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show.py
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import os
from matplotlib import pyplot as plt
from scipy.io import loadmat, savemat
plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签
plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号
plt.rcParams['font.family'] = 'SimHei'
plt.rcParams['axes.unicode_minus'] = False # 步骤二（解决坐标轴负数的负号显示问题）
def show(path):
data = loadmat(path)
mag = data['mag']
# g = data['g']
dir_path = path[:-4]
if not os.path.exists(dir_path):
os.mkdir(dir_path)
if 'ma' in data.keys():
ma = data['ma']
plt.imsave(dir_path + '/ma.png', ma, cmap='jet')
vmin, vmax = mag.min(), mag.max()
for i in range(mag.shape[2]):
plt.imsave(
dir_path + f'/mag{i:03d}.png',
mag[..., i],
cmap='jet',
vmin=vmin,
vmax=vmax)
savemat(f'{dir_path}/data.mat', data)
def show_zl():
# plt.rcParams['font.sans-serif']=['SimHei']
# plt.rcParams['axes.unicode_minus'] = False
pred = loadmat('data/field_data/field_ref_v2.mat')
ori = loadmat('data/field_data/field_data.mat')['ma']
ma = pred['ma']
mag = pred['mag']
plt.imshow(ma, cmap='gray')
plt.title('重建地表磁异常')
plt.yticks((0, 14, 29, 43, 57, 71, 86, 99), (0, 1, 2, 3, 4, 5, 6, 7))
plt.ylabel('Y / km')
plt.xticks((0, 15, 31, 46, 62), (0, 1, 2, 3, 4))
plt.xlabel('X / km')
plt.savefig('data/field_data/field_ref_v2/ma_.png')
plt.imshow(ori, cmap='gray')
plt.title('原始地表磁异常')
plt.yticks((0, 14, 29, 43, 57, 71, 86, 99), (0, 1, 2, 3, 4, 5, 6, 7))
plt.ylabel('Y / km')
plt.xticks((0, 15, 31, 46, 62), (0, 1, 2, 3, 4))
plt.xlabel('X / km')
plt.savefig('data/field_data/field_ref_v2/ma_ori.png')
plt.close()
print(mag.shape)
mag_data = mag[65].T
plt.imshow(mag_data, cmap='gray')
plt.title('磁化率剖面')
plt.xticks((0, 15, 31, 46, 61), (0, 1, 2, 3, 4))
plt.xlabel('X / km')
# plt.xticks((0, 14, 29, 43, 57, 71, 86, 99), (0, 1, 2, 3, 4, 5, 6, 7))
# plt.xlabel('Y / km')
plt.yticks((0, 9.5, 19), (0, 1, 2))
plt.ylabel('Depth / km')
plt.savefig('data/field_data/field_ref_v2/mag.png')
def draw_loss():
file1 = 'work_dirs/ref_v1_self/weights/log.txt'
with open(file1, 'r', encoding='utf-8') as f:
data1 = f.read().split('\n')
file2 = 'work_dirs/ref_v2_self/weights/log.txt'
with open(file2, 'r', encoding='utf-8') as f:
data2 = f.read().split('\n')
print(len(data1))
loss1 = []
for data in data1:
items = data.split('\t')
if len(items) < 2:
continue
loss1.append(float(items[2].split(' ')[1]))
loss2 = []
for data in data2:
items = data.split('\t')
if len(items) < 2:
continue
loss2.append(float(items[2].split(' ')[1]))
print(len(loss1), len(loss2))
x = list(range(1, 3001))
plt.plot(x, loss2, 'r')
plt.plot(x, loss1, 'b')
plt.axis([-10, 3001, -0.005, 5.8])
plt.title('The influence of `tan activate` on training')
plt.legend(['SSLMI', 'without `tan`'])
plt.xlabel('epoch')
plt.ylabel('loss')
plt.savefig('data/field_data/loss_en.png')
if __name__ == '__main__':
# show('data/field_data/field_ref_v1_65.mat')
# show('data/field_data/field_ref_v2_65.mat')
# show_zl()
draw_loss()