def prob():
df = pd.read_csv('bank-additional.csv', delimiter=',')
newdata = np.zeros((4119, 20))
for i in range(1, 4119):
newdata[i - 1, 0] = df.iloc[i, 0]
if df.iloc[i, 1] == 'unknown':
newdata[i - 1, 1] = 0
if df.iloc[i, 1] == 'admin.':
newdata[i - 1, 1] = 1
if df.iloc[i, 1] == 'blue-collar':
newdata[i - 1, 1] = 2
if df.iloc[i, 1] == 'entrepreneur':
newdata[i - 1, 1] = 3
if df.iloc[i, 1] == 'housemaid':
newdata[i - 1, 1] = 4
if df.iloc[i, 1] == 'management':
newdata[i - 1, 1] = 5
if df.iloc[i, 1] == 'retired':
newdata[i - 1, 1] = 6
if df.iloc[i, 1] == 'self-employed':
newdata[i - 1, 1] = 7
if df.iloc[i, 1] == 'services':
newdata[i - 1, 1] = 8
if df.iloc[i, 1] == 'student':
newdata[i - 1, 1] = 9
if df.iloc[i, 1] == 'technician':
newdata[i - 1, 1] = 10
if df.iloc[i, 1] == 'unemployed':
newdata[i - 1, 1] = 11
if df.iloc[i, 2] == 'unknown':
newdata[i - 1, 2] = 0
if df.iloc[i, 2] == 'married':
newdata[i - 1, 2] = 1
if df.iloc[i, 2] == 'single':
newdata[i - 1, 2] = 2
if df.iloc[i, 2] == 'divorced':
newdata[i - 1, 2] = 3
if df.iloc[i, 3] == 'unknown':
newdata[i - 1, 3] = 0
if df.iloc[i, 3] == 'basic.4y':
newdata[i - 1, 3] = 1
if df.iloc[i, 3] == 'basic.6y':
newdata[i - 1, 3] = 2
if df.iloc[i, 3] == 'basic.9y':
newdata[i - 1, 3] = 3
if df.iloc[i, 3] == 'high.school':
newdata[i - 1, 3] = 4
if df.iloc[i, 3] == 'illiterate':
newdata[i - 1, 3] = 5
if df.iloc[i, 3] == 'professional.course':
newdata[i - 1, 3] = 6
if df.iloc[i, 3] == 'university.degree':
newdata[i - 1, 3] = 7
if df.iloc[i, 4] == 'unknown':
newdata[i - 1, 4] = 0
if df.iloc[i, 4] == 'no':
newdata[i - 1, 4] = 1
if df.iloc[i, 4] == 'yes':
newdata[i - 1, 4] = 2
if df.iloc[i, 5] == 'unknown':
newdata[i - 1, 5] = 0
if df.iloc[i, 5] == 'no':
newdata[i - 1, 5] = 1
if df.iloc[i, 5] == 'yes':
newdata[i - 1, 5] = 2
if df.iloc[i, 6] == 'unknown':
newdata[i - 1, 6] = 0
if df.iloc[i, 6] == 'no':
newdata[i - 1, 6] = 1
if df.iloc[i, 6] == 'yes':
newdata[i - 1, 6] = 2
if df.iloc[i, 7] == 'cellular':
newdata[i - 1, 7] = 1
if df.iloc[i, 7] == 'telephone':
newdata[i - 1, 7] = 2
if df.iloc[i, 8] == 'apr':
newdata[i - 1, 8] = 1
if df.iloc[i, 8] == 'aug':
newdata[i - 1, 8] = 2
if df.iloc[i, 8] == 'dec':
newdata[i - 1, 8] = 3
if df.iloc[i, 8] == 'jul':
newdata[i - 1, 8] = 4
if df.iloc[i, 8] == 'jun':
newdata[i - 1, 8] = 5
if df.iloc[i, 8] == 'mar':
newdata[i - 1, 8] = 6
if df.iloc[i, 8] == 'may':
newdata[i - 1, 8] = 7
if df.iloc[i, 8] == 'nov':
newdata[i - 1, 8] = 8
if df.iloc[i, 8] == 'oct':
newdata[i - 1, 8] = 9
if df.iloc[i, 8] == 'sep':
newdata[i - 1, 8] = 10
if df.iloc[i, 9] == 'fri':
newdata[i - 1, 9] = 1
if df.iloc[i, 9] == 'mon':
newdata[i - 1, 9] = 2
if df.iloc[i, 9] == 'thu':
newdata[i - 1, 9] = 3
if df.iloc[i, 9] == 'tue':
newdata[i - 1, 9] = 4
if df.iloc[i, 9] == 'wed':
newdata[i - 1, 9] = 5
newdata[i - 1, 10] = df.iloc[i, 10]
newdata[i - 1, 11] = df.iloc[i, 11]
newdata[i - 1, 12] = df.iloc[i, 12]
if df.iloc[i, 13] == 'nonexistent':
newdata[i - 1, 13] = 0
if df.iloc[i, 13] == 'failure':
newdata[i - 1, 13] = 1
if df.iloc[i, 13] == 'success':
newdata[i - 1, 13] = 2
newdata[i - 1, 14] = df.iloc[i, 14]
newdata[i - 1, 15] = df.iloc[i, 15]
newdata[i - 1, 16] = df.iloc[i, 16]
newdata[i - 1, 17] = df.iloc[i, 17]
newdata[i - 1, 18] = df.iloc[i, 18]
if df.iloc[i, 19] == 'no':
newdata[i - 1, 19] = 0
else:
newdata[i - 1, 19] = 1
np.savetxt('see.csv', newdata, delimiter=',')
#select the test
Random = np.random.randint(4120, size=618)
Random = np.unique(Random)
m = Random.shape[0]
Test = np.zeros((m, 20))
Trainset = np.zeros((4119, 20))
for j in range(0, m - 1):
Test[j, :] = newdata[Random[j], :]
Test = Test[[i for i, x in enumerate(Test) if x.any()]]
test_num = Test.shape[0]
count1 = 0
for k in range(0, 4119):
flag = 0
for n in range(1, m):
if k == Random[n]:
flag = 1
if flag != 1:
Trainset[count1, :] = newdata[k, :]
count1 = count1 + 1
# enumerate the O
Trainset = Trainset[[i for i, x in enumerate(Trainset) if x.any()]]
train_num = Trainset.shape[0]
label = newdata[:, 19]
a = cnt.CNT(label, 0)
b = cnt.CNT(label, 1)
label1 = np.zeros((a, 20))
label2 = np.zeros((b, 20))
ratio = int(np.fix(a / b))
count1 = 0
count2 = 0
for j in range(0, 4119):
if newdata[j, 19] == 0:
label1[count1, :] = newdata[j, :]
count1 = count1 + 1
if newdata[j, 19] == 1:
label2[count2, :] = newdata[j, :]
count2 = count2 + 1
for k in range(0, 12):
L1 = cnt.CNT(Trainset[:, 1], k)
L2 = cnt.CNT(label2[:, 1], k)
Trainset[:, 1] = np.where(Trainset[:, 1] == k, L2 / (L1 + L2),
Trainset[:, 1])
Test[:, 1] = np.where(Test[:, 1] == k, L2 / (L1 + L2), Test[:, 1])
for k in range(0, 4):
L1 = cnt.CNT(Trainset[:, 2], k)
L2 = cnt.CNT(label2[:, 2], k)
Trainset[:, 2] = np.where(Trainset[:, 2] == k, L2 / (L1 + L2),
Trainset[:, 2])
Test[:, 2] = np.where(Test[:, 2] == k, L2 / (L1 + L2), Test[:, 2])
for k in range(0, 8):
L1 = cnt.CNT(Trainset[:, 3], k)
L2 = cnt.CNT(label2[:, 3], k)
Trainset[:, 3] = np.where(Trainset[:, 3] == k, L2 / (L1 + L2),
Trainset[:, 3])
Test[:, 3] = np.where(Test[:, 3] == k, L2 / (L1 + L2), Test[:, 3])
for k in range(0, 3):
L1 = cnt.CNT(Trainset[:, 4], k)
L2 = cnt.CNT(label2[:, 4], k)
Trainset[:, 4] = np.where(Trainset[:, 4] == k, L2 / (L1 + L2),
Trainset[:, 4])
Test[:, 4] = np.where(Test[:, 4] == k, L2 / (L1 + L2), Test[:, 4])
for k in range(0, 3):
L1 = cnt.CNT(Trainset[:, 5], k)
L2 = cnt.CNT(label2[:, 5], k)
Trainset[:, 5] = np.where(Trainset[:, 5] == k, L2 / (L1 + L2),
Trainset[:, 5])
Test[:, 5] = np.where(Test[:, 5] == k, L2 / (L1 + L2), Test[:, 5])
for k in range(0, 3):
L1 = cnt.CNT(Trainset[:, 6], k)
L2 = cnt.CNT(label2[:, 6], k)
Trainset[:, 6] = np.where(Trainset[:, 6] == k, L2 / (L1 + L2),
Trainset[:, 6])
Test[:, 6] = np.where(Test[:, 6] == k, L2 / (L1 + L2), Test[:, 6])
for k in range(1, 3):
L1 = cnt.CNT(Trainset[:, 7], k)
L2 = cnt.CNT(label2[:, 7], k)
Trainset[:, 7] = np.where(Trainset[:, 7] == k, L2 / (L1 + L2),
Trainset[:, 7])
Test[:, 7] = np.where(Test[:, 7] == k, L2 / (L1 + L2), Test[:, 7])
for k in range(1, 11):
L1 = cnt.CNT(Trainset[:, 8], k)
L2 = cnt.CNT(label2[:, 8], k)
Trainset[:, 8] = np.where(Trainset[:, 8] == k, L1 / (L1 + L2),
Trainset[:, 8])
Test[:, 8] = np.where(Test[:, 8] == k, L1 / (L1 + L2), Test[:, 8])
for k in range(1, 6):
L1 = cnt.CNT(Trainset[:, 9], k)
L2 = cnt.CNT(label2[:, 9], k)
Trainset[:, 9] = np.where(Trainset[:, 9] == k, L2 / (L1 + L2),
Trainset[:, 9])
Test[:, 9] = np.where(Test[:, 9] == k, L2 / (L1 + L2), Test[:, 9])
for k in range(0, 3):
L1 = cnt.CNT(Trainset[:, 13], k)
L2 = cnt.CNT(label2[:, 13], k)
Trainset[:, 13] = np.where(Trainset[:, 13] == k, L2 / (L1 + L2),
Trainset[:, 13])
Test[:, 13] = np.where(Test[:, 13] == k, L2 / (L1 + L2), Test[:, 13])
np.savetxt('Trainset.csv', Trainset, delimiter=',')
np.savetxt('Test.csv', Test, delimiter=',')
maxName = prefix + "max.dat" energy = 0.5 angleSamples = 200 offsetSamples = 2 rotSamples = 2 angleOffset = 0.05 * np.pi radius1 = 0.5 * const.A_CC * np.sqrt(3.0 * (n1**2 + n1 * m1 + m1**2)) / np.pi radius2 = 0.5 * const.A_CC * np.sqrt(3.0 * (n2**2 + n2 * m2 + m2**2)) / np.pi distance = radius1 + radius2 + 3.13 unitCell1 = cnt.CNT(n1, m1, 1) unitCell2 = cnt.CNT(n2, m2, 1) offsetMax1 = unitCell1.length offsetMax2 = unitCell2.length offsetStep1 = offsetMax1 / rotSamples offsetStep2 = offsetMax2 / rotSamples offsetMax1 -= offsetStep1 offsetMax2 -= offsetStep2 rotMax1 = 2.0 * np.pi / np.gcd(n1, m1) rotMax2 = 2.0 * np.pi / np.gcd(n2, m2) rotStep1 = rotMax1 / rotSamples rotStep2 = rotMax2 / rotSamples rotMax1 -= rotStep1 rotMax2 -= rotStep2
import kwant import matplotlib.pyplot as plt import numpy as np import constants as const import systems import cnt n1 = 6 m1 = 6 n2 = 6 m2 = 6 unitCellLength = cnt.CNT(n1, m1, 1).length radius = 1.5/np.pi*const.A_CC * 6 circumference = 2.0*np.pi*radius angle1 = -0.5*const.A_CC / radius angle2 = (circumference/12.0 - const.A_CC) / radius distance = 2.0*radius + 3.2 system = systems.InfiniteContact(n1, m1, distance, rot1=angle1, rot2=angle2, offset=0.0*unitCellLength) system.plotSystem() system.plotBandStructure()