我们从Python开源项目中，提取了以下17个代码示例，用于说明如何使用numpy.short()。
def read_wav_data(filename): ''' ????wav???????????????????? ''' wav = wave.open(filename,"rb") # ????wav???????? num_frame = wav.getnframes() # ???? num_channel=wav.getnchannels() # ????? framerate=wav.getframerate() # ????? num_sample_width=wav.getsampwidth() # ?????????????????? str_data = wav.readframes(num_frame) # ?????? wav.close() # ??? wave_data = np.fromstring(str_data, dtype = np.short) # ???????????????? wave_data.shape = -1, num_channel # ????????????????????????????????? wave_data = wave_data.T # ????? #wave_data = wave_data return wave_data, framerate
def read_gzip_wave_file(filename): if (not os.path.isfile(filename)): raise ValueError("File does not exist") with gzip.open(filename, 'rb') as wav_file: with wave.open(wav_file, 'rb') as s: if (s.getnchannels() != 1): raise ValueError("Wave file should be mono") #if (s.getframerate() != 22050): #raise ValueError("Sampling rate of wave file should be 16000") strsig = s.readframes(s.getnframes()) x = np.fromstring(strsig, np.short) fs = s.getframerate() s.close() return fs, x
def read_wave_file(filename): """ Read a wave file from disk # Arguments filename : the name of the wave file # Returns (fs, x) : (sampling frequency, signal) """ if (not os.path.isfile(filename)): raise ValueError("File does not exist") s = wave.open(filename, 'rb') if (s.getnchannels() != 1): raise ValueError("Wave file should be mono") # if (s.getframerate() != 22050): # raise ValueError("Sampling rate of wave file should be 16000") strsig = s.readframes(s.getnframes()) x = np.fromstring(strsig, np.short) fs = s.getframerate() s.close() x = x/32768.0 return fs, x
def read_wave_file_not_normalized(filename): """ Read a wave file from disk # Arguments filename : the name of the wave file # Returns (fs, x) : (sampling frequency, signal) """ if (not os.path.isfile(filename)): raise ValueError("File does not exist") s = wave.open(filename, 'rb') if (s.getnchannels() != 1): raise ValueError("Wave file should be mono") # if (s.getframerate() != 22050): # raise ValueError("Sampling rate of wave file should be 16000") strsig = s.readframes(s.getnframes()) x = np.fromstring(strsig, np.short) fs = s.getframerate() s.close() return fs, x
def initializeGL(self): self.sdf_shader = self.gls.shader_cache.get("image_vert", "tex_frag") self.buffer_dtype = numpy.dtype([ ("vertex", numpy.float32, 2), ("texpos", numpy.float32, 2) ]) self.b1 = vbobind(self.sdf_shader, self.buffer_dtype, "vertex") self.b2 = vbobind(self.sdf_shader, self.buffer_dtype, "texpos") self.tex = Texture() # TODO: implement short-int caching # build a De-bruijn sequence (shorted substring containing all substrings) # self.int_seq = de_bruijn(4) # self.int_seq += self.int_seq[:3]
def init_audio(self, wav=None, path=None, show_pic=False): """ ?????? :param wav: ????????wave?? :param path: ?????? :param show_pic: ????? :return: None """ if wav is None: if path is None: print('Error: ?????????') self.__wav = wave.open(path, 'rb') else: self.__wav = wav nframes = self.__wav.getnframes() '''byte??????????''' str_data = self.__wav.readframes(nframes) '''????????''' self.__wdata = np.fromstring(str_data, dtype=np.short) '''???????''' if self.__wav.getnchannels() == 2: '''??????2????????''' self.__wdata.shape = -1, 2 self.__wdata = self.__wdata.T '''???????????????wdata[0]?''' for _ in range(len(self.__wdata[0])): if self.__wdata[0][_] < self.__wdata[1][_]: self.__wdata[0][_] = self.__wdata[1][_] self.__wdata = self.__wdata[0] self.__wdata = np.delete(self.__wdata, np.where(self.__wdata == 0)) if show_pic: x = [_ for _ in range(len(self.__wdata))] pylab.plot(x, self.__wdata, 'b') pylab.show()
def recode(self): pa=PyAudio() stream=pa.open(format=paInt16,channels=1,rate=self.SAMPLES_RATE,input=True,frames_per_buffer=self.NUM_SAMPLES) save_count=0 save_buffer=[] time_count=self.TIME_COUNT print '\n\n\n??????????????' while True: time_count-=1 string_audio_data=stream.read(self.NUM_SAMPLES) audio_data=numpy.fromstring(string_audio_data,dtype=numpy.short) large_sample_count=numpy.sum(audio_data>self.LEVEL) print(numpy.max(audio_data)) if large_sample_count>self.COUNT_NUM: save_count=self.SAVE_LENGTH else: save_count-=1 if save_count<0: save_count=0 if save_count>0: save_buffer.append(string_audio_data) else: if len(save_buffer): self.Voice_String=save_buffer save_buffer=[] print '????????' return True if not time_count: if len(save_buffer): self.Voice_String=save_buffer save_buffer=[] print '????????' return True else: return False
def amean (inarray,dimension=None,keepdims=0): """ Calculates the arithmatic mean of the values in the passed array. That is: 1/n * (x1 + x2 + ... + xn). Defaults to ALL values in the passed array. Use dimension=None to flatten array first. REMEMBER: if dimension=0, it collapses over dimension 0 ('rows' in a 2D array) only, and if dimension is a sequence, it collapses over all specified dimensions. If keepdims is set to 1, the resulting array will have as many dimensions as inarray, with only 1 'level' per dim that was collapsed over. Usage: amean(inarray,dimension=None,keepdims=0) Returns: arithematic mean calculated over dim(s) in dimension """ if inarray.dtype in [N.int_, N.short,N.ubyte]: inarray = inarray.astype(N.float_) if dimension == None: inarray = N.ravel(inarray) sum = N.add.reduce(inarray) denom = float(len(inarray)) elif type(dimension) in [IntType,FloatType]: sum = asum(inarray,dimension) denom = float(inarray.shape[dimension]) if keepdims == 1: shp = list(inarray.shape) shp[dimension] = 1 sum = N.reshape(sum,shp) else: # must be a TUPLE of dims to average over dims = list(dimension) dims.sort() dims.reverse() sum = inarray *1.0 for dim in dims: sum = N.add.reduce(sum,dim) denom = N.array(N.multiply.reduce(N.take(inarray.shape,dims)),N.float_) if keepdims == 1: shp = list(inarray.shape) for dim in dims: shp[dim] = 1 sum = N.reshape(sum,shp) return sum/denom
def atmean(a,limits=None,inclusive=(1,1)): """ Returns the arithmetic mean of all values in an array, ignoring values strictly outside the sequence passed to 'limits'. Note: either limit in the sequence, or the value of limits itself, can be set to None. The inclusive list/tuple determines whether the lower and upper limiting bounds (respectively) are open/exclusive (0) or closed/inclusive (1). Usage: atmean(a,limits=None,inclusive=(1,1)) """ if a.dtype in [N.int_, N.short,N.ubyte]: a = a.astype(N.float_) if limits == None: return mean(a) assert type(limits) in [ListType,TupleType,N.ndarray], "Wrong type for limits in atmean" if inclusive[0]: lowerfcn = N.greater_equal else: lowerfcn = N.greater if inclusive[1]: upperfcn = N.less_equal else: upperfcn = N.less if limits[0] > N.maximum.reduce(N.ravel(a)) or limits[1] < N.minimum.reduce(N.ravel(a)): raise ValueError, "No array values within given limits (atmean)." elif limits[0]==None and limits[1]<>None: mask = upperfcn(a,limits[1]) elif limits[0]<>None and limits[1]==None: mask = lowerfcn(a,limits[0]) elif limits[0]<>None and limits[1]<>None: mask = lowerfcn(a,limits[0])*upperfcn(a,limits[1]) s = float(N.add.reduce(N.ravel(a*mask))) n = float(N.add.reduce(N.ravel(mask))) return s/n
def asum (a, dimension=None,keepdims=0): """ An alternative to the Numeric.add.reduce function, which allows one to (1) collapse over multiple dimensions at once, and/or (2) to retain all dimensions in the original array (squashing one down to size. Dimension can equal None (ravel array first), an integer (the dimension over which to operate), or a sequence (operate over multiple dimensions). If keepdims=1, the resulting array will have as many dimensions as the input array. Usage: asum(a, dimension=None, keepdims=0) Returns: array summed along 'dimension'(s), same _number_ of dims if keepdims=1 """ if type(a) == N.ndarray and a.dtype in [N.int_, N.short, N.ubyte]: a = a.astype(N.float_) if dimension == None: s = N.sum(N.ravel(a)) elif type(dimension) in [IntType,FloatType]: s = N.add.reduce(a, dimension) if keepdims == 1: shp = list(a.shape) shp[dimension] = 1 s = N.reshape(s,shp) else: # must be a SEQUENCE of dims to sum over dims = list(dimension) dims.sort() dims.reverse() s = a *1.0 for dim in dims: s = N.add.reduce(s,dim) if keepdims == 1: shp = list(a.shape) for dim in dims: shp[dim] = 1 s = N.reshape(s,shp) return s
def __init__(self, v_min, v_max, phase, pulse_shaper_interpolation): gr.interp_block.__init__(self, name="pulse_shaper_bs", in_sig=[numpy.int8], out_sig=[numpy.short], interp=pulse_shaper_interpolation) self.min = v_min self.max = v_max self.phase = phase self.ps_interpolation = pulse_shaper_interpolation
def my_record(self): while self.start_flag == 1: pa = PyAudio() stream = pa.open(format=paInt16, channels=1, rate=self.framerate, input=True, frames_per_buffer=self.NUM_SAMPLES) save_buffer = [] count = 0 while count < self.TIME * 20: string_audio_data = stream.read(self.NUM_SAMPLES) audio_data = np.fromstring(string_audio_data, dtype=np.short) large_sample_count = np.sum(audio_data > self.LEVEL) print large_sample_count if large_sample_count < self.mute_count_limit: self.mute_begin = 1 else: save_buffer.append(string_audio_data) self.mute_begin = 0 self.mute_end = 1 count += 1 if (self.mute_end - self.mute_begin) > 9: self.mute_begin = 0 self.mute_end = 1 break if self.mute_begin: self.mute_end += 1 print '.' save_buffer = save_buffer[:] # my_buf.append(string_audio_data) # count+=1 # print '.' if save_buffer: if self.file_name_index < 11: pass else: self.file_name_index = 1 filename = str(self.file_name_index) + '.wav' self.save_wave_file(filename=filename, data=save_buffer) self.writeQ(queue=self.wav_queue, data=filename) self.file_name_index += 1 print filename, 'saved' else: print 'file not saved!' # self.save_wave_file('01.wav',my_buf) save_buffer = [] stream.close()
def recoder(self): pa = PyAudio() stream = pa.open(format=paInt16, channels=1, rate=self.SAMPLING_RATE, input=True, frames_per_buffer=self.NUM_SAMPLES) save_count = 0 save_buffer = [] time_count = self.TIME_COUNT while True: time_count -= 1 # print time_count # ??NUM_SAMPLES??? string_audio_data = stream.read(self.NUM_SAMPLES) # ??????????? audio_data = np.fromstring(string_audio_data, dtype=np.short) # ????LEVEL?????? large_sample_count = np.sum( audio_data > self.LEVEL ) print(np.max(audio_data)) # ??????COUNT_NUM??????SAVE_LENGTH?? if large_sample_count > self.COUNT_NUM: save_count = self.SAVE_LENGTH else: save_count -= 1 if save_count < 0: save_count = 0 if save_count > 0 : # ??????????save_buffer? #print save_count > 0 and time_count >0 save_buffer.append( string_audio_data ) else: #print save_buffer # ?save_buffer??????WAV???WAV???????????? #print "debug" if len(save_buffer) > 0 : self.Voice_String = save_buffer save_buffer = [] print("Recode a piece of voice successfully!") return True if time_count==0: if len(save_buffer)>0: self.Voice_String = save_buffer save_buffer = [] print("Recode a piece of voice successfully!") return True else: return False
def record_wave(self,temp): while self.start_flag==1: pa=PyAudio() stream=pa.open(format=paInt16,channels=1, rate=framerate,input=True, frames_per_buffer=self.NUM_SAMPLES) my_buf=[] count=0 print "* start recoding *" while count<self.TIME*20: string_audio_data=stream.read(self.NUM_SAMPLES) audio_data=np.fromstring(string_audio_data,dtype=np.short) large_sample_count=np.sum(audio_data>self.LEVEL) print large_sample_count if large_sample_count<self.mute_count_limit: self.mute_begin=1 else: my_buf.append(string_audio_data) self.mute_begin=0 self.mute_end=1 count+=1 if(self.mute_end-self.mute_begin)>9: self.mute_begin=0 self.mute_end=1 break if self.mute_begin: self.mute_end+=1 print '.' my_buf=my_buf[:] if my_buf: if self.file_name_index<11: pass else: self.file_name_index=1 filename=str(self.file_name_index)+'.wav' self.save_wave_file(filename=filename,data=my_buf) self.writeQ(queue=self.wav_queue,data=filename) self.file_name_index+=1 print filename,"saved" else: print '* Error: file not saved! *' #self.save_wave_file(filename, my_buf) my_buf=[] stream.close()
def discretize(self, time_slice_length): self.time_slice_length = time_slice_length # clean the data directory if os.path.exists('corpus'): shutil.rmtree('corpus') os.makedirs('corpus') # compute the total number of time-slices time_delta = (self.end_date - self.start_date) time_delta = time_delta.total_seconds()/60 self.time_slice_count = int(time_delta // self.time_slice_length) + 1 self.tweet_count = np.zeros(self.time_slice_count) print(' Number of time-slices: %d' % self.time_slice_count) # create empty files for time_slice in range(self.time_slice_count): dummy_file = open('corpus/' + str(time_slice), 'w') dummy_file.write('') # compute word frequency self.global_freq = dok_matrix((len(self.vocabulary), self.time_slice_count), dtype=np.short) self.mention_freq = dok_matrix((len(self.vocabulary), self.time_slice_count), dtype=np.short) with open(self.source_file_path, 'r') as input_file: csv_reader = csv.reader(input_file, delimiter=self.separator) header = next(csv_reader) text_column_index = header.index('text') date_column_index = header.index('date') for line in csv_reader: tweet_date = datetime.strptime(line[date_column_index], "%Y-%m-%d %H:%M:%S") time_delta = (tweet_date - self.start_date) time_delta = time_delta.total_seconds() / 60 time_slice = int(time_delta / self.time_slice_length) self.tweet_count[time_slice] += 1 # tokenize the tweet and update word frequency tweet_text = line[text_column_index] words = self.tokenize(tweet_text) mention = '@' in tweet_text for word in set(words): word_id = self.vocabulary.get(word) if word_id is not None: self.global_freq[word_id, time_slice] += 1 if mention: self.mention_freq[word_id, time_slice] += 1 with open('corpus/' + str(time_slice), 'a') as time_slice_file: time_slice_file.write(tweet_text+'\n') self.global_freq = self.global_freq.tocsr() self.mention_freq = self.mention_freq.tocsr()
def startAccord(self, Times=100): # ?????? pa = PyAudio() if not self.SAMPLING_RATE: setAudio(self) stream = pa.open(format=paInt16, channels=1, rate=self.SAMPLING_RATE, input=True, frames_per_buffer=self.NUM_SAMPLES) save_count = 0 save_buffer = [] while True: #???? if Times != 100: Times -=1 # ??NUM_SAMPLES??? string_audio_data = stream.read(self.NUM_SAMPLES) # ??????????? audio_data = np.fromstring(string_audio_data, dtype=np.short) # ????LEVEL?????? large_sample_count = np.sum( audio_data > self.LEVEL ) if self.debug: print np.max(audio_data) # ??????COUNT_NUM??????SAVE_LENGTH?? if large_sample_count > self.COUNT_NUM: save_count = self.SAVE_LENGTH else: save_count -= 1 if save_count < 0: save_count = 0 if save_count > 0: # ??????????save_buffer? save_buffer.append( string_audio_data ) else: # ?save_buffer??????WAV???WAV???????????? if len(save_buffer) > 0: filename = "tmp.wav" cacheFile = save_wave_file(self, filename, save_buffer) save_buffer = [] stream.close() if self.debug: print "saved audio stream" return cacheFile break if Times < 0: stream.close() return False break
def recode(self): pa = PyAudio() stream = pa.open(format=paInt16, channels=self.nchannel, rate=self.SAMPLING_RATE, input=True, frames_per_buffer=self.NUM_SAMPLES) save_count = 0 save_buffer = [] time_out = self.TIME_OUT NO_WORDS=self.NO_WORDS while True and NO_WORDS: time_out -= 1 print 'time_out in', time_out # ??NUM_SAMPLES??? string_audio_data = stream.read(self.NUM_SAMPLES) # ??????????? audio_data = np.fromstring(string_audio_data, dtype=np.short) # ?????????? NO_WORDS -= 1 if np.max(audio_data) > self.UPPER_LEVEL: NO_WORDS=self.NO_WORDS print 'self.NO_WORDS ', NO_WORDS print 'np.max(audio_data) ', np.max(audio_data) # ????LOWER_LEVEL?????? large_sample_count = np.sum( audio_data > self.LOWER_LEVEL ) # ??????COUNT_NUM??????SAVE_LENGTH?? if large_sample_count > self.COUNT_NUM: save_count = self.SAVE_LENGTH else: save_count -= 1 #print 'save_count',save_count # ??????????save_buffer? if save_count < 0: save_count = 0 elif save_count > 0 : save_buffer.append( string_audio_data ) else: pass # ?save_buffer??????WAV???WAV???????????? if len(save_buffer) > 0 and NO_WORDS==0: self.Voice_String = save_buffer save_buffer = [] rospy.loginfo( "Recode a piece of voice successfully!") #return self.Voice_String elif len(save_buffer) > 0 and time_out==0: self.Voice_String = save_buffer save_buffer = [] rospy.loginfo( "Recode a piece of voice successfully!") #return self.Voice_String else: pass #rospy.loginfo( '\n\n')
