Python numpy.loads函数代码示例

 def k_means_distance(self, centers, result_name=None):
     """
         Computes the distance between each row and each of the given center vectors for k-means
     """
     if centers.shape[1] != self.__cols:
         raise BaseException('Dimensions of matrix and centers do not match')
     if result_name == None:
         result_name = MatrixFactory.getRandomMatrixName()
     
     redwrap = RedisWrapper(self.context.redis_master, self.context.key_manager)
     prefix = 'dist(' + self.__name + ',' + centers.name() + ')';
     dist_job = kmeans_jobs.KMeansDistanceJob(self.context, self, centers, prefix)
     
     parts = dist_job.run()
     
     for p in range(0,len(parts)):
         part_name = parts[p]
         m = self.context.redis_master.lpop(part_name)
         sum = None
         while m != None:
             if sum == None:
                 sum = numpy.loads(m)
             else:
                 sum += numpy.loads(m)
             m = self.context.redis_master.lpop(part_name)
             
         self.context.redis_master.delete(part_name)
         redwrap.create_block(self.context.key_manager.get_block_name(result_name, p, 0), numpy.sqrt(sum))
     
     res = Matrix(self.__rows, centers.shape[0], result_name, self.context)
     return res

    def run(self):
        video_input = self.get_input("videoInput")
        video_input_resized = self.get_input("videoInputResized")
        gif_data_output = self.get_output("gifData")
        script = FrameAnalyzer()

        while self.running():
            # read frames - full scale and thumb
            frame_obj = video_input.read()
            frame_obj_resized = video_input_resized.read()
            frame = np.loads(frame_obj)
            frame_resized = np.loads(frame_obj_resized)

            with self.filters_lock:
                script.change_settings(\
                    self.get_parameter("max_gif_length"),
                    self.get_parameter("min_gif_length"),
                    self.get_parameter("min_time_between_gifs"),
                    self.get_parameter("max_acceptable_distance") )

            loop_data = script(frame, frame_resized)
            if loop_data and len(loop_data)==4:
                file_path, w, h, frames_count = loop_data
                self.__send_to_next_service(gif_data_output, file_path, w, h, frames_count)
                self.__push_notification()

def socketcomm(port, pipec, flags, uarr, dat_size=156):
    """Callback function to deal with incoming tcp communication.
	pipec,pipeu and pipesoc are pipe objects
	pipec: describes position of camera
	pipeu: describes orientation of camera.
	           sends (True, data) for good data
	           sends (False,.......) for bad data or loop not running
	pipesoc: fill socket objects and send to __main__
	"""
    # Flags
    sockets = []
    waiting_for_data = True
    # initialise socket object
    serversocket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
    serversocket.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
    serversocket.bind((_TCP_SOCKET_HOST, port))
    serversocket.listen(1)
    serversocket.setblocking(0)
    # add server socket to list of sockets
    sockets.append(serversocket)
    # create epoll object
    _epoll = select.epoll()
    # register interest in read events on the server socket
    _epoll.register(serversocket.fileno(), select.EPOLLIN)

    while waiting_for_data:
        events = _epoll.poll(1)
        for fileno, event in events:
            if fileno == serversocket.fileno():
                # initialise client socket object
                clientsocket, clientaddr = serversocket.accept()
                clientsocket.setblocking(0)
                # register client socket on epoll
                _epoll.register(clientsocket.fileno(), select.EPOLLIN)
                sockets.append(clientsocket)
            elif event & select.EPOLLIN:
                # read event  on client socket
                content = clientsocket.recv(dat_size)
                if content[0] == "u":
                    # pipeu.send((True,True,np.loads(content[1:])))
                    with uarr.get_lock():
                        uarr.get_obj()[:3] = np.loads(content[1:]).ravel()
                    flags.value = 2
                elif content[0] == "d":
                    # pipeu.send((False,True))
                    flags.value = 1
                elif content[0] == "c":
                    pipec.send(np.loads(content[1:]))
                elif content[0] == "e":
                    # pipeu.send((False,False))
                    flags.value = 0
                    waiting_for_data = False
                    for i in sockets:
                        _epoll.unregister(i.fileno())
            elif event & select.EPOLLHUP:
                for i in sockets:
                    _epoll.unregister(i.fileno())
                    i.close()

 def get_correlation(self):
     pkt = self._request('') # raises NoCorrelation if none ready
     self.logger.debug('received: %r' % pkt)
     data = pkt[self._header_size:] # should be 3 arrays and 2 floats
     corr_time, left, right, current, total = self._header_struct.unpack(pkt[:self._header_size])
     lagss, visibss, fitss, m, c = self.unpacker.unpack(data)
     return (
         corr_time, left, right, current, total, # header information
         loads(lagss), loads(visibss), loads(fitss), m, c # data
         )

   def load_model(self, filename, delimiter = '<DELIMITER>'):
      """
      Load model parameters from the file provided
      """

      try:
         infile = open(filename, 'r')
      except IOError:
         print "Could not open filename '" + filename + "'."
         return

      # Get the number of units in each layer
      data = infile.read()
      data = data.split(delimiter)

      self.num_visible = int(data[0])
      self.num_hidden = int(data[1])
      self.num_rnn = int(data[2])

      # Read each line and convert to weight matrices
      self.Whv = np.loads(data[3])
      self.Wuh = np.loads(data[4])
      self.Wuv = np.loads(data[5])
      self.Wuu = np.loads(data[6])
      self.Wvu = np.loads(data[7])

      self.bias_visible = np.loads(data[8])
      self.bias_hidden = np.loads(data[9])
      self.bias_rnn = np.loads(data[10])

      self.initial_rnn = np.loads(data[11])

      infile.close()

def socket_cb1(socket,val):
    global u1,c1, running1,fr1,dat1
    if val[0] == 'u':
        u1 = np.loads(val[1:])
        fr1 += 1
        dat1 = True
    elif val[0] == 'c':
        c1 = np.loads(val[1:])
    elif val[0] == 'd':
        dat1 = False
    elif val[0] == 'e':
    	dat1 = False
        running1 = False

def socket_cb2(socket,val):
    global u2,c2, running2,fr2,dat2
    if val[0] == 'u':
        u2 = np.loads(val[1:])
        fr2 += 1
        dat2 = True
    elif val[0] == 'c':
        c2 = np.loads(val[1:])
    elif val[0] == 'd':
        dat2 = False
    elif val[0] == 'e':
    	dat2 = False
        running2 = False

 def _do_numeric(self, value, path):
     if PY_VER > 2:
         data = value['data']
         if isinstance(data, str):
             data = value['data'].encode('utf-8')
         junk = gunzip_string(base64.decodebytes(data))
         result = numpy.loads(junk, encoding='bytes')
     else:
         junk = gunzip_string(value['data'].decode('base64'))
         result = numpy.loads(junk)
     self._numeric[value['id']] = (path, result)
     self._obj_cache[value['id']] = result
     return result

 def show_frame(self, input_connector, video_frame_label):
     obj = input_connector.read()
     frame = np.loads(obj) # załadownaie ramki do obiektu NumPy
     img = Image.fromarray(frame)
     imgTk = ImageTk.PhotoImage(image=img)
     video_frame_label.imgTk = imgTk
     video_frame_label.configure(image=imgTk)

	def check(self):

		
		word_errors = 0
		file_errors = 0
		
		t0 = time.time()

		self.get_tree()
		

		files_read = 0
		
		for f in self.tree:
			
			try:
				arr = np.loads(f)
				files_read += 1
			except:
				file_errors += 1
				
			word_errors += np.sum(arr==self.chunk)
			
		t1 = time.time()
		speed = self.sweeplen / (1e6 * (t1-t0)) 



		msg = "\nread %.2f TB at %.2f MB/s"%(files_read*self.flen,speed)
		msg += "\n\t\t failed file reads = %i\n" %(file_errors)
		msg += "\n\t\t word errors = %i\n\n" %(word_errors)
		
		logging.info(msg)

    def run(self):  # główna metoda usługi
        video_input = self.get_input("videoInput")  # obiekt interfejsu wejściowego
        video_output = self.get_output("videoOutput")
        input_start = True
        out = None

        while self.running():  # pętla główna usługi
            try:
                frame_obj = video_input.read()  # odebranie danych z interfejsu wejściowego
                video_output.send(frame_obj)
            except Exception as e:
                video_input.close()
                if out != None:
                    out.release()
                break
            if input_start:
                video_format = self.get_parameter("videoFormat")
                print video_format
                fourcc = cv2.VideoWriter_fourcc(*"XVID")
                out = cv2.VideoWriter(
                    "output.avi", fourcc, video_format[0], (int(video_format[1]), int(video_format[2]))
                )
                input_start = False
            frame = np.loads(frame_obj)  # załadowanie ramki do obiektu NumPy
            out.write(frame)

def save_to_mat_file(init_data, outfile_name):
    def get_list(vocab_dict):
        l=np.zeros((len(vocab_dict),), dtype=np.object)
        for w, i in vocab_dict.iteritems():
            l[i]=w
        return l
    laff = lambda nm : np.loads(init_data[nm])
    tag_vocab=get_list(init_data["tag_vocab"])
    word_vocab=get_list(init_data["word_vocab"])
    with open(outfile_name, "wb") as f:
        savemat(f,
                dict(word_context_size=init_data["word_context_size"],
                     embedding_size=init_data["embedding_size"],
                     objective_type=init_data["objective_type"],
                     param_reg_type=init_data["param_reg_type"],
                     param_reg_weight=init_data["param_reg_weight"],
                     tag_vocab=tag_vocab,
                     word_vocab=word_vocab,
                     tagemb=laff("na_tag_emb"),
                     wordemb=laff("na_word_emb"),
                     T1=laff("nam_T1"),
                     T2=laff("nam_T2"),
                     Tt1=laff("nam_Ttld1"),
                     Tt2=laff("nam_Ttld2"),
                     W=laff("nat_W"),
                     Wt=laff("nat_Wtld"),
                     S=laff("na_S_emb")),
                oned_as='column',
                format='5',
                appendmat=False)

def k_means_recalc(cmd_ctx):
    m = _get_matrix_block(cmd_ctx, cmd_ctx.cmdArgs[0])
    d = _get_matrix_block(cmd_ctx, cmd_ctx.cmdArgs[1])
    result_prefix = cmd_ctx.cmdArgs[2]
    # Only count if prefix is given
    counter_prefix = None
    if len(cmd_ctx.cmdArgs) > 3:
        counter_prefix = cmd_ctx.cmdArgs[3]
    result = {}

    mincols = numpy.argmin(d, axis=1)

    # Find the nearest center for each record and add the record to the centers sum
    # Also count how many records are nearest to each center
    rowcount = 0
    for col in mincols:
        if counter_prefix != None:
            cmd_ctx.redis_master.incr(counter_prefix + str(col))
        if result.has_key(col):
            result[col] += m[rowcount]
        else:
            result[col] = m[rowcount]
        rowcount += 1

    for key in result.keys():
        k = result_prefix + str(key)
        tmp = cmd_ctx.redis_master.lpop(k)
        if tmp == None:
            cmd_ctx.redis_master.rpush(k, result[key].dumps())
        else:
            cmd_ctx.redis_master.rpush(k, (result[key] + numpy.loads(tmp)).dumps())

def data_from_pipeline(data, shape=None, dtype=None):
    if len(shape) == 0:
        return numpy.array(data, dtype=dtype)
    else:
        a = numpy.array([numpy.loads(x) for x in data], dtype=dtype)
        a.shape = (-1,)+shape
        return a

def socket_cb2(socket,val):
	global u1,running2,fr2
	if val[0] == 's':
		u1 = np.loads(val[1:])
		fr2 += 1
	elif val[0] == 'e':
		running2 = False