def encode_method_test():
a = VASP_DataExtract(vasp_dir="data\Pd_CH_s_fcc_stand.cif")
c = a.get_output_as_atom3Dspace()
e = a.get_energy_info()
coord, energy, atom_case = c.generate_data()
a = AtomGrid(coord, atom_case)
a.get_grid_border()
a.make_grid(minX=-0.5,
maxX=7,
minY=-3,
maxY=7.5,
minZ=-0.5,
maxZ=9.5,
resolutionX=100,
resolutionY=100,
resolutionZ=100)
train_index = [1]
en2 = a.grid_encode(train_index)[0]
en1 = a.grid_encode1(train_index)[0]
# en2 方法比en1快很多很多，同时要比较一下编码结果是否一致
print(en1)
print("_______________________")
print(en2)
print(en1 - en2)
print(np.sum(en1 - en2))
c = a.get_output_as_atom3Dspace()
e = a.get_energy_info()
coord, energy, atom_case = c.generate_data()
# 1 得到坐标的atom case的信息，输入Vasp dir
# print(coord)
a = AtomGrid(coord, atom_case)
# 2 得到边界信息，然后根据边界人为确定格子大小和边界
a.get_grid_border()
a.make_grid(minX=-6,
maxX=15,
minY=-6,
maxY=15,
minZ=-6,
maxZ=15,
resolutionX=60,
resolutionY=60,
resolutionZ=60)
# 3 进行编码，给出编码的index，打乱分数据集,然后存储不同的数据集
all_index = list(range(coord.shape[0]))
np.random.shuffle(all_index)
print(all_index)
sample_num = len(all_index)
train_test_ratio = 0.7
train_index = all_index[:int(sample_num * train_test_ratio)]
test_index = all_index[int(sample_num * train_test_ratio):]
def make_dataset(vasp_dir, atom_case, save=False, max_dataset_number=-1):
a = VASP_DataExtract(vasp_dir=vasp_dir)
vasp_dir_name = vasp_dir.split("/")[-1].split('\\')[-1]
c = a.get_output_as_atom3Dspace()
e = a.get_energy_info()
coord, energy, _atom_case = c.generate_data()
for i in _atom_case:
if i not in atom_case:
raise ValueError(
"Input Atom Case Didn't Contain %s, make sure all atom cases are in atom_case"
% _atom_case)
atom_case = atom_case
# 1 得到坐标的atom case的信息，输入Vasp dir
# print(coord)
a = AtomGrid(coord, atom_case)
# 2 得到边界信息，然后根据边界人为确定格子大小和边界
a.get_grid_border()
a.make_grid(minX=-6,
maxX=15,
minY=-6,
maxY=15,
minZ=-6,
maxZ=15,
resolutionX=60,
resolutionY=60,
resolutionZ=60)
# 3 进行编码，给出编码的index，打乱分数据集,然后存储不同的数据集
all_index = list(range(coord.shape[0]))[:max_dataset_number]
np.random.shuffle(all_index)
print(all_index)
sample_num = len(all_index)
train_test_ratio = 0.7
trainX = testX = None
train_index = all_index[:int(sample_num * train_test_ratio)]
test_index = all_index[int(sample_num * train_test_ratio):]
if (save != False):
a.grid_encode(train_index, "%s_trainX.npy" % vasp_dir_name)
a.grid_encode(test_index, "%s_testX.npy" % vasp_dir_name)
else:
trainX = a.grid_encode(train_index, save=False)
testX = a.grid_encode(test_index, save=False)
trainY = []
testY = []
for i in train_index:
trainY.append(energy[i])
for i in test_index:
testY.append(energy[i])
trainY = np.array(trainY)
testY = np.array(testY)
if save != False:
np.save("%s_trainY.npy" % vasp_dir_name, trainY)
np.save("%s_testY.npy" % vasp_dir_name, testY)
else:
return trainX, trainY, testX, testY