def simulate(df, seed_increment, switch_frequency, time_var):
np.random.seed(seed_increment)
print(seed_increment)
no_duplicates = df.set_index(['state', 'month_id',
'person']).index.is_unique
if not no_duplicates:
raise ValueError('Some months, the same person was in a state twice')
grouped = df.groupby('state')
df = grouped.apply(lambda x: reassign(x, time_var))
people = set(df['person'])
person_effect = dict(zip(people, burr.rvs(6, 10**5, size=len(people))))
df['person_effect'] = df['person'].map(person_effect)
df['person_effect'] = normalize(df['person_effect'])
return df[['person', 'person_effect']]
def check_trust_table(self, block, step):
if (block.owner_public_key in self.trust_table.keys()):
if (block.is_malicious):
self.trust_table[block.owner_public_key] = self.trust_table[
block.owner_public_key] - 1
if (self.trust_table[block.owner_public_key] == (limiar - 1)):
born_step = step + random.randint(0, 101)
time_to_validate = burr.rvs(19.207726887411624,
1.0485858296333794,
-0.1563851244724221,
9.799614489119339)
valid_step = born_step + int(round(time_to_validate))
new_transac = Transaction(4, born_step)
new_transac.target_public_key = block.owner_public_key
new_transac.origin_public_key = self.public_key.n
data = {}
data[self.topology.id_exp_trans] = []
data[self.topology.id_exp_trans].append({
"transac_type":
new_transac.transaction_type,
"born_step":
born_step,
"valid_step":
valid_step,
"target_public_key":
new_transac.target_public_key,
"origin_public_key":
new_transac.origin_public_key
})
content = "{}".format(data)
signature = rsa.sign(content.encode('utf-8'),
self.private_key, 'SHA-1')
self.topology.exp_transacts_json.append(data)
self.topology.exp_transacts[block.owner_public_key][0] += 1
self.topology.exp_transacts[
block.owner_public_key][1].append(new_transac)
self.topology.exp_transacts[
block.owner_public_key][3].append(str(signature))
self.topology.id_exp_trans += 1
else:
self.trust_table[block.owner_public_key] = self.trust_table[
block.owner_public_key] + 1
if (self.trust_table[block.owner_public_key] >= 10):
self.trust_table[block.owner_public_key] = 10
if (self.topology.bc_control_turn > len(self.topology.miners) - 1):
self.topology.bc_control_turn = 0
ax.hist(r, density=True, histtype='stepfilled', alpha=0.2) ax.legend(loc='best', frameon=False) plt.show() #burr Continuous distributions¶ from scipy.stats import burr import matplotlib.pyplot as plt import numpy as np fig, ax = plt.subplots(1, 1) #Calculate a few first moments: c, d = 10.5, 4.3 mean, var, skew, kurt = burr.stats(c, d, moments='mvsk') #Display the probability density function (pdf): x = np.linspace(burr.ppf(0.01, c, d), burr.ppf(0.99, c, d), 100) ax.plot(x, burr.pdf(x, c, d), 'r-', lw=5, alpha=0.6, label='burr pdf') #Alternatively, the distribution object can be called (as a function) to fix the shape, location and scale parameters. This returns a “frozen” RV object holding the given parameters fixed. #Freeze the distribution and display the frozen pdf: rv = burr(c, d) ax.plot(x, rv.pdf(x), 'k-', lw=2, label='frozen pdf') #Check accuracy of cdf and ppf: vals = burr.ppf([0.001, 0.5, 0.999], c, d) np.allclose([0.001, 0.5, 0.999], burr.cdf(vals, c, d)) True #Generate random numbers: r = burr.rvs(c, d, size=1000) #And compare the histogram: ax.hist(r, density=True, histtype='stepfilled', alpha=0.2) ax.legend(loc='best', frameon=False) plt.show()
def do_miner_step(self, step):
if (self.topology.miners[self.topology.bc_control_turn] == self):
block = Block(key=self.public_key.n)
if (self.is_malicious):
prob = random.random() * 100
if (block_malicious_rate > prob):
block.is_malicious = True
self.grades_flush.append(-1)
for node in self.topology.everything:
node.check_trust_table
else:
self.grades_flush.append(1)
if (len(self.topology.final_exp_trans) > 0):
for k in self.topology.final_exp_trans.keys():
if self.topology.final_exp_trans[k][0] == False:
for miner in self.topology.miners:
if (k == miner.public_key.n):
self.topology.data[
self.topology.iden] = [
miner.grades_flush,
miner.is_malicious
]
self.topology.iden += 1
self.topology.miners.remove(miner)
self.topology.everything.remove(miner)
self.topology.final_exp_trans[k][
3].signatures = self.topology.final_exp_trans[
k][2]
self.topology.final_exp_trans[k][0] = True
block.transactions.append(
self.topology.final_exp_trans[k][3])
self.topology.expss += 1
for key in self.topology.exp_transacts.keys():
if (self.topology.exp_transacts[key][2] == False
and (len(self.topology.exp_transacts[key][1]) >
(judges // 2 + 1))):
self.topology.exp_transacts[key][2] = True
born_step = step + random.randint(0, 101)
time_to_validate = burr.rvs(
19.207726887411624, 1.0485858296333794,
-0.1563851244724221, 9.799614489119339)
valid_step = born_step + int(
round(time_to_validate))
new_transac = Transaction(5, born_step)
new_transac.target_public_key = key
new_transac.origin_public_key = self.topology.exp_transacts[
key][1][0].origin_public_key
new_transac.validation_step = valid_step
self.topology.final_exp_trans[key] = [
False, self.topology.exp_transacts[key][1],
self.topology.exp_transacts[key][3],
new_transac
]
else:
self.grades_flush.append(1)
if (len(self.topology.final_exp_trans) > 0):
for k in self.topology.final_exp_trans.keys():
if self.topology.final_exp_trans[k][0] == False:
for miner in self.topology.miners:
if (k == miner.public_key.n):
self.topology.data[self.topology.iden] = [
miner.grades_flush, miner.is_malicious
]
self.topology.iden += 1
self.topology.miners.remove(miner)
self.topology.everything.remove(miner)
self.topology.final_exp_trans[k][
3].signatures = self.topology.final_exp_trans[
k][2]
self.topology.final_exp_trans[k][0] = True
block.transactions.append(
self.topology.final_exp_trans[k][3])
self.topology.expss += 1
for key in self.topology.exp_transacts.keys():
if (self.topology.exp_transacts[key][2] == False
and (len(self.topology.exp_transacts[key][1]) >
(judges // 2 + 1))):
self.topology.exp_transacts[key][2] = True
born_step = step + random.randint(0, 101)
time_to_validate = burr.rvs(19.207726887411624,
1.0485858296333794,
-0.1563851244724221,
9.799614489119339)
valid_step = born_step + int(round(time_to_validate))
new_transac = Transaction(5, born_step)
new_transac.target_public_key = key
new_transac.origin_public_key = self.topology.exp_transacts[
key][1][0].origin_public_key
new_transac.validation_step = valid_step
self.topology.final_exp_trans[key] = [
False, self.topology.exp_transacts[key][1],
self.topology.exp_transacts[key][3], new_transac
]
block_data = {}
array_of_transacs = []
block_data[self.topology.id_block_control] = []
if (len(block.transactions) > 0):
for trans in block.transactions:
transac = {}
transac[self.topology.trans_in_blocks_id] = []
transac[self.topology.trans_in_blocks_id].append({
"transac_type":
trans.transaction_type,
"miner_step":
step,
"valid_step":
trans.validation_step,
"born_step":
trans.born_step,
"target_public_key":
trans.target_public_key,
"origin_public_key":
trans.origin_public_key,
"signatures":
trans.signatures
})
self.topology.trans_in_blocks_id += 1
array_of_transacs.append(transac)
block_data[self.topology.id_block_control].append({
"miner_step":
step,
"miner_pub_key":
self.public_key.n,
"transactions":
array_of_transacs,
"is_malicious":
block.is_malicious
})
self.topology.id_block_control += 1
self.topology.blocks_from_controlbc_json.append(block_data)
if (self.topology.bc_turn > len(self.topology.miners) - 1):
self.topology.bc_turn = 0
""" import numpy as np from numpy import random from scipy.stats import burr import matplotlib.pyplot as plt fig, ax = plt.subplots(1, 1) c, d = 10.5, 4.3 mean, var, skew, kurt = burr.stats(c, d, moments='mvsk') x = np.linspace(burr.ppf(0.01, c, d), burr.ppf(0.99, c, d), 100) ax.plot(x, burr.pdf(x, c, d), 'r-', lw=5, alpha=0.6, label='burr pdf') rv = burr(c, d) ax.plot(x, rv.pdf(x), 'k-', lw=2, label='frozen pdf') vals = burr.ppf([0.001, 0.5, 0.999], c, d) np.allclose([0.001, 0.5, 0.999], burr.cdf(vals, c, d)) r = burr.rvs(c, d, size=1) print(r) ax.hist(r, density=True, histtype='stepfilled', alpha=0.2) ax.legend(loc='best', frameon=False) plt.show() fig, ax = plt.subplots(1, 1) c, d = 5.1, 3.8 mean, var, skew, kurt = burr.stats(c, d, moments='mvsk') x = np.linspace(burr.ppf(0.01, c, d), burr.ppf(0.99, c, d), 100) ax.plot(x, burr.pdf(x, c, d), 'r-', lw=5, alpha=0.6, label='burr pdf') rv = burr(c, d) ax.plot(x, rv.pdf(x), 'k-', lw=2, label='frozen pdf') vals = burr.ppf([0.001, 0.5, 0.999], c, d) np.allclose([0.001, 0.5, 0.999], burr.cdf(vals, c, d)) r = burr.rvs(c, d, size=5)