.append(np.concatenate(tmpNonComb[(-1)], tmp2))\\n used.append(i)\\n used.append(r)\\n\\n if sum(self.cID + self.dID + self.iID) != 0 and sum(self.xID) == 0:\\n children = tmpNonComb\\n return (\\n children, used)\",\n \"def test_push_pop(values):\\n test_stack = stack.Stack()\\n\\n for value in values:\\n test_stack.push(value)\\n\\n for expected_value in reversed(values):\\n value = test_stack.pop()\\n assert value == expected_value\\n\\n with pytest.raises(stack.StackEmptyError):\\n test_stack.pop()\",\n \"def run_simulation(env, pop, nr):\\n pop_f = []\\n pop_pl = []\\n pop_el = []\\n\\n\\n for individual in pop:\\n fitness, player_life, enemy_life, time = env.play(pcont=individual)\\n pop_f.append(fitness)\\n pop_pl.append(player_life)\\n pop_el.append(enemy_life)\\n\\n return pop_f, pop_pl, pop_el\",\n \"def reproduce(population:list):\\n new_gen = []\\n probs = []\\n for p in population:\\n probs.append(p[3])\\n while len(new_gen) != len(probs):\\n parents = selection(probs)\\n son,eval_son,daughter,eval_daughter = xo(population[parents[0]][0],population[parents[0]][1], population[parents[1]][0],population[parents[1]][1],2)\\n new_gen.append([son,eval_son])\\n new_gen.append([daughter,eval_daughter])\\n # mutation\\n # lets say 5% of the population gets mutated\\n how_many_to_mutate = int(NUM_OF_CHROMOZOMS * (1/100))\\n t = [i for i in range(NUM_OF_CHROMOZOMS)]\\n # choose percent of the population randomly, uniformly\\n indices_to_mutate = choice(t, how_many_to_mutate, replace=False)\\n for i in range(len(indices_to_mutate)):\\n mutate(new_gen[indices_to_mutate[i]])\\n\\n evaluateAll(new_gen)\\n return new_gen\",\n \"def crossover(population, kw=None, **kwargs):\\n future_population = []\\n while len(future_population) < len(population):\\n p1, p2 = random.choice(population)['notes'], random.choice(population)['notes']\\n split = random.randint(1, len(p1) - 1)\\n map(future_population.append, [p1[:split] + p2[split:], p2[:split] + p1[split:]])\\n return future_population\",\n \"def genPopulation(self):\\r\\n self.population_list = []\\r\\n for i in xrange(0, self.pop_size):\\r\\n individual = bitarray(self.indv_size)\\r\\n # Loop for randomizing the 'individual' string.\\r\\n for j in xrange(0, self.board_size):\\r\\n vert_pos = random.randint(0, self.board_size-1)\\r\\n vert_pos_bitnum = toBitArray(vert_pos, self.pos_bits_size)\\r\\n # print \\\"\\\\t\\\\t\\\", j, vert_pos_bitnum, vert_pos\\r\\n for k in range(0, self.pos_bits_size):\\r\\n individual[j * self.pos_bits_size + k] = vert_pos_bitnum[k]\\r\\n self.population_list.append(individual)\\r\\n # print \\\"\\\\t\\\", i, individual\\r\",\n \"def repeat_expt(epsilon, gamma,\\n result_nonprivate, \\n repetitions=10,\\n outfile_singles=None, outfile_aggregates=None, \\n data_blocker=1, windsorized=False):\\n \\n \\n blocker = gupt.GuptRunTime.get_data_blockers()[data_blocker-1]\\n # 1 NaiveDataBlocker\\n # 2 ResamplingDataBlockerConstantSize \\n # 3 ResamplingDataBlockerConstantBlocks\\n\\n if not windsorized:\\n DP_mode=\\\"standard_DP\\\"\\n else:\\n DP_mode=\\\"windsorized_DP\\\"\\n\\n logger.info(\\\"Running %d repetitions with data_blocker=%s\\\" % (repetitions, blocker))\\n logger.info(\\\"epsilon=%s gamma=%s, in mode %s\\\" % (epsilon, gamma, DP_mode))\\n \\n results, starttime = [], time.clock()\\n \\n # results = pickle.load( open( \\\"res.pickle\\\", \\\"rb\\\" ))\\n \\n \\n for i in range(repetitions):\\n\\n # TODO: Perhaps they DO or DO NOT have to be recreated in each run?\\n blocker = gupt.GuptRunTime.get_data_blockers()[data_blocker-1]\\n reader = censusdatadriver.get_reader()\\n runtime = gupt.GuptRunTime(MeanComputer, reader, epsilon, \\n blocker_name=blocker, blocker_args=gamma)\\n # end TODO\\n\\n if not windsorized:\\n res=runtime.start()\\n else:\\n res=runtime.start_windsorized()\\n \\n # artificial 2nd dimension, just for testing these routines:\\n # res = res + res\\n \\n print report_results(res, result_nonprivate, DP_mode, blocker, \\n epsilon, gamma, outfile_singles)\\n sleep_short()\\n \\n results.append(res)\\n\\n # pickle.dump(results, open( \\\"res.pickle\\\", \\\"wb\\\" ) )\\n \\n \\n duration = time.clock() - starttime\\n logger.info(\\\"%d repetitions took %.2f seconds\\\" % (repetitions, duration))\\n \\n mean, std = analyze_results(results) # , result_nonprivate)\\n \\n print report_results_repeated(mean, std, DP_mode, blocker,\\n epsilon, gamma, repetitions,\\n outfile=outfile_aggregates)\",\n \"def repeat(self, count):\\n x = _OSeq()\\n for i in range(count):\\n x = x.concatenate(self)\\n return x\",\n \"def pop():\",\n \"def run_expt_many_times(result_nonprivate, repetitions=10,\\n data_blocker=1, windsorized=False,\\n outfile_singles=None, outfile_aggregates=None):\\n for epsilon in numpy.arange(0.1, 2.2, 0.2):#(0.2, 10, 0.2):\\n for gamma in range(1, 10, 1): # (1, 7, 1):\\n \\n repeat_expt(epsilon, gamma,\\n data_blocker=data_blocker, windsorized=windsorized,\\n result_nonprivate=result_nonprivate,\\n repetitions=repetitions,\\n outfile_singles=outfile_singles, \\n outfile_aggregates=outfile_aggregates)\",\n \"def reset_next_population(self):\\n self.next_population = []\",\n \"def repeat_value(value: Any = None, repeat_count: int = None) -> ObservableBase:\\n from ..operators.observable.repeat import repeat_value\\n return repeat_value(value, repeat_count)\",\n \"def init_pop(self):\\n genes = np.random.randn( self.population_size * self.individual.gene_count )\\n self.population = genes.reshape((self.population_size, -1))\\n #print(self.population)\",\n \"def genNextPop(prevPop, masterList, popSize):\\n parSize = int(popSize / 10) #top 10%\\n parentPop = [Team(prevPop[i].roster) for i in range(parSize)]\\n parentPop = getStats(parentPop, masterList) # inefficent\\n rosterSize = len(parentPop[0].roster)\\n newPop = []\\n #parentsList = [] #debug\\n for i in range(popSize):\\n chromosome = doCrossover(parentPop, parSize, rosterSize)\\n newPop.append(Team(chromosome))\\n getStats(newPop, masterList)\\n #showStats(newPop, masterList, \\\"all\\\")\\n #debug code\\n #for playerInd in chromosome:\\n # print(masterList[playerInd].pos)\\n\\n return newPop\",\n \"def test_pop(self):\\n test_list = DoubleLinkedList()\\n test_list.push(15)\\n test_list.push(150)\\n test_list.push(13)\\n test_list.push(155)\\n test_list.push(1)\\n test_list.pop()\\n self.assertEqual(test_list.last().get_elem(), 155)\",\n \"def rand_pop(l: list):\\n i = randrange(len(l)) \\n l[i], l[-1] = l[-1], l[i] \\n return l.pop()\",\n \"def rep_pops(self):\\n return _PopFlag(self.rep_pop_flag)\",\n \"def repeated(self, *args, **kwargs):\\n return self.rep.RepeatBorders(self._trg, *args, **kwargs)\",\n \"def test_make_pop_exception(self, patch_log, patch_random):\\n patch_random.side_effect = ValueError\\n # Population is initialised with no households\\n pop_params = {\\\"population_size\\\": 10, \\\"cell_number\\\": 1,\\n \\\"microcell_number\\\": 1}\\n ToyPopulationFactory.make_pop(pop_params)\\n patch_log.assert_called_once_with(\\\"ValueError in ToyPopulation\\\"\\n + \\\"Factory.make_pop()\\\")\",\n \"def get_random_population():\\r\\n return [ get_random_individual() for _ in range(POPULATION_COUNT) ]\",\n \"def test_shift(doctest):\",\n \"def test_solo_cell():\\n cell = c6.Cell(loc=[1, 1])\\n for i in range(10):\\n cell.step()\",\n \"def runSimulation(numSteps):\\r\\n\\r\\n # TO DO\\r\\n #pass\\r\\n rabbits = []\\r\\n foxes = []\\r\\n for i in range(numSteps):\\r\\n rabbitGrowth()\\r\\n foxGrowth()\\r\\n rabbits.append(CURRENTRABBITPOP)\\r\\n foxes.append(CURRENTFOXPOP)\\r\\n return rabbits, foxes\",\n \"def runSimulation(numSteps):\\r\\n\\r\\n # TO DO\\r\\n #pass\\r\\n rabbits = []\\r\\n foxes = []\\r\\n for i in range(numSteps):\\r\\n rabbitGrowth()\\r\\n foxGrowth()\\r\\n rabbits.append(CURRENTRABBITPOP)\\r\\n foxes.append(CURRENTFOXPOP)\\r\\n return rabbits, foxes\",\n \"def popmany(self, num=1):\\n return [self.next() for i in range(num)]\",\n \"def ipset_x_repeating():\\n x = np.linspace(0, 10, 11)\\n x[5] = x[4]\\n return IPSet(x=x, y=np.linspace(-1, 1, 11), x_new=np.linspace(2, 5, 7))\",\n \"def test_reset(self):\\r\\n self.p += 8\\r\\n self.p.reset()\\r\\n self.assertEqual(str(self.p), '0% [....................]')\",\n \"def play():\\n decks = make_decks()\\n\\n deck_pulls = {deck: [] for deck in decks}\\n for i in range(100):\\n deck = random.choice(decks)\\n deck_pulls[deck].append(deck.pull())\\n\\n return decks, deck_pulls\",\n \"def test_endpointPOP3(self):\\n self._endpointTest(\\\"pop3\\\")\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6505946","0.6401242","0.58164656","0.54440105","0.53839743","0.5373807","0.53707576","0.53479195","0.5337674","0.5326383","0.5316677","0.53121865","0.53121865","0.5300285","0.5297226","0.52901715","0.5276409","0.5268856","0.5196304","0.5196304","0.5173073","0.51597667","0.51544595","0.5149219","0.51400036","0.51352286","0.5117778","0.5116364","0.51089984","0.51067406","0.50602293","0.5053365","0.5052107","0.50432307","0.50402075","0.5036144","0.5017667","0.50152904","0.5014456","0.5009249","0.5007194","0.49938065","0.49811885","0.49721584","0.49535653","0.49454007","0.49449232","0.4931532","0.49272776","0.49220848","0.49019122","0.48983014","0.48686028","0.4855172","0.48511168","0.4837923","0.48372665","0.48229888","0.48168114","0.48092508","0.48020446","0.47946322","0.47930422","0.4788318","0.47862095","0.47860527","0.47813416","0.47792202","0.4778324","0.47759062","0.47694802","0.47679576","0.47674227","0.4765635","0.47529393","0.47511858","0.4750886","0.4748555","0.47464865","0.473931","0.4717066","0.47106412","0.4705226","0.47019133","0.4699689","0.46994394","0.4684794","0.46788332","0.4677337","0.46694157","0.46677655","0.46671188","0.46560803","0.46535814","0.46535814","0.4648632","0.46437395","0.463997","0.46368384","0.46349326"],"string":"[\n \"0.6505946\",\n \"0.6401242\",\n \"0.58164656\",\n \"0.54440105\",\n \"0.53839743\",\n \"0.5373807\",\n \"0.53707576\",\n \"0.53479195\",\n \"0.5337674\",\n \"0.5326383\",\n \"0.5316677\",\n \"0.53121865\",\n \"0.53121865\",\n \"0.5300285\",\n \"0.5297226\",\n \"0.52901715\",\n \"0.5276409\",\n \"0.5268856\",\n \"0.5196304\",\n \"0.5196304\",\n \"0.5173073\",\n \"0.51597667\",\n \"0.51544595\",\n \"0.5149219\",\n \"0.51400036\",\n \"0.51352286\",\n \"0.5117778\",\n \"0.5116364\",\n \"0.51089984\",\n \"0.51067406\",\n \"0.50602293\",\n \"0.5053365\",\n \"0.5052107\",\n \"0.50432307\",\n \"0.50402075\",\n \"0.5036144\",\n \"0.5017667\",\n \"0.50152904\",\n \"0.5014456\",\n \"0.5009249\",\n \"0.5007194\",\n \"0.49938065\",\n \"0.49811885\",\n \"0.49721584\",\n \"0.49535653\",\n \"0.49454007\",\n \"0.49449232\",\n \"0.4931532\",\n \"0.49272776\",\n \"0.49220848\",\n \"0.49019122\",\n \"0.48983014\",\n \"0.48686028\",\n \"0.4855172\",\n \"0.48511168\",\n \"0.4837923\",\n \"0.48372665\",\n \"0.48229888\",\n \"0.48168114\",\n \"0.48092508\",\n \"0.48020446\",\n \"0.47946322\",\n \"0.47930422\",\n \"0.4788318\",\n \"0.47862095\",\n \"0.47860527\",\n \"0.47813416\",\n \"0.47792202\",\n \"0.4778324\",\n \"0.47759062\",\n \"0.47694802\",\n \"0.47679576\",\n \"0.47674227\",\n \"0.4765635\",\n \"0.47529393\",\n \"0.47511858\",\n \"0.4750886\",\n \"0.4748555\",\n \"0.47464865\",\n \"0.473931\",\n \"0.4717066\",\n \"0.47106412\",\n \"0.4705226\",\n \"0.47019133\",\n \"0.4699689\",\n \"0.46994394\",\n \"0.4684794\",\n \"0.46788332\",\n \"0.4677337\",\n \"0.46694157\",\n \"0.46677655\",\n \"0.46671188\",\n \"0.46560803\",\n \"0.46535814\",\n \"0.46535814\",\n \"0.4648632\",\n \"0.46437395\",\n \"0.463997\",\n \"0.46368384\",\n \"0.46349326\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6540377"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":6590,"cells":{"query":{"kind":"string","value":"Test the popxl subgraph in parent in repeat example"},"document":{"kind":"string","value":"def test_documentation_popxl_repeat_1(self):\n filename = \"repeat_graph_1.py\"\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)"},"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 test_documentation_popxl_basic_subgraph(self):\n filename = \"basic_graph.py\"\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)","def test_documentation_popxl_create_multi_subgraph(self):\n filename = \"create_multi_graphs_from_same_func.py\"\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)","def test_dummy3(self):\n xpb = XPathBuilder()\n xp = xpb.dummy()\n self.assertTrue(xp.parenthesize() is xp)","def test_documentation_popxl_repeat_2(self):\n filename = \"repeat_graph_2.py\"\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)","def test_documentation_popxl_repeat_0(self):\n filename = \"repeat_graph_0.py\"\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)","def sub_graph_merging(self):","def test_lacking_parent(self):\n pass","def crossover(self, pop):\n intDiscID = self.iID + self.dID\n varID = self.cID\n goldenRatio = (1.0 + sqrt(5)) / 2.0\n dx = np.zeros_like(pop[0])\n children = []\n used = []\n for i in range(0, int(self.fracElite * len(pop)), 1):\n r = int(rand() * self.population)\n while r in used or r == i:\n r = int(rand() * self.population)\n\n used.append(i)\n children.append(cp.deepcopy(pop[r]))\n dx = abs(pop[i] - children[i]) / goldenRatio\n children[i] = children[i] + dx * varID + np.round(dx * intDiscID)\n children[i] = simple_bounds(children[i], self.lb, self.ub)\n\n return (\n children, used)","def test_parent_label(self):\n l = self.d.label(1)\n l2 = self.d.label(31405)\n\n self.assertTrue(l.parent_label is None)\n self.assertTrue(l2 in l.sublabels)\n self.assertEqual(l2.parent_label, l)","def test_parent_with_iterables(self):\n def makeCubesAndGrp():\n cmds.file(new=1, f=1)\n cubes = []\n for x in range(10):\n cubes.append(pm.polyCube()[0])\n group = pm.group(empty=True)\n return cubes, group\n\n cubes, group = makeCubesAndGrp()\n res1 = pm.parent(cubes[:4], group)\n self.assertEqual(cubes[:4], res1)\n res2 = pm.parent(cubes, group)\n self.assertEqual(cubes[4:], res2)\n\n cubes, group = makeCubesAndGrp()\n res1 = pm.parent(cubes[:4] + [group])\n self.assertEqual(cubes[:4], res1)\n res2 = pm.parent(cubes, group)\n self.assertEqual(cubes[4:], res2)\n\n cubes, group = makeCubesAndGrp()\n res1 = pm.parent(cubes[0], cubes[1], cubes[2], cubes[3], group)\n self.assertEqual(cubes[:4], res1)\n res2 = pm.parent(cubes, group)\n self.assertEqual(cubes[4:], res2)\n\n cubes, group = makeCubesAndGrp()\n res1 = pm.parent(cubes[0], cubes[1], [cubes[2], cubes[3], group])\n self.assertEqual(cubes[:4], res1)\n res2 = pm.parent(cubes, group)\n self.assertEqual(cubes[4:], res2)\n\n cubes, group = makeCubesAndGrp()\n res1 = pm.parent([cubes[0], cubes[1]], cubes[2], [cubes[3], group])\n self.assertEqual(cubes[:4], res1)\n res2 = pm.parent(cubes, group)\n self.assertEqual(cubes[4:], res2)","def testParent(self):\n self.assertEqual(\n self,\n self.node.parent\n )","def testParent(self):\n self.assertEqual(\n self,\n self.node.parent\n )","def testParent(self):\n self.assertEqual(\n self.parent,\n self.mr.parent\n )","def test_simple(self):\n exp = [{'type': NewickEvents.OPEN_SUBTREE, 'comments': []},\n {'type': NewickEvents.OPEN_SUBTREE, 'comments': []},\n {'edge_info': None, 'type': NewickEvents.TIP, 'comments': [], 'label': 'h'},\n {'edge_info': None, 'type': NewickEvents.TIP, 'comments': [], 'label': 'p'},\n {'edge_info': '1', 'type': NewickEvents.CLOSE_SUBTREE,\n 'comments': [], 'label': 'hp'},\n {'edge_info': None, 'type': NewickEvents.TIP, 'comments': [], 'label': 'g'},\n {'edge_info': None, 'type': NewickEvents.CLOSE_SUBTREE,\n 'comments': [], 'label': 'hpg'}\n ]\n content = '((h,p)hp:1,g)hpg;'\n self._do_test(content, exp)\n content = '((h,[pretest]p[test][posttest])hp,g)hpg;'\n exp = [{'type': NewickEvents.OPEN_SUBTREE, 'comments': []},\n {'type': NewickEvents.OPEN_SUBTREE, 'comments': []},\n {'edge_info': None, 'type': NewickEvents.TIP, 'comments': [], 'label': 'h'},\n {'edge_info': None, 'type': NewickEvents.TIP,\n 'comments': ['pretest', 'test', 'posttest'], 'label': 'p'},\n {'edge_info': None, 'type': NewickEvents.CLOSE_SUBTREE,\n 'comments': [], 'label': 'hp'},\n {'edge_info': None, 'type': NewickEvents.TIP, 'comments': [], 'label': 'g'},\n {'edge_info': None, 'type': NewickEvents.CLOSE_SUBTREE,\n 'comments': [], 'label': 'hpg'}\n ]\n self._do_test(content, exp)","def reproduce(self, popDensity, activeDrugs):\r\n # TODO\r\n\r\n resistAll = True\r\n for drug in activeDrugs:\r\n if (self.resistances[drug]==False):resistAll = False\r\n \r\n\r\n\r\n maxReproduceProb = self.maxBirthProb * (1 - popDensity)\r\n \r\n \r\n if (resistAll and random.random() < maxReproduceProb):\r\n childResistances = {}\r\n for drug in self.resistances:\r\n if random.random() < self.mutProb:\r\n childResistances[drug] = (not self.resistances[drug])\r\n else:childResistances[drug] = (self.resistances[drug])\r\n\r\n \r\n childOfVirus = ResistantVirus(self.maxBirthProb, self.clearProb,childResistances,self.mutProb)\r\n return childOfVirus\r\n \r\n else: raise NoChildException('Child not created!')","def test_generator8(self):\n xpb = XPathBuilder()\n xp1 = xp2 = None\n base_xp = (xpb.foo.bar | xpb.x.y).parenthesize()\n base_gen = None\n with base_xp as b:\n base_gen = b\n xp1 = b() & xpb.c\n xp2 = b() & xpb.d\n xp1_exp = '(/foo/bar or /x/y) and /c'\n xp2_exp = '(/foo/bar or /x/y) and /d'\n base_exp = '(/foo/bar or /x/y)'\n # check tree structure\n self.assertTrue(base_xp._parent is None)\n self.assertTrue(base_gen._parent is None)\n # check xpath\n self.assertEqual(xp1.tostring(), xp1_exp)\n self.assertEqual(xp2.tostring(), xp2_exp)\n self.assertEqual(base_xp.tostring(), base_exp)\n self.assertEqual(base_gen.tostring(), base_exp)","def _cross_parents(self):\n while len(self.children_population) < self.children_count:\n parent_1, parent_2 = random.sample(self.population, k=2)\n self.children_population.extend(self.crossover.cross(parent_1, parent_2))","def test_documentation_popxl_multi_callsites_graph_input(self):\n filename = \"multi_call_graph_input.py\"\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)","def inversion_crossover(self, pop):\n children, tmpNonComb, used = ([] for i in range(3))\n for i in range(0, int(len(pop) * self.fracElite), 1):\n r = int(rand() * len(pop))\n while r == i:\n r = int(rand() * len(pop))\n\n if sum(self.cID + self.dID + self.iID) != 0:\n nonComb1 = pop[i][:np.where(self.cID + self.dID + self.iID == 1)[0][(-1)] + 1]\n nonComb2 = pop[r][:np.where(self.cID + self.dID + self.iID == 1)[0][(-1)] + 1]\n if sum(self.xID) != 0:\n comb1 = pop[i][:np.where(self.xID == 1)[0][(-1)] + 1]\n comb2 = pop[r][:np.where(self.xID == 1)[0][(-1)] + 1]\n if sum(self.cID + self.dID + self.iID) != 0:\n c = int(rand() * len(nonComb1))\n if rand() > 0.5:\n tmpNonComb.append(np.array(nonComb1[0:c + 1].tolist() + nonComb2[c + 1:].tolist()))\n else:\n tmpNonComb.append(np.array(nonComb2[0:c + 1].tolist() + nonComb1[c + 1:].tolist()))\n used.append(i)\n if sum(self.xID) != 0:\n c = int(rand() * len(comb1))\n for c1 in range(c, len(comb1), 1):\n d2 = (contains_sublist(comb2, comb1[c1]) + 1) % len(comb1)\n d1 = contains_sublist(comb1, comb2[d2])\n c2 = contains_sublist(comb2, comb1[((d1 + 1) % len(comb1))]) % len(comb1)\n tmp1 = cp.copy(comb1)\n if c1 < d1:\n tmp1[(c1 + 1):(d1 + 1)] = list(reversed(tmp1[c1 + 1:d1 + 1]))\n else:\n tmp1[d1:c1] = list(reversed(tmp1[d1:c1]))\n tmp2 = cp.copy(comb2)\n if c2 < d2:\n tmp2[c2:d2] = list(reversed(tmp2[c2:d2]))\n else:\n tmp2[(d2 + 1):(c2 + 1)] = list(reversed(tmp2[d2 + 1:c2 + 1]))\n if sum(self.cID + self.dID + self.iID) == 0 and sum(self.xID) != 0:\n children.append(tmp1)\n children.append(tmp2)\n elif sum(self.cID + self.dID + self.iID) != 0 and sum(self.xID) != 0:\n children.append(np.concatenate(tmpNonComb[(-1)], tmp1))\n children.append(np.concatenate(tmpNonComb[(-1)], tmp2))\n used.append(i)\n used.append(r)\n\n if sum(self.cID + self.dID + self.iID) != 0 and sum(self.xID) == 0:\n children = tmpNonComb\n return (\n children, used)","def mutate(self, pop):\n intDiscID = self.iID + self.dID\n varID = self.cID\n children = []\n k = rand(len(pop), len(pop[0])) > self.fracMutation * rand()\n childn1 = cp.copy(permutation(pop))\n childn2 = cp.copy(permutation(pop))\n r = rand()\n for j in range(0, len(pop), 1):\n n = np.array(childn1[j] - childn2[j])\n stepSize = r * n * varID + (n * intDiscID).astype(int)\n tmp = (pop[j] + stepSize * k[j, :]) * varID + (pop[j] + stepSize * k[j, :]) * intDiscID % (self.ub + 1 - self.lb)\n children.append(simple_bounds(tmp, self.lb, self.ub))\n\n return children","def test_aggregation(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.aggregation_default = 'a'\n cfg.genome.aggregation_options = {'a': 1, 'b': 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.aggregation == gene1.aggregation:\n p1 = True\n elif gene3.aggregation == gene2.aggregation:\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.aggregation, gene1.aggregation)\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.aggregation, gene2.aggregation)","def test_get_child():\n \n root_ts = TrackSegment(flow_dict=flow_dict) \n \n # ROOT MODULE\n start = root_ts.get_child(\"start\")\n # check depth\n assert(start.depth == root_ts.depth+1)\n # check parent\n assert(start.parent is root_ts)\n # check module_id\n assert(start.module_id == 'start')\n \n # CHILD (1,2,3)\n root_ts_get_child_result = root_ts.get_child((1,2,3))\n # check depth\n assert(root_ts_get_child_result.depth == root_ts.depth+1)\n # check parent\n assert(root_ts_get_child_result.parent is root_ts)\n \n # CHILD (2,3,4)\n root_ts_get_child_result2 = root_ts_get_child_result.get_child((2,3,4))\n # check depth\n assert(root_ts_get_child_result2.depth == root_ts.depth+2)\n # check parent \n assert(root_ts_get_child_result2.parent is root_ts_get_child_result)\n \n print(\"TEST GET_CHILD: success!\")","def test_has_children_property(mock_amg):\n\n # split a cell so we can be sure it should have children\n mock_amg.cells[4].split()\n\n assert mock_amg.cells[4].has_children()\n assert not mock_amg.cells[1].has_children()\n assert not mock_amg.cells[4].children['bl'].has_children()","def test_aggregation(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.aggregation_default = 'a'\n cfg.genome.aggregation_options = {'a': 1, 'b': 2}\n gene1, gene2 = get_output_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.aggregation == gene1.aggregation:\n p1 = True\n elif gene3.aggregation == gene2.aggregation:\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.aggregation, gene1.aggregation)\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.aggregation, gene2.aggregation)","def run_tests(g: Graph) -> None:\n print( g.nodes() , \"->\" , ', '.join([f\"{l}\" for l in g.scc()]) , f\"({g.cyclic()})\" )\n for n in g.nodes():\n for m in [m for m in g.nodes() if m != n]:\n p = g.path(n,m)\n if p is not None:\n assert p[0] == n\n assert p[-1] == m\n for i in range(1,len(p)):\n assert g.is_edge(p[i-1], p[i])\n print(\" \", n, \"->\", m, \":\", ' -> '.join([f\"{v}\" for v in p]))","def bclone():\n node = nuke.selectedNodes()\n if len(node)==1:\n clone1 = nuke.createNode(\"NoOp\", inpanel = False)\n clone1.setName(\"Bclone\")\n clone1['label'].setValue(node[0].name()+\"\\nClone_Parent\")\n clone1['tile_color'].setValue(2521651711)\n clone1['note_font_color'].setValue(1583243007)\n clone1xpos = clone1['xpos'].getValue()\n clone1ypos = clone1['ypos'].getValue()\n \n clone2 = nuke.createNode(\"NoOp\", inpanel = False)\n clone2.setName(\"Bclone\")\n clone2['label'].setValue(node[0].name()+\"\\nClone\")\n clone2['hide_input'].setValue(True)\n clone2['tile_color'].setValue(2521651711)\n clone2['note_font_color'].setValue(1583243007)\n clone2['xpos'].setValue(clone1xpos)\n clone2['ypos'].setValue(clone1ypos)\n\n if len(node)==0:\n clone1 = nuke.createNode(\"NoOp\", inpanel = False)\n clone1.setName(\"Bclone\")\n clone1['label'].setValue(\"Clone_Parent\")\n clone1['tile_color'].setValue(2521651711)\n clone1['note_font_color'].setValue(1583243007)\n clone1xpos = clone1['xpos'].getValue()\n clone1ypos = clone1['ypos'].getValue()\n \n clone2 = nuke.createNode(\"NoOp\", inpanel = False)\n clone2.setName(\"Bclone\")\n clone2['label'].setValue(\"Clone\")\n clone2['hide_input'].setValue(True)\n clone2['tile_color'].setValue(2521651711)\n clone2['note_font_color'].setValue(1583243007)\n clone2['xpos'].setValue(clone1xpos)\n clone2['ypos'].setValue(clone1ypos)\n if len(node)!=0 and len(node)!=1:\n nuke.message('Just select one node to clone !')","def show_subgraph(dfs_codes, nsupport, mapper):\n\tglobal __subgraph_count\n\n\tg = build_graph(dfs_codes)\n\tg.id = __subgraph_count\n\t__subgraph_count += 1\n\tg.gprint(nsupport, mapper)","def parents_loop(self):\r\n while len(self.parents) > 0:\r\n children = 0\r\n self.parent1 = random.choice(self.parents)\r\n index = self.parents.index(self.parent1)\r\n del self.parents[index]\r\n\r\n self.parent2 = random.choice(self.parents)\r\n index = self.parents.index(self.parent2)\r\n del self.parents[index]\r\n\r\n while children < 2:\r\n self.child = copy.deepcopy(self.parent1)\r\n \r\n self.battery_loop()\r\n\r\n childsolution = random_algo.Random(self.child, self.cable_cost, self.battery_cost)\r\n childsolution.change_battery_or_house('change_battery')\r\n childsolution.change_battery_or_house('change_house')\r\n\r\n if (self.child.valid_solution() and self.child not in self.district_population\r\n and self.child not in self.best_districts and self.child not in self.worst_districts):\r\n self.district_population.append(self.child)\r\n self.cost_populations.append(self.child.total_cost(self.battery_cost, self.cable_cost))\r\n children += 1","def test_restore_multiple_in_subgraph(self):\n subgraph = self._subgraph()\n subgraph['id'] = 15\n task1 = self._remote_task()\n task1['id'] = 1\n task2 = self._remote_task()\n task2['id'] = 2\n task1['parameters']['containing_subgraph'] = 15\n task2['parameters']['containing_subgraph'] = 15\n\n graph = self._restore_graph([subgraph, task1, task2])\n assert len(graph.tasks) == 3\n subgraphs = [op for op in graph.tasks if op.is_subgraph]\n remote_tasks = [op for op in graph.tasks if not op.is_subgraph]\n\n # those are all references to the same subgraph, the subgraph was\n # NOT restored multiple times\n assert remote_tasks[0].containing_subgraph \\\n is remote_tasks[1].containing_subgraph \\\n is subgraphs[0]\n\n assert len(subgraphs[0].tasks) == 2","def slice_graph_fwd( startea, reg ): \r\n\tgraph = vcg_Graph.vcgGraph({\"title\":'\"Slice for %s\"' % reg, \\\r\n\t\t\"manhattan_edges\":\"no\", \"layoutalgorithm\":\"maxdepth\"})\r\n\t#\r\n\t# Retrieve the name of the current basic block\r\n\t# \r\n\tworklist = []\r\n\tdata_bib = {}\r\n\tstartnode = slice_node( startea, 0, reg )\r\n\trootnode = graph.Add_Node( startnode.to_name() )\r\n\tdata_bib[ startnode.to_name() ] = startnode\r\n\tworklist.insert( 0, rootnode )\r\n\twhile len( worklist ) > 0:\r\n\t\tcurrnode = worklist.pop()\r\n\t\tcurrslice = data_bib[ currnode.get_name() ]\r\n\t\ttgt_reg = currslice.get_target_reg()\r\n\t\tif tgt_reg == \"END\":\r\n\t\t# Do not process this node any further\r\n\t\t\tpass\r\n\t\telif tgt_reg == \"\" or (( len( currslice.get_lines()) > 0) and \\\r\n\t\t\tcurrslice.endea != currslice.get_lines()[-1][0]):\r\n\t\t\t# Nothing much happening here, just proceed to parent bocks\r\n\t\t\tif ua_mnem( currslice.endea ) == \"call\":\r\n\t\t\t\txrefs = get_short_crefs_from( currslice.endea )\r\n\t\t\telse:\r\n\t\t\t\txrefs = get_crefs_from( currslice.endea )\r\n\t\t\tfor ref in xrefs:\r\n\t\t\t\tnewslice = slice_node( ref, 0, currslice.reg )\r\n\t\t\t\tif graph.Get_Node( newslice.to_name() ) == 0:\r\n\t\t\t\t\tnewnode = graph.Add_Node( newslice.to_name() )\r\n\t\t\t\t\tworklist.insert( 0, newnode )\r\n\t\t\t\t\tdata_bib[ newslice.to_name() ] = newslice\r\n\t\t\t\tgraph.Add_Link( currnode.get_name(), newslice.to_name())\r\n\t\telse:\r\n\t\t\t# Register was modified, use new register\r\n\t\t\txrefs = get_crefs_from( currslice.endea )\r\n\t\t\tfor ref in xrefs:\r\n\t\t\t\tnewslice = slice_node( ref, 0, tgt_reg )\r\n\t\t\t\tif graph.Get_Node( newslice.to_name() ) == 0:\r\n\t\t\t\t\tnewnode = graph.Add_Node( newslice.to_name() )\r\n\t\t\t\t\tworklist.insert( 0, newnode )\r\n\t\t\t\t\tdata_bib[ newslice.to_name() ] = newslice\r\n\t\t\t\tgraph.Add_Link( currnode.get_name(), newslice.to_name())\r\n\t\t\txrefs = get_crefs_from( currslice.endea )\r\n\t\t\tfor ref in xrefs:\r\n\t\t\t\tnewslice = slice_node( ref, 0, currslice.reg )\r\n\t\t\t\tif graph.Get_Node( newslice.to_name() ) == 0:\r\n\t\t\t\t\tnewnode = graph.Add_Node( newslice.to_name() )\r\n\t\t\t\t\tworklist.insert( 0, newnode )\r\n\t\t\t\t\tdata_bib[ newslice.to_name() ] = newslice\r\n\t\t\t\tgraph.Add_Link( currnode.get_name(), newslice.to_name())\r\n\treturn [ graph, data_bib ]","def testParentage(self):\n self.assertEqual(\n self.cd,\n self.media_ref.parent\n )\n\n self.assertEqual(\n self.cd,\n self.cc.parent\n )","def test_generator7(self):\n xpb = XPathBuilder()\n xp1 = xp2 = None\n base_xp = xpb.foo.bar & xpb.x.y\n base_gen = None\n with base_xp as b:\n base_gen = b\n xp1 = b() | xpb.c\n xp2 = b() | xpb.d\n xp1_exp = '/foo/bar and /x/y or /c'\n xp2_exp = '/foo/bar and /x/y or /d'\n base_exp = '/foo/bar and /x/y'\n # check tree structure\n self.assertTrue(base_xp._parent is None)\n self.assertTrue(base_gen._parent is None)\n # check xpath\n self.assertEqual(xp1.tostring(), xp1_exp)\n self.assertEqual(xp2.tostring(), xp2_exp)\n self.assertEqual(base_xp.tostring(), base_exp)\n self.assertEqual(base_gen.tostring(), base_exp)","def populate_graph(self):","def test_dag_preserves_superrep(dimension, conversion):\n qobj = conversion(rand_super_bcsz(dimension))\n assert qobj.superrep == qobj.dag().superrep","def test__graph_structure():\n assert PES_GRAPH == (\n ('CH2CH2+OH', 'CH2CH+H2O', 'C2H4OH', 'C2H5O', 'CH3CHO+H'),\n (frozenset({0, 1}), frozenset({0, 2}), frozenset({2, 3}),\n frozenset({3, 4}), frozenset({1, 2})))\n assert pgraph.species(PES_GRAPH) == (\n ('CH2CH2+OH', 'CH2CH+H2O', 'C2H4OH', 'C2H5O', 'CH3CHO+H'))\n assert pgraph.channels(PES_GRAPH) == (\n (frozenset({0, 1}), frozenset({0, 2}), frozenset({2, 3}),\n frozenset({3, 4}), frozenset({1, 2})))\n print('\\npes graph')\n print(PES_GRAPH)","def test_fpy_parent():\n\n data = \"\"\"\n<depletion_chain>\n <nuclide name=\"U235\" reactions=\"1\">\n <reaction type=\"fission\" Q=\"193405400.0\"/>\n <neutron_fission_yields>\n <energies>0.0253</energies>\n <fission_yields energy=\"0.0253\">\n <products>Te134 Zr100 Xe138</products>\n <data>0.062155 0.0497641 0.0481413</data>\n </fission_yields>\n </neutron_fission_yields>\n </nuclide>\n <nuclide name=\"U238\" reactions=\"1\">\n <reaction type=\"fission\" Q=\"200.0e6\"/>\n <neutron_fission_yields parent=\"U235\"/>\n </nuclide>\n</depletion_chain>\n \"\"\"\n\n root = ET.fromstring(data)\n elems = root.findall('nuclide')\n u235 = nuclide.Nuclide.from_xml(elems[0], root)\n u238 = nuclide.Nuclide.from_xml(elems[1], root)\n\n # Make sure U238 yield is same as U235\n assert np.array_equal(u238.yield_data.energies, u235.yield_data.energies)\n assert np.array_equal(u238.yield_data.yield_matrix, u235.yield_data.yield_matrix)\n\n # Make sure XML element created has single attribute\n elem = u238.to_xml_element()\n fpy_elem = elem.find('neutron_fission_yields')\n assert fpy_elem.get('parent') == 'U235'\n assert len(fpy_elem) == 0\n\n data = \"\"\"\n<depletion_chain>\n <nuclide name=\"U235\" reactions=\"1\">\n <reaction type=\"fission\" Q=\"193405400.0\"/>\n </nuclide>\n <nuclide name=\"U238\" reactions=\"1\">\n <reaction type=\"fission\" Q=\"200.0e6\"/>\n <neutron_fission_yields parent=\"U235\"/>\n </nuclide>\n</depletion_chain>\n \"\"\"\n\n # U235 yields are missing, so we should get an exception\n root = ET.fromstring(data)\n elems = root.findall('nuclide')\n with pytest.raises(ValueError, match=\"yields\"):\n u238 = nuclide.Nuclide.from_xml(elems[1], root)","def test_skirmish_parenting(self):\n root = SkirmishAction()\n a1 = SkirmishAction()\n a2 = SkirmishAction()\n self.sess.add_all([root, a1, a2])\n self.sess.commit()\n\n root.children.append(a1)\n root.children.append(a2)\n self.sess.commit()\n\n self.assertEqual(a1.parent_id, root.id)\n self.assertEqual(a2.parent_id, root.id)","def test_generator6(self):\n xpb = XPathBuilder()\n xp1 = xp2 = None\n base_xp = xpb.base.foo.bar\n base_gen = None\n with base_xp as b:\n base_gen = b\n xp1 = xpb.a.b.c.join(b())\n xp2 = xpb.test.join(b())\n xp1_exp = '/a/b/c/base/foo/bar'\n xp2_exp = '/test/base/foo/bar'\n base_exp = '/base/foo/bar'\n # check tree structure\n self.assertTrue(base_xp._parent is None)\n self.assertTrue(len(base_xp._children[0]._children[0]._children) == 0)\n self.assertTrue(base_gen._parent is None)\n self.assertTrue(len(base_gen._children) == 0)\n # check xpath\n self.assertEqual(xp1.tostring(), xp1_exp)\n self.assertEqual(xp2.tostring(), xp2_exp)\n self.assertEqual(base_xp.tostring(), base_exp)\n self.assertEqual(base_gen.tostring(), base_exp)","def test_grandchildren():\n\n # note c.upto(\"status\").desired.grandchildren\n # this is the same as *c.upto(\"status\").desired in python3.5+\n res = conf.status.conditions.choose(lambda c: (c.type, c.reason, c.upto(\"status\").desired.grandchildren))\n assert \"type\" in res\n assert \"reason\" in res\n assert \"version\" in res\n assert \"image\" in res\n assert \"force\" in res","def subplot_1(self, Graph, n_tabs):\n # The code below walks does a pre-order traversal of the tree\n # For exact details about the structure of self.Graph refer description in init function.\n\n attr_name = list(Graph.keys())[0]\n print(\"\\t\"*(n_tabs),\"feature name :\",attr_name)\n for val in list(Graph[attr_name].keys()):\n print(\"\\t\"*(n_tabs+1),\"feature value :\",val)\n sub_graph = Graph[attr_name][val]\n if (type(sub_graph)==dict):\n self.subplot_1(sub_graph, n_tabs+2)\n else:\n print(\"\\t\"*(n_tabs+2),\"class :\", sub_graph)","def hasSiblings():","def test_get_related_nodes(self):\n pass","def test_createSubLinkographWithoutCommands(self):\n self.performTestForParams()","def crossover (self, p1, p2, p_pop, c1, c2, c_pop) :\n assert self.crossover_count < self.pop_size\n assert self.get_iteration () == self.last_gen\n self.parents.append (p1)\n self.parents.append (p2)\n self.crossover_count += 2\n if self.crossover_count == self.pop_size :\n assert (self.get_iteration () == self.last_gen)\n print (self.get_iteration ())\n sys.stdout.flush ()\n self.build_model (p_pop)\n self.sample_model (c1, c2, c_pop)\n self.crossover_count = 0\n self.parents = []\n self.children = {}\n self.last_gen += 1\n self.clear_cache ()","def test_Tree():","def reproduce(self, popDensity, activeDrugs):\n\n # Checks the resistance of the mother virus to all drugs in activeDrugs list\n # if mother virus is resistant to all the drugs, reproduction will proceed\n for drug in activeDrugs:\n if not self.isResistantTo(drug):\n raise NoChildException()\n\n maxReproduceProb = self.maxBirthProb * (1 - popDensity)\n\n if random.random() < maxReproduceProb:\n resistance_trait = {}\n # Calculate the transfer of resistances property to child virus\n for drug in self.resistances.keys():\n if random.random() <= (1 - self.mutProb):\n resistance_trait[drug] = self.resistances[drug]\n else:\n resistance_trait[drug] = not self.resistances[drug]\n\n childOfVirus = ResistantVirus(self.maxBirthProb, self.clearProb, resistance_trait, self.mutProb)\n return childOfVirus\n else:\n raise NoChildException()","def dump_subgraph_for_debug(self):\n\n import pypipegraph2 as ppg\n\n nodes = []\n seen = set()\n edges = []\n counter = [0]\n node_to_counters = {}\n\n def descend(node):\n if node in seen:\n return\n seen.add(node)\n j = self.runner.jobs[node]\n if isinstance(j, ppg.FileInvariant):\n nodes.append(f\"Path('{counter[0]}').write_text('A')\")\n nodes.append(f\"job_{counter[0]} = ppg.FileInvariant('{counter[0]}')\")\n elif isinstance(j, ppg.ParameterInvariant):\n nodes.append(\n f\"job_{counter[0]} = ppg.ParameterInvariant('{counter[0]}', 55)\"\n )\n elif isinstance(j, ppg.FunctionInvariant):\n nodes.append(\n f\"job_{counter[0]} = ppg.FunctionInvariant('{counter[0]}', lambda: 55)\"\n )\n elif isinstance(j, ppg.SharedMultiFileGeneratingJob):\n nodes.append(\n f\"job_{counter[0]} = ppg.SharedMultiFileGeneratingJob('{counter[0]}', {[x.name for x in j.files]!r}, dummy_smfg, depend_on_function=False)\"\n )\n elif isinstance(j, ppg.TempFileGeneratingJob):\n nodes.append(\n f\"job_{counter[0]} = ppg.TempFileGeneratingJob('{counter[0]}', dummy_fg, depend_on_function=False)\"\n )\n elif isinstance(j, ppg.FileGeneratingJob):\n nodes.append(\n f\"job_{counter[0]} = ppg.FileGeneratingJob('{counter[0]}', dummy_fg, depend_on_function=False)\"\n )\n elif isinstance(j, ppg.MultiTempFileGeneratingJob):\n files = [counter[0] + \"/\" + x.name for x in j.files]\n nodes.append(\n f\"job_{counter[0]} = ppg.MultiTempFileGeneratingJob({files!r}, dummy_mfg, depend_on_function=False)\"\n )\n elif isinstance(j, ppg.MultiFileGeneratingJob):\n files = [str(counter[0]) + \"/\" + x.name for x in j.files]\n nodes.append(\n f\"job_{counter[0]} = ppg.MultiFileGeneratingJob({files!r}, dummy_mfg, depend_on_function=False)\"\n )\n elif isinstance(j, ppg.DataLoadingJob):\n nodes.append(\n f\"job_{counter[0]} = ppg.DataLoadingJob('{counter[0]}', lambda: None, depend_on_function=False)\"\n )\n elif isinstance(j, ppg.AttributeLoadingJob):\n nodes.append(\n f\"job_{counter[0]} = ppg.AttributeLoadingJob('{counter[0]}', DummyObject(), 'attr_{counter[0]}', lambda: None, depend_on_function=False)\"\n )\n else:\n raise ValueError(j)\n node_to_counters[node] = counter[0]\n counter[0] += 1\n for parent in self.runner.dag.predecessors(node):\n descend(parent)\n\n def build_edges(node):\n for parent in self.runner.dag.predecessors(node):\n edges.append(\n f\"edges.append(('{node_to_counters[node]}', '{node_to_counters[parent]}'))\"\n )\n build_edges(parent)\n\n descend(self.job_id)\n edges.append(\"edges = []\")\n build_edges(self.job_id)\n edges.extend(\n [\n \"for (a,b) in edges:\",\n \" if a in ppg.global_pipegraph.jobs and b in ppg.global_pipegraph.jobs:\",\n \" ppg.global_pipegraph.jobs[a].depends_on(ppg.global_pipegraph.jobs[b])\",\n ]\n )\n with open(\"subgraph_debug.py\", \"w\") as op:\n lines = \"\"\"\nclass DummyObject:\n pass\n\ndef dummy_smfg(files, prefix):\n Path(prefix).mkdir(exist_ok=True, parents=True)\n for f in files:\n f.write_text(\"hello\")\n\n\ndef dummy_mfg(files):\n for f in files:\n f.parent.mkdir(exist_ok=True, parents=True)\n f.write_text(\"hello\")\n\ndef dummy_fg(of):\n of.parent.mkdir(exist_ok=True, parents=True)\n of.write_text(\"fg\")\n\n\"\"\".split(\n \"\\n\"\n )\n lines += nodes\n lines += edges\n lines += [\"\", \"ppg.run()\", \"ppg.run\"]\n\n op.write(\"\\n\".join(\" \" + l for l in lines))","def test_tree_mode4(self):\n xpb = XPathBuilder()\n xp_1 = xpb.foo\n xp_2 = xpb.baz\n xp_and = xp_1 & xp_2\n self.assertTrue(xp_and._parent is None)\n self.assertTrue(len(xp_and._children) == 2)\n self.assertTrue(xp_and._children[0] is xp_1)\n self.assertTrue(xp_and._children[1] is xp_2)\n self.assertTrue(xp_1._parent is xp_and)\n self.assertTrue(len(xp_1._children) == 0)\n self.assertTrue(xp_2._parent is xp_and)\n self.assertTrue(len(xp_2._children) == 0)\n xp_2.reparent(None)\n # check references after remove\n self.assertTrue(xp_and._parent is None)\n self.assertTrue(len(xp_and._children) == 1)\n self.assertTrue(xp_and._children[0] is xp_1)\n self.assertTrue(xp_1._parent is xp_and)\n self.assertTrue(len(xp_1._children) == 0)\n # xp_2's references were changed\n self.assertTrue(xp_2._parent is None)\n self.assertTrue(len(xp_2._children) == 0)","def sample_from_subpop(instance, params, subpop):\n y = subpop\n x = np.random.choice([-1,+1], size=params['d'])\n x[instance['indices'][subpop]] = instance['values'][subpop]\n return x, y, subpop","def __init__(self, firstParent, secondParent):\n CrossOver.__init__(self, \"Group Point CrossOver\", firstParent, secondParent)","def setUp(self):\n self.complete = nx.Graph()\n self.complete.add_edge(1, 2)\n self.complete.add_edge(2, 3)\n self.complete.add_edge(1, 3)\n\n self.small_tree = nx.Graph()\n self.small_tree.add_edge(1, 2)\n self.small_tree.add_edge(2, 3)\n self.small_tree.add_edge(3, 4)\n self.small_tree.add_edge(1, 4)\n self.small_tree.add_edge(2, 4)\n self.small_tree.add_edge(4, 5)\n self.small_tree.add_edge(5, 6)\n self.small_tree.add_edge(5, 7)\n self.small_tree.add_edge(6, 7)\n\n self.deterministic_graph = nx.Graph()\n self.deterministic_graph.add_edge(1, 2)\n self.deterministic_graph.add_edge(1, 3)\n self.deterministic_graph.add_edge(3, 4)\n self.deterministic_graph.add_edge(2, 4)\n self.deterministic_graph.add_edge(3, 5)\n self.deterministic_graph.add_edge(4, 5)\n self.deterministic_graph.add_edge(3, 6)\n self.deterministic_graph.add_edge(5, 6)","def test_1():\n for _ in range(100):\n G = NetworkTopo()\n flows = MulticastFlows(G, 10, 40)\n\n spt = ShortestPathTree(G, flows)\n\n for T in spt.multicast_trees:\n assert len(nx.cycle_basis(T)) == 0","def reproduce(self, popDensity):\r\n\r\n # Does the virus reproduce? \r\n maxReproduceProb = self.maxBirthProb * (1 - popDensity)\r\n \r\n if random.random() < maxReproduceProb:\r\n childOfVirus = SimpleVirus(self.maxBirthProb, self.clearProb)\r\n return childOfVirus\r\n \r\n else: raise NoChildException('Child not created!')","def create_subbasin_graph():\n subbasin_to_downstream = pd.read_csv(module_dir + '/../data/simulations_shervan/test.rvh', sep='\\s+', skiprows=7, nrows=724, names=['subbasin', 'downstream_subbasin'], usecols=[1,2])\n subbasin_to_downstream['subbasin'] = subbasin_to_downstream['subbasin']\n subbasin_to_downstream['downstream_subbasin'] = 'sub' + subbasin_to_downstream['downstream_subbasin'].astype(str)\n subbasin_to_downstream['edge'] = 1\n\n for subbasin in subbasin_to_downstream['subbasin'].unique():\n is_sink = 1 if len(subbasin_to_downstream[(subbasin_to_downstream['subbasin'] == subbasin) & subbasin_to_downstream['edge'] == 1]) == 0 else 0\n subbasin_to_downstream = subbasin_to_downstream.append({'subbasin': subbasin, 'downstream_subbasin': subbasin, 'edge': is_sink}, ignore_index=True)\n subbasin_to_downstream = subbasin_to_downstream.append({'subbasin': 'sub-1', 'downstream_subbasin': 'sub-1', 'edge': 1}, ignore_index=True)\n \n adj = subbasin_to_downstream.pivot(index='subbasin', columns='downstream_subbasin', values='edge').fillna(0) \n adj = adj.sort_index(axis=0).sort_index(axis=1)\n \n G = nx.from_numpy_matrix(adj.values, parallel_edges=False, create_using=nx.DiGraph())\n label_mapping = dict(zip(range(len(adj.values)), adj.index))\n G = nx.relabel_nodes(G, label_mapping)\n \n return G","def test_input_not_subgraph(self, dim):\r\n with pytest.raises(ValueError, match=\"Input is not a valid subgraph\"):\r\n clique.grow([dim + 1], nx.empty_graph(dim))","def crossover(parent: list):\n ### GENERAL METHOD\n child1 = []\n child2 = []\n child = []\n geneA = int(random.random() * len(parent[0]))\n geneB = int(random.random() * len(parent[0]))\n startgene = min(geneA, geneB)\n endgene = max(geneA, geneB)\n for i in range(startgene, endgene):\n child1.append(parent[0][i])\n child2 = [item for item in parent[1] if item not in child1]\n child = child2[:startgene] + child1 + child2[startgene:]\n return child","def test_build_graph(self):\n insert_good_data()\n dataframe = get_dataframe()\n results = processing.build_graph(dataframe, figure_path, False)\n # 1\n self.assertEqual(results, \"Updated html File and Opened it\")","def test_10_parents_as_minus_1_0_0_1_2_3_3_4_6_6(self):\n parents = [ -1, 0, 0, 1, 2, 3, 3, 4, 6, 6 ]\n self.init(parents)\n self.assertEqual(5, self.method_under_test())","def reproduce(self,prob=None):\n for parent in self.agents:\n if parent.fledged(prob):\n fledgling = Agent()\n self._fledgling_move(fledgling,parent.hex)\n self.agents.append(fledgling)\n self.pop_size += 1","def genNextPop(prevPop, masterList, popSize):\n parSize = int(popSize / 10) #top 10%\n parentPop = [Team(prevPop[i].roster) for i in range(parSize)]\n parentPop = getStats(parentPop, masterList) # inefficent\n rosterSize = len(parentPop[0].roster)\n newPop = []\n #parentsList = [] #debug\n for i in range(popSize):\n chromosome = doCrossover(parentPop, parSize, rosterSize)\n newPop.append(Team(chromosome))\n getStats(newPop, masterList)\n #showStats(newPop, masterList, \"all\")\n #debug code\n #for playerInd in chromosome:\n # print(masterList[playerInd].pos)\n\n return newPop","def getChildren():","def test_restore_with_subgraph(self):\n subgraph = self._subgraph()\n task = self._remote_task()\n subgraph['id'] = 15\n task['parameters']['containing_subgraph'] = 15\n\n graph = self._restore_graph([subgraph, task])\n assert len(graph.tasks) == 2\n subgraphs = [op for op in graph.tasks if op.is_subgraph]\n remote_tasks = [op for op in graph.tasks if not op.is_subgraph]\n\n assert len(subgraphs) == 1\n assert len(remote_tasks) == 1\n\n assert len(subgraphs[0].tasks) == 1\n assert remote_tasks[0].containing_subgraph is subgraphs[0]","def test_removes_empty_subgraph(self):\n ctx = MockWorkflowContext()\n g = TaskDependencyGraph(ctx)\n\n # sg1 is just empty, no tasks inside it\n sg1 = g.subgraph(ctx)\n # sg2 contains only a NOPTask\n sg2 = g.subgraph(ctx)\n sg2.add_task(tasks.NOPLocalWorkflowTask(ctx))\n\n # sg3 contains sg4, which is empty behcause it only contains a NOPTask\n sg3 = g.subgraph(ctx)\n sg4 = g.subgraph(ctx)\n sg4.add_task(tasks.NOPLocalWorkflowTask(ctx))\n sg3.add_task(sg4)\n\n # sg5 is a subgraph that contains a real task! it is not removed\n sg5 = g.subgraph(ctx)\n real_task = tasks.WorkflowTask(ctx)\n sg5.add_task(real_task)\n\n assert set(g.tasks) > {sg1, sg2, sg3, sg4, sg5, real_task}\n g.optimize()\n assert set(g.tasks) == {sg5, real_task}","def test_dot(self):\n graph = graphviz.Graph(comment='The Round Table')\n graph.node('A', 'King Arthur')\n graph.node('B', 'Sir Bedevere the Wise')\n graph.edges(['AB'])\n\n st.graphviz_chart(graph)\n\n c = self.get_delta_from_queue().new_element.graphviz_chart\n self.assertEqual(hasattr(c, 'spec'), True)","def test_pod_valid_parent(self):\n session = self.login_to_apic()\n parent = PhysicalModel()\n pod = Pod.get(session, parent)\n children = parent.get_children()\n self.assertEqual(pod, children)","def test_ExplorePath_Simple( self ):\n links = []\n n1 = graph.Node( 10, 50 )\n n2 = graph.Node( 10, 50 )\n n3 = graph.Node( 10, 50 )\n n7 = graph.Node( 10, 50 )\n\n links.append( graph.Link( n1, n2 ) )\n links.append( graph.Link( n2, n3 ) )\n links.append( graph.Link( n3, n7 ) )\n roots = [n1]\n actual = nodes.explorePath( links, roots, n1 )\n expected = [ n1, n2, n3, n7 ]\n self.assertEqual( expected, actual )","def test_dummy4(self):\n xpb = XPathBuilder()\n xp = xpb.dummy()\n self.assertTrue(xp.log_not() is xp)","def test_child_link(self):\n def compare_func(obj, node):\n child_nodes = self.get_children_of_node(node)\n\n for (child_obj, child_node) in zip(obj.children, child_nodes):\n # parent-child link (children must have obj as their parent)\n self.assertEqual(child_obj.parent, obj)\n\n self.recursively_compare_tree_against_html(compare_func)","def _subgraph_isomorphism_matcher(digraph, nxpattern, node_pred, edge_pred):\n graph_matcher = iso.DiGraphMatcher(digraph, nxpattern, node_match=node_pred, edge_match=edge_pred)\n yield from graph_matcher.subgraph_isomorphisms_iter()","def sub_graph_merging(self):\n raise NotImplementedError()","def three_opt(self, pop):\n children, used = ([] for i in range(2))\n for i in range(0, self.population, 1):\n tmp = []\n breaks = np.sort(rand(3) * len(pop[i]) // 1)\n while breaks[1] == breaks[0] or breaks[1] == breaks[2]:\n breaks[1] = rand() * len(pop[i]) // 1\n breaks = np.sort(breaks)\n\n while breaks[2] == breaks[0]:\n breaks[2] = rand() * len(pop[i]) // 1\n\n breaks = np.sort(breaks)\n tmp[0:(int(breaks[0]))] = pop[i][0:int(breaks[0])]\n tmp[(len(tmp)):(int(len(tmp) + breaks[2] - breaks[1]))] = pop[i][int(breaks[1]):int(breaks[2])]\n tmp[(len(tmp)):(int(len(tmp) + breaks[1] - breaks[0]))] = pop[i][int(breaks[0]):int(breaks[1])]\n tmp[(len(tmp)):(int(len(tmp) + breaks[2] - len(pop[i])))] = pop[i][int(breaks[2]):len(pop[i])]\n children.append(tmp)\n used.append(i)\n tmp = []\n tmp[0:(int(breaks[0]))] = pop[i][0:int(breaks[0])]\n tmp[(len(tmp)):(int(len(tmp) + breaks[1] - breaks[0]))] = list(reversed(pop[i][int(breaks[0]):int(breaks[1])]))\n tmp[(len(tmp)):(int(len(tmp) + breaks[2] - breaks[1]))] = reversed(pop[i][int(breaks[1]):int(breaks[2])])\n tmp[(len(tmp)):(int(len(tmp) + breaks[2] - len(pop[i])))] = pop[i][int(breaks[2]):len(pop[i])]\n children.append(tmp)\n used.append(i)\n\n return (\n children, used)","def testSetParent(self):\n for child in self.color_corrections + self.color_decisions:\n self.assertEqual(\n None,\n child.parent\n )\n\n self.node.append_children(\n self.color_corrections + self.color_decisions\n )\n\n for child in self.node.all_children:\n self.assertEqual(\n self.node,\n child.parent\n )\n child.parent = 'banana'\n self.assertEqual(\n 'banana',\n child.parent\n )\n\n self.node.set_parentage()\n\n for child in self.node.all_children:\n self.assertEqual(\n self.node,\n child.parent\n )","def test_get_dependencies_subgraph_by_dfs(\n self, source_node, expected_nodes_in, expected_nodes_out\n ):\n graph = nx.DiGraph()\n graph.add_node(\"pack1\")\n graph.add_node(\"pack2\")\n graph.add_node(\"pack3\")\n graph.add_node(\"pack4\")\n graph.add_edge(\"pack1\", \"pack2\")\n graph.add_edge(\"pack2\", \"pack3\")\n dfs_graph = PackDependencies.get_dependencies_subgraph_by_dfs(\n graph, source_node\n )\n for i in expected_nodes_in:\n assert i in dfs_graph.nodes()\n for i in expected_nodes_out:\n assert i not in dfs_graph.nodes()","def test_case2(self):\n\n graph = BipartiteGraph()\n\n graph.addEdge(\"supervisor1\",\"student1\")\n graph.addEdge(\"supervisor2\",\"student4\")\n graph.addEdge(\"supervisor3\",\"student3\")\n\n val1 = graph.getSupervisorDegree(\"supervisor1\")\n\n graph.addEdge(\"supervisor1\",\"student2\")\n\n curr = graph.getSupervisorDegree(\"supervisor1\")\n val2 = graph.getSupervisors(\"student2\")\n expected2 = [\"supervisor1\"]\n\n self.assertEqual((curr-1,expected2),(val1,val2))","def test_12_parents_as_minus_1_0_0_1_1_2_3_4_4_5_7_7(self):\n parents = [ -1, 0, 0, 1, 1, 2, 3, 4, 4, 5, 7, 7 ]\n self.init(parents)\n self.assertEqual(5, self.method_under_test())","def test_node_info_popup(self):\n def test_popup(node):\n node.details.click()\n with NodeInfo() as details:\n self.assertEqual(\n node.name.text, details.header.text,\n 'Node name')\n details.close.click()\n details.wait_until_exists()\n\n with Nodes()as n:\n test_popup(n.nodes_discovered[0])\n test_popup(n.nodes_offline[0])\n test_popup(n.nodes_error[0])","def test_input_valid_subgraph(self, dim):\r\n with pytest.raises(ValueError, match=\"Input is not a valid subgraph\"):\r\n clique.swap([0, dim], nx.empty_graph(dim))","def test_spec(self):\n graph = graphviz.Graph(comment='The Round Table')\n graph.node('A', 'King Arthur')\n graph.node('B', 'Sir Bedevere the Wise')\n graph.edges(['AB'])\n\n st.graphviz_chart(graph)\n\n c = self.get_delta_from_queue().new_element.graphviz_chart\n self.assertEqual(hasattr(c, 'spec'), True)","def test_change_parent_location(self):\n pass","def test_4_parents_as_1_minus_1_1_0(self):\n parents = [ 1, -1, 1, 0 ]\n self.init(parents)\n self.assertEqual(3, self.method_under_test())","def test_6_parents_as_minus_1_0_1_2_3_4(self):\n parents = [ -1, 0, 1, 2, 3, 4 ]\n self.init(parents)\n self.assertEqual(6, self.method_under_test())","def test_7_parents_as_minus_1_0_0_1_2_3_4(self):\n parents = [ -1, 0, 0, 1, 2, 3, 4 ]\n self.init(parents)\n self.assertEqual(4, self.method_under_test())","def test_generator5(self):\n xpb = XPathBuilder()\n xp = None\n base_xp = xpb.base.foo.bar\n base_gen = None\n with base_xp as b:\n base_gen = b\n xp = b().join(xpb.a.b.c[3])\n exp = '/base/foo/bar/a/b/c[3]'\n base_exp = '/base/foo/bar'\n # check tree structure\n self.assertTrue(base_xp._parent is None)\n self.assertTrue(len(base_xp._children[0]._children[0]._children) == 0)\n self.assertTrue(base_gen._parent is None)\n self.assertTrue(len(base_gen._children) == 0)\n # check xpath\n self.assertEqual(xp.tostring(), exp)\n self.assertEqual(base_xp.tostring(), base_exp)\n self.assertEqual(base_gen.tostring(), base_exp)","def playout(self, state):\n node = self.root\n isTerminal = False\n depth = 0\n\n while not isTerminal and depth < self.playout_depth:\n #A = len(node.children) # num_children\n A = len(node.children[0]) + len(node.children[1]) \n if A < self.num_initActions:\n #if len(node.children[0]) < self.num_initActions:\n node, init_action_xy, init_spin = self.initChildren(node, state, depth)\n _, ShotVec = CreateShot(_ShotPos(init_action_xy[0], init_action_xy[1], init_spin))\n success, ResShot = Simulation(state, ShotVec, Config.RAND, -1)\n isTerminal = (state.ShotNum==0)\n \n depth += 1\n break\n \n n_a = [c.n_visits for c in node.children[0].values()] + [c.n_visits for c in node.children[1].values()]\n # progressive widening\n # if chilren node has been visited much times then expand\n #if np.sqrt(sum(n_a)) >= A:\n if sum(n_a) >= 10 * A: \n # expand\n node, expanded_action_xy, expanded_spin = self.expand(node)\n _, ShotVec = CreateShot(_ShotPos(expanded_action_xy[0],expanded_action_xy[1], expanded_spin))\n success, ResShot = Simulation(state, ShotVec, Config.RAND, -1)\n isTerminal = (state.ShotNum==0) # one end game\n \n depth += 1\n break\n\n # select\n node, selected_action_xy, selected_spin = self.ucb_select(node)\n _, ShotVec = CreateShot(_ShotPos(selected_action_xy[0], selected_action_xy[1], selected_spin))\n success, ResShot = Simulation(state, ShotVec, Config.RAND, -1)\n isTerminal = (state.ShotNum==0) # one end game\n \n depth += 1\n\n if isTerminal:\n break\n\n if not isTerminal and depth < self.playout_depth:\n # save the rollout_state for speed.\n #if node.rollout_state is None:\n state = self.rollOut(node, state, depth)\n #node.rollout_state = state\n #else:\n # state = node.rollout_state\n \n self.update(node, state)","def test_same_node_is_reachable(self):\n # G is an arbitrary tournament on ten nodes.\n G = DiGraph(sorted(p) for p in combinations(range(10), 2))\n assert_true(all(is_reachable(G, v, v) for v in G))","def test_wp_association_bp(self):\n test_graph = wikipathways_to_bel(WP2359, self.hgnc_manager)\n\n self.assertEqual(type(test_graph), BELGraph, msg='Error with graph type')\n\n self.assertEqual(test_graph.summary_dict()['Number of Nodes'], 2)\n self.assertEqual(test_graph.summary_dict()['Number of Edges'], 1)\n self.assertEqual(count_relations(test_graph)['regulates'], 1)","def test_set_node_second_level_component_with_first_level_parent(self):\n payload = {\n 'data': [\n {\n 'type': 'nodes',\n 'id': self.first_level_component._id\n },\n {\n 'type': 'nodes',\n 'id': self.second_level_component._id\n }\n ]\n }\n res = self.app.post_json_api(self.url, payload, auth=self.user.auth)\n self.view_only_link.reload()\n assert_equal(res.status_code, 201)\n assert_in(self.first_level_component, self.view_only_link.nodes.all())\n assert_in(self.second_level_component, self.view_only_link.nodes.all())","def test_n_path_reactions(self):\n self.assertEqual(self.Npath, 3)","def get_subgraphs(graph):\n nodes_powerset = get_nodes_combinations(graph)\n #print(\"Doing\")\n #draw_graph(graph)\n subgraphs = []\n for nodes in nodes_powerset:\n subg = graph.subgraph(nodes)\n nodes = subg.nodes(data=True)\n if nx.is_weakly_connected(subg):\n subgraphs.append(subg)\n return subgraphs","def test_ExplorePath( self ):\n links = []\n n1 = graph.Node( 10, 10 )\n n2 = graph.Node( 10, 20 )\n n3 = graph.Node( 10, 30 )\n n4a = graph.Node( 5, 40 )\n n4b = graph.Node( 15, 40 )\n n5a = graph.Node( 5, 50 )\n n5b = graph.Node( 15, 50 )\n n6a = graph.Node( 5, 60 )\n n6b = graph.Node( 15, 60 )\n n7 = graph.Node( 10, 70 )\n\n links.append( graph.Link( n1, n2 ) )\n links.append( graph.Link( n2, n3 ) )\n links.append( graph.Link( n3, n4a ) )\n links.append( graph.Link( n3, n4b ) )\n links.append( graph.Link( n4a, n5a ) )\n links.append( graph.Link( n4b, n5b ) )\n links.append( graph.Link( n5a, n6a ) )\n links.append( graph.Link( n6a, n7 ) )\n links.append( graph.Link( n5b, n7 ) )\n roots = [n1]\n actual = nodes.explorePath( links, n1, n1 )\n expected = [ n1, n2, n3, n4b, n5b, n7 ]\n self.assertEqual( expected, actual )","def test_cycle(self):\n g = Graph(3)\n g.add_edge(0, 1)\n g.add_edge(0, 2)\n # g.add_edge(0, 0)\n assert g.contains_cycle() is False\n g.add_edge(1, 2)\n assert g.contains_cycle() is True","def graph(self):\n ...","def iter_func(root_name, root, set_traverse, list_funcs, G, strings,\n plot_nodes, cur_pos, xgrain, min_weight, max_weight):\n set_traverse.append(root)\n nbs = G.neighbors(root)\n nbs = G[root]\n\n plot_nodes.append(cur_pos)\n xgrain = xgrain/2.0\n\n flag_pn = -1\n for nb in nbs.keys():\n if nb in set_traverse:\n continue\n\n next_pos = [0, 0, 0]\n if root.name == root_name:\n next_pos[0] = cur_pos[0]\n else:\n next_pos[0] = cur_pos[0] + xgrain*flag_pn*( 0.8+0.2*(nbs[nb][\"weight\"]-1.0/max_weight)/(1.0/min_weight-1.0/max_weight) ) #* (nbs[nb][\"weight\"]-1.0/max_weight)/(1.0/min_weight-1.0/max_weight)\n next_pos[1] = cur_pos[1] + 3.0*(nbs[nb][\"weight\"]-1.0/max_weight)/(1.0/min_weight-1.0/max_weight)\n next_pos[2] = nb.name\n\n flag_pn = flag_pn*(-1)\n\n strings.append([root, nb])\n set_traverse, strings, plot_nodes = iter_func(root_name, nb, set_traverse, list_funcs, G, strings, plot_nodes, next_pos, xgrain, min_weight, max_weight)\n\n return set_traverse, strings, plot_nodes","def pick_androdioecious_parents(simu, config):\n rng = simu.getRNG()\n runif = rng.randUniform\n rint = rng.randInt\n try:\n sstar = config.sstar\n def compound_generator(pop):\n \"\"\"\n Picks up parent(s) under androdioecy using a compound parameter.\n \"\"\"\n gen = -1\n while True:\n ngen = pop.dvars().gen\n if gen != ngen:\n # At the beginning of a generation, extract the\n # sex-specific subpopulations from a parental\n # population. The sex-specific subpopulations are used\n # throughout mating events in one generation.\n gen = ngen\n males = pop.extractSubPops(subPops=[(0, 0)])\n herms = pop.extractSubPops(subPops=[(0, 1)])\n nmale = males.popSize()\n nherm = herms.popSize()\n\n if runif() < sstar: # uniparental\n yield herms.individual(rint(nherm))\n else: # biparental\n yield [males.individual(rint(nmale)), herms.individual(rint(nherm))]\n return compound_generator\n except KeyError:\n stilde = config.stilde\n tau = config.tau\n def compound_generator(pop):\n \"\"\"\n Picks up parent(s) under androdioecy using fundamental parameters.\n \"\"\"\n gen = -1\n while True:\n ngen = pop.dvars().gen\n if gen != ngen:\n # At the beginning of a generation, extract the\n # sex-specific subpopulations from a parental\n # population. The sex-specific subpopulations are used\n # throughout mating events in one generation.\n gen = ngen\n males = pop.extractSubPops(subPops=[(0, 0)])\n herms = pop.extractSubPops(subPops=[(0, 1)])\n nmale = males.popSize()\n nherm = herms.popSize()\n\n if runif() < stilde: # proportion of self-fertlized egg\n if runif() < tau: # survival rate of a uniparental zygote rel to a biparental z.\n yield herms.individual(rint(nherm))\n else: # biparental\n yield [males.individual(rint(nmale)), herms.individual(rint(nherm))]\n return compound_generator","def test_12_parents_as_minus_1_0_0_1_1_2_3_4_4_5_7_7_9_12(self):\n parents = [ -1, 0, 0, 1, 1, 2, 3, 4, 4, 5, 7, 7, 9, 12 ]\n self.init(parents)\n self.assertEqual(6, self.method_under_test())","def crossover(parent1, parent2):\n path1 = random_subtree(parent1, \"\")\n path2 = random_subtree(parent2, \"\")\n parent1 = parent1.copy()\n parent2 = parent2.copy()\n loc1 = parent1\n loc1parent = parent1\n loc2 = parent2\n loc2parent = parent2\n for i in range(len(path1)):\n loc1parent = loc1\n if path1[i] == \"1\":\n loc1 = loc1parent.get_right()\n else:\n loc1 = loc1parent.get_left()\n for i in range(len(path2)):\n loc2parent = loc2\n if path2[i] == \"1\":\n loc2 = loc2parent.get_right()\n else:\n loc2 = loc2parent.get_left()\n if(len(path1)-1 >= 0):\n if path1[len(path1)-1] == \"1\":\n loc1parent.right = loc2\n else:\n loc1parent.left = loc2\n if(len(path2) - 1 >= 0):\n if path2[-1] == \"1\":\n loc2parent.right = loc1\n else:\n loc2parent.left = loc1\n return (parent1, parent2)","def test_4_parents_as_minus_1_0_1_2(self):\n parents = [ -1, 0, 1, 2 ]\n self.init(parents)\n self.assertEqual(4, self.method_under_test())","def testBasic1(self):\n nodes = self.G.nodes()\n assert len(nodes) == len( set(nodes) )","def test_5_parents_as_1_minus_1_1_0_0(self):\n parents = [ 1, -1, 1, 0, 0 ]\n self.init(parents)\n self.assertEqual(3, self.method_under_test())","def test_3_parents_as_1_minus_1_1(self):\n parents = [ 1, -1, 1 ]\n self.init(parents)\n self.assertEqual(2, self.method_under_test())"],"string":"[\n \"def test_documentation_popxl_basic_subgraph(self):\\n filename = \\\"basic_graph.py\\\"\\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)\",\n \"def test_documentation_popxl_create_multi_subgraph(self):\\n filename = \\\"create_multi_graphs_from_same_func.py\\\"\\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)\",\n \"def test_dummy3(self):\\n xpb = XPathBuilder()\\n xp = xpb.dummy()\\n self.assertTrue(xp.parenthesize() is xp)\",\n \"def test_documentation_popxl_repeat_2(self):\\n filename = \\\"repeat_graph_2.py\\\"\\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)\",\n \"def test_documentation_popxl_repeat_0(self):\\n filename = \\\"repeat_graph_0.py\\\"\\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)\",\n \"def sub_graph_merging(self):\",\n \"def test_lacking_parent(self):\\n pass\",\n \"def crossover(self, pop):\\n intDiscID = self.iID + self.dID\\n varID = self.cID\\n goldenRatio = (1.0 + sqrt(5)) / 2.0\\n dx = np.zeros_like(pop[0])\\n children = []\\n used = []\\n for i in range(0, int(self.fracElite * len(pop)), 1):\\n r = int(rand() * self.population)\\n while r in used or r == i:\\n r = int(rand() * self.population)\\n\\n used.append(i)\\n children.append(cp.deepcopy(pop[r]))\\n dx = abs(pop[i] - children[i]) / goldenRatio\\n children[i] = children[i] + dx * varID + np.round(dx * intDiscID)\\n children[i] = simple_bounds(children[i], self.lb, self.ub)\\n\\n return (\\n children, used)\",\n \"def test_parent_label(self):\\n l = self.d.label(1)\\n l2 = self.d.label(31405)\\n\\n self.assertTrue(l.parent_label is None)\\n self.assertTrue(l2 in l.sublabels)\\n self.assertEqual(l2.parent_label, l)\",\n \"def test_parent_with_iterables(self):\\n def makeCubesAndGrp():\\n cmds.file(new=1, f=1)\\n cubes = []\\n for x in range(10):\\n cubes.append(pm.polyCube()[0])\\n group = pm.group(empty=True)\\n return cubes, group\\n\\n cubes, group = makeCubesAndGrp()\\n res1 = pm.parent(cubes[:4], group)\\n self.assertEqual(cubes[:4], res1)\\n res2 = pm.parent(cubes, group)\\n self.assertEqual(cubes[4:], res2)\\n\\n cubes, group = makeCubesAndGrp()\\n res1 = pm.parent(cubes[:4] + [group])\\n self.assertEqual(cubes[:4], res1)\\n res2 = pm.parent(cubes, group)\\n self.assertEqual(cubes[4:], res2)\\n\\n cubes, group = makeCubesAndGrp()\\n res1 = pm.parent(cubes[0], cubes[1], cubes[2], cubes[3], group)\\n self.assertEqual(cubes[:4], res1)\\n res2 = pm.parent(cubes, group)\\n self.assertEqual(cubes[4:], res2)\\n\\n cubes, group = makeCubesAndGrp()\\n res1 = pm.parent(cubes[0], cubes[1], [cubes[2], cubes[3], group])\\n self.assertEqual(cubes[:4], res1)\\n res2 = pm.parent(cubes, group)\\n self.assertEqual(cubes[4:], res2)\\n\\n cubes, group = makeCubesAndGrp()\\n res1 = pm.parent([cubes[0], cubes[1]], cubes[2], [cubes[3], group])\\n self.assertEqual(cubes[:4], res1)\\n res2 = pm.parent(cubes, group)\\n self.assertEqual(cubes[4:], res2)\",\n \"def testParent(self):\\n self.assertEqual(\\n self,\\n self.node.parent\\n )\",\n \"def testParent(self):\\n self.assertEqual(\\n self,\\n self.node.parent\\n )\",\n \"def testParent(self):\\n self.assertEqual(\\n self.parent,\\n self.mr.parent\\n )\",\n \"def test_simple(self):\\n exp = [{'type': NewickEvents.OPEN_SUBTREE, 'comments': []},\\n {'type': NewickEvents.OPEN_SUBTREE, 'comments': []},\\n {'edge_info': None, 'type': NewickEvents.TIP, 'comments': [], 'label': 'h'},\\n {'edge_info': None, 'type': NewickEvents.TIP, 'comments': [], 'label': 'p'},\\n {'edge_info': '1', 'type': NewickEvents.CLOSE_SUBTREE,\\n 'comments': [], 'label': 'hp'},\\n {'edge_info': None, 'type': NewickEvents.TIP, 'comments': [], 'label': 'g'},\\n {'edge_info': None, 'type': NewickEvents.CLOSE_SUBTREE,\\n 'comments': [], 'label': 'hpg'}\\n ]\\n content = '((h,p)hp:1,g)hpg;'\\n self._do_test(content, exp)\\n content = '((h,[pretest]p[test][posttest])hp,g)hpg;'\\n exp = [{'type': NewickEvents.OPEN_SUBTREE, 'comments': []},\\n {'type': NewickEvents.OPEN_SUBTREE, 'comments': []},\\n {'edge_info': None, 'type': NewickEvents.TIP, 'comments': [], 'label': 'h'},\\n {'edge_info': None, 'type': NewickEvents.TIP,\\n 'comments': ['pretest', 'test', 'posttest'], 'label': 'p'},\\n {'edge_info': None, 'type': NewickEvents.CLOSE_SUBTREE,\\n 'comments': [], 'label': 'hp'},\\n {'edge_info': None, 'type': NewickEvents.TIP, 'comments': [], 'label': 'g'},\\n {'edge_info': None, 'type': NewickEvents.CLOSE_SUBTREE,\\n 'comments': [], 'label': 'hpg'}\\n ]\\n self._do_test(content, exp)\",\n \"def reproduce(self, popDensity, activeDrugs):\\r\\n # TODO\\r\\n\\r\\n resistAll = True\\r\\n for drug in activeDrugs:\\r\\n if (self.resistances[drug]==False):resistAll = False\\r\\n \\r\\n\\r\\n\\r\\n maxReproduceProb = self.maxBirthProb * (1 - popDensity)\\r\\n \\r\\n \\r\\n if (resistAll and random.random() < maxReproduceProb):\\r\\n childResistances = {}\\r\\n for drug in self.resistances:\\r\\n if random.random() < self.mutProb:\\r\\n childResistances[drug] = (not self.resistances[drug])\\r\\n else:childResistances[drug] = (self.resistances[drug])\\r\\n\\r\\n \\r\\n childOfVirus = ResistantVirus(self.maxBirthProb, self.clearProb,childResistances,self.mutProb)\\r\\n return childOfVirus\\r\\n \\r\\n else: raise NoChildException('Child not created!')\",\n \"def test_generator8(self):\\n xpb = XPathBuilder()\\n xp1 = xp2 = None\\n base_xp = (xpb.foo.bar | xpb.x.y).parenthesize()\\n base_gen = None\\n with base_xp as b:\\n base_gen = b\\n xp1 = b() & xpb.c\\n xp2 = b() & xpb.d\\n xp1_exp = '(/foo/bar or /x/y) and /c'\\n xp2_exp = '(/foo/bar or /x/y) and /d'\\n base_exp = '(/foo/bar or /x/y)'\\n # check tree structure\\n self.assertTrue(base_xp._parent is None)\\n self.assertTrue(base_gen._parent is None)\\n # check xpath\\n self.assertEqual(xp1.tostring(), xp1_exp)\\n self.assertEqual(xp2.tostring(), xp2_exp)\\n self.assertEqual(base_xp.tostring(), base_exp)\\n self.assertEqual(base_gen.tostring(), base_exp)\",\n \"def _cross_parents(self):\\n while len(self.children_population) < self.children_count:\\n parent_1, parent_2 = random.sample(self.population, k=2)\\n self.children_population.extend(self.crossover.cross(parent_1, parent_2))\",\n \"def test_documentation_popxl_multi_callsites_graph_input(self):\\n filename = \\\"multi_call_graph_input.py\\\"\\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)\",\n \"def inversion_crossover(self, pop):\\n children, tmpNonComb, used = ([] for i in range(3))\\n for i in range(0, int(len(pop) * self.fracElite), 1):\\n r = int(rand() * len(pop))\\n while r == i:\\n r = int(rand() * len(pop))\\n\\n if sum(self.cID + self.dID + self.iID) != 0:\\n nonComb1 = pop[i][:np.where(self.cID + self.dID + self.iID == 1)[0][(-1)] + 1]\\n nonComb2 = pop[r][:np.where(self.cID + self.dID + self.iID == 1)[0][(-1)] + 1]\\n if sum(self.xID) != 0:\\n comb1 = pop[i][:np.where(self.xID == 1)[0][(-1)] + 1]\\n comb2 = pop[r][:np.where(self.xID == 1)[0][(-1)] + 1]\\n if sum(self.cID + self.dID + self.iID) != 0:\\n c = int(rand() * len(nonComb1))\\n if rand() > 0.5:\\n tmpNonComb.append(np.array(nonComb1[0:c + 1].tolist() + nonComb2[c + 1:].tolist()))\\n else:\\n tmpNonComb.append(np.array(nonComb2[0:c + 1].tolist() + nonComb1[c + 1:].tolist()))\\n used.append(i)\\n if sum(self.xID) != 0:\\n c = int(rand() * len(comb1))\\n for c1 in range(c, len(comb1), 1):\\n d2 = (contains_sublist(comb2, comb1[c1]) + 1) % len(comb1)\\n d1 = contains_sublist(comb1, comb2[d2])\\n c2 = contains_sublist(comb2, comb1[((d1 + 1) % len(comb1))]) % len(comb1)\\n tmp1 = cp.copy(comb1)\\n if c1 < d1:\\n tmp1[(c1 + 1):(d1 + 1)] = list(reversed(tmp1[c1 + 1:d1 + 1]))\\n else:\\n tmp1[d1:c1] = list(reversed(tmp1[d1:c1]))\\n tmp2 = cp.copy(comb2)\\n if c2 < d2:\\n tmp2[c2:d2] = list(reversed(tmp2[c2:d2]))\\n else:\\n tmp2[(d2 + 1):(c2 + 1)] = list(reversed(tmp2[d2 + 1:c2 + 1]))\\n if sum(self.cID + self.dID + self.iID) == 0 and sum(self.xID) != 0:\\n children.append(tmp1)\\n children.append(tmp2)\\n elif sum(self.cID + self.dID + self.iID) != 0 and sum(self.xID) != 0:\\n children.append(np.concatenate(tmpNonComb[(-1)], tmp1))\\n children.append(np.concatenate(tmpNonComb[(-1)], tmp2))\\n used.append(i)\\n used.append(r)\\n\\n if sum(self.cID + self.dID + self.iID) != 0 and sum(self.xID) == 0:\\n children = tmpNonComb\\n return (\\n children, used)\",\n \"def mutate(self, pop):\\n intDiscID = self.iID + self.dID\\n varID = self.cID\\n children = []\\n k = rand(len(pop), len(pop[0])) > self.fracMutation * rand()\\n childn1 = cp.copy(permutation(pop))\\n childn2 = cp.copy(permutation(pop))\\n r = rand()\\n for j in range(0, len(pop), 1):\\n n = np.array(childn1[j] - childn2[j])\\n stepSize = r * n * varID + (n * intDiscID).astype(int)\\n tmp = (pop[j] + stepSize * k[j, :]) * varID + (pop[j] + stepSize * k[j, :]) * intDiscID % (self.ub + 1 - self.lb)\\n children.append(simple_bounds(tmp, self.lb, self.ub))\\n\\n return children\",\n \"def test_aggregation(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.aggregation_default = 'a'\\n cfg.genome.aggregation_options = {'a': 1, 'b': 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.aggregation == gene1.aggregation:\\n p1 = True\\n elif gene3.aggregation == gene2.aggregation:\\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.aggregation, gene1.aggregation)\\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.aggregation, gene2.aggregation)\",\n \"def test_get_child():\\n \\n root_ts = TrackSegment(flow_dict=flow_dict) \\n \\n # ROOT MODULE\\n start = root_ts.get_child(\\\"start\\\")\\n # check depth\\n assert(start.depth == root_ts.depth+1)\\n # check parent\\n assert(start.parent is root_ts)\\n # check module_id\\n assert(start.module_id == 'start')\\n \\n # CHILD (1,2,3)\\n root_ts_get_child_result = root_ts.get_child((1,2,3))\\n # check depth\\n assert(root_ts_get_child_result.depth == root_ts.depth+1)\\n # check parent\\n assert(root_ts_get_child_result.parent is root_ts)\\n \\n # CHILD (2,3,4)\\n root_ts_get_child_result2 = root_ts_get_child_result.get_child((2,3,4))\\n # check depth\\n assert(root_ts_get_child_result2.depth == root_ts.depth+2)\\n # check parent \\n assert(root_ts_get_child_result2.parent is root_ts_get_child_result)\\n \\n print(\\\"TEST GET_CHILD: success!\\\")\",\n \"def test_has_children_property(mock_amg):\\n\\n # split a cell so we can be sure it should have children\\n mock_amg.cells[4].split()\\n\\n assert mock_amg.cells[4].has_children()\\n assert not mock_amg.cells[1].has_children()\\n assert not mock_amg.cells[4].children['bl'].has_children()\",\n \"def test_aggregation(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.aggregation_default = 'a'\\n cfg.genome.aggregation_options = {'a': 1, 'b': 2}\\n gene1, gene2 = get_output_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.aggregation == gene1.aggregation:\\n p1 = True\\n elif gene3.aggregation == gene2.aggregation:\\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.aggregation, gene1.aggregation)\\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.aggregation, gene2.aggregation)\",\n \"def run_tests(g: Graph) -> None:\\n print( g.nodes() , \\\"->\\\" , ', '.join([f\\\"{l}\\\" for l in g.scc()]) , f\\\"({g.cyclic()})\\\" )\\n for n in g.nodes():\\n for m in [m for m in g.nodes() if m != n]:\\n p = g.path(n,m)\\n if p is not None:\\n assert p[0] == n\\n assert p[-1] == m\\n for i in range(1,len(p)):\\n assert g.is_edge(p[i-1], p[i])\\n print(\\\" \\\", n, \\\"->\\\", m, \\\":\\\", ' -> '.join([f\\\"{v}\\\" for v in p]))\",\n \"def bclone():\\n node = nuke.selectedNodes()\\n if len(node)==1:\\n clone1 = nuke.createNode(\\\"NoOp\\\", inpanel = False)\\n clone1.setName(\\\"Bclone\\\")\\n clone1['label'].setValue(node[0].name()+\\\"\\\\nClone_Parent\\\")\\n clone1['tile_color'].setValue(2521651711)\\n clone1['note_font_color'].setValue(1583243007)\\n clone1xpos = clone1['xpos'].getValue()\\n clone1ypos = clone1['ypos'].getValue()\\n \\n clone2 = nuke.createNode(\\\"NoOp\\\", inpanel = False)\\n clone2.setName(\\\"Bclone\\\")\\n clone2['label'].setValue(node[0].name()+\\\"\\\\nClone\\\")\\n clone2['hide_input'].setValue(True)\\n clone2['tile_color'].setValue(2521651711)\\n clone2['note_font_color'].setValue(1583243007)\\n clone2['xpos'].setValue(clone1xpos)\\n clone2['ypos'].setValue(clone1ypos)\\n\\n if len(node)==0:\\n clone1 = nuke.createNode(\\\"NoOp\\\", inpanel = False)\\n clone1.setName(\\\"Bclone\\\")\\n clone1['label'].setValue(\\\"Clone_Parent\\\")\\n clone1['tile_color'].setValue(2521651711)\\n clone1['note_font_color'].setValue(1583243007)\\n clone1xpos = clone1['xpos'].getValue()\\n clone1ypos = clone1['ypos'].getValue()\\n \\n clone2 = nuke.createNode(\\\"NoOp\\\", inpanel = False)\\n clone2.setName(\\\"Bclone\\\")\\n clone2['label'].setValue(\\\"Clone\\\")\\n clone2['hide_input'].setValue(True)\\n clone2['tile_color'].setValue(2521651711)\\n clone2['note_font_color'].setValue(1583243007)\\n clone2['xpos'].setValue(clone1xpos)\\n clone2['ypos'].setValue(clone1ypos)\\n if len(node)!=0 and len(node)!=1:\\n nuke.message('Just select one node to clone !')\",\n \"def show_subgraph(dfs_codes, nsupport, mapper):\\n\\tglobal __subgraph_count\\n\\n\\tg = build_graph(dfs_codes)\\n\\tg.id = __subgraph_count\\n\\t__subgraph_count += 1\\n\\tg.gprint(nsupport, mapper)\",\n \"def parents_loop(self):\\r\\n while len(self.parents) > 0:\\r\\n children = 0\\r\\n self.parent1 = random.choice(self.parents)\\r\\n index = self.parents.index(self.parent1)\\r\\n del self.parents[index]\\r\\n\\r\\n self.parent2 = random.choice(self.parents)\\r\\n index = self.parents.index(self.parent2)\\r\\n del self.parents[index]\\r\\n\\r\\n while children < 2:\\r\\n self.child = copy.deepcopy(self.parent1)\\r\\n \\r\\n self.battery_loop()\\r\\n\\r\\n childsolution = random_algo.Random(self.child, self.cable_cost, self.battery_cost)\\r\\n childsolution.change_battery_or_house('change_battery')\\r\\n childsolution.change_battery_or_house('change_house')\\r\\n\\r\\n if (self.child.valid_solution() and self.child not in self.district_population\\r\\n and self.child not in self.best_districts and self.child not in self.worst_districts):\\r\\n self.district_population.append(self.child)\\r\\n self.cost_populations.append(self.child.total_cost(self.battery_cost, self.cable_cost))\\r\\n children += 1\",\n \"def test_restore_multiple_in_subgraph(self):\\n subgraph = self._subgraph()\\n subgraph['id'] = 15\\n task1 = self._remote_task()\\n task1['id'] = 1\\n task2 = self._remote_task()\\n task2['id'] = 2\\n task1['parameters']['containing_subgraph'] = 15\\n task2['parameters']['containing_subgraph'] = 15\\n\\n graph = self._restore_graph([subgraph, task1, task2])\\n assert len(graph.tasks) == 3\\n subgraphs = [op for op in graph.tasks if op.is_subgraph]\\n remote_tasks = [op for op in graph.tasks if not op.is_subgraph]\\n\\n # those are all references to the same subgraph, the subgraph was\\n # NOT restored multiple times\\n assert remote_tasks[0].containing_subgraph \\\\\\n is remote_tasks[1].containing_subgraph \\\\\\n is subgraphs[0]\\n\\n assert len(subgraphs[0].tasks) == 2\",\n \"def slice_graph_fwd( startea, reg ): \\r\\n\\tgraph = vcg_Graph.vcgGraph({\\\"title\\\":'\\\"Slice for %s\\\"' % reg, \\\\\\r\\n\\t\\t\\\"manhattan_edges\\\":\\\"no\\\", \\\"layoutalgorithm\\\":\\\"maxdepth\\\"})\\r\\n\\t#\\r\\n\\t# Retrieve the name of the current basic block\\r\\n\\t# \\r\\n\\tworklist = []\\r\\n\\tdata_bib = {}\\r\\n\\tstartnode = slice_node( startea, 0, reg )\\r\\n\\trootnode = graph.Add_Node( startnode.to_name() )\\r\\n\\tdata_bib[ startnode.to_name() ] = startnode\\r\\n\\tworklist.insert( 0, rootnode )\\r\\n\\twhile len( worklist ) > 0:\\r\\n\\t\\tcurrnode = worklist.pop()\\r\\n\\t\\tcurrslice = data_bib[ currnode.get_name() ]\\r\\n\\t\\ttgt_reg = currslice.get_target_reg()\\r\\n\\t\\tif tgt_reg == \\\"END\\\":\\r\\n\\t\\t# Do not process this node any further\\r\\n\\t\\t\\tpass\\r\\n\\t\\telif tgt_reg == \\\"\\\" or (( len( currslice.get_lines()) > 0) and \\\\\\r\\n\\t\\t\\tcurrslice.endea != currslice.get_lines()[-1][0]):\\r\\n\\t\\t\\t# Nothing much happening here, just proceed to parent bocks\\r\\n\\t\\t\\tif ua_mnem( currslice.endea ) == \\\"call\\\":\\r\\n\\t\\t\\t\\txrefs = get_short_crefs_from( currslice.endea )\\r\\n\\t\\t\\telse:\\r\\n\\t\\t\\t\\txrefs = get_crefs_from( currslice.endea )\\r\\n\\t\\t\\tfor ref in xrefs:\\r\\n\\t\\t\\t\\tnewslice = slice_node( ref, 0, currslice.reg )\\r\\n\\t\\t\\t\\tif graph.Get_Node( newslice.to_name() ) == 0:\\r\\n\\t\\t\\t\\t\\tnewnode = graph.Add_Node( newslice.to_name() )\\r\\n\\t\\t\\t\\t\\tworklist.insert( 0, newnode )\\r\\n\\t\\t\\t\\t\\tdata_bib[ newslice.to_name() ] = newslice\\r\\n\\t\\t\\t\\tgraph.Add_Link( currnode.get_name(), newslice.to_name())\\r\\n\\t\\telse:\\r\\n\\t\\t\\t# Register was modified, use new register\\r\\n\\t\\t\\txrefs = get_crefs_from( currslice.endea )\\r\\n\\t\\t\\tfor ref in xrefs:\\r\\n\\t\\t\\t\\tnewslice = slice_node( ref, 0, tgt_reg )\\r\\n\\t\\t\\t\\tif graph.Get_Node( newslice.to_name() ) == 0:\\r\\n\\t\\t\\t\\t\\tnewnode = graph.Add_Node( newslice.to_name() )\\r\\n\\t\\t\\t\\t\\tworklist.insert( 0, newnode )\\r\\n\\t\\t\\t\\t\\tdata_bib[ newslice.to_name() ] = newslice\\r\\n\\t\\t\\t\\tgraph.Add_Link( currnode.get_name(), newslice.to_name())\\r\\n\\t\\t\\txrefs = get_crefs_from( currslice.endea )\\r\\n\\t\\t\\tfor ref in xrefs:\\r\\n\\t\\t\\t\\tnewslice = slice_node( ref, 0, currslice.reg )\\r\\n\\t\\t\\t\\tif graph.Get_Node( newslice.to_name() ) == 0:\\r\\n\\t\\t\\t\\t\\tnewnode = graph.Add_Node( newslice.to_name() )\\r\\n\\t\\t\\t\\t\\tworklist.insert( 0, newnode )\\r\\n\\t\\t\\t\\t\\tdata_bib[ newslice.to_name() ] = newslice\\r\\n\\t\\t\\t\\tgraph.Add_Link( currnode.get_name(), newslice.to_name())\\r\\n\\treturn [ graph, data_bib ]\",\n \"def testParentage(self):\\n self.assertEqual(\\n self.cd,\\n self.media_ref.parent\\n )\\n\\n self.assertEqual(\\n self.cd,\\n self.cc.parent\\n )\",\n \"def test_generator7(self):\\n xpb = XPathBuilder()\\n xp1 = xp2 = None\\n base_xp = xpb.foo.bar & xpb.x.y\\n base_gen = None\\n with base_xp as b:\\n base_gen = b\\n xp1 = b() | xpb.c\\n xp2 = b() | xpb.d\\n xp1_exp = '/foo/bar and /x/y or /c'\\n xp2_exp = '/foo/bar and /x/y or /d'\\n base_exp = '/foo/bar and /x/y'\\n # check tree structure\\n self.assertTrue(base_xp._parent is None)\\n self.assertTrue(base_gen._parent is None)\\n # check xpath\\n self.assertEqual(xp1.tostring(), xp1_exp)\\n self.assertEqual(xp2.tostring(), xp2_exp)\\n self.assertEqual(base_xp.tostring(), base_exp)\\n self.assertEqual(base_gen.tostring(), base_exp)\",\n \"def populate_graph(self):\",\n \"def test_dag_preserves_superrep(dimension, conversion):\\n qobj = conversion(rand_super_bcsz(dimension))\\n assert qobj.superrep == qobj.dag().superrep\",\n \"def test__graph_structure():\\n assert PES_GRAPH == (\\n ('CH2CH2+OH', 'CH2CH+H2O', 'C2H4OH', 'C2H5O', 'CH3CHO+H'),\\n (frozenset({0, 1}), frozenset({0, 2}), frozenset({2, 3}),\\n frozenset({3, 4}), frozenset({1, 2})))\\n assert pgraph.species(PES_GRAPH) == (\\n ('CH2CH2+OH', 'CH2CH+H2O', 'C2H4OH', 'C2H5O', 'CH3CHO+H'))\\n assert pgraph.channels(PES_GRAPH) == (\\n (frozenset({0, 1}), frozenset({0, 2}), frozenset({2, 3}),\\n frozenset({3, 4}), frozenset({1, 2})))\\n print('\\\\npes graph')\\n print(PES_GRAPH)\",\n \"def test_fpy_parent():\\n\\n data = \\\"\\\"\\\"\\n<depletion_chain>\\n <nuclide name=\\\"U235\\\" reactions=\\\"1\\\">\\n <reaction type=\\\"fission\\\" Q=\\\"193405400.0\\\"/>\\n <neutron_fission_yields>\\n <energies>0.0253</energies>\\n <fission_yields energy=\\\"0.0253\\\">\\n <products>Te134 Zr100 Xe138</products>\\n <data>0.062155 0.0497641 0.0481413</data>\\n </fission_yields>\\n </neutron_fission_yields>\\n </nuclide>\\n <nuclide name=\\\"U238\\\" reactions=\\\"1\\\">\\n <reaction type=\\\"fission\\\" Q=\\\"200.0e6\\\"/>\\n <neutron_fission_yields parent=\\\"U235\\\"/>\\n </nuclide>\\n</depletion_chain>\\n \\\"\\\"\\\"\\n\\n root = ET.fromstring(data)\\n elems = root.findall('nuclide')\\n u235 = nuclide.Nuclide.from_xml(elems[0], root)\\n u238 = nuclide.Nuclide.from_xml(elems[1], root)\\n\\n # Make sure U238 yield is same as U235\\n assert np.array_equal(u238.yield_data.energies, u235.yield_data.energies)\\n assert np.array_equal(u238.yield_data.yield_matrix, u235.yield_data.yield_matrix)\\n\\n # Make sure XML element created has single attribute\\n elem = u238.to_xml_element()\\n fpy_elem = elem.find('neutron_fission_yields')\\n assert fpy_elem.get('parent') == 'U235'\\n assert len(fpy_elem) == 0\\n\\n data = \\\"\\\"\\\"\\n<depletion_chain>\\n <nuclide name=\\\"U235\\\" reactions=\\\"1\\\">\\n <reaction type=\\\"fission\\\" Q=\\\"193405400.0\\\"/>\\n </nuclide>\\n <nuclide name=\\\"U238\\\" reactions=\\\"1\\\">\\n <reaction type=\\\"fission\\\" Q=\\\"200.0e6\\\"/>\\n <neutron_fission_yields parent=\\\"U235\\\"/>\\n </nuclide>\\n</depletion_chain>\\n \\\"\\\"\\\"\\n\\n # U235 yields are missing, so we should get an exception\\n root = ET.fromstring(data)\\n elems = root.findall('nuclide')\\n with pytest.raises(ValueError, match=\\\"yields\\\"):\\n u238 = nuclide.Nuclide.from_xml(elems[1], root)\",\n \"def test_skirmish_parenting(self):\\n root = SkirmishAction()\\n a1 = SkirmishAction()\\n a2 = SkirmishAction()\\n self.sess.add_all([root, a1, a2])\\n self.sess.commit()\\n\\n root.children.append(a1)\\n root.children.append(a2)\\n self.sess.commit()\\n\\n self.assertEqual(a1.parent_id, root.id)\\n self.assertEqual(a2.parent_id, root.id)\",\n \"def test_generator6(self):\\n xpb = XPathBuilder()\\n xp1 = xp2 = None\\n base_xp = xpb.base.foo.bar\\n base_gen = None\\n with base_xp as b:\\n base_gen = b\\n xp1 = xpb.a.b.c.join(b())\\n xp2 = xpb.test.join(b())\\n xp1_exp = '/a/b/c/base/foo/bar'\\n xp2_exp = '/test/base/foo/bar'\\n base_exp = '/base/foo/bar'\\n # check tree structure\\n self.assertTrue(base_xp._parent is None)\\n self.assertTrue(len(base_xp._children[0]._children[0]._children) == 0)\\n self.assertTrue(base_gen._parent is None)\\n self.assertTrue(len(base_gen._children) == 0)\\n # check xpath\\n self.assertEqual(xp1.tostring(), xp1_exp)\\n self.assertEqual(xp2.tostring(), xp2_exp)\\n self.assertEqual(base_xp.tostring(), base_exp)\\n self.assertEqual(base_gen.tostring(), base_exp)\",\n \"def test_grandchildren():\\n\\n # note c.upto(\\\"status\\\").desired.grandchildren\\n # this is the same as *c.upto(\\\"status\\\").desired in python3.5+\\n res = conf.status.conditions.choose(lambda c: (c.type, c.reason, c.upto(\\\"status\\\").desired.grandchildren))\\n assert \\\"type\\\" in res\\n assert \\\"reason\\\" in res\\n assert \\\"version\\\" in res\\n assert \\\"image\\\" in res\\n assert \\\"force\\\" in res\",\n \"def subplot_1(self, Graph, n_tabs):\\n # The code below walks does a pre-order traversal of the tree\\n # For exact details about the structure of self.Graph refer description in init function.\\n\\n attr_name = list(Graph.keys())[0]\\n print(\\\"\\\\t\\\"*(n_tabs),\\\"feature name :\\\",attr_name)\\n for val in list(Graph[attr_name].keys()):\\n print(\\\"\\\\t\\\"*(n_tabs+1),\\\"feature value :\\\",val)\\n sub_graph = Graph[attr_name][val]\\n if (type(sub_graph)==dict):\\n self.subplot_1(sub_graph, n_tabs+2)\\n else:\\n print(\\\"\\\\t\\\"*(n_tabs+2),\\\"class :\\\", sub_graph)\",\n \"def hasSiblings():\",\n \"def test_get_related_nodes(self):\\n pass\",\n \"def test_createSubLinkographWithoutCommands(self):\\n self.performTestForParams()\",\n \"def crossover (self, p1, p2, p_pop, c1, c2, c_pop) :\\n assert self.crossover_count < self.pop_size\\n assert self.get_iteration () == self.last_gen\\n self.parents.append (p1)\\n self.parents.append (p2)\\n self.crossover_count += 2\\n if self.crossover_count == self.pop_size :\\n assert (self.get_iteration () == self.last_gen)\\n print (self.get_iteration ())\\n sys.stdout.flush ()\\n self.build_model (p_pop)\\n self.sample_model (c1, c2, c_pop)\\n self.crossover_count = 0\\n self.parents = []\\n self.children = {}\\n self.last_gen += 1\\n self.clear_cache ()\",\n \"def test_Tree():\",\n \"def reproduce(self, popDensity, activeDrugs):\\n\\n # Checks the resistance of the mother virus to all drugs in activeDrugs list\\n # if mother virus is resistant to all the drugs, reproduction will proceed\\n for drug in activeDrugs:\\n if not self.isResistantTo(drug):\\n raise NoChildException()\\n\\n maxReproduceProb = self.maxBirthProb * (1 - popDensity)\\n\\n if random.random() < maxReproduceProb:\\n resistance_trait = {}\\n # Calculate the transfer of resistances property to child virus\\n for drug in self.resistances.keys():\\n if random.random() <= (1 - self.mutProb):\\n resistance_trait[drug] = self.resistances[drug]\\n else:\\n resistance_trait[drug] = not self.resistances[drug]\\n\\n childOfVirus = ResistantVirus(self.maxBirthProb, self.clearProb, resistance_trait, self.mutProb)\\n return childOfVirus\\n else:\\n raise NoChildException()\",\n \"def dump_subgraph_for_debug(self):\\n\\n import pypipegraph2 as ppg\\n\\n nodes = []\\n seen = set()\\n edges = []\\n counter = [0]\\n node_to_counters = {}\\n\\n def descend(node):\\n if node in seen:\\n return\\n seen.add(node)\\n j = self.runner.jobs[node]\\n if isinstance(j, ppg.FileInvariant):\\n nodes.append(f\\\"Path('{counter[0]}').write_text('A')\\\")\\n nodes.append(f\\\"job_{counter[0]} = ppg.FileInvariant('{counter[0]}')\\\")\\n elif isinstance(j, ppg.ParameterInvariant):\\n nodes.append(\\n f\\\"job_{counter[0]} = ppg.ParameterInvariant('{counter[0]}', 55)\\\"\\n )\\n elif isinstance(j, ppg.FunctionInvariant):\\n nodes.append(\\n f\\\"job_{counter[0]} = ppg.FunctionInvariant('{counter[0]}', lambda: 55)\\\"\\n )\\n elif isinstance(j, ppg.SharedMultiFileGeneratingJob):\\n nodes.append(\\n f\\\"job_{counter[0]} = ppg.SharedMultiFileGeneratingJob('{counter[0]}', {[x.name for x in j.files]!r}, dummy_smfg, depend_on_function=False)\\\"\\n )\\n elif isinstance(j, ppg.TempFileGeneratingJob):\\n nodes.append(\\n f\\\"job_{counter[0]} = ppg.TempFileGeneratingJob('{counter[0]}', dummy_fg, depend_on_function=False)\\\"\\n )\\n elif isinstance(j, ppg.FileGeneratingJob):\\n nodes.append(\\n f\\\"job_{counter[0]} = ppg.FileGeneratingJob('{counter[0]}', dummy_fg, depend_on_function=False)\\\"\\n )\\n elif isinstance(j, ppg.MultiTempFileGeneratingJob):\\n files = [counter[0] + \\\"/\\\" + x.name for x in j.files]\\n nodes.append(\\n f\\\"job_{counter[0]} = ppg.MultiTempFileGeneratingJob({files!r}, dummy_mfg, depend_on_function=False)\\\"\\n )\\n elif isinstance(j, ppg.MultiFileGeneratingJob):\\n files = [str(counter[0]) + \\\"/\\\" + x.name for x in j.files]\\n nodes.append(\\n f\\\"job_{counter[0]} = ppg.MultiFileGeneratingJob({files!r}, dummy_mfg, depend_on_function=False)\\\"\\n )\\n elif isinstance(j, ppg.DataLoadingJob):\\n nodes.append(\\n f\\\"job_{counter[0]} = ppg.DataLoadingJob('{counter[0]}', lambda: None, depend_on_function=False)\\\"\\n )\\n elif isinstance(j, ppg.AttributeLoadingJob):\\n nodes.append(\\n f\\\"job_{counter[0]} = ppg.AttributeLoadingJob('{counter[0]}', DummyObject(), 'attr_{counter[0]}', lambda: None, depend_on_function=False)\\\"\\n )\\n else:\\n raise ValueError(j)\\n node_to_counters[node] = counter[0]\\n counter[0] += 1\\n for parent in self.runner.dag.predecessors(node):\\n descend(parent)\\n\\n def build_edges(node):\\n for parent in self.runner.dag.predecessors(node):\\n edges.append(\\n f\\\"edges.append(('{node_to_counters[node]}', '{node_to_counters[parent]}'))\\\"\\n )\\n build_edges(parent)\\n\\n descend(self.job_id)\\n edges.append(\\\"edges = []\\\")\\n build_edges(self.job_id)\\n edges.extend(\\n [\\n \\\"for (a,b) in edges:\\\",\\n \\\" if a in ppg.global_pipegraph.jobs and b in ppg.global_pipegraph.jobs:\\\",\\n \\\" ppg.global_pipegraph.jobs[a].depends_on(ppg.global_pipegraph.jobs[b])\\\",\\n ]\\n )\\n with open(\\\"subgraph_debug.py\\\", \\\"w\\\") as op:\\n lines = \\\"\\\"\\\"\\nclass DummyObject:\\n pass\\n\\ndef dummy_smfg(files, prefix):\\n Path(prefix).mkdir(exist_ok=True, parents=True)\\n for f in files:\\n f.write_text(\\\"hello\\\")\\n\\n\\ndef dummy_mfg(files):\\n for f in files:\\n f.parent.mkdir(exist_ok=True, parents=True)\\n f.write_text(\\\"hello\\\")\\n\\ndef dummy_fg(of):\\n of.parent.mkdir(exist_ok=True, parents=True)\\n of.write_text(\\\"fg\\\")\\n\\n\\\"\\\"\\\".split(\\n \\\"\\\\n\\\"\\n )\\n lines += nodes\\n lines += edges\\n lines += [\\\"\\\", \\\"ppg.run()\\\", \\\"ppg.run\\\"]\\n\\n op.write(\\\"\\\\n\\\".join(\\\" \\\" + l for l in lines))\",\n \"def test_tree_mode4(self):\\n xpb = XPathBuilder()\\n xp_1 = xpb.foo\\n xp_2 = xpb.baz\\n xp_and = xp_1 & xp_2\\n self.assertTrue(xp_and._parent is None)\\n self.assertTrue(len(xp_and._children) == 2)\\n self.assertTrue(xp_and._children[0] is xp_1)\\n self.assertTrue(xp_and._children[1] is xp_2)\\n self.assertTrue(xp_1._parent is xp_and)\\n self.assertTrue(len(xp_1._children) == 0)\\n self.assertTrue(xp_2._parent is xp_and)\\n self.assertTrue(len(xp_2._children) == 0)\\n xp_2.reparent(None)\\n # check references after remove\\n self.assertTrue(xp_and._parent is None)\\n self.assertTrue(len(xp_and._children) == 1)\\n self.assertTrue(xp_and._children[0] is xp_1)\\n self.assertTrue(xp_1._parent is xp_and)\\n self.assertTrue(len(xp_1._children) == 0)\\n # xp_2's references were changed\\n self.assertTrue(xp_2._parent is None)\\n self.assertTrue(len(xp_2._children) == 0)\",\n \"def sample_from_subpop(instance, params, subpop):\\n y = subpop\\n x = np.random.choice([-1,+1], size=params['d'])\\n x[instance['indices'][subpop]] = instance['values'][subpop]\\n return x, y, subpop\",\n \"def __init__(self, firstParent, secondParent):\\n CrossOver.__init__(self, \\\"Group Point CrossOver\\\", firstParent, secondParent)\",\n \"def setUp(self):\\n self.complete = nx.Graph()\\n self.complete.add_edge(1, 2)\\n self.complete.add_edge(2, 3)\\n self.complete.add_edge(1, 3)\\n\\n self.small_tree = nx.Graph()\\n self.small_tree.add_edge(1, 2)\\n self.small_tree.add_edge(2, 3)\\n self.small_tree.add_edge(3, 4)\\n self.small_tree.add_edge(1, 4)\\n self.small_tree.add_edge(2, 4)\\n self.small_tree.add_edge(4, 5)\\n self.small_tree.add_edge(5, 6)\\n self.small_tree.add_edge(5, 7)\\n self.small_tree.add_edge(6, 7)\\n\\n self.deterministic_graph = nx.Graph()\\n self.deterministic_graph.add_edge(1, 2)\\n self.deterministic_graph.add_edge(1, 3)\\n self.deterministic_graph.add_edge(3, 4)\\n self.deterministic_graph.add_edge(2, 4)\\n self.deterministic_graph.add_edge(3, 5)\\n self.deterministic_graph.add_edge(4, 5)\\n self.deterministic_graph.add_edge(3, 6)\\n self.deterministic_graph.add_edge(5, 6)\",\n \"def test_1():\\n for _ in range(100):\\n G = NetworkTopo()\\n flows = MulticastFlows(G, 10, 40)\\n\\n spt = ShortestPathTree(G, flows)\\n\\n for T in spt.multicast_trees:\\n assert len(nx.cycle_basis(T)) == 0\",\n \"def reproduce(self, popDensity):\\r\\n\\r\\n # Does the virus reproduce? \\r\\n maxReproduceProb = self.maxBirthProb * (1 - popDensity)\\r\\n \\r\\n if random.random() < maxReproduceProb:\\r\\n childOfVirus = SimpleVirus(self.maxBirthProb, self.clearProb)\\r\\n return childOfVirus\\r\\n \\r\\n else: raise NoChildException('Child not created!')\",\n \"def create_subbasin_graph():\\n subbasin_to_downstream = pd.read_csv(module_dir + '/../data/simulations_shervan/test.rvh', sep='\\\\s+', skiprows=7, nrows=724, names=['subbasin', 'downstream_subbasin'], usecols=[1,2])\\n subbasin_to_downstream['subbasin'] = subbasin_to_downstream['subbasin']\\n subbasin_to_downstream['downstream_subbasin'] = 'sub' + subbasin_to_downstream['downstream_subbasin'].astype(str)\\n subbasin_to_downstream['edge'] = 1\\n\\n for subbasin in subbasin_to_downstream['subbasin'].unique():\\n is_sink = 1 if len(subbasin_to_downstream[(subbasin_to_downstream['subbasin'] == subbasin) & subbasin_to_downstream['edge'] == 1]) == 0 else 0\\n subbasin_to_downstream = subbasin_to_downstream.append({'subbasin': subbasin, 'downstream_subbasin': subbasin, 'edge': is_sink}, ignore_index=True)\\n subbasin_to_downstream = subbasin_to_downstream.append({'subbasin': 'sub-1', 'downstream_subbasin': 'sub-1', 'edge': 1}, ignore_index=True)\\n \\n adj = subbasin_to_downstream.pivot(index='subbasin', columns='downstream_subbasin', values='edge').fillna(0) \\n adj = adj.sort_index(axis=0).sort_index(axis=1)\\n \\n G = nx.from_numpy_matrix(adj.values, parallel_edges=False, create_using=nx.DiGraph())\\n label_mapping = dict(zip(range(len(adj.values)), adj.index))\\n G = nx.relabel_nodes(G, label_mapping)\\n \\n return G\",\n \"def test_input_not_subgraph(self, dim):\\r\\n with pytest.raises(ValueError, match=\\\"Input is not a valid subgraph\\\"):\\r\\n clique.grow([dim + 1], nx.empty_graph(dim))\",\n \"def crossover(parent: list):\\n ### GENERAL METHOD\\n child1 = []\\n child2 = []\\n child = []\\n geneA = int(random.random() * len(parent[0]))\\n geneB = int(random.random() * len(parent[0]))\\n startgene = min(geneA, geneB)\\n endgene = max(geneA, geneB)\\n for i in range(startgene, endgene):\\n child1.append(parent[0][i])\\n child2 = [item for item in parent[1] if item not in child1]\\n child = child2[:startgene] + child1 + child2[startgene:]\\n return child\",\n \"def test_build_graph(self):\\n insert_good_data()\\n dataframe = get_dataframe()\\n results = processing.build_graph(dataframe, figure_path, False)\\n # 1\\n self.assertEqual(results, \\\"Updated html File and Opened it\\\")\",\n \"def test_10_parents_as_minus_1_0_0_1_2_3_3_4_6_6(self):\\n parents = [ -1, 0, 0, 1, 2, 3, 3, 4, 6, 6 ]\\n self.init(parents)\\n self.assertEqual(5, self.method_under_test())\",\n \"def reproduce(self,prob=None):\\n for parent in self.agents:\\n if parent.fledged(prob):\\n fledgling = Agent()\\n self._fledgling_move(fledgling,parent.hex)\\n self.agents.append(fledgling)\\n self.pop_size += 1\",\n \"def genNextPop(prevPop, masterList, popSize):\\n parSize = int(popSize / 10) #top 10%\\n parentPop = [Team(prevPop[i].roster) for i in range(parSize)]\\n parentPop = getStats(parentPop, masterList) # inefficent\\n rosterSize = len(parentPop[0].roster)\\n newPop = []\\n #parentsList = [] #debug\\n for i in range(popSize):\\n chromosome = doCrossover(parentPop, parSize, rosterSize)\\n newPop.append(Team(chromosome))\\n getStats(newPop, masterList)\\n #showStats(newPop, masterList, \\\"all\\\")\\n #debug code\\n #for playerInd in chromosome:\\n # print(masterList[playerInd].pos)\\n\\n return newPop\",\n \"def getChildren():\",\n \"def test_restore_with_subgraph(self):\\n subgraph = self._subgraph()\\n task = self._remote_task()\\n subgraph['id'] = 15\\n task['parameters']['containing_subgraph'] = 15\\n\\n graph = self._restore_graph([subgraph, task])\\n assert len(graph.tasks) == 2\\n subgraphs = [op for op in graph.tasks if op.is_subgraph]\\n remote_tasks = [op for op in graph.tasks if not op.is_subgraph]\\n\\n assert len(subgraphs) == 1\\n assert len(remote_tasks) == 1\\n\\n assert len(subgraphs[0].tasks) == 1\\n assert remote_tasks[0].containing_subgraph is subgraphs[0]\",\n \"def test_removes_empty_subgraph(self):\\n ctx = MockWorkflowContext()\\n g = TaskDependencyGraph(ctx)\\n\\n # sg1 is just empty, no tasks inside it\\n sg1 = g.subgraph(ctx)\\n # sg2 contains only a NOPTask\\n sg2 = g.subgraph(ctx)\\n sg2.add_task(tasks.NOPLocalWorkflowTask(ctx))\\n\\n # sg3 contains sg4, which is empty behcause it only contains a NOPTask\\n sg3 = g.subgraph(ctx)\\n sg4 = g.subgraph(ctx)\\n sg4.add_task(tasks.NOPLocalWorkflowTask(ctx))\\n sg3.add_task(sg4)\\n\\n # sg5 is a subgraph that contains a real task! it is not removed\\n sg5 = g.subgraph(ctx)\\n real_task = tasks.WorkflowTask(ctx)\\n sg5.add_task(real_task)\\n\\n assert set(g.tasks) > {sg1, sg2, sg3, sg4, sg5, real_task}\\n g.optimize()\\n assert set(g.tasks) == {sg5, real_task}\",\n \"def test_dot(self):\\n graph = graphviz.Graph(comment='The Round Table')\\n graph.node('A', 'King Arthur')\\n graph.node('B', 'Sir Bedevere the Wise')\\n graph.edges(['AB'])\\n\\n st.graphviz_chart(graph)\\n\\n c = self.get_delta_from_queue().new_element.graphviz_chart\\n self.assertEqual(hasattr(c, 'spec'), True)\",\n \"def test_pod_valid_parent(self):\\n session = self.login_to_apic()\\n parent = PhysicalModel()\\n pod = Pod.get(session, parent)\\n children = parent.get_children()\\n self.assertEqual(pod, children)\",\n \"def test_ExplorePath_Simple( self ):\\n links = []\\n n1 = graph.Node( 10, 50 )\\n n2 = graph.Node( 10, 50 )\\n n3 = graph.Node( 10, 50 )\\n n7 = graph.Node( 10, 50 )\\n\\n links.append( graph.Link( n1, n2 ) )\\n links.append( graph.Link( n2, n3 ) )\\n links.append( graph.Link( n3, n7 ) )\\n roots = [n1]\\n actual = nodes.explorePath( links, roots, n1 )\\n expected = [ n1, n2, n3, n7 ]\\n self.assertEqual( expected, actual )\",\n \"def test_dummy4(self):\\n xpb = XPathBuilder()\\n xp = xpb.dummy()\\n self.assertTrue(xp.log_not() is xp)\",\n \"def test_child_link(self):\\n def compare_func(obj, node):\\n child_nodes = self.get_children_of_node(node)\\n\\n for (child_obj, child_node) in zip(obj.children, child_nodes):\\n # parent-child link (children must have obj as their parent)\\n self.assertEqual(child_obj.parent, obj)\\n\\n self.recursively_compare_tree_against_html(compare_func)\",\n \"def _subgraph_isomorphism_matcher(digraph, nxpattern, node_pred, edge_pred):\\n graph_matcher = iso.DiGraphMatcher(digraph, nxpattern, node_match=node_pred, edge_match=edge_pred)\\n yield from graph_matcher.subgraph_isomorphisms_iter()\",\n \"def sub_graph_merging(self):\\n raise NotImplementedError()\",\n \"def three_opt(self, pop):\\n children, used = ([] for i in range(2))\\n for i in range(0, self.population, 1):\\n tmp = []\\n breaks = np.sort(rand(3) * len(pop[i]) // 1)\\n while breaks[1] == breaks[0] or breaks[1] == breaks[2]:\\n breaks[1] = rand() * len(pop[i]) // 1\\n breaks = np.sort(breaks)\\n\\n while breaks[2] == breaks[0]:\\n breaks[2] = rand() * len(pop[i]) // 1\\n\\n breaks = np.sort(breaks)\\n tmp[0:(int(breaks[0]))] = pop[i][0:int(breaks[0])]\\n tmp[(len(tmp)):(int(len(tmp) + breaks[2] - breaks[1]))] = pop[i][int(breaks[1]):int(breaks[2])]\\n tmp[(len(tmp)):(int(len(tmp) + breaks[1] - breaks[0]))] = pop[i][int(breaks[0]):int(breaks[1])]\\n tmp[(len(tmp)):(int(len(tmp) + breaks[2] - len(pop[i])))] = pop[i][int(breaks[2]):len(pop[i])]\\n children.append(tmp)\\n used.append(i)\\n tmp = []\\n tmp[0:(int(breaks[0]))] = pop[i][0:int(breaks[0])]\\n tmp[(len(tmp)):(int(len(tmp) + breaks[1] - breaks[0]))] = list(reversed(pop[i][int(breaks[0]):int(breaks[1])]))\\n tmp[(len(tmp)):(int(len(tmp) + breaks[2] - breaks[1]))] = reversed(pop[i][int(breaks[1]):int(breaks[2])])\\n tmp[(len(tmp)):(int(len(tmp) + breaks[2] - len(pop[i])))] = pop[i][int(breaks[2]):len(pop[i])]\\n children.append(tmp)\\n used.append(i)\\n\\n return (\\n children, used)\",\n \"def testSetParent(self):\\n for child in self.color_corrections + self.color_decisions:\\n self.assertEqual(\\n None,\\n child.parent\\n )\\n\\n self.node.append_children(\\n self.color_corrections + self.color_decisions\\n )\\n\\n for child in self.node.all_children:\\n self.assertEqual(\\n self.node,\\n child.parent\\n )\\n child.parent = 'banana'\\n self.assertEqual(\\n 'banana',\\n child.parent\\n )\\n\\n self.node.set_parentage()\\n\\n for child in self.node.all_children:\\n self.assertEqual(\\n self.node,\\n child.parent\\n )\",\n \"def test_get_dependencies_subgraph_by_dfs(\\n self, source_node, expected_nodes_in, expected_nodes_out\\n ):\\n graph = nx.DiGraph()\\n graph.add_node(\\\"pack1\\\")\\n graph.add_node(\\\"pack2\\\")\\n graph.add_node(\\\"pack3\\\")\\n graph.add_node(\\\"pack4\\\")\\n graph.add_edge(\\\"pack1\\\", \\\"pack2\\\")\\n graph.add_edge(\\\"pack2\\\", \\\"pack3\\\")\\n dfs_graph = PackDependencies.get_dependencies_subgraph_by_dfs(\\n graph, source_node\\n )\\n for i in expected_nodes_in:\\n assert i in dfs_graph.nodes()\\n for i in expected_nodes_out:\\n assert i not in dfs_graph.nodes()\",\n \"def test_case2(self):\\n\\n graph = BipartiteGraph()\\n\\n graph.addEdge(\\\"supervisor1\\\",\\\"student1\\\")\\n graph.addEdge(\\\"supervisor2\\\",\\\"student4\\\")\\n graph.addEdge(\\\"supervisor3\\\",\\\"student3\\\")\\n\\n val1 = graph.getSupervisorDegree(\\\"supervisor1\\\")\\n\\n graph.addEdge(\\\"supervisor1\\\",\\\"student2\\\")\\n\\n curr = graph.getSupervisorDegree(\\\"supervisor1\\\")\\n val2 = graph.getSupervisors(\\\"student2\\\")\\n expected2 = [\\\"supervisor1\\\"]\\n\\n self.assertEqual((curr-1,expected2),(val1,val2))\",\n \"def test_12_parents_as_minus_1_0_0_1_1_2_3_4_4_5_7_7(self):\\n parents = [ -1, 0, 0, 1, 1, 2, 3, 4, 4, 5, 7, 7 ]\\n self.init(parents)\\n self.assertEqual(5, self.method_under_test())\",\n \"def test_node_info_popup(self):\\n def test_popup(node):\\n node.details.click()\\n with NodeInfo() as details:\\n self.assertEqual(\\n node.name.text, details.header.text,\\n 'Node name')\\n details.close.click()\\n details.wait_until_exists()\\n\\n with Nodes()as n:\\n test_popup(n.nodes_discovered[0])\\n test_popup(n.nodes_offline[0])\\n test_popup(n.nodes_error[0])\",\n \"def test_input_valid_subgraph(self, dim):\\r\\n with pytest.raises(ValueError, match=\\\"Input is not a valid subgraph\\\"):\\r\\n clique.swap([0, dim], nx.empty_graph(dim))\",\n \"def test_spec(self):\\n graph = graphviz.Graph(comment='The Round Table')\\n graph.node('A', 'King Arthur')\\n graph.node('B', 'Sir Bedevere the Wise')\\n graph.edges(['AB'])\\n\\n st.graphviz_chart(graph)\\n\\n c = self.get_delta_from_queue().new_element.graphviz_chart\\n self.assertEqual(hasattr(c, 'spec'), True)\",\n \"def test_change_parent_location(self):\\n pass\",\n \"def test_4_parents_as_1_minus_1_1_0(self):\\n parents = [ 1, -1, 1, 0 ]\\n self.init(parents)\\n self.assertEqual(3, self.method_under_test())\",\n \"def test_6_parents_as_minus_1_0_1_2_3_4(self):\\n parents = [ -1, 0, 1, 2, 3, 4 ]\\n self.init(parents)\\n self.assertEqual(6, self.method_under_test())\",\n \"def test_7_parents_as_minus_1_0_0_1_2_3_4(self):\\n parents = [ -1, 0, 0, 1, 2, 3, 4 ]\\n self.init(parents)\\n self.assertEqual(4, self.method_under_test())\",\n \"def test_generator5(self):\\n xpb = XPathBuilder()\\n xp = None\\n base_xp = xpb.base.foo.bar\\n base_gen = None\\n with base_xp as b:\\n base_gen = b\\n xp = b().join(xpb.a.b.c[3])\\n exp = '/base/foo/bar/a/b/c[3]'\\n base_exp = '/base/foo/bar'\\n # check tree structure\\n self.assertTrue(base_xp._parent is None)\\n self.assertTrue(len(base_xp._children[0]._children[0]._children) == 0)\\n self.assertTrue(base_gen._parent is None)\\n self.assertTrue(len(base_gen._children) == 0)\\n # check xpath\\n self.assertEqual(xp.tostring(), exp)\\n self.assertEqual(base_xp.tostring(), base_exp)\\n self.assertEqual(base_gen.tostring(), base_exp)\",\n \"def playout(self, state):\\n node = self.root\\n isTerminal = False\\n depth = 0\\n\\n while not isTerminal and depth < self.playout_depth:\\n #A = len(node.children) # num_children\\n A = len(node.children[0]) + len(node.children[1]) \\n if A < self.num_initActions:\\n #if len(node.children[0]) < self.num_initActions:\\n node, init_action_xy, init_spin = self.initChildren(node, state, depth)\\n _, ShotVec = CreateShot(_ShotPos(init_action_xy[0], init_action_xy[1], init_spin))\\n success, ResShot = Simulation(state, ShotVec, Config.RAND, -1)\\n isTerminal = (state.ShotNum==0)\\n \\n depth += 1\\n break\\n \\n n_a = [c.n_visits for c in node.children[0].values()] + [c.n_visits for c in node.children[1].values()]\\n # progressive widening\\n # if chilren node has been visited much times then expand\\n #if np.sqrt(sum(n_a)) >= A:\\n if sum(n_a) >= 10 * A: \\n # expand\\n node, expanded_action_xy, expanded_spin = self.expand(node)\\n _, ShotVec = CreateShot(_ShotPos(expanded_action_xy[0],expanded_action_xy[1], expanded_spin))\\n success, ResShot = Simulation(state, ShotVec, Config.RAND, -1)\\n isTerminal = (state.ShotNum==0) # one end game\\n \\n depth += 1\\n break\\n\\n # select\\n node, selected_action_xy, selected_spin = self.ucb_select(node)\\n _, ShotVec = CreateShot(_ShotPos(selected_action_xy[0], selected_action_xy[1], selected_spin))\\n success, ResShot = Simulation(state, ShotVec, Config.RAND, -1)\\n isTerminal = (state.ShotNum==0) # one end game\\n \\n depth += 1\\n\\n if isTerminal:\\n break\\n\\n if not isTerminal and depth < self.playout_depth:\\n # save the rollout_state for speed.\\n #if node.rollout_state is None:\\n state = self.rollOut(node, state, depth)\\n #node.rollout_state = state\\n #else:\\n # state = node.rollout_state\\n \\n self.update(node, state)\",\n \"def test_same_node_is_reachable(self):\\n # G is an arbitrary tournament on ten nodes.\\n G = DiGraph(sorted(p) for p in combinations(range(10), 2))\\n assert_true(all(is_reachable(G, v, v) for v in G))\",\n \"def test_wp_association_bp(self):\\n test_graph = wikipathways_to_bel(WP2359, self.hgnc_manager)\\n\\n self.assertEqual(type(test_graph), BELGraph, msg='Error with graph type')\\n\\n self.assertEqual(test_graph.summary_dict()['Number of Nodes'], 2)\\n self.assertEqual(test_graph.summary_dict()['Number of Edges'], 1)\\n self.assertEqual(count_relations(test_graph)['regulates'], 1)\",\n \"def test_set_node_second_level_component_with_first_level_parent(self):\\n payload = {\\n 'data': [\\n {\\n 'type': 'nodes',\\n 'id': self.first_level_component._id\\n },\\n {\\n 'type': 'nodes',\\n 'id': self.second_level_component._id\\n }\\n ]\\n }\\n res = self.app.post_json_api(self.url, payload, auth=self.user.auth)\\n self.view_only_link.reload()\\n assert_equal(res.status_code, 201)\\n assert_in(self.first_level_component, self.view_only_link.nodes.all())\\n assert_in(self.second_level_component, self.view_only_link.nodes.all())\",\n \"def test_n_path_reactions(self):\\n self.assertEqual(self.Npath, 3)\",\n \"def get_subgraphs(graph):\\n nodes_powerset = get_nodes_combinations(graph)\\n #print(\\\"Doing\\\")\\n #draw_graph(graph)\\n subgraphs = []\\n for nodes in nodes_powerset:\\n subg = graph.subgraph(nodes)\\n nodes = subg.nodes(data=True)\\n if nx.is_weakly_connected(subg):\\n subgraphs.append(subg)\\n return subgraphs\",\n \"def test_ExplorePath( self ):\\n links = []\\n n1 = graph.Node( 10, 10 )\\n n2 = graph.Node( 10, 20 )\\n n3 = graph.Node( 10, 30 )\\n n4a = graph.Node( 5, 40 )\\n n4b = graph.Node( 15, 40 )\\n n5a = graph.Node( 5, 50 )\\n n5b = graph.Node( 15, 50 )\\n n6a = graph.Node( 5, 60 )\\n n6b = graph.Node( 15, 60 )\\n n7 = graph.Node( 10, 70 )\\n\\n links.append( graph.Link( n1, n2 ) )\\n links.append( graph.Link( n2, n3 ) )\\n links.append( graph.Link( n3, n4a ) )\\n links.append( graph.Link( n3, n4b ) )\\n links.append( graph.Link( n4a, n5a ) )\\n links.append( graph.Link( n4b, n5b ) )\\n links.append( graph.Link( n5a, n6a ) )\\n links.append( graph.Link( n6a, n7 ) )\\n links.append( graph.Link( n5b, n7 ) )\\n roots = [n1]\\n actual = nodes.explorePath( links, n1, n1 )\\n expected = [ n1, n2, n3, n4b, n5b, n7 ]\\n self.assertEqual( expected, actual )\",\n \"def test_cycle(self):\\n g = Graph(3)\\n g.add_edge(0, 1)\\n g.add_edge(0, 2)\\n # g.add_edge(0, 0)\\n assert g.contains_cycle() is False\\n g.add_edge(1, 2)\\n assert g.contains_cycle() is True\",\n \"def graph(self):\\n ...\",\n \"def iter_func(root_name, root, set_traverse, list_funcs, G, strings,\\n plot_nodes, cur_pos, xgrain, min_weight, max_weight):\\n set_traverse.append(root)\\n nbs = G.neighbors(root)\\n nbs = G[root]\\n\\n plot_nodes.append(cur_pos)\\n xgrain = xgrain/2.0\\n\\n flag_pn = -1\\n for nb in nbs.keys():\\n if nb in set_traverse:\\n continue\\n\\n next_pos = [0, 0, 0]\\n if root.name == root_name:\\n next_pos[0] = cur_pos[0]\\n else:\\n next_pos[0] = cur_pos[0] + xgrain*flag_pn*( 0.8+0.2*(nbs[nb][\\\"weight\\\"]-1.0/max_weight)/(1.0/min_weight-1.0/max_weight) ) #* (nbs[nb][\\\"weight\\\"]-1.0/max_weight)/(1.0/min_weight-1.0/max_weight)\\n next_pos[1] = cur_pos[1] + 3.0*(nbs[nb][\\\"weight\\\"]-1.0/max_weight)/(1.0/min_weight-1.0/max_weight)\\n next_pos[2] = nb.name\\n\\n flag_pn = flag_pn*(-1)\\n\\n strings.append([root, nb])\\n set_traverse, strings, plot_nodes = iter_func(root_name, nb, set_traverse, list_funcs, G, strings, plot_nodes, next_pos, xgrain, min_weight, max_weight)\\n\\n return set_traverse, strings, plot_nodes\",\n \"def pick_androdioecious_parents(simu, config):\\n rng = simu.getRNG()\\n runif = rng.randUniform\\n rint = rng.randInt\\n try:\\n sstar = config.sstar\\n def compound_generator(pop):\\n \\\"\\\"\\\"\\n Picks up parent(s) under androdioecy using a compound parameter.\\n \\\"\\\"\\\"\\n gen = -1\\n while True:\\n ngen = pop.dvars().gen\\n if gen != ngen:\\n # At the beginning of a generation, extract the\\n # sex-specific subpopulations from a parental\\n # population. The sex-specific subpopulations are used\\n # throughout mating events in one generation.\\n gen = ngen\\n males = pop.extractSubPops(subPops=[(0, 0)])\\n herms = pop.extractSubPops(subPops=[(0, 1)])\\n nmale = males.popSize()\\n nherm = herms.popSize()\\n\\n if runif() < sstar: # uniparental\\n yield herms.individual(rint(nherm))\\n else: # biparental\\n yield [males.individual(rint(nmale)), herms.individual(rint(nherm))]\\n return compound_generator\\n except KeyError:\\n stilde = config.stilde\\n tau = config.tau\\n def compound_generator(pop):\\n \\\"\\\"\\\"\\n Picks up parent(s) under androdioecy using fundamental parameters.\\n \\\"\\\"\\\"\\n gen = -1\\n while True:\\n ngen = pop.dvars().gen\\n if gen != ngen:\\n # At the beginning of a generation, extract the\\n # sex-specific subpopulations from a parental\\n # population. The sex-specific subpopulations are used\\n # throughout mating events in one generation.\\n gen = ngen\\n males = pop.extractSubPops(subPops=[(0, 0)])\\n herms = pop.extractSubPops(subPops=[(0, 1)])\\n nmale = males.popSize()\\n nherm = herms.popSize()\\n\\n if runif() < stilde: # proportion of self-fertlized egg\\n if runif() < tau: # survival rate of a uniparental zygote rel to a biparental z.\\n yield herms.individual(rint(nherm))\\n else: # biparental\\n yield [males.individual(rint(nmale)), herms.individual(rint(nherm))]\\n return compound_generator\",\n \"def test_12_parents_as_minus_1_0_0_1_1_2_3_4_4_5_7_7_9_12(self):\\n parents = [ -1, 0, 0, 1, 1, 2, 3, 4, 4, 5, 7, 7, 9, 12 ]\\n self.init(parents)\\n self.assertEqual(6, self.method_under_test())\",\n \"def crossover(parent1, parent2):\\n path1 = random_subtree(parent1, \\\"\\\")\\n path2 = random_subtree(parent2, \\\"\\\")\\n parent1 = parent1.copy()\\n parent2 = parent2.copy()\\n loc1 = parent1\\n loc1parent = parent1\\n loc2 = parent2\\n loc2parent = parent2\\n for i in range(len(path1)):\\n loc1parent = loc1\\n if path1[i] == \\\"1\\\":\\n loc1 = loc1parent.get_right()\\n else:\\n loc1 = loc1parent.get_left()\\n for i in range(len(path2)):\\n loc2parent = loc2\\n if path2[i] == \\\"1\\\":\\n loc2 = loc2parent.get_right()\\n else:\\n loc2 = loc2parent.get_left()\\n if(len(path1)-1 >= 0):\\n if path1[len(path1)-1] == \\\"1\\\":\\n loc1parent.right = loc2\\n else:\\n loc1parent.left = loc2\\n if(len(path2) - 1 >= 0):\\n if path2[-1] == \\\"1\\\":\\n loc2parent.right = loc1\\n else:\\n loc2parent.left = loc1\\n return (parent1, parent2)\",\n \"def test_4_parents_as_minus_1_0_1_2(self):\\n parents = [ -1, 0, 1, 2 ]\\n self.init(parents)\\n self.assertEqual(4, self.method_under_test())\",\n \"def testBasic1(self):\\n nodes = self.G.nodes()\\n assert len(nodes) == len( set(nodes) )\",\n \"def test_5_parents_as_1_minus_1_1_0_0(self):\\n parents = [ 1, -1, 1, 0, 0 ]\\n self.init(parents)\\n self.assertEqual(3, self.method_under_test())\",\n \"def test_3_parents_as_1_minus_1_1(self):\\n parents = [ 1, -1, 1 ]\\n self.init(parents)\\n self.assertEqual(2, self.method_under_test())\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7205235","0.70020497","0.62084246","0.6038614","0.5988837","0.58964515","0.58667445","0.58020157","0.5726468","0.5667711","0.5539633","0.5539633","0.5504206","0.54905653","0.5482558","0.5460695","0.54255486","0.5419617","0.5398533","0.5355489","0.53264356","0.53245705","0.5312008","0.5307634","0.5303363","0.53014946","0.5294228","0.523673","0.5232471","0.5228123","0.52248925","0.52232295","0.5218298","0.52174497","0.52111673","0.52089536","0.51829195","0.51745975","0.51708","0.51655567","0.5164763","0.51581097","0.51574516","0.51436824","0.5132396","0.5131299","0.51306385","0.51233494","0.5118409","0.5116303","0.5108168","0.510709","0.5106759","0.510241","0.50905424","0.5089184","0.50854236","0.507906","0.5076508","0.50747967","0.50699407","0.5069547","0.50572777","0.5057174","0.5051708","0.50352585","0.50333","0.50322187","0.50287956","0.5022825","0.5014624","0.50122243","0.50086933","0.5006136","0.5001657","0.50005764","0.49920267","0.49919513","0.49910122","0.49844953","0.49769056","0.49724922","0.4971587","0.49644488","0.49549103","0.4952674","0.49509016","0.4941937","0.49410596","0.49405313","0.49402314","0.49369624","0.4928777","0.49261698","0.49260587","0.49215972","0.4920297","0.4917804","0.49171856","0.49147215"],"string":"[\n \"0.7205235\",\n \"0.70020497\",\n \"0.62084246\",\n \"0.6038614\",\n \"0.5988837\",\n \"0.58964515\",\n \"0.58667445\",\n \"0.58020157\",\n \"0.5726468\",\n \"0.5667711\",\n \"0.5539633\",\n \"0.5539633\",\n \"0.5504206\",\n \"0.54905653\",\n \"0.5482558\",\n \"0.5460695\",\n \"0.54255486\",\n \"0.5419617\",\n \"0.5398533\",\n \"0.5355489\",\n \"0.53264356\",\n \"0.53245705\",\n \"0.5312008\",\n \"0.5307634\",\n \"0.5303363\",\n \"0.53014946\",\n \"0.5294228\",\n \"0.523673\",\n \"0.5232471\",\n \"0.5228123\",\n \"0.52248925\",\n \"0.52232295\",\n \"0.5218298\",\n \"0.52174497\",\n \"0.52111673\",\n \"0.52089536\",\n \"0.51829195\",\n \"0.51745975\",\n \"0.51708\",\n \"0.51655567\",\n \"0.5164763\",\n \"0.51581097\",\n \"0.51574516\",\n \"0.51436824\",\n \"0.5132396\",\n \"0.5131299\",\n \"0.51306385\",\n \"0.51233494\",\n \"0.5118409\",\n \"0.5116303\",\n \"0.5108168\",\n \"0.510709\",\n \"0.5106759\",\n \"0.510241\",\n \"0.50905424\",\n \"0.5089184\",\n \"0.50854236\",\n \"0.507906\",\n \"0.5076508\",\n \"0.50747967\",\n \"0.50699407\",\n \"0.5069547\",\n \"0.50572777\",\n \"0.5057174\",\n \"0.5051708\",\n \"0.50352585\",\n \"0.50333\",\n \"0.50322187\",\n \"0.50287956\",\n \"0.5022825\",\n \"0.5014624\",\n \"0.50122243\",\n \"0.50086933\",\n \"0.5006136\",\n \"0.5001657\",\n \"0.50005764\",\n \"0.49920267\",\n \"0.49919513\",\n \"0.49910122\",\n \"0.49844953\",\n \"0.49769056\",\n \"0.49724922\",\n \"0.4971587\",\n \"0.49644488\",\n \"0.49549103\",\n \"0.4952674\",\n \"0.49509016\",\n \"0.4941937\",\n \"0.49410596\",\n \"0.49405313\",\n \"0.49402314\",\n \"0.49369624\",\n \"0.4928777\",\n \"0.49261698\",\n \"0.49260587\",\n \"0.49215972\",\n \"0.4920297\",\n \"0.4917804\",\n \"0.49171856\",\n \"0.49147215\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6087695"},"document_rank":{"kind":"string","value":"3"}}},{"rowIdx":6591,"cells":{"query":{"kind":"string","value":"Test the popxl subgraph in parent in repeat example"},"document":{"kind":"string","value":"def test_documentation_popxl_repeat_2(self):\n filename = \"repeat_graph_2.py\"\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)"},"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 test_documentation_popxl_basic_subgraph(self):\n filename = \"basic_graph.py\"\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)","def test_documentation_popxl_create_multi_subgraph(self):\n filename = \"create_multi_graphs_from_same_func.py\"\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)","def test_dummy3(self):\n xpb = XPathBuilder()\n xp = xpb.dummy()\n self.assertTrue(xp.parenthesize() is xp)","def test_documentation_popxl_repeat_1(self):\n filename = \"repeat_graph_1.py\"\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)","def test_documentation_popxl_repeat_0(self):\n filename = \"repeat_graph_0.py\"\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)","def sub_graph_merging(self):","def test_lacking_parent(self):\n pass","def crossover(self, pop):\n intDiscID = self.iID + self.dID\n varID = self.cID\n goldenRatio = (1.0 + sqrt(5)) / 2.0\n dx = np.zeros_like(pop[0])\n children = []\n used = []\n for i in range(0, int(self.fracElite * len(pop)), 1):\n r = int(rand() * self.population)\n while r in used or r == i:\n r = int(rand() * self.population)\n\n used.append(i)\n children.append(cp.deepcopy(pop[r]))\n dx = abs(pop[i] - children[i]) / goldenRatio\n children[i] = children[i] + dx * varID + np.round(dx * intDiscID)\n children[i] = simple_bounds(children[i], self.lb, self.ub)\n\n return (\n children, used)","def test_parent_label(self):\n l = self.d.label(1)\n l2 = self.d.label(31405)\n\n self.assertTrue(l.parent_label is None)\n self.assertTrue(l2 in l.sublabels)\n self.assertEqual(l2.parent_label, l)","def test_parent_with_iterables(self):\n def makeCubesAndGrp():\n cmds.file(new=1, f=1)\n cubes = []\n for x in range(10):\n cubes.append(pm.polyCube()[0])\n group = pm.group(empty=True)\n return cubes, group\n\n cubes, group = makeCubesAndGrp()\n res1 = pm.parent(cubes[:4], group)\n self.assertEqual(cubes[:4], res1)\n res2 = pm.parent(cubes, group)\n self.assertEqual(cubes[4:], res2)\n\n cubes, group = makeCubesAndGrp()\n res1 = pm.parent(cubes[:4] + [group])\n self.assertEqual(cubes[:4], res1)\n res2 = pm.parent(cubes, group)\n self.assertEqual(cubes[4:], res2)\n\n cubes, group = makeCubesAndGrp()\n res1 = pm.parent(cubes[0], cubes[1], cubes[2], cubes[3], group)\n self.assertEqual(cubes[:4], res1)\n res2 = pm.parent(cubes, group)\n self.assertEqual(cubes[4:], res2)\n\n cubes, group = makeCubesAndGrp()\n res1 = pm.parent(cubes[0], cubes[1], [cubes[2], cubes[3], group])\n self.assertEqual(cubes[:4], res1)\n res2 = pm.parent(cubes, group)\n self.assertEqual(cubes[4:], res2)\n\n cubes, group = makeCubesAndGrp()\n res1 = pm.parent([cubes[0], cubes[1]], cubes[2], [cubes[3], group])\n self.assertEqual(cubes[:4], res1)\n res2 = pm.parent(cubes, group)\n self.assertEqual(cubes[4:], res2)","def testParent(self):\n self.assertEqual(\n self,\n self.node.parent\n )","def testParent(self):\n self.assertEqual(\n self,\n self.node.parent\n )","def testParent(self):\n self.assertEqual(\n self.parent,\n self.mr.parent\n )","def test_simple(self):\n exp = [{'type': NewickEvents.OPEN_SUBTREE, 'comments': []},\n {'type': NewickEvents.OPEN_SUBTREE, 'comments': []},\n {'edge_info': None, 'type': NewickEvents.TIP, 'comments': [], 'label': 'h'},\n {'edge_info': None, 'type': NewickEvents.TIP, 'comments': [], 'label': 'p'},\n {'edge_info': '1', 'type': NewickEvents.CLOSE_SUBTREE,\n 'comments': [], 'label': 'hp'},\n {'edge_info': None, 'type': NewickEvents.TIP, 'comments': [], 'label': 'g'},\n {'edge_info': None, 'type': NewickEvents.CLOSE_SUBTREE,\n 'comments': [], 'label': 'hpg'}\n ]\n content = '((h,p)hp:1,g)hpg;'\n self._do_test(content, exp)\n content = '((h,[pretest]p[test][posttest])hp,g)hpg;'\n exp = [{'type': NewickEvents.OPEN_SUBTREE, 'comments': []},\n {'type': NewickEvents.OPEN_SUBTREE, 'comments': []},\n {'edge_info': None, 'type': NewickEvents.TIP, 'comments': [], 'label': 'h'},\n {'edge_info': None, 'type': NewickEvents.TIP,\n 'comments': ['pretest', 'test', 'posttest'], 'label': 'p'},\n {'edge_info': None, 'type': NewickEvents.CLOSE_SUBTREE,\n 'comments': [], 'label': 'hp'},\n {'edge_info': None, 'type': NewickEvents.TIP, 'comments': [], 'label': 'g'},\n {'edge_info': None, 'type': NewickEvents.CLOSE_SUBTREE,\n 'comments': [], 'label': 'hpg'}\n ]\n self._do_test(content, exp)","def reproduce(self, popDensity, activeDrugs):\r\n # TODO\r\n\r\n resistAll = True\r\n for drug in activeDrugs:\r\n if (self.resistances[drug]==False):resistAll = False\r\n \r\n\r\n\r\n maxReproduceProb = self.maxBirthProb * (1 - popDensity)\r\n \r\n \r\n if (resistAll and random.random() < maxReproduceProb):\r\n childResistances = {}\r\n for drug in self.resistances:\r\n if random.random() < self.mutProb:\r\n childResistances[drug] = (not self.resistances[drug])\r\n else:childResistances[drug] = (self.resistances[drug])\r\n\r\n \r\n childOfVirus = ResistantVirus(self.maxBirthProb, self.clearProb,childResistances,self.mutProb)\r\n return childOfVirus\r\n \r\n else: raise NoChildException('Child not created!')","def test_generator8(self):\n xpb = XPathBuilder()\n xp1 = xp2 = None\n base_xp = (xpb.foo.bar | xpb.x.y).parenthesize()\n base_gen = None\n with base_xp as b:\n base_gen = b\n xp1 = b() & xpb.c\n xp2 = b() & xpb.d\n xp1_exp = '(/foo/bar or /x/y) and /c'\n xp2_exp = '(/foo/bar or /x/y) and /d'\n base_exp = '(/foo/bar or /x/y)'\n # check tree structure\n self.assertTrue(base_xp._parent is None)\n self.assertTrue(base_gen._parent is None)\n # check xpath\n self.assertEqual(xp1.tostring(), xp1_exp)\n self.assertEqual(xp2.tostring(), xp2_exp)\n self.assertEqual(base_xp.tostring(), base_exp)\n self.assertEqual(base_gen.tostring(), base_exp)","def _cross_parents(self):\n while len(self.children_population) < self.children_count:\n parent_1, parent_2 = random.sample(self.population, k=2)\n self.children_population.extend(self.crossover.cross(parent_1, parent_2))","def test_documentation_popxl_multi_callsites_graph_input(self):\n filename = \"multi_call_graph_input.py\"\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)","def inversion_crossover(self, pop):\n children, tmpNonComb, used = ([] for i in range(3))\n for i in range(0, int(len(pop) * self.fracElite), 1):\n r = int(rand() * len(pop))\n while r == i:\n r = int(rand() * len(pop))\n\n if sum(self.cID + self.dID + self.iID) != 0:\n nonComb1 = pop[i][:np.where(self.cID + self.dID + self.iID == 1)[0][(-1)] + 1]\n nonComb2 = pop[r][:np.where(self.cID + self.dID + self.iID == 1)[0][(-1)] + 1]\n if sum(self.xID) != 0:\n comb1 = pop[i][:np.where(self.xID == 1)[0][(-1)] + 1]\n comb2 = pop[r][:np.where(self.xID == 1)[0][(-1)] + 1]\n if sum(self.cID + self.dID + self.iID) != 0:\n c = int(rand() * len(nonComb1))\n if rand() > 0.5:\n tmpNonComb.append(np.array(nonComb1[0:c + 1].tolist() + nonComb2[c + 1:].tolist()))\n else:\n tmpNonComb.append(np.array(nonComb2[0:c + 1].tolist() + nonComb1[c + 1:].tolist()))\n used.append(i)\n if sum(self.xID) != 0:\n c = int(rand() * len(comb1))\n for c1 in range(c, len(comb1), 1):\n d2 = (contains_sublist(comb2, comb1[c1]) + 1) % len(comb1)\n d1 = contains_sublist(comb1, comb2[d2])\n c2 = contains_sublist(comb2, comb1[((d1 + 1) % len(comb1))]) % len(comb1)\n tmp1 = cp.copy(comb1)\n if c1 < d1:\n tmp1[(c1 + 1):(d1 + 1)] = list(reversed(tmp1[c1 + 1:d1 + 1]))\n else:\n tmp1[d1:c1] = list(reversed(tmp1[d1:c1]))\n tmp2 = cp.copy(comb2)\n if c2 < d2:\n tmp2[c2:d2] = list(reversed(tmp2[c2:d2]))\n else:\n tmp2[(d2 + 1):(c2 + 1)] = list(reversed(tmp2[d2 + 1:c2 + 1]))\n if sum(self.cID + self.dID + self.iID) == 0 and sum(self.xID) != 0:\n children.append(tmp1)\n children.append(tmp2)\n elif sum(self.cID + self.dID + self.iID) != 0 and sum(self.xID) != 0:\n children.append(np.concatenate(tmpNonComb[(-1)], tmp1))\n children.append(np.concatenate(tmpNonComb[(-1)], tmp2))\n used.append(i)\n used.append(r)\n\n if sum(self.cID + self.dID + self.iID) != 0 and sum(self.xID) == 0:\n children = tmpNonComb\n return (\n children, used)","def mutate(self, pop):\n intDiscID = self.iID + self.dID\n varID = self.cID\n children = []\n k = rand(len(pop), len(pop[0])) > self.fracMutation * rand()\n childn1 = cp.copy(permutation(pop))\n childn2 = cp.copy(permutation(pop))\n r = rand()\n for j in range(0, len(pop), 1):\n n = np.array(childn1[j] - childn2[j])\n stepSize = r * n * varID + (n * intDiscID).astype(int)\n tmp = (pop[j] + stepSize * k[j, :]) * varID + (pop[j] + stepSize * k[j, :]) * intDiscID % (self.ub + 1 - self.lb)\n children.append(simple_bounds(tmp, self.lb, self.ub))\n\n return children","def test_aggregation(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.aggregation_default = 'a'\n cfg.genome.aggregation_options = {'a': 1, 'b': 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.aggregation == gene1.aggregation:\n p1 = True\n elif gene3.aggregation == gene2.aggregation:\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.aggregation, gene1.aggregation)\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.aggregation, gene2.aggregation)","def test_get_child():\n \n root_ts = TrackSegment(flow_dict=flow_dict) \n \n # ROOT MODULE\n start = root_ts.get_child(\"start\")\n # check depth\n assert(start.depth == root_ts.depth+1)\n # check parent\n assert(start.parent is root_ts)\n # check module_id\n assert(start.module_id == 'start')\n \n # CHILD (1,2,3)\n root_ts_get_child_result = root_ts.get_child((1,2,3))\n # check depth\n assert(root_ts_get_child_result.depth == root_ts.depth+1)\n # check parent\n assert(root_ts_get_child_result.parent is root_ts)\n \n # CHILD (2,3,4)\n root_ts_get_child_result2 = root_ts_get_child_result.get_child((2,3,4))\n # check depth\n assert(root_ts_get_child_result2.depth == root_ts.depth+2)\n # check parent \n assert(root_ts_get_child_result2.parent is root_ts_get_child_result)\n \n print(\"TEST GET_CHILD: success!\")","def test_has_children_property(mock_amg):\n\n # split a cell so we can be sure it should have children\n mock_amg.cells[4].split()\n\n assert mock_amg.cells[4].has_children()\n assert not mock_amg.cells[1].has_children()\n assert not mock_amg.cells[4].children['bl'].has_children()","def test_aggregation(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.aggregation_default = 'a'\n cfg.genome.aggregation_options = {'a': 1, 'b': 2}\n gene1, gene2 = get_output_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.aggregation == gene1.aggregation:\n p1 = True\n elif gene3.aggregation == gene2.aggregation:\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.aggregation, gene1.aggregation)\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.aggregation, gene2.aggregation)","def run_tests(g: Graph) -> None:\n print( g.nodes() , \"->\" , ', '.join([f\"{l}\" for l in g.scc()]) , f\"({g.cyclic()})\" )\n for n in g.nodes():\n for m in [m for m in g.nodes() if m != n]:\n p = g.path(n,m)\n if p is not None:\n assert p[0] == n\n assert p[-1] == m\n for i in range(1,len(p)):\n assert g.is_edge(p[i-1], p[i])\n print(\" \", n, \"->\", m, \":\", ' -> '.join([f\"{v}\" for v in p]))","def bclone():\n node = nuke.selectedNodes()\n if len(node)==1:\n clone1 = nuke.createNode(\"NoOp\", inpanel = False)\n clone1.setName(\"Bclone\")\n clone1['label'].setValue(node[0].name()+\"\\nClone_Parent\")\n clone1['tile_color'].setValue(2521651711)\n clone1['note_font_color'].setValue(1583243007)\n clone1xpos = clone1['xpos'].getValue()\n clone1ypos = clone1['ypos'].getValue()\n \n clone2 = nuke.createNode(\"NoOp\", inpanel = False)\n clone2.setName(\"Bclone\")\n clone2['label'].setValue(node[0].name()+\"\\nClone\")\n clone2['hide_input'].setValue(True)\n clone2['tile_color'].setValue(2521651711)\n clone2['note_font_color'].setValue(1583243007)\n clone2['xpos'].setValue(clone1xpos)\n clone2['ypos'].setValue(clone1ypos)\n\n if len(node)==0:\n clone1 = nuke.createNode(\"NoOp\", inpanel = False)\n clone1.setName(\"Bclone\")\n clone1['label'].setValue(\"Clone_Parent\")\n clone1['tile_color'].setValue(2521651711)\n clone1['note_font_color'].setValue(1583243007)\n clone1xpos = clone1['xpos'].getValue()\n clone1ypos = clone1['ypos'].getValue()\n \n clone2 = nuke.createNode(\"NoOp\", inpanel = False)\n clone2.setName(\"Bclone\")\n clone2['label'].setValue(\"Clone\")\n clone2['hide_input'].setValue(True)\n clone2['tile_color'].setValue(2521651711)\n clone2['note_font_color'].setValue(1583243007)\n clone2['xpos'].setValue(clone1xpos)\n clone2['ypos'].setValue(clone1ypos)\n if len(node)!=0 and len(node)!=1:\n nuke.message('Just select one node to clone !')","def show_subgraph(dfs_codes, nsupport, mapper):\n\tglobal __subgraph_count\n\n\tg = build_graph(dfs_codes)\n\tg.id = __subgraph_count\n\t__subgraph_count += 1\n\tg.gprint(nsupport, mapper)","def parents_loop(self):\r\n while len(self.parents) > 0:\r\n children = 0\r\n self.parent1 = random.choice(self.parents)\r\n index = self.parents.index(self.parent1)\r\n del self.parents[index]\r\n\r\n self.parent2 = random.choice(self.parents)\r\n index = self.parents.index(self.parent2)\r\n del self.parents[index]\r\n\r\n while children < 2:\r\n self.child = copy.deepcopy(self.parent1)\r\n \r\n self.battery_loop()\r\n\r\n childsolution = random_algo.Random(self.child, self.cable_cost, self.battery_cost)\r\n childsolution.change_battery_or_house('change_battery')\r\n childsolution.change_battery_or_house('change_house')\r\n\r\n if (self.child.valid_solution() and self.child not in self.district_population\r\n and self.child not in self.best_districts and self.child not in self.worst_districts):\r\n self.district_population.append(self.child)\r\n self.cost_populations.append(self.child.total_cost(self.battery_cost, self.cable_cost))\r\n children += 1","def test_restore_multiple_in_subgraph(self):\n subgraph = self._subgraph()\n subgraph['id'] = 15\n task1 = self._remote_task()\n task1['id'] = 1\n task2 = self._remote_task()\n task2['id'] = 2\n task1['parameters']['containing_subgraph'] = 15\n task2['parameters']['containing_subgraph'] = 15\n\n graph = self._restore_graph([subgraph, task1, task2])\n assert len(graph.tasks) == 3\n subgraphs = [op for op in graph.tasks if op.is_subgraph]\n remote_tasks = [op for op in graph.tasks if not op.is_subgraph]\n\n # those are all references to the same subgraph, the subgraph was\n # NOT restored multiple times\n assert remote_tasks[0].containing_subgraph \\\n is remote_tasks[1].containing_subgraph \\\n is subgraphs[0]\n\n assert len(subgraphs[0].tasks) == 2","def slice_graph_fwd( startea, reg ): \r\n\tgraph = vcg_Graph.vcgGraph({\"title\":'\"Slice for %s\"' % reg, \\\r\n\t\t\"manhattan_edges\":\"no\", \"layoutalgorithm\":\"maxdepth\"})\r\n\t#\r\n\t# Retrieve the name of the current basic block\r\n\t# \r\n\tworklist = []\r\n\tdata_bib = {}\r\n\tstartnode = slice_node( startea, 0, reg )\r\n\trootnode = graph.Add_Node( startnode.to_name() )\r\n\tdata_bib[ startnode.to_name() ] = startnode\r\n\tworklist.insert( 0, rootnode )\r\n\twhile len( worklist ) > 0:\r\n\t\tcurrnode = worklist.pop()\r\n\t\tcurrslice = data_bib[ currnode.get_name() ]\r\n\t\ttgt_reg = currslice.get_target_reg()\r\n\t\tif tgt_reg == \"END\":\r\n\t\t# Do not process this node any further\r\n\t\t\tpass\r\n\t\telif tgt_reg == \"\" or (( len( currslice.get_lines()) > 0) and \\\r\n\t\t\tcurrslice.endea != currslice.get_lines()[-1][0]):\r\n\t\t\t# Nothing much happening here, just proceed to parent bocks\r\n\t\t\tif ua_mnem( currslice.endea ) == \"call\":\r\n\t\t\t\txrefs = get_short_crefs_from( currslice.endea )\r\n\t\t\telse:\r\n\t\t\t\txrefs = get_crefs_from( currslice.endea )\r\n\t\t\tfor ref in xrefs:\r\n\t\t\t\tnewslice = slice_node( ref, 0, currslice.reg )\r\n\t\t\t\tif graph.Get_Node( newslice.to_name() ) == 0:\r\n\t\t\t\t\tnewnode = graph.Add_Node( newslice.to_name() )\r\n\t\t\t\t\tworklist.insert( 0, newnode )\r\n\t\t\t\t\tdata_bib[ newslice.to_name() ] = newslice\r\n\t\t\t\tgraph.Add_Link( currnode.get_name(), newslice.to_name())\r\n\t\telse:\r\n\t\t\t# Register was modified, use new register\r\n\t\t\txrefs = get_crefs_from( currslice.endea )\r\n\t\t\tfor ref in xrefs:\r\n\t\t\t\tnewslice = slice_node( ref, 0, tgt_reg )\r\n\t\t\t\tif graph.Get_Node( newslice.to_name() ) == 0:\r\n\t\t\t\t\tnewnode = graph.Add_Node( newslice.to_name() )\r\n\t\t\t\t\tworklist.insert( 0, newnode )\r\n\t\t\t\t\tdata_bib[ newslice.to_name() ] = newslice\r\n\t\t\t\tgraph.Add_Link( currnode.get_name(), newslice.to_name())\r\n\t\t\txrefs = get_crefs_from( currslice.endea )\r\n\t\t\tfor ref in xrefs:\r\n\t\t\t\tnewslice = slice_node( ref, 0, currslice.reg )\r\n\t\t\t\tif graph.Get_Node( newslice.to_name() ) == 0:\r\n\t\t\t\t\tnewnode = graph.Add_Node( newslice.to_name() )\r\n\t\t\t\t\tworklist.insert( 0, newnode )\r\n\t\t\t\t\tdata_bib[ newslice.to_name() ] = newslice\r\n\t\t\t\tgraph.Add_Link( currnode.get_name(), newslice.to_name())\r\n\treturn [ graph, data_bib ]","def testParentage(self):\n self.assertEqual(\n self.cd,\n self.media_ref.parent\n )\n\n self.assertEqual(\n self.cd,\n self.cc.parent\n )","def test_generator7(self):\n xpb = XPathBuilder()\n xp1 = xp2 = None\n base_xp = xpb.foo.bar & xpb.x.y\n base_gen = None\n with base_xp as b:\n base_gen = b\n xp1 = b() | xpb.c\n xp2 = b() | xpb.d\n xp1_exp = '/foo/bar and /x/y or /c'\n xp2_exp = '/foo/bar and /x/y or /d'\n base_exp = '/foo/bar and /x/y'\n # check tree structure\n self.assertTrue(base_xp._parent is None)\n self.assertTrue(base_gen._parent is None)\n # check xpath\n self.assertEqual(xp1.tostring(), xp1_exp)\n self.assertEqual(xp2.tostring(), xp2_exp)\n self.assertEqual(base_xp.tostring(), base_exp)\n self.assertEqual(base_gen.tostring(), base_exp)","def populate_graph(self):","def test_dag_preserves_superrep(dimension, conversion):\n qobj = conversion(rand_super_bcsz(dimension))\n assert qobj.superrep == qobj.dag().superrep","def test__graph_structure():\n assert PES_GRAPH == (\n ('CH2CH2+OH', 'CH2CH+H2O', 'C2H4OH', 'C2H5O', 'CH3CHO+H'),\n (frozenset({0, 1}), frozenset({0, 2}), frozenset({2, 3}),\n frozenset({3, 4}), frozenset({1, 2})))\n assert pgraph.species(PES_GRAPH) == (\n ('CH2CH2+OH', 'CH2CH+H2O', 'C2H4OH', 'C2H5O', 'CH3CHO+H'))\n assert pgraph.channels(PES_GRAPH) == (\n (frozenset({0, 1}), frozenset({0, 2}), frozenset({2, 3}),\n frozenset({3, 4}), frozenset({1, 2})))\n print('\\npes graph')\n print(PES_GRAPH)","def test_fpy_parent():\n\n data = \"\"\"\n<depletion_chain>\n <nuclide name=\"U235\" reactions=\"1\">\n <reaction type=\"fission\" Q=\"193405400.0\"/>\n <neutron_fission_yields>\n <energies>0.0253</energies>\n <fission_yields energy=\"0.0253\">\n <products>Te134 Zr100 Xe138</products>\n <data>0.062155 0.0497641 0.0481413</data>\n </fission_yields>\n </neutron_fission_yields>\n </nuclide>\n <nuclide name=\"U238\" reactions=\"1\">\n <reaction type=\"fission\" Q=\"200.0e6\"/>\n <neutron_fission_yields parent=\"U235\"/>\n </nuclide>\n</depletion_chain>\n \"\"\"\n\n root = ET.fromstring(data)\n elems = root.findall('nuclide')\n u235 = nuclide.Nuclide.from_xml(elems[0], root)\n u238 = nuclide.Nuclide.from_xml(elems[1], root)\n\n # Make sure U238 yield is same as U235\n assert np.array_equal(u238.yield_data.energies, u235.yield_data.energies)\n assert np.array_equal(u238.yield_data.yield_matrix, u235.yield_data.yield_matrix)\n\n # Make sure XML element created has single attribute\n elem = u238.to_xml_element()\n fpy_elem = elem.find('neutron_fission_yields')\n assert fpy_elem.get('parent') == 'U235'\n assert len(fpy_elem) == 0\n\n data = \"\"\"\n<depletion_chain>\n <nuclide name=\"U235\" reactions=\"1\">\n <reaction type=\"fission\" Q=\"193405400.0\"/>\n </nuclide>\n <nuclide name=\"U238\" reactions=\"1\">\n <reaction type=\"fission\" Q=\"200.0e6\"/>\n <neutron_fission_yields parent=\"U235\"/>\n </nuclide>\n</depletion_chain>\n \"\"\"\n\n # U235 yields are missing, so we should get an exception\n root = ET.fromstring(data)\n elems = root.findall('nuclide')\n with pytest.raises(ValueError, match=\"yields\"):\n u238 = nuclide.Nuclide.from_xml(elems[1], root)","def test_skirmish_parenting(self):\n root = SkirmishAction()\n a1 = SkirmishAction()\n a2 = SkirmishAction()\n self.sess.add_all([root, a1, a2])\n self.sess.commit()\n\n root.children.append(a1)\n root.children.append(a2)\n self.sess.commit()\n\n self.assertEqual(a1.parent_id, root.id)\n self.assertEqual(a2.parent_id, root.id)","def test_generator6(self):\n xpb = XPathBuilder()\n xp1 = xp2 = None\n base_xp = xpb.base.foo.bar\n base_gen = None\n with base_xp as b:\n base_gen = b\n xp1 = xpb.a.b.c.join(b())\n xp2 = xpb.test.join(b())\n xp1_exp = '/a/b/c/base/foo/bar'\n xp2_exp = '/test/base/foo/bar'\n base_exp = '/base/foo/bar'\n # check tree structure\n self.assertTrue(base_xp._parent is None)\n self.assertTrue(len(base_xp._children[0]._children[0]._children) == 0)\n self.assertTrue(base_gen._parent is None)\n self.assertTrue(len(base_gen._children) == 0)\n # check xpath\n self.assertEqual(xp1.tostring(), xp1_exp)\n self.assertEqual(xp2.tostring(), xp2_exp)\n self.assertEqual(base_xp.tostring(), base_exp)\n self.assertEqual(base_gen.tostring(), base_exp)","def test_grandchildren():\n\n # note c.upto(\"status\").desired.grandchildren\n # this is the same as *c.upto(\"status\").desired in python3.5+\n res = conf.status.conditions.choose(lambda c: (c.type, c.reason, c.upto(\"status\").desired.grandchildren))\n assert \"type\" in res\n assert \"reason\" in res\n assert \"version\" in res\n assert \"image\" in res\n assert \"force\" in res","def subplot_1(self, Graph, n_tabs):\n # The code below walks does a pre-order traversal of the tree\n # For exact details about the structure of self.Graph refer description in init function.\n\n attr_name = list(Graph.keys())[0]\n print(\"\\t\"*(n_tabs),\"feature name :\",attr_name)\n for val in list(Graph[attr_name].keys()):\n print(\"\\t\"*(n_tabs+1),\"feature value :\",val)\n sub_graph = Graph[attr_name][val]\n if (type(sub_graph)==dict):\n self.subplot_1(sub_graph, n_tabs+2)\n else:\n print(\"\\t\"*(n_tabs+2),\"class :\", sub_graph)","def hasSiblings():","def test_get_related_nodes(self):\n pass","def test_createSubLinkographWithoutCommands(self):\n self.performTestForParams()","def crossover (self, p1, p2, p_pop, c1, c2, c_pop) :\n assert self.crossover_count < self.pop_size\n assert self.get_iteration () == self.last_gen\n self.parents.append (p1)\n self.parents.append (p2)\n self.crossover_count += 2\n if self.crossover_count == self.pop_size :\n assert (self.get_iteration () == self.last_gen)\n print (self.get_iteration ())\n sys.stdout.flush ()\n self.build_model (p_pop)\n self.sample_model (c1, c2, c_pop)\n self.crossover_count = 0\n self.parents = []\n self.children = {}\n self.last_gen += 1\n self.clear_cache ()","def test_Tree():","def reproduce(self, popDensity, activeDrugs):\n\n # Checks the resistance of the mother virus to all drugs in activeDrugs list\n # if mother virus is resistant to all the drugs, reproduction will proceed\n for drug in activeDrugs:\n if not self.isResistantTo(drug):\n raise NoChildException()\n\n maxReproduceProb = self.maxBirthProb * (1 - popDensity)\n\n if random.random() < maxReproduceProb:\n resistance_trait = {}\n # Calculate the transfer of resistances property to child virus\n for drug in self.resistances.keys():\n if random.random() <= (1 - self.mutProb):\n resistance_trait[drug] = self.resistances[drug]\n else:\n resistance_trait[drug] = not self.resistances[drug]\n\n childOfVirus = ResistantVirus(self.maxBirthProb, self.clearProb, resistance_trait, self.mutProb)\n return childOfVirus\n else:\n raise NoChildException()","def dump_subgraph_for_debug(self):\n\n import pypipegraph2 as ppg\n\n nodes = []\n seen = set()\n edges = []\n counter = [0]\n node_to_counters = {}\n\n def descend(node):\n if node in seen:\n return\n seen.add(node)\n j = self.runner.jobs[node]\n if isinstance(j, ppg.FileInvariant):\n nodes.append(f\"Path('{counter[0]}').write_text('A')\")\n nodes.append(f\"job_{counter[0]} = ppg.FileInvariant('{counter[0]}')\")\n elif isinstance(j, ppg.ParameterInvariant):\n nodes.append(\n f\"job_{counter[0]} = ppg.ParameterInvariant('{counter[0]}', 55)\"\n )\n elif isinstance(j, ppg.FunctionInvariant):\n nodes.append(\n f\"job_{counter[0]} = ppg.FunctionInvariant('{counter[0]}', lambda: 55)\"\n )\n elif isinstance(j, ppg.SharedMultiFileGeneratingJob):\n nodes.append(\n f\"job_{counter[0]} = ppg.SharedMultiFileGeneratingJob('{counter[0]}', {[x.name for x in j.files]!r}, dummy_smfg, depend_on_function=False)\"\n )\n elif isinstance(j, ppg.TempFileGeneratingJob):\n nodes.append(\n f\"job_{counter[0]} = ppg.TempFileGeneratingJob('{counter[0]}', dummy_fg, depend_on_function=False)\"\n )\n elif isinstance(j, ppg.FileGeneratingJob):\n nodes.append(\n f\"job_{counter[0]} = ppg.FileGeneratingJob('{counter[0]}', dummy_fg, depend_on_function=False)\"\n )\n elif isinstance(j, ppg.MultiTempFileGeneratingJob):\n files = [counter[0] + \"/\" + x.name for x in j.files]\n nodes.append(\n f\"job_{counter[0]} = ppg.MultiTempFileGeneratingJob({files!r}, dummy_mfg, depend_on_function=False)\"\n )\n elif isinstance(j, ppg.MultiFileGeneratingJob):\n files = [str(counter[0]) + \"/\" + x.name for x in j.files]\n nodes.append(\n f\"job_{counter[0]} = ppg.MultiFileGeneratingJob({files!r}, dummy_mfg, depend_on_function=False)\"\n )\n elif isinstance(j, ppg.DataLoadingJob):\n nodes.append(\n f\"job_{counter[0]} = ppg.DataLoadingJob('{counter[0]}', lambda: None, depend_on_function=False)\"\n )\n elif isinstance(j, ppg.AttributeLoadingJob):\n nodes.append(\n f\"job_{counter[0]} = ppg.AttributeLoadingJob('{counter[0]}', DummyObject(), 'attr_{counter[0]}', lambda: None, depend_on_function=False)\"\n )\n else:\n raise ValueError(j)\n node_to_counters[node] = counter[0]\n counter[0] += 1\n for parent in self.runner.dag.predecessors(node):\n descend(parent)\n\n def build_edges(node):\n for parent in self.runner.dag.predecessors(node):\n edges.append(\n f\"edges.append(('{node_to_counters[node]}', '{node_to_counters[parent]}'))\"\n )\n build_edges(parent)\n\n descend(self.job_id)\n edges.append(\"edges = []\")\n build_edges(self.job_id)\n edges.extend(\n [\n \"for (a,b) in edges:\",\n \" if a in ppg.global_pipegraph.jobs and b in ppg.global_pipegraph.jobs:\",\n \" ppg.global_pipegraph.jobs[a].depends_on(ppg.global_pipegraph.jobs[b])\",\n ]\n )\n with open(\"subgraph_debug.py\", \"w\") as op:\n lines = \"\"\"\nclass DummyObject:\n pass\n\ndef dummy_smfg(files, prefix):\n Path(prefix).mkdir(exist_ok=True, parents=True)\n for f in files:\n f.write_text(\"hello\")\n\n\ndef dummy_mfg(files):\n for f in files:\n f.parent.mkdir(exist_ok=True, parents=True)\n f.write_text(\"hello\")\n\ndef dummy_fg(of):\n of.parent.mkdir(exist_ok=True, parents=True)\n of.write_text(\"fg\")\n\n\"\"\".split(\n \"\\n\"\n )\n lines += nodes\n lines += edges\n lines += [\"\", \"ppg.run()\", \"ppg.run\"]\n\n op.write(\"\\n\".join(\" \" + l for l in lines))","def test_tree_mode4(self):\n xpb = XPathBuilder()\n xp_1 = xpb.foo\n xp_2 = xpb.baz\n xp_and = xp_1 & xp_2\n self.assertTrue(xp_and._parent is None)\n self.assertTrue(len(xp_and._children) == 2)\n self.assertTrue(xp_and._children[0] is xp_1)\n self.assertTrue(xp_and._children[1] is xp_2)\n self.assertTrue(xp_1._parent is xp_and)\n self.assertTrue(len(xp_1._children) == 0)\n self.assertTrue(xp_2._parent is xp_and)\n self.assertTrue(len(xp_2._children) == 0)\n xp_2.reparent(None)\n # check references after remove\n self.assertTrue(xp_and._parent is None)\n self.assertTrue(len(xp_and._children) == 1)\n self.assertTrue(xp_and._children[0] is xp_1)\n self.assertTrue(xp_1._parent is xp_and)\n self.assertTrue(len(xp_1._children) == 0)\n # xp_2's references were changed\n self.assertTrue(xp_2._parent is None)\n self.assertTrue(len(xp_2._children) == 0)","def sample_from_subpop(instance, params, subpop):\n y = subpop\n x = np.random.choice([-1,+1], size=params['d'])\n x[instance['indices'][subpop]] = instance['values'][subpop]\n return x, y, subpop","def __init__(self, firstParent, secondParent):\n CrossOver.__init__(self, \"Group Point CrossOver\", firstParent, secondParent)","def setUp(self):\n self.complete = nx.Graph()\n self.complete.add_edge(1, 2)\n self.complete.add_edge(2, 3)\n self.complete.add_edge(1, 3)\n\n self.small_tree = nx.Graph()\n self.small_tree.add_edge(1, 2)\n self.small_tree.add_edge(2, 3)\n self.small_tree.add_edge(3, 4)\n self.small_tree.add_edge(1, 4)\n self.small_tree.add_edge(2, 4)\n self.small_tree.add_edge(4, 5)\n self.small_tree.add_edge(5, 6)\n self.small_tree.add_edge(5, 7)\n self.small_tree.add_edge(6, 7)\n\n self.deterministic_graph = nx.Graph()\n self.deterministic_graph.add_edge(1, 2)\n self.deterministic_graph.add_edge(1, 3)\n self.deterministic_graph.add_edge(3, 4)\n self.deterministic_graph.add_edge(2, 4)\n self.deterministic_graph.add_edge(3, 5)\n self.deterministic_graph.add_edge(4, 5)\n self.deterministic_graph.add_edge(3, 6)\n self.deterministic_graph.add_edge(5, 6)","def test_1():\n for _ in range(100):\n G = NetworkTopo()\n flows = MulticastFlows(G, 10, 40)\n\n spt = ShortestPathTree(G, flows)\n\n for T in spt.multicast_trees:\n assert len(nx.cycle_basis(T)) == 0","def reproduce(self, popDensity):\r\n\r\n # Does the virus reproduce? \r\n maxReproduceProb = self.maxBirthProb * (1 - popDensity)\r\n \r\n if random.random() < maxReproduceProb:\r\n childOfVirus = SimpleVirus(self.maxBirthProb, self.clearProb)\r\n return childOfVirus\r\n \r\n else: raise NoChildException('Child not created!')","def create_subbasin_graph():\n subbasin_to_downstream = pd.read_csv(module_dir + '/../data/simulations_shervan/test.rvh', sep='\\s+', skiprows=7, nrows=724, names=['subbasin', 'downstream_subbasin'], usecols=[1,2])\n subbasin_to_downstream['subbasin'] = subbasin_to_downstream['subbasin']\n subbasin_to_downstream['downstream_subbasin'] = 'sub' + subbasin_to_downstream['downstream_subbasin'].astype(str)\n subbasin_to_downstream['edge'] = 1\n\n for subbasin in subbasin_to_downstream['subbasin'].unique():\n is_sink = 1 if len(subbasin_to_downstream[(subbasin_to_downstream['subbasin'] == subbasin) & subbasin_to_downstream['edge'] == 1]) == 0 else 0\n subbasin_to_downstream = subbasin_to_downstream.append({'subbasin': subbasin, 'downstream_subbasin': subbasin, 'edge': is_sink}, ignore_index=True)\n subbasin_to_downstream = subbasin_to_downstream.append({'subbasin': 'sub-1', 'downstream_subbasin': 'sub-1', 'edge': 1}, ignore_index=True)\n \n adj = subbasin_to_downstream.pivot(index='subbasin', columns='downstream_subbasin', values='edge').fillna(0) \n adj = adj.sort_index(axis=0).sort_index(axis=1)\n \n G = nx.from_numpy_matrix(adj.values, parallel_edges=False, create_using=nx.DiGraph())\n label_mapping = dict(zip(range(len(adj.values)), adj.index))\n G = nx.relabel_nodes(G, label_mapping)\n \n return G","def test_input_not_subgraph(self, dim):\r\n with pytest.raises(ValueError, match=\"Input is not a valid subgraph\"):\r\n clique.grow([dim + 1], nx.empty_graph(dim))","def crossover(parent: list):\n ### GENERAL METHOD\n child1 = []\n child2 = []\n child = []\n geneA = int(random.random() * len(parent[0]))\n geneB = int(random.random() * len(parent[0]))\n startgene = min(geneA, geneB)\n endgene = max(geneA, geneB)\n for i in range(startgene, endgene):\n child1.append(parent[0][i])\n child2 = [item for item in parent[1] if item not in child1]\n child = child2[:startgene] + child1 + child2[startgene:]\n return child","def test_build_graph(self):\n insert_good_data()\n dataframe = get_dataframe()\n results = processing.build_graph(dataframe, figure_path, False)\n # 1\n self.assertEqual(results, \"Updated html File and Opened it\")","def test_10_parents_as_minus_1_0_0_1_2_3_3_4_6_6(self):\n parents = [ -1, 0, 0, 1, 2, 3, 3, 4, 6, 6 ]\n self.init(parents)\n self.assertEqual(5, self.method_under_test())","def reproduce(self,prob=None):\n for parent in self.agents:\n if parent.fledged(prob):\n fledgling = Agent()\n self._fledgling_move(fledgling,parent.hex)\n self.agents.append(fledgling)\n self.pop_size += 1","def genNextPop(prevPop, masterList, popSize):\n parSize = int(popSize / 10) #top 10%\n parentPop = [Team(prevPop[i].roster) for i in range(parSize)]\n parentPop = getStats(parentPop, masterList) # inefficent\n rosterSize = len(parentPop[0].roster)\n newPop = []\n #parentsList = [] #debug\n for i in range(popSize):\n chromosome = doCrossover(parentPop, parSize, rosterSize)\n newPop.append(Team(chromosome))\n getStats(newPop, masterList)\n #showStats(newPop, masterList, \"all\")\n #debug code\n #for playerInd in chromosome:\n # print(masterList[playerInd].pos)\n\n return newPop","def getChildren():","def test_restore_with_subgraph(self):\n subgraph = self._subgraph()\n task = self._remote_task()\n subgraph['id'] = 15\n task['parameters']['containing_subgraph'] = 15\n\n graph = self._restore_graph([subgraph, task])\n assert len(graph.tasks) == 2\n subgraphs = [op for op in graph.tasks if op.is_subgraph]\n remote_tasks = [op for op in graph.tasks if not op.is_subgraph]\n\n assert len(subgraphs) == 1\n assert len(remote_tasks) == 1\n\n assert len(subgraphs[0].tasks) == 1\n assert remote_tasks[0].containing_subgraph is subgraphs[0]","def test_removes_empty_subgraph(self):\n ctx = MockWorkflowContext()\n g = TaskDependencyGraph(ctx)\n\n # sg1 is just empty, no tasks inside it\n sg1 = g.subgraph(ctx)\n # sg2 contains only a NOPTask\n sg2 = g.subgraph(ctx)\n sg2.add_task(tasks.NOPLocalWorkflowTask(ctx))\n\n # sg3 contains sg4, which is empty behcause it only contains a NOPTask\n sg3 = g.subgraph(ctx)\n sg4 = g.subgraph(ctx)\n sg4.add_task(tasks.NOPLocalWorkflowTask(ctx))\n sg3.add_task(sg4)\n\n # sg5 is a subgraph that contains a real task! it is not removed\n sg5 = g.subgraph(ctx)\n real_task = tasks.WorkflowTask(ctx)\n sg5.add_task(real_task)\n\n assert set(g.tasks) > {sg1, sg2, sg3, sg4, sg5, real_task}\n g.optimize()\n assert set(g.tasks) == {sg5, real_task}","def test_dot(self):\n graph = graphviz.Graph(comment='The Round Table')\n graph.node('A', 'King Arthur')\n graph.node('B', 'Sir Bedevere the Wise')\n graph.edges(['AB'])\n\n st.graphviz_chart(graph)\n\n c = self.get_delta_from_queue().new_element.graphviz_chart\n self.assertEqual(hasattr(c, 'spec'), True)","def test_pod_valid_parent(self):\n session = self.login_to_apic()\n parent = PhysicalModel()\n pod = Pod.get(session, parent)\n children = parent.get_children()\n self.assertEqual(pod, children)","def test_ExplorePath_Simple( self ):\n links = []\n n1 = graph.Node( 10, 50 )\n n2 = graph.Node( 10, 50 )\n n3 = graph.Node( 10, 50 )\n n7 = graph.Node( 10, 50 )\n\n links.append( graph.Link( n1, n2 ) )\n links.append( graph.Link( n2, n3 ) )\n links.append( graph.Link( n3, n7 ) )\n roots = [n1]\n actual = nodes.explorePath( links, roots, n1 )\n expected = [ n1, n2, n3, n7 ]\n self.assertEqual( expected, actual )","def test_dummy4(self):\n xpb = XPathBuilder()\n xp = xpb.dummy()\n self.assertTrue(xp.log_not() is xp)","def test_child_link(self):\n def compare_func(obj, node):\n child_nodes = self.get_children_of_node(node)\n\n for (child_obj, child_node) in zip(obj.children, child_nodes):\n # parent-child link (children must have obj as their parent)\n self.assertEqual(child_obj.parent, obj)\n\n self.recursively_compare_tree_against_html(compare_func)","def _subgraph_isomorphism_matcher(digraph, nxpattern, node_pred, edge_pred):\n graph_matcher = iso.DiGraphMatcher(digraph, nxpattern, node_match=node_pred, edge_match=edge_pred)\n yield from graph_matcher.subgraph_isomorphisms_iter()","def sub_graph_merging(self):\n raise NotImplementedError()","def three_opt(self, pop):\n children, used = ([] for i in range(2))\n for i in range(0, self.population, 1):\n tmp = []\n breaks = np.sort(rand(3) * len(pop[i]) // 1)\n while breaks[1] == breaks[0] or breaks[1] == breaks[2]:\n breaks[1] = rand() * len(pop[i]) // 1\n breaks = np.sort(breaks)\n\n while breaks[2] == breaks[0]:\n breaks[2] = rand() * len(pop[i]) // 1\n\n breaks = np.sort(breaks)\n tmp[0:(int(breaks[0]))] = pop[i][0:int(breaks[0])]\n tmp[(len(tmp)):(int(len(tmp) + breaks[2] - breaks[1]))] = pop[i][int(breaks[1]):int(breaks[2])]\n tmp[(len(tmp)):(int(len(tmp) + breaks[1] - breaks[0]))] = pop[i][int(breaks[0]):int(breaks[1])]\n tmp[(len(tmp)):(int(len(tmp) + breaks[2] - len(pop[i])))] = pop[i][int(breaks[2]):len(pop[i])]\n children.append(tmp)\n used.append(i)\n tmp = []\n tmp[0:(int(breaks[0]))] = pop[i][0:int(breaks[0])]\n tmp[(len(tmp)):(int(len(tmp) + breaks[1] - breaks[0]))] = list(reversed(pop[i][int(breaks[0]):int(breaks[1])]))\n tmp[(len(tmp)):(int(len(tmp) + breaks[2] - breaks[1]))] = reversed(pop[i][int(breaks[1]):int(breaks[2])])\n tmp[(len(tmp)):(int(len(tmp) + breaks[2] - len(pop[i])))] = pop[i][int(breaks[2]):len(pop[i])]\n children.append(tmp)\n used.append(i)\n\n return (\n children, used)","def testSetParent(self):\n for child in self.color_corrections + self.color_decisions:\n self.assertEqual(\n None,\n child.parent\n )\n\n self.node.append_children(\n self.color_corrections + self.color_decisions\n )\n\n for child in self.node.all_children:\n self.assertEqual(\n self.node,\n child.parent\n )\n child.parent = 'banana'\n self.assertEqual(\n 'banana',\n child.parent\n )\n\n self.node.set_parentage()\n\n for child in self.node.all_children:\n self.assertEqual(\n self.node,\n child.parent\n )","def test_get_dependencies_subgraph_by_dfs(\n self, source_node, expected_nodes_in, expected_nodes_out\n ):\n graph = nx.DiGraph()\n graph.add_node(\"pack1\")\n graph.add_node(\"pack2\")\n graph.add_node(\"pack3\")\n graph.add_node(\"pack4\")\n graph.add_edge(\"pack1\", \"pack2\")\n graph.add_edge(\"pack2\", \"pack3\")\n dfs_graph = PackDependencies.get_dependencies_subgraph_by_dfs(\n graph, source_node\n )\n for i in expected_nodes_in:\n assert i in dfs_graph.nodes()\n for i in expected_nodes_out:\n assert i not in dfs_graph.nodes()","def test_case2(self):\n\n graph = BipartiteGraph()\n\n graph.addEdge(\"supervisor1\",\"student1\")\n graph.addEdge(\"supervisor2\",\"student4\")\n graph.addEdge(\"supervisor3\",\"student3\")\n\n val1 = graph.getSupervisorDegree(\"supervisor1\")\n\n graph.addEdge(\"supervisor1\",\"student2\")\n\n curr = graph.getSupervisorDegree(\"supervisor1\")\n val2 = graph.getSupervisors(\"student2\")\n expected2 = [\"supervisor1\"]\n\n self.assertEqual((curr-1,expected2),(val1,val2))","def test_12_parents_as_minus_1_0_0_1_1_2_3_4_4_5_7_7(self):\n parents = [ -1, 0, 0, 1, 1, 2, 3, 4, 4, 5, 7, 7 ]\n self.init(parents)\n self.assertEqual(5, self.method_under_test())","def test_node_info_popup(self):\n def test_popup(node):\n node.details.click()\n with NodeInfo() as details:\n self.assertEqual(\n node.name.text, details.header.text,\n 'Node name')\n details.close.click()\n details.wait_until_exists()\n\n with Nodes()as n:\n test_popup(n.nodes_discovered[0])\n test_popup(n.nodes_offline[0])\n test_popup(n.nodes_error[0])","def test_input_valid_subgraph(self, dim):\r\n with pytest.raises(ValueError, match=\"Input is not a valid subgraph\"):\r\n clique.swap([0, dim], nx.empty_graph(dim))","def test_spec(self):\n graph = graphviz.Graph(comment='The Round Table')\n graph.node('A', 'King Arthur')\n graph.node('B', 'Sir Bedevere the Wise')\n graph.edges(['AB'])\n\n st.graphviz_chart(graph)\n\n c = self.get_delta_from_queue().new_element.graphviz_chart\n self.assertEqual(hasattr(c, 'spec'), True)","def test_change_parent_location(self):\n pass","def test_4_parents_as_1_minus_1_1_0(self):\n parents = [ 1, -1, 1, 0 ]\n self.init(parents)\n self.assertEqual(3, self.method_under_test())","def test_6_parents_as_minus_1_0_1_2_3_4(self):\n parents = [ -1, 0, 1, 2, 3, 4 ]\n self.init(parents)\n self.assertEqual(6, self.method_under_test())","def test_7_parents_as_minus_1_0_0_1_2_3_4(self):\n parents = [ -1, 0, 0, 1, 2, 3, 4 ]\n self.init(parents)\n self.assertEqual(4, self.method_under_test())","def test_generator5(self):\n xpb = XPathBuilder()\n xp = None\n base_xp = xpb.base.foo.bar\n base_gen = None\n with base_xp as b:\n base_gen = b\n xp = b().join(xpb.a.b.c[3])\n exp = '/base/foo/bar/a/b/c[3]'\n base_exp = '/base/foo/bar'\n # check tree structure\n self.assertTrue(base_xp._parent is None)\n self.assertTrue(len(base_xp._children[0]._children[0]._children) == 0)\n self.assertTrue(base_gen._parent is None)\n self.assertTrue(len(base_gen._children) == 0)\n # check xpath\n self.assertEqual(xp.tostring(), exp)\n self.assertEqual(base_xp.tostring(), base_exp)\n self.assertEqual(base_gen.tostring(), base_exp)","def playout(self, state):\n node = self.root\n isTerminal = False\n depth = 0\n\n while not isTerminal and depth < self.playout_depth:\n #A = len(node.children) # num_children\n A = len(node.children[0]) + len(node.children[1]) \n if A < self.num_initActions:\n #if len(node.children[0]) < self.num_initActions:\n node, init_action_xy, init_spin = self.initChildren(node, state, depth)\n _, ShotVec = CreateShot(_ShotPos(init_action_xy[0], init_action_xy[1], init_spin))\n success, ResShot = Simulation(state, ShotVec, Config.RAND, -1)\n isTerminal = (state.ShotNum==0)\n \n depth += 1\n break\n \n n_a = [c.n_visits for c in node.children[0].values()] + [c.n_visits for c in node.children[1].values()]\n # progressive widening\n # if chilren node has been visited much times then expand\n #if np.sqrt(sum(n_a)) >= A:\n if sum(n_a) >= 10 * A: \n # expand\n node, expanded_action_xy, expanded_spin = self.expand(node)\n _, ShotVec = CreateShot(_ShotPos(expanded_action_xy[0],expanded_action_xy[1], expanded_spin))\n success, ResShot = Simulation(state, ShotVec, Config.RAND, -1)\n isTerminal = (state.ShotNum==0) # one end game\n \n depth += 1\n break\n\n # select\n node, selected_action_xy, selected_spin = self.ucb_select(node)\n _, ShotVec = CreateShot(_ShotPos(selected_action_xy[0], selected_action_xy[1], selected_spin))\n success, ResShot = Simulation(state, ShotVec, Config.RAND, -1)\n isTerminal = (state.ShotNum==0) # one end game\n \n depth += 1\n\n if isTerminal:\n break\n\n if not isTerminal and depth < self.playout_depth:\n # save the rollout_state for speed.\n #if node.rollout_state is None:\n state = self.rollOut(node, state, depth)\n #node.rollout_state = state\n #else:\n # state = node.rollout_state\n \n self.update(node, state)","def test_same_node_is_reachable(self):\n # G is an arbitrary tournament on ten nodes.\n G = DiGraph(sorted(p) for p in combinations(range(10), 2))\n assert_true(all(is_reachable(G, v, v) for v in G))","def test_wp_association_bp(self):\n test_graph = wikipathways_to_bel(WP2359, self.hgnc_manager)\n\n self.assertEqual(type(test_graph), BELGraph, msg='Error with graph type')\n\n self.assertEqual(test_graph.summary_dict()['Number of Nodes'], 2)\n self.assertEqual(test_graph.summary_dict()['Number of Edges'], 1)\n self.assertEqual(count_relations(test_graph)['regulates'], 1)","def test_set_node_second_level_component_with_first_level_parent(self):\n payload = {\n 'data': [\n {\n 'type': 'nodes',\n 'id': self.first_level_component._id\n },\n {\n 'type': 'nodes',\n 'id': self.second_level_component._id\n }\n ]\n }\n res = self.app.post_json_api(self.url, payload, auth=self.user.auth)\n self.view_only_link.reload()\n assert_equal(res.status_code, 201)\n assert_in(self.first_level_component, self.view_only_link.nodes.all())\n assert_in(self.second_level_component, self.view_only_link.nodes.all())","def test_n_path_reactions(self):\n self.assertEqual(self.Npath, 3)","def get_subgraphs(graph):\n nodes_powerset = get_nodes_combinations(graph)\n #print(\"Doing\")\n #draw_graph(graph)\n subgraphs = []\n for nodes in nodes_powerset:\n subg = graph.subgraph(nodes)\n nodes = subg.nodes(data=True)\n if nx.is_weakly_connected(subg):\n subgraphs.append(subg)\n return subgraphs","def test_ExplorePath( self ):\n links = []\n n1 = graph.Node( 10, 10 )\n n2 = graph.Node( 10, 20 )\n n3 = graph.Node( 10, 30 )\n n4a = graph.Node( 5, 40 )\n n4b = graph.Node( 15, 40 )\n n5a = graph.Node( 5, 50 )\n n5b = graph.Node( 15, 50 )\n n6a = graph.Node( 5, 60 )\n n6b = graph.Node( 15, 60 )\n n7 = graph.Node( 10, 70 )\n\n links.append( graph.Link( n1, n2 ) )\n links.append( graph.Link( n2, n3 ) )\n links.append( graph.Link( n3, n4a ) )\n links.append( graph.Link( n3, n4b ) )\n links.append( graph.Link( n4a, n5a ) )\n links.append( graph.Link( n4b, n5b ) )\n links.append( graph.Link( n5a, n6a ) )\n links.append( graph.Link( n6a, n7 ) )\n links.append( graph.Link( n5b, n7 ) )\n roots = [n1]\n actual = nodes.explorePath( links, n1, n1 )\n expected = [ n1, n2, n3, n4b, n5b, n7 ]\n self.assertEqual( expected, actual )","def test_cycle(self):\n g = Graph(3)\n g.add_edge(0, 1)\n g.add_edge(0, 2)\n # g.add_edge(0, 0)\n assert g.contains_cycle() is False\n g.add_edge(1, 2)\n assert g.contains_cycle() is True","def graph(self):\n ...","def iter_func(root_name, root, set_traverse, list_funcs, G, strings,\n plot_nodes, cur_pos, xgrain, min_weight, max_weight):\n set_traverse.append(root)\n nbs = G.neighbors(root)\n nbs = G[root]\n\n plot_nodes.append(cur_pos)\n xgrain = xgrain/2.0\n\n flag_pn = -1\n for nb in nbs.keys():\n if nb in set_traverse:\n continue\n\n next_pos = [0, 0, 0]\n if root.name == root_name:\n next_pos[0] = cur_pos[0]\n else:\n next_pos[0] = cur_pos[0] + xgrain*flag_pn*( 0.8+0.2*(nbs[nb][\"weight\"]-1.0/max_weight)/(1.0/min_weight-1.0/max_weight) ) #* (nbs[nb][\"weight\"]-1.0/max_weight)/(1.0/min_weight-1.0/max_weight)\n next_pos[1] = cur_pos[1] + 3.0*(nbs[nb][\"weight\"]-1.0/max_weight)/(1.0/min_weight-1.0/max_weight)\n next_pos[2] = nb.name\n\n flag_pn = flag_pn*(-1)\n\n strings.append([root, nb])\n set_traverse, strings, plot_nodes = iter_func(root_name, nb, set_traverse, list_funcs, G, strings, plot_nodes, next_pos, xgrain, min_weight, max_weight)\n\n return set_traverse, strings, plot_nodes","def pick_androdioecious_parents(simu, config):\n rng = simu.getRNG()\n runif = rng.randUniform\n rint = rng.randInt\n try:\n sstar = config.sstar\n def compound_generator(pop):\n \"\"\"\n Picks up parent(s) under androdioecy using a compound parameter.\n \"\"\"\n gen = -1\n while True:\n ngen = pop.dvars().gen\n if gen != ngen:\n # At the beginning of a generation, extract the\n # sex-specific subpopulations from a parental\n # population. The sex-specific subpopulations are used\n # throughout mating events in one generation.\n gen = ngen\n males = pop.extractSubPops(subPops=[(0, 0)])\n herms = pop.extractSubPops(subPops=[(0, 1)])\n nmale = males.popSize()\n nherm = herms.popSize()\n\n if runif() < sstar: # uniparental\n yield herms.individual(rint(nherm))\n else: # biparental\n yield [males.individual(rint(nmale)), herms.individual(rint(nherm))]\n return compound_generator\n except KeyError:\n stilde = config.stilde\n tau = config.tau\n def compound_generator(pop):\n \"\"\"\n Picks up parent(s) under androdioecy using fundamental parameters.\n \"\"\"\n gen = -1\n while True:\n ngen = pop.dvars().gen\n if gen != ngen:\n # At the beginning of a generation, extract the\n # sex-specific subpopulations from a parental\n # population. The sex-specific subpopulations are used\n # throughout mating events in one generation.\n gen = ngen\n males = pop.extractSubPops(subPops=[(0, 0)])\n herms = pop.extractSubPops(subPops=[(0, 1)])\n nmale = males.popSize()\n nherm = herms.popSize()\n\n if runif() < stilde: # proportion of self-fertlized egg\n if runif() < tau: # survival rate of a uniparental zygote rel to a biparental z.\n yield herms.individual(rint(nherm))\n else: # biparental\n yield [males.individual(rint(nmale)), herms.individual(rint(nherm))]\n return compound_generator","def test_12_parents_as_minus_1_0_0_1_1_2_3_4_4_5_7_7_9_12(self):\n parents = [ -1, 0, 0, 1, 1, 2, 3, 4, 4, 5, 7, 7, 9, 12 ]\n self.init(parents)\n self.assertEqual(6, self.method_under_test())","def crossover(parent1, parent2):\n path1 = random_subtree(parent1, \"\")\n path2 = random_subtree(parent2, \"\")\n parent1 = parent1.copy()\n parent2 = parent2.copy()\n loc1 = parent1\n loc1parent = parent1\n loc2 = parent2\n loc2parent = parent2\n for i in range(len(path1)):\n loc1parent = loc1\n if path1[i] == \"1\":\n loc1 = loc1parent.get_right()\n else:\n loc1 = loc1parent.get_left()\n for i in range(len(path2)):\n loc2parent = loc2\n if path2[i] == \"1\":\n loc2 = loc2parent.get_right()\n else:\n loc2 = loc2parent.get_left()\n if(len(path1)-1 >= 0):\n if path1[len(path1)-1] == \"1\":\n loc1parent.right = loc2\n else:\n loc1parent.left = loc2\n if(len(path2) - 1 >= 0):\n if path2[-1] == \"1\":\n loc2parent.right = loc1\n else:\n loc2parent.left = loc1\n return (parent1, parent2)","def test_4_parents_as_minus_1_0_1_2(self):\n parents = [ -1, 0, 1, 2 ]\n self.init(parents)\n self.assertEqual(4, self.method_under_test())","def testBasic1(self):\n nodes = self.G.nodes()\n assert len(nodes) == len( set(nodes) )","def test_5_parents_as_1_minus_1_1_0_0(self):\n parents = [ 1, -1, 1, 0, 0 ]\n self.init(parents)\n self.assertEqual(3, self.method_under_test())","def test_3_parents_as_1_minus_1_1(self):\n parents = [ 1, -1, 1 ]\n self.init(parents)\n self.assertEqual(2, self.method_under_test())"],"string":"[\n \"def test_documentation_popxl_basic_subgraph(self):\\n filename = \\\"basic_graph.py\\\"\\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)\",\n \"def test_documentation_popxl_create_multi_subgraph(self):\\n filename = \\\"create_multi_graphs_from_same_func.py\\\"\\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)\",\n \"def test_dummy3(self):\\n xpb = XPathBuilder()\\n xp = xpb.dummy()\\n self.assertTrue(xp.parenthesize() is xp)\",\n \"def test_documentation_popxl_repeat_1(self):\\n filename = \\\"repeat_graph_1.py\\\"\\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)\",\n \"def test_documentation_popxl_repeat_0(self):\\n filename = \\\"repeat_graph_0.py\\\"\\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)\",\n \"def sub_graph_merging(self):\",\n \"def test_lacking_parent(self):\\n pass\",\n \"def crossover(self, pop):\\n intDiscID = self.iID + self.dID\\n varID = self.cID\\n goldenRatio = (1.0 + sqrt(5)) / 2.0\\n dx = np.zeros_like(pop[0])\\n children = []\\n used = []\\n for i in range(0, int(self.fracElite * len(pop)), 1):\\n r = int(rand() * self.population)\\n while r in used or r == i:\\n r = int(rand() * self.population)\\n\\n used.append(i)\\n children.append(cp.deepcopy(pop[r]))\\n dx = abs(pop[i] - children[i]) / goldenRatio\\n children[i] = children[i] + dx * varID + np.round(dx * intDiscID)\\n children[i] = simple_bounds(children[i], self.lb, self.ub)\\n\\n return (\\n children, used)\",\n \"def test_parent_label(self):\\n l = self.d.label(1)\\n l2 = self.d.label(31405)\\n\\n self.assertTrue(l.parent_label is None)\\n self.assertTrue(l2 in l.sublabels)\\n self.assertEqual(l2.parent_label, l)\",\n \"def test_parent_with_iterables(self):\\n def makeCubesAndGrp():\\n cmds.file(new=1, f=1)\\n cubes = []\\n for x in range(10):\\n cubes.append(pm.polyCube()[0])\\n group = pm.group(empty=True)\\n return cubes, group\\n\\n cubes, group = makeCubesAndGrp()\\n res1 = pm.parent(cubes[:4], group)\\n self.assertEqual(cubes[:4], res1)\\n res2 = pm.parent(cubes, group)\\n self.assertEqual(cubes[4:], res2)\\n\\n cubes, group = makeCubesAndGrp()\\n res1 = pm.parent(cubes[:4] + [group])\\n self.assertEqual(cubes[:4], res1)\\n res2 = pm.parent(cubes, group)\\n self.assertEqual(cubes[4:], res2)\\n\\n cubes, group = makeCubesAndGrp()\\n res1 = pm.parent(cubes[0], cubes[1], cubes[2], cubes[3], group)\\n self.assertEqual(cubes[:4], res1)\\n res2 = pm.parent(cubes, group)\\n self.assertEqual(cubes[4:], res2)\\n\\n cubes, group = makeCubesAndGrp()\\n res1 = pm.parent(cubes[0], cubes[1], [cubes[2], cubes[3], group])\\n self.assertEqual(cubes[:4], res1)\\n res2 = pm.parent(cubes, group)\\n self.assertEqual(cubes[4:], res2)\\n\\n cubes, group = makeCubesAndGrp()\\n res1 = pm.parent([cubes[0], cubes[1]], cubes[2], [cubes[3], group])\\n self.assertEqual(cubes[:4], res1)\\n res2 = pm.parent(cubes, group)\\n self.assertEqual(cubes[4:], res2)\",\n \"def testParent(self):\\n self.assertEqual(\\n self,\\n self.node.parent\\n )\",\n \"def testParent(self):\\n self.assertEqual(\\n self,\\n self.node.parent\\n )\",\n \"def testParent(self):\\n self.assertEqual(\\n self.parent,\\n self.mr.parent\\n )\",\n \"def test_simple(self):\\n exp = [{'type': NewickEvents.OPEN_SUBTREE, 'comments': []},\\n {'type': NewickEvents.OPEN_SUBTREE, 'comments': []},\\n {'edge_info': None, 'type': NewickEvents.TIP, 'comments': [], 'label': 'h'},\\n {'edge_info': None, 'type': NewickEvents.TIP, 'comments': [], 'label': 'p'},\\n {'edge_info': '1', 'type': NewickEvents.CLOSE_SUBTREE,\\n 'comments': [], 'label': 'hp'},\\n {'edge_info': None, 'type': NewickEvents.TIP, 'comments': [], 'label': 'g'},\\n {'edge_info': None, 'type': NewickEvents.CLOSE_SUBTREE,\\n 'comments': [], 'label': 'hpg'}\\n ]\\n content = '((h,p)hp:1,g)hpg;'\\n self._do_test(content, exp)\\n content = '((h,[pretest]p[test][posttest])hp,g)hpg;'\\n exp = [{'type': NewickEvents.OPEN_SUBTREE, 'comments': []},\\n {'type': NewickEvents.OPEN_SUBTREE, 'comments': []},\\n {'edge_info': None, 'type': NewickEvents.TIP, 'comments': [], 'label': 'h'},\\n {'edge_info': None, 'type': NewickEvents.TIP,\\n 'comments': ['pretest', 'test', 'posttest'], 'label': 'p'},\\n {'edge_info': None, 'type': NewickEvents.CLOSE_SUBTREE,\\n 'comments': [], 'label': 'hp'},\\n {'edge_info': None, 'type': NewickEvents.TIP, 'comments': [], 'label': 'g'},\\n {'edge_info': None, 'type': NewickEvents.CLOSE_SUBTREE,\\n 'comments': [], 'label': 'hpg'}\\n ]\\n self._do_test(content, exp)\",\n \"def reproduce(self, popDensity, activeDrugs):\\r\\n # TODO\\r\\n\\r\\n resistAll = True\\r\\n for drug in activeDrugs:\\r\\n if (self.resistances[drug]==False):resistAll = False\\r\\n \\r\\n\\r\\n\\r\\n maxReproduceProb = self.maxBirthProb * (1 - popDensity)\\r\\n \\r\\n \\r\\n if (resistAll and random.random() < maxReproduceProb):\\r\\n childResistances = {}\\r\\n for drug in self.resistances:\\r\\n if random.random() < self.mutProb:\\r\\n childResistances[drug] = (not self.resistances[drug])\\r\\n else:childResistances[drug] = (self.resistances[drug])\\r\\n\\r\\n \\r\\n childOfVirus = ResistantVirus(self.maxBirthProb, self.clearProb,childResistances,self.mutProb)\\r\\n return childOfVirus\\r\\n \\r\\n else: raise NoChildException('Child not created!')\",\n \"def test_generator8(self):\\n xpb = XPathBuilder()\\n xp1 = xp2 = None\\n base_xp = (xpb.foo.bar | xpb.x.y).parenthesize()\\n base_gen = None\\n with base_xp as b:\\n base_gen = b\\n xp1 = b() & xpb.c\\n xp2 = b() & xpb.d\\n xp1_exp = '(/foo/bar or /x/y) and /c'\\n xp2_exp = '(/foo/bar or /x/y) and /d'\\n base_exp = '(/foo/bar or /x/y)'\\n # check tree structure\\n self.assertTrue(base_xp._parent is None)\\n self.assertTrue(base_gen._parent is None)\\n # check xpath\\n self.assertEqual(xp1.tostring(), xp1_exp)\\n self.assertEqual(xp2.tostring(), xp2_exp)\\n self.assertEqual(base_xp.tostring(), base_exp)\\n self.assertEqual(base_gen.tostring(), base_exp)\",\n \"def _cross_parents(self):\\n while len(self.children_population) < self.children_count:\\n parent_1, parent_2 = random.sample(self.population, k=2)\\n self.children_population.extend(self.crossover.cross(parent_1, parent_2))\",\n \"def test_documentation_popxl_multi_callsites_graph_input(self):\\n filename = \\\"multi_call_graph_input.py\\\"\\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)\",\n \"def inversion_crossover(self, pop):\\n children, tmpNonComb, used = ([] for i in range(3))\\n for i in range(0, int(len(pop) * self.fracElite), 1):\\n r = int(rand() * len(pop))\\n while r == i:\\n r = int(rand() * len(pop))\\n\\n if sum(self.cID + self.dID + self.iID) != 0:\\n nonComb1 = pop[i][:np.where(self.cID + self.dID + self.iID == 1)[0][(-1)] + 1]\\n nonComb2 = pop[r][:np.where(self.cID + self.dID + self.iID == 1)[0][(-1)] + 1]\\n if sum(self.xID) != 0:\\n comb1 = pop[i][:np.where(self.xID == 1)[0][(-1)] + 1]\\n comb2 = pop[r][:np.where(self.xID == 1)[0][(-1)] + 1]\\n if sum(self.cID + self.dID + self.iID) != 0:\\n c = int(rand() * len(nonComb1))\\n if rand() > 0.5:\\n tmpNonComb.append(np.array(nonComb1[0:c + 1].tolist() + nonComb2[c + 1:].tolist()))\\n else:\\n tmpNonComb.append(np.array(nonComb2[0:c + 1].tolist() + nonComb1[c + 1:].tolist()))\\n used.append(i)\\n if sum(self.xID) != 0:\\n c = int(rand() * len(comb1))\\n for c1 in range(c, len(comb1), 1):\\n d2 = (contains_sublist(comb2, comb1[c1]) + 1) % len(comb1)\\n d1 = contains_sublist(comb1, comb2[d2])\\n c2 = contains_sublist(comb2, comb1[((d1 + 1) % len(comb1))]) % len(comb1)\\n tmp1 = cp.copy(comb1)\\n if c1 < d1:\\n tmp1[(c1 + 1):(d1 + 1)] = list(reversed(tmp1[c1 + 1:d1 + 1]))\\n else:\\n tmp1[d1:c1] = list(reversed(tmp1[d1:c1]))\\n tmp2 = cp.copy(comb2)\\n if c2 < d2:\\n tmp2[c2:d2] = list(reversed(tmp2[c2:d2]))\\n else:\\n tmp2[(d2 + 1):(c2 + 1)] = list(reversed(tmp2[d2 + 1:c2 + 1]))\\n if sum(self.cID + self.dID + self.iID) == 0 and sum(self.xID) != 0:\\n children.append(tmp1)\\n children.append(tmp2)\\n elif sum(self.cID + self.dID + self.iID) != 0 and sum(self.xID) != 0:\\n children.append(np.concatenate(tmpNonComb[(-1)], tmp1))\\n children.append(np.concatenate(tmpNonComb[(-1)], tmp2))\\n used.append(i)\\n used.append(r)\\n\\n if sum(self.cID + self.dID + self.iID) != 0 and sum(self.xID) == 0:\\n children = tmpNonComb\\n return (\\n children, used)\",\n \"def mutate(self, pop):\\n intDiscID = self.iID + self.dID\\n varID = self.cID\\n children = []\\n k = rand(len(pop), len(pop[0])) > self.fracMutation * rand()\\n childn1 = cp.copy(permutation(pop))\\n childn2 = cp.copy(permutation(pop))\\n r = rand()\\n for j in range(0, len(pop), 1):\\n n = np.array(childn1[j] - childn2[j])\\n stepSize = r * n * varID + (n * intDiscID).astype(int)\\n tmp = (pop[j] + stepSize * k[j, :]) * varID + (pop[j] + stepSize * k[j, :]) * intDiscID % (self.ub + 1 - self.lb)\\n children.append(simple_bounds(tmp, self.lb, self.ub))\\n\\n return children\",\n \"def test_aggregation(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.aggregation_default = 'a'\\n cfg.genome.aggregation_options = {'a': 1, 'b': 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.aggregation == gene1.aggregation:\\n p1 = True\\n elif gene3.aggregation == gene2.aggregation:\\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.aggregation, gene1.aggregation)\\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.aggregation, gene2.aggregation)\",\n \"def test_get_child():\\n \\n root_ts = TrackSegment(flow_dict=flow_dict) \\n \\n # ROOT MODULE\\n start = root_ts.get_child(\\\"start\\\")\\n # check depth\\n assert(start.depth == root_ts.depth+1)\\n # check parent\\n assert(start.parent is root_ts)\\n # check module_id\\n assert(start.module_id == 'start')\\n \\n # CHILD (1,2,3)\\n root_ts_get_child_result = root_ts.get_child((1,2,3))\\n # check depth\\n assert(root_ts_get_child_result.depth == root_ts.depth+1)\\n # check parent\\n assert(root_ts_get_child_result.parent is root_ts)\\n \\n # CHILD (2,3,4)\\n root_ts_get_child_result2 = root_ts_get_child_result.get_child((2,3,4))\\n # check depth\\n assert(root_ts_get_child_result2.depth == root_ts.depth+2)\\n # check parent \\n assert(root_ts_get_child_result2.parent is root_ts_get_child_result)\\n \\n print(\\\"TEST GET_CHILD: success!\\\")\",\n \"def test_has_children_property(mock_amg):\\n\\n # split a cell so we can be sure it should have children\\n mock_amg.cells[4].split()\\n\\n assert mock_amg.cells[4].has_children()\\n assert not mock_amg.cells[1].has_children()\\n assert not mock_amg.cells[4].children['bl'].has_children()\",\n \"def test_aggregation(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.aggregation_default = 'a'\\n cfg.genome.aggregation_options = {'a': 1, 'b': 2}\\n gene1, gene2 = get_output_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.aggregation == gene1.aggregation:\\n p1 = True\\n elif gene3.aggregation == gene2.aggregation:\\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.aggregation, gene1.aggregation)\\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.aggregation, gene2.aggregation)\",\n \"def run_tests(g: Graph) -> None:\\n print( g.nodes() , \\\"->\\\" , ', '.join([f\\\"{l}\\\" for l in g.scc()]) , f\\\"({g.cyclic()})\\\" )\\n for n in g.nodes():\\n for m in [m for m in g.nodes() if m != n]:\\n p = g.path(n,m)\\n if p is not None:\\n assert p[0] == n\\n assert p[-1] == m\\n for i in range(1,len(p)):\\n assert g.is_edge(p[i-1], p[i])\\n print(\\\" \\\", n, \\\"->\\\", m, \\\":\\\", ' -> '.join([f\\\"{v}\\\" for v in p]))\",\n \"def bclone():\\n node = nuke.selectedNodes()\\n if len(node)==1:\\n clone1 = nuke.createNode(\\\"NoOp\\\", inpanel = False)\\n clone1.setName(\\\"Bclone\\\")\\n clone1['label'].setValue(node[0].name()+\\\"\\\\nClone_Parent\\\")\\n clone1['tile_color'].setValue(2521651711)\\n clone1['note_font_color'].setValue(1583243007)\\n clone1xpos = clone1['xpos'].getValue()\\n clone1ypos = clone1['ypos'].getValue()\\n \\n clone2 = nuke.createNode(\\\"NoOp\\\", inpanel = False)\\n clone2.setName(\\\"Bclone\\\")\\n clone2['label'].setValue(node[0].name()+\\\"\\\\nClone\\\")\\n clone2['hide_input'].setValue(True)\\n clone2['tile_color'].setValue(2521651711)\\n clone2['note_font_color'].setValue(1583243007)\\n clone2['xpos'].setValue(clone1xpos)\\n clone2['ypos'].setValue(clone1ypos)\\n\\n if len(node)==0:\\n clone1 = nuke.createNode(\\\"NoOp\\\", inpanel = False)\\n clone1.setName(\\\"Bclone\\\")\\n clone1['label'].setValue(\\\"Clone_Parent\\\")\\n clone1['tile_color'].setValue(2521651711)\\n clone1['note_font_color'].setValue(1583243007)\\n clone1xpos = clone1['xpos'].getValue()\\n clone1ypos = clone1['ypos'].getValue()\\n \\n clone2 = nuke.createNode(\\\"NoOp\\\", inpanel = False)\\n clone2.setName(\\\"Bclone\\\")\\n clone2['label'].setValue(\\\"Clone\\\")\\n clone2['hide_input'].setValue(True)\\n clone2['tile_color'].setValue(2521651711)\\n clone2['note_font_color'].setValue(1583243007)\\n clone2['xpos'].setValue(clone1xpos)\\n clone2['ypos'].setValue(clone1ypos)\\n if len(node)!=0 and len(node)!=1:\\n nuke.message('Just select one node to clone !')\",\n \"def show_subgraph(dfs_codes, nsupport, mapper):\\n\\tglobal __subgraph_count\\n\\n\\tg = build_graph(dfs_codes)\\n\\tg.id = __subgraph_count\\n\\t__subgraph_count += 1\\n\\tg.gprint(nsupport, mapper)\",\n \"def parents_loop(self):\\r\\n while len(self.parents) > 0:\\r\\n children = 0\\r\\n self.parent1 = random.choice(self.parents)\\r\\n index = self.parents.index(self.parent1)\\r\\n del self.parents[index]\\r\\n\\r\\n self.parent2 = random.choice(self.parents)\\r\\n index = self.parents.index(self.parent2)\\r\\n del self.parents[index]\\r\\n\\r\\n while children < 2:\\r\\n self.child = copy.deepcopy(self.parent1)\\r\\n \\r\\n self.battery_loop()\\r\\n\\r\\n childsolution = random_algo.Random(self.child, self.cable_cost, self.battery_cost)\\r\\n childsolution.change_battery_or_house('change_battery')\\r\\n childsolution.change_battery_or_house('change_house')\\r\\n\\r\\n if (self.child.valid_solution() and self.child not in self.district_population\\r\\n and self.child not in self.best_districts and self.child not in self.worst_districts):\\r\\n self.district_population.append(self.child)\\r\\n self.cost_populations.append(self.child.total_cost(self.battery_cost, self.cable_cost))\\r\\n children += 1\",\n \"def test_restore_multiple_in_subgraph(self):\\n subgraph = self._subgraph()\\n subgraph['id'] = 15\\n task1 = self._remote_task()\\n task1['id'] = 1\\n task2 = self._remote_task()\\n task2['id'] = 2\\n task1['parameters']['containing_subgraph'] = 15\\n task2['parameters']['containing_subgraph'] = 15\\n\\n graph = self._restore_graph([subgraph, task1, task2])\\n assert len(graph.tasks) == 3\\n subgraphs = [op for op in graph.tasks if op.is_subgraph]\\n remote_tasks = [op for op in graph.tasks if not op.is_subgraph]\\n\\n # those are all references to the same subgraph, the subgraph was\\n # NOT restored multiple times\\n assert remote_tasks[0].containing_subgraph \\\\\\n is remote_tasks[1].containing_subgraph \\\\\\n is subgraphs[0]\\n\\n assert len(subgraphs[0].tasks) == 2\",\n \"def slice_graph_fwd( startea, reg ): \\r\\n\\tgraph = vcg_Graph.vcgGraph({\\\"title\\\":'\\\"Slice for %s\\\"' % reg, \\\\\\r\\n\\t\\t\\\"manhattan_edges\\\":\\\"no\\\", \\\"layoutalgorithm\\\":\\\"maxdepth\\\"})\\r\\n\\t#\\r\\n\\t# Retrieve the name of the current basic block\\r\\n\\t# \\r\\n\\tworklist = []\\r\\n\\tdata_bib = {}\\r\\n\\tstartnode = slice_node( startea, 0, reg )\\r\\n\\trootnode = graph.Add_Node( startnode.to_name() )\\r\\n\\tdata_bib[ startnode.to_name() ] = startnode\\r\\n\\tworklist.insert( 0, rootnode )\\r\\n\\twhile len( worklist ) > 0:\\r\\n\\t\\tcurrnode = worklist.pop()\\r\\n\\t\\tcurrslice = data_bib[ currnode.get_name() ]\\r\\n\\t\\ttgt_reg = currslice.get_target_reg()\\r\\n\\t\\tif tgt_reg == \\\"END\\\":\\r\\n\\t\\t# Do not process this node any further\\r\\n\\t\\t\\tpass\\r\\n\\t\\telif tgt_reg == \\\"\\\" or (( len( currslice.get_lines()) > 0) and \\\\\\r\\n\\t\\t\\tcurrslice.endea != currslice.get_lines()[-1][0]):\\r\\n\\t\\t\\t# Nothing much happening here, just proceed to parent bocks\\r\\n\\t\\t\\tif ua_mnem( currslice.endea ) == \\\"call\\\":\\r\\n\\t\\t\\t\\txrefs = get_short_crefs_from( currslice.endea )\\r\\n\\t\\t\\telse:\\r\\n\\t\\t\\t\\txrefs = get_crefs_from( currslice.endea )\\r\\n\\t\\t\\tfor ref in xrefs:\\r\\n\\t\\t\\t\\tnewslice = slice_node( ref, 0, currslice.reg )\\r\\n\\t\\t\\t\\tif graph.Get_Node( newslice.to_name() ) == 0:\\r\\n\\t\\t\\t\\t\\tnewnode = graph.Add_Node( newslice.to_name() )\\r\\n\\t\\t\\t\\t\\tworklist.insert( 0, newnode )\\r\\n\\t\\t\\t\\t\\tdata_bib[ newslice.to_name() ] = newslice\\r\\n\\t\\t\\t\\tgraph.Add_Link( currnode.get_name(), newslice.to_name())\\r\\n\\t\\telse:\\r\\n\\t\\t\\t# Register was modified, use new register\\r\\n\\t\\t\\txrefs = get_crefs_from( currslice.endea )\\r\\n\\t\\t\\tfor ref in xrefs:\\r\\n\\t\\t\\t\\tnewslice = slice_node( ref, 0, tgt_reg )\\r\\n\\t\\t\\t\\tif graph.Get_Node( newslice.to_name() ) == 0:\\r\\n\\t\\t\\t\\t\\tnewnode = graph.Add_Node( newslice.to_name() )\\r\\n\\t\\t\\t\\t\\tworklist.insert( 0, newnode )\\r\\n\\t\\t\\t\\t\\tdata_bib[ newslice.to_name() ] = newslice\\r\\n\\t\\t\\t\\tgraph.Add_Link( currnode.get_name(), newslice.to_name())\\r\\n\\t\\t\\txrefs = get_crefs_from( currslice.endea )\\r\\n\\t\\t\\tfor ref in xrefs:\\r\\n\\t\\t\\t\\tnewslice = slice_node( ref, 0, currslice.reg )\\r\\n\\t\\t\\t\\tif graph.Get_Node( newslice.to_name() ) == 0:\\r\\n\\t\\t\\t\\t\\tnewnode = graph.Add_Node( newslice.to_name() )\\r\\n\\t\\t\\t\\t\\tworklist.insert( 0, newnode )\\r\\n\\t\\t\\t\\t\\tdata_bib[ newslice.to_name() ] = newslice\\r\\n\\t\\t\\t\\tgraph.Add_Link( currnode.get_name(), newslice.to_name())\\r\\n\\treturn [ graph, data_bib ]\",\n \"def testParentage(self):\\n self.assertEqual(\\n self.cd,\\n self.media_ref.parent\\n )\\n\\n self.assertEqual(\\n self.cd,\\n self.cc.parent\\n )\",\n \"def test_generator7(self):\\n xpb = XPathBuilder()\\n xp1 = xp2 = None\\n base_xp = xpb.foo.bar & xpb.x.y\\n base_gen = None\\n with base_xp as b:\\n base_gen = b\\n xp1 = b() | xpb.c\\n xp2 = b() | xpb.d\\n xp1_exp = '/foo/bar and /x/y or /c'\\n xp2_exp = '/foo/bar and /x/y or /d'\\n base_exp = '/foo/bar and /x/y'\\n # check tree structure\\n self.assertTrue(base_xp._parent is None)\\n self.assertTrue(base_gen._parent is None)\\n # check xpath\\n self.assertEqual(xp1.tostring(), xp1_exp)\\n self.assertEqual(xp2.tostring(), xp2_exp)\\n self.assertEqual(base_xp.tostring(), base_exp)\\n self.assertEqual(base_gen.tostring(), base_exp)\",\n \"def populate_graph(self):\",\n \"def test_dag_preserves_superrep(dimension, conversion):\\n qobj = conversion(rand_super_bcsz(dimension))\\n assert qobj.superrep == qobj.dag().superrep\",\n \"def test__graph_structure():\\n assert PES_GRAPH == (\\n ('CH2CH2+OH', 'CH2CH+H2O', 'C2H4OH', 'C2H5O', 'CH3CHO+H'),\\n (frozenset({0, 1}), frozenset({0, 2}), frozenset({2, 3}),\\n frozenset({3, 4}), frozenset({1, 2})))\\n assert pgraph.species(PES_GRAPH) == (\\n ('CH2CH2+OH', 'CH2CH+H2O', 'C2H4OH', 'C2H5O', 'CH3CHO+H'))\\n assert pgraph.channels(PES_GRAPH) == (\\n (frozenset({0, 1}), frozenset({0, 2}), frozenset({2, 3}),\\n frozenset({3, 4}), frozenset({1, 2})))\\n print('\\\\npes graph')\\n print(PES_GRAPH)\",\n \"def test_fpy_parent():\\n\\n data = \\\"\\\"\\\"\\n<depletion_chain>\\n <nuclide name=\\\"U235\\\" reactions=\\\"1\\\">\\n <reaction type=\\\"fission\\\" Q=\\\"193405400.0\\\"/>\\n <neutron_fission_yields>\\n <energies>0.0253</energies>\\n <fission_yields energy=\\\"0.0253\\\">\\n <products>Te134 Zr100 Xe138</products>\\n <data>0.062155 0.0497641 0.0481413</data>\\n </fission_yields>\\n </neutron_fission_yields>\\n </nuclide>\\n <nuclide name=\\\"U238\\\" reactions=\\\"1\\\">\\n <reaction type=\\\"fission\\\" Q=\\\"200.0e6\\\"/>\\n <neutron_fission_yields parent=\\\"U235\\\"/>\\n </nuclide>\\n</depletion_chain>\\n \\\"\\\"\\\"\\n\\n root = ET.fromstring(data)\\n elems = root.findall('nuclide')\\n u235 = nuclide.Nuclide.from_xml(elems[0], root)\\n u238 = nuclide.Nuclide.from_xml(elems[1], root)\\n\\n # Make sure U238 yield is same as U235\\n assert np.array_equal(u238.yield_data.energies, u235.yield_data.energies)\\n assert np.array_equal(u238.yield_data.yield_matrix, u235.yield_data.yield_matrix)\\n\\n # Make sure XML element created has single attribute\\n elem = u238.to_xml_element()\\n fpy_elem = elem.find('neutron_fission_yields')\\n assert fpy_elem.get('parent') == 'U235'\\n assert len(fpy_elem) == 0\\n\\n data = \\\"\\\"\\\"\\n<depletion_chain>\\n <nuclide name=\\\"U235\\\" reactions=\\\"1\\\">\\n <reaction type=\\\"fission\\\" Q=\\\"193405400.0\\\"/>\\n </nuclide>\\n <nuclide name=\\\"U238\\\" reactions=\\\"1\\\">\\n <reaction type=\\\"fission\\\" Q=\\\"200.0e6\\\"/>\\n <neutron_fission_yields parent=\\\"U235\\\"/>\\n </nuclide>\\n</depletion_chain>\\n \\\"\\\"\\\"\\n\\n # U235 yields are missing, so we should get an exception\\n root = ET.fromstring(data)\\n elems = root.findall('nuclide')\\n with pytest.raises(ValueError, match=\\\"yields\\\"):\\n u238 = nuclide.Nuclide.from_xml(elems[1], root)\",\n \"def test_skirmish_parenting(self):\\n root = SkirmishAction()\\n a1 = SkirmishAction()\\n a2 = SkirmishAction()\\n self.sess.add_all([root, a1, a2])\\n self.sess.commit()\\n\\n root.children.append(a1)\\n root.children.append(a2)\\n self.sess.commit()\\n\\n self.assertEqual(a1.parent_id, root.id)\\n self.assertEqual(a2.parent_id, root.id)\",\n \"def test_generator6(self):\\n xpb = XPathBuilder()\\n xp1 = xp2 = None\\n base_xp = xpb.base.foo.bar\\n base_gen = None\\n with base_xp as b:\\n base_gen = b\\n xp1 = xpb.a.b.c.join(b())\\n xp2 = xpb.test.join(b())\\n xp1_exp = '/a/b/c/base/foo/bar'\\n xp2_exp = '/test/base/foo/bar'\\n base_exp = '/base/foo/bar'\\n # check tree structure\\n self.assertTrue(base_xp._parent is None)\\n self.assertTrue(len(base_xp._children[0]._children[0]._children) == 0)\\n self.assertTrue(base_gen._parent is None)\\n self.assertTrue(len(base_gen._children) == 0)\\n # check xpath\\n self.assertEqual(xp1.tostring(), xp1_exp)\\n self.assertEqual(xp2.tostring(), xp2_exp)\\n self.assertEqual(base_xp.tostring(), base_exp)\\n self.assertEqual(base_gen.tostring(), base_exp)\",\n \"def test_grandchildren():\\n\\n # note c.upto(\\\"status\\\").desired.grandchildren\\n # this is the same as *c.upto(\\\"status\\\").desired in python3.5+\\n res = conf.status.conditions.choose(lambda c: (c.type, c.reason, c.upto(\\\"status\\\").desired.grandchildren))\\n assert \\\"type\\\" in res\\n assert \\\"reason\\\" in res\\n assert \\\"version\\\" in res\\n assert \\\"image\\\" in res\\n assert \\\"force\\\" in res\",\n \"def subplot_1(self, Graph, n_tabs):\\n # The code below walks does a pre-order traversal of the tree\\n # For exact details about the structure of self.Graph refer description in init function.\\n\\n attr_name = list(Graph.keys())[0]\\n print(\\\"\\\\t\\\"*(n_tabs),\\\"feature name :\\\",attr_name)\\n for val in list(Graph[attr_name].keys()):\\n print(\\\"\\\\t\\\"*(n_tabs+1),\\\"feature value :\\\",val)\\n sub_graph = Graph[attr_name][val]\\n if (type(sub_graph)==dict):\\n self.subplot_1(sub_graph, n_tabs+2)\\n else:\\n print(\\\"\\\\t\\\"*(n_tabs+2),\\\"class :\\\", sub_graph)\",\n \"def hasSiblings():\",\n \"def test_get_related_nodes(self):\\n pass\",\n \"def test_createSubLinkographWithoutCommands(self):\\n self.performTestForParams()\",\n \"def crossover (self, p1, p2, p_pop, c1, c2, c_pop) :\\n assert self.crossover_count < self.pop_size\\n assert self.get_iteration () == self.last_gen\\n self.parents.append (p1)\\n self.parents.append (p2)\\n self.crossover_count += 2\\n if self.crossover_count == self.pop_size :\\n assert (self.get_iteration () == self.last_gen)\\n print (self.get_iteration ())\\n sys.stdout.flush ()\\n self.build_model (p_pop)\\n self.sample_model (c1, c2, c_pop)\\n self.crossover_count = 0\\n self.parents = []\\n self.children = {}\\n self.last_gen += 1\\n self.clear_cache ()\",\n \"def test_Tree():\",\n \"def reproduce(self, popDensity, activeDrugs):\\n\\n # Checks the resistance of the mother virus to all drugs in activeDrugs list\\n # if mother virus is resistant to all the drugs, reproduction will proceed\\n for drug in activeDrugs:\\n if not self.isResistantTo(drug):\\n raise NoChildException()\\n\\n maxReproduceProb = self.maxBirthProb * (1 - popDensity)\\n\\n if random.random() < maxReproduceProb:\\n resistance_trait = {}\\n # Calculate the transfer of resistances property to child virus\\n for drug in self.resistances.keys():\\n if random.random() <= (1 - self.mutProb):\\n resistance_trait[drug] = self.resistances[drug]\\n else:\\n resistance_trait[drug] = not self.resistances[drug]\\n\\n childOfVirus = ResistantVirus(self.maxBirthProb, self.clearProb, resistance_trait, self.mutProb)\\n return childOfVirus\\n else:\\n raise NoChildException()\",\n \"def dump_subgraph_for_debug(self):\\n\\n import pypipegraph2 as ppg\\n\\n nodes = []\\n seen = set()\\n edges = []\\n counter = [0]\\n node_to_counters = {}\\n\\n def descend(node):\\n if node in seen:\\n return\\n seen.add(node)\\n j = self.runner.jobs[node]\\n if isinstance(j, ppg.FileInvariant):\\n nodes.append(f\\\"Path('{counter[0]}').write_text('A')\\\")\\n nodes.append(f\\\"job_{counter[0]} = ppg.FileInvariant('{counter[0]}')\\\")\\n elif isinstance(j, ppg.ParameterInvariant):\\n nodes.append(\\n f\\\"job_{counter[0]} = ppg.ParameterInvariant('{counter[0]}', 55)\\\"\\n )\\n elif isinstance(j, ppg.FunctionInvariant):\\n nodes.append(\\n f\\\"job_{counter[0]} = ppg.FunctionInvariant('{counter[0]}', lambda: 55)\\\"\\n )\\n elif isinstance(j, ppg.SharedMultiFileGeneratingJob):\\n nodes.append(\\n f\\\"job_{counter[0]} = ppg.SharedMultiFileGeneratingJob('{counter[0]}', {[x.name for x in j.files]!r}, dummy_smfg, depend_on_function=False)\\\"\\n )\\n elif isinstance(j, ppg.TempFileGeneratingJob):\\n nodes.append(\\n f\\\"job_{counter[0]} = ppg.TempFileGeneratingJob('{counter[0]}', dummy_fg, depend_on_function=False)\\\"\\n )\\n elif isinstance(j, ppg.FileGeneratingJob):\\n nodes.append(\\n f\\\"job_{counter[0]} = ppg.FileGeneratingJob('{counter[0]}', dummy_fg, depend_on_function=False)\\\"\\n )\\n elif isinstance(j, ppg.MultiTempFileGeneratingJob):\\n files = [counter[0] + \\\"/\\\" + x.name for x in j.files]\\n nodes.append(\\n f\\\"job_{counter[0]} = ppg.MultiTempFileGeneratingJob({files!r}, dummy_mfg, depend_on_function=False)\\\"\\n )\\n elif isinstance(j, ppg.MultiFileGeneratingJob):\\n files = [str(counter[0]) + \\\"/\\\" + x.name for x in j.files]\\n nodes.append(\\n f\\\"job_{counter[0]} = ppg.MultiFileGeneratingJob({files!r}, dummy_mfg, depend_on_function=False)\\\"\\n )\\n elif isinstance(j, ppg.DataLoadingJob):\\n nodes.append(\\n f\\\"job_{counter[0]} = ppg.DataLoadingJob('{counter[0]}', lambda: None, depend_on_function=False)\\\"\\n )\\n elif isinstance(j, ppg.AttributeLoadingJob):\\n nodes.append(\\n f\\\"job_{counter[0]} = ppg.AttributeLoadingJob('{counter[0]}', DummyObject(), 'attr_{counter[0]}', lambda: None, depend_on_function=False)\\\"\\n )\\n else:\\n raise ValueError(j)\\n node_to_counters[node] = counter[0]\\n counter[0] += 1\\n for parent in self.runner.dag.predecessors(node):\\n descend(parent)\\n\\n def build_edges(node):\\n for parent in self.runner.dag.predecessors(node):\\n edges.append(\\n f\\\"edges.append(('{node_to_counters[node]}', '{node_to_counters[parent]}'))\\\"\\n )\\n build_edges(parent)\\n\\n descend(self.job_id)\\n edges.append(\\\"edges = []\\\")\\n build_edges(self.job_id)\\n edges.extend(\\n [\\n \\\"for (a,b) in edges:\\\",\\n \\\" if a in ppg.global_pipegraph.jobs and b in ppg.global_pipegraph.jobs:\\\",\\n \\\" ppg.global_pipegraph.jobs[a].depends_on(ppg.global_pipegraph.jobs[b])\\\",\\n ]\\n )\\n with open(\\\"subgraph_debug.py\\\", \\\"w\\\") as op:\\n lines = \\\"\\\"\\\"\\nclass DummyObject:\\n pass\\n\\ndef dummy_smfg(files, prefix):\\n Path(prefix).mkdir(exist_ok=True, parents=True)\\n for f in files:\\n f.write_text(\\\"hello\\\")\\n\\n\\ndef dummy_mfg(files):\\n for f in files:\\n f.parent.mkdir(exist_ok=True, parents=True)\\n f.write_text(\\\"hello\\\")\\n\\ndef dummy_fg(of):\\n of.parent.mkdir(exist_ok=True, parents=True)\\n of.write_text(\\\"fg\\\")\\n\\n\\\"\\\"\\\".split(\\n \\\"\\\\n\\\"\\n )\\n lines += nodes\\n lines += edges\\n lines += [\\\"\\\", \\\"ppg.run()\\\", \\\"ppg.run\\\"]\\n\\n op.write(\\\"\\\\n\\\".join(\\\" \\\" + l for l in lines))\",\n \"def test_tree_mode4(self):\\n xpb = XPathBuilder()\\n xp_1 = xpb.foo\\n xp_2 = xpb.baz\\n xp_and = xp_1 & xp_2\\n self.assertTrue(xp_and._parent is None)\\n self.assertTrue(len(xp_and._children) == 2)\\n self.assertTrue(xp_and._children[0] is xp_1)\\n self.assertTrue(xp_and._children[1] is xp_2)\\n self.assertTrue(xp_1._parent is xp_and)\\n self.assertTrue(len(xp_1._children) == 0)\\n self.assertTrue(xp_2._parent is xp_and)\\n self.assertTrue(len(xp_2._children) == 0)\\n xp_2.reparent(None)\\n # check references after remove\\n self.assertTrue(xp_and._parent is None)\\n self.assertTrue(len(xp_and._children) == 1)\\n self.assertTrue(xp_and._children[0] is xp_1)\\n self.assertTrue(xp_1._parent is xp_and)\\n self.assertTrue(len(xp_1._children) == 0)\\n # xp_2's references were changed\\n self.assertTrue(xp_2._parent is None)\\n self.assertTrue(len(xp_2._children) == 0)\",\n \"def sample_from_subpop(instance, params, subpop):\\n y = subpop\\n x = np.random.choice([-1,+1], size=params['d'])\\n x[instance['indices'][subpop]] = instance['values'][subpop]\\n return x, y, subpop\",\n \"def __init__(self, firstParent, secondParent):\\n CrossOver.__init__(self, \\\"Group Point CrossOver\\\", firstParent, secondParent)\",\n \"def setUp(self):\\n self.complete = nx.Graph()\\n self.complete.add_edge(1, 2)\\n self.complete.add_edge(2, 3)\\n self.complete.add_edge(1, 3)\\n\\n self.small_tree = nx.Graph()\\n self.small_tree.add_edge(1, 2)\\n self.small_tree.add_edge(2, 3)\\n self.small_tree.add_edge(3, 4)\\n self.small_tree.add_edge(1, 4)\\n self.small_tree.add_edge(2, 4)\\n self.small_tree.add_edge(4, 5)\\n self.small_tree.add_edge(5, 6)\\n self.small_tree.add_edge(5, 7)\\n self.small_tree.add_edge(6, 7)\\n\\n self.deterministic_graph = nx.Graph()\\n self.deterministic_graph.add_edge(1, 2)\\n self.deterministic_graph.add_edge(1, 3)\\n self.deterministic_graph.add_edge(3, 4)\\n self.deterministic_graph.add_edge(2, 4)\\n self.deterministic_graph.add_edge(3, 5)\\n self.deterministic_graph.add_edge(4, 5)\\n self.deterministic_graph.add_edge(3, 6)\\n self.deterministic_graph.add_edge(5, 6)\",\n \"def test_1():\\n for _ in range(100):\\n G = NetworkTopo()\\n flows = MulticastFlows(G, 10, 40)\\n\\n spt = ShortestPathTree(G, flows)\\n\\n for T in spt.multicast_trees:\\n assert len(nx.cycle_basis(T)) == 0\",\n \"def reproduce(self, popDensity):\\r\\n\\r\\n # Does the virus reproduce? \\r\\n maxReproduceProb = self.maxBirthProb * (1 - popDensity)\\r\\n \\r\\n if random.random() < maxReproduceProb:\\r\\n childOfVirus = SimpleVirus(self.maxBirthProb, self.clearProb)\\r\\n return childOfVirus\\r\\n \\r\\n else: raise NoChildException('Child not created!')\",\n \"def create_subbasin_graph():\\n subbasin_to_downstream = pd.read_csv(module_dir + '/../data/simulations_shervan/test.rvh', sep='\\\\s+', skiprows=7, nrows=724, names=['subbasin', 'downstream_subbasin'], usecols=[1,2])\\n subbasin_to_downstream['subbasin'] = subbasin_to_downstream['subbasin']\\n subbasin_to_downstream['downstream_subbasin'] = 'sub' + subbasin_to_downstream['downstream_subbasin'].astype(str)\\n subbasin_to_downstream['edge'] = 1\\n\\n for subbasin in subbasin_to_downstream['subbasin'].unique():\\n is_sink = 1 if len(subbasin_to_downstream[(subbasin_to_downstream['subbasin'] == subbasin) & subbasin_to_downstream['edge'] == 1]) == 0 else 0\\n subbasin_to_downstream = subbasin_to_downstream.append({'subbasin': subbasin, 'downstream_subbasin': subbasin, 'edge': is_sink}, ignore_index=True)\\n subbasin_to_downstream = subbasin_to_downstream.append({'subbasin': 'sub-1', 'downstream_subbasin': 'sub-1', 'edge': 1}, ignore_index=True)\\n \\n adj = subbasin_to_downstream.pivot(index='subbasin', columns='downstream_subbasin', values='edge').fillna(0) \\n adj = adj.sort_index(axis=0).sort_index(axis=1)\\n \\n G = nx.from_numpy_matrix(adj.values, parallel_edges=False, create_using=nx.DiGraph())\\n label_mapping = dict(zip(range(len(adj.values)), adj.index))\\n G = nx.relabel_nodes(G, label_mapping)\\n \\n return G\",\n \"def test_input_not_subgraph(self, dim):\\r\\n with pytest.raises(ValueError, match=\\\"Input is not a valid subgraph\\\"):\\r\\n clique.grow([dim + 1], nx.empty_graph(dim))\",\n \"def crossover(parent: list):\\n ### GENERAL METHOD\\n child1 = []\\n child2 = []\\n child = []\\n geneA = int(random.random() * len(parent[0]))\\n geneB = int(random.random() * len(parent[0]))\\n startgene = min(geneA, geneB)\\n endgene = max(geneA, geneB)\\n for i in range(startgene, endgene):\\n child1.append(parent[0][i])\\n child2 = [item for item in parent[1] if item not in child1]\\n child = child2[:startgene] + child1 + child2[startgene:]\\n return child\",\n \"def test_build_graph(self):\\n insert_good_data()\\n dataframe = get_dataframe()\\n results = processing.build_graph(dataframe, figure_path, False)\\n # 1\\n self.assertEqual(results, \\\"Updated html File and Opened it\\\")\",\n \"def test_10_parents_as_minus_1_0_0_1_2_3_3_4_6_6(self):\\n parents = [ -1, 0, 0, 1, 2, 3, 3, 4, 6, 6 ]\\n self.init(parents)\\n self.assertEqual(5, self.method_under_test())\",\n \"def reproduce(self,prob=None):\\n for parent in self.agents:\\n if parent.fledged(prob):\\n fledgling = Agent()\\n self._fledgling_move(fledgling,parent.hex)\\n self.agents.append(fledgling)\\n self.pop_size += 1\",\n \"def genNextPop(prevPop, masterList, popSize):\\n parSize = int(popSize / 10) #top 10%\\n parentPop = [Team(prevPop[i].roster) for i in range(parSize)]\\n parentPop = getStats(parentPop, masterList) # inefficent\\n rosterSize = len(parentPop[0].roster)\\n newPop = []\\n #parentsList = [] #debug\\n for i in range(popSize):\\n chromosome = doCrossover(parentPop, parSize, rosterSize)\\n newPop.append(Team(chromosome))\\n getStats(newPop, masterList)\\n #showStats(newPop, masterList, \\\"all\\\")\\n #debug code\\n #for playerInd in chromosome:\\n # print(masterList[playerInd].pos)\\n\\n return newPop\",\n \"def getChildren():\",\n \"def test_restore_with_subgraph(self):\\n subgraph = self._subgraph()\\n task = self._remote_task()\\n subgraph['id'] = 15\\n task['parameters']['containing_subgraph'] = 15\\n\\n graph = self._restore_graph([subgraph, task])\\n assert len(graph.tasks) == 2\\n subgraphs = [op for op in graph.tasks if op.is_subgraph]\\n remote_tasks = [op for op in graph.tasks if not op.is_subgraph]\\n\\n assert len(subgraphs) == 1\\n assert len(remote_tasks) == 1\\n\\n assert len(subgraphs[0].tasks) == 1\\n assert remote_tasks[0].containing_subgraph is subgraphs[0]\",\n \"def test_removes_empty_subgraph(self):\\n ctx = MockWorkflowContext()\\n g = TaskDependencyGraph(ctx)\\n\\n # sg1 is just empty, no tasks inside it\\n sg1 = g.subgraph(ctx)\\n # sg2 contains only a NOPTask\\n sg2 = g.subgraph(ctx)\\n sg2.add_task(tasks.NOPLocalWorkflowTask(ctx))\\n\\n # sg3 contains sg4, which is empty behcause it only contains a NOPTask\\n sg3 = g.subgraph(ctx)\\n sg4 = g.subgraph(ctx)\\n sg4.add_task(tasks.NOPLocalWorkflowTask(ctx))\\n sg3.add_task(sg4)\\n\\n # sg5 is a subgraph that contains a real task! it is not removed\\n sg5 = g.subgraph(ctx)\\n real_task = tasks.WorkflowTask(ctx)\\n sg5.add_task(real_task)\\n\\n assert set(g.tasks) > {sg1, sg2, sg3, sg4, sg5, real_task}\\n g.optimize()\\n assert set(g.tasks) == {sg5, real_task}\",\n \"def test_dot(self):\\n graph = graphviz.Graph(comment='The Round Table')\\n graph.node('A', 'King Arthur')\\n graph.node('B', 'Sir Bedevere the Wise')\\n graph.edges(['AB'])\\n\\n st.graphviz_chart(graph)\\n\\n c = self.get_delta_from_queue().new_element.graphviz_chart\\n self.assertEqual(hasattr(c, 'spec'), True)\",\n \"def test_pod_valid_parent(self):\\n session = self.login_to_apic()\\n parent = PhysicalModel()\\n pod = Pod.get(session, parent)\\n children = parent.get_children()\\n self.assertEqual(pod, children)\",\n \"def test_ExplorePath_Simple( self ):\\n links = []\\n n1 = graph.Node( 10, 50 )\\n n2 = graph.Node( 10, 50 )\\n n3 = graph.Node( 10, 50 )\\n n7 = graph.Node( 10, 50 )\\n\\n links.append( graph.Link( n1, n2 ) )\\n links.append( graph.Link( n2, n3 ) )\\n links.append( graph.Link( n3, n7 ) )\\n roots = [n1]\\n actual = nodes.explorePath( links, roots, n1 )\\n expected = [ n1, n2, n3, n7 ]\\n self.assertEqual( expected, actual )\",\n \"def test_dummy4(self):\\n xpb = XPathBuilder()\\n xp = xpb.dummy()\\n self.assertTrue(xp.log_not() is xp)\",\n \"def test_child_link(self):\\n def compare_func(obj, node):\\n child_nodes = self.get_children_of_node(node)\\n\\n for (child_obj, child_node) in zip(obj.children, child_nodes):\\n # parent-child link (children must have obj as their parent)\\n self.assertEqual(child_obj.parent, obj)\\n\\n self.recursively_compare_tree_against_html(compare_func)\",\n \"def _subgraph_isomorphism_matcher(digraph, nxpattern, node_pred, edge_pred):\\n graph_matcher = iso.DiGraphMatcher(digraph, nxpattern, node_match=node_pred, edge_match=edge_pred)\\n yield from graph_matcher.subgraph_isomorphisms_iter()\",\n \"def sub_graph_merging(self):\\n raise NotImplementedError()\",\n \"def three_opt(self, pop):\\n children, used = ([] for i in range(2))\\n for i in range(0, self.population, 1):\\n tmp = []\\n breaks = np.sort(rand(3) * len(pop[i]) // 1)\\n while breaks[1] == breaks[0] or breaks[1] == breaks[2]:\\n breaks[1] = rand() * len(pop[i]) // 1\\n breaks = np.sort(breaks)\\n\\n while breaks[2] == breaks[0]:\\n breaks[2] = rand() * len(pop[i]) // 1\\n\\n breaks = np.sort(breaks)\\n tmp[0:(int(breaks[0]))] = pop[i][0:int(breaks[0])]\\n tmp[(len(tmp)):(int(len(tmp) + breaks[2] - breaks[1]))] = pop[i][int(breaks[1]):int(breaks[2])]\\n tmp[(len(tmp)):(int(len(tmp) + breaks[1] - breaks[0]))] = pop[i][int(breaks[0]):int(breaks[1])]\\n tmp[(len(tmp)):(int(len(tmp) + breaks[2] - len(pop[i])))] = pop[i][int(breaks[2]):len(pop[i])]\\n children.append(tmp)\\n used.append(i)\\n tmp = []\\n tmp[0:(int(breaks[0]))] = pop[i][0:int(breaks[0])]\\n tmp[(len(tmp)):(int(len(tmp) + breaks[1] - breaks[0]))] = list(reversed(pop[i][int(breaks[0]):int(breaks[1])]))\\n tmp[(len(tmp)):(int(len(tmp) + breaks[2] - breaks[1]))] = reversed(pop[i][int(breaks[1]):int(breaks[2])])\\n tmp[(len(tmp)):(int(len(tmp) + breaks[2] - len(pop[i])))] = pop[i][int(breaks[2]):len(pop[i])]\\n children.append(tmp)\\n used.append(i)\\n\\n return (\\n children, used)\",\n \"def testSetParent(self):\\n for child in self.color_corrections + self.color_decisions:\\n self.assertEqual(\\n None,\\n child.parent\\n )\\n\\n self.node.append_children(\\n self.color_corrections + self.color_decisions\\n )\\n\\n for child in self.node.all_children:\\n self.assertEqual(\\n self.node,\\n child.parent\\n )\\n child.parent = 'banana'\\n self.assertEqual(\\n 'banana',\\n child.parent\\n )\\n\\n self.node.set_parentage()\\n\\n for child in self.node.all_children:\\n self.assertEqual(\\n self.node,\\n child.parent\\n )\",\n \"def test_get_dependencies_subgraph_by_dfs(\\n self, source_node, expected_nodes_in, expected_nodes_out\\n ):\\n graph = nx.DiGraph()\\n graph.add_node(\\\"pack1\\\")\\n graph.add_node(\\\"pack2\\\")\\n graph.add_node(\\\"pack3\\\")\\n graph.add_node(\\\"pack4\\\")\\n graph.add_edge(\\\"pack1\\\", \\\"pack2\\\")\\n graph.add_edge(\\\"pack2\\\", \\\"pack3\\\")\\n dfs_graph = PackDependencies.get_dependencies_subgraph_by_dfs(\\n graph, source_node\\n )\\n for i in expected_nodes_in:\\n assert i in dfs_graph.nodes()\\n for i in expected_nodes_out:\\n assert i not in dfs_graph.nodes()\",\n \"def test_case2(self):\\n\\n graph = BipartiteGraph()\\n\\n graph.addEdge(\\\"supervisor1\\\",\\\"student1\\\")\\n graph.addEdge(\\\"supervisor2\\\",\\\"student4\\\")\\n graph.addEdge(\\\"supervisor3\\\",\\\"student3\\\")\\n\\n val1 = graph.getSupervisorDegree(\\\"supervisor1\\\")\\n\\n graph.addEdge(\\\"supervisor1\\\",\\\"student2\\\")\\n\\n curr = graph.getSupervisorDegree(\\\"supervisor1\\\")\\n val2 = graph.getSupervisors(\\\"student2\\\")\\n expected2 = [\\\"supervisor1\\\"]\\n\\n self.assertEqual((curr-1,expected2),(val1,val2))\",\n \"def test_12_parents_as_minus_1_0_0_1_1_2_3_4_4_5_7_7(self):\\n parents = [ -1, 0, 0, 1, 1, 2, 3, 4, 4, 5, 7, 7 ]\\n self.init(parents)\\n self.assertEqual(5, self.method_under_test())\",\n \"def test_node_info_popup(self):\\n def test_popup(node):\\n node.details.click()\\n with NodeInfo() as details:\\n self.assertEqual(\\n node.name.text, details.header.text,\\n 'Node name')\\n details.close.click()\\n details.wait_until_exists()\\n\\n with Nodes()as n:\\n test_popup(n.nodes_discovered[0])\\n test_popup(n.nodes_offline[0])\\n test_popup(n.nodes_error[0])\",\n \"def test_input_valid_subgraph(self, dim):\\r\\n with pytest.raises(ValueError, match=\\\"Input is not a valid subgraph\\\"):\\r\\n clique.swap([0, dim], nx.empty_graph(dim))\",\n \"def test_spec(self):\\n graph = graphviz.Graph(comment='The Round Table')\\n graph.node('A', 'King Arthur')\\n graph.node('B', 'Sir Bedevere the Wise')\\n graph.edges(['AB'])\\n\\n st.graphviz_chart(graph)\\n\\n c = self.get_delta_from_queue().new_element.graphviz_chart\\n self.assertEqual(hasattr(c, 'spec'), True)\",\n \"def test_change_parent_location(self):\\n pass\",\n \"def test_4_parents_as_1_minus_1_1_0(self):\\n parents = [ 1, -1, 1, 0 ]\\n self.init(parents)\\n self.assertEqual(3, self.method_under_test())\",\n \"def test_6_parents_as_minus_1_0_1_2_3_4(self):\\n parents = [ -1, 0, 1, 2, 3, 4 ]\\n self.init(parents)\\n self.assertEqual(6, self.method_under_test())\",\n \"def test_7_parents_as_minus_1_0_0_1_2_3_4(self):\\n parents = [ -1, 0, 0, 1, 2, 3, 4 ]\\n self.init(parents)\\n self.assertEqual(4, self.method_under_test())\",\n \"def test_generator5(self):\\n xpb = XPathBuilder()\\n xp = None\\n base_xp = xpb.base.foo.bar\\n base_gen = None\\n with base_xp as b:\\n base_gen = b\\n xp = b().join(xpb.a.b.c[3])\\n exp = '/base/foo/bar/a/b/c[3]'\\n base_exp = '/base/foo/bar'\\n # check tree structure\\n self.assertTrue(base_xp._parent is None)\\n self.assertTrue(len(base_xp._children[0]._children[0]._children) == 0)\\n self.assertTrue(base_gen._parent is None)\\n self.assertTrue(len(base_gen._children) == 0)\\n # check xpath\\n self.assertEqual(xp.tostring(), exp)\\n self.assertEqual(base_xp.tostring(), base_exp)\\n self.assertEqual(base_gen.tostring(), base_exp)\",\n \"def playout(self, state):\\n node = self.root\\n isTerminal = False\\n depth = 0\\n\\n while not isTerminal and depth < self.playout_depth:\\n #A = len(node.children) # num_children\\n A = len(node.children[0]) + len(node.children[1]) \\n if A < self.num_initActions:\\n #if len(node.children[0]) < self.num_initActions:\\n node, init_action_xy, init_spin = self.initChildren(node, state, depth)\\n _, ShotVec = CreateShot(_ShotPos(init_action_xy[0], init_action_xy[1], init_spin))\\n success, ResShot = Simulation(state, ShotVec, Config.RAND, -1)\\n isTerminal = (state.ShotNum==0)\\n \\n depth += 1\\n break\\n \\n n_a = [c.n_visits for c in node.children[0].values()] + [c.n_visits for c in node.children[1].values()]\\n # progressive widening\\n # if chilren node has been visited much times then expand\\n #if np.sqrt(sum(n_a)) >= A:\\n if sum(n_a) >= 10 * A: \\n # expand\\n node, expanded_action_xy, expanded_spin = self.expand(node)\\n _, ShotVec = CreateShot(_ShotPos(expanded_action_xy[0],expanded_action_xy[1], expanded_spin))\\n success, ResShot = Simulation(state, ShotVec, Config.RAND, -1)\\n isTerminal = (state.ShotNum==0) # one end game\\n \\n depth += 1\\n break\\n\\n # select\\n node, selected_action_xy, selected_spin = self.ucb_select(node)\\n _, ShotVec = CreateShot(_ShotPos(selected_action_xy[0], selected_action_xy[1], selected_spin))\\n success, ResShot = Simulation(state, ShotVec, Config.RAND, -1)\\n isTerminal = (state.ShotNum==0) # one end game\\n \\n depth += 1\\n\\n if isTerminal:\\n break\\n\\n if not isTerminal and depth < self.playout_depth:\\n # save the rollout_state for speed.\\n #if node.rollout_state is None:\\n state = self.rollOut(node, state, depth)\\n #node.rollout_state = state\\n #else:\\n # state = node.rollout_state\\n \\n self.update(node, state)\",\n \"def test_same_node_is_reachable(self):\\n # G is an arbitrary tournament on ten nodes.\\n G = DiGraph(sorted(p) for p in combinations(range(10), 2))\\n assert_true(all(is_reachable(G, v, v) for v in G))\",\n \"def test_wp_association_bp(self):\\n test_graph = wikipathways_to_bel(WP2359, self.hgnc_manager)\\n\\n self.assertEqual(type(test_graph), BELGraph, msg='Error with graph type')\\n\\n self.assertEqual(test_graph.summary_dict()['Number of Nodes'], 2)\\n self.assertEqual(test_graph.summary_dict()['Number of Edges'], 1)\\n self.assertEqual(count_relations(test_graph)['regulates'], 1)\",\n \"def test_set_node_second_level_component_with_first_level_parent(self):\\n payload = {\\n 'data': [\\n {\\n 'type': 'nodes',\\n 'id': self.first_level_component._id\\n },\\n {\\n 'type': 'nodes',\\n 'id': self.second_level_component._id\\n }\\n ]\\n }\\n res = self.app.post_json_api(self.url, payload, auth=self.user.auth)\\n self.view_only_link.reload()\\n assert_equal(res.status_code, 201)\\n assert_in(self.first_level_component, self.view_only_link.nodes.all())\\n assert_in(self.second_level_component, self.view_only_link.nodes.all())\",\n \"def test_n_path_reactions(self):\\n self.assertEqual(self.Npath, 3)\",\n \"def get_subgraphs(graph):\\n nodes_powerset = get_nodes_combinations(graph)\\n #print(\\\"Doing\\\")\\n #draw_graph(graph)\\n subgraphs = []\\n for nodes in nodes_powerset:\\n subg = graph.subgraph(nodes)\\n nodes = subg.nodes(data=True)\\n if nx.is_weakly_connected(subg):\\n subgraphs.append(subg)\\n return subgraphs\",\n \"def test_ExplorePath( self ):\\n links = []\\n n1 = graph.Node( 10, 10 )\\n n2 = graph.Node( 10, 20 )\\n n3 = graph.Node( 10, 30 )\\n n4a = graph.Node( 5, 40 )\\n n4b = graph.Node( 15, 40 )\\n n5a = graph.Node( 5, 50 )\\n n5b = graph.Node( 15, 50 )\\n n6a = graph.Node( 5, 60 )\\n n6b = graph.Node( 15, 60 )\\n n7 = graph.Node( 10, 70 )\\n\\n links.append( graph.Link( n1, n2 ) )\\n links.append( graph.Link( n2, n3 ) )\\n links.append( graph.Link( n3, n4a ) )\\n links.append( graph.Link( n3, n4b ) )\\n links.append( graph.Link( n4a, n5a ) )\\n links.append( graph.Link( n4b, n5b ) )\\n links.append( graph.Link( n5a, n6a ) )\\n links.append( graph.Link( n6a, n7 ) )\\n links.append( graph.Link( n5b, n7 ) )\\n roots = [n1]\\n actual = nodes.explorePath( links, n1, n1 )\\n expected = [ n1, n2, n3, n4b, n5b, n7 ]\\n self.assertEqual( expected, actual )\",\n \"def test_cycle(self):\\n g = Graph(3)\\n g.add_edge(0, 1)\\n g.add_edge(0, 2)\\n # g.add_edge(0, 0)\\n assert g.contains_cycle() is False\\n g.add_edge(1, 2)\\n assert g.contains_cycle() is True\",\n \"def graph(self):\\n ...\",\n \"def iter_func(root_name, root, set_traverse, list_funcs, G, strings,\\n plot_nodes, cur_pos, xgrain, min_weight, max_weight):\\n set_traverse.append(root)\\n nbs = G.neighbors(root)\\n nbs = G[root]\\n\\n plot_nodes.append(cur_pos)\\n xgrain = xgrain/2.0\\n\\n flag_pn = -1\\n for nb in nbs.keys():\\n if nb in set_traverse:\\n continue\\n\\n next_pos = [0, 0, 0]\\n if root.name == root_name:\\n next_pos[0] = cur_pos[0]\\n else:\\n next_pos[0] = cur_pos[0] + xgrain*flag_pn*( 0.8+0.2*(nbs[nb][\\\"weight\\\"]-1.0/max_weight)/(1.0/min_weight-1.0/max_weight) ) #* (nbs[nb][\\\"weight\\\"]-1.0/max_weight)/(1.0/min_weight-1.0/max_weight)\\n next_pos[1] = cur_pos[1] + 3.0*(nbs[nb][\\\"weight\\\"]-1.0/max_weight)/(1.0/min_weight-1.0/max_weight)\\n next_pos[2] = nb.name\\n\\n flag_pn = flag_pn*(-1)\\n\\n strings.append([root, nb])\\n set_traverse, strings, plot_nodes = iter_func(root_name, nb, set_traverse, list_funcs, G, strings, plot_nodes, next_pos, xgrain, min_weight, max_weight)\\n\\n return set_traverse, strings, plot_nodes\",\n \"def pick_androdioecious_parents(simu, config):\\n rng = simu.getRNG()\\n runif = rng.randUniform\\n rint = rng.randInt\\n try:\\n sstar = config.sstar\\n def compound_generator(pop):\\n \\\"\\\"\\\"\\n Picks up parent(s) under androdioecy using a compound parameter.\\n \\\"\\\"\\\"\\n gen = -1\\n while True:\\n ngen = pop.dvars().gen\\n if gen != ngen:\\n # At the beginning of a generation, extract the\\n # sex-specific subpopulations from a parental\\n # population. The sex-specific subpopulations are used\\n # throughout mating events in one generation.\\n gen = ngen\\n males = pop.extractSubPops(subPops=[(0, 0)])\\n herms = pop.extractSubPops(subPops=[(0, 1)])\\n nmale = males.popSize()\\n nherm = herms.popSize()\\n\\n if runif() < sstar: # uniparental\\n yield herms.individual(rint(nherm))\\n else: # biparental\\n yield [males.individual(rint(nmale)), herms.individual(rint(nherm))]\\n return compound_generator\\n except KeyError:\\n stilde = config.stilde\\n tau = config.tau\\n def compound_generator(pop):\\n \\\"\\\"\\\"\\n Picks up parent(s) under androdioecy using fundamental parameters.\\n \\\"\\\"\\\"\\n gen = -1\\n while True:\\n ngen = pop.dvars().gen\\n if gen != ngen:\\n # At the beginning of a generation, extract the\\n # sex-specific subpopulations from a parental\\n # population. The sex-specific subpopulations are used\\n # throughout mating events in one generation.\\n gen = ngen\\n males = pop.extractSubPops(subPops=[(0, 0)])\\n herms = pop.extractSubPops(subPops=[(0, 1)])\\n nmale = males.popSize()\\n nherm = herms.popSize()\\n\\n if runif() < stilde: # proportion of self-fertlized egg\\n if runif() < tau: # survival rate of a uniparental zygote rel to a biparental z.\\n yield herms.individual(rint(nherm))\\n else: # biparental\\n yield [males.individual(rint(nmale)), herms.individual(rint(nherm))]\\n return compound_generator\",\n \"def test_12_parents_as_minus_1_0_0_1_1_2_3_4_4_5_7_7_9_12(self):\\n parents = [ -1, 0, 0, 1, 1, 2, 3, 4, 4, 5, 7, 7, 9, 12 ]\\n self.init(parents)\\n self.assertEqual(6, self.method_under_test())\",\n \"def crossover(parent1, parent2):\\n path1 = random_subtree(parent1, \\\"\\\")\\n path2 = random_subtree(parent2, \\\"\\\")\\n parent1 = parent1.copy()\\n parent2 = parent2.copy()\\n loc1 = parent1\\n loc1parent = parent1\\n loc2 = parent2\\n loc2parent = parent2\\n for i in range(len(path1)):\\n loc1parent = loc1\\n if path1[i] == \\\"1\\\":\\n loc1 = loc1parent.get_right()\\n else:\\n loc1 = loc1parent.get_left()\\n for i in range(len(path2)):\\n loc2parent = loc2\\n if path2[i] == \\\"1\\\":\\n loc2 = loc2parent.get_right()\\n else:\\n loc2 = loc2parent.get_left()\\n if(len(path1)-1 >= 0):\\n if path1[len(path1)-1] == \\\"1\\\":\\n loc1parent.right = loc2\\n else:\\n loc1parent.left = loc2\\n if(len(path2) - 1 >= 0):\\n if path2[-1] == \\\"1\\\":\\n loc2parent.right = loc1\\n else:\\n loc2parent.left = loc1\\n return (parent1, parent2)\",\n \"def test_4_parents_as_minus_1_0_1_2(self):\\n parents = [ -1, 0, 1, 2 ]\\n self.init(parents)\\n self.assertEqual(4, self.method_under_test())\",\n \"def testBasic1(self):\\n nodes = self.G.nodes()\\n assert len(nodes) == len( set(nodes) )\",\n \"def test_5_parents_as_1_minus_1_1_0_0(self):\\n parents = [ 1, -1, 1, 0, 0 ]\\n self.init(parents)\\n self.assertEqual(3, self.method_under_test())\",\n \"def test_3_parents_as_1_minus_1_1(self):\\n parents = [ 1, -1, 1 ]\\n self.init(parents)\\n self.assertEqual(2, self.method_under_test())\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7205235","0.70020497","0.62084246","0.6087695","0.5988837","0.58964515","0.58667445","0.58020157","0.5726468","0.5667711","0.5539633","0.5539633","0.5504206","0.54905653","0.5482558","0.5460695","0.54255486","0.5419617","0.5398533","0.5355489","0.53264356","0.53245705","0.5312008","0.5307634","0.5303363","0.53014946","0.5294228","0.523673","0.5232471","0.5228123","0.52248925","0.52232295","0.5218298","0.52174497","0.52111673","0.52089536","0.51829195","0.51745975","0.51708","0.51655567","0.5164763","0.51581097","0.51574516","0.51436824","0.5132396","0.5131299","0.51306385","0.51233494","0.5118409","0.5116303","0.5108168","0.510709","0.5106759","0.510241","0.50905424","0.5089184","0.50854236","0.507906","0.5076508","0.50747967","0.50699407","0.5069547","0.50572777","0.5057174","0.5051708","0.50352585","0.50333","0.50322187","0.50287956","0.5022825","0.5014624","0.50122243","0.50086933","0.5006136","0.5001657","0.50005764","0.49920267","0.49919513","0.49910122","0.49844953","0.49769056","0.49724922","0.4971587","0.49644488","0.49549103","0.4952674","0.49509016","0.4941937","0.49410596","0.49405313","0.49402314","0.49369624","0.4928777","0.49261698","0.49260587","0.49215972","0.4920297","0.4917804","0.49171856","0.49147215"],"string":"[\n \"0.7205235\",\n \"0.70020497\",\n \"0.62084246\",\n \"0.6087695\",\n \"0.5988837\",\n \"0.58964515\",\n \"0.58667445\",\n \"0.58020157\",\n \"0.5726468\",\n \"0.5667711\",\n \"0.5539633\",\n \"0.5539633\",\n \"0.5504206\",\n \"0.54905653\",\n \"0.5482558\",\n \"0.5460695\",\n \"0.54255486\",\n \"0.5419617\",\n \"0.5398533\",\n \"0.5355489\",\n \"0.53264356\",\n \"0.53245705\",\n \"0.5312008\",\n \"0.5307634\",\n \"0.5303363\",\n \"0.53014946\",\n \"0.5294228\",\n \"0.523673\",\n \"0.5232471\",\n \"0.5228123\",\n \"0.52248925\",\n \"0.52232295\",\n \"0.5218298\",\n \"0.52174497\",\n \"0.52111673\",\n \"0.52089536\",\n \"0.51829195\",\n \"0.51745975\",\n \"0.51708\",\n \"0.51655567\",\n \"0.5164763\",\n \"0.51581097\",\n \"0.51574516\",\n \"0.51436824\",\n \"0.5132396\",\n \"0.5131299\",\n \"0.51306385\",\n \"0.51233494\",\n \"0.5118409\",\n \"0.5116303\",\n \"0.5108168\",\n \"0.510709\",\n \"0.5106759\",\n \"0.510241\",\n \"0.50905424\",\n \"0.5089184\",\n \"0.50854236\",\n \"0.507906\",\n \"0.5076508\",\n \"0.50747967\",\n \"0.50699407\",\n \"0.5069547\",\n \"0.50572777\",\n \"0.5057174\",\n \"0.5051708\",\n \"0.50352585\",\n \"0.50333\",\n \"0.50322187\",\n \"0.50287956\",\n \"0.5022825\",\n \"0.5014624\",\n \"0.50122243\",\n \"0.50086933\",\n \"0.5006136\",\n \"0.5001657\",\n \"0.50005764\",\n \"0.49920267\",\n \"0.49919513\",\n \"0.49910122\",\n \"0.49844953\",\n \"0.49769056\",\n \"0.49724922\",\n \"0.4971587\",\n \"0.49644488\",\n \"0.49549103\",\n \"0.4952674\",\n \"0.49509016\",\n \"0.4941937\",\n \"0.49410596\",\n \"0.49405313\",\n \"0.49402314\",\n \"0.49369624\",\n \"0.4928777\",\n \"0.49261698\",\n \"0.49260587\",\n \"0.49215972\",\n \"0.4920297\",\n \"0.4917804\",\n \"0.49171856\",\n \"0.49147215\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6038614"},"document_rank":{"kind":"string","value":"4"}}},{"rowIdx":6592,"cells":{"query":{"kind":"string","value":"Test the popxl getting / setting tensor data example"},"document":{"kind":"string","value":"def test_documentation_popxl_get_set_tensors(self):\n filename = \"tensor_get_write.py\"\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)"},"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 test_documentation_popxl_addition_variable(self):\n filename = \"tensor_addition.py\"\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)","def test_predictor():","def test_loadData():\n \n sys = LVsystem.Ecosystem()\n \n sys.loadSetup('2Prey1Predator')\n \n \n data = sys.create_data()\n \n assert data[0] == 3\n assert data[1] == ['rabbit', 'hen', 'fox']\n assert data[2] == [30,10,20]\n assert data[3] == [0.09,0.07,-0.06] \n assert data[4] == [10000,10000,1]\n assert data[5] == [400,500,250]\n assert data[6][1][2] == -data[6][2][1]\n assert data[6][2][2] == 0\n\n sys.removeSpecies('rabbit')\n sys.removeSpecies('fox')\n sys.removeSpecies('hen')","def test_data():\n batch_size = 10\n input_dim = 28\n test_data = np.random.rand(batch_size, input_dim)\n\n return test_data","def test_add_get_tensor(mock_data):\n dataset = Dataset(\"test-dataset\")\n\n # 1D tensors of all data types\n data = mock_data.create_data(10)\n add_get_arrays(dataset, data)","def test_documentation_popxl_adv_get_write(self):\n filename = \"tensor_get_write_adv.py\"\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)","def test_load_data(self):\n assert len(self._mnist.get()) == 10\n assert self._mnist.get()[0].label == 7\n pass","def test_add_get_tensor_3D(mock_data):\n dataset = Dataset(\"test-dataset\")\n\n # 3D tensors of all datatypes\n data_3D = mock_data.create_data((10, 10, 10))\n add_get_arrays(dataset, data_3D)","def test_getitem(self):\n obs = self.tester['1.SKM7.640188']\n exp = PrepSample('1.SKM7.640188', self.tester)\n self.assertEqual(obs, exp)","def test_documentation_popxl_mnist_rts_train_test(self):\n filename = \"mnist_rts.py --replication-factor 2 --rts --test\"\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)","def test_snow_pumps():\n test_path = tempfile.mkdtemp()\n x_train, metadata = snow_pumps(test_path)\n try:\n assert x_train.shape == (13, 4)\n except:\n shutil.rmtree(test_path)\n raise()","def test_machine_learning():","def test_synth_tr():\n test_path = tempfile.mkdtemp()\n x_train, metadata = synth_tr(test_path)\n try:\n assert x_train.shape == (250, 3)\n except:\n shutil.rmtree(test_path)\n raise()","def setUp(self):\n self.samples = 5\n self.otus = 10\n seed(0) # this will seed numpy prng at 0 before each test","def test_init_prediction_data(raw_data):\n prediction_data = PredictionData(**raw_data)\n assert prediction_data","def test_documentation_popxl_mnist_rts_train(self):\n filename = \"mnist_rts.py --replication-factor 2 --rts\"\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)","def rand_data():\n # 100 examples, with seq_len=10, each holding 300 features\n return torch.randn((100, 10, 300))","def set_data():\r\n #if not os.path.exists(filepath):\r\n #download_data()\r\n metadata = read(filepath + flist[-1])\r\n ndata = metadata['num_cases_per_batch']\r\n ndim = metadata['num_vis']\r\n\r\n data, train, test = {}, {}, {}\r\n data['labels'] = metadata['label_names']\r\n data['ntraindata'] = metadata['num_cases_per_batch'] * (len(flist) - 2)\r\n data['ntestdata'] = metadata['num_cases_per_batch']\r\n data['ndim'] = metadata['num_vis']\r\n\r\n train['x'], train['y'] = convert_train(data['ntraindata'], data['ndim'])\r\n\r\n testdata = read(filepath + flist[-2])\r\n test['x'] = testdata['data']\r\n test['y'] = testdata['labels']\r\n\r\n data['train'], data['test'] = train, test\r\n save_pkl(data)","def _creatExamplesTensorData(self, examples):\n\n images = []\n \n images2 = []\n images3 = []\n images4 = []\n images5 = [] \n labels = []\n for (img_idx, label) in examples:\n img = self.dataset[img_idx][0]\n #print(img)\n ##exit(0)\n if self.load:\n img = Image.fromarray(img)\n else:\n img = read_image(img)\n #print(img.size)\n #print(np.array(img).shape)\n #exit(0)\n if self.transform is not None:\n img1 = self.transform(img)\n\n img2 = self.transform_test(img)\n img3 = self.transform_test(img)\n img4 = self.transform_test(img)\n img5 = self.transform_test(img) \n #print((img2-img1).abs().sum(),(img3-img1).abs().sum(),(img2-img3).abs().sum())\n #print(img.shape,'located in test_loader.py at 146')\n #exit(0)\n images.append(img1)\n \n images2.append(img2)\n images3.append(img3)\n images4.append(img4)\n images5.append(img5) \n labels.append(label)\n images = torch.stack(images, dim=0)\n\n images2 = torch.stack(images2, dim=0)\n images3 = torch.stack(images3, dim=0)\n images4 = torch.stack(images4, dim=0)\n images5 = torch.stack(images5, dim=0) \n labels = torch.LongTensor(labels)\n return images, images2,images3,images4,images5,labels","def test_add_get_tensor_2D(mock_data):\n dataset = Dataset(\"test-dataset\")\n\n # 2D tensors of all data types\n data_2D = mock_data.create_data((10, 10))\n add_get_arrays(dataset, data_2D)","def test_meteo():\n test_path = tempfile.mkdtemp()\n x_train, metadata = meteo(test_path)\n try:\n assert x_train.shape == (11, 6)\n except:\n shutil.rmtree(test_path)\n raise()","def test_training(self):\n\t\tpass","def setUp(self):\n output = np.zeros((1, 5, 2))\n target = np.zeros((1, 5, 2))\n # first channel\n output[0, 0] = [10, 4]\n target[0, 0] = [10, 0]\n # second channel\n output[0, 1] = [10, 18]\n target[0, 1] = [10, 10]\n # third channel\n output[0, 2] = [0, 0]\n target[0, 2] = [0, -1]\n # fourth channel\n output[0, 3] = [40, 40]\n target[0, 3] = [30, 30]\n # fifth channel\n output[0, 4] = [20, 10]\n target[0, 4] = [0, 10]\n\n gt_instances = InstanceData()\n gt_instances.keypoints = target\n gt_instances.keypoints_visible = np.array(\n [[True, True, False, True, True]])\n\n pred_instances = InstanceData()\n pred_instances.keypoints = output\n\n data = {'inputs': None}\n data_sample = {\n 'gt_instances': gt_instances.to_dict(),\n 'pred_instances': pred_instances.to_dict()\n }\n\n self.data_batch = [data]\n self.data_samples = [data_sample]","def test_get_iris_setosa_data(self):\n iris = get_iris_setosa_data()\n self.assertEqual(len(iris.data), 150)\n self.assertEqual(len(iris.labels), 150)","def test_get_mnist_data(self):\n # TODO: Remove once get_mnist_data(...) is fixed.\n pass\n # mnist = get_mnist_data()\n # self.assertEqual(len(mnist.data), 60000)\n # self.assertEqual(len(mnist.labels), 60000)","def data_set_maker():\n\n # crate a folder in your code directory and name it: \"files\". put the .npy files iside that folder\n path = os.getcwd() # reads the current path\n x_train = np.load(path + '/files/tinyX.npy', 'r') # reads the input file\n y_train = np.load(path + '/files/tinyY.npy', 'r') # reads the input file\n x_test = np.load(path + '/files/tinyX_test.npy', 'r') # reads the input file\n x_train, y_train = shuffle(x_train, y_train)\n\n return x_train, y_train, x_test","def test_income():\n test_path = tempfile.mkdtemp()\n x_train, metadata = income(test_path)\n try:\n assert x_train.shape == (44, 4)\n except:\n shutil.rmtree(test_path)\n raise()","def test_gen():\n tpot_obj = TPOTClassifier()\n\n pipeline = tpot_obj._gen_grow_safe(tpot_obj._pset, 1, 3)\n\n assert len(pipeline) > 1\n assert pipeline[0].ret == Output_DF","def test_get(self):\n obs = self.tester.get('1.SKM7.640188')\n exp = PrepSample('1.SKM7.640188', self.tester)\n self.assertEqual(obs, exp)","def test_variational():\n # iris\n #pres = \"Test pour le data set Iris (facile, classique)\"\n #test_from_func_variational(pres, 15, 10, 3, True, Iris)\n\n # breast cancer\n pres = \"Test pour le data set Breast Cancer (facile, classique)\"\n test_from_func_variational(pres, 15, 10, 3, True, Breast_cancer)\n\n # digits\n # pres = \"Test pour le data set Digits (difficile, classique)\"\n # test_from_func(pres, 10, 10, 10, True, Digits, quantum_instance)\n\n # wine\n # pres = \"Test pour le data set Wine (moyen, classique)\"\n # test_from_func(pres, 15, 10, 5, True, Wine, quantum_instance)\n\n # gaussian\n pres = \"Test pour des données gaussiennes (moyen, classique)\"\n for _ in range(1):\n print(\"\\n\")\n print(\"New iteration\")\n test_from_func_variational(pres, 25, 10, 2, True, Gaussian)\n print(\"\\n\")\n\n # small adn strings\n pres = \"Test pour des séquences ADN courtes (difficile, classique)\"\n test_from_func_variational(pres, 10, 15, 14, True, Sequence)\n\n #Quantum data\n pres = \"Test pour des données générées par ordinateur quantique (facile, quantique)\"\n print(pres)\n _, samp_train, samp_test, labels = ad_hoc_data(15, 10, 2, 0.3, True)\n sample_m, sample_p = stock_get(20, 0.3)\n\n labels_me = [-1, 1]\n samp_train_me = {-1: np.array(sample_m[:15]), 1: np.array(sample_p[:15])}\n samp_test_me = {-1: np.array(sample_m[15:]), 1: np.array(sample_p[15:])}\n print(samp_train)\n print(samp_train_me)\n print(samp_test)\n print(samp_test_me)\n\n my_impl_variational(samp_train, samp_test, labels)\n print(\"Pour autres données quantiques\")\n my_impl_variational(samp_train_me, samp_test_me, labels_me)","def test_dataset_property():\n atom = ATOMClassifier(X_bin, y_bin, random_state=1)\n atom.run([\"MNB\", \"LR\"])\n assert atom.dataset.equals(atom.mnb.dataset)\n assert check_scaling(atom.lr.dataset)","def _get_data_for_tests():\n X = np.random.randn(100, input_dim)\n Y = np.random.randn(100, output_dim)\n X_new = np.random.randn(100, input_dim)\n return X, X_new, Y","def setUp(self):\n self.X_train, self.y_train = load_data(\"../data/traindata.mat.tar.gz\")\n self.nn = NN_hwr([len(self.X_train[0]), 50, 10])","def test_star():\n test_path = tempfile.mkdtemp()\n x_train, metadata = star(test_path)\n try:\n assert x_train.shape == (5748, 8)\n except:\n shutil.rmtree(test_path)\n raise()","def test_quartet():\n test_path = tempfile.mkdtemp()\n x_train, metadata = quartet(test_path)\n try:\n assert x_train.shape == (11, 6)\n except:\n shutil.rmtree(test_path)\n raise()","def test_show_examples():\n skip_if_no_matplotlib()\n skip_if_no_data()\n with open('temp.yaml', 'w') as f:\n f.write(\"\"\"\n!obj:pylearn2.datasets.mnist.MNIST {\n which_set: 'train'\n}\n\"\"\")\n show_examples('temp.yaml', 28, 28, out='garbage.png')\n os.remove('temp.yaml')","def test_affect():\n test_path = tempfile.mkdtemp()\n x_train, metadata = affect(test_path)\n try:\n assert x_train.shape == (330, 20)\n except:\n shutil.rmtree(test_path)\n raise()","def test_sample(self):\n seed = 5\n space = Space()\n probs = (0.1, 0.2, 0.3, 0.4)\n categories = (\"asdfa\", 2, 3, 4)\n dim1 = Categorical(\"yolo\", OrderedDict(zip(categories, probs)), shape=(2, 2))\n space.register(dim1)\n dim2 = Integer(\"yolo2\", \"uniform\", -3, 6)\n space.register(dim2)\n dim3 = Real(\"yolo3\", \"norm\", 0.9)\n space.register(dim3)\n\n point = space.sample(seed=seed)\n rng = check_random_state(seed)\n test_point = [\n dict(\n yolo=dim1.sample(seed=rng)[0],\n yolo2=dim2.sample(seed=rng)[0],\n yolo3=dim3.sample(seed=rng)[0],\n )\n ]\n assert len(point) == len(test_point) == 1\n assert len(point[0].params) == len(test_point[0]) == 3\n assert np.all(point[0].params[\"yolo\"] == test_point[0][\"yolo\"])\n assert point[0].params[\"yolo2\"] == test_point[0][\"yolo2\"]\n assert point[0].params[\"yolo3\"] == test_point[0][\"yolo3\"]\n\n points = space.sample(2, seed=seed)\n rng = check_random_state(seed)\n points1 = dim1.sample(2, seed=rng)\n points2 = dim2.sample(2, seed=rng)\n points3 = dim3.sample(2, seed=rng)\n test_points = [\n dict(yolo=points1[0], yolo2=points2[0], yolo3=points3[0]),\n dict(yolo=points1[1], yolo2=points2[1], yolo3=points3[1]),\n ]\n assert len(points) == len(test_points) == 2\n for i in range(2):\n assert len(points[i].params) == len(test_points[i]) == 3\n assert np.all(points[i].params[\"yolo\"] == test_points[i][\"yolo\"])\n assert points[i].params[\"yolo2\"] == test_points[i][\"yolo2\"]\n assert points[i].params[\"yolo3\"] == test_points[i][\"yolo3\"]","def test_init(self):\n xtal_model_data = XtalModelData(self.params)\n\n assert xtal_model_data.pdb == self.params.input.pdb\n\n assert xtal_model_data.mtz == self.params.input.mtz\n\n # TODO Assert utilised method calls of these classes\n # Assert is innstance causses issues if called from somewhere else\n\n self.assertIsInstance(xtal_model_data.xrs, cctbx.xray.structure)\n\n self.assertIsInstance(\n xtal_model_data.inputs, mmtbx.utils.process_command_line_args\n )\n\n self.assertIsInstance(\n xtal_model_data.crystal_gridding, cctbx.maptbx.crystal_gridding\n )\n\n self.assertIsInstance(xtal_model_data.fmodel, mmtbx.f_model.f_model.manager)","def setUp(self):\n self.dataset = get_test_dataset()","def test_mlp():\r\n datasets = gen_data()\r\n\r\n train_set_x, train_set_y = datasets[0]\r\n valid_set_x, valid_set_y = datasets[1]\r\n test_set_x , test_set_y = datasets[2]\r\n\r\n\r\n\r\n batch_size = 100 # size of the minibatch\r\n\r\n # compute number of minibatches for training, validation and testing\r\n n_train_batches = train_set_x.get_value(borrow=True).shape[0] / batch_size\r\n n_valid_batches = valid_set_x.get_value(borrow=True).shape[0] / batch_size\r\n n_test_batches = test_set_x.get_value(borrow=True).shape[0] / batch_size\r\n\r\n ######################\r\n # BUILD ACTUAL MODEL #\r\n ######################\r\n #print '... building the model'\r\n\r\n # allocate symbolic variables for the data\r\n index = T.lscalar() # index to a [mini]batch\r\n x = T.matrix('x') # the data is presented as rasterized images\r\n y = T.ivector('y') # the labels are presented as 1D vector of\r\n # [int] labels\r\n\r\n rng = numpy.random.RandomState(1234)\r\n\r\n # construct the MLP class\r\n classifier = MLP( rng = rng, input=x, n_in=28*28, n_hidden = 500, n_out=10)\r\n\r\n # the cost we minimize during training is the negative log likelihood of\r\n # the model.\r\n # We take the mean of the cost over each minibatch.\r\n cost = classifier.negative_log_likelihood(y).mean()\r\n\r\n # compute the gradient of cost with respect to theta (stored in params)\r\n # the resulting gradients will be stored in a list gparams\r\n gparams = []\r\n for param in classifier.params:\r\n gparam = T.grad(cost, param)\r\n gparams.append(gparam)\r\n\r\n # Some optimizations needed are tagged with 'fast_run'\r\n # TODO: refine that and include only those\r\n mode = theano.compile.get_default_mode().including('fast_run')\r\n\r\n updates2 = OrderedDict()\r\n\r\n updates2[classifier.hiddenLayer.params[0]]=T.grad(cost,classifier.hiddenLayer.params[0])\r\n train_model =theano.function( inputs = [index],\r\n updates = updates2,\r\n givens={\r\n x:train_set_x[index*batch_size:(index+1)*batch_size],\r\n y:train_set_y[index*batch_size:(index+1)*batch_size]},\r\n mode=mode)\r\n #print 'MODEL 1'\r\n #theano.printing.debugprint(train_model, print_type=True)\r\n assert any([isinstance(i.op,T.nnet.CrossentropySoftmax1HotWithBiasDx) for i in train_model.maker.fgraph.toposort()])\r\n\r\n # Even without FeatureShape\r\n train_model =theano.function( inputs = [index],\r\n updates = updates2,\r\n mode=mode.excluding('ShapeOpt'),\r\n givens={\r\n x:train_set_x[index*batch_size:(index+1)*batch_size],\r\n y:train_set_y[index*batch_size:(index+1)*batch_size]})\r\n #print\r\n #print 'MODEL 2'\r\n #theano.printing.debugprint(train_model, print_type=True)\r\n assert any([isinstance(i.op,T.nnet.CrossentropySoftmax1HotWithBiasDx) for i in train_model.maker.fgraph.toposort()])","def test_add_get_scalar(mock_data):\n dataset = Dataset(\"test-dataset\")\n\n # 1D tensors of all data types\n data = mock_data.create_metadata_scalars(10)\n add_get_scalars(dataset, data)","def test_significance_individual_timepoints(tensor_directory, analysis_name):\n\n \"\"\"\n # Open Analysis Dataframe\n analysis_file = h5py.File(os.path.join(tensor_directory, analysis_name + \".hdf5\"), \"r\")\n activity_dataset = analysis_file[\"Data\"]\n metadata_dataset = analysis_file[\"metadata\"]\n number_of_timepoints, number_of_trials, number_of_pixels = np.shape(activity_dataset)\n print(\"metadata_dataset\", np.shape(metadata_dataset))\n \"\"\"\n # Open Analysis Dataframe\n analysis_file = tables.open_file(os.path.join(tensor_directory, analysis_name + \"_Trialwise_.h5\"), mode=\"r\")\n activity_dataset = analysis_file.root[\"Data\"]\n metadata_dataset = analysis_file.root[\"Trial_Details\"]\n\n # Create P and Slope Tensors\n p_value_tensor = np.ones((number_of_timepoints, number_of_pixels))\n slope_tensor = np.zeros((number_of_timepoints, number_of_pixels))\n\n for timepoint_index in tqdm(range(number_of_timepoints), position=0, desc=\"Timepoint\"):\n\n # Get Timepoint Data\n timepoint_activity = activity_dataset[timepoint_index]\n\n for pixel_index in tqdm(range(number_of_pixels), position=1, desc=\"Pixel\", leave=True):\n\n # Package Into Dataframe\n pixel_activity = timepoint_activity[:, pixel_index]\n pixel_dataframe = repackage_data_into_dataframe(pixel_activity, metadata_dataset)\n\n # Fit Mixed Effects Model\n p_value, slope = mixed_effects_random_slope_and_intercept(pixel_dataframe)\n p_value_tensor[timepoint_index, pixel_index] = p_value\n slope_tensor[timepoint_index, pixel_index] = slope\n\n\n # Save These Tensors\n np.save(os.path.join(tensor_directory, analysis_name + \"_p_value_tensor.npy\"), p_value_tensor)\n np.save(os.path.join(tensor_directory, analysis_name + \"_slope_tensor.npy\"), slope_tensor)","def generate_x(number_dimensions, T_train, T_test, mu, feature_model):\n number_training_obeservations = T_train.shape[0]\n number_testing_obeservations = T_test.shape[0]\n\n X_train = np.zeros((number_training_obeservations,number_dimensions))\n X_test = np.zeros((number_testing_obeservations,number_dimensions))\n\n mixture_indicator_train = generate_mixture_indicator(number_training_obeservations)\n mixture_indicator_test = generate_mixture_indicator(number_testing_obeservations)\n\n G = np.random.normal(0,1,(number_dimensions,number_dimensions))\n q, r = np.linalg.qr(G)\n\n mu1 = mu*np.ones(number_dimensions)\n mu2 = -mu*np.ones(number_dimensions)\n\n if feature_model == \"A\":\n eigenvalues1 = np.random.uniform(0,1,(number_dimensions,1))\n eigenvalues1 = np.sort(eigenvalues1, axis = 0)/np.sum(eigenvalues1)\n lambda1 = np.identity(number_dimensions)\n np.fill_diagonal(lambda1,eigenvalues1)\n cov1 = q@lambda1@q.T\n\n for i in range(number_training_obeservations):\n if T_train[i] == 0:\n X_train[i,:] = np.random.multivariate_normal(mu1,cov1,1)\n else:\n X_train[i,:] = np.random.multivariate_normal(mu2,cov1,1)\n \n for i in range(number_testing_obeservations):\n if T_test[i] == 0:\n X_test[i,:] = np.random.multivariate_normal(mu1,cov1,1)\n else:\n X_test[i,:] = np.random.multivariate_normal(mu2,cov1,1)\n\n\n elif feature_model == \"B\":\n eigenvalues1 = np.random.uniform(0,1,(number_dimensions,1))\n eigenvalues1 = np.sort(eigenvalues1, axis = 0)/np.sum(eigenvalues1)\n lambda1 = np.identity(number_dimensions)\n np.fill_diagonal(lambda1,eigenvalues1)\n cov1 = q@lambda1@q.T\n\n eigenvalues2 = np.random.uniform(0,1,(number_dimensions,1))\n eigenvalues2 = np.sort(eigenvalues2, axis = 0)[::-1]/np.sum(eigenvalues2)\n lambda2 = np.identity(number_dimensions)\n np.fill_diagonal(lambda2,eigenvalues2)\n cov2 = q@lambda2@q.T\n\n\n for i in range(number_training_obeservations):\n if T_train[i] == 0:\n if mixture_indicator_train[i] == 0:\n X_train[i,:] = np.random.multivariate_normal(mu1,cov1,1)\n else:\n X_train[i,:] = np.random.multivariate_normal(mu1,cov2,1)\n else:\n if mixture_indicator_train[i] == 0:\n X_train[i,:] = np.random.multivariate_normal(mu2,cov1,1)\n else:\n X_train[i,:] = np.random.multivariate_normal(mu2,cov2,1)\n \n for i in range(number_testing_obeservations):\n if T_test[i] == 0:\n if mixture_indicator_test[i] == 0:\n X_test[i,:] = np.random.multivariate_normal(mu1,cov1,1)\n else:\n X_test[i,:] = np.random.multivariate_normal(mu1,cov2,1)\n else:\n if mixture_indicator_test[i] == 0:\n X_test[i,:] = np.random.multivariate_normal(mu2,cov1,1)\n else:\n X_test[i,:] = np.random.multivariate_normal(mu2,cov2,1)\n\n train_mean = np.mean(X_train, axis = 0)\n train_std = np.std(X_train, axis = 0)\n X_train = (X_train - train_mean)/train_std\n X_test = (X_test - train_mean)/train_std\n \n return X_train, X_test","def test_documentation_popxl_mnist_replication_train(self):\n filename = \"mnist_rts.py --replication-factor 2\"\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)","def test_getitem(self):\n obs = self.tester['1.SKM7.640188']\n exp = Sample('1.SKM7.640188', self.tester)\n self.assertEqual(obs, exp)","def test_get_random_data_basic(self):\n cdata = random_data(num_features=2,\n num_samples=4,\n labels=None)\n self.assertEqual(cdata.num_features, 2)\n self.assertEqual(cdata.num_samples, 4)","def tensor(data, **context):\n raise NotImplementedError","def test_000_basic_functionality() -> None:\n df = generate_test_data()\n skim(df)","def setUpTestData(cls):\n data_gen.run()","def setUpTestData(cls):\n data_gen.run()","def setUpTestData(cls):\n data_gen.run()","def setUpTestData(cls):\n data_gen.run()","def load_data(opt=\"mnist\"):\n if opt == \"mnist\":\n train, test = tf.keras.datasets.mnist.load_data()\n \n x_train, y_train = train\n x_test, y_test = test\n \n x_train = x_train.reshape(x_train.shape[0], 28 * 28)\n x_test = x_test.reshape(x_test.shape[0], 28 * 28)\n \n y_train = y_train.astype(np.int)\n y_test = y_test.astype(np.int)\n for i in range(len(y_train)):\n y_train[i] = 1 if y_train[i] % 2 == 0 else -1\n for i in range(len(y_test)):\n y_test[i] = 1 if y_test[i] % 2 == 0 else -1\n\n elif opt == \"covertype\":\n df = pd.read_csv(\"covtype.data\", header=None)\n x = df.iloc[:, 0:54].values\n y = df[54].values\n for i in range(len(y)):\n y[i] = 1 if y[i] % 2 == 0 else -1\n x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3)\n \n else:\n logging.error(\"Unknown dataset!!\")\n\n logging.info(\"train data shape: {}\".format(x_train.shape))\n logging.info(\"test data shape: {}\".format(x_test.shape))\n return (x_train, y_train), (x_test, y_test)","def test_model_sample(net, data_loader):\n net.eval()\n array = []\n with torch.no_grad():\n for data in data_loader:\n X = data['X']\n output = net(X)\n output = ToPILImage()(output)\n array.append(output)\n return array","def load_data_set():\n\n iris = pd.read_csv('/Users/michael/Codes/bitbucket/cs584-s18-kaiyue-ma/AS2/data/iris.csv', encoding=\"gbk\")\n iris = iris.sample(frac=1.0)\n\n dummy = pd.get_dummies(iris['Species'])\n iris = pd.concat([iris, dummy], axis=1)\n\n org_x = np.array(iris.iloc[:, 1:5])\n # org_y = np.array(iris['setosa']).reshape(len(iris), 1)\n org_y = np.array(iris['setosa'])\n\n # data_arr, test_arr, label_arr, test_label = train_test_split(org_x, org_y, test_size=0.2, random_state=42)\n #\n # data_arr = data_arr.tolist()\n # test_arr = test_arr.tolist()\n # label_arr = label_arr.tolist()\n # test_label = test_label.tolist()\n #\n # data_arr = np.dot(data_arr)\n # one = np.ones(len(data_arr))\n # data_arr = np.column_stack(one , data_arr)\n # data_arr = data_arr.tolist()\n #\n # test_arr = np.dot(test_arr)\n # one = np.ones(len(test_arr))\n # test_arr = np.column_stack(one, test_arr)\n # test_arr = test_arr.tolist()\n\n x = org_x.tolist()\n y = org_y.tolist()\n x = np.mat(x)\n one = np.ones(len(x))\n x = np.column_stack((one, x))\n x = x.tolist()\n\n # print(data_arr)\n # x1_2 = data_arr[:, 1]\n # print(x1_2)\n # print('.....')\n # print(label_arr)\n # return data_arr, test_arr, label_arr, test_label\n return x, y","def test_numpify_and_store(self):\n Nsamples = 9\n Ntimesteps = 10\n Ncolumns = 3\n X = [[[0 for a in range(Ncolumns)] for b in range(Ntimesteps)] \\\n for c in range(Nsamples)]\n y = [[0 for a in range(Ntimesteps)] for b in range(Nsamples)]\n xname = 'xname'\n yname = 'yname'\n outdatapath = os.getcwd()\n tutorial_pamap2.numpify_and_store(X, y, xname, yname, outdatapath, \\\n shuffle=True)\n filename = os.path.join(outdatapath, xname+ '.npy')\n test = os.path.isfile(filename)\n if test == True:\n os.remove(filename)\n os.remove(os.path.join(outdatapath, yname + '.npy'))\n assert test","def test_morley():\n test_path = tempfile.mkdtemp()\n x_train, metadata = morley(test_path)\n try:\n assert x_train.shape == (100, 3)\n except:\n shutil.rmtree(test_path)\n raise()","def test_add():\n data = io.create_sample_Dataset()\n tmp = data + data\n assert tmp[\"u\"][0, 0, 0] == 2.0","def fixture_example_data():\n import_example_data()","def init_tensors(self, sample, *args):\n raise NotImplementedError","def test(self, test):\r\n self.ml_data.set_target(test[0])\r\n self.ml_data.set_features(test[1])\r\n if self.ml_data.target_type.all() == np.float64 or self.ml_data.target_type.all() == np.int64:\r\n self.model_qua.open()\r\n else:\r\n self.model_quali.open()","def test_set_xT(self):\n s = State(substance=\"water\")\n s.xT = Q_(0.5, \"dimensionless\"), Q_(400.0, \"K\")\n # Pylance does not support NumPy ufuncs\n assert np.isclose(s.T, Q_(400.0, \"K\")) # type: ignore\n assert np.isclose(s.p, Q_(245769.34557103913, \"Pa\")) # type: ignore\n assert np.isclose(s.xT[1], Q_(400.0, \"K\")) # type: ignore\n assert np.isclose(s.xT[0], Q_(0.5, \"dimensionless\")) # type: ignore\n assert np.isclose(s.u, Q_(1534461.5163075812, \"J/kg\")) # type: ignore\n assert np.isclose(s.s, Q_(4329.703956664546, \"J/(kg*K)\")) # type: ignore\n assert np.isclose(s.cp, Q_(4056.471547685226, \"J/(kg*K)\")) # type: ignore\n assert np.isclose(s.cv, Q_(2913.7307270395363, \"J/(kg*K)\")) # type: ignore\n assert np.isclose(s.v, Q_(0.3656547423394701, \"m**3/kg\")) # type: ignore\n assert np.isclose(s.h, Q_(1624328.2430353598, \"J/kg\")) # type: ignore\n assert np.isclose(s.x, Q_(0.5, \"dimensionless\")) # type: ignore\n s.xT = Q_(50, \"percent\"), Q_(400.0, \"K\")\n assert np.isclose(s.T, Q_(400.0, \"K\")) # type: ignore\n assert np.isclose(s.p, Q_(245769.34557103913, \"Pa\")) # type: ignore\n assert np.isclose(s.xT[1], Q_(400.0, \"K\")) # type: ignore\n assert np.isclose(s.xT[0], Q_(0.5, \"dimensionless\")) # type: ignore\n assert np.isclose(s.u, Q_(1534461.5163075812, \"J/kg\")) # type: ignore\n assert np.isclose(s.s, Q_(4329.703956664546, \"J/(kg*K)\")) # type: ignore\n assert np.isclose(s.cp, Q_(4056.471547685226, \"J/(kg*K)\")) # type: ignore\n assert np.isclose(s.cv, Q_(2913.7307270395363, \"J/(kg*K)\")) # type: ignore\n assert np.isclose(s.v, Q_(0.3656547423394701, \"m**3/kg\")) # type: ignore\n assert np.isclose(s.h, Q_(1624328.2430353598, \"J/kg\")) # type: ignore\n assert np.isclose(s.x, Q_(0.5, \"dimensionless\")) # type: ignore","def run_experiments(data_set=\"\",compact=2,exp_name=\"\",x=\"\"):\n cwd = os.getcwd()\n results_path = cwd+'/data/geometric/'+exp_name+'_'\n dataset_path = cwd+'/data/geometric/'+data_set+'_'\n compact = int(compact)\n if \"m\" in x:\n # make new pxy\n pxy, Xdata, groups = gen_geometric_pxy()\n np.save(dataset_path+'Xdata',Xdata)\n np.save(dataset_path+'groups',groups)\n np.save(dataset_path+'pxy',pxy)\n else:\n # load existing pxy\n pxy = np.load(dataset_path+'pxy.npy')\n if \"r\" in x: # regular experiments\n if compact>1:\n metrics_stepwise, distributions_stepwise,\\\n metrics_converged, distributions_converged,\\\n metrics_stepwise_allreps, distributions_stepwise_allreps,\\\n metrics_converged_allreps, distributions_converged_allreps = test_IB(pxy,compact)\n metrics_converged.to_csv(results_path+'metrics_converged.csv')\n metrics_stepwise.to_csv(results_path+'metrics_stepwise.csv')\n distributions_converged.to_pickle(results_path+'distributions_converged.pkl')\n elif compact>0:\n metrics_stepwise,\\\n metrics_converged, distributions_converged,\\\n metrics_stepwise_allreps,\\\n metrics_converged_allreps, distributions_converged_allreps = test_IB(pxy,compact)\n metrics_converged.to_csv(results_path+'metrics_converged.csv')\n metrics_stepwise.to_csv(results_path+'metrics_stepwise.csv')\n distributions_converged.to_pickle(results_path+'distributions_converged.pkl')\n else:\n metrics_stepwise,\\\n metrics_converged,\\\n metrics_stepwise_allreps,\\\n metrics_converged_allreps = test_IB(pxy,compact)\n metrics_converged.to_csv(results_path+'metrics_converged.csv')\n metrics_stepwise.to_csv(results_path+'metrics_stepwise.csv')\n if \"ip\" in x: # initialization experiments - positive p0\n if compact>1:\n metrics_stepwise, distributions_stepwise,\\\n metrics_converged, distributions_converged,\\\n metrics_stepwise_allreps, distributions_stepwise_allreps,\\\n metrics_converged_allreps, distributions_converged_allreps = test_p0_pos(pxy,compact)\n metrics_converged.to_csv(results_path+'metrics_converged_p0_pos.csv')\n metrics_stepwise.to_csv(results_path+'metrics_stepwise_p0_pos.csv')\n distributions_converged.to_pickle(results_path+'distributions_converged_p0_pos.pkl')\n elif compact>0:\n metrics_stepwise,\\\n metrics_converged, distributions_converged,\\\n metrics_stepwise_allreps,\\\n metrics_converged_allreps, distributions_converged_allreps = test_p0_pos(pxy,compact)\n metrics_converged.to_csv(results_path+'metrics_converged_p0_pos.csv')\n metrics_stepwise.to_csv(results_path+'metrics_stepwise_p0_pos.csv')\n distributions_converged.to_pickle(results_path+'distributions_converged_p0_pos.pkl')\n else:\n metrics_stepwise,\\\n metrics_converged,\\\n metrics_stepwise_allreps,\\\n metrics_converged_allreps = test_p0_pos(pxy,compact)\n metrics_converged.to_csv(results_path+'metrics_converged_p0_pos.csv')\n metrics_stepwise.to_csv(results_path+'metrics_stepwise_p0_pos.csv')\n if \"in\" in x: # initialization experiments - negative p0\n if compact>1:\n metrics_stepwise, distributions_stepwise,\\\n metrics_converged, distributions_converged,\\\n metrics_stepwise_allreps, distributions_stepwise_allreps,\\\n metrics_converged_allreps, distributions_converged_allreps = test_p0_neg(pxy,compact)\n metrics_converged.to_csv(results_path+'metrics_converged_p0_neg.csv')\n metrics_stepwise.to_csv(results_path+'metrics_stepwise_p0_neg.csv')\n distributions_converged.to_pickle(results_path+'distributions_converged_p0_neg.pkl')\n elif compact>0:\n metrics_stepwise,\\\n metrics_converged, distributions_converged,\\\n metrics_stepwise_allreps,\\\n metrics_converged_allreps, distributions_converged_allreps = test_p0_neg(pxy,compact)\n metrics_converged.to_csv(results_path+'metrics_converged_p0_neg.csv')\n metrics_stepwise.to_csv(results_path+'metrics_stepwise_p0_neg.csv')\n distributions_converged.to_pickle(results_path+'distributions_converged_p0_neg.pkl')\n else:\n metrics_stepwise,\\\n metrics_converged,\\\n metrics_stepwise_allreps,\\\n metrics_converged_allreps = test_p0_neg(pxy,compact)\n metrics_converged.to_csv(results_path+'metrics_converged_p0_neg.csv')\n metrics_stepwise.to_csv(results_path+'metrics_stepwise_p0_neg.csv')\n if \"c\" in x: # convergence tolerance experiments\n if compact>1:\n metrics_stepwise, distributions_stepwise,\\\n metrics_converged, distributions_converged,\\\n metrics_stepwise_allreps, distributions_stepwise_allreps,\\\n metrics_converged_allreps, distributions_converged_allreps = test_ctol(pxy,compact)\n metrics_converged.to_csv(results_path+'metrics_converged_ctol.csv')\n metrics_stepwise.to_csv(results_path+'metrics_stepwise_ctol.csv')\n distributions_converged.to_pickle(results_path+'distributions_converged_ctol.pkl')\n elif compact>0:\n metrics_stepwise,\\\n metrics_converged, distributions_converged,\\\n metrics_stepwise_allreps,\\\n metrics_converged_allreps, distributions_converged_allreps = test_ctol(pxy,compact)\n metrics_converged.to_csv(results_path+'metrics_converged_ctol.csv')\n metrics_stepwise.to_csv(results_path+'metrics_stepwise_ctol.csv')\n distributions_converged.to_pickle(results_path+'distributions_converged_ctol.pkl')\n else:\n metrics_stepwise,\\\n metrics_converged,\\\n metrics_stepwise_allreps,\\\n metrics_converged_allreps = test_ctol(pxy,compact)\n metrics_converged.to_csv(results_path+'metrics_converged_ctol.csv')\n metrics_stepwise.to_csv(results_path+'metrics_stepwise_ctol.csv')\n if \"z\" in x: # zeroL tolerance experiments\n if compact>1:\n metrics_stepwise, distributions_stepwise,\\\n metrics_converged, distributions_converged,\\\n metrics_stepwise_allreps, distributions_stepwise_allreps,\\\n metrics_converged_allreps, distributions_converged_allreps = test_zeroLtol(pxy,compact)\n metrics_converged.to_csv(results_path+'metrics_converged_zeroLtol.csv')\n metrics_stepwise.to_csv(results_path+'metrics_stepwise_zeroLtol.csv')\n distributions_converged.to_pickle(results_path+'distributions_converged_zeroLtol.pkl')\n elif compact>0:\n metrics_stepwise,\\\n metrics_converged, distributions_converged,\\\n metrics_stepwise_allreps,\\\n metrics_converged_allreps, distributions_converged_allreps = test_zeroLtol(pxy,compact)\n metrics_converged.to_csv(results_path+'metrics_converged_zeroLtol.csv')\n metrics_stepwise.to_csv(results_path+'metrics_stepwise_zeroLtol.csv')\n distributions_converged.to_pickle(results_path+'distributions_converged_zeroLtol.pkl')\n else:\n metrics_stepwise,\\\n metrics_converged,\\\n metrics_stepwise_allreps,\\\n metrics_converged_allreps = test_zeroLtol(pxy,compact)\n metrics_converged.to_csv(results_path+'metrics_converged_zeroLtol.csv')\n metrics_stepwise.to_csv(results_path+'metrics_stepwise_zeroLtol.csv')\n if \"b\" in x: # trying proposed optimal beta\n if compact>1:\n metrics_stepwise, distributions_stepwise,\\\n metrics_converged, distributions_converged,\\\n metrics_stepwise_allreps, distributions_stepwise_allreps,\\\n metrics_converged_allreps, distributions_converged_allreps = test_IB(pxy,compact)\n metrics_converged.to_csv(results_path+'metrics_converged_bestbeta.csv')\n metrics_stepwise.to_csv(results_path+'metrics_stepwise_bestbeta.csv')\n distributions_converged.to_pickle(results_path+'distributions_converged_bestbeta.pkl')\n elif compact>0:\n metrics_stepwise,\\\n metrics_converged, distributions_converged,\\\n metrics_stepwise_allreps,\\\n metrics_converged_allreps, distributions_converged_allreps = test_IB(pxy,compact)\n metrics_converged.to_csv(results_path+'metrics_converged_bestbeta.csv')\n metrics_stepwise.to_csv(results_path+'metrics_stepwise_bestbeta.csv')\n distributions_converged.to_pickle(results_path+'distributions_converged_bestbeta.pkl')\n else:\n metrics_stepwise,\\\n metrics_converged,\\\n metrics_stepwise_allreps,\\\n metrics_converged_allreps = test_IB(pxy,compact)\n metrics_converged.to_csv(results_path+'metrics_converged_bestbeta.csv')\n metrics_stepwise.to_csv(results_path+'metrics_stepwise_bestbeta.csv')\n return 0","def test(self, dataset) -> None:\n raise NotImplementedError()","def test_neuron(self):\r\n # crear una lista 1-D (Horizontal, Entradas).\r\n Z = [1, 2, 3]\r\n # crear una lista 1-D (Vertical, Pesos de la red).\r\n W = [10, 20, 30]\r\n # Inicializamos la neurona, y obtenemos el valor que toma dado W * Z\r\n # X(k) = W * Z\r\n result = rhonn(W, Z).predict()\r\n # Comprobamos el resultado \r\n self.assertEqual(result, 140)","def test_data_manipulation(self):\n target_name = self.project['target']['name']\n self.api_mock.return_value.get_metadata.return_value = [\n {'_id': '0',\n 'pid': '1',\n 'created': datetime.datetime.now(),\n 'name':'universe',\n 'originalName': 'credit-sample-200.csv',\n 'varTypeString': 'NN',\n 'shape': [2, 100],\n 'controls':{},\n 'columns': [[1,target_name,0],[3,\"age\",0]],\n 'files': ['projects/' + str(self.pid) + '/raw/' + self.testdatafile],\n 'typeConvert': {}},\n {'_id': '1',\n 'pid': '1',\n 'name':'test',\n 'originalName': 'credit-sample-200.csv',\n 'created': datetime.datetime.now(),\n 'varTypeString': 'NN',\n 'shape': [2, 100],\n 'controls':{},\n 'columns': [[1,target_name,0],[3,\"age\",0]],\n 'files': ['projects/' + str(self.pid) + '/raw/' + self.testdatafile],\n 'typeConvert': {}},\n {'_id': '2',\n 'pid': '1',\n 'name':'new',\n 'created': datetime.datetime.now(),\n 'originalName': 'credit-sample-200.csv',\n 'newdata':True,\n 'controls':{},\n 'shape': [2, 100],\n 'varTypeString': 'NN',\n 'columns': [[1,target_name,0],[3,\"age\",0]],\n 'files': ['projects/' + str(self.pid) + '/raw/' + self.testdatafile],\n 'typeConvert': {}}]\n request = WorkerRequest({'pid': '1', 'uid': '1', 'dataset_id': '1',\n 'command': 'fit', 'max_reps': 0,\n 'samplepct': 100})\n\n #target\n #this will map the target values to (0,1) because target type is Binary\n target_vector = self.dataprocessor.target_vector()\n target_series = target_vector['main']\n self.assertItemsEqual(np.unique(target_series), [0,1])\n\n #this will be none because 'holdout_pct' isn't set in the project data\n self.assertIsNone(target_vector['holdout'])\n\n #prediction dataset\n predictors = self.dataprocessor.predictors()\n pred_dataframe = predictors['1']['main']\n self.assertItemsEqual(list(pred_dataframe.columns), [\"age\"])\n self.assertEqual(self.dataprocessor.get_vartypestring_without_target('1'), \"N\")\n\n request = WorkerRequest({'pid': '1', 'uid': '1', 'dataset_id': '1', 'scoring_dataset_id': '2', 'command': 'predict', 'max_reps': 0, 'samplepct':100})\n dp2 = DataProcessor(request)\n data = dp2.request_datasets()\n self.assertEqual(data.keys(), ['1'])\n self.assertEqual(data['1'].keys(), ['scoring', 'vartypes'])\n scoring_data = data['1']['scoring']\n vartypes = data['1']['vartypes']\n self.assertEqual(list(scoring_data.columns), [\"age\"])\n self.assertEqual(vartypes, \"N\")","def test_createData():\n\n sys = LVsystem.Ecosystem()\n\n sys.addSpecies('rabbit')\n sys.setInteraction('rabbit', 'hen', 0)\n sys.setInteraction('rabbit', 'fox', -1)\n sys.setInitialCond('rabbit', 30)\n sys.setGrowthRate('rabbit', 0.09)\n sys.setCarrCap('rabbit', 10000)\n sys.setChangeRate('rabbit', 400)\n\n sys.addSpecies('hen')\n sys.setInteraction('hen', 'rabbit', 0)\n sys.setInteraction('hen', 'fox', -1)\n sys.setInitialCond('hen', 10)\n sys.setGrowthRate('hen', 0.07)\n sys.setCarrCap('hen', 10000)\n sys.setChangeRate('hen', 500)\n\n sys.addSpecies('fox')\n sys.setInteraction('fox', 'rabbit', 1)\n sys.setInteraction('fox', 'hen', 1)\n sys.setInitialCond('fox', 20)\n sys.setGrowthRate('fox', -0.06)\n sys.setCarrCap('fox', 1)\n sys.setChangeRate('fox', 250)\n\n \n data = sys.create_data()\n \n assert data[0] == 3\n assert data[1] == ['rabbit', 'hen', 'fox']\n assert data[2] == [30,10,20]\n assert data[3] == [0.09,0.07,-0.06] \n assert data[4] == [10000,10000,1]\n assert data[5] == [400,500,250]\n assert data[6][1][2] == -data[6][2][1]\n assert data[6][2][2] == 0\n \n sys.removeSpecies('rabbit')\n sys.removeSpecies('fox')\n sys.removeSpecies('hen')","def test(batch_size=1, num_sample=16):\n return paddle.batch(_read_creater(num_sample=num_sample), batch_size)","def test(self, dataset):\n\n outputs, errors = self.use(dataset)\n\n ## PUT CODE HERE ##\n # I put the code in the \"use\" function, seems better :-)\n\n return outputs, errors","def test_X_test_property():\n atom = ATOMClassifier(X_bin, y_bin, random_state=1)\n atom.run([\"MNB\", \"LR\"])\n assert atom.X_test.equals(atom.mnb.X_test)\n assert check_scaling(atom.lr.X_test)","def test_op(self):\n name = \"my_mesh\"\n tensor_data = test_utils.get_random_mesh(\n 100, add_faces=True, add_colors=True\n )\n config_dict = {\"foo\": 1}\n with tf.compat.v1.Graph().as_default():\n tensor_summary = summary.op(\n name,\n tensor_data.vertices,\n faces=tensor_data.faces,\n colors=tensor_data.colors,\n config_dict=config_dict,\n )\n with self.test_session() as sess:\n proto = self.pb_via_op(tensor_summary)\n self.verify_proto(proto, name)\n plugin_metadata = metadata.parse_plugin_metadata(\n proto.value[0].metadata.plugin_data.content\n )\n self.assertEqual(\n json.dumps(config_dict, sort_keys=True),\n plugin_metadata.json_config,\n )","def setup(self):\n (self.X, self.Y) = load_iris(problem=\"label_ranking\")","def test_11_dataset(self, example):\n example.zonenumbers_i = np.array([100, 200, 300])\n example.groupnames_g = np.array(['Female', 'Male'], dtype='O')\n example.create_ds()\n assert isinstance(example.ds, xr.Dataset)\n for attr, dtype in example.dtypes.items():\n data_array = example.ds[attr]\n # test if the shapes are correct\n if dtype.shape:\n np.testing.assert_array_equal(dtype.get_shape(example),\n data_array.shape,\n 'shape not correct')\n else:\n # not initialized array\n assert not np.any(data_array.shape), 'shape not initialized'\n #test if the datatypes are correct\n assert np.dtype(dtype.dtype) == data_array.dtype, 'dtype not correct'\n print(example.ds)","def test_fish():\n test_path = tempfile.mkdtemp()\n x_train, metadata = fish(test_path)\n try:\n assert x_train.shape == (97, 20)\n except:\n shutil.rmtree(test_path)\n raise()","def test_active_inference_SPM_1b(self):","def test_okun():\n test_path = tempfile.mkdtemp()\n x_train, metadata = okun(test_path)\n try:\n assert x_train.shape == (47, 4)\n except:\n shutil.rmtree(test_path)\n raise()","def test_documentation_popxl_repeat_0(self):\n filename = \"repeat_graph_0.py\"\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)","def load_x(self):\n self.x = self.read_var(self.xvar)\n self.test_shape(self.xvar, self.x.shape, 1)","def testingData(self):\n\n #import SampleData\n #sampleDataLogic = SampleData.SampleDataLogic()\n #mrHead = sampleDataLogic.downloadMRHead()\n #dtiBrain = sampleDataLogic.downloadDTIBrain()\n \n # w = slicer.modules.SteeredFluidRegistrationWidget\n # w.fixedSelector.setCurrentNode(mrHead)\n # w.movingSelector.setCurrentNode(dtiBrain)\n \n if not slicer.util.getNodes('testbrain1*'):\n import os\n fileName = \"C:\\\\Work\\\\testbrain1.nrrd\"\n vl = slicer.modules.volumes.logic()\n brain1Node = vl.AddArchetypeVolume(fileName, \"testbrain1\", 0)\n else:\n nodes = slicer.util.getNodes('testbrain1.nrrd')\n brain1Node = nodes[0]\n\n if not slicer.util.getNodes('testbrain2*'):\n import os\n fileName = \"C:\\\\Work\\\\testbrain2.nrrd\"\n vl = slicer.modules.volumes.logic()\n brain2Node = vl.AddArchetypeVolume(fileName, \"testbrain2\", 0)\n #TODO else assign from list\n\n # if not slicer.util.getNodes('movingToFixed*'):\n # # Create transform node\n # transform = slicer.vtkMRMLLinearTransformNode()\n # transform.SetName('movingToFixed')\n # slicer.mrmlScene.AddNode(transform)\n\n # transform = slicer.util.getNode('movingToFixed')\n \n # ###\n # # neutral.SetAndObserveTransformNodeID(transform.GetID())\n # ###\n \n compositeNodes = slicer.util.getNodes('vtkMRMLSliceCompositeNode*')\n for compositeNode in compositeNodes.values():\n compositeNode.SetBackgroundVolumeID(brain1Node.GetID())\n compositeNode.SetForegroundVolumeID(brain2Node.GetID())\n compositeNode.SetForegroundOpacity(0.5)\n applicationLogic = slicer.app.applicationLogic()\n applicationLogic.FitSliceToAll()","def load_data(self):\n self.data = self.read_var(self.datavar)\n self.test_shape(self.datavar, self.data.shape, 2)","def test(self, dataset):\n model_path = os.path.join(self.check_point, 'model.pt')\n if not os.path.exists(model_path):\n raise Exception('Cannot find %s.' % model_path)\n\n self.model = torch.load(model_path)\n print(self.model)\n model_parameters = filter(lambda p: p.requires_grad, self.model.parameters())\n params = sum([np.prod(p.size()) for p in model_parameters])\n print(1.0 * params / (1000 * 1000))\n _, _, stats, outputs, names = self._check_PSNR(dataset, is_test=True)\n return stats, outputs, names","def get_data(generator, random, bench_id):\n x_train, y_train, x_test, y_test = generator(random, bench_id)\n x_train = np.c_[np.ones(len(x_train)), x_train]\n x_test = np.c_[np.ones(len(x_test)), x_test]\n return x_train, y_train, x_test, y_test","def test(self, dataset):\n model_path = os.path.join(self.check_point, 'model.pt')\n if not os.path.exists(model_path):\n raise Exception('Cannot find %s.' %model_path)\n \n self.model = torch.load(model_path)\n _, _, stats, outputs = self._check_PSNR(dataset, is_test=True)\n return stats, outputs","def test_significance_window(tensor_directory, analysis_name, window):\n\n \"\"\"\n # Open Analysis Dataframe\n analysis_file = h5py.File(os.path.join(tensor_directory, analysis_name + \".hdf5\"), \"r\")\n activity_dataset = analysis_file[\"Data\"]\n metadata_dataset = analysis_file[\"metadata\"]\n number_of_timepoints, number_of_trials, number_of_pixels = np.shape(activity_dataset)\n print(\"metadata_dataset\", np.shape(metadata_dataset))\n \"\"\"\n\n\n # Open Analysis Dataframe\n analysis_file = tables.open_file(os.path.join(tensor_directory, analysis_name + \"_Trialwise_.h5\"), mode=\"r\")\n activity_dataset = analysis_file.root[\"Data\"]\n metadata_dataset = analysis_file.root[\"Trial_Details\"]\n activity_dataset = np.array(activity_dataset)\n metadata_dataset = np.array(metadata_dataset)\n\n activity_dataset = np.nan_to_num(activity_dataset)\n\n number_of_trials, number_of_timepoints, number_of_pixels = np.shape(activity_dataset)\n\n\n print(\"Number of timepoints\", number_of_timepoints)\n print(\"number of pixels\", number_of_pixels)\n print(\"number of trials\", number_of_trials)\n\n # Create P and Slope Tensors\n p_value_tensor = np.ones(number_of_pixels)\n slope_tensor = np.zeros(number_of_pixels)\n\n # Get Timepoint Data\n timepoint_activity = activity_dataset[:, window]\n print(\"Timepoint activity shape\", np.shape(timepoint_activity))\n timepoint_activity = np.mean(timepoint_activity, axis=1)\n\n for pixel_index in tqdm(range(number_of_pixels), position=1, desc=\"Pixel\", leave=False):\n\n # Package Into Dataframe\n pixel_activity = timepoint_activity[:, pixel_index]\n pixel_dataframe = repackage_data_into_dataframe(pixel_activity, metadata_dataset)\n\n # Fit Mixed Effects Model\n p_value, slope = mixed_effects_random_slope_and_intercept(pixel_dataframe)\n p_value_tensor[pixel_index] = p_value\n slope_tensor[pixel_index] = slope\n\n return p_value_tensor, slope_tensor","def test_predict_from_examples():\n examples = []\n for i in range(len(DATA)):\n (value, attribute_map) = DATA[i]\n print 'tuple created : \\n1:' + str(value) + \"\\n2:\" + str(attribute_map)\n ex = VowpalExample(i, value)\n #Extracting the features from all of the namespaces in the map. \n for (featureNamespace, features) in attribute_map.items():\n #Each \"section\" is \n ex.add_section(featureNamespace, features)\n examples.append(ex)\n train = examples[:-2]\n test = examples[-2:]\n vw = Vowpal(PATH_VW, './vw.%s', {'--passes' : '10' })\n preds = vw.predict_from_examples(train, test)\n for (id, value) in preds:\n print 'prediction for %s is %s' % (id, value)","def test_api_predictors_get(self):\n pass","def setUp(self):\r\n self.value_for_seed = 20\r\n\r\n # Single comp.\r\n self.labels1 = ['foo', 'bar']\r\n self.dists1 = [[1, 2, 3], [7, 8]]\r\n\r\n # Multiple comps.\r\n self.labels2 = ['foo', 'bar', 'baz']\r\n self.dists2 = [[1, 2, 3], [7, 8], [9, 10, 11]]\r\n\r\n # Too few obs.\r\n self.labels3 = ['foo', 'bar', 'baz']\r\n self.dists3 = [[1], [7], [9, 10, 11]]","def test_education():\n test_path = tempfile.mkdtemp()\n x_train, metadata = education(test_path)\n try:\n assert x_train.shape == (50, 6)\n except:\n shutil.rmtree(test_path)\n raise()","def load_data():\n\n print('Loading and Visualizing Data ...')\n\n file_name = path.join(getcwd(), 'ex3', 'src', 'data', 'ex3data1')\n data = scipy.io.loadmat(file_name)\n\n # training data stored in arrays X, y\n # y should be a row vector of labels\n return data['X'], data['y'].T[0]","def test_getitem():\n atom = ATOMClassifier(X_class, y_class, random_state=1)\n atom.run(\"Tree\")\n assert atom.tree[\"alcohol\"].equals(atom.dataset[\"alcohol\"])\n with pytest.raises(TypeError, match=r\".*subscriptable with type str.*\"):\n print(atom.tree[2])","def setUp(self):\n self._m = 100\n self._n = 30\n self._k = 5\n self._increment = 20\n self._A = get_data(ExperimentType.ExampleNo2)(self._m, np.arange(2 * self._k).astype(float))\n self._approximation = random_id(self._A, self._k, self._increment)\n self._B = self._approximation.B\n self._P = np.array(self._approximation.P)\n self._A = self._A.as_numpy_arr()\n self._n = self._A.shape[1]\n self._approximation = self._approximation.as_numpy_arr()","def setUp(self):\n self.dataset = self.dataset_cls()","def mnist_testing(shuffled = True):\n mndata = MNIST(MNIST_PATH)\n test_ims, test_labels = mndata.load_testing()\n test_X = np.array(test_ims).T\n test_y = np.array(test_labels).T\n return test_X, test_y","def setUp(self):\n temperature = np.array([[185.0, 260.65, 273.15, 338.15]], dtype=np.float32)\n self.temperature = set_up_variable_cube(temperature)\n humidity = np.array([[60.0, 70.0, 75.0, 80.0]], dtype=np.float32)\n self.relative_humidity = set_up_variable_cube(\n humidity, name=\"relative_humidity\", units=\"%\"\n )\n pressure = np.array([[1.0e5, 9.9e4, 9.85e4, 9.8e4]], dtype=np.float32)\n self.pressure = set_up_variable_cube(pressure, name=\"air_pressure\", units=\"Pa\")\n self.expected_wbt_data = np.array(\n [[185.0, 259.88306, 271.78006, 333.96066]], dtype=np.float32\n )","def setUp(self):\n self._default_call_inputs = (\n np.array([[\"one\", \"two\", \"three\"],\n [\"four\", \"five\", \"six\"]]),\n None\n )\n\n self._hash_embedding_dim = 4\n self._embedding_dim = 2\n\n self._default_config = {\n \"hash_embedding_dim\": self._hash_embedding_dim,\n \"embedding_dim\": self._embedding_dim\n }","def setUp(self):\n self.testdatapath = os.path.join(mkdtemp())\n self.testfilenames = [os.path.join(self.testdatapath, \"0001.nc\")]\n\n self.gpis = [1, 10, 11, 12]\n self.lons = [0, 0, 1, 1]\n self.lats = [1, 1, 0, 0]\n self.cells = [1, 1, 1, 1]\n self.grid = grids.CellGrid(self.lons, self.lats, self.cells, self.gpis)","def test_np_memory_layout_add_input_tensor_pystepiocallback():\n\n def _test(transposedInput, transposedOutput):\n builder = popart.Builder()\n\n # Create a random constant and transpose it\n np.random.seed(1)\n input1 = builder.addInputTensor(\"INT32\", [2, 2])\n\n # Run a session to prove this\n output1 = builder.aiOnnx.identity([input1])\n builder.addOutputTensor(output1)\n anchorConfig = {output1: popart.AnchorReturnType(\"ALL\")}\n\n dataFlow = popart.DataFlow(1, anchorConfig)\n deviceConfig = {\"numIPUs\": 1}\n dm = popart.DeviceManager()\n device = dm.createIpuModelDevice(deviceConfig)\n session = popart.InferenceSession(\n fnModel=builder.getModelProto(), dataFlow=dataFlow, deviceInfo=device\n )\n\n # Compile graph and place weights onto it\n session.prepareDevice()\n session.weightsFromHost()\n\n # Feed the session with a transposed (non-contiguous) tensor.\n input1Value = np.random.randint(0, 100, size=(2, 2), dtype=\"int32\")\n if transposedInput:\n input1Value = np.transpose(input1Value, [1, 0])\n output1Value = np.random.randint(0, 100, size=(2, 2), dtype=\"int32\")\n if transposedOutput:\n output1Value = np.transpose(output1Value, [1, 0])\n\n with pytest.raises(\n (Exception, RuntimeError, popart.popart_exception)\n ) as e_info:\n\n # pylint: disable=unused-argument\n def input_callback(id, prefetch):\n return input1Value\n\n def input_complete_callback(_): # id is an unused parameter\n pass\n\n def output_callback(_): # id is an unused parameter\n return output1Value\n\n def output_complete_callback(_): # id is an unused parameter\n pass\n\n stepio = popart.PyStepIOCallback(\n input_callback,\n input_complete_callback,\n output_callback,\n output_complete_callback,\n )\n\n session.run(stepio)\n\n assert \"contiguous\" in e_info.value.args[0]\n\n _test(transposedInput=True, transposedOutput=False)\n _test(transposedInput=False, transposedOutput=True)","def demonstration_examples(kind):\n\n DIR = './data/demos/'\n RNN_DEMO = \"-demo_rnn_examples\"\n EXT = '.pkl'\n\n if kind == 'rnn':\n return pd.read_pickle(DIR+RNN_DEMO+EXT)","def test_0():\n sync.gen_multi_fake_data()#default is only one randomly selected data set\n sync.main(testing=True)"],"string":"[\n \"def test_documentation_popxl_addition_variable(self):\\n filename = \\\"tensor_addition.py\\\"\\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)\",\n \"def test_predictor():\",\n \"def test_loadData():\\n \\n sys = LVsystem.Ecosystem()\\n \\n sys.loadSetup('2Prey1Predator')\\n \\n \\n data = sys.create_data()\\n \\n assert data[0] == 3\\n assert data[1] == ['rabbit', 'hen', 'fox']\\n assert data[2] == [30,10,20]\\n assert data[3] == [0.09,0.07,-0.06] \\n assert data[4] == [10000,10000,1]\\n assert data[5] == [400,500,250]\\n assert data[6][1][2] == -data[6][2][1]\\n assert data[6][2][2] == 0\\n\\n sys.removeSpecies('rabbit')\\n sys.removeSpecies('fox')\\n sys.removeSpecies('hen')\",\n \"def test_data():\\n batch_size = 10\\n input_dim = 28\\n test_data = np.random.rand(batch_size, input_dim)\\n\\n return test_data\",\n \"def test_add_get_tensor(mock_data):\\n dataset = Dataset(\\\"test-dataset\\\")\\n\\n # 1D tensors of all data types\\n data = mock_data.create_data(10)\\n add_get_arrays(dataset, data)\",\n \"def test_documentation_popxl_adv_get_write(self):\\n filename = \\\"tensor_get_write_adv.py\\\"\\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)\",\n \"def test_load_data(self):\\n assert len(self._mnist.get()) == 10\\n assert self._mnist.get()[0].label == 7\\n pass\",\n \"def test_add_get_tensor_3D(mock_data):\\n dataset = Dataset(\\\"test-dataset\\\")\\n\\n # 3D tensors of all datatypes\\n data_3D = mock_data.create_data((10, 10, 10))\\n add_get_arrays(dataset, data_3D)\",\n \"def test_getitem(self):\\n obs = self.tester['1.SKM7.640188']\\n exp = PrepSample('1.SKM7.640188', self.tester)\\n self.assertEqual(obs, exp)\",\n \"def test_documentation_popxl_mnist_rts_train_test(self):\\n filename = \\\"mnist_rts.py --replication-factor 2 --rts --test\\\"\\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)\",\n \"def test_snow_pumps():\\n test_path = tempfile.mkdtemp()\\n x_train, metadata = snow_pumps(test_path)\\n try:\\n assert x_train.shape == (13, 4)\\n except:\\n shutil.rmtree(test_path)\\n raise()\",\n \"def test_machine_learning():\",\n \"def test_synth_tr():\\n test_path = tempfile.mkdtemp()\\n x_train, metadata = synth_tr(test_path)\\n try:\\n assert x_train.shape == (250, 3)\\n except:\\n shutil.rmtree(test_path)\\n raise()\",\n \"def setUp(self):\\n self.samples = 5\\n self.otus = 10\\n seed(0) # this will seed numpy prng at 0 before each test\",\n \"def test_init_prediction_data(raw_data):\\n prediction_data = PredictionData(**raw_data)\\n assert prediction_data\",\n \"def test_documentation_popxl_mnist_rts_train(self):\\n filename = \\\"mnist_rts.py --replication-factor 2 --rts\\\"\\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)\",\n \"def rand_data():\\n # 100 examples, with seq_len=10, each holding 300 features\\n return torch.randn((100, 10, 300))\",\n \"def set_data():\\r\\n #if not os.path.exists(filepath):\\r\\n #download_data()\\r\\n metadata = read(filepath + flist[-1])\\r\\n ndata = metadata['num_cases_per_batch']\\r\\n ndim = metadata['num_vis']\\r\\n\\r\\n data, train, test = {}, {}, {}\\r\\n data['labels'] = metadata['label_names']\\r\\n data['ntraindata'] = metadata['num_cases_per_batch'] * (len(flist) - 2)\\r\\n data['ntestdata'] = metadata['num_cases_per_batch']\\r\\n data['ndim'] = metadata['num_vis']\\r\\n\\r\\n train['x'], train['y'] = convert_train(data['ntraindata'], data['ndim'])\\r\\n\\r\\n testdata = read(filepath + flist[-2])\\r\\n test['x'] = testdata['data']\\r\\n test['y'] = testdata['labels']\\r\\n\\r\\n data['train'], data['test'] = train, test\\r\\n save_pkl(data)\",\n \"def _creatExamplesTensorData(self, examples):\\n\\n images = []\\n \\n images2 = []\\n images3 = []\\n images4 = []\\n images5 = [] \\n labels = []\\n for (img_idx, label) in examples:\\n img = self.dataset[img_idx][0]\\n #print(img)\\n ##exit(0)\\n if self.load:\\n img = Image.fromarray(img)\\n else:\\n img = read_image(img)\\n #print(img.size)\\n #print(np.array(img).shape)\\n #exit(0)\\n if self.transform is not None:\\n img1 = self.transform(img)\\n\\n img2 = self.transform_test(img)\\n img3 = self.transform_test(img)\\n img4 = self.transform_test(img)\\n img5 = self.transform_test(img) \\n #print((img2-img1).abs().sum(),(img3-img1).abs().sum(),(img2-img3).abs().sum())\\n #print(img.shape,'located in test_loader.py at 146')\\n #exit(0)\\n images.append(img1)\\n \\n images2.append(img2)\\n images3.append(img3)\\n images4.append(img4)\\n images5.append(img5) \\n labels.append(label)\\n images = torch.stack(images, dim=0)\\n\\n images2 = torch.stack(images2, dim=0)\\n images3 = torch.stack(images3, dim=0)\\n images4 = torch.stack(images4, dim=0)\\n images5 = torch.stack(images5, dim=0) \\n labels = torch.LongTensor(labels)\\n return images, images2,images3,images4,images5,labels\",\n \"def test_add_get_tensor_2D(mock_data):\\n dataset = Dataset(\\\"test-dataset\\\")\\n\\n # 2D tensors of all data types\\n data_2D = mock_data.create_data((10, 10))\\n add_get_arrays(dataset, data_2D)\",\n \"def test_meteo():\\n test_path = tempfile.mkdtemp()\\n x_train, metadata = meteo(test_path)\\n try:\\n assert x_train.shape == (11, 6)\\n except:\\n shutil.rmtree(test_path)\\n raise()\",\n \"def test_training(self):\\n\\t\\tpass\",\n \"def setUp(self):\\n output = np.zeros((1, 5, 2))\\n target = np.zeros((1, 5, 2))\\n # first channel\\n output[0, 0] = [10, 4]\\n target[0, 0] = [10, 0]\\n # second channel\\n output[0, 1] = [10, 18]\\n target[0, 1] = [10, 10]\\n # third channel\\n output[0, 2] = [0, 0]\\n target[0, 2] = [0, -1]\\n # fourth channel\\n output[0, 3] = [40, 40]\\n target[0, 3] = [30, 30]\\n # fifth channel\\n output[0, 4] = [20, 10]\\n target[0, 4] = [0, 10]\\n\\n gt_instances = InstanceData()\\n gt_instances.keypoints = target\\n gt_instances.keypoints_visible = np.array(\\n [[True, True, False, True, True]])\\n\\n pred_instances = InstanceData()\\n pred_instances.keypoints = output\\n\\n data = {'inputs': None}\\n data_sample = {\\n 'gt_instances': gt_instances.to_dict(),\\n 'pred_instances': pred_instances.to_dict()\\n }\\n\\n self.data_batch = [data]\\n self.data_samples = [data_sample]\",\n \"def test_get_iris_setosa_data(self):\\n iris = get_iris_setosa_data()\\n self.assertEqual(len(iris.data), 150)\\n self.assertEqual(len(iris.labels), 150)\",\n \"def test_get_mnist_data(self):\\n # TODO: Remove once get_mnist_data(...) is fixed.\\n pass\\n # mnist = get_mnist_data()\\n # self.assertEqual(len(mnist.data), 60000)\\n # self.assertEqual(len(mnist.labels), 60000)\",\n \"def data_set_maker():\\n\\n # crate a folder in your code directory and name it: \\\"files\\\". put the .npy files iside that folder\\n path = os.getcwd() # reads the current path\\n x_train = np.load(path + '/files/tinyX.npy', 'r') # reads the input file\\n y_train = np.load(path + '/files/tinyY.npy', 'r') # reads the input file\\n x_test = np.load(path + '/files/tinyX_test.npy', 'r') # reads the input file\\n x_train, y_train = shuffle(x_train, y_train)\\n\\n return x_train, y_train, x_test\",\n \"def test_income():\\n test_path = tempfile.mkdtemp()\\n x_train, metadata = income(test_path)\\n try:\\n assert x_train.shape == (44, 4)\\n except:\\n shutil.rmtree(test_path)\\n raise()\",\n \"def test_gen():\\n tpot_obj = TPOTClassifier()\\n\\n pipeline = tpot_obj._gen_grow_safe(tpot_obj._pset, 1, 3)\\n\\n assert len(pipeline) > 1\\n assert pipeline[0].ret == Output_DF\",\n \"def test_get(self):\\n obs = self.tester.get('1.SKM7.640188')\\n exp = PrepSample('1.SKM7.640188', self.tester)\\n self.assertEqual(obs, exp)\",\n \"def test_variational():\\n # iris\\n #pres = \\\"Test pour le data set Iris (facile, classique)\\\"\\n #test_from_func_variational(pres, 15, 10, 3, True, Iris)\\n\\n # breast cancer\\n pres = \\\"Test pour le data set Breast Cancer (facile, classique)\\\"\\n test_from_func_variational(pres, 15, 10, 3, True, Breast_cancer)\\n\\n # digits\\n # pres = \\\"Test pour le data set Digits (difficile, classique)\\\"\\n # test_from_func(pres, 10, 10, 10, True, Digits, quantum_instance)\\n\\n # wine\\n # pres = \\\"Test pour le data set Wine (moyen, classique)\\\"\\n # test_from_func(pres, 15, 10, 5, True, Wine, quantum_instance)\\n\\n # gaussian\\n pres = \\\"Test pour des données gaussiennes (moyen, classique)\\\"\\n for _ in range(1):\\n print(\\\"\\\\n\\\")\\n print(\\\"New iteration\\\")\\n test_from_func_variational(pres, 25, 10, 2, True, Gaussian)\\n print(\\\"\\\\n\\\")\\n\\n # small adn strings\\n pres = \\\"Test pour des séquences ADN courtes (difficile, classique)\\\"\\n test_from_func_variational(pres, 10, 15, 14, True, Sequence)\\n\\n #Quantum data\\n pres = \\\"Test pour des données générées par ordinateur quantique (facile, quantique)\\\"\\n print(pres)\\n _, samp_train, samp_test, labels = ad_hoc_data(15, 10, 2, 0.3, True)\\n sample_m, sample_p = stock_get(20, 0.3)\\n\\n labels_me = [-1, 1]\\n samp_train_me = {-1: np.array(sample_m[:15]), 1: np.array(sample_p[:15])}\\n samp_test_me = {-1: np.array(sample_m[15:]), 1: np.array(sample_p[15:])}\\n print(samp_train)\\n print(samp_train_me)\\n print(samp_test)\\n print(samp_test_me)\\n\\n my_impl_variational(samp_train, samp_test, labels)\\n print(\\\"Pour autres données quantiques\\\")\\n my_impl_variational(samp_train_me, samp_test_me, labels_me)\",\n \"def test_dataset_property():\\n atom = ATOMClassifier(X_bin, y_bin, random_state=1)\\n atom.run([\\\"MNB\\\", \\\"LR\\\"])\\n assert atom.dataset.equals(atom.mnb.dataset)\\n assert check_scaling(atom.lr.dataset)\",\n \"def _get_data_for_tests():\\n X = np.random.randn(100, input_dim)\\n Y = np.random.randn(100, output_dim)\\n X_new = np.random.randn(100, input_dim)\\n return X, X_new, Y\",\n \"def setUp(self):\\n self.X_train, self.y_train = load_data(\\\"../data/traindata.mat.tar.gz\\\")\\n self.nn = NN_hwr([len(self.X_train[0]), 50, 10])\",\n \"def test_star():\\n test_path = tempfile.mkdtemp()\\n x_train, metadata = star(test_path)\\n try:\\n assert x_train.shape == (5748, 8)\\n except:\\n shutil.rmtree(test_path)\\n raise()\",\n \"def test_quartet():\\n test_path = tempfile.mkdtemp()\\n x_train, metadata = quartet(test_path)\\n try:\\n assert x_train.shape == (11, 6)\\n except:\\n shutil.rmtree(test_path)\\n raise()\",\n \"def test_show_examples():\\n skip_if_no_matplotlib()\\n skip_if_no_data()\\n with open('temp.yaml', 'w') as f:\\n f.write(\\\"\\\"\\\"\\n!obj:pylearn2.datasets.mnist.MNIST {\\n which_set: 'train'\\n}\\n\\\"\\\"\\\")\\n show_examples('temp.yaml', 28, 28, out='garbage.png')\\n os.remove('temp.yaml')\",\n \"def test_affect():\\n test_path = tempfile.mkdtemp()\\n x_train, metadata = affect(test_path)\\n try:\\n assert x_train.shape == (330, 20)\\n except:\\n shutil.rmtree(test_path)\\n raise()\",\n \"def test_sample(self):\\n seed = 5\\n space = Space()\\n probs = (0.1, 0.2, 0.3, 0.4)\\n categories = (\\\"asdfa\\\", 2, 3, 4)\\n dim1 = Categorical(\\\"yolo\\\", OrderedDict(zip(categories, probs)), shape=(2, 2))\\n space.register(dim1)\\n dim2 = Integer(\\\"yolo2\\\", \\\"uniform\\\", -3, 6)\\n space.register(dim2)\\n dim3 = Real(\\\"yolo3\\\", \\\"norm\\\", 0.9)\\n space.register(dim3)\\n\\n point = space.sample(seed=seed)\\n rng = check_random_state(seed)\\n test_point = [\\n dict(\\n yolo=dim1.sample(seed=rng)[0],\\n yolo2=dim2.sample(seed=rng)[0],\\n yolo3=dim3.sample(seed=rng)[0],\\n )\\n ]\\n assert len(point) == len(test_point) == 1\\n assert len(point[0].params) == len(test_point[0]) == 3\\n assert np.all(point[0].params[\\\"yolo\\\"] == test_point[0][\\\"yolo\\\"])\\n assert point[0].params[\\\"yolo2\\\"] == test_point[0][\\\"yolo2\\\"]\\n assert point[0].params[\\\"yolo3\\\"] == test_point[0][\\\"yolo3\\\"]\\n\\n points = space.sample(2, seed=seed)\\n rng = check_random_state(seed)\\n points1 = dim1.sample(2, seed=rng)\\n points2 = dim2.sample(2, seed=rng)\\n points3 = dim3.sample(2, seed=rng)\\n test_points = [\\n dict(yolo=points1[0], yolo2=points2[0], yolo3=points3[0]),\\n dict(yolo=points1[1], yolo2=points2[1], yolo3=points3[1]),\\n ]\\n assert len(points) == len(test_points) == 2\\n for i in range(2):\\n assert len(points[i].params) == len(test_points[i]) == 3\\n assert np.all(points[i].params[\\\"yolo\\\"] == test_points[i][\\\"yolo\\\"])\\n assert points[i].params[\\\"yolo2\\\"] == test_points[i][\\\"yolo2\\\"]\\n assert points[i].params[\\\"yolo3\\\"] == test_points[i][\\\"yolo3\\\"]\",\n \"def test_init(self):\\n xtal_model_data = XtalModelData(self.params)\\n\\n assert xtal_model_data.pdb == self.params.input.pdb\\n\\n assert xtal_model_data.mtz == self.params.input.mtz\\n\\n # TODO Assert utilised method calls of these classes\\n # Assert is innstance causses issues if called from somewhere else\\n\\n self.assertIsInstance(xtal_model_data.xrs, cctbx.xray.structure)\\n\\n self.assertIsInstance(\\n xtal_model_data.inputs, mmtbx.utils.process_command_line_args\\n )\\n\\n self.assertIsInstance(\\n xtal_model_data.crystal_gridding, cctbx.maptbx.crystal_gridding\\n )\\n\\n self.assertIsInstance(xtal_model_data.fmodel, mmtbx.f_model.f_model.manager)\",\n \"def setUp(self):\\n self.dataset = get_test_dataset()\",\n \"def test_mlp():\\r\\n datasets = gen_data()\\r\\n\\r\\n train_set_x, train_set_y = datasets[0]\\r\\n valid_set_x, valid_set_y = datasets[1]\\r\\n test_set_x , test_set_y = datasets[2]\\r\\n\\r\\n\\r\\n\\r\\n batch_size = 100 # size of the minibatch\\r\\n\\r\\n # compute number of minibatches for training, validation and testing\\r\\n n_train_batches = train_set_x.get_value(borrow=True).shape[0] / batch_size\\r\\n n_valid_batches = valid_set_x.get_value(borrow=True).shape[0] / batch_size\\r\\n n_test_batches = test_set_x.get_value(borrow=True).shape[0] / batch_size\\r\\n\\r\\n ######################\\r\\n # BUILD ACTUAL MODEL #\\r\\n ######################\\r\\n #print '... building the model'\\r\\n\\r\\n # allocate symbolic variables for the data\\r\\n index = T.lscalar() # index to a [mini]batch\\r\\n x = T.matrix('x') # the data is presented as rasterized images\\r\\n y = T.ivector('y') # the labels are presented as 1D vector of\\r\\n # [int] labels\\r\\n\\r\\n rng = numpy.random.RandomState(1234)\\r\\n\\r\\n # construct the MLP class\\r\\n classifier = MLP( rng = rng, input=x, n_in=28*28, n_hidden = 500, n_out=10)\\r\\n\\r\\n # the cost we minimize during training is the negative log likelihood of\\r\\n # the model.\\r\\n # We take the mean of the cost over each minibatch.\\r\\n cost = classifier.negative_log_likelihood(y).mean()\\r\\n\\r\\n # compute the gradient of cost with respect to theta (stored in params)\\r\\n # the resulting gradients will be stored in a list gparams\\r\\n gparams = []\\r\\n for param in classifier.params:\\r\\n gparam = T.grad(cost, param)\\r\\n gparams.append(gparam)\\r\\n\\r\\n # Some optimizations needed are tagged with 'fast_run'\\r\\n # TODO: refine that and include only those\\r\\n mode = theano.compile.get_default_mode().including('fast_run')\\r\\n\\r\\n updates2 = OrderedDict()\\r\\n\\r\\n updates2[classifier.hiddenLayer.params[0]]=T.grad(cost,classifier.hiddenLayer.params[0])\\r\\n train_model =theano.function( inputs = [index],\\r\\n updates = updates2,\\r\\n givens={\\r\\n x:train_set_x[index*batch_size:(index+1)*batch_size],\\r\\n y:train_set_y[index*batch_size:(index+1)*batch_size]},\\r\\n mode=mode)\\r\\n #print 'MODEL 1'\\r\\n #theano.printing.debugprint(train_model, print_type=True)\\r\\n assert any([isinstance(i.op,T.nnet.CrossentropySoftmax1HotWithBiasDx) for i in train_model.maker.fgraph.toposort()])\\r\\n\\r\\n # Even without FeatureShape\\r\\n train_model =theano.function( inputs = [index],\\r\\n updates = updates2,\\r\\n mode=mode.excluding('ShapeOpt'),\\r\\n givens={\\r\\n x:train_set_x[index*batch_size:(index+1)*batch_size],\\r\\n y:train_set_y[index*batch_size:(index+1)*batch_size]})\\r\\n #print\\r\\n #print 'MODEL 2'\\r\\n #theano.printing.debugprint(train_model, print_type=True)\\r\\n assert any([isinstance(i.op,T.nnet.CrossentropySoftmax1HotWithBiasDx) for i in train_model.maker.fgraph.toposort()])\",\n \"def test_add_get_scalar(mock_data):\\n dataset = Dataset(\\\"test-dataset\\\")\\n\\n # 1D tensors of all data types\\n data = mock_data.create_metadata_scalars(10)\\n add_get_scalars(dataset, data)\",\n \"def test_significance_individual_timepoints(tensor_directory, analysis_name):\\n\\n \\\"\\\"\\\"\\n # Open Analysis Dataframe\\n analysis_file = h5py.File(os.path.join(tensor_directory, analysis_name + \\\".hdf5\\\"), \\\"r\\\")\\n activity_dataset = analysis_file[\\\"Data\\\"]\\n metadata_dataset = analysis_file[\\\"metadata\\\"]\\n number_of_timepoints, number_of_trials, number_of_pixels = np.shape(activity_dataset)\\n print(\\\"metadata_dataset\\\", np.shape(metadata_dataset))\\n \\\"\\\"\\\"\\n # Open Analysis Dataframe\\n analysis_file = tables.open_file(os.path.join(tensor_directory, analysis_name + \\\"_Trialwise_.h5\\\"), mode=\\\"r\\\")\\n activity_dataset = analysis_file.root[\\\"Data\\\"]\\n metadata_dataset = analysis_file.root[\\\"Trial_Details\\\"]\\n\\n # Create P and Slope Tensors\\n p_value_tensor = np.ones((number_of_timepoints, number_of_pixels))\\n slope_tensor = np.zeros((number_of_timepoints, number_of_pixels))\\n\\n for timepoint_index in tqdm(range(number_of_timepoints), position=0, desc=\\\"Timepoint\\\"):\\n\\n # Get Timepoint Data\\n timepoint_activity = activity_dataset[timepoint_index]\\n\\n for pixel_index in tqdm(range(number_of_pixels), position=1, desc=\\\"Pixel\\\", leave=True):\\n\\n # Package Into Dataframe\\n pixel_activity = timepoint_activity[:, pixel_index]\\n pixel_dataframe = repackage_data_into_dataframe(pixel_activity, metadata_dataset)\\n\\n # Fit Mixed Effects Model\\n p_value, slope = mixed_effects_random_slope_and_intercept(pixel_dataframe)\\n p_value_tensor[timepoint_index, pixel_index] = p_value\\n slope_tensor[timepoint_index, pixel_index] = slope\\n\\n\\n # Save These Tensors\\n np.save(os.path.join(tensor_directory, analysis_name + \\\"_p_value_tensor.npy\\\"), p_value_tensor)\\n np.save(os.path.join(tensor_directory, analysis_name + \\\"_slope_tensor.npy\\\"), slope_tensor)\",\n \"def generate_x(number_dimensions, T_train, T_test, mu, feature_model):\\n number_training_obeservations = T_train.shape[0]\\n number_testing_obeservations = T_test.shape[0]\\n\\n X_train = np.zeros((number_training_obeservations,number_dimensions))\\n X_test = np.zeros((number_testing_obeservations,number_dimensions))\\n\\n mixture_indicator_train = generate_mixture_indicator(number_training_obeservations)\\n mixture_indicator_test = generate_mixture_indicator(number_testing_obeservations)\\n\\n G = np.random.normal(0,1,(number_dimensions,number_dimensions))\\n q, r = np.linalg.qr(G)\\n\\n mu1 = mu*np.ones(number_dimensions)\\n mu2 = -mu*np.ones(number_dimensions)\\n\\n if feature_model == \\\"A\\\":\\n eigenvalues1 = np.random.uniform(0,1,(number_dimensions,1))\\n eigenvalues1 = np.sort(eigenvalues1, axis = 0)/np.sum(eigenvalues1)\\n lambda1 = np.identity(number_dimensions)\\n np.fill_diagonal(lambda1,eigenvalues1)\\n cov1 = q@lambda1@q.T\\n\\n for i in range(number_training_obeservations):\\n if T_train[i] == 0:\\n X_train[i,:] = np.random.multivariate_normal(mu1,cov1,1)\\n else:\\n X_train[i,:] = np.random.multivariate_normal(mu2,cov1,1)\\n \\n for i in range(number_testing_obeservations):\\n if T_test[i] == 0:\\n X_test[i,:] = np.random.multivariate_normal(mu1,cov1,1)\\n else:\\n X_test[i,:] = np.random.multivariate_normal(mu2,cov1,1)\\n\\n\\n elif feature_model == \\\"B\\\":\\n eigenvalues1 = np.random.uniform(0,1,(number_dimensions,1))\\n eigenvalues1 = np.sort(eigenvalues1, axis = 0)/np.sum(eigenvalues1)\\n lambda1 = np.identity(number_dimensions)\\n np.fill_diagonal(lambda1,eigenvalues1)\\n cov1 = q@lambda1@q.T\\n\\n eigenvalues2 = np.random.uniform(0,1,(number_dimensions,1))\\n eigenvalues2 = np.sort(eigenvalues2, axis = 0)[::-1]/np.sum(eigenvalues2)\\n lambda2 = np.identity(number_dimensions)\\n np.fill_diagonal(lambda2,eigenvalues2)\\n cov2 = q@lambda2@q.T\\n\\n\\n for i in range(number_training_obeservations):\\n if T_train[i] == 0:\\n if mixture_indicator_train[i] == 0:\\n X_train[i,:] = np.random.multivariate_normal(mu1,cov1,1)\\n else:\\n X_train[i,:] = np.random.multivariate_normal(mu1,cov2,1)\\n else:\\n if mixture_indicator_train[i] == 0:\\n X_train[i,:] = np.random.multivariate_normal(mu2,cov1,1)\\n else:\\n X_train[i,:] = np.random.multivariate_normal(mu2,cov2,1)\\n \\n for i in range(number_testing_obeservations):\\n if T_test[i] == 0:\\n if mixture_indicator_test[i] == 0:\\n X_test[i,:] = np.random.multivariate_normal(mu1,cov1,1)\\n else:\\n X_test[i,:] = np.random.multivariate_normal(mu1,cov2,1)\\n else:\\n if mixture_indicator_test[i] == 0:\\n X_test[i,:] = np.random.multivariate_normal(mu2,cov1,1)\\n else:\\n X_test[i,:] = np.random.multivariate_normal(mu2,cov2,1)\\n\\n train_mean = np.mean(X_train, axis = 0)\\n train_std = np.std(X_train, axis = 0)\\n X_train = (X_train - train_mean)/train_std\\n X_test = (X_test - train_mean)/train_std\\n \\n return X_train, X_test\",\n \"def test_documentation_popxl_mnist_replication_train(self):\\n filename = \\\"mnist_rts.py --replication-factor 2\\\"\\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)\",\n \"def test_getitem(self):\\n obs = self.tester['1.SKM7.640188']\\n exp = Sample('1.SKM7.640188', self.tester)\\n self.assertEqual(obs, exp)\",\n \"def test_get_random_data_basic(self):\\n cdata = random_data(num_features=2,\\n num_samples=4,\\n labels=None)\\n self.assertEqual(cdata.num_features, 2)\\n self.assertEqual(cdata.num_samples, 4)\",\n \"def tensor(data, **context):\\n raise NotImplementedError\",\n \"def test_000_basic_functionality() -> None:\\n df = generate_test_data()\\n skim(df)\",\n \"def setUpTestData(cls):\\n data_gen.run()\",\n \"def setUpTestData(cls):\\n data_gen.run()\",\n \"def setUpTestData(cls):\\n data_gen.run()\",\n \"def setUpTestData(cls):\\n data_gen.run()\",\n \"def load_data(opt=\\\"mnist\\\"):\\n if opt == \\\"mnist\\\":\\n train, test = tf.keras.datasets.mnist.load_data()\\n \\n x_train, y_train = train\\n x_test, y_test = test\\n \\n x_train = x_train.reshape(x_train.shape[0], 28 * 28)\\n x_test = x_test.reshape(x_test.shape[0], 28 * 28)\\n \\n y_train = y_train.astype(np.int)\\n y_test = y_test.astype(np.int)\\n for i in range(len(y_train)):\\n y_train[i] = 1 if y_train[i] % 2 == 0 else -1\\n for i in range(len(y_test)):\\n y_test[i] = 1 if y_test[i] % 2 == 0 else -1\\n\\n elif opt == \\\"covertype\\\":\\n df = pd.read_csv(\\\"covtype.data\\\", header=None)\\n x = df.iloc[:, 0:54].values\\n y = df[54].values\\n for i in range(len(y)):\\n y[i] = 1 if y[i] % 2 == 0 else -1\\n x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3)\\n \\n else:\\n logging.error(\\\"Unknown dataset!!\\\")\\n\\n logging.info(\\\"train data shape: {}\\\".format(x_train.shape))\\n logging.info(\\\"test data shape: {}\\\".format(x_test.shape))\\n return (x_train, y_train), (x_test, y_test)\",\n \"def test_model_sample(net, data_loader):\\n net.eval()\\n array = []\\n with torch.no_grad():\\n for data in data_loader:\\n X = data['X']\\n output = net(X)\\n output = ToPILImage()(output)\\n array.append(output)\\n return array\",\n \"def load_data_set():\\n\\n iris = pd.read_csv('/Users/michael/Codes/bitbucket/cs584-s18-kaiyue-ma/AS2/data/iris.csv', encoding=\\\"gbk\\\")\\n iris = iris.sample(frac=1.0)\\n\\n dummy = pd.get_dummies(iris['Species'])\\n iris = pd.concat([iris, dummy], axis=1)\\n\\n org_x = np.array(iris.iloc[:, 1:5])\\n # org_y = np.array(iris['setosa']).reshape(len(iris), 1)\\n org_y = np.array(iris['setosa'])\\n\\n # data_arr, test_arr, label_arr, test_label = train_test_split(org_x, org_y, test_size=0.2, random_state=42)\\n #\\n # data_arr = data_arr.tolist()\\n # test_arr = test_arr.tolist()\\n # label_arr = label_arr.tolist()\\n # test_label = test_label.tolist()\\n #\\n # data_arr = np.dot(data_arr)\\n # one = np.ones(len(data_arr))\\n # data_arr = np.column_stack(one , data_arr)\\n # data_arr = data_arr.tolist()\\n #\\n # test_arr = np.dot(test_arr)\\n # one = np.ones(len(test_arr))\\n # test_arr = np.column_stack(one, test_arr)\\n # test_arr = test_arr.tolist()\\n\\n x = org_x.tolist()\\n y = org_y.tolist()\\n x = np.mat(x)\\n one = np.ones(len(x))\\n x = np.column_stack((one, x))\\n x = x.tolist()\\n\\n # print(data_arr)\\n # x1_2 = data_arr[:, 1]\\n # print(x1_2)\\n # print('.....')\\n # print(label_arr)\\n # return data_arr, test_arr, label_arr, test_label\\n return x, y\",\n \"def test_numpify_and_store(self):\\n Nsamples = 9\\n Ntimesteps = 10\\n Ncolumns = 3\\n X = [[[0 for a in range(Ncolumns)] for b in range(Ntimesteps)] \\\\\\n for c in range(Nsamples)]\\n y = [[0 for a in range(Ntimesteps)] for b in range(Nsamples)]\\n xname = 'xname'\\n yname = 'yname'\\n outdatapath = os.getcwd()\\n tutorial_pamap2.numpify_and_store(X, y, xname, yname, outdatapath, \\\\\\n shuffle=True)\\n filename = os.path.join(outdatapath, xname+ '.npy')\\n test = os.path.isfile(filename)\\n if test == True:\\n os.remove(filename)\\n os.remove(os.path.join(outdatapath, yname + '.npy'))\\n assert test\",\n \"def test_morley():\\n test_path = tempfile.mkdtemp()\\n x_train, metadata = morley(test_path)\\n try:\\n assert x_train.shape == (100, 3)\\n except:\\n shutil.rmtree(test_path)\\n raise()\",\n \"def test_add():\\n data = io.create_sample_Dataset()\\n tmp = data + data\\n assert tmp[\\\"u\\\"][0, 0, 0] == 2.0\",\n \"def fixture_example_data():\\n import_example_data()\",\n \"def init_tensors(self, sample, *args):\\n raise NotImplementedError\",\n \"def test(self, test):\\r\\n self.ml_data.set_target(test[0])\\r\\n self.ml_data.set_features(test[1])\\r\\n if self.ml_data.target_type.all() == np.float64 or self.ml_data.target_type.all() == np.int64:\\r\\n self.model_qua.open()\\r\\n else:\\r\\n self.model_quali.open()\",\n \"def test_set_xT(self):\\n s = State(substance=\\\"water\\\")\\n s.xT = Q_(0.5, \\\"dimensionless\\\"), Q_(400.0, \\\"K\\\")\\n # Pylance does not support NumPy ufuncs\\n assert np.isclose(s.T, Q_(400.0, \\\"K\\\")) # type: ignore\\n assert np.isclose(s.p, Q_(245769.34557103913, \\\"Pa\\\")) # type: ignore\\n assert np.isclose(s.xT[1], Q_(400.0, \\\"K\\\")) # type: ignore\\n assert np.isclose(s.xT[0], Q_(0.5, \\\"dimensionless\\\")) # type: ignore\\n assert np.isclose(s.u, Q_(1534461.5163075812, \\\"J/kg\\\")) # type: ignore\\n assert np.isclose(s.s, Q_(4329.703956664546, \\\"J/(kg*K)\\\")) # type: ignore\\n assert np.isclose(s.cp, Q_(4056.471547685226, \\\"J/(kg*K)\\\")) # type: ignore\\n assert np.isclose(s.cv, Q_(2913.7307270395363, \\\"J/(kg*K)\\\")) # type: ignore\\n assert np.isclose(s.v, Q_(0.3656547423394701, \\\"m**3/kg\\\")) # type: ignore\\n assert np.isclose(s.h, Q_(1624328.2430353598, \\\"J/kg\\\")) # type: ignore\\n assert np.isclose(s.x, Q_(0.5, \\\"dimensionless\\\")) # type: ignore\\n s.xT = Q_(50, \\\"percent\\\"), Q_(400.0, \\\"K\\\")\\n assert np.isclose(s.T, Q_(400.0, \\\"K\\\")) # type: ignore\\n assert np.isclose(s.p, Q_(245769.34557103913, \\\"Pa\\\")) # type: ignore\\n assert np.isclose(s.xT[1], Q_(400.0, \\\"K\\\")) # type: ignore\\n assert np.isclose(s.xT[0], Q_(0.5, \\\"dimensionless\\\")) # type: ignore\\n assert np.isclose(s.u, Q_(1534461.5163075812, \\\"J/kg\\\")) # type: ignore\\n assert np.isclose(s.s, Q_(4329.703956664546, \\\"J/(kg*K)\\\")) # type: ignore\\n assert np.isclose(s.cp, Q_(4056.471547685226, \\\"J/(kg*K)\\\")) # type: ignore\\n assert np.isclose(s.cv, Q_(2913.7307270395363, \\\"J/(kg*K)\\\")) # type: ignore\\n assert np.isclose(s.v, Q_(0.3656547423394701, \\\"m**3/kg\\\")) # type: ignore\\n assert np.isclose(s.h, Q_(1624328.2430353598, \\\"J/kg\\\")) # type: ignore\\n assert np.isclose(s.x, Q_(0.5, \\\"dimensionless\\\")) # type: ignore\",\n \"def run_experiments(data_set=\\\"\\\",compact=2,exp_name=\\\"\\\",x=\\\"\\\"):\\n cwd = os.getcwd()\\n results_path = cwd+'/data/geometric/'+exp_name+'_'\\n dataset_path = cwd+'/data/geometric/'+data_set+'_'\\n compact = int(compact)\\n if \\\"m\\\" in x:\\n # make new pxy\\n pxy, Xdata, groups = gen_geometric_pxy()\\n np.save(dataset_path+'Xdata',Xdata)\\n np.save(dataset_path+'groups',groups)\\n np.save(dataset_path+'pxy',pxy)\\n else:\\n # load existing pxy\\n pxy = np.load(dataset_path+'pxy.npy')\\n if \\\"r\\\" in x: # regular experiments\\n if compact>1:\\n metrics_stepwise, distributions_stepwise,\\\\\\n metrics_converged, distributions_converged,\\\\\\n metrics_stepwise_allreps, distributions_stepwise_allreps,\\\\\\n metrics_converged_allreps, distributions_converged_allreps = test_IB(pxy,compact)\\n metrics_converged.to_csv(results_path+'metrics_converged.csv')\\n metrics_stepwise.to_csv(results_path+'metrics_stepwise.csv')\\n distributions_converged.to_pickle(results_path+'distributions_converged.pkl')\\n elif compact>0:\\n metrics_stepwise,\\\\\\n metrics_converged, distributions_converged,\\\\\\n metrics_stepwise_allreps,\\\\\\n metrics_converged_allreps, distributions_converged_allreps = test_IB(pxy,compact)\\n metrics_converged.to_csv(results_path+'metrics_converged.csv')\\n metrics_stepwise.to_csv(results_path+'metrics_stepwise.csv')\\n distributions_converged.to_pickle(results_path+'distributions_converged.pkl')\\n else:\\n metrics_stepwise,\\\\\\n metrics_converged,\\\\\\n metrics_stepwise_allreps,\\\\\\n metrics_converged_allreps = test_IB(pxy,compact)\\n metrics_converged.to_csv(results_path+'metrics_converged.csv')\\n metrics_stepwise.to_csv(results_path+'metrics_stepwise.csv')\\n if \\\"ip\\\" in x: # initialization experiments - positive p0\\n if compact>1:\\n metrics_stepwise, distributions_stepwise,\\\\\\n metrics_converged, distributions_converged,\\\\\\n metrics_stepwise_allreps, distributions_stepwise_allreps,\\\\\\n metrics_converged_allreps, distributions_converged_allreps = test_p0_pos(pxy,compact)\\n metrics_converged.to_csv(results_path+'metrics_converged_p0_pos.csv')\\n metrics_stepwise.to_csv(results_path+'metrics_stepwise_p0_pos.csv')\\n distributions_converged.to_pickle(results_path+'distributions_converged_p0_pos.pkl')\\n elif compact>0:\\n metrics_stepwise,\\\\\\n metrics_converged, distributions_converged,\\\\\\n metrics_stepwise_allreps,\\\\\\n metrics_converged_allreps, distributions_converged_allreps = test_p0_pos(pxy,compact)\\n metrics_converged.to_csv(results_path+'metrics_converged_p0_pos.csv')\\n metrics_stepwise.to_csv(results_path+'metrics_stepwise_p0_pos.csv')\\n distributions_converged.to_pickle(results_path+'distributions_converged_p0_pos.pkl')\\n else:\\n metrics_stepwise,\\\\\\n metrics_converged,\\\\\\n metrics_stepwise_allreps,\\\\\\n metrics_converged_allreps = test_p0_pos(pxy,compact)\\n metrics_converged.to_csv(results_path+'metrics_converged_p0_pos.csv')\\n metrics_stepwise.to_csv(results_path+'metrics_stepwise_p0_pos.csv')\\n if \\\"in\\\" in x: # initialization experiments - negative p0\\n if compact>1:\\n metrics_stepwise, distributions_stepwise,\\\\\\n metrics_converged, distributions_converged,\\\\\\n metrics_stepwise_allreps, distributions_stepwise_allreps,\\\\\\n metrics_converged_allreps, distributions_converged_allreps = test_p0_neg(pxy,compact)\\n metrics_converged.to_csv(results_path+'metrics_converged_p0_neg.csv')\\n metrics_stepwise.to_csv(results_path+'metrics_stepwise_p0_neg.csv')\\n distributions_converged.to_pickle(results_path+'distributions_converged_p0_neg.pkl')\\n elif compact>0:\\n metrics_stepwise,\\\\\\n metrics_converged, distributions_converged,\\\\\\n metrics_stepwise_allreps,\\\\\\n metrics_converged_allreps, distributions_converged_allreps = test_p0_neg(pxy,compact)\\n metrics_converged.to_csv(results_path+'metrics_converged_p0_neg.csv')\\n metrics_stepwise.to_csv(results_path+'metrics_stepwise_p0_neg.csv')\\n distributions_converged.to_pickle(results_path+'distributions_converged_p0_neg.pkl')\\n else:\\n metrics_stepwise,\\\\\\n metrics_converged,\\\\\\n metrics_stepwise_allreps,\\\\\\n metrics_converged_allreps = test_p0_neg(pxy,compact)\\n metrics_converged.to_csv(results_path+'metrics_converged_p0_neg.csv')\\n metrics_stepwise.to_csv(results_path+'metrics_stepwise_p0_neg.csv')\\n if \\\"c\\\" in x: # convergence tolerance experiments\\n if compact>1:\\n metrics_stepwise, distributions_stepwise,\\\\\\n metrics_converged, distributions_converged,\\\\\\n metrics_stepwise_allreps, distributions_stepwise_allreps,\\\\\\n metrics_converged_allreps, distributions_converged_allreps = test_ctol(pxy,compact)\\n metrics_converged.to_csv(results_path+'metrics_converged_ctol.csv')\\n metrics_stepwise.to_csv(results_path+'metrics_stepwise_ctol.csv')\\n distributions_converged.to_pickle(results_path+'distributions_converged_ctol.pkl')\\n elif compact>0:\\n metrics_stepwise,\\\\\\n metrics_converged, distributions_converged,\\\\\\n metrics_stepwise_allreps,\\\\\\n metrics_converged_allreps, distributions_converged_allreps = test_ctol(pxy,compact)\\n metrics_converged.to_csv(results_path+'metrics_converged_ctol.csv')\\n metrics_stepwise.to_csv(results_path+'metrics_stepwise_ctol.csv')\\n distributions_converged.to_pickle(results_path+'distributions_converged_ctol.pkl')\\n else:\\n metrics_stepwise,\\\\\\n metrics_converged,\\\\\\n metrics_stepwise_allreps,\\\\\\n metrics_converged_allreps = test_ctol(pxy,compact)\\n metrics_converged.to_csv(results_path+'metrics_converged_ctol.csv')\\n metrics_stepwise.to_csv(results_path+'metrics_stepwise_ctol.csv')\\n if \\\"z\\\" in x: # zeroL tolerance experiments\\n if compact>1:\\n metrics_stepwise, distributions_stepwise,\\\\\\n metrics_converged, distributions_converged,\\\\\\n metrics_stepwise_allreps, distributions_stepwise_allreps,\\\\\\n metrics_converged_allreps, distributions_converged_allreps = test_zeroLtol(pxy,compact)\\n metrics_converged.to_csv(results_path+'metrics_converged_zeroLtol.csv')\\n metrics_stepwise.to_csv(results_path+'metrics_stepwise_zeroLtol.csv')\\n distributions_converged.to_pickle(results_path+'distributions_converged_zeroLtol.pkl')\\n elif compact>0:\\n metrics_stepwise,\\\\\\n metrics_converged, distributions_converged,\\\\\\n metrics_stepwise_allreps,\\\\\\n metrics_converged_allreps, distributions_converged_allreps = test_zeroLtol(pxy,compact)\\n metrics_converged.to_csv(results_path+'metrics_converged_zeroLtol.csv')\\n metrics_stepwise.to_csv(results_path+'metrics_stepwise_zeroLtol.csv')\\n distributions_converged.to_pickle(results_path+'distributions_converged_zeroLtol.pkl')\\n else:\\n metrics_stepwise,\\\\\\n metrics_converged,\\\\\\n metrics_stepwise_allreps,\\\\\\n metrics_converged_allreps = test_zeroLtol(pxy,compact)\\n metrics_converged.to_csv(results_path+'metrics_converged_zeroLtol.csv')\\n metrics_stepwise.to_csv(results_path+'metrics_stepwise_zeroLtol.csv')\\n if \\\"b\\\" in x: # trying proposed optimal beta\\n if compact>1:\\n metrics_stepwise, distributions_stepwise,\\\\\\n metrics_converged, distributions_converged,\\\\\\n metrics_stepwise_allreps, distributions_stepwise_allreps,\\\\\\n metrics_converged_allreps, distributions_converged_allreps = test_IB(pxy,compact)\\n metrics_converged.to_csv(results_path+'metrics_converged_bestbeta.csv')\\n metrics_stepwise.to_csv(results_path+'metrics_stepwise_bestbeta.csv')\\n distributions_converged.to_pickle(results_path+'distributions_converged_bestbeta.pkl')\\n elif compact>0:\\n metrics_stepwise,\\\\\\n metrics_converged, distributions_converged,\\\\\\n metrics_stepwise_allreps,\\\\\\n metrics_converged_allreps, distributions_converged_allreps = test_IB(pxy,compact)\\n metrics_converged.to_csv(results_path+'metrics_converged_bestbeta.csv')\\n metrics_stepwise.to_csv(results_path+'metrics_stepwise_bestbeta.csv')\\n distributions_converged.to_pickle(results_path+'distributions_converged_bestbeta.pkl')\\n else:\\n metrics_stepwise,\\\\\\n metrics_converged,\\\\\\n metrics_stepwise_allreps,\\\\\\n metrics_converged_allreps = test_IB(pxy,compact)\\n metrics_converged.to_csv(results_path+'metrics_converged_bestbeta.csv')\\n metrics_stepwise.to_csv(results_path+'metrics_stepwise_bestbeta.csv')\\n return 0\",\n \"def test(self, dataset) -> None:\\n raise NotImplementedError()\",\n \"def test_neuron(self):\\r\\n # crear una lista 1-D (Horizontal, Entradas).\\r\\n Z = [1, 2, 3]\\r\\n # crear una lista 1-D (Vertical, Pesos de la red).\\r\\n W = [10, 20, 30]\\r\\n # Inicializamos la neurona, y obtenemos el valor que toma dado W * Z\\r\\n # X(k) = W * Z\\r\\n result = rhonn(W, Z).predict()\\r\\n # Comprobamos el resultado \\r\\n self.assertEqual(result, 140)\",\n \"def test_data_manipulation(self):\\n target_name = self.project['target']['name']\\n self.api_mock.return_value.get_metadata.return_value = [\\n {'_id': '0',\\n 'pid': '1',\\n 'created': datetime.datetime.now(),\\n 'name':'universe',\\n 'originalName': 'credit-sample-200.csv',\\n 'varTypeString': 'NN',\\n 'shape': [2, 100],\\n 'controls':{},\\n 'columns': [[1,target_name,0],[3,\\\"age\\\",0]],\\n 'files': ['projects/' + str(self.pid) + '/raw/' + self.testdatafile],\\n 'typeConvert': {}},\\n {'_id': '1',\\n 'pid': '1',\\n 'name':'test',\\n 'originalName': 'credit-sample-200.csv',\\n 'created': datetime.datetime.now(),\\n 'varTypeString': 'NN',\\n 'shape': [2, 100],\\n 'controls':{},\\n 'columns': [[1,target_name,0],[3,\\\"age\\\",0]],\\n 'files': ['projects/' + str(self.pid) + '/raw/' + self.testdatafile],\\n 'typeConvert': {}},\\n {'_id': '2',\\n 'pid': '1',\\n 'name':'new',\\n 'created': datetime.datetime.now(),\\n 'originalName': 'credit-sample-200.csv',\\n 'newdata':True,\\n 'controls':{},\\n 'shape': [2, 100],\\n 'varTypeString': 'NN',\\n 'columns': [[1,target_name,0],[3,\\\"age\\\",0]],\\n 'files': ['projects/' + str(self.pid) + '/raw/' + self.testdatafile],\\n 'typeConvert': {}}]\\n request = WorkerRequest({'pid': '1', 'uid': '1', 'dataset_id': '1',\\n 'command': 'fit', 'max_reps': 0,\\n 'samplepct': 100})\\n\\n #target\\n #this will map the target values to (0,1) because target type is Binary\\n target_vector = self.dataprocessor.target_vector()\\n target_series = target_vector['main']\\n self.assertItemsEqual(np.unique(target_series), [0,1])\\n\\n #this will be none because 'holdout_pct' isn't set in the project data\\n self.assertIsNone(target_vector['holdout'])\\n\\n #prediction dataset\\n predictors = self.dataprocessor.predictors()\\n pred_dataframe = predictors['1']['main']\\n self.assertItemsEqual(list(pred_dataframe.columns), [\\\"age\\\"])\\n self.assertEqual(self.dataprocessor.get_vartypestring_without_target('1'), \\\"N\\\")\\n\\n request = WorkerRequest({'pid': '1', 'uid': '1', 'dataset_id': '1', 'scoring_dataset_id': '2', 'command': 'predict', 'max_reps': 0, 'samplepct':100})\\n dp2 = DataProcessor(request)\\n data = dp2.request_datasets()\\n self.assertEqual(data.keys(), ['1'])\\n self.assertEqual(data['1'].keys(), ['scoring', 'vartypes'])\\n scoring_data = data['1']['scoring']\\n vartypes = data['1']['vartypes']\\n self.assertEqual(list(scoring_data.columns), [\\\"age\\\"])\\n self.assertEqual(vartypes, \\\"N\\\")\",\n \"def test_createData():\\n\\n sys = LVsystem.Ecosystem()\\n\\n sys.addSpecies('rabbit')\\n sys.setInteraction('rabbit', 'hen', 0)\\n sys.setInteraction('rabbit', 'fox', -1)\\n sys.setInitialCond('rabbit', 30)\\n sys.setGrowthRate('rabbit', 0.09)\\n sys.setCarrCap('rabbit', 10000)\\n sys.setChangeRate('rabbit', 400)\\n\\n sys.addSpecies('hen')\\n sys.setInteraction('hen', 'rabbit', 0)\\n sys.setInteraction('hen', 'fox', -1)\\n sys.setInitialCond('hen', 10)\\n sys.setGrowthRate('hen', 0.07)\\n sys.setCarrCap('hen', 10000)\\n sys.setChangeRate('hen', 500)\\n\\n sys.addSpecies('fox')\\n sys.setInteraction('fox', 'rabbit', 1)\\n sys.setInteraction('fox', 'hen', 1)\\n sys.setInitialCond('fox', 20)\\n sys.setGrowthRate('fox', -0.06)\\n sys.setCarrCap('fox', 1)\\n sys.setChangeRate('fox', 250)\\n\\n \\n data = sys.create_data()\\n \\n assert data[0] == 3\\n assert data[1] == ['rabbit', 'hen', 'fox']\\n assert data[2] == [30,10,20]\\n assert data[3] == [0.09,0.07,-0.06] \\n assert data[4] == [10000,10000,1]\\n assert data[5] == [400,500,250]\\n assert data[6][1][2] == -data[6][2][1]\\n assert data[6][2][2] == 0\\n \\n sys.removeSpecies('rabbit')\\n sys.removeSpecies('fox')\\n sys.removeSpecies('hen')\",\n \"def test(batch_size=1, num_sample=16):\\n return paddle.batch(_read_creater(num_sample=num_sample), batch_size)\",\n \"def test(self, dataset):\\n\\n outputs, errors = self.use(dataset)\\n\\n ## PUT CODE HERE ##\\n # I put the code in the \\\"use\\\" function, seems better :-)\\n\\n return outputs, errors\",\n \"def test_X_test_property():\\n atom = ATOMClassifier(X_bin, y_bin, random_state=1)\\n atom.run([\\\"MNB\\\", \\\"LR\\\"])\\n assert atom.X_test.equals(atom.mnb.X_test)\\n assert check_scaling(atom.lr.X_test)\",\n \"def test_op(self):\\n name = \\\"my_mesh\\\"\\n tensor_data = test_utils.get_random_mesh(\\n 100, add_faces=True, add_colors=True\\n )\\n config_dict = {\\\"foo\\\": 1}\\n with tf.compat.v1.Graph().as_default():\\n tensor_summary = summary.op(\\n name,\\n tensor_data.vertices,\\n faces=tensor_data.faces,\\n colors=tensor_data.colors,\\n config_dict=config_dict,\\n )\\n with self.test_session() as sess:\\n proto = self.pb_via_op(tensor_summary)\\n self.verify_proto(proto, name)\\n plugin_metadata = metadata.parse_plugin_metadata(\\n proto.value[0].metadata.plugin_data.content\\n )\\n self.assertEqual(\\n json.dumps(config_dict, sort_keys=True),\\n plugin_metadata.json_config,\\n )\",\n \"def setup(self):\\n (self.X, self.Y) = load_iris(problem=\\\"label_ranking\\\")\",\n \"def test_11_dataset(self, example):\\n example.zonenumbers_i = np.array([100, 200, 300])\\n example.groupnames_g = np.array(['Female', 'Male'], dtype='O')\\n example.create_ds()\\n assert isinstance(example.ds, xr.Dataset)\\n for attr, dtype in example.dtypes.items():\\n data_array = example.ds[attr]\\n # test if the shapes are correct\\n if dtype.shape:\\n np.testing.assert_array_equal(dtype.get_shape(example),\\n data_array.shape,\\n 'shape not correct')\\n else:\\n # not initialized array\\n assert not np.any(data_array.shape), 'shape not initialized'\\n #test if the datatypes are correct\\n assert np.dtype(dtype.dtype) == data_array.dtype, 'dtype not correct'\\n print(example.ds)\",\n \"def test_fish():\\n test_path = tempfile.mkdtemp()\\n x_train, metadata = fish(test_path)\\n try:\\n assert x_train.shape == (97, 20)\\n except:\\n shutil.rmtree(test_path)\\n raise()\",\n \"def test_active_inference_SPM_1b(self):\",\n \"def test_okun():\\n test_path = tempfile.mkdtemp()\\n x_train, metadata = okun(test_path)\\n try:\\n assert x_train.shape == (47, 4)\\n except:\\n shutil.rmtree(test_path)\\n raise()\",\n \"def test_documentation_popxl_repeat_0(self):\\n filename = \\\"repeat_graph_0.py\\\"\\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)\",\n \"def load_x(self):\\n self.x = self.read_var(self.xvar)\\n self.test_shape(self.xvar, self.x.shape, 1)\",\n \"def testingData(self):\\n\\n #import SampleData\\n #sampleDataLogic = SampleData.SampleDataLogic()\\n #mrHead = sampleDataLogic.downloadMRHead()\\n #dtiBrain = sampleDataLogic.downloadDTIBrain()\\n \\n # w = slicer.modules.SteeredFluidRegistrationWidget\\n # w.fixedSelector.setCurrentNode(mrHead)\\n # w.movingSelector.setCurrentNode(dtiBrain)\\n \\n if not slicer.util.getNodes('testbrain1*'):\\n import os\\n fileName = \\\"C:\\\\\\\\Work\\\\\\\\testbrain1.nrrd\\\"\\n vl = slicer.modules.volumes.logic()\\n brain1Node = vl.AddArchetypeVolume(fileName, \\\"testbrain1\\\", 0)\\n else:\\n nodes = slicer.util.getNodes('testbrain1.nrrd')\\n brain1Node = nodes[0]\\n\\n if not slicer.util.getNodes('testbrain2*'):\\n import os\\n fileName = \\\"C:\\\\\\\\Work\\\\\\\\testbrain2.nrrd\\\"\\n vl = slicer.modules.volumes.logic()\\n brain2Node = vl.AddArchetypeVolume(fileName, \\\"testbrain2\\\", 0)\\n #TODO else assign from list\\n\\n # if not slicer.util.getNodes('movingToFixed*'):\\n # # Create transform node\\n # transform = slicer.vtkMRMLLinearTransformNode()\\n # transform.SetName('movingToFixed')\\n # slicer.mrmlScene.AddNode(transform)\\n\\n # transform = slicer.util.getNode('movingToFixed')\\n \\n # ###\\n # # neutral.SetAndObserveTransformNodeID(transform.GetID())\\n # ###\\n \\n compositeNodes = slicer.util.getNodes('vtkMRMLSliceCompositeNode*')\\n for compositeNode in compositeNodes.values():\\n compositeNode.SetBackgroundVolumeID(brain1Node.GetID())\\n compositeNode.SetForegroundVolumeID(brain2Node.GetID())\\n compositeNode.SetForegroundOpacity(0.5)\\n applicationLogic = slicer.app.applicationLogic()\\n applicationLogic.FitSliceToAll()\",\n \"def load_data(self):\\n self.data = self.read_var(self.datavar)\\n self.test_shape(self.datavar, self.data.shape, 2)\",\n \"def test(self, dataset):\\n model_path = os.path.join(self.check_point, 'model.pt')\\n if not os.path.exists(model_path):\\n raise Exception('Cannot find %s.' % model_path)\\n\\n self.model = torch.load(model_path)\\n print(self.model)\\n model_parameters = filter(lambda p: p.requires_grad, self.model.parameters())\\n params = sum([np.prod(p.size()) for p in model_parameters])\\n print(1.0 * params / (1000 * 1000))\\n _, _, stats, outputs, names = self._check_PSNR(dataset, is_test=True)\\n return stats, outputs, names\",\n \"def get_data(generator, random, bench_id):\\n x_train, y_train, x_test, y_test = generator(random, bench_id)\\n x_train = np.c_[np.ones(len(x_train)), x_train]\\n x_test = np.c_[np.ones(len(x_test)), x_test]\\n return x_train, y_train, x_test, y_test\",\n \"def test(self, dataset):\\n model_path = os.path.join(self.check_point, 'model.pt')\\n if not os.path.exists(model_path):\\n raise Exception('Cannot find %s.' %model_path)\\n \\n self.model = torch.load(model_path)\\n _, _, stats, outputs = self._check_PSNR(dataset, is_test=True)\\n return stats, outputs\",\n \"def test_significance_window(tensor_directory, analysis_name, window):\\n\\n \\\"\\\"\\\"\\n # Open Analysis Dataframe\\n analysis_file = h5py.File(os.path.join(tensor_directory, analysis_name + \\\".hdf5\\\"), \\\"r\\\")\\n activity_dataset = analysis_file[\\\"Data\\\"]\\n metadata_dataset = analysis_file[\\\"metadata\\\"]\\n number_of_timepoints, number_of_trials, number_of_pixels = np.shape(activity_dataset)\\n print(\\\"metadata_dataset\\\", np.shape(metadata_dataset))\\n \\\"\\\"\\\"\\n\\n\\n # Open Analysis Dataframe\\n analysis_file = tables.open_file(os.path.join(tensor_directory, analysis_name + \\\"_Trialwise_.h5\\\"), mode=\\\"r\\\")\\n activity_dataset = analysis_file.root[\\\"Data\\\"]\\n metadata_dataset = analysis_file.root[\\\"Trial_Details\\\"]\\n activity_dataset = np.array(activity_dataset)\\n metadata_dataset = np.array(metadata_dataset)\\n\\n activity_dataset = np.nan_to_num(activity_dataset)\\n\\n number_of_trials, number_of_timepoints, number_of_pixels = np.shape(activity_dataset)\\n\\n\\n print(\\\"Number of timepoints\\\", number_of_timepoints)\\n print(\\\"number of pixels\\\", number_of_pixels)\\n print(\\\"number of trials\\\", number_of_trials)\\n\\n # Create P and Slope Tensors\\n p_value_tensor = np.ones(number_of_pixels)\\n slope_tensor = np.zeros(number_of_pixels)\\n\\n # Get Timepoint Data\\n timepoint_activity = activity_dataset[:, window]\\n print(\\\"Timepoint activity shape\\\", np.shape(timepoint_activity))\\n timepoint_activity = np.mean(timepoint_activity, axis=1)\\n\\n for pixel_index in tqdm(range(number_of_pixels), position=1, desc=\\\"Pixel\\\", leave=False):\\n\\n # Package Into Dataframe\\n pixel_activity = timepoint_activity[:, pixel_index]\\n pixel_dataframe = repackage_data_into_dataframe(pixel_activity, metadata_dataset)\\n\\n # Fit Mixed Effects Model\\n p_value, slope = mixed_effects_random_slope_and_intercept(pixel_dataframe)\\n p_value_tensor[pixel_index] = p_value\\n slope_tensor[pixel_index] = slope\\n\\n return p_value_tensor, slope_tensor\",\n \"def test_predict_from_examples():\\n examples = []\\n for i in range(len(DATA)):\\n (value, attribute_map) = DATA[i]\\n print 'tuple created : \\\\n1:' + str(value) + \\\"\\\\n2:\\\" + str(attribute_map)\\n ex = VowpalExample(i, value)\\n #Extracting the features from all of the namespaces in the map. \\n for (featureNamespace, features) in attribute_map.items():\\n #Each \\\"section\\\" is \\n ex.add_section(featureNamespace, features)\\n examples.append(ex)\\n train = examples[:-2]\\n test = examples[-2:]\\n vw = Vowpal(PATH_VW, './vw.%s', {'--passes' : '10' })\\n preds = vw.predict_from_examples(train, test)\\n for (id, value) in preds:\\n print 'prediction for %s is %s' % (id, value)\",\n \"def test_api_predictors_get(self):\\n pass\",\n \"def setUp(self):\\r\\n self.value_for_seed = 20\\r\\n\\r\\n # Single comp.\\r\\n self.labels1 = ['foo', 'bar']\\r\\n self.dists1 = [[1, 2, 3], [7, 8]]\\r\\n\\r\\n # Multiple comps.\\r\\n self.labels2 = ['foo', 'bar', 'baz']\\r\\n self.dists2 = [[1, 2, 3], [7, 8], [9, 10, 11]]\\r\\n\\r\\n # Too few obs.\\r\\n self.labels3 = ['foo', 'bar', 'baz']\\r\\n self.dists3 = [[1], [7], [9, 10, 11]]\",\n \"def test_education():\\n test_path = tempfile.mkdtemp()\\n x_train, metadata = education(test_path)\\n try:\\n assert x_train.shape == (50, 6)\\n except:\\n shutil.rmtree(test_path)\\n raise()\",\n \"def load_data():\\n\\n print('Loading and Visualizing Data ...')\\n\\n file_name = path.join(getcwd(), 'ex3', 'src', 'data', 'ex3data1')\\n data = scipy.io.loadmat(file_name)\\n\\n # training data stored in arrays X, y\\n # y should be a row vector of labels\\n return data['X'], data['y'].T[0]\",\n \"def test_getitem():\\n atom = ATOMClassifier(X_class, y_class, random_state=1)\\n atom.run(\\\"Tree\\\")\\n assert atom.tree[\\\"alcohol\\\"].equals(atom.dataset[\\\"alcohol\\\"])\\n with pytest.raises(TypeError, match=r\\\".*subscriptable with type str.*\\\"):\\n print(atom.tree[2])\",\n \"def setUp(self):\\n self._m = 100\\n self._n = 30\\n self._k = 5\\n self._increment = 20\\n self._A = get_data(ExperimentType.ExampleNo2)(self._m, np.arange(2 * self._k).astype(float))\\n self._approximation = random_id(self._A, self._k, self._increment)\\n self._B = self._approximation.B\\n self._P = np.array(self._approximation.P)\\n self._A = self._A.as_numpy_arr()\\n self._n = self._A.shape[1]\\n self._approximation = self._approximation.as_numpy_arr()\",\n \"def setUp(self):\\n self.dataset = self.dataset_cls()\",\n \"def mnist_testing(shuffled = True):\\n mndata = MNIST(MNIST_PATH)\\n test_ims, test_labels = mndata.load_testing()\\n test_X = np.array(test_ims).T\\n test_y = np.array(test_labels).T\\n return test_X, test_y\",\n \"def setUp(self):\\n temperature = np.array([[185.0, 260.65, 273.15, 338.15]], dtype=np.float32)\\n self.temperature = set_up_variable_cube(temperature)\\n humidity = np.array([[60.0, 70.0, 75.0, 80.0]], dtype=np.float32)\\n self.relative_humidity = set_up_variable_cube(\\n humidity, name=\\\"relative_humidity\\\", units=\\\"%\\\"\\n )\\n pressure = np.array([[1.0e5, 9.9e4, 9.85e4, 9.8e4]], dtype=np.float32)\\n self.pressure = set_up_variable_cube(pressure, name=\\\"air_pressure\\\", units=\\\"Pa\\\")\\n self.expected_wbt_data = np.array(\\n [[185.0, 259.88306, 271.78006, 333.96066]], dtype=np.float32\\n )\",\n \"def setUp(self):\\n self._default_call_inputs = (\\n np.array([[\\\"one\\\", \\\"two\\\", \\\"three\\\"],\\n [\\\"four\\\", \\\"five\\\", \\\"six\\\"]]),\\n None\\n )\\n\\n self._hash_embedding_dim = 4\\n self._embedding_dim = 2\\n\\n self._default_config = {\\n \\\"hash_embedding_dim\\\": self._hash_embedding_dim,\\n \\\"embedding_dim\\\": self._embedding_dim\\n }\",\n \"def setUp(self):\\n self.testdatapath = os.path.join(mkdtemp())\\n self.testfilenames = [os.path.join(self.testdatapath, \\\"0001.nc\\\")]\\n\\n self.gpis = [1, 10, 11, 12]\\n self.lons = [0, 0, 1, 1]\\n self.lats = [1, 1, 0, 0]\\n self.cells = [1, 1, 1, 1]\\n self.grid = grids.CellGrid(self.lons, self.lats, self.cells, self.gpis)\",\n \"def test_np_memory_layout_add_input_tensor_pystepiocallback():\\n\\n def _test(transposedInput, transposedOutput):\\n builder = popart.Builder()\\n\\n # Create a random constant and transpose it\\n np.random.seed(1)\\n input1 = builder.addInputTensor(\\\"INT32\\\", [2, 2])\\n\\n # Run a session to prove this\\n output1 = builder.aiOnnx.identity([input1])\\n builder.addOutputTensor(output1)\\n anchorConfig = {output1: popart.AnchorReturnType(\\\"ALL\\\")}\\n\\n dataFlow = popart.DataFlow(1, anchorConfig)\\n deviceConfig = {\\\"numIPUs\\\": 1}\\n dm = popart.DeviceManager()\\n device = dm.createIpuModelDevice(deviceConfig)\\n session = popart.InferenceSession(\\n fnModel=builder.getModelProto(), dataFlow=dataFlow, deviceInfo=device\\n )\\n\\n # Compile graph and place weights onto it\\n session.prepareDevice()\\n session.weightsFromHost()\\n\\n # Feed the session with a transposed (non-contiguous) tensor.\\n input1Value = np.random.randint(0, 100, size=(2, 2), dtype=\\\"int32\\\")\\n if transposedInput:\\n input1Value = np.transpose(input1Value, [1, 0])\\n output1Value = np.random.randint(0, 100, size=(2, 2), dtype=\\\"int32\\\")\\n if transposedOutput:\\n output1Value = np.transpose(output1Value, [1, 0])\\n\\n with pytest.raises(\\n (Exception, RuntimeError, popart.popart_exception)\\n ) as e_info:\\n\\n # pylint: disable=unused-argument\\n def input_callback(id, prefetch):\\n return input1Value\\n\\n def input_complete_callback(_): # id is an unused parameter\\n pass\\n\\n def output_callback(_): # id is an unused parameter\\n return output1Value\\n\\n def output_complete_callback(_): # id is an unused parameter\\n pass\\n\\n stepio = popart.PyStepIOCallback(\\n input_callback,\\n input_complete_callback,\\n output_callback,\\n output_complete_callback,\\n )\\n\\n session.run(stepio)\\n\\n assert \\\"contiguous\\\" in e_info.value.args[0]\\n\\n _test(transposedInput=True, transposedOutput=False)\\n _test(transposedInput=False, transposedOutput=True)\",\n \"def demonstration_examples(kind):\\n\\n DIR = './data/demos/'\\n RNN_DEMO = \\\"-demo_rnn_examples\\\"\\n EXT = '.pkl'\\n\\n if kind == 'rnn':\\n return pd.read_pickle(DIR+RNN_DEMO+EXT)\",\n \"def test_0():\\n sync.gen_multi_fake_data()#default is only one randomly selected data set\\n sync.main(testing=True)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6889487","0.65411484","0.62041","0.61115885","0.60792667","0.60625935","0.5973008","0.5798675","0.579797","0.57839084","0.5775764","0.5711267","0.57093495","0.5704983","0.5672286","0.5643146","0.5622259","0.5604334","0.5597279","0.5581926","0.558076","0.5573886","0.55467963","0.55462253","0.5545013","0.55389446","0.5538212","0.5531791","0.55170566","0.55123717","0.5501795","0.5493862","0.54827535","0.54823494","0.54781055","0.54713607","0.54701394","0.5457522","0.5445755","0.54388285","0.543875","0.5437019","0.54244787","0.54244757","0.54078555","0.54077554","0.5405763","0.54027545","0.5401663","0.5397861","0.5397861","0.5397861","0.5397861","0.5393307","0.5366809","0.5359045","0.53578895","0.53494453","0.5341475","0.5337517","0.53356194","0.5332557","0.5316538","0.53101254","0.5309958","0.5297681","0.5292696","0.5291052","0.52708805","0.5266438","0.52641845","0.5263294","0.52626467","0.52585566","0.5251969","0.5242423","0.5240648","0.5239713","0.5237465","0.52365184","0.5236094","0.5234752","0.523056","0.5218264","0.52179736","0.52177644","0.5213502","0.5211706","0.5209398","0.5209125","0.52076596","0.520705","0.52041197","0.5200524","0.5194009","0.51924425","0.51919067","0.51887023","0.5187254","0.5185873"],"string":"[\n \"0.6889487\",\n \"0.65411484\",\n \"0.62041\",\n \"0.61115885\",\n \"0.60792667\",\n \"0.60625935\",\n \"0.5973008\",\n \"0.5798675\",\n \"0.579797\",\n \"0.57839084\",\n \"0.5775764\",\n \"0.5711267\",\n \"0.57093495\",\n \"0.5704983\",\n \"0.5672286\",\n \"0.5643146\",\n \"0.5622259\",\n \"0.5604334\",\n \"0.5597279\",\n \"0.5581926\",\n \"0.558076\",\n \"0.5573886\",\n \"0.55467963\",\n \"0.55462253\",\n \"0.5545013\",\n \"0.55389446\",\n \"0.5538212\",\n \"0.5531791\",\n \"0.55170566\",\n \"0.55123717\",\n \"0.5501795\",\n \"0.5493862\",\n \"0.54827535\",\n \"0.54823494\",\n \"0.54781055\",\n \"0.54713607\",\n \"0.54701394\",\n \"0.5457522\",\n \"0.5445755\",\n \"0.54388285\",\n \"0.543875\",\n \"0.5437019\",\n \"0.54244787\",\n \"0.54244757\",\n \"0.54078555\",\n \"0.54077554\",\n \"0.5405763\",\n \"0.54027545\",\n \"0.5401663\",\n \"0.5397861\",\n \"0.5397861\",\n \"0.5397861\",\n \"0.5397861\",\n \"0.5393307\",\n \"0.5366809\",\n \"0.5359045\",\n \"0.53578895\",\n \"0.53494453\",\n \"0.5341475\",\n \"0.5337517\",\n \"0.53356194\",\n \"0.5332557\",\n \"0.5316538\",\n \"0.53101254\",\n \"0.5309958\",\n \"0.5297681\",\n \"0.5292696\",\n \"0.5291052\",\n \"0.52708805\",\n \"0.5266438\",\n \"0.52641845\",\n \"0.5263294\",\n \"0.52626467\",\n \"0.52585566\",\n \"0.5251969\",\n \"0.5242423\",\n \"0.5240648\",\n \"0.5239713\",\n \"0.5237465\",\n \"0.52365184\",\n \"0.5236094\",\n \"0.5234752\",\n \"0.523056\",\n \"0.5218264\",\n \"0.52179736\",\n \"0.52177644\",\n \"0.5213502\",\n \"0.5211706\",\n \"0.5209398\",\n \"0.5209125\",\n \"0.52076596\",\n \"0.520705\",\n \"0.52041197\",\n \"0.5200524\",\n \"0.5194009\",\n \"0.51924425\",\n \"0.51919067\",\n \"0.51887023\",\n \"0.5187254\",\n \"0.5185873\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7280651"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":6593,"cells":{"query":{"kind":"string","value":"Test the popxl advanced getting / writing tensor data example that shows exactly when devicehost transfers occur"},"document":{"kind":"string","value":"def test_documentation_popxl_adv_get_write(self):\n filename = \"tensor_get_write_adv.py\"\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)"},"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 test_documentation_popxl_get_set_tensors(self):\n filename = \"tensor_get_write.py\"\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)","def test_remote_buffer() -> None:\n # Prepare the input and output data\n shape_1 = (1, 3, 5)\n shape_2 = (7, 11)\n d_type_1 = np.dtype(\"float32\")\n d_type_2 = np.dtype(\"float16\")\n\n data: Dict[str, np.ndarray] = {}\n\n # Store and load data for the first tensor\n data[\"store_in_1\"] = np.random.rand(*shape_1).astype(d_type_1)\n data[\"load_in_1\"] = np.zeros(shape_1).astype(d_type_1)\n data[\"load_in_1_inplace\"] = np.zeros(shape_1).astype(d_type_1)\n # Store and load data for the second tensor\n data[\"store_in_2\"] = np.random.rand(*shape_2).astype(d_type_2)\n data[\"load_in_2\"] = np.zeros(shape_2).astype(d_type_2)\n # Store and load data for the third tensor\n data[\"store_in_3\"] = np.random.rand(*shape_2).astype(d_type_2)\n data[\"load_in_3_inplace\"] = np.zeros(shape_2).astype(d_type_2)\n\n ir, d2h_streams = build_model(data)\n\n # Get the tensor_ids\n labels = (\n \"load_in_1\",\n \"load_in_1_inplace\",\n \"load_out_1\",\n \"load_out_1_inplace\",\n \"load_in_2\",\n \"load_in_3_inplace\",\n \"load_out_2\",\n \"load_out_3_inplace\",\n )\n tensor_d2h = {label: d2h_streams[label] for label in labels}\n\n session = popxl.Session(ir, \"ipu_model\")\n with session:\n outputs = session.run()\n\n # Assert that the tensors are correct\n remote_load_scenarios = (\n \"1\",\n \"1_inplace\",\n \"2\",\n \"3_inplace\",\n )\n for scenario in remote_load_scenarios:\n print(f\"Now asserting remote load scenario {scenario}\")\n # Get data to assert\n store_in_data = data[f\"store_in_{scenario.replace('_inplace', '')}\"]\n load_in_data_before_op_call = data[f\"load_in_{scenario}\"]\n load_in_data_after_op_call = outputs[tensor_d2h[f\"load_in_{scenario}\"]]\n load_out_data = outputs[tensor_d2h[f\"load_out_{scenario}\"]]\n shape = shape_1 if \"1\" in scenario else shape_2\n d_type = d_type_1 if \"1\" in scenario else d_type_2\n inplace = True if \"inplace\" in scenario else False\n # Assert shape and type\n assert load_in_data_after_op_call.shape == shape\n assert load_in_data_after_op_call.dtype == d_type\n assert load_out_data.shape == shape\n assert load_out_data.dtype == d_type\n\n # Assert that the data has been loaded\n assert np.allclose(store_in_data, load_out_data)\n if inplace:\n # Assert that the load in data has been overwritten\n assert np.allclose(load_in_data_after_op_call, store_in_data)\n else:\n # Assert that the load in data has not been overwritten\n assert np.allclose(load_in_data_after_op_call, load_in_data_before_op_call)","def build_model(\n data: Dict[str, np.array]\n) -> Tuple[popxl.Ir, Dict[str, DeviceToHostStream]]:\n ir = popxl.Ir()\n main = ir.main_graph\n\n with main:\n # Placeholder for tensor ids\n tensors = {}\n # Create variable tensors from the data\n for name in data.keys():\n tensors[name] = popxl.variable(data[name], name=name)\n\n # Placeholder for device to host streams\n d2h_streams = {}\n\n # Store and load the first tensor\n remote_buffer_1 = RemoteBuffer(\n tensor_shape=tensors[\"store_in_1\"]._pb_tensor.info.shape(),\n tensor_dtype=dtype.as_dtype(\n tensors[\"store_in_1\"]._pb_tensor.info.data_type_lcase()\n ),\n entries=1,\n )\n offset_tensor_1 = popxl.constant(0, name=\"offset_1\")\n # Ensure that the ops are in the order we define them in\n with popxl.in_sequence(True):\n ops.remote_store(\n remote_buffer=remote_buffer_1,\n offset=offset_tensor_1,\n t=tensors[\"store_in_1\"],\n )\n tensors[\"load_out_1\"] = ops.remote_load(\n remote_buffer=remote_buffer_1, offset=offset_tensor_1, name=\"load_out_1\"\n )\n tensors[\"load_out_1_inplace\"] = ops.remote_load_(\n remote_buffer=remote_buffer_1,\n offset=offset_tensor_1,\n t=tensors[\"load_in_1_inplace\"],\n )\n # Anchor the input tensors to the load operator\n d2h_streams = make_stream(d2h_streams, tensors, \"load_in_1\")\n d2h_streams = make_stream(d2h_streams, tensors, \"load_in_1_inplace\")\n # Anchor the output tensors of the load operator\n d2h_streams = make_stream(d2h_streams, tensors, \"load_out_1\")\n d2h_streams = make_stream(d2h_streams, tensors, \"load_out_1_inplace\")\n\n # Store and load the second and third tensor using a new buffer id\n remote_buffer_2 = RemoteBuffer(\n tensor_shape=tensors[\"store_in_2\"]._pb_tensor.info.shape(),\n tensor_dtype=dtype.as_dtype(\n tensors[\"store_in_2\"]._pb_tensor.info.data_type_lcase()\n ),\n entries=2,\n )\n # Index starts at 0\n offset_tensor_2 = popxl.constant(0, name=\"offset_2\")\n offset_tensor_3 = 1 # Test that the int version of offset works\n ops.remote_store(\n remote_buffer=remote_buffer_2,\n offset=offset_tensor_2,\n t=tensors[\"store_in_2\"],\n )\n ops.remote_store(\n remote_buffer=remote_buffer_2,\n offset=offset_tensor_3,\n t=tensors[\"store_in_3\"],\n )\n tensors[\"load_out_2\"] = ops.remote_load(\n remote_buffer=remote_buffer_2, offset=offset_tensor_2, name=\"load_out_2\"\n )\n tensors[\"load_out_3_inplace\"] = ops.remote_load_(\n remote_buffer=remote_buffer_2,\n offset=offset_tensor_3,\n t=tensors[\"load_in_3_inplace\"],\n )\n\n # Anchor the input tensors to the load operator\n d2h_streams = make_stream(d2h_streams, tensors, \"load_in_2\")\n d2h_streams = make_stream(d2h_streams, tensors, \"load_in_3_inplace\")\n # Anchor the output tensors of the load operator\n d2h_streams = make_stream(d2h_streams, tensors, \"load_out_2\")\n d2h_streams = make_stream(d2h_streams, tensors, \"load_out_3_inplace\")\n\n return ir, d2h_streams","def test_np_memory_layout_add_input_tensor_pystepiocallback():\n\n def _test(transposedInput, transposedOutput):\n builder = popart.Builder()\n\n # Create a random constant and transpose it\n np.random.seed(1)\n input1 = builder.addInputTensor(\"INT32\", [2, 2])\n\n # Run a session to prove this\n output1 = builder.aiOnnx.identity([input1])\n builder.addOutputTensor(output1)\n anchorConfig = {output1: popart.AnchorReturnType(\"ALL\")}\n\n dataFlow = popart.DataFlow(1, anchorConfig)\n deviceConfig = {\"numIPUs\": 1}\n dm = popart.DeviceManager()\n device = dm.createIpuModelDevice(deviceConfig)\n session = popart.InferenceSession(\n fnModel=builder.getModelProto(), dataFlow=dataFlow, deviceInfo=device\n )\n\n # Compile graph and place weights onto it\n session.prepareDevice()\n session.weightsFromHost()\n\n # Feed the session with a transposed (non-contiguous) tensor.\n input1Value = np.random.randint(0, 100, size=(2, 2), dtype=\"int32\")\n if transposedInput:\n input1Value = np.transpose(input1Value, [1, 0])\n output1Value = np.random.randint(0, 100, size=(2, 2), dtype=\"int32\")\n if transposedOutput:\n output1Value = np.transpose(output1Value, [1, 0])\n\n with pytest.raises(\n (Exception, RuntimeError, popart.popart_exception)\n ) as e_info:\n\n # pylint: disable=unused-argument\n def input_callback(id, prefetch):\n return input1Value\n\n def input_complete_callback(_): # id is an unused parameter\n pass\n\n def output_callback(_): # id is an unused parameter\n return output1Value\n\n def output_complete_callback(_): # id is an unused parameter\n pass\n\n stepio = popart.PyStepIOCallback(\n input_callback,\n input_complete_callback,\n output_callback,\n output_complete_callback,\n )\n\n session.run(stepio)\n\n assert \"contiguous\" in e_info.value.args[0]\n\n _test(transposedInput=True, transposedOutput=False)\n _test(transposedInput=False, transposedOutput=True)","def test_byteps_push_pull(self):\n dtypes = ['float16', 'float32', 'float64']\n dims = [1, 2, 3]\n count = 0\n ctx = self._current_context()\n shapes = [(), (17), (17, 17), (17, 17, 17)]\n for dtype, dim in itertools.product(dtypes, dims):\n # MXNet uses gpu_id as part of the seed, so to get identical seeds\n # we must set a context.\n mx.random.seed(10 + 10 * bps.rank(), ctx=ctx)\n tensor = mx.nd.random.uniform(-100, 100, shape=shapes[dim],\n ctx=ctx)\n tensor = tensor.astype(dtype)\n input = tensor.asnumpy()\n\n bps.byteps_declare_tensor(\"tensor_\" + str(count))\n bps.byteps_push_pull(tensor, name=\"tensor_\"+str(count))\n tensor.wait_to_read()\n output = tensor.asnumpy()\n assert np.allclose(input, output)\n count += 1\n\n print('test_byteps_push_pull passed')","def test_co_transfer():\n test_path = tempfile.mkdtemp()\n x_train, metadata = co_transfer(test_path)\n try:\n assert x_train.shape == (7, 2)\n except:\n shutil.rmtree(test_path)\n raise()","def test_create_device_data(self):\n pass","def test_np_memory_layout_add_input_tensor_pystepio():\n\n builder = popart.Builder()\n\n # Create a random constant and transpose it\n np.random.seed(1)\n input1 = builder.addInputTensor(\"INT32\", [2, 2])\n\n # Run a session to prove this\n output1 = builder.aiOnnx.identity([input1])\n builder.addOutputTensor(output1)\n anchorConfig = {output1: popart.AnchorReturnType(\"ALL\")}\n\n dataFlow = popart.DataFlow(1, anchorConfig)\n deviceConfig = {\"numIPUs\": 1}\n dm = popart.DeviceManager()\n device = dm.createIpuModelDevice(deviceConfig)\n session = popart.InferenceSession(\n fnModel=builder.getModelProto(), dataFlow=dataFlow, deviceInfo=device\n )\n\n # Compile graph and place weights onto it\n session.prepareDevice()\n session.weightsFromHost()\n anchors = session.initAnchorArrays()\n\n # Feed the session with a transposed (non-contiguous) tensor.\n input1Value = np.random.randint(0, 100, size=(2, 2), dtype=\"int32\")\n input1Value = np.transpose(input1Value, [1, 0])\n\n with pytest.raises((RuntimeError, popart.popart_exception)) as e_info:\n stepio = popart.PyStepIO({input1: input1Value}, anchors)\n session.run(stepio)\n\n assert \"contiguous\" in e_info.value.args[0]","def test():\n quant_handle = QuantAndDeQuantGPU()\n import torch\n logger = logging.getLogger(__name__)\n logger.setLevel(logging.INFO)\n tensor = torch.Tensor(np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])).cuda()\n logging.info(\"Origin Data: \")\n logging.info(tensor)\n\n start_time = datetime.datetime.now()\n quant_tensor = quant_handle(tensor)\n end_time = datetime.datetime.now()\n\n logging.info(\"Quant Data: \")\n logging.info(quant_tensor)\n\n data_expected = np.array([\n 0.0000000000, 1.0000000000, 2.0000000000, 2.9536523819, 4.0000000000,\n 4.9674310684, 5.9073047638, 7.0250086784, 8.0000000000, 8.7240619659\n ])\n\n logging.info(\"Data expected: \")\n logging.info(\" \".join([str(v) for v in data_expected]))\n\n data_diff = quant_tensor.data.detach().cpu().numpy() - data_expected\n flag = \"success.\"\n for num in data_diff:\n if abs(num) > 0.000000001:\n flag = \"failed.\"\n\n run_time = end_time - start_time\n logging.info(\"QuantAndDeQuantGPU time: %s\", str(run_time))\n logging.info(\"QuantAndDeQuantGPU %s\", flag)","def test_significance_individual_timepoints(tensor_directory, analysis_name):\n\n \"\"\"\n # Open Analysis Dataframe\n analysis_file = h5py.File(os.path.join(tensor_directory, analysis_name + \".hdf5\"), \"r\")\n activity_dataset = analysis_file[\"Data\"]\n metadata_dataset = analysis_file[\"metadata\"]\n number_of_timepoints, number_of_trials, number_of_pixels = np.shape(activity_dataset)\n print(\"metadata_dataset\", np.shape(metadata_dataset))\n \"\"\"\n # Open Analysis Dataframe\n analysis_file = tables.open_file(os.path.join(tensor_directory, analysis_name + \"_Trialwise_.h5\"), mode=\"r\")\n activity_dataset = analysis_file.root[\"Data\"]\n metadata_dataset = analysis_file.root[\"Trial_Details\"]\n\n # Create P and Slope Tensors\n p_value_tensor = np.ones((number_of_timepoints, number_of_pixels))\n slope_tensor = np.zeros((number_of_timepoints, number_of_pixels))\n\n for timepoint_index in tqdm(range(number_of_timepoints), position=0, desc=\"Timepoint\"):\n\n # Get Timepoint Data\n timepoint_activity = activity_dataset[timepoint_index]\n\n for pixel_index in tqdm(range(number_of_pixels), position=1, desc=\"Pixel\", leave=True):\n\n # Package Into Dataframe\n pixel_activity = timepoint_activity[:, pixel_index]\n pixel_dataframe = repackage_data_into_dataframe(pixel_activity, metadata_dataset)\n\n # Fit Mixed Effects Model\n p_value, slope = mixed_effects_random_slope_and_intercept(pixel_dataframe)\n p_value_tensor[timepoint_index, pixel_index] = p_value\n slope_tensor[timepoint_index, pixel_index] = slope\n\n\n # Save These Tensors\n np.save(os.path.join(tensor_directory, analysis_name + \"_p_value_tensor.npy\"), p_value_tensor)\n np.save(os.path.join(tensor_directory, analysis_name + \"_slope_tensor.npy\"), slope_tensor)","def test_output_data():\n output_params = dict(\n type=\"geodetic\",\n format=\"GeoTIFF\",\n path=OUT_DIR,\n pixelbuffer=0,\n metatiling=1,\n bands=1,\n dtype=\"int16\"\n )\n output = gtiff.OutputData(output_params)\n assert output.path == OUT_DIR\n assert output.file_extension == \".tif\"\n tp = BufferedTilePyramid(\"geodetic\")\n tile = tp.tile(5, 5, 5)\n # get_path\n assert output.get_path(tile) == os.path.join(*[\n OUT_DIR, \"5\", \"5\", \"5\"+\".tif\"])\n # prepare_path\n try:\n temp_dir = os.path.join(*[OUT_DIR, \"5\", \"5\"])\n output.prepare_path(tile)\n assert os.path.isdir(temp_dir)\n finally:\n shutil.rmtree(temp_dir, ignore_errors=True)\n # profile\n assert isinstance(output.profile(tile), dict)\n # write\n try:\n tile.data = np.ones((1, ) + tile.shape)*128\n output.write(tile)\n # tiles_exist\n assert output.tiles_exist(tile)\n # read\n data = output.read(tile).data\n assert isinstance(data, np.ndarray)\n assert not data[0].mask.any()\n finally:\n shutil.rmtree(temp_dir, ignore_errors=True)\n # read empty\n data = output.read(tile).data\n assert isinstance(data, np.ndarray)\n assert data[0].mask.all()\n # empty\n empty = output.empty(tile)\n assert isinstance(empty, ma.MaskedArray)\n assert not empty.any()\n # deflate with predictor\n output_params.update(compression=\"deflate\", predictor=2)\n output = gtiff.OutputData(output_params)\n assert output.profile(tile)[\"compress\"] == \"deflate\"\n assert output.profile(tile)[\"predictor\"] == 2","def test_add_get_tensor(mock_data):\n dataset = Dataset(\"test-dataset\")\n\n # 1D tensors of all data types\n data = mock_data.create_data(10)\n add_get_arrays(dataset, data)","def test_documentation_popxl_addition_variable(self):\n filename = \"tensor_addition.py\"\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)","def sample_batch(pid, args, batch_queue, port_dict, device, actor_id_to_ip_dataport, local_size, cache_array):\n def recv_data(k, data_stream, actor_set, real_data_tasks_i):\n for real_data in data_stream:\n tmp = []\n tmp.append(real_data.state)\n tmp.append(real_data.action)\n tmp.append(real_data.reward)\n tmp.append(real_data.next_state)\n tmp.append(real_data.done)\n tmp.append(actor_set[k]['w'][real_data.idx])\n tmp.append(actor_set[k]['i'][real_data.idx])\n tmp.append(actor_set[k]['t'][real_data.idx])\n tmp.append(real_data.timestamp)\n local_dict[actor_set[k]['i'][real_data.idx]] = tmp\n cache_array[actor_set[k]['i'][real_data.idx]] |= 2**pid\n decom_state = torch.FloatTensor(np.frombuffer(zlib.decompress(real_data.state), dtype=np.uint8).reshape((1, 4, 84, 84)))\n real_data_tasks_i['states'].append(decom_state) #.to(device))\n real_data_tasks_i['actions'].append(torch.LongTensor([real_data.action])) #.to(device))\n real_data_tasks_i['rewards'].append(torch.FloatTensor([real_data.reward])) #.to(device))\n decom_next_state = torch.FloatTensor(np.frombuffer(zlib.decompress(real_data.next_state), dtype=np.uint8).reshape((1, 4, 84, 84)))\n real_data_tasks_i['next_states'].append(decom_next_state) #.to(device))\n real_data_tasks_i['dones'].append(torch.FloatTensor([real_data.done])) #.to(device))\n real_data_tasks_i['batch_weights'].append(torch.FloatTensor([actor_set[k]['w'][real_data.idx]])) #.to(device))\n real_data_tasks_i['batch_idxes'].append(actor_set[k]['i'][real_data.idx])\n # is the data overwrited?\n real_data_tasks_i['batch_timestamp_store'].append(actor_set[k]['t'][real_data.idx])\n real_data_tasks_i['batch_timestamp_real'].append(real_data.timestamp)\n conn = grpc.insecure_channel(port_dict['replay_ip'] + ':' + port_dict['sampleDataPort'])\n client = apex_data_pb2_grpc.SampleDataStub(channel=conn)\n local_dict = {}\n while True:\n batch_timestamp_real = []\n batch_timestamp_store = []\n batch_weights = []\n batch_idxes = []\n\n states, actions, rewards, next_states, dones = [], [], [], [], []\n\n res_batch = client.Send(apex_data_pb2.SampleDataRequest(batch_size=args.batch_size, beta = args.beta))\n actor_ids, data_ids, timestamps, weights, idxes = res_batch.actor_ids, res_batch.data_ids, res_batch.timestamp, res_batch.weights, res_batch.idxes\n actor_set = {}\n cached_value = {'states':{},'actions':{},'rewards':{},'next_states':{},'dones':{},'batch_weights':{},'batch_idxes':{},'batch_timestamp_store':{},'batch_timestamp_real':{}}\n for i in range(len(actor_ids)):\n set_a = actor_set.get(actor_ids[i], False)\n if set_a == False:\n actor_set[actor_ids[i]] = {}\n set_a = actor_set[actor_ids[i]]\n set_a['d'] = []\n set_a['w'] = []\n set_a['i'] = []\n set_a['t'] = []\n cached_value['states'][actor_ids[i]] = []\n cached_value['actions'][actor_ids[i]] = []\n cached_value['rewards'][actor_ids[i]] = []\n cached_value['next_states'][actor_ids[i]] = []\n cached_value['dones'][actor_ids[i]] = []\n cached_value['batch_weights'][actor_ids[i]] = []\n cached_value['batch_idxes'][actor_ids[i]] = []\n cached_value['batch_timestamp_store'][actor_ids[i]] = []\n cached_value['batch_timestamp_real'][actor_ids[i]] = []\n cache_id = actor_ids[i]*local_size+data_ids[i]\n cache_trans = cache_array[cache_id]\n if cache_trans & 2**pid == 0:\n set_a['d'].append(data_ids[i])\n set_a['w'].append(weights[i])\n set_a['i'].append(idxes[i])\n set_a['t'].append(timestamps[i])\n if cache_trans == 0 and local_dict.get(cache_id, False) != False:\n del local_dict[cache_id]\n else:\n try:\n state_tmp = local_dict[cache_id][0]\n action_tmp = local_dict[cache_id][1]\n reward_tmp = local_dict[cache_id][2] \n next_state_tmp = local_dict[cache_id][3] \n done_tmp = local_dict[cache_id][4] \n batch_weight_tmp = local_dict[cache_id][5] \n batch_idx_tmp = local_dict[cache_id][6] \n batch_store_tmp = local_dict[cache_id][7] \n batch_real_tmp = local_dict[cache_id][8] \n decom_state = torch.FloatTensor(np.frombuffer(zlib.decompress(state_tmp), dtype=np.uint8).reshape((1, 4, 84, 84)))\n cached_value['states'][actor_ids[i]].append(decom_state)\n cached_value['actions'][actor_ids[i]].append(torch.LongTensor([action_tmp]))\n cached_value['rewards'][actor_ids[i]].append(torch.FloatTensor([reward_tmp]))\n decom_next_state = torch.FloatTensor(np.frombuffer(zlib.decompress(next_state_tmp), dtype=np.uint8).reshape((1, 4, 84, 84)))\n cached_value['next_states'][actor_ids[i]].append(decom_next_state)\n cached_value['dones'][actor_ids[i]].append(torch.FloatTensor([done_tmp]))\n cached_value['batch_weights'][actor_ids[i]].append(torch.FloatTensor([batch_weight_tmp]))\n cached_value['batch_idxes'][actor_ids[i]].append(batch_idx_tmp)\n cached_value['batch_timestamp_store'][actor_ids[i]].append(batch_store_tmp)\n cached_value['batch_timestamp_real'][actor_ids[i]].append(batch_real_tmp)\n except:\n set_a['d'].append(data_ids[i])\n set_a['w'].append(weights[i])\n set_a['i'].append(idxes[i])\n set_a['t'].append(timestamps[i])\n real_data_links = {}\n real_data_tasks = {}\n for k, v in actor_set.items():\n actor_ip, data_port = actor_id_to_ip_dataport[k]\n conn_actor = grpc.insecure_channel(actor_ip + ':' + data_port)\n client_actor = apex_data_pb2_grpc.SendRealDataStub(channel=conn_actor)\n real_data_links[k] = client_actor.Send(apex_data_pb2.RealBatchRequest(idxes=v['d']))\n real_data_tasks[k] = {}\n real_data_tasks[k]['states'] = cached_value['states'][k]\n real_data_tasks[k]['actions'] = cached_value['actions'][k]\n real_data_tasks[k]['rewards'] = cached_value['rewards'][k]\n real_data_tasks[k]['next_states'] = cached_value['next_states'][k]\n real_data_tasks[k]['dones'] = cached_value['dones'][k]\n real_data_tasks[k]['batch_weights'] = cached_value['batch_weights'][k]\n real_data_tasks[k]['batch_idxes'] = cached_value['batch_idxes'][k]\n real_data_tasks[k]['batch_timestamp_store'] = cached_value['batch_timestamp_store'][k]\n real_data_tasks[k]['batch_timestamp_real'] = cached_value['batch_timestamp_real'][k]\n threads = []\n for k, v in real_data_links.items():\n t = threading.Thread(target=recv_data, args=(k, v, actor_set, real_data_tasks[k],))\n threads.append(t)\n t.start()\n\n for t in threads:\n t.join()\n\n for k, v in real_data_tasks.items():\n states += v['states']\n actions += v['actions']\n rewards += v['rewards']\n next_states += v['next_states']\n dones += v['dones']\n batch_weights += v['batch_weights']\n batch_idxes += v['batch_idxes']\n batch_timestamp_real += v['batch_timestamp_real']\n batch_timestamp_store += v['batch_timestamp_store']\n\n states = torch.cat(states,0).to(device)\n actions = torch.cat(actions,0).to(device)\n rewards = torch.cat(rewards,0).to(device)\n next_states = torch.cat(next_states,0).to(device)\n dones = torch.cat(dones,0).to(device)\n batch_weights = torch.cat(batch_weights,0).to(device)\n\n batch = [states, actions, rewards, next_states, dones, batch_weights, batch_idxes]\n batch_queue.put(batch)\n data, batch = None, None","def test(model, dataloader, params, args, val):\n\n # evaluation mode\n model.eval()\n\n # initialise buffers\n dice_lv_buffer = []\n dice_myo_buffer = []\n dice_rv_buffer = []\n\n mcd_lv_buffer = []\n hd_lv_buffer = []\n mcd_myo_buffer = []\n hd_myo_buffer = []\n mcd_rv_buffer = []\n hd_rv_buffer = []\n\n mean_mag_grad_detJ_buffer = []\n negative_detJ_buffer = []\n\n\n with tqdm(total=len(dataloader)) as t:\n # iterate over validation subjects\n for idx, (image_ed_batch, image_es_batch, label_ed_batch, label_es_batch) in enumerate(dataloader):\n # (data all in shape of (c, N, H, W))\n\n # extend to (N, c, H, W)\n image_ed_batch = image_ed_batch.permute(1, 0, 2, 3).to(device=args.device)\n image_es_batch = image_es_batch.permute(1, 0, 2, 3).to(device=args.device)\n label_es_batch = label_es_batch.permute(1, 0, 2, 3).to(device=args.device)\n\n with torch.no_grad():\n # compute optical flow and warped ED images towards ES\n dvf = model(image_ed_batch, image_es_batch)\n\n # transform label mask of ES frame\n warped_label_es_batch = resample_transform(label_es_batch.float(), dvf, interp='nearest')\n\n\n \"\"\" Move data to device \"\"\"\n if args.cuda:\n # move data to cpu to calculate metrics\n # (the axis permutation is to comply with metric calculation code which takes input shape H, W, N)\n warped_label_es_batch = warped_label_es_batch.squeeze(1).cpu().numpy().transpose(1, 2, 0)\n label_ed_batch = label_ed_batch.squeeze(0).numpy().transpose(1, 2, 0)\n dvf = dvf.data.cpu().numpy().transpose(0, 2, 3, 1) # (N, H, W, 2)\n else:\n # CPU version of the code\n warped_label_es_batch = warped_label_es_batch.squeeze(1).numpy().transpose(1, 2, 0)\n label_ed_batch = label_ed_batch.squeeze(0).numpy().transpose(1, 2, 0)\n dvf = dvf.data.numpy().transpose(0, 2, 3, 1) # (N, H, W, 2)\n \"\"\"\"\"\"\n\n \"\"\" Calculate the metrics (only works with SAX images) \"\"\"\n # (optional) extract 3 slices (apical, mid-ventricle and basal)\n if not args.all_slices:\n num_slices = label_ed_batch.shape[-1]\n apical_idx = int(round((num_slices - 1) * 0.75)) # 75% from basal\n mid_ven_idx = int(round((num_slices - 1) * 0.5)) # 50% from basal\n basal_idx = int(round((num_slices - 1) * 0.25)) # 25% from basal\n slices_idx = [apical_idx, mid_ven_idx, basal_idx]\n\n warped_label_es_batch = warped_label_es_batch[:, :, slices_idx]\n label_ed_batch = label_ed_batch[:, :, slices_idx]\n dvf = dvf[slices_idx, :, :, :] # needed for detJac\n\n # dice\n dice_lv = categorical_dice_stack(warped_label_es_batch, label_ed_batch, label_class=1)\n dice_myo = categorical_dice_stack(warped_label_es_batch, label_ed_batch, label_class=2)\n dice_rv = categorical_dice_stack(warped_label_es_batch, label_ed_batch, label_class=3)\n\n dice_lv_buffer += [dice_lv]\n dice_myo_buffer += [dice_myo]\n dice_rv_buffer += [dice_rv]\n\n # contour distances\n mcd_lv, hd_lv = contour_distances_stack(warped_label_es_batch, label_ed_batch, label_class=1, dx=params.pixel_size)\n mcd_myo, hd_myo = contour_distances_stack(warped_label_es_batch, label_ed_batch, label_class=2, dx=params.pixel_size)\n mcd_rv, hd_rv = contour_distances_stack(warped_label_es_batch, label_ed_batch, label_class=3, dx=params.pixel_size)\n\n # determinant of Jacobian\n mean_grad_detJ, mean_negative_detJ = detJac_stack(dvf)\n\n\n # update buffers\n mcd_lv_buffer += [mcd_lv]\n hd_lv_buffer += [hd_lv]\n mcd_myo_buffer += [mcd_myo]\n hd_myo_buffer += [hd_myo]\n mcd_rv_buffer += [mcd_rv]\n hd_rv_buffer += [hd_rv]\n\n mean_mag_grad_detJ_buffer += [mean_grad_detJ]\n negative_detJ_buffer += [mean_negative_detJ]\n\n t.update()\n\n # construct metrics dict\n metrics = {'dice_lv_mean': np.mean(dice_lv_buffer), 'dice_lv_std': np.std(dice_lv_buffer),\n 'dice_myo_mean': np.mean(dice_myo_buffer), 'dice_myo_std': np.std(dice_myo_buffer),\n 'dice_rv_mean': np.mean(dice_rv_buffer), 'dice_rv_std': np.std(dice_rv_buffer),\n\n 'mcd_lv_mean': np.mean(mcd_lv_buffer), 'mcd_lv_std': np.std(mcd_lv_buffer),\n 'mcd_myo_mean': np.mean(mcd_myo_buffer), 'mcd_myo_std': np.std(mcd_myo_buffer),\n 'mcd_rv_mean': np.mean(mcd_rv_buffer), 'mcd_rv_std': np.std(mcd_rv_buffer),\n\n 'hd_lv_mean': np.mean(hd_lv_buffer), 'hd_lv_std': np.std(hd_lv_buffer),\n 'hd_myo_mean': np.mean(hd_myo_buffer), 'hd_myo_std': np.std(hd_myo_buffer),\n 'hd_rv_mean': np.mean(hd_rv_buffer), 'hd_rv_std': np.std(hd_rv_buffer),\n\n 'mean_mag_grad_detJ_mean': np.mean(mean_mag_grad_detJ_buffer),\n 'mean_mag_grad_detJ_std': np.std(mean_mag_grad_detJ_buffer),\n\n 'negative_detJ_mean': np.mean(negative_detJ_buffer),\n 'negative_detJ_std': np.std(negative_detJ_buffer)\n }\n\n\n if not val:\n # testing only: save all metrics evaluated for all test subjects in pandas dataframe\n test_result_dir = os.path.join(args.model_dir, \"test_results\")\n if not os.path.exists(test_result_dir):\n os.makedirs(test_result_dir)\n\n # save metrics results mean & std\n xutils.save_dict_to_json(metrics,\n f\"{test_result_dir}/test_results_3slices_{not args.all_slices}.json\")\n\n # save accuracy metrics of every subject\n subj_id_buffer = dataloader.dataset.dir_list\n df_buffer = []\n column_method = ['DL'] * len(subj_id_buffer)\n for struct in ['LV', 'MYO', 'RV']:\n if struct == 'LV':\n ls_dice = dice_lv_buffer\n ls_mcd = mcd_lv_buffer\n ls_hd = hd_lv_buffer\n elif struct == 'MYO':\n ls_dice = dice_myo_buffer\n ls_mcd = mcd_myo_buffer\n ls_hd = hd_myo_buffer\n elif struct == 'RV':\n ls_dice = dice_rv_buffer\n ls_mcd = mcd_rv_buffer\n ls_hd = hd_rv_buffer\n\n ls_struct = [struct] * len(subj_id_buffer)\n data = {'Method': column_method,\n 'ID': subj_id_buffer,\n 'Structure': ls_struct,\n 'Dice': ls_dice,\n 'MCD': ls_mcd,\n 'HD': ls_hd}\n df_buffer += [pd.DataFrame(data=data)]\n # concatenate df and save\n metrics_df = pd.concat(df_buffer, axis=0)\n metrics_df.to_pickle(f\"{test_result_dir}/test_accuracy_results_3slices_{not args.all_slices}.pkl\")\n\n # save detJac metrics for every subject\n jac_data = {'Method': column_method,\n 'ID': subj_id_buffer,\n 'GradDetJac': mean_mag_grad_detJ_buffer,\n 'NegDetJac': negative_detJ_buffer}\n jac_df = pd.DataFrame(data=jac_data)\n jac_df.to_pickle(f\"{test_result_dir}/test_Jacobian_results_3slices{not args.all_slices}.pkl\")\n\n return metrics","def testAllWrite(self):\n import time,copy\n time.sleep(2)\n to_config = self.config['vdevs']['slave']['icsifaces'][0]\n from_config = self.config['vdevs']['master']['clientifaces'][0]\n points = self.config['vdevs']['slave']['points']\n client = ModbusRTU(to_config, points, from_config)\n\n pts = copy.deepcopy(self.config['vdevs']['slave']['points'])\n pts = [pt for pt in pts if pt['name']!='pressure'] #Can't write to pres\n for i in xrange(50):\n ptnames = [ pt['name'] for pt in pts ]\n pointsvalues = dict(zip(ptnames, [0]*len(ptnames)))\n reply = client.writePoints(pointsvalues)\n assert reply is None, \"Write returned value other than None: \" + str(reply)\n reply = client.readPoints(ptnames)\n #print \"Reply: \", reply\n for pt in ptnames:\n #assert value == reply[ptnames.index(pt)]\n if not 0 == reply[ptnames.index(pt)]: \n print pt, ' was not read properly.'","def testAllWrite(self):\n import time,copy\n time.sleep(2)\n client = ModbusTCP(self.config['vdevs']['slave']['icsifaces'][0],\n self.config['vdevs']['slave']['points']) \n\n pts = copy.deepcopy(self.config['vdevs']['slave']['points'])\n pts = [pt for pt in pts if pt['name']!='pressure'] #Can't write to pres\n ptnames = [ pt['name'] for pt in pts ]\n pointsvalues = dict(zip(ptnames, [0]*len(ptnames)))\n reply = client.writePoints(pointsvalues)\n assert reply is None, \"Write returned value other than None: \" + str(reply)\n reply = client.readPoints(ptnames)\n #print \"Reply: \", reply\n for pt in ptnames:\n #assert value == reply[ptnames.index(pt)]\n if not 0 == reply[ptnames.index(pt)]: \n print pt, ' was not read properly.'","def test_mstgcn():\n node_count = 307\n num_classes = 10\n edge_per_node = 15\n\n num_for_predict = 12\n len_input = 12\n nb_time_strides = 1\n\n device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n node_features = 2\n nb_block = 2\n K = 3\n nb_chev_filter = 64\n nb_time_filter = 64\n batch_size = 32\n\n model = MSTGCN(\n nb_block,\n node_features,\n K,\n nb_chev_filter,\n nb_time_filter,\n nb_time_strides,\n num_for_predict,\n len_input,\n ).to(device)\n T = len_input\n x_seq = torch.zeros([batch_size, node_count, node_features, T]).to(device)\n target_seq = torch.zeros([batch_size, node_count, T]).to(device)\n edge_index_seq = []\n\n for b in range(batch_size):\n for t in range(T):\n x, edge_index = create_mock_data(node_count, edge_per_node, node_features)\n x_seq[b, :, :, t] = x.to(device)\n if b == 0:\n edge_index_seq.append(edge_index.to(device))\n target = create_mock_target(node_count, num_classes).to(device)\n target_seq[b, :, t] = target\n\n shuffle = True\n train_dataset = torch.utils.data.TensorDataset(x_seq, target_seq)\n train_loader = torch.utils.data.DataLoader(\n train_dataset, batch_size=batch_size, shuffle=shuffle\n )\n\n for batch_data in train_loader:\n encoder_inputs, _ = batch_data\n outputs1 = model(encoder_inputs, edge_index_seq)\n outputs2 = model(encoder_inputs, edge_index_seq[0])\n\n assert outputs1.shape == (batch_size, node_count, num_for_predict)\n assert outputs2.shape == (batch_size, node_count, num_for_predict)","def test_synth_tr():\n test_path = tempfile.mkdtemp()\n x_train, metadata = synth_tr(test_path)\n try:\n assert x_train.shape == (250, 3)\n except:\n shutil.rmtree(test_path)\n raise()","def test_data_manipulation(self):\n target_name = self.project['target']['name']\n self.api_mock.return_value.get_metadata.return_value = [\n {'_id': '0',\n 'pid': '1',\n 'created': datetime.datetime.now(),\n 'name':'universe',\n 'originalName': 'credit-sample-200.csv',\n 'varTypeString': 'NN',\n 'shape': [2, 100],\n 'controls':{},\n 'columns': [[1,target_name,0],[3,\"age\",0]],\n 'files': ['projects/' + str(self.pid) + '/raw/' + self.testdatafile],\n 'typeConvert': {}},\n {'_id': '1',\n 'pid': '1',\n 'name':'test',\n 'originalName': 'credit-sample-200.csv',\n 'created': datetime.datetime.now(),\n 'varTypeString': 'NN',\n 'shape': [2, 100],\n 'controls':{},\n 'columns': [[1,target_name,0],[3,\"age\",0]],\n 'files': ['projects/' + str(self.pid) + '/raw/' + self.testdatafile],\n 'typeConvert': {}},\n {'_id': '2',\n 'pid': '1',\n 'name':'new',\n 'created': datetime.datetime.now(),\n 'originalName': 'credit-sample-200.csv',\n 'newdata':True,\n 'controls':{},\n 'shape': [2, 100],\n 'varTypeString': 'NN',\n 'columns': [[1,target_name,0],[3,\"age\",0]],\n 'files': ['projects/' + str(self.pid) + '/raw/' + self.testdatafile],\n 'typeConvert': {}}]\n request = WorkerRequest({'pid': '1', 'uid': '1', 'dataset_id': '1',\n 'command': 'fit', 'max_reps': 0,\n 'samplepct': 100})\n\n #target\n #this will map the target values to (0,1) because target type is Binary\n target_vector = self.dataprocessor.target_vector()\n target_series = target_vector['main']\n self.assertItemsEqual(np.unique(target_series), [0,1])\n\n #this will be none because 'holdout_pct' isn't set in the project data\n self.assertIsNone(target_vector['holdout'])\n\n #prediction dataset\n predictors = self.dataprocessor.predictors()\n pred_dataframe = predictors['1']['main']\n self.assertItemsEqual(list(pred_dataframe.columns), [\"age\"])\n self.assertEqual(self.dataprocessor.get_vartypestring_without_target('1'), \"N\")\n\n request = WorkerRequest({'pid': '1', 'uid': '1', 'dataset_id': '1', 'scoring_dataset_id': '2', 'command': 'predict', 'max_reps': 0, 'samplepct':100})\n dp2 = DataProcessor(request)\n data = dp2.request_datasets()\n self.assertEqual(data.keys(), ['1'])\n self.assertEqual(data['1'].keys(), ['scoring', 'vartypes'])\n scoring_data = data['1']['scoring']\n vartypes = data['1']['vartypes']\n self.assertEqual(list(scoring_data.columns), [\"age\"])\n self.assertEqual(vartypes, \"N\")","def test_gen():\n tpot_obj = TPOTClassifier()\n\n pipeline = tpot_obj._gen_grow_safe(tpot_obj._pset, 1, 3)\n\n assert len(pipeline) > 1\n assert pipeline[0].ret == Output_DF","def test_publish_path(self):\n log.debug(\"======================= creating telem data\")\n self.create_sample_data_set_dir(\n 'node59p1_step1.dat',\n TELEM_DIR,\n \"node59p1.dat\",\n copy_metadata=False\n )\n log.debug(\"======================= creating recov data\")\n self.create_sample_data_set_dir(\n \"DOS15908_1st7_step1.DAT\",\n RECOV_DIR,\n \"DOS15908.DAT\",\n copy_metadata=False\n )\n\n self.assert_initialize()\n\n # get the telemetered metadata particle\n result = self.data_subscribers.get_samples(DataParticleType.METADATA_TELEMETERED, 1)\n log.debug(\"***************************************************** metadata result %s\", result)\n #get the telemetered instrument particles\n result1 = self.data_subscribers.get_samples(DataParticleType.SAMPLE_TELEMETERED, 1)\n result.extend(result1)\n log.debug(\"RESULT: %s\", result)\n\n # Verify values\n self.assert_data_values(result, 'test_data_1.txt.result.yml')\n \n \n # get the telemetered metadata particle\n result = self.data_subscribers.get_samples(DataParticleType.METADATA_RECOVERED, 1)\n log.debug(\"***************************************************** metadata result %s\", result)\n #get the telemetered instrument particles\n result1 = self.data_subscribers.get_samples(DataParticleType.SAMPLE_RECOVERED, 1)\n result.extend(result1)\n log.debug(\"RESULT: %s\", result)\n\n # Verify values\n self.assert_data_values(result, 'test_data_1r.txt.result.yml')","def test_significance_window(tensor_directory, analysis_name, window):\n\n \"\"\"\n # Open Analysis Dataframe\n analysis_file = h5py.File(os.path.join(tensor_directory, analysis_name + \".hdf5\"), \"r\")\n activity_dataset = analysis_file[\"Data\"]\n metadata_dataset = analysis_file[\"metadata\"]\n number_of_timepoints, number_of_trials, number_of_pixels = np.shape(activity_dataset)\n print(\"metadata_dataset\", np.shape(metadata_dataset))\n \"\"\"\n\n\n # Open Analysis Dataframe\n analysis_file = tables.open_file(os.path.join(tensor_directory, analysis_name + \"_Trialwise_.h5\"), mode=\"r\")\n activity_dataset = analysis_file.root[\"Data\"]\n metadata_dataset = analysis_file.root[\"Trial_Details\"]\n activity_dataset = np.array(activity_dataset)\n metadata_dataset = np.array(metadata_dataset)\n\n activity_dataset = np.nan_to_num(activity_dataset)\n\n number_of_trials, number_of_timepoints, number_of_pixels = np.shape(activity_dataset)\n\n\n print(\"Number of timepoints\", number_of_timepoints)\n print(\"number of pixels\", number_of_pixels)\n print(\"number of trials\", number_of_trials)\n\n # Create P and Slope Tensors\n p_value_tensor = np.ones(number_of_pixels)\n slope_tensor = np.zeros(number_of_pixels)\n\n # Get Timepoint Data\n timepoint_activity = activity_dataset[:, window]\n print(\"Timepoint activity shape\", np.shape(timepoint_activity))\n timepoint_activity = np.mean(timepoint_activity, axis=1)\n\n for pixel_index in tqdm(range(number_of_pixels), position=1, desc=\"Pixel\", leave=False):\n\n # Package Into Dataframe\n pixel_activity = timepoint_activity[:, pixel_index]\n pixel_dataframe = repackage_data_into_dataframe(pixel_activity, metadata_dataset)\n\n # Fit Mixed Effects Model\n p_value, slope = mixed_effects_random_slope_and_intercept(pixel_dataframe)\n p_value_tensor[pixel_index] = p_value\n slope_tensor[pixel_index] = slope\n\n return p_value_tensor, slope_tensor","def test_active_inference_SPM_1b(self):","def test_predictor():","def testNanotecSharedMemory():\n\t\n\tmemoryClient = NanotecSharedMemoryClient()\n\t\n\t\n\t\n\tserialPort1 = memoryClient.sendInstruction(\"NanotecMotor,/dev/ttyACM0,17\")\n\t\n\tID1 = memoryClient.sendInstruction(\"getID,\" + serialPort1)\n\tprint(\"ID1 (actual): \" + ID1 + \", expected: 17\")\n\tprint(\"\")\n\t\n\tstartTime = time.time()\n\tnumCalls = 1000\n\tfor _ in range(numCalls):\n\t\tID1 = memoryClient.sendInstruction(\"getSerialPort,\" + serialPort1)\n\tendTime = time.time()\n\ttotalTime = endTime - startTime\n\tprint(\"\")\n\tprint(\"ID Call\")\n\tprint(\"numCalls: \" + str(numCalls))\n\tprint(\"total time: \" + str(totalTime))\n\tprint(\"time per call: \" + str(totalTime/numCalls))\n\t\n\t\n\tstartTime = time.time()\n\tnumCalls = 1000\n\tfor _ in range(numCalls):\n\t\tposition = memoryClient.sendInstruction(\"getAbsoluteAngularPosition,\" + serialPort1)\n\tendTime = time.time()\n\ttotalTime = endTime - startTime\n\tprint(\"\")\n\tprint(\"Position Call\")\n\tprint(\"numCalls: \" + str(numCalls))\n\tprint(\"total time: \" + str(totalTime))\n\tprint(\"time per call: \" + str(totalTime/numCalls))\n\t\n\treturn","def setUp(self):\n\n serial_times = {295: '1971-07-31T01:24:11.754',\n 296: '1971-07-31T01:24:36.970',\n 297: '1971-07-31T01:25:02.243',\n 298: '1971-07-31T01:25:27.457',\n 299: '1971-07-31T01:25:52.669',\n 300: '1971-07-31T01:26:17.923'}\n self.serials = ['APOLLO15/METRIC/{}'.format(i) for i in serial_times.values()]\n\n\n x = list(range(5))\n y = list(range(5))\n pid = [0,0,1,1,1]\n idx = pid\n serials = [self.serials[0], self.serials[1], self.serials[2],\n self.serials[2], self.serials[3]]\n\n\n columns = ['x', 'y', 'idx', 'pid', 'nid']\n self.data_length = 5\n\n data = [x,y, idx, pid, serials]\n\n self.creation_time = strftime(\"%Y-%m-%d %H:%M:%S\", gmtime())\n cnet = C(data, index=columns).T\n\n io_controlnetwork.to_isis('test.net', cnet, mode='wb', targetname='Moon')\n\n self.header_message_size = 85\n self.point_start_byte = 65621","def test_anchor_output():\n anchorDict = {\n \"ReplicationFactor\": 2,\n # Accl factor must divide batch size\n \"AccumulationFactor\": 4,\n \"Pipelining\": True,\n \"ReturnType\": \"ALL\",\n }\n label_array = np.ones([BATCH_SIZE]).astype(np.int32)\n\n micro_batch_size = BATCH_SIZE // (\n anchorDict[\"AccumulationFactor\"] * anchorDict[\"ReplicationFactor\"]\n )\n\n builder = popart.Builder()\n input_shape = [micro_batch_size, CHANNELS, DATA_LEN, DATA_LEN]\n\n data_shape = popart.TensorInfo(\"FLOAT\", input_shape)\n lbl_shape = popart.TensorInfo(\"INT32\", [micro_batch_size])\n w = builder.addInitializedInputTensor(\n np.random.random_sample(input_shape).astype(np.float32)\n )\n\n ip = builder.addInputTensor(data_shape)\n lb = builder.addInputTensor(lbl_shape)\n\n a = builder.aiOnnx.matmul([ip, w])\n o = builder.reshape_const(\n builder.aiOnnx, [a], [micro_batch_size, CHANNELS * DATA_LEN * DATA_LEN]\n )\n o = builder.aiOnnx.relu([o])\n o = builder.aiOnnx.softmax([o])\n nll = builder.aiGraphcore.nllloss([o, lb])\n\n GRAD = popart.reservedGradientPrefix() + w\n ACCL = popart.reservedAccumPrefix() + w\n art = popart.AnchorReturnType(\"All\")\n data_flow = popart.DataFlow(\n BATCHES_PER_STEP, {o: art, a: art, ip: art, w: art, GRAD: art, ACCL: art}\n )\n\n opts, deviceContext = return_options(anchorDict)\n with deviceContext as device:\n if device is None:\n pytest.skip(\"Test needs to run on IPU, but none are available\")\n\n session = popart.TrainingSession(\n fnModel=builder.getModelProto(),\n dataFlow=data_flow,\n loss=nll,\n optimizer=popart.ConstSGD(LEARNING_RATE),\n userOptions=opts,\n deviceInfo=device,\n )\n\n session.prepareDevice()\n\n if anchorDict[\"ReplicationFactor\"] > 1:\n input_shape = [anchorDict[\"ReplicationFactor\"]] + input_shape\n label_array = label_array.reshape([anchorDict[\"ReplicationFactor\"], -1])\n if anchorDict[\"AccumulationFactor\"] > 1:\n input_shape = [anchorDict[\"AccumulationFactor\"]] + input_shape\n label_array = label_array.reshape([anchorDict[\"AccumulationFactor\"], -1])\n if BATCHES_PER_STEP > 1:\n input_shape = [BATCHES_PER_STEP] + input_shape\n label_array = np.repeat(label_array[np.newaxis], BATCHES_PER_STEP, 0)\n\n anchors = session.initAnchorArrays()\n in_array = np.random.random_sample(input_shape).astype(np.float32)\n\n stepio = popart.PyStepIO({ip: in_array, lb: label_array}, anchors)\n session.weightsFromHost()\n\n session.run(stepio)\n\n # Returned anchors will be of shape\n # [bps, grad_accl_factor, repl_factor, micro_batch_size, channels, data_len, data_len]\n for batch in range(anchors[w].shape[0]):\n for replica in range(anchors[w].shape[1]):\n # Weights should not change over the gradient accumulation\n # dimension - only after gradAccl steps.\n assert np.allclose(\n anchors[w][batch, 0, :, :, :, :, :],\n anchors[w][batch, replica, :, :, :, :, :],\n )\n\n # Check that the accumulated gradient plus the weights for the current batch\n # equals the weights for the next batch.\n # Batch loop\n for batch in range(anchors[w].shape[0] - 1):\n calc_weight = {}\n # Replica loop.\n for replica in range(anchors[w].shape[2]):\n # For each replica in each batch, take the relevant replica's\n # last weight tensor in the accumulation loop minus\n # the sum of the accumulated gradients across replicas\n calc_weight[replica] = anchors[w][\n batch, -1, replica, :, :, :, :\n ] - np.sum(anchors[ACCL][batch, -1, :, :, :, :, :], axis=0)\n # Then compare against the last weight tensor of the next batch,\n # for the relevant replica. These should match.\n assert np.allclose(\n calc_weight[replica], anchors[w][batch + 1, -1, replica, :, :, :, :]\n )","def test_add_get_tensor_2D(mock_data):\n dataset = Dataset(\"test-dataset\")\n\n # 2D tensors of all data types\n data_2D = mock_data.create_data((10, 10))\n add_get_arrays(dataset, data_2D)","def test_large_import(self):\n self.create_sample_data_set_dir(\"node59p1.dat\", TELEM_DIR)\n self.assert_initialize()\n result = self.data_subscribers.get_samples(DataParticleType.METADATA_TELEMETERED,1,60)\n result = self.data_subscribers.get_samples(DataParticleType.SAMPLE_TELEMETERED,750,400)","def test_case_store_data_memory( self ):\n MEM_AUX = memory.fetch_system_memory()\n def test( clk, chip_select, address, load_store, data_in, data_out ):\n count = len( MEM_AUX )\n for i in range( count+10 , count+100 ):\n chip_select.next = 1\n load_store.next = 1\n address.next = i\n data_in.next = i*32\n MEM_AUX[i]=i*32\n yield clk.posedge\n yield clk.posedge\n #chip_select.next = 1\n load_store.next = 0\n #address.next = i\n data_in.next = 0\n yield clk.posedge\n yield clk.posedge\n chip_select.next = 0\n self.assertEqual( data_out, MEM_AUX[i] )\n\n raise StopSimulation\n\n clk_s = Signal( bool( 1 ) )\n clkgen = clk_gen( clk_s )\n address, data_in, data_out =[Signal( intbv( 0 )[32:] ) for i in range( 3 )]\n chip_select, load_store = [Signal( bool( 0 ) ) for i in range( 2 )]\n\n mem_test = memory.memory( clk_s, chip_select, address, load_store, data_in, data_out )\n #check = test( clk_s, chip_select, address, load_store, data_in, data_out )\n #sim = Simulation( mem_test, check, clkgen )\n te = traceSignals(test, clk_s, chip_select, address, load_store, data_in, data_out)\n sim = Simulation( mem_test, te, clkgen )\n sim.run( quiet=1 )","def test(args, device, test_generator, model):\n model.eval()\n\n with torch.no_grad():\n # Get inputs and labels\n inputs, inputs_prev, labels, image, _, omit = test_generator.generate_batch()\n\n # Send to device\n inputs = torch.from_numpy(inputs).to(device)\n inputs_prev = torch.from_numpy(inputs_prev).to(device)\n labels = torch.from_numpy(labels).to(device)\n\n # Initialize syn_x or hidden state\n model.syn_x = model.init_syn_x(args.batch_size).to(device)\n model.hidden = model.init_hidden(args.batch_size).to(device)\n\n output, hidden, inputs = model(inputs, inputs_prev)\n # Convert to binary prediction\n output = torch.sigmoid(output)\n pred = torch.bernoulli(output).byte()\n\n # Compute hit rate and false alarm rate\n hit_rate = (pred * (labels == 1)).sum().float().item() / \\\n (labels == 1).sum().item()\n fa_rate = (pred * (labels == -1)).sum().float().item() / \\\n (labels == -1).sum().item()\n\n # Compute dprime\n # dprime_true = dprime(hit_rate, fa_rate)\n go = (labels == 1).sum().item()\n catch = (labels == -1).sum().item()\n num_trials = (labels != 0).sum().item()\n assert (go + catch) == num_trials\n\n # dprime_true = compute_dprime(hit_rate, fa_rate, go, catch, num_trials)\n # dprime_old = dprime(hit_rate, fa_rate)\n dprime_true = dprime(hit_rate, fa_rate)\n # try:\n # assert dprime_true == dprime_old\n # except:\n # print(hit_rate, fa_rate)\n # print(dprime_true, dprime_old)\n\n return dprime_true.item(), hit_rate, fa_rate, inputs, hidden, output, pred, image, labels, omit","def make_request(dataset, path, out, session):\n reader = tf.python_io.tf_record_iterator(\n path, options=tf.python_io.TFRecordOptions(\n compression_type=TFRecordCompressionType.GZIP))\n print >> sys.stderr, path\n for buf in reader:\n examples_tensor = json.loads(\n session.graph.get_collection('inputs')[0])['examples']\n output_tensor = json.loads(\n session.graph.get_collection('outputs')[0])[FLAGS.fetch]\n outputs = session.run(fetches=[output_tensor],\n feed_dict={examples_tensor: [buf]})\n example = tf.train.Example.FromString(buf)\n sample_name = example.features.feature['sample_name'].bytes_list.value[0]\n super_population = example.features.feature[\n 'super_population_string'].bytes_list.value[0]\n population = example.features.feature[\n 'population_string'].bytes_list.value[0]\n print >> out, '%s\\t%s\\t%s\\t%s\\t%s' % (\n dataset, sample_name, super_population, population, '\\t'.join(\n str(value) for value in outputs[0][0]))","def step(self, observation, last_state):\n # We are omitting the details of network inference here.\n # ...\n feature_screen = observation[3]['feature_screen']\n feature_minimap = observation[3]['feature_minimap']\n feature_units = observation[3]['feature_units']\n feature_player = observation[3]['player']\n available_actions = observation[3]['available_actions']\n score_by_category = observation[3]['score_by_category']\n game_loop = observation[3]['game_loop']\n\n unit_type = feature_screen.unit_type\n empty_space = np.where(unit_type == 0)\n empty_space = np.vstack((empty_space[0], empty_space[1])).T\n random_point = random.choice(empty_space)\n #target = [random_point[0], random_point[1]]\n #action = [actions.FunctionCall(_BUILD_SUPPLY_DEPOT, [_NOT_QUEUED, target])]\n policy_logits = None\n new_state = None\n\n spatial_encoder_output = self.spatial_encoder(np.reshape(feature_screen, [1,128,128,27]))\n\n agent_statistics = get_agent_statistics(score_by_category)\n\n home_race = 'Terran'\n away_race = 'Terran'\n race = get_race_onehot(home_race, away_race)\n\n time = get_gameloop_obs(game_loop)\n\n upgrade_value = get_upgrade_obs(feature_units)\n if upgrade_value != -1:\n self.home_upgrade_array[np.where(upgrade_value[0] == 1)] = 1\n self.away_upgrade_array[np.where(upgrade_value[1] == 1)] = 1\n\n embedded_scalar = np.concatenate((agent_statistics, race, time, self.home_upgrade_array, self.away_upgrade_array), axis=0)\n scalar_encoder_output = self.scalar_encoder(np.reshape(embedded_scalar, [1,307]))\n embedded_feature_units = get_entity_obs(feature_units)\n entity_encoder_output = self.entity_encoder(np.reshape(embedded_feature_units, [1,512,464]))\n encoder_input = np.concatenate((spatial_encoder_output, scalar_encoder_output, entity_encoder_output), axis=1)\n\n core_input = np.reshape(encoder_input, [16, 8, 131])\n whole_seq_output, final_memory_state, final_carry_state = self.core(core_input)\n print(whole_seq_output.shape)\n print(final_memory_state.shape)\n print(final_carry_state.shape)\n\n action = [actions.FUNCTIONS.no_op()]\n\n return action, policy_logits, new_state","def test_create_device1(self):\n pass","def test_add_get_tensor_3D(mock_data):\n dataset = Dataset(\"test-dataset\")\n\n # 3D tensors of all datatypes\n data_3D = mock_data.create_data((10, 10, 10))\n add_get_arrays(dataset, data_3D)","def setUp(self):\n output = np.zeros((1, 5, 2))\n target = np.zeros((1, 5, 2))\n # first channel\n output[0, 0] = [10, 4]\n target[0, 0] = [10, 0]\n # second channel\n output[0, 1] = [10, 18]\n target[0, 1] = [10, 10]\n # third channel\n output[0, 2] = [0, 0]\n target[0, 2] = [0, -1]\n # fourth channel\n output[0, 3] = [40, 40]\n target[0, 3] = [30, 30]\n # fifth channel\n output[0, 4] = [20, 10]\n target[0, 4] = [0, 10]\n\n gt_instances = InstanceData()\n gt_instances.keypoints = target\n gt_instances.keypoints_visible = np.array(\n [[True, True, False, True, True]])\n\n pred_instances = InstanceData()\n pred_instances.keypoints = output\n\n data = {'inputs': None}\n data_sample = {\n 'gt_instances': gt_instances.to_dict(),\n 'pred_instances': pred_instances.to_dict()\n }\n\n self.data_batch = [data]\n self.data_samples = [data_sample]","def test5():\n setLogLevel(\"info\")\n info(\"Configuracion Unidad experimental\")\n \"\"\" 1 -> Definicion de la topologia \"\"\"\n t1 = Topologia1()\n ue1 = UnidadExperimental()\n ue1.setTopo(t1)\n ue1.definirNodosClaves(A = 'h1', C='h2', V='h3') # Caso solo para trafico normal\n ue1.setController('ryu', 'simple_switch_13.py,ofctl_rest.py')\n info(\"Configuracion del experimento\")\n \"\"\" 3. Confiracion del experimento \"\"\"\n exp1 = Experimento()\n exp1.configureParams(ue1)\n exp1.configurarTrafico('ataque')\n \"\"\" 4. Inicio del experimento \"\"\"\n exp1.startTest()\n exp1.pingAllTest() # **************** Parece que es necesario que se de un arranque al controlador\n # **************** para que aprenda las reglas antes del ataque.\n\n \"\"\" 5. Aplicacion de pruebas \"\"\"\n exp1.trafico.pingMeasure()\n #exp1.trafico.pingMeasure(filename='ping_ataque_test.log')\n \"\"\" 6. Fin del experimento \"\"\"\n exp1.endTest()\n info(\"Removiendo la topologia\\n\")\n exp1.killTest()\n info(\"Removiendo el controlador\\n\")\n exp1.killController() # Si no se pone no se finaliza el controlador","def test_IODimensions(self):\n tasks = [(1,1,100,True),(10,1,100,True),(1,10,100,True),(10,10,100,True),\n (1,1,100,False),(10,1,100,False),(1,10,100,False),(10,10,100,False)]\n for t in tasks:\n N_in ,N_out, N_samples, tf = t\n X = np.random.randn(N_samples,N_in) if N_in > 1 else np.random.randn(N_samples)\n y = np.random.randn(N_samples,N_out) if N_out > 1 else np.random.randn(N_samples)\n Xp = np.random.randn(N_samples,N_in) if N_in > 1 else np.random.randn(N_samples)\n esn = ESN(N_in,N_out,teacher_forcing=tf)\n prediction_tr = esn.fit(X,y)\n prediction_t = esn.predict(Xp)\n self.assertEqual(prediction_tr.shape,(N_samples,N_out))\n self.assertEqual(prediction_t.shape,(N_samples,N_out))","def gen_ep_data(self,ntrials,trlen):\n self.resort_emat()\n ## instruction\n # for each trial, generate random instruction encoding sequence\n i_encoding_input = np.array([\n np.random.permutation(np.arange(1,self.nmaps+1)) \n for i in range(ntrials)\n ])\n i_test_input = np.zeros([ntrials,trlen])\n i_input = np.concatenate([\n i_encoding_input,i_test_input],\n 1).astype(int).reshape(-1) # (ntrials,trlen+)\n ## stimulus\n x_encoding_input = i_encoding_input\n x_test_input = np.random.randint(1,self.nmaps+1,[ntrials,trlen])\n x_input = np.concatenate([x_encoding_input,x_test_input],1)\n # print('X',x_input)\n ''' \n embed x_input: \n [ntrials,nmaps+trlen] -> s_input [ntrials*(nmaps+trlen),edim]\n explicit loop required for flatten and embedd x_input\n because if switchmaps=1, matrix is resorted between trials\n and therefore same stimulus token integers correspond to\n different stimulus embeddings on different trials\n '''\n s_input = -np.ones([ntrials,(self.nmaps+trlen),self.stimdim])\n for trialn,x_input_trial in enumerate(x_input): \n if self.switchmaps: self.resort_emat()\n s_input[trialn] = self.emat[x_input_trial]\n # format output\n i_input = tr.unsqueeze(tr.LongTensor(i_input),1)\n s_input = tr.unsqueeze(tr.Tensor(np.concatenate(s_input)),1)\n yseq = tr.unsqueeze(tr.LongTensor(x_input.reshape(-1)),1)\n return i_input,s_input,yseq","def test_qpu_0_shots():\n _aws_device(wires=2, shots=0)","def test_data():\n batch_size = 10\n input_dim = 28\n test_data = np.random.rand(batch_size, input_dim)\n\n return test_data","def test_get_node_sensors(self):\n pass","def Test(self):\n print('Testing:')\n # set mode eval\n torch.cuda.empty_cache()\n self.network.eval()\n transform = transforms.Compose([Rescale(params.rescale_size),\n RandomCrop(params.image_size),\n \n ToTensor()\n ])\n dataset = Cityscapes(params.dataset_root, mode='test', transforms = transform)\n test_loader = DataLoader(dataset,\n batch_size=params.test_batch,\n shuffle=params.shuffle,\n num_workers=params.dataloader_workers)\n # prepare test data\n recal = 0\n precision = 0\n F_one = 0\n IOU = 0\n accuracy_new = 0\n test_size = 1124\n if test_size % self.params.test_batch != 0:\n total_batch = test_size // self.params.test_batch + 1\n else:\n total_batch = test_size // self.params.test_batch\n\n # test for one epoch\n for batch_idx, batch in enumerate(test_loader):\n self.pb.click(batch_idx, total_batch)\n image, label, name = batch['image'], batch['label'], batch['label_name']\n image_cuda, label_cuda = image.cuda(), label.cuda()\n pred = image_cuda\n pred = pred.to(torch.device(\"cpu\"))\n pred = pred.detach()\n img_grid = pred[0]\n #img_grid = torchvision.utils.make_grid(out) \n img_grid = img_grid.numpy().transpose(1, 2, 0)*255\n cv2.imwrite(\"/content/drive/My Drive/Test_images/original%d.jpg\" % batch_idx, img_grid)\n if self.params.should_split:\n image_cuda.requires_grad_()\n out = checkpoint_sequential(self.network, self.params.split, image_cuda)\n else:\n out = self.network(image_cuda)\n TP, FP, TN, FN = confusion(out, label_cuda)\n recal = recal+TP\n precision = precision+FP\n F_one = F_one +TN\n IOU = IOU+ FN \n _,predict = torch.max(out.data,1)\n predict = predict.to(torch.device(\"cpu\"))\n predict = predict.detach()\n img = predict[0]\n img = img.numpy()*255\n #img_grid = torchvision.utils.make_grid(out) \n cv2.imwrite(\"/content/drive/My Drive/Test_images/predict_label%d.png\" % batch_idx, img)\n label = label_cuda.to(torch.device(\"cpu\"))\n label = label.detach()\n label = label[0].numpy()*255\n cv2.imwrite(\"/content/drive/My Drive/Test_images/original_label%d.png\" % batch_idx, label)\n\n accuracy_final = accuracy(out, label_cuda)\n accuracy_new = accuracy_new + accuracy_final\n print(\"\\t\")\n print(recal/total_batch, precision/ total_batch, F_one/ total_batch, IOU/ total_batch)\n print(\"\\t\")\n print(accuracy_new/total_batch)","def _sample(self, rnn_output, temperature):\n pass","def test_teleport(self):\n self.log.info('test_teleport')\n pi = np.pi\n shots = 2000\n qr = QuantumRegister(3, 'qr')\n cr0 = ClassicalRegister(1, 'cr0')\n cr1 = ClassicalRegister(1, 'cr1')\n cr2 = ClassicalRegister(1, 'cr2')\n circuit = QuantumCircuit(qr, cr0, cr1, cr2, name='teleport')\n circuit.h(qr[1])\n circuit.cx(qr[1], qr[2])\n circuit.ry(pi/4, qr[0])\n circuit.cx(qr[0], qr[1])\n circuit.h(qr[0])\n circuit.barrier(qr)\n circuit.measure(qr[0], cr0[0])\n circuit.measure(qr[1], cr1[0])\n circuit.z(qr[2]).c_if(cr0, 1)\n circuit.x(qr[2]).c_if(cr1, 1)\n circuit.measure(qr[2], cr2[0])\n job = execute(circuit, backend=self.backend, shots=shots, seed_simulator=self.seed)\n results = job.result()\n data = results.get_counts('teleport')\n alice = {\n '00': data['0 0 0'] + data['1 0 0'],\n '01': data['0 1 0'] + data['1 1 0'],\n '10': data['0 0 1'] + data['1 0 1'],\n '11': data['0 1 1'] + data['1 1 1']\n }\n bob = {\n '0': data['0 0 0'] + data['0 1 0'] + data['0 0 1'] + data['0 1 1'],\n '1': data['1 0 0'] + data['1 1 0'] + data['1 0 1'] + data['1 1 1']\n }\n self.log.info('test_teleport: circuit:')\n self.log.info(circuit.qasm())\n self.log.info('test_teleport: data %s', data)\n self.log.info('test_teleport: alice %s', alice)\n self.log.info('test_teleport: bob %s', bob)\n alice_ratio = 1/np.tan(pi/8)**2\n bob_ratio = bob['0']/float(bob['1'])\n error = abs(alice_ratio - bob_ratio) / alice_ratio\n self.log.info('test_teleport: relative error = %s', error)\n self.assertLess(error, 0.05)","def testOutputs(self):\n # Remember original (correct) example outputs\n old_files = self.read_outputs()\n\n # Set up and run Xanthos\n ini = 'example/pm_abcd_mrtm.ini'\n xth = Xanthos(ini)\n res = xth.execute()\n\n # Check result dimensions\n self.assertEqual(res.Q.shape, (67420, 372))\n\n # Test that new outputs equal old outputs.\n new_files = self.read_outputs()\n for k in new_files.keys():\n pd.testing.assert_frame_equal(new_files[k], old_files[k])","def simulatesensor_mqtt_device_demo(args):\n # [START iot_mqtt_run]\n global minimum_backoff_time\n global MAXIMUM_BACKOFF_TIME\n\n # Publish to the events or state topic based on the flag.\n sub_topic = 'events' if args.message_type == 'event' else 'state'\n\n mqtt_topic = '/devices/{}/{}'.format(args.device_id, sub_topic)\n\n jwt_iat = datetime.datetime.utcnow()\n jwt_exp_mins = args.jwt_expires_minutes\n client = get_client(\n args.project_id, args.cloud_region, args.registry_id,\n args.device_id, args.private_key_file, args.algorithm,\n args.ca_certs, args.mqtt_bridge_hostname, args.mqtt_bridge_port)\n\n # Publish num_messages messages to the MQTT bridge once per second.\n for i in range(1, args.num_messages + 1):\n client.loop()\n\n currentTime = datetime.datetime.utcnow().strftime('%Y-%m-%d %H:%M:%S')\n (ram_usage, cpu_usage, number_of_cores, number_of_processes, battery_percentage) = read_sensor(i)\n\n payloadJSON = createJSON(args.registry_id, args.device_id, currentTime, ram_usage, cpu_usage, number_of_cores, number_of_processes, battery_percentage)\n\n #payload = '{}/{}-image-{}'.format(args.registry_id, args.device_id, i)\n print('Publishing message {}/: \\'{}\\''.format(\n i, payloadJSON))\n\n # Publish \"payload\" to the MQTT topic. qos=1 means at least once\n # delivery. Cloud IoT Core also supports qos=0 for at most once\n # delivery.\n client.publish(mqtt_topic, payloadJSON, qos=1)\n\n \n # Send events every second. State should not be updated as often\n time.sleep(1)","def test_create_device(self):\n pass","def test_create_device(self):\n pass","def get_data(datapath,n=10):\n device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n input_path = datapath + \"/maze_data_test_59/inputs.npy\"\n target_path = datapath + \"/maze_data_test_59/solutions.npy\"\n data = np.load(input_path)\n target = np.load(target_path)\n a = data[:n]\n a = torch.from_numpy(a)\n input = a.to(device, dtype=torch.float)\n b = target[:n]\n t = torch.from_numpy(b)\n t = t.to(device, dtype=torch.float)\n target = t\n return input, target","def test_mlp():\r\n datasets = gen_data()\r\n\r\n train_set_x, train_set_y = datasets[0]\r\n valid_set_x, valid_set_y = datasets[1]\r\n test_set_x , test_set_y = datasets[2]\r\n\r\n\r\n\r\n batch_size = 100 # size of the minibatch\r\n\r\n # compute number of minibatches for training, validation and testing\r\n n_train_batches = train_set_x.get_value(borrow=True).shape[0] / batch_size\r\n n_valid_batches = valid_set_x.get_value(borrow=True).shape[0] / batch_size\r\n n_test_batches = test_set_x.get_value(borrow=True).shape[0] / batch_size\r\n\r\n ######################\r\n # BUILD ACTUAL MODEL #\r\n ######################\r\n #print '... building the model'\r\n\r\n # allocate symbolic variables for the data\r\n index = T.lscalar() # index to a [mini]batch\r\n x = T.matrix('x') # the data is presented as rasterized images\r\n y = T.ivector('y') # the labels are presented as 1D vector of\r\n # [int] labels\r\n\r\n rng = numpy.random.RandomState(1234)\r\n\r\n # construct the MLP class\r\n classifier = MLP( rng = rng, input=x, n_in=28*28, n_hidden = 500, n_out=10)\r\n\r\n # the cost we minimize during training is the negative log likelihood of\r\n # the model.\r\n # We take the mean of the cost over each minibatch.\r\n cost = classifier.negative_log_likelihood(y).mean()\r\n\r\n # compute the gradient of cost with respect to theta (stored in params)\r\n # the resulting gradients will be stored in a list gparams\r\n gparams = []\r\n for param in classifier.params:\r\n gparam = T.grad(cost, param)\r\n gparams.append(gparam)\r\n\r\n # Some optimizations needed are tagged with 'fast_run'\r\n # TODO: refine that and include only those\r\n mode = theano.compile.get_default_mode().including('fast_run')\r\n\r\n updates2 = OrderedDict()\r\n\r\n updates2[classifier.hiddenLayer.params[0]]=T.grad(cost,classifier.hiddenLayer.params[0])\r\n train_model =theano.function( inputs = [index],\r\n updates = updates2,\r\n givens={\r\n x:train_set_x[index*batch_size:(index+1)*batch_size],\r\n y:train_set_y[index*batch_size:(index+1)*batch_size]},\r\n mode=mode)\r\n #print 'MODEL 1'\r\n #theano.printing.debugprint(train_model, print_type=True)\r\n assert any([isinstance(i.op,T.nnet.CrossentropySoftmax1HotWithBiasDx) for i in train_model.maker.fgraph.toposort()])\r\n\r\n # Even without FeatureShape\r\n train_model =theano.function( inputs = [index],\r\n updates = updates2,\r\n mode=mode.excluding('ShapeOpt'),\r\n givens={\r\n x:train_set_x[index*batch_size:(index+1)*batch_size],\r\n y:train_set_y[index*batch_size:(index+1)*batch_size]})\r\n #print\r\n #print 'MODEL 2'\r\n #theano.printing.debugprint(train_model, print_type=True)\r\n assert any([isinstance(i.op,T.nnet.CrossentropySoftmax1HotWithBiasDx) for i in train_model.maker.fgraph.toposort()])","def test():\n parser = argparse.ArgumentParser()\n parser.add_argument('-n', '--name', type=str, help='name of the model',\n default='model_new_o')\n parser.add_argument('-f', '--filename', type=str,\n help='name of the dataset (.h5 file)', default='./dataset.h5')\n parser.add_argument('-bs', '--batch-size', type=int,\n help='size of the batches of the training data', default=256)\n args = parser.parse_args()\n\n name = args.name\n filename = args.filename\n batch_size = args.batch_size\n\n out_channels = 400\n model_path = './model/' + name\n checkpoint_path = model_path + '/checkpoints'\n device = 'cuda' if torch.cuda.is_available() else 'cpu'\n\n for k, v in vars(args).items():\n print('{0} = \"{1}\"'.format(k, v))\n print('device = \"' + device + '\"')\n\n if not os.path.exists(checkpoint_path):\n print('Model parameters not found: ' + checkpoint_path)\n exit()\n\n # Dataset\n\n input_cols = ['camera', 'pos_x', 'pos_y', 'theta']\n target_cols = ['target_map']\n train_test_split = 11\n\n dataset = get_dataset(filename, device=device, augment=False,\n input_cols=input_cols, target_cols=target_cols)\n split_index = dataset.cumulative_sizes[train_test_split]\n\n # Model\n\n device = 'cuda' if torch.cuda.is_available() else 'cpu'\n model = NN(in_channels=3, out_channels=out_channels).to(device)\n model.load_state_dict(torch.load(checkpoint_path + '/best.pth'))\n summary(model, (3, 64, 80), device=device)\n\n auc_function = MaskedAUROC()\n\n # Testing\n\n aucs = []\n for x, px, py, pt, y in dataset.batches(batch_size, start=split_index, shuffle=False):\n pose = torch.stack([px, py, pt], dim=-1).to(device)\n mask = y > -1\n\n preds = model(x)\n\n aucs.append(auc_function(preds, y, mask).cpu().numpy())\n\n auc = np.nanmean(aucs, axis=0).reshape(20, 20)\n auc = np.rot90(auc, 1)\n auc = np.fliplr(auc) * 100\n\n print('AUC: ' + str(auc.mean().item()))\n\n print(auc)\n\n rounded = (100 * coords).round(2).astype(int)\n fig, ax = plt.subplots(nrows=2, ncols=1, figsize=(7, 5.8))\n sns.distplot(auc, bins=int(np.ceil(auc.max() - auc.min())),\n ax=ax[0], kde=False, rug=False, color='red', hist_kws={'rwidth': 0.75})\n sns.heatmap(auc, cmap='gray', annot=True, cbar_kws={'shrink': .8},\n vmin=50, vmax=100, linewidths=0, ax=ax[1])\n plt.yticks(.5 + np.arange(20), np.unique(rounded[:, 0])[::-1])\n plt.xticks(.5 + np.arange(20), np.unique(rounded[:, 1]))\n plt.xlabel('Y [cm]')\n plt.ylabel('X [cm]')\n plt.setp(ax[1].xaxis.get_majorticklabels(), rotation=0)\n plt.setp(ax[1].yaxis.get_majorticklabels(), rotation=0)\n plt.axis('equal')\n plt.tight_layout()\n plt.show()","def test_full_house_flush_ind(self):","def test_batch(self):\n pass","def test_op(self):\n name = \"my_mesh\"\n tensor_data = test_utils.get_random_mesh(\n 100, add_faces=True, add_colors=True\n )\n config_dict = {\"foo\": 1}\n with tf.compat.v1.Graph().as_default():\n tensor_summary = summary.op(\n name,\n tensor_data.vertices,\n faces=tensor_data.faces,\n colors=tensor_data.colors,\n config_dict=config_dict,\n )\n with self.test_session() as sess:\n proto = self.pb_via_op(tensor_summary)\n self.verify_proto(proto, name)\n plugin_metadata = metadata.parse_plugin_metadata(\n proto.value[0].metadata.plugin_data.content\n )\n self.assertEqual(\n json.dumps(config_dict, sort_keys=True),\n plugin_metadata.json_config,\n )","def test_auto_transfer_correct_device(ray_start_4_cpus_2_gpus):\n import nvidia_smi\n\n nvidia_smi.nvmlInit()\n\n def get_gpu_used_mem(i):\n handle = nvidia_smi.nvmlDeviceGetHandleByIndex(i)\n info = nvidia_smi.nvmlDeviceGetMemoryInfo(handle)\n return info.used\n\n start_gpu_memory = get_gpu_used_mem(1)\n\n device = torch.device(\"cuda:1\")\n small_dataloader = [(torch.randn((1024 * 4, 1024 * 4)),) for _ in range(10)]\n wrapped_dataloader = ( # noqa: F841\n ray.train.torch.train_loop_utils._WrappedDataLoader(\n small_dataloader, device, True\n )\n )\n\n end_gpu_memory = get_gpu_used_mem(1)\n\n # Verify GPU memory usage increases on the right cuda device\n assert end_gpu_memory > start_gpu_memory","def test_output(data,idx,model):\n x,y = data[idx]\n out = model(x)\n return y.data.cpu().numpy(), out.data.cpu().numpy()","def gen_ep_data(self,ntrials,trlen):\n ## instruction\n # for each trial, generate random instruction encoding sequence\n i_encoding_input = np.array([\n np.random.permutation(np.arange(1,self.nmaps+1)) \n for i in range(ntrials)\n ])\n i_test_input = np.zeros([ntrials,trlen])\n i_input = np.concatenate([\n i_encoding_input,i_test_input],\n 1).astype(int).reshape(-1) # (ntrials,trlen+)\n ## stimulus\n x_encoding_input = i_encoding_input\n x_test_input = np.random.randint(1,self.nmaps+1,[ntrials,trlen])\n x_input = np.concatenate([i_encoding_input,x_test_input],1)\n ''' \n embed x_input: \n [ntrials,nmaps+trlen] -> s_input [ntrials*(nmaps+trlen),edim]\n explicit loop required for flatten and embedd x_input\n because if switchmaps=1, matrix is resorted between trials\n and therefore same stimulus token integers correspond to\n different stimulus embeddings on different trials\n '''\n s_input = -np.ones([ntrials,(self.nmaps+trlen),self.stimdim])\n for trialn,x_input_trial in enumerate(x_input): \n if self.switchmaps: self.resort_emat()\n s_input[trialn] = self.emat[x_input_trial]\n \n # format output\n i_input = tr.unsqueeze(tr.LongTensor(i_input),1)\n s_input = tr.unsqueeze(tr.Tensor(np.concatenate(s_input)),1)\n yseq = tr.unsqueeze(tr.LongTensor(x_input.reshape(-1)),1)\n if return_trial_flag:\n tr_flag = np.concatenate([i*np.ones(self.nmaps+trlen) for i in range(ntrials)])\n tr_flag = tr.unsqueeze(tr.LongTensor(tr_flag),1)\n return tr_flag,i_input,s_input,yseq,\n else:\n return i_input,s_input,yseq","def test_get_device_template(self):\n pass","def test_tiff_io_tensor():\n width = 560\n height = 320\n channels = 4\n\n images = []\n for filename in [\n \"small-00.png\",\n \"small-01.png\",\n \"small-02.png\",\n \"small-03.png\",\n \"small-04.png\"]:\n with open(\n os.path.join(os.path.dirname(os.path.abspath(__file__)),\n \"test_image\",\n filename), 'rb') as f:\n png_contents = f.read()\n image_v = tf.image.decode_png(png_contents, channels=channels)\n assert image_v.shape == [height, width, channels]\n images.append(image_v)\n\n filename = os.path.join(\n os.path.dirname(os.path.abspath(__file__)), \"test_image\", \"small.tiff\")\n filename = \"file://\" + filename\n\n tiff = tfio.IOTensor.from_tiff(filename)\n assert tiff.keys == list(range(5))\n for i in tiff.keys:\n assert np.all(images[i].numpy() == tiff(i).to_tensor().numpy())","def test_get_device(self):\n pass","def test_get_device(self):\n pass","def test(self):\n # Load the trained models.\n self.train_ev_ea()\n self.restore_model(self.test_iters)\n self.encoder_v.eval()\n # Set data loader.\n data_loader = self.data_loader\n empty = torch.FloatTensor(1, 3,self.image_size,self.image_size).to(self.device) \n empty.fill_(1)\n noise = torch.FloatTensor(self.batch_size, self.nz_num)\n noise = noise.to(self.device)\n step = 0\n data_loader.test.reinitialize_index()\n with torch.no_grad():\n while True:\n try:\n x_real, wrong_images, attributes, _, label_org = data_loader.test.next_batch_test(self.batch_size,10)\n except:\n break\n x_real = x_real.to(self.device) \n label_org = label_org.to(self.device)\n attributes = attributes.to(self.device)\n \n \n ev_x = self.encoder_v(x_real)\n noise.normal_(0, 1)\n ea_a = self.encoder_a(attributes, noise)\n \n out_A2B_results = [empty]\n out_A2B_results_a = [empty]\n\n for idx1 in range(label_org.size(0)):\n out_A2B_results.append(x_real[idx1:idx1+1])\n out_A2B_results_a.append(x_real[idx1:idx1+1])\n\n for idx2 in range(label_org.size(0)):\n out_A2B_results.append(x_real[idx2:idx2+1])\n out_A2B_results_a.append(x_real[idx2:idx2+1])\n \n for idx1 in range(label_org.size(0)):\n x_fake = self.decoder(self.encoder(x_real[idx2:idx2+1]), ev_x[idx1:idx1+1])\n out_A2B_results.append(x_fake)\n \n x_fake_a = self.decoder(self.encoder(x_real[idx2:idx2+1]), ea_a[idx1:idx1+1])\n out_A2B_results_a.append(x_fake_a)\n results_concat = torch.cat(out_A2B_results)\n x_AB_results_path = os.path.join(self.result_dir, '{}_x_AB_results_test_v.jpg'.format(step+1)) \n save_image(self.denorm(results_concat.data.cpu()), x_AB_results_path, nrow=label_org.size(0)+1,padding=0)\n print('Saved real and fake images into {}...'.format(x_AB_results_path))\n \n results_concat = torch.cat(out_A2B_results_a)\n x_AB_results_path = os.path.join(self.result_dir, '{}_x_AB_results_test_a.jpg'.format(step+1)) \n save_image(self.denorm(results_concat.data.cpu()), x_AB_results_path, nrow=label_org.size(0)+1,padding=0)\n print('Saved real and fake images into {}...'.format(x_AB_results_path))\n \n step += 1","def import_measurements():\n\n print('Receive a transfer...')","def main():\n \"\"\"\n This is just for testing the functions\n \"\"\"\n\n x1 = np.array([1, 1, 1, 1, -1, -1, 1, 1, 1])\n x2 = np.array([1, -1, 1, 1, 1, 1, 1, -1, 1])\n x3 = np.array([-1, 1, -1, -1, 1, -1, -1, 1, -1])\n train_set = np.vstack((x1, x2))\n train_set = np.vstack((train_set, x3))\n\n\n params = {\n \"epochs\": 100,\n \"neurons\": len(x1),\n \"learn_method\": 'classic'\n }\n\n hop = hop_net.HopfieldNet(train_set, **params)\n hop.batch_train()\n show_trained(train_set)\n\n x4d = [1,1,1,1,1,1,1,1,1]\n x5d = [1,1,1,1,-1,-1,1,-1,-1]\n x45d = np.vstack((x4d, x5d))\n test_set = np.vstack((x45d, train_set))\n recalled_set = hop.recall(test_set)\n for i in range(test_set.shape[0]):\n show_tested(test_set[i], recalled_set[i])","def test_write_broadcast(setup_teardown_file):\n f = setup_teardown_file[3]\n\n dt = np.dtype('(3,)i')\n\n dset = f.create_dataset('x', (10,), dtype=dt)\n dset[...] = 42","def simple_unet_data():\n return tf.constant(value=1.0, shape=(1, 256, 256, 1))","def test_gpu_memory(self):\n m = pyflamegpu.ModelDescription(\"test_gpu_memory_test\")\n a = m.newAgent(\"agent\")\n a.newVariableInt(\"id\")\n p = pyflamegpu.AgentVector(a, AGENT_COUNT)\n for i in range(AGENT_COUNT):\n instance = p[i]\n instance.setVariableInt(\"id\", i)\n cm = pyflamegpu.CUDASimulation(m)\n # copy to device then back by setting and getting population data\n cm.setPopulationData(p)\n cm.getPopulationData(p)\n # check values are the same\n for i in range(AGENT_COUNT):\n instance = p[i]\n assert instance.getVariableInt(\"id\") == i","def _creatExamplesTensorData(self, examples):\n\n images = []\n \n images2 = []\n images3 = []\n images4 = []\n images5 = [] \n labels = []\n for (img_idx, label) in examples:\n img = self.dataset[img_idx][0]\n #print(img)\n ##exit(0)\n if self.load:\n img = Image.fromarray(img)\n else:\n img = read_image(img)\n #print(img.size)\n #print(np.array(img).shape)\n #exit(0)\n if self.transform is not None:\n img1 = self.transform(img)\n\n img2 = self.transform_test(img)\n img3 = self.transform_test(img)\n img4 = self.transform_test(img)\n img5 = self.transform_test(img) \n #print((img2-img1).abs().sum(),(img3-img1).abs().sum(),(img2-img3).abs().sum())\n #print(img.shape,'located in test_loader.py at 146')\n #exit(0)\n images.append(img1)\n \n images2.append(img2)\n images3.append(img3)\n images4.append(img4)\n images5.append(img5) \n labels.append(label)\n images = torch.stack(images, dim=0)\n\n images2 = torch.stack(images2, dim=0)\n images3 = torch.stack(images3, dim=0)\n images4 = torch.stack(images4, dim=0)\n images5 = torch.stack(images5, dim=0) \n labels = torch.LongTensor(labels)\n return images, images2,images3,images4,images5,labels","def test_flmb(self):\n self.create_sample_data_set_dir(\"node10p1.dat\", TELEM_DIR, \"node59p1.dat\")\n self.assert_initialize()\n result = self.data_subscribers.get_samples(DataParticleType.METADATA_TELEMETERED,1,30)\n result = self.data_subscribers.get_samples(DataParticleType.SAMPLE_TELEMETERED,5,30)","def testSampleOutput(self):\n beam_width = 3\n max_decode_length = 2\n\n smart_compose_model = model.create_smart_compose_model(self.embedding_layer_param, self.empty_url, self.min_len, self.max_len,\n beam_width, max_decode_length, self.feature_type_2_name, self.min_seq_prob,\n self.length_norm_power)\n\n # {'exist_prefix': True,\n # 'predicted_scores': [[-2.7357671, -2.7361841, -2.7503903]] (could vary due to random initialization),\n # 'predicted_texts': [[b'[CLS] build is', b'[CLS] build source', b'[CLS] build token']]}\n print(smart_compose_model.prefix_aware_beam_search({\n InputFtrType.TARGET_COLUMN_NAME: tf.constant(['bui'])\n }))\n # {'exist_prefix': True,\n # 'predicted_scores': [[-2.7357671, -2.7361841, -2.7503903]] (could vary due to random initialization),\n # 'predicted_texts': [[b'[CLS] build is', b'[CLS] build source', b'[CLS] build token']]}\n print(smart_compose_model.prefix_aware_beam_search({\n InputFtrType.TARGET_COLUMN_NAME: tf.constant(['build'])\n }))\n # {'exist_prefix': True,\n # 'predicted_scores': [[-2.7357671, -2.7361841, -2.7503903]] (could vary due to random initialization),\n # 'predicted_texts': [[b'build is [PAD]', b'build source [PAD]', b'build token [PAD]']]}\n print(smart_compose_model.prefix_aware_beam_search({\n InputFtrType.TARGET_COLUMN_NAME: tf.constant(['build '])\n }))\n # {'exist_prefix': True,\n # 'predicted_scores': [[-2.711434 , -2.7171993, -2.7329462]] (could vary due to random initialization),\n # 'predicted_texts': [[b'build function token', b'build function test', b'build function is']]\n print(smart_compose_model.prefix_aware_beam_search({\n InputFtrType.TARGET_COLUMN_NAME: tf.constant(['build f'])\n }))","def test(self):\n img_gen, self.loss_reg, self.parsav = self.net_G(self.input_P1, self.input_P2, self.input_BP1, self.input_BP2, self.input_SPL1, self.input_SPL2)\n ## test flow ##\n\n self.save_results(img_gen, data_name='vis')\n if self.opt.save_input or self.opt.phase == 'val':\n self.save_results(self.input_P1, data_name='ref')\n self.save_results(self.input_P2, data_name='gt')\n result = torch.cat([self.input_P1, img_gen, self.input_P2], 3)\n self.save_results(result, data_name='all')","def test_tensor_network_bell(self):\n circuit = jet.Circuit(num_wires=2)\n circuit.append_gate(jet.GateFactory.create(\"H\"), wire_ids=[0])\n circuit.append_gate(jet.GateFactory.create(\"CNOT\"), wire_ids=[0, 1])\n\n tn = circuit.tensor_network()\n tensor = tn.contract()\n\n assert tensor.indices == [\"0-2\", \"1-1\"]\n assert tensor.shape == [2, 2]\n assert tensor.data == pytest.approx([1 / sqrt(2), 0, 0, 1 / sqrt(2)])","def test_get_node_outputs(self):\n pass","def test_verify_connection_to_a_device():","def test_download_terrascope():\n\n s1_belgium.download(\"sigma0_cube_terrascope.nc\",format=\"NetCDF\")","def main():\n\n # set up output directory and file\n output_file_folder = \"output/{}\".format(args.experiment_name)\n Path(output_file_folder).mkdir(parents=True, exist_ok=True)\n args.output_file_name = \"{}/{}.csv\".format(output_file_folder, args.model_name)\n args.checkpoint_name = \"{}/{}.pt\".format(output_file_folder, args.model_name + \"_best_model\")\n\n # read lcquad merged data\n if args.dataset_name == \"lcquad\":\n df_train = pd.read_csv(\"./data/lcquad/gold_db/train_gold.csv\")\n df_valid = pd.read_csv(\"./data/lcquad/gold_db/valid_gold.csv\")\n df_test = pd.read_csv(\"./data/lcquad/gold_db/lcquad_test_sorted.csv\")\n args.gold_file_name = \"lcquad/lcquad_gt_5000.csv\"\n # elif args.dataset_name == \"qald9\":\n # df_train = pd.read_csv(\"./data/qald-9/train_gold.csv\")\n # df_valid = pd.read_csv(\"./data/qald-9/valid_gold.csv\")\n # df_test = pd.read_csv(\"./data/qald-9/test_gold.csv\")\n # args.gold_file_name = \"qald/qald_data_gt.csv\"\n # elif args.dataset_name == \"webqsp\":\n # df_train = pd.read_csv(\"./data/webqsp/train_gold.csv\")\n # df_valid = pd.read_csv(\"./data/webqsp/valid_gold.csv\")\n # df_test = pd.read_csv(\"./data/webqsp/test_gold.csv\")\n # args.gold_file_name = \"webqsp/webqsp_data_gt.csv\"\n\n train_data = read_data_file(df_train, device, \"train\")\n valid_data = read_data_file(df_valid, device, \"valid\")\n test_data = read_data_file(df_test, device, \"test\")\n\n # train model and evaluate\n if args.model_name == \"pure\":\n model = PureNameLNN(args.alpha, -1, False)\n elif args.model_name == \"ctx\":\n model = None\n elif args.model_name == 'type':\n model = None\n elif args.model_name == \"pure_ctx\":\n model = None\n elif args.model_name == \"pure_type\":\n model = None\n elif args.model_name == \"ctx_type\":\n model = None\n\n model = model.to(device)\n print(\"model: \", args.model_name, args.alpha)\n\n # training\n train(model, train_data, valid_data, test_data, args.checkpoint_name, args.num_epoch, args.margin, args.learning_rate)","def test_compute_glycemic_load(self):\n pass","def test_snow_pumps():\n test_path = tempfile.mkdtemp()\n x_train, metadata = snow_pumps(test_path)\n try:\n assert x_train.shape == (13, 4)\n except:\n shutil.rmtree(test_path)\n raise()","def test_with_matrix_and_pointer(tmp_path):\n trans_man = _create_transform_manager()\n\n out_dir = tmp_path\n pointer_tip = None\n\n pointer_writer = pointer.BardPointerWriter(trans_man, out_dir, pointer_tip)\n\n pointer_writer.write_pointer_tip()\n\n pointer_tip = [0, 0, 100]\n\n pointer_writer = pointer.BardPointerWriter(trans_man, out_dir, pointer_tip)\n\n pointer_writer.write_pointer_tip()","def test_meteo():\n test_path = tempfile.mkdtemp()\n x_train, metadata = meteo(test_path)\n try:\n assert x_train.shape == (11, 6)\n except:\n shutil.rmtree(test_path)\n raise()","def test_chunk_memory(self):\n layer = tl.Serial(tl.Dense(1024*1024), tl.Dense(128))\n chunked = tl.Chunk(layer, 256)\n x = np.random.uniform(size=(16*1024, 16))\n chunked.init(shapes.signature(x))\n y = chunked(x)\n z = tl.Accelerate(chunked)(x)\n self.assertEqual(y.shape, (16*1024, 128))\n self.assertEqual(z.shape, (16*1024, 128))","def test_update_device(self):\n pass","def test_update_device(self):\n pass","def create_mock_transformer_xl_data(\n batch_size,\n num_heads,\n head_size,\n hidden_size,\n seq_length,\n memory_length=0,\n num_predictions=2,\n two_stream=False,\n num_layers=1,\n include_biases=True,\n include_state=False,\n include_mask=False,\n include_segment=False):\n encoding_shape = (batch_size, seq_length * 2, hidden_size)\n\n data = dict(\n relative_position_encoding=tf.random.normal(shape=encoding_shape),\n content_stream=tf.random.normal(\n shape=(batch_size, seq_length, hidden_size)))\n\n if include_biases:\n attention_bias_shape = (num_heads, head_size)\n data.update(dict(\n content_attention_bias=tf.random.normal(shape=attention_bias_shape),\n segment_attention_bias=tf.random.normal(shape=attention_bias_shape),\n positional_attention_bias=tf.random.normal(shape=attention_bias_shape)))\n\n if two_stream:\n data.update(dict(\n query_stream=tf.random.normal(\n shape=(batch_size, num_predictions, hidden_size)),\n target_mapping=tf.random.normal(\n shape=(batch_size, num_predictions, seq_length))))\n\n if include_state:\n total_seq_length = seq_length + memory_length\n if num_layers > 1:\n state_shape = (num_layers, batch_size, memory_length, hidden_size)\n else:\n state_shape = (batch_size, memory_length, hidden_size)\n data.update(dict(\n state=tf.random.normal(shape=state_shape)))\n else:\n total_seq_length = seq_length\n\n if include_mask:\n mask_shape = (batch_size, num_heads, seq_length, total_seq_length)\n mask_data = np.random.randint(2, size=mask_shape).astype(\"float32\")\n data[\"content_attention_mask\"] = mask_data\n if two_stream:\n data[\"query_attention_mask\"] = mask_data\n\n if include_segment:\n # A transformer XL block takes an individual segment \"encoding\" from the\n # entirety of the Transformer XL segment \"embedding\".\n if num_layers > 1:\n segment_encoding_shape = (num_layers, 2, num_heads, head_size)\n segment_encoding_name = \"segment_embedding\"\n else:\n segment_encoding_shape = (2, num_heads, head_size)\n segment_encoding_name = \"segment_encoding\"\n\n segment_matrix = np.random.randint(\n 2, size=(batch_size, seq_length, total_seq_length))\n data[\"segment_matrix\"] = tf.math.equal(segment_matrix, 1)\n data[segment_encoding_name] = tf.random.normal(shape=segment_encoding_shape)\n\n return data","def test_read(self):\n with HTTMock(spark_cloud_mock):\n for v in self.device.variables.keys():\n expected = self.cloud_device.read(v)\n self.assertEqual(self.device.read(v), expected)","def _generate(device, env, qnet, ob_scale,\n number_timesteps, param_noise,\n exploration_fraction, exploration_final_eps,\n atom_num, min_value, max_value):\n noise_scale = 1e-2\n action_dim = env.action_space.n\n explore_steps = number_timesteps * exploration_fraction\n if atom_num > 1:\n vrange = torch.linspace(min_value, max_value, atom_num).to(device)\n\n o = env.reset()\n infos = dict()\n for n in range(1, number_timesteps + 1):\n epsilon = 1.0 - (1.0 - exploration_final_eps) * n / explore_steps\n epsilon = max(exploration_final_eps, epsilon)\n\n # sample action\n with torch.no_grad():\n ob = scale_ob(np.expand_dims(o, 0), device, ob_scale)\n q = qnet(ob)\n if atom_num > 1:\n q = (q.exp() * vrange).sum(2)\n if not param_noise:\n if random.random() < epsilon:\n a = int(random.random() * action_dim)\n else:\n a = q.argmax(1).cpu().numpy()[0]\n else:\n # see Appendix C of `https://arxiv.org/abs/1706.01905`\n q_dict = deepcopy(qnet.state_dict())\n for _, m in qnet.named_modules():\n if isinstance(m, nn.Linear):\n std = torch.empty_like(m.weight).fill_(noise_scale)\n m.weight.data.add_(torch.normal(0, std).to(device))\n std = torch.empty_like(m.bias).fill_(noise_scale)\n m.bias.data.add_(torch.normal(0, std).to(device))\n q_perturb = qnet(ob)\n if atom_num > 1:\n q_perturb = (q_perturb.exp() * vrange).sum(2)\n kl_perturb = ((log_softmax(q, 1) - log_softmax(q_perturb, 1)) *\n softmax(q, 1)).sum(-1).mean()\n kl_explore = -math.log(1 - epsilon + epsilon / action_dim)\n if kl_perturb < kl_explore:\n noise_scale *= 1.01\n else:\n noise_scale /= 1.01\n qnet.load_state_dict(q_dict)\n if random.random() < epsilon:\n a = int(random.random() * action_dim)\n else:\n a = q_perturb.argmax(1).cpu().numpy()[0]\n\n # take action in env\n o_, r, done, info = env.step(a)\n if info.get('episode'):\n infos = {\n 'eplenmean': info['episode']['l'],\n 'eprewmean': info['episode']['r'],\n }\n # return data and update observation\n yield (o, [a], [r], o_, [int(done)], infos)\n infos = dict()\n o = o_ if not done else env.reset()","def test(cfg):\n # Set up environment.\n distributed.init_distributed_training(cfg)\n\n # Set random seed from configs.\n np.random.seed(cfg.RNG_SEED)\n torch.manual_seed(cfg.RNG_SEED)\n\n # Print config.\n if distributed.is_master_proc():\n print(\"Test with config:\")\n print(cfg)\n\n # Build the model and print model statistics.\n # Use cuda if available\n device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n # Construct the model\n model = PanopticNarrativeGroundingBaseline(cfg, device=device)\n # Determine the GPU used by the current process\n cur_device = torch.cuda.current_device()\n # Transfer the model to the current GPU device\n model = model.cuda(device=cur_device)\n if cfg.NUM_GPUS > 1:\n # Make model replica operate on the current device\n model = torch.nn.parallel.DistributedDataParallel(\n module=model, device_ids=[cur_device], output_device=cur_device,\n find_unused_parameters=True\n )\n if cfg.LOG_MODEL_INFO and distributed.is_master_proc():\n print(\"Model:\\n{}\".format(model))\n print(\"Params: {:,}\".format(np.sum([p.numel() for p in model.parameters()]).item()))\n print(\"Mem: {:,} MB\".format(torch.cuda.max_memory_allocated() / 1024 ** 3))\n print(\"nvidia-smi\")\n os.system(\"nvidia-smi\")\n\n # Load a checkpoint to test if applicable.\n checkpoint_path = osp.join(cfg.OUTPUT_DIR, 'model_final.pth')\n if cfg.TEST.CHECKPOINT_FILE_PATH != \"\":\n checkpoint_path = cfg.TEST.CHECKPOINT_FILE_PATH\n if osp.exists(checkpoint_path):\n if distributed.is_master_proc():\n print('Loading model from: {0}'.format(checkpoint_path))\n checkpoint = torch.load(checkpoint_path, map_location=\"cpu\")\n if cfg.NUM_GPUS > 1:\n model.module.load_state_dict(checkpoint['model_state'])\n else:\n model.load_state_dict(checkpoint['model_state'])\n elif cfg.TRAIN.CHECKPOINT_FILE_PATH != \"\":\n # If no checkpoint found in TEST.CHECKPOINT_FILE_PATH or in the current\n # checkpoint folder, try to load checkpoint from\n # TRAIN.CHECKPOINT_FILE_PATH and test it.\n checkpoint_path = cfg.TRAIN.CHECKPOINT_FILE_PATH\n checkpoint = torch.load(checkpoint_path, map_location=\"cpu\")\n if cfg.NUM_GPUS > 1:\n model.module.load_state_dict(checkpoint['model_state'])\n else:\n model.load_state_dict(checkpoint['model_state'])\n else:\n if distributed.is_master_proc():\n print(\"Testing with random initialization. Only for debugging.\")\n\n # Create testing loaders.\n test_dataset = PanopticNarrativeGroundingDataset(cfg, cfg.DATA.VAL_SPLIT, train=False)\n test_loader = DataLoader(\n test_dataset,\n batch_size=int(cfg.TRAIN.BATCH_SIZE / max(1, cfg.NUM_GPUS)),\n shuffle=False,\n sampler=(DistributedSampler(test_dataset) if cfg.NUM_GPUS > 1 else None),\n num_workers=cfg.DATA_LOADER.NUM_WORKERS,\n pin_memory=cfg.DATA_LOADER.PIN_MEMORY\n )\n \n if distributed.is_master_proc():\n print(\"Testing model for {} iterations\".format(len(test_loader)))\n\n # Perform test on the entire dataset.\n perform_test(test_loader, model, cfg)","def get_GP_samples(Y,T,X,ind_kf,ind_kt,num_obs_times,num_obs_values,\n num_tcn_grid_times, cov_grid, input_dim,method, gp_params, lab_vitals_only, pad_before): ##,med_cov_grid \n\n n_mc_smps, M = gp_params.n_mc_smps, gp_params.M\n grid_max = tf.shape(X)[1]\n Z = tf.zeros([0,grid_max,input_dim])\n \n N = tf.shape(T)[0] #number of observations\n \n #setup tf while loop (have to use this bc loop size is variable)\n def cond(i,Z):\n return i<N\n \n def body(i,Z):\n Yi = tf.reshape(tf.slice(Y,[i,0],[1,num_obs_values[i]]),[-1]) #MM: tf.reshape(x, [-1]) flattens tensor x (e.g. [2,3,1] to [6]), slice cuts out all Y data of one patient\n Ti = tf.reshape(tf.slice(T,[i,0],[1,num_obs_times[i]]),[-1])\n ind_kfi = tf.reshape(tf.slice(ind_kf,[i,0],[1,num_obs_values[i]]),[-1])\n ind_kti = tf.reshape(tf.slice(ind_kt,[i,0],[1,num_obs_values[i]]),[-1])\n Xi = tf.reshape(tf.slice(X,[i,0],[1,num_tcn_grid_times[i]]),[-1])\n X_len = num_tcn_grid_times[i]\n \n GP_draws = draw_GP(Yi,Ti,Xi,ind_kfi,ind_kti,method=method, gp_params=gp_params)\n pad_len = grid_max-X_len #pad by this much\n #padding direction:\n if pad_before:\n print('Padding GP_draws before observed data..')\n padded_GP_draws = tf.concat([tf.zeros((n_mc_smps,pad_len,M)), GP_draws],1) \n else:\n padded_GP_draws = tf.concat([GP_draws,tf.zeros((n_mc_smps,pad_len,M))],1) \n\n if lab_vitals_only:\n Z = tf.concat([Z,padded_GP_draws],0) #without covs\n else: #with covs\n medcovs = tf.slice(cov_grid,[i,0,0],[1,-1,-1])\n tiled_medcovs = tf.tile(medcovs,[n_mc_smps,1,1])\n padded_GPdraws_medcovs = tf.concat([padded_GP_draws,tiled_medcovs],2)\n Z = tf.concat([Z,padded_GPdraws_medcovs],0) #with covs\n \n return i+1,Z \n \n i = tf.constant(0)\n #with tf.control_dependencies([tf.Print(tf.shape(ind_kf), [tf.shape(ind_kf), tf.shape(ind_kt), num_obs_values], 'ind_kf & ind_kt & num_obs_values')]):\n i,Z = tf.while_loop(cond,body,loop_vars=[i,Z],\n shape_invariants=[i.get_shape(),tf.TensorShape([None,None,None])])\n\n return Z","def unit_test():\n test_net = SqueezeNetSqueezeLSTM(20, 6)\n test_net_output = test_net(\n Variable(torch.randn(5, 36, 94, 168)),\n Variable(torch.randn(5, 8, 23, 41)))\n logging.debug('Net Test Output = {}'.format(test_net_output))\n logging.debug('Network was Unit Tested')\n print(test_net.num_params())\n # for param in test_net.parameters():","def test_write(self):\n dset = self.f.create_dataset('x2', (10, 2))\n\n x = np.zeros((10, 1))\n dset[:, 0] = x[:, 0]\n with self.assertRaises(TypeError):\n dset[:, 1] = x","def test_input_data():\n mp = mapchete.open(\n os.path.join(SCRIPTDIR, \"testdata/cleantopo_br.mapchete\"))\n tp = BufferedTilePyramid(\"geodetic\")\n # TODO tile with existing but empty data\n tile = tp.tile(5, 5, 5)\n output_params = dict(\n type=\"geodetic\",\n format=\"GeoTIFF\",\n path=OUT_DIR,\n pixelbuffer=0,\n metatiling=1,\n bands=2,\n dtype=\"int16\"\n )\n output = gtiff.OutputData(output_params)\n with output.open(tile, mp, resampling=\"nearest\") as input_tile:\n assert input_tile.resampling == \"nearest\"\n for data in [\n input_tile.read(), input_tile.read(1), input_tile.read([1]),\n # TODO assert valid indexes are passed input_tile.read([1, 2])\n ]:\n assert isinstance(data, ma.masked_array)\n assert input_tile.is_empty()\n # open without resampling\n with output.open(tile, mp) as input_tile:\n pass","def test():\n args = parse_args()\n\n devid = int(os.getenv('DEVICE_ID')) if os.getenv('DEVICE_ID') else 0\n context.set_context(mode=context.GRAPH_MODE, device_target='Ascend', save_graphs=True, device_id=devid)\n\n # logger\n args.outputs_dir = os.path.join(args.log_path,\n datetime.datetime.now().strftime('%Y-%m-%d_time_%H_%M_%S'))\n rank_id = int(os.environ.get('RANK_ID')) if os.environ.get('RANK_ID') else 0\n args.logger = get_logger(args.outputs_dir, rank_id)\n\n context.reset_auto_parallel_context()\n parallel_mode = ParallelMode.STAND_ALONE\n context.set_auto_parallel_context(parallel_mode=parallel_mode, gradients_mean=True, device_num=1)\n\n args.logger.info('Creating Network....')\n network = SolveOutput(YOLOV3DarkNet53(is_training=False))\n\n data_root = args.data_root\n ann_file = args.annFile\n\n args.logger.info(args.pretrained)\n if os.path.isfile(args.pretrained):\n param_dict = load_checkpoint(args.pretrained)\n param_dict_new = {}\n for key, values in param_dict.items():\n if key.startswith('moments.'):\n continue\n elif key.startswith('yolo_network.'):\n param_dict_new[key[13:]] = values\n else:\n param_dict_new[key] = values\n load_param_into_net(network, param_dict_new)\n args.logger.info('load_model {} success'.format(args.pretrained))\n else:\n args.logger.info('{} not exists or not a pre-trained file'.format(args.pretrained))\n assert FileNotFoundError('{} not exists or not a pre-trained file'.format(args.pretrained))\n exit(1)\n\n config = ConfigYOLOV3DarkNet53()\n if args.testing_shape:\n config.test_img_shape = conver_testing_shape(args)\n\n ds, data_size = create_yolo_dataset(data_root, ann_file, is_training=False, batch_size=1,\n max_epoch=1, device_num=1, rank=rank_id, shuffle=False,\n config=config)\n\n args.logger.info('testing shape : {}'.format(config.test_img_shape))\n args.logger.info('totol {} images to eval'.format(data_size))\n\n network.set_train(False)\n # build attacker\n attack = DeepFool(network, num_classes=80, model_type='detection', reserve_ratio=0.9, bounds=(0, 1))\n input_shape = Tensor(tuple(config.test_img_shape), ms.float32)\n\n args.logger.info('Start inference....')\n batch_num = args.samples_num\n adv_example = []\n for i, data in enumerate(ds.create_dict_iterator(num_epochs=1)):\n if i >= batch_num:\n break\n image = data[\"image\"]\n image_shape = data[\"image_shape\"]\n\n gt_boxes, gt_logits = network(image, input_shape)\n gt_boxes, gt_logits = gt_boxes.asnumpy(), gt_logits.asnumpy()\n gt_labels = np.argmax(gt_logits, axis=2)\n\n adv_img = attack.generate((image.asnumpy(), image_shape.asnumpy()), (gt_boxes, gt_labels))\n adv_example.append(adv_img)\n np.save('adv_example.npy', adv_example)","def model_and_data(request, hyperparams, estep_conf):\n if tvo.get_run_policy() == \"mpi\":\n init_processes()\n\n precision, N, D, H, batch_size = get(hyperparams, \"precision\", \"N\", \"D\", \"H\", \"batch_size\")\n\n if request.param == \"BSC\":\n W_gt = generate_bars(H, bar_amp=10.0, precision=precision)\n sigma2_gt = to.ones((1,), dtype=precision, device=tvo.get_device())\n pies_gt = to.full((H,), 2.0 / H, dtype=precision, device=tvo.get_device())\n\n to.manual_seed(999)\n W_init = to.rand((D, H), dtype=precision)\n W_init = W_init.to(device=tvo.get_device())\n broadcast(W_init)\n\n sigma2_init = to.tensor([1.0], dtype=precision, device=tvo.get_device())\n pies_init = to.full((H,), 1.0 / H, dtype=precision, device=tvo.get_device())\n\n model = BSC(\n H=H, D=D, W_init=W_gt, sigma2_init=sigma2_gt, pies_init=pies_gt, precision=precision\n )\n\n fname = \"bars_test_data_bsc.h5\"\n\n write_dataset(fname, N, D, np.float32, model)\n\n model.theta[\"W\"] = W_init\n model.theta[\"sigma2\"] = sigma2_init\n model.theta[\"pies\"] = pies_init\n\n elif request.param == \"NoisyOR\":\n W_gt = generate_bars(H, bar_amp=0.8, bg_amp=0.1, precision=precision)\n pies_gt = to.full((H,), 2.0 / H, dtype=precision, device=tvo.get_device())\n\n to.manual_seed(999)\n W_init = to.rand((D, H), dtype=precision)\n W_init = W_init.to(device=tvo.get_device())\n broadcast(W_init)\n pies_init = to.full((H,), 1.0 / H, dtype=precision, device=tvo.get_device())\n\n model = NoisyOR(H=H, D=D, W_init=W_gt, pi_init=pies_gt, precision=precision)\n\n fname = \"bars_test_data_nor.h5\"\n\n write_dataset(fname, N, D, np.uint8, model)\n\n model.theta[\"W\"] = W_init\n model.theta[\"pies\"] = pies_init\n\n if tvo.get_run_policy() == \"mpi\":\n dist.barrier()\n\n return model, fname","def test_deviceX_1():\n assert 0","def test(batch_size=1, num_sample=16):\n return paddle.batch(_read_creater(num_sample=num_sample), batch_size)","def test_forfatal_functions(self):\n logging.getLogger(\"tensorflow\").setLevel(logging.ERROR)\n logging.getLogger(\"batchglm\").setLevel(logging.WARNING)\n logging.getLogger(\"diffxpy\").setLevel(logging.WARNING)\n\n num_observations = 10\n num_features = 2\n\n sim = Simulator(num_observations=num_observations, num_features=num_features)\n sim.generate_sample_description(num_batches=0, num_conditions=0)\n sim.generate()\n\n random_sample_description = pd.DataFrame({\n \"pseudotime\": np.random.random(size=sim.nobs),\n \"batch\": np.random.randint(2, size=sim.nobs)\n })\n\n test = de.test.continuous_1d(\n data=sim.X,\n continuous=\"pseudotime\",\n df=3,\n formula_loc=\"~ 1 + pseudotime + batch\",\n formula_scale=\"~ 1\",\n factor_loc_totest=\"pseudotime\",\n test=\"wald\",\n sample_description=random_sample_description,\n quick_scale=True,\n batch_size=None,\n training_strategy=\"DEFAULT\",\n dtype=\"float64\"\n )\n\n summary = test.summary()\n ids = test.gene_ids\n\n # 1. Test all additional functions which depend on model computation:\n # 1.1. Only continuous model:\n temp = test.log_fold_change(genes=ids, nonnumeric=False)\n temp = test.max(genes=ids, nonnumeric=False)\n temp = test.min(genes=ids, nonnumeric=False)\n temp = test.argmax(genes=ids, nonnumeric=False)\n temp = test.argmin(genes=ids, nonnumeric=False)\n temp = test.summary(nonnumeric=False)\n # 1.2. Full model:\n temp = test.log_fold_change(genes=ids, nonnumeric=True)\n temp = test.max(genes=ids, nonnumeric=True)\n temp = test.min(genes=ids, nonnumeric=True)\n temp = test.argmax(genes=ids, nonnumeric=True)\n temp = test.argmin(genes=ids, nonnumeric=True)\n temp = test.summary(nonnumeric=True)\n\n return True","def test_wool():\n test_path = tempfile.mkdtemp()\n x_train, metadata = wool(test_path)\n try:\n assert x_train.shape == (309, 2)\n except:\n shutil.rmtree(test_path)\n raise()","def test_numpify_and_store(self):\n Nsamples = 9\n Ntimesteps = 10\n Ncolumns = 3\n X = [[[0 for a in range(Ncolumns)] for b in range(Ntimesteps)] \\\n for c in range(Nsamples)]\n y = [[0 for a in range(Ntimesteps)] for b in range(Nsamples)]\n xname = 'xname'\n yname = 'yname'\n outdatapath = os.getcwd()\n tutorial_pamap2.numpify_and_store(X, y, xname, yname, outdatapath, \\\n shuffle=True)\n filename = os.path.join(outdatapath, xname+ '.npy')\n test = os.path.isfile(filename)\n if test == True:\n os.remove(filename)\n os.remove(os.path.join(outdatapath, yname + '.npy'))\n assert test"],"string":"[\n \"def test_documentation_popxl_get_set_tensors(self):\\n filename = \\\"tensor_get_write.py\\\"\\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)\",\n \"def test_remote_buffer() -> None:\\n # Prepare the input and output data\\n shape_1 = (1, 3, 5)\\n shape_2 = (7, 11)\\n d_type_1 = np.dtype(\\\"float32\\\")\\n d_type_2 = np.dtype(\\\"float16\\\")\\n\\n data: Dict[str, np.ndarray] = {}\\n\\n # Store and load data for the first tensor\\n data[\\\"store_in_1\\\"] = np.random.rand(*shape_1).astype(d_type_1)\\n data[\\\"load_in_1\\\"] = np.zeros(shape_1).astype(d_type_1)\\n data[\\\"load_in_1_inplace\\\"] = np.zeros(shape_1).astype(d_type_1)\\n # Store and load data for the second tensor\\n data[\\\"store_in_2\\\"] = np.random.rand(*shape_2).astype(d_type_2)\\n data[\\\"load_in_2\\\"] = np.zeros(shape_2).astype(d_type_2)\\n # Store and load data for the third tensor\\n data[\\\"store_in_3\\\"] = np.random.rand(*shape_2).astype(d_type_2)\\n data[\\\"load_in_3_inplace\\\"] = np.zeros(shape_2).astype(d_type_2)\\n\\n ir, d2h_streams = build_model(data)\\n\\n # Get the tensor_ids\\n labels = (\\n \\\"load_in_1\\\",\\n \\\"load_in_1_inplace\\\",\\n \\\"load_out_1\\\",\\n \\\"load_out_1_inplace\\\",\\n \\\"load_in_2\\\",\\n \\\"load_in_3_inplace\\\",\\n \\\"load_out_2\\\",\\n \\\"load_out_3_inplace\\\",\\n )\\n tensor_d2h = {label: d2h_streams[label] for label in labels}\\n\\n session = popxl.Session(ir, \\\"ipu_model\\\")\\n with session:\\n outputs = session.run()\\n\\n # Assert that the tensors are correct\\n remote_load_scenarios = (\\n \\\"1\\\",\\n \\\"1_inplace\\\",\\n \\\"2\\\",\\n \\\"3_inplace\\\",\\n )\\n for scenario in remote_load_scenarios:\\n print(f\\\"Now asserting remote load scenario {scenario}\\\")\\n # Get data to assert\\n store_in_data = data[f\\\"store_in_{scenario.replace('_inplace', '')}\\\"]\\n load_in_data_before_op_call = data[f\\\"load_in_{scenario}\\\"]\\n load_in_data_after_op_call = outputs[tensor_d2h[f\\\"load_in_{scenario}\\\"]]\\n load_out_data = outputs[tensor_d2h[f\\\"load_out_{scenario}\\\"]]\\n shape = shape_1 if \\\"1\\\" in scenario else shape_2\\n d_type = d_type_1 if \\\"1\\\" in scenario else d_type_2\\n inplace = True if \\\"inplace\\\" in scenario else False\\n # Assert shape and type\\n assert load_in_data_after_op_call.shape == shape\\n assert load_in_data_after_op_call.dtype == d_type\\n assert load_out_data.shape == shape\\n assert load_out_data.dtype == d_type\\n\\n # Assert that the data has been loaded\\n assert np.allclose(store_in_data, load_out_data)\\n if inplace:\\n # Assert that the load in data has been overwritten\\n assert np.allclose(load_in_data_after_op_call, store_in_data)\\n else:\\n # Assert that the load in data has not been overwritten\\n assert np.allclose(load_in_data_after_op_call, load_in_data_before_op_call)\",\n \"def build_model(\\n data: Dict[str, np.array]\\n) -> Tuple[popxl.Ir, Dict[str, DeviceToHostStream]]:\\n ir = popxl.Ir()\\n main = ir.main_graph\\n\\n with main:\\n # Placeholder for tensor ids\\n tensors = {}\\n # Create variable tensors from the data\\n for name in data.keys():\\n tensors[name] = popxl.variable(data[name], name=name)\\n\\n # Placeholder for device to host streams\\n d2h_streams = {}\\n\\n # Store and load the first tensor\\n remote_buffer_1 = RemoteBuffer(\\n tensor_shape=tensors[\\\"store_in_1\\\"]._pb_tensor.info.shape(),\\n tensor_dtype=dtype.as_dtype(\\n tensors[\\\"store_in_1\\\"]._pb_tensor.info.data_type_lcase()\\n ),\\n entries=1,\\n )\\n offset_tensor_1 = popxl.constant(0, name=\\\"offset_1\\\")\\n # Ensure that the ops are in the order we define them in\\n with popxl.in_sequence(True):\\n ops.remote_store(\\n remote_buffer=remote_buffer_1,\\n offset=offset_tensor_1,\\n t=tensors[\\\"store_in_1\\\"],\\n )\\n tensors[\\\"load_out_1\\\"] = ops.remote_load(\\n remote_buffer=remote_buffer_1, offset=offset_tensor_1, name=\\\"load_out_1\\\"\\n )\\n tensors[\\\"load_out_1_inplace\\\"] = ops.remote_load_(\\n remote_buffer=remote_buffer_1,\\n offset=offset_tensor_1,\\n t=tensors[\\\"load_in_1_inplace\\\"],\\n )\\n # Anchor the input tensors to the load operator\\n d2h_streams = make_stream(d2h_streams, tensors, \\\"load_in_1\\\")\\n d2h_streams = make_stream(d2h_streams, tensors, \\\"load_in_1_inplace\\\")\\n # Anchor the output tensors of the load operator\\n d2h_streams = make_stream(d2h_streams, tensors, \\\"load_out_1\\\")\\n d2h_streams = make_stream(d2h_streams, tensors, \\\"load_out_1_inplace\\\")\\n\\n # Store and load the second and third tensor using a new buffer id\\n remote_buffer_2 = RemoteBuffer(\\n tensor_shape=tensors[\\\"store_in_2\\\"]._pb_tensor.info.shape(),\\n tensor_dtype=dtype.as_dtype(\\n tensors[\\\"store_in_2\\\"]._pb_tensor.info.data_type_lcase()\\n ),\\n entries=2,\\n )\\n # Index starts at 0\\n offset_tensor_2 = popxl.constant(0, name=\\\"offset_2\\\")\\n offset_tensor_3 = 1 # Test that the int version of offset works\\n ops.remote_store(\\n remote_buffer=remote_buffer_2,\\n offset=offset_tensor_2,\\n t=tensors[\\\"store_in_2\\\"],\\n )\\n ops.remote_store(\\n remote_buffer=remote_buffer_2,\\n offset=offset_tensor_3,\\n t=tensors[\\\"store_in_3\\\"],\\n )\\n tensors[\\\"load_out_2\\\"] = ops.remote_load(\\n remote_buffer=remote_buffer_2, offset=offset_tensor_2, name=\\\"load_out_2\\\"\\n )\\n tensors[\\\"load_out_3_inplace\\\"] = ops.remote_load_(\\n remote_buffer=remote_buffer_2,\\n offset=offset_tensor_3,\\n t=tensors[\\\"load_in_3_inplace\\\"],\\n )\\n\\n # Anchor the input tensors to the load operator\\n d2h_streams = make_stream(d2h_streams, tensors, \\\"load_in_2\\\")\\n d2h_streams = make_stream(d2h_streams, tensors, \\\"load_in_3_inplace\\\")\\n # Anchor the output tensors of the load operator\\n d2h_streams = make_stream(d2h_streams, tensors, \\\"load_out_2\\\")\\n d2h_streams = make_stream(d2h_streams, tensors, \\\"load_out_3_inplace\\\")\\n\\n return ir, d2h_streams\",\n \"def test_np_memory_layout_add_input_tensor_pystepiocallback():\\n\\n def _test(transposedInput, transposedOutput):\\n builder = popart.Builder()\\n\\n # Create a random constant and transpose it\\n np.random.seed(1)\\n input1 = builder.addInputTensor(\\\"INT32\\\", [2, 2])\\n\\n # Run a session to prove this\\n output1 = builder.aiOnnx.identity([input1])\\n builder.addOutputTensor(output1)\\n anchorConfig = {output1: popart.AnchorReturnType(\\\"ALL\\\")}\\n\\n dataFlow = popart.DataFlow(1, anchorConfig)\\n deviceConfig = {\\\"numIPUs\\\": 1}\\n dm = popart.DeviceManager()\\n device = dm.createIpuModelDevice(deviceConfig)\\n session = popart.InferenceSession(\\n fnModel=builder.getModelProto(), dataFlow=dataFlow, deviceInfo=device\\n )\\n\\n # Compile graph and place weights onto it\\n session.prepareDevice()\\n session.weightsFromHost()\\n\\n # Feed the session with a transposed (non-contiguous) tensor.\\n input1Value = np.random.randint(0, 100, size=(2, 2), dtype=\\\"int32\\\")\\n if transposedInput:\\n input1Value = np.transpose(input1Value, [1, 0])\\n output1Value = np.random.randint(0, 100, size=(2, 2), dtype=\\\"int32\\\")\\n if transposedOutput:\\n output1Value = np.transpose(output1Value, [1, 0])\\n\\n with pytest.raises(\\n (Exception, RuntimeError, popart.popart_exception)\\n ) as e_info:\\n\\n # pylint: disable=unused-argument\\n def input_callback(id, prefetch):\\n return input1Value\\n\\n def input_complete_callback(_): # id is an unused parameter\\n pass\\n\\n def output_callback(_): # id is an unused parameter\\n return output1Value\\n\\n def output_complete_callback(_): # id is an unused parameter\\n pass\\n\\n stepio = popart.PyStepIOCallback(\\n input_callback,\\n input_complete_callback,\\n output_callback,\\n output_complete_callback,\\n )\\n\\n session.run(stepio)\\n\\n assert \\\"contiguous\\\" in e_info.value.args[0]\\n\\n _test(transposedInput=True, transposedOutput=False)\\n _test(transposedInput=False, transposedOutput=True)\",\n \"def test_byteps_push_pull(self):\\n dtypes = ['float16', 'float32', 'float64']\\n dims = [1, 2, 3]\\n count = 0\\n ctx = self._current_context()\\n shapes = [(), (17), (17, 17), (17, 17, 17)]\\n for dtype, dim in itertools.product(dtypes, dims):\\n # MXNet uses gpu_id as part of the seed, so to get identical seeds\\n # we must set a context.\\n mx.random.seed(10 + 10 * bps.rank(), ctx=ctx)\\n tensor = mx.nd.random.uniform(-100, 100, shape=shapes[dim],\\n ctx=ctx)\\n tensor = tensor.astype(dtype)\\n input = tensor.asnumpy()\\n\\n bps.byteps_declare_tensor(\\\"tensor_\\\" + str(count))\\n bps.byteps_push_pull(tensor, name=\\\"tensor_\\\"+str(count))\\n tensor.wait_to_read()\\n output = tensor.asnumpy()\\n assert np.allclose(input, output)\\n count += 1\\n\\n print('test_byteps_push_pull passed')\",\n \"def test_co_transfer():\\n test_path = tempfile.mkdtemp()\\n x_train, metadata = co_transfer(test_path)\\n try:\\n assert x_train.shape == (7, 2)\\n except:\\n shutil.rmtree(test_path)\\n raise()\",\n \"def test_create_device_data(self):\\n pass\",\n \"def test_np_memory_layout_add_input_tensor_pystepio():\\n\\n builder = popart.Builder()\\n\\n # Create a random constant and transpose it\\n np.random.seed(1)\\n input1 = builder.addInputTensor(\\\"INT32\\\", [2, 2])\\n\\n # Run a session to prove this\\n output1 = builder.aiOnnx.identity([input1])\\n builder.addOutputTensor(output1)\\n anchorConfig = {output1: popart.AnchorReturnType(\\\"ALL\\\")}\\n\\n dataFlow = popart.DataFlow(1, anchorConfig)\\n deviceConfig = {\\\"numIPUs\\\": 1}\\n dm = popart.DeviceManager()\\n device = dm.createIpuModelDevice(deviceConfig)\\n session = popart.InferenceSession(\\n fnModel=builder.getModelProto(), dataFlow=dataFlow, deviceInfo=device\\n )\\n\\n # Compile graph and place weights onto it\\n session.prepareDevice()\\n session.weightsFromHost()\\n anchors = session.initAnchorArrays()\\n\\n # Feed the session with a transposed (non-contiguous) tensor.\\n input1Value = np.random.randint(0, 100, size=(2, 2), dtype=\\\"int32\\\")\\n input1Value = np.transpose(input1Value, [1, 0])\\n\\n with pytest.raises((RuntimeError, popart.popart_exception)) as e_info:\\n stepio = popart.PyStepIO({input1: input1Value}, anchors)\\n session.run(stepio)\\n\\n assert \\\"contiguous\\\" in e_info.value.args[0]\",\n \"def test():\\n quant_handle = QuantAndDeQuantGPU()\\n import torch\\n logger = logging.getLogger(__name__)\\n logger.setLevel(logging.INFO)\\n tensor = torch.Tensor(np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])).cuda()\\n logging.info(\\\"Origin Data: \\\")\\n logging.info(tensor)\\n\\n start_time = datetime.datetime.now()\\n quant_tensor = quant_handle(tensor)\\n end_time = datetime.datetime.now()\\n\\n logging.info(\\\"Quant Data: \\\")\\n logging.info(quant_tensor)\\n\\n data_expected = np.array([\\n 0.0000000000, 1.0000000000, 2.0000000000, 2.9536523819, 4.0000000000,\\n 4.9674310684, 5.9073047638, 7.0250086784, 8.0000000000, 8.7240619659\\n ])\\n\\n logging.info(\\\"Data expected: \\\")\\n logging.info(\\\" \\\".join([str(v) for v in data_expected]))\\n\\n data_diff = quant_tensor.data.detach().cpu().numpy() - data_expected\\n flag = \\\"success.\\\"\\n for num in data_diff:\\n if abs(num) > 0.000000001:\\n flag = \\\"failed.\\\"\\n\\n run_time = end_time - start_time\\n logging.info(\\\"QuantAndDeQuantGPU time: %s\\\", str(run_time))\\n logging.info(\\\"QuantAndDeQuantGPU %s\\\", flag)\",\n \"def test_significance_individual_timepoints(tensor_directory, analysis_name):\\n\\n \\\"\\\"\\\"\\n # Open Analysis Dataframe\\n analysis_file = h5py.File(os.path.join(tensor_directory, analysis_name + \\\".hdf5\\\"), \\\"r\\\")\\n activity_dataset = analysis_file[\\\"Data\\\"]\\n metadata_dataset = analysis_file[\\\"metadata\\\"]\\n number_of_timepoints, number_of_trials, number_of_pixels = np.shape(activity_dataset)\\n print(\\\"metadata_dataset\\\", np.shape(metadata_dataset))\\n \\\"\\\"\\\"\\n # Open Analysis Dataframe\\n analysis_file = tables.open_file(os.path.join(tensor_directory, analysis_name + \\\"_Trialwise_.h5\\\"), mode=\\\"r\\\")\\n activity_dataset = analysis_file.root[\\\"Data\\\"]\\n metadata_dataset = analysis_file.root[\\\"Trial_Details\\\"]\\n\\n # Create P and Slope Tensors\\n p_value_tensor = np.ones((number_of_timepoints, number_of_pixels))\\n slope_tensor = np.zeros((number_of_timepoints, number_of_pixels))\\n\\n for timepoint_index in tqdm(range(number_of_timepoints), position=0, desc=\\\"Timepoint\\\"):\\n\\n # Get Timepoint Data\\n timepoint_activity = activity_dataset[timepoint_index]\\n\\n for pixel_index in tqdm(range(number_of_pixels), position=1, desc=\\\"Pixel\\\", leave=True):\\n\\n # Package Into Dataframe\\n pixel_activity = timepoint_activity[:, pixel_index]\\n pixel_dataframe = repackage_data_into_dataframe(pixel_activity, metadata_dataset)\\n\\n # Fit Mixed Effects Model\\n p_value, slope = mixed_effects_random_slope_and_intercept(pixel_dataframe)\\n p_value_tensor[timepoint_index, pixel_index] = p_value\\n slope_tensor[timepoint_index, pixel_index] = slope\\n\\n\\n # Save These Tensors\\n np.save(os.path.join(tensor_directory, analysis_name + \\\"_p_value_tensor.npy\\\"), p_value_tensor)\\n np.save(os.path.join(tensor_directory, analysis_name + \\\"_slope_tensor.npy\\\"), slope_tensor)\",\n \"def test_output_data():\\n output_params = dict(\\n type=\\\"geodetic\\\",\\n format=\\\"GeoTIFF\\\",\\n path=OUT_DIR,\\n pixelbuffer=0,\\n metatiling=1,\\n bands=1,\\n dtype=\\\"int16\\\"\\n )\\n output = gtiff.OutputData(output_params)\\n assert output.path == OUT_DIR\\n assert output.file_extension == \\\".tif\\\"\\n tp = BufferedTilePyramid(\\\"geodetic\\\")\\n tile = tp.tile(5, 5, 5)\\n # get_path\\n assert output.get_path(tile) == os.path.join(*[\\n OUT_DIR, \\\"5\\\", \\\"5\\\", \\\"5\\\"+\\\".tif\\\"])\\n # prepare_path\\n try:\\n temp_dir = os.path.join(*[OUT_DIR, \\\"5\\\", \\\"5\\\"])\\n output.prepare_path(tile)\\n assert os.path.isdir(temp_dir)\\n finally:\\n shutil.rmtree(temp_dir, ignore_errors=True)\\n # profile\\n assert isinstance(output.profile(tile), dict)\\n # write\\n try:\\n tile.data = np.ones((1, ) + tile.shape)*128\\n output.write(tile)\\n # tiles_exist\\n assert output.tiles_exist(tile)\\n # read\\n data = output.read(tile).data\\n assert isinstance(data, np.ndarray)\\n assert not data[0].mask.any()\\n finally:\\n shutil.rmtree(temp_dir, ignore_errors=True)\\n # read empty\\n data = output.read(tile).data\\n assert isinstance(data, np.ndarray)\\n assert data[0].mask.all()\\n # empty\\n empty = output.empty(tile)\\n assert isinstance(empty, ma.MaskedArray)\\n assert not empty.any()\\n # deflate with predictor\\n output_params.update(compression=\\\"deflate\\\", predictor=2)\\n output = gtiff.OutputData(output_params)\\n assert output.profile(tile)[\\\"compress\\\"] == \\\"deflate\\\"\\n assert output.profile(tile)[\\\"predictor\\\"] == 2\",\n \"def test_add_get_tensor(mock_data):\\n dataset = Dataset(\\\"test-dataset\\\")\\n\\n # 1D tensors of all data types\\n data = mock_data.create_data(10)\\n add_get_arrays(dataset, data)\",\n \"def test_documentation_popxl_addition_variable(self):\\n filename = \\\"tensor_addition.py\\\"\\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)\",\n \"def sample_batch(pid, args, batch_queue, port_dict, device, actor_id_to_ip_dataport, local_size, cache_array):\\n def recv_data(k, data_stream, actor_set, real_data_tasks_i):\\n for real_data in data_stream:\\n tmp = []\\n tmp.append(real_data.state)\\n tmp.append(real_data.action)\\n tmp.append(real_data.reward)\\n tmp.append(real_data.next_state)\\n tmp.append(real_data.done)\\n tmp.append(actor_set[k]['w'][real_data.idx])\\n tmp.append(actor_set[k]['i'][real_data.idx])\\n tmp.append(actor_set[k]['t'][real_data.idx])\\n tmp.append(real_data.timestamp)\\n local_dict[actor_set[k]['i'][real_data.idx]] = tmp\\n cache_array[actor_set[k]['i'][real_data.idx]] |= 2**pid\\n decom_state = torch.FloatTensor(np.frombuffer(zlib.decompress(real_data.state), dtype=np.uint8).reshape((1, 4, 84, 84)))\\n real_data_tasks_i['states'].append(decom_state) #.to(device))\\n real_data_tasks_i['actions'].append(torch.LongTensor([real_data.action])) #.to(device))\\n real_data_tasks_i['rewards'].append(torch.FloatTensor([real_data.reward])) #.to(device))\\n decom_next_state = torch.FloatTensor(np.frombuffer(zlib.decompress(real_data.next_state), dtype=np.uint8).reshape((1, 4, 84, 84)))\\n real_data_tasks_i['next_states'].append(decom_next_state) #.to(device))\\n real_data_tasks_i['dones'].append(torch.FloatTensor([real_data.done])) #.to(device))\\n real_data_tasks_i['batch_weights'].append(torch.FloatTensor([actor_set[k]['w'][real_data.idx]])) #.to(device))\\n real_data_tasks_i['batch_idxes'].append(actor_set[k]['i'][real_data.idx])\\n # is the data overwrited?\\n real_data_tasks_i['batch_timestamp_store'].append(actor_set[k]['t'][real_data.idx])\\n real_data_tasks_i['batch_timestamp_real'].append(real_data.timestamp)\\n conn = grpc.insecure_channel(port_dict['replay_ip'] + ':' + port_dict['sampleDataPort'])\\n client = apex_data_pb2_grpc.SampleDataStub(channel=conn)\\n local_dict = {}\\n while True:\\n batch_timestamp_real = []\\n batch_timestamp_store = []\\n batch_weights = []\\n batch_idxes = []\\n\\n states, actions, rewards, next_states, dones = [], [], [], [], []\\n\\n res_batch = client.Send(apex_data_pb2.SampleDataRequest(batch_size=args.batch_size, beta = args.beta))\\n actor_ids, data_ids, timestamps, weights, idxes = res_batch.actor_ids, res_batch.data_ids, res_batch.timestamp, res_batch.weights, res_batch.idxes\\n actor_set = {}\\n cached_value = {'states':{},'actions':{},'rewards':{},'next_states':{},'dones':{},'batch_weights':{},'batch_idxes':{},'batch_timestamp_store':{},'batch_timestamp_real':{}}\\n for i in range(len(actor_ids)):\\n set_a = actor_set.get(actor_ids[i], False)\\n if set_a == False:\\n actor_set[actor_ids[i]] = {}\\n set_a = actor_set[actor_ids[i]]\\n set_a['d'] = []\\n set_a['w'] = []\\n set_a['i'] = []\\n set_a['t'] = []\\n cached_value['states'][actor_ids[i]] = []\\n cached_value['actions'][actor_ids[i]] = []\\n cached_value['rewards'][actor_ids[i]] = []\\n cached_value['next_states'][actor_ids[i]] = []\\n cached_value['dones'][actor_ids[i]] = []\\n cached_value['batch_weights'][actor_ids[i]] = []\\n cached_value['batch_idxes'][actor_ids[i]] = []\\n cached_value['batch_timestamp_store'][actor_ids[i]] = []\\n cached_value['batch_timestamp_real'][actor_ids[i]] = []\\n cache_id = actor_ids[i]*local_size+data_ids[i]\\n cache_trans = cache_array[cache_id]\\n if cache_trans & 2**pid == 0:\\n set_a['d'].append(data_ids[i])\\n set_a['w'].append(weights[i])\\n set_a['i'].append(idxes[i])\\n set_a['t'].append(timestamps[i])\\n if cache_trans == 0 and local_dict.get(cache_id, False) != False:\\n del local_dict[cache_id]\\n else:\\n try:\\n state_tmp = local_dict[cache_id][0]\\n action_tmp = local_dict[cache_id][1]\\n reward_tmp = local_dict[cache_id][2] \\n next_state_tmp = local_dict[cache_id][3] \\n done_tmp = local_dict[cache_id][4] \\n batch_weight_tmp = local_dict[cache_id][5] \\n batch_idx_tmp = local_dict[cache_id][6] \\n batch_store_tmp = local_dict[cache_id][7] \\n batch_real_tmp = local_dict[cache_id][8] \\n decom_state = torch.FloatTensor(np.frombuffer(zlib.decompress(state_tmp), dtype=np.uint8).reshape((1, 4, 84, 84)))\\n cached_value['states'][actor_ids[i]].append(decom_state)\\n cached_value['actions'][actor_ids[i]].append(torch.LongTensor([action_tmp]))\\n cached_value['rewards'][actor_ids[i]].append(torch.FloatTensor([reward_tmp]))\\n decom_next_state = torch.FloatTensor(np.frombuffer(zlib.decompress(next_state_tmp), dtype=np.uint8).reshape((1, 4, 84, 84)))\\n cached_value['next_states'][actor_ids[i]].append(decom_next_state)\\n cached_value['dones'][actor_ids[i]].append(torch.FloatTensor([done_tmp]))\\n cached_value['batch_weights'][actor_ids[i]].append(torch.FloatTensor([batch_weight_tmp]))\\n cached_value['batch_idxes'][actor_ids[i]].append(batch_idx_tmp)\\n cached_value['batch_timestamp_store'][actor_ids[i]].append(batch_store_tmp)\\n cached_value['batch_timestamp_real'][actor_ids[i]].append(batch_real_tmp)\\n except:\\n set_a['d'].append(data_ids[i])\\n set_a['w'].append(weights[i])\\n set_a['i'].append(idxes[i])\\n set_a['t'].append(timestamps[i])\\n real_data_links = {}\\n real_data_tasks = {}\\n for k, v in actor_set.items():\\n actor_ip, data_port = actor_id_to_ip_dataport[k]\\n conn_actor = grpc.insecure_channel(actor_ip + ':' + data_port)\\n client_actor = apex_data_pb2_grpc.SendRealDataStub(channel=conn_actor)\\n real_data_links[k] = client_actor.Send(apex_data_pb2.RealBatchRequest(idxes=v['d']))\\n real_data_tasks[k] = {}\\n real_data_tasks[k]['states'] = cached_value['states'][k]\\n real_data_tasks[k]['actions'] = cached_value['actions'][k]\\n real_data_tasks[k]['rewards'] = cached_value['rewards'][k]\\n real_data_tasks[k]['next_states'] = cached_value['next_states'][k]\\n real_data_tasks[k]['dones'] = cached_value['dones'][k]\\n real_data_tasks[k]['batch_weights'] = cached_value['batch_weights'][k]\\n real_data_tasks[k]['batch_idxes'] = cached_value['batch_idxes'][k]\\n real_data_tasks[k]['batch_timestamp_store'] = cached_value['batch_timestamp_store'][k]\\n real_data_tasks[k]['batch_timestamp_real'] = cached_value['batch_timestamp_real'][k]\\n threads = []\\n for k, v in real_data_links.items():\\n t = threading.Thread(target=recv_data, args=(k, v, actor_set, real_data_tasks[k],))\\n threads.append(t)\\n t.start()\\n\\n for t in threads:\\n t.join()\\n\\n for k, v in real_data_tasks.items():\\n states += v['states']\\n actions += v['actions']\\n rewards += v['rewards']\\n next_states += v['next_states']\\n dones += v['dones']\\n batch_weights += v['batch_weights']\\n batch_idxes += v['batch_idxes']\\n batch_timestamp_real += v['batch_timestamp_real']\\n batch_timestamp_store += v['batch_timestamp_store']\\n\\n states = torch.cat(states,0).to(device)\\n actions = torch.cat(actions,0).to(device)\\n rewards = torch.cat(rewards,0).to(device)\\n next_states = torch.cat(next_states,0).to(device)\\n dones = torch.cat(dones,0).to(device)\\n batch_weights = torch.cat(batch_weights,0).to(device)\\n\\n batch = [states, actions, rewards, next_states, dones, batch_weights, batch_idxes]\\n batch_queue.put(batch)\\n data, batch = None, None\",\n \"def test(model, dataloader, params, args, val):\\n\\n # evaluation mode\\n model.eval()\\n\\n # initialise buffers\\n dice_lv_buffer = []\\n dice_myo_buffer = []\\n dice_rv_buffer = []\\n\\n mcd_lv_buffer = []\\n hd_lv_buffer = []\\n mcd_myo_buffer = []\\n hd_myo_buffer = []\\n mcd_rv_buffer = []\\n hd_rv_buffer = []\\n\\n mean_mag_grad_detJ_buffer = []\\n negative_detJ_buffer = []\\n\\n\\n with tqdm(total=len(dataloader)) as t:\\n # iterate over validation subjects\\n for idx, (image_ed_batch, image_es_batch, label_ed_batch, label_es_batch) in enumerate(dataloader):\\n # (data all in shape of (c, N, H, W))\\n\\n # extend to (N, c, H, W)\\n image_ed_batch = image_ed_batch.permute(1, 0, 2, 3).to(device=args.device)\\n image_es_batch = image_es_batch.permute(1, 0, 2, 3).to(device=args.device)\\n label_es_batch = label_es_batch.permute(1, 0, 2, 3).to(device=args.device)\\n\\n with torch.no_grad():\\n # compute optical flow and warped ED images towards ES\\n dvf = model(image_ed_batch, image_es_batch)\\n\\n # transform label mask of ES frame\\n warped_label_es_batch = resample_transform(label_es_batch.float(), dvf, interp='nearest')\\n\\n\\n \\\"\\\"\\\" Move data to device \\\"\\\"\\\"\\n if args.cuda:\\n # move data to cpu to calculate metrics\\n # (the axis permutation is to comply with metric calculation code which takes input shape H, W, N)\\n warped_label_es_batch = warped_label_es_batch.squeeze(1).cpu().numpy().transpose(1, 2, 0)\\n label_ed_batch = label_ed_batch.squeeze(0).numpy().transpose(1, 2, 0)\\n dvf = dvf.data.cpu().numpy().transpose(0, 2, 3, 1) # (N, H, W, 2)\\n else:\\n # CPU version of the code\\n warped_label_es_batch = warped_label_es_batch.squeeze(1).numpy().transpose(1, 2, 0)\\n label_ed_batch = label_ed_batch.squeeze(0).numpy().transpose(1, 2, 0)\\n dvf = dvf.data.numpy().transpose(0, 2, 3, 1) # (N, H, W, 2)\\n \\\"\\\"\\\"\\\"\\\"\\\"\\n\\n \\\"\\\"\\\" Calculate the metrics (only works with SAX images) \\\"\\\"\\\"\\n # (optional) extract 3 slices (apical, mid-ventricle and basal)\\n if not args.all_slices:\\n num_slices = label_ed_batch.shape[-1]\\n apical_idx = int(round((num_slices - 1) * 0.75)) # 75% from basal\\n mid_ven_idx = int(round((num_slices - 1) * 0.5)) # 50% from basal\\n basal_idx = int(round((num_slices - 1) * 0.25)) # 25% from basal\\n slices_idx = [apical_idx, mid_ven_idx, basal_idx]\\n\\n warped_label_es_batch = warped_label_es_batch[:, :, slices_idx]\\n label_ed_batch = label_ed_batch[:, :, slices_idx]\\n dvf = dvf[slices_idx, :, :, :] # needed for detJac\\n\\n # dice\\n dice_lv = categorical_dice_stack(warped_label_es_batch, label_ed_batch, label_class=1)\\n dice_myo = categorical_dice_stack(warped_label_es_batch, label_ed_batch, label_class=2)\\n dice_rv = categorical_dice_stack(warped_label_es_batch, label_ed_batch, label_class=3)\\n\\n dice_lv_buffer += [dice_lv]\\n dice_myo_buffer += [dice_myo]\\n dice_rv_buffer += [dice_rv]\\n\\n # contour distances\\n mcd_lv, hd_lv = contour_distances_stack(warped_label_es_batch, label_ed_batch, label_class=1, dx=params.pixel_size)\\n mcd_myo, hd_myo = contour_distances_stack(warped_label_es_batch, label_ed_batch, label_class=2, dx=params.pixel_size)\\n mcd_rv, hd_rv = contour_distances_stack(warped_label_es_batch, label_ed_batch, label_class=3, dx=params.pixel_size)\\n\\n # determinant of Jacobian\\n mean_grad_detJ, mean_negative_detJ = detJac_stack(dvf)\\n\\n\\n # update buffers\\n mcd_lv_buffer += [mcd_lv]\\n hd_lv_buffer += [hd_lv]\\n mcd_myo_buffer += [mcd_myo]\\n hd_myo_buffer += [hd_myo]\\n mcd_rv_buffer += [mcd_rv]\\n hd_rv_buffer += [hd_rv]\\n\\n mean_mag_grad_detJ_buffer += [mean_grad_detJ]\\n negative_detJ_buffer += [mean_negative_detJ]\\n\\n t.update()\\n\\n # construct metrics dict\\n metrics = {'dice_lv_mean': np.mean(dice_lv_buffer), 'dice_lv_std': np.std(dice_lv_buffer),\\n 'dice_myo_mean': np.mean(dice_myo_buffer), 'dice_myo_std': np.std(dice_myo_buffer),\\n 'dice_rv_mean': np.mean(dice_rv_buffer), 'dice_rv_std': np.std(dice_rv_buffer),\\n\\n 'mcd_lv_mean': np.mean(mcd_lv_buffer), 'mcd_lv_std': np.std(mcd_lv_buffer),\\n 'mcd_myo_mean': np.mean(mcd_myo_buffer), 'mcd_myo_std': np.std(mcd_myo_buffer),\\n 'mcd_rv_mean': np.mean(mcd_rv_buffer), 'mcd_rv_std': np.std(mcd_rv_buffer),\\n\\n 'hd_lv_mean': np.mean(hd_lv_buffer), 'hd_lv_std': np.std(hd_lv_buffer),\\n 'hd_myo_mean': np.mean(hd_myo_buffer), 'hd_myo_std': np.std(hd_myo_buffer),\\n 'hd_rv_mean': np.mean(hd_rv_buffer), 'hd_rv_std': np.std(hd_rv_buffer),\\n\\n 'mean_mag_grad_detJ_mean': np.mean(mean_mag_grad_detJ_buffer),\\n 'mean_mag_grad_detJ_std': np.std(mean_mag_grad_detJ_buffer),\\n\\n 'negative_detJ_mean': np.mean(negative_detJ_buffer),\\n 'negative_detJ_std': np.std(negative_detJ_buffer)\\n }\\n\\n\\n if not val:\\n # testing only: save all metrics evaluated for all test subjects in pandas dataframe\\n test_result_dir = os.path.join(args.model_dir, \\\"test_results\\\")\\n if not os.path.exists(test_result_dir):\\n os.makedirs(test_result_dir)\\n\\n # save metrics results mean & std\\n xutils.save_dict_to_json(metrics,\\n f\\\"{test_result_dir}/test_results_3slices_{not args.all_slices}.json\\\")\\n\\n # save accuracy metrics of every subject\\n subj_id_buffer = dataloader.dataset.dir_list\\n df_buffer = []\\n column_method = ['DL'] * len(subj_id_buffer)\\n for struct in ['LV', 'MYO', 'RV']:\\n if struct == 'LV':\\n ls_dice = dice_lv_buffer\\n ls_mcd = mcd_lv_buffer\\n ls_hd = hd_lv_buffer\\n elif struct == 'MYO':\\n ls_dice = dice_myo_buffer\\n ls_mcd = mcd_myo_buffer\\n ls_hd = hd_myo_buffer\\n elif struct == 'RV':\\n ls_dice = dice_rv_buffer\\n ls_mcd = mcd_rv_buffer\\n ls_hd = hd_rv_buffer\\n\\n ls_struct = [struct] * len(subj_id_buffer)\\n data = {'Method': column_method,\\n 'ID': subj_id_buffer,\\n 'Structure': ls_struct,\\n 'Dice': ls_dice,\\n 'MCD': ls_mcd,\\n 'HD': ls_hd}\\n df_buffer += [pd.DataFrame(data=data)]\\n # concatenate df and save\\n metrics_df = pd.concat(df_buffer, axis=0)\\n metrics_df.to_pickle(f\\\"{test_result_dir}/test_accuracy_results_3slices_{not args.all_slices}.pkl\\\")\\n\\n # save detJac metrics for every subject\\n jac_data = {'Method': column_method,\\n 'ID': subj_id_buffer,\\n 'GradDetJac': mean_mag_grad_detJ_buffer,\\n 'NegDetJac': negative_detJ_buffer}\\n jac_df = pd.DataFrame(data=jac_data)\\n jac_df.to_pickle(f\\\"{test_result_dir}/test_Jacobian_results_3slices{not args.all_slices}.pkl\\\")\\n\\n return metrics\",\n \"def testAllWrite(self):\\n import time,copy\\n time.sleep(2)\\n to_config = self.config['vdevs']['slave']['icsifaces'][0]\\n from_config = self.config['vdevs']['master']['clientifaces'][0]\\n points = self.config['vdevs']['slave']['points']\\n client = ModbusRTU(to_config, points, from_config)\\n\\n pts = copy.deepcopy(self.config['vdevs']['slave']['points'])\\n pts = [pt for pt in pts if pt['name']!='pressure'] #Can't write to pres\\n for i in xrange(50):\\n ptnames = [ pt['name'] for pt in pts ]\\n pointsvalues = dict(zip(ptnames, [0]*len(ptnames)))\\n reply = client.writePoints(pointsvalues)\\n assert reply is None, \\\"Write returned value other than None: \\\" + str(reply)\\n reply = client.readPoints(ptnames)\\n #print \\\"Reply: \\\", reply\\n for pt in ptnames:\\n #assert value == reply[ptnames.index(pt)]\\n if not 0 == reply[ptnames.index(pt)]: \\n print pt, ' was not read properly.'\",\n \"def testAllWrite(self):\\n import time,copy\\n time.sleep(2)\\n client = ModbusTCP(self.config['vdevs']['slave']['icsifaces'][0],\\n self.config['vdevs']['slave']['points']) \\n\\n pts = copy.deepcopy(self.config['vdevs']['slave']['points'])\\n pts = [pt for pt in pts if pt['name']!='pressure'] #Can't write to pres\\n ptnames = [ pt['name'] for pt in pts ]\\n pointsvalues = dict(zip(ptnames, [0]*len(ptnames)))\\n reply = client.writePoints(pointsvalues)\\n assert reply is None, \\\"Write returned value other than None: \\\" + str(reply)\\n reply = client.readPoints(ptnames)\\n #print \\\"Reply: \\\", reply\\n for pt in ptnames:\\n #assert value == reply[ptnames.index(pt)]\\n if not 0 == reply[ptnames.index(pt)]: \\n print pt, ' was not read properly.'\",\n \"def test_mstgcn():\\n node_count = 307\\n num_classes = 10\\n edge_per_node = 15\\n\\n num_for_predict = 12\\n len_input = 12\\n nb_time_strides = 1\\n\\n device = torch.device(\\\"cuda\\\" if torch.cuda.is_available() else \\\"cpu\\\")\\n node_features = 2\\n nb_block = 2\\n K = 3\\n nb_chev_filter = 64\\n nb_time_filter = 64\\n batch_size = 32\\n\\n model = MSTGCN(\\n nb_block,\\n node_features,\\n K,\\n nb_chev_filter,\\n nb_time_filter,\\n nb_time_strides,\\n num_for_predict,\\n len_input,\\n ).to(device)\\n T = len_input\\n x_seq = torch.zeros([batch_size, node_count, node_features, T]).to(device)\\n target_seq = torch.zeros([batch_size, node_count, T]).to(device)\\n edge_index_seq = []\\n\\n for b in range(batch_size):\\n for t in range(T):\\n x, edge_index = create_mock_data(node_count, edge_per_node, node_features)\\n x_seq[b, :, :, t] = x.to(device)\\n if b == 0:\\n edge_index_seq.append(edge_index.to(device))\\n target = create_mock_target(node_count, num_classes).to(device)\\n target_seq[b, :, t] = target\\n\\n shuffle = True\\n train_dataset = torch.utils.data.TensorDataset(x_seq, target_seq)\\n train_loader = torch.utils.data.DataLoader(\\n train_dataset, batch_size=batch_size, shuffle=shuffle\\n )\\n\\n for batch_data in train_loader:\\n encoder_inputs, _ = batch_data\\n outputs1 = model(encoder_inputs, edge_index_seq)\\n outputs2 = model(encoder_inputs, edge_index_seq[0])\\n\\n assert outputs1.shape == (batch_size, node_count, num_for_predict)\\n assert outputs2.shape == (batch_size, node_count, num_for_predict)\",\n \"def test_synth_tr():\\n test_path = tempfile.mkdtemp()\\n x_train, metadata = synth_tr(test_path)\\n try:\\n assert x_train.shape == (250, 3)\\n except:\\n shutil.rmtree(test_path)\\n raise()\",\n \"def test_data_manipulation(self):\\n target_name = self.project['target']['name']\\n self.api_mock.return_value.get_metadata.return_value = [\\n {'_id': '0',\\n 'pid': '1',\\n 'created': datetime.datetime.now(),\\n 'name':'universe',\\n 'originalName': 'credit-sample-200.csv',\\n 'varTypeString': 'NN',\\n 'shape': [2, 100],\\n 'controls':{},\\n 'columns': [[1,target_name,0],[3,\\\"age\\\",0]],\\n 'files': ['projects/' + str(self.pid) + '/raw/' + self.testdatafile],\\n 'typeConvert': {}},\\n {'_id': '1',\\n 'pid': '1',\\n 'name':'test',\\n 'originalName': 'credit-sample-200.csv',\\n 'created': datetime.datetime.now(),\\n 'varTypeString': 'NN',\\n 'shape': [2, 100],\\n 'controls':{},\\n 'columns': [[1,target_name,0],[3,\\\"age\\\",0]],\\n 'files': ['projects/' + str(self.pid) + '/raw/' + self.testdatafile],\\n 'typeConvert': {}},\\n {'_id': '2',\\n 'pid': '1',\\n 'name':'new',\\n 'created': datetime.datetime.now(),\\n 'originalName': 'credit-sample-200.csv',\\n 'newdata':True,\\n 'controls':{},\\n 'shape': [2, 100],\\n 'varTypeString': 'NN',\\n 'columns': [[1,target_name,0],[3,\\\"age\\\",0]],\\n 'files': ['projects/' + str(self.pid) + '/raw/' + self.testdatafile],\\n 'typeConvert': {}}]\\n request = WorkerRequest({'pid': '1', 'uid': '1', 'dataset_id': '1',\\n 'command': 'fit', 'max_reps': 0,\\n 'samplepct': 100})\\n\\n #target\\n #this will map the target values to (0,1) because target type is Binary\\n target_vector = self.dataprocessor.target_vector()\\n target_series = target_vector['main']\\n self.assertItemsEqual(np.unique(target_series), [0,1])\\n\\n #this will be none because 'holdout_pct' isn't set in the project data\\n self.assertIsNone(target_vector['holdout'])\\n\\n #prediction dataset\\n predictors = self.dataprocessor.predictors()\\n pred_dataframe = predictors['1']['main']\\n self.assertItemsEqual(list(pred_dataframe.columns), [\\\"age\\\"])\\n self.assertEqual(self.dataprocessor.get_vartypestring_without_target('1'), \\\"N\\\")\\n\\n request = WorkerRequest({'pid': '1', 'uid': '1', 'dataset_id': '1', 'scoring_dataset_id': '2', 'command': 'predict', 'max_reps': 0, 'samplepct':100})\\n dp2 = DataProcessor(request)\\n data = dp2.request_datasets()\\n self.assertEqual(data.keys(), ['1'])\\n self.assertEqual(data['1'].keys(), ['scoring', 'vartypes'])\\n scoring_data = data['1']['scoring']\\n vartypes = data['1']['vartypes']\\n self.assertEqual(list(scoring_data.columns), [\\\"age\\\"])\\n self.assertEqual(vartypes, \\\"N\\\")\",\n \"def test_gen():\\n tpot_obj = TPOTClassifier()\\n\\n pipeline = tpot_obj._gen_grow_safe(tpot_obj._pset, 1, 3)\\n\\n assert len(pipeline) > 1\\n assert pipeline[0].ret == Output_DF\",\n \"def test_publish_path(self):\\n log.debug(\\\"======================= creating telem data\\\")\\n self.create_sample_data_set_dir(\\n 'node59p1_step1.dat',\\n TELEM_DIR,\\n \\\"node59p1.dat\\\",\\n copy_metadata=False\\n )\\n log.debug(\\\"======================= creating recov data\\\")\\n self.create_sample_data_set_dir(\\n \\\"DOS15908_1st7_step1.DAT\\\",\\n RECOV_DIR,\\n \\\"DOS15908.DAT\\\",\\n copy_metadata=False\\n )\\n\\n self.assert_initialize()\\n\\n # get the telemetered metadata particle\\n result = self.data_subscribers.get_samples(DataParticleType.METADATA_TELEMETERED, 1)\\n log.debug(\\\"***************************************************** metadata result %s\\\", result)\\n #get the telemetered instrument particles\\n result1 = self.data_subscribers.get_samples(DataParticleType.SAMPLE_TELEMETERED, 1)\\n result.extend(result1)\\n log.debug(\\\"RESULT: %s\\\", result)\\n\\n # Verify values\\n self.assert_data_values(result, 'test_data_1.txt.result.yml')\\n \\n \\n # get the telemetered metadata particle\\n result = self.data_subscribers.get_samples(DataParticleType.METADATA_RECOVERED, 1)\\n log.debug(\\\"***************************************************** metadata result %s\\\", result)\\n #get the telemetered instrument particles\\n result1 = self.data_subscribers.get_samples(DataParticleType.SAMPLE_RECOVERED, 1)\\n result.extend(result1)\\n log.debug(\\\"RESULT: %s\\\", result)\\n\\n # Verify values\\n self.assert_data_values(result, 'test_data_1r.txt.result.yml')\",\n \"def test_significance_window(tensor_directory, analysis_name, window):\\n\\n \\\"\\\"\\\"\\n # Open Analysis Dataframe\\n analysis_file = h5py.File(os.path.join(tensor_directory, analysis_name + \\\".hdf5\\\"), \\\"r\\\")\\n activity_dataset = analysis_file[\\\"Data\\\"]\\n metadata_dataset = analysis_file[\\\"metadata\\\"]\\n number_of_timepoints, number_of_trials, number_of_pixels = np.shape(activity_dataset)\\n print(\\\"metadata_dataset\\\", np.shape(metadata_dataset))\\n \\\"\\\"\\\"\\n\\n\\n # Open Analysis Dataframe\\n analysis_file = tables.open_file(os.path.join(tensor_directory, analysis_name + \\\"_Trialwise_.h5\\\"), mode=\\\"r\\\")\\n activity_dataset = analysis_file.root[\\\"Data\\\"]\\n metadata_dataset = analysis_file.root[\\\"Trial_Details\\\"]\\n activity_dataset = np.array(activity_dataset)\\n metadata_dataset = np.array(metadata_dataset)\\n\\n activity_dataset = np.nan_to_num(activity_dataset)\\n\\n number_of_trials, number_of_timepoints, number_of_pixels = np.shape(activity_dataset)\\n\\n\\n print(\\\"Number of timepoints\\\", number_of_timepoints)\\n print(\\\"number of pixels\\\", number_of_pixels)\\n print(\\\"number of trials\\\", number_of_trials)\\n\\n # Create P and Slope Tensors\\n p_value_tensor = np.ones(number_of_pixels)\\n slope_tensor = np.zeros(number_of_pixels)\\n\\n # Get Timepoint Data\\n timepoint_activity = activity_dataset[:, window]\\n print(\\\"Timepoint activity shape\\\", np.shape(timepoint_activity))\\n timepoint_activity = np.mean(timepoint_activity, axis=1)\\n\\n for pixel_index in tqdm(range(number_of_pixels), position=1, desc=\\\"Pixel\\\", leave=False):\\n\\n # Package Into Dataframe\\n pixel_activity = timepoint_activity[:, pixel_index]\\n pixel_dataframe = repackage_data_into_dataframe(pixel_activity, metadata_dataset)\\n\\n # Fit Mixed Effects Model\\n p_value, slope = mixed_effects_random_slope_and_intercept(pixel_dataframe)\\n p_value_tensor[pixel_index] = p_value\\n slope_tensor[pixel_index] = slope\\n\\n return p_value_tensor, slope_tensor\",\n \"def test_active_inference_SPM_1b(self):\",\n \"def test_predictor():\",\n \"def testNanotecSharedMemory():\\n\\t\\n\\tmemoryClient = NanotecSharedMemoryClient()\\n\\t\\n\\t\\n\\t\\n\\tserialPort1 = memoryClient.sendInstruction(\\\"NanotecMotor,/dev/ttyACM0,17\\\")\\n\\t\\n\\tID1 = memoryClient.sendInstruction(\\\"getID,\\\" + serialPort1)\\n\\tprint(\\\"ID1 (actual): \\\" + ID1 + \\\", expected: 17\\\")\\n\\tprint(\\\"\\\")\\n\\t\\n\\tstartTime = time.time()\\n\\tnumCalls = 1000\\n\\tfor _ in range(numCalls):\\n\\t\\tID1 = memoryClient.sendInstruction(\\\"getSerialPort,\\\" + serialPort1)\\n\\tendTime = time.time()\\n\\ttotalTime = endTime - startTime\\n\\tprint(\\\"\\\")\\n\\tprint(\\\"ID Call\\\")\\n\\tprint(\\\"numCalls: \\\" + str(numCalls))\\n\\tprint(\\\"total time: \\\" + str(totalTime))\\n\\tprint(\\\"time per call: \\\" + str(totalTime/numCalls))\\n\\t\\n\\t\\n\\tstartTime = time.time()\\n\\tnumCalls = 1000\\n\\tfor _ in range(numCalls):\\n\\t\\tposition = memoryClient.sendInstruction(\\\"getAbsoluteAngularPosition,\\\" + serialPort1)\\n\\tendTime = time.time()\\n\\ttotalTime = endTime - startTime\\n\\tprint(\\\"\\\")\\n\\tprint(\\\"Position Call\\\")\\n\\tprint(\\\"numCalls: \\\" + str(numCalls))\\n\\tprint(\\\"total time: \\\" + str(totalTime))\\n\\tprint(\\\"time per call: \\\" + str(totalTime/numCalls))\\n\\t\\n\\treturn\",\n \"def setUp(self):\\n\\n serial_times = {295: '1971-07-31T01:24:11.754',\\n 296: '1971-07-31T01:24:36.970',\\n 297: '1971-07-31T01:25:02.243',\\n 298: '1971-07-31T01:25:27.457',\\n 299: '1971-07-31T01:25:52.669',\\n 300: '1971-07-31T01:26:17.923'}\\n self.serials = ['APOLLO15/METRIC/{}'.format(i) for i in serial_times.values()]\\n\\n\\n x = list(range(5))\\n y = list(range(5))\\n pid = [0,0,1,1,1]\\n idx = pid\\n serials = [self.serials[0], self.serials[1], self.serials[2],\\n self.serials[2], self.serials[3]]\\n\\n\\n columns = ['x', 'y', 'idx', 'pid', 'nid']\\n self.data_length = 5\\n\\n data = [x,y, idx, pid, serials]\\n\\n self.creation_time = strftime(\\\"%Y-%m-%d %H:%M:%S\\\", gmtime())\\n cnet = C(data, index=columns).T\\n\\n io_controlnetwork.to_isis('test.net', cnet, mode='wb', targetname='Moon')\\n\\n self.header_message_size = 85\\n self.point_start_byte = 65621\",\n \"def test_anchor_output():\\n anchorDict = {\\n \\\"ReplicationFactor\\\": 2,\\n # Accl factor must divide batch size\\n \\\"AccumulationFactor\\\": 4,\\n \\\"Pipelining\\\": True,\\n \\\"ReturnType\\\": \\\"ALL\\\",\\n }\\n label_array = np.ones([BATCH_SIZE]).astype(np.int32)\\n\\n micro_batch_size = BATCH_SIZE // (\\n anchorDict[\\\"AccumulationFactor\\\"] * anchorDict[\\\"ReplicationFactor\\\"]\\n )\\n\\n builder = popart.Builder()\\n input_shape = [micro_batch_size, CHANNELS, DATA_LEN, DATA_LEN]\\n\\n data_shape = popart.TensorInfo(\\\"FLOAT\\\", input_shape)\\n lbl_shape = popart.TensorInfo(\\\"INT32\\\", [micro_batch_size])\\n w = builder.addInitializedInputTensor(\\n np.random.random_sample(input_shape).astype(np.float32)\\n )\\n\\n ip = builder.addInputTensor(data_shape)\\n lb = builder.addInputTensor(lbl_shape)\\n\\n a = builder.aiOnnx.matmul([ip, w])\\n o = builder.reshape_const(\\n builder.aiOnnx, [a], [micro_batch_size, CHANNELS * DATA_LEN * DATA_LEN]\\n )\\n o = builder.aiOnnx.relu([o])\\n o = builder.aiOnnx.softmax([o])\\n nll = builder.aiGraphcore.nllloss([o, lb])\\n\\n GRAD = popart.reservedGradientPrefix() + w\\n ACCL = popart.reservedAccumPrefix() + w\\n art = popart.AnchorReturnType(\\\"All\\\")\\n data_flow = popart.DataFlow(\\n BATCHES_PER_STEP, {o: art, a: art, ip: art, w: art, GRAD: art, ACCL: art}\\n )\\n\\n opts, deviceContext = return_options(anchorDict)\\n with deviceContext as device:\\n if device is None:\\n pytest.skip(\\\"Test needs to run on IPU, but none are available\\\")\\n\\n session = popart.TrainingSession(\\n fnModel=builder.getModelProto(),\\n dataFlow=data_flow,\\n loss=nll,\\n optimizer=popart.ConstSGD(LEARNING_RATE),\\n userOptions=opts,\\n deviceInfo=device,\\n )\\n\\n session.prepareDevice()\\n\\n if anchorDict[\\\"ReplicationFactor\\\"] > 1:\\n input_shape = [anchorDict[\\\"ReplicationFactor\\\"]] + input_shape\\n label_array = label_array.reshape([anchorDict[\\\"ReplicationFactor\\\"], -1])\\n if anchorDict[\\\"AccumulationFactor\\\"] > 1:\\n input_shape = [anchorDict[\\\"AccumulationFactor\\\"]] + input_shape\\n label_array = label_array.reshape([anchorDict[\\\"AccumulationFactor\\\"], -1])\\n if BATCHES_PER_STEP > 1:\\n input_shape = [BATCHES_PER_STEP] + input_shape\\n label_array = np.repeat(label_array[np.newaxis], BATCHES_PER_STEP, 0)\\n\\n anchors = session.initAnchorArrays()\\n in_array = np.random.random_sample(input_shape).astype(np.float32)\\n\\n stepio = popart.PyStepIO({ip: in_array, lb: label_array}, anchors)\\n session.weightsFromHost()\\n\\n session.run(stepio)\\n\\n # Returned anchors will be of shape\\n # [bps, grad_accl_factor, repl_factor, micro_batch_size, channels, data_len, data_len]\\n for batch in range(anchors[w].shape[0]):\\n for replica in range(anchors[w].shape[1]):\\n # Weights should not change over the gradient accumulation\\n # dimension - only after gradAccl steps.\\n assert np.allclose(\\n anchors[w][batch, 0, :, :, :, :, :],\\n anchors[w][batch, replica, :, :, :, :, :],\\n )\\n\\n # Check that the accumulated gradient plus the weights for the current batch\\n # equals the weights for the next batch.\\n # Batch loop\\n for batch in range(anchors[w].shape[0] - 1):\\n calc_weight = {}\\n # Replica loop.\\n for replica in range(anchors[w].shape[2]):\\n # For each replica in each batch, take the relevant replica's\\n # last weight tensor in the accumulation loop minus\\n # the sum of the accumulated gradients across replicas\\n calc_weight[replica] = anchors[w][\\n batch, -1, replica, :, :, :, :\\n ] - np.sum(anchors[ACCL][batch, -1, :, :, :, :, :], axis=0)\\n # Then compare against the last weight tensor of the next batch,\\n # for the relevant replica. These should match.\\n assert np.allclose(\\n calc_weight[replica], anchors[w][batch + 1, -1, replica, :, :, :, :]\\n )\",\n \"def test_add_get_tensor_2D(mock_data):\\n dataset = Dataset(\\\"test-dataset\\\")\\n\\n # 2D tensors of all data types\\n data_2D = mock_data.create_data((10, 10))\\n add_get_arrays(dataset, data_2D)\",\n \"def test_large_import(self):\\n self.create_sample_data_set_dir(\\\"node59p1.dat\\\", TELEM_DIR)\\n self.assert_initialize()\\n result = self.data_subscribers.get_samples(DataParticleType.METADATA_TELEMETERED,1,60)\\n result = self.data_subscribers.get_samples(DataParticleType.SAMPLE_TELEMETERED,750,400)\",\n \"def test_case_store_data_memory( self ):\\n MEM_AUX = memory.fetch_system_memory()\\n def test( clk, chip_select, address, load_store, data_in, data_out ):\\n count = len( MEM_AUX )\\n for i in range( count+10 , count+100 ):\\n chip_select.next = 1\\n load_store.next = 1\\n address.next = i\\n data_in.next = i*32\\n MEM_AUX[i]=i*32\\n yield clk.posedge\\n yield clk.posedge\\n #chip_select.next = 1\\n load_store.next = 0\\n #address.next = i\\n data_in.next = 0\\n yield clk.posedge\\n yield clk.posedge\\n chip_select.next = 0\\n self.assertEqual( data_out, MEM_AUX[i] )\\n\\n raise StopSimulation\\n\\n clk_s = Signal( bool( 1 ) )\\n clkgen = clk_gen( clk_s )\\n address, data_in, data_out =[Signal( intbv( 0 )[32:] ) for i in range( 3 )]\\n chip_select, load_store = [Signal( bool( 0 ) ) for i in range( 2 )]\\n\\n mem_test = memory.memory( clk_s, chip_select, address, load_store, data_in, data_out )\\n #check = test( clk_s, chip_select, address, load_store, data_in, data_out )\\n #sim = Simulation( mem_test, check, clkgen )\\n te = traceSignals(test, clk_s, chip_select, address, load_store, data_in, data_out)\\n sim = Simulation( mem_test, te, clkgen )\\n sim.run( quiet=1 )\",\n \"def test(args, device, test_generator, model):\\n model.eval()\\n\\n with torch.no_grad():\\n # Get inputs and labels\\n inputs, inputs_prev, labels, image, _, omit = test_generator.generate_batch()\\n\\n # Send to device\\n inputs = torch.from_numpy(inputs).to(device)\\n inputs_prev = torch.from_numpy(inputs_prev).to(device)\\n labels = torch.from_numpy(labels).to(device)\\n\\n # Initialize syn_x or hidden state\\n model.syn_x = model.init_syn_x(args.batch_size).to(device)\\n model.hidden = model.init_hidden(args.batch_size).to(device)\\n\\n output, hidden, inputs = model(inputs, inputs_prev)\\n # Convert to binary prediction\\n output = torch.sigmoid(output)\\n pred = torch.bernoulli(output).byte()\\n\\n # Compute hit rate and false alarm rate\\n hit_rate = (pred * (labels == 1)).sum().float().item() / \\\\\\n (labels == 1).sum().item()\\n fa_rate = (pred * (labels == -1)).sum().float().item() / \\\\\\n (labels == -1).sum().item()\\n\\n # Compute dprime\\n # dprime_true = dprime(hit_rate, fa_rate)\\n go = (labels == 1).sum().item()\\n catch = (labels == -1).sum().item()\\n num_trials = (labels != 0).sum().item()\\n assert (go + catch) == num_trials\\n\\n # dprime_true = compute_dprime(hit_rate, fa_rate, go, catch, num_trials)\\n # dprime_old = dprime(hit_rate, fa_rate)\\n dprime_true = dprime(hit_rate, fa_rate)\\n # try:\\n # assert dprime_true == dprime_old\\n # except:\\n # print(hit_rate, fa_rate)\\n # print(dprime_true, dprime_old)\\n\\n return dprime_true.item(), hit_rate, fa_rate, inputs, hidden, output, pred, image, labels, omit\",\n \"def make_request(dataset, path, out, session):\\n reader = tf.python_io.tf_record_iterator(\\n path, options=tf.python_io.TFRecordOptions(\\n compression_type=TFRecordCompressionType.GZIP))\\n print >> sys.stderr, path\\n for buf in reader:\\n examples_tensor = json.loads(\\n session.graph.get_collection('inputs')[0])['examples']\\n output_tensor = json.loads(\\n session.graph.get_collection('outputs')[0])[FLAGS.fetch]\\n outputs = session.run(fetches=[output_tensor],\\n feed_dict={examples_tensor: [buf]})\\n example = tf.train.Example.FromString(buf)\\n sample_name = example.features.feature['sample_name'].bytes_list.value[0]\\n super_population = example.features.feature[\\n 'super_population_string'].bytes_list.value[0]\\n population = example.features.feature[\\n 'population_string'].bytes_list.value[0]\\n print >> out, '%s\\\\t%s\\\\t%s\\\\t%s\\\\t%s' % (\\n dataset, sample_name, super_population, population, '\\\\t'.join(\\n str(value) for value in outputs[0][0]))\",\n \"def step(self, observation, last_state):\\n # We are omitting the details of network inference here.\\n # ...\\n feature_screen = observation[3]['feature_screen']\\n feature_minimap = observation[3]['feature_minimap']\\n feature_units = observation[3]['feature_units']\\n feature_player = observation[3]['player']\\n available_actions = observation[3]['available_actions']\\n score_by_category = observation[3]['score_by_category']\\n game_loop = observation[3]['game_loop']\\n\\n unit_type = feature_screen.unit_type\\n empty_space = np.where(unit_type == 0)\\n empty_space = np.vstack((empty_space[0], empty_space[1])).T\\n random_point = random.choice(empty_space)\\n #target = [random_point[0], random_point[1]]\\n #action = [actions.FunctionCall(_BUILD_SUPPLY_DEPOT, [_NOT_QUEUED, target])]\\n policy_logits = None\\n new_state = None\\n\\n spatial_encoder_output = self.spatial_encoder(np.reshape(feature_screen, [1,128,128,27]))\\n\\n agent_statistics = get_agent_statistics(score_by_category)\\n\\n home_race = 'Terran'\\n away_race = 'Terran'\\n race = get_race_onehot(home_race, away_race)\\n\\n time = get_gameloop_obs(game_loop)\\n\\n upgrade_value = get_upgrade_obs(feature_units)\\n if upgrade_value != -1:\\n self.home_upgrade_array[np.where(upgrade_value[0] == 1)] = 1\\n self.away_upgrade_array[np.where(upgrade_value[1] == 1)] = 1\\n\\n embedded_scalar = np.concatenate((agent_statistics, race, time, self.home_upgrade_array, self.away_upgrade_array), axis=0)\\n scalar_encoder_output = self.scalar_encoder(np.reshape(embedded_scalar, [1,307]))\\n embedded_feature_units = get_entity_obs(feature_units)\\n entity_encoder_output = self.entity_encoder(np.reshape(embedded_feature_units, [1,512,464]))\\n encoder_input = np.concatenate((spatial_encoder_output, scalar_encoder_output, entity_encoder_output), axis=1)\\n\\n core_input = np.reshape(encoder_input, [16, 8, 131])\\n whole_seq_output, final_memory_state, final_carry_state = self.core(core_input)\\n print(whole_seq_output.shape)\\n print(final_memory_state.shape)\\n print(final_carry_state.shape)\\n\\n action = [actions.FUNCTIONS.no_op()]\\n\\n return action, policy_logits, new_state\",\n \"def test_create_device1(self):\\n pass\",\n \"def test_add_get_tensor_3D(mock_data):\\n dataset = Dataset(\\\"test-dataset\\\")\\n\\n # 3D tensors of all datatypes\\n data_3D = mock_data.create_data((10, 10, 10))\\n add_get_arrays(dataset, data_3D)\",\n \"def setUp(self):\\n output = np.zeros((1, 5, 2))\\n target = np.zeros((1, 5, 2))\\n # first channel\\n output[0, 0] = [10, 4]\\n target[0, 0] = [10, 0]\\n # second channel\\n output[0, 1] = [10, 18]\\n target[0, 1] = [10, 10]\\n # third channel\\n output[0, 2] = [0, 0]\\n target[0, 2] = [0, -1]\\n # fourth channel\\n output[0, 3] = [40, 40]\\n target[0, 3] = [30, 30]\\n # fifth channel\\n output[0, 4] = [20, 10]\\n target[0, 4] = [0, 10]\\n\\n gt_instances = InstanceData()\\n gt_instances.keypoints = target\\n gt_instances.keypoints_visible = np.array(\\n [[True, True, False, True, True]])\\n\\n pred_instances = InstanceData()\\n pred_instances.keypoints = output\\n\\n data = {'inputs': None}\\n data_sample = {\\n 'gt_instances': gt_instances.to_dict(),\\n 'pred_instances': pred_instances.to_dict()\\n }\\n\\n self.data_batch = [data]\\n self.data_samples = [data_sample]\",\n \"def test5():\\n setLogLevel(\\\"info\\\")\\n info(\\\"Configuracion Unidad experimental\\\")\\n \\\"\\\"\\\" 1 -> Definicion de la topologia \\\"\\\"\\\"\\n t1 = Topologia1()\\n ue1 = UnidadExperimental()\\n ue1.setTopo(t1)\\n ue1.definirNodosClaves(A = 'h1', C='h2', V='h3') # Caso solo para trafico normal\\n ue1.setController('ryu', 'simple_switch_13.py,ofctl_rest.py')\\n info(\\\"Configuracion del experimento\\\")\\n \\\"\\\"\\\" 3. Confiracion del experimento \\\"\\\"\\\"\\n exp1 = Experimento()\\n exp1.configureParams(ue1)\\n exp1.configurarTrafico('ataque')\\n \\\"\\\"\\\" 4. Inicio del experimento \\\"\\\"\\\"\\n exp1.startTest()\\n exp1.pingAllTest() # **************** Parece que es necesario que se de un arranque al controlador\\n # **************** para que aprenda las reglas antes del ataque.\\n\\n \\\"\\\"\\\" 5. Aplicacion de pruebas \\\"\\\"\\\"\\n exp1.trafico.pingMeasure()\\n #exp1.trafico.pingMeasure(filename='ping_ataque_test.log')\\n \\\"\\\"\\\" 6. Fin del experimento \\\"\\\"\\\"\\n exp1.endTest()\\n info(\\\"Removiendo la topologia\\\\n\\\")\\n exp1.killTest()\\n info(\\\"Removiendo el controlador\\\\n\\\")\\n exp1.killController() # Si no se pone no se finaliza el controlador\",\n \"def test_IODimensions(self):\\n tasks = [(1,1,100,True),(10,1,100,True),(1,10,100,True),(10,10,100,True),\\n (1,1,100,False),(10,1,100,False),(1,10,100,False),(10,10,100,False)]\\n for t in tasks:\\n N_in ,N_out, N_samples, tf = t\\n X = np.random.randn(N_samples,N_in) if N_in > 1 else np.random.randn(N_samples)\\n y = np.random.randn(N_samples,N_out) if N_out > 1 else np.random.randn(N_samples)\\n Xp = np.random.randn(N_samples,N_in) if N_in > 1 else np.random.randn(N_samples)\\n esn = ESN(N_in,N_out,teacher_forcing=tf)\\n prediction_tr = esn.fit(X,y)\\n prediction_t = esn.predict(Xp)\\n self.assertEqual(prediction_tr.shape,(N_samples,N_out))\\n self.assertEqual(prediction_t.shape,(N_samples,N_out))\",\n \"def gen_ep_data(self,ntrials,trlen):\\n self.resort_emat()\\n ## instruction\\n # for each trial, generate random instruction encoding sequence\\n i_encoding_input = np.array([\\n np.random.permutation(np.arange(1,self.nmaps+1)) \\n for i in range(ntrials)\\n ])\\n i_test_input = np.zeros([ntrials,trlen])\\n i_input = np.concatenate([\\n i_encoding_input,i_test_input],\\n 1).astype(int).reshape(-1) # (ntrials,trlen+)\\n ## stimulus\\n x_encoding_input = i_encoding_input\\n x_test_input = np.random.randint(1,self.nmaps+1,[ntrials,trlen])\\n x_input = np.concatenate([x_encoding_input,x_test_input],1)\\n # print('X',x_input)\\n ''' \\n embed x_input: \\n [ntrials,nmaps+trlen] -> s_input [ntrials*(nmaps+trlen),edim]\\n explicit loop required for flatten and embedd x_input\\n because if switchmaps=1, matrix is resorted between trials\\n and therefore same stimulus token integers correspond to\\n different stimulus embeddings on different trials\\n '''\\n s_input = -np.ones([ntrials,(self.nmaps+trlen),self.stimdim])\\n for trialn,x_input_trial in enumerate(x_input): \\n if self.switchmaps: self.resort_emat()\\n s_input[trialn] = self.emat[x_input_trial]\\n # format output\\n i_input = tr.unsqueeze(tr.LongTensor(i_input),1)\\n s_input = tr.unsqueeze(tr.Tensor(np.concatenate(s_input)),1)\\n yseq = tr.unsqueeze(tr.LongTensor(x_input.reshape(-1)),1)\\n return i_input,s_input,yseq\",\n \"def test_qpu_0_shots():\\n _aws_device(wires=2, shots=0)\",\n \"def test_data():\\n batch_size = 10\\n input_dim = 28\\n test_data = np.random.rand(batch_size, input_dim)\\n\\n return test_data\",\n \"def test_get_node_sensors(self):\\n pass\",\n \"def Test(self):\\n print('Testing:')\\n # set mode eval\\n torch.cuda.empty_cache()\\n self.network.eval()\\n transform = transforms.Compose([Rescale(params.rescale_size),\\n RandomCrop(params.image_size),\\n \\n ToTensor()\\n ])\\n dataset = Cityscapes(params.dataset_root, mode='test', transforms = transform)\\n test_loader = DataLoader(dataset,\\n batch_size=params.test_batch,\\n shuffle=params.shuffle,\\n num_workers=params.dataloader_workers)\\n # prepare test data\\n recal = 0\\n precision = 0\\n F_one = 0\\n IOU = 0\\n accuracy_new = 0\\n test_size = 1124\\n if test_size % self.params.test_batch != 0:\\n total_batch = test_size // self.params.test_batch + 1\\n else:\\n total_batch = test_size // self.params.test_batch\\n\\n # test for one epoch\\n for batch_idx, batch in enumerate(test_loader):\\n self.pb.click(batch_idx, total_batch)\\n image, label, name = batch['image'], batch['label'], batch['label_name']\\n image_cuda, label_cuda = image.cuda(), label.cuda()\\n pred = image_cuda\\n pred = pred.to(torch.device(\\\"cpu\\\"))\\n pred = pred.detach()\\n img_grid = pred[0]\\n #img_grid = torchvision.utils.make_grid(out) \\n img_grid = img_grid.numpy().transpose(1, 2, 0)*255\\n cv2.imwrite(\\\"/content/drive/My Drive/Test_images/original%d.jpg\\\" % batch_idx, img_grid)\\n if self.params.should_split:\\n image_cuda.requires_grad_()\\n out = checkpoint_sequential(self.network, self.params.split, image_cuda)\\n else:\\n out = self.network(image_cuda)\\n TP, FP, TN, FN = confusion(out, label_cuda)\\n recal = recal+TP\\n precision = precision+FP\\n F_one = F_one +TN\\n IOU = IOU+ FN \\n _,predict = torch.max(out.data,1)\\n predict = predict.to(torch.device(\\\"cpu\\\"))\\n predict = predict.detach()\\n img = predict[0]\\n img = img.numpy()*255\\n #img_grid = torchvision.utils.make_grid(out) \\n cv2.imwrite(\\\"/content/drive/My Drive/Test_images/predict_label%d.png\\\" % batch_idx, img)\\n label = label_cuda.to(torch.device(\\\"cpu\\\"))\\n label = label.detach()\\n label = label[0].numpy()*255\\n cv2.imwrite(\\\"/content/drive/My Drive/Test_images/original_label%d.png\\\" % batch_idx, label)\\n\\n accuracy_final = accuracy(out, label_cuda)\\n accuracy_new = accuracy_new + accuracy_final\\n print(\\\"\\\\t\\\")\\n print(recal/total_batch, precision/ total_batch, F_one/ total_batch, IOU/ total_batch)\\n print(\\\"\\\\t\\\")\\n print(accuracy_new/total_batch)\",\n \"def _sample(self, rnn_output, temperature):\\n pass\",\n \"def test_teleport(self):\\n self.log.info('test_teleport')\\n pi = np.pi\\n shots = 2000\\n qr = QuantumRegister(3, 'qr')\\n cr0 = ClassicalRegister(1, 'cr0')\\n cr1 = ClassicalRegister(1, 'cr1')\\n cr2 = ClassicalRegister(1, 'cr2')\\n circuit = QuantumCircuit(qr, cr0, cr1, cr2, name='teleport')\\n circuit.h(qr[1])\\n circuit.cx(qr[1], qr[2])\\n circuit.ry(pi/4, qr[0])\\n circuit.cx(qr[0], qr[1])\\n circuit.h(qr[0])\\n circuit.barrier(qr)\\n circuit.measure(qr[0], cr0[0])\\n circuit.measure(qr[1], cr1[0])\\n circuit.z(qr[2]).c_if(cr0, 1)\\n circuit.x(qr[2]).c_if(cr1, 1)\\n circuit.measure(qr[2], cr2[0])\\n job = execute(circuit, backend=self.backend, shots=shots, seed_simulator=self.seed)\\n results = job.result()\\n data = results.get_counts('teleport')\\n alice = {\\n '00': data['0 0 0'] + data['1 0 0'],\\n '01': data['0 1 0'] + data['1 1 0'],\\n '10': data['0 0 1'] + data['1 0 1'],\\n '11': data['0 1 1'] + data['1 1 1']\\n }\\n bob = {\\n '0': data['0 0 0'] + data['0 1 0'] + data['0 0 1'] + data['0 1 1'],\\n '1': data['1 0 0'] + data['1 1 0'] + data['1 0 1'] + data['1 1 1']\\n }\\n self.log.info('test_teleport: circuit:')\\n self.log.info(circuit.qasm())\\n self.log.info('test_teleport: data %s', data)\\n self.log.info('test_teleport: alice %s', alice)\\n self.log.info('test_teleport: bob %s', bob)\\n alice_ratio = 1/np.tan(pi/8)**2\\n bob_ratio = bob['0']/float(bob['1'])\\n error = abs(alice_ratio - bob_ratio) / alice_ratio\\n self.log.info('test_teleport: relative error = %s', error)\\n self.assertLess(error, 0.05)\",\n \"def testOutputs(self):\\n # Remember original (correct) example outputs\\n old_files = self.read_outputs()\\n\\n # Set up and run Xanthos\\n ini = 'example/pm_abcd_mrtm.ini'\\n xth = Xanthos(ini)\\n res = xth.execute()\\n\\n # Check result dimensions\\n self.assertEqual(res.Q.shape, (67420, 372))\\n\\n # Test that new outputs equal old outputs.\\n new_files = self.read_outputs()\\n for k in new_files.keys():\\n pd.testing.assert_frame_equal(new_files[k], old_files[k])\",\n \"def simulatesensor_mqtt_device_demo(args):\\n # [START iot_mqtt_run]\\n global minimum_backoff_time\\n global MAXIMUM_BACKOFF_TIME\\n\\n # Publish to the events or state topic based on the flag.\\n sub_topic = 'events' if args.message_type == 'event' else 'state'\\n\\n mqtt_topic = '/devices/{}/{}'.format(args.device_id, sub_topic)\\n\\n jwt_iat = datetime.datetime.utcnow()\\n jwt_exp_mins = args.jwt_expires_minutes\\n client = get_client(\\n args.project_id, args.cloud_region, args.registry_id,\\n args.device_id, args.private_key_file, args.algorithm,\\n args.ca_certs, args.mqtt_bridge_hostname, args.mqtt_bridge_port)\\n\\n # Publish num_messages messages to the MQTT bridge once per second.\\n for i in range(1, args.num_messages + 1):\\n client.loop()\\n\\n currentTime = datetime.datetime.utcnow().strftime('%Y-%m-%d %H:%M:%S')\\n (ram_usage, cpu_usage, number_of_cores, number_of_processes, battery_percentage) = read_sensor(i)\\n\\n payloadJSON = createJSON(args.registry_id, args.device_id, currentTime, ram_usage, cpu_usage, number_of_cores, number_of_processes, battery_percentage)\\n\\n #payload = '{}/{}-image-{}'.format(args.registry_id, args.device_id, i)\\n print('Publishing message {}/: \\\\'{}\\\\''.format(\\n i, payloadJSON))\\n\\n # Publish \\\"payload\\\" to the MQTT topic. qos=1 means at least once\\n # delivery. Cloud IoT Core also supports qos=0 for at most once\\n # delivery.\\n client.publish(mqtt_topic, payloadJSON, qos=1)\\n\\n \\n # Send events every second. State should not be updated as often\\n time.sleep(1)\",\n \"def test_create_device(self):\\n pass\",\n \"def test_create_device(self):\\n pass\",\n \"def get_data(datapath,n=10):\\n device = \\\"cuda\\\" if torch.cuda.is_available() else \\\"cpu\\\"\\n input_path = datapath + \\\"/maze_data_test_59/inputs.npy\\\"\\n target_path = datapath + \\\"/maze_data_test_59/solutions.npy\\\"\\n data = np.load(input_path)\\n target = np.load(target_path)\\n a = data[:n]\\n a = torch.from_numpy(a)\\n input = a.to(device, dtype=torch.float)\\n b = target[:n]\\n t = torch.from_numpy(b)\\n t = t.to(device, dtype=torch.float)\\n target = t\\n return input, target\",\n \"def test_mlp():\\r\\n datasets = gen_data()\\r\\n\\r\\n train_set_x, train_set_y = datasets[0]\\r\\n valid_set_x, valid_set_y = datasets[1]\\r\\n test_set_x , test_set_y = datasets[2]\\r\\n\\r\\n\\r\\n\\r\\n batch_size = 100 # size of the minibatch\\r\\n\\r\\n # compute number of minibatches for training, validation and testing\\r\\n n_train_batches = train_set_x.get_value(borrow=True).shape[0] / batch_size\\r\\n n_valid_batches = valid_set_x.get_value(borrow=True).shape[0] / batch_size\\r\\n n_test_batches = test_set_x.get_value(borrow=True).shape[0] / batch_size\\r\\n\\r\\n ######################\\r\\n # BUILD ACTUAL MODEL #\\r\\n ######################\\r\\n #print '... building the model'\\r\\n\\r\\n # allocate symbolic variables for the data\\r\\n index = T.lscalar() # index to a [mini]batch\\r\\n x = T.matrix('x') # the data is presented as rasterized images\\r\\n y = T.ivector('y') # the labels are presented as 1D vector of\\r\\n # [int] labels\\r\\n\\r\\n rng = numpy.random.RandomState(1234)\\r\\n\\r\\n # construct the MLP class\\r\\n classifier = MLP( rng = rng, input=x, n_in=28*28, n_hidden = 500, n_out=10)\\r\\n\\r\\n # the cost we minimize during training is the negative log likelihood of\\r\\n # the model.\\r\\n # We take the mean of the cost over each minibatch.\\r\\n cost = classifier.negative_log_likelihood(y).mean()\\r\\n\\r\\n # compute the gradient of cost with respect to theta (stored in params)\\r\\n # the resulting gradients will be stored in a list gparams\\r\\n gparams = []\\r\\n for param in classifier.params:\\r\\n gparam = T.grad(cost, param)\\r\\n gparams.append(gparam)\\r\\n\\r\\n # Some optimizations needed are tagged with 'fast_run'\\r\\n # TODO: refine that and include only those\\r\\n mode = theano.compile.get_default_mode().including('fast_run')\\r\\n\\r\\n updates2 = OrderedDict()\\r\\n\\r\\n updates2[classifier.hiddenLayer.params[0]]=T.grad(cost,classifier.hiddenLayer.params[0])\\r\\n train_model =theano.function( inputs = [index],\\r\\n updates = updates2,\\r\\n givens={\\r\\n x:train_set_x[index*batch_size:(index+1)*batch_size],\\r\\n y:train_set_y[index*batch_size:(index+1)*batch_size]},\\r\\n mode=mode)\\r\\n #print 'MODEL 1'\\r\\n #theano.printing.debugprint(train_model, print_type=True)\\r\\n assert any([isinstance(i.op,T.nnet.CrossentropySoftmax1HotWithBiasDx) for i in train_model.maker.fgraph.toposort()])\\r\\n\\r\\n # Even without FeatureShape\\r\\n train_model =theano.function( inputs = [index],\\r\\n updates = updates2,\\r\\n mode=mode.excluding('ShapeOpt'),\\r\\n givens={\\r\\n x:train_set_x[index*batch_size:(index+1)*batch_size],\\r\\n y:train_set_y[index*batch_size:(index+1)*batch_size]})\\r\\n #print\\r\\n #print 'MODEL 2'\\r\\n #theano.printing.debugprint(train_model, print_type=True)\\r\\n assert any([isinstance(i.op,T.nnet.CrossentropySoftmax1HotWithBiasDx) for i in train_model.maker.fgraph.toposort()])\",\n \"def test():\\n parser = argparse.ArgumentParser()\\n parser.add_argument('-n', '--name', type=str, help='name of the model',\\n default='model_new_o')\\n parser.add_argument('-f', '--filename', type=str,\\n help='name of the dataset (.h5 file)', default='./dataset.h5')\\n parser.add_argument('-bs', '--batch-size', type=int,\\n help='size of the batches of the training data', default=256)\\n args = parser.parse_args()\\n\\n name = args.name\\n filename = args.filename\\n batch_size = args.batch_size\\n\\n out_channels = 400\\n model_path = './model/' + name\\n checkpoint_path = model_path + '/checkpoints'\\n device = 'cuda' if torch.cuda.is_available() else 'cpu'\\n\\n for k, v in vars(args).items():\\n print('{0} = \\\"{1}\\\"'.format(k, v))\\n print('device = \\\"' + device + '\\\"')\\n\\n if not os.path.exists(checkpoint_path):\\n print('Model parameters not found: ' + checkpoint_path)\\n exit()\\n\\n # Dataset\\n\\n input_cols = ['camera', 'pos_x', 'pos_y', 'theta']\\n target_cols = ['target_map']\\n train_test_split = 11\\n\\n dataset = get_dataset(filename, device=device, augment=False,\\n input_cols=input_cols, target_cols=target_cols)\\n split_index = dataset.cumulative_sizes[train_test_split]\\n\\n # Model\\n\\n device = 'cuda' if torch.cuda.is_available() else 'cpu'\\n model = NN(in_channels=3, out_channels=out_channels).to(device)\\n model.load_state_dict(torch.load(checkpoint_path + '/best.pth'))\\n summary(model, (3, 64, 80), device=device)\\n\\n auc_function = MaskedAUROC()\\n\\n # Testing\\n\\n aucs = []\\n for x, px, py, pt, y in dataset.batches(batch_size, start=split_index, shuffle=False):\\n pose = torch.stack([px, py, pt], dim=-1).to(device)\\n mask = y > -1\\n\\n preds = model(x)\\n\\n aucs.append(auc_function(preds, y, mask).cpu().numpy())\\n\\n auc = np.nanmean(aucs, axis=0).reshape(20, 20)\\n auc = np.rot90(auc, 1)\\n auc = np.fliplr(auc) * 100\\n\\n print('AUC: ' + str(auc.mean().item()))\\n\\n print(auc)\\n\\n rounded = (100 * coords).round(2).astype(int)\\n fig, ax = plt.subplots(nrows=2, ncols=1, figsize=(7, 5.8))\\n sns.distplot(auc, bins=int(np.ceil(auc.max() - auc.min())),\\n ax=ax[0], kde=False, rug=False, color='red', hist_kws={'rwidth': 0.75})\\n sns.heatmap(auc, cmap='gray', annot=True, cbar_kws={'shrink': .8},\\n vmin=50, vmax=100, linewidths=0, ax=ax[1])\\n plt.yticks(.5 + np.arange(20), np.unique(rounded[:, 0])[::-1])\\n plt.xticks(.5 + np.arange(20), np.unique(rounded[:, 1]))\\n plt.xlabel('Y [cm]')\\n plt.ylabel('X [cm]')\\n plt.setp(ax[1].xaxis.get_majorticklabels(), rotation=0)\\n plt.setp(ax[1].yaxis.get_majorticklabels(), rotation=0)\\n plt.axis('equal')\\n plt.tight_layout()\\n plt.show()\",\n \"def test_full_house_flush_ind(self):\",\n \"def test_batch(self):\\n pass\",\n \"def test_op(self):\\n name = \\\"my_mesh\\\"\\n tensor_data = test_utils.get_random_mesh(\\n 100, add_faces=True, add_colors=True\\n )\\n config_dict = {\\\"foo\\\": 1}\\n with tf.compat.v1.Graph().as_default():\\n tensor_summary = summary.op(\\n name,\\n tensor_data.vertices,\\n faces=tensor_data.faces,\\n colors=tensor_data.colors,\\n config_dict=config_dict,\\n )\\n with self.test_session() as sess:\\n proto = self.pb_via_op(tensor_summary)\\n self.verify_proto(proto, name)\\n plugin_metadata = metadata.parse_plugin_metadata(\\n proto.value[0].metadata.plugin_data.content\\n )\\n self.assertEqual(\\n json.dumps(config_dict, sort_keys=True),\\n plugin_metadata.json_config,\\n )\",\n \"def test_auto_transfer_correct_device(ray_start_4_cpus_2_gpus):\\n import nvidia_smi\\n\\n nvidia_smi.nvmlInit()\\n\\n def get_gpu_used_mem(i):\\n handle = nvidia_smi.nvmlDeviceGetHandleByIndex(i)\\n info = nvidia_smi.nvmlDeviceGetMemoryInfo(handle)\\n return info.used\\n\\n start_gpu_memory = get_gpu_used_mem(1)\\n\\n device = torch.device(\\\"cuda:1\\\")\\n small_dataloader = [(torch.randn((1024 * 4, 1024 * 4)),) for _ in range(10)]\\n wrapped_dataloader = ( # noqa: F841\\n ray.train.torch.train_loop_utils._WrappedDataLoader(\\n small_dataloader, device, True\\n )\\n )\\n\\n end_gpu_memory = get_gpu_used_mem(1)\\n\\n # Verify GPU memory usage increases on the right cuda device\\n assert end_gpu_memory > start_gpu_memory\",\n \"def test_output(data,idx,model):\\n x,y = data[idx]\\n out = model(x)\\n return y.data.cpu().numpy(), out.data.cpu().numpy()\",\n \"def gen_ep_data(self,ntrials,trlen):\\n ## instruction\\n # for each trial, generate random instruction encoding sequence\\n i_encoding_input = np.array([\\n np.random.permutation(np.arange(1,self.nmaps+1)) \\n for i in range(ntrials)\\n ])\\n i_test_input = np.zeros([ntrials,trlen])\\n i_input = np.concatenate([\\n i_encoding_input,i_test_input],\\n 1).astype(int).reshape(-1) # (ntrials,trlen+)\\n ## stimulus\\n x_encoding_input = i_encoding_input\\n x_test_input = np.random.randint(1,self.nmaps+1,[ntrials,trlen])\\n x_input = np.concatenate([i_encoding_input,x_test_input],1)\\n ''' \\n embed x_input: \\n [ntrials,nmaps+trlen] -> s_input [ntrials*(nmaps+trlen),edim]\\n explicit loop required for flatten and embedd x_input\\n because if switchmaps=1, matrix is resorted between trials\\n and therefore same stimulus token integers correspond to\\n different stimulus embeddings on different trials\\n '''\\n s_input = -np.ones([ntrials,(self.nmaps+trlen),self.stimdim])\\n for trialn,x_input_trial in enumerate(x_input): \\n if self.switchmaps: self.resort_emat()\\n s_input[trialn] = self.emat[x_input_trial]\\n \\n # format output\\n i_input = tr.unsqueeze(tr.LongTensor(i_input),1)\\n s_input = tr.unsqueeze(tr.Tensor(np.concatenate(s_input)),1)\\n yseq = tr.unsqueeze(tr.LongTensor(x_input.reshape(-1)),1)\\n if return_trial_flag:\\n tr_flag = np.concatenate([i*np.ones(self.nmaps+trlen) for i in range(ntrials)])\\n tr_flag = tr.unsqueeze(tr.LongTensor(tr_flag),1)\\n return tr_flag,i_input,s_input,yseq,\\n else:\\n return i_input,s_input,yseq\",\n \"def test_get_device_template(self):\\n pass\",\n \"def test_tiff_io_tensor():\\n width = 560\\n height = 320\\n channels = 4\\n\\n images = []\\n for filename in [\\n \\\"small-00.png\\\",\\n \\\"small-01.png\\\",\\n \\\"small-02.png\\\",\\n \\\"small-03.png\\\",\\n \\\"small-04.png\\\"]:\\n with open(\\n os.path.join(os.path.dirname(os.path.abspath(__file__)),\\n \\\"test_image\\\",\\n filename), 'rb') as f:\\n png_contents = f.read()\\n image_v = tf.image.decode_png(png_contents, channels=channels)\\n assert image_v.shape == [height, width, channels]\\n images.append(image_v)\\n\\n filename = os.path.join(\\n os.path.dirname(os.path.abspath(__file__)), \\\"test_image\\\", \\\"small.tiff\\\")\\n filename = \\\"file://\\\" + filename\\n\\n tiff = tfio.IOTensor.from_tiff(filename)\\n assert tiff.keys == list(range(5))\\n for i in tiff.keys:\\n assert np.all(images[i].numpy() == tiff(i).to_tensor().numpy())\",\n \"def test_get_device(self):\\n pass\",\n \"def test_get_device(self):\\n pass\",\n \"def test(self):\\n # Load the trained models.\\n self.train_ev_ea()\\n self.restore_model(self.test_iters)\\n self.encoder_v.eval()\\n # Set data loader.\\n data_loader = self.data_loader\\n empty = torch.FloatTensor(1, 3,self.image_size,self.image_size).to(self.device) \\n empty.fill_(1)\\n noise = torch.FloatTensor(self.batch_size, self.nz_num)\\n noise = noise.to(self.device)\\n step = 0\\n data_loader.test.reinitialize_index()\\n with torch.no_grad():\\n while True:\\n try:\\n x_real, wrong_images, attributes, _, label_org = data_loader.test.next_batch_test(self.batch_size,10)\\n except:\\n break\\n x_real = x_real.to(self.device) \\n label_org = label_org.to(self.device)\\n attributes = attributes.to(self.device)\\n \\n \\n ev_x = self.encoder_v(x_real)\\n noise.normal_(0, 1)\\n ea_a = self.encoder_a(attributes, noise)\\n \\n out_A2B_results = [empty]\\n out_A2B_results_a = [empty]\\n\\n for idx1 in range(label_org.size(0)):\\n out_A2B_results.append(x_real[idx1:idx1+1])\\n out_A2B_results_a.append(x_real[idx1:idx1+1])\\n\\n for idx2 in range(label_org.size(0)):\\n out_A2B_results.append(x_real[idx2:idx2+1])\\n out_A2B_results_a.append(x_real[idx2:idx2+1])\\n \\n for idx1 in range(label_org.size(0)):\\n x_fake = self.decoder(self.encoder(x_real[idx2:idx2+1]), ev_x[idx1:idx1+1])\\n out_A2B_results.append(x_fake)\\n \\n x_fake_a = self.decoder(self.encoder(x_real[idx2:idx2+1]), ea_a[idx1:idx1+1])\\n out_A2B_results_a.append(x_fake_a)\\n results_concat = torch.cat(out_A2B_results)\\n x_AB_results_path = os.path.join(self.result_dir, '{}_x_AB_results_test_v.jpg'.format(step+1)) \\n save_image(self.denorm(results_concat.data.cpu()), x_AB_results_path, nrow=label_org.size(0)+1,padding=0)\\n print('Saved real and fake images into {}...'.format(x_AB_results_path))\\n \\n results_concat = torch.cat(out_A2B_results_a)\\n x_AB_results_path = os.path.join(self.result_dir, '{}_x_AB_results_test_a.jpg'.format(step+1)) \\n save_image(self.denorm(results_concat.data.cpu()), x_AB_results_path, nrow=label_org.size(0)+1,padding=0)\\n print('Saved real and fake images into {}...'.format(x_AB_results_path))\\n \\n step += 1\",\n \"def import_measurements():\\n\\n print('Receive a transfer...')\",\n \"def main():\\n \\\"\\\"\\\"\\n This is just for testing the functions\\n \\\"\\\"\\\"\\n\\n x1 = np.array([1, 1, 1, 1, -1, -1, 1, 1, 1])\\n x2 = np.array([1, -1, 1, 1, 1, 1, 1, -1, 1])\\n x3 = np.array([-1, 1, -1, -1, 1, -1, -1, 1, -1])\\n train_set = np.vstack((x1, x2))\\n train_set = np.vstack((train_set, x3))\\n\\n\\n params = {\\n \\\"epochs\\\": 100,\\n \\\"neurons\\\": len(x1),\\n \\\"learn_method\\\": 'classic'\\n }\\n\\n hop = hop_net.HopfieldNet(train_set, **params)\\n hop.batch_train()\\n show_trained(train_set)\\n\\n x4d = [1,1,1,1,1,1,1,1,1]\\n x5d = [1,1,1,1,-1,-1,1,-1,-1]\\n x45d = np.vstack((x4d, x5d))\\n test_set = np.vstack((x45d, train_set))\\n recalled_set = hop.recall(test_set)\\n for i in range(test_set.shape[0]):\\n show_tested(test_set[i], recalled_set[i])\",\n \"def test_write_broadcast(setup_teardown_file):\\n f = setup_teardown_file[3]\\n\\n dt = np.dtype('(3,)i')\\n\\n dset = f.create_dataset('x', (10,), dtype=dt)\\n dset[...] = 42\",\n \"def simple_unet_data():\\n return tf.constant(value=1.0, shape=(1, 256, 256, 1))\",\n \"def test_gpu_memory(self):\\n m = pyflamegpu.ModelDescription(\\\"test_gpu_memory_test\\\")\\n a = m.newAgent(\\\"agent\\\")\\n a.newVariableInt(\\\"id\\\")\\n p = pyflamegpu.AgentVector(a, AGENT_COUNT)\\n for i in range(AGENT_COUNT):\\n instance = p[i]\\n instance.setVariableInt(\\\"id\\\", i)\\n cm = pyflamegpu.CUDASimulation(m)\\n # copy to device then back by setting and getting population data\\n cm.setPopulationData(p)\\n cm.getPopulationData(p)\\n # check values are the same\\n for i in range(AGENT_COUNT):\\n instance = p[i]\\n assert instance.getVariableInt(\\\"id\\\") == i\",\n \"def _creatExamplesTensorData(self, examples):\\n\\n images = []\\n \\n images2 = []\\n images3 = []\\n images4 = []\\n images5 = [] \\n labels = []\\n for (img_idx, label) in examples:\\n img = self.dataset[img_idx][0]\\n #print(img)\\n ##exit(0)\\n if self.load:\\n img = Image.fromarray(img)\\n else:\\n img = read_image(img)\\n #print(img.size)\\n #print(np.array(img).shape)\\n #exit(0)\\n if self.transform is not None:\\n img1 = self.transform(img)\\n\\n img2 = self.transform_test(img)\\n img3 = self.transform_test(img)\\n img4 = self.transform_test(img)\\n img5 = self.transform_test(img) \\n #print((img2-img1).abs().sum(),(img3-img1).abs().sum(),(img2-img3).abs().sum())\\n #print(img.shape,'located in test_loader.py at 146')\\n #exit(0)\\n images.append(img1)\\n \\n images2.append(img2)\\n images3.append(img3)\\n images4.append(img4)\\n images5.append(img5) \\n labels.append(label)\\n images = torch.stack(images, dim=0)\\n\\n images2 = torch.stack(images2, dim=0)\\n images3 = torch.stack(images3, dim=0)\\n images4 = torch.stack(images4, dim=0)\\n images5 = torch.stack(images5, dim=0) \\n labels = torch.LongTensor(labels)\\n return images, images2,images3,images4,images5,labels\",\n \"def test_flmb(self):\\n self.create_sample_data_set_dir(\\\"node10p1.dat\\\", TELEM_DIR, \\\"node59p1.dat\\\")\\n self.assert_initialize()\\n result = self.data_subscribers.get_samples(DataParticleType.METADATA_TELEMETERED,1,30)\\n result = self.data_subscribers.get_samples(DataParticleType.SAMPLE_TELEMETERED,5,30)\",\n \"def testSampleOutput(self):\\n beam_width = 3\\n max_decode_length = 2\\n\\n smart_compose_model = model.create_smart_compose_model(self.embedding_layer_param, self.empty_url, self.min_len, self.max_len,\\n beam_width, max_decode_length, self.feature_type_2_name, self.min_seq_prob,\\n self.length_norm_power)\\n\\n # {'exist_prefix': True,\\n # 'predicted_scores': [[-2.7357671, -2.7361841, -2.7503903]] (could vary due to random initialization),\\n # 'predicted_texts': [[b'[CLS] build is', b'[CLS] build source', b'[CLS] build token']]}\\n print(smart_compose_model.prefix_aware_beam_search({\\n InputFtrType.TARGET_COLUMN_NAME: tf.constant(['bui'])\\n }))\\n # {'exist_prefix': True,\\n # 'predicted_scores': [[-2.7357671, -2.7361841, -2.7503903]] (could vary due to random initialization),\\n # 'predicted_texts': [[b'[CLS] build is', b'[CLS] build source', b'[CLS] build token']]}\\n print(smart_compose_model.prefix_aware_beam_search({\\n InputFtrType.TARGET_COLUMN_NAME: tf.constant(['build'])\\n }))\\n # {'exist_prefix': True,\\n # 'predicted_scores': [[-2.7357671, -2.7361841, -2.7503903]] (could vary due to random initialization),\\n # 'predicted_texts': [[b'build is [PAD]', b'build source [PAD]', b'build token [PAD]']]}\\n print(smart_compose_model.prefix_aware_beam_search({\\n InputFtrType.TARGET_COLUMN_NAME: tf.constant(['build '])\\n }))\\n # {'exist_prefix': True,\\n # 'predicted_scores': [[-2.711434 , -2.7171993, -2.7329462]] (could vary due to random initialization),\\n # 'predicted_texts': [[b'build function token', b'build function test', b'build function is']]\\n print(smart_compose_model.prefix_aware_beam_search({\\n InputFtrType.TARGET_COLUMN_NAME: tf.constant(['build f'])\\n }))\",\n \"def test(self):\\n img_gen, self.loss_reg, self.parsav = self.net_G(self.input_P1, self.input_P2, self.input_BP1, self.input_BP2, self.input_SPL1, self.input_SPL2)\\n ## test flow ##\\n\\n self.save_results(img_gen, data_name='vis')\\n if self.opt.save_input or self.opt.phase == 'val':\\n self.save_results(self.input_P1, data_name='ref')\\n self.save_results(self.input_P2, data_name='gt')\\n result = torch.cat([self.input_P1, img_gen, self.input_P2], 3)\\n self.save_results(result, data_name='all')\",\n \"def test_tensor_network_bell(self):\\n circuit = jet.Circuit(num_wires=2)\\n circuit.append_gate(jet.GateFactory.create(\\\"H\\\"), wire_ids=[0])\\n circuit.append_gate(jet.GateFactory.create(\\\"CNOT\\\"), wire_ids=[0, 1])\\n\\n tn = circuit.tensor_network()\\n tensor = tn.contract()\\n\\n assert tensor.indices == [\\\"0-2\\\", \\\"1-1\\\"]\\n assert tensor.shape == [2, 2]\\n assert tensor.data == pytest.approx([1 / sqrt(2), 0, 0, 1 / sqrt(2)])\",\n \"def test_get_node_outputs(self):\\n pass\",\n \"def test_verify_connection_to_a_device():\",\n \"def test_download_terrascope():\\n\\n s1_belgium.download(\\\"sigma0_cube_terrascope.nc\\\",format=\\\"NetCDF\\\")\",\n \"def main():\\n\\n # set up output directory and file\\n output_file_folder = \\\"output/{}\\\".format(args.experiment_name)\\n Path(output_file_folder).mkdir(parents=True, exist_ok=True)\\n args.output_file_name = \\\"{}/{}.csv\\\".format(output_file_folder, args.model_name)\\n args.checkpoint_name = \\\"{}/{}.pt\\\".format(output_file_folder, args.model_name + \\\"_best_model\\\")\\n\\n # read lcquad merged data\\n if args.dataset_name == \\\"lcquad\\\":\\n df_train = pd.read_csv(\\\"./data/lcquad/gold_db/train_gold.csv\\\")\\n df_valid = pd.read_csv(\\\"./data/lcquad/gold_db/valid_gold.csv\\\")\\n df_test = pd.read_csv(\\\"./data/lcquad/gold_db/lcquad_test_sorted.csv\\\")\\n args.gold_file_name = \\\"lcquad/lcquad_gt_5000.csv\\\"\\n # elif args.dataset_name == \\\"qald9\\\":\\n # df_train = pd.read_csv(\\\"./data/qald-9/train_gold.csv\\\")\\n # df_valid = pd.read_csv(\\\"./data/qald-9/valid_gold.csv\\\")\\n # df_test = pd.read_csv(\\\"./data/qald-9/test_gold.csv\\\")\\n # args.gold_file_name = \\\"qald/qald_data_gt.csv\\\"\\n # elif args.dataset_name == \\\"webqsp\\\":\\n # df_train = pd.read_csv(\\\"./data/webqsp/train_gold.csv\\\")\\n # df_valid = pd.read_csv(\\\"./data/webqsp/valid_gold.csv\\\")\\n # df_test = pd.read_csv(\\\"./data/webqsp/test_gold.csv\\\")\\n # args.gold_file_name = \\\"webqsp/webqsp_data_gt.csv\\\"\\n\\n train_data = read_data_file(df_train, device, \\\"train\\\")\\n valid_data = read_data_file(df_valid, device, \\\"valid\\\")\\n test_data = read_data_file(df_test, device, \\\"test\\\")\\n\\n # train model and evaluate\\n if args.model_name == \\\"pure\\\":\\n model = PureNameLNN(args.alpha, -1, False)\\n elif args.model_name == \\\"ctx\\\":\\n model = None\\n elif args.model_name == 'type':\\n model = None\\n elif args.model_name == \\\"pure_ctx\\\":\\n model = None\\n elif args.model_name == \\\"pure_type\\\":\\n model = None\\n elif args.model_name == \\\"ctx_type\\\":\\n model = None\\n\\n model = model.to(device)\\n print(\\\"model: \\\", args.model_name, args.alpha)\\n\\n # training\\n train(model, train_data, valid_data, test_data, args.checkpoint_name, args.num_epoch, args.margin, args.learning_rate)\",\n \"def test_compute_glycemic_load(self):\\n pass\",\n \"def test_snow_pumps():\\n test_path = tempfile.mkdtemp()\\n x_train, metadata = snow_pumps(test_path)\\n try:\\n assert x_train.shape == (13, 4)\\n except:\\n shutil.rmtree(test_path)\\n raise()\",\n \"def test_with_matrix_and_pointer(tmp_path):\\n trans_man = _create_transform_manager()\\n\\n out_dir = tmp_path\\n pointer_tip = None\\n\\n pointer_writer = pointer.BardPointerWriter(trans_man, out_dir, pointer_tip)\\n\\n pointer_writer.write_pointer_tip()\\n\\n pointer_tip = [0, 0, 100]\\n\\n pointer_writer = pointer.BardPointerWriter(trans_man, out_dir, pointer_tip)\\n\\n pointer_writer.write_pointer_tip()\",\n \"def test_meteo():\\n test_path = tempfile.mkdtemp()\\n x_train, metadata = meteo(test_path)\\n try:\\n assert x_train.shape == (11, 6)\\n except:\\n shutil.rmtree(test_path)\\n raise()\",\n \"def test_chunk_memory(self):\\n layer = tl.Serial(tl.Dense(1024*1024), tl.Dense(128))\\n chunked = tl.Chunk(layer, 256)\\n x = np.random.uniform(size=(16*1024, 16))\\n chunked.init(shapes.signature(x))\\n y = chunked(x)\\n z = tl.Accelerate(chunked)(x)\\n self.assertEqual(y.shape, (16*1024, 128))\\n self.assertEqual(z.shape, (16*1024, 128))\",\n \"def test_update_device(self):\\n pass\",\n \"def test_update_device(self):\\n pass\",\n \"def create_mock_transformer_xl_data(\\n batch_size,\\n num_heads,\\n head_size,\\n hidden_size,\\n seq_length,\\n memory_length=0,\\n num_predictions=2,\\n two_stream=False,\\n num_layers=1,\\n include_biases=True,\\n include_state=False,\\n include_mask=False,\\n include_segment=False):\\n encoding_shape = (batch_size, seq_length * 2, hidden_size)\\n\\n data = dict(\\n relative_position_encoding=tf.random.normal(shape=encoding_shape),\\n content_stream=tf.random.normal(\\n shape=(batch_size, seq_length, hidden_size)))\\n\\n if include_biases:\\n attention_bias_shape = (num_heads, head_size)\\n data.update(dict(\\n content_attention_bias=tf.random.normal(shape=attention_bias_shape),\\n segment_attention_bias=tf.random.normal(shape=attention_bias_shape),\\n positional_attention_bias=tf.random.normal(shape=attention_bias_shape)))\\n\\n if two_stream:\\n data.update(dict(\\n query_stream=tf.random.normal(\\n shape=(batch_size, num_predictions, hidden_size)),\\n target_mapping=tf.random.normal(\\n shape=(batch_size, num_predictions, seq_length))))\\n\\n if include_state:\\n total_seq_length = seq_length + memory_length\\n if num_layers > 1:\\n state_shape = (num_layers, batch_size, memory_length, hidden_size)\\n else:\\n state_shape = (batch_size, memory_length, hidden_size)\\n data.update(dict(\\n state=tf.random.normal(shape=state_shape)))\\n else:\\n total_seq_length = seq_length\\n\\n if include_mask:\\n mask_shape = (batch_size, num_heads, seq_length, total_seq_length)\\n mask_data = np.random.randint(2, size=mask_shape).astype(\\\"float32\\\")\\n data[\\\"content_attention_mask\\\"] = mask_data\\n if two_stream:\\n data[\\\"query_attention_mask\\\"] = mask_data\\n\\n if include_segment:\\n # A transformer XL block takes an individual segment \\\"encoding\\\" from the\\n # entirety of the Transformer XL segment \\\"embedding\\\".\\n if num_layers > 1:\\n segment_encoding_shape = (num_layers, 2, num_heads, head_size)\\n segment_encoding_name = \\\"segment_embedding\\\"\\n else:\\n segment_encoding_shape = (2, num_heads, head_size)\\n segment_encoding_name = \\\"segment_encoding\\\"\\n\\n segment_matrix = np.random.randint(\\n 2, size=(batch_size, seq_length, total_seq_length))\\n data[\\\"segment_matrix\\\"] = tf.math.equal(segment_matrix, 1)\\n data[segment_encoding_name] = tf.random.normal(shape=segment_encoding_shape)\\n\\n return data\",\n \"def test_read(self):\\n with HTTMock(spark_cloud_mock):\\n for v in self.device.variables.keys():\\n expected = self.cloud_device.read(v)\\n self.assertEqual(self.device.read(v), expected)\",\n \"def _generate(device, env, qnet, ob_scale,\\n number_timesteps, param_noise,\\n exploration_fraction, exploration_final_eps,\\n atom_num, min_value, max_value):\\n noise_scale = 1e-2\\n action_dim = env.action_space.n\\n explore_steps = number_timesteps * exploration_fraction\\n if atom_num > 1:\\n vrange = torch.linspace(min_value, max_value, atom_num).to(device)\\n\\n o = env.reset()\\n infos = dict()\\n for n in range(1, number_timesteps + 1):\\n epsilon = 1.0 - (1.0 - exploration_final_eps) * n / explore_steps\\n epsilon = max(exploration_final_eps, epsilon)\\n\\n # sample action\\n with torch.no_grad():\\n ob = scale_ob(np.expand_dims(o, 0), device, ob_scale)\\n q = qnet(ob)\\n if atom_num > 1:\\n q = (q.exp() * vrange).sum(2)\\n if not param_noise:\\n if random.random() < epsilon:\\n a = int(random.random() * action_dim)\\n else:\\n a = q.argmax(1).cpu().numpy()[0]\\n else:\\n # see Appendix C of `https://arxiv.org/abs/1706.01905`\\n q_dict = deepcopy(qnet.state_dict())\\n for _, m in qnet.named_modules():\\n if isinstance(m, nn.Linear):\\n std = torch.empty_like(m.weight).fill_(noise_scale)\\n m.weight.data.add_(torch.normal(0, std).to(device))\\n std = torch.empty_like(m.bias).fill_(noise_scale)\\n m.bias.data.add_(torch.normal(0, std).to(device))\\n q_perturb = qnet(ob)\\n if atom_num > 1:\\n q_perturb = (q_perturb.exp() * vrange).sum(2)\\n kl_perturb = ((log_softmax(q, 1) - log_softmax(q_perturb, 1)) *\\n softmax(q, 1)).sum(-1).mean()\\n kl_explore = -math.log(1 - epsilon + epsilon / action_dim)\\n if kl_perturb < kl_explore:\\n noise_scale *= 1.01\\n else:\\n noise_scale /= 1.01\\n qnet.load_state_dict(q_dict)\\n if random.random() < epsilon:\\n a = int(random.random() * action_dim)\\n else:\\n a = q_perturb.argmax(1).cpu().numpy()[0]\\n\\n # take action in env\\n o_, r, done, info = env.step(a)\\n if info.get('episode'):\\n infos = {\\n 'eplenmean': info['episode']['l'],\\n 'eprewmean': info['episode']['r'],\\n }\\n # return data and update observation\\n yield (o, [a], [r], o_, [int(done)], infos)\\n infos = dict()\\n o = o_ if not done else env.reset()\",\n \"def test(cfg):\\n # Set up environment.\\n distributed.init_distributed_training(cfg)\\n\\n # Set random seed from configs.\\n np.random.seed(cfg.RNG_SEED)\\n torch.manual_seed(cfg.RNG_SEED)\\n\\n # Print config.\\n if distributed.is_master_proc():\\n print(\\\"Test with config:\\\")\\n print(cfg)\\n\\n # Build the model and print model statistics.\\n # Use cuda if available\\n device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\\n # Construct the model\\n model = PanopticNarrativeGroundingBaseline(cfg, device=device)\\n # Determine the GPU used by the current process\\n cur_device = torch.cuda.current_device()\\n # Transfer the model to the current GPU device\\n model = model.cuda(device=cur_device)\\n if cfg.NUM_GPUS > 1:\\n # Make model replica operate on the current device\\n model = torch.nn.parallel.DistributedDataParallel(\\n module=model, device_ids=[cur_device], output_device=cur_device,\\n find_unused_parameters=True\\n )\\n if cfg.LOG_MODEL_INFO and distributed.is_master_proc():\\n print(\\\"Model:\\\\n{}\\\".format(model))\\n print(\\\"Params: {:,}\\\".format(np.sum([p.numel() for p in model.parameters()]).item()))\\n print(\\\"Mem: {:,} MB\\\".format(torch.cuda.max_memory_allocated() / 1024 ** 3))\\n print(\\\"nvidia-smi\\\")\\n os.system(\\\"nvidia-smi\\\")\\n\\n # Load a checkpoint to test if applicable.\\n checkpoint_path = osp.join(cfg.OUTPUT_DIR, 'model_final.pth')\\n if cfg.TEST.CHECKPOINT_FILE_PATH != \\\"\\\":\\n checkpoint_path = cfg.TEST.CHECKPOINT_FILE_PATH\\n if osp.exists(checkpoint_path):\\n if distributed.is_master_proc():\\n print('Loading model from: {0}'.format(checkpoint_path))\\n checkpoint = torch.load(checkpoint_path, map_location=\\\"cpu\\\")\\n if cfg.NUM_GPUS > 1:\\n model.module.load_state_dict(checkpoint['model_state'])\\n else:\\n model.load_state_dict(checkpoint['model_state'])\\n elif cfg.TRAIN.CHECKPOINT_FILE_PATH != \\\"\\\":\\n # If no checkpoint found in TEST.CHECKPOINT_FILE_PATH or in the current\\n # checkpoint folder, try to load checkpoint from\\n # TRAIN.CHECKPOINT_FILE_PATH and test it.\\n checkpoint_path = cfg.TRAIN.CHECKPOINT_FILE_PATH\\n checkpoint = torch.load(checkpoint_path, map_location=\\\"cpu\\\")\\n if cfg.NUM_GPUS > 1:\\n model.module.load_state_dict(checkpoint['model_state'])\\n else:\\n model.load_state_dict(checkpoint['model_state'])\\n else:\\n if distributed.is_master_proc():\\n print(\\\"Testing with random initialization. Only for debugging.\\\")\\n\\n # Create testing loaders.\\n test_dataset = PanopticNarrativeGroundingDataset(cfg, cfg.DATA.VAL_SPLIT, train=False)\\n test_loader = DataLoader(\\n test_dataset,\\n batch_size=int(cfg.TRAIN.BATCH_SIZE / max(1, cfg.NUM_GPUS)),\\n shuffle=False,\\n sampler=(DistributedSampler(test_dataset) if cfg.NUM_GPUS > 1 else None),\\n num_workers=cfg.DATA_LOADER.NUM_WORKERS,\\n pin_memory=cfg.DATA_LOADER.PIN_MEMORY\\n )\\n \\n if distributed.is_master_proc():\\n print(\\\"Testing model for {} iterations\\\".format(len(test_loader)))\\n\\n # Perform test on the entire dataset.\\n perform_test(test_loader, model, cfg)\",\n \"def get_GP_samples(Y,T,X,ind_kf,ind_kt,num_obs_times,num_obs_values,\\n num_tcn_grid_times, cov_grid, input_dim,method, gp_params, lab_vitals_only, pad_before): ##,med_cov_grid \\n\\n n_mc_smps, M = gp_params.n_mc_smps, gp_params.M\\n grid_max = tf.shape(X)[1]\\n Z = tf.zeros([0,grid_max,input_dim])\\n \\n N = tf.shape(T)[0] #number of observations\\n \\n #setup tf while loop (have to use this bc loop size is variable)\\n def cond(i,Z):\\n return i<N\\n \\n def body(i,Z):\\n Yi = tf.reshape(tf.slice(Y,[i,0],[1,num_obs_values[i]]),[-1]) #MM: tf.reshape(x, [-1]) flattens tensor x (e.g. [2,3,1] to [6]), slice cuts out all Y data of one patient\\n Ti = tf.reshape(tf.slice(T,[i,0],[1,num_obs_times[i]]),[-1])\\n ind_kfi = tf.reshape(tf.slice(ind_kf,[i,0],[1,num_obs_values[i]]),[-1])\\n ind_kti = tf.reshape(tf.slice(ind_kt,[i,0],[1,num_obs_values[i]]),[-1])\\n Xi = tf.reshape(tf.slice(X,[i,0],[1,num_tcn_grid_times[i]]),[-1])\\n X_len = num_tcn_grid_times[i]\\n \\n GP_draws = draw_GP(Yi,Ti,Xi,ind_kfi,ind_kti,method=method, gp_params=gp_params)\\n pad_len = grid_max-X_len #pad by this much\\n #padding direction:\\n if pad_before:\\n print('Padding GP_draws before observed data..')\\n padded_GP_draws = tf.concat([tf.zeros((n_mc_smps,pad_len,M)), GP_draws],1) \\n else:\\n padded_GP_draws = tf.concat([GP_draws,tf.zeros((n_mc_smps,pad_len,M))],1) \\n\\n if lab_vitals_only:\\n Z = tf.concat([Z,padded_GP_draws],0) #without covs\\n else: #with covs\\n medcovs = tf.slice(cov_grid,[i,0,0],[1,-1,-1])\\n tiled_medcovs = tf.tile(medcovs,[n_mc_smps,1,1])\\n padded_GPdraws_medcovs = tf.concat([padded_GP_draws,tiled_medcovs],2)\\n Z = tf.concat([Z,padded_GPdraws_medcovs],0) #with covs\\n \\n return i+1,Z \\n \\n i = tf.constant(0)\\n #with tf.control_dependencies([tf.Print(tf.shape(ind_kf), [tf.shape(ind_kf), tf.shape(ind_kt), num_obs_values], 'ind_kf & ind_kt & num_obs_values')]):\\n i,Z = tf.while_loop(cond,body,loop_vars=[i,Z],\\n shape_invariants=[i.get_shape(),tf.TensorShape([None,None,None])])\\n\\n return Z\",\n \"def unit_test():\\n test_net = SqueezeNetSqueezeLSTM(20, 6)\\n test_net_output = test_net(\\n Variable(torch.randn(5, 36, 94, 168)),\\n Variable(torch.randn(5, 8, 23, 41)))\\n logging.debug('Net Test Output = {}'.format(test_net_output))\\n logging.debug('Network was Unit Tested')\\n print(test_net.num_params())\\n # for param in test_net.parameters():\",\n \"def test_write(self):\\n dset = self.f.create_dataset('x2', (10, 2))\\n\\n x = np.zeros((10, 1))\\n dset[:, 0] = x[:, 0]\\n with self.assertRaises(TypeError):\\n dset[:, 1] = x\",\n \"def test_input_data():\\n mp = mapchete.open(\\n os.path.join(SCRIPTDIR, \\\"testdata/cleantopo_br.mapchete\\\"))\\n tp = BufferedTilePyramid(\\\"geodetic\\\")\\n # TODO tile with existing but empty data\\n tile = tp.tile(5, 5, 5)\\n output_params = dict(\\n type=\\\"geodetic\\\",\\n format=\\\"GeoTIFF\\\",\\n path=OUT_DIR,\\n pixelbuffer=0,\\n metatiling=1,\\n bands=2,\\n dtype=\\\"int16\\\"\\n )\\n output = gtiff.OutputData(output_params)\\n with output.open(tile, mp, resampling=\\\"nearest\\\") as input_tile:\\n assert input_tile.resampling == \\\"nearest\\\"\\n for data in [\\n input_tile.read(), input_tile.read(1), input_tile.read([1]),\\n # TODO assert valid indexes are passed input_tile.read([1, 2])\\n ]:\\n assert isinstance(data, ma.masked_array)\\n assert input_tile.is_empty()\\n # open without resampling\\n with output.open(tile, mp) as input_tile:\\n pass\",\n \"def test():\\n args = parse_args()\\n\\n devid = int(os.getenv('DEVICE_ID')) if os.getenv('DEVICE_ID') else 0\\n context.set_context(mode=context.GRAPH_MODE, device_target='Ascend', save_graphs=True, device_id=devid)\\n\\n # logger\\n args.outputs_dir = os.path.join(args.log_path,\\n datetime.datetime.now().strftime('%Y-%m-%d_time_%H_%M_%S'))\\n rank_id = int(os.environ.get('RANK_ID')) if os.environ.get('RANK_ID') else 0\\n args.logger = get_logger(args.outputs_dir, rank_id)\\n\\n context.reset_auto_parallel_context()\\n parallel_mode = ParallelMode.STAND_ALONE\\n context.set_auto_parallel_context(parallel_mode=parallel_mode, gradients_mean=True, device_num=1)\\n\\n args.logger.info('Creating Network....')\\n network = SolveOutput(YOLOV3DarkNet53(is_training=False))\\n\\n data_root = args.data_root\\n ann_file = args.annFile\\n\\n args.logger.info(args.pretrained)\\n if os.path.isfile(args.pretrained):\\n param_dict = load_checkpoint(args.pretrained)\\n param_dict_new = {}\\n for key, values in param_dict.items():\\n if key.startswith('moments.'):\\n continue\\n elif key.startswith('yolo_network.'):\\n param_dict_new[key[13:]] = values\\n else:\\n param_dict_new[key] = values\\n load_param_into_net(network, param_dict_new)\\n args.logger.info('load_model {} success'.format(args.pretrained))\\n else:\\n args.logger.info('{} not exists or not a pre-trained file'.format(args.pretrained))\\n assert FileNotFoundError('{} not exists or not a pre-trained file'.format(args.pretrained))\\n exit(1)\\n\\n config = ConfigYOLOV3DarkNet53()\\n if args.testing_shape:\\n config.test_img_shape = conver_testing_shape(args)\\n\\n ds, data_size = create_yolo_dataset(data_root, ann_file, is_training=False, batch_size=1,\\n max_epoch=1, device_num=1, rank=rank_id, shuffle=False,\\n config=config)\\n\\n args.logger.info('testing shape : {}'.format(config.test_img_shape))\\n args.logger.info('totol {} images to eval'.format(data_size))\\n\\n network.set_train(False)\\n # build attacker\\n attack = DeepFool(network, num_classes=80, model_type='detection', reserve_ratio=0.9, bounds=(0, 1))\\n input_shape = Tensor(tuple(config.test_img_shape), ms.float32)\\n\\n args.logger.info('Start inference....')\\n batch_num = args.samples_num\\n adv_example = []\\n for i, data in enumerate(ds.create_dict_iterator(num_epochs=1)):\\n if i >= batch_num:\\n break\\n image = data[\\\"image\\\"]\\n image_shape = data[\\\"image_shape\\\"]\\n\\n gt_boxes, gt_logits = network(image, input_shape)\\n gt_boxes, gt_logits = gt_boxes.asnumpy(), gt_logits.asnumpy()\\n gt_labels = np.argmax(gt_logits, axis=2)\\n\\n adv_img = attack.generate((image.asnumpy(), image_shape.asnumpy()), (gt_boxes, gt_labels))\\n adv_example.append(adv_img)\\n np.save('adv_example.npy', adv_example)\",\n \"def model_and_data(request, hyperparams, estep_conf):\\n if tvo.get_run_policy() == \\\"mpi\\\":\\n init_processes()\\n\\n precision, N, D, H, batch_size = get(hyperparams, \\\"precision\\\", \\\"N\\\", \\\"D\\\", \\\"H\\\", \\\"batch_size\\\")\\n\\n if request.param == \\\"BSC\\\":\\n W_gt = generate_bars(H, bar_amp=10.0, precision=precision)\\n sigma2_gt = to.ones((1,), dtype=precision, device=tvo.get_device())\\n pies_gt = to.full((H,), 2.0 / H, dtype=precision, device=tvo.get_device())\\n\\n to.manual_seed(999)\\n W_init = to.rand((D, H), dtype=precision)\\n W_init = W_init.to(device=tvo.get_device())\\n broadcast(W_init)\\n\\n sigma2_init = to.tensor([1.0], dtype=precision, device=tvo.get_device())\\n pies_init = to.full((H,), 1.0 / H, dtype=precision, device=tvo.get_device())\\n\\n model = BSC(\\n H=H, D=D, W_init=W_gt, sigma2_init=sigma2_gt, pies_init=pies_gt, precision=precision\\n )\\n\\n fname = \\\"bars_test_data_bsc.h5\\\"\\n\\n write_dataset(fname, N, D, np.float32, model)\\n\\n model.theta[\\\"W\\\"] = W_init\\n model.theta[\\\"sigma2\\\"] = sigma2_init\\n model.theta[\\\"pies\\\"] = pies_init\\n\\n elif request.param == \\\"NoisyOR\\\":\\n W_gt = generate_bars(H, bar_amp=0.8, bg_amp=0.1, precision=precision)\\n pies_gt = to.full((H,), 2.0 / H, dtype=precision, device=tvo.get_device())\\n\\n to.manual_seed(999)\\n W_init = to.rand((D, H), dtype=precision)\\n W_init = W_init.to(device=tvo.get_device())\\n broadcast(W_init)\\n pies_init = to.full((H,), 1.0 / H, dtype=precision, device=tvo.get_device())\\n\\n model = NoisyOR(H=H, D=D, W_init=W_gt, pi_init=pies_gt, precision=precision)\\n\\n fname = \\\"bars_test_data_nor.h5\\\"\\n\\n write_dataset(fname, N, D, np.uint8, model)\\n\\n model.theta[\\\"W\\\"] = W_init\\n model.theta[\\\"pies\\\"] = pies_init\\n\\n if tvo.get_run_policy() == \\\"mpi\\\":\\n dist.barrier()\\n\\n return model, fname\",\n \"def test_deviceX_1():\\n assert 0\",\n \"def test(batch_size=1, num_sample=16):\\n return paddle.batch(_read_creater(num_sample=num_sample), batch_size)\",\n \"def test_forfatal_functions(self):\\n logging.getLogger(\\\"tensorflow\\\").setLevel(logging.ERROR)\\n logging.getLogger(\\\"batchglm\\\").setLevel(logging.WARNING)\\n logging.getLogger(\\\"diffxpy\\\").setLevel(logging.WARNING)\\n\\n num_observations = 10\\n num_features = 2\\n\\n sim = Simulator(num_observations=num_observations, num_features=num_features)\\n sim.generate_sample_description(num_batches=0, num_conditions=0)\\n sim.generate()\\n\\n random_sample_description = pd.DataFrame({\\n \\\"pseudotime\\\": np.random.random(size=sim.nobs),\\n \\\"batch\\\": np.random.randint(2, size=sim.nobs)\\n })\\n\\n test = de.test.continuous_1d(\\n data=sim.X,\\n continuous=\\\"pseudotime\\\",\\n df=3,\\n formula_loc=\\\"~ 1 + pseudotime + batch\\\",\\n formula_scale=\\\"~ 1\\\",\\n factor_loc_totest=\\\"pseudotime\\\",\\n test=\\\"wald\\\",\\n sample_description=random_sample_description,\\n quick_scale=True,\\n batch_size=None,\\n training_strategy=\\\"DEFAULT\\\",\\n dtype=\\\"float64\\\"\\n )\\n\\n summary = test.summary()\\n ids = test.gene_ids\\n\\n # 1. Test all additional functions which depend on model computation:\\n # 1.1. Only continuous model:\\n temp = test.log_fold_change(genes=ids, nonnumeric=False)\\n temp = test.max(genes=ids, nonnumeric=False)\\n temp = test.min(genes=ids, nonnumeric=False)\\n temp = test.argmax(genes=ids, nonnumeric=False)\\n temp = test.argmin(genes=ids, nonnumeric=False)\\n temp = test.summary(nonnumeric=False)\\n # 1.2. Full model:\\n temp = test.log_fold_change(genes=ids, nonnumeric=True)\\n temp = test.max(genes=ids, nonnumeric=True)\\n temp = test.min(genes=ids, nonnumeric=True)\\n temp = test.argmax(genes=ids, nonnumeric=True)\\n temp = test.argmin(genes=ids, nonnumeric=True)\\n temp = test.summary(nonnumeric=True)\\n\\n return True\",\n \"def test_wool():\\n test_path = tempfile.mkdtemp()\\n x_train, metadata = wool(test_path)\\n try:\\n assert x_train.shape == (309, 2)\\n except:\\n shutil.rmtree(test_path)\\n raise()\",\n \"def test_numpify_and_store(self):\\n Nsamples = 9\\n Ntimesteps = 10\\n Ncolumns = 3\\n X = [[[0 for a in range(Ncolumns)] for b in range(Ntimesteps)] \\\\\\n for c in range(Nsamples)]\\n y = [[0 for a in range(Ntimesteps)] for b in range(Nsamples)]\\n xname = 'xname'\\n yname = 'yname'\\n outdatapath = os.getcwd()\\n tutorial_pamap2.numpify_and_store(X, y, xname, yname, outdatapath, \\\\\\n shuffle=True)\\n filename = os.path.join(outdatapath, xname+ '.npy')\\n test = os.path.isfile(filename)\\n if test == True:\\n os.remove(filename)\\n os.remove(os.path.join(outdatapath, yname + '.npy'))\\n assert test\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.66837543","0.66518664","0.6184355","0.6113137","0.58957744","0.5887892","0.58565533","0.5855714","0.5791288","0.5715855","0.56533664","0.5649407","0.5645982","0.5569726","0.5525689","0.5520632","0.5512734","0.5490553","0.5452423","0.54212606","0.53811026","0.53693086","0.5343723","0.53103983","0.5287458","0.5286752","0.5275774","0.52635837","0.524238","0.524214","0.5221695","0.5210801","0.5190939","0.51863724","0.51765555","0.5174389","0.5170602","0.5150499","0.51463205","0.5139448","0.5134264","0.5132594","0.5131977","0.5130938","0.51215065","0.5118143","0.511803","0.5111194","0.5110172","0.5110172","0.5107281","0.5107077","0.5105265","0.51017976","0.50974506","0.50901556","0.50872433","0.5083166","0.5081412","0.50746423","0.50719565","0.50713134","0.50713134","0.5069252","0.50670874","0.5053582","0.505035","0.50497043","0.5049095","0.5046092","0.5043105","0.50355804","0.5035223","0.5028129","0.5026561","0.502063","0.5015126","0.5015065","0.5010907","0.5010162","0.5010162","0.5005787","0.50048065","0.5004115","0.5004115","0.5003922","0.50038576","0.500312","0.4999934","0.49998915","0.49954575","0.49934638","0.49934524","0.4989839","0.4987235","0.49862424","0.49831748","0.49819127","0.49784067","0.4976468"],"string":"[\n \"0.66837543\",\n \"0.66518664\",\n \"0.6184355\",\n \"0.6113137\",\n \"0.58957744\",\n \"0.5887892\",\n \"0.58565533\",\n \"0.5855714\",\n \"0.5791288\",\n \"0.5715855\",\n \"0.56533664\",\n \"0.5649407\",\n \"0.5645982\",\n \"0.5569726\",\n \"0.5525689\",\n \"0.5520632\",\n \"0.5512734\",\n \"0.5490553\",\n \"0.5452423\",\n \"0.54212606\",\n \"0.53811026\",\n \"0.53693086\",\n \"0.5343723\",\n \"0.53103983\",\n \"0.5287458\",\n \"0.5286752\",\n \"0.5275774\",\n \"0.52635837\",\n \"0.524238\",\n \"0.524214\",\n \"0.5221695\",\n \"0.5210801\",\n \"0.5190939\",\n \"0.51863724\",\n \"0.51765555\",\n \"0.5174389\",\n \"0.5170602\",\n \"0.5150499\",\n \"0.51463205\",\n \"0.5139448\",\n \"0.5134264\",\n \"0.5132594\",\n \"0.5131977\",\n \"0.5130938\",\n \"0.51215065\",\n \"0.5118143\",\n \"0.511803\",\n \"0.5111194\",\n \"0.5110172\",\n \"0.5110172\",\n \"0.5107281\",\n \"0.5107077\",\n \"0.5105265\",\n \"0.51017976\",\n \"0.50974506\",\n \"0.50901556\",\n \"0.50872433\",\n \"0.5083166\",\n \"0.5081412\",\n \"0.50746423\",\n \"0.50719565\",\n \"0.50713134\",\n \"0.50713134\",\n \"0.5069252\",\n \"0.50670874\",\n \"0.5053582\",\n \"0.505035\",\n \"0.50497043\",\n \"0.5049095\",\n \"0.5046092\",\n \"0.5043105\",\n \"0.50355804\",\n \"0.5035223\",\n \"0.5028129\",\n \"0.5026561\",\n \"0.502063\",\n \"0.5015126\",\n \"0.5015065\",\n \"0.5010907\",\n \"0.5010162\",\n \"0.5010162\",\n \"0.5005787\",\n \"0.50048065\",\n \"0.5004115\",\n \"0.5004115\",\n \"0.5003922\",\n \"0.50038576\",\n \"0.500312\",\n \"0.4999934\",\n \"0.49998915\",\n \"0.49954575\",\n \"0.49934638\",\n \"0.49934524\",\n \"0.4989839\",\n \"0.4987235\",\n \"0.49862424\",\n \"0.49831748\",\n \"0.49819127\",\n \"0.49784067\",\n \"0.4976468\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.65643054"},"document_rank":{"kind":"string","value":"2"}}},{"rowIdx":6594,"cells":{"query":{"kind":"string","value":"Test the popxl autodiff op"},"document":{"kind":"string","value":"def test_documentation_popxl_autodiff(self):\n filename = \"autodiff.py\"\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)"},"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 test_data_norange(self):\n ex = self.ex\n m = self.m\n n = self.n\n\n nreps = random.randint(1, 10)\n lensumrange = random.randint(1, 10)\n\n ex.nreps = nreps\n ex.sumrange = [\"j\", range(lensumrange)]\n ex.vary[\"X\"][\"with\"].add(\"rep\")\n ex.vary[\"Y\"][\"with\"].add(\"j\")\n ex.vary[\"Y\"][\"along\"] = 0\n ex.vary[\"Z\"][\"with\"].update([\"rep\", \"j\"])\n ex.infer_lds()\n\n cmds = ex.generate_cmds()\n\n self.assertIn([\"smalloc\", \"X\", nreps * m * n], cmds)\n idx = random.randint(0, nreps - 1)\n self.assertIn([\"soffset\", \"X\", idx * m * n, \"X_%d\" % idx], cmds)\n\n self.assertIn([\n \"dmalloc\", \"Y\", lensumrange * m * m + (lensumrange - 1) * m\n ], cmds)\n idx = random.randint(0, lensumrange - 1)\n self.assertIn([\"doffset\", \"Y\", idx * m, \"Y_%d\" % idx], cmds)\n\n self.assertIn([\"cmalloc\", \"Z\", nreps * lensumrange * n * n], cmds)\n idxrep = random.randint(0, nreps - 1)\n idxrange = random.randint(0, lensumrange - 1)\n self.assertIn([\"coffset\", \"Z\",\n (idxrep * lensumrange + idxrange) * n * n,\n \"Z_%d_%d\" % (idxrep, idxrange)], cmds)","def test_57o_correct_preflop_odds(self):\n self.assertEqual(self.hand.preFlopOdds10, 7.9)","def test_is_old_papernum(self):\n self.assertFalse(util.is_old_papernum(\"9106001\"))\n self.assertTrue(util.is_old_papernum(\"9107001\"))\n self.assertFalse(util.is_old_papernum(\"9200001\"))\n self.assertTrue(util.is_old_papernum(\"9201001\"))\n self.assertTrue(util.is_old_papernum(\"0703999\"))\n self.assertFalse(util.is_old_papernum(\"0704001\"))","def test_99_correct_preflop_odds(self):\n self.assertEqual(self.hand.preFlopOdds10, 15.6)","def test_pressure_increasing_check_some_decreasing(mocker, pressure_values, expected):\n profile = mocker.patch.object(argortqcpy.profile, \"Profile\")\n profile.get_property_data = mocker.Mock(return_value=ma.masked_array(pressure_values))\n\n pic = PressureIncreasingCheck(profile, None)\n output = pic.run()\n\n assert np.all(output.get_output_flags_for_property(\"PRES\").data == expected)","def test_g_asignar_rol(self):","def test_open_fill(self):","def test31_clear_air():\n assert not is_precip_mode(31), 'VCP 31 is not precip'","def test_modexp(self):\n self.assertEqual(MathFunctions.modexp(2, 5, 7), 4)\n self.assertEqual(MathFunctions.modexp(2, 10, 8), 0)","def test_AKs_correct_preflop_odds(self):\n self.assertEqual(self.hand.preFlopOdds10, 20.7)","def test_analytical_vs_numerical():\n pass","def test_devide_int(self):\n self.assertEqual(operations.devide(8,4), 2)","def test_pressure_increasing_check_some_bad(mocker, pressure_values, expected):\n profile = mocker.patch.object(argortqcpy.profile, \"Profile\")\n profile.get_property_data = mocker.Mock(return_value=ma.masked_array(pressure_values))\n\n pic = PressureIncreasingCheck(profile, None)\n output = pic.run()\n\n assert np.all(output.get_output_flags_for_property(\"PRES\").data == expected)","def test_without_orographic_enhancement(self):\n input_cube = self.precip_cube.copy()\n input_cube.rename(\"air_temperature\")\n input_cube.units = \"K\"\n plugin = CreateExtrapolationForecast(input_cube, self.vel_x, self.vel_y)\n result = plugin.extrapolate(10)\n expected_result = np.array(\n [[np.nan, np.nan, np.nan], [np.nan, 1, 2], [np.nan, 1, 1], [np.nan, 0, 2]],\n dtype=np.float32,\n )\n expected_result = np.ma.masked_invalid(expected_result)\n expected_forecast_period = np.array([600], dtype=np.int64)\n # Check we get the expected result, and the correct time coordinates.\n self.assertArrayEqual(\n np.ma.getmask(expected_result), np.ma.getmask(result.data)\n )\n self.assertArrayAlmostEqual(expected_result.data, result.data.data)\n self.assertArrayAlmostEqual(\n result.coord(\"forecast_period\").points, expected_forecast_period\n )\n self.assertEqual(result.coord(\"forecast_period\").units, \"seconds\")\n self.assertEqual(\n result.coord(\"forecast_reference_time\").points,\n input_cube.coord(\"time\").points,\n )\n self.assertEqual(\n result.coord(\"time\").points, input_cube.coord(\"time\").points + 600\n )","def test_one_pop(data_: tuple, _is_pop: bool):\n x_bar = cls.get_mean(data_)\n s_x = cls.get_stdev(data_, is_population=_is_pop)\n n_x = cls.get_n(data_)\n return (x_bar - h0) / (s_x / sqrt(n_x))","def test_get_meta_range(self):\n pass","def test_with_orographic_enhancement(self):\n plugin = CreateExtrapolationForecast(\n self.precip_cube,\n self.vel_x,\n self.vel_y,\n orographic_enhancement_cube=self.oe_cube,\n )\n result = plugin.extrapolate(10)\n expected_result = np.array(\n [\n [np.nan, np.nan, np.nan],\n [np.nan, 1.03125, 1.0],\n [np.nan, 1.0, 0.03125],\n [np.nan, 0, 2.0],\n ],\n dtype=np.float32,\n )\n expected_result = np.ma.masked_invalid(expected_result)\n expected_forecast_period = np.array([600], dtype=np.int64)\n # Check we get the expected result, and the correct time coordinates.\n self.assertArrayEqual(\n np.ma.getmask(expected_result), np.ma.getmask(result.data)\n )\n self.assertArrayAlmostEqual(expected_result.data, result.data.data)\n self.assertArrayAlmostEqual(\n result.coord(\"forecast_period\").points, expected_forecast_period\n )\n self.assertEqual(result.coord(\"forecast_period\").units, \"seconds\")\n self.assertEqual(\n result.coord(\"forecast_reference_time\").points,\n self.precip_cube.coord(\"time\").points,\n )\n self.assertEqual(\n result.coord(\"time\").points, self.precip_cube.coord(\"time\").points + 600\n )","def testspec(arr: list[int]) -> None:\n\n print(50*'-')\n print(arr)\n print_rem(arr)\n rev_dupes(arr)\n print(arr)","def test_FlexCrop1(self):","def clean_data(self, opz):\n# pdb.set_trace()\n mask = (opz['Opzetstuk Noord (°)']<-1) | (opz['Opzetstuk Noord (°)']>100)\n opz = opz.drop(opz.loc[mask].index)\n opz['open'] = opz[\"Opzetstuk Noord (°)\"].apply(lambda x: 1 if x < 80 else 0)\n #Deze klopt niet. We hebben het moment nodig van opengaan en het moment van dichtgaat. Moment van openen is: wanneer de verandering van de aantal graden >1 graad is. Moment van sluiten is de laatste verandering totdat het niet meer veranderd. Zie ook code van Pieter in C#.\n opz['diff'] = opz['open'].diff()\n beweegt=opz[opz['diff']!=0]\n return beweegt","def c_test_population_function(self, function):\r\n return 1","def test_get_range(self):\n pass","def test_170329_notimp(self):\n spc = parser(get_file('PTSDY2_notimp.txt'))\n # spc.draw_outlooks()\n outlook = spc.get_outlook('CATEGORICAL', 'MRGL')\n self.assertAlmostEqual(outlook.geometry.area, 110.24, 2)","def testMedicationsImmunosupp(self):\n attr = self.session.create_visit_attr()\n\n self.util.boolTypeTest(self, attr, \"immunosupp\")\n\n self.util.boolPropertyTest(self, attr, \"immunosupp\")","def test_wb(self):\n df = dep.read_wb(get_path('wb.txt'))\n self.assertAlmostEquals(df['precip'].max(), 162.04, 2)","def test21_precip():\n assert is_precip_mode(21), 'VCP 21 is precip'","def test_for_more_flags(self):\n shorter = REFRESH_COMMANDS.calculate_refresh_commands(\"Rainmeter.exe\", \"test-config\", \"file.inc\", True, False)\n longer = REFRESH_COMMANDS.calculate_refresh_commands(\"Rainmeter.exe\", \"test-config\", \"file.ini\", True, True)\n\n self.assertGreater(longer, shorter)","def test_4_4_1_1(self):\n pass","def test_reset(self):\r\n self.p += 8\r\n self.p.reset()\r\n self.assertEqual(str(self.p), '[>............] 0%')","def test_documentation_popxl_replication(self):\n filename = \"replication.py\"\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)","def test_is_ramped_using_int(self):\n self.feature_test.set_percentage(100)\n self.assertTrue(self.feature_test._is_ramped(5))","def test_untar(self):","def test_10(self, test):\r\n return test.MANUAL()","def test_issue_reset_time(self):\n pass","def test_ncols_gtiff_object(self):\n self.assertEqual(_test_object(landsat_gtiff)[2], 235)","def test_right(self):\n x = np.array([-100, -2, -1, 0, 1, 1.1])\n self.assertEqual(npinterval.half_sample_mode(x), +1.05)","def test_dewpoint_specific_humidity_old_signature():\n p = 1013.25 * units.mbar\n temperature = 20. * units.degC\n q = 0.012 * units.dimensionless\n with pytest.raises(ValueError, match='changed in 1.0'):\n dewpoint_from_specific_humidity(q, temperature, p)","def test_unroll_patch_true(self):\r\n self.validate((3, 2, 7, 5), (5, 2, 2, 3), 'valid', unroll_patch=True)\r\n self.validate((3, 2, 7, 5), (5, 2, 2, 3), 'full', unroll_patch=True)\r\n self.validate((3, 2, 3, 3), (4, 2, 3, 3), 'valid',\r\n unroll_patch=True, verify_grad=False)","def test_osd(self):\n self.assertEqual(osd(self.TestData), (9,3,3))","def test_nrows_gtiff_object(self):\n self.assertEqual(_test_object(landsat_gtiff)[1], 224)","def test_pressure_increasing_check_some_constants(mocker, pressure_values, expected):\n profile = mocker.patch.object(argortqcpy.profile, \"Profile\")\n profile.get_property_data = mocker.Mock(return_value=ma.masked_array(pressure_values))\n\n pic = PressureIncreasingCheck(profile, None)\n output = pic.run()\n\n assert np.all(output.get_output_flags_for_property(\"PRES\").data == expected)","def test_of_agreement(self): \n \n pop_t = world.pop_t \n pop_sub = world.pop_sub \n \n \n Ns = world.subtime.length\n \n dt = world.time.step\n ds = world.subtime.step\n N = round(ds/dt)\n for i in range(Ns):\n numpy.testing.assert_allclose(pop_sub[:,i],pop_t[i*N,:])","def test_pickable_propertysheet(self):\n from Acquisition import aq_base\n import pickle\n\n self.assertGreater(len(pickle.dumps(aq_base(self.ldap))), 170)","def test_left(self):\n x = np.array([-1.1, -1, 0, 1, 2, 100])\n self.assertEqual(npinterval.half_sample_mode(x), -1.05)","def test_MINX_pass(self):\n self.assertTrue(self.mod.minx.isset)","def testPerfilCasoInterseccionDescendente(self):\n if self.TESTALL:\n perfilOriginal = [1,9,5,0,3,7,12,0]\n resultadoEsperado = [1,9,5,7,12]\n perfil = Perfil.Perfil()\n resultado = perfil.calcularPerfil(perfilOriginal,0)\n self.assertEqual(resultadoEsperado, resultado)","def _test_totalpop(self, obj, known_2000, known_2010):\n known_delta = known_2010 - known_2000\n known_pct = float(known_delta) / float(known_2000)\n\n self.assertEqual(float(obj['data']['2000'][\"P1\"]['P001001']), known_2000)\n self.assertEqual(float(obj['data']['2010'][\"P1\"]['P001001']), known_2010)\n self.assertEqual(float(obj['data']['delta'][\"P1\"]['P001001']), known_delta)\n self.assertAlmostEqual(\n float(obj['data']['pct_change'][\"P1\"]['P001001']),\n known_pct\n )","def test_pressure_increasing_check_all_pass(mocker, pressure_values):\n profile = mocker.patch.object(argortqcpy.profile, \"Profile\")\n profile.get_property_data = mocker.Mock(return_value=ma.masked_array(pressure_values))\n\n pic = PressureIncreasingCheck(profile, None)\n output = pic.run()\n\n assert np.all(output.get_output_flags_for_property(\"PRES\").data == ArgoQcFlag.GOOD.value)","def test_cliford(generator, paulixops, result):\n u = clifford(generator, paulixops)\n assert u.compare(result)","def test_binops(self):","def test_floor(doctest):","def test_margalef(self):\n self.assertEqual(margalef(self.TestData), 8/log(22))","def test_delete_o(self):\n number = 6.0\n number_return = self.new_calculation.delete_o(number)\n self.assertEqual(number_return, 6)\n self.assertNotEqual(type(number), int)\n self.assertEqual(type(number_return), int)\n\n number = 10.565\n number_return = self.new_calculation.delete_o(number)\n self.assertEqual(number_return, 10.565)\n self.assertEqual(type(number_return), float)\n\n number = 10\n number_return = self.new_calculation.delete_o(number)\n self.assertEqual(number_return, 10)\n self.assertEqual(type(number_return), int)","def test_flceiling(doctest):","def testCorrectForTwoAtomCellWithoutPeriodicityNEEDED(self):\n\t\texpDist = 0.01*10\n\t\tself._checkExpMatchesActual(expDist)","def test_unroll_patch_false(self):\r\n self.validate((3, 2, 7, 5), (5, 2, 2, 3), 'valid', unroll_patch=False)\r\n self.validate((3, 2, 7, 5), (5, 2, 2, 3), 'full', unroll_patch=False)\r\n self.validate((3, 2, 3, 3), (4, 2, 3, 3), 'valid',\r\n unroll_patch=False, verify_grad=False)","def test_iimi1():\n iimi = interactive_intrinsic_mutual_information(n_mod_m(3, 2), rvs=[[0], [1]], crvs=[2], rounds=1)\n assert iimi == pytest.approx(0.0)","def test_fltruncate(doctest):","def test_T4():","def test_T4():","def test_reset(self):\r\n self.p += 8\r\n self.p.reset()\r\n self.assertEqual(str(self.p), '0% [....................]')","def test_rolling_before_analysis(self):\n cheese = TomoCheese.from_demo_images()\n cheese.analyze()\n original_roi_1 = copy.copy(cheese.module.rois[\"1\"].pixel_value)\n for img in cheese.dicom_stack:\n img.roll(direction=\"x\", amount=20)\n cheese.analyze()\n new_roi_1 = cheese.module.rois[\"1\"].pixel_value\n assert math.isclose(original_roi_1, new_roi_1, abs_tol=3)","def test_y_before_x(self):","def test_minus(self):\n print('test_minus');\n self.assertEqual(90, minus(100, 10))","def test_screenip_unit_det(self):\n #\n # '''\n # Dose Equiv. Toxicity:\n #\n # The FI value (kg-diet) is multiplied by the reported NOAEC (mg/kg-diet) and then divided by\n # the test animal's body weight to derive the dose-equivalent chronic toxicity value (mg/kg-bw):\n #\n # Dose Equiv. Toxicity = (NOAEC * FI) / BW\n #\n # NOTE: The user enters the lowest available NOAEC for the mallard duck, for the bobwhite quail,\n # and for any other test species. The model calculates the dose equivalent toxicity values for\n # all of the modeled values (Cells F20-24 and results worksheet) and then selects the lowest dose\n # equivalent toxicity value to represent the chronic toxicity of the chemical to birds.\n # '''\n # try:\n # # result =\n # # self.assertEquals(result, )\n # pass\n # finally:\n # pass\n # return\n #\n #\n # def test_det_duck(self):\n # \"\"\"\n # unittest for function screenip.det_duck:\n # :return:\n # \"\"\"\n # try:\n # # det_duck = (self.noaec_duck * self.fi_bird(1580.)) / (1580. / 1000.)\n # screenip_empty.noaec_duck = pd.Series([1.], dtype='int')\n # screenip_empty.fi_bird = pd.Series([1.], dtype='int')\n # result = screenip_empty.det_duck()\n # npt.assert_array_almost_equal(result, 1000., 4, '', True)\n # finally:\n # pass\n # return\n #\n # def test_det_quail(self):\n # \"\"\"\n # unittest for function screenip.det_quail:\n # :return:\n # \"\"\"\n # try:\n # # det_quail = (self.noaec_quail * self.fi_bird(178.)) / (178. / 1000.)\n # screenip_empty.noaec_quail = pd.Series([1.], dtype='int')\n # screenip_empty.fi_bird = pd.Series([1.], dtype='int')\n # result = screenip_empty.det_quail()\n # npt.assert_array_almost_equal(result, 1000., 4, '', True)\n # finally:\n # pass\n # return\n #\n # def test_det_other_1(self):\n # \"\"\"\n # unittest for function screenip.det_other_1:\n # :return:\n # \"\"\"\n # try:\n # #det_other_1 = (self.noaec_bird_other_1 * self.fi_bird(self.bodyweight_bird_other_1)) / (self.bodyweight_bird_other_1 / 1000.)\n # #det_other_2 = (self.noaec_bird_other_2 * self.fi_bird(self.bodyweight_bird_other_1)) / (self.bodyweight_bird_other_1 / 1000.)\n # screenip_empty.noaec_bird_other_1 = pd.Series([400.]) # mg/kg-diet\n # screenip_empty.bodyweight_bird_other_1 = pd.Series([100]) # grams\n # result = screenip_empty.det_other_1()\n # npt.assert_array_almost_equal(result, 4666, 4)\n # finally:\n # pass\n # return\n #\n # The following tests are configured such that:\n # 1. four values are provided for each needed input\n # 2. the four input values generate four values of out_det_* per bird type\n # 3. the inputs per bird type are set so that calculations of out_det_* will result in\n # each bird type having one minimum among the bird types;\n # thus all four calculations result in one minimum per bird type\n\n # create empty pandas dataframes to create empty object for this unittest\n screenip_empty = self.create_screenip_object()\n\n expected_results = pd.Series([4.2174, 4.96125, 7.97237, 10.664648], dtype='float')\n result = pd.Series([], dtype='float')\n\n try:\n screenip_empty.bodyweight_bobwhite_quail = 178.\n screenip_empty.bodyweight_mallard_duck = 1580.\n screenip_empty.noaec_quail = pd.Series([100., 300., 75., 150.], dtype='float')\n screenip_empty.noaec_duck = pd.Series([400., 100., 200., 350.], dtype='float')\n screenip_empty.noaec_bird_other_1 = pd.Series([50., 200., 300., 250.], dtype='float')\n screenip_empty.noaec_bird_other_2 = pd.Series([350., 400., 250., 100.], dtype='float')\n screenip_empty.noaec_bodyweight_bird_other_1 = pd.Series([345.34, 453.54, 649.29, 294.56], dtype='float')\n screenip_empty.noaec_bodyweight_bird_other_2 = pd.Series([123.84, 85.743, 127.884, 176.34], dtype='float')\n screenip_empty.no_of_runs = len(expected_results)\n result = screenip_empty.det()\n npt.assert_allclose(result, expected_results, rtol=1e-4, atol=0, err_msg='', verbose=True )\n finally:\n tab = [result, expected_results]\n print(\"\\n\")\n print(inspect.currentframe().f_code.co_name)\n print(tabulate(tab, headers='keys', tablefmt='rst'))\n return","def test11_precip():\n assert is_precip_mode(11), 'VCP 11 is precip'","def untruncatedMode(self, x):\n self.raiseAnError(NotImplementedError,'untruncatedMode not yet implemented for ' + self.type)","def test_case03(self):\n version1 = versions.get_version_power(\"1.1.1\")\n version2 = versions.get_version_power(\"0.2.1\")\n self.assertGreater(version1, version2)","def test_documentation_popxl_addition(self):\n filename = \"simple_addition.py\"\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)","def test_active_pen_range(get_touchmat):\n touchmat = get_touchmat\n touchmat_model = check_device_types.get_device_model(touchmat)\n\n if touchmat_model == Devices.touchmat_g1:\n with pytest.raises(PySproutError) as execinfo:\n touchmat.active_pen_range()\n assert 'Functionality not available' in str(execinfo.value)\n\n with pytest.raises(PySproutError) as execinfo:\n touchmat.active_pen_range(\"ten_mm\")\n assert 'Functionality not available' in str(execinfo.value)\n return\n\n cur_range = touchmat.active_pen_range()\n assert isinstance(cur_range, touchmat.ActivePenRange)\n assert isinstance(cur_range.value, str)\n\n for item in TouchMat.ActivePenRange:\n pen_range = touchmat.active_pen_range(item)\n assert pen_range == item\n assert touchmat.active_pen_range() == item\n pen_range = touchmat.active_pen_range(item.value)\n assert pen_range == item\n assert touchmat.active_pen_range() == item\n\n ranges = [\"five_mm\", \"ten_mm\", \"fifteen_mm\", \"twenty_mm\"]\n for item in ranges:\n pen_range = touchmat.active_pen_range(item)\n assert pen_range.value == item\n assert touchmat.active_pen_range().value == item\n\n # Verify incorrect values return errors\n with pytest.raises(ValueError) as execinfo:\n touchmat.active_pen_range(\"thirty_mm\")\n assert 'is not a valid ActivePenRange' in str(execinfo.value)\n with pytest.raises(ValueError) as execinfo:\n touchmat.active_pen_range(3)\n assert 'is not a valid ActivePenRange' in str(execinfo.value)\n\n # Send a bad value to SoHal (bypassing the hippy enum check) and makes\n # sure SoHal throws an error...\n with pytest.raises(PySproutError) as execinfo:\n touchmat._send_msg('active_pen_range', 1) # pylint: disable=protected-access\n assert 'Invalid parameter' in execinfo.value.message\n with pytest.raises(PySproutError) as execinfo:\n touchmat._send_msg('active_pen_range', 'moo') # pylint: disable=protected-access\n assert 'Invalid parameter' in execinfo.value.message\n with pytest.raises(PySproutError) as execinfo:\n touchmat._send_msg('active_pen_range', {}) # pylint: disable=protected-access\n assert 'Invalid parameter' in execinfo.value.message","def _test_out_of_range(self):\n self.cdbconf.setup('KKG')\n self.cdbconf.setConfiguration('CUSTOM_OPT')\n az, el, latitude = [radians(50)] * 3\n site_info = {'latitude': latitude}\n self.p.setup(site_info, self.source, self.device)\n self.p.setRewindingMode('AUTO')\n offset = 20\n max_limit = self.device.getMaxLimit() \n min_limit = self.device.getMinLimit()\n Pis = max_limit - offset/2\n time.sleep(0.2) if self.using_mock else time.sleep(3)\n self.p.setPosition(Pis)\n time.sleep(0.2) # Wait a bit for the setup\n max_rewinding_steps = (max_limit - min_limit) // self.device.getStep()\n expected = Pis - max_rewinding_steps*self.device.getStep() + offset\n self.source.setAzimuth(az)\n self.source.setElevation(el)\n self.p.startUpdating('MNG_TRACK', 'ANT_NORTH', az, el, None, None)\n time.sleep(0.2) if self.using_mock else time.sleep(3)\n self.p.setOffset(offset)\n time.sleep(0.2) if self.using_mock else time.sleep(3)\n self.assertEqual(self.device.getActPosition(), expected)","def test_right_mode(self):\n self.dp.setRewindingMode('AUTO')\n self.assertEqual(self.dp.getRewindingMode(), 'AUTO')\n self.dp.setRewindingMode('MANUAL')","def test_excel(test_data,tmp_path):\n\n for d in test_data:\n\n gpm = GenotypePhenotypeMap(genotype=d[\"genotype\"],\n wildtype=d[\"wildtype\"],\n phenotype=d[\"phenotype\"],\n uncertainty=d[\"uncertainty\"])\n\n # Write excel file\n excel_file = os.path.join(tmp_path,\"tmp.xlsx\")\n gpm.to_excel(filename=excel_file)\n assert os.path.isfile(excel_file)\n\n # Read in and make sure it worked.\n new_gpm = gpmap.read_excel(filename=excel_file,wildtype=d[\"wildtype\"])\n conftest.compare_gpmap(gpm,new_gpm)\n\n # Do not give wildtype. Should still work because the wildtype was\n # inferred.\n gpm_read = gpmap.read_excel(filename=excel_file)\n conftest.compare_gpmap(gpm,gpm_read)\n\n # Check ability to read labels back in\n site_labels = [f\"{x}\" for x in range(10,10+len(d[\"wildtype\"]),1)]\n gpm = GenotypePhenotypeMap(genotype=d[\"genotype\"],\n wildtype=d[\"wildtype\"],\n site_labels=site_labels)\n out_file = os.path.join(tmp_path,\"tmp.xlsx\")\n gpm.to_excel(out_file)\n\n gpm_read = gpmap.read_excel(out_file)\n\n for i in range(len(gpm_read.site_labels)):\n\n # Skip virtual site_labels added for invariant sites\n if len(d[\"mutations\"][i]) == 1:\n continue\n\n assert gpm_read.site_labels[i] == gpm.site_labels[i]\n\n # Read in with bad wildtype. Should throw warning and then have\n # sequential site labels.\n with pytest.warns(UserWarning):\n gpm_read = gpmap.read_excel(out_file,wildtype=d[\"mutant\"])\n\n assert np.array_equal(gpm_read.site_labels,range(len(d[\"wildtype\"])))","def test_restore_backup():","def testsub_Y_X ( self ):\r\n\t\tr = re.compile ( 'frac' )\r\n\t\tfor fracTupX, fracTupY, dictAdd, dictSub, dictMul, dictDiv in self.knownArithResultValues:\r\n\t\t\tfracX = eval ( r.sub ( 'frac.frac', fracTupX ) )\r\n\t\t\tfracY = eval ( r.sub ( 'frac.frac', fracTupY ) )\r\n\t\t\tsub_fracY_fracX = fracY - fracX\r\n\t\t\tself.assertEqual ( sub_fracY_fracX.toString ().split ()[0], dictSub ['Y-X'] )","def test_thermallyExpands(self):\n self.assertFalse(self.component.THERMAL_EXPANSION_DIMS)","def testRefresh(self):\n \n pass","def test_rmg_mode(self):\n self.assertEqual(self.rmgmode, False)","def exo2():","def test_full(self):\n x = np.array([-5, -3, -2, -2, 100])\n self.assertEqual(\n npinterval.interval(x, 1),\n (-5, 100, 0, 5))","def test_flonum_special(doctest):","def test_thermallyExpands(self):\n self.assertTrue(self.component.THERMAL_EXPANSION_DIMS)","def test_thermallyExpands(self):\n self.assertTrue(self.component.THERMAL_EXPANSION_DIMS)","def test_thermallyExpands(self):\n self.assertTrue(self.component.THERMAL_EXPANSION_DIMS)","def test_thermallyExpands(self):\n self.assertTrue(self.component.THERMAL_EXPANSION_DIMS)","def test_thermallyExpands(self):\n self.assertTrue(self.component.THERMAL_EXPANSION_DIMS)","def test_thermallyExpands(self):\n self.assertTrue(self.component.THERMAL_EXPANSION_DIMS)","def test_thermallyExpands(self):\n self.assertTrue(self.component.THERMAL_EXPANSION_DIMS)","def test_thermallyExpands(self):\n self.assertTrue(self.component.THERMAL_EXPANSION_DIMS)","def test_post_nveto_pmts(self):\n pass","def test_diferencia_porcentual_menor(self):\r\n valorNuevo = 10\r\n valorAnterior = 0\r\n self.assertEqual(diferenciaPorcentual(valorNuevo, valorAnterior), -999999)","def test_REPLICATE_INTERVAL(self):\n self.assertIsInstance(constants.REPLICATE_INTERVAL, int,\n \"constants.REPLICATE_INTERVAL must be an \" +\n \"integer.\")","def test_get_speed_limit():\n center = Coordinates(1 , 1)\n radius = 10\n speed_limit = 20\n\n assert get_speed_limit(center, radius, speed_limit) != center\n assert get_speed_limit(center, radius, speed_limit) != radius\n assert get_speed_limit(center, radius, speed_limit) == speed_limit","def test_get_game_diff(self):\n pass","def test_step_to_midi_04():\n # This should produce 131, above range.\n step = 'C'\n alter = -1\n octave = 10\n with pytest.raises(Exception):\n U.step_to_midi(step, octave, alter)\n # This should produce 128, 1 above range.\n step = 'G'\n alter = 1\n octave = 9\n with pytest.raises(Exception):\n U.step_to_midi(step, octave, alter)","def test_damage(self):\n\n for op in self.veh.operators:\n op.experience = 10\n self.assertEqual(self.veh.damage, 0.4)","def test_popleft_when_equal(self, mock_datetime_obj):\n max_hour_count = None\n mock_datetime_obj.return_value = self.datetime_obj + timedelta(minutes=70)\n self.deque.append((self.datetime_obj, self.timestamp))\n self.deque.append((self.datetime_obj, self.timestamp))\n deque_length = len(self.deque)\n result = feature_5(self.deque,\n self.heap,\n self.expected_dict,\n self.top_n,\n max_hour_count,\n self.time_rollover_queue)\n self.assertEqual(result, (self.datetime_obj,\n self.timestamp,\n deque_length))","def test_parcel_profile_drop_duplicates():\n pressure = np.array([962., 951., 937.9, 925., 908., 905.7, 894., 875.,\n 41.3, 40.8, 37., 36.8, 32., 30., 27.7, 27.7, 26.4]) * units.hPa\n\n temperature = units.Quantity(19.6, 'degC')\n\n dewpoint = units.Quantity(18.6, 'degC')\n\n truth = np.array([292.75, 291.78965331, 291.12778784, 290.61996294,\n 289.93681828, 289.84313902, 289.36183185, 288.5626898,\n 135.46280886, 134.99220142, 131.27369084, 131.07055878,\n 125.93977169, 123.63877507, 120.85291224, 120.85291224,\n 119.20448296]) * units.kelvin\n\n with pytest.warns(UserWarning, match='Duplicate pressure'):\n profile = parcel_profile(pressure, temperature, dewpoint)\n assert_almost_equal(profile, truth, 5)","def testPerfilCasoInterseccionIgnorada(self):\n if self.TESTALL:\n perfilOriginal = [0,7,5,0,1,3,2,0]\n resultadoEsperado = [0,7,5]\n perfil = Perfil.Perfil()\n resultado = perfil.calcularPerfil(perfilOriginal,0)\n self.assertEqual(resultadoEsperado, resultado)","def test_data_range(self):\n ex = self.ex\n m = self.m\n n = self.n\n\n lenrange = random.randint(1, 10)\n nreps = random.randint(1, 10)\n\n ex.range = [\"i\", range(lenrange)]\n ex.nreps = nreps\n\n ex.vary[\"X\"][\"along\"] = 0\n ex.vary[\"X\"][\"with\"].add(\"rep\")\n ex.infer_lds()\n\n cmds = ex.generate_cmds()\n\n self.assertIn([\"smalloc\", \"X\", nreps * m * n + (nreps - 1) * m], cmds)\n rangeidx = random.randint(0, lenrange - 1)\n repidx = random.randint(0, nreps - 1)\n self.assertIn([\"soffset\", \"X\", repidx * m,\n \"X_%d_%d\" % (rangeidx, repidx)], cmds)"],"string":"[\n \"def test_data_norange(self):\\n ex = self.ex\\n m = self.m\\n n = self.n\\n\\n nreps = random.randint(1, 10)\\n lensumrange = random.randint(1, 10)\\n\\n ex.nreps = nreps\\n ex.sumrange = [\\\"j\\\", range(lensumrange)]\\n ex.vary[\\\"X\\\"][\\\"with\\\"].add(\\\"rep\\\")\\n ex.vary[\\\"Y\\\"][\\\"with\\\"].add(\\\"j\\\")\\n ex.vary[\\\"Y\\\"][\\\"along\\\"] = 0\\n ex.vary[\\\"Z\\\"][\\\"with\\\"].update([\\\"rep\\\", \\\"j\\\"])\\n ex.infer_lds()\\n\\n cmds = ex.generate_cmds()\\n\\n self.assertIn([\\\"smalloc\\\", \\\"X\\\", nreps * m * n], cmds)\\n idx = random.randint(0, nreps - 1)\\n self.assertIn([\\\"soffset\\\", \\\"X\\\", idx * m * n, \\\"X_%d\\\" % idx], cmds)\\n\\n self.assertIn([\\n \\\"dmalloc\\\", \\\"Y\\\", lensumrange * m * m + (lensumrange - 1) * m\\n ], cmds)\\n idx = random.randint(0, lensumrange - 1)\\n self.assertIn([\\\"doffset\\\", \\\"Y\\\", idx * m, \\\"Y_%d\\\" % idx], cmds)\\n\\n self.assertIn([\\\"cmalloc\\\", \\\"Z\\\", nreps * lensumrange * n * n], cmds)\\n idxrep = random.randint(0, nreps - 1)\\n idxrange = random.randint(0, lensumrange - 1)\\n self.assertIn([\\\"coffset\\\", \\\"Z\\\",\\n (idxrep * lensumrange + idxrange) * n * n,\\n \\\"Z_%d_%d\\\" % (idxrep, idxrange)], cmds)\",\n \"def test_57o_correct_preflop_odds(self):\\n self.assertEqual(self.hand.preFlopOdds10, 7.9)\",\n \"def test_is_old_papernum(self):\\n self.assertFalse(util.is_old_papernum(\\\"9106001\\\"))\\n self.assertTrue(util.is_old_papernum(\\\"9107001\\\"))\\n self.assertFalse(util.is_old_papernum(\\\"9200001\\\"))\\n self.assertTrue(util.is_old_papernum(\\\"9201001\\\"))\\n self.assertTrue(util.is_old_papernum(\\\"0703999\\\"))\\n self.assertFalse(util.is_old_papernum(\\\"0704001\\\"))\",\n \"def test_99_correct_preflop_odds(self):\\n self.assertEqual(self.hand.preFlopOdds10, 15.6)\",\n \"def test_pressure_increasing_check_some_decreasing(mocker, pressure_values, expected):\\n profile = mocker.patch.object(argortqcpy.profile, \\\"Profile\\\")\\n profile.get_property_data = mocker.Mock(return_value=ma.masked_array(pressure_values))\\n\\n pic = PressureIncreasingCheck(profile, None)\\n output = pic.run()\\n\\n assert np.all(output.get_output_flags_for_property(\\\"PRES\\\").data == expected)\",\n \"def test_g_asignar_rol(self):\",\n \"def test_open_fill(self):\",\n \"def test31_clear_air():\\n assert not is_precip_mode(31), 'VCP 31 is not precip'\",\n \"def test_modexp(self):\\n self.assertEqual(MathFunctions.modexp(2, 5, 7), 4)\\n self.assertEqual(MathFunctions.modexp(2, 10, 8), 0)\",\n \"def test_AKs_correct_preflop_odds(self):\\n self.assertEqual(self.hand.preFlopOdds10, 20.7)\",\n \"def test_analytical_vs_numerical():\\n pass\",\n \"def test_devide_int(self):\\n self.assertEqual(operations.devide(8,4), 2)\",\n \"def test_pressure_increasing_check_some_bad(mocker, pressure_values, expected):\\n profile = mocker.patch.object(argortqcpy.profile, \\\"Profile\\\")\\n profile.get_property_data = mocker.Mock(return_value=ma.masked_array(pressure_values))\\n\\n pic = PressureIncreasingCheck(profile, None)\\n output = pic.run()\\n\\n assert np.all(output.get_output_flags_for_property(\\\"PRES\\\").data == expected)\",\n \"def test_without_orographic_enhancement(self):\\n input_cube = self.precip_cube.copy()\\n input_cube.rename(\\\"air_temperature\\\")\\n input_cube.units = \\\"K\\\"\\n plugin = CreateExtrapolationForecast(input_cube, self.vel_x, self.vel_y)\\n result = plugin.extrapolate(10)\\n expected_result = np.array(\\n [[np.nan, np.nan, np.nan], [np.nan, 1, 2], [np.nan, 1, 1], [np.nan, 0, 2]],\\n dtype=np.float32,\\n )\\n expected_result = np.ma.masked_invalid(expected_result)\\n expected_forecast_period = np.array([600], dtype=np.int64)\\n # Check we get the expected result, and the correct time coordinates.\\n self.assertArrayEqual(\\n np.ma.getmask(expected_result), np.ma.getmask(result.data)\\n )\\n self.assertArrayAlmostEqual(expected_result.data, result.data.data)\\n self.assertArrayAlmostEqual(\\n result.coord(\\\"forecast_period\\\").points, expected_forecast_period\\n )\\n self.assertEqual(result.coord(\\\"forecast_period\\\").units, \\\"seconds\\\")\\n self.assertEqual(\\n result.coord(\\\"forecast_reference_time\\\").points,\\n input_cube.coord(\\\"time\\\").points,\\n )\\n self.assertEqual(\\n result.coord(\\\"time\\\").points, input_cube.coord(\\\"time\\\").points + 600\\n )\",\n \"def test_one_pop(data_: tuple, _is_pop: bool):\\n x_bar = cls.get_mean(data_)\\n s_x = cls.get_stdev(data_, is_population=_is_pop)\\n n_x = cls.get_n(data_)\\n return (x_bar - h0) / (s_x / sqrt(n_x))\",\n \"def test_get_meta_range(self):\\n pass\",\n \"def test_with_orographic_enhancement(self):\\n plugin = CreateExtrapolationForecast(\\n self.precip_cube,\\n self.vel_x,\\n self.vel_y,\\n orographic_enhancement_cube=self.oe_cube,\\n )\\n result = plugin.extrapolate(10)\\n expected_result = np.array(\\n [\\n [np.nan, np.nan, np.nan],\\n [np.nan, 1.03125, 1.0],\\n [np.nan, 1.0, 0.03125],\\n [np.nan, 0, 2.0],\\n ],\\n dtype=np.float32,\\n )\\n expected_result = np.ma.masked_invalid(expected_result)\\n expected_forecast_period = np.array([600], dtype=np.int64)\\n # Check we get the expected result, and the correct time coordinates.\\n self.assertArrayEqual(\\n np.ma.getmask(expected_result), np.ma.getmask(result.data)\\n )\\n self.assertArrayAlmostEqual(expected_result.data, result.data.data)\\n self.assertArrayAlmostEqual(\\n result.coord(\\\"forecast_period\\\").points, expected_forecast_period\\n )\\n self.assertEqual(result.coord(\\\"forecast_period\\\").units, \\\"seconds\\\")\\n self.assertEqual(\\n result.coord(\\\"forecast_reference_time\\\").points,\\n self.precip_cube.coord(\\\"time\\\").points,\\n )\\n self.assertEqual(\\n result.coord(\\\"time\\\").points, self.precip_cube.coord(\\\"time\\\").points + 600\\n )\",\n \"def testspec(arr: list[int]) -> None:\\n\\n print(50*'-')\\n print(arr)\\n print_rem(arr)\\n rev_dupes(arr)\\n print(arr)\",\n \"def test_FlexCrop1(self):\",\n \"def clean_data(self, opz):\\n# pdb.set_trace()\\n mask = (opz['Opzetstuk Noord (°)']<-1) | (opz['Opzetstuk Noord (°)']>100)\\n opz = opz.drop(opz.loc[mask].index)\\n opz['open'] = opz[\\\"Opzetstuk Noord (°)\\\"].apply(lambda x: 1 if x < 80 else 0)\\n #Deze klopt niet. We hebben het moment nodig van opengaan en het moment van dichtgaat. Moment van openen is: wanneer de verandering van de aantal graden >1 graad is. Moment van sluiten is de laatste verandering totdat het niet meer veranderd. Zie ook code van Pieter in C#.\\n opz['diff'] = opz['open'].diff()\\n beweegt=opz[opz['diff']!=0]\\n return beweegt\",\n \"def c_test_population_function(self, function):\\r\\n return 1\",\n \"def test_get_range(self):\\n pass\",\n \"def test_170329_notimp(self):\\n spc = parser(get_file('PTSDY2_notimp.txt'))\\n # spc.draw_outlooks()\\n outlook = spc.get_outlook('CATEGORICAL', 'MRGL')\\n self.assertAlmostEqual(outlook.geometry.area, 110.24, 2)\",\n \"def testMedicationsImmunosupp(self):\\n attr = self.session.create_visit_attr()\\n\\n self.util.boolTypeTest(self, attr, \\\"immunosupp\\\")\\n\\n self.util.boolPropertyTest(self, attr, \\\"immunosupp\\\")\",\n \"def test_wb(self):\\n df = dep.read_wb(get_path('wb.txt'))\\n self.assertAlmostEquals(df['precip'].max(), 162.04, 2)\",\n \"def test21_precip():\\n assert is_precip_mode(21), 'VCP 21 is precip'\",\n \"def test_for_more_flags(self):\\n shorter = REFRESH_COMMANDS.calculate_refresh_commands(\\\"Rainmeter.exe\\\", \\\"test-config\\\", \\\"file.inc\\\", True, False)\\n longer = REFRESH_COMMANDS.calculate_refresh_commands(\\\"Rainmeter.exe\\\", \\\"test-config\\\", \\\"file.ini\\\", True, True)\\n\\n self.assertGreater(longer, shorter)\",\n \"def test_4_4_1_1(self):\\n pass\",\n \"def test_reset(self):\\r\\n self.p += 8\\r\\n self.p.reset()\\r\\n self.assertEqual(str(self.p), '[>............] 0%')\",\n \"def test_documentation_popxl_replication(self):\\n filename = \\\"replication.py\\\"\\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)\",\n \"def test_is_ramped_using_int(self):\\n self.feature_test.set_percentage(100)\\n self.assertTrue(self.feature_test._is_ramped(5))\",\n \"def test_untar(self):\",\n \"def test_10(self, test):\\r\\n return test.MANUAL()\",\n \"def test_issue_reset_time(self):\\n pass\",\n \"def test_ncols_gtiff_object(self):\\n self.assertEqual(_test_object(landsat_gtiff)[2], 235)\",\n \"def test_right(self):\\n x = np.array([-100, -2, -1, 0, 1, 1.1])\\n self.assertEqual(npinterval.half_sample_mode(x), +1.05)\",\n \"def test_dewpoint_specific_humidity_old_signature():\\n p = 1013.25 * units.mbar\\n temperature = 20. * units.degC\\n q = 0.012 * units.dimensionless\\n with pytest.raises(ValueError, match='changed in 1.0'):\\n dewpoint_from_specific_humidity(q, temperature, p)\",\n \"def test_unroll_patch_true(self):\\r\\n self.validate((3, 2, 7, 5), (5, 2, 2, 3), 'valid', unroll_patch=True)\\r\\n self.validate((3, 2, 7, 5), (5, 2, 2, 3), 'full', unroll_patch=True)\\r\\n self.validate((3, 2, 3, 3), (4, 2, 3, 3), 'valid',\\r\\n unroll_patch=True, verify_grad=False)\",\n \"def test_osd(self):\\n self.assertEqual(osd(self.TestData), (9,3,3))\",\n \"def test_nrows_gtiff_object(self):\\n self.assertEqual(_test_object(landsat_gtiff)[1], 224)\",\n \"def test_pressure_increasing_check_some_constants(mocker, pressure_values, expected):\\n profile = mocker.patch.object(argortqcpy.profile, \\\"Profile\\\")\\n profile.get_property_data = mocker.Mock(return_value=ma.masked_array(pressure_values))\\n\\n pic = PressureIncreasingCheck(profile, None)\\n output = pic.run()\\n\\n assert np.all(output.get_output_flags_for_property(\\\"PRES\\\").data == expected)\",\n \"def test_of_agreement(self): \\n \\n pop_t = world.pop_t \\n pop_sub = world.pop_sub \\n \\n \\n Ns = world.subtime.length\\n \\n dt = world.time.step\\n ds = world.subtime.step\\n N = round(ds/dt)\\n for i in range(Ns):\\n numpy.testing.assert_allclose(pop_sub[:,i],pop_t[i*N,:])\",\n \"def test_pickable_propertysheet(self):\\n from Acquisition import aq_base\\n import pickle\\n\\n self.assertGreater(len(pickle.dumps(aq_base(self.ldap))), 170)\",\n \"def test_left(self):\\n x = np.array([-1.1, -1, 0, 1, 2, 100])\\n self.assertEqual(npinterval.half_sample_mode(x), -1.05)\",\n \"def test_MINX_pass(self):\\n self.assertTrue(self.mod.minx.isset)\",\n \"def testPerfilCasoInterseccionDescendente(self):\\n if self.TESTALL:\\n perfilOriginal = [1,9,5,0,3,7,12,0]\\n resultadoEsperado = [1,9,5,7,12]\\n perfil = Perfil.Perfil()\\n resultado = perfil.calcularPerfil(perfilOriginal,0)\\n self.assertEqual(resultadoEsperado, resultado)\",\n \"def _test_totalpop(self, obj, known_2000, known_2010):\\n known_delta = known_2010 - known_2000\\n known_pct = float(known_delta) / float(known_2000)\\n\\n self.assertEqual(float(obj['data']['2000'][\\\"P1\\\"]['P001001']), known_2000)\\n self.assertEqual(float(obj['data']['2010'][\\\"P1\\\"]['P001001']), known_2010)\\n self.assertEqual(float(obj['data']['delta'][\\\"P1\\\"]['P001001']), known_delta)\\n self.assertAlmostEqual(\\n float(obj['data']['pct_change'][\\\"P1\\\"]['P001001']),\\n known_pct\\n )\",\n \"def test_pressure_increasing_check_all_pass(mocker, pressure_values):\\n profile = mocker.patch.object(argortqcpy.profile, \\\"Profile\\\")\\n profile.get_property_data = mocker.Mock(return_value=ma.masked_array(pressure_values))\\n\\n pic = PressureIncreasingCheck(profile, None)\\n output = pic.run()\\n\\n assert np.all(output.get_output_flags_for_property(\\\"PRES\\\").data == ArgoQcFlag.GOOD.value)\",\n \"def test_cliford(generator, paulixops, result):\\n u = clifford(generator, paulixops)\\n assert u.compare(result)\",\n \"def test_binops(self):\",\n \"def test_floor(doctest):\",\n \"def test_margalef(self):\\n self.assertEqual(margalef(self.TestData), 8/log(22))\",\n \"def test_delete_o(self):\\n number = 6.0\\n number_return = self.new_calculation.delete_o(number)\\n self.assertEqual(number_return, 6)\\n self.assertNotEqual(type(number), int)\\n self.assertEqual(type(number_return), int)\\n\\n number = 10.565\\n number_return = self.new_calculation.delete_o(number)\\n self.assertEqual(number_return, 10.565)\\n self.assertEqual(type(number_return), float)\\n\\n number = 10\\n number_return = self.new_calculation.delete_o(number)\\n self.assertEqual(number_return, 10)\\n self.assertEqual(type(number_return), int)\",\n \"def test_flceiling(doctest):\",\n \"def testCorrectForTwoAtomCellWithoutPeriodicityNEEDED(self):\\n\\t\\texpDist = 0.01*10\\n\\t\\tself._checkExpMatchesActual(expDist)\",\n \"def test_unroll_patch_false(self):\\r\\n self.validate((3, 2, 7, 5), (5, 2, 2, 3), 'valid', unroll_patch=False)\\r\\n self.validate((3, 2, 7, 5), (5, 2, 2, 3), 'full', unroll_patch=False)\\r\\n self.validate((3, 2, 3, 3), (4, 2, 3, 3), 'valid',\\r\\n unroll_patch=False, verify_grad=False)\",\n \"def test_iimi1():\\n iimi = interactive_intrinsic_mutual_information(n_mod_m(3, 2), rvs=[[0], [1]], crvs=[2], rounds=1)\\n assert iimi == pytest.approx(0.0)\",\n \"def test_fltruncate(doctest):\",\n \"def test_T4():\",\n \"def test_T4():\",\n \"def test_reset(self):\\r\\n self.p += 8\\r\\n self.p.reset()\\r\\n self.assertEqual(str(self.p), '0% [....................]')\",\n \"def test_rolling_before_analysis(self):\\n cheese = TomoCheese.from_demo_images()\\n cheese.analyze()\\n original_roi_1 = copy.copy(cheese.module.rois[\\\"1\\\"].pixel_value)\\n for img in cheese.dicom_stack:\\n img.roll(direction=\\\"x\\\", amount=20)\\n cheese.analyze()\\n new_roi_1 = cheese.module.rois[\\\"1\\\"].pixel_value\\n assert math.isclose(original_roi_1, new_roi_1, abs_tol=3)\",\n \"def test_y_before_x(self):\",\n \"def test_minus(self):\\n print('test_minus');\\n self.assertEqual(90, minus(100, 10))\",\n \"def test_screenip_unit_det(self):\\n #\\n # '''\\n # Dose Equiv. Toxicity:\\n #\\n # The FI value (kg-diet) is multiplied by the reported NOAEC (mg/kg-diet) and then divided by\\n # the test animal's body weight to derive the dose-equivalent chronic toxicity value (mg/kg-bw):\\n #\\n # Dose Equiv. Toxicity = (NOAEC * FI) / BW\\n #\\n # NOTE: The user enters the lowest available NOAEC for the mallard duck, for the bobwhite quail,\\n # and for any other test species. The model calculates the dose equivalent toxicity values for\\n # all of the modeled values (Cells F20-24 and results worksheet) and then selects the lowest dose\\n # equivalent toxicity value to represent the chronic toxicity of the chemical to birds.\\n # '''\\n # try:\\n # # result =\\n # # self.assertEquals(result, )\\n # pass\\n # finally:\\n # pass\\n # return\\n #\\n #\\n # def test_det_duck(self):\\n # \\\"\\\"\\\"\\n # unittest for function screenip.det_duck:\\n # :return:\\n # \\\"\\\"\\\"\\n # try:\\n # # det_duck = (self.noaec_duck * self.fi_bird(1580.)) / (1580. / 1000.)\\n # screenip_empty.noaec_duck = pd.Series([1.], dtype='int')\\n # screenip_empty.fi_bird = pd.Series([1.], dtype='int')\\n # result = screenip_empty.det_duck()\\n # npt.assert_array_almost_equal(result, 1000., 4, '', True)\\n # finally:\\n # pass\\n # return\\n #\\n # def test_det_quail(self):\\n # \\\"\\\"\\\"\\n # unittest for function screenip.det_quail:\\n # :return:\\n # \\\"\\\"\\\"\\n # try:\\n # # det_quail = (self.noaec_quail * self.fi_bird(178.)) / (178. / 1000.)\\n # screenip_empty.noaec_quail = pd.Series([1.], dtype='int')\\n # screenip_empty.fi_bird = pd.Series([1.], dtype='int')\\n # result = screenip_empty.det_quail()\\n # npt.assert_array_almost_equal(result, 1000., 4, '', True)\\n # finally:\\n # pass\\n # return\\n #\\n # def test_det_other_1(self):\\n # \\\"\\\"\\\"\\n # unittest for function screenip.det_other_1:\\n # :return:\\n # \\\"\\\"\\\"\\n # try:\\n # #det_other_1 = (self.noaec_bird_other_1 * self.fi_bird(self.bodyweight_bird_other_1)) / (self.bodyweight_bird_other_1 / 1000.)\\n # #det_other_2 = (self.noaec_bird_other_2 * self.fi_bird(self.bodyweight_bird_other_1)) / (self.bodyweight_bird_other_1 / 1000.)\\n # screenip_empty.noaec_bird_other_1 = pd.Series([400.]) # mg/kg-diet\\n # screenip_empty.bodyweight_bird_other_1 = pd.Series([100]) # grams\\n # result = screenip_empty.det_other_1()\\n # npt.assert_array_almost_equal(result, 4666, 4)\\n # finally:\\n # pass\\n # return\\n #\\n # The following tests are configured such that:\\n # 1. four values are provided for each needed input\\n # 2. the four input values generate four values of out_det_* per bird type\\n # 3. the inputs per bird type are set so that calculations of out_det_* will result in\\n # each bird type having one minimum among the bird types;\\n # thus all four calculations result in one minimum per bird type\\n\\n # create empty pandas dataframes to create empty object for this unittest\\n screenip_empty = self.create_screenip_object()\\n\\n expected_results = pd.Series([4.2174, 4.96125, 7.97237, 10.664648], dtype='float')\\n result = pd.Series([], dtype='float')\\n\\n try:\\n screenip_empty.bodyweight_bobwhite_quail = 178.\\n screenip_empty.bodyweight_mallard_duck = 1580.\\n screenip_empty.noaec_quail = pd.Series([100., 300., 75., 150.], dtype='float')\\n screenip_empty.noaec_duck = pd.Series([400., 100., 200., 350.], dtype='float')\\n screenip_empty.noaec_bird_other_1 = pd.Series([50., 200., 300., 250.], dtype='float')\\n screenip_empty.noaec_bird_other_2 = pd.Series([350., 400., 250., 100.], dtype='float')\\n screenip_empty.noaec_bodyweight_bird_other_1 = pd.Series([345.34, 453.54, 649.29, 294.56], dtype='float')\\n screenip_empty.noaec_bodyweight_bird_other_2 = pd.Series([123.84, 85.743, 127.884, 176.34], dtype='float')\\n screenip_empty.no_of_runs = len(expected_results)\\n result = screenip_empty.det()\\n npt.assert_allclose(result, expected_results, rtol=1e-4, atol=0, err_msg='', verbose=True )\\n finally:\\n tab = [result, expected_results]\\n print(\\\"\\\\n\\\")\\n print(inspect.currentframe().f_code.co_name)\\n print(tabulate(tab, headers='keys', tablefmt='rst'))\\n return\",\n \"def test11_precip():\\n assert is_precip_mode(11), 'VCP 11 is precip'\",\n \"def untruncatedMode(self, x):\\n self.raiseAnError(NotImplementedError,'untruncatedMode not yet implemented for ' + self.type)\",\n \"def test_case03(self):\\n version1 = versions.get_version_power(\\\"1.1.1\\\")\\n version2 = versions.get_version_power(\\\"0.2.1\\\")\\n self.assertGreater(version1, version2)\",\n \"def test_documentation_popxl_addition(self):\\n filename = \\\"simple_addition.py\\\"\\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)\",\n \"def test_active_pen_range(get_touchmat):\\n touchmat = get_touchmat\\n touchmat_model = check_device_types.get_device_model(touchmat)\\n\\n if touchmat_model == Devices.touchmat_g1:\\n with pytest.raises(PySproutError) as execinfo:\\n touchmat.active_pen_range()\\n assert 'Functionality not available' in str(execinfo.value)\\n\\n with pytest.raises(PySproutError) as execinfo:\\n touchmat.active_pen_range(\\\"ten_mm\\\")\\n assert 'Functionality not available' in str(execinfo.value)\\n return\\n\\n cur_range = touchmat.active_pen_range()\\n assert isinstance(cur_range, touchmat.ActivePenRange)\\n assert isinstance(cur_range.value, str)\\n\\n for item in TouchMat.ActivePenRange:\\n pen_range = touchmat.active_pen_range(item)\\n assert pen_range == item\\n assert touchmat.active_pen_range() == item\\n pen_range = touchmat.active_pen_range(item.value)\\n assert pen_range == item\\n assert touchmat.active_pen_range() == item\\n\\n ranges = [\\\"five_mm\\\", \\\"ten_mm\\\", \\\"fifteen_mm\\\", \\\"twenty_mm\\\"]\\n for item in ranges:\\n pen_range = touchmat.active_pen_range(item)\\n assert pen_range.value == item\\n assert touchmat.active_pen_range().value == item\\n\\n # Verify incorrect values return errors\\n with pytest.raises(ValueError) as execinfo:\\n touchmat.active_pen_range(\\\"thirty_mm\\\")\\n assert 'is not a valid ActivePenRange' in str(execinfo.value)\\n with pytest.raises(ValueError) as execinfo:\\n touchmat.active_pen_range(3)\\n assert 'is not a valid ActivePenRange' in str(execinfo.value)\\n\\n # Send a bad value to SoHal (bypassing the hippy enum check) and makes\\n # sure SoHal throws an error...\\n with pytest.raises(PySproutError) as execinfo:\\n touchmat._send_msg('active_pen_range', 1) # pylint: disable=protected-access\\n assert 'Invalid parameter' in execinfo.value.message\\n with pytest.raises(PySproutError) as execinfo:\\n touchmat._send_msg('active_pen_range', 'moo') # pylint: disable=protected-access\\n assert 'Invalid parameter' in execinfo.value.message\\n with pytest.raises(PySproutError) as execinfo:\\n touchmat._send_msg('active_pen_range', {}) # pylint: disable=protected-access\\n assert 'Invalid parameter' in execinfo.value.message\",\n \"def _test_out_of_range(self):\\n self.cdbconf.setup('KKG')\\n self.cdbconf.setConfiguration('CUSTOM_OPT')\\n az, el, latitude = [radians(50)] * 3\\n site_info = {'latitude': latitude}\\n self.p.setup(site_info, self.source, self.device)\\n self.p.setRewindingMode('AUTO')\\n offset = 20\\n max_limit = self.device.getMaxLimit() \\n min_limit = self.device.getMinLimit()\\n Pis = max_limit - offset/2\\n time.sleep(0.2) if self.using_mock else time.sleep(3)\\n self.p.setPosition(Pis)\\n time.sleep(0.2) # Wait a bit for the setup\\n max_rewinding_steps = (max_limit - min_limit) // self.device.getStep()\\n expected = Pis - max_rewinding_steps*self.device.getStep() + offset\\n self.source.setAzimuth(az)\\n self.source.setElevation(el)\\n self.p.startUpdating('MNG_TRACK', 'ANT_NORTH', az, el, None, None)\\n time.sleep(0.2) if self.using_mock else time.sleep(3)\\n self.p.setOffset(offset)\\n time.sleep(0.2) if self.using_mock else time.sleep(3)\\n self.assertEqual(self.device.getActPosition(), expected)\",\n \"def test_right_mode(self):\\n self.dp.setRewindingMode('AUTO')\\n self.assertEqual(self.dp.getRewindingMode(), 'AUTO')\\n self.dp.setRewindingMode('MANUAL')\",\n \"def test_excel(test_data,tmp_path):\\n\\n for d in test_data:\\n\\n gpm = GenotypePhenotypeMap(genotype=d[\\\"genotype\\\"],\\n wildtype=d[\\\"wildtype\\\"],\\n phenotype=d[\\\"phenotype\\\"],\\n uncertainty=d[\\\"uncertainty\\\"])\\n\\n # Write excel file\\n excel_file = os.path.join(tmp_path,\\\"tmp.xlsx\\\")\\n gpm.to_excel(filename=excel_file)\\n assert os.path.isfile(excel_file)\\n\\n # Read in and make sure it worked.\\n new_gpm = gpmap.read_excel(filename=excel_file,wildtype=d[\\\"wildtype\\\"])\\n conftest.compare_gpmap(gpm,new_gpm)\\n\\n # Do not give wildtype. Should still work because the wildtype was\\n # inferred.\\n gpm_read = gpmap.read_excel(filename=excel_file)\\n conftest.compare_gpmap(gpm,gpm_read)\\n\\n # Check ability to read labels back in\\n site_labels = [f\\\"{x}\\\" for x in range(10,10+len(d[\\\"wildtype\\\"]),1)]\\n gpm = GenotypePhenotypeMap(genotype=d[\\\"genotype\\\"],\\n wildtype=d[\\\"wildtype\\\"],\\n site_labels=site_labels)\\n out_file = os.path.join(tmp_path,\\\"tmp.xlsx\\\")\\n gpm.to_excel(out_file)\\n\\n gpm_read = gpmap.read_excel(out_file)\\n\\n for i in range(len(gpm_read.site_labels)):\\n\\n # Skip virtual site_labels added for invariant sites\\n if len(d[\\\"mutations\\\"][i]) == 1:\\n continue\\n\\n assert gpm_read.site_labels[i] == gpm.site_labels[i]\\n\\n # Read in with bad wildtype. Should throw warning and then have\\n # sequential site labels.\\n with pytest.warns(UserWarning):\\n gpm_read = gpmap.read_excel(out_file,wildtype=d[\\\"mutant\\\"])\\n\\n assert np.array_equal(gpm_read.site_labels,range(len(d[\\\"wildtype\\\"])))\",\n \"def test_restore_backup():\",\n \"def testsub_Y_X ( self ):\\r\\n\\t\\tr = re.compile ( 'frac' )\\r\\n\\t\\tfor fracTupX, fracTupY, dictAdd, dictSub, dictMul, dictDiv in self.knownArithResultValues:\\r\\n\\t\\t\\tfracX = eval ( r.sub ( 'frac.frac', fracTupX ) )\\r\\n\\t\\t\\tfracY = eval ( r.sub ( 'frac.frac', fracTupY ) )\\r\\n\\t\\t\\tsub_fracY_fracX = fracY - fracX\\r\\n\\t\\t\\tself.assertEqual ( sub_fracY_fracX.toString ().split ()[0], dictSub ['Y-X'] )\",\n \"def test_thermallyExpands(self):\\n self.assertFalse(self.component.THERMAL_EXPANSION_DIMS)\",\n \"def testRefresh(self):\\n \\n pass\",\n \"def test_rmg_mode(self):\\n self.assertEqual(self.rmgmode, False)\",\n \"def exo2():\",\n \"def test_full(self):\\n x = np.array([-5, -3, -2, -2, 100])\\n self.assertEqual(\\n npinterval.interval(x, 1),\\n (-5, 100, 0, 5))\",\n \"def test_flonum_special(doctest):\",\n \"def test_thermallyExpands(self):\\n self.assertTrue(self.component.THERMAL_EXPANSION_DIMS)\",\n \"def test_thermallyExpands(self):\\n self.assertTrue(self.component.THERMAL_EXPANSION_DIMS)\",\n \"def test_thermallyExpands(self):\\n self.assertTrue(self.component.THERMAL_EXPANSION_DIMS)\",\n \"def test_thermallyExpands(self):\\n self.assertTrue(self.component.THERMAL_EXPANSION_DIMS)\",\n \"def test_thermallyExpands(self):\\n self.assertTrue(self.component.THERMAL_EXPANSION_DIMS)\",\n \"def test_thermallyExpands(self):\\n self.assertTrue(self.component.THERMAL_EXPANSION_DIMS)\",\n \"def test_thermallyExpands(self):\\n self.assertTrue(self.component.THERMAL_EXPANSION_DIMS)\",\n \"def test_thermallyExpands(self):\\n self.assertTrue(self.component.THERMAL_EXPANSION_DIMS)\",\n \"def test_post_nveto_pmts(self):\\n pass\",\n \"def test_diferencia_porcentual_menor(self):\\r\\n valorNuevo = 10\\r\\n valorAnterior = 0\\r\\n self.assertEqual(diferenciaPorcentual(valorNuevo, valorAnterior), -999999)\",\n \"def test_REPLICATE_INTERVAL(self):\\n self.assertIsInstance(constants.REPLICATE_INTERVAL, int,\\n \\\"constants.REPLICATE_INTERVAL must be an \\\" +\\n \\\"integer.\\\")\",\n \"def test_get_speed_limit():\\n center = Coordinates(1 , 1)\\n radius = 10\\n speed_limit = 20\\n\\n assert get_speed_limit(center, radius, speed_limit) != center\\n assert get_speed_limit(center, radius, speed_limit) != radius\\n assert get_speed_limit(center, radius, speed_limit) == speed_limit\",\n \"def test_get_game_diff(self):\\n pass\",\n \"def test_step_to_midi_04():\\n # This should produce 131, above range.\\n step = 'C'\\n alter = -1\\n octave = 10\\n with pytest.raises(Exception):\\n U.step_to_midi(step, octave, alter)\\n # This should produce 128, 1 above range.\\n step = 'G'\\n alter = 1\\n octave = 9\\n with pytest.raises(Exception):\\n U.step_to_midi(step, octave, alter)\",\n \"def test_damage(self):\\n\\n for op in self.veh.operators:\\n op.experience = 10\\n self.assertEqual(self.veh.damage, 0.4)\",\n \"def test_popleft_when_equal(self, mock_datetime_obj):\\n max_hour_count = None\\n mock_datetime_obj.return_value = self.datetime_obj + timedelta(minutes=70)\\n self.deque.append((self.datetime_obj, self.timestamp))\\n self.deque.append((self.datetime_obj, self.timestamp))\\n deque_length = len(self.deque)\\n result = feature_5(self.deque,\\n self.heap,\\n self.expected_dict,\\n self.top_n,\\n max_hour_count,\\n self.time_rollover_queue)\\n self.assertEqual(result, (self.datetime_obj,\\n self.timestamp,\\n deque_length))\",\n \"def test_parcel_profile_drop_duplicates():\\n pressure = np.array([962., 951., 937.9, 925., 908., 905.7, 894., 875.,\\n 41.3, 40.8, 37., 36.8, 32., 30., 27.7, 27.7, 26.4]) * units.hPa\\n\\n temperature = units.Quantity(19.6, 'degC')\\n\\n dewpoint = units.Quantity(18.6, 'degC')\\n\\n truth = np.array([292.75, 291.78965331, 291.12778784, 290.61996294,\\n 289.93681828, 289.84313902, 289.36183185, 288.5626898,\\n 135.46280886, 134.99220142, 131.27369084, 131.07055878,\\n 125.93977169, 123.63877507, 120.85291224, 120.85291224,\\n 119.20448296]) * units.kelvin\\n\\n with pytest.warns(UserWarning, match='Duplicate pressure'):\\n profile = parcel_profile(pressure, temperature, dewpoint)\\n assert_almost_equal(profile, truth, 5)\",\n \"def testPerfilCasoInterseccionIgnorada(self):\\n if self.TESTALL:\\n perfilOriginal = [0,7,5,0,1,3,2,0]\\n resultadoEsperado = [0,7,5]\\n perfil = Perfil.Perfil()\\n resultado = perfil.calcularPerfil(perfilOriginal,0)\\n self.assertEqual(resultadoEsperado, resultado)\",\n \"def test_data_range(self):\\n ex = self.ex\\n m = self.m\\n n = self.n\\n\\n lenrange = random.randint(1, 10)\\n nreps = random.randint(1, 10)\\n\\n ex.range = [\\\"i\\\", range(lenrange)]\\n ex.nreps = nreps\\n\\n ex.vary[\\\"X\\\"][\\\"along\\\"] = 0\\n ex.vary[\\\"X\\\"][\\\"with\\\"].add(\\\"rep\\\")\\n ex.infer_lds()\\n\\n cmds = ex.generate_cmds()\\n\\n self.assertIn([\\\"smalloc\\\", \\\"X\\\", nreps * m * n + (nreps - 1) * m], cmds)\\n rangeidx = random.randint(0, lenrange - 1)\\n repidx = random.randint(0, nreps - 1)\\n self.assertIn([\\\"soffset\\\", \\\"X\\\", repidx * m,\\n \\\"X_%d_%d\\\" % (rangeidx, repidx)], cmds)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.49731633","0.49007177","0.4868027","0.48571473","0.48517647","0.48516244","0.4770387","0.47574338","0.474696","0.4732291","0.47281486","0.47268423","0.47249767","0.47073162","0.46844417","0.46804345","0.46610695","0.4651014","0.4639932","0.46340024","0.46131676","0.45845267","0.45754358","0.45751292","0.45408538","0.45391086","0.45374107","0.4537361","0.45317215","0.45218405","0.45209447","0.45156255","0.4502764","0.45013598","0.44934025","0.44912276","0.44861484","0.44816777","0.4479247","0.4470395","0.44607255","0.4459262","0.44508436","0.44495746","0.44465953","0.44459653","0.444419","0.4442795","0.44417977","0.4435355","0.44348645","0.4433324","0.44314036","0.44260013","0.44217736","0.44216588","0.44216043","0.44201776","0.44113117","0.44113117","0.44088137","0.44038793","0.44009084","0.4396257","0.43895966","0.4382958","0.4381791","0.43668365","0.43648317","0.43611997","0.43597952","0.4359033","0.43564856","0.43551147","0.43379694","0.43284866","0.43277636","0.4327682","0.4322788","0.4322088","0.4321747","0.43192428","0.43192428","0.43192428","0.43192428","0.43192428","0.43192428","0.43192428","0.43192428","0.43178454","0.43116987","0.43106747","0.43071315","0.4305771","0.43054962","0.4299678","0.42984834","0.42958894","0.4293386","0.4292231"],"string":"[\n \"0.49731633\",\n \"0.49007177\",\n \"0.4868027\",\n \"0.48571473\",\n \"0.48517647\",\n \"0.48516244\",\n \"0.4770387\",\n \"0.47574338\",\n \"0.474696\",\n \"0.4732291\",\n \"0.47281486\",\n \"0.47268423\",\n \"0.47249767\",\n \"0.47073162\",\n \"0.46844417\",\n \"0.46804345\",\n \"0.46610695\",\n \"0.4651014\",\n \"0.4639932\",\n \"0.46340024\",\n \"0.46131676\",\n \"0.45845267\",\n \"0.45754358\",\n \"0.45751292\",\n \"0.45408538\",\n \"0.45391086\",\n \"0.45374107\",\n \"0.4537361\",\n \"0.45317215\",\n \"0.45218405\",\n \"0.45209447\",\n \"0.45156255\",\n \"0.4502764\",\n \"0.45013598\",\n \"0.44934025\",\n \"0.44912276\",\n \"0.44861484\",\n \"0.44816777\",\n \"0.4479247\",\n \"0.4470395\",\n \"0.44607255\",\n \"0.4459262\",\n \"0.44508436\",\n \"0.44495746\",\n \"0.44465953\",\n \"0.44459653\",\n \"0.444419\",\n \"0.4442795\",\n \"0.44417977\",\n \"0.4435355\",\n \"0.44348645\",\n \"0.4433324\",\n \"0.44314036\",\n \"0.44260013\",\n \"0.44217736\",\n \"0.44216588\",\n \"0.44216043\",\n \"0.44201776\",\n \"0.44113117\",\n \"0.44113117\",\n \"0.44088137\",\n \"0.44038793\",\n \"0.44009084\",\n \"0.4396257\",\n \"0.43895966\",\n \"0.4382958\",\n \"0.4381791\",\n \"0.43668365\",\n \"0.43648317\",\n \"0.43611997\",\n \"0.43597952\",\n \"0.4359033\",\n \"0.43564856\",\n \"0.43551147\",\n \"0.43379694\",\n \"0.43284866\",\n \"0.43277636\",\n \"0.4327682\",\n \"0.4322788\",\n \"0.4322088\",\n \"0.4321747\",\n \"0.43192428\",\n \"0.43192428\",\n \"0.43192428\",\n \"0.43192428\",\n \"0.43192428\",\n \"0.43192428\",\n \"0.43192428\",\n \"0.43192428\",\n \"0.43178454\",\n \"0.43116987\",\n \"0.43106747\",\n \"0.43071315\",\n \"0.4305771\",\n \"0.43054962\",\n \"0.4299678\",\n \"0.42984834\",\n \"0.42958894\",\n \"0.4293386\",\n \"0.4292231\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6394983"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":6595,"cells":{"query":{"kind":"string","value":"Test the popxl in sequence context manager"},"document":{"kind":"string","value":"def test_documentation_popxl_in_sequence(self):\n filename = \"in_sequence.py\"\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)"},"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 test_pop_methods(self):\n\n batch = Batch(Mock())\n\n # mock BatchRequests\n mock_obj = Mock()\n mock_ref = Mock()\n batch._objects_batch = mock_obj\n batch._reference_batch = mock_ref\n\n mock_obj.pop.assert_not_called()\n mock_ref.pop.assert_not_called()\n\n # pop object default value\n batch.pop_object()\n mock_obj.pop.assert_called_with(-1)\n mock_ref.pop.assert_not_called()\n # reset mock objects\n mock_obj.reset_mock()\n mock_ref.reset_mock()\n\n # pop object at index\n batch.pop_object(10)\n mock_obj.pop.assert_called_with(10)\n mock_ref.pop.assert_not_called()\n # reset mock objects\n mock_obj.reset_mock()\n mock_ref.reset_mock()\n\n # pop reference default value\n batch.pop_reference()\n mock_obj.pop.assert_not_called()\n mock_ref.pop.assert_called_with(-1)\n # reset mock objects\n mock_obj.reset_mock()\n mock_ref.reset_mock()\n\n # pop reference at index\n batch.pop_reference(9)\n mock_obj.pop.assert_not_called()\n mock_ref.pop.assert_called_with(9)","def test_pop_returns_value(new_dll):\n assert new_dll.pop() == 3","def test_close():\n while True:\n yield","def test_pop(self):\n sched = Schedule()\n inst_map = InstructionScheduleMap()\n\n inst_map.add(\"tmp\", 100, sched)\n self.assertEqual(inst_map.pop(\"tmp\", 100), sched)\n self.assertFalse(inst_map.has(\"tmp\", 100))\n\n self.assertEqual(inst_map.qubit_instructions(100), [])\n self.assertEqual(inst_map.qubits_with_instruction(\"tmp\"), [])\n with self.assertRaises(PulseError):\n inst_map.pop(\"not_there\", (0,))","def testGetSequence():\r\n\t\r\n\t#a few of hand-tested genome positions\r\n\ttest_data = [\t('1',500,520,'GTCTGACCTGAGGAGAACTGT'),\r\n\t\t\t\t\t('2',500,520,'CCCGACCCCGACCCCGACCCA'),\r\n\t\t\t\t\t('3',50000,50020,'TCTTCTTTTATGAAAAAGGAT'),\r\n\t\t\t\t\t('4',50000,50020,'AGAGCCCTGCAATTTGAAGAT'),\r\n\t\t\t\t\t('5',100000,100020,'AATGTTCACCAGTATATTTTA'),\r\n\t\t\t\t\t('X',100000,100020,'TAGGTCTCATTGAGGACAGAT'),\r\n\t\t\t\t\t('Y',100000,100020,'TAGGTCTCATTGAGGACAGAT')]\r\n\t\t\t\t\t\r\n\tfor this_check in test_data:\r\n\t\tyield CheckGetSequence, this_check","def tearDown(self):\n del self.pop","def pop():","def test_push_pop(values):\n test_stack = stack.Stack()\n\n for value in values:\n test_stack.push(value)\n\n for expected_value in reversed(values):\n value = test_stack.pop()\n assert value == expected_value\n\n with pytest.raises(stack.StackEmptyError):\n test_stack.pop()","def test_pop_on_empty_raises_error(sample_priorityq):\n with pytest.raises(IndexError):\n sample_priorityq.pop()","def test_pop_gate(self):\n sched = Schedule()\n inst_map = InstructionScheduleMap()\n\n inst_map.add(XGate(), 100, sched)\n self.assertEqual(inst_map.pop(XGate(), 100), sched)\n self.assertFalse(inst_map.has(XGate(), 100))\n\n self.assertEqual(inst_map.qubit_instructions(100), [])\n self.assertEqual(inst_map.qubits_with_instruction(XGate()), [])\n with self.assertRaises(PulseError):\n inst_map.pop(\"not_there\", (0,))","def test_pop_no_args(self):\r\n msg_list = messages.MessageList()\r\n # Adds 5 Message objects to the list.\r\n msg_list.push(messages.StringMessage(\"a\"))\r\n msg_list.push(messages.StringMessage(\"b\"))\r\n msg_list.push(messages.StringMessage(\"c\"))\r\n msg_list.push(messages.StringMessage(\"d\"))\r\n msg_list.push(messages.StringMessage(\"e\"))\r\n\r\n self.assertEqual(msg_list.length(), 5)\r\n popped = msg_list.pop()\r\n self.assertEqual(msg_list.length(), 4)\r\n self.assertEqual(popped.msg, \"e\")\r\n msg_list.pop()\r\n msg_list.pop()\r\n msg_list.pop()\r\n msg_list.pop()\r\n self.assertRaises(IndexError, msg_list.pop)","def test_open_fill(self):","def test_peek_empty():\n test_stack = stack.Stack()\n\n with pytest.raises(stack.StackEmptyError):\n test_stack.peek()","def test_ignore_close():\n try:\n yield\n except GeneratorExit:\n yield","def test_pop_on_small_stack(small_stack):\n assert small_stack.pop().val == 3\n assert small_stack.pop().val == 2\n assert small_stack._size == 1","def _pop(self, actual_call):\n try:\n expected_call, mock_result = self._queue.popleft()\n except IndexError as ex:\n error = UnexpectedCall(\n \"Queue is empty. call: {0}\"\n .format(actual_call)\n )\n self._store_pop_error(error)\n raise error\n if actual_call != expected_call:\n error = UnexpectedCall(\n \"Call does not match expectation. actual: {0}; expected: {1}\"\n .format(actual_call, expected_call)\n )\n self._store_pop_error(error)\n raise error\n # let it raise if the result is an exception or exception type.\n return mock_result()","def testSeq(self, mock_gs):\n self.mr._sequences = ['apple', 'banana']\n\n self.assertEqual(\n 'apple',\n self.mr.seq\n )\n\n mock_gs.assert_called_once_with()\n mock_gs.reset_mock()\n\n self.mr._is_seq = False\n\n self.assertEqual(\n None,\n self.mr.seq\n )\n\n # Test that we pulled from the cache\n self.assertFalse(\n mock_gs.called\n )","def test_pop_left_check_head(dq_3):\n dq_3.pop_left()\n assert dq_3._dll.head.data == 4","def test_pop_returns_value_of_tail(dq_3):\n assert dq_3.pop() == 'ragtime'","def test_appended(self):\n genFn = Mock(return_value=None)\n expected = 123\n \n wrapper = KaoGenerator(genFn)\n wrapper.queue(expected)\n actual = wrapper._queue.pop()\n self.assertEqual(expected, actual)","def testPushPopItem(self):\n test_queue = multi_process.MultiProcessingQueue()\n\n for item in self._ITEMS:\n test_queue.PushItem(item)\n\n test_queue.SignalEndOfInput()\n test_queue_consumer = test_lib.TestQueueConsumer(test_queue)\n test_queue_consumer.ConsumeItems()\n\n self.assertEqual(test_queue_consumer.number_of_items, len(self._ITEMS))","def test_peek_single(values, expected_value):\n test_stack = stack.Stack()\n for value in values:\n test_stack.push(value)\n\n returned_value = test_stack.peek()\n\n assert returned_value == expected_value\n\n for value in reversed(values):\n assert test_stack.pop() == value","def test_sequence(self):\n seq_name = 'test_seq'\n\n with self.dbh.sequence_recreate(seq_name):\n try:\n self.assertEqual(self.dbh.get_seq_next_value(seq_name), 1)\n self.assertEqual(self.dbh.get_seq_next_value(seq_name), 2)\n self.assertEqual(self.dbh.get_seq_next_value(seq_name), 3)\n except Exception:\n self.dbh.rollback()\n raise","def test_valueInQueue(self):\n genFn = Mock(return_value=None)\n expected = 123\n \n wrapper = KaoGenerator(genFn)\n wrapper.queue(expected)\n actual = wrapper.pop()\n self.assertEqual(expected, actual)","def do_test():\n for x in execute_helper(test_info,crossmap_tests):\n yield x","def test_populator_aborts_early():\n o1, o2 = MediaBag(media=1), MediaBag(media=2)\n\n def multi_get(*keys):\n raise AssertionError('tried calling multi_get')\n\n results = media.build_populator('id', multi_get)([o1, o2])\n assert results == [o1, o2]","def test_generator_cleanup():\n try:\n yield 1\n finally:\n print('cleanup')","def pop_write(self):\n ...","def test_pushpop2_dir(self):\n TempfileManager.push()\n os.mkdir(tempdir + 'pushpop2')\n TempfileManager.add_tempfile(tempdir + 'pushpop2')\n\n TempfileManager.push()\n os.mkdir(tempdir + 'pushpop2a')\n TempfileManager.add_tempfile(tempdir + 'pushpop2a')\n TempfileManager.pop()\n if not os.path.exists(tempdir + 'pushpop2'):\n self.fail(\"pop() clean out all files\")\n if os.path.exists(tempdir + 'pushpop2a'):\n self.fail(\"pop() failed to clean out files\")\n\n TempfileManager.pop()\n if os.path.exists(tempdir + 'pushpop2'):\n self.fail(\"pop() failed to clean out files\")","def popitem(self): # real signature unknown; restored from __doc__\n pass","def testSeqs(self, mock_gs):\n self.mr._sequences = ['apple', 'banana']\n\n self.assertEqual(\n ['apple', 'banana'],\n self.mr.seqs\n )\n\n mock_gs.assert_called_once_with()\n mock_gs.reset_mock()\n\n self.mr._is_seq = False\n\n self.assertEqual(\n [],\n self.mr.seqs\n )\n\n # Test that we pulled from the cache\n self.assertFalse(\n mock_gs.called\n )","def test_reading_missing(self, mock_pop, mock_shell):\n mock_result = mock.Mock()\n mock_result.communicate = mock.Mock()\n mock_result.returncode = 0\n mock_result.communicate.return_value = (self.dir, None)\n mock_pop.return_value = mock_result\n mock_shell.return_value = self.gitconf_str\n config = load_configuration(package_dir=\"womp\")\n\n self.failUnless(mock_result.communicate.called)\n mock_pop.assert_has_calls(mock.call(['git', 'rev-parse', '--show-toplevel'], stdout=-1))\n self.assertEqual(config.package_version(), '1.2.3')\n self.assertEqual(config.package_name(), 'cirrus_tests')\n self.failUnless(mock_shell.called)\n mock_shell.assert_has_calls([\n mock.call('git config --file {} -l'.format(self.gitconfig))\n ])","def test_sequence_done(self):\n self.t(\"1,2 done\")\n code, out, err = self.t(\"_get 1.status 2.status\")\n self.assertEqual(\"completed completed\\n\", out)","def test_tail_call(self):","def test_priority_que_pop(priority_queue_full):\n # import pdb; pdb.set_trace()\n assert (priority_queue_full.pop(),\n priority_queue_full.pop(),\n priority_queue_full.pop(),\n priority_queue_full.pop()) == (11, 6, 12, 15)","def test_pop_left_for_length(dq_3):\n dq_3.pop_left()\n assert dq_3.length == 2","def test_invoke_anonymous_pipe():\n\n def processor_a(app, items):\n items = list(items)\n assert items == [holocron.Item({\"a\": \"b\"})]\n items[0][\"x\"] = 42\n yield from items\n\n def processor_b(app, items):\n items = list(items)\n assert items == [holocron.Item({\"a\": \"b\", \"x\": 42})]\n items.append(holocron.Item({\"z\": 13}))\n yield from items\n\n def processor_c(app, items):\n items = list(items)\n assert items == [\n holocron.Item({\"a\": \"b\", \"x\": 42}),\n holocron.Item({\"z\": 13}),\n ]\n yield from items\n\n testapp = holocron.Application()\n testapp.add_processor(\"processor_a\", processor_a)\n testapp.add_processor(\"processor_b\", processor_b)\n testapp.add_processor(\"processor_c\", processor_c)\n\n stream = testapp.invoke(\n [\n {\"name\": \"processor_a\"},\n {\"name\": \"processor_b\"},\n {\"name\": \"processor_c\"},\n ],\n [holocron.Item({\"a\": \"b\"})],\n )\n\n assert next(stream) == holocron.Item({\"a\": \"b\", \"x\": 42})\n assert next(stream) == holocron.Item({\"z\": 13})\n\n with pytest.raises(StopIteration):\n next(stream)","def test_pop(self):\n self.assertRaises(EmptyHeapException, self.minheap.pop)\n self.minheap.heap = [0, 1, 4, 7, 9]\n assert self.minheap.pop() == 1\n assert self.minheap.heap == [0, 4, 9, 7]","def test_INVOKE_repeat(self, propose):\n self.rep.proposals[1] = PROPOSAL1\n self.failIf(propose.called)","def test_peek_on_empty_raises_error(sample_priorityq):\n with pytest.raises(IndexError):\n sample_priorityq.peek()","def unit_test():\n queue = Queue()\n stack = Stack()\n\n for i in range(17):\n queue.push(i)\n stack.push(i)\n\n print(stack.pop())\n print(queue.pop())\n\n print(stack.peek())\n print(queue.peek())\n\n print(\"STACK: \", stack)\n print(\"QUEUE: \", queue)\n print(\"EMPTY?: QUEUE: \", queue.is_empty(), \"STACK: \", stack.is_empty())\n\n while not queue.is_empty():\n queue.pop()\n\n print(\"EMPTY?: QUEUE: \", queue.is_empty(), \"STACK: \", stack.is_empty())\n\n # print(\"POP EMPTY: \", queue.pop())","def test_pop_sets_new_head(new_dll):\n new_dll.pop()\n assert new_dll.head.value == 4","def test_pop(self):\n test_list = DoubleLinkedList()\n test_list.push(15)\n test_list.push(150)\n test_list.push(13)\n test_list.push(155)\n test_list.push(1)\n test_list.pop()\n self.assertEqual(test_list.last().get_elem(), 155)","def test_teardown(self):\n assert self.search_behaviour.teardown() is None\n self.assert_quantity_in_outbox(0)","def test_teardown(self):\n assert self.transaction_behaviour.teardown() is None\n self.assert_quantity_in_outbox(0)","def pop(self):","def pop(self):","def test_multiple_commands_at_same_time(self):","def test_sequence_modify(self):\n self.t(\"1,2 modify +xyz\")\n code, out, err = self.t(\"_get 1.tags 2.tags\")\n self.assertEqual(\"xyz xyz\\n\", out)","def pop(self, *args, **kwargs): # real signature unknown\n pass","def test_run_ended(self):","def test_generator(self):\n args = Args()\n args.files = ['tests/xproto/test.xproto']\n args.target = 'tests/xtarget/test.xtarget'\n args.output = 'tests/out/dir/'\n args.write_to_file = \"target\"\n args.dest_file = None\n args.dest_extension = None\n\n expected_args = Args()\n expected_args.files = [os.path.abspath(os.getcwd() + '/' + args.files[0])]\n expected_args.target = os.path.abspath(os.getcwd() + '/' + args.target)\n expected_args.output = os.path.abspath(os.getcwd() + '/' + args.output)\n\n with patch(\"xosgenx.xosgen.XOSGenerator.generate\") as generator:\n XosGen.init(args)\n actual_args = generator.call_args[0][0]\n self.assertEqual(actual_args.files, expected_args.files)\n self.assertEqual(actual_args.target, expected_args.target)\n self.assertEqual(actual_args.output, expected_args.output)","def test_pushpop1_dir(self):\n TempfileManager.push()\n os.mkdir(tempdir + 'pushpop1')\n TempfileManager.add_tempfile(tempdir + 'pushpop1')\n TempfileManager.pop()\n if os.path.exists(tempdir + 'pushpop1'):\n self.fail(\"pop() failed to clean out directories\")","def test_pop_sets_new_head_next(new_dll):\n new_dll.pop()\n assert new_dll.head.next.value == 5","def teardown(self):","def teardown(self):","def teardown(self):","def popitem(self):\n pass","def mp_test(target, args=(), kwargs={}, timeout=None):\n\n if os.name == 'nt':\n return target(*args,**kwargs)\n\n r, w = mp.Pipe(duplex=False)\n def wrap(target=None, args=(), kwargs={}):\n r.close()\n res=target(*args, **kwargs)\n try:\n w.send(res)\n except:\n os.kill(os.getpid(),signal.SIGTERM)\n else:\n w.close()\n \n p = mp.Process(target=wrap, args=[target, args, kwargs])\n p.start()\n w.close()\n p.join(timeout) # Can't timeout pipe recv, so risking block on send.\n if p.is_alive():\n p.terminate()\n raise Error(\"timeout\",\"Timeout limit was \"+str(timeout)+\" seconds\")\n if p.exitcode:\n raise Error(\"signaled\",\"Exited following signal -\"+str(p.exitcode))\n output = r.recv()\n r.close()\n return output\n #return (None,output)","def test_invoke_processor_errors():\n\n def processor(app, documents):\n raise ValueError(\"something bad happened\")\n yield\n\n testapp = holocron.Application()\n testapp.add_processor(\"processor\", processor)\n testapp.add_pipe(\"test\", [{\"name\": \"processor\"}])\n\n stream = testapp.invoke(\"test\")\n\n with pytest.raises(ValueError, match=r\"^something bad happened$\"):\n next(stream)\n\n with pytest.raises(StopIteration):\n next(stream)","def test_redoer():\n tock = 1.0\n redoer = doing.ReDoer()\n assert redoer.tock == 0.0\n\n tymist = tyming.Tymist()\n redoer = doing.ReDoer(tymth=tymist.tymen(), tock=tock)\n assert redoer.tock == tock == 1.0\n redoer.tock = 0.0\n assert redoer.tock == 0.0\n\n # create generator use send and run until normal exit. emulates Doist.ready\n args = {}\n dog = redoer(tymth=redoer.tymth, tock=redoer.tock, **args)\n assert inspect.isgenerator(dog)\n\n result = dog.send(None)\n assert result == redoer.tock == 0.0\n\n tymist.tick()\n result = dog.send(tymist.tyme)\n assert result == redoer.tock == 0.0\n\n tymist.tick()\n result = dog.send(tymist.tyme)\n assert result == redoer.tock == 0.0\n\n tymist.tick()\n with pytest.raises(StopIteration):\n try:\n result = dog.send(tymist.tyme)\n except StopIteration as ex:\n assert ex.value == True\n raise\n\n tymist.tick()\n with pytest.raises(StopIteration): # send after break\n try:\n result = dog.send(tymist.tyme)\n except StopIteration as ex:\n assert ex.value == None\n raise\n\n # create generator use send and then explicit close. emulates Doist.ready\n args = {}\n dog = redoer(tymth=redoer.tymth, tock=redoer.tock, **args)\n assert inspect.isgenerator(dog)\n\n result = dog.send(None)\n assert result == redoer.tock == 0.0\n\n tymist.tick()\n result = dog.send(tymist.tyme)\n assert result == redoer.tock == 0.0\n\n result = dog.close()\n assert result == None # no yielded value on close\n\n tymist.tick()\n with pytest.raises(StopIteration): # send after close\n try:\n result = dog.send(tymist.tyme)\n except StopIteration as ex:\n assert ex.value == None\n raise\n\n\n # use next instead of send\n args = {}\n dog = redoer(tymth=redoer.tymth, tock=redoer.tock, **args)\n assert inspect.isgenerator(dog)\n\n result = next(dog)\n assert result == redoer.tock == 0.0\n\n result = next(dog)\n assert result == redoer.tock == 0.0\n\n result = dog.close()\n assert result == None # no yielded value on close\n\n tymist.tick()\n with pytest.raises(StopIteration): # send after close\n try:\n result = dog.send(tymist.tyme)\n except StopIteration as ex:\n assert ex.value == None\n raise\n\n \"\"\"End Test \"\"\"","def test_invoke_passthrough_items():\n\n def processor(app, items):\n yield from items\n\n testapp = holocron.Application()\n testapp.add_processor(\"processor\", processor)\n testapp.add_pipe(\"test\", [{\"name\": \"processor\"}])\n\n stream = testapp.invoke(\n \"test\",\n [\n holocron.Item(name=\"yoda\", rank=\"master\"),\n holocron.Item(name=\"skywalker\"),\n ],\n )\n\n assert next(stream) == holocron.Item(name=\"yoda\", rank=\"master\")\n assert next(stream) == holocron.Item(name=\"skywalker\")\n\n with pytest.raises(StopIteration):\n next(testapp.invoke(\"test\"))","def test_invoke_propagates_processor_args(processor_args):\n\n def processor(app, items, **args):\n assert args == processor_args\n yield from items\n\n testapp = holocron.Application()\n testapp.add_processor(\"processor\", processor)\n testapp.add_pipe(\"test\", [{\"name\": \"processor\", \"args\": processor_args}])\n\n with pytest.raises(StopIteration):\n next(testapp.invoke(\"test\"))","def teardown(self):\n pos.close()","def teardown(self):\n pos.close()","def test_enter_exit(self, mock_flush):\n\n batch = Batch(Mock())\n\n with batch as b:\n self.assertEqual(batch, b)\n mock_flush.assert_called()","def test_unload(install_mockery, mock_fetch, mock_archive, mock_packages, working_env):\n install(\"mpileaks\")\n mpileaks_spec = spack.spec.Spec(\"mpileaks\").concretized()\n\n # Set so unload has something to do\n os.environ[\"FOOBAR\"] = \"mpileaks\"\n os.environ[uenv.spack_loaded_hashes_var] = \"%s:%s\" % (mpileaks_spec.dag_hash(), \"garbage\")\n\n sh_out = unload(\"--sh\", \"mpileaks\")\n csh_out = unload(\"--csh\", \"mpileaks\")\n\n assert \"unset FOOBAR\" in sh_out\n assert \"unsetenv FOOBAR\" in csh_out\n\n assert \"export %s=garbage\" % uenv.spack_loaded_hashes_var in sh_out\n assert \"setenv %s garbage\" % uenv.spack_loaded_hashes_var in csh_out","def test_documentation_popxl_replication(self):\n filename = \"replication.py\"\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)","def pop(self):\n return self.new_dll.shift()","def popitem(self):\n pass","def test_pop_empty_list():\n from dll import DbLinkedList\n dll = DbLinkedList()\n msg = \"Cannot pop from an empty list\"\n with pytest.raises(ValueError, message=msg):\n dll.pop()","def test_peek_on_small_stack(small_stack):\n assert small_stack.peek().val == 3","def test_invoke_passthrough_items_empty_pipe():\n\n testapp = holocron.Application()\n testapp.add_pipe(\"test\", [])\n\n stream = testapp.invoke(\n \"test\",\n [\n holocron.Item(name=\"yoda\", rank=\"master\"),\n holocron.Item(name=\"skywalker\"),\n ],\n )\n\n assert next(stream) == holocron.Item(name=\"yoda\", rank=\"master\")\n assert next(stream) == holocron.Item(name=\"skywalker\")\n\n with pytest.raises(StopIteration):\n next(testapp.invoke(\"test\"))","def teardown(self) -> None:","def teardown(self) -> None:","def teardown(self) -> None:","def test_sequence(self):\n self.assertEqual([1, -3, 9, -27, 81, -243],\n [x for x in GeometricProgression(6, 1, -3)])\n\n self.assertEqual([1, 1, 1, 1, 1],\n [x for x in GeometricProgression(5, 1, 1)])\n\n self.assertEqual([4, 40, 400, 4000, 40000],\n [x for x in GeometricProgression(5, 4, 10)])","def test_pop_from_empty_list(new_empty_ll):\n from linked_list import Linked_List\n with pytest.raises(IndexError):\n new_empty_ll.pop()","def test_lose(self):\n self.choice.return_value = \"ant\" \n self.input.side_effect = list(\"bcdefg\" \"n\")\n\n gallows.main()\n\n self.xprint.assert_any_call('You have run out of guesses!')","def tearDown(self):\n self.popen_patcher.stop()","def tearDown(self):\n self.popen_patcher.stop()","def pop_sm(self):\r\n while True:\r\n # wait to receive a read request\r\n req = yield self.r_in_pipe.get()\r\n # model read latency\r\n # for i in range(self.read_latency):\r\n yield self.wait_sys_clks(self.read_latency)\r\n # try to read head element\r\n if len(self.items) > 0:\r\n data = self.items[0]\r\n self.items = self.items[1:]\r\n else:\r\n print >> sys.stderr, \"ERROR: PIFO pop_sm: attempted to read from empty PIFO\"\r\n data = None\r\n # write data back\r\n self.r_out_pipe.put(data)","def test_main_gc_2(test):\n answers = (i for i in (test, '1 1 1 1', 'q'))\n with mock.patch.object(builtins, 'input', lambda _: next(answers)):\n g_c.main()","def test_excel(test_data,tmp_path):\n\n for d in test_data:\n\n gpm = GenotypePhenotypeMap(genotype=d[\"genotype\"],\n wildtype=d[\"wildtype\"],\n phenotype=d[\"phenotype\"],\n uncertainty=d[\"uncertainty\"])\n\n # Write excel file\n excel_file = os.path.join(tmp_path,\"tmp.xlsx\")\n gpm.to_excel(filename=excel_file)\n assert os.path.isfile(excel_file)\n\n # Read in and make sure it worked.\n new_gpm = gpmap.read_excel(filename=excel_file,wildtype=d[\"wildtype\"])\n conftest.compare_gpmap(gpm,new_gpm)\n\n # Do not give wildtype. Should still work because the wildtype was\n # inferred.\n gpm_read = gpmap.read_excel(filename=excel_file)\n conftest.compare_gpmap(gpm,gpm_read)\n\n # Check ability to read labels back in\n site_labels = [f\"{x}\" for x in range(10,10+len(d[\"wildtype\"]),1)]\n gpm = GenotypePhenotypeMap(genotype=d[\"genotype\"],\n wildtype=d[\"wildtype\"],\n site_labels=site_labels)\n out_file = os.path.join(tmp_path,\"tmp.xlsx\")\n gpm.to_excel(out_file)\n\n gpm_read = gpmap.read_excel(out_file)\n\n for i in range(len(gpm_read.site_labels)):\n\n # Skip virtual site_labels added for invariant sites\n if len(d[\"mutations\"][i]) == 1:\n continue\n\n assert gpm_read.site_labels[i] == gpm.site_labels[i]\n\n # Read in with bad wildtype. Should throw warning and then have\n # sequential site labels.\n with pytest.warns(UserWarning):\n gpm_read = gpmap.read_excel(out_file,wildtype=d[\"mutant\"])\n\n assert np.array_equal(gpm_read.site_labels,range(len(d[\"wildtype\"])))","def test_make_pop_exception(self, patch_log, patch_random):\n patch_random.side_effect = ValueError\n # Population is initialised with no households\n pop_params = {\"population_size\": 10, \"cell_number\": 1,\n \"microcell_number\": 1}\n ToyPopulationFactory.make_pop(pop_params)\n patch_log.assert_called_once_with(\"ValueError in ToyPopulation\"\n + \"Factory.make_pop()\")","def test_main_gc_1(test):\n answers = (i for i in (test, 'b', 'q'))\n with mock.patch.object(builtins, 'input', lambda _: next(answers)):\n g_c.main()","def test_generator3(self):\n xpb = XPathBuilder()\n xp1 = xp2 = None\n base_xp = xpb.base.foo[xpb.attr('abc') == 'x']\n with base_xp as b:\n xp1 = b().bar.text() == 'foo'\n xp2 = b().x.y.z[42]\n base_exp = '/base/foo[@abc = \"x\"]'\n xp1_exp = '/base/foo[@abc = \"x\"]/bar/text() = \"foo\"'\n xp2_exp = '/base/foo[@abc = \"x\"]/x/y/z[42]'\n self.assertEqual(base_xp.tostring(), base_exp)\n self.assertEqual(xp1.tostring(), xp1_exp)\n self.assertEqual(xp2.tostring(), xp2_exp)","def test_empty_pop(self, populated_collection):\n test_list, plain_list = populated_collection\n item = test_list.pop()\n assert self.is_equal(self.to_base(item), plain_list[-1])\n plain_list.pop()\n self.check_equivalent(test_list, plain_list)\n self.final_check(test_list)","def test_run_loop_success(self):\n found = False\n pyint = Interpreter(limit=15)\n try:\n pyint.run(code=BF_CODE_LOOP_TWICE)\n except SystemExit: \n found = True\n self.assertFalse(found)","def test_shift_returns_value(new_dll):\n assert new_dll.shift() == 5","def test_reset(self):\n ran = []\n def foo():\n ran.append(None)\n\n c = task.Clock()\n lc = TestableLoopingCall(c, foo)\n lc.start(2, now=False)\n c.advance(1)\n lc.reset()\n c.advance(1)\n self.assertEqual(ran, [])\n c.advance(1)\n self.assertEqual(ran, [None])","def test_population_movements_with_compilation(self):\n self._pystepx = PySTEPXIsland(nb_islands=4, init_script=init_script)\n print self._pystepx._rc[0]['gp_engine']\n self._pystepx._rc[0].execute('elems = gp_engine.get_evolver().select_and_remove_individuals(0.01)',\n block=True)\n print self._pystepx._rc[0]['elems']","def test_documentation_popxl_addition(self):\n filename = \"simple_addition.py\"\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)","def pop(self):\n pass","def pop(self, arguments):\n puts_err(colored.red(\"Not implemented!\"))","def test(self):\n # 0x13 is nop\n self.gdb.command(\"p *((int*) 0x%x)=0x13\" % self.target.ram)\n self.gdb.command(\"p *((int*) 0x%x)=0x13\" % (self.target.ram + 4))\n self.gdb.command(\"p *((int*) 0x%x)=0x13\" % (self.target.ram + 8))\n self.gdb.p(\"$pc=0x%x\" % self.target.ram)\n self.gdb.stepi()\n assertEqual((self.target.ram + 4), self.gdb.p(\"$pc\"))\n self.gdb.stepi()\n assertEqual((self.target.ram + 8), self.gdb.p(\"$pc\"))","def test__get_xtraback_prepare_cmd(self):\n mb = self.maria_backup\n self.assertEqual(mb._get_xtraback_prepare_cmd('/a/dir'), ['xtrabackup', '--prepare',\n '--target-dir', '/a/dir/test',\n '--use-memory', '40G',\n '--open-files-limit', '200000'])","async def my_job():\n\n while test_plan:\n to_yield = test_plan[-1][0]\n test_plan[-1][0] = None\n yield to_yield","def TransferMemorySequence():\r\n pass","def exp_pop(self) -> Any:\n return self.exp_stack.popleft()"],"string":"[\n \"def test_pop_methods(self):\\n\\n batch = Batch(Mock())\\n\\n # mock BatchRequests\\n mock_obj = Mock()\\n mock_ref = Mock()\\n batch._objects_batch = mock_obj\\n batch._reference_batch = mock_ref\\n\\n mock_obj.pop.assert_not_called()\\n mock_ref.pop.assert_not_called()\\n\\n # pop object default value\\n batch.pop_object()\\n mock_obj.pop.assert_called_with(-1)\\n mock_ref.pop.assert_not_called()\\n # reset mock objects\\n mock_obj.reset_mock()\\n mock_ref.reset_mock()\\n\\n # pop object at index\\n batch.pop_object(10)\\n mock_obj.pop.assert_called_with(10)\\n mock_ref.pop.assert_not_called()\\n # reset mock objects\\n mock_obj.reset_mock()\\n mock_ref.reset_mock()\\n\\n # pop reference default value\\n batch.pop_reference()\\n mock_obj.pop.assert_not_called()\\n mock_ref.pop.assert_called_with(-1)\\n # reset mock objects\\n mock_obj.reset_mock()\\n mock_ref.reset_mock()\\n\\n # pop reference at index\\n batch.pop_reference(9)\\n mock_obj.pop.assert_not_called()\\n mock_ref.pop.assert_called_with(9)\",\n \"def test_pop_returns_value(new_dll):\\n assert new_dll.pop() == 3\",\n \"def test_close():\\n while True:\\n yield\",\n \"def test_pop(self):\\n sched = Schedule()\\n inst_map = InstructionScheduleMap()\\n\\n inst_map.add(\\\"tmp\\\", 100, sched)\\n self.assertEqual(inst_map.pop(\\\"tmp\\\", 100), sched)\\n self.assertFalse(inst_map.has(\\\"tmp\\\", 100))\\n\\n self.assertEqual(inst_map.qubit_instructions(100), [])\\n self.assertEqual(inst_map.qubits_with_instruction(\\\"tmp\\\"), [])\\n with self.assertRaises(PulseError):\\n inst_map.pop(\\\"not_there\\\", (0,))\",\n \"def testGetSequence():\\r\\n\\t\\r\\n\\t#a few of hand-tested genome positions\\r\\n\\ttest_data = [\\t('1',500,520,'GTCTGACCTGAGGAGAACTGT'),\\r\\n\\t\\t\\t\\t\\t('2',500,520,'CCCGACCCCGACCCCGACCCA'),\\r\\n\\t\\t\\t\\t\\t('3',50000,50020,'TCTTCTTTTATGAAAAAGGAT'),\\r\\n\\t\\t\\t\\t\\t('4',50000,50020,'AGAGCCCTGCAATTTGAAGAT'),\\r\\n\\t\\t\\t\\t\\t('5',100000,100020,'AATGTTCACCAGTATATTTTA'),\\r\\n\\t\\t\\t\\t\\t('X',100000,100020,'TAGGTCTCATTGAGGACAGAT'),\\r\\n\\t\\t\\t\\t\\t('Y',100000,100020,'TAGGTCTCATTGAGGACAGAT')]\\r\\n\\t\\t\\t\\t\\t\\r\\n\\tfor this_check in test_data:\\r\\n\\t\\tyield CheckGetSequence, this_check\",\n \"def tearDown(self):\\n del self.pop\",\n \"def pop():\",\n \"def test_push_pop(values):\\n test_stack = stack.Stack()\\n\\n for value in values:\\n test_stack.push(value)\\n\\n for expected_value in reversed(values):\\n value = test_stack.pop()\\n assert value == expected_value\\n\\n with pytest.raises(stack.StackEmptyError):\\n test_stack.pop()\",\n \"def test_pop_on_empty_raises_error(sample_priorityq):\\n with pytest.raises(IndexError):\\n sample_priorityq.pop()\",\n \"def test_pop_gate(self):\\n sched = Schedule()\\n inst_map = InstructionScheduleMap()\\n\\n inst_map.add(XGate(), 100, sched)\\n self.assertEqual(inst_map.pop(XGate(), 100), sched)\\n self.assertFalse(inst_map.has(XGate(), 100))\\n\\n self.assertEqual(inst_map.qubit_instructions(100), [])\\n self.assertEqual(inst_map.qubits_with_instruction(XGate()), [])\\n with self.assertRaises(PulseError):\\n inst_map.pop(\\\"not_there\\\", (0,))\",\n \"def test_pop_no_args(self):\\r\\n msg_list = messages.MessageList()\\r\\n # Adds 5 Message objects to the list.\\r\\n msg_list.push(messages.StringMessage(\\\"a\\\"))\\r\\n msg_list.push(messages.StringMessage(\\\"b\\\"))\\r\\n msg_list.push(messages.StringMessage(\\\"c\\\"))\\r\\n msg_list.push(messages.StringMessage(\\\"d\\\"))\\r\\n msg_list.push(messages.StringMessage(\\\"e\\\"))\\r\\n\\r\\n self.assertEqual(msg_list.length(), 5)\\r\\n popped = msg_list.pop()\\r\\n self.assertEqual(msg_list.length(), 4)\\r\\n self.assertEqual(popped.msg, \\\"e\\\")\\r\\n msg_list.pop()\\r\\n msg_list.pop()\\r\\n msg_list.pop()\\r\\n msg_list.pop()\\r\\n self.assertRaises(IndexError, msg_list.pop)\",\n \"def test_open_fill(self):\",\n \"def test_peek_empty():\\n test_stack = stack.Stack()\\n\\n with pytest.raises(stack.StackEmptyError):\\n test_stack.peek()\",\n \"def test_ignore_close():\\n try:\\n yield\\n except GeneratorExit:\\n yield\",\n \"def test_pop_on_small_stack(small_stack):\\n assert small_stack.pop().val == 3\\n assert small_stack.pop().val == 2\\n assert small_stack._size == 1\",\n \"def _pop(self, actual_call):\\n try:\\n expected_call, mock_result = self._queue.popleft()\\n except IndexError as ex:\\n error = UnexpectedCall(\\n \\\"Queue is empty. call: {0}\\\"\\n .format(actual_call)\\n )\\n self._store_pop_error(error)\\n raise error\\n if actual_call != expected_call:\\n error = UnexpectedCall(\\n \\\"Call does not match expectation. actual: {0}; expected: {1}\\\"\\n .format(actual_call, expected_call)\\n )\\n self._store_pop_error(error)\\n raise error\\n # let it raise if the result is an exception or exception type.\\n return mock_result()\",\n \"def testSeq(self, mock_gs):\\n self.mr._sequences = ['apple', 'banana']\\n\\n self.assertEqual(\\n 'apple',\\n self.mr.seq\\n )\\n\\n mock_gs.assert_called_once_with()\\n mock_gs.reset_mock()\\n\\n self.mr._is_seq = False\\n\\n self.assertEqual(\\n None,\\n self.mr.seq\\n )\\n\\n # Test that we pulled from the cache\\n self.assertFalse(\\n mock_gs.called\\n )\",\n \"def test_pop_left_check_head(dq_3):\\n dq_3.pop_left()\\n assert dq_3._dll.head.data == 4\",\n \"def test_pop_returns_value_of_tail(dq_3):\\n assert dq_3.pop() == 'ragtime'\",\n \"def test_appended(self):\\n genFn = Mock(return_value=None)\\n expected = 123\\n \\n wrapper = KaoGenerator(genFn)\\n wrapper.queue(expected)\\n actual = wrapper._queue.pop()\\n self.assertEqual(expected, actual)\",\n \"def testPushPopItem(self):\\n test_queue = multi_process.MultiProcessingQueue()\\n\\n for item in self._ITEMS:\\n test_queue.PushItem(item)\\n\\n test_queue.SignalEndOfInput()\\n test_queue_consumer = test_lib.TestQueueConsumer(test_queue)\\n test_queue_consumer.ConsumeItems()\\n\\n self.assertEqual(test_queue_consumer.number_of_items, len(self._ITEMS))\",\n \"def test_peek_single(values, expected_value):\\n test_stack = stack.Stack()\\n for value in values:\\n test_stack.push(value)\\n\\n returned_value = test_stack.peek()\\n\\n assert returned_value == expected_value\\n\\n for value in reversed(values):\\n assert test_stack.pop() == value\",\n \"def test_sequence(self):\\n seq_name = 'test_seq'\\n\\n with self.dbh.sequence_recreate(seq_name):\\n try:\\n self.assertEqual(self.dbh.get_seq_next_value(seq_name), 1)\\n self.assertEqual(self.dbh.get_seq_next_value(seq_name), 2)\\n self.assertEqual(self.dbh.get_seq_next_value(seq_name), 3)\\n except Exception:\\n self.dbh.rollback()\\n raise\",\n \"def test_valueInQueue(self):\\n genFn = Mock(return_value=None)\\n expected = 123\\n \\n wrapper = KaoGenerator(genFn)\\n wrapper.queue(expected)\\n actual = wrapper.pop()\\n self.assertEqual(expected, actual)\",\n \"def do_test():\\n for x in execute_helper(test_info,crossmap_tests):\\n yield x\",\n \"def test_populator_aborts_early():\\n o1, o2 = MediaBag(media=1), MediaBag(media=2)\\n\\n def multi_get(*keys):\\n raise AssertionError('tried calling multi_get')\\n\\n results = media.build_populator('id', multi_get)([o1, o2])\\n assert results == [o1, o2]\",\n \"def test_generator_cleanup():\\n try:\\n yield 1\\n finally:\\n print('cleanup')\",\n \"def pop_write(self):\\n ...\",\n \"def test_pushpop2_dir(self):\\n TempfileManager.push()\\n os.mkdir(tempdir + 'pushpop2')\\n TempfileManager.add_tempfile(tempdir + 'pushpop2')\\n\\n TempfileManager.push()\\n os.mkdir(tempdir + 'pushpop2a')\\n TempfileManager.add_tempfile(tempdir + 'pushpop2a')\\n TempfileManager.pop()\\n if not os.path.exists(tempdir + 'pushpop2'):\\n self.fail(\\\"pop() clean out all files\\\")\\n if os.path.exists(tempdir + 'pushpop2a'):\\n self.fail(\\\"pop() failed to clean out files\\\")\\n\\n TempfileManager.pop()\\n if os.path.exists(tempdir + 'pushpop2'):\\n self.fail(\\\"pop() failed to clean out files\\\")\",\n \"def popitem(self): # real signature unknown; restored from __doc__\\n pass\",\n \"def testSeqs(self, mock_gs):\\n self.mr._sequences = ['apple', 'banana']\\n\\n self.assertEqual(\\n ['apple', 'banana'],\\n self.mr.seqs\\n )\\n\\n mock_gs.assert_called_once_with()\\n mock_gs.reset_mock()\\n\\n self.mr._is_seq = False\\n\\n self.assertEqual(\\n [],\\n self.mr.seqs\\n )\\n\\n # Test that we pulled from the cache\\n self.assertFalse(\\n mock_gs.called\\n )\",\n \"def test_reading_missing(self, mock_pop, mock_shell):\\n mock_result = mock.Mock()\\n mock_result.communicate = mock.Mock()\\n mock_result.returncode = 0\\n mock_result.communicate.return_value = (self.dir, None)\\n mock_pop.return_value = mock_result\\n mock_shell.return_value = self.gitconf_str\\n config = load_configuration(package_dir=\\\"womp\\\")\\n\\n self.failUnless(mock_result.communicate.called)\\n mock_pop.assert_has_calls(mock.call(['git', 'rev-parse', '--show-toplevel'], stdout=-1))\\n self.assertEqual(config.package_version(), '1.2.3')\\n self.assertEqual(config.package_name(), 'cirrus_tests')\\n self.failUnless(mock_shell.called)\\n mock_shell.assert_has_calls([\\n mock.call('git config --file {} -l'.format(self.gitconfig))\\n ])\",\n \"def test_sequence_done(self):\\n self.t(\\\"1,2 done\\\")\\n code, out, err = self.t(\\\"_get 1.status 2.status\\\")\\n self.assertEqual(\\\"completed completed\\\\n\\\", out)\",\n \"def test_tail_call(self):\",\n \"def test_priority_que_pop(priority_queue_full):\\n # import pdb; pdb.set_trace()\\n assert (priority_queue_full.pop(),\\n priority_queue_full.pop(),\\n priority_queue_full.pop(),\\n priority_queue_full.pop()) == (11, 6, 12, 15)\",\n \"def test_pop_left_for_length(dq_3):\\n dq_3.pop_left()\\n assert dq_3.length == 2\",\n \"def test_invoke_anonymous_pipe():\\n\\n def processor_a(app, items):\\n items = list(items)\\n assert items == [holocron.Item({\\\"a\\\": \\\"b\\\"})]\\n items[0][\\\"x\\\"] = 42\\n yield from items\\n\\n def processor_b(app, items):\\n items = list(items)\\n assert items == [holocron.Item({\\\"a\\\": \\\"b\\\", \\\"x\\\": 42})]\\n items.append(holocron.Item({\\\"z\\\": 13}))\\n yield from items\\n\\n def processor_c(app, items):\\n items = list(items)\\n assert items == [\\n holocron.Item({\\\"a\\\": \\\"b\\\", \\\"x\\\": 42}),\\n holocron.Item({\\\"z\\\": 13}),\\n ]\\n yield from items\\n\\n testapp = holocron.Application()\\n testapp.add_processor(\\\"processor_a\\\", processor_a)\\n testapp.add_processor(\\\"processor_b\\\", processor_b)\\n testapp.add_processor(\\\"processor_c\\\", processor_c)\\n\\n stream = testapp.invoke(\\n [\\n {\\\"name\\\": \\\"processor_a\\\"},\\n {\\\"name\\\": \\\"processor_b\\\"},\\n {\\\"name\\\": \\\"processor_c\\\"},\\n ],\\n [holocron.Item({\\\"a\\\": \\\"b\\\"})],\\n )\\n\\n assert next(stream) == holocron.Item({\\\"a\\\": \\\"b\\\", \\\"x\\\": 42})\\n assert next(stream) == holocron.Item({\\\"z\\\": 13})\\n\\n with pytest.raises(StopIteration):\\n next(stream)\",\n \"def test_pop(self):\\n self.assertRaises(EmptyHeapException, self.minheap.pop)\\n self.minheap.heap = [0, 1, 4, 7, 9]\\n assert self.minheap.pop() == 1\\n assert self.minheap.heap == [0, 4, 9, 7]\",\n \"def test_INVOKE_repeat(self, propose):\\n self.rep.proposals[1] = PROPOSAL1\\n self.failIf(propose.called)\",\n \"def test_peek_on_empty_raises_error(sample_priorityq):\\n with pytest.raises(IndexError):\\n sample_priorityq.peek()\",\n \"def unit_test():\\n queue = Queue()\\n stack = Stack()\\n\\n for i in range(17):\\n queue.push(i)\\n stack.push(i)\\n\\n print(stack.pop())\\n print(queue.pop())\\n\\n print(stack.peek())\\n print(queue.peek())\\n\\n print(\\\"STACK: \\\", stack)\\n print(\\\"QUEUE: \\\", queue)\\n print(\\\"EMPTY?: QUEUE: \\\", queue.is_empty(), \\\"STACK: \\\", stack.is_empty())\\n\\n while not queue.is_empty():\\n queue.pop()\\n\\n print(\\\"EMPTY?: QUEUE: \\\", queue.is_empty(), \\\"STACK: \\\", stack.is_empty())\\n\\n # print(\\\"POP EMPTY: \\\", queue.pop())\",\n \"def test_pop_sets_new_head(new_dll):\\n new_dll.pop()\\n assert new_dll.head.value == 4\",\n \"def test_pop(self):\\n test_list = DoubleLinkedList()\\n test_list.push(15)\\n test_list.push(150)\\n test_list.push(13)\\n test_list.push(155)\\n test_list.push(1)\\n test_list.pop()\\n self.assertEqual(test_list.last().get_elem(), 155)\",\n \"def test_teardown(self):\\n assert self.search_behaviour.teardown() is None\\n self.assert_quantity_in_outbox(0)\",\n \"def test_teardown(self):\\n assert self.transaction_behaviour.teardown() is None\\n self.assert_quantity_in_outbox(0)\",\n \"def pop(self):\",\n \"def pop(self):\",\n \"def test_multiple_commands_at_same_time(self):\",\n \"def test_sequence_modify(self):\\n self.t(\\\"1,2 modify +xyz\\\")\\n code, out, err = self.t(\\\"_get 1.tags 2.tags\\\")\\n self.assertEqual(\\\"xyz xyz\\\\n\\\", out)\",\n \"def pop(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def test_run_ended(self):\",\n \"def test_generator(self):\\n args = Args()\\n args.files = ['tests/xproto/test.xproto']\\n args.target = 'tests/xtarget/test.xtarget'\\n args.output = 'tests/out/dir/'\\n args.write_to_file = \\\"target\\\"\\n args.dest_file = None\\n args.dest_extension = None\\n\\n expected_args = Args()\\n expected_args.files = [os.path.abspath(os.getcwd() + '/' + args.files[0])]\\n expected_args.target = os.path.abspath(os.getcwd() + '/' + args.target)\\n expected_args.output = os.path.abspath(os.getcwd() + '/' + args.output)\\n\\n with patch(\\\"xosgenx.xosgen.XOSGenerator.generate\\\") as generator:\\n XosGen.init(args)\\n actual_args = generator.call_args[0][0]\\n self.assertEqual(actual_args.files, expected_args.files)\\n self.assertEqual(actual_args.target, expected_args.target)\\n self.assertEqual(actual_args.output, expected_args.output)\",\n \"def test_pushpop1_dir(self):\\n TempfileManager.push()\\n os.mkdir(tempdir + 'pushpop1')\\n TempfileManager.add_tempfile(tempdir + 'pushpop1')\\n TempfileManager.pop()\\n if os.path.exists(tempdir + 'pushpop1'):\\n self.fail(\\\"pop() failed to clean out directories\\\")\",\n \"def test_pop_sets_new_head_next(new_dll):\\n new_dll.pop()\\n assert new_dll.head.next.value == 5\",\n \"def teardown(self):\",\n \"def teardown(self):\",\n \"def teardown(self):\",\n \"def popitem(self):\\n pass\",\n \"def mp_test(target, args=(), kwargs={}, timeout=None):\\n\\n if os.name == 'nt':\\n return target(*args,**kwargs)\\n\\n r, w = mp.Pipe(duplex=False)\\n def wrap(target=None, args=(), kwargs={}):\\n r.close()\\n res=target(*args, **kwargs)\\n try:\\n w.send(res)\\n except:\\n os.kill(os.getpid(),signal.SIGTERM)\\n else:\\n w.close()\\n \\n p = mp.Process(target=wrap, args=[target, args, kwargs])\\n p.start()\\n w.close()\\n p.join(timeout) # Can't timeout pipe recv, so risking block on send.\\n if p.is_alive():\\n p.terminate()\\n raise Error(\\\"timeout\\\",\\\"Timeout limit was \\\"+str(timeout)+\\\" seconds\\\")\\n if p.exitcode:\\n raise Error(\\\"signaled\\\",\\\"Exited following signal -\\\"+str(p.exitcode))\\n output = r.recv()\\n r.close()\\n return output\\n #return (None,output)\",\n \"def test_invoke_processor_errors():\\n\\n def processor(app, documents):\\n raise ValueError(\\\"something bad happened\\\")\\n yield\\n\\n testapp = holocron.Application()\\n testapp.add_processor(\\\"processor\\\", processor)\\n testapp.add_pipe(\\\"test\\\", [{\\\"name\\\": \\\"processor\\\"}])\\n\\n stream = testapp.invoke(\\\"test\\\")\\n\\n with pytest.raises(ValueError, match=r\\\"^something bad happened$\\\"):\\n next(stream)\\n\\n with pytest.raises(StopIteration):\\n next(stream)\",\n \"def test_redoer():\\n tock = 1.0\\n redoer = doing.ReDoer()\\n assert redoer.tock == 0.0\\n\\n tymist = tyming.Tymist()\\n redoer = doing.ReDoer(tymth=tymist.tymen(), tock=tock)\\n assert redoer.tock == tock == 1.0\\n redoer.tock = 0.0\\n assert redoer.tock == 0.0\\n\\n # create generator use send and run until normal exit. emulates Doist.ready\\n args = {}\\n dog = redoer(tymth=redoer.tymth, tock=redoer.tock, **args)\\n assert inspect.isgenerator(dog)\\n\\n result = dog.send(None)\\n assert result == redoer.tock == 0.0\\n\\n tymist.tick()\\n result = dog.send(tymist.tyme)\\n assert result == redoer.tock == 0.0\\n\\n tymist.tick()\\n result = dog.send(tymist.tyme)\\n assert result == redoer.tock == 0.0\\n\\n tymist.tick()\\n with pytest.raises(StopIteration):\\n try:\\n result = dog.send(tymist.tyme)\\n except StopIteration as ex:\\n assert ex.value == True\\n raise\\n\\n tymist.tick()\\n with pytest.raises(StopIteration): # send after break\\n try:\\n result = dog.send(tymist.tyme)\\n except StopIteration as ex:\\n assert ex.value == None\\n raise\\n\\n # create generator use send and then explicit close. emulates Doist.ready\\n args = {}\\n dog = redoer(tymth=redoer.tymth, tock=redoer.tock, **args)\\n assert inspect.isgenerator(dog)\\n\\n result = dog.send(None)\\n assert result == redoer.tock == 0.0\\n\\n tymist.tick()\\n result = dog.send(tymist.tyme)\\n assert result == redoer.tock == 0.0\\n\\n result = dog.close()\\n assert result == None # no yielded value on close\\n\\n tymist.tick()\\n with pytest.raises(StopIteration): # send after close\\n try:\\n result = dog.send(tymist.tyme)\\n except StopIteration as ex:\\n assert ex.value == None\\n raise\\n\\n\\n # use next instead of send\\n args = {}\\n dog = redoer(tymth=redoer.tymth, tock=redoer.tock, **args)\\n assert inspect.isgenerator(dog)\\n\\n result = next(dog)\\n assert result == redoer.tock == 0.0\\n\\n result = next(dog)\\n assert result == redoer.tock == 0.0\\n\\n result = dog.close()\\n assert result == None # no yielded value on close\\n\\n tymist.tick()\\n with pytest.raises(StopIteration): # send after close\\n try:\\n result = dog.send(tymist.tyme)\\n except StopIteration as ex:\\n assert ex.value == None\\n raise\\n\\n \\\"\\\"\\\"End Test \\\"\\\"\\\"\",\n \"def test_invoke_passthrough_items():\\n\\n def processor(app, items):\\n yield from items\\n\\n testapp = holocron.Application()\\n testapp.add_processor(\\\"processor\\\", processor)\\n testapp.add_pipe(\\\"test\\\", [{\\\"name\\\": \\\"processor\\\"}])\\n\\n stream = testapp.invoke(\\n \\\"test\\\",\\n [\\n holocron.Item(name=\\\"yoda\\\", rank=\\\"master\\\"),\\n holocron.Item(name=\\\"skywalker\\\"),\\n ],\\n )\\n\\n assert next(stream) == holocron.Item(name=\\\"yoda\\\", rank=\\\"master\\\")\\n assert next(stream) == holocron.Item(name=\\\"skywalker\\\")\\n\\n with pytest.raises(StopIteration):\\n next(testapp.invoke(\\\"test\\\"))\",\n \"def test_invoke_propagates_processor_args(processor_args):\\n\\n def processor(app, items, **args):\\n assert args == processor_args\\n yield from items\\n\\n testapp = holocron.Application()\\n testapp.add_processor(\\\"processor\\\", processor)\\n testapp.add_pipe(\\\"test\\\", [{\\\"name\\\": \\\"processor\\\", \\\"args\\\": processor_args}])\\n\\n with pytest.raises(StopIteration):\\n next(testapp.invoke(\\\"test\\\"))\",\n \"def teardown(self):\\n pos.close()\",\n \"def teardown(self):\\n pos.close()\",\n \"def test_enter_exit(self, mock_flush):\\n\\n batch = Batch(Mock())\\n\\n with batch as b:\\n self.assertEqual(batch, b)\\n mock_flush.assert_called()\",\n \"def test_unload(install_mockery, mock_fetch, mock_archive, mock_packages, working_env):\\n install(\\\"mpileaks\\\")\\n mpileaks_spec = spack.spec.Spec(\\\"mpileaks\\\").concretized()\\n\\n # Set so unload has something to do\\n os.environ[\\\"FOOBAR\\\"] = \\\"mpileaks\\\"\\n os.environ[uenv.spack_loaded_hashes_var] = \\\"%s:%s\\\" % (mpileaks_spec.dag_hash(), \\\"garbage\\\")\\n\\n sh_out = unload(\\\"--sh\\\", \\\"mpileaks\\\")\\n csh_out = unload(\\\"--csh\\\", \\\"mpileaks\\\")\\n\\n assert \\\"unset FOOBAR\\\" in sh_out\\n assert \\\"unsetenv FOOBAR\\\" in csh_out\\n\\n assert \\\"export %s=garbage\\\" % uenv.spack_loaded_hashes_var in sh_out\\n assert \\\"setenv %s garbage\\\" % uenv.spack_loaded_hashes_var in csh_out\",\n \"def test_documentation_popxl_replication(self):\\n filename = \\\"replication.py\\\"\\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)\",\n \"def pop(self):\\n return self.new_dll.shift()\",\n \"def popitem(self):\\n pass\",\n \"def test_pop_empty_list():\\n from dll import DbLinkedList\\n dll = DbLinkedList()\\n msg = \\\"Cannot pop from an empty list\\\"\\n with pytest.raises(ValueError, message=msg):\\n dll.pop()\",\n \"def test_peek_on_small_stack(small_stack):\\n assert small_stack.peek().val == 3\",\n \"def test_invoke_passthrough_items_empty_pipe():\\n\\n testapp = holocron.Application()\\n testapp.add_pipe(\\\"test\\\", [])\\n\\n stream = testapp.invoke(\\n \\\"test\\\",\\n [\\n holocron.Item(name=\\\"yoda\\\", rank=\\\"master\\\"),\\n holocron.Item(name=\\\"skywalker\\\"),\\n ],\\n )\\n\\n assert next(stream) == holocron.Item(name=\\\"yoda\\\", rank=\\\"master\\\")\\n assert next(stream) == holocron.Item(name=\\\"skywalker\\\")\\n\\n with pytest.raises(StopIteration):\\n next(testapp.invoke(\\\"test\\\"))\",\n \"def teardown(self) -> None:\",\n \"def teardown(self) -> None:\",\n \"def teardown(self) -> None:\",\n \"def test_sequence(self):\\n self.assertEqual([1, -3, 9, -27, 81, -243],\\n [x for x in GeometricProgression(6, 1, -3)])\\n\\n self.assertEqual([1, 1, 1, 1, 1],\\n [x for x in GeometricProgression(5, 1, 1)])\\n\\n self.assertEqual([4, 40, 400, 4000, 40000],\\n [x for x in GeometricProgression(5, 4, 10)])\",\n \"def test_pop_from_empty_list(new_empty_ll):\\n from linked_list import Linked_List\\n with pytest.raises(IndexError):\\n new_empty_ll.pop()\",\n \"def test_lose(self):\\n self.choice.return_value = \\\"ant\\\" \\n self.input.side_effect = list(\\\"bcdefg\\\" \\\"n\\\")\\n\\n gallows.main()\\n\\n self.xprint.assert_any_call('You have run out of guesses!')\",\n \"def tearDown(self):\\n self.popen_patcher.stop()\",\n \"def tearDown(self):\\n self.popen_patcher.stop()\",\n \"def pop_sm(self):\\r\\n while True:\\r\\n # wait to receive a read request\\r\\n req = yield self.r_in_pipe.get()\\r\\n # model read latency\\r\\n # for i in range(self.read_latency):\\r\\n yield self.wait_sys_clks(self.read_latency)\\r\\n # try to read head element\\r\\n if len(self.items) > 0:\\r\\n data = self.items[0]\\r\\n self.items = self.items[1:]\\r\\n else:\\r\\n print >> sys.stderr, \\\"ERROR: PIFO pop_sm: attempted to read from empty PIFO\\\"\\r\\n data = None\\r\\n # write data back\\r\\n self.r_out_pipe.put(data)\",\n \"def test_main_gc_2(test):\\n answers = (i for i in (test, '1 1 1 1', 'q'))\\n with mock.patch.object(builtins, 'input', lambda _: next(answers)):\\n g_c.main()\",\n \"def test_excel(test_data,tmp_path):\\n\\n for d in test_data:\\n\\n gpm = GenotypePhenotypeMap(genotype=d[\\\"genotype\\\"],\\n wildtype=d[\\\"wildtype\\\"],\\n phenotype=d[\\\"phenotype\\\"],\\n uncertainty=d[\\\"uncertainty\\\"])\\n\\n # Write excel file\\n excel_file = os.path.join(tmp_path,\\\"tmp.xlsx\\\")\\n gpm.to_excel(filename=excel_file)\\n assert os.path.isfile(excel_file)\\n\\n # Read in and make sure it worked.\\n new_gpm = gpmap.read_excel(filename=excel_file,wildtype=d[\\\"wildtype\\\"])\\n conftest.compare_gpmap(gpm,new_gpm)\\n\\n # Do not give wildtype. Should still work because the wildtype was\\n # inferred.\\n gpm_read = gpmap.read_excel(filename=excel_file)\\n conftest.compare_gpmap(gpm,gpm_read)\\n\\n # Check ability to read labels back in\\n site_labels = [f\\\"{x}\\\" for x in range(10,10+len(d[\\\"wildtype\\\"]),1)]\\n gpm = GenotypePhenotypeMap(genotype=d[\\\"genotype\\\"],\\n wildtype=d[\\\"wildtype\\\"],\\n site_labels=site_labels)\\n out_file = os.path.join(tmp_path,\\\"tmp.xlsx\\\")\\n gpm.to_excel(out_file)\\n\\n gpm_read = gpmap.read_excel(out_file)\\n\\n for i in range(len(gpm_read.site_labels)):\\n\\n # Skip virtual site_labels added for invariant sites\\n if len(d[\\\"mutations\\\"][i]) == 1:\\n continue\\n\\n assert gpm_read.site_labels[i] == gpm.site_labels[i]\\n\\n # Read in with bad wildtype. Should throw warning and then have\\n # sequential site labels.\\n with pytest.warns(UserWarning):\\n gpm_read = gpmap.read_excel(out_file,wildtype=d[\\\"mutant\\\"])\\n\\n assert np.array_equal(gpm_read.site_labels,range(len(d[\\\"wildtype\\\"])))\",\n \"def test_make_pop_exception(self, patch_log, patch_random):\\n patch_random.side_effect = ValueError\\n # Population is initialised with no households\\n pop_params = {\\\"population_size\\\": 10, \\\"cell_number\\\": 1,\\n \\\"microcell_number\\\": 1}\\n ToyPopulationFactory.make_pop(pop_params)\\n patch_log.assert_called_once_with(\\\"ValueError in ToyPopulation\\\"\\n + \\\"Factory.make_pop()\\\")\",\n \"def test_main_gc_1(test):\\n answers = (i for i in (test, 'b', 'q'))\\n with mock.patch.object(builtins, 'input', lambda _: next(answers)):\\n g_c.main()\",\n \"def test_generator3(self):\\n xpb = XPathBuilder()\\n xp1 = xp2 = None\\n base_xp = xpb.base.foo[xpb.attr('abc') == 'x']\\n with base_xp as b:\\n xp1 = b().bar.text() == 'foo'\\n xp2 = b().x.y.z[42]\\n base_exp = '/base/foo[@abc = \\\"x\\\"]'\\n xp1_exp = '/base/foo[@abc = \\\"x\\\"]/bar/text() = \\\"foo\\\"'\\n xp2_exp = '/base/foo[@abc = \\\"x\\\"]/x/y/z[42]'\\n self.assertEqual(base_xp.tostring(), base_exp)\\n self.assertEqual(xp1.tostring(), xp1_exp)\\n self.assertEqual(xp2.tostring(), xp2_exp)\",\n \"def test_empty_pop(self, populated_collection):\\n test_list, plain_list = populated_collection\\n item = test_list.pop()\\n assert self.is_equal(self.to_base(item), plain_list[-1])\\n plain_list.pop()\\n self.check_equivalent(test_list, plain_list)\\n self.final_check(test_list)\",\n \"def test_run_loop_success(self):\\n found = False\\n pyint = Interpreter(limit=15)\\n try:\\n pyint.run(code=BF_CODE_LOOP_TWICE)\\n except SystemExit: \\n found = True\\n self.assertFalse(found)\",\n \"def test_shift_returns_value(new_dll):\\n assert new_dll.shift() == 5\",\n \"def test_reset(self):\\n ran = []\\n def foo():\\n ran.append(None)\\n\\n c = task.Clock()\\n lc = TestableLoopingCall(c, foo)\\n lc.start(2, now=False)\\n c.advance(1)\\n lc.reset()\\n c.advance(1)\\n self.assertEqual(ran, [])\\n c.advance(1)\\n self.assertEqual(ran, [None])\",\n \"def test_population_movements_with_compilation(self):\\n self._pystepx = PySTEPXIsland(nb_islands=4, init_script=init_script)\\n print self._pystepx._rc[0]['gp_engine']\\n self._pystepx._rc[0].execute('elems = gp_engine.get_evolver().select_and_remove_individuals(0.01)',\\n block=True)\\n print self._pystepx._rc[0]['elems']\",\n \"def test_documentation_popxl_addition(self):\\n filename = \\\"simple_addition.py\\\"\\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)\",\n \"def pop(self):\\n pass\",\n \"def pop(self, arguments):\\n puts_err(colored.red(\\\"Not implemented!\\\"))\",\n \"def test(self):\\n # 0x13 is nop\\n self.gdb.command(\\\"p *((int*) 0x%x)=0x13\\\" % self.target.ram)\\n self.gdb.command(\\\"p *((int*) 0x%x)=0x13\\\" % (self.target.ram + 4))\\n self.gdb.command(\\\"p *((int*) 0x%x)=0x13\\\" % (self.target.ram + 8))\\n self.gdb.p(\\\"$pc=0x%x\\\" % self.target.ram)\\n self.gdb.stepi()\\n assertEqual((self.target.ram + 4), self.gdb.p(\\\"$pc\\\"))\\n self.gdb.stepi()\\n assertEqual((self.target.ram + 8), self.gdb.p(\\\"$pc\\\"))\",\n \"def test__get_xtraback_prepare_cmd(self):\\n mb = self.maria_backup\\n self.assertEqual(mb._get_xtraback_prepare_cmd('/a/dir'), ['xtrabackup', '--prepare',\\n '--target-dir', '/a/dir/test',\\n '--use-memory', '40G',\\n '--open-files-limit', '200000'])\",\n \"async def my_job():\\n\\n while test_plan:\\n to_yield = test_plan[-1][0]\\n test_plan[-1][0] = None\\n yield to_yield\",\n \"def TransferMemorySequence():\\r\\n pass\",\n \"def exp_pop(self) -> Any:\\n return self.exp_stack.popleft()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.58948493","0.5882581","0.5770228","0.54232585","0.54092556","0.54075843","0.5373329","0.5303253","0.53030944","0.5287886","0.52534574","0.5221736","0.52018183","0.5189823","0.5175281","0.5172834","0.5172073","0.51521003","0.51421815","0.5127545","0.51249427","0.51089585","0.5103546","0.5087489","0.5045468","0.5040717","0.5031918","0.5026089","0.50086796","0.4999565","0.49845994","0.4983298","0.49627236","0.4959022","0.49560332","0.49143422","0.4894076","0.48857024","0.4881891","0.48814523","0.48599333","0.48598775","0.48365682","0.48338538","0.48286676","0.4826836","0.4826836","0.48268154","0.48260146","0.48238036","0.48233682","0.48144883","0.48111746","0.48098588","0.4806326","0.4806326","0.4806326","0.48044452","0.4801096","0.47967568","0.47932225","0.47905892","0.47885412","0.47874752","0.47874752","0.47869822","0.47862285","0.4782183","0.47803375","0.47777343","0.476481","0.47645095","0.47635064","0.47543886","0.47543886","0.47543886","0.47509274","0.47483334","0.47483286","0.47397968","0.47397968","0.47386912","0.4728878","0.4728151","0.47263145","0.4724572","0.47004166","0.46942502","0.46938148","0.4691006","0.46903977","0.46903297","0.4686174","0.46684793","0.46645814","0.4662198","0.46619257","0.4660861","0.4655052","0.46534914"],"string":"[\n \"0.58948493\",\n \"0.5882581\",\n \"0.5770228\",\n \"0.54232585\",\n \"0.54092556\",\n \"0.54075843\",\n \"0.5373329\",\n \"0.5303253\",\n \"0.53030944\",\n \"0.5287886\",\n \"0.52534574\",\n \"0.5221736\",\n \"0.52018183\",\n \"0.5189823\",\n \"0.5175281\",\n \"0.5172834\",\n \"0.5172073\",\n \"0.51521003\",\n \"0.51421815\",\n \"0.5127545\",\n \"0.51249427\",\n \"0.51089585\",\n \"0.5103546\",\n \"0.5087489\",\n \"0.5045468\",\n \"0.5040717\",\n \"0.5031918\",\n \"0.5026089\",\n \"0.50086796\",\n \"0.4999565\",\n \"0.49845994\",\n \"0.4983298\",\n \"0.49627236\",\n \"0.4959022\",\n \"0.49560332\",\n \"0.49143422\",\n \"0.4894076\",\n \"0.48857024\",\n \"0.4881891\",\n \"0.48814523\",\n \"0.48599333\",\n \"0.48598775\",\n \"0.48365682\",\n \"0.48338538\",\n \"0.48286676\",\n \"0.4826836\",\n \"0.4826836\",\n \"0.48268154\",\n \"0.48260146\",\n \"0.48238036\",\n \"0.48233682\",\n \"0.48144883\",\n \"0.48111746\",\n \"0.48098588\",\n \"0.4806326\",\n \"0.4806326\",\n \"0.4806326\",\n \"0.48044452\",\n \"0.4801096\",\n \"0.47967568\",\n \"0.47932225\",\n \"0.47905892\",\n \"0.47885412\",\n \"0.47874752\",\n \"0.47874752\",\n \"0.47869822\",\n \"0.47862285\",\n \"0.4782183\",\n \"0.47803375\",\n \"0.47777343\",\n \"0.476481\",\n \"0.47645095\",\n \"0.47635064\",\n \"0.47543886\",\n \"0.47543886\",\n \"0.47543886\",\n \"0.47509274\",\n \"0.47483334\",\n \"0.47483286\",\n \"0.47397968\",\n \"0.47397968\",\n \"0.47386912\",\n \"0.4728878\",\n \"0.4728151\",\n \"0.47263145\",\n \"0.4724572\",\n \"0.47004166\",\n \"0.46942502\",\n \"0.46938148\",\n \"0.4691006\",\n \"0.46903977\",\n \"0.46903297\",\n \"0.4686174\",\n \"0.46684793\",\n \"0.46645814\",\n \"0.4662198\",\n \"0.46619257\",\n \"0.4660861\",\n \"0.4655052\",\n \"0.46534914\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.65936136"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":6596,"cells":{"query":{"kind":"string","value":"Test the popxl remote variable"},"document":{"kind":"string","value":"def test_documentation_popxl_remote_var(self):\n filename = \"remote_variable.py\"\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)"},"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 test_documentation_popxl_remote_rts_var(self):\n filename = \"remote_rts_var.py\"\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)","def test_documentation_popxl_rts_var(self):\n filename = \"rts_var.py\"\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)","def test_pop_returns_value(new_dll):\n assert new_dll.pop() == 3","def _is_pop(self, words):\n if words[0] == 'pop':\n if len(words) != 3:\n raise SyntaxError(\"File line {}: Invalid number of arguments for C_POP command.\".format(self._file_line))\n if words[1] not in ['temp', 'pointer', 'static', 'local', 'argument', 'this', 'that']:\n raise SyntaxError(\"File line {}: Invalid second argument.\".format(self._file_line))\n return True\n else:\n return False","def test_endpointPOP3(self):\n self._endpointTest(\"pop3\")","def _platformix_get(self, context, fake_reply, prop):\r\n if hasattr(self.host, prop):\r\n self._reply(context, proto_success(getattr(self.host, prop), prop), fake_reply)\r\n else:\r\n self._reply(context, proto_failure(\"Property {} not found on {}\".format(prop, self.host.name)), fake_reply)","def test_check_replication_unknown_valueerror2(self, mock_urlopen):\n base_url = 'http://localhost:6000/recon/'\n jdata = PropertyMock(return_value=b'X')\n mock_urlopen.return_value = MagicMock(read=jdata)\n result = check_replication(base_url, [4, 10, 4, 10])\n self.assertEqual(result,\n 3*[(STATUS_UNKNOWN,\n \"Can't parse status data\")])","def remote_status():","def test_documentation_popxl_replication(self):\n filename = \"replication.py\"\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)","def test_get_property():\n\n sdk = '23'\n contents = (\"[Info]\\n\"\n \"sdk = %s\" % sdk)\n\n testutils.deploy_config_raw(contents)\n\n assert prop.get_prop('info', 'sdk') == sdk\n\n testutils.undeploy()\n\n return 0","def test_z_remote_command(self):\n\t\ttheResult = False\n\t\ttry:\n\t\t\timport subprocess\n\t\t\ttheOutputtext = subprocess.check_output([\"which\", \"check_nrpe\"])\n\t\t\tif (str(\"/check_nrpe\") in str(theOutputtext)):\n\t\t\t\ttheResult = True\n\t\texcept Exception:\n\t\t\ttheResult = False\n\t\t\ttry:\n\t\t\t\ttheOutputtext = subprocess.check_output([\"which\", \"ssh\"])\n\t\t\t\tif (str(\"/ssh\") in str(theOutputtext)):\n\t\t\t\t\ttheResult = True\n\t\t\texcept Exception:\n\t\t\t\ttheResult = False\n\t\tassert theResult","def remote_installed(self):\n gxp_dir = os.environ[\"GXP_DIR\"]\n flag = os.path.join(gxp_dir, \"REMOTE_INSTALLED\")\n if dbg>=2: \n ioman.LOG(\"checking remote flag %s/%s\\n\" % (gxp_dir, flag))\n if os.path.exists(flag):\n if dbg>=2: ioman.LOG(\"exists, remotely installed\\n\")\n return 1\n else:\n if dbg>=2: ioman.LOG(\"does not exit, locally installed\\n\")\n return 0","def remote():\n pass","def test_pop3(self):\n self._endpointServerTest(\"pop3\", protocols.POP3Factory)","def _is_pop_command(self):\n return self._match_memory_pattern(\"pop\")","def test_check_nip(client):\n is_assigned, request_id = client.check_nip(\n \"8655104670\", \"41146786026458860703735932\"\n )\n\n assert is_assigned","def hasGridProxy():\n import os\n from subprocess import Popen, PIPE\n \n arguments = 'dirac-proxy-info --checkvalid'\n arguments = ['dirac-command'] + arguments.split()\n logger.verbose ( 'hasGridProxy:use Popen(%s)' % arguments)\n\n p = Popen(arguments, stdout=PIPE, stderr=PIPE)\n (cout, cerr) = p.communicate()\n #\n if 0 != p.returncode: return False\n #\n if py3 :\n cout = cout.decode ( 'utf-8' ) if cout else cout \n cerr = cerr.decode ( 'utf-8' ) if cerr else cerr \n # \n\n if 'expired' in cout : return False\n if 'Insane' in cout : return False\n if 'Error' in cout : return False\n #\n return 0 == p.returncode and cout and not cerr","def test_set_get(self):\n self.shell.onecmd(\"create %s/one 'hello'\" % (self.tests_path))\n self.shell.onecmd(\"set %s/one 'bye'\" % (self.tests_path))\n self.shell.onecmd(\"get %s/one\" % (self.tests_path))\n self.assertEqual(\"bye\\n\", self.output.getvalue())","def test_test_property():\n\n contents = (\"[Info]\\n\"\n \"sdk = 23\")\n\n testutils.deploy_config_raw(contents)\n\n assert prop.test_prop('info', 'sdk') == 1\n\n testutils.undeploy()\n\n return 0","def on_pullVar(self):\n if not os.path.exists(grapher.binPath):\n mess = \"!!! ERROR: rndBin path not found, check user pref !!!\"\n self.mainUi._defaultErrorDialog(mess, self.mainUi)\n else:\n tmpPath = os.path.join(self.grapher.userBinPath, 'tmp')\n tmpFile = os.path.join(tmpPath, 'varBuffer.py')\n varDict = pFile.readPyFile(tmpFile)\n for var in sorted(varDict.keys()):\n if var.startswith('selVar_'):\n newItem = self.on_addVar()\n self.setItem(newItem, **varDict[var])","def SEMIHook(p):\n x = p['sy']['pop']()\n if (x == ';'):\n p['sy']['push'](p['OK'])\n else:\n p['sy']['push'](p['NOK'])\n #endif","def check_remote_rpm_install(self, rpm_package_name, host):\n results = run_remote_command(\"rpm -q %s --dbpath %s\" % (rpm_package_name, RPM_DATABASE), host)\n self.assertEqual(results, rpm_package_name)","def test_expect_status_property_about_registry_process(client, start_call_fx):\n\n url = reverse_lazy('calls:registry-list')\n\n response = client.post(url, start_call_fx, content_type='application/json')\n\n job_id = response.data.get('job_id')\n\n job = client.get(job_id)\n\n assert job.data.get('status') == 'DONE'","def test_existing_value(self):\n var_name = \"PICCOLO_TEST_2\"\n initial_value = \"hello\"\n new_value = \"goodbye\"\n\n os.environ[var_name] = initial_value\n\n with set_env_var(var_name=var_name, temp_value=new_value):\n self.assertEqual(os.environ.get(var_name), new_value)\n\n self.assertEqual(os.environ.get(var_name), initial_value)","def test_ipcrm():\n IPCComm.ipcrm()","def nremote(self):","def test_ip(response):\n \n # from comeon_core import update\n ip = getIP()\n print(ip)\n #init_db(engine)\n #update()\n assert True","def is_remote(self): # -> Any | bool:\n ...","def get_xcom(**context):\n ti = context['ti']\n data = ti.xcom_pull(task_ids='xcom_from_bash', key='return_value')\n logging.info(data)","def test_remote(self):\n\n self.assertEqual(description.RepositoryDescription(\n 'git@github.com:/example/remote', '/path/to/local').remote,\n implementation.RemoteRepository(\n 'git@github.com:/example/remote'))","def test_get_property_success(self):\r\n self.assertEqual(self.config.option1, 1337)","def fetch():\n return True","def PHook(p):\n x = p['sy']['pop']()\n if (x == '.'):\n p['sy']['push'](p['OK'])\n else:\n p['sy']['push'](p['NOK'])\n #endif","def is_remote(self):\n if socket.gethostbyname(socket.gethostname()).startswith('10.7'):\n return False\n else:\n return True","def sendPullResponse(remote, request, txpk,sock):\r\n remote = (remote[0], remote[1])\r\n\r\n m = GatewayMessage(version=request.version, token=request.token,\r\n identifier=PULL_RESP, gatewayEUI=b'9079494338994186168',\r\n remote=remote, ptype='txpk', txpk=txpk)\r\n print(\"Sending PULL_RESP message to %s:%d\" % remote)\r\n\r\n sock.sendto(m.encode(), remote)\r\n data, address = sock.recvfrom(4096)\r\n print(data)\r\n print('received %s bytes from %s' % (len(data), address))","def test_postfix(self):\n test_sensordef = {\n \"kind\": self.test_postfix.get_kind(),\n \"name\": \"Postfix Mailqueue\",\n \"description\": \"Monitors the mailqueue of a postfix server\",\n \"help\": \"Monitors the mailqueue of a postfix server for active, deferred, hold or corrupt mail\",\n \"tag\": \"mppostfixsensor\",\n \"fields\": [],\n \"groups\": []\n }\n assert_equal(self.test_postfix.get_sensordef(), test_sensordef)","def remote_pull(*keys):","def check_available():\n\n rm = current_app.config['rm_object']\n\n return rm.check_availability()","def do_local(self, *args):\n return self.do_scpi(':communicate:remote 0')","def checkGetHostByName(self, result):\n self.assertEquals(result, '127.0.0.1')","def unset():\n rino.remote.unset()","def test_get_address(self):\n with self.subprocess_getoutput_patch:\n ret = self.inst._get_address()\n self.assertEqual(ret, \"http://example\")","def test_get_value(self) -> None:\n\n expected = False\n actual = self.helper.set_name(self.test_name).exists()\n\n self.assertEqual(expected, actual)\n\n expected = \"Hello\"\n actual = self.helper.get_value(default=\"Hello\")\n\n self.assertEqual(expected, actual)\n\n expected = \"This is a test.\"\n\n os.environ[self.test_name] = \"This is a test.\"\n\n actual = self.helper.get_value(default=\"Hello\")\n\n self.assertEqual(expected, actual)\n\n del os.environ[self.test_name]","def test_getSiblingExistsRemote(self):\n self.store.powerUp(self.contentStore1, ISiblingStore)\n self._retrievalTest()","def test_script_p2_sh_address(self):\n pass","def testIP(self):\n self.assertEqual([\"http://234.234.234.234\"], grab('http://234.234.234.234', self.needScheme))","def SetPop(self, fname, var):\n\n\t\tself._pop_fname = fname\n\t\tself._pop_var = var","def test_get_backup_command(self):\n mb = self.maria_backup\n mock = mock_open(read_data='')\n with patch('builtins.open', mock): # skip reading the port list section\n self.assertEqual(mb.get_backup_cmd('test_dir'),\n ['xtrabackup', '--backup',\n '--target-dir', 'test_dir/test',\n '--datadir', '/srv/sqldata',\n '--socket', '/run/mysqld/mysqld.sock'])","def testProtocolReturn(self):\n self.assertEqual(\n self.protocol,\n self.mr.protocol\n )\n\n self.mr._protocol = 'burp'\n\n self.assertEqual(\n 'burp',\n self.mr.protocol\n )","def test_get(self, publish_mock: mock.Mock) -> None:\n\n def side_effect(*args: str, **_: str) -> Any:\n if args[0] == \"registry:search:dict\":\n return [{\"host1\": \"mac1\", \"host2\": \"mac2\"}]\n\n if args[0] == \"app_url\":\n return [\"/registry\"]\n\n if args[0] == \"jinja:render\":\n return [\"\"]\n\n return mock.DEFAULT\n\n publish_mock.side_effect = side_effect\n\n self.request(\"/\")\n\n self.assertEqual(\n helpers.template_var(publish_mock, \"registry_url\"),\n \"/registry\"\n )\n self.assertEqual(\n helpers.template_var(publish_mock, \"hosts\").get(\"host1\"),\n \"mac1\"\n )\n\n self.assertNotIn(\n \"host3\",\n helpers.template_var(publish_mock, \"hosts\")\n )\n\n self.assertFalse(\n helpers.template_var(publish_mock, \"send\")\n )","def test_variables_get(self):\n pass","def test_get_host(self):\n pass","def test_is_remote(self):\n self.assertEqual(self.project.is_remote(), False)","def pop_payload(self):\n payload = self.rdb.lpop(self.key)\n if payload:\n self.pool.spawn(self.send, json.loads(payload.decode(\"utf-8\")))\n else:\n gevent.sleep(5)","def test_retrieving_variables(self):\n\t\turl = reverse('variables', args = ('b'))\n\t\tresponse = self.client.get(url, format='json')\n\t\tself.assertEqual(response.data, {'b': 567})","def remote_kill():","def test_preferred_zip_precedence(self):\n subscriber = Subscriber.objects.get(id=4)\n create_subscriber_in_session(self, subscriber)\n factory = RequestFactory()\n self.assemble_session(self.session) \n request = factory.get('/hudson-valley/', follow=True) \n request.session = self.session\n site = Site.objects.get(id=3)\n preferred_zip = get_preferred_zip(request, site)\n self.assertEqual(preferred_zip, '27604')","def rpc_ping(self):\n\t\treturn True","def test_check_status_code_returns_true():\n plugin_instance = PluginVipCustomisation()\n assert plugin_instance._check_status_code('replace me with real xml') == True","def test_master_address(self):\n from supvisors.rpcinterface import RPCInterface\n # prepare context\n self.supervisor.supvisors.context.master_address = '10.0.0.1'\n # create RPC instance\n rpc = RPCInterface(self.supervisor)\n self.assertEqual('10.0.0.1', rpc.get_master_address())","def check_remote_rpm_uninstall(self, rpm_package_name, host):\n with self.assertRaisesRegexp(ExecutionError, \"%s is not installed\" % rpm_package_name):\n results = run_remote_command(\"rpm -q %s --dbpath %s\" % (rpm_package_name, RPM_DATABASE), host)","def c_test_population_function(self, function):\r\n return 1","def test_package(response):\n \n # from comeon_core import update\n ip = getIP()\n print(ip)\n db = connect()\n engine = db.connect() \n init_db(engine)\n update(engine)\n assert True","def test_no_existing_value(self):\n var_name = \"PICCOLO_TEST_1\"\n\n # Make sure it definitely doesn't exist already\n if os.environ.get(var_name) is not None:\n del os.environ[var_name]\n\n new_value = \"hello world\"\n\n with set_env_var(var_name=var_name, temp_value=new_value):\n self.assertEqual(os.environ.get(var_name), new_value)\n\n self.assertEqual(os.environ.get(var_name), None)","def testPowerOnResponse(self):\n message = (mavutil.mavlink.GOPRO_COMMAND_POWER, mavutil.mavlink.GOPRO_REQUEST_SUCCESS)\n self.mgr.set_response_callback('vehicle','name', message)\n self.mgr.processMsgQueue.assert_called_with()","def test_rpcCall(self):\n pass","def test_plc_read_val(plc_ip, tag_name):\n\n plc = ClxDriver()\n if plc.open(plc_ip):\n tagg = plc.read_tag(tag_name)\n plc.close()\n return (tagg)\n \n else:\n print(\"Unable to open\", plc_ip)","def test_get(self):\n self.assertEqual(self.config.get('basic','greeting'),'hello')","def test_preferred_zip_defaulted(self):\n factory = RequestFactory() \n request = factory.get('/hudson-valley/', follow=True)\n request.session = self.session\n request.META['site_id'] = 2\n preferred_zip = get_preferred_zip(request)\n self.assertEqual(preferred_zip, '12601')","def test_del_property():\n\n contents = (\"[Info]\\n\"\n \"sdk = 23\")\n\n testutils.deploy_config_raw(contents)\n\n prop.del_prop('info', 'sdk')\n\n testutils.undeploy()\n\n return 0","def is_return(self, pick):\n return ('-' in pick.name and sorted(pick.name.split('-'), reverse=True)[0].startswith('ret'))","def test_check_regon(client):\n is_assigned, request_id = client.check_regon(\n \"730371613\", \"41146786026458860703735932\"\n )\n\n assert is_assigned","def test_z_z_func_command_local(self):\n\t\ttheResult = False\n\t\ttry:\n\t\t\ttheOutputtext = checkPythonCommand([getPythonCommand(),\n\t\t\t\tstr(\"code/restart_service.py\"),\n\t\t\t\tstr(\"--use-local\"),\n\t\t\t\tstr(\"-E\"),\n\t\t\t\tstr(\"5\"),\n\t\t\t\tstr(\"CRITICAL\"),\n\t\t\t\tstr(\"HARD\"),\n\t\t\t\tstr(\"5\"),\n\t\t\t\tstr(\"localhost\"),\n\t\t\t\tstr(\"true\"),\n\t\t\t\tstr(\"\\\"test\\\"\")\n\t\t\t], stderr=subprocess.STDOUT)\n\t\t\tif (str(\"OK - Service test restarted on host localhost\") in str(theOutputtext)):\n\t\t\t\ttheResult = True\n\t\t\telif (str(\"OK - Service \\\"test\\\" restarted on host localhost\") in str(theOutputtext)):\n\t\t\t\ttheResult = True\n\t\t\telse:\n\t\t\t\t print(str(theOutputtext))\n\t\texcept Exception:\n\t\t\ttheResult = False\n\t\tassert theResult","def test_ping(self):\n status, output = commands.getstatusoutput('ping -c 5 %s' % self.known_ip)\n assert status == 0","def test_check_replication_unknown_valueerror1(self, mock_urlopen):\n base_url = '.'\n mock_urlopen.side_effect = ValueError(Mock(return_value=''))\n result = check_replication(base_url, [4, 10, 4, 10])\n self.assertEqual(result,\n 3*[(STATUS_UNKNOWN,\n \"Can't parse status data\")])","def test_vcmp(self):\r\n if self.flask_app.config.get('VMCP_KEY'):\r\n self.flask_app.config.pop('VMCP_KEY')\r\n res = self.app.get('api/vmcp', follow_redirects=True)\r\n err = json.loads(res.data)\r\n assert res.status_code == 501, err\r\n assert err['status_code'] == 501, err\r\n assert err['status'] == \"failed\", err\r\n assert err['target'] == \"vmcp\", err\r\n assert err['action'] == \"GET\", err","def hxlinfo():\n run_script(hxlinfo_main)","def send_xdisploc(self):\n return self.shell.xdisploc","def test_request(self):\n self.assertIn('list', self.api.request('sys.settings.get').data,\n msg=\"request() doesn't work properly. 'list' is not found in the response\")","def test_select_var(self):\n self.check_metadata.side_effect = lambda: self.cube\n with patch('esmvalcore.cmor._fixes.fix.Fix.get_fixes',\n return_value=[]):\n with patch('esmvalcore.cmor.fix._get_cmor_checker',\n return_value=self.checker):\n cube_returned = fix_metadata(\n cubes=[self.cube,\n self._create_mock_cube('extra')],\n short_name='short_name',\n project='CMIP6',\n dataset='model',\n mip='mip',\n )[0]\n self.checker.assert_called_once_with(self.cube)\n self.check_metadata.assert_called_once_with()\n assert cube_returned is self.cube","def remotes():","def test_get_cell(workbook):\n assert workbook.get_cell(3,1) == '507906000030242007'","def _exists_remote(self, host):\n # This file gets written after cloudinit is done\n # path = '/var/lib/cloud/instance/boot-finished'\n path = '/home/ubuntu/SETUP_COMPLETE'\n t = 0\n sleep_len = 10\n while True:\n status = subprocess.call(\n ['ssh', '-oStrictHostKeyChecking=no', '-i', '/home/ubuntu/.ssh/id_rsa', 'ubuntu@'+host, 'test -f {}'.format(pipes.quote(path))])\n if status == 0:\n return True\n else:\n return False","def test_documentation_popxl_call_with_info(self):\n filename = \"call_with_info.py\"\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)","def get_response(hf, var):\n psweep = hf.attrs['UQtype']\n return unpickle(hf['/%s/%s/response' % (psweep, var)].value)","def fingertip_no_remote(self) -> bool:\n hcell = self._get_hcell2()\n return hcell.get(\"fingertip_no_remote\", False)","def test_unload(install_mockery, mock_fetch, mock_archive, mock_packages, working_env):\n install(\"mpileaks\")\n mpileaks_spec = spack.spec.Spec(\"mpileaks\").concretized()\n\n # Set so unload has something to do\n os.environ[\"FOOBAR\"] = \"mpileaks\"\n os.environ[uenv.spack_loaded_hashes_var] = \"%s:%s\" % (mpileaks_spec.dag_hash(), \"garbage\")\n\n sh_out = unload(\"--sh\", \"mpileaks\")\n csh_out = unload(\"--csh\", \"mpileaks\")\n\n assert \"unset FOOBAR\" in sh_out\n assert \"unsetenv FOOBAR\" in csh_out\n\n assert \"export %s=garbage\" % uenv.spack_loaded_hashes_var in sh_out\n assert \"setenv %s garbage\" % uenv.spack_loaded_hashes_var in csh_out","def test_variablepresentations_get(self):\n pass","def test_broadcast_setting_change():\n assert win_functions.broadcast_setting_change()","def pull(self, pull: Optional[int] = None) -> Optional[int]:\n ...","def test(self,version=''):\n p5cmd = ['srvinfo', 'lexxvers']\n try:\n res = self.nsdchat_call(p5cmd,5)\n p5_version = singlevalue(res)\n if (p5_version >= str(version)):\n return True\n return False\n except subprocess.TimeoutExpired:\n print(\"Could not connect to the archiware p5 server.\\nPlease review\"\n \"the connection and firewall settings.\")\n raise","def test_delete_on_background_response_descriptor_variables_library_variable_set_library_variable_set_resource(self):\n pass","def test_set_value(self) -> None:\n\n expected = False\n actual = self.helper.set_name(self.test_name).exists()\n\n self.assertEqual(expected, actual)\n\n self.helper.set_value(\"Hello, World!\")\n\n expected = \"Hello, World!\"\n actual = self.helper.get_value()\n\n self.assertEqual(expected, actual)\n\n del os.environ[self.test_name]","def remote_push(self, pNamespace):","def test_get_temp(loaded_fridge):\n assert loaded_fridge.get_temp() == loaded_fridge.temp","def do_remote(self, *args):\n return self.do_scpi(':communicate:remote 1')","def test_constants(self):\n self.assertTrue(getattr(unshare, 'CLONE_NEWNS'))\n self.assertTrue(getattr(unshare, 'CLONE_NEWNET'))","def test_popen(self):\n self.executor.command(['grep', 'foo']).popen()","def test_preferred_zip_consumer(self):\n factory = RequestFactory()\n consumer = Consumer.objects.get(id=115)\n self.assertEqual(consumer.consumer_zip_postal, '12589')\n create_consumer_in_session(self, consumer)\n self.assemble_session(self.session) \n request = factory.get('/hudson-valley/', follow=True)\n request.session = self.session\n site = Site.objects.get(id=2)\n preferred_zip = get_preferred_zip(request, site)\n self.assertEqual(preferred_zip, '12589')","def is_remote(self):\n\t\treturn bool(call_sdk_function('PrlVmDev_IsRemote', self.handle))"],"string":"[\n \"def test_documentation_popxl_remote_rts_var(self):\\n filename = \\\"remote_rts_var.py\\\"\\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)\",\n \"def test_documentation_popxl_rts_var(self):\\n filename = \\\"rts_var.py\\\"\\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)\",\n \"def test_pop_returns_value(new_dll):\\n assert new_dll.pop() == 3\",\n \"def _is_pop(self, words):\\n if words[0] == 'pop':\\n if len(words) != 3:\\n raise SyntaxError(\\\"File line {}: Invalid number of arguments for C_POP command.\\\".format(self._file_line))\\n if words[1] not in ['temp', 'pointer', 'static', 'local', 'argument', 'this', 'that']:\\n raise SyntaxError(\\\"File line {}: Invalid second argument.\\\".format(self._file_line))\\n return True\\n else:\\n return False\",\n \"def test_endpointPOP3(self):\\n self._endpointTest(\\\"pop3\\\")\",\n \"def _platformix_get(self, context, fake_reply, prop):\\r\\n if hasattr(self.host, prop):\\r\\n self._reply(context, proto_success(getattr(self.host, prop), prop), fake_reply)\\r\\n else:\\r\\n self._reply(context, proto_failure(\\\"Property {} not found on {}\\\".format(prop, self.host.name)), fake_reply)\",\n \"def test_check_replication_unknown_valueerror2(self, mock_urlopen):\\n base_url = 'http://localhost:6000/recon/'\\n jdata = PropertyMock(return_value=b'X')\\n mock_urlopen.return_value = MagicMock(read=jdata)\\n result = check_replication(base_url, [4, 10, 4, 10])\\n self.assertEqual(result,\\n 3*[(STATUS_UNKNOWN,\\n \\\"Can't parse status data\\\")])\",\n \"def remote_status():\",\n \"def test_documentation_popxl_replication(self):\\n filename = \\\"replication.py\\\"\\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)\",\n \"def test_get_property():\\n\\n sdk = '23'\\n contents = (\\\"[Info]\\\\n\\\"\\n \\\"sdk = %s\\\" % sdk)\\n\\n testutils.deploy_config_raw(contents)\\n\\n assert prop.get_prop('info', 'sdk') == sdk\\n\\n testutils.undeploy()\\n\\n return 0\",\n \"def test_z_remote_command(self):\\n\\t\\ttheResult = False\\n\\t\\ttry:\\n\\t\\t\\timport subprocess\\n\\t\\t\\ttheOutputtext = subprocess.check_output([\\\"which\\\", \\\"check_nrpe\\\"])\\n\\t\\t\\tif (str(\\\"/check_nrpe\\\") in str(theOutputtext)):\\n\\t\\t\\t\\ttheResult = True\\n\\t\\texcept Exception:\\n\\t\\t\\ttheResult = False\\n\\t\\t\\ttry:\\n\\t\\t\\t\\ttheOutputtext = subprocess.check_output([\\\"which\\\", \\\"ssh\\\"])\\n\\t\\t\\t\\tif (str(\\\"/ssh\\\") in str(theOutputtext)):\\n\\t\\t\\t\\t\\ttheResult = True\\n\\t\\t\\texcept Exception:\\n\\t\\t\\t\\ttheResult = False\\n\\t\\tassert theResult\",\n \"def remote_installed(self):\\n gxp_dir = os.environ[\\\"GXP_DIR\\\"]\\n flag = os.path.join(gxp_dir, \\\"REMOTE_INSTALLED\\\")\\n if dbg>=2: \\n ioman.LOG(\\\"checking remote flag %s/%s\\\\n\\\" % (gxp_dir, flag))\\n if os.path.exists(flag):\\n if dbg>=2: ioman.LOG(\\\"exists, remotely installed\\\\n\\\")\\n return 1\\n else:\\n if dbg>=2: ioman.LOG(\\\"does not exit, locally installed\\\\n\\\")\\n return 0\",\n \"def remote():\\n pass\",\n \"def test_pop3(self):\\n self._endpointServerTest(\\\"pop3\\\", protocols.POP3Factory)\",\n \"def _is_pop_command(self):\\n return self._match_memory_pattern(\\\"pop\\\")\",\n \"def test_check_nip(client):\\n is_assigned, request_id = client.check_nip(\\n \\\"8655104670\\\", \\\"41146786026458860703735932\\\"\\n )\\n\\n assert is_assigned\",\n \"def hasGridProxy():\\n import os\\n from subprocess import Popen, PIPE\\n \\n arguments = 'dirac-proxy-info --checkvalid'\\n arguments = ['dirac-command'] + arguments.split()\\n logger.verbose ( 'hasGridProxy:use Popen(%s)' % arguments)\\n\\n p = Popen(arguments, stdout=PIPE, stderr=PIPE)\\n (cout, cerr) = p.communicate()\\n #\\n if 0 != p.returncode: return False\\n #\\n if py3 :\\n cout = cout.decode ( 'utf-8' ) if cout else cout \\n cerr = cerr.decode ( 'utf-8' ) if cerr else cerr \\n # \\n\\n if 'expired' in cout : return False\\n if 'Insane' in cout : return False\\n if 'Error' in cout : return False\\n #\\n return 0 == p.returncode and cout and not cerr\",\n \"def test_set_get(self):\\n self.shell.onecmd(\\\"create %s/one 'hello'\\\" % (self.tests_path))\\n self.shell.onecmd(\\\"set %s/one 'bye'\\\" % (self.tests_path))\\n self.shell.onecmd(\\\"get %s/one\\\" % (self.tests_path))\\n self.assertEqual(\\\"bye\\\\n\\\", self.output.getvalue())\",\n \"def test_test_property():\\n\\n contents = (\\\"[Info]\\\\n\\\"\\n \\\"sdk = 23\\\")\\n\\n testutils.deploy_config_raw(contents)\\n\\n assert prop.test_prop('info', 'sdk') == 1\\n\\n testutils.undeploy()\\n\\n return 0\",\n \"def on_pullVar(self):\\n if not os.path.exists(grapher.binPath):\\n mess = \\\"!!! ERROR: rndBin path not found, check user pref !!!\\\"\\n self.mainUi._defaultErrorDialog(mess, self.mainUi)\\n else:\\n tmpPath = os.path.join(self.grapher.userBinPath, 'tmp')\\n tmpFile = os.path.join(tmpPath, 'varBuffer.py')\\n varDict = pFile.readPyFile(tmpFile)\\n for var in sorted(varDict.keys()):\\n if var.startswith('selVar_'):\\n newItem = self.on_addVar()\\n self.setItem(newItem, **varDict[var])\",\n \"def SEMIHook(p):\\n x = p['sy']['pop']()\\n if (x == ';'):\\n p['sy']['push'](p['OK'])\\n else:\\n p['sy']['push'](p['NOK'])\\n #endif\",\n \"def check_remote_rpm_install(self, rpm_package_name, host):\\n results = run_remote_command(\\\"rpm -q %s --dbpath %s\\\" % (rpm_package_name, RPM_DATABASE), host)\\n self.assertEqual(results, rpm_package_name)\",\n \"def test_expect_status_property_about_registry_process(client, start_call_fx):\\n\\n url = reverse_lazy('calls:registry-list')\\n\\n response = client.post(url, start_call_fx, content_type='application/json')\\n\\n job_id = response.data.get('job_id')\\n\\n job = client.get(job_id)\\n\\n assert job.data.get('status') == 'DONE'\",\n \"def test_existing_value(self):\\n var_name = \\\"PICCOLO_TEST_2\\\"\\n initial_value = \\\"hello\\\"\\n new_value = \\\"goodbye\\\"\\n\\n os.environ[var_name] = initial_value\\n\\n with set_env_var(var_name=var_name, temp_value=new_value):\\n self.assertEqual(os.environ.get(var_name), new_value)\\n\\n self.assertEqual(os.environ.get(var_name), initial_value)\",\n \"def test_ipcrm():\\n IPCComm.ipcrm()\",\n \"def nremote(self):\",\n \"def test_ip(response):\\n \\n # from comeon_core import update\\n ip = getIP()\\n print(ip)\\n #init_db(engine)\\n #update()\\n assert True\",\n \"def is_remote(self): # -> Any | bool:\\n ...\",\n \"def get_xcom(**context):\\n ti = context['ti']\\n data = ti.xcom_pull(task_ids='xcom_from_bash', key='return_value')\\n logging.info(data)\",\n \"def test_remote(self):\\n\\n self.assertEqual(description.RepositoryDescription(\\n 'git@github.com:/example/remote', '/path/to/local').remote,\\n implementation.RemoteRepository(\\n 'git@github.com:/example/remote'))\",\n \"def test_get_property_success(self):\\r\\n self.assertEqual(self.config.option1, 1337)\",\n \"def fetch():\\n return True\",\n \"def PHook(p):\\n x = p['sy']['pop']()\\n if (x == '.'):\\n p['sy']['push'](p['OK'])\\n else:\\n p['sy']['push'](p['NOK'])\\n #endif\",\n \"def is_remote(self):\\n if socket.gethostbyname(socket.gethostname()).startswith('10.7'):\\n return False\\n else:\\n return True\",\n \"def sendPullResponse(remote, request, txpk,sock):\\r\\n remote = (remote[0], remote[1])\\r\\n\\r\\n m = GatewayMessage(version=request.version, token=request.token,\\r\\n identifier=PULL_RESP, gatewayEUI=b'9079494338994186168',\\r\\n remote=remote, ptype='txpk', txpk=txpk)\\r\\n print(\\\"Sending PULL_RESP message to %s:%d\\\" % remote)\\r\\n\\r\\n sock.sendto(m.encode(), remote)\\r\\n data, address = sock.recvfrom(4096)\\r\\n print(data)\\r\\n print('received %s bytes from %s' % (len(data), address))\",\n \"def test_postfix(self):\\n test_sensordef = {\\n \\\"kind\\\": self.test_postfix.get_kind(),\\n \\\"name\\\": \\\"Postfix Mailqueue\\\",\\n \\\"description\\\": \\\"Monitors the mailqueue of a postfix server\\\",\\n \\\"help\\\": \\\"Monitors the mailqueue of a postfix server for active, deferred, hold or corrupt mail\\\",\\n \\\"tag\\\": \\\"mppostfixsensor\\\",\\n \\\"fields\\\": [],\\n \\\"groups\\\": []\\n }\\n assert_equal(self.test_postfix.get_sensordef(), test_sensordef)\",\n \"def remote_pull(*keys):\",\n \"def check_available():\\n\\n rm = current_app.config['rm_object']\\n\\n return rm.check_availability()\",\n \"def do_local(self, *args):\\n return self.do_scpi(':communicate:remote 0')\",\n \"def checkGetHostByName(self, result):\\n self.assertEquals(result, '127.0.0.1')\",\n \"def unset():\\n rino.remote.unset()\",\n \"def test_get_address(self):\\n with self.subprocess_getoutput_patch:\\n ret = self.inst._get_address()\\n self.assertEqual(ret, \\\"http://example\\\")\",\n \"def test_get_value(self) -> None:\\n\\n expected = False\\n actual = self.helper.set_name(self.test_name).exists()\\n\\n self.assertEqual(expected, actual)\\n\\n expected = \\\"Hello\\\"\\n actual = self.helper.get_value(default=\\\"Hello\\\")\\n\\n self.assertEqual(expected, actual)\\n\\n expected = \\\"This is a test.\\\"\\n\\n os.environ[self.test_name] = \\\"This is a test.\\\"\\n\\n actual = self.helper.get_value(default=\\\"Hello\\\")\\n\\n self.assertEqual(expected, actual)\\n\\n del os.environ[self.test_name]\",\n \"def test_getSiblingExistsRemote(self):\\n self.store.powerUp(self.contentStore1, ISiblingStore)\\n self._retrievalTest()\",\n \"def test_script_p2_sh_address(self):\\n pass\",\n \"def testIP(self):\\n self.assertEqual([\\\"http://234.234.234.234\\\"], grab('http://234.234.234.234', self.needScheme))\",\n \"def SetPop(self, fname, var):\\n\\n\\t\\tself._pop_fname = fname\\n\\t\\tself._pop_var = var\",\n \"def test_get_backup_command(self):\\n mb = self.maria_backup\\n mock = mock_open(read_data='')\\n with patch('builtins.open', mock): # skip reading the port list section\\n self.assertEqual(mb.get_backup_cmd('test_dir'),\\n ['xtrabackup', '--backup',\\n '--target-dir', 'test_dir/test',\\n '--datadir', '/srv/sqldata',\\n '--socket', '/run/mysqld/mysqld.sock'])\",\n \"def testProtocolReturn(self):\\n self.assertEqual(\\n self.protocol,\\n self.mr.protocol\\n )\\n\\n self.mr._protocol = 'burp'\\n\\n self.assertEqual(\\n 'burp',\\n self.mr.protocol\\n )\",\n \"def test_get(self, publish_mock: mock.Mock) -> None:\\n\\n def side_effect(*args: str, **_: str) -> Any:\\n if args[0] == \\\"registry:search:dict\\\":\\n return [{\\\"host1\\\": \\\"mac1\\\", \\\"host2\\\": \\\"mac2\\\"}]\\n\\n if args[0] == \\\"app_url\\\":\\n return [\\\"/registry\\\"]\\n\\n if args[0] == \\\"jinja:render\\\":\\n return [\\\"\\\"]\\n\\n return mock.DEFAULT\\n\\n publish_mock.side_effect = side_effect\\n\\n self.request(\\\"/\\\")\\n\\n self.assertEqual(\\n helpers.template_var(publish_mock, \\\"registry_url\\\"),\\n \\\"/registry\\\"\\n )\\n self.assertEqual(\\n helpers.template_var(publish_mock, \\\"hosts\\\").get(\\\"host1\\\"),\\n \\\"mac1\\\"\\n )\\n\\n self.assertNotIn(\\n \\\"host3\\\",\\n helpers.template_var(publish_mock, \\\"hosts\\\")\\n )\\n\\n self.assertFalse(\\n helpers.template_var(publish_mock, \\\"send\\\")\\n )\",\n \"def test_variables_get(self):\\n pass\",\n \"def test_get_host(self):\\n pass\",\n \"def test_is_remote(self):\\n self.assertEqual(self.project.is_remote(), False)\",\n \"def pop_payload(self):\\n payload = self.rdb.lpop(self.key)\\n if payload:\\n self.pool.spawn(self.send, json.loads(payload.decode(\\\"utf-8\\\")))\\n else:\\n gevent.sleep(5)\",\n \"def test_retrieving_variables(self):\\n\\t\\turl = reverse('variables', args = ('b'))\\n\\t\\tresponse = self.client.get(url, format='json')\\n\\t\\tself.assertEqual(response.data, {'b': 567})\",\n \"def remote_kill():\",\n \"def test_preferred_zip_precedence(self):\\n subscriber = Subscriber.objects.get(id=4)\\n create_subscriber_in_session(self, subscriber)\\n factory = RequestFactory()\\n self.assemble_session(self.session) \\n request = factory.get('/hudson-valley/', follow=True) \\n request.session = self.session\\n site = Site.objects.get(id=3)\\n preferred_zip = get_preferred_zip(request, site)\\n self.assertEqual(preferred_zip, '27604')\",\n \"def rpc_ping(self):\\n\\t\\treturn True\",\n \"def test_check_status_code_returns_true():\\n plugin_instance = PluginVipCustomisation()\\n assert plugin_instance._check_status_code('replace me with real xml') == True\",\n \"def test_master_address(self):\\n from supvisors.rpcinterface import RPCInterface\\n # prepare context\\n self.supervisor.supvisors.context.master_address = '10.0.0.1'\\n # create RPC instance\\n rpc = RPCInterface(self.supervisor)\\n self.assertEqual('10.0.0.1', rpc.get_master_address())\",\n \"def check_remote_rpm_uninstall(self, rpm_package_name, host):\\n with self.assertRaisesRegexp(ExecutionError, \\\"%s is not installed\\\" % rpm_package_name):\\n results = run_remote_command(\\\"rpm -q %s --dbpath %s\\\" % (rpm_package_name, RPM_DATABASE), host)\",\n \"def c_test_population_function(self, function):\\r\\n return 1\",\n \"def test_package(response):\\n \\n # from comeon_core import update\\n ip = getIP()\\n print(ip)\\n db = connect()\\n engine = db.connect() \\n init_db(engine)\\n update(engine)\\n assert True\",\n \"def test_no_existing_value(self):\\n var_name = \\\"PICCOLO_TEST_1\\\"\\n\\n # Make sure it definitely doesn't exist already\\n if os.environ.get(var_name) is not None:\\n del os.environ[var_name]\\n\\n new_value = \\\"hello world\\\"\\n\\n with set_env_var(var_name=var_name, temp_value=new_value):\\n self.assertEqual(os.environ.get(var_name), new_value)\\n\\n self.assertEqual(os.environ.get(var_name), None)\",\n \"def testPowerOnResponse(self):\\n message = (mavutil.mavlink.GOPRO_COMMAND_POWER, mavutil.mavlink.GOPRO_REQUEST_SUCCESS)\\n self.mgr.set_response_callback('vehicle','name', message)\\n self.mgr.processMsgQueue.assert_called_with()\",\n \"def test_rpcCall(self):\\n pass\",\n \"def test_plc_read_val(plc_ip, tag_name):\\n\\n plc = ClxDriver()\\n if plc.open(plc_ip):\\n tagg = plc.read_tag(tag_name)\\n plc.close()\\n return (tagg)\\n \\n else:\\n print(\\\"Unable to open\\\", plc_ip)\",\n \"def test_get(self):\\n self.assertEqual(self.config.get('basic','greeting'),'hello')\",\n \"def test_preferred_zip_defaulted(self):\\n factory = RequestFactory() \\n request = factory.get('/hudson-valley/', follow=True)\\n request.session = self.session\\n request.META['site_id'] = 2\\n preferred_zip = get_preferred_zip(request)\\n self.assertEqual(preferred_zip, '12601')\",\n \"def test_del_property():\\n\\n contents = (\\\"[Info]\\\\n\\\"\\n \\\"sdk = 23\\\")\\n\\n testutils.deploy_config_raw(contents)\\n\\n prop.del_prop('info', 'sdk')\\n\\n testutils.undeploy()\\n\\n return 0\",\n \"def is_return(self, pick):\\n return ('-' in pick.name and sorted(pick.name.split('-'), reverse=True)[0].startswith('ret'))\",\n \"def test_check_regon(client):\\n is_assigned, request_id = client.check_regon(\\n \\\"730371613\\\", \\\"41146786026458860703735932\\\"\\n )\\n\\n assert is_assigned\",\n \"def test_z_z_func_command_local(self):\\n\\t\\ttheResult = False\\n\\t\\ttry:\\n\\t\\t\\ttheOutputtext = checkPythonCommand([getPythonCommand(),\\n\\t\\t\\t\\tstr(\\\"code/restart_service.py\\\"),\\n\\t\\t\\t\\tstr(\\\"--use-local\\\"),\\n\\t\\t\\t\\tstr(\\\"-E\\\"),\\n\\t\\t\\t\\tstr(\\\"5\\\"),\\n\\t\\t\\t\\tstr(\\\"CRITICAL\\\"),\\n\\t\\t\\t\\tstr(\\\"HARD\\\"),\\n\\t\\t\\t\\tstr(\\\"5\\\"),\\n\\t\\t\\t\\tstr(\\\"localhost\\\"),\\n\\t\\t\\t\\tstr(\\\"true\\\"),\\n\\t\\t\\t\\tstr(\\\"\\\\\\\"test\\\\\\\"\\\")\\n\\t\\t\\t], stderr=subprocess.STDOUT)\\n\\t\\t\\tif (str(\\\"OK - Service test restarted on host localhost\\\") in str(theOutputtext)):\\n\\t\\t\\t\\ttheResult = True\\n\\t\\t\\telif (str(\\\"OK - Service \\\\\\\"test\\\\\\\" restarted on host localhost\\\") in str(theOutputtext)):\\n\\t\\t\\t\\ttheResult = True\\n\\t\\t\\telse:\\n\\t\\t\\t\\t print(str(theOutputtext))\\n\\t\\texcept Exception:\\n\\t\\t\\ttheResult = False\\n\\t\\tassert theResult\",\n \"def test_ping(self):\\n status, output = commands.getstatusoutput('ping -c 5 %s' % self.known_ip)\\n assert status == 0\",\n \"def test_check_replication_unknown_valueerror1(self, mock_urlopen):\\n base_url = '.'\\n mock_urlopen.side_effect = ValueError(Mock(return_value=''))\\n result = check_replication(base_url, [4, 10, 4, 10])\\n self.assertEqual(result,\\n 3*[(STATUS_UNKNOWN,\\n \\\"Can't parse status data\\\")])\",\n \"def test_vcmp(self):\\r\\n if self.flask_app.config.get('VMCP_KEY'):\\r\\n self.flask_app.config.pop('VMCP_KEY')\\r\\n res = self.app.get('api/vmcp', follow_redirects=True)\\r\\n err = json.loads(res.data)\\r\\n assert res.status_code == 501, err\\r\\n assert err['status_code'] == 501, err\\r\\n assert err['status'] == \\\"failed\\\", err\\r\\n assert err['target'] == \\\"vmcp\\\", err\\r\\n assert err['action'] == \\\"GET\\\", err\",\n \"def hxlinfo():\\n run_script(hxlinfo_main)\",\n \"def send_xdisploc(self):\\n return self.shell.xdisploc\",\n \"def test_request(self):\\n self.assertIn('list', self.api.request('sys.settings.get').data,\\n msg=\\\"request() doesn't work properly. 'list' is not found in the response\\\")\",\n \"def test_select_var(self):\\n self.check_metadata.side_effect = lambda: self.cube\\n with patch('esmvalcore.cmor._fixes.fix.Fix.get_fixes',\\n return_value=[]):\\n with patch('esmvalcore.cmor.fix._get_cmor_checker',\\n return_value=self.checker):\\n cube_returned = fix_metadata(\\n cubes=[self.cube,\\n self._create_mock_cube('extra')],\\n short_name='short_name',\\n project='CMIP6',\\n dataset='model',\\n mip='mip',\\n )[0]\\n self.checker.assert_called_once_with(self.cube)\\n self.check_metadata.assert_called_once_with()\\n assert cube_returned is self.cube\",\n \"def remotes():\",\n \"def test_get_cell(workbook):\\n assert workbook.get_cell(3,1) == '507906000030242007'\",\n \"def _exists_remote(self, host):\\n # This file gets written after cloudinit is done\\n # path = '/var/lib/cloud/instance/boot-finished'\\n path = '/home/ubuntu/SETUP_COMPLETE'\\n t = 0\\n sleep_len = 10\\n while True:\\n status = subprocess.call(\\n ['ssh', '-oStrictHostKeyChecking=no', '-i', '/home/ubuntu/.ssh/id_rsa', 'ubuntu@'+host, 'test -f {}'.format(pipes.quote(path))])\\n if status == 0:\\n return True\\n else:\\n return False\",\n \"def test_documentation_popxl_call_with_info(self):\\n filename = \\\"call_with_info.py\\\"\\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)\",\n \"def get_response(hf, var):\\n psweep = hf.attrs['UQtype']\\n return unpickle(hf['/%s/%s/response' % (psweep, var)].value)\",\n \"def fingertip_no_remote(self) -> bool:\\n hcell = self._get_hcell2()\\n return hcell.get(\\\"fingertip_no_remote\\\", False)\",\n \"def test_unload(install_mockery, mock_fetch, mock_archive, mock_packages, working_env):\\n install(\\\"mpileaks\\\")\\n mpileaks_spec = spack.spec.Spec(\\\"mpileaks\\\").concretized()\\n\\n # Set so unload has something to do\\n os.environ[\\\"FOOBAR\\\"] = \\\"mpileaks\\\"\\n os.environ[uenv.spack_loaded_hashes_var] = \\\"%s:%s\\\" % (mpileaks_spec.dag_hash(), \\\"garbage\\\")\\n\\n sh_out = unload(\\\"--sh\\\", \\\"mpileaks\\\")\\n csh_out = unload(\\\"--csh\\\", \\\"mpileaks\\\")\\n\\n assert \\\"unset FOOBAR\\\" in sh_out\\n assert \\\"unsetenv FOOBAR\\\" in csh_out\\n\\n assert \\\"export %s=garbage\\\" % uenv.spack_loaded_hashes_var in sh_out\\n assert \\\"setenv %s garbage\\\" % uenv.spack_loaded_hashes_var in csh_out\",\n \"def test_variablepresentations_get(self):\\n pass\",\n \"def test_broadcast_setting_change():\\n assert win_functions.broadcast_setting_change()\",\n \"def pull(self, pull: Optional[int] = None) -> Optional[int]:\\n ...\",\n \"def test(self,version=''):\\n p5cmd = ['srvinfo', 'lexxvers']\\n try:\\n res = self.nsdchat_call(p5cmd,5)\\n p5_version = singlevalue(res)\\n if (p5_version >= str(version)):\\n return True\\n return False\\n except subprocess.TimeoutExpired:\\n print(\\\"Could not connect to the archiware p5 server.\\\\nPlease review\\\"\\n \\\"the connection and firewall settings.\\\")\\n raise\",\n \"def test_delete_on_background_response_descriptor_variables_library_variable_set_library_variable_set_resource(self):\\n pass\",\n \"def test_set_value(self) -> None:\\n\\n expected = False\\n actual = self.helper.set_name(self.test_name).exists()\\n\\n self.assertEqual(expected, actual)\\n\\n self.helper.set_value(\\\"Hello, World!\\\")\\n\\n expected = \\\"Hello, World!\\\"\\n actual = self.helper.get_value()\\n\\n self.assertEqual(expected, actual)\\n\\n del os.environ[self.test_name]\",\n \"def remote_push(self, pNamespace):\",\n \"def test_get_temp(loaded_fridge):\\n assert loaded_fridge.get_temp() == loaded_fridge.temp\",\n \"def do_remote(self, *args):\\n return self.do_scpi(':communicate:remote 1')\",\n \"def test_constants(self):\\n self.assertTrue(getattr(unshare, 'CLONE_NEWNS'))\\n self.assertTrue(getattr(unshare, 'CLONE_NEWNET'))\",\n \"def test_popen(self):\\n self.executor.command(['grep', 'foo']).popen()\",\n \"def test_preferred_zip_consumer(self):\\n factory = RequestFactory()\\n consumer = Consumer.objects.get(id=115)\\n self.assertEqual(consumer.consumer_zip_postal, '12589')\\n create_consumer_in_session(self, consumer)\\n self.assemble_session(self.session) \\n request = factory.get('/hudson-valley/', follow=True)\\n request.session = self.session\\n site = Site.objects.get(id=2)\\n preferred_zip = get_preferred_zip(request, site)\\n self.assertEqual(preferred_zip, '12589')\",\n \"def is_remote(self):\\n\\t\\treturn bool(call_sdk_function('PrlVmDev_IsRemote', self.handle))\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.62325144","0.53577113","0.53224456","0.5209967","0.51961607","0.5094487","0.50844604","0.5058393","0.5020116","0.49631917","0.49456343","0.49408296","0.4912187","0.48899758","0.48812777","0.48232034","0.47519946","0.46955076","0.46878415","0.46760944","0.46738386","0.46679908","0.46611106","0.4651345","0.4638157","0.46368185","0.4623075","0.4617728","0.46174634","0.46088743","0.46008083","0.45800346","0.4579057","0.45739767","0.45700228","0.45691937","0.45599997","0.45450374","0.45440426","0.45414668","0.4534699","0.45326477","0.4526917","0.45260498","0.4511431","0.4495912","0.4493665","0.44913954","0.4489914","0.44856313","0.44757453","0.4450531","0.44503233","0.44452298","0.44390523","0.4430514","0.442883","0.44237134","0.44169253","0.44092146","0.44072923","0.44000322","0.43992734","0.43976614","0.43970418","0.43956864","0.43902853","0.43872306","0.43826932","0.4381051","0.43806627","0.4370733","0.43697423","0.4359181","0.43581724","0.43568087","0.43543255","0.4348264","0.43452635","0.4343514","0.43326026","0.43297276","0.43288898","0.4323902","0.43234098","0.43066952","0.43031117","0.43030474","0.42980474","0.4297566","0.42948997","0.42943197","0.4285817","0.42813385","0.42808366","0.42795014","0.42719218","0.42645165","0.42644444","0.42641416"],"string":"[\n \"0.62325144\",\n \"0.53577113\",\n \"0.53224456\",\n \"0.5209967\",\n \"0.51961607\",\n \"0.5094487\",\n \"0.50844604\",\n \"0.5058393\",\n \"0.5020116\",\n \"0.49631917\",\n \"0.49456343\",\n \"0.49408296\",\n \"0.4912187\",\n \"0.48899758\",\n \"0.48812777\",\n \"0.48232034\",\n \"0.47519946\",\n \"0.46955076\",\n \"0.46878415\",\n \"0.46760944\",\n \"0.46738386\",\n \"0.46679908\",\n \"0.46611106\",\n \"0.4651345\",\n \"0.4638157\",\n \"0.46368185\",\n \"0.4623075\",\n \"0.4617728\",\n \"0.46174634\",\n \"0.46088743\",\n \"0.46008083\",\n \"0.45800346\",\n \"0.4579057\",\n \"0.45739767\",\n \"0.45700228\",\n \"0.45691937\",\n \"0.45599997\",\n \"0.45450374\",\n \"0.45440426\",\n \"0.45414668\",\n \"0.4534699\",\n \"0.45326477\",\n \"0.4526917\",\n \"0.45260498\",\n \"0.4511431\",\n \"0.4495912\",\n \"0.4493665\",\n \"0.44913954\",\n \"0.4489914\",\n \"0.44856313\",\n \"0.44757453\",\n \"0.4450531\",\n \"0.44503233\",\n \"0.44452298\",\n \"0.44390523\",\n \"0.4430514\",\n \"0.442883\",\n \"0.44237134\",\n \"0.44169253\",\n \"0.44092146\",\n \"0.44072923\",\n \"0.44000322\",\n \"0.43992734\",\n \"0.43976614\",\n \"0.43970418\",\n \"0.43956864\",\n \"0.43902853\",\n \"0.43872306\",\n \"0.43826932\",\n \"0.4381051\",\n \"0.43806627\",\n \"0.4370733\",\n \"0.43697423\",\n \"0.4359181\",\n \"0.43581724\",\n \"0.43568087\",\n \"0.43543255\",\n \"0.4348264\",\n \"0.43452635\",\n \"0.4343514\",\n \"0.43326026\",\n \"0.43297276\",\n \"0.43288898\",\n \"0.4323902\",\n \"0.43234098\",\n \"0.43066952\",\n \"0.43031117\",\n \"0.43030474\",\n \"0.42980474\",\n \"0.4297566\",\n \"0.42948997\",\n \"0.42943197\",\n \"0.4285817\",\n \"0.42813385\",\n \"0.42808366\",\n \"0.42795014\",\n \"0.42719218\",\n \"0.42645165\",\n \"0.42644444\",\n \"0.42641416\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.70022154"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":6597,"cells":{"query":{"kind":"string","value":"Test the popxl remote rts variable"},"document":{"kind":"string","value":"def test_documentation_popxl_remote_rts_var(self):\n filename = \"remote_rts_var.py\"\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)"},"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 test_documentation_popxl_remote_var(self):\n filename = \"remote_variable.py\"\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)","def test_documentation_popxl_rts_var(self):\n filename = \"rts_var.py\"\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)","def test_ipcrm():\n IPCComm.ipcrm()","def TestRetract(portVXM,portArd):\n commandString = \"F\"\n portVXM.write(commandString)\n commandString = \"PM-3,C,SA1M400,SA3M100,LM0,I3M400,P5,I1M-90,P5,L3,R \"\n portVXM.write(commandString)\n\tresp='abs'\n\twhile( '^' not in resp ):\n\t resp = portVXM.read(1000)\n\t\n\tprint \"Moving to Standard Operation.\"\t\n commandString = \"PM-2,C,SA1M400,SA3M100,LM0,I3M400,P5,I1M-90,P5,L0,R \"\n portVXM.write(commandString)\n\tt0 = time.time() \n\tt = time.time() \n\tportArd.flushInput()\n\tresp='abs'\n\t\n #while( ('21,1,1' not in resp) and ((t-t0)>30.0)):\n while( not('21,0,1\\n\\r21,1,1\\n\\r21,0,0\\n\\r' in resp) ):\n\t resp = portArd.read(10000)\n\t t = time.time()\n\n\t#print \"CONDITION: \\t\" ,('21,1,1' not in resp),'\\t'\n\tportVXM.write(\"D,\")\n resp = portVXM.read(1000)\n\tXpos, Zpos = GetXZ(portVXM)\n localtime = time.asctime(time.localtime(time.time()))\n\tprint \"Source Fully Retracted at (X,Y) : \",Xpos, \"\\t\", Zpos ,\"\\t\", \"at localtime: \", localtime\n\tprint abs(t-t0) , \"\\t Seconds \\r\"\n\tWaitUntilReady(portVXM)\n\tportVXM.write(\"C,IA1M-0,IA3M-0,R \")\n\tportVXM.write(\"Q, \")\n\tportArd.flush()","def test_plc_read_val(plc_ip, tag_name):\n\n plc = ClxDriver()\n if plc.open(plc_ip):\n tagg = plc.read_tag(tag_name)\n plc.close()\n return (tagg)\n \n else:\n print(\"Unable to open\", plc_ip)","def TestResponse(port):\n\tcommandString = \"F\"\n\tport.write(commandString)\n\tcommandString = \"PM3,C,I1M500,I3M-500,I3M500,I1M-500,R\"\n\tport.write(commandString)\n\tWaitUntilReady(port)\n\tport.write(\"R\")\n\tresp=WaitUntilReady(port)\n\tcount=0\n\tprint(\"starting loop:\")\n\twhile('^' in resp):\n \tport.write(\"X\")\n\t\txpos=port.read(9)\n\t\tprint(xpos)\n\t\tport.write(\"R\")\n\t\ttime.sleep(5)\n\t\tresp=WaitUntilReady(port)\n\t\tcount = count+1\n\t\tprint(count)","def test(self):\n # 0x13 is nop\n self.gdb.command(\"p *((int*) 0x%x)=0x13\" % self.target.ram)\n self.gdb.command(\"p *((int*) 0x%x)=0x13\" % (self.target.ram + 4))\n self.gdb.command(\"p *((int*) 0x%x)=0x13\" % (self.target.ram + 8))\n self.gdb.p(\"$pc=0x%x\" % self.target.ram)\n self.gdb.stepi()\n assertEqual((self.target.ram + 4), self.gdb.p(\"$pc\"))\n self.gdb.stepi()\n assertEqual((self.target.ram + 8), self.gdb.p(\"$pc\"))","def remote_status():","def test_rpcCall(self):\n pass","def testProtocolReturn(self):\n self.assertEqual(\n self.protocol,\n self.mr.protocol\n )\n\n self.mr._protocol = 'burp'\n\n self.assertEqual(\n 'burp',\n self.mr.protocol\n )","def test_check_nip(client):\n is_assigned, request_id = client.check_nip(\n \"8655104670\", \"41146786026458860703735932\"\n )\n\n assert is_assigned","def test_check_replication_unknown_valueerror2(self, mock_urlopen):\n base_url = 'http://localhost:6000/recon/'\n jdata = PropertyMock(return_value=b'X')\n mock_urlopen.return_value = MagicMock(read=jdata)\n result = check_replication(base_url, [4, 10, 4, 10])\n self.assertEqual(result,\n 3*[(STATUS_UNKNOWN,\n \"Can't parse status data\")])","def test_returnCar(self):\n with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:\n s.connect(ADDRESS)\n self.assertTrue(Functions.returnCar(s, '38'))","def test_emirp_check():\r\n pass","def test_rsp_unknown_status(self):\n\n def handle(event):\n return 0xFFF0, event.modification_list\n\n self.ae = ae = AE()\n ae.acse_timeout = 5\n ae.dimse_timeout = 5\n ae.network_timeout = 5\n ae.add_supported_context(ModalityPerformedProcedureStep)\n scp = ae.start_server(\n (\"localhost\", 11112), block=False, evt_handlers=[(evt.EVT_N_SET, handle)]\n )\n\n ae.add_requested_context(ModalityPerformedProcedureStep)\n assoc = ae.associate(\"localhost\", 11112)\n assert assoc.is_established\n\n ds = Dataset()\n ds.PatientName = \"Test^test\"\n status, ds = assoc.send_n_set(\n ds, ModalityPerformedProcedureStep, \"1.2.840.10008.5.1.1.40.1\"\n )\n assert status.Status == 0xFFF0\n assert ds is None\n assoc.release()\n assert assoc.is_released\n\n scp.shutdown()","def test_connect():\n PV_name = \"TEST:TEST.VAL\"\n casput(PV_name, 1)\n assert caget(PV_name) == 1","def test_get_property():\n\n sdk = '23'\n contents = (\"[Info]\\n\"\n \"sdk = %s\" % sdk)\n\n testutils.deploy_config_raw(contents)\n\n assert prop.get_prop('info', 'sdk') == sdk\n\n testutils.undeploy()\n\n return 0","def testAllRead(self):\n import time,copy\n time.sleep(2)\n client = ModbusTCP(self.config['vdevs']['slave']['icsifaces'][0],\n self.config['vdevs']['slave']['points']) \n\n pts = copy.deepcopy(self.config['vdevs']['slave']['points'])\n ptnames = [ pt['name'] for pt in pts ]\n reply = client.readPoints(ptnames)\n #print \"Reply: \", reply\n for pt in ptnames:\n value = filter(lambda x: x['name']==pt, pts)[0]['value']\n #assert value == reply[ptnames.index(pt)]\n received = reply[ptnames.index(pt)]\n if not value == received: \n print pt, ' was %s but should be %s'%(str(received),str(value))","def test_send_network(self) :\n symbol = 'A' \n oProtocol = Protocol(symbol,mode=\"client\",debug=self.debug)\n command = \"N200\"\n message = oProtocol.send(command)\n #if message['status'] is False :\n #print(\"\\n*** ERROR : test_send_network : {}\".format(message['notify']))\n\n #Pour enregistrer les traces d'appels de fonctions dans le fichier log/client_calltrack_sorted.txt\n client_tracker_print()\n self.assertTrue( (message['status'] is not True) )","def test_rsp_failure(self):\n\n def handle(event):\n return 0x0112, None\n\n self.ae = ae = AE()\n ae.acse_timeout = 5\n ae.dimse_timeout = 5\n ae.network_timeout = 5\n ae.add_supported_context(DisplaySystem)\n scp = ae.start_server(\n (\"localhost\", 11112), block=False, evt_handlers=[(evt.EVT_N_GET, handle)]\n )\n\n ae.add_requested_context(DisplaySystem)\n assoc = ae.associate(\"localhost\", 11112)\n assert assoc.is_established\n\n status, ds = assoc.send_n_get(\n [(0x7FE0, 0x0010)], DisplaySystem, \"1.2.840.10008.5.1.1.40.1\"\n )\n assert status.Status == 0x0112\n assert ds is None\n assoc.release()\n assert assoc.is_released\n\n scp.shutdown()","def test_set_get_value_1(self):\n value = 23.0\n port = cn.Port(value)\n self.assertEqual(port.value, value)","def test_check_query_response(self, node_id):\n\n print('\\n### Testing query node status RESPONSE ###')\n print('Remember that node_id must be the same 3 characters string that in test_query_node_id(node_id)')\n\n received_bytes = self.serport.readline()\n if received_bytes == b'E\\r\\n':\n print(\"You received Error Msg!\")\n print(f'Did not receive correct query status response from node {node_id}')\n print(f'Query again the node {node_id} if required')\n return False\n\n elif (len(received_bytes) == 13) and (received_bytes[0:8] == b'#B' + node_id.encode() + b'06V'):\n supply_voltage = received_bytes.decode()[8:13]\n print(f\"supply_voltage of {node_id} is {supply_voltage}\")\n print(\"response from the remote node SUCCESS\")\n return True\n else:\n print(f'Did not receive correct query status response from node {node_id}')\n print(f'Query again the node {node_id} if required')\n return False","def test_ip(response):\n \n # from comeon_core import update\n ip = getIP()\n print(ip)\n #init_db(engine)\n #update()\n assert True","def testSingleRead(self, point = 'pressure', expectedValue = 17.0 ):\n import time\n time.sleep(2)\n client = ModbusTCP(self.config['vdevs']['slave']['icsifaces'][0],\n self.config['vdevs']['slave']['points']) \n\n reply = client.readPoints(point)\n #print \"Slave pressure: \", reply, \"Expected:\", expectedValue\n assert reply[0] == expectedValue","def test_rsp_unknown_status(self):\n\n def handle(event):\n ds = Dataset()\n ds.PatientName = \"Test\"\n ds.SOPClassUID = DisplaySystem\n ds.SOPInstanceUID = \"1.2.3.4\"\n return 0xFFF0, ds\n\n self.ae = ae = AE()\n ae.acse_timeout = 5\n ae.dimse_timeout = 5\n ae.network_timeout = 5\n ae.add_supported_context(DisplaySystem)\n scp = ae.start_server(\n (\"localhost\", 11112), block=False, evt_handlers=[(evt.EVT_N_GET, handle)]\n )\n\n ae.add_requested_context(DisplaySystem)\n assoc = ae.associate(\"localhost\", 11112)\n assert assoc.is_established\n\n status, ds = assoc.send_n_get(\n [(0x7FE0, 0x0010)], DisplaySystem, \"1.2.840.10008.5.1.1.40.1\"\n )\n assert status.Status == 0xFFF0\n assert ds is None\n assoc.release()\n assert assoc.is_released\n\n scp.shutdown()","def test_rangetocidr_command(runner):\n\n result = runner.invoke(command, ['rangetocidr', '127.0.0.0', '128.0.0.3'])\n assert result.exit_code == 0\n\n assert '127.0.0.0/8' in result.output\n assert '128.0.0.0/30' in result.output","def test(self,version=''):\n p5cmd = ['srvinfo', 'lexxvers']\n try:\n res = self.nsdchat_call(p5cmd,5)\n p5_version = singlevalue(res)\n if (p5_version >= str(version)):\n return True\n return False\n except subprocess.TimeoutExpired:\n print(\"Could not connect to the archiware p5 server.\\nPlease review\"\n \"the connection and firewall settings.\")\n raise","def test_set_get_value_2(self):\n value = (1,)\n port = cn.Port()\n port.value = value\n self.assertEqual(port.value, value)","def test_ipcrm_not_isntalled(): # pragma: windows\n IPCComm.ipcrm()","def test_pop_returns_value(new_dll):\n assert new_dll.pop() == 3","def test_plc_read(plc_ip, tag_name):\n\n plc = ClxDriver()\n if plc.open(plc_ip):\n\n print(plc.read_tag(tag_name))\n\t#plc.read_tag(tag_name)\n plc.close()\n else:\n print(\"Unable to open\", plc_ip)","def test_getbinarystate(\n fauxmo_server: pytest.fixture, simplehttpplugin_target: pytest.fixture\n) -> None:\n data = b'Soapaction: \"urn:Belkin:service:basicevent:1#GetBinaryState\"'\n\n resp = requests.post(\n \"http://127.0.0.1:12345/upnp/control/basicevent1\", data=data\n )\n assert resp.status_code == 200\n\n root = ET.fromstring(resp.text)\n val = root.find(\".//BinaryState\").text\n assert val in [\"0\", \"1\"]","def read_test(self, cmd):\n w_bytes = [random.randrange(0, 128) for i in range(0, 16)]\n self._pyb.send(w_bytes)\n self._serial.reset_input_buffer()\n self._serial.write('\\r\\n'.encode('utf-8'))\n self._serial.write(cmd.encode('utf-8'))\n self._serial.write('\\r\\n'.encode('utf-8'))\n\n res = self._serial.read_until(terminator=serial.to_bytes([ord(c) for c in 'Ignored '])).decode('utf-8')\n self._pyb.deinit()\n\n r_bytes = []\n for x in re.sub('\\r', '', res).split('\\n'):\n if x.find('IGNORE') != -1:\n r_bytes = [int(s, 16) for s in x.split(',') if len(s) == 2]\n break\n\n if self.compare_host_dut_result(w_bytes, r_bytes) == -1:\n print(repr(res))\n return \"Fail\"\n\n return \"Pass\"","def test_incoming_k(self):\n m_interface = Mock()\n m_interface.callback.return_value = True\n m_interface.read.return_value = ''\n upb = UPB(m_interface)\n upb.onCommand(address=(22,255), callback=m_interface.callback)\n m_interface.read.return_value = \"PU07141610FF3090\\x0DPU07151610FF308F\\x0D\"\n# time.sleep(4000)\n time.sleep(2)\n m_interface.callback.assert_called_with(address=(22,255), command='status', source=upb)\n m_interface.read.return_value = ''","def testSingleRead(self, point = 'pressure', expectedValue = 17.0 ):\n import time\n time.sleep(2)\n to_config = self.config['vdevs']['slave']['icsifaces'][0]\n from_config = self.config['vdevs']['master']['clientifaces'][0]\n points = self.config['vdevs']['slave']['points']\n client = ModbusRTU(to_config, points, from_config)\n\n reply = client.readPoints(point)\n #print \"Slave pressure: \", reply\n assert reply[0] == expectedValue","def _platformix_get(self, context, fake_reply, prop):\r\n if hasattr(self.host, prop):\r\n self._reply(context, proto_success(getattr(self.host, prop), prop), fake_reply)\r\n else:\r\n self._reply(context, proto_failure(\"Property {} not found on {}\".format(prop, self.host.name)), fake_reply)","def check_relay_status():\n \n query_cmd_packet = b'\\x04\\x18\\x00\\x00\\x00\\x1b\\x0f'\n ser_relay.write(query_cmd_packet)\n time.sleep(1)\n resp_array = array('B', ser_relay.read(7))\n \n return resp_array","def test_expect_status_property_about_registry_process(client, start_call_fx):\n\n url = reverse_lazy('calls:registry-list')\n\n response = client.post(url, start_call_fx, content_type='application/json')\n\n job_id = response.data.get('job_id')\n\n job = client.get(job_id)\n\n assert job.data.get('status') == 'DONE'","def test_test_property():\n\n contents = (\"[Info]\\n\"\n \"sdk = 23\")\n\n testutils.deploy_config_raw(contents)\n\n assert prop.test_prop('info', 'sdk') == 1\n\n testutils.undeploy()\n\n return 0","def testAllRead(self):\n import time,copy\n time.sleep(2)\n to_config = self.config['vdevs']['slave']['icsifaces'][0]\n from_config = self.config['vdevs']['master']['clientifaces'][0]\n points = self.config['vdevs']['slave']['points']\n client = ModbusRTU(to_config, points, from_config)\n\n\n pts = copy.deepcopy(self.config['vdevs']['slave']['points'])\n for i in xrange(50):\n ptnames = [ pt['name'] for pt in pts ]\n reply = client.readPoints(ptnames)\n #print \"Reply: \", reply\n for pt in ptnames:\n value = filter(lambda x: x['name']==pt, pts)[0]['value']\n #assert value == reply[ptnames.index(pt)]\n received = reply[ptnames.index(pt)]\n if not value == received: \n print pt, ' was %s but should be %s'%(str(received),str(value))","def test_rsp_failure(self):\n\n def handle(event):\n return 0x0112, None\n\n self.ae = ae = AE()\n ae.acse_timeout = 5\n ae.dimse_timeout = 5\n ae.network_timeout = 5\n ae.add_supported_context(ModalityPerformedProcedureStep)\n scp = ae.start_server(\n (\"localhost\", 11112), block=False, evt_handlers=[(evt.EVT_N_SET, handle)]\n )\n\n ae.add_requested_context(ModalityPerformedProcedureStep)\n assoc = ae.associate(\"localhost\", 11112)\n assert assoc.is_established\n\n ds = Dataset()\n ds.PatientName = \"Test^test\"\n status, ds = assoc.send_n_set(\n ds, ModalityPerformedProcedureStep, \"1.2.840.10008.5.1.1.40.1\"\n )\n assert status.Status == 0x0112\n assert ds is None\n assoc.release()\n assert assoc.is_released\n\n scp.shutdown()","def test_documentation_popxl_replication(self):\n filename = \"replication.py\"\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)","def test_xmlrpc(text):\r\n return \"Here's a response! %s\" % str(text)","def test_verify_connection_to_a_device():","def test_host_port_expression_props(self):\n block = SNMPBase()\n block._create_data = MagicMock()\n block._execute_snmp_request = MagicMock(\n return_value=SAMPLE_SNMP_RESPONSE)\n block._handle_data = MagicMock()\n myOID = \"1.3.6.1.2.1.31.1.1.1.6.2\"\n ip = \"0.0.0.0\"\n port = 1611\n starting_signal = Signal({\n \"ip\": ip,\n \"port\": port\n })\n self.configure_block(block, {\n \"oids\": [{\"oid\": myOID}],\n \"agent_host\": \"{{ $ip }}\",\n \"agent_port\": \"{{ $port }}\"\n })\n block.start()\n # Send the starting signal, make sure everything was called correctly\n block.process_signals([starting_signal])\n args, kwargs = block._execute_snmp_request.call_args\n self.assertEqual(args[1], [myOID])\n block._handle_data.assert_called_once_with([], starting_signal)\n block.stop()","def rpc_ping(self):\n\t\treturn True","def test_invalid_port_expression_prop(self):\n block = SNMPBase()\n block._create_data = MagicMock()\n block.execute_request = MagicMock()\n myOID = \"1.3.6.1.2.1.31.1.1.1.6.2\"\n ip = \"0.0.0.0\"\n port = 1611\n starting_signal = Signal({\n \"ip\": ip,\n \"port\": port\n })\n self.configure_block(block, {\n \"oids\": [{\"oid\": myOID}],\n \"agent_port\": \"{{ port }}\"\n })\n block.start()\n # Send the starting signal, make sure everything was called correctly\n block.process_signals([starting_signal])\n self.assertEqual(0, block.execute_request.call_count)\n block.stop()","def rpc_match():","def on_pullVar(self):\n if not os.path.exists(grapher.binPath):\n mess = \"!!! ERROR: rndBin path not found, check user pref !!!\"\n self.mainUi._defaultErrorDialog(mess, self.mainUi)\n else:\n tmpPath = os.path.join(self.grapher.userBinPath, 'tmp')\n tmpFile = os.path.join(tmpPath, 'varBuffer.py')\n varDict = pFile.readPyFile(tmpFile)\n for var in sorted(varDict.keys()):\n if var.startswith('selVar_'):\n newItem = self.on_addVar()\n self.setItem(newItem, **varDict[var])","def test_variables_get(self):\n pass","def test_device_status(self):\n #071031031E3067\n self.ms.add_response({'\\x14071031031E3067\\x0D': 'PA\\x0D'})\n # Network / Device ID\n response = self.upb.status((49, 3))\n self.assertTrue(response)","def test_package(response):\n \n # from comeon_core import update\n ip = getIP()\n print(ip)\n db = connect()\n engine = db.connect() \n init_db(engine)\n update(engine)\n assert True","def test_snmpset_return_structure():\n result = snmpset(ipaddress=SNMP_SRV_ADDR, community='public',\n oid='SNMPv2-MIB::sysName.0', value_type='s',\n value='Test Description', port=SNMP_SRV_PORT)\n assert 'Test Description' in result","def test_invalid_host_expression_prop(self):\n block = SNMPBase()\n block._create_data = MagicMock()\n block.execute_request = MagicMock()\n myOID = \"1.3.6.1.2.1.31.1.1.1.6.2\"\n ip = \"0.0.0.0\"\n port = 1611\n starting_signal = Signal({\n \"ip\": ip,\n \"port\": port\n })\n self.configure_block(block, {\n \"oids\": [{\"oid\": myOID}],\n \"agent_host\": \"{{ ip }}\",\n })\n block.start()\n # Send the starting signal, make sure everything was called correctly\n block.process_signals([starting_signal])\n self.assertEqual(0, block.execute_request.call_count)\n block.stop()","def test_ipcs():\n IPCComm.ipcs()","def test_port_get_set_value(self):\n v1 = [1, 2, 3]\n p1 = cn.Port()\n p1.value = v1\n\n p2 = cn.Port()\n p2.value = v1\n self.assertEqual(\n (p1.value == p2.value).all(),\n True\n )","def test_query_self_status(self):\n print('\\n### Testing query self status ###')\n\n command = b'$?'\n self.serport.write(command)\n received_bytes = self.serport.readline()\n index = received_bytes.find(b'#A')\n\n if (index != -1) and (len(received_bytes) - index == 10):\n if received_bytes[index+5:index+6] == b'V':\n # print(\"check_for_valid_ack_signal SELF_STATUS V was spot on\")\n for i in range(index+6, index+10):\n if b'0' <= received_bytes[i:i+1] <= b'9':\n pass\n else:\n print(\"query self status FAILURE 1\")\n return False\n node_id = received_bytes.decode()[index+2:index+5]\n print(\"SELF_STATUS node is :\", node_id)\n\n voltage = round(float(received_bytes[index+6:index+10]) * (15.0/65536.0), 3)\n print(\"SELF_STATUS voltage is :\", voltage)\n print(\"query self status SUCCESS\")\n return True\n else:\n print(\" query self status FAILURE 2\")\n return False","def Read_Response(self, expected = bytes([0x01])):\r\n data = self.Port.read(1)\r\n if data == expected: return True\r\n return False","def test_rsp_unknown_status(self):\n\n def handle(event):\n return 0xFFF0, event.attribute_list\n\n self.ae = ae = AE()\n ae.acse_timeout = 5\n ae.dimse_timeout = 5\n ae.network_timeout = 5\n ae.add_supported_context(ModalityPerformedProcedureStep)\n\n handlers = [(evt.EVT_N_CREATE, handle)]\n scp = ae.start_server((\"localhost\", 11112), evt_handlers=handlers, block=False)\n\n ae.add_requested_context(ModalityPerformedProcedureStep)\n assoc = ae.associate(\"localhost\", 11112)\n assert assoc.is_established\n\n ds = Dataset()\n ds.PatientName = \"Test^test\"\n status, ds = assoc.send_n_create(\n ds, ModalityPerformedProcedureStep, \"1.2.840.10008.5.1.1.40.1\"\n )\n assert status.Status == 0xFFF0\n assert ds is None\n assoc.release()\n assert assoc.is_released\n\n scp.shutdown()","def test_rsp_warning(self):\n\n def handle(event):\n ds = Dataset()\n ds.PatientName = \"Test\"\n ds.SOPClassUID = DisplaySystem\n ds.SOPInstanceUID = \"1.2.3.4\"\n return 0x0116, ds\n\n self.ae = ae = AE()\n ae.acse_timeout = 5\n ae.dimse_timeout = 5\n ae.network_timeout = 5\n ae.add_supported_context(DisplaySystem)\n scp = ae.start_server(\n (\"localhost\", 11112), block=False, evt_handlers=[(evt.EVT_N_GET, handle)]\n )\n\n ae.add_requested_context(DisplaySystem)\n assoc = ae.associate(\"localhost\", 11112)\n assert assoc.is_established\n\n status, ds = assoc.send_n_get(\n [(0x7FE0, 0x0010)], DisplaySystem, \"1.2.840.10008.5.1.1.40.1\"\n )\n assert status.Status == 0x0116\n assert ds is not None\n assert isinstance(ds, Dataset)\n assert ds.PatientName == \"Test\"\n assert ds.SOPClassUID == DisplaySystem\n assert ds.SOPInstanceUID == \"1.2.3.4\"\n assoc.release()\n assert assoc.is_released\n\n scp.shutdown()","def get_xcom(**context):\n ti = context['ti']\n data = ti.xcom_pull(task_ids='xcom_from_bash', key='return_value')\n logging.info(data)","def test_script_p2_sh_address(self):\n pass","def test_set_get(self):\n self.shell.onecmd(\"create %s/one 'hello'\" % (self.tests_path))\n self.shell.onecmd(\"set %s/one 'bye'\" % (self.tests_path))\n self.shell.onecmd(\"get %s/one\" % (self.tests_path))\n self.assertEqual(\"bye\\n\", self.output.getvalue())","def sendPullResponse(remote, request, txpk,sock):\r\n remote = (remote[0], remote[1])\r\n\r\n m = GatewayMessage(version=request.version, token=request.token,\r\n identifier=PULL_RESP, gatewayEUI=b'9079494338994186168',\r\n remote=remote, ptype='txpk', txpk=txpk)\r\n print(\"Sending PULL_RESP message to %s:%d\" % remote)\r\n\r\n sock.sendto(m.encode(), remote)\r\n data, address = sock.recvfrom(4096)\r\n print(data)\r\n print('received %s bytes from %s' % (len(data), address))","def test_postfix(self):\n test_sensordef = {\n \"kind\": self.test_postfix.get_kind(),\n \"name\": \"Postfix Mailqueue\",\n \"description\": \"Monitors the mailqueue of a postfix server\",\n \"help\": \"Monitors the mailqueue of a postfix server for active, deferred, hold or corrupt mail\",\n \"tag\": \"mppostfixsensor\",\n \"fields\": [],\n \"groups\": []\n }\n assert_equal(self.test_postfix.get_sensordef(), test_sensordef)","def test_t10_message():\n send_json_message_to_t10(\"10.89.130.68\", \"cisco\", \"cisco\", request.get_json())\n return \"ok\"","def test_get_status(self):\n pass","def test_get_status(self):\n pass","def nremote(self):","def test_verify_state_of_a_device():","def test_process_newly_public_domain_gj(self):\n self.page_mock.text = \"\"\"{{REDaten\n|BAND=S I\n|KEINE_SCHÖPFUNGSHÖHE=ON\n|GEBURTSJAHR=1870\n}}\nbla\n{{REAutor|Stein.}}\"\"\"\n re_page = RePage(self.page_mock)\n task = PDKSTask(None, self.logger)\n compare({\"success\": True, \"changed\": True}, task.run(re_page))\n compare(\"\", re_page[0][\"GEBURTSJAHR\"].value)","def test_snmpget_return_structure():\n result = snmpget(community='public', ipaddress=SNMP_SRV_ADDR,\n oid=SYSDESCR_OID, port=SNMP_SRV_PORT)\n assert 'Linux' in result\n assert isinstance(result, str)","def test_rsp_warning(self):\n\n def handle(event):\n return 0x0116, event.modification_list\n\n self.ae = ae = AE()\n ae.acse_timeout = 5\n ae.dimse_timeout = 5\n ae.network_timeout = 5\n ae.add_supported_context(ModalityPerformedProcedureStep)\n scp = ae.start_server(\n (\"localhost\", 11112), block=False, evt_handlers=[(evt.EVT_N_SET, handle)]\n )\n\n ae.add_requested_context(ModalityPerformedProcedureStep)\n assoc = ae.associate(\"localhost\", 11112)\n assert assoc.is_established\n\n ds = Dataset()\n ds.PatientName = \"Test^test\"\n status, ds = assoc.send_n_set(\n ds, ModalityPerformedProcedureStep, \"1.2.840.10008.5.1.1.40.1\"\n )\n assert status.Status == 0x0116\n assert ds.PatientName == \"Test^test\"\n assoc.release()\n assert assoc.is_released\n\n scp.shutdown()","def test_process_newly_public_domain_gj_not_yet(self):\n self.page_mock.text = \"\"\"{{REDaten\n|BAND=S I\n|KEINE_SCHÖPFUNGSHÖHE=ON\n|GEBURTSJAHR=1871\n}}\nbla\n{{REAutor|Stein.}}\"\"\"\n re_page = RePage(self.page_mock)\n task = PDKSTask(None, self.logger)\n compare({\"success\": True, \"changed\": False}, task.run(re_page))\n compare(\"1871\", re_page[0][\"GEBURTSJAHR\"].value)","def test_system(cli_request, msg):\n from client import client\n response = client(cli_request)\n response_parts = response.split('\\r\\n')\n assert response_parts[0] == msg\n assert '' in response_parts","def test_get(self):\n obs = self.tester.get('1.SKM7.640188')\n exp = PrepSample('1.SKM7.640188', self.tester)\n self.assertEqual(obs, exp)","def test_get_property_success(self):\r\n self.assertEqual(self.config.option1, 1337)","def testPowerOnResponse(self):\n message = (mavutil.mavlink.GOPRO_COMMAND_POWER, mavutil.mavlink.GOPRO_REQUEST_SUCCESS)\n self.mgr.set_response_callback('vehicle','name', message)\n self.mgr.processMsgQueue.assert_called_with()","def test_get_address():\n\n # Wait for workspace to be initialized\n time.sleep(40)\n bambi = create_test_bambi()\n address = bambi.get_address_to_workspace()\n assert address","def test_cmd_field():\n FNAME = 'xonsh-SESSIONID.json'\n FNAME += '.cmdfield'\n hist = History(filename=FNAME, here='yup', **HIST_TEST_KWARGS)\n # in-memory\n with mock_xonsh_env({'HISTCONTROL': set()}):\n hf = hist.append({'rtn': 1})\n yield assert_is_none, hf\n yield assert_equal, 1, hist.rtns[0]\n yield assert_equal, 1, hist.rtns[-1]\n yield assert_equal, None, hist.outs[-1]\n # slice\n yield assert_equal, [1], hist.rtns[:]\n # on disk\n hf = hist.flush()\n yield assert_is_not_none, hf\n yield assert_equal, 1, hist.rtns[0]\n yield assert_equal, 1, hist.rtns[-1]\n yield assert_equal, None, hist.outs[-1]\n os.remove(FNAME)","def test_rpcSendRecv(self):\n cli_send = self.client_msg\n srv_send = self.server_msg\n # Send message to driver\n flag = self.client_comm.send(cli_send)\n assert(flag)\n flag, msg_recv = self.server_comm.recv(timeout=self.timeout)\n assert(flag)\n nt.assert_equal(msg_recv, srv_send)\n # Send response back to instance\n flag = self.server_comm.send(srv_send)\n assert(flag)\n # self.driver.sleep(1)\n flag, msg_recv = self.client_comm.recv(timeout=self.timeout)\n assert(flag)\n nt.assert_equal(msg_recv, cli_send)","def test_script_p2_s_address(self):\n pass","def test_recv(self):\n # Required to get useful test names\n super(TestCisPlyInput_local, self).test_recv()","def ping():\n return 'pong'","def test_rsp_success(self):\n\n def handle(event):\n ds = Dataset()\n ds.PatientName = \"Test\"\n ds.SOPClassUID = DisplaySystem\n ds.SOPInstanceUID = \"1.2.3.4\"\n return 0x0000, ds\n\n self.ae = ae = AE()\n ae.acse_timeout = 5\n ae.dimse_timeout = 5\n ae.network_timeout = 5\n ae.add_supported_context(DisplaySystem)\n scp = ae.start_server(\n (\"localhost\", 11112), block=False, evt_handlers=[(evt.EVT_N_GET, handle)]\n )\n\n ae.add_requested_context(DisplaySystem)\n assoc = ae.associate(\"localhost\", 11112)\n assert assoc.is_established\n\n status, ds = assoc.send_n_get(\n [(0x7FE0, 0x0010)], DisplaySystem, \"1.2.840.10008.5.1.1.40.1\"\n )\n assert status.Status == 0x0000\n assert ds is not None\n assert isinstance(ds, Dataset)\n assert ds.PatientName == \"Test\"\n assert ds.SOPClassUID == DisplaySystem\n assert ds.SOPInstanceUID == \"1.2.3.4\"\n assoc.release()\n assert assoc.is_released\n\n scp.shutdown()","def read_and_response(self, vsr, address_h, address_l):\n # time.sleep(0.2)\n self.send_cmd([vsr, 0x41, address_h, address_l])\n # time.sleep(0.2)\n resp = self.read_response() # ie resp = [42, 144, 48, 49, 13]\n reply = resp[2:-1] # Omit start char, vsr address and end char\n reply = \"{}\".format(''.join([chr(x) for x in reply])) # Turn list of integers into ASCII string\n # print(\" RR. reply: {} (resp: {})\".format(reply, resp)) # ie reply = '01'\n return resp, reply","def test_store_property_after_reconnecting_to_the_device():","def test_get_requests(self):\n response = self.client.open('/api/provisioning/port',\n method='GET')\n self.assert200(response, \"Response body is : \" + response.data.decode('utf-8'))","def main():\n test_exchange()\n print(\"2.1589225 Euros\")","def test_get_address(self):\n with self.subprocess_getoutput_patch:\n ret = self.inst._get_address()\n self.assertEqual(ret, \"http://example\")","def test_check_regon(client):\n is_assigned, request_id = client.check_regon(\n \"730371613\", \"41146786026458860703735932\"\n )\n\n assert is_assigned","def _test_con(self) -> bool:\n self.reset_buffers()\n try:\n serial_number = self.get_exact(b\"<GETSERIAL>>\", size=7)\n except Exception as e:\n # Unsure of exception types.\n logger.warning(f\"{e}\", exc_info=True)\n return False\n # timeout error if wrong\n if len(serial_number) == 7:\n # Not 100% sure... no prescribed method of confirming\n # we're connected to a GMC device in specs\n logger.debug(f\"Test connection serial: {serial_number}\")\n return True\n else:\n logger.warning(f\"Unexpected response: {serial_number}\")\n return False","def test_getirramp():\n d = gini.get_ir_ramp()\n assert len(d) == 256","def testFailure(self):\n request = b'hello'\n reply = self.sendAndReceive(request)\n self.assertEqual(2, reply[0])","def test_get_robax_tostring_correct(self):\n got_var, const_jtar = rapid_datatypes.get_rapid_data(self.controller, 'T_ROB1', 'MainModule', 'const_jtarget')\n if not got_var:\n print 'Couldn\\'t get variable. Test will not run.'\n sys.exit()\n robax = rapid_jointtarget.get_robax_tostring(const_jtar)\n self.assertEqual(robax, 'RobAx: [Rax_1,Rax_2,Rax_3,Rax_4,Rax_5,Rax_6] = [0,0,0,10,0,0]')","def test_fetch_otp(self):\n otp = self.api.fetch_otp()\n self.assertIn('code', otp)","def test_repr(self):\n namespace = {}\n exec('tunneling = {0!r}'.format(self.tunneling), globals(), namespace)\n self.assertIn('tunneling', namespace)\n tunneling = namespace['tunneling']\n self.assertAlmostEqual(self.tunneling.frequency.value, tunneling.frequency.value, 2)\n self.assertEqual(self.tunneling.frequency.units, tunneling.frequency.units)","def test_query_node_id(self,node_id):\n\n print('\\n### Testing query node status ACK ###')\n print('Remember that node_id must be a 3 characters string')\n\n command = b'$V' + node_id.encode()\n self.serport.write(command)\n received_bytes = self.serport.readline()\n index = received_bytes.find(b'$V')\n\n #ACK COMMAND\n if (index != -1) and (len(received_bytes) - index == 5 and received_bytes.decode()[1] == 'V'):\n #print(\"SET_ADDRESS V was spot on\")\n if received_bytes[1:4] == command[1:4]:\n node_id = received_bytes.decode()[2:5]\n print(\"command has well been sent to node :\"+ node_id)\n print(\"acknowledgement of the command SUCCESS\")\n return True\n else: \n print(\"acknowledgement of the command FAILURE\")\n return False","def PHook(p):\n x = p['sy']['pop']()\n if (x == '.'):\n p['sy']['push'](p['OK'])\n else:\n p['sy']['push'](p['NOK'])\n #endif","def test_wrapper_processing(self):\r\n result = mib2pysnmp('conpot/tests/data/VOGON-POEM-MIB.mib')\r\n self.assertTrue('mibBuilder.exportSymbols(\"VOGON-POEM-MIB\"' in result,\r\n 'mib2pysnmp did not generate the expected output. Output: {0}'.format(result))"],"string":"[\n \"def test_documentation_popxl_remote_var(self):\\n filename = \\\"remote_variable.py\\\"\\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)\",\n \"def test_documentation_popxl_rts_var(self):\\n filename = \\\"rts_var.py\\\"\\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)\",\n \"def test_ipcrm():\\n IPCComm.ipcrm()\",\n \"def TestRetract(portVXM,portArd):\\n commandString = \\\"F\\\"\\n portVXM.write(commandString)\\n commandString = \\\"PM-3,C,SA1M400,SA3M100,LM0,I3M400,P5,I1M-90,P5,L3,R \\\"\\n portVXM.write(commandString)\\n\\tresp='abs'\\n\\twhile( '^' not in resp ):\\n\\t resp = portVXM.read(1000)\\n\\t\\n\\tprint \\\"Moving to Standard Operation.\\\"\\t\\n commandString = \\\"PM-2,C,SA1M400,SA3M100,LM0,I3M400,P5,I1M-90,P5,L0,R \\\"\\n portVXM.write(commandString)\\n\\tt0 = time.time() \\n\\tt = time.time() \\n\\tportArd.flushInput()\\n\\tresp='abs'\\n\\t\\n #while( ('21,1,1' not in resp) and ((t-t0)>30.0)):\\n while( not('21,0,1\\\\n\\\\r21,1,1\\\\n\\\\r21,0,0\\\\n\\\\r' in resp) ):\\n\\t resp = portArd.read(10000)\\n\\t t = time.time()\\n\\n\\t#print \\\"CONDITION: \\\\t\\\" ,('21,1,1' not in resp),'\\\\t'\\n\\tportVXM.write(\\\"D,\\\")\\n resp = portVXM.read(1000)\\n\\tXpos, Zpos = GetXZ(portVXM)\\n localtime = time.asctime(time.localtime(time.time()))\\n\\tprint \\\"Source Fully Retracted at (X,Y) : \\\",Xpos, \\\"\\\\t\\\", Zpos ,\\\"\\\\t\\\", \\\"at localtime: \\\", localtime\\n\\tprint abs(t-t0) , \\\"\\\\t Seconds \\\\r\\\"\\n\\tWaitUntilReady(portVXM)\\n\\tportVXM.write(\\\"C,IA1M-0,IA3M-0,R \\\")\\n\\tportVXM.write(\\\"Q, \\\")\\n\\tportArd.flush()\",\n \"def test_plc_read_val(plc_ip, tag_name):\\n\\n plc = ClxDriver()\\n if plc.open(plc_ip):\\n tagg = plc.read_tag(tag_name)\\n plc.close()\\n return (tagg)\\n \\n else:\\n print(\\\"Unable to open\\\", plc_ip)\",\n \"def TestResponse(port):\\n\\tcommandString = \\\"F\\\"\\n\\tport.write(commandString)\\n\\tcommandString = \\\"PM3,C,I1M500,I3M-500,I3M500,I1M-500,R\\\"\\n\\tport.write(commandString)\\n\\tWaitUntilReady(port)\\n\\tport.write(\\\"R\\\")\\n\\tresp=WaitUntilReady(port)\\n\\tcount=0\\n\\tprint(\\\"starting loop:\\\")\\n\\twhile('^' in resp):\\n \\tport.write(\\\"X\\\")\\n\\t\\txpos=port.read(9)\\n\\t\\tprint(xpos)\\n\\t\\tport.write(\\\"R\\\")\\n\\t\\ttime.sleep(5)\\n\\t\\tresp=WaitUntilReady(port)\\n\\t\\tcount = count+1\\n\\t\\tprint(count)\",\n \"def test(self):\\n # 0x13 is nop\\n self.gdb.command(\\\"p *((int*) 0x%x)=0x13\\\" % self.target.ram)\\n self.gdb.command(\\\"p *((int*) 0x%x)=0x13\\\" % (self.target.ram + 4))\\n self.gdb.command(\\\"p *((int*) 0x%x)=0x13\\\" % (self.target.ram + 8))\\n self.gdb.p(\\\"$pc=0x%x\\\" % self.target.ram)\\n self.gdb.stepi()\\n assertEqual((self.target.ram + 4), self.gdb.p(\\\"$pc\\\"))\\n self.gdb.stepi()\\n assertEqual((self.target.ram + 8), self.gdb.p(\\\"$pc\\\"))\",\n \"def remote_status():\",\n \"def test_rpcCall(self):\\n pass\",\n \"def testProtocolReturn(self):\\n self.assertEqual(\\n self.protocol,\\n self.mr.protocol\\n )\\n\\n self.mr._protocol = 'burp'\\n\\n self.assertEqual(\\n 'burp',\\n self.mr.protocol\\n )\",\n \"def test_check_nip(client):\\n is_assigned, request_id = client.check_nip(\\n \\\"8655104670\\\", \\\"41146786026458860703735932\\\"\\n )\\n\\n assert is_assigned\",\n \"def test_check_replication_unknown_valueerror2(self, mock_urlopen):\\n base_url = 'http://localhost:6000/recon/'\\n jdata = PropertyMock(return_value=b'X')\\n mock_urlopen.return_value = MagicMock(read=jdata)\\n result = check_replication(base_url, [4, 10, 4, 10])\\n self.assertEqual(result,\\n 3*[(STATUS_UNKNOWN,\\n \\\"Can't parse status data\\\")])\",\n \"def test_returnCar(self):\\n with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:\\n s.connect(ADDRESS)\\n self.assertTrue(Functions.returnCar(s, '38'))\",\n \"def test_emirp_check():\\r\\n pass\",\n \"def test_rsp_unknown_status(self):\\n\\n def handle(event):\\n return 0xFFF0, event.modification_list\\n\\n self.ae = ae = AE()\\n ae.acse_timeout = 5\\n ae.dimse_timeout = 5\\n ae.network_timeout = 5\\n ae.add_supported_context(ModalityPerformedProcedureStep)\\n scp = ae.start_server(\\n (\\\"localhost\\\", 11112), block=False, evt_handlers=[(evt.EVT_N_SET, handle)]\\n )\\n\\n ae.add_requested_context(ModalityPerformedProcedureStep)\\n assoc = ae.associate(\\\"localhost\\\", 11112)\\n assert assoc.is_established\\n\\n ds = Dataset()\\n ds.PatientName = \\\"Test^test\\\"\\n status, ds = assoc.send_n_set(\\n ds, ModalityPerformedProcedureStep, \\\"1.2.840.10008.5.1.1.40.1\\\"\\n )\\n assert status.Status == 0xFFF0\\n assert ds is None\\n assoc.release()\\n assert assoc.is_released\\n\\n scp.shutdown()\",\n \"def test_connect():\\n PV_name = \\\"TEST:TEST.VAL\\\"\\n casput(PV_name, 1)\\n assert caget(PV_name) == 1\",\n \"def test_get_property():\\n\\n sdk = '23'\\n contents = (\\\"[Info]\\\\n\\\"\\n \\\"sdk = %s\\\" % sdk)\\n\\n testutils.deploy_config_raw(contents)\\n\\n assert prop.get_prop('info', 'sdk') == sdk\\n\\n testutils.undeploy()\\n\\n return 0\",\n \"def testAllRead(self):\\n import time,copy\\n time.sleep(2)\\n client = ModbusTCP(self.config['vdevs']['slave']['icsifaces'][0],\\n self.config['vdevs']['slave']['points']) \\n\\n pts = copy.deepcopy(self.config['vdevs']['slave']['points'])\\n ptnames = [ pt['name'] for pt in pts ]\\n reply = client.readPoints(ptnames)\\n #print \\\"Reply: \\\", reply\\n for pt in ptnames:\\n value = filter(lambda x: x['name']==pt, pts)[0]['value']\\n #assert value == reply[ptnames.index(pt)]\\n received = reply[ptnames.index(pt)]\\n if not value == received: \\n print pt, ' was %s but should be %s'%(str(received),str(value))\",\n \"def test_send_network(self) :\\n symbol = 'A' \\n oProtocol = Protocol(symbol,mode=\\\"client\\\",debug=self.debug)\\n command = \\\"N200\\\"\\n message = oProtocol.send(command)\\n #if message['status'] is False :\\n #print(\\\"\\\\n*** ERROR : test_send_network : {}\\\".format(message['notify']))\\n\\n #Pour enregistrer les traces d'appels de fonctions dans le fichier log/client_calltrack_sorted.txt\\n client_tracker_print()\\n self.assertTrue( (message['status'] is not True) )\",\n \"def test_rsp_failure(self):\\n\\n def handle(event):\\n return 0x0112, None\\n\\n self.ae = ae = AE()\\n ae.acse_timeout = 5\\n ae.dimse_timeout = 5\\n ae.network_timeout = 5\\n ae.add_supported_context(DisplaySystem)\\n scp = ae.start_server(\\n (\\\"localhost\\\", 11112), block=False, evt_handlers=[(evt.EVT_N_GET, handle)]\\n )\\n\\n ae.add_requested_context(DisplaySystem)\\n assoc = ae.associate(\\\"localhost\\\", 11112)\\n assert assoc.is_established\\n\\n status, ds = assoc.send_n_get(\\n [(0x7FE0, 0x0010)], DisplaySystem, \\\"1.2.840.10008.5.1.1.40.1\\\"\\n )\\n assert status.Status == 0x0112\\n assert ds is None\\n assoc.release()\\n assert assoc.is_released\\n\\n scp.shutdown()\",\n \"def test_set_get_value_1(self):\\n value = 23.0\\n port = cn.Port(value)\\n self.assertEqual(port.value, value)\",\n \"def test_check_query_response(self, node_id):\\n\\n print('\\\\n### Testing query node status RESPONSE ###')\\n print('Remember that node_id must be the same 3 characters string that in test_query_node_id(node_id)')\\n\\n received_bytes = self.serport.readline()\\n if received_bytes == b'E\\\\r\\\\n':\\n print(\\\"You received Error Msg!\\\")\\n print(f'Did not receive correct query status response from node {node_id}')\\n print(f'Query again the node {node_id} if required')\\n return False\\n\\n elif (len(received_bytes) == 13) and (received_bytes[0:8] == b'#B' + node_id.encode() + b'06V'):\\n supply_voltage = received_bytes.decode()[8:13]\\n print(f\\\"supply_voltage of {node_id} is {supply_voltage}\\\")\\n print(\\\"response from the remote node SUCCESS\\\")\\n return True\\n else:\\n print(f'Did not receive correct query status response from node {node_id}')\\n print(f'Query again the node {node_id} if required')\\n return False\",\n \"def test_ip(response):\\n \\n # from comeon_core import update\\n ip = getIP()\\n print(ip)\\n #init_db(engine)\\n #update()\\n assert True\",\n \"def testSingleRead(self, point = 'pressure', expectedValue = 17.0 ):\\n import time\\n time.sleep(2)\\n client = ModbusTCP(self.config['vdevs']['slave']['icsifaces'][0],\\n self.config['vdevs']['slave']['points']) \\n\\n reply = client.readPoints(point)\\n #print \\\"Slave pressure: \\\", reply, \\\"Expected:\\\", expectedValue\\n assert reply[0] == expectedValue\",\n \"def test_rsp_unknown_status(self):\\n\\n def handle(event):\\n ds = Dataset()\\n ds.PatientName = \\\"Test\\\"\\n ds.SOPClassUID = DisplaySystem\\n ds.SOPInstanceUID = \\\"1.2.3.4\\\"\\n return 0xFFF0, ds\\n\\n self.ae = ae = AE()\\n ae.acse_timeout = 5\\n ae.dimse_timeout = 5\\n ae.network_timeout = 5\\n ae.add_supported_context(DisplaySystem)\\n scp = ae.start_server(\\n (\\\"localhost\\\", 11112), block=False, evt_handlers=[(evt.EVT_N_GET, handle)]\\n )\\n\\n ae.add_requested_context(DisplaySystem)\\n assoc = ae.associate(\\\"localhost\\\", 11112)\\n assert assoc.is_established\\n\\n status, ds = assoc.send_n_get(\\n [(0x7FE0, 0x0010)], DisplaySystem, \\\"1.2.840.10008.5.1.1.40.1\\\"\\n )\\n assert status.Status == 0xFFF0\\n assert ds is None\\n assoc.release()\\n assert assoc.is_released\\n\\n scp.shutdown()\",\n \"def test_rangetocidr_command(runner):\\n\\n result = runner.invoke(command, ['rangetocidr', '127.0.0.0', '128.0.0.3'])\\n assert result.exit_code == 0\\n\\n assert '127.0.0.0/8' in result.output\\n assert '128.0.0.0/30' in result.output\",\n \"def test(self,version=''):\\n p5cmd = ['srvinfo', 'lexxvers']\\n try:\\n res = self.nsdchat_call(p5cmd,5)\\n p5_version = singlevalue(res)\\n if (p5_version >= str(version)):\\n return True\\n return False\\n except subprocess.TimeoutExpired:\\n print(\\\"Could not connect to the archiware p5 server.\\\\nPlease review\\\"\\n \\\"the connection and firewall settings.\\\")\\n raise\",\n \"def test_set_get_value_2(self):\\n value = (1,)\\n port = cn.Port()\\n port.value = value\\n self.assertEqual(port.value, value)\",\n \"def test_ipcrm_not_isntalled(): # pragma: windows\\n IPCComm.ipcrm()\",\n \"def test_pop_returns_value(new_dll):\\n assert new_dll.pop() == 3\",\n \"def test_plc_read(plc_ip, tag_name):\\n\\n plc = ClxDriver()\\n if plc.open(plc_ip):\\n\\n print(plc.read_tag(tag_name))\\n\\t#plc.read_tag(tag_name)\\n plc.close()\\n else:\\n print(\\\"Unable to open\\\", plc_ip)\",\n \"def test_getbinarystate(\\n fauxmo_server: pytest.fixture, simplehttpplugin_target: pytest.fixture\\n) -> None:\\n data = b'Soapaction: \\\"urn:Belkin:service:basicevent:1#GetBinaryState\\\"'\\n\\n resp = requests.post(\\n \\\"http://127.0.0.1:12345/upnp/control/basicevent1\\\", data=data\\n )\\n assert resp.status_code == 200\\n\\n root = ET.fromstring(resp.text)\\n val = root.find(\\\".//BinaryState\\\").text\\n assert val in [\\\"0\\\", \\\"1\\\"]\",\n \"def read_test(self, cmd):\\n w_bytes = [random.randrange(0, 128) for i in range(0, 16)]\\n self._pyb.send(w_bytes)\\n self._serial.reset_input_buffer()\\n self._serial.write('\\\\r\\\\n'.encode('utf-8'))\\n self._serial.write(cmd.encode('utf-8'))\\n self._serial.write('\\\\r\\\\n'.encode('utf-8'))\\n\\n res = self._serial.read_until(terminator=serial.to_bytes([ord(c) for c in 'Ignored '])).decode('utf-8')\\n self._pyb.deinit()\\n\\n r_bytes = []\\n for x in re.sub('\\\\r', '', res).split('\\\\n'):\\n if x.find('IGNORE') != -1:\\n r_bytes = [int(s, 16) for s in x.split(',') if len(s) == 2]\\n break\\n\\n if self.compare_host_dut_result(w_bytes, r_bytes) == -1:\\n print(repr(res))\\n return \\\"Fail\\\"\\n\\n return \\\"Pass\\\"\",\n \"def test_incoming_k(self):\\n m_interface = Mock()\\n m_interface.callback.return_value = True\\n m_interface.read.return_value = ''\\n upb = UPB(m_interface)\\n upb.onCommand(address=(22,255), callback=m_interface.callback)\\n m_interface.read.return_value = \\\"PU07141610FF3090\\\\x0DPU07151610FF308F\\\\x0D\\\"\\n# time.sleep(4000)\\n time.sleep(2)\\n m_interface.callback.assert_called_with(address=(22,255), command='status', source=upb)\\n m_interface.read.return_value = ''\",\n \"def testSingleRead(self, point = 'pressure', expectedValue = 17.0 ):\\n import time\\n time.sleep(2)\\n to_config = self.config['vdevs']['slave']['icsifaces'][0]\\n from_config = self.config['vdevs']['master']['clientifaces'][0]\\n points = self.config['vdevs']['slave']['points']\\n client = ModbusRTU(to_config, points, from_config)\\n\\n reply = client.readPoints(point)\\n #print \\\"Slave pressure: \\\", reply\\n assert reply[0] == expectedValue\",\n \"def _platformix_get(self, context, fake_reply, prop):\\r\\n if hasattr(self.host, prop):\\r\\n self._reply(context, proto_success(getattr(self.host, prop), prop), fake_reply)\\r\\n else:\\r\\n self._reply(context, proto_failure(\\\"Property {} not found on {}\\\".format(prop, self.host.name)), fake_reply)\",\n \"def check_relay_status():\\n \\n query_cmd_packet = b'\\\\x04\\\\x18\\\\x00\\\\x00\\\\x00\\\\x1b\\\\x0f'\\n ser_relay.write(query_cmd_packet)\\n time.sleep(1)\\n resp_array = array('B', ser_relay.read(7))\\n \\n return resp_array\",\n \"def test_expect_status_property_about_registry_process(client, start_call_fx):\\n\\n url = reverse_lazy('calls:registry-list')\\n\\n response = client.post(url, start_call_fx, content_type='application/json')\\n\\n job_id = response.data.get('job_id')\\n\\n job = client.get(job_id)\\n\\n assert job.data.get('status') == 'DONE'\",\n \"def test_test_property():\\n\\n contents = (\\\"[Info]\\\\n\\\"\\n \\\"sdk = 23\\\")\\n\\n testutils.deploy_config_raw(contents)\\n\\n assert prop.test_prop('info', 'sdk') == 1\\n\\n testutils.undeploy()\\n\\n return 0\",\n \"def testAllRead(self):\\n import time,copy\\n time.sleep(2)\\n to_config = self.config['vdevs']['slave']['icsifaces'][0]\\n from_config = self.config['vdevs']['master']['clientifaces'][0]\\n points = self.config['vdevs']['slave']['points']\\n client = ModbusRTU(to_config, points, from_config)\\n\\n\\n pts = copy.deepcopy(self.config['vdevs']['slave']['points'])\\n for i in xrange(50):\\n ptnames = [ pt['name'] for pt in pts ]\\n reply = client.readPoints(ptnames)\\n #print \\\"Reply: \\\", reply\\n for pt in ptnames:\\n value = filter(lambda x: x['name']==pt, pts)[0]['value']\\n #assert value == reply[ptnames.index(pt)]\\n received = reply[ptnames.index(pt)]\\n if not value == received: \\n print pt, ' was %s but should be %s'%(str(received),str(value))\",\n \"def test_rsp_failure(self):\\n\\n def handle(event):\\n return 0x0112, None\\n\\n self.ae = ae = AE()\\n ae.acse_timeout = 5\\n ae.dimse_timeout = 5\\n ae.network_timeout = 5\\n ae.add_supported_context(ModalityPerformedProcedureStep)\\n scp = ae.start_server(\\n (\\\"localhost\\\", 11112), block=False, evt_handlers=[(evt.EVT_N_SET, handle)]\\n )\\n\\n ae.add_requested_context(ModalityPerformedProcedureStep)\\n assoc = ae.associate(\\\"localhost\\\", 11112)\\n assert assoc.is_established\\n\\n ds = Dataset()\\n ds.PatientName = \\\"Test^test\\\"\\n status, ds = assoc.send_n_set(\\n ds, ModalityPerformedProcedureStep, \\\"1.2.840.10008.5.1.1.40.1\\\"\\n )\\n assert status.Status == 0x0112\\n assert ds is None\\n assoc.release()\\n assert assoc.is_released\\n\\n scp.shutdown()\",\n \"def test_documentation_popxl_replication(self):\\n filename = \\\"replication.py\\\"\\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)\",\n \"def test_xmlrpc(text):\\r\\n return \\\"Here's a response! %s\\\" % str(text)\",\n \"def test_verify_connection_to_a_device():\",\n \"def test_host_port_expression_props(self):\\n block = SNMPBase()\\n block._create_data = MagicMock()\\n block._execute_snmp_request = MagicMock(\\n return_value=SAMPLE_SNMP_RESPONSE)\\n block._handle_data = MagicMock()\\n myOID = \\\"1.3.6.1.2.1.31.1.1.1.6.2\\\"\\n ip = \\\"0.0.0.0\\\"\\n port = 1611\\n starting_signal = Signal({\\n \\\"ip\\\": ip,\\n \\\"port\\\": port\\n })\\n self.configure_block(block, {\\n \\\"oids\\\": [{\\\"oid\\\": myOID}],\\n \\\"agent_host\\\": \\\"{{ $ip }}\\\",\\n \\\"agent_port\\\": \\\"{{ $port }}\\\"\\n })\\n block.start()\\n # Send the starting signal, make sure everything was called correctly\\n block.process_signals([starting_signal])\\n args, kwargs = block._execute_snmp_request.call_args\\n self.assertEqual(args[1], [myOID])\\n block._handle_data.assert_called_once_with([], starting_signal)\\n block.stop()\",\n \"def rpc_ping(self):\\n\\t\\treturn True\",\n \"def test_invalid_port_expression_prop(self):\\n block = SNMPBase()\\n block._create_data = MagicMock()\\n block.execute_request = MagicMock()\\n myOID = \\\"1.3.6.1.2.1.31.1.1.1.6.2\\\"\\n ip = \\\"0.0.0.0\\\"\\n port = 1611\\n starting_signal = Signal({\\n \\\"ip\\\": ip,\\n \\\"port\\\": port\\n })\\n self.configure_block(block, {\\n \\\"oids\\\": [{\\\"oid\\\": myOID}],\\n \\\"agent_port\\\": \\\"{{ port }}\\\"\\n })\\n block.start()\\n # Send the starting signal, make sure everything was called correctly\\n block.process_signals([starting_signal])\\n self.assertEqual(0, block.execute_request.call_count)\\n block.stop()\",\n \"def rpc_match():\",\n \"def on_pullVar(self):\\n if not os.path.exists(grapher.binPath):\\n mess = \\\"!!! ERROR: rndBin path not found, check user pref !!!\\\"\\n self.mainUi._defaultErrorDialog(mess, self.mainUi)\\n else:\\n tmpPath = os.path.join(self.grapher.userBinPath, 'tmp')\\n tmpFile = os.path.join(tmpPath, 'varBuffer.py')\\n varDict = pFile.readPyFile(tmpFile)\\n for var in sorted(varDict.keys()):\\n if var.startswith('selVar_'):\\n newItem = self.on_addVar()\\n self.setItem(newItem, **varDict[var])\",\n \"def test_variables_get(self):\\n pass\",\n \"def test_device_status(self):\\n #071031031E3067\\n self.ms.add_response({'\\\\x14071031031E3067\\\\x0D': 'PA\\\\x0D'})\\n # Network / Device ID\\n response = self.upb.status((49, 3))\\n self.assertTrue(response)\",\n \"def test_package(response):\\n \\n # from comeon_core import update\\n ip = getIP()\\n print(ip)\\n db = connect()\\n engine = db.connect() \\n init_db(engine)\\n update(engine)\\n assert True\",\n \"def test_snmpset_return_structure():\\n result = snmpset(ipaddress=SNMP_SRV_ADDR, community='public',\\n oid='SNMPv2-MIB::sysName.0', value_type='s',\\n value='Test Description', port=SNMP_SRV_PORT)\\n assert 'Test Description' in result\",\n \"def test_invalid_host_expression_prop(self):\\n block = SNMPBase()\\n block._create_data = MagicMock()\\n block.execute_request = MagicMock()\\n myOID = \\\"1.3.6.1.2.1.31.1.1.1.6.2\\\"\\n ip = \\\"0.0.0.0\\\"\\n port = 1611\\n starting_signal = Signal({\\n \\\"ip\\\": ip,\\n \\\"port\\\": port\\n })\\n self.configure_block(block, {\\n \\\"oids\\\": [{\\\"oid\\\": myOID}],\\n \\\"agent_host\\\": \\\"{{ ip }}\\\",\\n })\\n block.start()\\n # Send the starting signal, make sure everything was called correctly\\n block.process_signals([starting_signal])\\n self.assertEqual(0, block.execute_request.call_count)\\n block.stop()\",\n \"def test_ipcs():\\n IPCComm.ipcs()\",\n \"def test_port_get_set_value(self):\\n v1 = [1, 2, 3]\\n p1 = cn.Port()\\n p1.value = v1\\n\\n p2 = cn.Port()\\n p2.value = v1\\n self.assertEqual(\\n (p1.value == p2.value).all(),\\n True\\n )\",\n \"def test_query_self_status(self):\\n print('\\\\n### Testing query self status ###')\\n\\n command = b'$?'\\n self.serport.write(command)\\n received_bytes = self.serport.readline()\\n index = received_bytes.find(b'#A')\\n\\n if (index != -1) and (len(received_bytes) - index == 10):\\n if received_bytes[index+5:index+6] == b'V':\\n # print(\\\"check_for_valid_ack_signal SELF_STATUS V was spot on\\\")\\n for i in range(index+6, index+10):\\n if b'0' <= received_bytes[i:i+1] <= b'9':\\n pass\\n else:\\n print(\\\"query self status FAILURE 1\\\")\\n return False\\n node_id = received_bytes.decode()[index+2:index+5]\\n print(\\\"SELF_STATUS node is :\\\", node_id)\\n\\n voltage = round(float(received_bytes[index+6:index+10]) * (15.0/65536.0), 3)\\n print(\\\"SELF_STATUS voltage is :\\\", voltage)\\n print(\\\"query self status SUCCESS\\\")\\n return True\\n else:\\n print(\\\" query self status FAILURE 2\\\")\\n return False\",\n \"def Read_Response(self, expected = bytes([0x01])):\\r\\n data = self.Port.read(1)\\r\\n if data == expected: return True\\r\\n return False\",\n \"def test_rsp_unknown_status(self):\\n\\n def handle(event):\\n return 0xFFF0, event.attribute_list\\n\\n self.ae = ae = AE()\\n ae.acse_timeout = 5\\n ae.dimse_timeout = 5\\n ae.network_timeout = 5\\n ae.add_supported_context(ModalityPerformedProcedureStep)\\n\\n handlers = [(evt.EVT_N_CREATE, handle)]\\n scp = ae.start_server((\\\"localhost\\\", 11112), evt_handlers=handlers, block=False)\\n\\n ae.add_requested_context(ModalityPerformedProcedureStep)\\n assoc = ae.associate(\\\"localhost\\\", 11112)\\n assert assoc.is_established\\n\\n ds = Dataset()\\n ds.PatientName = \\\"Test^test\\\"\\n status, ds = assoc.send_n_create(\\n ds, ModalityPerformedProcedureStep, \\\"1.2.840.10008.5.1.1.40.1\\\"\\n )\\n assert status.Status == 0xFFF0\\n assert ds is None\\n assoc.release()\\n assert assoc.is_released\\n\\n scp.shutdown()\",\n \"def test_rsp_warning(self):\\n\\n def handle(event):\\n ds = Dataset()\\n ds.PatientName = \\\"Test\\\"\\n ds.SOPClassUID = DisplaySystem\\n ds.SOPInstanceUID = \\\"1.2.3.4\\\"\\n return 0x0116, ds\\n\\n self.ae = ae = AE()\\n ae.acse_timeout = 5\\n ae.dimse_timeout = 5\\n ae.network_timeout = 5\\n ae.add_supported_context(DisplaySystem)\\n scp = ae.start_server(\\n (\\\"localhost\\\", 11112), block=False, evt_handlers=[(evt.EVT_N_GET, handle)]\\n )\\n\\n ae.add_requested_context(DisplaySystem)\\n assoc = ae.associate(\\\"localhost\\\", 11112)\\n assert assoc.is_established\\n\\n status, ds = assoc.send_n_get(\\n [(0x7FE0, 0x0010)], DisplaySystem, \\\"1.2.840.10008.5.1.1.40.1\\\"\\n )\\n assert status.Status == 0x0116\\n assert ds is not None\\n assert isinstance(ds, Dataset)\\n assert ds.PatientName == \\\"Test\\\"\\n assert ds.SOPClassUID == DisplaySystem\\n assert ds.SOPInstanceUID == \\\"1.2.3.4\\\"\\n assoc.release()\\n assert assoc.is_released\\n\\n scp.shutdown()\",\n \"def get_xcom(**context):\\n ti = context['ti']\\n data = ti.xcom_pull(task_ids='xcom_from_bash', key='return_value')\\n logging.info(data)\",\n \"def test_script_p2_sh_address(self):\\n pass\",\n \"def test_set_get(self):\\n self.shell.onecmd(\\\"create %s/one 'hello'\\\" % (self.tests_path))\\n self.shell.onecmd(\\\"set %s/one 'bye'\\\" % (self.tests_path))\\n self.shell.onecmd(\\\"get %s/one\\\" % (self.tests_path))\\n self.assertEqual(\\\"bye\\\\n\\\", self.output.getvalue())\",\n \"def sendPullResponse(remote, request, txpk,sock):\\r\\n remote = (remote[0], remote[1])\\r\\n\\r\\n m = GatewayMessage(version=request.version, token=request.token,\\r\\n identifier=PULL_RESP, gatewayEUI=b'9079494338994186168',\\r\\n remote=remote, ptype='txpk', txpk=txpk)\\r\\n print(\\\"Sending PULL_RESP message to %s:%d\\\" % remote)\\r\\n\\r\\n sock.sendto(m.encode(), remote)\\r\\n data, address = sock.recvfrom(4096)\\r\\n print(data)\\r\\n print('received %s bytes from %s' % (len(data), address))\",\n \"def test_postfix(self):\\n test_sensordef = {\\n \\\"kind\\\": self.test_postfix.get_kind(),\\n \\\"name\\\": \\\"Postfix Mailqueue\\\",\\n \\\"description\\\": \\\"Monitors the mailqueue of a postfix server\\\",\\n \\\"help\\\": \\\"Monitors the mailqueue of a postfix server for active, deferred, hold or corrupt mail\\\",\\n \\\"tag\\\": \\\"mppostfixsensor\\\",\\n \\\"fields\\\": [],\\n \\\"groups\\\": []\\n }\\n assert_equal(self.test_postfix.get_sensordef(), test_sensordef)\",\n \"def test_t10_message():\\n send_json_message_to_t10(\\\"10.89.130.68\\\", \\\"cisco\\\", \\\"cisco\\\", request.get_json())\\n return \\\"ok\\\"\",\n \"def test_get_status(self):\\n pass\",\n \"def test_get_status(self):\\n pass\",\n \"def nremote(self):\",\n \"def test_verify_state_of_a_device():\",\n \"def test_process_newly_public_domain_gj(self):\\n self.page_mock.text = \\\"\\\"\\\"{{REDaten\\n|BAND=S I\\n|KEINE_SCHÖPFUNGSHÖHE=ON\\n|GEBURTSJAHR=1870\\n}}\\nbla\\n{{REAutor|Stein.}}\\\"\\\"\\\"\\n re_page = RePage(self.page_mock)\\n task = PDKSTask(None, self.logger)\\n compare({\\\"success\\\": True, \\\"changed\\\": True}, task.run(re_page))\\n compare(\\\"\\\", re_page[0][\\\"GEBURTSJAHR\\\"].value)\",\n \"def test_snmpget_return_structure():\\n result = snmpget(community='public', ipaddress=SNMP_SRV_ADDR,\\n oid=SYSDESCR_OID, port=SNMP_SRV_PORT)\\n assert 'Linux' in result\\n assert isinstance(result, str)\",\n \"def test_rsp_warning(self):\\n\\n def handle(event):\\n return 0x0116, event.modification_list\\n\\n self.ae = ae = AE()\\n ae.acse_timeout = 5\\n ae.dimse_timeout = 5\\n ae.network_timeout = 5\\n ae.add_supported_context(ModalityPerformedProcedureStep)\\n scp = ae.start_server(\\n (\\\"localhost\\\", 11112), block=False, evt_handlers=[(evt.EVT_N_SET, handle)]\\n )\\n\\n ae.add_requested_context(ModalityPerformedProcedureStep)\\n assoc = ae.associate(\\\"localhost\\\", 11112)\\n assert assoc.is_established\\n\\n ds = Dataset()\\n ds.PatientName = \\\"Test^test\\\"\\n status, ds = assoc.send_n_set(\\n ds, ModalityPerformedProcedureStep, \\\"1.2.840.10008.5.1.1.40.1\\\"\\n )\\n assert status.Status == 0x0116\\n assert ds.PatientName == \\\"Test^test\\\"\\n assoc.release()\\n assert assoc.is_released\\n\\n scp.shutdown()\",\n \"def test_process_newly_public_domain_gj_not_yet(self):\\n self.page_mock.text = \\\"\\\"\\\"{{REDaten\\n|BAND=S I\\n|KEINE_SCHÖPFUNGSHÖHE=ON\\n|GEBURTSJAHR=1871\\n}}\\nbla\\n{{REAutor|Stein.}}\\\"\\\"\\\"\\n re_page = RePage(self.page_mock)\\n task = PDKSTask(None, self.logger)\\n compare({\\\"success\\\": True, \\\"changed\\\": False}, task.run(re_page))\\n compare(\\\"1871\\\", re_page[0][\\\"GEBURTSJAHR\\\"].value)\",\n \"def test_system(cli_request, msg):\\n from client import client\\n response = client(cli_request)\\n response_parts = response.split('\\\\r\\\\n')\\n assert response_parts[0] == msg\\n assert '' in response_parts\",\n \"def test_get(self):\\n obs = self.tester.get('1.SKM7.640188')\\n exp = PrepSample('1.SKM7.640188', self.tester)\\n self.assertEqual(obs, exp)\",\n \"def test_get_property_success(self):\\r\\n self.assertEqual(self.config.option1, 1337)\",\n \"def testPowerOnResponse(self):\\n message = (mavutil.mavlink.GOPRO_COMMAND_POWER, mavutil.mavlink.GOPRO_REQUEST_SUCCESS)\\n self.mgr.set_response_callback('vehicle','name', message)\\n self.mgr.processMsgQueue.assert_called_with()\",\n \"def test_get_address():\\n\\n # Wait for workspace to be initialized\\n time.sleep(40)\\n bambi = create_test_bambi()\\n address = bambi.get_address_to_workspace()\\n assert address\",\n \"def test_cmd_field():\\n FNAME = 'xonsh-SESSIONID.json'\\n FNAME += '.cmdfield'\\n hist = History(filename=FNAME, here='yup', **HIST_TEST_KWARGS)\\n # in-memory\\n with mock_xonsh_env({'HISTCONTROL': set()}):\\n hf = hist.append({'rtn': 1})\\n yield assert_is_none, hf\\n yield assert_equal, 1, hist.rtns[0]\\n yield assert_equal, 1, hist.rtns[-1]\\n yield assert_equal, None, hist.outs[-1]\\n # slice\\n yield assert_equal, [1], hist.rtns[:]\\n # on disk\\n hf = hist.flush()\\n yield assert_is_not_none, hf\\n yield assert_equal, 1, hist.rtns[0]\\n yield assert_equal, 1, hist.rtns[-1]\\n yield assert_equal, None, hist.outs[-1]\\n os.remove(FNAME)\",\n \"def test_rpcSendRecv(self):\\n cli_send = self.client_msg\\n srv_send = self.server_msg\\n # Send message to driver\\n flag = self.client_comm.send(cli_send)\\n assert(flag)\\n flag, msg_recv = self.server_comm.recv(timeout=self.timeout)\\n assert(flag)\\n nt.assert_equal(msg_recv, srv_send)\\n # Send response back to instance\\n flag = self.server_comm.send(srv_send)\\n assert(flag)\\n # self.driver.sleep(1)\\n flag, msg_recv = self.client_comm.recv(timeout=self.timeout)\\n assert(flag)\\n nt.assert_equal(msg_recv, cli_send)\",\n \"def test_script_p2_s_address(self):\\n pass\",\n \"def test_recv(self):\\n # Required to get useful test names\\n super(TestCisPlyInput_local, self).test_recv()\",\n \"def ping():\\n return 'pong'\",\n \"def test_rsp_success(self):\\n\\n def handle(event):\\n ds = Dataset()\\n ds.PatientName = \\\"Test\\\"\\n ds.SOPClassUID = DisplaySystem\\n ds.SOPInstanceUID = \\\"1.2.3.4\\\"\\n return 0x0000, ds\\n\\n self.ae = ae = AE()\\n ae.acse_timeout = 5\\n ae.dimse_timeout = 5\\n ae.network_timeout = 5\\n ae.add_supported_context(DisplaySystem)\\n scp = ae.start_server(\\n (\\\"localhost\\\", 11112), block=False, evt_handlers=[(evt.EVT_N_GET, handle)]\\n )\\n\\n ae.add_requested_context(DisplaySystem)\\n assoc = ae.associate(\\\"localhost\\\", 11112)\\n assert assoc.is_established\\n\\n status, ds = assoc.send_n_get(\\n [(0x7FE0, 0x0010)], DisplaySystem, \\\"1.2.840.10008.5.1.1.40.1\\\"\\n )\\n assert status.Status == 0x0000\\n assert ds is not None\\n assert isinstance(ds, Dataset)\\n assert ds.PatientName == \\\"Test\\\"\\n assert ds.SOPClassUID == DisplaySystem\\n assert ds.SOPInstanceUID == \\\"1.2.3.4\\\"\\n assoc.release()\\n assert assoc.is_released\\n\\n scp.shutdown()\",\n \"def read_and_response(self, vsr, address_h, address_l):\\n # time.sleep(0.2)\\n self.send_cmd([vsr, 0x41, address_h, address_l])\\n # time.sleep(0.2)\\n resp = self.read_response() # ie resp = [42, 144, 48, 49, 13]\\n reply = resp[2:-1] # Omit start char, vsr address and end char\\n reply = \\\"{}\\\".format(''.join([chr(x) for x in reply])) # Turn list of integers into ASCII string\\n # print(\\\" RR. reply: {} (resp: {})\\\".format(reply, resp)) # ie reply = '01'\\n return resp, reply\",\n \"def test_store_property_after_reconnecting_to_the_device():\",\n \"def test_get_requests(self):\\n response = self.client.open('/api/provisioning/port',\\n method='GET')\\n self.assert200(response, \\\"Response body is : \\\" + response.data.decode('utf-8'))\",\n \"def main():\\n test_exchange()\\n print(\\\"2.1589225 Euros\\\")\",\n \"def test_get_address(self):\\n with self.subprocess_getoutput_patch:\\n ret = self.inst._get_address()\\n self.assertEqual(ret, \\\"http://example\\\")\",\n \"def test_check_regon(client):\\n is_assigned, request_id = client.check_regon(\\n \\\"730371613\\\", \\\"41146786026458860703735932\\\"\\n )\\n\\n assert is_assigned\",\n \"def _test_con(self) -> bool:\\n self.reset_buffers()\\n try:\\n serial_number = self.get_exact(b\\\"<GETSERIAL>>\\\", size=7)\\n except Exception as e:\\n # Unsure of exception types.\\n logger.warning(f\\\"{e}\\\", exc_info=True)\\n return False\\n # timeout error if wrong\\n if len(serial_number) == 7:\\n # Not 100% sure... no prescribed method of confirming\\n # we're connected to a GMC device in specs\\n logger.debug(f\\\"Test connection serial: {serial_number}\\\")\\n return True\\n else:\\n logger.warning(f\\\"Unexpected response: {serial_number}\\\")\\n return False\",\n \"def test_getirramp():\\n d = gini.get_ir_ramp()\\n assert len(d) == 256\",\n \"def testFailure(self):\\n request = b'hello'\\n reply = self.sendAndReceive(request)\\n self.assertEqual(2, reply[0])\",\n \"def test_get_robax_tostring_correct(self):\\n got_var, const_jtar = rapid_datatypes.get_rapid_data(self.controller, 'T_ROB1', 'MainModule', 'const_jtarget')\\n if not got_var:\\n print 'Couldn\\\\'t get variable. Test will not run.'\\n sys.exit()\\n robax = rapid_jointtarget.get_robax_tostring(const_jtar)\\n self.assertEqual(robax, 'RobAx: [Rax_1,Rax_2,Rax_3,Rax_4,Rax_5,Rax_6] = [0,0,0,10,0,0]')\",\n \"def test_fetch_otp(self):\\n otp = self.api.fetch_otp()\\n self.assertIn('code', otp)\",\n \"def test_repr(self):\\n namespace = {}\\n exec('tunneling = {0!r}'.format(self.tunneling), globals(), namespace)\\n self.assertIn('tunneling', namespace)\\n tunneling = namespace['tunneling']\\n self.assertAlmostEqual(self.tunneling.frequency.value, tunneling.frequency.value, 2)\\n self.assertEqual(self.tunneling.frequency.units, tunneling.frequency.units)\",\n \"def test_query_node_id(self,node_id):\\n\\n print('\\\\n### Testing query node status ACK ###')\\n print('Remember that node_id must be a 3 characters string')\\n\\n command = b'$V' + node_id.encode()\\n self.serport.write(command)\\n received_bytes = self.serport.readline()\\n index = received_bytes.find(b'$V')\\n\\n #ACK COMMAND\\n if (index != -1) and (len(received_bytes) - index == 5 and received_bytes.decode()[1] == 'V'):\\n #print(\\\"SET_ADDRESS V was spot on\\\")\\n if received_bytes[1:4] == command[1:4]:\\n node_id = received_bytes.decode()[2:5]\\n print(\\\"command has well been sent to node :\\\"+ node_id)\\n print(\\\"acknowledgement of the command SUCCESS\\\")\\n return True\\n else: \\n print(\\\"acknowledgement of the command FAILURE\\\")\\n return False\",\n \"def PHook(p):\\n x = p['sy']['pop']()\\n if (x == '.'):\\n p['sy']['push'](p['OK'])\\n else:\\n p['sy']['push'](p['NOK'])\\n #endif\",\n \"def test_wrapper_processing(self):\\r\\n result = mib2pysnmp('conpot/tests/data/VOGON-POEM-MIB.mib')\\r\\n self.assertTrue('mibBuilder.exportSymbols(\\\"VOGON-POEM-MIB\\\"' in result,\\r\\n 'mib2pysnmp did not generate the expected output. Output: {0}'.format(result))\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6584408","0.6459491","0.5550423","0.54029787","0.53768945","0.5342253","0.528288","0.52809453","0.5237307","0.51818335","0.5181425","0.50585806","0.5056922","0.50432426","0.503633","0.50314456","0.50298536","0.50265086","0.5013454","0.500096","0.49899554","0.49847114","0.49791014","0.49777964","0.4974934","0.49557754","0.4948672","0.4935801","0.49343306","0.4926512","0.49236172","0.4919985","0.49150056","0.49105394","0.4904867","0.4904291","0.49010172","0.4898389","0.48974383","0.48780486","0.48673916","0.4863756","0.48636496","0.48394468","0.48366937","0.48347008","0.48331434","0.4819641","0.48079807","0.48011705","0.47969285","0.4785952","0.4782586","0.47802556","0.47782713","0.47748664","0.47715926","0.4771568","0.47712806","0.47706273","0.47621697","0.4743293","0.47427595","0.473364","0.47276682","0.47233862","0.471872","0.471872","0.4713776","0.47128528","0.47074708","0.47063836","0.46983287","0.46880034","0.4684406","0.4682207","0.46793804","0.46788722","0.4678213","0.46779972","0.4667929","0.4666138","0.4663983","0.46614596","0.46612293","0.46523303","0.46520817","0.46464512","0.4644334","0.46440127","0.46426818","0.464051","0.4636325","0.46345738","0.4630314","0.4630307","0.46298337","0.46290416","0.46263152","0.46213162"],"string":"[\n \"0.6584408\",\n \"0.6459491\",\n \"0.5550423\",\n \"0.54029787\",\n \"0.53768945\",\n \"0.5342253\",\n \"0.528288\",\n \"0.52809453\",\n \"0.5237307\",\n \"0.51818335\",\n \"0.5181425\",\n \"0.50585806\",\n \"0.5056922\",\n \"0.50432426\",\n \"0.503633\",\n \"0.50314456\",\n \"0.50298536\",\n \"0.50265086\",\n \"0.5013454\",\n \"0.500096\",\n \"0.49899554\",\n \"0.49847114\",\n \"0.49791014\",\n \"0.49777964\",\n \"0.4974934\",\n \"0.49557754\",\n \"0.4948672\",\n \"0.4935801\",\n \"0.49343306\",\n \"0.4926512\",\n \"0.49236172\",\n \"0.4919985\",\n \"0.49150056\",\n \"0.49105394\",\n \"0.4904867\",\n \"0.4904291\",\n \"0.49010172\",\n \"0.4898389\",\n \"0.48974383\",\n \"0.48780486\",\n \"0.48673916\",\n \"0.4863756\",\n \"0.48636496\",\n \"0.48394468\",\n \"0.48366937\",\n \"0.48347008\",\n \"0.48331434\",\n \"0.4819641\",\n \"0.48079807\",\n \"0.48011705\",\n \"0.47969285\",\n \"0.4785952\",\n \"0.4782586\",\n \"0.47802556\",\n \"0.47782713\",\n \"0.47748664\",\n \"0.47715926\",\n \"0.4771568\",\n \"0.47712806\",\n \"0.47706273\",\n \"0.47621697\",\n \"0.4743293\",\n \"0.47427595\",\n \"0.473364\",\n \"0.47276682\",\n \"0.47233862\",\n \"0.471872\",\n \"0.471872\",\n \"0.4713776\",\n \"0.47128528\",\n \"0.47074708\",\n \"0.47063836\",\n \"0.46983287\",\n \"0.46880034\",\n \"0.4684406\",\n \"0.4682207\",\n \"0.46793804\",\n \"0.46788722\",\n \"0.4678213\",\n \"0.46779972\",\n \"0.4667929\",\n \"0.4666138\",\n \"0.4663983\",\n \"0.46614596\",\n \"0.46612293\",\n \"0.46523303\",\n \"0.46520817\",\n \"0.46464512\",\n \"0.4644334\",\n \"0.46440127\",\n \"0.46426818\",\n \"0.464051\",\n \"0.4636325\",\n \"0.46345738\",\n \"0.4630314\",\n \"0.4630307\",\n \"0.46298337\",\n \"0.46290416\",\n \"0.46263152\",\n \"0.46213162\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6953812"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":6598,"cells":{"query":{"kind":"string","value":"Test the popxl rts variable"},"document":{"kind":"string","value":"def test_documentation_popxl_rts_var(self):\n filename = \"rts_var.py\"\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)"},"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 test(self):\n # 0x13 is nop\n self.gdb.command(\"p *((int*) 0x%x)=0x13\" % self.target.ram)\n self.gdb.command(\"p *((int*) 0x%x)=0x13\" % (self.target.ram + 4))\n self.gdb.command(\"p *((int*) 0x%x)=0x13\" % (self.target.ram + 8))\n self.gdb.p(\"$pc=0x%x\" % self.target.ram)\n self.gdb.stepi()\n assertEqual((self.target.ram + 4), self.gdb.p(\"$pc\"))\n self.gdb.stepi()\n assertEqual((self.target.ram + 8), self.gdb.p(\"$pc\"))","def test_documentation_popxl_remote_rts_var(self):\n filename = \"remote_rts_var.py\"\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)","def test_get_robax_tostring_incorrect(self):\n got_var, const_number = rapid_datatypes.get_rapid_data(self.controller, 'T_ROB1', 'MainModule', 'const_number')\n if not got_var:\n print 'Couldn\\'t get variable. Test will not run.'\n sys.exit()\n # Checks if wrong rapid data is inserted.\n robax = rapid_jointtarget.get_robax_tostring(const_number)\n self.assertEqual(robax, 'DataType is num and not jointtarget.')\n # Checks if wrong data is inserted.\n robax = rapid_jointtarget.get_robax_tostring(10)\n self.assertIsInstance(robax, Exception)","def test_get_extax_tostring_incorrect(self):\n got_var, const_number = rapid_datatypes.get_rapid_data(self.controller, 'T_ROB1', 'MainModule', 'const_number')\n if not got_var:\n print 'Couldn\\'t get variable. Test will not run.'\n sys.exit()\n # Checks if wrong rapid data is inserted.\n extax = rapid_jointtarget.get_extax_tostring(const_number)\n self.assertEqual(extax, 'DataType is num and not jointtarget.')\n # Checks if wrong data is inserted.\n extax = rapid_jointtarget.get_extax_tostring(10)\n self.assertIsInstance(extax, Exception)","def has_regvar(*args):\n return _ida_frame.has_regvar(*args)","def PHook(p):\n x = p['sy']['pop']()\n if (x == '.'):\n p['sy']['push'](p['OK'])\n else:\n p['sy']['push'](p['NOK'])\n #endif","def test_get_robax_tostring_correct(self):\n got_var, const_jtar = rapid_datatypes.get_rapid_data(self.controller, 'T_ROB1', 'MainModule', 'const_jtarget')\n if not got_var:\n print 'Couldn\\'t get variable. Test will not run.'\n sys.exit()\n robax = rapid_jointtarget.get_robax_tostring(const_jtar)\n self.assertEqual(robax, 'RobAx: [Rax_1,Rax_2,Rax_3,Rax_4,Rax_5,Rax_6] = [0,0,0,10,0,0]')","def pintest(self, barcode, pin):\n u = self.dump(barcode)\n if 'ERRNUM' in u:\n return False\n return len(barcode) == 14 or pin == barcode[0] * 4","def test_documentation_popxl_remote_var(self):\n filename = \"remote_variable.py\"\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)","def test_pslx_34_002(self, testf=\"pslx_34_002.pslx\"):\n BlatPslCases.test_psl_34_002(self, \"pslx_34_002.pslx\", pslx=True)","def test_is_rock_valid():\n\n\tassert game.is_val('rock') is True # muze byt jenom 'True', vsechny True stejne ID i False","def test_pop_returns_value(new_dll):\n assert new_dll.pop() == 3","def _is_pop(self, words):\n if words[0] == 'pop':\n if len(words) != 3:\n raise SyntaxError(\"File line {}: Invalid number of arguments for C_POP command.\".format(self._file_line))\n if words[1] not in ['temp', 'pointer', 'static', 'local', 'argument', 'this', 'that']:\n raise SyntaxError(\"File line {}: Invalid second argument.\".format(self._file_line))\n return True\n else:\n return False","def test_prodcode_4(self):\n self.assertEqual(prodcode.functio(), \"production value\")","def test_03_pass(self):\n if x==1:\n pass","def test_02_pass(self):\n if x==1:\n pass","def check():","def test_get_extax_tostring_correct(self):\n got_var, const_jtar = rapid_datatypes.get_rapid_data(self.controller, 'T_ROB1', 'MainModule', 'const_jtarget')\n if not got_var:\n print 'Couldn\\'t get variable. Test will not run.'\n sys.exit()\n extax = rapid_jointtarget.get_extax_tostring(const_jtar)\n self.assertEqual(extax, 'Extax: [Eax_a,Eax_b,Eax_c,Eax_d,Eax_e,Eax_f] = [9E9,9E9,9E9,9E9,9E9,9E9]')","def test_func(self):\n rol_nu = rol_get_huidige(self.request)\n return rol_nu == Rollen.ROL_RCL","def test_01_pass(self):\n if x==1:\n pass","def test_01_pass(self):\n if x==1:\n pass","def TestRetract(portVXM,portArd):\n commandString = \"F\"\n portVXM.write(commandString)\n commandString = \"PM-3,C,SA1M400,SA3M100,LM0,I3M400,P5,I1M-90,P5,L3,R \"\n portVXM.write(commandString)\n\tresp='abs'\n\twhile( '^' not in resp ):\n\t resp = portVXM.read(1000)\n\t\n\tprint \"Moving to Standard Operation.\"\t\n commandString = \"PM-2,C,SA1M400,SA3M100,LM0,I3M400,P5,I1M-90,P5,L0,R \"\n portVXM.write(commandString)\n\tt0 = time.time() \n\tt = time.time() \n\tportArd.flushInput()\n\tresp='abs'\n\t\n #while( ('21,1,1' not in resp) and ((t-t0)>30.0)):\n while( not('21,0,1\\n\\r21,1,1\\n\\r21,0,0\\n\\r' in resp) ):\n\t resp = portArd.read(10000)\n\t t = time.time()\n\n\t#print \"CONDITION: \\t\" ,('21,1,1' not in resp),'\\t'\n\tportVXM.write(\"D,\")\n resp = portVXM.read(1000)\n\tXpos, Zpos = GetXZ(portVXM)\n localtime = time.asctime(time.localtime(time.time()))\n\tprint \"Source Fully Retracted at (X,Y) : \",Xpos, \"\\t\", Zpos ,\"\\t\", \"at localtime: \", localtime\n\tprint abs(t-t0) , \"\\t Seconds \\r\"\n\tWaitUntilReady(portVXM)\n\tportVXM.write(\"C,IA1M-0,IA3M-0,R \")\n\tportVXM.write(\"Q, \")\n\tportArd.flush()","def test_plc_read_val(plc_ip, tag_name):\n\n plc = ClxDriver()\n if plc.open(plc_ip):\n tagg = plc.read_tag(tag_name)\n plc.close()\n return (tagg)\n \n else:\n print(\"Unable to open\", plc_ip)","def test_variablepresentations_get(self):\n pass","def test_ge(self):\n # Success\n script = self.write_script(\"\"\"\n variable = 5\n if variable >= 5:\n check = 130\n else:\n check = 0\n end\n \"\"\")\n check = script.get_variable_or_attribute(\"check\")\n self.assertEqual(check, 130)\n\n # Failure\n script = self.write_script(\"\"\"\n variable = 5\n if variable >= 8:\n check = 130\n else:\n check = 0\n end\n \"\"\")\n check = script.get_variable_or_attribute(\"check\")\n self.assertEqual(check, 0)","def test_le(self):\n # Success\n script = self.write_script(\"\"\"\n variable = 10\n if variable <= 10:\n check = 80\n else:\n check = 10\n end\n \"\"\")\n check = script.get_variable_or_attribute(\"check\")\n self.assertEqual(check, 80)\n\n # Failure\n script = self.write_script(\"\"\"\n variable = 10\n if variable <= 8:\n check = 80\n else:\n check = 10\n end\n \"\"\")\n check = script.get_variable_or_attribute(\"check\")\n self.assertEqual(check, 10)","def test_getx(self):\n point = (1,2)\n x = utils.getx(point)\n self.assertEqual(1, x)","def test_MINX_pass(self):\n self.assertTrue(self.mod.minx.isset)","def test_contains_true(self):\n self.assertTrue('1.SKM7.640188' in self.tester)","def test_contains_true(self):\n self.assertTrue('1.SKM7.640188' in self.tester)","def test_pickle_within_drs(free_alg):\n\n dr = free_alg\n env = dr.exec_drs(TEST_PICKLE_DRS)\n\n assert env['good_symb']\n assert env['good_indexed']\n assert env['def_'] == env['def_back']","def test_boolean_and_selection(self):\n\n # The selection loop:\n sel = list(mol_res_spin.residue_loop(\"#Ap4Aase:4 & :Pro\"))\n\n # Test:\n self.assertEqual(len(sel), 1)\n for res in sel:\n self.assert_(res.name == \"Pro\" and res.num == 4)","def test_31_if(self):\n\t\tinput = \"\"\"procedure main(); var x:integer; begin x:=foo(); end\n\t\tfunction foo():integer; var a:real; begin\n\t\tif a>0 then return 0;\n\t\tend\"\"\"\n\t\texpect = \"Function foo Not Return\"\n\t\tself.assertTrue(TestChecker.test(input,expect,431))","def is_return(self, pick):\n return ('-' in pick.name and sorted(pick.name.split('-'), reverse=True)[0].startswith('ret'))","def test_reffs(self):\n self.assertEqual((\"1\" in list(map(lambda x: str(x), self.TEI.reffs))), True)\n self.assertEqual((\"1.pr\" in list(map(lambda x: str(x), self.TEI.reffs))), True)\n self.assertEqual((\"2.40.8\" in list(map(lambda x: str(x), self.TEI.reffs))), True)","def test_dummy4(self):\n xpb = XPathBuilder()\n xp = xpb.dummy()\n self.assertTrue(xp.log_not() is xp)","def test_func(self):\n self.rol_nu = rol_get_huidige(self.request)\n return self.rol_nu in (Rollen.ROL_BB, Rollen.ROL_BKO, Rollen.ROL_RKO, Rollen.ROL_RCL, Rollen.ROL_HWL)","def test_func(self):\n self.rol_nu = rol_get_huidige(self.request)\n return self.rol_nu in (Rollen.ROL_BB, Rollen.ROL_BKO, Rollen.ROL_RKO, Rollen.ROL_RCL, Rollen.ROL_HWL)","def test_func(self):\n self.rol_nu = rol_get_huidige(self.request)\n return self.rol_nu in (Rollen.ROL_BB, Rollen.ROL_BKO, Rollen.ROL_RKO, Rollen.ROL_RCL, Rollen.ROL_HWL)","def read_pin10(self):\n return self.PIN_10_SIGNAL_TEST","def testHealthAssessPyoGangrenosum(self):\n attr = self.session.create_visit_attr()\n\n self.util.boolTypeTest(self, attr, \"pyo_gangrenosum\")\n\n self.util.boolPropertyTest(self, attr, \"pyo_gangrenosum\")","def c_test_population_function(self, function):\r\n return 1","def test_9_call_expression(self):\n\t\tinput = \"\"\"var x,y:integer;\n\t\tfunction foo(a:real;b:boolean):real; begin return 1; end\n\t\tprocedure main(); var x:real; begin x:=foo(1,false); {OK} end\n\t\tprocedure f(); var y:integer; y:real; begin end\"\"\"\n\t\texpect = \"Redeclared Variable: y\"\n\t\tself.assertTrue(TestChecker.test(input,expect,409))","def test_get(self):\n obs = self.tester.get('1.SKM7.640188')\n exp = PrepSample('1.SKM7.640188', self.tester)\n self.assertEqual(obs, exp)","def test_is_99_preHandSimple_correct(self):\n self.assertEqual(self.hand.getPreHandSimple(), '99')","def test_get(self):\n self.assertEqual(self.tester.get('barcodesequence'), 'AGCGCTCACATC')","def test_getint(self):\n self.assertEqual(self.config.getint('advanced','n'),12)","def test_variables_get(self):\n pass","def event_m10_29_80000():\r\n \"\"\"State 0,2: [Lib] [Preset] Reproduction fire _SubState\"\"\"\r\n assert event_m10_29_x9(z58=1029000, z59=1029099)\r\n \"\"\"State 1: Finish\"\"\"\r\n EndMachine()\r\n Quit()","def event_m20_11_81000():\n \"\"\"State 0,2: [Lib] [Preset] Reproduction fire _SubState\"\"\"\n assert event_m20_11_x53(z73=2011100, z74=2011199)\n \"\"\"State 1: Finish\"\"\"\n EndMachine()","def true(symbol):\n return True","def test_emirp_check():\r\n pass","def test_32_if(self):\n\t\tinput = \"\"\"function foo():integer; var a:real; begin\n\t\twith a:integer;b:real; do begin if a=0 then return 1; else return a; end\n\t\tend\n\t\tprocedure main(); var x:integer; begin x:=foo(); foo(); end\"\"\"\n\t\texpect = \"Undeclared Procedure: foo\"\n\t\tself.assertTrue(TestChecker.test(input,expect,432))","def test_data(clear, data):\r\n cmd = ShdlcCmdGetErrorState(clear=clear)\r\n assert type(cmd.data) is bytes\r\n assert cmd.data == data","def test_test_property():\n\n contents = (\"[Info]\\n\"\n \"sdk = 23\")\n\n testutils.deploy_config_raw(contents)\n\n assert prop.test_prop('info', 'sdk') == 1\n\n testutils.undeploy()\n\n return 0","def c_test_eval_inp(self, population, run_locals):\r\n return 1","def test01getNumber(self):\n self.assertEqual( calc.getNumber(), 1234 )","def test_get_property():\n\n sdk = '23'\n contents = (\"[Info]\\n\"\n \"sdk = %s\" % sdk)\n\n testutils.deploy_config_raw(contents)\n\n assert prop.get_prop('info', 'sdk') == sdk\n\n testutils.undeploy()\n\n return 0","def test_get_cell(workbook):\n assert workbook.get_cell(3,1) == '507906000030242007'","def test_pop_returns_value_of_tail(dq_3):\n assert dq_3.pop() == 'ragtime'","def test_id():\r\n cmd = ShdlcCmdGetErrorState(clear=False)\r\n assert type(cmd.id) is int\r\n assert cmd.id == 0xD2","def test_RUMI():\r\n\r\n start = 11051\r\n end = 11902\r\n read_poems(start, end)","def test_stub(self):\n self.assertEqual(self._value, True)","def test_brackets_success(self):\n found = False\n pyint = Interpreter()\n try:\n pyint.run(code=BF_CORRECT_BRACK)\n except SystemExit: \n found = True\n self.assertFalse(found)","def test_extracting_one_value(self):\n\t\tself.assertEqual([\"b\"], au.extract_variables(bf.Var(\"b\")), \"Invalid variables extracted, expected [b].\")","def test04(self):\n model = self.setup_model02()\n x = model.x\n x[1].fix(1)\n wts = StoreSpec.value_isfixed_isactive(only_fixed=True)\n to_json(model, fname=self.fname, human_read=True, wts=wts)\n x[1].unfix()\n x[1].value = 2\n x[2].value = 10\n model.g.deactivate()\n from_json(model, fname=self.fname, wts=wts)\n assert x[1].fixed\n assert value(x[1]) == 1\n assert value(x[2]) == 10\n assert model.g.active","def test_oncvpsp_pseudo(self):\n ger = Pseudo.from_file(ref_file(\"ge.oncvpsp\"))\n print(repr(ger))\n print(ger)\n print(ger.as_dict())\n ger.as_tmpfile()\n\n self.assertTrue(ger.symbol == \"Ge\")\n self.assert_equal(ger.Z, 32.0)\n self.assert_equal(ger.Z_val, 4.0)\n self.assertTrue(ger.isnc)\n self.assertFalse(ger.ispaw)\n self.assert_equal(ger.l_max, 2)\n self.assert_equal(ger.l_local, 4)\n self.assert_equal(ger.rcore, None)\n self.assertFalse(ger.has_dojo_report)","def test(self):\n res = self._dll.JLINKARM_Test()\n return (res == 0)","def test_getirramp():\n d = gini.get_ir_ramp()\n assert len(d) == 256","def testCheck(self):\r\n from pydsl.Check import PLYChecker\r\n from pydsl.contrib.grammar import example_ply\r\n from pydsl.Grammar.Definition import PLYGrammar\r\n grammardef = PLYGrammar(example_ply)\r\n checker = PLYChecker(grammardef)\r\n self.assertTrue(checker.check(\"O\"))\r\n self.assertTrue(checker.check([\"O\"]))\r\n self.assertFalse(checker.check(\"FALSE\"))\r\n #self.assertFalse(checker.check(\"\")) #FIXME\r","def test_get(self):\n obs = self.tester.get('1.SKM7.640188')\n exp = Sample('1.SKM7.640188', self.tester)\n self.assertEqual(obs, exp)","def check_register(dut, expected):\n for key, value in expected.items():\n val = dut.DUT_apple_riscv_soc.soc_cpu_core.regfile_inst.ram[key].value.integer\n assert value == val, f\"RAM1: Register {key}, Expected: {value}, Actual: {val}\"\n print(f\"RAM1: Register {key}, Expected: {value}, Actual: {val}\")","def event_m20_11_82000():\n \"\"\"State 0,2: [Lib] [Preset] Reproduction fire _SubState\"\"\"\n assert event_m20_11_x53(z73=2011200, z74=2011299)\n \"\"\"State 1: Finish\"\"\"\n EndMachine()","def test_contains_true(self):\n self.assertTrue('BarcodeSequence' in self.tester)\n self.assertTrue('barcodesequence' in self.tester)","def test_dummy1(self):\n xpb = XPathBuilder()\n xp = xpb.dummy()\n self.assertFalse(xp)\n xp = xp & xpb.foo.bar\n self.assertTrue(xp)\n exp = '/foo/bar'\n self.assertEqual(xp.tostring(), exp)","def event_m20_11_x110(z24=211000081, z25=802, z26=211020082):\n \"\"\"State 0,1: [Reproduction] Voice frog singing voice_flag_SubState\"\"\"\n call = event_m20_11_x107(z24=z24, z26=z26, z25=z25)\n if call.Get() == 1:\n pass\n elif call.Get() == 0:\n \"\"\"State 3: [Condition] Singing voice frog_flag_SubState\"\"\"\n assert event_m20_11_x108(z24=z24, z25=z25)\n \"\"\"State 2: [Execution] Voice frog singing voice_flag_SubState\"\"\"\n assert event_m20_11_x109(z26=z26, z25=z25)\n \"\"\"State 4: Finish\"\"\"\n return 0","def test_check_nip(client):\n is_assigned, request_id = client.check_nip(\n \"8655104670\", \"41146786026458860703735932\"\n )\n\n assert is_assigned","def test_57o_Properties(self):\n self.assertEqual(self.hand.premInd, 0)\n self.assertEqual(self.hand.connectedInd, 0)\n self.assertEqual(self.hand.oneSpaceConnectedInd, 1)\n self.assertEqual(self.hand.suitedInd, 0)\n self.assertEqual(self.hand.PPInd, 0)\n self.assertEqual(self.hand.PPCard, 0)","def testProtocolReturn(self):\n self.assertEqual(\n self.protocol,\n self.mr.protocol\n )\n\n self.mr._protocol = 'burp'\n\n self.assertEqual(\n 'burp',\n self.mr.protocol\n )","def verify_payload():\n return True","def Xchg(self, String, infix):\r\n\r\n tmp1 = self.Check_code_operand(infix[0])\r\n tmp2 = self.Check_code_operand(infix[1])\r\n if (tmp1 == False) or (tmp2 == False):\r\n return False\r\n if (tmp1[0] == 'imm') or (tmp1[2] == 0) or ((tmp1[0] == 'imm') and (tmp2[0] == 'imm')):\r\n if (tmp1[2]==0)and(tmp2[2]!=0)and (tmp2[0] != 'imm'):\r\n tmp1[2]=tmp2[2]\r\n else:\r\n return False\r\n if ((tmp1[0] == 'add') and (tmp2[0] == 'add')) or ((tmp1[0] == 'imm') and (tmp2[0] == 'imm')) or ((tmp1[2] != tmp2[2]) and (tmp2[2] != 0)):\r\n return False\r\n\r\n a = 0\r\n if (tmp1[0] != 'add'):\r\n a = tmp1[1]\r\n else:\r\n a = self.Get_value_from_memory(tmp1[1], tmp1[2])\r\n b = 0\r\n if (tmp2[0] != 'add'):\r\n b = tmp2[1]\r\n else:\r\n b = self.Get_value_from_memory(tmp2[1], tmp1[2])\r\n\r\n if tmp1[0] == 'reg':\r\n if len(infix[0][0]) == 3:\r\n self.Registers[infix[0][0]] = b\r\n else:\r\n self.Save_value_in_reg_X(infix[0][0], b)\r\n else:\r\n if not self.Save_value_in_memory(tmp1[1], b, tmp1[2]):\r\n return False\r\n\r\n if tmp2[0] == 'reg':\r\n if len(infix[1][0]) == 3:\r\n self.Registers[infix[1][0]] = a\r\n else:\r\n self.Save_value_in_reg_X(infix[1][0], a)\r\n else:\r\n if not self.Save_value_in_memory(tmp2[1], a, tmp1[2]):\r\n return False\r\n return True","def test_29_if(self):\n\t\tinput = \"\"\"procedure main(); var x:integer; begin x:=foo(); end\n\t\tfunction foo():integer; var a:real; begin\n\t\twith a:integer;b:real; do begin if a>0 then return 1; else b:=0; end\n\t\tend\"\"\"\n\t\texpect = \"Function foo Not Return\"\n\t\tself.assertTrue(TestChecker.test(input,expect,429))","def test_read_lxyr(self):\n cwd = os.path.dirname(os.path.abspath(__file__))\n # gt_file = os.path.join(cwd, 'test_files/test_gt.lxyr')\n # ground_truths = read_lxyr(gt_file)\n test_dir = os.path.join(cwd, 'test_files/')\n ground_truths = read_lxyr(test_dir, 'test_gt')\n # print ground_truths\n self.assertTrue(any(\n gt for gt in ground_truths\n if gt.x == 553 and gt.y == 132\n and gt.radius == 16.64 and gt.class_value == 3))\n self.assertTrue(any(\n gt for gt in ground_truths\n if gt.x == 119 and gt.y == 631\n and gt.radius == 15.0 and gt.class_value == 4))","def event_m20_11_80000():\n \"\"\"State 0,2: [Lib] [Preset] Reproduction fire _SubState\"\"\"\n assert event_m20_11_x53(z73=2011000, z74=2011099)\n \"\"\"State 1: Finish\"\"\"\n EndMachine()","def test04_boolean_operator(self):\n\n import _cppyy\n number = _cppyy.gbl.number\n\n n = number(20)\n assert n\n\n n = number(0)\n assert not n","def test_dummy2(self):\n xpb = XPathBuilder()\n xp = xpb.dummy()\n self.assertFalse(xp)\n xp = xp & (xpb.attr('foo') == 'xyz')\n self.assertTrue(xp)\n exp = '@foo = \"xyz\"'\n self.assertEqual(xp.tostring(), exp)","def test_30_if(self):\n\t\tinput = \"\"\"procedure main(); var x:integer; begin x:=foo(); end\n\t\tfunction foo():integer; var a:real; begin\n\t\twith a:integer;b:real; do begin if a>0 then a:=1; else return 0; end\n\t\tend\"\"\"\n\t\texpect = \"Function foo Not Return\"\n\t\tself.assertTrue(TestChecker.test(input,expect,430))","def test_is_57o_preHandSimple_correct(self):\n self.assertEqual(self.hand.getPreHandSimple(), '75o')","def test_substgrpexcl00402m7_positive(mode, save_output, output_format):\n assert_bindings(\n schema=\"sunData/ElemDecl/substGroupExclusions/substGrpExcl00402m/substGrpExcl00402m7.xsd\",\n instance=\"sunData/ElemDecl/substGroupExclusions/substGrpExcl00402m/substGrpExcl00402m7_p.xml\",\n class_name=\"Root\",\n version=\"1.1\",\n mode=mode,\n save_output=save_output,\n output_format=output_format,\n structure_style=\"filenames\",\n )","def event_m20_11_83000():\n \"\"\"State 0,2: [Lib] [Preset] Reproduction fire _SubState\"\"\"\n assert event_m20_11_x53(z73=2011300, z74=2011399)\n \"\"\"State 1: Finish\"\"\"\n EndMachine()","def CHK( UDStat ):\n if UDStat != NOERRORS:\n raise UniversalLibraryError( UDStat )","def test_get_temp(loaded_fridge):\n assert loaded_fridge.get_temp() == loaded_fridge.temp","def test_stub() -> None:\n test_val = 3\n assert test_val == 3","def test_27_if(self):\n\t\tinput = \"\"\"procedure main(); var x:integer; begin x:=foo(); end\n\t\tfunction foo():integer; var a:real; begin\n\t\twith a:integer;b:real; do begin if a>0 then a:=0; else b:=0; end\n\t\tend\"\"\"\n\t\texpect = \"Function foo Not Return\"\n\t\tself.assertTrue(TestChecker.test(input,expect,427))","def test_rf_value(self):\n\t\tdetails = self.watcher.describe()\t\t\n\t\tself.assertTrue((details.rf.to_numpy()[0:8] == 9.0).all())","def testnatom(self):\r\n assert self.data.natom == 20","def unitTest(self):\n\n if 'y' in self.snmp_obj.dbg:\n print(self.snmp_obj.mib_dict)\n for k in self.snmp_obj.mib_dict:\n print(k, \":\", self.snmp_obj.mib_dict[k])\n\n print(\"Testing get mib oid\")\n\n for i in self.mibs:\n oid = self.snmp_obj.get_mib_oid(i)\n print('mib: %s - oid=%s' % (i, oid))\n\n return True","def test_id():\n assert Packet106.id == 106","def test_getting(self):\n self.assertEqual(self.test_notes['C'], self.C)","def test_getitem(self):\n obs = self.tester['1.SKM7.640188']\n exp = PrepSample('1.SKM7.640188', self.tester)\n self.assertEqual(obs, exp)"],"string":"[\n \"def test(self):\\n # 0x13 is nop\\n self.gdb.command(\\\"p *((int*) 0x%x)=0x13\\\" % self.target.ram)\\n self.gdb.command(\\\"p *((int*) 0x%x)=0x13\\\" % (self.target.ram + 4))\\n self.gdb.command(\\\"p *((int*) 0x%x)=0x13\\\" % (self.target.ram + 8))\\n self.gdb.p(\\\"$pc=0x%x\\\" % self.target.ram)\\n self.gdb.stepi()\\n assertEqual((self.target.ram + 4), self.gdb.p(\\\"$pc\\\"))\\n self.gdb.stepi()\\n assertEqual((self.target.ram + 8), self.gdb.p(\\\"$pc\\\"))\",\n \"def test_documentation_popxl_remote_rts_var(self):\\n filename = \\\"remote_rts_var.py\\\"\\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)\",\n \"def test_get_robax_tostring_incorrect(self):\\n got_var, const_number = rapid_datatypes.get_rapid_data(self.controller, 'T_ROB1', 'MainModule', 'const_number')\\n if not got_var:\\n print 'Couldn\\\\'t get variable. Test will not run.'\\n sys.exit()\\n # Checks if wrong rapid data is inserted.\\n robax = rapid_jointtarget.get_robax_tostring(const_number)\\n self.assertEqual(robax, 'DataType is num and not jointtarget.')\\n # Checks if wrong data is inserted.\\n robax = rapid_jointtarget.get_robax_tostring(10)\\n self.assertIsInstance(robax, Exception)\",\n \"def test_get_extax_tostring_incorrect(self):\\n got_var, const_number = rapid_datatypes.get_rapid_data(self.controller, 'T_ROB1', 'MainModule', 'const_number')\\n if not got_var:\\n print 'Couldn\\\\'t get variable. Test will not run.'\\n sys.exit()\\n # Checks if wrong rapid data is inserted.\\n extax = rapid_jointtarget.get_extax_tostring(const_number)\\n self.assertEqual(extax, 'DataType is num and not jointtarget.')\\n # Checks if wrong data is inserted.\\n extax = rapid_jointtarget.get_extax_tostring(10)\\n self.assertIsInstance(extax, Exception)\",\n \"def has_regvar(*args):\\n return _ida_frame.has_regvar(*args)\",\n \"def PHook(p):\\n x = p['sy']['pop']()\\n if (x == '.'):\\n p['sy']['push'](p['OK'])\\n else:\\n p['sy']['push'](p['NOK'])\\n #endif\",\n \"def test_get_robax_tostring_correct(self):\\n got_var, const_jtar = rapid_datatypes.get_rapid_data(self.controller, 'T_ROB1', 'MainModule', 'const_jtarget')\\n if not got_var:\\n print 'Couldn\\\\'t get variable. Test will not run.'\\n sys.exit()\\n robax = rapid_jointtarget.get_robax_tostring(const_jtar)\\n self.assertEqual(robax, 'RobAx: [Rax_1,Rax_2,Rax_3,Rax_4,Rax_5,Rax_6] = [0,0,0,10,0,0]')\",\n \"def pintest(self, barcode, pin):\\n u = self.dump(barcode)\\n if 'ERRNUM' in u:\\n return False\\n return len(barcode) == 14 or pin == barcode[0] * 4\",\n \"def test_documentation_popxl_remote_var(self):\\n filename = \\\"remote_variable.py\\\"\\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)\",\n \"def test_pslx_34_002(self, testf=\\\"pslx_34_002.pslx\\\"):\\n BlatPslCases.test_psl_34_002(self, \\\"pslx_34_002.pslx\\\", pslx=True)\",\n \"def test_is_rock_valid():\\n\\n\\tassert game.is_val('rock') is True # muze byt jenom 'True', vsechny True stejne ID i False\",\n \"def test_pop_returns_value(new_dll):\\n assert new_dll.pop() == 3\",\n \"def _is_pop(self, words):\\n if words[0] == 'pop':\\n if len(words) != 3:\\n raise SyntaxError(\\\"File line {}: Invalid number of arguments for C_POP command.\\\".format(self._file_line))\\n if words[1] not in ['temp', 'pointer', 'static', 'local', 'argument', 'this', 'that']:\\n raise SyntaxError(\\\"File line {}: Invalid second argument.\\\".format(self._file_line))\\n return True\\n else:\\n return False\",\n \"def test_prodcode_4(self):\\n self.assertEqual(prodcode.functio(), \\\"production value\\\")\",\n \"def test_03_pass(self):\\n if x==1:\\n pass\",\n \"def test_02_pass(self):\\n if x==1:\\n pass\",\n \"def check():\",\n \"def test_get_extax_tostring_correct(self):\\n got_var, const_jtar = rapid_datatypes.get_rapid_data(self.controller, 'T_ROB1', 'MainModule', 'const_jtarget')\\n if not got_var:\\n print 'Couldn\\\\'t get variable. Test will not run.'\\n sys.exit()\\n extax = rapid_jointtarget.get_extax_tostring(const_jtar)\\n self.assertEqual(extax, 'Extax: [Eax_a,Eax_b,Eax_c,Eax_d,Eax_e,Eax_f] = [9E9,9E9,9E9,9E9,9E9,9E9]')\",\n \"def test_func(self):\\n rol_nu = rol_get_huidige(self.request)\\n return rol_nu == Rollen.ROL_RCL\",\n \"def test_01_pass(self):\\n if x==1:\\n pass\",\n \"def test_01_pass(self):\\n if x==1:\\n pass\",\n \"def TestRetract(portVXM,portArd):\\n commandString = \\\"F\\\"\\n portVXM.write(commandString)\\n commandString = \\\"PM-3,C,SA1M400,SA3M100,LM0,I3M400,P5,I1M-90,P5,L3,R \\\"\\n portVXM.write(commandString)\\n\\tresp='abs'\\n\\twhile( '^' not in resp ):\\n\\t resp = portVXM.read(1000)\\n\\t\\n\\tprint \\\"Moving to Standard Operation.\\\"\\t\\n commandString = \\\"PM-2,C,SA1M400,SA3M100,LM0,I3M400,P5,I1M-90,P5,L0,R \\\"\\n portVXM.write(commandString)\\n\\tt0 = time.time() \\n\\tt = time.time() \\n\\tportArd.flushInput()\\n\\tresp='abs'\\n\\t\\n #while( ('21,1,1' not in resp) and ((t-t0)>30.0)):\\n while( not('21,0,1\\\\n\\\\r21,1,1\\\\n\\\\r21,0,0\\\\n\\\\r' in resp) ):\\n\\t resp = portArd.read(10000)\\n\\t t = time.time()\\n\\n\\t#print \\\"CONDITION: \\\\t\\\" ,('21,1,1' not in resp),'\\\\t'\\n\\tportVXM.write(\\\"D,\\\")\\n resp = portVXM.read(1000)\\n\\tXpos, Zpos = GetXZ(portVXM)\\n localtime = time.asctime(time.localtime(time.time()))\\n\\tprint \\\"Source Fully Retracted at (X,Y) : \\\",Xpos, \\\"\\\\t\\\", Zpos ,\\\"\\\\t\\\", \\\"at localtime: \\\", localtime\\n\\tprint abs(t-t0) , \\\"\\\\t Seconds \\\\r\\\"\\n\\tWaitUntilReady(portVXM)\\n\\tportVXM.write(\\\"C,IA1M-0,IA3M-0,R \\\")\\n\\tportVXM.write(\\\"Q, \\\")\\n\\tportArd.flush()\",\n \"def test_plc_read_val(plc_ip, tag_name):\\n\\n plc = ClxDriver()\\n if plc.open(plc_ip):\\n tagg = plc.read_tag(tag_name)\\n plc.close()\\n return (tagg)\\n \\n else:\\n print(\\\"Unable to open\\\", plc_ip)\",\n \"def test_variablepresentations_get(self):\\n pass\",\n \"def test_ge(self):\\n # Success\\n script = self.write_script(\\\"\\\"\\\"\\n variable = 5\\n if variable >= 5:\\n check = 130\\n else:\\n check = 0\\n end\\n \\\"\\\"\\\")\\n check = script.get_variable_or_attribute(\\\"check\\\")\\n self.assertEqual(check, 130)\\n\\n # Failure\\n script = self.write_script(\\\"\\\"\\\"\\n variable = 5\\n if variable >= 8:\\n check = 130\\n else:\\n check = 0\\n end\\n \\\"\\\"\\\")\\n check = script.get_variable_or_attribute(\\\"check\\\")\\n self.assertEqual(check, 0)\",\n \"def test_le(self):\\n # Success\\n script = self.write_script(\\\"\\\"\\\"\\n variable = 10\\n if variable <= 10:\\n check = 80\\n else:\\n check = 10\\n end\\n \\\"\\\"\\\")\\n check = script.get_variable_or_attribute(\\\"check\\\")\\n self.assertEqual(check, 80)\\n\\n # Failure\\n script = self.write_script(\\\"\\\"\\\"\\n variable = 10\\n if variable <= 8:\\n check = 80\\n else:\\n check = 10\\n end\\n \\\"\\\"\\\")\\n check = script.get_variable_or_attribute(\\\"check\\\")\\n self.assertEqual(check, 10)\",\n \"def test_getx(self):\\n point = (1,2)\\n x = utils.getx(point)\\n self.assertEqual(1, x)\",\n \"def test_MINX_pass(self):\\n self.assertTrue(self.mod.minx.isset)\",\n \"def test_contains_true(self):\\n self.assertTrue('1.SKM7.640188' in self.tester)\",\n \"def test_contains_true(self):\\n self.assertTrue('1.SKM7.640188' in self.tester)\",\n \"def test_pickle_within_drs(free_alg):\\n\\n dr = free_alg\\n env = dr.exec_drs(TEST_PICKLE_DRS)\\n\\n assert env['good_symb']\\n assert env['good_indexed']\\n assert env['def_'] == env['def_back']\",\n \"def test_boolean_and_selection(self):\\n\\n # The selection loop:\\n sel = list(mol_res_spin.residue_loop(\\\"#Ap4Aase:4 & :Pro\\\"))\\n\\n # Test:\\n self.assertEqual(len(sel), 1)\\n for res in sel:\\n self.assert_(res.name == \\\"Pro\\\" and res.num == 4)\",\n \"def test_31_if(self):\\n\\t\\tinput = \\\"\\\"\\\"procedure main(); var x:integer; begin x:=foo(); end\\n\\t\\tfunction foo():integer; var a:real; begin\\n\\t\\tif a>0 then return 0;\\n\\t\\tend\\\"\\\"\\\"\\n\\t\\texpect = \\\"Function foo Not Return\\\"\\n\\t\\tself.assertTrue(TestChecker.test(input,expect,431))\",\n \"def is_return(self, pick):\\n return ('-' in pick.name and sorted(pick.name.split('-'), reverse=True)[0].startswith('ret'))\",\n \"def test_reffs(self):\\n self.assertEqual((\\\"1\\\" in list(map(lambda x: str(x), self.TEI.reffs))), True)\\n self.assertEqual((\\\"1.pr\\\" in list(map(lambda x: str(x), self.TEI.reffs))), True)\\n self.assertEqual((\\\"2.40.8\\\" in list(map(lambda x: str(x), self.TEI.reffs))), True)\",\n \"def test_dummy4(self):\\n xpb = XPathBuilder()\\n xp = xpb.dummy()\\n self.assertTrue(xp.log_not() is xp)\",\n \"def test_func(self):\\n self.rol_nu = rol_get_huidige(self.request)\\n return self.rol_nu in (Rollen.ROL_BB, Rollen.ROL_BKO, Rollen.ROL_RKO, Rollen.ROL_RCL, Rollen.ROL_HWL)\",\n \"def test_func(self):\\n self.rol_nu = rol_get_huidige(self.request)\\n return self.rol_nu in (Rollen.ROL_BB, Rollen.ROL_BKO, Rollen.ROL_RKO, Rollen.ROL_RCL, Rollen.ROL_HWL)\",\n \"def test_func(self):\\n self.rol_nu = rol_get_huidige(self.request)\\n return self.rol_nu in (Rollen.ROL_BB, Rollen.ROL_BKO, Rollen.ROL_RKO, Rollen.ROL_RCL, Rollen.ROL_HWL)\",\n \"def read_pin10(self):\\n return self.PIN_10_SIGNAL_TEST\",\n \"def testHealthAssessPyoGangrenosum(self):\\n attr = self.session.create_visit_attr()\\n\\n self.util.boolTypeTest(self, attr, \\\"pyo_gangrenosum\\\")\\n\\n self.util.boolPropertyTest(self, attr, \\\"pyo_gangrenosum\\\")\",\n \"def c_test_population_function(self, function):\\r\\n return 1\",\n \"def test_9_call_expression(self):\\n\\t\\tinput = \\\"\\\"\\\"var x,y:integer;\\n\\t\\tfunction foo(a:real;b:boolean):real; begin return 1; end\\n\\t\\tprocedure main(); var x:real; begin x:=foo(1,false); {OK} end\\n\\t\\tprocedure f(); var y:integer; y:real; begin end\\\"\\\"\\\"\\n\\t\\texpect = \\\"Redeclared Variable: y\\\"\\n\\t\\tself.assertTrue(TestChecker.test(input,expect,409))\",\n \"def test_get(self):\\n obs = self.tester.get('1.SKM7.640188')\\n exp = PrepSample('1.SKM7.640188', self.tester)\\n self.assertEqual(obs, exp)\",\n \"def test_is_99_preHandSimple_correct(self):\\n self.assertEqual(self.hand.getPreHandSimple(), '99')\",\n \"def test_get(self):\\n self.assertEqual(self.tester.get('barcodesequence'), 'AGCGCTCACATC')\",\n \"def test_getint(self):\\n self.assertEqual(self.config.getint('advanced','n'),12)\",\n \"def test_variables_get(self):\\n pass\",\n \"def event_m10_29_80000():\\r\\n \\\"\\\"\\\"State 0,2: [Lib] [Preset] Reproduction fire _SubState\\\"\\\"\\\"\\r\\n assert event_m10_29_x9(z58=1029000, z59=1029099)\\r\\n \\\"\\\"\\\"State 1: Finish\\\"\\\"\\\"\\r\\n EndMachine()\\r\\n Quit()\",\n \"def event_m20_11_81000():\\n \\\"\\\"\\\"State 0,2: [Lib] [Preset] Reproduction fire _SubState\\\"\\\"\\\"\\n assert event_m20_11_x53(z73=2011100, z74=2011199)\\n \\\"\\\"\\\"State 1: Finish\\\"\\\"\\\"\\n EndMachine()\",\n \"def true(symbol):\\n return True\",\n \"def test_emirp_check():\\r\\n pass\",\n \"def test_32_if(self):\\n\\t\\tinput = \\\"\\\"\\\"function foo():integer; var a:real; begin\\n\\t\\twith a:integer;b:real; do begin if a=0 then return 1; else return a; end\\n\\t\\tend\\n\\t\\tprocedure main(); var x:integer; begin x:=foo(); foo(); end\\\"\\\"\\\"\\n\\t\\texpect = \\\"Undeclared Procedure: foo\\\"\\n\\t\\tself.assertTrue(TestChecker.test(input,expect,432))\",\n \"def test_data(clear, data):\\r\\n cmd = ShdlcCmdGetErrorState(clear=clear)\\r\\n assert type(cmd.data) is bytes\\r\\n assert cmd.data == data\",\n \"def test_test_property():\\n\\n contents = (\\\"[Info]\\\\n\\\"\\n \\\"sdk = 23\\\")\\n\\n testutils.deploy_config_raw(contents)\\n\\n assert prop.test_prop('info', 'sdk') == 1\\n\\n testutils.undeploy()\\n\\n return 0\",\n \"def c_test_eval_inp(self, population, run_locals):\\r\\n return 1\",\n \"def test01getNumber(self):\\n self.assertEqual( calc.getNumber(), 1234 )\",\n \"def test_get_property():\\n\\n sdk = '23'\\n contents = (\\\"[Info]\\\\n\\\"\\n \\\"sdk = %s\\\" % sdk)\\n\\n testutils.deploy_config_raw(contents)\\n\\n assert prop.get_prop('info', 'sdk') == sdk\\n\\n testutils.undeploy()\\n\\n return 0\",\n \"def test_get_cell(workbook):\\n assert workbook.get_cell(3,1) == '507906000030242007'\",\n \"def test_pop_returns_value_of_tail(dq_3):\\n assert dq_3.pop() == 'ragtime'\",\n \"def test_id():\\r\\n cmd = ShdlcCmdGetErrorState(clear=False)\\r\\n assert type(cmd.id) is int\\r\\n assert cmd.id == 0xD2\",\n \"def test_RUMI():\\r\\n\\r\\n start = 11051\\r\\n end = 11902\\r\\n read_poems(start, end)\",\n \"def test_stub(self):\\n self.assertEqual(self._value, True)\",\n \"def test_brackets_success(self):\\n found = False\\n pyint = Interpreter()\\n try:\\n pyint.run(code=BF_CORRECT_BRACK)\\n except SystemExit: \\n found = True\\n self.assertFalse(found)\",\n \"def test_extracting_one_value(self):\\n\\t\\tself.assertEqual([\\\"b\\\"], au.extract_variables(bf.Var(\\\"b\\\")), \\\"Invalid variables extracted, expected [b].\\\")\",\n \"def test04(self):\\n model = self.setup_model02()\\n x = model.x\\n x[1].fix(1)\\n wts = StoreSpec.value_isfixed_isactive(only_fixed=True)\\n to_json(model, fname=self.fname, human_read=True, wts=wts)\\n x[1].unfix()\\n x[1].value = 2\\n x[2].value = 10\\n model.g.deactivate()\\n from_json(model, fname=self.fname, wts=wts)\\n assert x[1].fixed\\n assert value(x[1]) == 1\\n assert value(x[2]) == 10\\n assert model.g.active\",\n \"def test_oncvpsp_pseudo(self):\\n ger = Pseudo.from_file(ref_file(\\\"ge.oncvpsp\\\"))\\n print(repr(ger))\\n print(ger)\\n print(ger.as_dict())\\n ger.as_tmpfile()\\n\\n self.assertTrue(ger.symbol == \\\"Ge\\\")\\n self.assert_equal(ger.Z, 32.0)\\n self.assert_equal(ger.Z_val, 4.0)\\n self.assertTrue(ger.isnc)\\n self.assertFalse(ger.ispaw)\\n self.assert_equal(ger.l_max, 2)\\n self.assert_equal(ger.l_local, 4)\\n self.assert_equal(ger.rcore, None)\\n self.assertFalse(ger.has_dojo_report)\",\n \"def test(self):\\n res = self._dll.JLINKARM_Test()\\n return (res == 0)\",\n \"def test_getirramp():\\n d = gini.get_ir_ramp()\\n assert len(d) == 256\",\n \"def testCheck(self):\\r\\n from pydsl.Check import PLYChecker\\r\\n from pydsl.contrib.grammar import example_ply\\r\\n from pydsl.Grammar.Definition import PLYGrammar\\r\\n grammardef = PLYGrammar(example_ply)\\r\\n checker = PLYChecker(grammardef)\\r\\n self.assertTrue(checker.check(\\\"O\\\"))\\r\\n self.assertTrue(checker.check([\\\"O\\\"]))\\r\\n self.assertFalse(checker.check(\\\"FALSE\\\"))\\r\\n #self.assertFalse(checker.check(\\\"\\\")) #FIXME\\r\",\n \"def test_get(self):\\n obs = self.tester.get('1.SKM7.640188')\\n exp = Sample('1.SKM7.640188', self.tester)\\n self.assertEqual(obs, exp)\",\n \"def check_register(dut, expected):\\n for key, value in expected.items():\\n val = dut.DUT_apple_riscv_soc.soc_cpu_core.regfile_inst.ram[key].value.integer\\n assert value == val, f\\\"RAM1: Register {key}, Expected: {value}, Actual: {val}\\\"\\n print(f\\\"RAM1: Register {key}, Expected: {value}, Actual: {val}\\\")\",\n \"def event_m20_11_82000():\\n \\\"\\\"\\\"State 0,2: [Lib] [Preset] Reproduction fire _SubState\\\"\\\"\\\"\\n assert event_m20_11_x53(z73=2011200, z74=2011299)\\n \\\"\\\"\\\"State 1: Finish\\\"\\\"\\\"\\n EndMachine()\",\n \"def test_contains_true(self):\\n self.assertTrue('BarcodeSequence' in self.tester)\\n self.assertTrue('barcodesequence' in self.tester)\",\n \"def test_dummy1(self):\\n xpb = XPathBuilder()\\n xp = xpb.dummy()\\n self.assertFalse(xp)\\n xp = xp & xpb.foo.bar\\n self.assertTrue(xp)\\n exp = '/foo/bar'\\n self.assertEqual(xp.tostring(), exp)\",\n \"def event_m20_11_x110(z24=211000081, z25=802, z26=211020082):\\n \\\"\\\"\\\"State 0,1: [Reproduction] Voice frog singing voice_flag_SubState\\\"\\\"\\\"\\n call = event_m20_11_x107(z24=z24, z26=z26, z25=z25)\\n if call.Get() == 1:\\n pass\\n elif call.Get() == 0:\\n \\\"\\\"\\\"State 3: [Condition] Singing voice frog_flag_SubState\\\"\\\"\\\"\\n assert event_m20_11_x108(z24=z24, z25=z25)\\n \\\"\\\"\\\"State 2: [Execution] Voice frog singing voice_flag_SubState\\\"\\\"\\\"\\n assert event_m20_11_x109(z26=z26, z25=z25)\\n \\\"\\\"\\\"State 4: Finish\\\"\\\"\\\"\\n return 0\",\n \"def test_check_nip(client):\\n is_assigned, request_id = client.check_nip(\\n \\\"8655104670\\\", \\\"41146786026458860703735932\\\"\\n )\\n\\n assert is_assigned\",\n \"def test_57o_Properties(self):\\n self.assertEqual(self.hand.premInd, 0)\\n self.assertEqual(self.hand.connectedInd, 0)\\n self.assertEqual(self.hand.oneSpaceConnectedInd, 1)\\n self.assertEqual(self.hand.suitedInd, 0)\\n self.assertEqual(self.hand.PPInd, 0)\\n self.assertEqual(self.hand.PPCard, 0)\",\n \"def testProtocolReturn(self):\\n self.assertEqual(\\n self.protocol,\\n self.mr.protocol\\n )\\n\\n self.mr._protocol = 'burp'\\n\\n self.assertEqual(\\n 'burp',\\n self.mr.protocol\\n )\",\n \"def verify_payload():\\n return True\",\n \"def Xchg(self, String, infix):\\r\\n\\r\\n tmp1 = self.Check_code_operand(infix[0])\\r\\n tmp2 = self.Check_code_operand(infix[1])\\r\\n if (tmp1 == False) or (tmp2 == False):\\r\\n return False\\r\\n if (tmp1[0] == 'imm') or (tmp1[2] == 0) or ((tmp1[0] == 'imm') and (tmp2[0] == 'imm')):\\r\\n if (tmp1[2]==0)and(tmp2[2]!=0)and (tmp2[0] != 'imm'):\\r\\n tmp1[2]=tmp2[2]\\r\\n else:\\r\\n return False\\r\\n if ((tmp1[0] == 'add') and (tmp2[0] == 'add')) or ((tmp1[0] == 'imm') and (tmp2[0] == 'imm')) or ((tmp1[2] != tmp2[2]) and (tmp2[2] != 0)):\\r\\n return False\\r\\n\\r\\n a = 0\\r\\n if (tmp1[0] != 'add'):\\r\\n a = tmp1[1]\\r\\n else:\\r\\n a = self.Get_value_from_memory(tmp1[1], tmp1[2])\\r\\n b = 0\\r\\n if (tmp2[0] != 'add'):\\r\\n b = tmp2[1]\\r\\n else:\\r\\n b = self.Get_value_from_memory(tmp2[1], tmp1[2])\\r\\n\\r\\n if tmp1[0] == 'reg':\\r\\n if len(infix[0][0]) == 3:\\r\\n self.Registers[infix[0][0]] = b\\r\\n else:\\r\\n self.Save_value_in_reg_X(infix[0][0], b)\\r\\n else:\\r\\n if not self.Save_value_in_memory(tmp1[1], b, tmp1[2]):\\r\\n return False\\r\\n\\r\\n if tmp2[0] == 'reg':\\r\\n if len(infix[1][0]) == 3:\\r\\n self.Registers[infix[1][0]] = a\\r\\n else:\\r\\n self.Save_value_in_reg_X(infix[1][0], a)\\r\\n else:\\r\\n if not self.Save_value_in_memory(tmp2[1], a, tmp1[2]):\\r\\n return False\\r\\n return True\",\n \"def test_29_if(self):\\n\\t\\tinput = \\\"\\\"\\\"procedure main(); var x:integer; begin x:=foo(); end\\n\\t\\tfunction foo():integer; var a:real; begin\\n\\t\\twith a:integer;b:real; do begin if a>0 then return 1; else b:=0; end\\n\\t\\tend\\\"\\\"\\\"\\n\\t\\texpect = \\\"Function foo Not Return\\\"\\n\\t\\tself.assertTrue(TestChecker.test(input,expect,429))\",\n \"def test_read_lxyr(self):\\n cwd = os.path.dirname(os.path.abspath(__file__))\\n # gt_file = os.path.join(cwd, 'test_files/test_gt.lxyr')\\n # ground_truths = read_lxyr(gt_file)\\n test_dir = os.path.join(cwd, 'test_files/')\\n ground_truths = read_lxyr(test_dir, 'test_gt')\\n # print ground_truths\\n self.assertTrue(any(\\n gt for gt in ground_truths\\n if gt.x == 553 and gt.y == 132\\n and gt.radius == 16.64 and gt.class_value == 3))\\n self.assertTrue(any(\\n gt for gt in ground_truths\\n if gt.x == 119 and gt.y == 631\\n and gt.radius == 15.0 and gt.class_value == 4))\",\n \"def event_m20_11_80000():\\n \\\"\\\"\\\"State 0,2: [Lib] [Preset] Reproduction fire _SubState\\\"\\\"\\\"\\n assert event_m20_11_x53(z73=2011000, z74=2011099)\\n \\\"\\\"\\\"State 1: Finish\\\"\\\"\\\"\\n EndMachine()\",\n \"def test04_boolean_operator(self):\\n\\n import _cppyy\\n number = _cppyy.gbl.number\\n\\n n = number(20)\\n assert n\\n\\n n = number(0)\\n assert not n\",\n \"def test_dummy2(self):\\n xpb = XPathBuilder()\\n xp = xpb.dummy()\\n self.assertFalse(xp)\\n xp = xp & (xpb.attr('foo') == 'xyz')\\n self.assertTrue(xp)\\n exp = '@foo = \\\"xyz\\\"'\\n self.assertEqual(xp.tostring(), exp)\",\n \"def test_30_if(self):\\n\\t\\tinput = \\\"\\\"\\\"procedure main(); var x:integer; begin x:=foo(); end\\n\\t\\tfunction foo():integer; var a:real; begin\\n\\t\\twith a:integer;b:real; do begin if a>0 then a:=1; else return 0; end\\n\\t\\tend\\\"\\\"\\\"\\n\\t\\texpect = \\\"Function foo Not Return\\\"\\n\\t\\tself.assertTrue(TestChecker.test(input,expect,430))\",\n \"def test_is_57o_preHandSimple_correct(self):\\n self.assertEqual(self.hand.getPreHandSimple(), '75o')\",\n \"def test_substgrpexcl00402m7_positive(mode, save_output, output_format):\\n assert_bindings(\\n schema=\\\"sunData/ElemDecl/substGroupExclusions/substGrpExcl00402m/substGrpExcl00402m7.xsd\\\",\\n instance=\\\"sunData/ElemDecl/substGroupExclusions/substGrpExcl00402m/substGrpExcl00402m7_p.xml\\\",\\n class_name=\\\"Root\\\",\\n version=\\\"1.1\\\",\\n mode=mode,\\n save_output=save_output,\\n output_format=output_format,\\n structure_style=\\\"filenames\\\",\\n )\",\n \"def event_m20_11_83000():\\n \\\"\\\"\\\"State 0,2: [Lib] [Preset] Reproduction fire _SubState\\\"\\\"\\\"\\n assert event_m20_11_x53(z73=2011300, z74=2011399)\\n \\\"\\\"\\\"State 1: Finish\\\"\\\"\\\"\\n EndMachine()\",\n \"def CHK( UDStat ):\\n if UDStat != NOERRORS:\\n raise UniversalLibraryError( UDStat )\",\n \"def test_get_temp(loaded_fridge):\\n assert loaded_fridge.get_temp() == loaded_fridge.temp\",\n \"def test_stub() -> None:\\n test_val = 3\\n assert test_val == 3\",\n \"def test_27_if(self):\\n\\t\\tinput = \\\"\\\"\\\"procedure main(); var x:integer; begin x:=foo(); end\\n\\t\\tfunction foo():integer; var a:real; begin\\n\\t\\twith a:integer;b:real; do begin if a>0 then a:=0; else b:=0; end\\n\\t\\tend\\\"\\\"\\\"\\n\\t\\texpect = \\\"Function foo Not Return\\\"\\n\\t\\tself.assertTrue(TestChecker.test(input,expect,427))\",\n \"def test_rf_value(self):\\n\\t\\tdetails = self.watcher.describe()\\t\\t\\n\\t\\tself.assertTrue((details.rf.to_numpy()[0:8] == 9.0).all())\",\n \"def testnatom(self):\\r\\n assert self.data.natom == 20\",\n \"def unitTest(self):\\n\\n if 'y' in self.snmp_obj.dbg:\\n print(self.snmp_obj.mib_dict)\\n for k in self.snmp_obj.mib_dict:\\n print(k, \\\":\\\", self.snmp_obj.mib_dict[k])\\n\\n print(\\\"Testing get mib oid\\\")\\n\\n for i in self.mibs:\\n oid = self.snmp_obj.get_mib_oid(i)\\n print('mib: %s - oid=%s' % (i, oid))\\n\\n return True\",\n \"def test_id():\\n assert Packet106.id == 106\",\n \"def test_getting(self):\\n self.assertEqual(self.test_notes['C'], self.C)\",\n \"def test_getitem(self):\\n obs = self.tester['1.SKM7.640188']\\n exp = PrepSample('1.SKM7.640188', self.tester)\\n self.assertEqual(obs, exp)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.5512495","0.54203445","0.5234334","0.5186235","0.513492","0.5132137","0.5124492","0.5119325","0.5085518","0.50540435","0.50286376","0.5028228","0.50170285","0.50151104","0.50103855","0.4989811","0.49670818","0.49645856","0.49455714","0.493474","0.493474","0.49287537","0.49034783","0.49002767","0.48985684","0.48946097","0.4867602","0.48639008","0.48600543","0.48600543","0.48566508","0.48364127","0.4805699","0.47914377","0.47877735","0.47874647","0.4786578","0.4786578","0.4786578","0.47830084","0.47660437","0.47647673","0.4764395","0.47620922","0.47511968","0.47476676","0.47472867","0.47345355","0.47344595","0.47325748","0.47173956","0.47136986","0.4705302","0.4695226","0.46940032","0.46931794","0.46907532","0.4688725","0.4681009","0.46766368","0.46735662","0.4657959","0.4655685","0.4653598","0.46529362","0.4652088","0.46496466","0.46438643","0.46414793","0.46404964","0.46353337","0.4634045","0.4633901","0.46326703","0.46309495","0.46300173","0.4626585","0.46230108","0.46209654","0.46205476","0.4619603","0.46148315","0.46131375","0.46074024","0.4606707","0.4606233","0.46035278","0.4603216","0.46000305","0.45996812","0.45978743","0.45956957","0.45882395","0.45879036","0.45857877","0.45850226","0.45849124","0.45809078","0.45779765","0.45745224"],"string":"[\n \"0.5512495\",\n \"0.54203445\",\n \"0.5234334\",\n \"0.5186235\",\n \"0.513492\",\n \"0.5132137\",\n \"0.5124492\",\n \"0.5119325\",\n \"0.5085518\",\n \"0.50540435\",\n \"0.50286376\",\n \"0.5028228\",\n \"0.50170285\",\n \"0.50151104\",\n \"0.50103855\",\n \"0.4989811\",\n \"0.49670818\",\n \"0.49645856\",\n \"0.49455714\",\n \"0.493474\",\n \"0.493474\",\n \"0.49287537\",\n \"0.49034783\",\n \"0.49002767\",\n \"0.48985684\",\n \"0.48946097\",\n \"0.4867602\",\n \"0.48639008\",\n \"0.48600543\",\n \"0.48600543\",\n \"0.48566508\",\n \"0.48364127\",\n \"0.4805699\",\n \"0.47914377\",\n \"0.47877735\",\n \"0.47874647\",\n \"0.4786578\",\n \"0.4786578\",\n \"0.4786578\",\n \"0.47830084\",\n \"0.47660437\",\n \"0.47647673\",\n \"0.4764395\",\n \"0.47620922\",\n \"0.47511968\",\n \"0.47476676\",\n \"0.47472867\",\n \"0.47345355\",\n \"0.47344595\",\n \"0.47325748\",\n \"0.47173956\",\n \"0.47136986\",\n \"0.4705302\",\n \"0.4695226\",\n \"0.46940032\",\n \"0.46931794\",\n \"0.46907532\",\n \"0.4688725\",\n \"0.4681009\",\n \"0.46766368\",\n \"0.46735662\",\n \"0.4657959\",\n \"0.4655685\",\n \"0.4653598\",\n \"0.46529362\",\n \"0.4652088\",\n \"0.46496466\",\n \"0.46438643\",\n \"0.46414793\",\n \"0.46404964\",\n \"0.46353337\",\n \"0.4634045\",\n \"0.4633901\",\n \"0.46326703\",\n \"0.46309495\",\n \"0.46300173\",\n \"0.4626585\",\n \"0.46230108\",\n \"0.46209654\",\n \"0.46205476\",\n \"0.4619603\",\n \"0.46148315\",\n \"0.46131375\",\n \"0.46074024\",\n \"0.4606707\",\n \"0.4606233\",\n \"0.46035278\",\n \"0.4603216\",\n \"0.46000305\",\n \"0.45996812\",\n \"0.45978743\",\n \"0.45956957\",\n \"0.45882395\",\n \"0.45879036\",\n \"0.45857877\",\n \"0.45850226\",\n \"0.45849124\",\n \"0.45809078\",\n \"0.45779765\",\n \"0.45745224\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6135992"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":6599,"cells":{"query":{"kind":"string","value":"Test the popxl basic mnist example"},"document":{"kind":"string","value":"def test_documentation_popxl_mnist(self):\n filename = \"mnist.py\"\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)"},"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 main():\n\n os.system(\"rm -rf images; mkdir images\")\n\n if (len(sys.argv) > 1):\n N = int(sys.argv[1])\n else:\n N = 10\n\n x_test = np.load(\"../../../../data/mnist/mnist_test_images.npy\")\n\n for i in range(N):\n r,c = random.randint(6,12), random.randint(6,12)\n g = np.zeros(r*c)\n for j in range(r*c):\n if (random.random() < 0.15):\n g[j] = 1\n g = g.reshape((r,c))\n g[:,0] = g[0,:] = g[:,-1] = g[-1,:] = 0\n\n img = np.zeros((28*r,28*c), dtype=\"uint8\")\n for x in range(r):\n for y in range(c):\n if (g[x,y] == 1):\n n = random.randint(0, x_test.shape[0])\n im = x_test[n]\n img[28*x:(28*x+28), 28*y:(28*y+28)] = im\n \n Image.fromarray(img).save(\"images/image_%04d.png\" % i)","def mnist_testing(shuffled = True):\n mndata = MNIST(MNIST_PATH)\n test_ims, test_labels = mndata.load_testing()\n test_X = np.array(test_ims).T\n test_y = np.array(test_labels).T\n return test_X, test_y","def test_keras_mnist():\n data = fetch(\"mnist\")\n check(data, n_samples_train=60000, n_samples_test=10000, n_features=28 * 28)","def test_keras_mnist_return_X_y():\n X, y = fetch(\"mnist\", return_X_y=True)\n assert X.shape == (70000, 28 * 28)\n assert y.shape == (70000,)","def test_mnist(args):\n # type: () -> None\n\n # Build dataset and model\n dataset = MNIST(path=args.path)\n model = MEMMNIST(input_shape=dataset.shape, code_length=64, cpd_channels=100, mem_dim=100, shrink_thres=0.5/100).cuda().eval()\n\n # Set up result helper and perform test\n helper = MEMResultHelper(dataset, model, checkpoints_dir=args.checkpoints, output_file='mem_mnist.txt')\n helper.test_one_class_classification()","def test_rand(self):\n assert len(self._mnist.random()[:5]) == 5\n pass","def test_get_mnist_data(self):\n # TODO: Remove once get_mnist_data(...) is fixed.\n pass\n # mnist = get_mnist_data()\n # self.assertEqual(len(mnist.data), 60000)\n # self.assertEqual(len(mnist.labels), 60000)","def mnist(path=None):\r\n url = 'http://yann.lecun.com/exdb/mnist/'\r\n files = ['train-images-idx3-ubyte.gz',\r\n 'train-labels-idx1-ubyte.gz',\r\n 't10k-images-idx3-ubyte.gz',\r\n 't10k-labels-idx1-ubyte.gz']\r\n\r\n if path is None:\r\n # Set path to /home/USER/data/mnist or C:\\Users\\USER\\data\\mnist\r\n path = os.path.join(os.path.expanduser('~'), 'data', 'mnist')\r\n\r\n # Create path if it doesn't exist\r\n os.makedirs(path, exist_ok=True)\r\n\r\n # Download any missing files\r\n for file in files:\r\n if file not in os.listdir(path):\r\n urlretrieve(url + file, os.path.join(path, file))\r\n print(\"Downloaded %s to %s\" % (file, path))\r\n\r\n def _images(path):\r\n \"\"\"Return images loaded locally.\"\"\"\r\n with gzip.open(path) as f:\r\n # First 16 bytes are magic_number, n_imgs, n_rows, n_cols\r\n pixels = np.frombuffer(f.read(), 'B', offset=16)\r\n return pixels.reshape(-1, 784).astype('float32') / 255\r\n\r\n def _labels(path):\r\n \"\"\"Return labels loaded locally.\"\"\"\r\n with gzip.open(path) as f:\r\n # First 8 bytes are magic_number, n_labels\r\n integer_labels = np.frombuffer(f.read(), 'B', offset=8)\r\n\r\n def _onehot(integer_labels):\r\n \"\"\"Return matrix whose rows are onehot encodings of integers.\"\"\"\r\n n_rows = len(integer_labels)\r\n n_cols = integer_labels.max() + 1\r\n onehot = np.zeros((n_rows, n_cols), dtype='uint8')\r\n onehot[np.arange(n_rows), integer_labels] = 1\r\n return onehot\r\n\r\n return _onehot(integer_labels)\r\n\r\n train_images = _images(os.path.join(path, files[0]))\r\n train_labels = _labels(os.path.join(path, files[1]))\r\n test_images = _images(os.path.join(path, files[2]))\r\n test_labels = _labels(os.path.join(path, files[3]))\r\n\r\n return train_images, train_labels, test_images, test_labels","def test_dataset():\n X,Y = get_MNIST_training_normalized()\n digits_test_truth = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 632, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 166, 0, 0, 0, 0, 0]\n digits_test = []\n for example in itertools.islice(X,30):\n digits_test.append(sum(example[1:100]))\n assert(example.shape == (28*28,))\n\n show_as_image(X[0,:], 28, 28)\n print digits_test\n print digits_test_truth\n assert(digits_test_truth == digits_test)\n assert(X.shape == (60000, 28*28))\n assert(Y.shape == (60000,))\n return \"Dziala :)\"","def MNIST_data():\n\n # Pobieramy macierze numpy z cyframi\n # images[i,j,k] <=> piksel (j,k) z i-tego obrazka w zbiorze danych\n images, labels = get_MNIST_dataset(range(10), \"training\") #pierwszy argument to\n\n # a) Ilosc przykladow i rozmiary danych\n print \"Raw training data dimensions \", images.shape\n print \"Labels dimensions \",labels.shape\n\n # b) Ile jest cyfr 2?\n print \"Counting 2 in training dataset \",len(filter(lambda x: x == 2, labels))\n\n # c) Jaki jest sredni obrazek 2 ? (Usrednienie wszystkich macierzy ktore sa 2)\n\n #1. Pobierzmy wszystkie dwojki, fajny sposob indeksowania\n print labels == 2\n only_2 = images[labels == 2, :, :]\n print \"Checking number of 2s \", only_2.shape\n\n #2. TODO: Usrednienie (matrix.mean moze byc przydatne)\n\n #3. TODO: narysowanie usrednionej cyfry (zobacz pl.imshow)\n\n # d) Ostatnie - przetworzmy ostatnia cyfre do 1 wymiarowego wektora\n vectorized = np.reshape(images[-1], newshape=(images[-1].shape[0]*images[-1].shape[1]))\n print \"Vectorized last digit \", vectorized","def test_show_examples():\n skip_if_no_matplotlib()\n skip_if_no_data()\n with open('temp.yaml', 'w') as f:\n f.write(\"\"\"\n!obj:pylearn2.datasets.mnist.MNIST {\n which_set: 'train'\n}\n\"\"\")\n show_examples('temp.yaml', 28, 28, out='garbage.png')\n os.remove('temp.yaml')","def mnist_training():\n mndata = MNIST(MNIST_PATH)\n train_ims, train_labels = mndata.load_training()\n train_X = np.array(train_ims).T\n train_y = np.array(train_labels).T\n return train_X, train_y","def create_mnistm(X: Any) -> Any:\n\n bst_path = \"./data/MNIST_M/BSR_bsds500.tgz\"\n\n rand = np.random.RandomState(42)\n train_files = []\n\n with tarfile.open(bst_path, \"r\") as bsr_file:\n for name in bsr_file.getnames():\n if name.startswith(\"BSR/BSDS500/data/images/train/\"):\n train_files.append(name)\n\n print(\"Loading BSR training images\")\n background_data = []\n for name in train_files:\n try:\n fp = bsr_file.extractfile(name)\n bg_img = skimage.io.imread(fp)\n background_data.append(bg_img)\n except:\n continue\n\n X_ = np.zeros([X.shape[0], 28, 28, 3], np.uint8)\n for i in range(X.shape[0]):\n if i % 1000 == 0:\n print(\"Processing example\", i)\n\n bg_img = rand.choice(background_data)\n d = mnist_to_img(X[i])\n d = compose_image(d, bg_img)\n X_[i] = d\n\n return X_","def test_load_data(self):\n assert len(self._mnist.get()) == 10\n assert self._mnist.get()[0].label == 7\n pass","def MNIST_experiment():\n tsetlin_machine = TsetlinMachine(number_clauses=1000,\n number_action_states=1000,\n precision=3.0,\n threshold=10)\n\n X, y, val_X, val_y = MNIST()\n\n tsetlin_machine.fit(X, y, val_X, val_y, 300)\n print('Final training accuracy:', tsetlin_machine.accuracy(X, y))\n print('Final validation accuracy:', tsetlin_machine.accuracy(val_X, val_y))","def get_mnist():\n from keras.datasets import mnist\n\n # input image dimensions\n img_rows, img_cols = 28, 28\n num_classes = 10\n # the data, shuffled and split between train and test sets\n (x_train, y_train), (x_test, y_test) = mnist.load_data()\n\n if K.image_data_format() == 'channels_first':\n print (\"Using Channels first\")\n x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)\n x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)\n input_shape = (1, img_rows, img_cols)\n else:\n print(\"Channels last\")\n x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)\n x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)\n input_shape = (img_rows, img_cols, 1)\n\n return (x_train, y_train), (x_test, y_test)","def test_readme_minimal():\n # Data sampler that generates balanced batches from MNIST dataset\n sampler = TFDatasetMultiShotMemorySampler(\n dataset_name='mnist',\n classes_per_batch=10\n )\n\n # Build a Similarity model using standard Keras layers\n inputs = layers.Input(shape=(28, 28, 1))\n x = layers.experimental.preprocessing.Rescaling(1/255)(inputs)\n x = layers.Conv2D(64, 3, activation='relu')(x)\n x = layers.Flatten()(x)\n x = layers.Dense(64, activation='relu')(x)\n outputs = MetricEmbedding(64)(x)\n\n # Build a specialized Similarity model\n model = SimilarityModel(inputs, outputs)\n\n # Train Similarity model using contrastive loss\n model.compile('adam', loss=MultiSimilarityLoss())\n model.fit(sampler, epochs=5)\n\n # Index 100 embedded MNIST examples to make them searchable\n sx, sy = sampler.get_slice(0, 100)\n model.index(x=sx, y=sy, data=sx)\n\n # Find the top 5 most similar indexed MNIST examples for a given example\n qx, qy = sampler.get_slice(3713, 1)\n nns = model.single_lookup(qx[0]) # noqa\n\n # ! don't add viz its block the test in certain env.\n # Visualize the query example and its top 5 neighbors\n # viz_neigbors_imgs(qx[0], qy[0], nns)","def get_mnist_mlp():\n # Set defaults.\n nb_classes = 10 #dataset dependent \n batch_size = 64\n epochs = 4\n input_shape = (784,)\n\n # Get the data.\n (x_train, y_train), (x_test, y_test) = mnist.load_data()\n x_train = x_train.reshape(60000, 784)\n x_test = x_test.reshape(10000, 784)\n x_train = x_train.astype('float32')\n x_test = x_test.astype('float32')\n x_train /= 255\n x_test /= 255\n\n # convert class vectors to binary class matrices\n y_train = to_categorical(y_train, nb_classes)\n y_test = to_categorical(y_test, nb_classes)\n\n return (nb_classes, batch_size, input_shape, x_train, x_test, y_train, y_test, epochs)","def load_mnist(path='./', test_size=0.3, random_state = 123):\n \n np.random.seed(random_state)\n if 'X_train.npy' not in os.listdir(path=path) or 'y_train.npy' not in os.listdir(path=path):\n print (\"Train dataset not found. Downloading...\")\n os.system(\"curl -L -o train.zip {}\".format(TRAIN_DATA_LINK))\n os.system(\"unzip train.zip\")\n os.system(\"tar -xf trainingSet.tar.gz\")\n images = []\n labels = []\n for class_name in os.listdir('./trainingSet'):\n if 'ipynb' not in class_name and '.DS' not in class_name:\n for image_name in os.listdir('./trainingSet/{}'.format(class_name)):\n image = imread('./trainingSet/{}/{}'.format(class_name, image_name))\n images.append(image)\n labels.append(int(class_name))\n X_train = np.array(images)\n y_train = np.array(labels)\n\n permutation = np.random.permutation(X_train.shape[0])\n X_train = X_train[permutation]\n y_train = y_train[permutation]\n\n with open('X_train.npy', 'wb') as f:\n np.save(f, X_train)\n with open('y_train.npy', 'wb') as f:\n np.save(f, y_train)\n os.system(\"rm -rf trainingSet\")\n os.system(\"rm -rf train.zip\")\n os.system(\"rm -rf trainingSet.tar.gz\")\n else:\n X_train = np.load('X_train.npy')\n y_train = np.load('y_train.npy')\n\n if 'X_test.npy' not in os.listdir(path=path) or 'y_test.npy' not in os.listdir(path=path):\n print (\"Test dataset not found. Downloading...\")\n os.system(\"curl -L -o test.zip {}\".format(TEST_DATA_LINK))\n os.system(\"unzip test.zip\")\n os.system(\"tar -xf trainingSample.tar.gz\")\n images = []\n labels = []\n for class_name in os.listdir('./trainingSample'):\n if 'ipynb' not in class_name and '.DS' not in class_name:\n for image_name in os.listdir('./trainingSample/{}'.format(class_name)):\n image = imread('./trainingSample/{}/{}'.format(class_name, image_name))\n images.append(image)\n labels.append(int(class_name))\n X_test = np.array(images)\n y_test = np.array(labels)\n with open('X_test.npy', 'wb') as f:\n np.save(f, X_test)\n with open('y_test.npy', 'wb') as f:\n np.save(f, y_test)\n\n os.system(\"rm -rf trainingSample\")\n os.system(\"rm -rf test.zip\")\n os.system(\"rm -rf trainingSet.tar.gz\")\n\n else:\n X_test = np.load('X_test.npy')\n y_test = np.load('y_test.npy')\n\n return X_train, X_test, y_train, y_test","def main():\n # Import or download the mnist data, from target file path.\n mnist = input_data.read_data_sets(\"Data/\", one_hot=True)\n\n # Train and test model.\n train(mnist)","def test_neuron(self):\r\n # crear una lista 1-D (Horizontal, Entradas).\r\n Z = [1, 2, 3]\r\n # crear una lista 1-D (Vertical, Pesos de la red).\r\n W = [10, 20, 30]\r\n # Inicializamos la neurona, y obtenemos el valor que toma dado W * Z\r\n # X(k) = W * Z\r\n result = rhonn(W, Z).predict()\r\n # Comprobamos el resultado \r\n self.assertEqual(result, 140)","def mnist(path):\n with open(path, 'r') as f:\n for line in f:\n data = line.strip().split(',')\n\n # Label is a vector with one element per class\n label = [0.0] * 10\n label[int(data[0])] = 1.0 \n\n # The data are images of 28x28 pixels\n image_array = np.asfarray(data[1:]).reshape((28, 28))\n # Normalize the pictures \n image_array = image_array / 255.0\n\n #plt.imshow(image_array, cmap='Greys', interpolation='None')\n yield (image_array, label)","def mnist(path):\n with open(path, 'r') as f:\n for line in f:\n data = line.strip().split(',')\n\n # Label is a vector with one element per class\n label = [0.01] * 10\n label[int(data[0])] = 0.99\n\n # The data are images of 28x28 pixels\n #image_array = np.asfarray(data[1:]).reshape((28, 28))\n image_array = np.asfarray(data[1:])\n # Normalize all values between [0.01, 1.0]\n image_array = ((image_array) / 255.0 * 0.99) + 0.01\n\n #plt.imshow(image_array, cmap='Greys', interpolation='None')\n yield (image_array, label)","def test_documentation_popxl_mnist_rts_train_test(self):\n filename = \"mnist_rts.py --replication-factor 2 --rts --test\"\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)","def create_mnistm(X):\r\n X_ = np.zeros([X.shape[0], 28, 28, 3], np.uint8)\r\n for i in range(X.shape[0]):\r\n bg_img = rand.choice(background_data)\r\n d = mnist_to_img(X[i])\r\n d = compose_image(d, bg_img)\r\n X_[i] = d\r\n return X_","def load_mnist(path='mnist/mnist.npz'):\n\n with np.load(path) as f:\n x_train, y_train = f['x_train'], f['y_train']\n x_test, y_test = f['x_test'], f['y_test']\n x_train = x_train.astype(np.float32) / 255.\n y_train = y_train.astype(np.int32)\n x_test = x_test.astype(np.float32) / 255.\n y_test = y_test.astype(np.int32)\n \n return (x_train, y_train), (x_test, y_test)","def test_documentation_popxl_mnist_rts_train(self):\n filename = \"mnist_rts.py --replication-factor 2 --rts\"\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)","def import_mnist():\n\turl_mnist = \"http://deeplearning.net/data/mnist/mnist.pkl.gz\"\n\tfile_name = \"mnist.pkl.gz\"\n\twork_directory = \"mnist\"\n\tfile_path = maybe_download(url=url_mnist, file_name=file_name, work_directory=work_directory)\n\n\timport pickle\n\twith gzip.open(file_path,'rb') as ff :\n\t\tu = pickle._Unpickler( ff )\n\t\tu.encoding = 'latin1'\n\t\ttrain, val, test = u.load()\n\t\ttrainX = np.array(train[0])\n\t\ttrainY = np.reshape(train[1], [50000, 1])\n\t\tvalX = np.array(val[0])\n\t\tvalY = np.reshape(val[1], [10000, 1])\n\t\ttestX = np.array(test[0])\n\t\ttestY = np.reshape(test[1], [10000, 1])\n\t\ttrainX = np.concatenate((trainX, valX), axis = 0)\n\t\ttrainY = np.concatenate((trainY, valY), axis = 0)\n\treturn trainX, trainY, testX, testY","def load_mnist_dataset(shape=(-1,784)):\n # We first define a download function, supporting both Python 2 and 3.\n if sys.version_info[0] == 2:\n from urllib import urlretrieve\n else:\n from urllib.request import urlretrieve\n\n def download(filename, source='http://yann.lecun.com/exdb/mnist/'):\n print(\"Downloading %s\" % filename)\n urlretrieve(source + filename, filename)\n\n # We then define functions for loading MNIST images and labels.\n # For convenience, they also download the requested files if needed.\n import gzip\n\n def load_mnist_images(filename):\n if not os.path.exists(filename):\n download(filename)\n # Read the inputs in Yann LeCun's binary format.\n with gzip.open(filename, 'rb') as f:\n data = np.frombuffer(f.read(), np.uint8, offset=16)\n # The inputs are vectors now, we reshape them to monochrome 2D images,\n # following the shape convention: (examples, channels, rows, columns)\n data = data.reshape(shape)\n # data = data.reshape(-1, 1, 28, 28) # for lasagne\n # data = data.reshape(-1, 28, 28, 1) # for tensorflow\n # data = data.reshape(-1, 784) # for tensorflow\n # The inputs come as bytes, we convert them to float32 in range [0,1].\n # (Actually to range [0, 255/256], for compatibility to the version\n # provided at http://deeplearning.net/data/mnist/mnist.pkl.gz.)\n return data / np.float32(256)\n\n def load_mnist_labels(filename):\n if not os.path.exists(filename):\n download(filename)\n # Read the labels in Yann LeCun's binary format.\n with gzip.open(filename, 'rb') as f:\n data = np.frombuffer(f.read(), np.uint8, offset=8)\n # The labels are vectors of integers now, that's exactly what we want.\n return data\n\n # We can now download and read the training and test set images and labels.\n ## you may want to change the path\n data_dir = '' #os.getcwd() + '/lasagne_tutorial/'\n # print('data_dir > %s' % data_dir)\n\n X_train = load_mnist_images(data_dir+'train-images-idx3-ubyte.gz')\n y_train = load_mnist_labels(data_dir+'train-labels-idx1-ubyte.gz')\n X_test = load_mnist_images(data_dir+'t10k-images-idx3-ubyte.gz')\n y_test = load_mnist_labels(data_dir+'t10k-labels-idx1-ubyte.gz')\n\n # We reserve the last 10000 training examples for validation.\n X_train, X_val = X_train[:-10000], X_train[-10000:]\n y_train, y_val = y_train[:-10000], y_train[-10000:]\n\n ## you may want to plot one example\n # print('X_train[0][0] >', X_train[0][0].shape, type(X_train[0][0])) # for lasagne\n # print('X_train[0] >', X_train[0].shape, type(X_train[0])) # for tensorflow\n # # exit()\n # # [[..],[..]] (28, 28) numpy.ndarray\n # # plt.imshow 只支持 (28, 28)格式，不支持 (1, 28, 28),所以用 [0][0]\n # fig = plt.figure()\n # #plotwindow = fig.add_subplot(111)\n # # plt.imshow(X_train[0][0], cmap='gray') # for lasagne (-1, 1, 28, 28)\n # plt.imshow(X_train[0].reshape(28,28), cmap='gray') # for tensorflow (-1, 28, 28, 1)\n # plt.title('A training image')\n # plt.show()\n\n # We just return all the arrays in order, as expected in main().\n # (It doesn't matter how we do this as long as we can read them again.)\n return X_train, y_train, X_val, y_val, X_test, y_test","def test_machine_learning():","def test_documentation_popxl_mnist_replication_train(self):\n filename = \"mnist_rts.py --replication-factor 2\"\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)","def get_mnist(self):\n # Set defaults.\n nb_classes = 10\n batch_size = 100\n input_shape = (784,)\n\n # Get the data.\n (x_train, y_train), (x_test, y_test) = mnist.load_data()\n x_train = x_train.reshape(60000, 784)\n x_test = x_test.reshape(10000, 784)\n x_train = x_train.astype('float32')\n x_test = x_test.astype('float32')\n x_train /= 255\n x_test /= 255\n\n # convert class vectors to binary class matrices\n y_train = to_categorical(y_train, nb_classes)\n y_test = to_categorical(y_test, nb_classes)\n\n return (nb_classes, batch_size, input_shape, x_train, x_test, y_train, y_test)","def readMNISTData():\n mnist = input_data.read_data_sets(\"MNIST_data\",one_hot=True) \n return mnist","def load_mnist(path, kind='train'):\n '''ref: http://yann.lecun.com/exdb/mnist/ '''\n ''' each hand write is 28x28 = 784, a 1 dim vector'''\n labels_path = os.path.join(path,\n '%s-labels-idx1-ubyte'\n % kind)\n images_path = os.path.join(path,\n '%s-images-idx3-ubyte'\n % kind)\n\n # check the offical doc to know how to extract the content\n '''\n [offset] [type] [value] [description]\n 0000 32 bit integer 0x00000801(2049) magic number (MSB first)\n 0004 32 bit integer 60000 number of items\n 0008 unsigned byte ?? label\n 0009 unsigned byte ?? label\n ........\n xxxx unsigned byte ?? label\n The labels values are 0 to 9.\n '''\n with open(labels_path, 'rb') as lbpath:\n magic, n = struct.unpack('>II',\n lbpath.read(8))\n labels = np.fromfile(lbpath,\n dtype=np.uint8)\n\n '''\n [offset] [type] [value] [description]\n 0000 32 bit integer 0x00000803(2051) magic number\n 0004 32 bit integer 60000 number of images\n 0008 32 bit integer 28 number of rows\n 0012 32 bit integer 28 number of columns\n 0016 unsigned byte ?? pixel\n 0017 unsigned byte ?? pixel\n ........\n xxxx unsigned byte ?? pixel\n Pixels are organized row-wise. Pixel values are 0 to 255. 0 means background (white), 255 means foreground (black).\n '''\n with open(images_path, 'rb') as imgpath:\n magic, num, rows, cols = struct.unpack(\">IIII\",\n imgpath.read(16))\n ''' each hand write is 28x28 = 784, a 1 dim vector'''\n images = np.fromfile(imgpath,\n dtype=np.uint8).reshape(len(labels), 784)\n\n return images, labels","def load_mnist(dataset=\"training\", digits=np.arange(10), path=\".\"):\n\n if dataset == \"training\":\n fname_img = os.path.join(path, 'train-images-idx3-ubyte')\n fname_lbl = os.path.join(path, 'train-labels-idx1-ubyte')\n elif dataset == \"testing\":\n fname_img = os.path.join(path, 't10k-images-idx3-ubyte')\n fname_lbl = os.path.join(path, 't10k-labels-idx1-ubyte')\n else:\n raise ValueError(\"dataset must be 'testing' or 'training'\")\n\n flbl = open(fname_lbl, 'rb')\n magic_nr, size = struct.unpack(\">II\", flbl.read(8))\n lbl = pyarray(\"b\", flbl.read())\n flbl.close()\n\n fimg = open(fname_img, 'rb')\n magic_nr, size, rows, cols = struct.unpack(\">IIII\", fimg.read(16))\n img = pyarray(\"B\", fimg.read())\n fimg.close()\n\n ind = [ k for k in range(size) if lbl[k] in digits ]\n N = len(ind)\n\n images = zeros((N, rows, cols), dtype=uint8)\n labels = zeros((N, 1), dtype=int8)\n for i in range(len(ind)):\n images[i] = array(img[ ind[i]*rows*cols : (ind[i]+1)*rows*cols ]).reshape((rows, cols))\n labels[i] = lbl[ind[i]]\n\n return images, labels","def test_train():\n set_seed(42) # Noqa\n transform = transforms.Compose([\n transforms.ToTensor(),\n ])\n mnist_train = MNIST(\"./\", download=True, train=False, transform=transform)\n model = SimpleNet()\n\n optimizer = torch.optim.SGD(model.parameters(), lr=1e-2, momentum=0.5)\n criterion = nn.CrossEntropyLoss()\n\n train_loader = DataLoader(mnist_train, batch_size=64, shuffle=True,\n num_workers=0)\n loss, accuracy = train(model, optimizer, criterion, train_loader,\n imshape=(-1, 28*28))\n\n assert type(loss) == torch.Tensor\n assert type(accuracy) == np.float64\n assert len(loss.shape) == 0","def test_mnist():\n skip_if_no_data()\n mode = get_default_mode()\n if hasattr(mode, 'check_py_code'):\n old_value = mode.check_py_code\n mode.check_py_code = False\n try:\n if config.mode == \"DEBUG_MODE\":\n yaml_file = 'mnist_fast'\n else:\n yaml_file = 'mnist'\n limited_epoch_train(os.path.join(yaml_file_path, '%s.yaml'\n % yaml_file))\n try:\n os.remove(os.path.join(save_path, '%s.pkl' % yaml_file))\n os.remove(os.path.join(save_path, '%s_best.pkl' % yaml_file))\n except Exception:\n pass\n finally:\n if hasattr(mode, 'check_py_code'):\n mode.check_py_code = old_value","def main():\n\n dataset = ConvMNIST(64)\n print(dataset.get_train().x.shape)\n\n\n inputs = Value(type=tf.float32, shape=(None, 28, 28, 1), cls = None)\n targets = Value(type=tf.int64, shape=(None), cls = 10)\n learning_rate = 0.0001\n\n fc_hidden = [1024, 500]\n c_h = [\n (3, 3, 1, 32),\n (3, 3, 32, 64)\n ]\n conv_hidden = ConvHidden(conv_weights=c_h, fc_weights=fc_hidden)\n\n config = Config(inputs, targets, conv_hidden, learning_rate)\n\n network = ConvNetworkBuilder(config)\n hidden = FFConvHiddenBuilder()\n _ = network.build_network(hidden)\n\n\n train_config = TrainerConfig(\n epochs = EPOCHS, display_after = DISPLAY_STEP, \n keep_prob = KEEP_PROB,checkpoint_path=None, \n summary_path=None\n )\n\n trainer = Trainer(network, train_config)\n trainer.train(dataset)","def test_X_test_property():\n atom = ATOMClassifier(X_bin, y_bin, random_state=1)\n atom.run([\"MNB\", \"LR\"])\n assert atom.X_test.equals(atom.mnb.X_test)\n assert check_scaling(atom.lr.X_test)","def cg_optimization_mnist(n_epochs=50, mnist_pkl_gz='mnist.pkl.gz'):\r\n #############\r\n # LOAD DATA #\r\n #############\r\n datasets = load_data(mnist_pkl_gz)\r\n\r\n train_set_x, train_set_y = datasets[0]\r\n valid_set_x, valid_set_y = datasets[1]\r\n test_set_x, test_set_y = datasets[2]\r\n\r\n batch_size = 600 # size of the minibatch\r\n\r\n n_train_batches = train_set_x.get_value(borrow=True).shape[0] / batch_size\r\n n_valid_batches = valid_set_x.get_value(borrow=True).shape[0] / batch_size\r\n n_test_batches = test_set_x.get_value(borrow=True).shape[0] / batch_size\r\n\r\n ishape = (28, 28) # this is the size of MNIST images\r\n n_in = 28 * 28 # number of input units\r\n n_out = 10 # number of output units\r\n\r\n ######################\r\n # BUILD ACTUAL MODEL #\r\n ######################\r\n print '... building the model'\r\n\r\n # allocate symbolic variables for the data\r\n minibatch_offset = T.lscalar() # offset to the start of a [mini]batch\r\n x = T.matrix() # the data is presented as rasterized images\r\n y = T.ivector() # the labels are presented as 1D vector of\r\n # [int] labels\r\n\r\n # construct the logistic regression class\r\n classifier = LogisticRegression(input=x, n_in=28 * 28, n_out=10)\r\n\r\n # the cost we minimize during training is the negative log likelihood of\r\n # the model in symbolic format\r\n cost = classifier.negative_log_likelihood(y).mean()\r\n\r\n # compile a theano function that computes the mistakes that are made by\r\n # the model on a minibatch\r\n test_model = theano.function([minibatch_offset], classifier.errors(y),\r\n givens={\r\n x: test_set_x[minibatch_offset:minibatch_offset + batch_size],\r\n y: test_set_y[minibatch_offset:minibatch_offset + batch_size]},\r\n name=\"test\")\r\n\r\n validate_model = theano.function([minibatch_offset], classifier.errors(y),\r\n givens={\r\n x: valid_set_x[minibatch_offset:\r\n minibatch_offset + batch_size],\r\n y: valid_set_y[minibatch_offset:\r\n minibatch_offset + batch_size]},\r\n name=\"validate\")\r\n\r\n # compile a thenao function that returns the cost of a minibatch\r\n batch_cost = theano.function([minibatch_offset], cost,\r\n givens={\r\n x: train_set_x[minibatch_offset:\r\n minibatch_offset + batch_size],\r\n y: train_set_y[minibatch_offset:\r\n minibatch_offset + batch_size]},\r\n name=\"batch_cost\")\r\n\r\n # compile a theano function that returns the gradient of the minibatch\r\n # with respect to theta\r\n batch_grad = theano.function([minibatch_offset],\r\n T.grad(cost, classifier.theta),\r\n givens={\r\n x: train_set_x[minibatch_offset:\r\n minibatch_offset + batch_size],\r\n y: train_set_y[minibatch_offset:\r\n minibatch_offset + batch_size]},\r\n name=\"batch_grad\")\r\n\r\n # creates a function that computes the average cost on the training set\r\n def train_fn(theta_value):\r\n classifier.theta.set_value(theta_value, borrow=True)\r\n train_losses = [batch_cost(i * batch_size)\r\n for i in xrange(n_train_batches)]\r\n return numpy.mean(train_losses)\r\n\r\n # creates a function that computes the average gradient of cost with\r\n # respect to theta\r\n def train_fn_grad(theta_value):\r\n classifier.theta.set_value(theta_value, borrow=True)\r\n grad = batch_grad(0)\r\n for i in xrange(1, n_train_batches):\r\n grad += batch_grad(i * batch_size)\r\n return grad / n_train_batches\r\n\r\n validation_scores = [numpy.inf, 0]\r\n\r\n # creates the validation function\r\n def callback(theta_value):\r\n classifier.theta.set_value(theta_value, borrow=True)\r\n #compute the validation loss\r\n validation_losses = [validate_model(i * batch_size)\r\n for i in xrange(n_valid_batches)]\r\n this_validation_loss = numpy.mean(validation_losses)\r\n print('validation error %f %%' % (this_validation_loss * 100.,))\r\n\r\n # check if it is better then best validation score got until now\r\n if this_validation_loss < validation_scores[0]:\r\n # if so, replace the old one, and compute the score on the\r\n # testing dataset\r\n validation_scores[0] = this_validation_loss\r\n test_losses = [test_model(i * batch_size)\r\n for i in xrange(n_test_batches)]\r\n validation_scores[1] = numpy.mean(test_losses)\r\n\r\n ###############\r\n # TRAIN MODEL #\r\n ###############\r\n\r\n # using scipy conjugate gradient optimizer\r\n import scipy.optimize\r\n print (\"Optimizing using scipy.optimize.fmin_cg...\")\r\n start_time = time.clock()\r\n best_w_b = scipy.optimize.fmin_cg(\r\n f=train_fn,\r\n x0=numpy.zeros((n_in + 1) * n_out, dtype=x.dtype),\r\n fprime=train_fn_grad,\r\n callback=callback,\r\n disp=0,\r\n maxiter=n_epochs)\r\n end_time = time.clock()\r\n print(('Optimization complete with best validation score of %f %%, with '\r\n 'test performance %f %%') %\r\n (validation_scores[0] * 100., validation_scores[1] * 100.))\r\n\r\n print >> sys.stderr, ('The code for file ' +\r\n os.path.split(__file__)[1] +\r\n ' ran for %.1fs' % ((end_time - start_time)))","def train_mnist():\r\n # type: () -> None\r\n\r\n # Build dataset and model\r\n dataset = MNIST_TRAIN(path=Config.video_folder)\r\n model = LSAMNIST(input_shape=dataset.shape, code_length=64,\r\n cpd_channels=100).to(device).train()\r\n\r\n # Set up result helper and perform test\r\n helper = OneClassResultHelper(dataset, model,\r\n checkpoints_dir=Config.model_ckpt,\r\n output_file='mnist.txt')\r\n helper.train_one_class_classification()","def test_custom_relu_mnist():\n loss1 = mnist()\n loss2 = custom_mnist()\n assert np.allclose(loss1, loss2, equal_nan=True)","def pick_data(ns, digits):\n f = gzip.open('data/mnist.pkl.gz', 'rb')\n train_set, valid_set, test_set = cPickle.load(f)\n f.close()\n images, labels = train_set\n\n originals = []; \n shapes = []; \n true_labels = [];\n i = 0\n for n, d in zip(ns, digits):\n # picking n elements with digit d\n x = np.where(labels==d)[0]\n idx = np.random.choice(x, n, replace=False)\n imgs = images[idx]\n originals.append(imgs)\n contours = [mnistshape.get_shape2(im.reshape((28,28)), n=30, s=5, ir=2)\n for im in imgs]\n shapes.append(contours)\n true_labels.append([i]*n)\n i += 1\n originals = np.concatenate(originals)\n true_labels = np.concatenate(true_labels)\n \n new_shapes = []\n for cluster in shapes:\n for shape in cluster:\n new_shapes.append(shape)\n new_shapes = np.array(new_shapes)\n\n # return shuffled data\n idx = range(len(originals))\n np.random.shuffle(idx)\n return originals[idx], new_shapes[idx], true_labels[idx]","def test_Gaussian_NB_estimators():","def test_n_iris(self):\r\n n = NeuronNetwork(1,\r\n [3],\r\n [[[0.2,0.2,0.2,0.2]]*3],\r\n [[-1.0,-1.0,-1.0]],learningRate=0.3)\r\n print(n)\r\n \r\n data = load_iris()\r\n\r\n inputs = data.data\r\n target = []\r\n for x in data.target:\r\n empty = [0,0,0]\r\n empty[x] = 1\r\n target.append(empty)\r\n \r\n n.train(inputs, target, 2000, 10*60)\r\n print(n)\r\n\r\n total = 0\r\n error = 0\r\n for i, x in enumerate(target, 0):\r\n out = n.feed_forward(inputs[i])\r\n if i < 50:\r\n error += self.mse(out, [1,0,0])\r\n if np.argmax(out) == 0:\r\n total +=1\r\n print(i, out, 1)\r\n elif i >= 50 and i < 100:\r\n error += self.mse(out, [0,1,0])\r\n if np.argmax(out) == 1:\r\n total +=1\r\n print(i, out, 2)\r\n elif i >= 100 and i < 150:\r\n error += self.mse(out, [0,0,1])\r\n if np.argmax(out) == 2:\r\n total +=1\r\n print(i, out, 3)\r\n\r\n print(f'MSE: {error/150}, RMSE:{math.sqrt(error/150)}')\r\n print(f'Accuracy:{total/len(target)}')","def load_data():\n f = gzip.open('mnist.pkl.gz', 'rb')\n training_data, validation_data, test_data = pickle.load(f, encoding=\"latin1\")\n f.close()\n \n X_train = [np.reshape(x, (784, 1)) for x in training_data[0]]\n Y_train = [vectorized_result(y) for y in training_data[1]]\n \n X_validation = [np.reshape(x, (784, 1)) for x in validation_data[0]]\n Y_validation = validation_data[1]\n \n X_test = [np.reshape(x, (784, 1)) for x in test_data[0]]\n Y_test = test_data[1]\n \n return (X_train, Y_train, X_validation, Y_validation, X_test, Y_test)","def load_data():\n # Load image data from MNIST.\n (train_x, train_y),(eval_x, eval_y) = keras.datasets.mnist.load_data()\n\n # We convert the input data to (60000, 28, 28, 1), float32 and normalize our data values to the range [0, 1].\n train_x = train_x.reshape(train_x.shape[0], train_x.shape[1], train_x.shape[2], 1)\n eval_x = eval_x.reshape(eval_x.shape[0], eval_x.shape[1], eval_x.shape[2], 1)\n\n train_x = train_x.astype('float32')\n eval_x = eval_x.astype('float32')\n train_x /= 255\n eval_x /= 255\n\n # Preprocess class labels \n train_y = train_y.astype(np.int32)\n eval_y = eval_y.astype(np.int32)\n\n train_y = np_utils.to_categorical(train_y, 10)\n eval_y = np_utils.to_categorical(eval_y, 10)\n\n return train_x, train_y, eval_x, eval_y","def main():\n # construct the argument parse and parse the arguments\n args = argparse.ArgumentParser()\n args.add_argument(\"-o\", \"--output\", required=True, help=\"path to the output loss/accuracy plot\")\n args = vars(args.parse_args())\n\n # grab the MNIST dataset (if this is your first time using this\n # dataset then the 11MB download may take a minute)\n print(\"[INFO] accessing MNIST...\")\n ((train_x, train_y), (test_x, test_y)) = mnist.load_data()\n\n # each image in the MNIST dataset is represented as a 28x28x1\n # image, but in order to apply a standard neural network we must\n # first \"flatten\" the image to be simple list of 28x28=784 pixels\n train_x = train_x.reshape((train_x.shape[0], 28 * 28 * 1))\n test_x = test_x.reshape((test_x.shape[0], 28 * 28 * 1))\n # scale data to the range of [0, 1]\n train_x = train_x.astype(\"float32\") / 255.0\n test_x = test_x.astype(\"float32\") / 255.0\n\n # convert the labels from integers to vectors\n label_binarizer = LabelBinarizer()\n train_y = label_binarizer.fit_transform(train_y)\n test_y = label_binarizer.transform(test_y)\n\n # define the 784-256-128-10 architecture using Keras\n model = Sequential()\n model.add(Dense(256, input_shape=(784,), activation=\"sigmoid\"))\n model.add(Dense(128, activation=\"sigmoid\"))\n model.add(Dense(10, activation=\"softmax\"))\n\n # train the model using SGD\n print(\"[INFO] training network...\")\n sgd = SGD(0.01)\n model.compile(loss=\"categorical_crossentropy\", optimizer=sgd, metrics=[\"accuracy\"])\n model_fit = model.fit(train_x, train_y, validation_data=(test_x, test_y), epochs=100, batch_size=128)\n\n # evaluate the network\n print(\"[INFO] evaluating network...\")\n predictions = model.predict(test_x, batch_size=128)\n print(\n classification_report(\n test_y.argmax(axis=1), predictions.argmax(axis=1), target_names=[str(x) for x in label_binarizer.classes_]\n )\n )\n\n # plot the training loss and accuracy\n plt.style.use(\"ggplot\")\n plt.figure()\n plt.plot(np.arange(0, 100), model_fit.history[\"loss\"], label=\"train_loss\")\n plt.plot(np.arange(0, 100), model_fit.history[\"val_loss\"], label=\"val_loss\")\n plt.plot(np.arange(0, 100), model_fit.history[\"acc\"], label=\"train_acc\")\n plt.plot(np.arange(0, 100), model_fit.history[\"val_acc\"], label=\"val_acc\")\n plt.title(\"Training Loss and Accuracy\")\n plt.xlabel(\"Epoch #\")\n plt.ylabel(\"Loss/Accuracy\")\n plt.legend()\n plt.savefig(args[\"output\"])","def get_data(numbers):\r\n numbers = numbers\r\n n_classes = len(numbers)\r\n z = zipfile.ZipFile('lab3/mnist.pkl.zip', 'r')\r\n k = z.extract('mnist.pkl') # Извлечь файл из архива\r\n with open(k, 'rb') as f:\r\n train_set, _, test_set = pickle.load(f, encoding=\"bytes\")\r\n x_train = train_set[0]\r\n x_test = test_set[0]\r\n x_train[x_train >= 0.5] = 1\r\n x_train[x_train < 0.5] = 0\r\n x_test[x_test >= 0.5] = 1\r\n x_test[x_test < 0.5] = 0\r\n y_train = train_set[1]\r\n y_test = test_set[1]\r\n idx_train = [[np.where(y_train == i)] for i in numbers]\r\n idx_test = [[np.where(y_test == i)] for i in numbers]\r\n idx_x_train = [x_train[idx_train[i][0]] for i in range(len(idx_train))]\r\n idx_x_test = [x_test[idx_test[i][0]] for i in range(len(idx_test))]\r\n idx_y_test = [y_test[idx_test[i][0]] for i in range(len(idx_test))]\r\n x_train_new = shuffle(np.concatenate(idx_x_train))\r\n x_test_new = shuffle(np.concatenate(idx_x_test))\r\n y_test_new = shuffle(np.concatenate(idx_y_test))\r\n return x_train_new, x_test_new, y_test_new, numbers, n_classes","def test_model_evaluation(model, mnist, idx, label):\n expected_probabilities = np.zeros((10,))\n expected_probabilities[label] = 1.0\n assert_array_almost_equal(\n model.classify(mnist.get_test_image(idx)),\n expected_probabilities\n )","def load_data(m=5000, n=100, path='D:/file/vscode/py/data/mnist.npz'):\r\n f = np.load(path)\r\n x_train, y_train = f['x_train'], f['y_train']\r\n\r\n x_test, y_test = f['x_test'], f['y_test']\r\n\r\n f.close()\r\n return (x_train, y_train), (x_test, y_test)","def __init__(self):\n\n TEST_RATIO = 0.05\n mnist_trainset = datasets.MNIST(root='./data', train=True, download=True, transform=None)\n idxs = np.arange(mnist_trainset.train_data.size(0))\n np.random.shuffle(idxs)\n\n #print(torch.min(mnist_trainset.train_labels), torch.max(mnist_trainset.train_labels))\n #print(mnist_trainset.train_labels.size())\n \n # reshape input data to (1, 784) and normalize to range [0., 1.]\n self.train_data = torch.reshape(\n mnist_trainset.train_data[idxs].float(), (-1,1,28,28))/255.\n self.data_size = self.train_data.size(0)\n self.train_len = self.train_data.size(0)\n self.train_label = torch.Tensor([1]).float() # since there is only one class - 'real' image\n\n print('Train images -- {}'.format(self.train_data.size()))","def test_menhinick(self):\n self.assertEqual(menhinick(self.TestData), 9/sqrt(22))","def get_mnist_cnn():\n # Set defaults.\n nb_classes = 10 #dataset dependent \n batch_size = 128\n epochs = 4\n \n # Input image dimensions\n img_rows, img_cols = 28, 28\n\n # Get the data.\n # the data, shuffled and split between train and test sets\n (x_train, y_train), (x_test, y_test) = mnist.load_data()\n \n if K.image_data_format() == 'channels_first':\n x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)\n x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)\n input_shape = (1, img_rows, img_cols)\n else:\n x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)\n x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)\n input_shape = (img_rows, img_cols, 1)\n\n #x_train = x_train.reshape(60000, 784)\n #x_test = x_test.reshape(10000, 784)\n \n x_train = x_train.astype('float32')\n x_test = x_test.astype('float32')\n x_train /= 255\n x_test /= 255\n\n #print('x_train shape:', x_train.shape)\n #print(x_train.shape[0], 'train samples')\n #print(x_test.shape[0], 'test samples')\n\n # convert class vectors to binary class matrices\n y_train = to_categorical(y_train, nb_classes)\n y_test = to_categorical(y_test, nb_classes)\n\n # convert class vectors to binary class matrices\n #y_train = keras.utils.to_categorical(y_train, nb_classes)\n #y_test = keras.utils.to_categorical(y_test, nb_classes)\n\n return (nb_classes, batch_size, input_shape, x_train, x_test, y_train, y_test, epochs)","def get_mini_samples():\n if GLOBALS['project_root']=='':\n print('please initialize project_root in GLOBALS first')\n return None\n data_path = os.path.join(GLOBALS['project_root'], 'data/MNIST/')\n pickle_path = os.path.join(data_path, 'mnist_mini_samples.pickle')\n if os.path.exists(pickle_path):\n with open(pickle_path, 'rb') as f:\n mini_samples = pickle.load(f)\n else:\n mnist = get_mnist()\n mini_samples = mnist.train.next_batch(50)\n with open(pickle_path, 'wb') as f:\n pickle.dump(mini_samples, f, pickle.HIGHEST_PROTOCOL)\n\n return mini_samples","def main():\n training_data, validation_data, test_data = mnist.load()\n\n model = nn.NeuralNetwork([784, 100, 10], learning_rate=0.01, batch_size=50)\n\n model_training = training.EarlyStoppingRegularization(model,\n training_data,\n validation_data,\n test_data,\n max_steps_without_progression=2)\n result = model_training.train()\n\n result.save('models/mnist')","def test_X_train_property():\n atom = ATOMClassifier(X_bin, y_bin, random_state=1)\n atom.run([\"MNB\", \"LR\"])\n assert atom.X_train.equals(atom.mnb.X_train)\n assert check_scaling(atom.lr.X_train)","def sgd_optimization_mnist(learning_rate=0.13, n_epochs=1000,\r\n dataset='mnist.pkl.gz',\r\n batch_size=600):\r\n datasets = load_data(dataset)\r\n\r\n train_set_x, train_set_y = datasets[0]\r\n valid_set_x, valid_set_y = datasets[1]\r\n test_set_x, test_set_y = datasets[2]\r\n\r\n # compute number of minibatches for training, validation and testing\r\n n_train_batches = train_set_x.get_value(borrow=True).shape[0] / batch_size\r\n n_valid_batches = valid_set_x.get_value(borrow=True).shape[0] / batch_size\r\n n_test_batches = test_set_x.get_value(borrow=True).shape[0] / batch_size\r\n\r\n ######################\r\n # BUILD ACTUAL MODEL #\r\n ######################\r\n print '... building the model'\r\n\r\n # allocate symbolic variables for the data\r\n index = T.lscalar() # index to a [mini]batch\r\n x = T.matrix('x') # the data is presented as rasterized images\r\n y = T.ivector('y') # the labels are presented as 1D vector of\r\n # [int] labels\r\n\r\n # construct the logistic regression class\r\n # Each MNIST image has size 28*28\r\n classifier = LogisticRegression(input=x, n_in=28 * 28, n_out=10)\r\n\r\n # the cost we minimize during training is the negative log likelihood of\r\n # the model in symbolic format\r\n cost = classifier.negative_log_likelihood(y)\r\n\r\n # compiling a Theano function that computes the mistakes that are made by\r\n # the model on a minibatch\r\n test_model = theano.function(inputs=[index],\r\n outputs=classifier.errors(y),\r\n givens={\r\n x: test_set_x[index * batch_size: (index + 1) * batch_size],\r\n y: test_set_y[index * batch_size: (index + 1) * batch_size]})\r\n\r\n validate_model = theano.function(inputs=[index],\r\n outputs=classifier.errors(y),\r\n givens={\r\n x: valid_set_x[index * batch_size:(index + 1) * batch_size],\r\n y: valid_set_y[index * batch_size:(index + 1) * batch_size]})\r\n\r\n # compute the gradient of cost with respect to theta = (W,b)\r\n g_W = T.grad(cost=cost, wrt=classifier.W)\r\n g_b = T.grad(cost=cost, wrt=classifier.b)\r\n\r\n # specify how to update the parameters of the model as a list of\r\n # (variable, update expression) pairs.\r\n updates = [(classifier.W, classifier.W - learning_rate * g_W),\r\n (classifier.b, classifier.b - learning_rate * g_b)]\r\n\r\n # compiling a Theano function `train_model` that returns the cost, but in\r\n # the same time updates the parameter of the model based on the rules\r\n # defined in `updates`\r\n train_model = theano.function(inputs=[index],\r\n outputs=cost,\r\n updates=updates,\r\n givens={\r\n x: train_set_x[index * batch_size:(index + 1) * batch_size],\r\n y: train_set_y[index * batch_size:(index + 1) * batch_size]})\r\n\r\n ###############\r\n # TRAIN MODEL #\r\n ###############\r\n print '... training the model'\r\n # early-stopping parameters\r\n patience = 5000 # look as this many examples regardless\r\n patience_increase = 2 # wait this much longer when a new best is\r\n # found\r\n improvement_threshold = 0.995 # a relative improvement of this much is\r\n # considered significant\r\n validation_frequency = min(n_train_batches, patience / 2)\r\n # go through this many\r\n # minibatche before checking the network\r\n # on the validation set; in this case we\r\n # check every epoch\r\n\r\n best_params = None\r\n best_validation_loss = numpy.inf\r\n test_score = 0.\r\n start_time = time.clock()\r\n\r\n done_looping = False\r\n epoch = 0\r\n while (epoch < n_epochs) and (not done_looping):\r\n epoch = epoch + 1\r\n for minibatch_index in xrange(n_train_batches):\r\n\r\n minibatch_avg_cost = train_model(minibatch_index)\r\n # iteration number\r\n iter = (epoch - 1) * n_train_batches + minibatch_index\r\n\r\n if (iter + 1) % validation_frequency == 0:\r\n # compute zero-one loss on validation set\r\n validation_losses = [validate_model(i)\r\n for i in xrange(n_valid_batches)]\r\n this_validation_loss = numpy.mean(validation_losses)\r\n\r\n print('epoch %i, minibatch %i/%i, validation error %f %%' % \\\r\n (epoch, minibatch_index + 1, n_train_batches,\r\n this_validation_loss * 100.))\r\n\r\n # if we got the best validation score until now\r\n if this_validation_loss < best_validation_loss:\r\n #improve patience if loss improvement is good enough\r\n if this_validation_loss < best_validation_loss * \\\r\n improvement_threshold:\r\n patience = max(patience, iter * patience_increase)\r\n\r\n best_validation_loss = this_validation_loss\r\n # test it on the test set\r\n\r\n test_losses = [test_model(i)\r\n for i in xrange(n_test_batches)]\r\n test_score = numpy.mean(test_losses)\r\n\r\n print((' epoch %i, minibatch %i/%i, test error of best'\r\n ' model %f %%') %\r\n (epoch, minibatch_index + 1, n_train_batches,\r\n test_score * 100.))\r\n\r\n if patience <= iter:\r\n done_looping = True\r\n break\r\n\r\n end_time = time.clock()\r\n print(('Optimization complete with best validation score of %f %%,'\r\n 'with test performance %f %%') %\r\n (best_validation_loss * 100., test_score * 100.))\r\n print 'The code run for %d epochs, with %f epochs/sec' % (\r\n epoch, 1. * epoch / (end_time - start_time))\r\n print >> sys.stderr, ('The code for file ' +\r\n os.path.split(__file__)[1] +\r\n ' ran for %.1fs' % ((end_time - start_time)))","def neural_network(X, Y, Xs_test, Ys_test):\n ## YOUR CODE HERE\n #################\n return 0","def use_mnist_model(self):\n\n\t\t# load the model\n\t\tnumber_recognizer_MNIST = load_model('models/MNIST_digits_recognition.h5', compile=False)\n\n\t\t# create empty ndarray\n\t\tnumbers_mnist = np.ones(shape=(self.sudoku_size, self.sudoku_size))\n\n\t\tpics = deepcopy(self.list_of_number_pictures)\n\t\tfor i in range(self.sudoku_size):\n\t\t\tfor j in range(self.sudoku_size):\n\t\t\t\tpics[i][j] = self.preprocess_cell(pics[i][j], mnist=True, resize=True, clean_remains=True)\n\t\t\t\tif self.empty_cells[i][j] != 0:\n\t\t\t\t\tnumbers_mnist[i][j] = np.argmax(number_recognizer_MNIST.predict([[pics[i][j].reshape(28,28,1)]]))\n\n\t\treturn numbers_mnist","def test_star():\n test_path = tempfile.mkdtemp()\n x_train, metadata = star(test_path)\n try:\n assert x_train.shape == (5748, 8)\n except:\n shutil.rmtree(test_path)\n raise()","def mnist_v1(batch_size=128, epochs=20, kernel_size=3):\n (X_train, Y_train), (X_test, Y_test) = mnist.load_data()\n\n # Data preparation\n X_train = prepare(X_train)\n X_test = prepare(X_test)\n Y_train = np_utils.to_categorical(Y_train, 10) # 0..9\n Y_test = np_utils.to_categorical(Y_test, 10) # 0..9\n\n # Fitting the data to the augmentation data generator\n datagen = augmentedData(X_train)\n\n # --------------------\n # NEURAL NETWORK MODEL\n # --------------------\n\n # Model architecture\n model = Sequential()\n\n model.add(Conv2D(32, (kernel_size, kernel_size), activation='relu', input_shape=(1, 28, 28)))\n model.add(Conv2D(32, (kernel_size, kernel_size), activation='relu'))\n model.add(MaxPooling2D(pool_size=(2, 2)))\n model.add(Dropout(0.25))\n\n model.add(Flatten())\n model.add(Dense(128, activation='relu'))\n model.add(Dropout(0.5))\n model.add(Dense(10, activation='softmax'))\n\n # Model compilation\n model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n\n #Tensor board saves\n now = datetime.datetime.now()\n tensorboard = TensorBoard(log_dir=\"logs_first/kernel_size:{}\".format(kernel_size))\n\n model.fit_generator(datagen.flow(X_train, Y_train, batch_size=batch_size), epochs=epochs, verbose=1, callbacks=[tensorboard])\n\n # Model saves\n now = datetime.datetime.now()\n model.save(\"sirr_HYPERPARAMETERS_mnist_first_\" + str(now.hour) + \"h\" + str(now.minute) + \".h5\")\n\n # Model evaluation\n return model.evaluate(X_test, Y_test, verbose=1)","def fetch_mnist():\n data_path = check_fetch_mnist()\n f = gzip.open(data_path, 'rb')\n try:\n train_set, valid_set, test_set = pickle.load(f, encoding=\"latin1\")\n except TypeError:\n train_set, valid_set, test_set = pickle.load(f)\n f.close()\n train_indices = np.arange(0, len(train_set[0]))\n valid_indices = np.arange(0, len(valid_set[0])) + train_indices[-1] + 1\n test_indices = np.arange(0, len(test_set[0])) + valid_indices[-1] + 1\n return {\"data\": np.concatenate((train_set[0], valid_set[0], test_set[0]),\n axis=0).astype(theano.config.floatX),\n \"target\": np.concatenate((train_set[1], valid_set[1], test_set[1]),\n axis=0).astype(np.int32),\n \"train_indices\": train_indices.astype(np.int32),\n \"valid_indices\": valid_indices.astype(np.int32),\n \"test_indices\": test_indices.astype(np.int32)}","def test_predictor():","def wild_test(img, mod):\n img = cv2.imread(img)\n img_gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)\n img_resize = cv2.resize(img_gray, (28, 28))\n img_resize = img_resize.reshape((1, 28, 28))\n print (\"Image size\", img_resize.shape)\n # it is ugly, you can make this much better\n data = np.asarray([img_resize]*100)\n test_iter = mx.io.NDArrayIter(data, None, 100)\n prob = mod.predict(test_iter)\n print (\"The prediction is :\", np.argmax(prob.asnumpy()[0]))","def test_X_property():\n atom = ATOMClassifier(X_bin, y_bin, random_state=1)\n atom.run([\"MNB\", \"LR\"])\n assert atom.X.equals(atom.mnb.X)\n assert check_scaling(atom.lr.X)","def testvis(layers='first'):\n\n\tfrom scipy.io import loadmat\n\tfrom setup import NeuralNetwork as nnsetup\n\n\tresult = loadmat(\"goodmatx.mat\")\n\tw1 = result['v1']\n\tw0 = result['v0']\n\tx,y = result['train_x'], result['train_y']\n\n\t# result = loadmat(\"ducky.mat\")\n\t# x = result['train_x']\n\t# y = result['train_y']\n\n\tsize = [x.shape[1], 1000, y.shape[1]]\n\n\tnn = nnsetup([size[0],size[1],size[0]],output='sigm')\n\n\tnn.W[0] = w0\n\tnn.W[1] = w1\n\t\n\tfor i in range(50):\n\t\tvisualize( nn, x, k = 3000+i*4, layers=layers, mode='save' )","def main():\n # \"\"\"Prepare neuromorphic MNIST image datasets for use in caffe\n # Each dataset will be generated with different number of unique spikes\n # \"\"\"\n # initial_size = 1e6 #best to make this big enough avoid expensive\n # re-allocation\n # test_dir = os.path.abspath('testFull')\n # train_dir = os.path.abspath('trainFull')\n\n # for num_spikes in range(150, 260, 10):\n # #test directory\n # image_dataset = generate_nmnist_dataset(initial_size, test_dir,\n # num_spikes, 0.75)\n # output_lmdb = 'testlmdb' + str(num_spikes)\n # database = save_to_lmdb(image_dataset, output_lmdb)\n # #database.process_all_data(show_lmdb_datum)\n\n # #train directory\n # image_dataset = generate_nmnist_dataset(initial_size, train_dir,\n # num_spikes, 0.75)\n # output_lmdb = 'trainlmdb' + str(num_spikes)\n # save_to_lmdb(image_dataset, output_lmdb)\n\n # TD = ev.read_dataset(os.path.abspath('trainReduced/0/00002.bin'))\n # best to make this big enough avoid expensive re-allocation\n initial_size = 6e5\n test_dir = os.path.abspath('testFull')\n train_dir = os.path.abspath('trainFull')\n\n # test directory\n image_dataset = generate_nmnist_continuous_dataset(initial_size, test_dir)\n save_to_lmdb(image_dataset, 'testlmdb_continuous', True)\n save_to_mat(image_dataset, 'MNIST_continuous_test.mat')\n # database.process_all_data(show_lmdb_datum)\n\n # train directory\n image_dataset = generate_nmnist_continuous_dataset(initial_size, train_dir)\n save_to_lmdb(image_dataset, 'trainlmdb_continuous', True)\n save_to_mat(image_dataset, 'MNIST_continuous_train.mat')\n\n # TD = ev.read_dataset(os.path.abspath('trainReduced/0/00002.bin'))","def codeepneat_mnist_example(_):\n # Set standard configuration specific to TFNE but not the neuroevolution process\n logging_level = logging.INFO\n config_file_path = './codeepneat_mnist_example_config.cfg'\n backup_dir_path = './tfne_state_backups/'\n max_generations = 20\n max_fitness = None\n\n # Read in optionally supplied flags, changing the just set standard configuration\n if flags.FLAGS.logging_level is not None:\n logging_level = flags.FLAGS.logging_level\n if flags.FLAGS.config_file is not None:\n config_file_path = flags.FLAGS.config_file\n if flags.FLAGS.backup_dir is not None:\n backup_dir_path = flags.FLAGS.backup_dir\n if flags.FLAGS.max_generations is not None:\n max_generations = flags.FLAGS.max_generations\n if flags.FLAGS.max_fitness is not None:\n max_fitness = flags.FLAGS.max_fitness\n\n # Set logging, parse config\n logging.set_verbosity(logging_level)\n config = tfne.parse_configuration(config_file_path)\n\n # Initialize the environment and the specific NE algorithm\n environment = tfne.environments.MNISTEnvironment(weight_training=True, config=config, verbosity=logging_level)\n ne_algorithm = tfne.algorithms.CoDeepNEAT(config)\n\n # Initialize evolution engine and supply config as well as initialized NE algorithm and evaluation environment.\n engine = tfne.EvolutionEngine(ne_algorithm=ne_algorithm,\n environment=environment,\n backup_dir_path=backup_dir_path,\n max_generations=max_generations,\n max_fitness=max_fitness)\n\n # Start training process, returning the best genome when training ends\n best_genome = engine.train()\n print(\"Best genome returned by evolution:\\n\")\n print(best_genome)\n\n # Increase epoch count in environment for a final training of the best genome. Train the genome and then replay it.\n print(\"Training best genome for 200 epochs...\\n\")\n environment.epochs = 20\n environment.eval_genome_fitness(best_genome)\n environment.replay_genome(best_genome)\n\n # Serialize and save genotype and Tensorflow model to demonstrate serialization\n best_genome.save_genotype(save_dir_path='./best_genome_genotype/')\n best_genome.save_model(file_path='./best_genome_model/')","def mnist_noniid(dataset, num_users):\n # num_shards, num_imgs = 2*num_users, int(dataset.data.size()[0]/2/num_users) # choose two number from a set with num_shards, each client has 2*num_imgs images\n # idx_shard = [i for i in range(num_shards)]\n # dict_users = {i: np.array([], dtype='int64') for i in range(num_users)}\n # idxs = np.arange(dataset.data.size()[0])\n # labels = dataset.train_labels.numpy()\n #\n # # sort labels\n # idxs_labels = np.vstack((idxs, labels))\n # idxs_labels = idxs_labels[:,idxs_labels[1,:].argsort()]\n # idxs = idxs_labels[0,:]\n #\n # # divide and assign\n # for i in range(num_users):\n # rand_set = set(np.random.choice(idx_shard, 2, replace=False))\n # idx_shard = list(set(idx_shard) - rand_set)\n # for rand in rand_set:\n # dict_users[i] = np.concatenate((dict_users[i], idxs[rand*num_imgs:(rand+1)*num_imgs]), axis=0)\n # return dict_users\n\n label_list = dataset.targets.numpy()\n minLabel = min(label_list)\n numLabels = len(dataset.classes)\n\n dict_users = {i: np.array([], dtype='int64') for i in range(num_users)}\n for i in range(0, len(label_list)):\n tmp_target_node = int((label_list[i] - minLabel) % num_users)\n if num_users > numLabels:\n tmpMinIndex = 0\n tmpMinVal = math.inf\n for n in range(0, num_users):\n if (n) % numLabels == tmp_target_node and len(dict_users[n]) < tmpMinVal:\n tmpMinVal = len(dict_users[n])\n tmpMinIndex = n\n tmp_target_node = tmpMinIndex\n dict_users[tmp_target_node] = np.concatenate((dict_users[tmp_target_node], [i]), axis=0)\n return dict_users","def get_MNIST_testing_normalized():\n X, Y = get_dataset(\"testing\")\n X/=255.0\n X-=0.5\n return X,Y\n\n global MNIST_testing\n if MNIST_testing is None:\n if os.path.exists(\"./data/MNIST_testing.pkl\"):\n MNIST_testing = cPickle.load(open(\"./data/MNIST_testing.pkl\", \"r\"))\n else:\n MNIST_testing = get_dataset(\"testing\")\n MNIST_testing = normalize_data(MNIST_testing[0], MNIST_testing[1])\n cPickle.dump(MNIST_training, open(\"./data/MNIST_testing.pkl\",\"w\"))\n return MNIST_testing","def test_data():\n batch_size = 10\n input_dim = 28\n test_data = np.random.rand(batch_size, input_dim)\n\n return test_data","def load_mnist(train_data=True, test_data=False):\n os.chdir(ROOT_DIR)\n RESOURCES = [\n 'train-images-idx3-ubyte.gz', 'train-labels-idx1-ubyte.gz',\n 't10k-images-idx3-ubyte.gz', 't10k-labels-idx1-ubyte.gz'\n ]\n\n if (os.path.isdir('data') == 0):\n os.mkdir('data')\n if (os.path.isdir('data/mnist') == 0):\n os.mkdir('data/mnist')\n for name in RESOURCES:\n if (os.path.isfile('data/mnist/' + name) == 0):\n url = 'https://github.com/HIPS/hypergrad/raw/master/data/mnist/' + name\n r = requests.get(url, allow_redirects=True)\n open('data/mnist/' + name, 'wb').write(r.content)\n\n return get_images(train_data, test_data), get_labels(train_data, test_data)","def test_snow_pumps():\n test_path = tempfile.mkdtemp()\n x_train, metadata = snow_pumps(test_path)\n try:\n assert x_train.shape == (13, 4)\n except:\n shutil.rmtree(test_path)\n raise()","def run_test():\n # Get the sets of images and labels for training, validation, and\n # test on MNIST.\n train ,validation,test = datasets_mnist.read_data_sets(FLAGS.input_data_dir, FLAGS.fake_data)\n # Tell TensorFlow that the model will be built into the default Graph.\n with tf.Graph().as_default():\n # Generate placeholders for the images and labels.\n images_placeholder, labels_placeholder, phase_pl = placeholder_inputs(\n FLAGS.batch_size)\n\n # Build a Graph that computes predictions from the inference model.\n logits = mnist.inference(images_placeholder,\n FLAGS.hidden1,\n FLAGS.hidden2, \n phase_pl)\n\n eval_correct = mnist.evaluation(logits, labels_placeholder)\n # Add the variable initializer Op.\n all_variable = tf.global_variables()\n \n # Create a saver for writing training checkpoints.\n saver = tf.train.Saver()\n\n # Create a session for running Ops on the Graph.\n with tf.Session() as sess:\n\n saver.restore(sess, \"log/model.ckpt-1999\")\n for variable in all_variable:\n if \"moving\" in variable.name:\n print(variable.name, variable.eval())\n do_eval(sess,\n eval_correct,\n images_placeholder,\n labels_placeholder,\n phase_pl,\n test)","def as_mnist(filename, imwidth):\n\n images = []\n labels = []\n \n if filename.find(\"devel\") != -1:\n print(\"we're working with the development set: \" + filename)\n\n for cls, data in enumerate(load(filename)):\n for example in data:\n labels.append(cls)\n image = numpy.zeros(shape=(imwidth, imwidth), dtype='uint8')\n for (x, y) in example:\n x_ = int(round(imwidth * x))\n y_ = int(round(1-(imwidth * y)))\n image[y_, x_] = 255\n images.append(image.flatten())\n\n return numpy.vstack(images).T.copy(), numpy.array(labels)","def get_mnist_data(batch=128):\n \n def transformer(data, label):\n data = data.flatten().expand_dims(0).astype(np.float32)/255\n data = data-0.13/0.31\n label = label.astype(np.float32)\n return data, label\n\n train_dataset = gluon.data.vision.datasets.MNIST(root=M5_IMAGES, train=True, transform=transformer)\n validation_dataset = gluon.data.vision.datasets.MNIST(root=M5_IMAGES, train=False, transform=transformer)\n train_dataloader = gluon.data.DataLoader(train_dataset, batch_size=batch, last_batch='keep',shuffle=True)\n validation_dataloader = gluon.data.DataLoader(validation_dataset, batch_size=batch, last_batch='keep')\n \n return train_dataloader, validation_dataloader","def ex_3_b(x_train, y_train, x_test, y_test):\n ###########\n ## TODO:\n ## Train multi-class SVMs with a LINEAR kernel\n ## Use the sklearn.metrics.confusion_matrix to plot the confusion matrix.\n ## Find the index for which you get the highest error rate.\n ## Plot the confusion matrix with plot_confusion_matrix.\n ## Plot the first 10 images classified as the most misclassified digit using plot_mnist.\n ###########\n\n labels = range(1, 6)\n\n lin = svm.SVC(decision_function_shape='ovr', kernel='linear')\n lin.fit(x_train, y_train)\n\n y_test_predict =lin.predict(x_test)\n\n score_train = lin.score(x_train, y_train)\n score_test = lin.score(x_test, y_test)\n\n cm = confusion_matrix(y_test, y_test_predict)\n plot_confusion_matrix(cm, labels)\n #print(cm)\n\n diff_list = y_test_predict == y_test\n\n # indexes of all missclassiefied images\n misclassifieds = [i for i, val in enumerate(diff_list) if val == False]\n\n # remove diagonal elements from cm for later processing\n cm_no_diagonal = cm\n np.fill_diagonal(cm_no_diagonal, 0)\n #print(cm_no_diagonal)\n\n errors_per_class = np.sum(cm_no_diagonal, axis=0)\n #print(errors_per_class)\n\n sel_err = np.array(misclassifieds) # CHANGE ME! Numpy indices to select all images that are misclassified.\n i = np.argmax(errors_per_class) # CHANGE ME! Should be the label number corresponding the largest classification error.\n #print(i)\n\n # Plot with mnist plot\n plot_mnist(x_test[sel_err], y_test_predict[sel_err], labels=labels[i], k_plots=10, prefix='Predicted class')","def get_mnist():\n if GLOBALS['project_root']=='':\n print('please initialize project_root in GLOBALS first')\n return None\n data_path = os.path.join(GLOBALS['project_root'], 'data/MNIST/')\n pickle_path = os.path.join(data_path, 'mnist.pickle')\n if os.path.exists(pickle_path):\n with open(pickle_path, 'rb') as f:\n mnist = pickle.load(f)\n else:\n from tensorflow.examples.tutorials.mnist import input_data\n mnist = input_data.read_data_sets(data_path, one_hot=True)\n with open(pickle_path, 'wb') as f:\n pickle.dump(mnist, f, pickle.HIGHEST_PROTOCOL)\n\n return mnist","def load_mnist(dataset_name='mnist', **kwargs):\n dataset_name = dataset_name.strip().lower().replace('minist', 'mnist')\n\n if dataset_name.lower() not in ['mnist', 'fashion-mnist']:\n raise ValueError('Only mnist or fashion-mnist are valid dataset_name.')\n\n base = 'http://yann.lecun.com/exdb/mnist/'\n if dataset_name == 'fashion-mnist':\n base = 'http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/'\n\n dirname = os.path.join(_trident_dir, dataset_name)\n make_dir_if_need(dirname)\n\n \"\"\"Load MNIST data from `path`\"\"\"\n trainData = None\n testData = None\n for kind in ['train', 'test']:\n labels_file = '{0}-labels-idx1-ubyte.gz'.format( 't10k' if dataset_name in ('mnist', 'fashion-mnist') and kind == 'test' else kind)\n images_file = '{0}-images-idx3-ubyte.gz'.format( 't10k' if dataset_name in ('mnist', 'fashion-mnist') and kind == 'test' else kind)\n # if dataset_name == 'emnist' :\n # labels_file='emnist-balanced-'+labels_file\n # images_file = 'emnist-balanced-' + images_file\n\n is_data_download = download_file(base + labels_file, dirname, labels_file, dataset_name + '_labels_{0}'.format(kind))\n is_label_download = download_file(base + images_file, dirname, images_file, dataset_name + '_images_{0}'.format(kind))\n if is_data_download and is_label_download:\n labels_path = os.path.join(dirname, labels_file)\n images_path = os.path.join(dirname, images_file)\n labeldata = None\n imagedata = None\n with gzip.open(labels_path, 'rb') as lbpath:\n labels = np.frombuffer(lbpath.read(), dtype=np.uint8, offset=8)\n labels = np.squeeze(labels).astype(np.int64)\n labeldata = LabelDataset(labels.tolist(),object_type=ObjectType.classification_label)\n\n with gzip.open(images_path, 'rb') as imgpath:\n images = np.frombuffer(imgpath.read(), dtype=np.uint8, offset=16)\n images = np.reshape(images, (len(labels), 784)).astype(dtype=_session.floatx)\n images = np.reshape(images, (-1, 28, 28))\n imagedata = ImageDataset(images, object_type=ObjectType.gray)\n if kind == 'train':\n trainData = Iterator(data=imagedata, label=labeldata)\n else:\n testData = Iterator(data=imagedata, label=labeldata)\n\n dataset = DataProvider(dataset_name, traindata=trainData, testdata=testData)\n dataset.binding_class_names(\n [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] if dataset_name == 'mnist' else ['T-shirt/top', 'Trouser', 'Pullover',\n 'Dress', 'Coat', 'Sandal', 'Shirt',\n 'Sneaker', 'Bag', 'Ankle boot'],\n 'en-US')\n\n return dataset\n return None","def test_mlp():\r\n datasets = gen_data()\r\n\r\n train_set_x, train_set_y = datasets[0]\r\n valid_set_x, valid_set_y = datasets[1]\r\n test_set_x , test_set_y = datasets[2]\r\n\r\n\r\n\r\n batch_size = 100 # size of the minibatch\r\n\r\n # compute number of minibatches for training, validation and testing\r\n n_train_batches = train_set_x.get_value(borrow=True).shape[0] / batch_size\r\n n_valid_batches = valid_set_x.get_value(borrow=True).shape[0] / batch_size\r\n n_test_batches = test_set_x.get_value(borrow=True).shape[0] / batch_size\r\n\r\n ######################\r\n # BUILD ACTUAL MODEL #\r\n ######################\r\n #print '... building the model'\r\n\r\n # allocate symbolic variables for the data\r\n index = T.lscalar() # index to a [mini]batch\r\n x = T.matrix('x') # the data is presented as rasterized images\r\n y = T.ivector('y') # the labels are presented as 1D vector of\r\n # [int] labels\r\n\r\n rng = numpy.random.RandomState(1234)\r\n\r\n # construct the MLP class\r\n classifier = MLP( rng = rng, input=x, n_in=28*28, n_hidden = 500, n_out=10)\r\n\r\n # the cost we minimize during training is the negative log likelihood of\r\n # the model.\r\n # We take the mean of the cost over each minibatch.\r\n cost = classifier.negative_log_likelihood(y).mean()\r\n\r\n # compute the gradient of cost with respect to theta (stored in params)\r\n # the resulting gradients will be stored in a list gparams\r\n gparams = []\r\n for param in classifier.params:\r\n gparam = T.grad(cost, param)\r\n gparams.append(gparam)\r\n\r\n # Some optimizations needed are tagged with 'fast_run'\r\n # TODO: refine that and include only those\r\n mode = theano.compile.get_default_mode().including('fast_run')\r\n\r\n updates2 = OrderedDict()\r\n\r\n updates2[classifier.hiddenLayer.params[0]]=T.grad(cost,classifier.hiddenLayer.params[0])\r\n train_model =theano.function( inputs = [index],\r\n updates = updates2,\r\n givens={\r\n x:train_set_x[index*batch_size:(index+1)*batch_size],\r\n y:train_set_y[index*batch_size:(index+1)*batch_size]},\r\n mode=mode)\r\n #print 'MODEL 1'\r\n #theano.printing.debugprint(train_model, print_type=True)\r\n assert any([isinstance(i.op,T.nnet.CrossentropySoftmax1HotWithBiasDx) for i in train_model.maker.fgraph.toposort()])\r\n\r\n # Even without FeatureShape\r\n train_model =theano.function( inputs = [index],\r\n updates = updates2,\r\n mode=mode.excluding('ShapeOpt'),\r\n givens={\r\n x:train_set_x[index*batch_size:(index+1)*batch_size],\r\n y:train_set_y[index*batch_size:(index+1)*batch_size]})\r\n #print\r\n #print 'MODEL 2'\r\n #theano.printing.debugprint(train_model, print_type=True)\r\n assert any([isinstance(i.op,T.nnet.CrossentropySoftmax1HotWithBiasDx) for i in train_model.maker.fgraph.toposort()])","def test_generate_nb(self):\n pass","def neural_net_ex4_ng():\n # ==================\n # read data\n dataset = loadmat('data/ex4data1.mat')\n print(dataset.keys())\n\n y = dataset['y'] # 5000 x 1\n print('dims y: ', y.shape)\n # print('y[0]: ', y[0])\n\n X = dataset['X'] # 5000 x 400\n print('dims X: ', X.shape)\n # print('X[0]: ', X[0])\n\n X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n\n num_samples_test = X_test.shape[0]\n\n # ==================\n # display data\n\n # pick 20 examples and visualize them\n fig = plt.figure(figsize=(10, 8), facecolor='white')\n fig.add_subplot(651)\n samples = np.random.choice(num_samples_test, 10)\n print('samples:', samples)\n plt.imshow(X_test[samples, :].reshape(-1, 20).T, cmap=\"Greys\")\n plt.axis('off')\n\n # ==================\n # run neural net\n hidden_layer_size = 25\n\n mlp = MLPClassifier(hidden_layer_sizes=(25,), max_iter=20, alpha=1e-4,\n solver='sgd', verbose=False, tol=1e-4, random_state=1,\n learning_rate_init=.1)\n mlp.fit(X_train, y_train.ravel())\n\n predictions = mlp.predict(X_test)\n print('Test set accuracy: {} %'.format(np.mean(predictions == y_test.ravel())*100))\n\n # print(confusion_matrix(y_test, predictions))\n # print(classification_report(y_test, predictions))\n print(\"Training set score: %f\" % mlp.score(X_train, y_train))\n print(\"Test set score: %f\" % mlp.score(X_test, y_test))\n print('coeffs shape', (mlp.coefs_[0]).shape)\n\n # ==================\n # display coefficients of hidden layer\n fig.add_subplot(652)\n plt.imshow(mlp.coefs_[0][:, 0].reshape(20, 20))\n plt.axis('off')\n\n gs = gridspec.GridSpec(6, 5)\n cur_img_idx = 5\n\n # use global min / max to ensure all weights are shown on the same scale\n vmin, vmax = mlp.coefs_[0].min(), mlp.coefs_[0].max()\n for coef, ax in zip(mlp.coefs_[0].T, range(hidden_layer_size)):\n fig.add_subplot(gs[cur_img_idx])\n plt.imshow(coef.reshape(20, 20), cmap=plt.cm.gray, vmin=.5 * vmin, vmax=.5 * vmax)\n plt.axis('off')\n cur_img_idx += 1\n\n plt.show()","def demo():\n def load_data():\n train = open(\"csv/svd_train.csv\", \"r\")\n r = csv.reader(train)\n next(r)\n\n data = []\n target = []\n\n print \"Prepping data...\"\n for row in r:\n aux = [0 for x in xrange(10)]\n aux[int(row[0])] = 1\n target.append(aux)\n data.append([float(x) for x in row[1:]])\n\n train.close()\n\n data = np.array(data)\n\n target = np.array(target)\n\n #train = [target[:35000],data[:35000]]\n #test = [target[35000:],data[35000:]]\n\n return [target, data]\n\n NN = MLP_NeuralNetwork(101, 75, 35, 10,\n iterations = 200,\n learning_rate = 0.5,\n momentum = 0.05,\n rate_decay = 0.005)\n\n train = load_data()\n\n NN.train(train)\n #NN.test_cross(test)\n #NN.test()\n NN.test_against()","def gen_train_val_test_images(data_dir, seed=131):\n np.random.seed(seed)\n\n # Load SVHN Dataset (single digits)\n train_data = scipy_io.loadmat(data_dir + '/train_32x32.mat')\n test_data = scipy_io.loadmat(data_dir + '/test_32x32.mat')\n extra_data = scipy_io.loadmat(data_dir + '/extra_32x32.mat')\n\n train_X, train_y = train_data['X'], train_data['y']\n test_X, test_y = test_data['X'], test_data['y']\n extra_X, extra_y = extra_data['X'], extra_data['y']\n\n train_y = train_y.squeeze()\n test_y = test_y.squeeze()\n extra_y = extra_y.squeeze()\n\n # Change labels for '0' digit from 10 to 0\n train_y[train_y == 10] = 0\n test_y[test_y == 10] = 0\n extra_y[extra_y == 10] = 0\n\n del extra_data\n\n num_classes = 10\n\n train_val_sample_idxs = np.array([], int)\n for i in range(num_classes):\n class_idxs = np.arange(len(train_y))[train_y == i]\n sel_class_idxs = np.random.choice(class_idxs, size=400)\n train_val_sample_idxs = np.concatenate((train_val_sample_idxs,\n sel_class_idxs))\n not_train_val_sample_idxs = np.setdiff1d(np.arange(len(train_y)),\n train_val_sample_idxs)\n\n val_X = train_X[:, :, :, train_val_sample_idxs]\n val_y = train_y[train_val_sample_idxs]\n\n extra_val_sample_idxs = np.array([], int)\n for i in range(num_classes):\n class_idxs = np.arange(len(extra_y))[extra_y == i]\n sel_class_idxs = np.random.choice(class_idxs, size=200)\n extra_val_sample_idxs = np.concatenate((extra_val_sample_idxs,\n sel_class_idxs))\n not_extra_val_sample_idxs = np.setdiff1d(np.arange(len(extra_y)),\n extra_val_sample_idxs)\n\n val_X = np.concatenate((val_X, extra_X[:, :, :, extra_val_sample_idxs]), axis=3)\n val_y = np.hstack((val_y, extra_y[extra_val_sample_idxs]))\n\n train_X = np.concatenate((train_X[:, :, :, not_train_val_sample_idxs],\n extra_X[:, :, :, not_extra_val_sample_idxs]), axis=3)\n train_y = np.hstack((train_y[not_train_val_sample_idxs],\n extra_y[not_extra_val_sample_idxs]))\n\n # Create directories and save images\n train_dir = data_dir + '/imgs/train'\n test_dir = data_dir + '/imgs/test'\n validation_dir = data_dir + '/imgs/validation'\n\n if not os.path.exists(train_dir):\n os.makedirs(train_dir)\n\n if not os.path.exists(validation_dir):\n os.makedirs(validation_dir)\n\n if not os.path.exists(test_dir):\n os.makedirs(test_dir)\n\n for i in range(num_classes):\n if not os.path.exists(train_dir + '/' + str(i)):\n os.makedirs(train_dir + '/' + str(i))\n\n if not os.path.exists(validation_dir + '/' + str(i)):\n os.makedirs(validation_dir + '/' + str(i))\n\n if not os.path.exists(test_dir + '/' + str(i)):\n os.makedirs(test_dir + '/' + str(i))\n\n print \"Creating train images ... \"\n for i in range(len(train_y)):\n filename = train_dir + '/' + str(train_y[i]) + '/' + str(i) + '.png'\n skimage_io.imsave(filename, train_X[:, :, :, i])\n\n print \"Creating validation images ... \"\n for i in range(len(val_y)):\n filename = validation_dir + '/' + str(val_y[i]) + '/' + str(i) + '.png'\n skimage_io.imsave(filename, val_X[:, :, :, i])\n\n print \"Creating test images ... \"\n for i in range(len(test_y)):\n filename = test_dir + '/' + str(test_y[i]) + '/' + str(i) + '.png'\n skimage_io.imsave(filename, test_X[:, :, :, i])","def test_on_all(self) -> None:\n x_test, y_test = self.mnist.test.images, self.mnist.test.labels\n N = self.mnist.test.num_examples\n\n # I have replaced all -1 with self.mb_size to be sure about exact shapes of all layers.\n assert N % self.mb_size == 0,\\\n \"Sorry, mb_size must divide the number of images in test set\"\n\n results = np.array([0., 0.])\n for batch_no in range(N // self.mb_size):\n beg = batch_no * self.mb_size\n end = min(N, (batch_no + 1) * self.mb_size)\n len_batch = end - beg\n batch_results = np.array(self.test_on_batch(x_test[beg:end], y_test[beg:end]))\n results += batch_results * len_batch\n results /= N\n self.logger.info(\"(Test(final): Loss: {0[0]}, accuracy: {0[1]}\".format(results))","def fetch_multimnist_image(label):\n dataset = MultiMNIST('./data', train=False, download=True,\n transform=transforms.ToTensor(),\n target_transform=charlist_tensor)\n images = dataset.test_data\n labels = dataset.test_labels\n n_rows = len(images)\n\n images = []\n for i in xrange(n_rows):\n image = images[i]\n text = labels[i]\n if tensor_to_string(text.squeeze(0)) == label:\n images.append(image)\n\n if len(images) == 0:\n sys.exit('No images with label (%s) found.' % label)\n\n images = torch.cat(images).cpu().numpy()\n ix = np.random.choice(np.arange(images.shape[0]))\n image = images[ix]\n image = torch.from_numpy(image).float() \n image = image.unsqueeze(0)\n return Variable(image, volatile=True)","def _load_mnist(path, dataset=\"training\", digits=None, asbytes=False,\n selection=None, return_labels=True, return_indices=False):\n\n # The files are assumed to have these names and should be found in 'path'\n files = {\n 'training': ('train-images-idx3-ubyte', 'train-labels-idx1-ubyte'),\n 'testing': ('t10k-images-idx3-ubyte', 't10k-labels-idx1-ubyte'),\n }\n\n try:\n images_fname = os.path.join(path, files[dataset][0])\n labels_fname = os.path.join(path, files[dataset][1])\n except KeyError:\n raise ValueError(\"Data set must be 'testing' or 'training'\")\n\n # We can skip the labels file only if digits aren't specified and labels\n # aren't asked for\n if return_labels or digits is not None:\n flbl = open(labels_fname, 'rb')\n magic_nr, size = struct.unpack(\">II\", flbl.read(8))\n labels_raw = pyarray(\"b\", flbl.read())\n flbl.close()\n\n fimg = open(images_fname, 'rb')\n magic_nr, size, rows, cols = struct.unpack(\">IIII\", fimg.read(16))\n images_raw = pyarray(\"B\", fimg.read())\n fimg.close()\n\n if digits:\n indices = [k for k in range(size) if labels_raw[k] in digits]\n else:\n indices = range(size)\n\n if selection:\n indices = indices[selection]\n\n images = np.zeros((len(indices), rows, cols), dtype=np.uint8)\n\n if return_labels:\n labels = np.zeros((len(indices)), dtype=np.int8)\n for i in range(len(indices)):\n images[i] = np.array(images_raw[indices[i] * rows * cols:(indices[i] + 1) * rows * cols]).reshape((rows, cols))\n if return_labels:\n labels[i] = labels_raw[indices[i]]\n\n if not asbytes:\n images = images.astype(float)/255.0\n\n ret = (images,)\n if return_labels:\n ret += (labels,)\n if return_indices:\n ret += (indices,)\n\n if len(ret) == 1:\n return ret[0] # Don't return a tuple of one\n\n return ret","def fetch_binarized_mnist():\n mnist = fetch_mnist()\n random_state = np.random.RandomState(1999)\n\n def get_sampled(arr):\n # make sure that a pixel can always be turned off\n return random_state.binomial(1, arr * 255 / 256., size=arr.shape)\n\n data = get_sampled(mnist[\"data\"]).astype(theano.config.floatX)\n return {\"data\": data,\n \"target\": mnist[\"target\"],\n \"train_indices\": mnist[\"train_indices\"],\n \"valid_indices\": mnist[\"valid_indices\"],\n \"test_indices\": mnist[\"test_indices\"]}","def get_data():\n\t(X_train, y_train), (X_val, y_val) = mnist.load_data()\n\tn_features = X_train.shape[1]*X_train.shape[1]\n\tn_train = X_train.shape[0]\n\tn_val = X_val.shape[0]\n\tX_train = X_train.reshape(n_train, n_features)\n\tX_val = X_val.reshape(n_val, n_features)\n\tX_train = X_train.astype(\"float32\") / 255\n\tX_val = X_val.astype(\"float32\") / 255\n\n\treturn (X_train, y_train), (X_val, y_val)","def try4():\n path = '/Users/mayankkejriwal/git-projects/bioExperiments/tsne_python/'\n mnist = path+'mnist2500_X.txt'\n X = numpy.loadtxt(mnist)\n labels = numpy.loadtxt(path+\"mnist2500_labels.txt\")\n Y = tsne.tsne(X, 2, 50, 20.0)\n pylab.scatter(Y[:,0], Y[:,1], 20, labels)\n pylab.show()","def test_mlp(learning_rate=0.01, L1_reg=0.00, L2_reg=0.0001, n_epochs=1000,\r\n dataset='mnist.pkl.gz', batch_size=20, n_hidden=500):\r\n datasets = load_data(dataset)\r\n\r\n train_set_x, train_set_y = datasets[0]\r\n valid_set_x, valid_set_y = datasets[1]\r\n test_set_x, test_set_y = datasets[2]\r\n\r\n # compute number of minibatches for training, validation and testing\r\n n_train_batches = train_set_x.get_value(borrow=True).shape[0] / batch_size\r\n n_valid_batches = valid_set_x.get_value(borrow=True).shape[0] / batch_size\r\n n_test_batches = test_set_x.get_value(borrow=True).shape[0] / batch_size\r\n\r\n ######################\r\n # BUILD ACTUAL MODEL #\r\n ######################\r\n print '... building the model'\r\n\r\n # allocate symbolic variables for the data\r\n index = T.lscalar() # index to a [mini]batch\r\n x = T.matrix('x') # the data is presented as rasterized images\r\n y = T.ivector('y') # the labels are presented as 1D vector of\r\n # [int] labels\r\n\r\n rng = numpy.random.RandomState(1234)\r\n\r\n # construct the MLP class\r\n classifier = MLP(rng=rng, input=x, n_in=28 * 28,\r\n n_hidden=n_hidden, n_out=10)\r\n\r\n # the cost we minimize during training is the negative log likelihood of\r\n # the model plus the regularization terms (L1 and L2); cost is expressed\r\n # here symbolically\r\n cost = classifier.negative_log_likelihood(y) \\\r\n + L1_reg * classifier.L1 \\\r\n + L2_reg * classifier.L2_sqr\r\n\r\n # compiling a Theano function that computes the mistakes that are made\r\n # by the model on a minibatch\r\n test_model = theano.function(inputs=[index],\r\n outputs=classifier.errors(y),\r\n givens={\r\n x: test_set_x[index * batch_size:(index + 1) * batch_size],\r\n y: test_set_y[index * batch_size:(index + 1) * batch_size]})\r\n\r\n validate_model = theano.function(inputs=[index],\r\n outputs=classifier.errors(y),\r\n givens={\r\n x: valid_set_x[index * batch_size:(index + 1) * batch_size],\r\n y: valid_set_y[index * batch_size:(index + 1) * batch_size]})\r\n\r\n # compute the gradient of cost with respect to theta (sotred in params)\r\n # the resulting gradients will be stored in a list gparams\r\n gparams = []\r\n for param in classifier.params:\r\n gparam = T.grad(cost, param)\r\n gparams.append(gparam)\r\n\r\n # specify how to update the parameters of the model as a list of\r\n # (variable, update expression) pairs\r\n updates = []\r\n # given two list the zip A = [a1, a2, a3, a4] and B = [b1, b2, b3, b4] of\r\n # same length, zip generates a list C of same size, where each element\r\n # is a pair formed from the two lists :\r\n # C = [(a1, b1), (a2, b2), (a3, b3), (a4, b4)]\r\n for param, gparam in zip(classifier.params, gparams):\r\n updates.append((param, param - learning_rate * gparam))\r\n\r\n # compiling a Theano function `train_model` that returns the cost, but\r\n # in the same time updates the parameter of the model based on the rules\r\n # defined in `updates`\r\n train_model = theano.function(inputs=[index], outputs=cost,\r\n updates=updates,\r\n givens={\r\n x: train_set_x[index * batch_size:(index + 1) * batch_size],\r\n y: train_set_y[index * batch_size:(index + 1) * batch_size]})\r\n\r\n ###############\r\n # TRAIN MODEL #\r\n ###############\r\n print '... training'\r\n\r\n # early-stopping parameters\r\n patience = 10000 # look as this many examples regardless\r\n patience_increase = 2 # wait this much longer when a new best is\r\n # found\r\n improvement_threshold = 0.995 # a relative improvement of this much is\r\n # considered significant\r\n validation_frequency = min(n_train_batches, patience / 2)\r\n # go through this many\r\n # minibatche before checking the network\r\n # on the validation set; in this case we\r\n # check every epoch\r\n\r\n best_params = None\r\n best_validation_loss = numpy.inf\r\n best_iter = 0\r\n test_score = 0.\r\n start_time = time.clock()\r\n\r\n epoch = 0\r\n done_looping = False\r\n\r\n while (epoch < n_epochs) and (not done_looping):\r\n epoch = epoch + 1\r\n for minibatch_index in xrange(n_train_batches):\r\n\r\n minibatch_avg_cost = train_model(minibatch_index)\r\n # iteration number\r\n iter = (epoch - 1) * n_train_batches + minibatch_index\r\n\r\n if (iter + 1) % validation_frequency == 0:\r\n # compute zero-one loss on validation set\r\n validation_losses = [validate_model(i) for i\r\n in xrange(n_valid_batches)]\r\n this_validation_loss = numpy.mean(validation_losses)\r\n\r\n print('epoch %i, minibatch %i/%i, validation error %f %%' %\r\n (epoch, minibatch_index + 1, n_train_batches,\r\n this_validation_loss * 100.))\r\n\r\n # if we got the best validation score until now\r\n if this_validation_loss < best_validation_loss:\r\n #improve patience if loss improvement is good enough\r\n if this_validation_loss < best_validation_loss * \\\r\n improvement_threshold:\r\n patience = max(patience, iter * patience_increase)\r\n\r\n best_validation_loss = this_validation_loss\r\n best_iter = iter\r\n\r\n # test it on the test set\r\n test_losses = [test_model(i) for i\r\n in xrange(n_test_batches)]\r\n test_score = numpy.mean(test_losses)\r\n\r\n print((' epoch %i, minibatch %i/%i, test error of '\r\n 'best model %f %%') %\r\n (epoch, minibatch_index + 1, n_train_batches,\r\n test_score * 100.))\r\n\r\n if patience <= iter:\r\n done_looping = True\r\n break\r\n\r\n end_time = time.clock()\r\n print(('Optimization complete. Best validation score of %f %% '\r\n 'obtained at iteration %i, with test performance %f %%') %\r\n (best_validation_loss * 100., best_iter + 1, test_score * 100.))\r\n print >> sys.stderr, ('The code for file ' +\r\n os.path.split(__file__)[1] +\r\n ' ran for %.2fm' % ((end_time - start_time) / 60.))","def test_data():\n global _MEAN # pylint: disable=global-statement\n _np.random.seed(1)\n view = _skdc10.view.OfficialImageClassificationTask()\n permutation = _np.random.permutation(range(10000))\n if _MEAN is None:\n _MEAN = view.train.x.reshape((50000 * 32 * 32, 3)).mean(axis=0)\n return ((view.test.x[:10000, :][permutation, :] - _MEAN).\n transpose((0, 3, 1, 2)).astype('float32'),\n view.test.y[:10000][permutation].reshape((10000, 1)).astype('float32'))","def load_data():\n prefix = 'mnist_data/'\n train_data = np.load(prefix + 'mnist_train_images.npy')\n train_labels = np.load(prefix + 'mnist_train_labels.npy')\n val_data = np.load(prefix + 'mnist_validation_images.npy')\n val_labels = np.load(prefix + 'mnist_validation_labels.npy')\n test_data = np.load(prefix + 'mnist_test_images.npy')\n test_labels = np.load(prefix + 'mnist_test_labels.npy')\n assert train_data.shape == (55000, 784) and train_labels.shape == (55000, 10)\n assert val_data.shape == (5000, 784) and val_labels.shape == (5000, 10)\n assert test_data.shape == (10000, 784) and test_labels.shape == (10000, 10)\n return train_data, train_labels, val_data, val_labels, test_data, test_labels","def test_basic(self):\n result = NonLinearWeights(0.85).nonlinear_weights(3)\n self.assertIsInstance(result, np.ndarray)","def test(ndigit, elambda, showSamples, showConfusion):\n Data, Label = getData()\n trainX, trainY, testX, testY = splitData(Data, Label, ndigit)\n trainX_mean = np.mean(trainX, axis=0)\n trainX_new = trainX - trainX_mean\n eigenvectors = getEigenVectors(trainX_new, elambda)\n trainX_eigen = trainX_new.dot(eigenvectors)\n testX_new = testX - trainX_mean\n testX_eigen = testX_new.dot(eigenvectors)\n testO = []\n if showSamples:\n correct_samples = []\n correct_samples_nearest = []\n correct_samples_eigen = []\n correct_samples_nearest_eigen = []\n correct_samples_labels = []\n correct_samples_predictions = []\n wrong_samples = []\n wrong_samples_nearest = []\n wrong_samples_eigen = []\n wrong_samples_nearest_eigen = []\n wrong_samples_labels = []\n wrong_samples_predictions = []\n if showConfusion:\n conf = np.zeros((ndigit, ndigit))\n for i in xrange(testX_eigen.shape[0]):\n t = testX_eigen[i]\n j = getNearestSampleIndex(t, trainX_eigen)\n p = int(trainY[j])\n y = int(testY[i])\n if showConfusion:\n conf[p, y] += 1\n if showSamples:\n if p == y:\n if len(correct_samples) < y + 1:\n correct_samples.append(testX[i])\n correct_samples_nearest.append(trainX[j])\n correct_samples_eigen.append(testX_eigen[i])\n correct_samples_nearest_eigen.append(trainX_eigen[j])\n correct_samples_labels.append(y)\n correct_samples_predictions.append(p)\n else:\n if len(wrong_samples) < y + 1:\n wrong_samples.append(testX[i])\n wrong_samples_nearest.append(trainX[j])\n wrong_samples_eigen.append(testX_eigen[i])\n wrong_samples_nearest_eigen.append(trainX_eigen[j])\n wrong_samples_labels.append(y)\n wrong_samples_predictions.append(p)\n testO.append(p)\n testO = np.array(testO)\n train0 = []\n for i in xrange(trainX_eigen.shape[0]):\n t = trainX_eigen[i]\n j = getNearestSampleIndex(t, trainX_eigen)\n min_class = trainY[j]\n train0.append(min_class)\n train0 = np.array(train0)\n print \"for digits = %d lambda = %.2f train = %.6f test = %.6f \" % (\n ndigit, elambda, (train0 == trainY).mean(), (testO == testY).mean())\n if showConfusion:\n print conf\n if showSamples:\n displaySamples(correct_samples_labels, correct_samples_predictions,\n correct_samples, correct_samples_nearest,\n correct_samples_eigen, correct_samples_nearest_eigen,\n eigenvectors, trainX_mean, elambda, 'Correct')\n displaySamples(wrong_samples_labels, wrong_samples_predictions,\n wrong_samples, wrong_samples_nearest,\n wrong_samples_eigen, wrong_samples_nearest_eigen,\n eigenvectors, trainX_mean, elambda, 'Wrong')","def load_mnist(fashion, onehot_encode=True, flatten_x=False, crop_x=0, classes=None):\n\tif not fashion:\n\t\t(x_train, y_train),(x_test, y_test) = tf.keras.datasets.mnist.load_data()\n\t\tx_train, x_test = x_train / 255.0, x_test / 255.0\n\telse:\n\t\t(x_train, y_train),(x_test, y_test) = tf.keras.datasets.fashion_mnist.load_data()\n\t\tx_train, x_test = x_train / 255.0, x_test / 255.0 \n \n\tdef crop(X, crop_size):\n\t\tassert crop_x < X.shape[1]/2\n\t\tassert crop_x < X.shape[2]/2\n\t\treturn X[:,crop_size:-crop_size,crop_size:-crop_size]\n\n\tif crop_x > 0:\n\t\tx_train = crop(x_train, crop_x)\n\t\tx_test = crop(x_test, crop_x)\n\n\t# Flatten to 2d arrays (each example 1d)\n\tdef flatten_image(X):\n\t return X.reshape(X.shape[0], X.shape[1]*X.shape[1])\n\tif flatten_x:\n\t\tx_train = flatten_image(x_train)\n\t\tx_test = flatten_image(x_test)\n\n\tif onehot_encode:\n\t\ty_train = onehot_encode_labels(y_train)\n\t\ty_test = onehot_encode_labels(y_test)\n\n\tif classes is not None:\n\t\tassert len(classes) == 2\n\t\tc0, c1 = classes\n\t\ttrain_idxs_to_keep = np.logical_or(y_train==c0, y_train==c1)\n\t\tx_train, y_train = x_train[train_idxs_to_keep,:], y_train[train_idxs_to_keep]\n\t\ttest_idxs_to_keep = np.logical_or(y_test==c0, y_test==c1)\n\t\tx_test, y_test = x_test[test_idxs_to_keep,:], y_test[test_idxs_to_keep]\n\n\t\ty_train = (y_train==c1).astype(int)[:,np.newaxis]\n\t\ty_test = (y_test==c1).astype(int)[:,np.newaxis]\n\n\treturn x_train, y_train, x_test, y_test","def test_metric_learning(smote_class):\n nn_params = {'metric': 'precomputed',\n 'metric_learning_method': 'ITML'}\n X, y = smote_class(nn_params=nn_params).sample(dataset['data'],\n dataset['target'])\n\n assert np.unique(y).shape[0] == 2\n assert X.shape[0] > 0","def load_EMNIST_data(file, verbose = False, standarized = False): \n mat = sio.loadmat(file)\n data = mat[\"dataset\"]\n \n X_train = data['train'][0,0]['images'][0,0]\n X_train = X_train.reshape((X_train.shape[0], 28, 28), order = \"F\")\n y_train = data['train'][0,0]['labels'][0,0]\n y_train = np.squeeze(y_train)\n y_train -= 1 #y_train is zero-based\n \n X_test = data['test'][0,0]['images'][0,0]\n X_test= X_test.reshape((X_test.shape[0], 28, 28), order = \"F\")\n y_test = data['test'][0,0]['labels'][0,0]\n y_test = np.squeeze(y_test)\n y_test -= 1 #y_test is zero-based\n \n if standarized: \n X_train = X_train/255\n X_test = X_test/255\n mean_image = np.mean(X_train, axis=0)\n X_train -= mean_image\n X_test -= mean_image\n \n\n if verbose == True: \n print(\"EMNIST-letter dataset ... \")\n print(\"X_train shape :\", X_train.shape)\n print(\"X_test shape :\", X_test.shape)\n print(\"y_train shape :\", y_train.shape)\n print(\"y_test shape :\", y_test.shape)\n \n return X_train, y_train, X_test, y_test","def test_000_basic_functionality() -> None:\n df = generate_test_data()\n skim(df)"],"string":"[\n \"def main():\\n\\n os.system(\\\"rm -rf images; mkdir images\\\")\\n\\n if (len(sys.argv) > 1):\\n N = int(sys.argv[1])\\n else:\\n N = 10\\n\\n x_test = np.load(\\\"../../../../data/mnist/mnist_test_images.npy\\\")\\n\\n for i in range(N):\\n r,c = random.randint(6,12), random.randint(6,12)\\n g = np.zeros(r*c)\\n for j in range(r*c):\\n if (random.random() < 0.15):\\n g[j] = 1\\n g = g.reshape((r,c))\\n g[:,0] = g[0,:] = g[:,-1] = g[-1,:] = 0\\n\\n img = np.zeros((28*r,28*c), dtype=\\\"uint8\\\")\\n for x in range(r):\\n for y in range(c):\\n if (g[x,y] == 1):\\n n = random.randint(0, x_test.shape[0])\\n im = x_test[n]\\n img[28*x:(28*x+28), 28*y:(28*y+28)] = im\\n \\n Image.fromarray(img).save(\\\"images/image_%04d.png\\\" % i)\",\n \"def mnist_testing(shuffled = True):\\n mndata = MNIST(MNIST_PATH)\\n test_ims, test_labels = mndata.load_testing()\\n test_X = np.array(test_ims).T\\n test_y = np.array(test_labels).T\\n return test_X, test_y\",\n \"def test_keras_mnist():\\n data = fetch(\\\"mnist\\\")\\n check(data, n_samples_train=60000, n_samples_test=10000, n_features=28 * 28)\",\n \"def test_keras_mnist_return_X_y():\\n X, y = fetch(\\\"mnist\\\", return_X_y=True)\\n assert X.shape == (70000, 28 * 28)\\n assert y.shape == (70000,)\",\n \"def test_mnist(args):\\n # type: () -> None\\n\\n # Build dataset and model\\n dataset = MNIST(path=args.path)\\n model = MEMMNIST(input_shape=dataset.shape, code_length=64, cpd_channels=100, mem_dim=100, shrink_thres=0.5/100).cuda().eval()\\n\\n # Set up result helper and perform test\\n helper = MEMResultHelper(dataset, model, checkpoints_dir=args.checkpoints, output_file='mem_mnist.txt')\\n helper.test_one_class_classification()\",\n \"def test_rand(self):\\n assert len(self._mnist.random()[:5]) == 5\\n pass\",\n \"def test_get_mnist_data(self):\\n # TODO: Remove once get_mnist_data(...) is fixed.\\n pass\\n # mnist = get_mnist_data()\\n # self.assertEqual(len(mnist.data), 60000)\\n # self.assertEqual(len(mnist.labels), 60000)\",\n \"def mnist(path=None):\\r\\n url = 'http://yann.lecun.com/exdb/mnist/'\\r\\n files = ['train-images-idx3-ubyte.gz',\\r\\n 'train-labels-idx1-ubyte.gz',\\r\\n 't10k-images-idx3-ubyte.gz',\\r\\n 't10k-labels-idx1-ubyte.gz']\\r\\n\\r\\n if path is None:\\r\\n # Set path to /home/USER/data/mnist or C:\\\\Users\\\\USER\\\\data\\\\mnist\\r\\n path = os.path.join(os.path.expanduser('~'), 'data', 'mnist')\\r\\n\\r\\n # Create path if it doesn't exist\\r\\n os.makedirs(path, exist_ok=True)\\r\\n\\r\\n # Download any missing files\\r\\n for file in files:\\r\\n if file not in os.listdir(path):\\r\\n urlretrieve(url + file, os.path.join(path, file))\\r\\n print(\\\"Downloaded %s to %s\\\" % (file, path))\\r\\n\\r\\n def _images(path):\\r\\n \\\"\\\"\\\"Return images loaded locally.\\\"\\\"\\\"\\r\\n with gzip.open(path) as f:\\r\\n # First 16 bytes are magic_number, n_imgs, n_rows, n_cols\\r\\n pixels = np.frombuffer(f.read(), 'B', offset=16)\\r\\n return pixels.reshape(-1, 784).astype('float32') / 255\\r\\n\\r\\n def _labels(path):\\r\\n \\\"\\\"\\\"Return labels loaded locally.\\\"\\\"\\\"\\r\\n with gzip.open(path) as f:\\r\\n # First 8 bytes are magic_number, n_labels\\r\\n integer_labels = np.frombuffer(f.read(), 'B', offset=8)\\r\\n\\r\\n def _onehot(integer_labels):\\r\\n \\\"\\\"\\\"Return matrix whose rows are onehot encodings of integers.\\\"\\\"\\\"\\r\\n n_rows = len(integer_labels)\\r\\n n_cols = integer_labels.max() + 1\\r\\n onehot = np.zeros((n_rows, n_cols), dtype='uint8')\\r\\n onehot[np.arange(n_rows), integer_labels] = 1\\r\\n return onehot\\r\\n\\r\\n return _onehot(integer_labels)\\r\\n\\r\\n train_images = _images(os.path.join(path, files[0]))\\r\\n train_labels = _labels(os.path.join(path, files[1]))\\r\\n test_images = _images(os.path.join(path, files[2]))\\r\\n test_labels = _labels(os.path.join(path, files[3]))\\r\\n\\r\\n return train_images, train_labels, test_images, test_labels\",\n \"def test_dataset():\\n X,Y = get_MNIST_training_normalized()\\n digits_test_truth = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 632, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 166, 0, 0, 0, 0, 0]\\n digits_test = []\\n for example in itertools.islice(X,30):\\n digits_test.append(sum(example[1:100]))\\n assert(example.shape == (28*28,))\\n\\n show_as_image(X[0,:], 28, 28)\\n print digits_test\\n print digits_test_truth\\n assert(digits_test_truth == digits_test)\\n assert(X.shape == (60000, 28*28))\\n assert(Y.shape == (60000,))\\n return \\\"Dziala :)\\\"\",\n \"def MNIST_data():\\n\\n # Pobieramy macierze numpy z cyframi\\n # images[i,j,k] <=> piksel (j,k) z i-tego obrazka w zbiorze danych\\n images, labels = get_MNIST_dataset(range(10), \\\"training\\\") #pierwszy argument to\\n\\n # a) Ilosc przykladow i rozmiary danych\\n print \\\"Raw training data dimensions \\\", images.shape\\n print \\\"Labels dimensions \\\",labels.shape\\n\\n # b) Ile jest cyfr 2?\\n print \\\"Counting 2 in training dataset \\\",len(filter(lambda x: x == 2, labels))\\n\\n # c) Jaki jest sredni obrazek 2 ? (Usrednienie wszystkich macierzy ktore sa 2)\\n\\n #1. Pobierzmy wszystkie dwojki, fajny sposob indeksowania\\n print labels == 2\\n only_2 = images[labels == 2, :, :]\\n print \\\"Checking number of 2s \\\", only_2.shape\\n\\n #2. TODO: Usrednienie (matrix.mean moze byc przydatne)\\n\\n #3. TODO: narysowanie usrednionej cyfry (zobacz pl.imshow)\\n\\n # d) Ostatnie - przetworzmy ostatnia cyfre do 1 wymiarowego wektora\\n vectorized = np.reshape(images[-1], newshape=(images[-1].shape[0]*images[-1].shape[1]))\\n print \\\"Vectorized last digit \\\", vectorized\",\n \"def test_show_examples():\\n skip_if_no_matplotlib()\\n skip_if_no_data()\\n with open('temp.yaml', 'w') as f:\\n f.write(\\\"\\\"\\\"\\n!obj:pylearn2.datasets.mnist.MNIST {\\n which_set: 'train'\\n}\\n\\\"\\\"\\\")\\n show_examples('temp.yaml', 28, 28, out='garbage.png')\\n os.remove('temp.yaml')\",\n \"def mnist_training():\\n mndata = MNIST(MNIST_PATH)\\n train_ims, train_labels = mndata.load_training()\\n train_X = np.array(train_ims).T\\n train_y = np.array(train_labels).T\\n return train_X, train_y\",\n \"def create_mnistm(X: Any) -> Any:\\n\\n bst_path = \\\"./data/MNIST_M/BSR_bsds500.tgz\\\"\\n\\n rand = np.random.RandomState(42)\\n train_files = []\\n\\n with tarfile.open(bst_path, \\\"r\\\") as bsr_file:\\n for name in bsr_file.getnames():\\n if name.startswith(\\\"BSR/BSDS500/data/images/train/\\\"):\\n train_files.append(name)\\n\\n print(\\\"Loading BSR training images\\\")\\n background_data = []\\n for name in train_files:\\n try:\\n fp = bsr_file.extractfile(name)\\n bg_img = skimage.io.imread(fp)\\n background_data.append(bg_img)\\n except:\\n continue\\n\\n X_ = np.zeros([X.shape[0], 28, 28, 3], np.uint8)\\n for i in range(X.shape[0]):\\n if i % 1000 == 0:\\n print(\\\"Processing example\\\", i)\\n\\n bg_img = rand.choice(background_data)\\n d = mnist_to_img(X[i])\\n d = compose_image(d, bg_img)\\n X_[i] = d\\n\\n return X_\",\n \"def test_load_data(self):\\n assert len(self._mnist.get()) == 10\\n assert self._mnist.get()[0].label == 7\\n pass\",\n \"def MNIST_experiment():\\n tsetlin_machine = TsetlinMachine(number_clauses=1000,\\n number_action_states=1000,\\n precision=3.0,\\n threshold=10)\\n\\n X, y, val_X, val_y = MNIST()\\n\\n tsetlin_machine.fit(X, y, val_X, val_y, 300)\\n print('Final training accuracy:', tsetlin_machine.accuracy(X, y))\\n print('Final validation accuracy:', tsetlin_machine.accuracy(val_X, val_y))\",\n \"def get_mnist():\\n from keras.datasets import mnist\\n\\n # input image dimensions\\n img_rows, img_cols = 28, 28\\n num_classes = 10\\n # the data, shuffled and split between train and test sets\\n (x_train, y_train), (x_test, y_test) = mnist.load_data()\\n\\n if K.image_data_format() == 'channels_first':\\n print (\\\"Using Channels first\\\")\\n x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)\\n x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)\\n input_shape = (1, img_rows, img_cols)\\n else:\\n print(\\\"Channels last\\\")\\n x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)\\n x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)\\n input_shape = (img_rows, img_cols, 1)\\n\\n return (x_train, y_train), (x_test, y_test)\",\n \"def test_readme_minimal():\\n # Data sampler that generates balanced batches from MNIST dataset\\n sampler = TFDatasetMultiShotMemorySampler(\\n dataset_name='mnist',\\n classes_per_batch=10\\n )\\n\\n # Build a Similarity model using standard Keras layers\\n inputs = layers.Input(shape=(28, 28, 1))\\n x = layers.experimental.preprocessing.Rescaling(1/255)(inputs)\\n x = layers.Conv2D(64, 3, activation='relu')(x)\\n x = layers.Flatten()(x)\\n x = layers.Dense(64, activation='relu')(x)\\n outputs = MetricEmbedding(64)(x)\\n\\n # Build a specialized Similarity model\\n model = SimilarityModel(inputs, outputs)\\n\\n # Train Similarity model using contrastive loss\\n model.compile('adam', loss=MultiSimilarityLoss())\\n model.fit(sampler, epochs=5)\\n\\n # Index 100 embedded MNIST examples to make them searchable\\n sx, sy = sampler.get_slice(0, 100)\\n model.index(x=sx, y=sy, data=sx)\\n\\n # Find the top 5 most similar indexed MNIST examples for a given example\\n qx, qy = sampler.get_slice(3713, 1)\\n nns = model.single_lookup(qx[0]) # noqa\\n\\n # ! don't add viz its block the test in certain env.\\n # Visualize the query example and its top 5 neighbors\\n # viz_neigbors_imgs(qx[0], qy[0], nns)\",\n \"def get_mnist_mlp():\\n # Set defaults.\\n nb_classes = 10 #dataset dependent \\n batch_size = 64\\n epochs = 4\\n input_shape = (784,)\\n\\n # Get the data.\\n (x_train, y_train), (x_test, y_test) = mnist.load_data()\\n x_train = x_train.reshape(60000, 784)\\n x_test = x_test.reshape(10000, 784)\\n x_train = x_train.astype('float32')\\n x_test = x_test.astype('float32')\\n x_train /= 255\\n x_test /= 255\\n\\n # convert class vectors to binary class matrices\\n y_train = to_categorical(y_train, nb_classes)\\n y_test = to_categorical(y_test, nb_classes)\\n\\n return (nb_classes, batch_size, input_shape, x_train, x_test, y_train, y_test, epochs)\",\n \"def load_mnist(path='./', test_size=0.3, random_state = 123):\\n \\n np.random.seed(random_state)\\n if 'X_train.npy' not in os.listdir(path=path) or 'y_train.npy' not in os.listdir(path=path):\\n print (\\\"Train dataset not found. Downloading...\\\")\\n os.system(\\\"curl -L -o train.zip {}\\\".format(TRAIN_DATA_LINK))\\n os.system(\\\"unzip train.zip\\\")\\n os.system(\\\"tar -xf trainingSet.tar.gz\\\")\\n images = []\\n labels = []\\n for class_name in os.listdir('./trainingSet'):\\n if 'ipynb' not in class_name and '.DS' not in class_name:\\n for image_name in os.listdir('./trainingSet/{}'.format(class_name)):\\n image = imread('./trainingSet/{}/{}'.format(class_name, image_name))\\n images.append(image)\\n labels.append(int(class_name))\\n X_train = np.array(images)\\n y_train = np.array(labels)\\n\\n permutation = np.random.permutation(X_train.shape[0])\\n X_train = X_train[permutation]\\n y_train = y_train[permutation]\\n\\n with open('X_train.npy', 'wb') as f:\\n np.save(f, X_train)\\n with open('y_train.npy', 'wb') as f:\\n np.save(f, y_train)\\n os.system(\\\"rm -rf trainingSet\\\")\\n os.system(\\\"rm -rf train.zip\\\")\\n os.system(\\\"rm -rf trainingSet.tar.gz\\\")\\n else:\\n X_train = np.load('X_train.npy')\\n y_train = np.load('y_train.npy')\\n\\n if 'X_test.npy' not in os.listdir(path=path) or 'y_test.npy' not in os.listdir(path=path):\\n print (\\\"Test dataset not found. Downloading...\\\")\\n os.system(\\\"curl -L -o test.zip {}\\\".format(TEST_DATA_LINK))\\n os.system(\\\"unzip test.zip\\\")\\n os.system(\\\"tar -xf trainingSample.tar.gz\\\")\\n images = []\\n labels = []\\n for class_name in os.listdir('./trainingSample'):\\n if 'ipynb' not in class_name and '.DS' not in class_name:\\n for image_name in os.listdir('./trainingSample/{}'.format(class_name)):\\n image = imread('./trainingSample/{}/{}'.format(class_name, image_name))\\n images.append(image)\\n labels.append(int(class_name))\\n X_test = np.array(images)\\n y_test = np.array(labels)\\n with open('X_test.npy', 'wb') as f:\\n np.save(f, X_test)\\n with open('y_test.npy', 'wb') as f:\\n np.save(f, y_test)\\n\\n os.system(\\\"rm -rf trainingSample\\\")\\n os.system(\\\"rm -rf test.zip\\\")\\n os.system(\\\"rm -rf trainingSet.tar.gz\\\")\\n\\n else:\\n X_test = np.load('X_test.npy')\\n y_test = np.load('y_test.npy')\\n\\n return X_train, X_test, y_train, y_test\",\n \"def main():\\n # Import or download the mnist data, from target file path.\\n mnist = input_data.read_data_sets(\\\"Data/\\\", one_hot=True)\\n\\n # Train and test model.\\n train(mnist)\",\n \"def test_neuron(self):\\r\\n # crear una lista 1-D (Horizontal, Entradas).\\r\\n Z = [1, 2, 3]\\r\\n # crear una lista 1-D (Vertical, Pesos de la red).\\r\\n W = [10, 20, 30]\\r\\n # Inicializamos la neurona, y obtenemos el valor que toma dado W * Z\\r\\n # X(k) = W * Z\\r\\n result = rhonn(W, Z).predict()\\r\\n # Comprobamos el resultado \\r\\n self.assertEqual(result, 140)\",\n \"def mnist(path):\\n with open(path, 'r') as f:\\n for line in f:\\n data = line.strip().split(',')\\n\\n # Label is a vector with one element per class\\n label = [0.0] * 10\\n label[int(data[0])] = 1.0 \\n\\n # The data are images of 28x28 pixels\\n image_array = np.asfarray(data[1:]).reshape((28, 28))\\n # Normalize the pictures \\n image_array = image_array / 255.0\\n\\n #plt.imshow(image_array, cmap='Greys', interpolation='None')\\n yield (image_array, label)\",\n \"def mnist(path):\\n with open(path, 'r') as f:\\n for line in f:\\n data = line.strip().split(',')\\n\\n # Label is a vector with one element per class\\n label = [0.01] * 10\\n label[int(data[0])] = 0.99\\n\\n # The data are images of 28x28 pixels\\n #image_array = np.asfarray(data[1:]).reshape((28, 28))\\n image_array = np.asfarray(data[1:])\\n # Normalize all values between [0.01, 1.0]\\n image_array = ((image_array) / 255.0 * 0.99) + 0.01\\n\\n #plt.imshow(image_array, cmap='Greys', interpolation='None')\\n yield (image_array, label)\",\n \"def test_documentation_popxl_mnist_rts_train_test(self):\\n filename = \\\"mnist_rts.py --replication-factor 2 --rts --test\\\"\\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)\",\n \"def create_mnistm(X):\\r\\n X_ = np.zeros([X.shape[0], 28, 28, 3], np.uint8)\\r\\n for i in range(X.shape[0]):\\r\\n bg_img = rand.choice(background_data)\\r\\n d = mnist_to_img(X[i])\\r\\n d = compose_image(d, bg_img)\\r\\n X_[i] = d\\r\\n return X_\",\n \"def load_mnist(path='mnist/mnist.npz'):\\n\\n with np.load(path) as f:\\n x_train, y_train = f['x_train'], f['y_train']\\n x_test, y_test = f['x_test'], f['y_test']\\n x_train = x_train.astype(np.float32) / 255.\\n y_train = y_train.astype(np.int32)\\n x_test = x_test.astype(np.float32) / 255.\\n y_test = y_test.astype(np.int32)\\n \\n return (x_train, y_train), (x_test, y_test)\",\n \"def test_documentation_popxl_mnist_rts_train(self):\\n filename = \\\"mnist_rts.py --replication-factor 2 --rts\\\"\\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)\",\n \"def import_mnist():\\n\\turl_mnist = \\\"http://deeplearning.net/data/mnist/mnist.pkl.gz\\\"\\n\\tfile_name = \\\"mnist.pkl.gz\\\"\\n\\twork_directory = \\\"mnist\\\"\\n\\tfile_path = maybe_download(url=url_mnist, file_name=file_name, work_directory=work_directory)\\n\\n\\timport pickle\\n\\twith gzip.open(file_path,'rb') as ff :\\n\\t\\tu = pickle._Unpickler( ff )\\n\\t\\tu.encoding = 'latin1'\\n\\t\\ttrain, val, test = u.load()\\n\\t\\ttrainX = np.array(train[0])\\n\\t\\ttrainY = np.reshape(train[1], [50000, 1])\\n\\t\\tvalX = np.array(val[0])\\n\\t\\tvalY = np.reshape(val[1], [10000, 1])\\n\\t\\ttestX = np.array(test[0])\\n\\t\\ttestY = np.reshape(test[1], [10000, 1])\\n\\t\\ttrainX = np.concatenate((trainX, valX), axis = 0)\\n\\t\\ttrainY = np.concatenate((trainY, valY), axis = 0)\\n\\treturn trainX, trainY, testX, testY\",\n \"def load_mnist_dataset(shape=(-1,784)):\\n # We first define a download function, supporting both Python 2 and 3.\\n if sys.version_info[0] == 2:\\n from urllib import urlretrieve\\n else:\\n from urllib.request import urlretrieve\\n\\n def download(filename, source='http://yann.lecun.com/exdb/mnist/'):\\n print(\\\"Downloading %s\\\" % filename)\\n urlretrieve(source + filename, filename)\\n\\n # We then define functions for loading MNIST images and labels.\\n # For convenience, they also download the requested files if needed.\\n import gzip\\n\\n def load_mnist_images(filename):\\n if not os.path.exists(filename):\\n download(filename)\\n # Read the inputs in Yann LeCun's binary format.\\n with gzip.open(filename, 'rb') as f:\\n data = np.frombuffer(f.read(), np.uint8, offset=16)\\n # The inputs are vectors now, we reshape them to monochrome 2D images,\\n # following the shape convention: (examples, channels, rows, columns)\\n data = data.reshape(shape)\\n # data = data.reshape(-1, 1, 28, 28) # for lasagne\\n # data = data.reshape(-1, 28, 28, 1) # for tensorflow\\n # data = data.reshape(-1, 784) # for tensorflow\\n # The inputs come as bytes, we convert them to float32 in range [0,1].\\n # (Actually to range [0, 255/256], for compatibility to the version\\n # provided at http://deeplearning.net/data/mnist/mnist.pkl.gz.)\\n return data / np.float32(256)\\n\\n def load_mnist_labels(filename):\\n if not os.path.exists(filename):\\n download(filename)\\n # Read the labels in Yann LeCun's binary format.\\n with gzip.open(filename, 'rb') as f:\\n data = np.frombuffer(f.read(), np.uint8, offset=8)\\n # The labels are vectors of integers now, that's exactly what we want.\\n return data\\n\\n # We can now download and read the training and test set images and labels.\\n ## you may want to change the path\\n data_dir = '' #os.getcwd() + '/lasagne_tutorial/'\\n # print('data_dir > %s' % data_dir)\\n\\n X_train = load_mnist_images(data_dir+'train-images-idx3-ubyte.gz')\\n y_train = load_mnist_labels(data_dir+'train-labels-idx1-ubyte.gz')\\n X_test = load_mnist_images(data_dir+'t10k-images-idx3-ubyte.gz')\\n y_test = load_mnist_labels(data_dir+'t10k-labels-idx1-ubyte.gz')\\n\\n # We reserve the last 10000 training examples for validation.\\n X_train, X_val = X_train[:-10000], X_train[-10000:]\\n y_train, y_val = y_train[:-10000], y_train[-10000:]\\n\\n ## you may want to plot one example\\n # print('X_train[0][0] >', X_train[0][0].shape, type(X_train[0][0])) # for lasagne\\n # print('X_train[0] >', X_train[0].shape, type(X_train[0])) # for tensorflow\\n # # exit()\\n # # [[..],[..]] (28, 28) numpy.ndarray\\n # # plt.imshow 只支持 (28, 28)格式，不支持 (1, 28, 28),所以用 [0][0]\\n # fig = plt.figure()\\n # #plotwindow = fig.add_subplot(111)\\n # # plt.imshow(X_train[0][0], cmap='gray') # for lasagne (-1, 1, 28, 28)\\n # plt.imshow(X_train[0].reshape(28,28), cmap='gray') # for tensorflow (-1, 28, 28, 1)\\n # plt.title('A training image')\\n # plt.show()\\n\\n # We just return all the arrays in order, as expected in main().\\n # (It doesn't matter how we do this as long as we can read them again.)\\n return X_train, y_train, X_val, y_val, X_test, y_test\",\n \"def test_machine_learning():\",\n \"def test_documentation_popxl_mnist_replication_train(self):\\n filename = \\\"mnist_rts.py --replication-factor 2\\\"\\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)\",\n \"def get_mnist(self):\\n # Set defaults.\\n nb_classes = 10\\n batch_size = 100\\n input_shape = (784,)\\n\\n # Get the data.\\n (x_train, y_train), (x_test, y_test) = mnist.load_data()\\n x_train = x_train.reshape(60000, 784)\\n x_test = x_test.reshape(10000, 784)\\n x_train = x_train.astype('float32')\\n x_test = x_test.astype('float32')\\n x_train /= 255\\n x_test /= 255\\n\\n # convert class vectors to binary class matrices\\n y_train = to_categorical(y_train, nb_classes)\\n y_test = to_categorical(y_test, nb_classes)\\n\\n return (nb_classes, batch_size, input_shape, x_train, x_test, y_train, y_test)\",\n \"def readMNISTData():\\n mnist = input_data.read_data_sets(\\\"MNIST_data\\\",one_hot=True) \\n return mnist\",\n \"def load_mnist(path, kind='train'):\\n '''ref: http://yann.lecun.com/exdb/mnist/ '''\\n ''' each hand write is 28x28 = 784, a 1 dim vector'''\\n labels_path = os.path.join(path,\\n '%s-labels-idx1-ubyte'\\n % kind)\\n images_path = os.path.join(path,\\n '%s-images-idx3-ubyte'\\n % kind)\\n\\n # check the offical doc to know how to extract the content\\n '''\\n [offset] [type] [value] [description]\\n 0000 32 bit integer 0x00000801(2049) magic number (MSB first)\\n 0004 32 bit integer 60000 number of items\\n 0008 unsigned byte ?? label\\n 0009 unsigned byte ?? label\\n ........\\n xxxx unsigned byte ?? label\\n The labels values are 0 to 9.\\n '''\\n with open(labels_path, 'rb') as lbpath:\\n magic, n = struct.unpack('>II',\\n lbpath.read(8))\\n labels = np.fromfile(lbpath,\\n dtype=np.uint8)\\n\\n '''\\n [offset] [type] [value] [description]\\n 0000 32 bit integer 0x00000803(2051) magic number\\n 0004 32 bit integer 60000 number of images\\n 0008 32 bit integer 28 number of rows\\n 0012 32 bit integer 28 number of columns\\n 0016 unsigned byte ?? pixel\\n 0017 unsigned byte ?? pixel\\n ........\\n xxxx unsigned byte ?? pixel\\n Pixels are organized row-wise. Pixel values are 0 to 255. 0 means background (white), 255 means foreground (black).\\n '''\\n with open(images_path, 'rb') as imgpath:\\n magic, num, rows, cols = struct.unpack(\\\">IIII\\\",\\n imgpath.read(16))\\n ''' each hand write is 28x28 = 784, a 1 dim vector'''\\n images = np.fromfile(imgpath,\\n dtype=np.uint8).reshape(len(labels), 784)\\n\\n return images, labels\",\n \"def load_mnist(dataset=\\\"training\\\", digits=np.arange(10), path=\\\".\\\"):\\n\\n if dataset == \\\"training\\\":\\n fname_img = os.path.join(path, 'train-images-idx3-ubyte')\\n fname_lbl = os.path.join(path, 'train-labels-idx1-ubyte')\\n elif dataset == \\\"testing\\\":\\n fname_img = os.path.join(path, 't10k-images-idx3-ubyte')\\n fname_lbl = os.path.join(path, 't10k-labels-idx1-ubyte')\\n else:\\n raise ValueError(\\\"dataset must be 'testing' or 'training'\\\")\\n\\n flbl = open(fname_lbl, 'rb')\\n magic_nr, size = struct.unpack(\\\">II\\\", flbl.read(8))\\n lbl = pyarray(\\\"b\\\", flbl.read())\\n flbl.close()\\n\\n fimg = open(fname_img, 'rb')\\n magic_nr, size, rows, cols = struct.unpack(\\\">IIII\\\", fimg.read(16))\\n img = pyarray(\\\"B\\\", fimg.read())\\n fimg.close()\\n\\n ind = [ k for k in range(size) if lbl[k] in digits ]\\n N = len(ind)\\n\\n images = zeros((N, rows, cols), dtype=uint8)\\n labels = zeros((N, 1), dtype=int8)\\n for i in range(len(ind)):\\n images[i] = array(img[ ind[i]*rows*cols : (ind[i]+1)*rows*cols ]).reshape((rows, cols))\\n labels[i] = lbl[ind[i]]\\n\\n return images, labels\",\n \"def test_train():\\n set_seed(42) # Noqa\\n transform = transforms.Compose([\\n transforms.ToTensor(),\\n ])\\n mnist_train = MNIST(\\\"./\\\", download=True, train=False, transform=transform)\\n model = SimpleNet()\\n\\n optimizer = torch.optim.SGD(model.parameters(), lr=1e-2, momentum=0.5)\\n criterion = nn.CrossEntropyLoss()\\n\\n train_loader = DataLoader(mnist_train, batch_size=64, shuffle=True,\\n num_workers=0)\\n loss, accuracy = train(model, optimizer, criterion, train_loader,\\n imshape=(-1, 28*28))\\n\\n assert type(loss) == torch.Tensor\\n assert type(accuracy) == np.float64\\n assert len(loss.shape) == 0\",\n \"def test_mnist():\\n skip_if_no_data()\\n mode = get_default_mode()\\n if hasattr(mode, 'check_py_code'):\\n old_value = mode.check_py_code\\n mode.check_py_code = False\\n try:\\n if config.mode == \\\"DEBUG_MODE\\\":\\n yaml_file = 'mnist_fast'\\n else:\\n yaml_file = 'mnist'\\n limited_epoch_train(os.path.join(yaml_file_path, '%s.yaml'\\n % yaml_file))\\n try:\\n os.remove(os.path.join(save_path, '%s.pkl' % yaml_file))\\n os.remove(os.path.join(save_path, '%s_best.pkl' % yaml_file))\\n except Exception:\\n pass\\n finally:\\n if hasattr(mode, 'check_py_code'):\\n mode.check_py_code = old_value\",\n \"def main():\\n\\n dataset = ConvMNIST(64)\\n print(dataset.get_train().x.shape)\\n\\n\\n inputs = Value(type=tf.float32, shape=(None, 28, 28, 1), cls = None)\\n targets = Value(type=tf.int64, shape=(None), cls = 10)\\n learning_rate = 0.0001\\n\\n fc_hidden = [1024, 500]\\n c_h = [\\n (3, 3, 1, 32),\\n (3, 3, 32, 64)\\n ]\\n conv_hidden = ConvHidden(conv_weights=c_h, fc_weights=fc_hidden)\\n\\n config = Config(inputs, targets, conv_hidden, learning_rate)\\n\\n network = ConvNetworkBuilder(config)\\n hidden = FFConvHiddenBuilder()\\n _ = network.build_network(hidden)\\n\\n\\n train_config = TrainerConfig(\\n epochs = EPOCHS, display_after = DISPLAY_STEP, \\n keep_prob = KEEP_PROB,checkpoint_path=None, \\n summary_path=None\\n )\\n\\n trainer = Trainer(network, train_config)\\n trainer.train(dataset)\",\n \"def test_X_test_property():\\n atom = ATOMClassifier(X_bin, y_bin, random_state=1)\\n atom.run([\\\"MNB\\\", \\\"LR\\\"])\\n assert atom.X_test.equals(atom.mnb.X_test)\\n assert check_scaling(atom.lr.X_test)\",\n \"def cg_optimization_mnist(n_epochs=50, mnist_pkl_gz='mnist.pkl.gz'):\\r\\n #############\\r\\n # LOAD DATA #\\r\\n #############\\r\\n datasets = load_data(mnist_pkl_gz)\\r\\n\\r\\n train_set_x, train_set_y = datasets[0]\\r\\n valid_set_x, valid_set_y = datasets[1]\\r\\n test_set_x, test_set_y = datasets[2]\\r\\n\\r\\n batch_size = 600 # size of the minibatch\\r\\n\\r\\n n_train_batches = train_set_x.get_value(borrow=True).shape[0] / batch_size\\r\\n n_valid_batches = valid_set_x.get_value(borrow=True).shape[0] / batch_size\\r\\n n_test_batches = test_set_x.get_value(borrow=True).shape[0] / batch_size\\r\\n\\r\\n ishape = (28, 28) # this is the size of MNIST images\\r\\n n_in = 28 * 28 # number of input units\\r\\n n_out = 10 # number of output units\\r\\n\\r\\n ######################\\r\\n # BUILD ACTUAL MODEL #\\r\\n ######################\\r\\n print '... building the model'\\r\\n\\r\\n # allocate symbolic variables for the data\\r\\n minibatch_offset = T.lscalar() # offset to the start of a [mini]batch\\r\\n x = T.matrix() # the data is presented as rasterized images\\r\\n y = T.ivector() # the labels are presented as 1D vector of\\r\\n # [int] labels\\r\\n\\r\\n # construct the logistic regression class\\r\\n classifier = LogisticRegression(input=x, n_in=28 * 28, n_out=10)\\r\\n\\r\\n # the cost we minimize during training is the negative log likelihood of\\r\\n # the model in symbolic format\\r\\n cost = classifier.negative_log_likelihood(y).mean()\\r\\n\\r\\n # compile a theano function that computes the mistakes that are made by\\r\\n # the model on a minibatch\\r\\n test_model = theano.function([minibatch_offset], classifier.errors(y),\\r\\n givens={\\r\\n x: test_set_x[minibatch_offset:minibatch_offset + batch_size],\\r\\n y: test_set_y[minibatch_offset:minibatch_offset + batch_size]},\\r\\n name=\\\"test\\\")\\r\\n\\r\\n validate_model = theano.function([minibatch_offset], classifier.errors(y),\\r\\n givens={\\r\\n x: valid_set_x[minibatch_offset:\\r\\n minibatch_offset + batch_size],\\r\\n y: valid_set_y[minibatch_offset:\\r\\n minibatch_offset + batch_size]},\\r\\n name=\\\"validate\\\")\\r\\n\\r\\n # compile a thenao function that returns the cost of a minibatch\\r\\n batch_cost = theano.function([minibatch_offset], cost,\\r\\n givens={\\r\\n x: train_set_x[minibatch_offset:\\r\\n minibatch_offset + batch_size],\\r\\n y: train_set_y[minibatch_offset:\\r\\n minibatch_offset + batch_size]},\\r\\n name=\\\"batch_cost\\\")\\r\\n\\r\\n # compile a theano function that returns the gradient of the minibatch\\r\\n # with respect to theta\\r\\n batch_grad = theano.function([minibatch_offset],\\r\\n T.grad(cost, classifier.theta),\\r\\n givens={\\r\\n x: train_set_x[minibatch_offset:\\r\\n minibatch_offset + batch_size],\\r\\n y: train_set_y[minibatch_offset:\\r\\n minibatch_offset + batch_size]},\\r\\n name=\\\"batch_grad\\\")\\r\\n\\r\\n # creates a function that computes the average cost on the training set\\r\\n def train_fn(theta_value):\\r\\n classifier.theta.set_value(theta_value, borrow=True)\\r\\n train_losses = [batch_cost(i * batch_size)\\r\\n for i in xrange(n_train_batches)]\\r\\n return numpy.mean(train_losses)\\r\\n\\r\\n # creates a function that computes the average gradient of cost with\\r\\n # respect to theta\\r\\n def train_fn_grad(theta_value):\\r\\n classifier.theta.set_value(theta_value, borrow=True)\\r\\n grad = batch_grad(0)\\r\\n for i in xrange(1, n_train_batches):\\r\\n grad += batch_grad(i * batch_size)\\r\\n return grad / n_train_batches\\r\\n\\r\\n validation_scores = [numpy.inf, 0]\\r\\n\\r\\n # creates the validation function\\r\\n def callback(theta_value):\\r\\n classifier.theta.set_value(theta_value, borrow=True)\\r\\n #compute the validation loss\\r\\n validation_losses = [validate_model(i * batch_size)\\r\\n for i in xrange(n_valid_batches)]\\r\\n this_validation_loss = numpy.mean(validation_losses)\\r\\n print('validation error %f %%' % (this_validation_loss * 100.,))\\r\\n\\r\\n # check if it is better then best validation score got until now\\r\\n if this_validation_loss < validation_scores[0]:\\r\\n # if so, replace the old one, and compute the score on the\\r\\n # testing dataset\\r\\n validation_scores[0] = this_validation_loss\\r\\n test_losses = [test_model(i * batch_size)\\r\\n for i in xrange(n_test_batches)]\\r\\n validation_scores[1] = numpy.mean(test_losses)\\r\\n\\r\\n ###############\\r\\n # TRAIN MODEL #\\r\\n ###############\\r\\n\\r\\n # using scipy conjugate gradient optimizer\\r\\n import scipy.optimize\\r\\n print (\\\"Optimizing using scipy.optimize.fmin_cg...\\\")\\r\\n start_time = time.clock()\\r\\n best_w_b = scipy.optimize.fmin_cg(\\r\\n f=train_fn,\\r\\n x0=numpy.zeros((n_in + 1) * n_out, dtype=x.dtype),\\r\\n fprime=train_fn_grad,\\r\\n callback=callback,\\r\\n disp=0,\\r\\n maxiter=n_epochs)\\r\\n end_time = time.clock()\\r\\n print(('Optimization complete with best validation score of %f %%, with '\\r\\n 'test performance %f %%') %\\r\\n (validation_scores[0] * 100., validation_scores[1] * 100.))\\r\\n\\r\\n print >> sys.stderr, ('The code for file ' +\\r\\n os.path.split(__file__)[1] +\\r\\n ' ran for %.1fs' % ((end_time - start_time)))\",\n \"def train_mnist():\\r\\n # type: () -> None\\r\\n\\r\\n # Build dataset and model\\r\\n dataset = MNIST_TRAIN(path=Config.video_folder)\\r\\n model = LSAMNIST(input_shape=dataset.shape, code_length=64,\\r\\n cpd_channels=100).to(device).train()\\r\\n\\r\\n # Set up result helper and perform test\\r\\n helper = OneClassResultHelper(dataset, model,\\r\\n checkpoints_dir=Config.model_ckpt,\\r\\n output_file='mnist.txt')\\r\\n helper.train_one_class_classification()\",\n \"def test_custom_relu_mnist():\\n loss1 = mnist()\\n loss2 = custom_mnist()\\n assert np.allclose(loss1, loss2, equal_nan=True)\",\n \"def pick_data(ns, digits):\\n f = gzip.open('data/mnist.pkl.gz', 'rb')\\n train_set, valid_set, test_set = cPickle.load(f)\\n f.close()\\n images, labels = train_set\\n\\n originals = []; \\n shapes = []; \\n true_labels = [];\\n i = 0\\n for n, d in zip(ns, digits):\\n # picking n elements with digit d\\n x = np.where(labels==d)[0]\\n idx = np.random.choice(x, n, replace=False)\\n imgs = images[idx]\\n originals.append(imgs)\\n contours = [mnistshape.get_shape2(im.reshape((28,28)), n=30, s=5, ir=2)\\n for im in imgs]\\n shapes.append(contours)\\n true_labels.append([i]*n)\\n i += 1\\n originals = np.concatenate(originals)\\n true_labels = np.concatenate(true_labels)\\n \\n new_shapes = []\\n for cluster in shapes:\\n for shape in cluster:\\n new_shapes.append(shape)\\n new_shapes = np.array(new_shapes)\\n\\n # return shuffled data\\n idx = range(len(originals))\\n np.random.shuffle(idx)\\n return originals[idx], new_shapes[idx], true_labels[idx]\",\n \"def test_Gaussian_NB_estimators():\",\n \"def test_n_iris(self):\\r\\n n = NeuronNetwork(1,\\r\\n [3],\\r\\n [[[0.2,0.2,0.2,0.2]]*3],\\r\\n [[-1.0,-1.0,-1.0]],learningRate=0.3)\\r\\n print(n)\\r\\n \\r\\n data = load_iris()\\r\\n\\r\\n inputs = data.data\\r\\n target = []\\r\\n for x in data.target:\\r\\n empty = [0,0,0]\\r\\n empty[x] = 1\\r\\n target.append(empty)\\r\\n \\r\\n n.train(inputs, target, 2000, 10*60)\\r\\n print(n)\\r\\n\\r\\n total = 0\\r\\n error = 0\\r\\n for i, x in enumerate(target, 0):\\r\\n out = n.feed_forward(inputs[i])\\r\\n if i < 50:\\r\\n error += self.mse(out, [1,0,0])\\r\\n if np.argmax(out) == 0:\\r\\n total +=1\\r\\n print(i, out, 1)\\r\\n elif i >= 50 and i < 100:\\r\\n error += self.mse(out, [0,1,0])\\r\\n if np.argmax(out) == 1:\\r\\n total +=1\\r\\n print(i, out, 2)\\r\\n elif i >= 100 and i < 150:\\r\\n error += self.mse(out, [0,0,1])\\r\\n if np.argmax(out) == 2:\\r\\n total +=1\\r\\n print(i, out, 3)\\r\\n\\r\\n print(f'MSE: {error/150}, RMSE:{math.sqrt(error/150)}')\\r\\n print(f'Accuracy:{total/len(target)}')\",\n \"def load_data():\\n f = gzip.open('mnist.pkl.gz', 'rb')\\n training_data, validation_data, test_data = pickle.load(f, encoding=\\\"latin1\\\")\\n f.close()\\n \\n X_train = [np.reshape(x, (784, 1)) for x in training_data[0]]\\n Y_train = [vectorized_result(y) for y in training_data[1]]\\n \\n X_validation = [np.reshape(x, (784, 1)) for x in validation_data[0]]\\n Y_validation = validation_data[1]\\n \\n X_test = [np.reshape(x, (784, 1)) for x in test_data[0]]\\n Y_test = test_data[1]\\n \\n return (X_train, Y_train, X_validation, Y_validation, X_test, Y_test)\",\n \"def load_data():\\n # Load image data from MNIST.\\n (train_x, train_y),(eval_x, eval_y) = keras.datasets.mnist.load_data()\\n\\n # We convert the input data to (60000, 28, 28, 1), float32 and normalize our data values to the range [0, 1].\\n train_x = train_x.reshape(train_x.shape[0], train_x.shape[1], train_x.shape[2], 1)\\n eval_x = eval_x.reshape(eval_x.shape[0], eval_x.shape[1], eval_x.shape[2], 1)\\n\\n train_x = train_x.astype('float32')\\n eval_x = eval_x.astype('float32')\\n train_x /= 255\\n eval_x /= 255\\n\\n # Preprocess class labels \\n train_y = train_y.astype(np.int32)\\n eval_y = eval_y.astype(np.int32)\\n\\n train_y = np_utils.to_categorical(train_y, 10)\\n eval_y = np_utils.to_categorical(eval_y, 10)\\n\\n return train_x, train_y, eval_x, eval_y\",\n \"def main():\\n # construct the argument parse and parse the arguments\\n args = argparse.ArgumentParser()\\n args.add_argument(\\\"-o\\\", \\\"--output\\\", required=True, help=\\\"path to the output loss/accuracy plot\\\")\\n args = vars(args.parse_args())\\n\\n # grab the MNIST dataset (if this is your first time using this\\n # dataset then the 11MB download may take a minute)\\n print(\\\"[INFO] accessing MNIST...\\\")\\n ((train_x, train_y), (test_x, test_y)) = mnist.load_data()\\n\\n # each image in the MNIST dataset is represented as a 28x28x1\\n # image, but in order to apply a standard neural network we must\\n # first \\\"flatten\\\" the image to be simple list of 28x28=784 pixels\\n train_x = train_x.reshape((train_x.shape[0], 28 * 28 * 1))\\n test_x = test_x.reshape((test_x.shape[0], 28 * 28 * 1))\\n # scale data to the range of [0, 1]\\n train_x = train_x.astype(\\\"float32\\\") / 255.0\\n test_x = test_x.astype(\\\"float32\\\") / 255.0\\n\\n # convert the labels from integers to vectors\\n label_binarizer = LabelBinarizer()\\n train_y = label_binarizer.fit_transform(train_y)\\n test_y = label_binarizer.transform(test_y)\\n\\n # define the 784-256-128-10 architecture using Keras\\n model = Sequential()\\n model.add(Dense(256, input_shape=(784,), activation=\\\"sigmoid\\\"))\\n model.add(Dense(128, activation=\\\"sigmoid\\\"))\\n model.add(Dense(10, activation=\\\"softmax\\\"))\\n\\n # train the model using SGD\\n print(\\\"[INFO] training network...\\\")\\n sgd = SGD(0.01)\\n model.compile(loss=\\\"categorical_crossentropy\\\", optimizer=sgd, metrics=[\\\"accuracy\\\"])\\n model_fit = model.fit(train_x, train_y, validation_data=(test_x, test_y), epochs=100, batch_size=128)\\n\\n # evaluate the network\\n print(\\\"[INFO] evaluating network...\\\")\\n predictions = model.predict(test_x, batch_size=128)\\n print(\\n classification_report(\\n test_y.argmax(axis=1), predictions.argmax(axis=1), target_names=[str(x) for x in label_binarizer.classes_]\\n )\\n )\\n\\n # plot the training loss and accuracy\\n plt.style.use(\\\"ggplot\\\")\\n plt.figure()\\n plt.plot(np.arange(0, 100), model_fit.history[\\\"loss\\\"], label=\\\"train_loss\\\")\\n plt.plot(np.arange(0, 100), model_fit.history[\\\"val_loss\\\"], label=\\\"val_loss\\\")\\n plt.plot(np.arange(0, 100), model_fit.history[\\\"acc\\\"], label=\\\"train_acc\\\")\\n plt.plot(np.arange(0, 100), model_fit.history[\\\"val_acc\\\"], label=\\\"val_acc\\\")\\n plt.title(\\\"Training Loss and Accuracy\\\")\\n plt.xlabel(\\\"Epoch #\\\")\\n plt.ylabel(\\\"Loss/Accuracy\\\")\\n plt.legend()\\n plt.savefig(args[\\\"output\\\"])\",\n \"def get_data(numbers):\\r\\n numbers = numbers\\r\\n n_classes = len(numbers)\\r\\n z = zipfile.ZipFile('lab3/mnist.pkl.zip', 'r')\\r\\n k = z.extract('mnist.pkl') # Извлечь файл из архива\\r\\n with open(k, 'rb') as f:\\r\\n train_set, _, test_set = pickle.load(f, encoding=\\\"bytes\\\")\\r\\n x_train = train_set[0]\\r\\n x_test = test_set[0]\\r\\n x_train[x_train >= 0.5] = 1\\r\\n x_train[x_train < 0.5] = 0\\r\\n x_test[x_test >= 0.5] = 1\\r\\n x_test[x_test < 0.5] = 0\\r\\n y_train = train_set[1]\\r\\n y_test = test_set[1]\\r\\n idx_train = [[np.where(y_train == i)] for i in numbers]\\r\\n idx_test = [[np.where(y_test == i)] for i in numbers]\\r\\n idx_x_train = [x_train[idx_train[i][0]] for i in range(len(idx_train))]\\r\\n idx_x_test = [x_test[idx_test[i][0]] for i in range(len(idx_test))]\\r\\n idx_y_test = [y_test[idx_test[i][0]] for i in range(len(idx_test))]\\r\\n x_train_new = shuffle(np.concatenate(idx_x_train))\\r\\n x_test_new = shuffle(np.concatenate(idx_x_test))\\r\\n y_test_new = shuffle(np.concatenate(idx_y_test))\\r\\n return x_train_new, x_test_new, y_test_new, numbers, n_classes\",\n \"def test_model_evaluation(model, mnist, idx, label):\\n expected_probabilities = np.zeros((10,))\\n expected_probabilities[label] = 1.0\\n assert_array_almost_equal(\\n model.classify(mnist.get_test_image(idx)),\\n expected_probabilities\\n )\",\n \"def load_data(m=5000, n=100, path='D:/file/vscode/py/data/mnist.npz'):\\r\\n f = np.load(path)\\r\\n x_train, y_train = f['x_train'], f['y_train']\\r\\n\\r\\n x_test, y_test = f['x_test'], f['y_test']\\r\\n\\r\\n f.close()\\r\\n return (x_train, y_train), (x_test, y_test)\",\n \"def __init__(self):\\n\\n TEST_RATIO = 0.05\\n mnist_trainset = datasets.MNIST(root='./data', train=True, download=True, transform=None)\\n idxs = np.arange(mnist_trainset.train_data.size(0))\\n np.random.shuffle(idxs)\\n\\n #print(torch.min(mnist_trainset.train_labels), torch.max(mnist_trainset.train_labels))\\n #print(mnist_trainset.train_labels.size())\\n \\n # reshape input data to (1, 784) and normalize to range [0., 1.]\\n self.train_data = torch.reshape(\\n mnist_trainset.train_data[idxs].float(), (-1,1,28,28))/255.\\n self.data_size = self.train_data.size(0)\\n self.train_len = self.train_data.size(0)\\n self.train_label = torch.Tensor([1]).float() # since there is only one class - 'real' image\\n\\n print('Train images -- {}'.format(self.train_data.size()))\",\n \"def test_menhinick(self):\\n self.assertEqual(menhinick(self.TestData), 9/sqrt(22))\",\n \"def get_mnist_cnn():\\n # Set defaults.\\n nb_classes = 10 #dataset dependent \\n batch_size = 128\\n epochs = 4\\n \\n # Input image dimensions\\n img_rows, img_cols = 28, 28\\n\\n # Get the data.\\n # the data, shuffled and split between train and test sets\\n (x_train, y_train), (x_test, y_test) = mnist.load_data()\\n \\n if K.image_data_format() == 'channels_first':\\n x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)\\n x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)\\n input_shape = (1, img_rows, img_cols)\\n else:\\n x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)\\n x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)\\n input_shape = (img_rows, img_cols, 1)\\n\\n #x_train = x_train.reshape(60000, 784)\\n #x_test = x_test.reshape(10000, 784)\\n \\n x_train = x_train.astype('float32')\\n x_test = x_test.astype('float32')\\n x_train /= 255\\n x_test /= 255\\n\\n #print('x_train shape:', x_train.shape)\\n #print(x_train.shape[0], 'train samples')\\n #print(x_test.shape[0], 'test samples')\\n\\n # convert class vectors to binary class matrices\\n y_train = to_categorical(y_train, nb_classes)\\n y_test = to_categorical(y_test, nb_classes)\\n\\n # convert class vectors to binary class matrices\\n #y_train = keras.utils.to_categorical(y_train, nb_classes)\\n #y_test = keras.utils.to_categorical(y_test, nb_classes)\\n\\n return (nb_classes, batch_size, input_shape, x_train, x_test, y_train, y_test, epochs)\",\n \"def get_mini_samples():\\n if GLOBALS['project_root']=='':\\n print('please initialize project_root in GLOBALS first')\\n return None\\n data_path = os.path.join(GLOBALS['project_root'], 'data/MNIST/')\\n pickle_path = os.path.join(data_path, 'mnist_mini_samples.pickle')\\n if os.path.exists(pickle_path):\\n with open(pickle_path, 'rb') as f:\\n mini_samples = pickle.load(f)\\n else:\\n mnist = get_mnist()\\n mini_samples = mnist.train.next_batch(50)\\n with open(pickle_path, 'wb') as f:\\n pickle.dump(mini_samples, f, pickle.HIGHEST_PROTOCOL)\\n\\n return mini_samples\",\n \"def main():\\n training_data, validation_data, test_data = mnist.load()\\n\\n model = nn.NeuralNetwork([784, 100, 10], learning_rate=0.01, batch_size=50)\\n\\n model_training = training.EarlyStoppingRegularization(model,\\n training_data,\\n validation_data,\\n test_data,\\n max_steps_without_progression=2)\\n result = model_training.train()\\n\\n result.save('models/mnist')\",\n \"def test_X_train_property():\\n atom = ATOMClassifier(X_bin, y_bin, random_state=1)\\n atom.run([\\\"MNB\\\", \\\"LR\\\"])\\n assert atom.X_train.equals(atom.mnb.X_train)\\n assert check_scaling(atom.lr.X_train)\",\n \"def sgd_optimization_mnist(learning_rate=0.13, n_epochs=1000,\\r\\n dataset='mnist.pkl.gz',\\r\\n batch_size=600):\\r\\n datasets = load_data(dataset)\\r\\n\\r\\n train_set_x, train_set_y = datasets[0]\\r\\n valid_set_x, valid_set_y = datasets[1]\\r\\n test_set_x, test_set_y = datasets[2]\\r\\n\\r\\n # compute number of minibatches for training, validation and testing\\r\\n n_train_batches = train_set_x.get_value(borrow=True).shape[0] / batch_size\\r\\n n_valid_batches = valid_set_x.get_value(borrow=True).shape[0] / batch_size\\r\\n n_test_batches = test_set_x.get_value(borrow=True).shape[0] / batch_size\\r\\n\\r\\n ######################\\r\\n # BUILD ACTUAL MODEL #\\r\\n ######################\\r\\n print '... building the model'\\r\\n\\r\\n # allocate symbolic variables for the data\\r\\n index = T.lscalar() # index to a [mini]batch\\r\\n x = T.matrix('x') # the data is presented as rasterized images\\r\\n y = T.ivector('y') # the labels are presented as 1D vector of\\r\\n # [int] labels\\r\\n\\r\\n # construct the logistic regression class\\r\\n # Each MNIST image has size 28*28\\r\\n classifier = LogisticRegression(input=x, n_in=28 * 28, n_out=10)\\r\\n\\r\\n # the cost we minimize during training is the negative log likelihood of\\r\\n # the model in symbolic format\\r\\n cost = classifier.negative_log_likelihood(y)\\r\\n\\r\\n # compiling a Theano function that computes the mistakes that are made by\\r\\n # the model on a minibatch\\r\\n test_model = theano.function(inputs=[index],\\r\\n outputs=classifier.errors(y),\\r\\n givens={\\r\\n x: test_set_x[index * batch_size: (index + 1) * batch_size],\\r\\n y: test_set_y[index * batch_size: (index + 1) * batch_size]})\\r\\n\\r\\n validate_model = theano.function(inputs=[index],\\r\\n outputs=classifier.errors(y),\\r\\n givens={\\r\\n x: valid_set_x[index * batch_size:(index + 1) * batch_size],\\r\\n y: valid_set_y[index * batch_size:(index + 1) * batch_size]})\\r\\n\\r\\n # compute the gradient of cost with respect to theta = (W,b)\\r\\n g_W = T.grad(cost=cost, wrt=classifier.W)\\r\\n g_b = T.grad(cost=cost, wrt=classifier.b)\\r\\n\\r\\n # specify how to update the parameters of the model as a list of\\r\\n # (variable, update expression) pairs.\\r\\n updates = [(classifier.W, classifier.W - learning_rate * g_W),\\r\\n (classifier.b, classifier.b - learning_rate * g_b)]\\r\\n\\r\\n # compiling a Theano function `train_model` that returns the cost, but in\\r\\n # the same time updates the parameter of the model based on the rules\\r\\n # defined in `updates`\\r\\n train_model = theano.function(inputs=[index],\\r\\n outputs=cost,\\r\\n updates=updates,\\r\\n givens={\\r\\n x: train_set_x[index * batch_size:(index + 1) * batch_size],\\r\\n y: train_set_y[index * batch_size:(index + 1) * batch_size]})\\r\\n\\r\\n ###############\\r\\n # TRAIN MODEL #\\r\\n ###############\\r\\n print '... training the model'\\r\\n # early-stopping parameters\\r\\n patience = 5000 # look as this many examples regardless\\r\\n patience_increase = 2 # wait this much longer when a new best is\\r\\n # found\\r\\n improvement_threshold = 0.995 # a relative improvement of this much is\\r\\n # considered significant\\r\\n validation_frequency = min(n_train_batches, patience / 2)\\r\\n # go through this many\\r\\n # minibatche before checking the network\\r\\n # on the validation set; in this case we\\r\\n # check every epoch\\r\\n\\r\\n best_params = None\\r\\n best_validation_loss = numpy.inf\\r\\n test_score = 0.\\r\\n start_time = time.clock()\\r\\n\\r\\n done_looping = False\\r\\n epoch = 0\\r\\n while (epoch < n_epochs) and (not done_looping):\\r\\n epoch = epoch + 1\\r\\n for minibatch_index in xrange(n_train_batches):\\r\\n\\r\\n minibatch_avg_cost = train_model(minibatch_index)\\r\\n # iteration number\\r\\n iter = (epoch - 1) * n_train_batches + minibatch_index\\r\\n\\r\\n if (iter + 1) % validation_frequency == 0:\\r\\n # compute zero-one loss on validation set\\r\\n validation_losses = [validate_model(i)\\r\\n for i in xrange(n_valid_batches)]\\r\\n this_validation_loss = numpy.mean(validation_losses)\\r\\n\\r\\n print('epoch %i, minibatch %i/%i, validation error %f %%' % \\\\\\r\\n (epoch, minibatch_index + 1, n_train_batches,\\r\\n this_validation_loss * 100.))\\r\\n\\r\\n # if we got the best validation score until now\\r\\n if this_validation_loss < best_validation_loss:\\r\\n #improve patience if loss improvement is good enough\\r\\n if this_validation_loss < best_validation_loss * \\\\\\r\\n improvement_threshold:\\r\\n patience = max(patience, iter * patience_increase)\\r\\n\\r\\n best_validation_loss = this_validation_loss\\r\\n # test it on the test set\\r\\n\\r\\n test_losses = [test_model(i)\\r\\n for i in xrange(n_test_batches)]\\r\\n test_score = numpy.mean(test_losses)\\r\\n\\r\\n print((' epoch %i, minibatch %i/%i, test error of best'\\r\\n ' model %f %%') %\\r\\n (epoch, minibatch_index + 1, n_train_batches,\\r\\n test_score * 100.))\\r\\n\\r\\n if patience <= iter:\\r\\n done_looping = True\\r\\n break\\r\\n\\r\\n end_time = time.clock()\\r\\n print(('Optimization complete with best validation score of %f %%,'\\r\\n 'with test performance %f %%') %\\r\\n (best_validation_loss * 100., test_score * 100.))\\r\\n print 'The code run for %d epochs, with %f epochs/sec' % (\\r\\n epoch, 1. * epoch / (end_time - start_time))\\r\\n print >> sys.stderr, ('The code for file ' +\\r\\n os.path.split(__file__)[1] +\\r\\n ' ran for %.1fs' % ((end_time - start_time)))\",\n \"def neural_network(X, Y, Xs_test, Ys_test):\\n ## YOUR CODE HERE\\n #################\\n return 0\",\n \"def use_mnist_model(self):\\n\\n\\t\\t# load the model\\n\\t\\tnumber_recognizer_MNIST = load_model('models/MNIST_digits_recognition.h5', compile=False)\\n\\n\\t\\t# create empty ndarray\\n\\t\\tnumbers_mnist = np.ones(shape=(self.sudoku_size, self.sudoku_size))\\n\\n\\t\\tpics = deepcopy(self.list_of_number_pictures)\\n\\t\\tfor i in range(self.sudoku_size):\\n\\t\\t\\tfor j in range(self.sudoku_size):\\n\\t\\t\\t\\tpics[i][j] = self.preprocess_cell(pics[i][j], mnist=True, resize=True, clean_remains=True)\\n\\t\\t\\t\\tif self.empty_cells[i][j] != 0:\\n\\t\\t\\t\\t\\tnumbers_mnist[i][j] = np.argmax(number_recognizer_MNIST.predict([[pics[i][j].reshape(28,28,1)]]))\\n\\n\\t\\treturn numbers_mnist\",\n \"def test_star():\\n test_path = tempfile.mkdtemp()\\n x_train, metadata = star(test_path)\\n try:\\n assert x_train.shape == (5748, 8)\\n except:\\n shutil.rmtree(test_path)\\n raise()\",\n \"def mnist_v1(batch_size=128, epochs=20, kernel_size=3):\\n (X_train, Y_train), (X_test, Y_test) = mnist.load_data()\\n\\n # Data preparation\\n X_train = prepare(X_train)\\n X_test = prepare(X_test)\\n Y_train = np_utils.to_categorical(Y_train, 10) # 0..9\\n Y_test = np_utils.to_categorical(Y_test, 10) # 0..9\\n\\n # Fitting the data to the augmentation data generator\\n datagen = augmentedData(X_train)\\n\\n # --------------------\\n # NEURAL NETWORK MODEL\\n # --------------------\\n\\n # Model architecture\\n model = Sequential()\\n\\n model.add(Conv2D(32, (kernel_size, kernel_size), activation='relu', input_shape=(1, 28, 28)))\\n model.add(Conv2D(32, (kernel_size, kernel_size), activation='relu'))\\n model.add(MaxPooling2D(pool_size=(2, 2)))\\n model.add(Dropout(0.25))\\n\\n model.add(Flatten())\\n model.add(Dense(128, activation='relu'))\\n model.add(Dropout(0.5))\\n model.add(Dense(10, activation='softmax'))\\n\\n # Model compilation\\n model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\\n\\n #Tensor board saves\\n now = datetime.datetime.now()\\n tensorboard = TensorBoard(log_dir=\\\"logs_first/kernel_size:{}\\\".format(kernel_size))\\n\\n model.fit_generator(datagen.flow(X_train, Y_train, batch_size=batch_size), epochs=epochs, verbose=1, callbacks=[tensorboard])\\n\\n # Model saves\\n now = datetime.datetime.now()\\n model.save(\\\"sirr_HYPERPARAMETERS_mnist_first_\\\" + str(now.hour) + \\\"h\\\" + str(now.minute) + \\\".h5\\\")\\n\\n # Model evaluation\\n return model.evaluate(X_test, Y_test, verbose=1)\",\n \"def fetch_mnist():\\n data_path = check_fetch_mnist()\\n f = gzip.open(data_path, 'rb')\\n try:\\n train_set, valid_set, test_set = pickle.load(f, encoding=\\\"latin1\\\")\\n except TypeError:\\n train_set, valid_set, test_set = pickle.load(f)\\n f.close()\\n train_indices = np.arange(0, len(train_set[0]))\\n valid_indices = np.arange(0, len(valid_set[0])) + train_indices[-1] + 1\\n test_indices = np.arange(0, len(test_set[0])) + valid_indices[-1] + 1\\n return {\\\"data\\\": np.concatenate((train_set[0], valid_set[0], test_set[0]),\\n axis=0).astype(theano.config.floatX),\\n \\\"target\\\": np.concatenate((train_set[1], valid_set[1], test_set[1]),\\n axis=0).astype(np.int32),\\n \\\"train_indices\\\": train_indices.astype(np.int32),\\n \\\"valid_indices\\\": valid_indices.astype(np.int32),\\n \\\"test_indices\\\": test_indices.astype(np.int32)}\",\n \"def test_predictor():\",\n \"def wild_test(img, mod):\\n img = cv2.imread(img)\\n img_gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)\\n img_resize = cv2.resize(img_gray, (28, 28))\\n img_resize = img_resize.reshape((1, 28, 28))\\n print (\\\"Image size\\\", img_resize.shape)\\n # it is ugly, you can make this much better\\n data = np.asarray([img_resize]*100)\\n test_iter = mx.io.NDArrayIter(data, None, 100)\\n prob = mod.predict(test_iter)\\n print (\\\"The prediction is :\\\", np.argmax(prob.asnumpy()[0]))\",\n \"def test_X_property():\\n atom = ATOMClassifier(X_bin, y_bin, random_state=1)\\n atom.run([\\\"MNB\\\", \\\"LR\\\"])\\n assert atom.X.equals(atom.mnb.X)\\n assert check_scaling(atom.lr.X)\",\n \"def testvis(layers='first'):\\n\\n\\tfrom scipy.io import loadmat\\n\\tfrom setup import NeuralNetwork as nnsetup\\n\\n\\tresult = loadmat(\\\"goodmatx.mat\\\")\\n\\tw1 = result['v1']\\n\\tw0 = result['v0']\\n\\tx,y = result['train_x'], result['train_y']\\n\\n\\t# result = loadmat(\\\"ducky.mat\\\")\\n\\t# x = result['train_x']\\n\\t# y = result['train_y']\\n\\n\\tsize = [x.shape[1], 1000, y.shape[1]]\\n\\n\\tnn = nnsetup([size[0],size[1],size[0]],output='sigm')\\n\\n\\tnn.W[0] = w0\\n\\tnn.W[1] = w1\\n\\t\\n\\tfor i in range(50):\\n\\t\\tvisualize( nn, x, k = 3000+i*4, layers=layers, mode='save' )\",\n \"def main():\\n # \\\"\\\"\\\"Prepare neuromorphic MNIST image datasets for use in caffe\\n # Each dataset will be generated with different number of unique spikes\\n # \\\"\\\"\\\"\\n # initial_size = 1e6 #best to make this big enough avoid expensive\\n # re-allocation\\n # test_dir = os.path.abspath('testFull')\\n # train_dir = os.path.abspath('trainFull')\\n\\n # for num_spikes in range(150, 260, 10):\\n # #test directory\\n # image_dataset = generate_nmnist_dataset(initial_size, test_dir,\\n # num_spikes, 0.75)\\n # output_lmdb = 'testlmdb' + str(num_spikes)\\n # database = save_to_lmdb(image_dataset, output_lmdb)\\n # #database.process_all_data(show_lmdb_datum)\\n\\n # #train directory\\n # image_dataset = generate_nmnist_dataset(initial_size, train_dir,\\n # num_spikes, 0.75)\\n # output_lmdb = 'trainlmdb' + str(num_spikes)\\n # save_to_lmdb(image_dataset, output_lmdb)\\n\\n # TD = ev.read_dataset(os.path.abspath('trainReduced/0/00002.bin'))\\n # best to make this big enough avoid expensive re-allocation\\n initial_size = 6e5\\n test_dir = os.path.abspath('testFull')\\n train_dir = os.path.abspath('trainFull')\\n\\n # test directory\\n image_dataset = generate_nmnist_continuous_dataset(initial_size, test_dir)\\n save_to_lmdb(image_dataset, 'testlmdb_continuous', True)\\n save_to_mat(image_dataset, 'MNIST_continuous_test.mat')\\n # database.process_all_data(show_lmdb_datum)\\n\\n # train directory\\n image_dataset = generate_nmnist_continuous_dataset(initial_size, train_dir)\\n save_to_lmdb(image_dataset, 'trainlmdb_continuous', True)\\n save_to_mat(image_dataset, 'MNIST_continuous_train.mat')\\n\\n # TD = ev.read_dataset(os.path.abspath('trainReduced/0/00002.bin'))\",\n \"def codeepneat_mnist_example(_):\\n # Set standard configuration specific to TFNE but not the neuroevolution process\\n logging_level = logging.INFO\\n config_file_path = './codeepneat_mnist_example_config.cfg'\\n backup_dir_path = './tfne_state_backups/'\\n max_generations = 20\\n max_fitness = None\\n\\n # Read in optionally supplied flags, changing the just set standard configuration\\n if flags.FLAGS.logging_level is not None:\\n logging_level = flags.FLAGS.logging_level\\n if flags.FLAGS.config_file is not None:\\n config_file_path = flags.FLAGS.config_file\\n if flags.FLAGS.backup_dir is not None:\\n backup_dir_path = flags.FLAGS.backup_dir\\n if flags.FLAGS.max_generations is not None:\\n max_generations = flags.FLAGS.max_generations\\n if flags.FLAGS.max_fitness is not None:\\n max_fitness = flags.FLAGS.max_fitness\\n\\n # Set logging, parse config\\n logging.set_verbosity(logging_level)\\n config = tfne.parse_configuration(config_file_path)\\n\\n # Initialize the environment and the specific NE algorithm\\n environment = tfne.environments.MNISTEnvironment(weight_training=True, config=config, verbosity=logging_level)\\n ne_algorithm = tfne.algorithms.CoDeepNEAT(config)\\n\\n # Initialize evolution engine and supply config as well as initialized NE algorithm and evaluation environment.\\n engine = tfne.EvolutionEngine(ne_algorithm=ne_algorithm,\\n environment=environment,\\n backup_dir_path=backup_dir_path,\\n max_generations=max_generations,\\n max_fitness=max_fitness)\\n\\n # Start training process, returning the best genome when training ends\\n best_genome = engine.train()\\n print(\\\"Best genome returned by evolution:\\\\n\\\")\\n print(best_genome)\\n\\n # Increase epoch count in environment for a final training of the best genome. Train the genome and then replay it.\\n print(\\\"Training best genome for 200 epochs...\\\\n\\\")\\n environment.epochs = 20\\n environment.eval_genome_fitness(best_genome)\\n environment.replay_genome(best_genome)\\n\\n # Serialize and save genotype and Tensorflow model to demonstrate serialization\\n best_genome.save_genotype(save_dir_path='./best_genome_genotype/')\\n best_genome.save_model(file_path='./best_genome_model/')\",\n \"def mnist_noniid(dataset, num_users):\\n # num_shards, num_imgs = 2*num_users, int(dataset.data.size()[0]/2/num_users) # choose two number from a set with num_shards, each client has 2*num_imgs images\\n # idx_shard = [i for i in range(num_shards)]\\n # dict_users = {i: np.array([], dtype='int64') for i in range(num_users)}\\n # idxs = np.arange(dataset.data.size()[0])\\n # labels = dataset.train_labels.numpy()\\n #\\n # # sort labels\\n # idxs_labels = np.vstack((idxs, labels))\\n # idxs_labels = idxs_labels[:,idxs_labels[1,:].argsort()]\\n # idxs = idxs_labels[0,:]\\n #\\n # # divide and assign\\n # for i in range(num_users):\\n # rand_set = set(np.random.choice(idx_shard, 2, replace=False))\\n # idx_shard = list(set(idx_shard) - rand_set)\\n # for rand in rand_set:\\n # dict_users[i] = np.concatenate((dict_users[i], idxs[rand*num_imgs:(rand+1)*num_imgs]), axis=0)\\n # return dict_users\\n\\n label_list = dataset.targets.numpy()\\n minLabel = min(label_list)\\n numLabels = len(dataset.classes)\\n\\n dict_users = {i: np.array([], dtype='int64') for i in range(num_users)}\\n for i in range(0, len(label_list)):\\n tmp_target_node = int((label_list[i] - minLabel) % num_users)\\n if num_users > numLabels:\\n tmpMinIndex = 0\\n tmpMinVal = math.inf\\n for n in range(0, num_users):\\n if (n) % numLabels == tmp_target_node and len(dict_users[n]) < tmpMinVal:\\n tmpMinVal = len(dict_users[n])\\n tmpMinIndex = n\\n tmp_target_node = tmpMinIndex\\n dict_users[tmp_target_node] = np.concatenate((dict_users[tmp_target_node], [i]), axis=0)\\n return dict_users\",\n \"def get_MNIST_testing_normalized():\\n X, Y = get_dataset(\\\"testing\\\")\\n X/=255.0\\n X-=0.5\\n return X,Y\\n\\n global MNIST_testing\\n if MNIST_testing is None:\\n if os.path.exists(\\\"./data/MNIST_testing.pkl\\\"):\\n MNIST_testing = cPickle.load(open(\\\"./data/MNIST_testing.pkl\\\", \\\"r\\\"))\\n else:\\n MNIST_testing = get_dataset(\\\"testing\\\")\\n MNIST_testing = normalize_data(MNIST_testing[0], MNIST_testing[1])\\n cPickle.dump(MNIST_training, open(\\\"./data/MNIST_testing.pkl\\\",\\\"w\\\"))\\n return MNIST_testing\",\n \"def test_data():\\n batch_size = 10\\n input_dim = 28\\n test_data = np.random.rand(batch_size, input_dim)\\n\\n return test_data\",\n \"def load_mnist(train_data=True, test_data=False):\\n os.chdir(ROOT_DIR)\\n RESOURCES = [\\n 'train-images-idx3-ubyte.gz', 'train-labels-idx1-ubyte.gz',\\n 't10k-images-idx3-ubyte.gz', 't10k-labels-idx1-ubyte.gz'\\n ]\\n\\n if (os.path.isdir('data') == 0):\\n os.mkdir('data')\\n if (os.path.isdir('data/mnist') == 0):\\n os.mkdir('data/mnist')\\n for name in RESOURCES:\\n if (os.path.isfile('data/mnist/' + name) == 0):\\n url = 'https://github.com/HIPS/hypergrad/raw/master/data/mnist/' + name\\n r = requests.get(url, allow_redirects=True)\\n open('data/mnist/' + name, 'wb').write(r.content)\\n\\n return get_images(train_data, test_data), get_labels(train_data, test_data)\",\n \"def test_snow_pumps():\\n test_path = tempfile.mkdtemp()\\n x_train, metadata = snow_pumps(test_path)\\n try:\\n assert x_train.shape == (13, 4)\\n except:\\n shutil.rmtree(test_path)\\n raise()\",\n \"def run_test():\\n # Get the sets of images and labels for training, validation, and\\n # test on MNIST.\\n train ,validation,test = datasets_mnist.read_data_sets(FLAGS.input_data_dir, FLAGS.fake_data)\\n # Tell TensorFlow that the model will be built into the default Graph.\\n with tf.Graph().as_default():\\n # Generate placeholders for the images and labels.\\n images_placeholder, labels_placeholder, phase_pl = placeholder_inputs(\\n FLAGS.batch_size)\\n\\n # Build a Graph that computes predictions from the inference model.\\n logits = mnist.inference(images_placeholder,\\n FLAGS.hidden1,\\n FLAGS.hidden2, \\n phase_pl)\\n\\n eval_correct = mnist.evaluation(logits, labels_placeholder)\\n # Add the variable initializer Op.\\n all_variable = tf.global_variables()\\n \\n # Create a saver for writing training checkpoints.\\n saver = tf.train.Saver()\\n\\n # Create a session for running Ops on the Graph.\\n with tf.Session() as sess:\\n\\n saver.restore(sess, \\\"log/model.ckpt-1999\\\")\\n for variable in all_variable:\\n if \\\"moving\\\" in variable.name:\\n print(variable.name, variable.eval())\\n do_eval(sess,\\n eval_correct,\\n images_placeholder,\\n labels_placeholder,\\n phase_pl,\\n test)\",\n \"def as_mnist(filename, imwidth):\\n\\n images = []\\n labels = []\\n \\n if filename.find(\\\"devel\\\") != -1:\\n print(\\\"we're working with the development set: \\\" + filename)\\n\\n for cls, data in enumerate(load(filename)):\\n for example in data:\\n labels.append(cls)\\n image = numpy.zeros(shape=(imwidth, imwidth), dtype='uint8')\\n for (x, y) in example:\\n x_ = int(round(imwidth * x))\\n y_ = int(round(1-(imwidth * y)))\\n image[y_, x_] = 255\\n images.append(image.flatten())\\n\\n return numpy.vstack(images).T.copy(), numpy.array(labels)\",\n \"def get_mnist_data(batch=128):\\n \\n def transformer(data, label):\\n data = data.flatten().expand_dims(0).astype(np.float32)/255\\n data = data-0.13/0.31\\n label = label.astype(np.float32)\\n return data, label\\n\\n train_dataset = gluon.data.vision.datasets.MNIST(root=M5_IMAGES, train=True, transform=transformer)\\n validation_dataset = gluon.data.vision.datasets.MNIST(root=M5_IMAGES, train=False, transform=transformer)\\n train_dataloader = gluon.data.DataLoader(train_dataset, batch_size=batch, last_batch='keep',shuffle=True)\\n validation_dataloader = gluon.data.DataLoader(validation_dataset, batch_size=batch, last_batch='keep')\\n \\n return train_dataloader, validation_dataloader\",\n \"def ex_3_b(x_train, y_train, x_test, y_test):\\n ###########\\n ## TODO:\\n ## Train multi-class SVMs with a LINEAR kernel\\n ## Use the sklearn.metrics.confusion_matrix to plot the confusion matrix.\\n ## Find the index for which you get the highest error rate.\\n ## Plot the confusion matrix with plot_confusion_matrix.\\n ## Plot the first 10 images classified as the most misclassified digit using plot_mnist.\\n ###########\\n\\n labels = range(1, 6)\\n\\n lin = svm.SVC(decision_function_shape='ovr', kernel='linear')\\n lin.fit(x_train, y_train)\\n\\n y_test_predict =lin.predict(x_test)\\n\\n score_train = lin.score(x_train, y_train)\\n score_test = lin.score(x_test, y_test)\\n\\n cm = confusion_matrix(y_test, y_test_predict)\\n plot_confusion_matrix(cm, labels)\\n #print(cm)\\n\\n diff_list = y_test_predict == y_test\\n\\n # indexes of all missclassiefied images\\n misclassifieds = [i for i, val in enumerate(diff_list) if val == False]\\n\\n # remove diagonal elements from cm for later processing\\n cm_no_diagonal = cm\\n np.fill_diagonal(cm_no_diagonal, 0)\\n #print(cm_no_diagonal)\\n\\n errors_per_class = np.sum(cm_no_diagonal, axis=0)\\n #print(errors_per_class)\\n\\n sel_err = np.array(misclassifieds) # CHANGE ME! Numpy indices to select all images that are misclassified.\\n i = np.argmax(errors_per_class) # CHANGE ME! Should be the label number corresponding the largest classification error.\\n #print(i)\\n\\n # Plot with mnist plot\\n plot_mnist(x_test[sel_err], y_test_predict[sel_err], labels=labels[i], k_plots=10, prefix='Predicted class')\",\n \"def get_mnist():\\n if GLOBALS['project_root']=='':\\n print('please initialize project_root in GLOBALS first')\\n return None\\n data_path = os.path.join(GLOBALS['project_root'], 'data/MNIST/')\\n pickle_path = os.path.join(data_path, 'mnist.pickle')\\n if os.path.exists(pickle_path):\\n with open(pickle_path, 'rb') as f:\\n mnist = pickle.load(f)\\n else:\\n from tensorflow.examples.tutorials.mnist import input_data\\n mnist = input_data.read_data_sets(data_path, one_hot=True)\\n with open(pickle_path, 'wb') as f:\\n pickle.dump(mnist, f, pickle.HIGHEST_PROTOCOL)\\n\\n return mnist\",\n \"def load_mnist(dataset_name='mnist', **kwargs):\\n dataset_name = dataset_name.strip().lower().replace('minist', 'mnist')\\n\\n if dataset_name.lower() not in ['mnist', 'fashion-mnist']:\\n raise ValueError('Only mnist or fashion-mnist are valid dataset_name.')\\n\\n base = 'http://yann.lecun.com/exdb/mnist/'\\n if dataset_name == 'fashion-mnist':\\n base = 'http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/'\\n\\n dirname = os.path.join(_trident_dir, dataset_name)\\n make_dir_if_need(dirname)\\n\\n \\\"\\\"\\\"Load MNIST data from `path`\\\"\\\"\\\"\\n trainData = None\\n testData = None\\n for kind in ['train', 'test']:\\n labels_file = '{0}-labels-idx1-ubyte.gz'.format( 't10k' if dataset_name in ('mnist', 'fashion-mnist') and kind == 'test' else kind)\\n images_file = '{0}-images-idx3-ubyte.gz'.format( 't10k' if dataset_name in ('mnist', 'fashion-mnist') and kind == 'test' else kind)\\n # if dataset_name == 'emnist' :\\n # labels_file='emnist-balanced-'+labels_file\\n # images_file = 'emnist-balanced-' + images_file\\n\\n is_data_download = download_file(base + labels_file, dirname, labels_file, dataset_name + '_labels_{0}'.format(kind))\\n is_label_download = download_file(base + images_file, dirname, images_file, dataset_name + '_images_{0}'.format(kind))\\n if is_data_download and is_label_download:\\n labels_path = os.path.join(dirname, labels_file)\\n images_path = os.path.join(dirname, images_file)\\n labeldata = None\\n imagedata = None\\n with gzip.open(labels_path, 'rb') as lbpath:\\n labels = np.frombuffer(lbpath.read(), dtype=np.uint8, offset=8)\\n labels = np.squeeze(labels).astype(np.int64)\\n labeldata = LabelDataset(labels.tolist(),object_type=ObjectType.classification_label)\\n\\n with gzip.open(images_path, 'rb') as imgpath:\\n images = np.frombuffer(imgpath.read(), dtype=np.uint8, offset=16)\\n images = np.reshape(images, (len(labels), 784)).astype(dtype=_session.floatx)\\n images = np.reshape(images, (-1, 28, 28))\\n imagedata = ImageDataset(images, object_type=ObjectType.gray)\\n if kind == 'train':\\n trainData = Iterator(data=imagedata, label=labeldata)\\n else:\\n testData = Iterator(data=imagedata, label=labeldata)\\n\\n dataset = DataProvider(dataset_name, traindata=trainData, testdata=testData)\\n dataset.binding_class_names(\\n [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] if dataset_name == 'mnist' else ['T-shirt/top', 'Trouser', 'Pullover',\\n 'Dress', 'Coat', 'Sandal', 'Shirt',\\n 'Sneaker', 'Bag', 'Ankle boot'],\\n 'en-US')\\n\\n return dataset\\n return None\",\n \"def test_mlp():\\r\\n datasets = gen_data()\\r\\n\\r\\n train_set_x, train_set_y = datasets[0]\\r\\n valid_set_x, valid_set_y = datasets[1]\\r\\n test_set_x , test_set_y = datasets[2]\\r\\n\\r\\n\\r\\n\\r\\n batch_size = 100 # size of the minibatch\\r\\n\\r\\n # compute number of minibatches for training, validation and testing\\r\\n n_train_batches = train_set_x.get_value(borrow=True).shape[0] / batch_size\\r\\n n_valid_batches = valid_set_x.get_value(borrow=True).shape[0] / batch_size\\r\\n n_test_batches = test_set_x.get_value(borrow=True).shape[0] / batch_size\\r\\n\\r\\n ######################\\r\\n # BUILD ACTUAL MODEL #\\r\\n ######################\\r\\n #print '... building the model'\\r\\n\\r\\n # allocate symbolic variables for the data\\r\\n index = T.lscalar() # index to a [mini]batch\\r\\n x = T.matrix('x') # the data is presented as rasterized images\\r\\n y = T.ivector('y') # the labels are presented as 1D vector of\\r\\n # [int] labels\\r\\n\\r\\n rng = numpy.random.RandomState(1234)\\r\\n\\r\\n # construct the MLP class\\r\\n classifier = MLP( rng = rng, input=x, n_in=28*28, n_hidden = 500, n_out=10)\\r\\n\\r\\n # the cost we minimize during training is the negative log likelihood of\\r\\n # the model.\\r\\n # We take the mean of the cost over each minibatch.\\r\\n cost = classifier.negative_log_likelihood(y).mean()\\r\\n\\r\\n # compute the gradient of cost with respect to theta (stored in params)\\r\\n # the resulting gradients will be stored in a list gparams\\r\\n gparams = []\\r\\n for param in classifier.params:\\r\\n gparam = T.grad(cost, param)\\r\\n gparams.append(gparam)\\r\\n\\r\\n # Some optimizations needed are tagged with 'fast_run'\\r\\n # TODO: refine that and include only those\\r\\n mode = theano.compile.get_default_mode().including('fast_run')\\r\\n\\r\\n updates2 = OrderedDict()\\r\\n\\r\\n updates2[classifier.hiddenLayer.params[0]]=T.grad(cost,classifier.hiddenLayer.params[0])\\r\\n train_model =theano.function( inputs = [index],\\r\\n updates = updates2,\\r\\n givens={\\r\\n x:train_set_x[index*batch_size:(index+1)*batch_size],\\r\\n y:train_set_y[index*batch_size:(index+1)*batch_size]},\\r\\n mode=mode)\\r\\n #print 'MODEL 1'\\r\\n #theano.printing.debugprint(train_model, print_type=True)\\r\\n assert any([isinstance(i.op,T.nnet.CrossentropySoftmax1HotWithBiasDx) for i in train_model.maker.fgraph.toposort()])\\r\\n\\r\\n # Even without FeatureShape\\r\\n train_model =theano.function( inputs = [index],\\r\\n updates = updates2,\\r\\n mode=mode.excluding('ShapeOpt'),\\r\\n givens={\\r\\n x:train_set_x[index*batch_size:(index+1)*batch_size],\\r\\n y:train_set_y[index*batch_size:(index+1)*batch_size]})\\r\\n #print\\r\\n #print 'MODEL 2'\\r\\n #theano.printing.debugprint(train_model, print_type=True)\\r\\n assert any([isinstance(i.op,T.nnet.CrossentropySoftmax1HotWithBiasDx) for i in train_model.maker.fgraph.toposort()])\",\n \"def test_generate_nb(self):\\n pass\",\n \"def neural_net_ex4_ng():\\n # ==================\\n # read data\\n dataset = loadmat('data/ex4data1.mat')\\n print(dataset.keys())\\n\\n y = dataset['y'] # 5000 x 1\\n print('dims y: ', y.shape)\\n # print('y[0]: ', y[0])\\n\\n X = dataset['X'] # 5000 x 400\\n print('dims X: ', X.shape)\\n # print('X[0]: ', X[0])\\n\\n X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\\n\\n num_samples_test = X_test.shape[0]\\n\\n # ==================\\n # display data\\n\\n # pick 20 examples and visualize them\\n fig = plt.figure(figsize=(10, 8), facecolor='white')\\n fig.add_subplot(651)\\n samples = np.random.choice(num_samples_test, 10)\\n print('samples:', samples)\\n plt.imshow(X_test[samples, :].reshape(-1, 20).T, cmap=\\\"Greys\\\")\\n plt.axis('off')\\n\\n # ==================\\n # run neural net\\n hidden_layer_size = 25\\n\\n mlp = MLPClassifier(hidden_layer_sizes=(25,), max_iter=20, alpha=1e-4,\\n solver='sgd', verbose=False, tol=1e-4, random_state=1,\\n learning_rate_init=.1)\\n mlp.fit(X_train, y_train.ravel())\\n\\n predictions = mlp.predict(X_test)\\n print('Test set accuracy: {} %'.format(np.mean(predictions == y_test.ravel())*100))\\n\\n # print(confusion_matrix(y_test, predictions))\\n # print(classification_report(y_test, predictions))\\n print(\\\"Training set score: %f\\\" % mlp.score(X_train, y_train))\\n print(\\\"Test set score: %f\\\" % mlp.score(X_test, y_test))\\n print('coeffs shape', (mlp.coefs_[0]).shape)\\n\\n # ==================\\n # display coefficients of hidden layer\\n fig.add_subplot(652)\\n plt.imshow(mlp.coefs_[0][:, 0].reshape(20, 20))\\n plt.axis('off')\\n\\n gs = gridspec.GridSpec(6, 5)\\n cur_img_idx = 5\\n\\n # use global min / max to ensure all weights are shown on the same scale\\n vmin, vmax = mlp.coefs_[0].min(), mlp.coefs_[0].max()\\n for coef, ax in zip(mlp.coefs_[0].T, range(hidden_layer_size)):\\n fig.add_subplot(gs[cur_img_idx])\\n plt.imshow(coef.reshape(20, 20), cmap=plt.cm.gray, vmin=.5 * vmin, vmax=.5 * vmax)\\n plt.axis('off')\\n cur_img_idx += 1\\n\\n plt.show()\",\n \"def demo():\\n def load_data():\\n train = open(\\\"csv/svd_train.csv\\\", \\\"r\\\")\\n r = csv.reader(train)\\n next(r)\\n\\n data = []\\n target = []\\n\\n print \\\"Prepping data...\\\"\\n for row in r:\\n aux = [0 for x in xrange(10)]\\n aux[int(row[0])] = 1\\n target.append(aux)\\n data.append([float(x) for x in row[1:]])\\n\\n train.close()\\n\\n data = np.array(data)\\n\\n target = np.array(target)\\n\\n #train = [target[:35000],data[:35000]]\\n #test = [target[35000:],data[35000:]]\\n\\n return [target, data]\\n\\n NN = MLP_NeuralNetwork(101, 75, 35, 10,\\n iterations = 200,\\n learning_rate = 0.5,\\n momentum = 0.05,\\n rate_decay = 0.005)\\n\\n train = load_data()\\n\\n NN.train(train)\\n #NN.test_cross(test)\\n #NN.test()\\n NN.test_against()\",\n \"def gen_train_val_test_images(data_dir, seed=131):\\n np.random.seed(seed)\\n\\n # Load SVHN Dataset (single digits)\\n train_data = scipy_io.loadmat(data_dir + '/train_32x32.mat')\\n test_data = scipy_io.loadmat(data_dir + '/test_32x32.mat')\\n extra_data = scipy_io.loadmat(data_dir + '/extra_32x32.mat')\\n\\n train_X, train_y = train_data['X'], train_data['y']\\n test_X, test_y = test_data['X'], test_data['y']\\n extra_X, extra_y = extra_data['X'], extra_data['y']\\n\\n train_y = train_y.squeeze()\\n test_y = test_y.squeeze()\\n extra_y = extra_y.squeeze()\\n\\n # Change labels for '0' digit from 10 to 0\\n train_y[train_y == 10] = 0\\n test_y[test_y == 10] = 0\\n extra_y[extra_y == 10] = 0\\n\\n del extra_data\\n\\n num_classes = 10\\n\\n train_val_sample_idxs = np.array([], int)\\n for i in range(num_classes):\\n class_idxs = np.arange(len(train_y))[train_y == i]\\n sel_class_idxs = np.random.choice(class_idxs, size=400)\\n train_val_sample_idxs = np.concatenate((train_val_sample_idxs,\\n sel_class_idxs))\\n not_train_val_sample_idxs = np.setdiff1d(np.arange(len(train_y)),\\n train_val_sample_idxs)\\n\\n val_X = train_X[:, :, :, train_val_sample_idxs]\\n val_y = train_y[train_val_sample_idxs]\\n\\n extra_val_sample_idxs = np.array([], int)\\n for i in range(num_classes):\\n class_idxs = np.arange(len(extra_y))[extra_y == i]\\n sel_class_idxs = np.random.choice(class_idxs, size=200)\\n extra_val_sample_idxs = np.concatenate((extra_val_sample_idxs,\\n sel_class_idxs))\\n not_extra_val_sample_idxs = np.setdiff1d(np.arange(len(extra_y)),\\n extra_val_sample_idxs)\\n\\n val_X = np.concatenate((val_X, extra_X[:, :, :, extra_val_sample_idxs]), axis=3)\\n val_y = np.hstack((val_y, extra_y[extra_val_sample_idxs]))\\n\\n train_X = np.concatenate((train_X[:, :, :, not_train_val_sample_idxs],\\n extra_X[:, :, :, not_extra_val_sample_idxs]), axis=3)\\n train_y = np.hstack((train_y[not_train_val_sample_idxs],\\n extra_y[not_extra_val_sample_idxs]))\\n\\n # Create directories and save images\\n train_dir = data_dir + '/imgs/train'\\n test_dir = data_dir + '/imgs/test'\\n validation_dir = data_dir + '/imgs/validation'\\n\\n if not os.path.exists(train_dir):\\n os.makedirs(train_dir)\\n\\n if not os.path.exists(validation_dir):\\n os.makedirs(validation_dir)\\n\\n if not os.path.exists(test_dir):\\n os.makedirs(test_dir)\\n\\n for i in range(num_classes):\\n if not os.path.exists(train_dir + '/' + str(i)):\\n os.makedirs(train_dir + '/' + str(i))\\n\\n if not os.path.exists(validation_dir + '/' + str(i)):\\n os.makedirs(validation_dir + '/' + str(i))\\n\\n if not os.path.exists(test_dir + '/' + str(i)):\\n os.makedirs(test_dir + '/' + str(i))\\n\\n print \\\"Creating train images ... \\\"\\n for i in range(len(train_y)):\\n filename = train_dir + '/' + str(train_y[i]) + '/' + str(i) + '.png'\\n skimage_io.imsave(filename, train_X[:, :, :, i])\\n\\n print \\\"Creating validation images ... \\\"\\n for i in range(len(val_y)):\\n filename = validation_dir + '/' + str(val_y[i]) + '/' + str(i) + '.png'\\n skimage_io.imsave(filename, val_X[:, :, :, i])\\n\\n print \\\"Creating test images ... \\\"\\n for i in range(len(test_y)):\\n filename = test_dir + '/' + str(test_y[i]) + '/' + str(i) + '.png'\\n skimage_io.imsave(filename, test_X[:, :, :, i])\",\n \"def test_on_all(self) -> None:\\n x_test, y_test = self.mnist.test.images, self.mnist.test.labels\\n N = self.mnist.test.num_examples\\n\\n # I have replaced all -1 with self.mb_size to be sure about exact shapes of all layers.\\n assert N % self.mb_size == 0,\\\\\\n \\\"Sorry, mb_size must divide the number of images in test set\\\"\\n\\n results = np.array([0., 0.])\\n for batch_no in range(N // self.mb_size):\\n beg = batch_no * self.mb_size\\n end = min(N, (batch_no + 1) * self.mb_size)\\n len_batch = end - beg\\n batch_results = np.array(self.test_on_batch(x_test[beg:end], y_test[beg:end]))\\n results += batch_results * len_batch\\n results /= N\\n self.logger.info(\\\"(Test(final): Loss: {0[0]}, accuracy: {0[1]}\\\".format(results))\",\n \"def fetch_multimnist_image(label):\\n dataset = MultiMNIST('./data', train=False, download=True,\\n transform=transforms.ToTensor(),\\n target_transform=charlist_tensor)\\n images = dataset.test_data\\n labels = dataset.test_labels\\n n_rows = len(images)\\n\\n images = []\\n for i in xrange(n_rows):\\n image = images[i]\\n text = labels[i]\\n if tensor_to_string(text.squeeze(0)) == label:\\n images.append(image)\\n\\n if len(images) == 0:\\n sys.exit('No images with label (%s) found.' % label)\\n\\n images = torch.cat(images).cpu().numpy()\\n ix = np.random.choice(np.arange(images.shape[0]))\\n image = images[ix]\\n image = torch.from_numpy(image).float() \\n image = image.unsqueeze(0)\\n return Variable(image, volatile=True)\",\n \"def _load_mnist(path, dataset=\\\"training\\\", digits=None, asbytes=False,\\n selection=None, return_labels=True, return_indices=False):\\n\\n # The files are assumed to have these names and should be found in 'path'\\n files = {\\n 'training': ('train-images-idx3-ubyte', 'train-labels-idx1-ubyte'),\\n 'testing': ('t10k-images-idx3-ubyte', 't10k-labels-idx1-ubyte'),\\n }\\n\\n try:\\n images_fname = os.path.join(path, files[dataset][0])\\n labels_fname = os.path.join(path, files[dataset][1])\\n except KeyError:\\n raise ValueError(\\\"Data set must be 'testing' or 'training'\\\")\\n\\n # We can skip the labels file only if digits aren't specified and labels\\n # aren't asked for\\n if return_labels or digits is not None:\\n flbl = open(labels_fname, 'rb')\\n magic_nr, size = struct.unpack(\\\">II\\\", flbl.read(8))\\n labels_raw = pyarray(\\\"b\\\", flbl.read())\\n flbl.close()\\n\\n fimg = open(images_fname, 'rb')\\n magic_nr, size, rows, cols = struct.unpack(\\\">IIII\\\", fimg.read(16))\\n images_raw = pyarray(\\\"B\\\", fimg.read())\\n fimg.close()\\n\\n if digits:\\n indices = [k for k in range(size) if labels_raw[k] in digits]\\n else:\\n indices = range(size)\\n\\n if selection:\\n indices = indices[selection]\\n\\n images = np.zeros((len(indices), rows, cols), dtype=np.uint8)\\n\\n if return_labels:\\n labels = np.zeros((len(indices)), dtype=np.int8)\\n for i in range(len(indices)):\\n images[i] = np.array(images_raw[indices[i] * rows * cols:(indices[i] + 1) * rows * cols]).reshape((rows, cols))\\n if return_labels:\\n labels[i] = labels_raw[indices[i]]\\n\\n if not asbytes:\\n images = images.astype(float)/255.0\\n\\n ret = (images,)\\n if return_labels:\\n ret += (labels,)\\n if return_indices:\\n ret += (indices,)\\n\\n if len(ret) == 1:\\n return ret[0] # Don't return a tuple of one\\n\\n return ret\",\n \"def fetch_binarized_mnist():\\n mnist = fetch_mnist()\\n random_state = np.random.RandomState(1999)\\n\\n def get_sampled(arr):\\n # make sure that a pixel can always be turned off\\n return random_state.binomial(1, arr * 255 / 256., size=arr.shape)\\n\\n data = get_sampled(mnist[\\\"data\\\"]).astype(theano.config.floatX)\\n return {\\\"data\\\": data,\\n \\\"target\\\": mnist[\\\"target\\\"],\\n \\\"train_indices\\\": mnist[\\\"train_indices\\\"],\\n \\\"valid_indices\\\": mnist[\\\"valid_indices\\\"],\\n \\\"test_indices\\\": mnist[\\\"test_indices\\\"]}\",\n \"def get_data():\\n\\t(X_train, y_train), (X_val, y_val) = mnist.load_data()\\n\\tn_features = X_train.shape[1]*X_train.shape[1]\\n\\tn_train = X_train.shape[0]\\n\\tn_val = X_val.shape[0]\\n\\tX_train = X_train.reshape(n_train, n_features)\\n\\tX_val = X_val.reshape(n_val, n_features)\\n\\tX_train = X_train.astype(\\\"float32\\\") / 255\\n\\tX_val = X_val.astype(\\\"float32\\\") / 255\\n\\n\\treturn (X_train, y_train), (X_val, y_val)\",\n \"def try4():\\n path = '/Users/mayankkejriwal/git-projects/bioExperiments/tsne_python/'\\n mnist = path+'mnist2500_X.txt'\\n X = numpy.loadtxt(mnist)\\n labels = numpy.loadtxt(path+\\\"mnist2500_labels.txt\\\")\\n Y = tsne.tsne(X, 2, 50, 20.0)\\n pylab.scatter(Y[:,0], Y[:,1], 20, labels)\\n pylab.show()\",\n \"def test_mlp(learning_rate=0.01, L1_reg=0.00, L2_reg=0.0001, n_epochs=1000,\\r\\n dataset='mnist.pkl.gz', batch_size=20, n_hidden=500):\\r\\n datasets = load_data(dataset)\\r\\n\\r\\n train_set_x, train_set_y = datasets[0]\\r\\n valid_set_x, valid_set_y = datasets[1]\\r\\n test_set_x, test_set_y = datasets[2]\\r\\n\\r\\n # compute number of minibatches for training, validation and testing\\r\\n n_train_batches = train_set_x.get_value(borrow=True).shape[0] / batch_size\\r\\n n_valid_batches = valid_set_x.get_value(borrow=True).shape[0] / batch_size\\r\\n n_test_batches = test_set_x.get_value(borrow=True).shape[0] / batch_size\\r\\n\\r\\n ######################\\r\\n # BUILD ACTUAL MODEL #\\r\\n ######################\\r\\n print '... building the model'\\r\\n\\r\\n # allocate symbolic variables for the data\\r\\n index = T.lscalar() # index to a [mini]batch\\r\\n x = T.matrix('x') # the data is presented as rasterized images\\r\\n y = T.ivector('y') # the labels are presented as 1D vector of\\r\\n # [int] labels\\r\\n\\r\\n rng = numpy.random.RandomState(1234)\\r\\n\\r\\n # construct the MLP class\\r\\n classifier = MLP(rng=rng, input=x, n_in=28 * 28,\\r\\n n_hidden=n_hidden, n_out=10)\\r\\n\\r\\n # the cost we minimize during training is the negative log likelihood of\\r\\n # the model plus the regularization terms (L1 and L2); cost is expressed\\r\\n # here symbolically\\r\\n cost = classifier.negative_log_likelihood(y) \\\\\\r\\n + L1_reg * classifier.L1 \\\\\\r\\n + L2_reg * classifier.L2_sqr\\r\\n\\r\\n # compiling a Theano function that computes the mistakes that are made\\r\\n # by the model on a minibatch\\r\\n test_model = theano.function(inputs=[index],\\r\\n outputs=classifier.errors(y),\\r\\n givens={\\r\\n x: test_set_x[index * batch_size:(index + 1) * batch_size],\\r\\n y: test_set_y[index * batch_size:(index + 1) * batch_size]})\\r\\n\\r\\n validate_model = theano.function(inputs=[index],\\r\\n outputs=classifier.errors(y),\\r\\n givens={\\r\\n x: valid_set_x[index * batch_size:(index + 1) * batch_size],\\r\\n y: valid_set_y[index * batch_size:(index + 1) * batch_size]})\\r\\n\\r\\n # compute the gradient of cost with respect to theta (sotred in params)\\r\\n # the resulting gradients will be stored in a list gparams\\r\\n gparams = []\\r\\n for param in classifier.params:\\r\\n gparam = T.grad(cost, param)\\r\\n gparams.append(gparam)\\r\\n\\r\\n # specify how to update the parameters of the model as a list of\\r\\n # (variable, update expression) pairs\\r\\n updates = []\\r\\n # given two list the zip A = [a1, a2, a3, a4] and B = [b1, b2, b3, b4] of\\r\\n # same length, zip generates a list C of same size, where each element\\r\\n # is a pair formed from the two lists :\\r\\n # C = [(a1, b1), (a2, b2), (a3, b3), (a4, b4)]\\r\\n for param, gparam in zip(classifier.params, gparams):\\r\\n updates.append((param, param - learning_rate * gparam))\\r\\n\\r\\n # compiling a Theano function `train_model` that returns the cost, but\\r\\n # in the same time updates the parameter of the model based on the rules\\r\\n # defined in `updates`\\r\\n train_model = theano.function(inputs=[index], outputs=cost,\\r\\n updates=updates,\\r\\n givens={\\r\\n x: train_set_x[index * batch_size:(index + 1) * batch_size],\\r\\n y: train_set_y[index * batch_size:(index + 1) * batch_size]})\\r\\n\\r\\n ###############\\r\\n # TRAIN MODEL #\\r\\n ###############\\r\\n print '... training'\\r\\n\\r\\n # early-stopping parameters\\r\\n patience = 10000 # look as this many examples regardless\\r\\n patience_increase = 2 # wait this much longer when a new best is\\r\\n # found\\r\\n improvement_threshold = 0.995 # a relative improvement of this much is\\r\\n # considered significant\\r\\n validation_frequency = min(n_train_batches, patience / 2)\\r\\n # go through this many\\r\\n # minibatche before checking the network\\r\\n # on the validation set; in this case we\\r\\n # check every epoch\\r\\n\\r\\n best_params = None\\r\\n best_validation_loss = numpy.inf\\r\\n best_iter = 0\\r\\n test_score = 0.\\r\\n start_time = time.clock()\\r\\n\\r\\n epoch = 0\\r\\n done_looping = False\\r\\n\\r\\n while (epoch < n_epochs) and (not done_looping):\\r\\n epoch = epoch + 1\\r\\n for minibatch_index in xrange(n_train_batches):\\r\\n\\r\\n minibatch_avg_cost = train_model(minibatch_index)\\r\\n # iteration number\\r\\n iter = (epoch - 1) * n_train_batches + minibatch_index\\r\\n\\r\\n if (iter + 1) % validation_frequency == 0:\\r\\n # compute zero-one loss on validation set\\r\\n validation_losses = [validate_model(i) for i\\r\\n in xrange(n_valid_batches)]\\r\\n this_validation_loss = numpy.mean(validation_losses)\\r\\n\\r\\n print('epoch %i, minibatch %i/%i, validation error %f %%' %\\r\\n (epoch, minibatch_index + 1, n_train_batches,\\r\\n this_validation_loss * 100.))\\r\\n\\r\\n # if we got the best validation score until now\\r\\n if this_validation_loss < best_validation_loss:\\r\\n #improve patience if loss improvement is good enough\\r\\n if this_validation_loss < best_validation_loss * \\\\\\r\\n improvement_threshold:\\r\\n patience = max(patience, iter * patience_increase)\\r\\n\\r\\n best_validation_loss = this_validation_loss\\r\\n best_iter = iter\\r\\n\\r\\n # test it on the test set\\r\\n test_losses = [test_model(i) for i\\r\\n in xrange(n_test_batches)]\\r\\n test_score = numpy.mean(test_losses)\\r\\n\\r\\n print((' epoch %i, minibatch %i/%i, test error of '\\r\\n 'best model %f %%') %\\r\\n (epoch, minibatch_index + 1, n_train_batches,\\r\\n test_score * 100.))\\r\\n\\r\\n if patience <= iter:\\r\\n done_looping = True\\r\\n break\\r\\n\\r\\n end_time = time.clock()\\r\\n print(('Optimization complete. Best validation score of %f %% '\\r\\n 'obtained at iteration %i, with test performance %f %%') %\\r\\n (best_validation_loss * 100., best_iter + 1, test_score * 100.))\\r\\n print >> sys.stderr, ('The code for file ' +\\r\\n os.path.split(__file__)[1] +\\r\\n ' ran for %.2fm' % ((end_time - start_time) / 60.))\",\n \"def test_data():\\n global _MEAN # pylint: disable=global-statement\\n _np.random.seed(1)\\n view = _skdc10.view.OfficialImageClassificationTask()\\n permutation = _np.random.permutation(range(10000))\\n if _MEAN is None:\\n _MEAN = view.train.x.reshape((50000 * 32 * 32, 3)).mean(axis=0)\\n return ((view.test.x[:10000, :][permutation, :] - _MEAN).\\n transpose((0, 3, 1, 2)).astype('float32'),\\n view.test.y[:10000][permutation].reshape((10000, 1)).astype('float32'))\",\n \"def load_data():\\n prefix = 'mnist_data/'\\n train_data = np.load(prefix + 'mnist_train_images.npy')\\n train_labels = np.load(prefix + 'mnist_train_labels.npy')\\n val_data = np.load(prefix + 'mnist_validation_images.npy')\\n val_labels = np.load(prefix + 'mnist_validation_labels.npy')\\n test_data = np.load(prefix + 'mnist_test_images.npy')\\n test_labels = np.load(prefix + 'mnist_test_labels.npy')\\n assert train_data.shape == (55000, 784) and train_labels.shape == (55000, 10)\\n assert val_data.shape == (5000, 784) and val_labels.shape == (5000, 10)\\n assert test_data.shape == (10000, 784) and test_labels.shape == (10000, 10)\\n return train_data, train_labels, val_data, val_labels, test_data, test_labels\",\n \"def test_basic(self):\\n result = NonLinearWeights(0.85).nonlinear_weights(3)\\n self.assertIsInstance(result, np.ndarray)\",\n \"def test(ndigit, elambda, showSamples, showConfusion):\\n Data, Label = getData()\\n trainX, trainY, testX, testY = splitData(Data, Label, ndigit)\\n trainX_mean = np.mean(trainX, axis=0)\\n trainX_new = trainX - trainX_mean\\n eigenvectors = getEigenVectors(trainX_new, elambda)\\n trainX_eigen = trainX_new.dot(eigenvectors)\\n testX_new = testX - trainX_mean\\n testX_eigen = testX_new.dot(eigenvectors)\\n testO = []\\n if showSamples:\\n correct_samples = []\\n correct_samples_nearest = []\\n correct_samples_eigen = []\\n correct_samples_nearest_eigen = []\\n correct_samples_labels = []\\n correct_samples_predictions = []\\n wrong_samples = []\\n wrong_samples_nearest = []\\n wrong_samples_eigen = []\\n wrong_samples_nearest_eigen = []\\n wrong_samples_labels = []\\n wrong_samples_predictions = []\\n if showConfusion:\\n conf = np.zeros((ndigit, ndigit))\\n for i in xrange(testX_eigen.shape[0]):\\n t = testX_eigen[i]\\n j = getNearestSampleIndex(t, trainX_eigen)\\n p = int(trainY[j])\\n y = int(testY[i])\\n if showConfusion:\\n conf[p, y] += 1\\n if showSamples:\\n if p == y:\\n if len(correct_samples) < y + 1:\\n correct_samples.append(testX[i])\\n correct_samples_nearest.append(trainX[j])\\n correct_samples_eigen.append(testX_eigen[i])\\n correct_samples_nearest_eigen.append(trainX_eigen[j])\\n correct_samples_labels.append(y)\\n correct_samples_predictions.append(p)\\n else:\\n if len(wrong_samples) < y + 1:\\n wrong_samples.append(testX[i])\\n wrong_samples_nearest.append(trainX[j])\\n wrong_samples_eigen.append(testX_eigen[i])\\n wrong_samples_nearest_eigen.append(trainX_eigen[j])\\n wrong_samples_labels.append(y)\\n wrong_samples_predictions.append(p)\\n testO.append(p)\\n testO = np.array(testO)\\n train0 = []\\n for i in xrange(trainX_eigen.shape[0]):\\n t = trainX_eigen[i]\\n j = getNearestSampleIndex(t, trainX_eigen)\\n min_class = trainY[j]\\n train0.append(min_class)\\n train0 = np.array(train0)\\n print \\\"for digits = %d lambda = %.2f train = %.6f test = %.6f \\\" % (\\n ndigit, elambda, (train0 == trainY).mean(), (testO == testY).mean())\\n if showConfusion:\\n print conf\\n if showSamples:\\n displaySamples(correct_samples_labels, correct_samples_predictions,\\n correct_samples, correct_samples_nearest,\\n correct_samples_eigen, correct_samples_nearest_eigen,\\n eigenvectors, trainX_mean, elambda, 'Correct')\\n displaySamples(wrong_samples_labels, wrong_samples_predictions,\\n wrong_samples, wrong_samples_nearest,\\n wrong_samples_eigen, wrong_samples_nearest_eigen,\\n eigenvectors, trainX_mean, elambda, 'Wrong')\",\n \"def load_mnist(fashion, onehot_encode=True, flatten_x=False, crop_x=0, classes=None):\\n\\tif not fashion:\\n\\t\\t(x_train, y_train),(x_test, y_test) = tf.keras.datasets.mnist.load_data()\\n\\t\\tx_train, x_test = x_train / 255.0, x_test / 255.0\\n\\telse:\\n\\t\\t(x_train, y_train),(x_test, y_test) = tf.keras.datasets.fashion_mnist.load_data()\\n\\t\\tx_train, x_test = x_train / 255.0, x_test / 255.0 \\n \\n\\tdef crop(X, crop_size):\\n\\t\\tassert crop_x < X.shape[1]/2\\n\\t\\tassert crop_x < X.shape[2]/2\\n\\t\\treturn X[:,crop_size:-crop_size,crop_size:-crop_size]\\n\\n\\tif crop_x > 0:\\n\\t\\tx_train = crop(x_train, crop_x)\\n\\t\\tx_test = crop(x_test, crop_x)\\n\\n\\t# Flatten to 2d arrays (each example 1d)\\n\\tdef flatten_image(X):\\n\\t return X.reshape(X.shape[0], X.shape[1]*X.shape[1])\\n\\tif flatten_x:\\n\\t\\tx_train = flatten_image(x_train)\\n\\t\\tx_test = flatten_image(x_test)\\n\\n\\tif onehot_encode:\\n\\t\\ty_train = onehot_encode_labels(y_train)\\n\\t\\ty_test = onehot_encode_labels(y_test)\\n\\n\\tif classes is not None:\\n\\t\\tassert len(classes) == 2\\n\\t\\tc0, c1 = classes\\n\\t\\ttrain_idxs_to_keep = np.logical_or(y_train==c0, y_train==c1)\\n\\t\\tx_train, y_train = x_train[train_idxs_to_keep,:], y_train[train_idxs_to_keep]\\n\\t\\ttest_idxs_to_keep = np.logical_or(y_test==c0, y_test==c1)\\n\\t\\tx_test, y_test = x_test[test_idxs_to_keep,:], y_test[test_idxs_to_keep]\\n\\n\\t\\ty_train = (y_train==c1).astype(int)[:,np.newaxis]\\n\\t\\ty_test = (y_test==c1).astype(int)[:,np.newaxis]\\n\\n\\treturn x_train, y_train, x_test, y_test\",\n \"def test_metric_learning(smote_class):\\n nn_params = {'metric': 'precomputed',\\n 'metric_learning_method': 'ITML'}\\n X, y = smote_class(nn_params=nn_params).sample(dataset['data'],\\n dataset['target'])\\n\\n assert np.unique(y).shape[0] == 2\\n assert X.shape[0] > 0\",\n \"def load_EMNIST_data(file, verbose = False, standarized = False): \\n mat = sio.loadmat(file)\\n data = mat[\\\"dataset\\\"]\\n \\n X_train = data['train'][0,0]['images'][0,0]\\n X_train = X_train.reshape((X_train.shape[0], 28, 28), order = \\\"F\\\")\\n y_train = data['train'][0,0]['labels'][0,0]\\n y_train = np.squeeze(y_train)\\n y_train -= 1 #y_train is zero-based\\n \\n X_test = data['test'][0,0]['images'][0,0]\\n X_test= X_test.reshape((X_test.shape[0], 28, 28), order = \\\"F\\\")\\n y_test = data['test'][0,0]['labels'][0,0]\\n y_test = np.squeeze(y_test)\\n y_test -= 1 #y_test is zero-based\\n \\n if standarized: \\n X_train = X_train/255\\n X_test = X_test/255\\n mean_image = np.mean(X_train, axis=0)\\n X_train -= mean_image\\n X_test -= mean_image\\n \\n\\n if verbose == True: \\n print(\\\"EMNIST-letter dataset ... \\\")\\n print(\\\"X_train shape :\\\", X_train.shape)\\n print(\\\"X_test shape :\\\", X_test.shape)\\n print(\\\"y_train shape :\\\", y_train.shape)\\n print(\\\"y_test shape :\\\", y_test.shape)\\n \\n return X_train, y_train, X_test, y_test\",\n \"def test_000_basic_functionality() -> None:\\n df = generate_test_data()\\n skim(df)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7192644","0.7088527","0.6992389","0.6823488","0.6820499","0.65735173","0.6531278","0.6529289","0.6502993","0.63889843","0.6371827","0.6368664","0.63274115","0.6315902","0.62861085","0.62725544","0.6220246","0.6188648","0.6144985","0.61362416","0.6119027","0.6045903","0.6035213","0.6018793","0.6013625","0.6011223","0.6001034","0.5967618","0.5955806","0.5953214","0.59335077","0.5884085","0.58810836","0.5856891","0.58554476","0.58467555","0.5846364","0.58420515","0.5834867","0.5833051","0.5830527","0.58132356","0.5802065","0.57884526","0.57714105","0.5755312","0.57360905","0.57294244","0.57265216","0.57239056","0.5703129","0.5699589","0.5678674","0.5674968","0.56662554","0.56531256","0.56452096","0.5642889","0.563169","0.56229085","0.56223816","0.5610885","0.55906284","0.55791396","0.5569076","0.5564868","0.55626106","0.5559306","0.555815","0.5556102","0.5549294","0.55482215","0.55392706","0.55255824","0.5516632","0.55111265","0.5501207","0.5490707","0.5485328","0.5485075","0.5480144","0.5476179","0.5468571","0.5462391","0.5462032","0.54609066","0.5451851","0.54505485","0.5434967","0.54316974","0.5430205","0.5429624","0.54293615","0.54290414","0.541637","0.5412779","0.5411554","0.54080427","0.5401129","0.5398112"],"string":"[\n \"0.7192644\",\n \"0.7088527\",\n \"0.6992389\",\n \"0.6823488\",\n \"0.6820499\",\n \"0.65735173\",\n \"0.6531278\",\n \"0.6529289\",\n \"0.6502993\",\n \"0.63889843\",\n \"0.6371827\",\n \"0.6368664\",\n \"0.63274115\",\n \"0.6315902\",\n \"0.62861085\",\n \"0.62725544\",\n \"0.6220246\",\n \"0.6188648\",\n \"0.6144985\",\n \"0.61362416\",\n \"0.6119027\",\n \"0.6045903\",\n \"0.6035213\",\n \"0.6018793\",\n \"0.6013625\",\n \"0.6011223\",\n \"0.6001034\",\n \"0.5967618\",\n \"0.5955806\",\n \"0.5953214\",\n \"0.59335077\",\n \"0.5884085\",\n \"0.58810836\",\n \"0.5856891\",\n \"0.58554476\",\n \"0.58467555\",\n \"0.5846364\",\n \"0.58420515\",\n \"0.5834867\",\n \"0.5833051\",\n \"0.5830527\",\n \"0.58132356\",\n \"0.5802065\",\n \"0.57884526\",\n \"0.57714105\",\n \"0.5755312\",\n \"0.57360905\",\n \"0.57294244\",\n \"0.57265216\",\n \"0.57239056\",\n \"0.5703129\",\n \"0.5699589\",\n \"0.5678674\",\n \"0.5674968\",\n \"0.56662554\",\n \"0.56531256\",\n \"0.56452096\",\n \"0.5642889\",\n \"0.563169\",\n \"0.56229085\",\n \"0.56223816\",\n \"0.5610885\",\n \"0.55906284\",\n \"0.55791396\",\n \"0.5569076\",\n \"0.5564868\",\n \"0.55626106\",\n \"0.5559306\",\n \"0.555815\",\n \"0.5556102\",\n \"0.5549294\",\n \"0.55482215\",\n \"0.55392706\",\n \"0.55255824\",\n \"0.5516632\",\n \"0.55111265\",\n \"0.5501207\",\n \"0.5490707\",\n \"0.5485328\",\n \"0.5485075\",\n \"0.5480144\",\n \"0.5476179\",\n \"0.5468571\",\n \"0.5462391\",\n \"0.5462032\",\n \"0.54609066\",\n \"0.5451851\",\n \"0.54505485\",\n \"0.5434967\",\n \"0.54316974\",\n \"0.5430205\",\n \"0.5429624\",\n \"0.54293615\",\n \"0.54290414\",\n \"0.541637\",\n \"0.5412779\",\n \"0.5411554\",\n \"0.54080427\",\n \"0.5401129\",\n \"0.5398112\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6742171"},"document_rank":{"kind":"string","value":"5"}}}],"truncated":false,"partial":true},"paginationData":{"pageIndex":65,"numItemsPerPage":100,"numTotalItems":95772,"offset":6500,"length":100}},"jwt":"eyJhbGciOiJFZERTQSJ9.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc3Mzk3MjIxMCwic3ViIjoiL2RhdGFzZXRzL25vbWljLWFpL2Nvcm5zdGFjay1weXRob24tdjEiLCJleHAiOjE3NzM5NzU4MTAsImlzcyI6Imh0dHBzOi8vaHVnZ2luZ2ZhY2UuY28ifQ.pk5jgRgbZCQ9Y3Oa17EsIsvJKl4-6fr9Yb0CwVBuJW4KSzCCkIbvGykMf9CPOy85MFuy65a9i70w38x9UH2FAQ","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 |
|---|---|---|---|---|---|---|
Returns the set of all objects instead of just live objects | def queryset(self, request):
qs = self.model.all_objects.get_query_set()
ordering = self.ordering or ()
if ordering:
qs = qs.order_by(*ordering)
return qs | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def objects_in_use(self):\n return set()",
"def all(self):\n return self.__objects",
"def all(self):\n return self.__objects",
"def all(self):\n return self.__objects",
"def all(self):\n return self.__objects",
"def all(self):\n return self.__objects",
"def all(sel... | [
"0.80215394",
"0.7922002",
"0.7922002",
"0.7922002",
"0.7922002",
"0.7922002",
"0.7922002",
"0.7845179",
"0.7754362",
"0.7715909",
"0.7468645",
"0.74566925",
"0.7332164",
"0.7307938",
"0.72778046",
"0.71881217",
"0.707433",
"0.7026089",
"0.7010139",
"0.7000168",
"0.6991465",
... | 0.0 | -1 |
Creating a custom time entry, minimum must is hour duration and project param | def createTimeEntry(self, hourduration, description=None, projectid=None, projectname=None,
taskid=None, clientname=None, year=None, month=None, day=None, hour=None,
billable=False, hourdiff=-2):
data = {
"time_entry": {}
}
if not projectid:
if projectname and clientname:
projectid = (self.getClientProject(clientname, projectname))['data']['id']
elif projectname:
projectid = (self.searchClientProject(projectname))['data']['id']
else:
print('Too many missing parameters for query')
exit(1)
if description:
data['time_entry']['description'] = description
if taskid:
data['time_entry']['tid'] = taskid
year = datetime.now().year if not year else year
month = datetime.now().month if not month else month
day = datetime.now().day if not day else day
hour = datetime.now().hour if not hour else hour
timestruct = datetime(year, month, day, hour + hourdiff).isoformat() + '.000Z'
data['time_entry']['start'] = timestruct
data['time_entry']['duration'] = hourduration * 3600
data['time_entry']['pid'] = projectid
data['time_entry']['created_with'] = 'NAME'
data['time_entry']['billable'] = billable
response = self.postRequest(Endpoints.TIME_ENTRIES, parameters=data)
return self.decodeJSON(response) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def running_custom_hour(arg):\n pass",
"def __init__(self, hour, minute=0, second=0, microsecond=0, tzinfo=None):",
"def timeField(*args, annotation: Union[AnyStr, bool]=\"\", backgroundColor: Union[List[float,\n float, float], bool]=None, changeCommand: Script=None, defineTemplate: AnyStr=\"\"... | [
"0.63917255",
"0.63114077",
"0.5988722",
"0.5984777",
"0.59670573",
"0.59660786",
"0.59379464",
"0.5916419",
"0.58417237",
"0.58348185",
"0.5833585",
"0.5783263",
"0.5764132",
"0.5757329",
"0.5752414",
"0.57444865",
"0.57442397",
"0.5735035",
"0.57336044",
"0.5732334",
"0.572... | 0.68871313 | 0 |
Return all of the projects for a given Workspace | def getWorkspaceProjects(self, id):
return self.request(Endpoints.WORKSPACES + '/{0}'.format(id) + '/projects') | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_projects(self):\n projects = []\n page = 1\n while not len(projects) % 100:\n projects += self._get('/projects?{0}'.format(urllib.urlencode({'per_page': 100, 'page': page})))\n if not projects:\n break\n page += 1\n return projects... | [
"0.74932694",
"0.7458052",
"0.7392975",
"0.73838377",
"0.737569",
"0.7349293",
"0.730423",
"0.7239146",
"0.72044575",
"0.7192277",
"0.71775115",
"0.71413183",
"0.71402687",
"0.713788",
"0.71083647",
"0.7094535",
"0.7094002",
"0.7023004",
"0.6982993",
"0.6981023",
"0.6948548",... | 0.7177935 | 10 |
Provide only a projects name for query and search through entire available names | def searchClientProject(self, name):
for client in self.getClients():
try:
for project in self.getClientProjects(client['id']):
if project['name'] == name:
return project
except Exception:
continue
print('Could not find client by the name')
return None | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def find_projects_by_name(self, name):\n projects = []\n for i in storage_utils.get_proj_ids(self._storage_location):\n project = self.find_project_by_id(i)\n if name.upper() in project.name.upper():\n projects.append(project)\n return projects",
"def tes... | [
"0.711047",
"0.6944019",
"0.6936128",
"0.6779344",
"0.6688481",
"0.66640425",
"0.6621097",
"0.66138625",
"0.6602506",
"0.6593444",
"0.6547937",
"0.6496374",
"0.6491965",
"0.6417066",
"0.6392265",
"0.636261",
"0.635305",
"0.6313007",
"0.62534356",
"0.6243291",
"0.62387365",
... | 0.6892241 | 3 |
Fast query given the Client's name and Project's name | def getClientProject(self, clientName, projectName):
for client in self.getClients():
if client['name'] == clientName:
cid = client['id']
if not cid:
print('Could not find such client name')
return None
for projct in self.getClientProjects(cid):
if projct['name'] == projectName:
pid = projct['id']
if not pid:
print('Could not find such project name')
return None
return self.getProject(pid) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def searchClientProject(self, name):\n for client in self.getClients():\n try:\n for project in self.getClientProjects(client['id']):\n if project['name'] == name:\n return project\n except Exception:\n continue\n\... | [
"0.68639714",
"0.61412024",
"0.5989478",
"0.5867039",
"0.5781275",
"0.5674645",
"0.566956",
"0.56622523",
"0.5642613",
"0.5634638",
"0.55459017",
"0.5535563",
"0.5528021",
"0.5521917",
"0.5521528",
"0.54804444",
"0.5467839",
"0.5415431",
"0.5400909",
"0.53992367",
"0.53678286... | 0.6326702 | 1 |
return all tasks of a given project | def getProjectTasks(self, pid, archived=False):
return self.request(Endpoints.PROJECTS + '/{0}'.format(pid) + '/tasks') | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_tasks_of_project(self, project_id):\n res = self.conn.cursor().execute(\"SELECT * FROM tasks WHERE project_id=? ORDER BY project_order\", (project_id,))\n return res.fetchall()",
"def get_tasks_list(project_id):\n project = Project.query.filter_by(id=project_id).first()\n if not proje... | [
"0.7955744",
"0.75765634",
"0.7526731",
"0.7374001",
"0.735808",
"0.73461324",
"0.7285441",
"0.72829795",
"0.7264477",
"0.72372395",
"0.722166",
"0.7205922",
"0.714417",
"0.7095142",
"0.70789766",
"0.70438063",
"0.6978584",
"0.6959845",
"0.6954135",
"0.6882514",
"0.68778336",... | 0.7401329 | 3 |
create a new client | def createClient(self, name, wid, notes=None):
data = {}
data['client'] = {}
data['client']['name'] = name
data['client']['wid'] = wid
data['client']['notes'] = notes
response = self.postRequest(Endpoints.CLIENTS, parameters=data)
return self.decodeJSON(response) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_client(self) -> None:\n pass",
"def create_client(name):\n client = Client(name=name)\n print(client.client_secret)\n db.session.add(client)\n db.session.commit()\n return client",
"def create(ctx, name, company, mail, age):\n client = Client(name,company,mail,age)\n client_s... | [
"0.84618115",
"0.7922506",
"0.76412994",
"0.7560049",
"0.72630185",
"0.7126018",
"0.70866805",
"0.7063268",
"0.7053907",
"0.70391697",
"0.7030181",
"0.702756",
"0.69890064",
"0.6928916",
"0.6843426",
"0.68396825",
"0.6788722",
"0.676113",
"0.67482334",
"0.67339486",
"0.672684... | 0.69494456 | 13 |
Update data for an existing client. If the name or notes parameter is not supplied, the existing data on the Toggl server will not be changed. | def updateClient(self, id, name=None, notes=None):
data = {}
data['client'] = {}
data['client']['name'] = name
data['client']['notes'] = notes
response = self.postRequest(Endpoints.CLIENTS + '/{0}'.format(id), parameters=data, method='PUT')
return self.decodeJSON(response) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def update_client(client_name, updated_client_name): # Operacion modificar\n global clients\n\n if client_name in clients:\n index = clients.index(client_name)\n clients[index] = updated_client_name\n else:\n print(\"Client isn\\'t in the client list\")",
"def test_update_client(se... | [
"0.67937523",
"0.65493184",
"0.6435495",
"0.62011623",
"0.6047726",
"0.5984437",
"0.5956502",
"0.5945212",
"0.5844981",
"0.57898957",
"0.5773103",
"0.57370985",
"0.5677678",
"0.55478555",
"0.5545997",
"0.55407304",
"0.55150896",
"0.55113846",
"0.5479551",
"0.5476245",
"0.5462... | 0.7835998 | 0 |
Delete the specified client | def deleteClient(self, id):
response = self.postRequest(Endpoints.CLIENTS + '/{0}'.format(id), method='DELETE')
return response | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def delete(self, client):\n log(\"Deleting %s\" % self, self.opt)\n client.delete(self.path)",
"def delete_client(self, client):\n for c in self.clients:\n if client == c:\n self.clients.remove(c)",
"def delete(self, **kwargs):\n self.dbdel('client', kwargs... | [
"0.8223102",
"0.81987244",
"0.81581897",
"0.80933565",
"0.8074244",
"0.7924584",
"0.79122",
"0.75041306",
"0.74830085",
"0.7278975",
"0.7202662",
"0.7177086",
"0.7141298",
"0.7069998",
"0.7028738",
"0.6995486",
"0.68752044",
"0.6845381",
"0.6844797",
"0.6833574",
"0.68101287"... | 0.78178424 | 7 |
Returns the items that overlap a bounding rectangle. Returns the set of all items in the quadtree that overlap with a bounding rectangle. | def hit(self, rect):
# Find the hits at the current level
hits = set(item for item in self.items if rect.contains(item.location))
# Recursively check the lower quadrants
if self.nw and rect.left < self.cx and rect.top < self.cy:
hits |= self.nw.hit(rect)
if self.sw and rect.left < self.cx and rect.bottom >= self.cy:
hits |= self.sw.hit(rect)
if self.ne and rect.right >= self.cx and rect.top < self.cy:
hits |= self.ne.hit(rect)
if self.se and rect.right >= self.cx and rect.bottom >= self.cy:
hits |= self.se.hit(rect)
return hits | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def query(self, rect):\n if hasattr(self, 'leaves'):\n return self.leaves.values()\n else:\n results = []\n rect = Rect(rect)\n for c in self.children:\n if c.rect.intersects(rect):\n results.extend(c.query(rect))\n ... | [
"0.6644186",
"0.6522617",
"0.6465768",
"0.6435466",
"0.59305274",
"0.59259313",
"0.5883582",
"0.58389163",
"0.5806691",
"0.5789564",
"0.5719773",
"0.56912875",
"0.5672954",
"0.5642597",
"0.56199247",
"0.5603152",
"0.5598976",
"0.55976313",
"0.5585401",
"0.55301195",
"0.550351... | 0.6004383 | 4 |
This method will run all the episodes with epsilon greedy strategy | def run_epsilon(env, num_of_bandits, iterations, episodes):
# Initialize total mean rewards array per episode by zero
epsilon_rewards = np.zeros(iterations)
for i in range(episodes):
print(f"Running Epsilon episode:{i}")
n = 1
action_count_per_bandit = np.ones(num_of_bandits)
mean_reward = 0
total_rewards = np.zeros(iterations)
mean_reward_per_bandit = np.zeros(num_of_bandits)
env.reset()
epsilon = 0.5
for j in range(iterations):
a = get_epsilon_action(epsilon, env, mean_reward_per_bandit)
observation, reward, done, info = env.step(a)
# Update counts
n += 1
action_count_per_bandit[a] += 1
# Update mean rewards
mean_reward = mean_reward + (
reward - mean_reward) / n
# Update mean rewards per bandit
mean_reward_per_bandit[a] = mean_reward_per_bandit[a] + (
reward - mean_reward_per_bandit[a]) / action_count_per_bandit[a]
# Capture mean rewards per iteration
total_rewards[j] = mean_reward
# Update mean episode rewards once all the iterations of the episode are done
epsilon_rewards = epsilon_rewards + (total_rewards - epsilon_rewards) / (i + 1)
return epsilon_rewards | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def run(self) -> None:\n for episode in range(1, self.episodes + 1):\n print('Episode:', episode)\n steps, state_action_history = self.run_one_episode()\n self.steps_per_episode.append(steps)\n if episode % parameters.CACHING_INTERVAL == 0 or steps < 1000:\n ... | [
"0.7450819",
"0.7309439",
"0.71770984",
"0.7124558",
"0.7124558",
"0.7051895",
"0.703537",
"0.69566625",
"0.684824",
"0.68205166",
"0.6802857",
"0.68013686",
"0.67991036",
"0.6790752",
"0.675303",
"0.67489773",
"0.67405975",
"0.6739792",
"0.67348915",
"0.6732355",
"0.6706088"... | 0.74749076 | 0 |
This method will return action by epsilon greedy | def get_epsilon_action(epsilon, env, mean_reward_per_bandit):
explore = np.random.uniform() < epsilon
if explore:
return env.action_space.sample()
else:
return np.argmax(mean_reward_per_bandit) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def greedy(self) -> Action:\n return NotImplemented",
"def eps_greedy(Q, epsilon, num_actions):\n if np.random.uniform(0,1,1) > epsilon:\n action = np.argmax(Q)\n else:\n action = np.random.randint(low=0, high=num_actions)\n \n Q_value = Q[action]\n return action, Q_value",
... | [
"0.7672569",
"0.732197",
"0.7154065",
"0.71332085",
"0.70703125",
"0.6964753",
"0.69215",
"0.6916414",
"0.68847233",
"0.68748766",
"0.6836524",
"0.6821661",
"0.677791",
"0.6760553",
"0.6743109",
"0.6672397",
"0.66714406",
"0.65827435",
"0.6575905",
"0.6562029",
"0.65613985",
... | 0.6494198 | 24 |
Main method that is called by the user to calculate the manipulated variable. | def control(self, state, reference):
self.ref[-1] = reference[self.ref_idx] # Set the reference
epsilon_d = state[self.eps_idx] * self.limit[self.eps_idx] + self.dead_time * self.tau * state[self.omega_idx] * \
self.limit[self.omega_idx] * self.mp['p'] # Calculate delta epsilon
# Iterate through high-level controller
if self.omega_control:
for i in range(len(self.overlaid_controller) + 1, 1, -1):
# Calculate reference
self.ref[i] = self.overlaid_controller[i-2].control(state[self.ref_state_idx[i + 1]], self.ref[i + 1])
# Check limits and integrate
if (0.85 * self.state_space.low[self.ref_state_idx[i]] <= self.ref[i] <= 0.85 *
self.state_space.high[self.ref_state_idx[i]]) and self.overlaid_type[i - 2]:
self.overlaid_controller[i - 2].integrate(state[self.ref_state_idx[i + 1]], self.ref[i + 1])
else:
self.ref[i] = np.clip(self.ref[i], self.nominal_values[self.ref_state_idx[i]] / self.limit[
self.ref_state_idx[i]] * self.state_space.low[self.ref_state_idx[i]],
self.nominal_values[self.ref_state_idx[i]] / self.limit[
self.ref_state_idx[i]] * self.state_space.high[self.ref_state_idx[i]])
# Calculate reference values for i_d and i_q
if self.torque_control:
torque = self.ref[2] * self.limit[self.torque_idx]
self.ref[0], self.ref[1] = self.torque_controller.control(state, torque)
# Calculate action for continuous action space
if self.has_cont_action_space:
# Decouple the two current components
if self.decoupling:
self.u_sd_0 = -state[self.omega_idx] * self.mp['p'] * self.mp['l_q'] * state[self.i_sq_idx]\
* self.limit[self.i_sq_idx] / self.limit[self.u_sd_idx] * self.limit[self.omega_idx]
self.u_sq_0 = state[self.omega_idx] * self.mp['p'] * (
state[self.i_sd_idx] * self.mp['l_d'] * self.limit[self.u_sd_idx] + self.psi_p) / self.limit[
self.u_sq_idx] * self.limit[self.omega_idx]
# Calculate action for u_sd
if self.torque_control:
u_sd = self.d_controller.control(state[self.i_sd_idx], self.ref[1]) + self.u_sd_0
else:
u_sd = self.d_controller.control(state[self.i_sd_idx], reference[self.ref_d_idx]) + self.u_sd_0
# Calculate action for u_sq
u_sq = self.q_controller.control(state[self.i_sq_idx], self.ref[0]) + self.u_sq_0
# Shifting the reference potential
action_temp = self.backward_transformation((u_sd, u_sq), epsilon_d)
action_temp = action_temp - 0.5 * (max(action_temp) + min(action_temp))
# Check limit and integrate
action = np.clip(action_temp, self.action_space.low[0], self.action_space.high[0])
if (action == action_temp).all():
if self.torque_control:
self.d_controller.integrate(state[self.i_sd_idx], self.ref[1])
else:
self.d_controller.integrate(state[self.i_sd_idx], reference[self.ref_d_idx])
self.q_controller.integrate(state[self.i_sq_idx], self.ref[0])
# Calculate action for discrete action space
else:
ref = self.ref[1] if self.torque_control else reference[self.ref_d_idx]
ref_abc = self.backward_transformation((ref, self.ref[0]), epsilon_d)
action = 0
for i in range(3):
action += (2 ** (2 - i)) * self.abc_controller[i].control(state[self.i_abc_idx[i]], ref_abc[i])
# Plot overlaid reference values
plot(external_reference_plots=self.external_ref_plots, state_names=self.state_names, external_data=self.get_plot_data(),
visualization=True)
return action | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def cmd_calculation():",
"def execute(self):\n \n self.outvar = self.invar + .01",
"def main():\n user_input_name()\n user_input_age()\n choose_unit()\n user_input_weight()\n user_input_height()\n bmi_calculator()\n bmi_categories()\n restart_calculator()",
"def main():\... | [
"0.6921429",
"0.6770756",
"0.67365",
"0.669798",
"0.6433398",
"0.64304745",
"0.64164007",
"0.6388115",
"0.6387682",
"0.6349823",
"0.6285624",
"0.6284488",
"0.62795794",
"0.6257987",
"0.6253564",
"0.62122047",
"0.61981833",
"0.61678576",
"0.61678576",
"0.6157993",
"0.6157345",... | 0.0 | -1 |
Generate training data by playing games vs self. Gathers experiece tuples over n_episodes and pushes them to agent replay buffer. | def self_play(self, n_episodes):
eps = self.eps(self.agent.learning_iters)
experiences = self_play_episodes(self.mdp, self.agent, n_episodes, eps)
for state, action, reward, next_state, done in experiences:
self.agent.replay_buffer.push(state, action, reward, next_state, done) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_data(self, episodes, agents, batch_index):\n\n\t\tstate_dict = {}\n\t\talpha = 0.95\n\n\t\t# iterate over episodes\n\t\tfor e in range(episodes):\n\t\n\t\t\t# choose 2 random agents to play\n\t\t\tperm = np.random.permutation(len(agents))\n\n\t\t\tagent1 = agents[perm[0]]\n\t\t\tagent2 = agents[perm[1]]... | [
"0.7142396",
"0.7135517",
"0.68600905",
"0.6848414",
"0.683487",
"0.68314207",
"0.68152994",
"0.68056613",
"0.6796379",
"0.6748984",
"0.67470056",
"0.671317",
"0.6709892",
"0.6708906",
"0.66968066",
"0.6685076",
"0.66660935",
"0.66479623",
"0.6647209",
"0.6645968",
"0.6634925... | 0.68460876 | 4 |
Update model with random batch from agent replay buffer. | def learn(self):
batch = self.agent.replay_buffer.sample(self.batch_size)
states = torch.tensor([x.state for x in batch], dtype=torch.float32).to(self.agent.device) # shape == (batch_size, 3, 6, 7)
actions = [x.action for x in batch]
rewards = torch.tensor([x.reward for x in batch], dtype=torch.float32).to(self.agent.device)
next_states = torch.tensor([x.next_state for x in batch], dtype=torch.float32).to(self.agent.device)
dones = [x.done for x in batch]
self.optimizer.zero_grad()
q_vals = self.agent.policy_net(states)[range(len(actions)), actions] # Q vals for actions taken
q_next_vals = self.agent.target_net(next_states).detach() # we don't care about grad wrt target net
q_next_vals[dones] = 0.0 # terminal states have no future expected value
q_targets = rewards + self.gamma * torch.max(q_next_vals, dim=1)[0]
# all_q_vals = self.agent.policy_net(states)
# print()
# print('actions')
# print(actions)
# print()
# print('original all q vals')
# print(self.agent.policy_net(states))
# print(self.agent.policy_net(states).shape)
# print()
# print('QVALS:', q_vals)
# print(q_vals.shape)
# print('\n\n')
# print('QTARGETS:', q_targets)
# print(q_targets.shape)
# breakpoint()
loss = self.loss_fn(q_targets, q_vals).to(self.agent.device)
loss.backward()
# for layer in self.agent.policy_net.named_parameters():
# # print(f'layer: {layer[0]}')
# # print(f'grad:', layer[1].grad)
# # print('loss', loss)
# # print('q_vals grad:', q_vals.grad)
# # print('states:', )
self.optimizer.step()
self.agent.learning_iters += 1
if self.agent.learning_iters % self.target_update_freq == 0:
self.agent.update_target_net()
# logger.info('Updated target net') | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def replay(self):\n # Start only have enough memories\n if len(self.memory) < self.train_start:\n return\n\n batch_size = min(self.batch_size, len(self.memory))\n\n # Use mini_batch, sampling form the memory\n mini_batch = random.sample(self.memory, batch_size)\n\n ... | [
"0.6248699",
"0.6233435",
"0.6136998",
"0.6025851",
"0.6016958",
"0.596585",
"0.5935107",
"0.5765406",
"0.5739933",
"0.57378775",
"0.5711635",
"0.5678842",
"0.5633065",
"0.5633065",
"0.55791724",
"0.55476403",
"0.55153525",
"0.54968476",
"0.54914474",
"0.54901624",
"0.5484997... | 0.0 | -1 |
Train agent over given number of iterations. Each iteration consists of self play over n_episodes and then a learn step where agent updates network based on random sample from replay buffer | def train(self, iters, n_episodes):
for i in range(iters):
self.self_play(n_episodes)
self.learn() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def learn(self, num_episodes=10000):\n for i in range(num_episodes):\n self.actor()\n self.learner()",
"def trainAgent(self):\r\n\t\tfor episode in range(self.TOT_EPISODES):\r\n\t\t\t#reset environment, stacked frames every episode.\r\n\t\t\tstate = self.env.reset()\r\n\t\t\trewards ... | [
"0.7785768",
"0.76625186",
"0.76138806",
"0.7555034",
"0.7430718",
"0.7399803",
"0.73953015",
"0.73881173",
"0.7361592",
"0.72675776",
"0.72441524",
"0.7235535",
"0.72085416",
"0.71934515",
"0.71493644",
"0.71422166",
"0.7102493",
"0.7057307",
"0.70422393",
"0.70264834",
"0.7... | 0.81745845 | 0 |
generate pair message between node Hv and Hw. since the cat operation, msgs from hv > hw and hw > hv are different | def __init__(self, dim_hv, dim_hw, msg_dim):
super(PairMessageGenerator, self).__init__()
self.dim_hv, self.dim_hw, self.msg_dim = dim_hv, dim_hw, msg_dim
self.in_dim = dim_hv + dim_hw # row * feature_dim, 2048
self.mlp = nn.Sequential(
nn.LayerNorm(self.in_dim), # this layer norm is important to create diversity
nn.Linear(self.in_dim, self.msg_dim),
nn.LeakyReLU(0.2)
) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_HHH(self, msg):\n self.prev_head = self.head\n self.head = msg.headx, msg.heady, msg.headz\n # self.torso = msg.torsox, msg.torsoy, msg.torsoz\n # self.Rhand = msg.Rhandx, msg.Rhandy, msg.Rhandz\n # self.Lhand = msg.Lhandx, msg.Lhandy, msg.Lhandz\n\n # if the dista... | [
"0.54044586",
"0.5386834",
"0.5290445",
"0.51632214",
"0.51632214",
"0.51127476",
"0.51023054",
"0.50814384",
"0.50715846",
"0.50035816",
"0.499038",
"0.4964276",
"0.4964276",
"0.4964276",
"0.49613354",
"0.49603233",
"0.49329245",
"0.48807725",
"0.48755825",
"0.48755825",
"0.... | 0.58693963 | 0 |
for n graph instances given edge index number this connected_msgs_list contains all connnected edges msg, say m connected edges | def aggregate_msgs(self, connected_msgs_list):
msg_num = len(connected_msgs_list)
agg_msg = connected_msgs_list[0]
for i in range(1, msg_num):
agg_msg += connected_msgs_list[i]
if self.msg_aggrgt == 'AVG':
return agg_msg / msg_num
elif self.msg_aggrgt == 'SUM':
return agg_msg | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def collect_messages(j, i):\n j_neighbors_except_i = [k for k in edges[j] if k != i]\n \n for k in j_neighbors_except_i: # No effect when j_neighbors_except_i is empty []\n collect_messages(k, j)\n send_message(j, i, j_neighbors_except_i)",
"def distribute_messages(i, j):... | [
"0.66461325",
"0.6315994",
"0.5970758",
"0.594913",
"0.57439035",
"0.57150644",
"0.5673093",
"0.56705296",
"0.56621045",
"0.56380904",
"0.5598652",
"0.55890334",
"0.55843794",
"0.5572508",
"0.55645573",
"0.5527443",
"0.5512135",
"0.54903924",
"0.54806775",
"0.5466238",
"0.545... | 0.0 | -1 |
deep geometric feature set as state representation | def __init__(self, _conv_encoder=None, _encoder_out_channels=32, _vi_key_pointer=None, key_point_num=20, _debug_tool=None, _debug_frequency=None, _vi_mode=True):
super(M6EKViEncoder, self).__init__()
self.encoder = _conv_encoder
self.k_heatmaps_layer = nn.Sequential(
nn.Conv2d(_encoder_out_channels, key_point_num, kernel_size=(1, 1), stride=(1, 1)),
nn.BatchNorm2d(key_point_num),
nn.ReLU())
self.vi_key_pointer = _vi_key_pointer
self.debug_tool = _debug_tool
self.debug_frequency = _debug_frequency
self.it_count = 0
self.vi_mode = _vi_mode | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getStates(self):\n feature_states = []\n for i, sim in enumerate(self.sims):\n state = sim.getState()\n features = []\n for j in range(36):\n long_id = self._make_id(state.scanId, state.location.viewpointId,str(j+1))\n feature = self.... | [
"0.63558125",
"0.6102643",
"0.606456",
"0.6011864",
"0.5996851",
"0.59706515",
"0.5943621",
"0.59378874",
"0.5885338",
"0.5885338",
"0.5870441",
"0.5870441",
"0.5846847",
"0.5846655",
"0.57815313",
"0.5768379",
"0.5745745",
"0.5655412",
"0.5653317",
"0.56334156",
"0.5630338",... | 0.0 | -1 |
Note that for training, you can keep training the model after it's already been trained because it automatically remembers the last error weights. So self.train(n=2) is equivalent to self.train(n=1); self.train(n=1) | def train(self, data, data_class, n=1):
# Initialize the sample weights
for _ in range(n):
# Train the weak estimator
new_estimator = BinaryDecisionTree(self.max_depth)
new_estimator.train(data, data_class, self.error_weights)
y_pred, _ = new_estimator.validate(data, data_class)
# Check misclassifications and update the error weights accordingly
incorrect = y_pred != data_class
error = self.error_weights[incorrect].sum()
alpha = 0.5 * np.log((1.0 - error) / error)
sign = incorrect * 2 - 1 # Maps incorrect to 1, correct to -1
self.error_weights *= np.e ** (sign * alpha)
self.error_weights /= self.error_weights.sum()
# Save down alpha and estimator for prediction
self.alphas.append(alpha)
self.estimators.append(new_estimator) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def train_one_epoch(self):\n raise NotImplementedError",
"def train(self, training_steps=10):",
"def TrainOneStep(self):\n pass",
"def train(self, ):\n raise NotImplementedError",
"def train(self):\n raise NotImplementedError",
"def train2(self):\n for epoch in range(se... | [
"0.7506583",
"0.73702186",
"0.7218326",
"0.71550184",
"0.7129135",
"0.7113297",
"0.7095796",
"0.7077056",
"0.7067491",
"0.70636654",
"0.70636654",
"0.70636654",
"0.70636654",
"0.70636654",
"0.70617354",
"0.7049623",
"0.703932",
"0.70169145",
"0.70089275",
"0.69914174",
"0.696... | 0.0 | -1 |
Fastest way to just consume an iterator. | def consume(iterator):
deque(iterator, maxlen=0) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def consume(iterator, n=None):\n # Use functions that consume iterators at C speed.\n if n is None:\n # feed the entire iterator into a zero-length deque\n collections.deque(iterator, maxlen=0)\n else:\n # advance to the empty slice starting at position n\n next(islice(iterator... | [
"0.72329783",
"0.71654475",
"0.67598706",
"0.606488",
"0.60311234",
"0.60123855",
"0.5925557",
"0.592464",
"0.5869508",
"0.5843964",
"0.5843164",
"0.58100826",
"0.57714164",
"0.5746189",
"0.5739034",
"0.5733739",
"0.57284236",
"0.5727265",
"0.5726913",
"0.5722843",
"0.5719147... | 0.7133738 | 2 |
Create an instance of this store with a loader object. | def __init__(self, loader):
self.loader = loader
self.models = [] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self, loader, *args, **kw):\n self._loader = loader",
"def create(self):\n\t\tif self.isInitialized():\n\t\t\tself.Loaded = self.loader.create()",
"def __init__(self, loader, *args, **kw):\r\n self._loader = loader",
"def create_loader(self, *args, **kwargs):\n def loader():... | [
"0.7120568",
"0.71177053",
"0.70920056",
"0.6825259",
"0.65534455",
"0.6519639",
"0.6312017",
"0.6218765",
"0.60434765",
"0.58976614",
"0.5878757",
"0.5864052",
"0.5856109",
"0.58553034",
"0.57751334",
"0.5761484",
"0.57599795",
"0.5753757",
"0.5739945",
"0.57246524",
"0.5698... | 0.6796559 | 4 |
Import all model data using the loader. | def import_data(self):
self.models = []
for o in self.loader.load():
klass = self.type_for(o)
if hasattr(klass, "from_api"):
self.models.append(klass.from_api(o))
else:
self.models.append(klass(o))
return self.models | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def import_all():\n\n # count the number of files loaded\n count = 0\n\n # get model name\n model_name_list = [model for data_models in settings.OBJECT_DATA_MODELS\n for model in data_models]\n\n model_name_list += [model for model in settings.OTHER_DATA_MODELS]\n\n # import... | [
"0.7716706",
"0.6954782",
"0.6742698",
"0.67201203",
"0.6513403",
"0.64677274",
"0.64014745",
"0.6369573",
"0.6368797",
"0.63448185",
"0.633244",
"0.6321181",
"0.62659895",
"0.6258032",
"0.6243625",
"0.6167777",
"0.6158548",
"0.6115253",
"0.608663",
"0.608182",
"0.6074489",
... | 0.7674908 | 1 |
Return the class type to be used for a given event name. | def type_for(data):
switcher = {
# Startup
"FileHeader": models.FileHeader,
"ClearSavedGame": models.ClearSavedGame,
"NewCommander": models.NewCommander,
"LoadGame": models.LoadGame,
"Progress": models.Progress,
"Rank": models.Rank,
# Travel
"Docked": models.Docked,
"DockingCancelled": models.DockingCancelled,
"DockingDenied": models.DockingDenied,
"DockingGranted": models.DockingGranted,
"DockingRequested": models.DockingRequested,
"DockingTimeout": models.DockingTimeout,
"FSDJump": models.FSDJump,
"Liftoff": models.Liftoff,
"Location": models.Location,
"SupercruiseEntry": models.SupercruiseEntry,
"SupercruiseExit": models.SupercruiseExit,
"Touchdown": models.Touchdown,
"Undocked": models.Undocked,
# Combat
"Bounty": models.Bounty,
"CapShipBond": models.CapShipBond,
"Died": models.Died,
"EscapeInterdiction": models.EscapeInterdiction,
"FactionKillBond": models.FactionKillBond,
"HeatDamage": models.HeatDamage,
"HeatWarning": models.HeatWarning,
"HullDamage": models.HullDamage,
"Interdicted": models.Interdicted,
"Interdiction": models.Interdiction,
"PVPKill": models.PVPKill,
"ShieldState": models.ShieldState,
# Exploration
"Scan": models.Scan,
"MaterialCollected": models.MaterialCollected,
"MaterialDiscarded": models.MaterialDiscarded,
"MaterialDiscovered": models.MaterialDiscovered,
"BuyExplorationData": models.BuyExplorationData,
"SellExplorationData": models.SellExplorationData,
"Screenshot": models.Screenshot,
# Trade
"BuyTradeData": models.BuyTradeData,
"CollectCargo": models.CollectCargo,
"EjectCargo": models.EjectCargo,
"MarketBuy": models.MarketBuy,
"MarketSell": models.MarketSell,
"MiningRefined": models.MiningRefined,
# Station Services
"BuyAmmo": models.BuyAmmo,
"BuyDrones": models.BuyDrones,
"CommunityGoalDiscard": models.CommunityGoalDiscard,
"CommunityGoalJoin": models.CommunityGoalJoin,
"CommunityGoalReward": models.CommunityGoalReward,
"CrewAssign": models.CrewAssign,
"CrewFire": models.CrewFire,
"CrewHire": models.CrewHire,
"EngineerApply": models.EngineerApply,
"EngineerCraft": models.EngineerCraft,
"EngineerProgress": models.EngineerProgress,
"FetchRemoteModule": models.FetchRemoteModule,
"MassModuleStore": models.MassModuleStore,
"MissionAbandoned": models.MissionAbandoned,
"MissionAccepted": models.MissionAccepted,
"MissionCompleted": models.MissionCompleted,
"MissionFailed": models.MissionFailed,
"ModuleBuy": models.ModuleBuy,
"ModuleRetrieve": models.ModuleRetrieve,
"ModuleSell": models.ModuleSell,
"ModuleSellRemote": models.ModuleSellRemote,
"ModuleStore": models.ModuleStore,
"ModuleSwap": models.ModuleSwap,
"PayFines": models.PayFines,
"PayLegacyFines": models.PayLegacyFines,
"RedeemVoucher": models.RedeemVoucher,
"RefuelAll": models.RefuelAll,
"RefuelPartial": models.RefuelPartial,
"Repair": models.Repair,
"RepairAll": models.RepairAll,
"RestockVehicle": models.RestockVehicle,
"ScientificResearch": models.ScientificResearch,
"SellDrones": models.SellDrones,
"ShipyardBuy": models.ShipyardBuy,
"ShipyardNew": models.ShipyardNew,
"ShipyardSell": models.ShipyardSell,
"ShipyardTransfer": models.ShipyardTransfer,
"ShipyardSwap": models.ShipyardSwap,
# Powerplay
"PowerplayCollect": models.PowerplayCollect,
"PowerplayDefect": models.PowerplayDefect,
"PowerplayDeliver": models.PowerplayDeliver,
"PowerplayFastTrack": models.PowerplayFastTrack,
"PowerplayJoin": models.PowerplayJoin,
"PowerplayLeave": models.PowerplayLeave,
"PowerplaySalary": models.PowerplaySalary,
"PowerplayVote": models.PowerplayVote,
"PowerplayVoucher": models.PowerplayVoucher,
# Other Events
"ApproachSettlement": models.ApproachSettlement,
"CockpitBreached": models.CockpitBreached,
"CommitCrime": models.CommitCrime,
"Continued": models.Continued,
"DatalinkScan": models.DatalinkScan,
"DatalinkVoucher": models.DatalinkVoucher,
"DataScanned": models.DataScanned,
"DockFighter": models.DockFighter,
"DockSRV": models.DockSRV,
"FuelScoop": models.FuelScoop,
"JetConeBoost": models.JetConeBoost,
"JetConeDamage": models.JetConeDamage,
"LaunchFighter": models.LaunchFighter,
"LaunchSRV": models.LaunchSRV,
"Promotion": models.Promotion,
"RebootRepair": models.RebootRepair,
"ReceiveText": models.ReceiveText,
"Resurrect": models.Resurrect,
"SelfDestruct": models.SelfDestruct,
"SendText": models.SendText,
"Synthesis": models.Synthesis,
"USSDrop": models.USSDrop,
"VehicleSwitch": models.VehicleSwitch,
"WingAdd": models.WingAdd,
"WingJoin": models.WingJoin,
"WingLeave": models.WingLeave,
}
return switcher.get(data["event"], models.BaseModel) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_event_class_by_type(type):\n event_module = importlib.import_module('.'.join(type.split('.')[:-1]))\n return getattr(event_module, type.split('.')[-1])",
"def _get_event_type(event):\n return event.type()",
"def _get_event_type(event):\n return event.type()",
"def class_name(name:... | [
"0.7802693",
"0.70439726",
"0.70439726",
"0.6722869",
"0.6596402",
"0.65878123",
"0.65594405",
"0.65270084",
"0.645151",
"0.64356005",
"0.64065075",
"0.6406168",
"0.6361024",
"0.63387644",
"0.6334703",
"0.62997335",
"0.6297802",
"0.6282378",
"0.625796",
"0.6205886",
"0.618207... | 0.0 | -1 |
Force cancellations when owners of predictions are bankrupt | def admin_bankruptcy(self, with_report=False):
name = self.client.srandmember(self._NAMES)
self.admin_bankruptcy_forced_cancellation(name=name, with_report=with_report)
self.admin_bankruptcy_notification(name=name) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def cancel_dummy(self):\n if self.state != 'authorized':\n self.raise_user_error('cancel_only_authorized')\n else:\n self.state = 'cancel'\n self.save()",
"def cancel():",
"def cancel(self):",
"def cancel(self):",
"def cancel(self):",
"def do_uncancel(self):... | [
"0.6585705",
"0.6542071",
"0.64821595",
"0.64821595",
"0.64821595",
"0.6472716",
"0.6299607",
"0.6293841",
"0.6284326",
"0.6247299",
"0.6247299",
"0.6156231",
"0.6093092",
"0.6050534",
"0.60215384",
"0.59936893",
"0.59936893",
"0.59936893",
"0.59936893",
"0.59633005",
"0.5921... | 0.0 | -1 |
Notify folks and set attribute so it only happens once | def admin_bankruptcy_notification(self, name:str):
write_keys = list()
for delay in self.DELAYS:
write_keys.extend( self._get_sample_owners(name=name, delay=delay) )
for write_key in write_keys:
if self.bankrupt(write_key):
notified = self.get_attribute(attribute_type=AttributeType.update,
granularity=AttributeGranularity.write_key,
write_key=write_key) or False
if not notified:
self.set_attribute(attribute_type=AttributeType.update,
granularity=AttributeGranularity.write_key,
write_key=write_key, value=1)
message = 'Your write_key is bankrupt. Consider topping it up with put_balance()'
self.add_owner_alert_message(write_key=write_key, message=message) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __setattr__(self, name, value):\n super(Message, self).__setattr__(name, value)\n if name not in ('bcc', '_dirty', '_processed'): \n self.__dict__['_dirty'] = True",
"def dummy_update( self ):\r\n pass",
"def _set_silent(self, model_instance, value):\n setattr(model_i... | [
"0.6458744",
"0.6421269",
"0.63834894",
"0.627908",
"0.627908",
"0.627908",
"0.627908",
"0.627908",
"0.627908",
"0.627908",
"0.627908",
"0.627908",
"0.627908",
"0.627908",
"0.627908",
"0.627908",
"0.627908",
"0.627908",
"0.627908",
"0.627908",
"0.6269448",
"0.62105566",
"... | 0.0 | -1 |
Force cancellation of submissions for poorly performing bankrupt algorithms | def admin_bankruptcy_forced_cancellation(self, name, with_report):
discards = list()
for delay in self.DELAYS:
write_keys = self._get_sample_owners(name=name, delay=delay)
leaderboard = self.get_leaderboard(granularity=LeaderboardGranularity.name_and_delay, count=10000,
name=name, delay=delay, readable=False)
losers = [key for key in write_keys if
(self.shash(key) in leaderboard) and (leaderboard[self.shash(key)] < -1.0)]
for write_key in losers:
if self.bankrupt(write_key=write_key):
code = self.shash(write_key)
memo = Memo(activity=Activity.cancel, context=ActivityContext.bankruptcy, name=name, code=code,
write_key=write_key, message='Initiating cancellation of submissions due to bankruptcy')
self.add_memo_as_owner_confirm(memo=memo)
self.cancel(name=name, write_key=write_key, delay=delay)
discards.append((name, write_key))
report_data = dict(discards)
report_memo = Memo(activity=Activity.daemon, context=ActivityContext.bankruptcy, count=len(report_data), data=report_data)
self.add_memo_as_system_confirm(memo=report_memo,private_actor=PrivateActor.bankruptcy_daemon)
return len(discards) if not with_report else report_memo.as_dict() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def cancel():",
"def cancel(self):",
"def cancel(self):",
"def cancel(self):",
"def cancel(self):\n pass",
"def cancel_workers(self):\n pass",
"def do_uncancel(self):\r\n self.write({'cancelled': False})",
"def kill_pending(self):\n for req in self._outbox:\n if... | [
"0.7574049",
"0.72383225",
"0.72383225",
"0.72383225",
"0.7049807",
"0.6872498",
"0.6760143",
"0.6673882",
"0.660217",
"0.660217",
"0.659783",
"0.659783",
"0.659783",
"0.659783",
"0.64751947",
"0.6469023",
"0.6427791",
"0.6421745",
"0.6420724",
"0.64180344",
"0.6395012",
"0... | 0.64316154 | 16 |
Test running artifact rejection. | def test_temporal_derivative_distribution_repair(fname, tmp_path):
raw = read_raw_nirx(fname)
raw_od = optical_density(raw)
raw_hb = beer_lambert_law(raw_od)
# With optical densities
# Add a baseline shift artifact about half way through data
max_shift = np.max(np.diff(raw_od._data[0]))
shift_amp = 5 * max_shift
raw_od._data[0, 0:30] = raw_od._data[0, 0:30] - shift_amp
# make one channel zero std
raw_od._data[1] = 0.0
raw_od._data[2] = 1.0
assert np.max(np.diff(raw_od._data[0])) > shift_amp
# Ensure that applying the algorithm reduces the step change
raw_od = tddr(raw_od)
assert np.max(np.diff(raw_od._data[0])) < shift_amp
assert_allclose(raw_od._data[1], 0.0) # unchanged
assert_allclose(raw_od._data[2], 1.0) # unchanged
# With Hb
# Add a baseline shift artifact about half way through data
max_shift = np.max(np.diff(raw_hb._data[0]))
shift_amp = 5 * max_shift
raw_hb._data[0, 0:30] = raw_hb._data[0, 0:30] - (1.1 * shift_amp)
# make one channel zero std
raw_hb._data[1] = 0.0
raw_hb._data[2] = 1.0
assert np.max(np.diff(raw_hb._data[0])) > shift_amp
# Ensure that applying the algorithm reduces the step change
raw_hb = tddr(raw_hb)
assert np.max(np.diff(raw_hb._data[0])) < shift_amp
assert_allclose(raw_hb._data[1], 0.0) # unchanged
assert_allclose(raw_hb._data[2], 1.0) # unchanged | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_itar_restrict_software_asset(self):\n pass",
"def test_reject_proposal_demand(self):\n pass",
"async def test_finish_release_no_release(doof, repo_info, event_loop, mocker):\n get_release_pr_mock = mocker.patch('bot.get_release_pr', autospec=True, return_value=None)\n with pytest.r... | [
"0.6357305",
"0.6324513",
"0.6190728",
"0.6092561",
"0.5969728",
"0.5934762",
"0.5933897",
"0.59015524",
"0.58819264",
"0.5881338",
"0.58585626",
"0.5845605",
"0.5825424",
"0.577213",
"0.5720475",
"0.5704311",
"0.56921726",
"0.56621385",
"0.56546986",
"0.56332654",
"0.5630342... | 0.0 | -1 |
Convert from camera_frame to world_frame | def cam_to_world(cam_point, world_to_cam):
# cam_point = np.array([cam_pose[0], cam_pose[1], cam_pose[2]])
obj_vector = np.concatenate((cam_point, np.ones(1))).reshape((4, 1))
world_point = np.dot(world_to_cam, obj_vector)
world_point = [p[0] for p in world_point]
return world_point[0:3] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def camera_to_world(self, X):\n raise NotImplementedError",
"def world_to_camera(self, X):\n raise NotImplementedError",
"def cameraToWorld(self, p):\n result = self.camPos\n result += p[2] * self.camZ # result is now in the middle of the view-plane\n result += p[0] * sel... | [
"0.77324754",
"0.7427758",
"0.6999884",
"0.6942515",
"0.68882954",
"0.630541",
"0.62254226",
"0.6142121",
"0.6108579",
"0.60515493",
"0.6012601",
"0.6009316",
"0.60076267",
"0.5937143",
"0.5847855",
"0.58359575",
"0.58199376",
"0.57920545",
"0.57797766",
"0.5773777",
"0.57097... | 0.7209461 | 2 |
Test GenBank parsing invalid product line raises ValueError | def test_invalid_product_line_raises_value_error(self):
def parse_invalid_product_line():
rec = SeqIO.read(path.join('GenBank', 'invalid_product.gb'),
'genbank')
self.assertRaises(ValueError, parse_invalid_product_line) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def testBadLine(self):\n\n self.assertRaises(\n ValueError,\n tools._trackInfo,\n 'not a real line'\n )",
"def test_readbadformat(self):\n\n self.assertRaises(ParseError, self.hw, self.badfile)",
"def test_invalid_regref(self, parse_input_mocked_metadata):\... | [
"0.6412361",
"0.63490635",
"0.6333902",
"0.6294638",
"0.62427443",
"0.6210081",
"0.6208923",
"0.6203845",
"0.6167732",
"0.61114794",
"0.6096026",
"0.60889727",
"0.6073056",
"0.60688126",
"0.6063661",
"0.60588723",
"0.6050436",
"0.6005442",
"0.5976354",
"0.5972584",
"0.5971466... | 0.8058622 | 0 |
Publish apps to the 21 Marketplace. \b Usage _____ Publish your app to the 21 Marketplace. $ 21 publish submit path_to_manifest/manifest.yaml To update your published listing, run the above command after modifying your manifest.yaml. \b Publish your app to the 21 Marketplace without strict checking of the manifest against your current IP. $ 21 publish submit s path_to_manifest/manifest.yaml \b See the help for submit. $ 21 publish submit help \b View all of your published apps. $ 21 publish list \b See the help for list. $ 21 publish list help \b Remove one of your published apps from the marketplace. $ 21 publish remove {app_id} \b See the help for remove. $ 21 publish remove help | def publish():
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _publish(client, manifest_path, marketplace, skip, overrides):\n try:\n manifest_json = check_app_manifest(manifest_path, overrides, marketplace)\n app_url = \"{}://{}\".format(manifest_json[\"schemes\"][0], manifest_json[\"host\"])\n app_ip = urlparse(app_url).hostname\n\n if no... | [
"0.70976746",
"0.6921972",
"0.6737742",
"0.61851317",
"0.60648894",
"0.5921255",
"0.59202874",
"0.57908696",
"0.5747466",
"0.5709409",
"0.56947315",
"0.5662847",
"0.5600021",
"0.55838025",
"0.55629",
"0.5550522",
"0.55285674",
"0.55028474",
"0.54907167",
"0.54901814",
"0.5488... | 0.6171967 | 4 |
\b Lists all your published apps. $ 21 publish list Results from the list command are paginated. Use 'n' to move to the next page and 'p' to move to the previous page. You can view detailed admin information about an app by specifying it's id at the prompt. | def list(ctx):
# pylint: disable=redefined-builtin
_list_apps(ctx.obj['config'], ctx.obj['client']) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _list_apps(config, client):\n logger.info(\"Listing all the published apps by {}: \".format(config.username), fg=\"green\")\n current_page = 0\n total_pages = get_search_results(config, client, current_page)\n if total_pages < 1:\n return\n\n while 0 <= current_page < total_pages:\n ... | [
"0.71768415",
"0.6380013",
"0.6237213",
"0.61973405",
"0.6124043",
"0.60990864",
"0.60854894",
"0.5944503",
"0.5898133",
"0.58913547",
"0.5877315",
"0.5836518",
"0.5767014",
"0.5754838",
"0.5688991",
"0.56420964",
"0.56412876",
"0.55870926",
"0.5555698",
"0.5543384",
"0.55005... | 0.64636713 | 1 |
\b Removes a published app from the Marketplace. $ 21 publish remove [yes] {app_id} \b Removes all published apps from the Marketplace. $ 21 publish remove [yes] all \b | def remove(ctx, app_id, all, assume_yes):
if all and not app_id:
for _app_id in _get_all_app_ids(ctx.obj['config'], ctx.obj['client']):
_delete_app(ctx.obj['config'], ctx.obj['client'], _app_id, assume_yes)
elif app_id and not all:
_delete_app(ctx.obj['config'], ctx.obj['client'], app_id, assume_yes)
else:
logger.info(ctx.command.get_help(ctx))
sys.exit(1) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def remove():\n run('pew rm {0}'.format(package_name()))",
"def app_delete(self, name):\n self.core.api.os.shell.cmd('{0} delete app /app.name:\"{1}\"'.format(self.APP_CMD, name))",
"def remove_app(self):\n \n pass",
"def delete_app(AppId=None):\n pass",
"def delete_app(short_na... | [
"0.6542366",
"0.63983655",
"0.635603",
"0.62508076",
"0.6105173",
"0.60880226",
"0.5995787",
"0.59228504",
"0.5885536",
"0.58168525",
"0.57712317",
"0.5742939",
"0.56851757",
"0.56586546",
"0.5657139",
"0.5622254",
"0.5595414",
"0.5593344",
"0.55384624",
"0.55296105",
"0.5524... | 0.67598 | 0 |
\b Publishes an app to 21 Marketplace. $ 21 publish submit path_to_manifest/manifest.yaml The contents of the manifest file should follow the guidelines specified at | def submit(ctx, manifest_path, marketplace, skip, parameters):
if parameters is not None:
try:
parameters = _parse_parameters(parameters)
except:
logger.error(
"Manifest parameter overrides should be in the form 'key1=\"value1\" "
"key2=\"value2\".",
fg="red")
return
_publish(ctx.obj['client'], manifest_path, marketplace, skip, parameters) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _publish(client, manifest_path, marketplace, skip, overrides):\n try:\n manifest_json = check_app_manifest(manifest_path, overrides, marketplace)\n app_url = \"{}://{}\".format(manifest_json[\"schemes\"][0], manifest_json[\"host\"])\n app_ip = urlparse(app_url).hostname\n\n if no... | [
"0.7300059",
"0.67660546",
"0.6331592",
"0.6170584",
"0.60555214",
"0.6053848",
"0.5974832",
"0.58902156",
"0.5870017",
"0.58022076",
"0.56858075",
"0.56083417",
"0.5599532",
"0.5585206",
"0.5573604",
"0.5570114",
"0.5532284",
"0.5491696",
"0.5456021",
"0.5449518",
"0.5443404... | 0.69711465 | 1 |
Parses parameters string and returns a dict of overrides. This function assumes that parameters string is in the form of '"key1="value1" key2="value2"'. Use of single quotes is optional but is helpful for strings that contain spaces. | def _parse_parameters(parameters):
if not re.match(r'^(\w+)="([^=]+)"(\s{1}(\w+)="([^=]+)")*$', parameters):
raise ValueError
# first we add tokens that separate key/value pairs.
# in case of key='ss sss ss', we skip tokenizing when we se the first single quote
# and resume when we see the second
replace_space = True
tokenized = ""
for c in parameters:
if c == '\"':
replace_space = not replace_space
elif c == ' ' and replace_space:
tokenized += "$$"
else:
tokenized += c
# now get the tokens
tokens = tokenized.split('$$')
result = {}
for token in tokens:
# separate key/values
key_value = token.split("=")
result[key_value[0]] = key_value[1]
return result | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _parse_params( self ):\n paramDic={}\n # Parameters are on the 3rd arg passed to the script\n paramStr=sys.argv[2]\n print paramStr\n if len(paramStr)>1:\n paramStr = paramStr.replace('?','')\n \n # Ignore last char if it is a '/'\n ... | [
"0.65870714",
"0.65518653",
"0.65437955",
"0.65169543",
"0.63385206",
"0.6322526",
"0.63106567",
"0.6298732",
"0.6283422",
"0.6264256",
"0.62140757",
"0.6202048",
"0.6163683",
"0.60954064",
"0.6093808",
"0.6043404",
"0.60339767",
"0.6015657",
"0.5971249",
"0.5954985",
"0.5940... | 0.74429375 | 0 |
Lists all apps that have been published to the 21 marketplace | def _list_apps(config, client):
logger.info("Listing all the published apps by {}: ".format(config.username), fg="green")
current_page = 0
total_pages = get_search_results(config, client, current_page)
if total_pages < 1:
return
while 0 <= current_page < total_pages:
try:
prompt_resp = click.prompt(uxstring.UxString.pagination,
type=str)
next_page = get_next_page(prompt_resp, current_page)
if next_page == -1:
model_id = prompt_resp
display_app_info(config, client, model_id)
elif next_page >= total_pages or next_page < 0:
continue
else:
get_search_results(config, client, next_page)
current_page = next_page
except click.exceptions.Abort:
return | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_app_list(self):\n return self.get_setting('applications', 'installed_apps')",
"def list_apps(self) -> list:\n apps = self.app.list_apps()\n app_list = [app[\"title\"] for app in apps]\n return app_list",
"def ListApps(self, request, context):\n context.code(beta_interfa... | [
"0.70584273",
"0.69872224",
"0.69679195",
"0.69105625",
"0.66437083",
"0.66229135",
"0.657946",
"0.64917386",
"0.6440265",
"0.63986486",
"0.6345584",
"0.63214445",
"0.6319517",
"0.63044786",
"0.6269743",
"0.6268732",
"0.6233685",
"0.61907285",
"0.6159065",
"0.6152657",
"0.608... | 0.6918819 | 3 |
Gets all apps published by the current user to the 21 marketplace | def _get_all_app_ids(config, client):
rv = set()
total_pages = client.get_published_apps(config.username, 0).json()["total_pages"]
for current_page in range(total_pages):
current_page_results = client.get_published_apps(config.username, current_page).json()['results']
for result in current_page_results:
rv.add(result['id'])
return rv | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_all_apps(self):\n return list(self.apps.values())",
"async def get_apps(self, params: Optional = None) -> dict:\r\n return await self.get_items(API_APPS, params=params)",
"def get_app_list(self):\n return self.get_setting('applications', 'installed_apps')",
"def get_apps(self):\n... | [
"0.71890974",
"0.71757436",
"0.70074826",
"0.68211126",
"0.6804829",
"0.6794968",
"0.67782676",
"0.6769873",
"0.67512155",
"0.6689302",
"0.66789335",
"0.66459394",
"0.6527662",
"0.64864933",
"0.6406882",
"0.63923347",
"0.6377203",
"0.63365346",
"0.63237804",
"0.62257147",
"0.... | 0.5995079 | 28 |
Deletes an app that has been published to the 21 marketplace | def _delete_app(config, client, app_id, assume_yes):
title = client.get_app_full_info(config.username, app_id).json()['app_info']['title']
if assume_yes or click.confirm(
"Are you sure that you want to delete App '{} ({})'?".format(app_id, title)):
try:
resp = client.delete_app(config.username, app_id)
resp_json = resp.json()
deleted_title = resp_json["deleted_title"]
logger.info("App {} ({}) was successfully removed from the marketplace.".format(app_id, deleted_title))
except ServerRequestError as e:
if e.status_code == 404:
logger.info("The app with id '{}' does not exist in the marketplace.".format(app_id), fg="red")
elif e.status_code == 403:
logger.info(
"You don't have permission to delete the app with id '{}'. You "
"can only delete apps that you have published.".format(app_id), fg="red") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def delete_app(AppId=None):\n pass",
"def app_delete(self, name):\n self.core.api.os.shell.cmd('{0} delete app /app.name:\"{1}\"'.format(self.APP_CMD, name))",
"def delete_app(self, name):\n raise NotImplementedError",
"def delete_app(self):\n contract = jc.Contract()\n return... | [
"0.77574676",
"0.73077077",
"0.7193325",
"0.6995305",
"0.6962425",
"0.69525915",
"0.69459826",
"0.68724674",
"0.6756195",
"0.6666602",
"0.6640245",
"0.6541444",
"0.65118265",
"0.64288366",
"0.64217",
"0.63251245",
"0.6289479",
"0.62437224",
"0.62267774",
"0.62075216",
"0.6170... | 0.73056835 | 2 |
Publishes application by uploading the manifest to the given marketplace | def _publish(client, manifest_path, marketplace, skip, overrides):
try:
manifest_json = check_app_manifest(manifest_path, overrides, marketplace)
app_url = "{}://{}".format(manifest_json["schemes"][0], manifest_json["host"])
app_ip = urlparse(app_url).hostname
if not skip:
address = get_zerotier_address(marketplace)
if address != app_ip:
wrong_ip = click.style("It seems that the IP address that you put in your manifest file (") +\
click.style("{}", bold=True) +\
click.style(") is different than your current 21market IP (") +\
click.style("{}", bold=True) +\
click.style(")\nAre you sure you want to continue publishing with ") +\
click.style("{}", bold=True) +\
click.style("?")
if not click.confirm(wrong_ip.format(app_ip, address, app_ip)):
switch_host = click.style("Please edit ") +\
click.style("{}", bold=True) +\
click.style(" and replace ") +\
click.style("{}", bold=True) +\
click.style(" with ") +\
click.style("[{}].", bold=True)
logger.info(switch_host.format(manifest_path, app_ip, address))
return
except exceptions.ValidationError as ex:
# catches and re-raises the same exception to enhance the error message
publish_docs_url = click.style("https://21.co/learn/21-publish/", bold=True)
publish_instructions = "For instructions on publishing your app, please refer to {}".format(publish_docs_url)
raise exceptions.ValidationError(
"The following error occurred while reading your manifest file at {}:\n{}\n\n{}"
.format(manifest_path, ex.args[0], publish_instructions),
json=ex.json)
app_name = manifest_json["info"]["title"]
app_endpoint = "{}://{}{}".format(manifest_json["schemes"][0],
manifest_json["host"],
manifest_json["basePath"])
logger.info(
(click.style("Publishing {} at ") + click.style("{}", bold=True) + click.style(" to {}."))
.format(app_name, app_endpoint, marketplace))
payload = {"manifest": manifest_json, "marketplace": marketplace}
try:
response = client.publish(payload)
except ServerRequestError as e:
if e.status_code == 403 and e.data.get("error") == "TO600":
logger.info(
"The endpoint {} specified in your manifest has already been registered in "
"the marketplace by another user.\nPlease check your manifest file and make "
"sure your 'host' field is correct.\nIf the problem persists please contact "
"support@21.co.".format(app_endpoint), fg="red")
return
else:
raise e
if response.status_code == 201:
response_data = response.json()
mkt_url = response_data['mkt_url']
permalink = response_data['permalink']
logger.info(
click.style(
"\n"
"You have successfully published {} to {}. "
"You should be able to view the listing within a few minutes at {}\n\n"
"Users will be able to purchase it, using 21 buy, at {} ",
fg="magenta")
.format(app_name, marketplace, permalink, mkt_url)
) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def submit(ctx, manifest_path, marketplace, skip, parameters):\n if parameters is not None:\n try:\n parameters = _parse_parameters(parameters)\n except:\n logger.error(\n \"Manifest parameter overrides should be in the form 'key1=\\\"value1\\\" \"\n ... | [
"0.68291336",
"0.6059394",
"0.6052995",
"0.6010262",
"0.5933436",
"0.5847316",
"0.5775076",
"0.57516134",
"0.5748247",
"0.5703469",
"0.56963223",
"0.56790406",
"0.5647197",
"0.56322503",
"0.5598916",
"0.5595371",
"0.5584658",
"0.5561256",
"0.54689205",
"0.5453668",
"0.5450249... | 0.7366084 | 0 |
Queries the marketplace for published apps | def get_search_results(config, client, page):
resp = client.get_published_apps(config.username, page)
resp_json = resp.json()
search_results = resp_json["results"]
if search_results is None or len(search_results) == 0:
logger.info(
click.style("You haven't published any apps to the marketplace yet. Use ", fg="blue") +
click.style("21 publish submit {PATH_TO_MANIFEST_FILE}", bold=True, fg="blue") +
click.style(" to publish your apps to the marketplace.", fg="blue"), fg="blue")
return 0
total_pages = resp_json["total_pages"]
logger.info("\nPage {}/{}".format(page + 1, total_pages), fg="green")
headers = ["id", "Title", "Url", "Rating", "Is up", "Is healthy", "Average Uptime",
"Last Update"]
rows = []
for r in search_results:
rating = "Not yet Rated"
if r["rating_count"] > 0:
rating = "{:.1f} ({} rating".format(r["average_rating"],
int(r["rating_count"]))
if r["rating_count"] > 1:
rating += "s"
rating += ")"
rows.append([r["id"],
r["title"],
r["app_url"],
rating,
str(r["is_up"]),
str(r["is_healthy"]),
"{:.2f}%".format(r["average_uptime"] * 100),
util.format_date(r["last_update"])])
logger.info(tabulate(rows, headers, tablefmt="simple"))
return total_pages | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_apps(provider, query):\n\n workdir = os.path.dirname(os.path.realpath(__file__))\n with open(os.path.join(workdir, '..', 'config.yml')) as f:\n config = yaml.load(f)\n ex = Explorer()\n logging.info('Read bucket: %s', config['SCOOP_BUCKET'])\n apps = ex.get_apps(os.path.expandvars(con... | [
"0.6542337",
"0.65027314",
"0.64569217",
"0.6388464",
"0.618664",
"0.6088148",
"0.6048241",
"0.60335594",
"0.60146016",
"0.6012545",
"0.5858998",
"0.5831379",
"0.57853997",
"0.5776328",
"0.5764713",
"0.57061666",
"0.5654142",
"0.56492996",
"0.56406236",
"0.56354034",
"0.56090... | 0.66877913 | 0 |
Displays info about the application selected | def display_app_info(config, client, app_id):
try:
resp = client.get_app_full_info(config.username, app_id)
result = resp.json()
app_info = result["app_info"]
title = click.style("App Name : ", fg="blue") + click.style(
"{}".format(app_info["title"]))
if app_info["rating_count"] == 0:
rating = "Not yet rated"
else:
rating = "{:.1f} ({} rating".format(app_info["average_rating"],
int(app_info["rating_count"]))
if app_info["rating_count"] > 1:
rating += "s"
rating += ")"
rating_row = click.style("Rating : ", fg="blue") + click.style("{}".format(rating))
up_status = click.style("Status : ", fg="blue")
if app_info["is_up"]:
up_status += click.style("Up")
else:
up_status += click.style("Down")
last_crawl_str = "Not yet crawled"
if "last_crawl" in app_info:
last_crawl_str = util.format_date(app_info["last_crawl"])
last_crawl = click.style("Last Crawl Time : ", fg="blue") + click.style(
"{}".format(last_crawl_str))
version = click.style("Version : ", fg="blue") + click.style(
"{}".format(app_info["version"]))
last_updated_str = util.format_date(app_info["updated"])
last_update = click.style("Last Update : ", fg="blue") + click.style(
"{}".format(last_updated_str))
availability = click.style("Availability : ", fg="blue") + click.style(
"{:.2f}%".format(app_info["average_uptime"] * 100))
app_url = click.style("Public App URL : ", fg="blue") + click.style(
"{}".format(app_info["app_url"]))
original_url = click.style("Private App URL : ", fg="blue") + click.style(
"{}".format(app_info["original_url"]))
category = click.style("Category : ", fg="blue") + click.style(
"{}".format(app_info["category"]))
desc = click.style("Description : ", fg="blue") + click.style(
"{}".format(app_info["description"]))
price = click.style("Price Range : ", fg="blue") + click.style(
"{} - {} Satoshis").format(
app_info["min_price"], app_info["max_price"])
doc_url = click.style("Docs URL : ", fg="blue") + click.style(
"{}".format(app_info["docs_url"]))
quick_start = click.style("Quick Start\n\n", fg="blue") + click.style(
app_info["quick_buy"])
usage_docs = None
if "usage_docs" in app_info:
usage_docs = click.style("Detailed usage\n\n", fg="blue") + click.style(
app_info["usage_docs"])
page_components = [title, "\n",
rating_row, up_status, availability, last_crawl, last_update, version,
"\n",
desc, app_url, original_url, doc_url, "\n",
category, price, "\n", quick_start, "\n"]
if usage_docs:
page_components.append(usage_docs + "\n")
final_str = "\n".join(page_components)
logger.info(final_str, pager=True)
except ServerRequestError as e:
if e.status_code == 404:
logger.info(
"The specified id for the app ({}) does not match any apps in the "
"marketplace.".format(app_id))
else:
raise e | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_info():\r\n app = application.Application()\r\n\r\n app.start(r\"C:\\\\AL50022\\\\Circ\\\\bin\\\\Circ.exe\")\r\n\r\n app.Circ.menu_select(\"View\")",
"def on_about(self):\n MessageBox.showinfo(\"SuperSID\", self.controller.about_app())",
"def program_info():\n\n print(\n color... | [
"0.7198173",
"0.6656836",
"0.66274256",
"0.6559104",
"0.6542044",
"0.6500156",
"0.6425056",
"0.6402444",
"0.63951284",
"0.6386804",
"0.6373861",
"0.63630366",
"0.6341547",
"0.63411224",
"0.63381755",
"0.63182807",
"0.63173515",
"0.62924206",
"0.62774503",
"0.6272408",
"0.6257... | 0.66377944 | 2 |
Runs validate_manifest and handles any errors that could occur | def check_app_manifest(api_docs_path, overrides, marketplace):
if not os.path.exists(api_docs_path):
raise exceptions.ValidationError(
click.style("Could not find the manifest file at {}.", fg="red").format(api_docs_path))
if os.path.isdir(api_docs_path):
raise exceptions.ValidationError(
click.style("{} is a directory. Please enter the direct path to the manifest file.",
fg="red").format(api_docs_path))
file_size = os.path.getsize(api_docs_path) / 1e6
if file_size > 2:
raise exceptions.ValidationError(
click.style("The size of the manifest file at {} exceeds the maximum limit of 2MB.", fg="red")
.format(api_docs_path))
try:
with open(api_docs_path, "r") as f:
original_manifest_dict = yaml.load(f.read())
manifest_dict = transform_manifest(original_manifest_dict, overrides, marketplace)
# write back the manifest in case some clean up or overriding has happend
with open(api_docs_path, "w") as f:
yaml.dump(manifest_dict, f)
return manifest_dict
except (YAMLError, ValueError):
raise exceptions.ValidationError(
click.style("Your manifest file at {} is not valid YAML.", fg="red")
.format(api_docs_path)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def validate_manifest(parser, options):\n if not options.manifest:\n return\n\n template = \"When specifying --manifest, {0} is also required\"\n\n if not options.manifest_id:\n parser.error(template.format(\"--manifest-id\"))\n \n if not options.manifest_service:\n parser.error(template.format(\"--m... | [
"0.69982684",
"0.67911905",
"0.6616815",
"0.65430355",
"0.63983166",
"0.6289555",
"0.62729937",
"0.6270612",
"0.61467505",
"0.6103869",
"0.6071787",
"0.5995637",
"0.59809095",
"0.5950012",
"0.5940859",
"0.5853178",
"0.57848716",
"0.5735434",
"0.5723771",
"0.56745976",
"0.5658... | 0.61277246 | 9 |
cleans up possible errors in the user manifest. | def clean_manifest(manifest_json):
manifest_json = copy.deepcopy(manifest_json)
host = manifest_json["host"]
host = host.strip("/").lstrip("http://").lstrip("https://")
manifest_json["host"] = host
return manifest_json | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def clean():\n user_init.clean_setup()",
"def clear_errors(self) -> None:",
"def clear_errors(self) -> None:",
"def clean_up(self):\n\t\tpass",
"def cleanUp(self):\r\n # All intermediates should be removed by app controller\r\n pass",
"def clean_up(self):\n pass",
"def clean_up(... | [
"0.6784651",
"0.64837694",
"0.64837694",
"0.64458686",
"0.6421459",
"0.6405516",
"0.6405516",
"0.63899606",
"0.6353433",
"0.63306385",
"0.63012415",
"0.62639797",
"0.6250906",
"0.6223817",
"0.619754",
"0.6186283",
"0.61667925",
"0.6079713",
"0.6040983",
"0.60129434",
"0.59378... | 0.0 | -1 |
Overrides fields in the manifest file. | def apply_overrides(manifest_json, overrides, marketplace):
manifest_json = copy.deepcopy(manifest_json)
if overrides is None:
return manifest_json
if "title" in overrides:
manifest_json["info"]["title"] = overrides["title"]
if "description" in overrides:
manifest_json["info"]["description"] = overrides["description"]
if "price" in overrides:
invalid_price_format = "Price should be a non-negative integer."
try:
price = int(overrides["price"])
manifest_json["info"]["x-21-total-price"]["min"] = price
manifest_json["info"]["x-21-total-price"]["max"] = price
if price < 0:
raise exceptions.ValidationError(invalid_price_format)
except ValueError:
raise exceptions.ValidationError(invalid_price_format)
if "name" in overrides:
manifest_json["info"]["contact"]["name"] = overrides["name"]
if "email" in overrides:
manifest_json["info"]["contact"]["email"] = overrides["email"]
if "host" in overrides:
manifest_json["host"] = overrides["host"]
if "port" in overrides:
host = manifest_json["host"]
# if the host is in the form of https://x.com/ remove the trailing slash
host = host.strip("/")
invalid_port_format = "Port should be an integer between 0 and 65536."
try:
port = int(overrides["port"])
if port <= 0 or port > 65536:
raise exceptions.ValidationError(invalid_port_format)
except ValueError:
raise exceptions.ValidationError(invalid_port_format)
host += ":{}".format(port)
manifest_json["host"] = host
if "basePath" in overrides:
manifest_json["basePath"] = overrides["basePath"]
return manifest_json | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _set_manifest(self, manifest: Dict) -> None:\n if \"metadata\" not in manifest:\n manifest[\"metadata\"] = {}\n\n if \"files\" not in manifest:\n manifest[\"files\"] = {\n \"includes\": [],\n \"excludes\": [],\n }\n\n with open... | [
"0.61127824",
"0.57904774",
"0.57481503",
"0.54889697",
"0.54140353",
"0.5328529",
"0.5295124",
"0.52817696",
"0.5250707",
"0.52453095",
"0.5233346",
"0.5206509",
"0.5180013",
"0.5170564",
"0.51529664",
"0.51508546",
"0.5138381",
"0.51179296",
"0.5108538",
"0.50936186",
"0.50... | 0.53681576 | 5 |
Replace "AUTO" in the host and quickbuy with the ZeroTier IP. The server subsequently replaces, in the displayed quickbuy, instances of the manifest host value with a mkt.21.co address. | def replace_auto(manifest_dict, marketplace):
manifest_dict = copy.deepcopy(manifest_dict)
def get_formatted_zerotier_address(marketplace):
host = get_zerotier_address(marketplace)
if "." not in host:
return "[{}]".format(host)
else:
return host
if 'AUTO' in manifest_dict['host']:
manifest_dict['host'] = manifest_dict['host'].replace(
'AUTO', get_formatted_zerotier_address(marketplace))
if 'AUTO' in manifest_dict['info']['x-21-quick-buy']:
manifest_dict['info']['x-21-quick-buy'] = manifest_dict['info']['x-21-quick-buy'].replace(
'AUTO', get_formatted_zerotier_address(marketplace))
return manifest_dict | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _post_task_update_advertise_address():\n default_network_interface = None\n\n with open(KUBE_APISERVER_CONFIG) as f:\n lines = f.read()\n m = re.search(REGEXPR_ADVERTISE_ADDRESS, lines)\n if m:\n default_network_interface = m.group(1)\n LOG.debug(' ... | [
"0.60582787",
"0.6001809",
"0.57579035",
"0.5625545",
"0.5491891",
"0.5420629",
"0.5379404",
"0.5277771",
"0.52422714",
"0.52219576",
"0.52120817",
"0.52100813",
"0.52078915",
"0.5189471",
"0.5156395",
"0.51266915",
"0.5107505",
"0.5092903",
"0.5082044",
"0.5071935",
"0.50623... | 0.7036063 | 0 |
Validates the manifest file Ensures that the required fields in the manifest are present and valid | def validate_manifest(manifest_json):
manifest_json = copy.deepcopy(manifest_json)
for field in ["schemes", "host", "basePath", "info"]:
if field not in manifest_json:
raise exceptions.ValidationError(
click.style("Field '{}' is missing from the manifest file.", fg="red").format(field),
json=manifest_json)
for field in ["contact", "title", "description", "x-21-total-price", "x-21-quick-buy", "x-21-category"]:
if field not in manifest_json["info"]:
raise exceptions.ValidationError(
click.style(
"Field '{}' is missing from the manifest file under the 'info' section.",
fg="red").format(field),
json=manifest_json)
for field in {"name", "email"}:
if field not in manifest_json["info"]["contact"]:
raise exceptions.ValidationError(
click.style(
"Field '{}' is missing from the manifest file under the 'contact' section.", fg="red")
.format(field),
json=manifest_json)
for field in ["min", "max"]:
if field not in manifest_json["info"]["x-21-total-price"]:
raise exceptions.ValidationError(
click.style("Field '{}' is missing from the manifest file under the "
"'x-21-total-price' section.",
fg="red"),
json=manifest_json)
if len(manifest_json["schemes"]) == 0:
raise exceptions.ValidationError(
click.style(
"You have to specify either HTTP or HTTPS for your endpoint under the "
"`schemes` section.",
fg="red"),
json=manifest_json)
valid_app_categories = {'blockchain', 'entertainment', 'social', 'markets', 'utilities', 'iot'}
if manifest_json["info"]["x-21-category"].lower() not in valid_app_categories:
valid_categories = ", ".join(valid_app_categories)
raise exceptions.ValidationError(
click.style("'{}' is not a valid category for the 21 marketplace. Valid categories are {}.",
fg="red").format(
manifest_json["info"]["x-21-category"], valid_categories),
json=manifest_json) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def validate_manifest(parser, options):\n if not options.manifest:\n return\n\n template = \"When specifying --manifest, {0} is also required\"\n\n if not options.manifest_id:\n parser.error(template.format(\"--manifest-id\"))\n \n if not options.manifest_service:\n parser.error(template.format(\"--m... | [
"0.73052067",
"0.6979145",
"0.69215554",
"0.68080264",
"0.6786265",
"0.6757262",
"0.6450811",
"0.6440165",
"0.640244",
"0.6351523",
"0.63133943",
"0.62944144",
"0.62856257",
"0.62499046",
"0.62444276",
"0.62436587",
"0.6221382",
"0.61910504",
"0.6152198",
"0.60909945",
"0.606... | 0.741697 | 0 |
Gets the zerotier IP address from the given marketplace name | def get_zerotier_address(marketplace):
logger.info("You might need to enter your superuser password.")
address = zerotier.get_address(marketplace)
if not address:
join_cmd = click.style("21 join", bold=True, reset=False)
no_zt_network = click.style(
"You are not part of the {}. Use {} to join the market.",
fg="red")
raise UnloggedException(no_zt_network.format(marketplace, join_cmd))
return address | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_vip_address(self, vip_name):\n networks = self.nailgun_client.get_networks(self.cluster_id)\n vip = networks.get('vips').get(vip_name, {}).get('ipaddr', None)\n asserts.assert_is_not_none(\n vip, \"Failed to get the IP of {} server\".format(vip_name))\n\n logger.debug... | [
"0.64503324",
"0.63069993",
"0.6197815",
"0.618933",
"0.61700016",
"0.609886",
"0.5992339",
"0.5982672",
"0.5972976",
"0.5954624",
"0.5945781",
"0.5938082",
"0.5936992",
"0.5928978",
"0.59271926",
"0.59166557",
"0.58961654",
"0.5854242",
"0.581978",
"0.58141637",
"0.5809872",... | 0.6974309 | 0 |
Checks the state of a field and its neighbors to decide whether the field should die or live in the next tick | def _should_cell_live(self, cell: Cell) -> bool:
living_neighbours_count = self._count_living_neighbors(cell)
# Any live cell with two or three live neighbours survives
if cell.is_alive and living_neighbours_count in [2, 3]:
return True
# Any dead cell with three live neighbours becomes a live cell
if not cell.is_alive and living_neighbours_count == 3:
return True
# All other live cells die in the next generation. Similarly, all other dead cells stay dead
return False | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def live_or_die(self, x, y):\n neighbors = self.get_neighbors(x, y)\n num_neighbors = 0\n for val in neighbors:\n if val:\n num_neighbors+=1\n\n\n # cell dies if less than 2 neighbors\n if num_neighbors < 2:\n return False\n\n # cell li... | [
"0.604368",
"0.5863431",
"0.584722",
"0.5698167",
"0.567636",
"0.5640735",
"0.5575544",
"0.5574422",
"0.55377287",
"0.5520766",
"0.55079025",
"0.5434514",
"0.54343575",
"0.5425291",
"0.5410013",
"0.5398703",
"0.5397574",
"0.5384121",
"0.53724176",
"0.5368251",
"0.53623694",
... | 0.0 | -1 |
Returns a count of horizontally, vertically and diagonally adjacent living cells | def _count_living_neighbors(self, cell: Cell) -> int:
count = 0
# borders of the area in which we are trying to find neighbors
# Let's assume y axis directs downside and x axis directs to the left
for x in range(cell.x - 1, cell.x + 2):
for y in range(cell.y - 1, cell.y + 2):
if cell.x == x and cell.y == y:
continue
if (x, y) in self.living_cells.keys():
count += 1
return count | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getAdjacentCount(grid, x, y, X, Y, char):\n count = 0\n try{\n if x == 0:\n\n if y == 0:\n\n if x == X-1:\n\n if y == Y-1:\n }",
"def _count_adj_occupied(grid: List[List[str]], row: int, col: int) -> int:\n count = 0\n if row - 1 >= 0:\n if col - 1 >= 0:\n count... | [
"0.75605994",
"0.7353794",
"0.7319598",
"0.7206011",
"0.6943233",
"0.6866241",
"0.68430185",
"0.68314976",
"0.6813723",
"0.6796741",
"0.67812294",
"0.6732968",
"0.6658989",
"0.6624076",
"0.65973485",
"0.658886",
"0.65862817",
"0.65752614",
"0.652818",
"0.6520708",
"0.645585",... | 0.68710995 | 5 |
Generates an initial state of the game | def _create_living_cells(self, living_cells_positions: List[Tuple[int, int]]) -> None:
for x, y in living_cells_positions:
self.living_cells[x, y] = Cell(x, y, True) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def initial_state(self):\n state = GameState(self.size)\n return state",
"def make_initial_state(self,seed,scrambles):\n seen = {}\n ns=0\n x = range(self.N*self.N)\n\n for r in range(self.N):\n for c in range(self.N):\n if x[r*self.N+c]==0:\n ... | [
"0.75943387",
"0.7504709",
"0.7235902",
"0.7209142",
"0.6989828",
"0.6983098",
"0.6977527",
"0.68630457",
"0.68396306",
"0.68289196",
"0.6774058",
"0.6772746",
"0.67558104",
"0.67219126",
"0.6709018",
"0.669816",
"0.6664396",
"0.6647363",
"0.6641459",
"0.65971696",
"0.6497779... | 0.0 | -1 |
Creates a game with a random initial state | def randomize(self, count, deviation: int = 5):
cells = []
for i in range(count):
cells.append(
(random.randint(-deviation, deviation - 1),
random.randint(-deviation, deviation - 1))
)
self._create_living_cells(cells)
return self | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def initial_state(self):\n state = GameState(self.size)\n return state",
"def make_state() -> state.GameState:\r\n dung: world.Dungeon = worldgen.EmptyDungeonGenerator(20, 20).spawn_dungeon(0)\r\n p1x, p1y = dung.get_random_unblocked()\r\n p2x, p2y = dung.get_random_unblocked()\r\n whil... | [
"0.7173411",
"0.7049228",
"0.6956561",
"0.6915384",
"0.6877471",
"0.67299753",
"0.67241377",
"0.6658237",
"0.6629494",
"0.66283655",
"0.66206324",
"0.65918475",
"0.6558591",
"0.6555488",
"0.6528986",
"0.65245974",
"0.6520028",
"0.64893174",
"0.6486757",
"0.64732444",
"0.64720... | 0.0 | -1 |
Returns the next state of the game | def generate_next_state(self) -> Dict[Tuple[int, int], Cell]:
next_state: Dict[Tuple[int, int], Cell] = {}
for living_cell in self.living_cells.values():
for x in range(living_cell.x - 1, living_cell.x + 2):
for y in range(living_cell.y - 1, living_cell.y + 2):
cell = Cell(x, y)
if (x, y) in self.living_cells.keys():
cell = self.living_cells[x, y]
if self._should_cell_live(cell):
next_state[x, y] = Cell(x, y, True)
self.living_cells = next_state
return self.living_cells | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_next(self):\n\t\tassert(len(self.past_values) < 256**3)\n\t\twhile self.advance_state() in self.past_values:\n\t\t\tpass\n\t\tself.past_values.add(self.state)\n\t\treturn self.state",
"def getNextState(self):\n if self.__mode == \"AlphaBeta\":\n next_state = self.startAlphaBeta()\n ... | [
"0.81304115",
"0.8000824",
"0.7832929",
"0.78188527",
"0.7760601",
"0.7588468",
"0.75123686",
"0.7505072",
"0.7259888",
"0.72233593",
"0.7170211",
"0.7106312",
"0.70742047",
"0.7022712",
"0.7002653",
"0.69881517",
"0.6954046",
"0.69376624",
"0.69316417",
"0.69316417",
"0.6925... | 0.6383667 | 59 |
Prints the current state in the console | def display_cli(self) -> None:
if len(self.living_cells.keys()) == 0:
print('.')
return
min_x, min_y = math.inf, math.inf
max_x, max_y = -math.inf, -math.inf
for x, y in self.living_cells.keys():
min_x = min(min_x, x)
min_y = min(min_y, y)
max_x = max(max_x, x)
max_y = max(max_y, y)
for y in range(min_y, max_y + 1):
chars = ""
for x in range(min_x, max_x + 1):
chars += '*' if (x, y) in self.living_cells.keys() else '.'
print(chars)
print() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def print_state(self):\n print('\\nthe current state is: ' + str(self.state) + '\\n')",
"def display_state(self):\r\n\r\n print('\\n')\r\n print('>>CURRENT STATE')\r\n ct = 0\r\n for i in self.state:\r\n for j in i:\r\n if j == -1:\r\n ... | [
"0.88597125",
"0.8146921",
"0.7986526",
"0.7926085",
"0.7904914",
"0.7863573",
"0.7799375",
"0.7672214",
"0.7600998",
"0.7504073",
"0.750387",
"0.73954594",
"0.7305813",
"0.7303963",
"0.724449",
"0.71872765",
"0.71179473",
"0.7104623",
"0.7040808",
"0.70034736",
"0.6951544",
... | 0.0 | -1 |
This program plays hangman game | def main():
# initial condition
ans = random_word()
remaining_guess_num = N_TURNS
guess_word = ''
for i in range(len(ans)):
guess_word += '-'
# start to play hangman game
while (remaining_guess_num > 0) and (guess_word != ans):
print('The word looks like: ' + str(guess_word))
print('You have ' + str(remaining_guess_num) + ' guesses left.')
input_ch = str(input('Your guess: '))
# illegal format
if not input_ch.isalpha():
print('illegal format.')
elif len(input_ch) != 1:
print('illegal format.')
# correct format
else:
# case-insensitive
input_ch = input_ch.upper()
# wrong guess
if ans.find(input_ch) == -1:
print('There is no ' + str(input_ch) + '\'s in the word.')
remaining_guess_num -= 1
# correct guess
else:
print('You are correct!')
ans_slice = ans
# replace all the correct guessed letter(s)
while ans_slice.find(input_ch) != -1:
replace_loc = len(ans) - len(ans_slice) + ans_slice.find(input_ch)
guess_word = replace_letter(input_ch, replace_loc, guess_word)
ans_slice = ans_slice[ans_slice.find(input_ch)+1:]
# win
if guess_word == ans:
print('You win!!')
# lose
else:
print('You are completely hung : (')
print('The word was: ' + str(ans)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def main():\n game = Hangman()\n game.play_hangman()",
"def play_hangman(self):\n while self.stage < 6:\n self.display_hangman()\n guess = input(f'{Fore.YELLOW}Choose a letter: {Style.RESET_ALL}').lower().strip() # noqa\n print('\\n')\n if guess.isalpha()... | [
"0.8373847",
"0.8304962",
"0.82357603",
"0.8165628",
"0.7786699",
"0.7716054",
"0.7695237",
"0.7678341",
"0.7639744",
"0.7586323",
"0.7568711",
"0.75466156",
"0.7424747",
"0.7330501",
"0.7276859",
"0.72238797",
"0.72146386",
"0.71630037",
"0.7157477",
"0.714775",
"0.7065392",... | 0.77418953 | 5 |
Initialize the view provider | def __init__(self, obj):
obj.Proxy = self | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def init_view(self):\n self.view_map = self.ctx.clientmap",
"def initialize(self, view, request, *args, **kwargs):\n view.request = request\n view.args = args\n view.kwargs = kwargs\n return view",
"def setUp(self):\n self.theView = View()",
"def initView(self):\n ... | [
"0.74448436",
"0.70989877",
"0.69125175",
"0.67591083",
"0.66585654",
"0.653166",
"0.6484332",
"0.63898385",
"0.63898385",
"0.6375199",
"0.63615674",
"0.63615674",
"0.63615674",
"0.6273222",
"0.62673444",
"0.6202396",
"0.61920524",
"0.6179315",
"0.6178323",
"0.61032915",
"0.6... | 0.0 | -1 |
View provider scene graph initialization | def attach(self, obj):
self.Object = obj.Object | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def init_view(self):\n self.view_map = self.ctx.clientmap",
"def viewer_setup(self):\n pass",
"def viewer_setup(self):\n pass",
"def __init__(self, scene: Scene):\n self.scene = scene",
"def __init__(self, scene): # type: (Scene) -> None\n self.scene = scene",
"def on_s... | [
"0.6847052",
"0.6840577",
"0.6840577",
"0.66847914",
"0.65465605",
"0.63439626",
"0.63439626",
"0.63439626",
"0.6235359",
"0.6225565",
"0.6199402",
"0.61638135",
"0.61520505",
"0.6145105",
"0.6108402",
"0.6104385",
"0.6099813",
"0.6098735",
"0.6098318",
"0.6096668",
"0.606917... | 0.0 | -1 |
Return default display mode | def getDefaultDisplayMode(self):
return "Wireframe" | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getCurrentDisplay():\n # Windows reports the active display as 'console', so we hardcode it\n return \"console\"",
"def mode(self) -> Optional[str]:\n return pulumi.get(self, \"mode\")",
"def mode(self) -> Optional[str]:\n return pulumi.get(self, \"mode\")",
"def get_mode(self):\r\n ... | [
"0.7362568",
"0.72185713",
"0.72185713",
"0.7218308",
"0.7204967",
"0.7182178",
"0.7182178",
"0.7181349",
"0.71671027",
"0.7127637",
"0.7036331",
"0.7016391",
"0.70013005",
"0.6981956",
"0.6971124",
"0.6963615",
"0.6962245",
"0.6953157",
"0.69366914",
"0.6927507",
"0.69018614... | 0.7002804 | 12 |
Set mode wireframe only | def setDisplayMode(self, mode):
return "Wireframe" | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def toggle_wireframe(self):\n self.view['wireframe'] = not self.view['wireframe']\n self.update_flags()",
"def wireframe_only(self):\n return self._wireframe_only",
"def setSurfaceShadingMode(mode='flat'):\n sdict = {'flat':'FLAT','smooth':'SMOOTH'}\n dislin.shdmod(sdict[mode], 'SURF... | [
"0.8165864",
"0.7190839",
"0.67805",
"0.6758721",
"0.65666986",
"0.6441041",
"0.6434427",
"0.6336882",
"0.6314271",
"0.6215526",
"0.61872584",
"0.6129271",
"0.60960007",
"0.6085161",
"0.5989606",
"0.5981274",
"0.59671307",
"0.5965656",
"0.5920257",
"0.58588403",
"0.5820351",
... | 0.7538678 | 1 |
Handle individual property changes | def onChanged(self, vp, prop):
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def onPropertiesChange(self, data):\n pass",
"def propertyChanged(self, p_str, Any): # real signature unknown; restored from __doc__\n pass",
"def on_property_change(self, td, name):\n\n raise NotImplementedError()",
"def onChanged(self, fp, prop):\n if prop == \"Length\" or prop ... | [
"0.75981975",
"0.71719784",
"0.7011226",
"0.69150203",
"0.66239375",
"0.6611883",
"0.6571908",
"0.65436894",
"0.6386289",
"0.63312656",
"0.6329722",
"0.632825",
"0.6243396",
"0.6038764",
"0.6026384",
"0.60087305",
"0.60087305",
"0.60087305",
"0.596836",
"0.5836696",
"0.582731... | 0.74094045 | 2 |
Check internet connection Raise exception if none | def check_connection(url="http://example.com/"):
try:
requests.head(url)
return True
except requests.ConnectionError:
spinner.warn("No internet connecction 🤭")
sys.exit(1) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"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 check_internet_connection():\n logging.debug('Checking internet connection')\n try:\n urlopen(config.api_base_url,\... | [
"0.8113887",
"0.8006314",
"0.798462",
"0.78738654",
"0.7798628",
"0.7766045",
"0.7750547",
"0.7554359",
"0.7195896",
"0.7195208",
"0.7083016",
"0.70552427",
"0.69422174",
"0.69266725",
"0.6923615",
"0.69057584",
"0.6864931",
"0.67980105",
"0.67922544",
"0.6775535",
"0.6751784... | 0.7992725 | 2 |
Creates a Flask application. | def _create_app(self, config={}):
app = Flask('signac-dashboard')
app.config.update({
'SECRET_KEY': os.urandom(24),
'SEND_FILE_MAX_AGE_DEFAULT': 300, # Cache control for static files
})
# Load the provided config
app.config.update(config)
# Enable profiling
if app.config.get('PROFILE'):
logger.warning("Application profiling is enabled.")
from werkzeug.contrib.profiler import ProfilerMiddleware
app.wsgi_app = ProfilerMiddleware(app.wsgi_app, restrictions=[10])
# Set up default signac-dashboard static and template paths
signac_dashboard_path = os.path.dirname(__file__)
app.static_folder = signac_dashboard_path + '/static'
app.template_folder = signac_dashboard_path + '/templates'
# Set up custom template paths
# The paths in DASHBOARD_PATHS give the preferred order of template
# loading
loader_list = []
for dashpath in list(app.config.get('DASHBOARD_PATHS', [])):
logger.warning("Adding '{}' to dashboard paths.".format(dashpath))
loader_list.append(
jinja2.FileSystemLoader(dashpath + '/templates'))
# The default loader goes last and is overridden by any custom paths
loader_list.append(app.jinja_loader)
app.jinja_loader = jinja2.ChoiceLoader(loader_list)
turbolinks(app)
return app | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def make_app() -> Flask:\n logger.info('creating flask application')\n app = Flask(\n 'pasta',\n static_url_path='/static',\n static_folder='./static',\n template_folder='./views')\n config.flask.SECRET_KEY = os.urandom(32)\n config.flask.SERVER_NAME = None\n app.config.f... | [
"0.85773337",
"0.84702384",
"0.8426545",
"0.84196174",
"0.84109616",
"0.8407618",
"0.83920527",
"0.8390233",
"0.83386177",
"0.8315099",
"0.83083576",
"0.8303583",
"0.8263485",
"0.8250034",
"0.8220532",
"0.82113147",
"0.82048184",
"0.81779504",
"0.81760377",
"0.8132556",
"0.81... | 0.0 | -1 |
Add assets for inclusion in the dashboard HTML. | def _create_assets(self):
assets = Environment(self.app)
# jQuery is served as a standalone file
jquery = Bundle('js/jquery-*.min.js', output='gen/jquery.min.js')
# JavaScript is combined into one file and minified
js_all = Bundle('js/js_all/*.js',
filters='jsmin',
output='gen/app.min.js')
# SCSS (Sassy CSS) is compiled to CSS
scss_all = Bundle('scss/app.scss',
filters='libsass',
output='gen/app.css')
assets.register('jquery', jquery)
assets.register('js_all', js_all)
assets.register('scss_all', scss_all)
return assets | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def assets():",
"def assets():\n pass",
"def compile_static_assets(assets):\n assets.auto_build = True\n assets.debug = False\n\n css = Bundle(\n \"css/*.css\",\n # filters=\"less,cssmin\",\n output=\"gen/avantui.css\",\n # extra={\"rel\": \"stylesheet/less\"},\n )\n\... | [
"0.7301845",
"0.69285274",
"0.6680427",
"0.6377354",
"0.60470253",
"0.59089947",
"0.5860708",
"0.5840234",
"0.5829316",
"0.5822734",
"0.5812532",
"0.57396126",
"0.57066774",
"0.56420225",
"0.556743",
"0.5532212",
"0.5530811",
"0.5510526",
"0.5499899",
"0.548234",
"0.54720134"... | 0.6160059 | 4 |
Register an asset required by a dashboard module. Some modules require special scripts or stylesheets, like the | def register_module_asset(self, asset):
self._module_assets.append(asset) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def process_xmodule_assets():\r\n sh('xmodule_assets common/static/xmodule')",
"def assets():\n pass",
"def assets():",
"def script_info_assets(app, static_dir, testcss):\n InvenioAssets(app)\n\n blueprint = Blueprint(__name__, \"test_bp\", static_folder=static_dir)\n\n class Ext(object):\n ... | [
"0.6323401",
"0.6234134",
"0.5986547",
"0.57688546",
"0.57355887",
"0.57000977",
"0.5596467",
"0.5571452",
"0.55544966",
"0.55511576",
"0.5550094",
"0.54957074",
"0.548881",
"0.54596376",
"0.5429053",
"0.537195",
"0.5304316",
"0.52905613",
"0.52882594",
"0.5239327",
"0.523754... | 0.70824754 | 0 |
Prepare this dashboard instance to run. | def _prepare(self):
# Set configuration defaults and save to the project document
self.config.setdefault('PAGINATION', True)
self.config.setdefault('PER_PAGE', 25)
# Create and configure the Flask application
self.app = self._create_app(self.config)
# Add assets and routes
self.assets = self._create_assets()
self._register_routes()
# Add module assets and routes
self._module_assets = []
for module in self.modules:
try:
module.register(self)
except Exception as e:
logger.error('Error while registering {} module: {}'.format(
module.name, e))
logger.error('Removing module {} from dashboard.'.format(
module.name))
self.modules.remove(module)
# Clear dashboard and project caches.
self.update_cache() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self):\n # Wipe the db\n self.wipe_db()\n\n # Set some global things\n try:\n dashboard_configuration = DashboardConfiguration(type=\"default\")\n dashboard_configuration.save()\n except IntegrityError:\n dashboard_configuration = Das... | [
"0.62227356",
"0.61323386",
"0.6124156",
"0.6124156",
"0.6124156",
"0.603257",
"0.6021469",
"0.6008526",
"0.59598756",
"0.5919939",
"0.5891619",
"0.58891463",
"0.5887354",
"0.5885531",
"0.58851296",
"0.58174354",
"0.57714",
"0.57629365",
"0.57341665",
"0.57147014",
"0.5703350... | 0.6481295 | 0 |
Runs the dashboard webserver. | def run(self, *args, **kwargs):
host = self.config.get('HOST', 'localhost')
port = self.config.get('PORT', 8888)
max_retries = 5
for _ in range(max_retries):
try:
self.app.run(host, port, *args, **kwargs)
break
except OSError as e:
logger.warning(e)
if port:
port += 1
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def webserver_start():\n run(_webserver_command())",
"def run_dashboard(\n database_path,\n no_browser,\n port,\n updating_options,\n):\n port = _find_free_port() if port is None else port\n port = int(port)\n\n if not isinstance(database_path, (str, pathlib.Path)):\n raise TypeErr... | [
"0.7217987",
"0.7090126",
"0.6873404",
"0.6859352",
"0.6825107",
"0.6763319",
"0.67429936",
"0.6691408",
"0.6641341",
"0.655142",
"0.6534171",
"0.6508218",
"0.6484628",
"0.64228106",
"0.6421459",
"0.64109594",
"0.6343112",
"0.63255435",
"0.63101995",
"0.63063806",
"0.6303844"... | 0.0 | -1 |
Override this method for custom job titles. This method generates job titles. By default, the title is a pretty (but verbose) form of the job state point, based on the project schema. | def job_title(self, job):
def _format_num(num):
if isinstance(num, bool):
return str(num)
elif isinstance(num, Real):
return str(round(num, 2))
return str(num)
try:
s = []
for keys in sorted(self._schema_variables()):
v = job.statepoint()[keys[0]]
try:
for key in keys[1:]:
v = v[key]
except KeyError: # Particular key is present in overall
continue # schema, but not this state point.
else:
s.append('{}={}'.format('.'.join(keys), _format_num(v)))
return ' '.join(s)
except Exception as error:
logger.debug(
"Error while generating job title: '{}'. "
"Returning job-id as fallback.".format(error))
return str(job) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_job_title(self, job_name):\n return ''",
"def _make_title(self):\n ret = self.properties['reason'].capitalize()\n ret += ' has been reported near ' + self.properties['address'].split(',')[0]\n time = datetime.strptime(self.properties['when'], '%Y-%m-%dT%H:%M:%S')\n time... | [
"0.6774112",
"0.6684482",
"0.6545176",
"0.65105885",
"0.64988434",
"0.6343814",
"0.6343415",
"0.6338762",
"0.62752765",
"0.6266623",
"0.6256214",
"0.6256214",
"0.6229947",
"0.6229947",
"0.6226944",
"0.61802864",
"0.61802864",
"0.6152143",
"0.6079925",
"0.60740346",
"0.6057321... | 0.756392 | 0 |
Override this method for custom job subtitles. This method generates job subtitles. By default, the subtitle is a minimal unique substring of the job id. | def job_subtitle(self, job):
return str(job)[:max(8, self._project_min_len_unique_id())] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def subtitle(self, txt):\n num = len(txt)\n ticks = \"-\" * num\n print(txt)\n print(ticks)",
"def getSubtitleURL(self):\n\n # If it is a movie, we use this methodology -\n try:\n IndexingParameters = [\"subtitleUrls\", 0, \"url\"]\n TitleParamters ... | [
"0.6298904",
"0.5891134",
"0.57354397",
"0.57253325",
"0.57177824",
"0.5635711",
"0.5478623",
"0.5465625",
"0.5414711",
"0.54018885",
"0.53634965",
"0.53630906",
"0.535014",
"0.5312166",
"0.5256253",
"0.5206248",
"0.5183688",
"0.5179891",
"0.517678",
"0.516628",
"0.51632917",... | 0.74129283 | 0 |
Override this method for custom job sorting. This method returns a key that can be compared to sort jobs. By | def job_sorter(self, job):
key = natsort.natsort_keygen(key=self.job_title, alg=natsort.REAL)
return key(job) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def job_priority_key(self, job):\n raise NotImplemented",
"def sort_key(self):\n ...",
"def get_sort_key(self) -> str:\n return self.name",
"def job_priority_key(self, job):\n camp, user = job.camp, job.user\n end = camp.time_left / user.shares # lower value -> higher prio... | [
"0.75236887",
"0.74009395",
"0.71635604",
"0.7031687",
"0.64361453",
"0.6359381",
"0.62978643",
"0.6156635",
"0.61132455",
"0.6011168",
"0.59698707",
"0.59516037",
"0.5946783",
"0.592785",
"0.5922575",
"0.59023565",
"0.58991647",
"0.58991647",
"0.5893522",
"0.5864389",
"0.584... | 0.78755414 | 0 |
Add a route to the dashboard. This method allows custom view functions to be triggered for specified routes. These view functions are imported lazily, when their route | def add_url(self, import_name, url_rules=[],
import_file='signac_dashboard', **options):
if import_file is not None:
import_name = import_file + '.' + import_name
for url_rule in url_rules:
self.app.add_url_rule(
rule=url_rule,
view_func=LazyView(dashboard=self, import_name=import_name),
**options) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def add_routes(self):\n pass",
"def add_route(config, route, view, route_name=None, renderer='json'):\n route_name = route_name or view.__name__\n config.add_route(route_name, route)\n config.add_view(view, route_name=route_name, renderer=renderer)",
"def add_route(self, pattern: str, view: Cal... | [
"0.70428646",
"0.69580984",
"0.6819496",
"0.6740185",
"0.65938735",
"0.6558933",
"0.6456988",
"0.643927",
"0.64224243",
"0.6417979",
"0.6417649",
"0.6408866",
"0.6398267",
"0.63959163",
"0.638843",
"0.6382129",
"0.63491535",
"0.63472974",
"0.63417435",
"0.6259017",
"0.6238091... | 0.0 | -1 |
Registers routes with the Flask application. This method configures context processors, templates, and sets up routes for a basic Dashboard instance. Additionally, routes declared by modules are registered by this method. | def _register_routes(self):
dashboard = self
@dashboard.app.after_request
def prevent_caching(response):
if 'Cache-Control' not in response.headers:
response.headers['Cache-Control'] = 'no-store'
return response
@dashboard.app.context_processor
def injections():
session.setdefault('enabled_modules',
[i for i in range(len(self.modules))
if self.modules[i].enabled])
return {
'APP_NAME': 'signac-dashboard',
'APP_VERSION': __version__,
'PROJECT_NAME': self.project.config['project'],
'PROJECT_DIR': self.project.config['project_dir'],
'modules': self.modules,
'enabled_modules': session['enabled_modules'],
'module_assets': self._module_assets
}
# Add pagination support from http://flask.pocoo.org/snippets/44/
@dashboard.app.template_global()
def url_for_other_page(page):
args = request.args.copy()
args['page'] = page
return url_for(request.endpoint, **args)
@dashboard.app.template_global()
def modify_query(**new_values):
args = request.args.copy()
for key, value in new_values.items():
args[key] = value
return '{}?{}'.format(request.path, url_encode(args))
@dashboard.app.errorhandler(404)
def page_not_found(error):
return self._render_error(str(error))
self.add_url('views.home', ['/'])
self.add_url('views.settings', ['/settings'])
self.add_url('views.search', ['/search'])
self.add_url('views.jobs_list', ['/jobs/'])
self.add_url('views.show_job', ['/jobs/<jobid>'])
self.add_url('views.get_file', ['/jobs/<jobid>/file/<path:filename>'])
self.add_url('views.change_modules', ['/modules'], methods=['POST']) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def add_routes(self):\n# from server.flask import views as flask_views\n# flask_views_custom_methods = filter(lambda x: x.startswith(\"view_\"), dir(flask_views))\n# for custom_method in flask_views_custom_methods:\n# # Retrieve data needed to add the URL rule to the Flask app\n# ... | [
"0.72350115",
"0.6712779",
"0.67111504",
"0.66817683",
"0.6653695",
"0.6587917",
"0.6505443",
"0.64713275",
"0.6450273",
"0.63990045",
"0.63630635",
"0.63256997",
"0.63007975",
"0.6283785",
"0.6241679",
"0.6237398",
"0.62150884",
"0.61836624",
"0.6177383",
"0.6159926",
"0.612... | 0.7511353 | 0 |
Clear project and dashboard server caches. The dashboard relies on caching for performance. If the data space is altered, this method may need to be called before the dashboard reflects those changes. | def update_cache(self):
# Try to update signac project cache. Requires signac 0.9.2 or later.
with warnings.catch_warnings():
warnings.simplefilter(action='ignore', category=FutureWarning)
try:
self.project.update_cache()
except Exception:
pass
# Clear caches of all dashboard methods
members = inspect.getmembers(self, predicate=inspect.ismethod)
for func in filter(lambda f: hasattr(f, 'cache_clear'),
map(lambda x: x[1], members)):
func.cache_clear() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def clear_cache():\n # TODO\n pass",
"def clear_cache(self):\n\n for dataset in self._datasets:\n dataset.clear_cache()",
"def clear_cache(self):\n pass",
"def _clear_cache(self):\n keys = [\"nodes\", \"availability\", \"capacity\", \"cost\"]\n for key in ... | [
"0.67237765",
"0.67170703",
"0.6651066",
"0.65608287",
"0.6541997",
"0.64387864",
"0.642453",
"0.63758636",
"0.6363097",
"0.6251716",
"0.62509453",
"0.62315863",
"0.6219807",
"0.6218337",
"0.6209813",
"0.62015516",
"0.61923134",
"0.61808413",
"0.6152699",
"0.61314887",
"0.611... | 0.72654325 | 0 |
Call the dashboard as a WSGI application. | def __call__(self, environ, start_response):
return self.app(environ, start_response) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def main():\r\n run_wsgi_app(app)",
"def main():\n run_wsgi_app(APP)",
"def Main():\n wsgiref.handlers.CGIHandler().run(application)",
"def main(_, **settings):\n config = Configurator(settings=settings)\n register_includes(config)\n register_json_renderer(config)\n register_routes(config)... | [
"0.74744",
"0.72382814",
"0.69897854",
"0.6828364",
"0.66787845",
"0.6616081",
"0.6616081",
"0.658992",
"0.6578323",
"0.65606695",
"0.65123874",
"0.6492135",
"0.64652956",
"0.64537275",
"0.6407495",
"0.6406011",
"0.640311",
"0.63525355",
"0.635047",
"0.6333705",
"0.6266929",
... | 0.6479684 | 12 |
Runs the command line interface. Call this function to use signacdashboard from its command line | def main(self):
def _run(args):
kwargs = vars(args)
if kwargs.get('host', None) is not None:
self.config['HOST'] = kwargs.pop('host')
if kwargs.get('port', None) is not None:
self.config['PORT'] = kwargs.pop('port')
self.config['PROFILE'] = kwargs.pop('profile')
self.config['DEBUG'] = kwargs.pop('debug')
self.run()
parser = argparse.ArgumentParser(
description="signac-dashboard is a web-based data visualization "
"and analysis tool, part of the signac framework.")
parser.add_argument(
'--debug',
action='store_true',
help="Show traceback on error for debugging.")
parser.add_argument(
'--version',
action='store_true',
help="Display the version number and exit.")
subparsers = parser.add_subparsers()
parser_run = subparsers.add_parser('run')
parser_run.add_argument(
'-p', '--profile',
action='store_true',
help='Enable flask performance profiling.')
parser_run.add_argument(
'-d', '--debug',
action='store_true',
help='Enable flask debug mode.')
parser_run.add_argument(
'--host', type=str,
help='Host (binding address). Default: localhost')
parser_run.add_argument(
'--port', type=int,
help='Port to listen on. Default: 8888')
parser_run.set_defaults(func=_run)
# This is a hack, as argparse itself does not
# allow to parse only --version without any
# of the other required arguments.
if '--version' in sys.argv:
print('signac-dashboard', __version__)
sys.exit(0)
args = parser.parse_args()
if args.debug:
logger.setLevel(logging.DEBUG)
if not hasattr(args, 'func'):
parser.print_usage()
sys.exit(2)
try:
self.observer.start()
args.func(args)
except RuntimeWarning as warning:
logger.warning("Warning: {}".format(warning))
if args.debug:
raise
sys.exit(1)
except Exception as error:
logger.error('Error: {}'.format(error))
if args.debug:
raise
sys.exit(1)
finally:
self.observer.stop()
self.observer.join() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def cli():\n config, auth, execute_now = read_command_line_arguments()\n main(config, auth, execute_now)",
"def cli():\n pass",
"def main(args):\n cli = CLI()\n # Check arguments\n cli.parse_arguments(args)",
"def main_cli():\n pass",
"def main():\n CLI_APP.run()",
"def cli():... | [
"0.73368174",
"0.6704575",
"0.66263586",
"0.6620892",
"0.65879875",
"0.65856606",
"0.6576689",
"0.6504645",
"0.64861155",
"0.6461405",
"0.6461405",
"0.6461405",
"0.6461405",
"0.6461405",
"0.6461405",
"0.6461405",
"0.6461405",
"0.6461405",
"0.6461405",
"0.6461405",
"0.6461405"... | 0.70329654 | 1 |
Compresses a tensor with the given compression context, and then returns it with the context needed to decompress it. | def compress(self, tensor): | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def compress(self, tensor, *args, **kwargs):\n return self.compressor.compress(tensor)",
"def compress(self, tensor, *args, **kwargs):\n return self.compressor.compress(tensor)",
"def decompress(self, tensor, ctx, *args, **kwargs):\n return tensor",
"def compress(self, tensor, *args, **k... | [
"0.7178514",
"0.7178514",
"0.6956097",
"0.68778074",
"0.6652845",
"0.64798415",
"0.6468104",
"0.61270404",
"0.60731876",
"0.5983608",
"0.56424665",
"0.5547921",
"0.5408176",
"0.5383199",
"0.53278595",
"0.5266407",
"0.5179403",
"0.5145993",
"0.5139783",
"0.51359445",
"0.513499... | 0.67097557 | 4 |
Decompress the tensor with the given decompression context. | def decompress(self, tensors): | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def decompress(self, tensor, ctx, *args, **kwargs):\n pass",
"def decompress(self, tensor, ctx, *args, **kwargs):\n return tensor",
"def decompress(self, tensor, ctx, *args, **kwargs):\n tensor = self.compressor.decompress(tensor, ctx, *args, **kwargs)\n \n # uncompressed gra... | [
"0.8031564",
"0.7931041",
"0.75796515",
"0.7512713",
"0.71366394",
"0.68911856",
"0.66436976",
"0.6177685",
"0.60084254",
"0.5998883",
"0.5772824",
"0.5761908",
"0.5596383",
"0.55957705",
"0.5553985",
"0.54953575",
"0.5484418",
"0.54572964",
"0.54572964",
"0.5454716",
"0.5433... | 0.71033555 | 5 |
Compresses a tensor with the given compression context, and then returns it with the context needed to decompress it. | def encode(self, seq): | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def compress(self, tensor, *args, **kwargs):\n return self.compressor.compress(tensor)",
"def compress(self, tensor, *args, **kwargs):\n return self.compressor.compress(tensor)",
"def decompress(self, tensor, ctx, *args, **kwargs):\n return tensor",
"def compress(self, tensor, *args, **k... | [
"0.7178514",
"0.7178514",
"0.6956097",
"0.68778074",
"0.67097557",
"0.6652845",
"0.64798415",
"0.6468104",
"0.61270404",
"0.60731876",
"0.5983608",
"0.56424665",
"0.5547921",
"0.5408176",
"0.5383199",
"0.53278595",
"0.5266407",
"0.5179403",
"0.5145993",
"0.5139783",
"0.513594... | 0.0 | -1 |
Decompress the tensor with the given decompression context. | def decode(self, coded_set): | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def decompress(self, tensor, ctx, *args, **kwargs):\n pass",
"def decompress(self, tensor, ctx, *args, **kwargs):\n return tensor",
"def decompress(self, tensor, ctx, *args, **kwargs):\n tensor = self.compressor.decompress(tensor, ctx, *args, **kwargs)\n \n # uncompressed gra... | [
"0.8031564",
"0.7931041",
"0.75796515",
"0.7512713",
"0.71366394",
"0.71033555",
"0.68911856",
"0.66436976",
"0.6177685",
"0.60084254",
"0.5998883",
"0.5772824",
"0.5761908",
"0.5596383",
"0.55957705",
"0.5553985",
"0.54953575",
"0.5484418",
"0.54572964",
"0.54572964",
"0.545... | 0.0 | -1 |
Runs optimization over n rounds of k sequential trials. | def test_run_experiment_locally(self) -> None:
experiment = Experiment(
name="torchx_booth_sequential_demo",
search_space=SearchSpace(parameters=self._parameters),
optimization_config=OptimizationConfig(objective=self._objective),
runner=self._runner,
is_test=True,
properties={Keys.IMMUTABLE_SEARCH_SPACE_AND_OPT_CONF: True},
)
scheduler = Scheduler(
experiment=experiment,
generation_strategy=(
choose_generation_strategy(
search_space=experiment.search_space,
)
),
options=SchedulerOptions(),
)
try:
for _ in range(3):
scheduler.run_n_trials(max_trials=2)
# TorchXMetric always returns trial index; hence the best experiment
# for min objective will be the params for trial 0.
scheduler.report_results()
except FailureRateExceededError:
pass # TODO(ehotaj): Figure out why this test fails in OSS.
# Nothing to assert, just make sure experiment runs. | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def run(self, n_trials=10):\n # Create study object\n if self.study is None:\n self.study = optuna.create_study(\n direction=\"minimize\",\n sampler=optuna.samplers.RandomSampler(seed=123)\n )\n # Run trials\n self.study.optimize(\n ... | [
"0.62938315",
"0.6189227",
"0.6007014",
"0.5923211",
"0.58506715",
"0.58172965",
"0.58066565",
"0.58060586",
"0.57466394",
"0.5720371",
"0.5709016",
"0.57055277",
"0.56977755",
"0.56941396",
"0.5682223",
"0.5663299",
"0.56584656",
"0.5658233",
"0.56204224",
"0.5607117",
"0.55... | 0.0 | -1 |
Runs optimization over k x n rounds of k parallel trials. This asks Ax to run up to max_parallelism_cap trials in parallel by submitting them to the scheduler at the same time. | def test_run_experiment_locally_in_batches(self) -> None:
parallelism = 2
rounds = 3
experiment = Experiment(
name="torchx_booth_parallel_demo",
search_space=SearchSpace(parameters=self._parameters),
optimization_config=OptimizationConfig(objective=self._objective),
runner=self._runner,
is_test=True,
properties={Keys.IMMUTABLE_SEARCH_SPACE_AND_OPT_CONF: True},
)
scheduler = Scheduler(
experiment=experiment,
generation_strategy=(
choose_generation_strategy(
search_space=experiment.search_space,
max_parallelism_cap=parallelism,
)
),
options=SchedulerOptions(
run_trials_in_batches=True, total_trials=(parallelism * rounds)
),
)
try:
scheduler.run_all_trials()
# TorchXMetric always returns trial index; hence the best experiment
# for min objective will be the params for trial 0.
scheduler.report_results()
except FailureRateExceededError:
pass # TODO(ehotaj): Figure out why this test fails in OSS.
# Nothing to assert, just make sure experiment runs. | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def run(self, n_steps: int, n_parallel: int = 1, **kwargs):\n batch_size = kwargs.get(\"batch_size\", 1)\n n_parallel = min(n_parallel, batch_size)\n with self.scheduler(n_parallel=n_parallel) as scheduler:\n for i in range(n_steps):\n samples = self.optimiser.run_ste... | [
"0.58023125",
"0.5677729",
"0.55972874",
"0.55697495",
"0.5547481",
"0.5508865",
"0.54776615",
"0.5470444",
"0.5464197",
"0.53902745",
"0.53773487",
"0.53718054",
"0.53490144",
"0.53486687",
"0.5348121",
"0.5320192",
"0.5305906",
"0.52872264",
"0.5229914",
"0.52269125",
"0.52... | 0.54965633 | 6 |
Test the popxl simple addition example | def test_documentation_popxl_addition(self):
filename = "simple_addition.py"
self.run_python(filename, file_dir=working_dir, working_dir=working_dir) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_add_numbers(self):\n self.assertEqual(add(3, 8), 11)",
"def test_add_numbers(self):\n self.assertEqual(add(3, 8), 11)",
"def test_add_numbers(self):\n self.assertEqual(add(3, 8), 11)",
"def test_and_numbers(self):\n self.assertEqual(add(3,8), 11)",
"def test_add_two_num... | [
"0.65502137",
"0.65502137",
"0.65502137",
"0.64431727",
"0.63226366",
"0.6308587",
"0.628632",
"0.6280519",
"0.6253776",
"0.6226606",
"0.62078625",
"0.61861867",
"0.61754084",
"0.6170412",
"0.61692727",
"0.615465",
"0.6137472",
"0.61353207",
"0.6135082",
"0.6111764",
"0.60554... | 0.70790774 | 0 |
Test the popxl simple addition example | def test_documentation_popxl_addition_variable(self):
filename = "tensor_addition.py"
self.run_python(filename, file_dir=working_dir, working_dir=working_dir) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_documentation_popxl_addition(self):\n filename = \"simple_addition.py\"\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)",
"def test_add_numbers(self):\n self.assertEqual(add(3, 8), 11)",
"def test_add_numbers(self):\n self.assertEqual(add(3, 8), 1... | [
"0.70790774",
"0.65502137",
"0.65502137",
"0.65502137",
"0.64431727",
"0.63226366",
"0.6308587",
"0.628632",
"0.6280519",
"0.6253776",
"0.6226606",
"0.62078625",
"0.61861867",
"0.61754084",
"0.6170412",
"0.61692727",
"0.615465",
"0.6137472",
"0.61353207",
"0.6135082",
"0.6111... | 0.5998734 | 25 |
Test the popxl basic subgraph example | def test_documentation_popxl_basic_subgraph(self):
filename = "basic_graph.py"
self.run_python(filename, file_dir=working_dir, working_dir=working_dir) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_documentation_popxl_create_multi_subgraph(self):\n filename = \"create_multi_graphs_from_same_func.py\"\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)",
"def sub_graph_merging(self):",
"def show_subgraph(dfs_codes, nsupport, mapper):\n\tglobal __subgraph_count... | [
"0.75176567",
"0.62960446",
"0.60611916",
"0.6042744",
"0.6038377",
"0.5965643",
"0.5938931",
"0.58985436",
"0.587293",
"0.5843227",
"0.58157665",
"0.5796282",
"0.5735608",
"0.5665891",
"0.5604234",
"0.5579183",
"0.5560783",
"0.5559308",
"0.5536412",
"0.5530756",
"0.5525885",... | 0.8505168 | 0 |
Test the popxl replication example | def test_documentation_popxl_replication(self):
filename = "replication.py"
self.run_python(filename, file_dir=working_dir, working_dir=working_dir) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_documentation_popxl_mnist_replication_train(self):\n filename = \"mnist_rts.py --replication-factor 2\"\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)",
"def test_replicate_pg_to_pg(self):\n # TODO - Real and more complex e2e tests will be added here\n ... | [
"0.6849515",
"0.5858241",
"0.567002",
"0.5621691",
"0.5603418",
"0.5590965",
"0.55826133",
"0.55337054",
"0.55278075",
"0.5499861",
"0.5477127",
"0.539578",
"0.53619903",
"0.5334775",
"0.5324242",
"0.53177917",
"0.53116417",
"0.53056175",
"0.52885664",
"0.5232968",
"0.5212479... | 0.8200137 | 0 |
Test the popxl create multiple subgraph example | def test_documentation_popxl_create_multi_subgraph(self):
filename = "create_multi_graphs_from_same_func.py"
self.run_python(filename, file_dir=working_dir, working_dir=working_dir) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_documentation_popxl_basic_subgraph(self):\n filename = \"basic_graph.py\"\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)",
"def sub_graph_merging(self):",
"def populate_graph(self):",
"def test_documentation_popxl_repeat_2(self):\n filename = \"repeat... | [
"0.79100144",
"0.65580076",
"0.64052117",
"0.63540345",
"0.6348667",
"0.61608034",
"0.6133355",
"0.5854858",
"0.5819954",
"0.5817296",
"0.5729944",
"0.57231635",
"0.5651493",
"0.5646747",
"0.5616531",
"0.5610424",
"0.56057763",
"0.5573422",
"0.55470526",
"0.5529936",
"0.55223... | 0.8254092 | 0 |
Test the popxl create multiple callsites for a subgraph input example | def test_documentation_popxl_multi_callsites_graph_input(self):
filename = "multi_call_graph_input.py"
self.run_python(filename, file_dir=working_dir, working_dir=working_dir) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_documentation_popxl_create_multi_subgraph(self):\n filename = \"create_multi_graphs_from_same_func.py\"\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)",
"def test_documentation_popxl_basic_subgraph(self):\n filename = \"basic_graph.py\"\n self.run_... | [
"0.81625026",
"0.75757235",
"0.63863367",
"0.63754267",
"0.61888385",
"0.5910345",
"0.5906325",
"0.568147",
"0.56611234",
"0.5634181",
"0.5622761",
"0.5596353",
"0.5533244",
"0.5507174",
"0.54756355",
"0.5458438",
"0.5447056",
"0.53142405",
"0.5302135",
"0.5267153",
"0.525309... | 0.69611263 | 2 |
Test the code loading example | def test_documentation_popxl_code_loading(self):
filename = "code_loading.py"
self.run_python(filename, file_dir=working_dir, working_dir=working_dir) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_example(decorated_example):\n import visual_coding_2p_analysis",
"def test_examples():\n import airconics\n # pytest runs test files in ./__pycache__: need to go up two levels\n example_dir = os.path.abspath(\n os.path.join(__file__, '..', '..', 'examples', 'core'))\n example_scrip... | [
"0.7195194",
"0.7059802",
"0.6890149",
"0.6869557",
"0.6869557",
"0.6869557",
"0.6869557",
"0.67875314",
"0.6767249",
"0.6749942",
"0.67483723",
"0.67235684",
"0.6716485",
"0.6689319",
"0.66765344",
"0.66539055",
"0.66304076",
"0.6627514",
"0.65707266",
"0.6563701",
"0.654868... | 0.74833703 | 0 |
Test the nested code loading example | def test_documentation_popxl_nested_code_loading(self):
filename = "code_loading_nested.py"
self.run_python(filename, file_dir=working_dir, working_dir=working_dir) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_example(decorated_example):\n import visual_coding_2p_analysis",
"def inner_test():\n pass",
"def inner_test():\n pass",
"def test_documentation_popxl_code_loading(self):\n filename = \"code_loading.py\"\n self.run_python(filename, file_dir=working_dir, working... | [
"0.6656562",
"0.6570933",
"0.6570933",
"0.6489614",
"0.6259519",
"0.6246904",
"0.61843383",
"0.61584216",
"0.61110884",
"0.61107814",
"0.6097114",
"0.6087612",
"0.60835487",
"0.60752654",
"0.60712975",
"0.6059714",
"0.60356414",
"0.60127175",
"0.60122466",
"0.601197",
"0.5992... | 0.83801514 | 0 |
Test the nested Session contexts example | def test_documentation_popxl_nested_session_contexts(self):
filename = "nested_session_contexts.py"
self.run_python(filename, file_dir=working_dir, working_dir=working_dir) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_set_session():",
"def test_resource(data_manager):\n sessions = set([])\n with data_manager.dal():\n context1 = current_context._get_current_object()\n session = context1.sqlalchemy\n assert isinstance(session, orm.Session)\n sessions.add(session)\n\n with data_m... | [
"0.67018175",
"0.6668321",
"0.6427891",
"0.63258743",
"0.62819177",
"0.6264992",
"0.6244206",
"0.6197762",
"0.6128129",
"0.60963386",
"0.59784377",
"0.59195083",
"0.5882602",
"0.58783644",
"0.5873574",
"0.58591706",
"0.5849194",
"0.584282",
"0.5837098",
"0.58088905",
"0.58034... | 0.8149533 | 0 |
Test the popxl call_with_info example | def test_documentation_popxl_call_with_info(self):
filename = "call_with_info.py"
self.run_python(filename, file_dir=working_dir, working_dir=working_dir) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def hxlinfo():\n run_script(hxlinfo_main)",
"def test_get_info(self):\n pass",
"def test_application_info(self, mocked_serial, mocked_check):\n from supvisors.rpcinterface import RPCInterface\n # create RPC instance\n rpc = RPCInterface(self.supervisor)\n # test RPC call\n... | [
"0.6383911",
"0.5714342",
"0.55663514",
"0.5375583",
"0.532167",
"0.52700174",
"0.5258182",
"0.5240253",
"0.51907164",
"0.5187251",
"0.51472026",
"0.50939924",
"0.5084021",
"0.50806963",
"0.507654",
"0.5028689",
"0.50142485",
"0.49954024",
"0.4964053",
"0.49308",
"0.49162632"... | 0.82054126 | 0 |
Test the popxl basic repeat example | def test_documentation_popxl_repeat_0(self):
filename = "repeat_graph_0.py"
self.run_python(filename, file_dir=working_dir, working_dir=working_dir) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_documentation_popxl_repeat_1(self):\n filename = \"repeat_graph_1.py\"\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)",
"def test_documentation_popxl_repeat_2(self):\n filename = \"repeat_graph_2.py\"\n self.run_python(filename, file_dir=working_di... | [
"0.6505946",
"0.6401242",
"0.58164656",
"0.54440105",
"0.53839743",
"0.5373807",
"0.53707576",
"0.53479195",
"0.5337674",
"0.5326383",
"0.5316677",
"0.53121865",
"0.53121865",
"0.5300285",
"0.5297226",
"0.52901715",
"0.5276409",
"0.5268856",
"0.5196304",
"0.5196304",
"0.51730... | 0.6540377 | 0 |
Test the popxl subgraph in parent in repeat example | def test_documentation_popxl_repeat_1(self):
filename = "repeat_graph_1.py"
self.run_python(filename, file_dir=working_dir, working_dir=working_dir) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_documentation_popxl_basic_subgraph(self):\n filename = \"basic_graph.py\"\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)",
"def test_documentation_popxl_create_multi_subgraph(self):\n filename = \"create_multi_graphs_from_same_func.py\"\n self.run_... | [
"0.7205235",
"0.70020497",
"0.62084246",
"0.6038614",
"0.5988837",
"0.58964515",
"0.58667445",
"0.58020157",
"0.5726468",
"0.5667711",
"0.5539633",
"0.5539633",
"0.5504206",
"0.54905653",
"0.5482558",
"0.5460695",
"0.54255486",
"0.5419617",
"0.5398533",
"0.5355489",
"0.532643... | 0.6087695 | 3 |
Test the popxl subgraph in parent in repeat example | def test_documentation_popxl_repeat_2(self):
filename = "repeat_graph_2.py"
self.run_python(filename, file_dir=working_dir, working_dir=working_dir) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_documentation_popxl_basic_subgraph(self):\n filename = \"basic_graph.py\"\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)",
"def test_documentation_popxl_create_multi_subgraph(self):\n filename = \"create_multi_graphs_from_same_func.py\"\n self.run_... | [
"0.7205235",
"0.70020497",
"0.62084246",
"0.6087695",
"0.5988837",
"0.58964515",
"0.58667445",
"0.58020157",
"0.5726468",
"0.5667711",
"0.5539633",
"0.5539633",
"0.5504206",
"0.54905653",
"0.5482558",
"0.5460695",
"0.54255486",
"0.5419617",
"0.5398533",
"0.5355489",
"0.532643... | 0.6038614 | 4 |
Test the popxl getting / setting tensor data example | def test_documentation_popxl_get_set_tensors(self):
filename = "tensor_get_write.py"
self.run_python(filename, file_dir=working_dir, working_dir=working_dir) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_documentation_popxl_addition_variable(self):\n filename = \"tensor_addition.py\"\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)",
"def test_predictor():",
"def test_loadData():\n \n sys = LVsystem.Ecosystem()\n \n sys.loadSetup('2Prey1Predator')\n ... | [
"0.6889487",
"0.65411484",
"0.62041",
"0.61115885",
"0.60792667",
"0.60625935",
"0.5973008",
"0.5798675",
"0.579797",
"0.57839084",
"0.5775764",
"0.5711267",
"0.57093495",
"0.5704983",
"0.5672286",
"0.5643146",
"0.5622259",
"0.5604334",
"0.5597279",
"0.5581926",
"0.558076",
... | 0.7280651 | 0 |
Test the popxl advanced getting / writing tensor data example that shows exactly when devicehost transfers occur | def test_documentation_popxl_adv_get_write(self):
filename = "tensor_get_write_adv.py"
self.run_python(filename, file_dir=working_dir, working_dir=working_dir) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_documentation_popxl_get_set_tensors(self):\n filename = \"tensor_get_write.py\"\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)",
"def test_remote_buffer() -> None:\n # Prepare the input and output data\n shape_1 = (1, 3, 5)\n shape_2 = (7, 11)\n d_ty... | [
"0.66837543",
"0.66518664",
"0.6184355",
"0.6113137",
"0.58957744",
"0.5887892",
"0.58565533",
"0.5855714",
"0.5791288",
"0.5715855",
"0.56533664",
"0.5649407",
"0.5645982",
"0.5569726",
"0.5525689",
"0.5520632",
"0.5512734",
"0.5490553",
"0.5452423",
"0.54212606",
"0.5381102... | 0.65643054 | 2 |
Test the popxl autodiff op | def test_documentation_popxl_autodiff(self):
filename = "autodiff.py"
self.run_python(filename, file_dir=working_dir, working_dir=working_dir) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_data_norange(self):\n ex = self.ex\n m = self.m\n n = self.n\n\n nreps = random.randint(1, 10)\n lensumrange = random.randint(1, 10)\n\n ex.nreps = nreps\n ex.sumrange = [\"j\", range(lensumrange)]\n ex.vary[\"X\"][\"with\"].add(\"rep\")\n ex.... | [
"0.49731633",
"0.49007177",
"0.4868027",
"0.48571473",
"0.48517647",
"0.48516244",
"0.4770387",
"0.47574338",
"0.474696",
"0.4732291",
"0.47281486",
"0.47268423",
"0.47249767",
"0.47073162",
"0.46844417",
"0.46804345",
"0.46610695",
"0.4651014",
"0.4639932",
"0.46340024",
"0.... | 0.6394983 | 0 |
Test the popxl in sequence context manager | def test_documentation_popxl_in_sequence(self):
filename = "in_sequence.py"
self.run_python(filename, file_dir=working_dir, working_dir=working_dir) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_pop_methods(self):\n\n batch = Batch(Mock())\n\n # mock BatchRequests\n mock_obj = Mock()\n mock_ref = Mock()\n batch._objects_batch = mock_obj\n batch._reference_batch = mock_ref\n\n mock_obj.pop.assert_not_called()\n mock_ref.pop.assert_not_called(... | [
"0.58948493",
"0.5882581",
"0.5770228",
"0.54232585",
"0.54092556",
"0.54075843",
"0.5373329",
"0.5303253",
"0.53030944",
"0.5287886",
"0.52534574",
"0.5221736",
"0.52018183",
"0.5189823",
"0.5175281",
"0.5172834",
"0.5172073",
"0.51521003",
"0.51421815",
"0.5127545",
"0.5124... | 0.65936136 | 0 |
Test the popxl remote variable | def test_documentation_popxl_remote_var(self):
filename = "remote_variable.py"
self.run_python(filename, file_dir=working_dir, working_dir=working_dir) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_documentation_popxl_remote_rts_var(self):\n filename = \"remote_rts_var.py\"\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)",
"def test_documentation_popxl_rts_var(self):\n filename = \"rts_var.py\"\n self.run_python(filename, file_dir=working_dir,... | [
"0.62325144",
"0.53577113",
"0.53224456",
"0.5209967",
"0.51961607",
"0.5094487",
"0.50844604",
"0.5058393",
"0.5020116",
"0.49631917",
"0.49456343",
"0.49408296",
"0.4912187",
"0.48899758",
"0.48812777",
"0.48232034",
"0.47519946",
"0.46955076",
"0.46878415",
"0.46760944",
"... | 0.70022154 | 0 |
Test the popxl remote rts variable | def test_documentation_popxl_remote_rts_var(self):
filename = "remote_rts_var.py"
self.run_python(filename, file_dir=working_dir, working_dir=working_dir) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_documentation_popxl_remote_var(self):\n filename = \"remote_variable.py\"\n self.run_python(filename, file_dir=working_dir, working_dir=working_dir)",
"def test_documentation_popxl_rts_var(self):\n filename = \"rts_var.py\"\n self.run_python(filename, file_dir=working_dir, wo... | [
"0.6584408",
"0.6459491",
"0.5550423",
"0.54029787",
"0.53768945",
"0.5342253",
"0.528288",
"0.52809453",
"0.5237307",
"0.51818335",
"0.5181425",
"0.50585806",
"0.5056922",
"0.50432426",
"0.503633",
"0.50314456",
"0.50298536",
"0.50265086",
"0.5013454",
"0.500096",
"0.4989955... | 0.6953812 | 0 |
Test the popxl rts variable | def test_documentation_popxl_rts_var(self):
filename = "rts_var.py"
self.run_python(filename, file_dir=working_dir, working_dir=working_dir) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test(self):\n # 0x13 is nop\n self.gdb.command(\"p *((int*) 0x%x)=0x13\" % self.target.ram)\n self.gdb.command(\"p *((int*) 0x%x)=0x13\" % (self.target.ram + 4))\n self.gdb.command(\"p *((int*) 0x%x)=0x13\" % (self.target.ram + 8))\n self.gdb.p(\"$pc=0x%x\" % self.target.ram)... | [
"0.5512495",
"0.54203445",
"0.5234334",
"0.5186235",
"0.513492",
"0.5132137",
"0.5124492",
"0.5119325",
"0.5085518",
"0.50540435",
"0.50286376",
"0.5028228",
"0.50170285",
"0.50151104",
"0.50103855",
"0.4989811",
"0.49670818",
"0.49645856",
"0.49455714",
"0.493474",
"0.493474... | 0.6135992 | 0 |
Test the popxl basic mnist example | def test_documentation_popxl_mnist(self):
filename = "mnist.py"
self.run_python(filename, file_dir=working_dir, working_dir=working_dir) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def main():\n\n os.system(\"rm -rf images; mkdir images\")\n\n if (len(sys.argv) > 1):\n N = int(sys.argv[1])\n else:\n N = 10\n\n x_test = np.load(\"../../../../data/mnist/mnist_test_images.npy\")\n\n for i in range(N):\n r,c = random.randint(6,12), random.randint(6,12)\n ... | [
"0.7192644",
"0.7088527",
"0.6992389",
"0.6823488",
"0.6820499",
"0.65735173",
"0.6531278",
"0.6529289",
"0.6502993",
"0.63889843",
"0.6371827",
"0.6368664",
"0.63274115",
"0.6315902",
"0.62861085",
"0.62725544",
"0.6220246",
"0.6188648",
"0.6144985",
"0.61362416",
"0.6119027... | 0.6742171 | 5 |
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