Python numpy 模块，short() 实例源码
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')