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| import numpy as np
from matplotlib import pyplot as plt
from pathos.multiprocessing import ProcessingPool as newPool
def test_bz(bzzs, bzxls, V):
print("测试")
bzz = bzzs[0]
bzxl = bzxls[0]
test_ = (bzz * bzxl) - np.dot(bzxl, V)
print(test_)
if (sum(test_) < 10**-8):
print("本征值没问题")
def plot_n(xst, vst, n_see=16):
legends = []
plt.ylim(-1.5, 1.5)
for i in [0]:
s = "-."
if (i == 0):
s = "-"
plt.plot(xst[:, n_see + i], s)
legends.append("n=%d" % (n_see + i))
plt.legend(legends)
plt.savefig("test/nt")
plt.close()
def plot_t(xst, jump=100):
for xs in xst[::jump]:
plt.plot(xs)
plt.savefig("test/xst")
plt.close()
def get_Ets(xst, vst, bzzs, bzxls):
bzxls = np.transpose(bzxls)
xst_ = np.dot(xst, bzxls)
vst_ = np.dot(vst, bzxls)
Ets = vst_ ** 2 + xst_**2 * np.abs(bzzs)
np.save("cache/data/ets.npy", Ets)
return Ets
def ty_xst(tys, bzxls, log=True):
plt.figure()
if (log):
plt.axes(yscale="log")
print(tys)
ys = np.dot(tys, np.transpose(bzxls))
plt.scatter(np.linspace(1, len(tys), len(tys)), ys)
plt.savefig("cache/imgs/ty_xst")
plt.close()
def plot_ek(Ets, t=1, onlyOdd=True, log=True):
es = Ets[t, :]
N = len(es)
xs = np.linspace(1, N, N)
if(onlyOdd):
es = es[::2]
xs = xs[::2]
print(xs)
plt.figure()
if(log):
plt.axes(yscale="log")
plt.scatter(xs, es)
for i in range(len(es)):
plt.annotate(str(int(xs[i])), xy=(xs[i], es[i]), xytext=(xs[i], es[i]))
plt.savefig("test/eks")
plt.close()
def plot_et(Es, ks=[0, 1, 2, 3, 4, 5], log=False):
Es = np.array(Es)
print(np.shape(Es))
plt.figure()
if (log):
plt.axes(yscale="log")
Es_ = []
legend = []
for k in ks:
k = k
print(k)
legend.append(k)
Es_.append(Es[:, k])
ts = np.arange(len(Es[:, 0])) * t_
for j in range(len(ks)):
y = Es_[j]
plt.plot(ts, y)
plt.legend(legend)
plt.savefig("test/ets")
plt.close()
def plt_bz(k, bzxl, bzz):
N = len(bzxls)
x = np.linspace(1, N, N)
print(x)
js = np.linspace(0, N, N + 1)
print(js)
file = "cache/imgs/bz/%d" % k
plt.scatter(x, bzxl)
plt.plot((2 / (N + 1))**0.5 * np.sin(np.pi * k * js / (N + 1)))
title = "%d=%f" % (k, bzz)
plt.title(title)
print(title)
plt.savefig(file)
plt.close()
def plt_bzs(bzzs, bzxls):
plt.plot(bzzs)
plt.savefig("cache/imgs/bz/bz")
plt.close()
pool = newPool()
N = len(bzzs)
pool.map(plt_bz, np.linspace(1, N, N), bzxls, bzzs)
pool.close()
pool.join()
file_add = "_alpha"
N = 32
step_start = 0
steps = 1000000
t_ = 0.01
xst = np.load("cache/data/xst%s.npy" % file_add)[step_start:step_start +
steps, :]
vst = np.load("cache/data/vst%s.npy" % file_add)[step_start:step_start +
steps, :]
print(np.shape(xst))
bzzs = np.load("data/bzzs.npy")
bzxls = np.load("data/bzxls.npy")
V = np.load("data/V.npy")
test_bz(bzzs, bzxls, V)
Ets = get_Ets(xst, vst, bzzs, bzxls)
print(np.shape(Ets))
plot_et(Ets)
|
