def update_figure(self):
if GlobalMap().get('userdefined') == 'Null_': #检测从自定义对话框传递过来的数据
return
if not self.begin: #防止重绘
return
self.begin = 0
GlobalMap().set(time_point=self.times, )
self.times += 1
if self.times == 2:
self.begin = 1
self.temp0 = NN.main([], [],
GlobalMap().get('userdefined')) #传入BP网络预测
return
else:
temp01 = self.temp0
self.temp0 = NN.main([], [], temp01)
imgP = imread('core/map.png', )
self.axes.imshow(imgP,
extent=[112.1495, 112.288, 30.308, 30.391],
aspect='auto')
x = np.array(self.temp0[0])
y = np.array(self.temp0[1])
z = gaussian_kde(self.temp0)(self.temp0) #生成密度颜色
self.axes.hold(True)
self.axes.scatter(x, y, marker='.', s=100, c=z, edgecolors='')
self.axes.grid(True)
self.axes.set_xlim(112.150, 112.284)
self.axes.set_ylim(30.309, 30.385)
self.axes.set(xlabel="longitude", ylabel="latitude")
self.axes.set_title('The ' + str(self.times) +
':00 ’s distribution prediction')
self.draw()
self.begin = 1
self.axes.hold(False)
if self.times >= 2:
self.pause()
def run(self):
if self.working == True:
times = GlobalMap().get('time_point')
GlobalMap().set(progress_num=0)
lists = []
for i in range(24):
lists += [i]
GlobalMap().set(progress_num=2)
if exists('db/temp/20180515.csv') and exists(
'db/temp/20180516.csv'):
temp1 = DataFrame(read_csv('db/temp/20180515.csv', header=0))
temp2 = DataFrame(read_csv('db/temp/20180516.csv', header=0))
else:
temp1 = dataclear(20180515).run()
temp2 = dataclear(20180516).run()
GlobalMap().set(progress_num=5)
temp11 = temp1[temp1['time'] == lists[times - 1]]
temp12 = temp1[temp1['time'] == lists[times]]
temp1112 = merge(temp11, temp12, how='outer', on='id').dropna()
temp21 = temp2[temp2['time'] == lists[times - 1]]
temp22 = temp2[temp2['time'] == lists[times]]
temp2122 = merge(temp21, temp22, how='outer', on='id').dropna()
temp000 = np.vstack((temp1112['x_x'], temp1112['y_x']))
temp001 = np.vstack((temp1112['x_y'], temp1112['y_y']))
temp020 = np.vstack((temp2122['x_x'], temp2122['y_x']))
#temp021 = np.vstack((temp2122['x_y'],temp2122['y_y']))
cells_array = np.array(NN.main(temp000, temp001, temp020))
np.save(GlobalMap().get('temp_path') + '/test' + str(times),
cells_array)
GlobalMap().set(progress_num=100, running_global=0)
self.changed2_signal.emit(1)
def run(self):
if self.working == True:
times = GlobalMap().get('time_point')
GlobalMap().set(progress_num=0)
lists = []
for i in range(24):
lists += [i]
GlobalMap().set(progress_num=2)
if exists('db/temp/20180515.csv') and exists(
'db/temp/20180516.csv'):
temp1 = DataFrame(read_csv('db/temp/20180515.csv', header=0))
temp2 = DataFrame(read_csv('db/temp/20180516.csv', header=0))
else:
temp1 = dataclear(20180515).run()
temp2 = dataclear(20180516).run()
GlobalMap().set(progress_num=5)
temp11 = temp1[temp1['time'] == lists[times - 1]]
temp12 = temp1[temp1['time'] == lists[times]]
temp1112 = merge(temp11, temp12, how='outer', on='id').dropna()
'''begin 另一种数据处理方式'''
# temp1_x = DataFrame(read_csv('db/temp/20180515-x.csv' ,header=0))
# temp1_y = DataFrame(read_csv('db/temp/20180515-y.csv' ,header=0))
# temp2_x = DataFrame(read_csv('db/temp/20180516-x.csv' ,header=0))
# temp2_y = DataFrame(read_csv('db/temp/20180516-y.csv' ,header=0))
#
# temp11_x = temp1_x[str(lists[times])]
# temp11_y = temp1_y[str(lists[times])]
# temp12_x = temp2_x[str(lists[times])]
# temp12_y = temp2_y[str(lists[times])]
#
# temp21_x = temp1_x[str(lists[times+1])]
# temp21_y = temp1_y[str(lists[times+1])]
# temp22_x = temp2_x[str(lists[times+1])]
# temp22_y = temp2_y[str(lists[times+1])]
#
# cc01 = np.vstack((temp11_x, temp11_y))
# cc02 = np.vstack((temp21_x, temp21_y))
# cc03 = np.vstack((temp12_x, temp12_y))
# cc04 = np.vstack((temp22_x, temp22_y))
'''end'''
temp21 = temp2[temp2['time'] == lists[times - 1]]
temp22 = temp2[temp2['time'] == lists[times]]
temp2122 = merge(temp21, temp22, how='outer', on='id').dropna()
temp000 = np.vstack((temp1112['x_x'], temp1112['y_x']))
temp001 = np.vstack((temp1112['x_y'], temp1112['y_y']))
temp020 = np.vstack((temp2122['x_x'], temp2122['y_x']))
temp021 = np.vstack((temp2122['x_y'], temp2122['y_y']))
#用20180515 T 和 T+1 时刻的训练 来预测 20180516 T 时刻 在 T+1 时刻的分布情况
cells_array = np.array(NN.main(temp000, temp001, temp020))
#cells_array = np.array(NN.main(cc01, cc02, cc03 ))
np.save(GlobalMap().get('temp_path') + '/test' + str(times),
cells_array)
GlobalMap().set(progress_num=100)
self.changed_signal.emit(cells_array, temp021)