numpy – 複素数に関係する関数について

import numpy as np

a = np.array([1.2, 3.4 + 5j, 6j])

# numpy.real()
print(np.real(a))  # [1.2 3.4 0. ]

# ndarray.real
print(a.real)  # [1.2 3.4 0. ]

a = np.array([1.2, 6 + 4e-15j])
print(np.real_if_close(a))  # [1.2 6. ]

# すべての虚部の値が閾値以下でない場合、実数を返さない。
a = np.array([1.2, 2 + 4j, 6 + 4e-15j])
print(np.real_if_close(a))  # [1.2+0.e+00j 2. +4.e+00j 6. +4.e-15j]

a = np.array([1.2, 3.4 + 5j, 6j])

# numpy.imag()
print(np.imag(a))  # [0. 5. 6.]

# ndarray.imag
print(a.imag)  # [0. 5. 6.]

a = np.array([1.2, 3.4 + 5j, 6j])

print(np.conj(a))  # [1.2-0.j 3.4-5.j 0. -6.j]

import numpy as np
from matplotlib import pyplot as plt

arr = np.array([1.2, 3.4 + 5j, 2 - 3j])
print("angle (radian)", np.angle(arr))
print("angle (degree)", np.angle(arr, deg=True))

fig = plt.figure(figsize=(6, 6))
ax = fig.add_subplot(111, projection="polar")
ax.grid(True)
for x in arr:
    ax.plot([0, np.angle(x)], [0, np.abs(x)], label=x, marker="o")
ax.legend()
plt.show()

a = np.array([1.2, 3.4 + 5j, 6j])

print(np.isreal(a))

a = np.array([1.2, 3.4 + 5j, 6j])

print(np.iscomplex(a))

print(np.iscomplexobj([1.2, 3.4 + 5j, 6j]))
print(np.iscomplexobj([1.2, 3.4, 6]))
print(np.iscomplexobj(np.array([1.2, 3.4, 6], dtype=np.complex64)))

print(np.isrealobj([1.2, 3.4 + 5j, 6j]))
print(np.isrealobj([1.2, 3.4, 6]))
print(np.isrealobj(np.array([1.2, 3.4, 6], dtype=np.complex64)))