Python numpy.getbuffer() Examples
The following are 16
code examples of numpy.getbuffer().
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Example #1
Source File: cache.py From heamy with MIT License | 6 votes |
def numpy_buffer(ndarray): """Creates a buffer from c_contiguous numpy ndarray.""" # Credits to: https://git.io/fjC5g if isinstance(ndarray, (pd.Series, pd.DataFrame)): ndarray = ndarray.values if ndarray.flags.c_contiguous: obj_c_contiguous = ndarray elif ndarray.flags.f_contiguous: obj_c_contiguous = ndarray.T else: obj_c_contiguous = ndarray.flatten() obj_c_contiguous = obj_c_contiguous.view(np.uint8) if hasattr(np, "getbuffer"): return np.getbuffer(obj_c_contiguous) else: return memoryview(obj_c_contiguous)
Example #2
Source File: hashing.py From estimators with MIT License | 6 votes |
def __init__(self, hash_name='md5', coerce_mmap=False): """ Parameters ---------- hash_name: string The hash algorithm to be used coerce_mmap: boolean Make no difference between np.memmap and np.ndarray objects. """ self.coerce_mmap = coerce_mmap Hasher.__init__(self, hash_name=hash_name) # delayed import of numpy, to avoid tight coupling import numpy as np self.np = np if hasattr(np, 'getbuffer'): self._getbuffer = np.getbuffer else: self._getbuffer = memoryview
Example #3
Source File: hashing.py From mlens with MIT License | 6 votes |
def __init__(self, hash_name='md5', coerce_mmap=False): """ Parameters ---------- hash_name: string The hash algorithm to be used coerce_mmap: boolean Make no difference between np.memmap and np.ndarray objects. """ self.coerce_mmap = coerce_mmap Hasher.__init__(self, hash_name=hash_name) # delayed import of numpy, to avoid tight coupling import numpy as np self.np = np if hasattr(np, 'getbuffer'): self._getbuffer = np.getbuffer else: self._getbuffer = memoryview
Example #4
Source File: hashing.py From django-estimators with MIT License | 6 votes |
def __init__(self, hash_name='md5', coerce_mmap=False): """ Parameters ---------- hash_name: string The hash algorithm to be used coerce_mmap: boolean Make no difference between np.memmap and np.ndarray objects. """ self.coerce_mmap = coerce_mmap Hasher.__init__(self, hash_name=hash_name) # delayed import of numpy, to avoid tight coupling import numpy as np self.np = np if hasattr(np, 'getbuffer'): self._getbuffer = np.getbuffer else: self._getbuffer = memoryview
Example #5
Source File: hashing.py From Splunking-Crime with GNU Affero General Public License v3.0 | 6 votes |
def __init__(self, hash_name='md5', coerce_mmap=False): """ Parameters ---------- hash_name: string The hash algorithm to be used coerce_mmap: boolean Make no difference between np.memmap and np.ndarray objects. """ self.coerce_mmap = coerce_mmap Hasher.__init__(self, hash_name=hash_name) # delayed import of numpy, to avoid tight coupling import numpy as np self.np = np if hasattr(np, 'getbuffer'): self._getbuffer = np.getbuffer else: self._getbuffer = memoryview
Example #6
Source File: hashing.py From abu with GNU General Public License v3.0 | 6 votes |
def __init__(self, hash_name='md5', coerce_mmap=False): """ Parameters ---------- hash_name: string The hash algorithm to be used coerce_mmap: boolean Make no difference between np.memmap and np.ndarray objects. """ self.coerce_mmap = coerce_mmap Hasher.__init__(self, hash_name=hash_name) # delayed import of numpy, to avoid tight coupling import numpy as np self.np = np if hasattr(np, 'getbuffer'): self._getbuffer = np.getbuffer else: self._getbuffer = memoryview
Example #7
Source File: hashing.py From SqueezeMeta with GNU General Public License v3.0 | 6 votes |
def __init__(self, hash_name='md5', coerce_mmap=False): """ Parameters ---------- hash_name: string The hash algorithm to be used coerce_mmap: boolean Make no difference between np.memmap and np.ndarray objects. """ self.coerce_mmap = coerce_mmap Hasher.__init__(self, hash_name=hash_name) # delayed import of numpy, to avoid tight coupling import numpy as np self.np = np if hasattr(np, 'getbuffer'): self._getbuffer = np.getbuffer else: self._getbuffer = memoryview
Example #8
Source File: hashing.py From twitter-stock-recommendation with MIT License | 6 votes |
def __init__(self, hash_name='md5', coerce_mmap=False): """ Parameters ---------- hash_name: string The hash algorithm to be used coerce_mmap: boolean Make no difference between np.memmap and np.ndarray objects. """ self.coerce_mmap = coerce_mmap Hasher.__init__(self, hash_name=hash_name) # delayed import of numpy, to avoid tight coupling import numpy as np self.np = np if hasattr(np, 'getbuffer'): self._getbuffer = np.getbuffer else: self._getbuffer = memoryview
Example #9
Source File: utils.py From D-VAE with MIT License | 5 votes |
def hash_from_code(msg): try: return hashlib.md5(msg).hexdigest() except TypeError: assert isinstance(msg, numpy.ndarray) return hashlib.md5(numpy.getbuffer(msg)).hexdigest()
Example #10
Source File: utils.py From attention-lvcsr with MIT License | 5 votes |
def hash_from_code(msg): try: return hashlib.md5(msg).hexdigest() except TypeError: assert isinstance(msg, numpy.ndarray) return hashlib.md5(numpy.getbuffer(msg)).hexdigest()
Example #11
Source File: test_converters.py From attention-lvcsr with MIT License | 5 votes |
def setUp(self): MNIST_IMAGE_MAGIC = 2051 MNIST_LABEL_MAGIC = 2049 numpy.random.seed(9 + 5 + 2015) self.train_features_mock = numpy.random.randint( 0, 256, (10, 1, 28, 28)).astype('uint8') self.train_targets_mock = numpy.random.randint( 0, 10, (10, 1)).astype('uint8') self.test_features_mock = numpy.random.randint( 0, 256, (10, 1, 28, 28)).astype('uint8') self.test_targets_mock = numpy.random.randint( 0, 10, (10, 1)).astype('uint8') self.tempdir = tempfile.mkdtemp() self.train_images_path = os.path.join( self.tempdir, 'train-images-idx3-ubyte.gz') self.train_labels_path = os.path.join( self.tempdir, 'train-labels-idx1-ubyte.gz') self.test_images_path = os.path.join( self.tempdir, 't10k-images-idx3-ubyte.gz') self.test_labels_path = os.path.join( self.tempdir, 't10k-labels-idx1-ubyte.gz') self.wrong_images_path = os.path.join(self.tempdir, 'wrong_images.gz') self.wrong_labels_path = os.path.join(self.tempdir, 'wrong_labels.gz') with gzip.open(self.train_images_path, 'wb') as f: f.write(struct.pack('>iiii', *(MNIST_IMAGE_MAGIC, 10, 28, 28))) f.write(getbuffer(self.train_features_mock.flatten())) with gzip.open(self.train_labels_path, 'wb') as f: f.write(struct.pack('>ii', *(MNIST_LABEL_MAGIC, 10))) f.write(getbuffer(self.train_targets_mock.flatten())) with gzip.open(self.test_images_path, 'wb') as f: f.write(struct.pack('>iiii', *(MNIST_IMAGE_MAGIC, 10, 28, 28))) f.write(getbuffer(self.test_features_mock.flatten())) with gzip.open(self.test_labels_path, 'wb') as f: f.write(struct.pack('>ii', *(MNIST_LABEL_MAGIC, 10))) f.write(getbuffer(self.test_targets_mock.flatten())) with gzip.open(self.wrong_images_path, 'wb') as f: f.write(struct.pack('>iiii', *(2000, 10, 28, 28))) with gzip.open(self.wrong_labels_path, 'wb') as f: f.write(struct.pack('>ii', *(2000, 10)))
Example #12
Source File: test_converters.py From fuel with MIT License | 5 votes |
def setUp(self): MNIST_IMAGE_MAGIC = 2051 MNIST_LABEL_MAGIC = 2049 numpy.random.seed(9 + 5 + 2015) self.train_features_mock = numpy.random.randint( 0, 256, (10, 1, 28, 28)).astype('uint8') self.train_targets_mock = numpy.random.randint( 0, 10, (10, 1)).astype('uint8') self.test_features_mock = numpy.random.randint( 0, 256, (10, 1, 28, 28)).astype('uint8') self.test_targets_mock = numpy.random.randint( 0, 10, (10, 1)).astype('uint8') self.tempdir = tempfile.mkdtemp() self.train_images_path = os.path.join( self.tempdir, 'train-images-idx3-ubyte.gz') self.train_labels_path = os.path.join( self.tempdir, 'train-labels-idx1-ubyte.gz') self.test_images_path = os.path.join( self.tempdir, 't10k-images-idx3-ubyte.gz') self.test_labels_path = os.path.join( self.tempdir, 't10k-labels-idx1-ubyte.gz') self.wrong_images_path = os.path.join(self.tempdir, 'wrong_images.gz') self.wrong_labels_path = os.path.join(self.tempdir, 'wrong_labels.gz') with gzip.open(self.train_images_path, 'wb') as f: f.write(struct.pack('>iiii', *(MNIST_IMAGE_MAGIC, 10, 28, 28))) f.write(getbuffer(self.train_features_mock.flatten())) with gzip.open(self.train_labels_path, 'wb') as f: f.write(struct.pack('>ii', *(MNIST_LABEL_MAGIC, 10))) f.write(getbuffer(self.train_targets_mock.flatten())) with gzip.open(self.test_images_path, 'wb') as f: f.write(struct.pack('>iiii', *(MNIST_IMAGE_MAGIC, 10, 28, 28))) f.write(getbuffer(self.test_features_mock.flatten())) with gzip.open(self.test_labels_path, 'wb') as f: f.write(struct.pack('>ii', *(MNIST_LABEL_MAGIC, 10))) f.write(getbuffer(self.test_targets_mock.flatten())) with gzip.open(self.wrong_images_path, 'wb') as f: f.write(struct.pack('>iiii', *(2000, 10, 28, 28))) with gzip.open(self.wrong_labels_path, 'wb') as f: f.write(struct.pack('>ii', *(2000, 10)))
Example #13
Source File: sampler.py From eegsynth with GNU General Public License v3.0 | 5 votes |
def callback(in_data, frame_count, time_info, status): global lock, debug, stack, channels, prefix, current_channel, current_value with lock: begsample = 0 endsample = min(frame_count, stack.shape[0]) dat = stack[begsample:endsample] # add zero-padding if required pad = np.zeros((frame_count - endsample, channels), dtype=np.float32) dat = np.concatenate((dat, pad), axis=0) # remove the current samples from the stack stack = stack[endsample:] if stack.shape[0] == 0 and current_channel != None: # send a trigger to indicate that the sample finished playing patch.setvalue("%s.%s" % (finished, current_channel), current_value) current_channel = None current_value = 0 try: # this is for Python 2 buf = np.getbuffer(dat) except: # this is for Python 3 buf = dat.tobytes() return buf, pyaudio.paContinue
Example #14
Source File: db_handling.py From hfnet with MIT License | 5 votes |
def array_to_blob(array): if IS_PYTHON3: return array.tostring() else: return np.getbuffer(array)
Example #15
Source File: audioProcessor.py From gcc-nmf with MIT License | 5 votes |
def filePlayerCallback(self, in_data, numFrames, time_info, status): startTime = tm.time() if self.sampleIndex+numFrames >= self.numFrames: self.sampleIndex = 0 inputBuffer = self.samples[self.sampleIndex*self.bytesPerFrameAllChannels:(self.sampleIndex+numFrames)*self.bytesPerFrameAllChannels] inputIntArray = np.frombuffer(inputBuffer, dtype='<i2') self.inputFrames[:] = pcm2float(inputIntArray).reshape(-1, self.numChannels).T self.sampleIndex += numFrames #logging.info('AudioStreamProcessor: setting processFramesEvent') self.processFramesDoneEvent.clear() self.processFramesEvent.set() #logging.info('AudioStreamProcessor: waiting for processFramesDoneEvent') self.processFramesDoneEvent.wait() #logging.info('AudioStreamProcessor: done waiting for processFramesDoneEvent') outputIntArray = float2pcm(self.outputFrames.T.flatten()) try: outputBuffer = np.getbuffer(outputIntArray) except: outputBuffer = outputIntArray.tobytes() self.processingTimes.append(tm.time() - startTime) return outputBuffer, self.paContinue
Example #16
Source File: outputaudio.py From eegsynth with GNU General Public License v3.0 | 4 votes |
def callback(in_data, frame_count, time_info, status): global stack, window, firstsample, stretch, inputrate, outputrate, outputblock, prevoutput, b, a, zi now = time.time() duration = now - prevoutput prevoutput = now if outputblock > 5 and duration > 0: old = outputrate new = frame_count / duration if old/new > 0.1 or old/new < 10: outputrate = (1 - lrate) * old + lrate * new # estimate the required stretch between input and output rate old = stretch new = outputrate / inputrate stretch = (1 - lrate) * old + lrate * new # linearly interpolate the selection of samples, i.e. stretch or compress the time axis when needed begsample = firstsample endsample = round(firstsample + frame_count / stretch) selection = np.linspace(begsample, endsample, frame_count).astype(np.int32) # remember where to continue the next time firstsample = (endsample + 1) % window with lock: lenstack = len(stack) if endsample > (window - 1) and lenstack>1: # the selection passes the boundary, concatenate the first two blocks dat = np.append(stack[0], stack[1], axis=0) elif lenstack>0: # the selection can be made in the first block dat = stack[0] # select the samples that will be written to the audio card try: dat = dat[selection] except: dat = np.zeros((frame_count,1), dtype=float) if endsample > window: # it is time to remove data from the stack with lock: stack = stack[1:] # remove the first block try: # this is for Python 2 buf = np.getbuffer(dat) except: # this is for Python 3 buf = dat.tobytes() outputblock += 1 return buf, pyaudio.paContinue