def generate_csv_contrast(self, data_set, x_standard, f_standard):
loadfile = load.LoadData(self.filepath)
file = loadfile.read_csv()
maxlength = len(file)
pre = osp.splitext(osp.basename(self.filepath))[0]
f_max = max(np.array(file)[:, 1])
print(pre + ' start, max_f = ' + str(f_max))
# split
cnt = 0
idx = 0
while maxlength - 200 * cnt > 0:
temp = file[idx:idx + 200]
data = np.array(temp)
x = data[:, 0]
f = data[:, 1]
idx += 200
cnt += 1
name = pre + '-' + str(cnt) + '.png'
if util.zero_data(x, f):
continue
if name in data_set:
x_add, f_add = normalization_each_device(x, f, f_max, False)
# standard_name = standard.replace('.png')
save_path = 'image2\\plus\\' + pre + '-' + str(cnt) + 'vs.png'
process_contrast(x_add, f_add, x_standard, f_standard,
save_path)
def generate_excel_map(self, content_list):
loadfile = load.LoadData(self.filepath)
data = loadfile.read_excel()
pre = osp.splitext(osp.basename(self.filepath))[0]
for cnt in range(len(data)):
# for cnt in range(2):
# skip exception
if util.nan_data(data[cnt, 0]) | util.nan_data(data[cnt, 1]):
continue
x = data[cnt, 0].split(',')
f = data[cnt, 1].split(',')
if not util.match_data(x, f):
continue
x_array = np.array(x, dtype=float)
f_array = np.array(f, dtype=float)
if util.zero_data(x_array, f_array):
continue
# normalization_max
x_new, f_new = normalization_each_image(x_array, f_array)
image_dict = {
"image_name":
pre + '-' + str(cnt) + '.png',
"x":
','.join(
str(x_new.tolist()).replace(']',
'').replace('[',
'').split(',')),
"f":
','.join(
str(f_new.tolist()).replace(']',
'').replace('[',
'').split(','))
}
content_list.append(image_dict)
def generate_csv_with_axis(self, base_path):
loadfile = load.LoadData(self.filepath)
file = loadfile.read_csv()
maxlength = len(file)
exception_zero = 0
pre = osp.splitext(osp.basename(self.filepath))[0]
f_max = max(np.array(file)[:, 1])
print(pre + ' start, max_f = ' + str(f_max))
# split
cnt = 0
idx = 0
# image_cnt = 0
while maxlength - 200 * cnt > 0:
temp = file[idx:idx + 200]
data = np.array(temp)
x = data[:, 0]
f = data[:, 1]
idx += 200
cnt += 1
if cnt == 3:
if util.zero_data(x, f):
exception_zero += 1
continue
# x_new = np.empty([200, 1], dtype=int)
# f_new = np.empty([200, 1], dtype=int)
# for i in range(200):
# x_new[i] = int(x[i])
# f_new[i] = int(f[i])
save_path = osp.join(base_path, pre + '-' + str(cnt) + '.png')
process_with_axis(x, f, save_path, f_max)
def generate_csv(self, base_path):
loadfile = load.LoadData(self.filepath)
file = loadfile.read_csv()
maxlength = len(file)
exception_zero = 0
pre = osp.splitext(osp.basename(self.filepath))[0]
f_max = max(np.array(file)[:, 1])
print(pre + ' start, max_f = ' + str(f_max))
# split
cnt = 0
idx = 0
# image_cnt = 0
while maxlength - 200 * cnt > 0:
# while cnt < 5:
temp = file[idx:idx + 200]
data = np.array(temp)
x = data[:, 0]
f = data[:, 1]
idx += 200
cnt += 1
# skip exception
if util.zero_data(x, f):
exception_zero += 1
# print(pre + '-' + str(cnt) + ':zero')
continue
# normalization_max
# x_new, f_new = self.normalization_each_image(x, f)
# normalization_100
x_new, f_new = normalization_each_device(x, f, f_max, False)
# draw and save
save_path = osp.join(base_path, pre + '-' + str(cnt) + '.png')
process(x_new, f_new, save_path)
def augmentation_csv(filepath, data_set, num, base_path):
loadfile = load.LoadData(filepath)
file = loadfile.read_csv()
maxlength = len(file)
pre = osp.splitext(osp.basename(filepath))[0]
f_max = max(np.array(file)[:, 1])
print(pre + ' start, max_f = ' + str(f_max))
# split
cnt = 0
idx = 0
while maxlength - 200 * cnt > 0:
# while cnt < 5:
temp = file[idx:idx + 200]
data = np.array(temp)
x = data[:, 0]
f = data[:, 1]
idx += 200
cnt += 1
name = pre + '-' + str(cnt) + '.png'
if util.zero_data(x, f):
continue
if name in data_set:
# print(name)
for k in range(num):
x_add, f_add = normalization_each_device(x, f, f_max, True)
add_path = osp.join(
base_path, pre + '-' + str(cnt) + '-' + str(k) + '.png')
process(x_add, f_add, add_path)
def generate_csv_map(self, content_list):
loadfile = load.LoadData(self.filepath)
file = loadfile.read_csv()
maxlength = len(file)
exception_zero = 0
pre = osp.splitext(osp.basename(self.filepath))[0]
f_max = max(np.array(file)[:, 1])
print(pre + ' start, max_f = ' + str(f_max))
# split
cnt = 0
idx = 0
while maxlength - 200 * cnt > 0:
# while cnt < 5:
image_name = pre + '-' + str(cnt) + '.png'
temp = file[idx:idx + 200]
data = np.array(temp)
x = data[:, 0]
f = data[:, 1]
idx += 200
cnt += 1
# skip exception
if util.zero_data(x, f):
exception_zero += 1
print(pre + '-' + str(cnt) + ':zero')
continue
# normalization_max
# x_new, f_new = self.normalization_each_image(x, f)
# normalization_100
x_new, f_new = normalization_each_device(x, f, f_max, False)
image_dict = {
"image_name":
pre + '-' + str(cnt) + '.png',
"x":
','.join(
str(x_new.tolist()).replace(']',
'').replace('[',
'').split(',')),
"f":
','.join(
str(f_new.tolist()).replace(']',
'').replace('[',
'').split(','))
}
content_list.append(image_dict)
def get_standard_excel(self, standard):
loadfile = load.LoadData(self.filepath)
data = loadfile.read_excel()
pre = osp.splitext(osp.basename(self.filepath))[0]
for cnt in range(len(data)):
# skip exception
if util.nan_data(data[cnt, 0]) | util.nan_data(data[cnt, 1]):
continue
x = data[cnt, 0].split(',')
f = data[cnt, 1].split(',')
if not util.match_data(x, f):
continue
x_array = np.array(x, dtype=float)
f_array = np.array(f, dtype=float)
if util.zero_data(x_array, f_array):
continue
name = pre + '-' + str(cnt) + '.png'
if name == standard:
x_standard, f_standard = normalization_each_image(
x_array, f_array)
return x_standard, f_standard
def generate_excel(self, base_path):
loadfile = load.LoadData(self.filepath)
data = loadfile.read_excel()
exception_not_match = 0
exception_null = 0
exception_zero = 0
pre = osp.splitext(osp.basename(self.filepath))[0]
image_cnt = 0
for cnt in range(len(data)):
# for cnt in range(2):
# skip exception
if util.nan_data(data[cnt, 0]) | util.nan_data(data[cnt, 1]):
exception_null += 1
print(pre + '-' + str(cnt) + ':null')
continue
x = data[cnt, 0].split(',')
f = data[cnt, 1].split(',')
if not util.match_data(x, f):
exception_not_match += 1
print(pre + '-' + str(cnt) + ':not match, x has ' +
str(len(x)) + 'points and f has ' + str(len(f)) +
'points')
continue
x_array = np.array(x, dtype=float)
f_array = np.array(f, dtype=float)
if util.zero_data(x_array, f_array):
exception_zero += 1
print(pre + '-' + str(cnt) + ':zero')
continue
# normalization_max
x_new, f_new = normalization_each_image(x_array, f_array)
# normalization_100
# x_new, f_new = self.normalization_each_device(x_array, f_array, 1000, False)
# draw and save
save_path = osp.join(base_path, pre + '-' + str(cnt) + '.png')
process(x_new, f_new, save_path)
image_cnt += 1
print(pre + ' num = ' + str(exception_not_match) + ' ; ' +
str(exception_null) + ' ; ' + str(exception_zero) + ' ; ' +
str(image_cnt))
def generate_excel_contrast(self, data_set, x_standard, f_standard):
loadfile = load.LoadData(self.filepath)
data = loadfile.read_excel()
pre = osp.splitext(osp.basename(self.filepath))[0]
for cnt in range(len(data)):
# skip exception
if util.nan_data(data[cnt, 0]) | util.nan_data(data[cnt, 1]):
continue
x = data[cnt, 0].split(',')
f = data[cnt, 1].split(',')
if not util.match_data(x, f):
continue
x_array = np.array(x, dtype=float)
f_array = np.array(f, dtype=float)
if util.zero_data(x_array, f_array):
continue
name = pre + '-' + str(cnt) + '.png'
if name in data_set:
x_new, f_new = normalization_each_image(x_array, f_array)
# standard_name = standard.replace('.png')
save_path = 'image2\\plus\\' + pre + '-' + str(cnt) + 'vs.png'
process_contrast(x_new, f_new, x_standard, f_standard,
save_path)
def get_standard_csv(self, standard):
loadfile = load.LoadData(self.filepath)
file = loadfile.read_csv()
maxlength = len(file)
pre = osp.splitext(osp.basename(self.filepath))[0]
f_max = max(np.array(file)[:, 1])
# print('standard image source is' + pre + ', max_f is ' + str(f_max))
# split
cnt = 0
idx = 0
while maxlength - 200 * cnt > 0:
temp = file[idx:idx + 200]
data = np.array(temp)
x = data[:, 0]
f = data[:, 1]
idx += 200
cnt += 1
name = pre + '-' + str(cnt) + '.png'
if util.zero_data(x, f):
continue
if name == standard:
x_standard, f_standard = normalization_each_device(
x, f, f_max, False)
return x_standard, f_standard
