def temp_query_data(request):
dic={}
data=get_from_queue()
print("query_data", data)
dic['data']=data
json_dump=json.dumps(dic, cls=NpEncoder)
return JsonResponse(json_dump, safe=False)
def query_data(requset):
dic = {}
data = get_from_queue()
data = data[:, ::32]
print(data.shape, get_queue_remainder())
dic["data"] = data.tolist()
json_dump = json.dumps(dic)
return JsonResponse(json_dump, safe=False)
def query_data(request):
dic = {}
data = get_from_queue() # data.shape => (8, 2048)
# print("data:", data)
# sample the data for display
sampling_interval_for_display = 1024
time_interval = 1 / 2048 * sampling_interval_for_display
data_for_display = data[:,::sampling_interval_for_display]
now_time = time.time()
print("data for display", data_for_display)
print(now_time)
temp_display_dic = []
for i in range(data_for_display.shape[0]):
_temp_dic = []
for j in range(data_for_display.shape[1]):
_temp_dic.append({
"x" : now_time + time_interval * j,
"y" : data_for_display[i][j]
})
temp_display_dic.append(_temp_dic)
dic["data"] = temp_display_dic
print("data", temp_display_dic)
# predict the label
data_for_predict = data.T.reshape([-1, 128, 8])
result = predict_label(data_for_predict)
result = np.argmax(result, axis=1)
print("predicted result",result)
dic["label"] = result.tolist()
# build return value
json_dump = json.dumps(dic)
return JsonResponse(json_dump, safe=False)
def query_data_new(request):
temp_dic = get_from_queue()
json_dump = json.dumps(temp_dic)
return JsonResponse(json_dump, safe=False)
def temp_output():
while True:
time.sleep(1 / 32)
print(get_from_queue().shape)
print(get_queue_remainder(), time.time())