modified to support COO

.gitattributes

@@ -0,0 +1,2 @@
+# Auto detect text files and perform LF normalization
+* text=auto

.gitignore

@@ -1,14 +1,19 @@
 # python cache
 *.pyc
-
+*.csv
 # virtual env
 venv
 
 # pycharm
 .idea/
 
+# vscode
+.vscode/
+
+# simulation data
+SimuData/
+
 # DS_Store
 .DS_Store
-
-# Huge files
-indirect_refs.csv
+indirect_refs.csv
+indirect_refs.csv

README.md

@@ -1,6 +1,6 @@
 # IOTA TANGLE SIMULATION
 
-This is originally a single- and multi-agent simulation of the IOTA Tangle, as described in the white-paper, which is extended to support Coodinator and transaction confirmation simulations. This extended simulator is used to build a hybrid model in this M.Sc thesis project.
+This is a single- and multi-agent simulation of the IOTA Tangle, as described in the white-paper.
 
 ## Getting Started
 
@@ -55,12 +55,7 @@ python core.py
 
 In this file you can also change the configurations of the simulation.
 
-## Modified by
-
-* Sara Ghaemi
-* Caixiang Fan
-
-## Original Authors
+## Authors
 
 * Manuel Zander
 
@@ -70,4 +65,4 @@ See LICENSE.txt
 
 ## Acknowledgments
 
-Many thanks to Dominik Harz (nud3l) for his help and suggestions during development of this software.
+Many thanks to Dominik Harz (nud3l) for his help and suggestions during development of this software.

cal_layered_conf_num.py

@@ -0,0 +1,177 @@
+import pandas as pd
+import matplotlib.pyplot as plt
+import csv
+import numpy as np
+import time
+
+new_indirect_ref_list = []
+index = 0
+lambdas = [8, 9, 10]
+# simulation_num = [i for i in range(1, 43)]
+average_start = 1
+layer = {}
+layer_num = 1
+
+
+def find_indirect_refs(loc, refs):
+ """
+ This function finds all the indirect references for all of our transactions.
+ :param loc: Current transaction
+ :param refs: References
+ """
+ global index
+ global new_indirect_ref_list
+ if loc == 0:
+ return
+ elif loc in indirect_references.keys():
+ new_indirect_ref_list = list(set((new_indirect_ref_list + indirect_references[loc])))
+ return
+ else:
+ num_of_refs = len(refs[loc])
+ if refs[loc][0] not in new_indirect_ref_list:
+ new_indirect_ref_list.append(refs[loc][0])
+ find_indirect_refs(refs[loc][0], refs)
+ if num_of_refs == 2:
+ if refs[loc][1] not in new_indirect_ref_list:
+ new_indirect_ref_list.append(refs[loc][1])
+ find_indirect_refs(refs[loc][1], refs)
+
+
+def find_layers_refs(loc, refs, confirmed_by_milestone):
+ global layer_num
+ if loc not in confirmed_by_milestone or loc == 0:
+ return
+ else:
+ num_of_refs = len(refs[loc])
+ if refs[loc][0] in list(layer.keys()):
+ layer[refs[loc][0]] = min(layer[refs[loc][0]], layer_num)
+ else:
+ layer[refs[loc][0]] = layer_num
+ layer_num += 1
+ find_layers_refs(refs[loc][0], refs, confirmed_by_milestone)
+ layer_num -= 1
+ if num_of_refs == 2:
+ if refs[loc][1] in list(layer.keys()):
+ layer[refs[loc][1]] = min(layer[refs[loc][1]], layer_num)
+ else:
+ layer[refs[loc][1]] = layer_num
+ layer_num += 1
+ find_layers_refs(refs[loc][1], refs, confirmed_by_milestone)
+ layer_num -= 1
+
+
+mean_confirmed = []
+mean_confirmed_in_each_simulation = []
+
+# Calculate all lambdas corresponding layered confirmed tx and store into csv files
+for current_lambda in lambdas:
+ begin_time = time.time()
+ # Reading data from simulation file
+ data = pd.read_csv("SimuData/layered_conf_num_20_Milestones/lambda_{}_tansactions_{}.csv".format(current_lambda,
+ current_lambda * 1200))
+
+ data = data.sort_index()
+ print('read main csv file: ', time.time() - begin_time)
+ references = data['references'].apply(eval).to_dict()
+
+ # Finding all the indirect references
+ begin_time = time.time()
+ indirect_references = {}
+ for j in references:
+ new_indirect_ref_list = []
+ find_indirect_refs(j, references)
+ indirect_references[j] = new_indirect_ref_list
+ print('recursive function: ', time.time() - begin_time)
+
+ # Storing all indirect references in a csv file
+ begin_time = time.time()
+ with open('indirect_refs.csv', 'w', newline='') as file:
+ writer = csv.writer(file, dialect='excel')
+ writer.writerow(['index', 'indirect_references'])
+ writer.writerow([0, []])
+ for transaction in indirect_references.keys():
+ if transaction != 0:
+ line = []
+ line.append(transaction)
+ line.append(indirect_references[transaction])
+ writer.writerow(line)
+ print('write to indirect_refs.csv: ', time.time() - begin_time)
+
+ # Finding all milestones
+ begin_time = time.time()
+ milestone_times = [i for i in range(int(max(data['time']))) if i % 60 == 0]
+ print(milestone_times)
+ milestone_indexes = []
+ for i in milestone_times:
+ try:
+ milestone_indexes.append((data[data['time'] == i].index[0]))
+ except:
+ pass
+ print(milestone_indexes)
+ print('find milestones from data frame: ', time.time() - begin_time)
+
+ # Reading from the indirect references file
+ begin_time = time.time()
+ indirect_refs_data = pd.read_csv("indirect_refs.csv")
+ milestone_indirect = [indirect_refs_data.loc[i][1] for i in milestone_indexes]
+ print('read indirect_refs.csv and find milestones indirect from data frame: ', time.time() - begin_time)
+
+ # Find all the transactions that are confirmed by each milestone and their number.
+ conf_by_milestone = {}
+ num_conf_by_milestone = {}
+ for trans in range(len(milestone_indirect)):
+ already_confirmed = []
+ for ms in range(0, trans):
+ already_confirmed = list(set(already_confirmed + indirect_references[milestone_indexes[ms]]))
+
+ if trans == 0:
+ num_conf_by_milestone[trans] = len(indirect_references[milestone_indexes[trans]])
+ conf_by_milestone[milestone_indexes[trans]] = indirect_references[milestone_indexes[trans]]
+ else:
+ conf_by_milestone[milestone_indexes[trans]] = []
+ for ref in indirect_references[milestone_indexes[trans]]:
+ if ref not in already_confirmed:
+ conf_by_milestone[milestone_indexes[trans]].append(ref)
+ num_conf_by_milestone[trans] = len(conf_by_milestone[milestone_indexes[trans]])
+ print(num_conf_by_milestone)
+ print(conf_by_milestone)
+
+ for i in range(5, 16):
+ print('Starting', milestone_indexes[i])
+
+ begin_time = time.time()
+ layer_num = 1
+ layer = {}
+ find_layers_refs(milestone_indexes[i], references,
+ conf_by_milestone[milestone_indexes[i]] + [milestone_indexes[i]])
+ print('second recursive took: ', time.time() - begin_time)
+
+ divided_by_layer = {}
+ all_layers = list(set(list(layer.values())))
+ conf_in_layer = []
+ for k in all_layers:
+ divided_by_layer[k] = [l for l, j in layer.items() if j == k]
+ print(divided_by_layer[k])
+ conf_in_layer.append(len(divided_by_layer[k]))
+ x = np.array(all_layers)
+ y = np.array(conf_in_layer)
+
+ # # # # Store transactions confirmed by each milestone on each layer into csv files
+ with open('SimuData/layered_conf_num_20_Milestones/layered_conf_num/lambda_{}_layer_conf_{}.csv'.format(current_lambda, i), 'w',
+ newline='') as file2:
+ writer = csv.writer(file2, dialect='excel')
+ writer.writerow(['layer_index', 'conf_txs_num'])
+ for layer in x:
+ line = []
+ line.append(layer)
+ line.append(y[layer - 1])
+ writer.writerow(line)
+
+ # ax.set_xticks(np.arange(0, 20, step=1))
+ # ax.set_yticks(np.arange(0, y.max(), step=1))
+ # ax.plot(x, y, label='Milestone {}'.format(i), marker='o')
+ # plt.rcParams.update({'font.size': 22})
+ print('done with {} conf in layer is: {}'.format(i, conf_in_layer))
+
+ # legend = ax.legend(loc='upper right')
+ # plt.show()