def __init__(self, data_list=[]):
self.data_list = data_list
self.avr_list = []
self.var_list = []
self.kalman_list = []
self.split_list = []
self.data_filter = Filter()
# print("ok")
return
warnValue = WarnValue(0)
# 串口数据
rt = Seriar()
SerStatus = 0
SerCount = 0
SerCom = 'COM11'
dataQueue = queue.Queue(1000)
listArr = List()
file_handle = open('.1.txt', mode='w')
myFilter = Filter()
top = tk.Tk()
top.geometry('600x400')
top.resizable(0, 0)
# line
cv = tk.Canvas(top, bg='white', width=480, height=286)
cv.place(x=5, y=105, anchor=tk.NW)
cv.create_text(10, 10, text=str(warnValue.getUpValue()))
cv.create_text(10, 270, text=str(warnValue.getDownValue()))
CValue = 1
RStatus = 0
def ReceiveTask():
global rt
class DataTest:
def __init__(self, data_list=[]):
self.data_list = data_list
self.avr_list = []
self.var_list = []
self.kalman_list = []
self.split_list = []
self.data_filter = Filter()
def data_read(self, file_dir):
for root, dirs, files in os.walk(file_dir):
self.data_list = [0] * len(files)
for file in files:
if os.path.splitext(file)[1] == '.txt':
print(os.path.join(root, file))
f = open(os.path.join(root, file), "r", encoding="utf-8")
lines = f.readlines()
for i, item in enumerate(lines):
if int(os.path.splitext(file)[0]) in range(37, 48):
lines[i] = float(item.split(':')[1])
else:
lines[i] = float(item)
print(lines)
self.data_list[int(os.path.splitext(file)[0])] = lines
f.close()
return True
def data_split(self, n):
self.split_list = [[]] * len(self.data_list)
for i, item in enumerate(self.data_list):
print(i)
print(item)
self.split_list[i] = []
dpcopy_row = copy.deepcopy(item)
random.shuffle(dpcopy_row) # 重排序
m = int(len(dpcopy_row) / n)
for j in range(0, len(dpcopy_row), m):
self.split_list[i].append(dpcopy_row[j:j + m])
ret = []
for i in range(0, n):
ret_data_list = []
for item in self.split_list:
ret_data_list.append(item[i])
ret.append(DataTest(ret_data_list))
return ret
def data_average(self):
self.avr_list = [0] * len(self.data_list)
self.var_list = [0] * len(self.data_list)
for i, row in enumerate(self.data_list):
self.avr_list[i] = np.average(row)
self.var_list[i] = np.var(row)
def data_kalman(self, phase):
n = int(phase / 5)
self.kalman_list = [0] * len(self.avr_list)
for i, item in enumerate(self.avr_list):
cnum = self.data_filter.filter(item)
if i >= n:
self.kalman_list[i - n] = cnum
else:
self.kalman_list[len(self.kalman_list) - n + i] = cnum
def data_plot(self, phase=0):
self.data_average()
self.data_kalman(phase)
fig = plt.figure(figsize=(15, 5))
ax1 = fig.add_subplot(1, 3, 1)
plt.title('average')
ax2 = fig.add_subplot(1, 3, 2)
plt.title('var')
ax3 = fig.add_subplot(1, 3, 3)
plt.title('kalman result')
ax1.plot(range(0, 360, 5), self.avr_list)
ax2.plot(range(0, 360, 5), self.var_list)
ax3.plot(range(0, 360, 5), self.kalman_list)
plt.show()
def data_write(self, file_dir):
if not os.path.exists(file_dir):
os.mkdir(file_dir)
f = open(file_dir + '/avr.txt', 'w')
for item in self.avr_list:
f.writelines(str(item))
f.write('\n')
f.close()
f = open(file_dir + '/var.txt', 'w')
for item in self.var_list:
f.writelines(str(item))
f.write('\n')
f.close()
f = open(file_dir + '/kalman.txt', 'w')
for item in self.kalman_list:
f.writelines(str(item))
f.write('\n')
f.close()
def filter_process(self):
logger.info("run filter")
"""Filter each detection models and predict resistome(s)."""
filter_obj = Filter(self.input_type, self.loose, self.input_sequence, self.blast_results_xml_file, \
os.path.join(self.dp,"card.json"),os.path.basename(self.input_sequence) ,self.output_file,self.threads, self)
filter_obj.run()