def main():
data_path = "../../data/train/before/LINE_100_dbdt.dat"
with fluid.dygraph.guard():
model = AlexNet()
# Load static
min_dict, _ = fluid.load_dygraph(model_path='min_polyfit')
# print(min_dict)
model.set_dict(stat_dict=min_dict)
model.eval()
data_file = SingleFile(data_path)
one_point = data_file.get_one_point()
data = one_point.get_data()
data = np.array(data, 'float32').reshape(1, 2, 1, 100)
# teacher.res
data = fluid.dygraph.to_variable(data)
logits = model(data)
result = logits.numpy()
result = back_change(result)
x_data = one_point.x
print("RESULT: \n", result)
one_point.plot(show=False, label='origin')
plt.plot(x_data, [exponenial_func(x, *result[0]) for x in x_data], label='predict')
plt.show()
def read_data(data_path, placeholder, max_input_length=500):
logger.info("Logging data {}...".format(placeholder))
sf = SingleFile(data_path)
data = []
all_point_number = 500
# all_point_number = sf.point_number
with tqdm(total=all_point_number) as pbar:
pbar.set_description("Reading {}".format(placeholder))
for i in range(all_point_number):
point = sf.get_one_point()
x_data, y_data = point.get_data()
output = data_handle(x_data, y_data, MAX_INPUT_LENGTH)
data.append(output.tolist())
pbar.update(1)
return K.cast_to_floatx(np.array(data))
def generateAllPictures(data_dir):
father_dir = os.path.dirname(data_dir)
before_data_dir = os.path.join(data_dir, 'before')
after_data_dir = os.path.join(data_dir, 'after')
batch_size = 10
figures_dir = os.path.join(father_dir, 'figures')
if not os.path.exists(figures_dir):
os.mkdir(figures_dir)
for path_dir in [before_data_dir, after_data_dir]:
for filename in os.listdir(path_dir):
filepath = os.path.join(path_dir, filename)
datafile = SingleFile(filepath=filepath)
data_reader = datafile.get_reader(batch_size=batch_size)
if path_dir is before_data_dir:
single_file_path = os.path.join(figures_dir, 'before',
f'{datafile.filename}')
else:
single_file_path = os.path.join(figures_dir, 'after',
f'{datafile.filename}')
if not os.path.exists(single_file_path):
os.makedirs(single_file_path)
for batch_id, points in enumerate(data_reader()):
for point_id, point in enumerate(points):
point_id = batch_id * batch_size + point_id
print(f'[INFO] drawing {filename}--point_{point_id}...')
plt.figure()
# plt configs
plt.ylim(0, 30 * 1e-8)
point.plot(show=False)
plt.savefig(
os.path.join(single_file_path,
f'point_{point_id}.jpg'))
plt.close()
def reader():
points = []
labels = []
for file_name in os.listdir(data_dir):
if debug:
print('[INFO] getting teacher from {}'.format(file_name))
file_path = os.path.join(data_dir, file_name)
file_name, ext = os.path.splitext(file_name)
if debug:
print("[INFO] Reading file {}".format(file_name))
try:
file = SingleFile(file_path)
except Exception as e:
print(e)
continue
point_reader = file.get_reader(batch_size=1)
label_df = pd.read_csv(os.path.join(csv_dir,
'new_' + file_name + '.csv'),
index_col=0)
# print(label_df)
# print(label_df.iloc[0, :])
for point_id, point in enumerate(point_reader()):
point = point[0]
label_series = label_df.iloc[point_id, :]
points.append(point.get_data())
labels.append(label_series.to_list())
if len(points) is batch_size:
points = np.array(points,
dtype).reshape(len(points), 2, 1, 100)
labels = np.array(labels, dtype).reshape(len(labels), -1)
yield points, labels
points = []
labels = []
if len(labels):
points = np.array(points, dtype).reshape(len(points), 2, 1, 100)
labels = np.array(labels, dtype).reshape(len(labels), -1)
yield points, labels
def read_data(data_path, placeholder, max_input_length=500):
logger.info("Logging data {}...".format(placeholder))
sf = SingleFile(data_path)
data = []
all_point_number = 50
# all_point_number = sf.point_number
with tqdm(total=all_point_number) as pbar:
pbar.set_description("Reading {}".format(placeholder))
for i in range(all_point_number):
point = sf.get_one_point()
one_data = point.get_data()
one_data = np.array(one_data).reshape(1, -1, 2)
one_data = data_handle(one_data)
length = point.size
output = np.pad(one_data,
pad_width=((0, 0), (0, max_input_length - length), (0, 0)),
constant_values=(0, 0),
)
output = np.tile(output, reps=[3, 1, 1])
data.append(output.tolist())
pbar.update(1)
return K.cast_to_floatx(np.array(data))
if len(points):
yield points
return reader
def get_one_point(self) -> SinglePoint:
point = next(self.point_reader())
if point is None:
raise ValueError('No more point in this file')
return point
def __describe(self):
# TODO：ADD FILE BASIC DESCRIPTION
var = (f"""
============================{self.filename}============================
point_number: {self._point_number}
============================{'=' * len(self.filename)}============================"
""")
if __name__ == '__main__':
from ele_common.units import SingleFile
singlefile = SingleFile(
filepath='../../data/origin/before/LINE_120_dbdt.dat')
print(singlefile._date)
print(singlefile.filename)
point = singlefile.get_one_point()
point.plot()
from ele_common.units import SingleFile from ele_common.functions import fit_point before_filepath = "../data/origin/before/LINE_100_dbdt.dat" after_filepath = "../data/origin/after/new_LINE_100_dbdt.dat" before_file = SingleFile(before_filepath) after_file = SingleFile(after_filepath) fit_point(before_file.get_one_point(), show=True) fit_point(after_file.get_one_point(), show=True)
import sys
sys.path.append("../..")
import numpy as np
from tensorflow.keras.models import load_model
from solutions.cnn_coder.train import data_handle
from ele_common.units import SingleFile
import matplotlib.pyplot as plt
from constants import *
from tensorflow.keras import backend as K
model = load_model("ed_model")
max_input_length = 500
tf = SingleFile("../../data/generate/concat/teacher_result.dat")
df = SingleFile("../../data/generate/concat/data_result.dat")
for i in range(10):
tpoint = tf.get_one_point()
point = df.get_one_point()
tdata = data_handle(tpoint.x, tpoint.y, MAX_INPUT_LENGTH)
ddata = data_handle(point.x, point.y, MAX_INPUT_LENGTH)
tx = [t[0] for t in tdata[0]]
ty = [t[1] for t in tdata[0]]
dx = [t[0] for t in ddata[0]]
dy = [t[1] for t in ddata[0]]
x_data, y_data = point.get_data()
output = data_handle(x_data, y_data, MAX_INPUT_LENGTH)