def step3():
file = open('test_list.txt', 'r')
dataset = [float(x.strip()) for x in file]
file.close()
print(dataset)
t = np.linspace(start = -4, stop = 4, num = len(dataset))
#y = np.sin(t) + np.random.randn(len(t)) * 0.1
y=np.array(dataset)
#print(y)
a = dataset.copy()
#===============loop start==============
for loop_time in range(0,1):
print("================= Loop times : ",loop_time+1,"================= ")
total_list=[]
temp_list=[]
for i in range(0,len(a)-1):
temp_list.append(a[i])
fall=a[i+1]-a[i]
if abs(fall)>4:
total_list.append(temp_list)
temp_list=[]
if i == len(a)-2:
temp_list.append(dataset[-1])
total_list.append(temp_list)
print("一共分为 ",len(total_list)," 组",total_list)
new_list=total_list.copy()
print("初始化")
print("old : ",total_list)
for loop_time_1 in range(0,15):
start_point_in_original_list = find_longest_element_index(total_list)
error_detect=0
try:
for i in range(0,5):
fall_list=round(new_list[i+1][0]-new_list[i][-1])
if abs(fall_list)>6 and len(new_list[i+1])<=6 or np.mean(new_list[i+1])>=60 and len(new_list[i+1])>=3:
outlier_list=new_list[i+1]
makeup_length=len(outlier_list)
new_list.remove(outlier_list)
for m in range(1,makeup_length+1):
makeup_part=new_list[i][-1]
new_list[i].append(makeup_part)
error_detect=1
print("### after this combine, list number is: ",len(new_list))
except:
pass
c = []
for i in range(0, len(new_list)):
for m in range(0, len(new_list[i])):
c.append(new_list[i][m])
plt.plot(t, y, "b.-", t, c, "r.-")
plt.title("loop times: " + str(loop_time_1 + 1))
plt.xlabel('Time')
plt.ylabel('Value')
plt.legend(['original data', 'smooth data'])
plt.grid(True)
plt.show()
if loop_time_1!=1 and error_detect==0:
print("The loop is over!")
break
print("new : ",new_list)
new_list=sum(new_list,[])
print("total group: ",len(new_list),new_list)
a=new_list
#============Loop End============
#output
file = open('data_smooth_output.txt', 'w')
for fp in new_list:
file.write(str(fp))
file.write('\n')
file.close()
def step3(cell_id):
file = open('test_list.txt', 'r')
dataset = [float(x.strip()) for x in file]
file.close()
t = np.linspace(start=-4, stop=4, num=len(dataset))
y = np.array(dataset)
a = dataset.copy()
#===============loop start==============
for loop_time in range(0, 1):
print("================= Loop times : ", loop_time + 1,
"================= ")
total_list = []
temp_list = []
for i in range(0, len(a) - 1):
temp_list.append(a[i])
fall = a[i + 1] - a[i]
if abs(fall) > 4:
total_list.append(temp_list)
temp_list = []
if i == len(a) - 2:
temp_list.append(dataset[-1])
total_list.append(temp_list)
new_list = total_list.copy()
for loop_time_1 in range(0, 20):
start_point_in_original_list = find_longest_element_index(
total_list)
i4 = relocate_start_point(len(total_list),
start_point_in_original_list)
error_detect = 0
try:
for i in range(0, 30):
fall_list = round(new_list[i + 1][0] - new_list[i][-1])
if (abs(fall_list) > 6 and len(new_list[i + 1]) <= 6) or (
np.mean(new_list[i + 1]) >= 50
and len(new_list[i + 1]) >= 3):
outlier_list = new_list[i + 1]
makeup_length = len(outlier_list)
new_list.remove(outlier_list)
for m in range(1, makeup_length + 1):
makeup_part = new_list[i][-1]
new_list[i].append(makeup_part)
error_detect = 1
except:
pass
c = []
for i in range(0, len(new_list)):
for m in range(0, len(new_list[i])):
c.append(new_list[i][m])
#test on
#plt.show()
if loop_time_1 != 1 and error_detect == 0:
standard_circle_list = [round(np.mean(sum(new_list, [])), 1)
] * len(dataset)
plt.savefig(
"G:\\2020summer\\Project\\Cell_classfication_1.0.0\\output_smooth\\"
+ str(cell_id) + ".jpg")
break
new_list = sum(new_list, [])
#data_smooth_update
def moving_average(interval, window_size):
window = np.ones(int(window_size)) / float(window_size)
return np.convolve(interval, window, 'same') # numpy的卷积函数
t = np.linspace(start=-4, stop=4, num=len(new_list))
window_size_number = 4
y_av = moving_average(interval=new_list,
window_size=window_size_number)
y_av[0:round(window_size_number / 2)] = y[0:round(window_size_number /
2)]
y_av[-round(window_size_number / 2):] = y[-round(window_size_number /
2):]
#test_mode
#plt.title("Smoothed_Data",size=20)
standard_circle_list = [round(np.mean(y), 1)] * len(dataset)
plt.plot(t, y, "r.-", t, y_av, "g.-")
plt.plot(t, standard_circle_list, "y.-")
plt.legend(['original data', 'smooth data', 'standard circle'],
prop={'size': 15})
font1 = {
'weight': 'normal',
'size': 20,
}
plt.tick_params(labelsize=15)
plt.xlabel('Number of Points', font1)
plt.ylabel('Distance to Center', font1)
plt.show()
new_list = y_av
a = new_list
#============Loop End============
#output
file = open('data_smooth_output.txt', 'w')
for fp in new_list:
file.write(str(fp))
file.write('\n')
file.close()