def popOneDayData(self):
if self.md_date is None or self.md_date.shape[0] < 1:
return extracts.md_extracts(np.array([]), np.array([]),
np.array([]), np.array([]),
np.array([]), np.array([]),
np.array([]))
currentDay = self.md_date[0]
nextDayIndex = 0
for dayIndex in range(self.md_date.shape[0]):
if currentDay != self.md_date[dayIndex]:
nextDayIndex = dayIndex
break
if nextDayIndex == 0:
nextDayIndex = self.md_date.shape[0]
oneDayDate, self.md_date = np.split(self.md_date, [nextDayIndex])
oneDayTime, self.md_time = np.split(self.md_time, [nextDayIndex])
oneDayOpen, self.md_open = np.split(self.md_open, [nextDayIndex])
oneDayClose, self.md_close = np.split(self.md_close, [nextDayIndex])
oneDayHigh, self.md_high = np.split(self.md_high, [nextDayIndex])
oneDayLow, self.md_low = np.split(self.md_low, [nextDayIndex])
oneDayVolume, self.md_volume = np.split(self.md_volume, [nextDayIndex])
extract_obj = extracts.md_extracts(oneDayDate, oneDayTime, oneDayOpen,
oneDayClose, oneDayHigh, oneDayLow,
oneDayVolume)
return extract_obj
def split(self):
if self.extract_obj.md_date.shape[0] > self.threshold:
input_date, output_date = np.split(self.extract_obj.md_date,
[self.threshold])
input_time, output_time = np.split(self.extract_obj.md_time,
[self.threshold])
input_open, output_open = np.split(self.extract_obj.md_open,
[self.threshold])
input_close, output_close = np.split(self.extract_obj.md_close,
[self.threshold])
input_high, output_high = np.split(self.extract_obj.md_high,
[self.threshold])
input_low, output_low = np.split(self.extract_obj.md_low,
[self.threshold])
input_volume, output_volume = np.split(self.extract_obj.md_volume,
[self.threshold])
input_extract_obj = extracts.md_extracts(input_date, input_time,
input_open, input_close,
input_high, input_low,
input_volume)
output_extract_obj = extracts.md_extracts(output_date, output_time,
output_open,
output_close,
output_high, output_low,
output_volume)
return True, input_extract_obj, output_extract_obj
else:
input_extract_obj = extracts.md_extracts(np.array([]), np.array(
[]), np.array([]), np.array([]), np.array([]), np.array([]),
np.array([]))
output_extract_obj = extracts.md_extracts(np.array([]), np.array(
[]), np.array([]), np.array([]), np.array([]), np.array([]),
np.array([]))
return False, input_extract_obj, output_extract_obj
def extract_file(train_file):
data_file = open(train_file)
data_file_header = data_file.readline()
file_data = []
for line in data_file:
if (len(line.strip()) > 0):
file_data.append(line)
data_file_header = [x for x in data_file_header.split(',') if len(x) >= 1]
data_by_field = [[x for x in y.split(',') if len(x) >= 1]
for y in file_data if len(y) >= 1]
md_date = np.array([x[0].strip() for x in data_by_field])
md_time = np.array([x[1].strip() for x in data_by_field])
md_open = np.array([x[5].strip() for x in data_by_field])
md_close = np.array([x[6].strip() for x in data_by_field])
md_high = np.array([x[7].strip() for x in data_by_field])
md_low = np.array([x[8].strip() for x in data_by_field])
md_volume = np.array([x[9].strip() for x in data_by_field])
extracts_obj = extracts.md_extracts(md_date, md_time, md_open, md_close,
md_high, md_low, md_volume)
data_file.close()
return extracts_obj
def setUp(self):
self.md_date = np.array([
'03/06/2012', '03/06/2012', '03/06/2012', '03/06/2012',
'03/06/2012', '03/06/2012'
])
self.md_time = np.array([
'09:15:00 AM', '09:16:00 AM', '09:17:00 AM', '09:18:00 AM',
'09:19:00 AM', '09:20:00 AM'
])
self.md_open = np.array(
['21119', '21119', '21126', '22119', '22119', '22126'])
self.md_high = np.array(
['21130', '21127', '21126', '22130', '22127', '22126'])
self.md_low = np.array(
['21101', '21111', '21105', '22101', '22111', '22105'])
self.md_close = np.array(
['21115', '21125', '21108', '22115', '22125', '22108'])
self.md_volume = np.array(
['662', '243', '392', '2662', '2243', '2392'])
self.extract_obj = extracts.md_extracts(self.md_date, self.md_time,
self.md_open, self.md_close,
self.md_high, self.md_low,
self.md_volume)