python中dtype的使用规范_Python numpy.dtype() 使用实例

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Example 1

def extract_images(filename):

“””Extract the images into a 4D uint8 numpy array [index, y, x, depth].”””

print(‘Extracting’, filename)

with gzip.open(filename) as bytestream:

magic = _read32(bytestream)

if magic != 2051:

raise ValueError(

‘Invalid magic number %d in MNIST image file: %s’ %

(magic, filename))

num_images = _read32(bytestream)

rows = _read32(bytestream)

cols = _read32(bytestream)

buf = bytestream.read(rows * cols * num_images)

data = numpy.frombuffer(buf, dtype=numpy.uint8)

data = data.reshape(num_images, rows, cols, 1)

return data

Example 2

def gl_init(self,array_table):

self.gl_hide = False

self.gl_vertex_array = gl.VertexArray()

glBindVertexArray(self.gl_vertex_array)

self.gl_vertex_buffer = gl.Buffer()

glBindBuffer(GL_ARRAY_BUFFER,self.gl_vertex_buffer)

self.gl_element_count = 3*gl_count_triangles(self)

self.gl_element_buffer = gl.Buffer()

glBindBuffer(GL_ELEMENT_ARRAY_BUFFER,self.gl_element_buffer)

vertex_type = numpy.dtype([array_table[attribute].field() for attribute in self.attributes])

vertex_count = sum(len(primitive.vertices) for primitive in self.primitives)

vertex_array = numpy.empty(vertex_count,vertex_type)

for attribute in self.attributes:

array_table[attribute].load(self,vertex_array)

vertex_array,element_map = numpy.unique(vertex_array,return_inverse=True)

element_array = gl_create_element_array(self,element_map,self.gl_element_count)

glBufferData(GL_ARRAY_BUFFER,vertex_array.nbytes,vertex_array,GL_STATIC_DRAW)

glBufferData(GL_ELEMENT_ARRAY_BUFFER,element_array.nbytes,element_array,GL_STATIC_DRAW)

Example 3

def extract_images(filename):

“””Extract the images into a 4D uint8 numpy array [index, y, x, depth].”””

print(‘Extracting’, filename)

with gzip.open(filename) as bytestream:

magic = _read32(bytestream)

if magic != 2051:

raise ValueError(

‘Invalid magic number %d in MNIST image file: %s’ %

(magic, filename))

num_images = _read32(bytestream)

rows = _read32(bytestream)

cols = _read32(bytestream)

buf = bytestream.read(rows * cols * num_images)

data = numpy.frombuffer(buf, dtype=numpy.uint8)

data = data.reshape(num_images, rows, cols, 1)

return data

Example 4

def __keytransform__(self, key):

if isinstance(key[0], np.ndarray):

shape = key[0].shape

dtype = key[0].dtype

i = key[1]

zero = True if len(key) == 2 else key[2]

elif isinstance(key[0], tuple):

if len(key) == 3:

shape, dtype, i = key

zero = True

elif len(key) == 4:

shape, dtype, i, zero = key

else:

raise TypeError(“Wrong type of key for work array”)

assert isinstance(zero, bool)

assert isinstance(i, int)

self.fillzero = zero

return (shape, np.dtype(dtype), i)

Example 5

def accumulate_strings(values, name=”strings”):

“””Accumulates strings into a vector.

Args:

values: A 1-d string tensor that contains values to add to the accumulator.

Returns:

A tuple (value_tensor, update_op).

“””

tf.assert_type(values, tf.string)

strings = tf.Variable(

name=name,

initial_value=[],

dtype=tf.string,

trainable=False,

collections=[],

validate_shape=True)

value_tensor = tf.identity(strings)

update_op = tf.assign(

ref=strings, value=tf.concat([strings, values], 0), validate_shape=False)

return value_tensor, update_op

Example 6

def test_expect_dtypes_with_tuple(self):

allowed_dtypes = (dtype(‘datetime64[ns]’), dtype(‘float’))

@expect_dtypes(a=allowed_dtypes)

def foo(a, b):

return a, b

for d in allowed_dtypes:

good_a = arange(3).astype(d)

good_b = object()

ret_a, ret_b = foo(good_a, good_b)

self.assertIs(good_a, ret_a)

self.assertIs(good_b, ret_b)

with self.assertRaises(TypeError) as e:

foo(arange(3, dtype=’uint32′), object())

expected_message = (

“{qualname}() expected a value with dtype ‘datetime64[ns]’ “

“or ‘float64’ for argument ‘a’, but got ‘uint32’ instead.”

).format(qualname=qualname(foo))

self.assertEqual(e.exception.args[0], expected_message)

Example 7

def _classify_gems(counts0, counts1):

“”” Infer number of distinct transcriptomes present in each GEM (1 or 2) and

report cr_constants.GEM_CLASS_GENOME0 for a single cell w/ transcriptome 0,

report cr_constants.GEM_CLASS_GENOME1 for a single cell w/ transcriptome 1,

report cr_constants.GEM_CLASS_MULTIPLET for multiple transcriptomes “””

# Assumes that most of the GEMs are single-cell; model counts independently

thresh0, thresh1 = [cr_constants.DEFAULT_MULTIPLET_THRESHOLD] * 2

if sum(counts0 > counts1) >= 1 and sum(counts1 > counts0) >= 1:

thresh0 = np.percentile(counts0[counts0 > counts1], cr_constants.MULTIPLET_PROB_THRESHOLD)

thresh1 = np.percentile(counts1[counts1 > counts0], cr_constants.MULTIPLET_PROB_THRESHOLD)

doublet = np.logical_and(counts0 >= thresh0, counts1 >= thresh1)

dtype = np.dtype(‘|S%d’ % max(len(cls) for cls in cr_constants.GEM_CLASSES))

result = np.where(doublet, cr_constants.GEM_CLASS_MULTIPLET, cr_constants.GEM_CLASS_GENOME0).astype(dtype)

result[np.logical_and(np.logical_not(result == cr_constants.GEM_CLASS_MULTIPLET), counts1 > counts0)] = cr_constants.GEM_CLASS_GENOME1

return result

Example 8

def widen_cat_column(old_ds, new_type):

name = old_ds.name

tmp_name = “__tmp_” + old_ds.name

grp = old_ds.parent

ds = grp.create_dataset(tmp_name,

data = old_ds[:],

shape = old_ds.shape,

maxshape = (None,),

dtype = new_type,

compression = COMPRESSION,

shuffle = True,

chunks = (CHUNK_SIZE,))

del grp[name]

grp.move(tmp_name, name)

return ds

Example 9

def create_levels(ds, levels):

# Create a dataset in the LEVEL_GROUP

# and store as native numpy / h5py types

level_grp = ds.file.get(LEVEL_GROUP)

if level_grp is None:

# Create a LEVEL_GROUP

level_grp = ds.file.create_group(LEVEL_GROUP)

ds_name = ds.name.split(“/”)[-1]

dt = h5py.special_dtype(vlen=str)

level_grp.create_dataset(ds_name,

shape = [len(levels)],

maxshape = (None,),

dtype = dt,

data = levels,

compression = COMPRESSION,

chunks = (CHUNK_SIZE,))

Example 10

def reg2bin_vector(begin, end):

”’Vectorized tabix reg2bin — much faster than reg2bin”’

result = np.zeros(begin.shape)

# Entries filled

done = np.zeros(begin.shape, dtype=np.bool)

for (bits, bins) in rev_bit_bins:

begin_shift = begin >> bits

new_done = (begin >> bits) == (end >> bits)

mask = np.logical_and(new_done, np.logical_not(done))

offset = ((1 << (29 – bits)) – 1) / 7

result[mask] = offset + begin_shift[mask]

done = new_done

return result.astype(np.int32)

Example 11

def flip_code(code):

if isinstance(code, (numpy.dtype,type)):

# since several things map to complex64 we must carefully select

# the opposite that is an exact match (ticket 1518)

if code == numpy.int8:

return gdalconst.GDT_Byte

if code == numpy.complex64:

return gdalconst.GDT_CFloat32

for key, value in codes.items():

if value == code:

return key

return None

else:

try:

return codes[code]

except KeyError:

return None

Example 12

def make2d(array, cols=None, dtype=None):

”’

Make a 2D array from an array of arrays. The `cols’ and `dtype’

arguments can be omitted if the array is not empty.

”’

if (cols is None or dtype is None) and not len(array):

raise RuntimeError(“cols and dtype must be specified for empty “

“array”)

if cols is None:

cols = len(array[0])

if dtype is None:

dtype = array[0].dtype

return _np.fromiter(array, [(‘_’, dtype, (cols,))],

count=len(array))[‘_’]

Example 13

def _read(self, stream, text, byte_order):

”’

Read the actual data from a PLY file.

”’

if text:

self._read_txt(stream)

else:

if self._have_list:

# There are list properties, so a simple load is

# impossible.

self._read_bin(stream, byte_order)

else:

# There are no list properties, so loading the data is

# much more straightforward.

self._data = _np.fromfile(stream,

self.dtype(byte_order),

self.count)

if len(self._data) < self.count:

k = len(self._data)

del self._data

raise PlyParseError(“early end-of-file”, self, k)

self._check_sanity()

Example 14

def _read_bin(self, stream, byte_order):

”’

Load a PLY element from a binary PLY file. The element may

contain list properties.

”’

self._data = _np.empty(self.count, dtype=self.dtype(byte_order))

for k in _range(self.count):

for prop in self.properties:

try:

self._data[prop.name][k] = \

prop._read_bin(stream, byte_order)

except StopIteration:

raise PlyParseError(“early end-of-file”,

self, k, prop)

Example 15

def _merge_all(parts, dtype):

if len(parts) == 1:

return parts[0]

else:

nparts = []

for i in xrange(0, len(parts), 2):

if i+1 < len(parts):

npart = numpy.empty((len(parts[i])+len(parts[i+1]), 2), dtype)

merge_elements = index_merge(parts[i], parts[i+1], npart)

if merge_elements != len(npart):

npart = npart[:merge_elements]

nparts.append(npart)

else:

nparts.append(parts[i])

del parts

return _merge_all(nparts, dtype)

Example 16

def __init__(self, buf, offset = 0):

# Accelerate class attributes

self._encode = self.encode

self._dtype = self.dtype

self._xxh = self.xxh

# Initialize buffer

if offset:

self._buf = self._likebuf = buffer(buf, offset)

else:

self._buf = buf

self._likebuf = _likebuffer(buf)

# Parse header and map index

self.index_elements, self.index_offset = self._Header.unpack_from(self._buf, 0)

self.index = numpy.ndarray(buffer = self._buf,

offset = self.index_offset,

dtype = self.dtype,

shape = (self.index_elements, 3))

Example 17

def test_rescaleData():

dtypes = map(np.dtype, (‘ubyte’, ‘uint16’, ‘byte’, ‘int16’, ‘int’, ‘float’))

for dtype1 in dtypes:

for dtype2 in dtypes:

data = (np.random.random(size=10) * 2**32 – 2**31).astype(dtype1)

for scale, offset in [(10, 0), (10., 0.), (1, -50), (0.2, 0.5), (0.001, 0)]:

if dtype2.kind in ‘iu’:

lim = np.iinfo(dtype2)

lim = lim.min, lim.max

else:

lim = (-np.inf, np.inf)

s1 = np.clip(float(scale) * (data-float(offset)), *lim).astype(dtype2)

s2 = pg.rescaleData(data, scale, offset, dtype2)

assert s1.dtype == s2.dtype

if dtype2.kind in ‘iu’:

assert np.all(s1 == s2)

else:

assert np.allclose(s1, s2)

Example 18

def solve3DTransform(points1, points2):

“””

Find a 3D transformation matrix that maps points1 onto points2.

Points must be specified as either lists of 4 Vectors or

(4, 3) arrays.

“””

import numpy.linalg

pts = []

for inp in (points1, points2):

if isinstance(inp, np.ndarray):

A = np.empty((4,4), dtype=float)

A[:,:3] = inp[:,:3]

A[:,3] = 1.0

else:

A = np.array([[inp[i].x(), inp[i].y(), inp[i].z(), 1] for i in range(4)])

pts.append(A)

## solve 3 sets of linear equations to determine transformation matrix elements

matrix = np.zeros((4,4))

for i in range(3):

## solve Ax = B; x is one row of the desired transformation matrix

matrix[i] = numpy.linalg.solve(pts[0], pts[1][:,i])

return matrix

Example 19

def __init__(self, index, channel_names=None, channel_ids=None,

name=None, description=None, file_origin=None,

coordinates=None, **annotations):

”’

Initialize a new :class:`ChannelIndex` instance.

”’

# Inherited initialization

# Sets universally recommended attributes, and places all others

# in annotations

super(ChannelIndex, self).__init__(name=name,

description=description,

file_origin=file_origin,

**annotations)

# Defaults

if channel_names is None:

channel_names = np.array([], dtype=’S’)

if channel_ids is None:

channel_ids = np.array([], dtype=’i’)

# Store recommended attributes

self.channel_names = np.array(channel_names)

self.channel_ids = np.array(channel_ids)

self.index = np.array(index)

self.coordinates = coordinates

Example 20

def load_bytes(self, data_blocks, dtype=’

“””

Return list of bytes contained

in the specified set of blocks.

NB : load all data as files cannot exceed 4Gb

find later other solutions to spare memory.

“””

chunks = list()

raw = ”

# keep only data blocks having

# a size greater than zero

blocks = [k for k in data_blocks if k.size > 0]

for data_block in blocks :

self.file.seek(data_block.start)

raw = self.file.read(data_block.size)[0:expected_size]

databytes = np.frombuffer(raw, dtype=dtype)

chunks.append(databytes)

# concatenate all chunks and return

# the specified slice

if len(chunks)>0 :

databytes = np.concatenate(chunks)

return databytes[start:end]

else :

return np.array([])

Example 21

def load_channel_data(self, ep, ch):

“””

Return a numpy array containing the

list of bytes corresponding to the

specified episode and channel.

“””

#memorise the sample size and symbol

sample_size = self.sample_size(ep, ch)

sample_symbol = self.sample_symbol(ep, ch)

#create a bit mask to define which

#sample to keep from the file

bit_mask = self.create_bit_mask(ep, ch)

#load all bytes contained in an episode

data_blocks = self.get_data_blocks(ep)

databytes = self.load_bytes(data_blocks)

raw = self.filter_bytes(databytes, bit_mask)

#reshape bytes from the sample size

dt = np.dtype(numpy_map[sample_symbol])

dt.newbyteorder(‘

return np.frombuffer(raw.reshape([len(raw) / sample_size, sample_size]), dt)

Example 22

def get_signal_data(self, ep, ch):

“””

Return a numpy array containing all samples of a

signal, acquired on an Elphy analog channel, formatted

as a list of (time, value) tuples.

“””

#get data from the file

y_data = self.load_encoded_data(ep, ch)

x_data = np.arange(0, len(y_data))

#create a recarray

data = np.recarray(len(y_data), dtype=[(‘x’, b_float), (‘y’, b_float)])

#put in the recarray the scaled data

x_factors = self.x_scale_factors(ep, ch)

y_factors = self.y_scale_factors(ep, ch)

data[‘x’] = x_factors.scale(x_data)

data[‘y’] = y_factors.scale(y_data)

return data

Example 23

def get_tag_data(self, ep, tag_ch):

“””

Return a numpy array containing all samples of a

signal, acquired on an Elphy tag channel, formatted

as a list of (time, value) tuples.

“””

#get data from the file

y_data = self.load_encoded_tags(ep, tag_ch)

x_data = np.arange(0, len(y_data))

#create a recarray

data = np.recarray(len(y_data), dtype=[(‘x’, b_float), (‘y’, b_int)])

#put in the recarray the scaled data

factors = self.x_tag_scale_factors(ep)

data[‘x’] = factors.scale(x_data)

data[‘y’] = y_data

return data

Example 24

def get_event(self, ep, ch, marked_ks):

“””

Return a :class:`ElphyEvent` which is a

descriptor of the specified event channel.

“””

assert ep in range(1, self.n_episodes + 1)

assert ch in range(1, self.n_channels + 1)

# find the event channel number

evt_channel = np.where(marked_ks == -1)[0][0]

assert evt_channel in range(1, self.n_events(ep) + 1)

block = self.episode_block(ep)

ep_blocks = self.get_blocks_stored_in_episode(ep)

evt_blocks = [k for k in ep_blocks if k.identifier == ‘REVT’]

n_events = np.sum([k.n_events[evt_channel – 1] for k in evt_blocks], dtype=int)

x_unit = block.ep_block.x_unit

return ElphyEvent(self, ep, evt_channel, x_unit, n_events, ch_number=ch)

Example 25

def load_encoded_events(self, episode, evt_channel, identifier):

“””

Return times stored as a 4-bytes integer

in the specified event channel.

“””

data_blocks = self.group_blocks_of_type(episode, identifier)

ep_blocks = self.get_blocks_stored_in_episode(episode)

evt_blocks = [k for k in ep_blocks if k.identifier == identifier]

#compute events on each channel

n_events = np.sum([k.n_events for k in evt_blocks], dtype=int, axis=0)

pre_events = np.sum(n_events[0:evt_channel – 1], dtype=int)

start = pre_events

end = start + n_events[evt_channel – 1]

expected_size = 4 * np.sum(n_events, dtype=int)

return self.load_bytes(data_blocks, dtype=’

Example 26

def load_encoded_spikes(self, episode, evt_channel, identifier):

“””

Return times stored as a 4-bytes integer

in the specified spike channel.

NB: it is meant for Blackrock-type, having an additional byte for each event time as spike sorting label.

These additiona bytes are appended trailing the times.

“””

# to load the requested spikes for the specified episode and event channel:

# get all the elphy blocks having as identifier ‘RSPK’ (or whatever)

all_rspk_blocks = [k for k in self.blocks if k.identifier == identifier]

rspk_block = all_rspk_blocks[episode-1]

# RDATA(h?dI) REVT(NbVeV:I, NbEv:256I … spike data are 4byte integers

rspk_header = 4*( rspk_block.size – rspk_block.data_size-2 + len(rspk_block.n_events))

pre_events = np.sum(rspk_block.n_events[0:evt_channel-1], dtype=int, axis=0)

# the real start is after header, preceeding events (which are 4byte) and preceeding labels (1byte)

start = rspk_header + (4*pre_events) + pre_events

end = start + 4*rspk_block.n_events[evt_channel-1]

raw = self.load_bytes( [rspk_block], dtype=’

# re-encoding after reading byte by byte

res = np.frombuffer(raw[0:(4*rspk_block.n_events[evt_channel-1])], dtype=’

res.sort() # sometimes timings are not sorted

#print “load_encoded_data() – spikes:”,res

return res

Example 27

def get_waveform_data(self, episode, electrode_id):

“””

Return waveforms corresponding to the specified

spike channel. This function is triggered when the

“waveforms“ property of an :class:`Spike` descriptor

instance is accessed.

“””

block = self.episode_block(episode)

times, databytes = self.load_encoded_waveforms(episode, electrode_id)

n_events, = databytes.shape

wf_samples = databytes[‘waveform’].shape[1]

dtype = [

(‘time’, float),

(‘electrode_id’, int),

(‘unit_id’, int),

(‘waveform’, float, (wf_samples, 2))

]

data = np.empty(n_events, dtype=dtype)

data[‘electrode_id’] = databytes[‘channel_id’][:, 0]

data[‘unit_id’] = databytes[‘unit_id’][:, 0]

data[‘time’] = databytes[‘elphy_time’][:, 0] * block.ep_block.dX

data[‘waveform’][:, :, 0] = times * block.ep_block.dX

data[‘waveform’][:, :, 1] = databytes[‘waveform’] * block.ep_block.dY_wf + block.ep_block.Y0_wf

return data

Example 28

def get_rspk_data(self, spk_channel):

“””

Return times stored as a 4-bytes integer

in the specified event channel.

“””

evt_blocks = self.get_blocks_of_type(‘RSPK’)

#compute events on each channel

n_events = np.sum([k.n_events for k in evt_blocks], dtype=int, axis=0)

pre_events = np.sum(n_events[0:spk_channel], dtype=int) # sum of array values up to spk_channel-1!!!!

start = pre_events + (7 + len(n_events))# rspk header

end = start + n_events[spk_channel]

expected_size = 4 * np.sum(n_events, dtype=int) # constant

return self.load_bytes(evt_blocks, dtype=’

# ———————————————————

# factories.py

Example 29

def __mmap_ncs_packet_headers(self, filename):

“””

Memory map of the Neuralynx .ncs file optimized for extraction of

data packet headers

Reading standard dtype improves speed, but timestamps need to be

reconstructed

“””

filesize = getsize(self.sessiondir + sep + filename) # in byte

if filesize > 16384:

data = np.memmap(self.sessiondir + sep + filename,

dtype=’

shape=((filesize – 16384) / 4 / 261, 261),

mode=’r’, offset=16384)

ts = data[:, 0:2]

multi = np.repeat(np.array([1, 2 ** 32], ndmin=2), len(data),

axis=0)

timestamps = np.sum(ts * multi, axis=1)

# timestamps = data[:,0] + (data[:,1] *2**32)

header_u4 = data[:, 2:5]

return timestamps, header_u4

else:

return None

Example 30

def __mmap_ncs_packet_timestamps(self, filename):

“””

Memory map of the Neuralynx .ncs file optimized for extraction of

data packet headers

Reading standard dtype improves speed, but timestamps need to be

reconstructed

“””

filesize = getsize(self.sessiondir + sep + filename) # in byte

if filesize > 16384:

data = np.memmap(self.sessiondir + sep + filename,

dtype=’

shape=(int((filesize – 16384) / 4 / 261), 261),

mode=’r’, offset=16384)

ts = data[:, 0:2]

multi = np.repeat(np.array([1, 2 ** 32], ndmin=2), len(data),

axis=0)

timestamps = np.sum(ts * multi, axis=1)

# timestamps = data[:,0] + data[:,1]*2**32

return timestamps

else:

return None

Example 31

def __mmap_nev_file(self, filename):

“”” Memory map the Neuralynx .nev file “””

nev_dtype = np.dtype([

(‘reserved’, ‘

(‘system_id’, ‘

(‘data_size’, ‘

(‘timestamp’, ‘

(‘event_id’, ‘

(‘ttl_input’, ‘

(‘crc_check’, ‘

(‘dummy1’, ‘

(‘dummy2’, ‘

(‘extra’, ‘

(‘event_string’, ‘a128’),

])

if getsize(self.sessiondir + sep + filename) > 16384:

return np.memmap(self.sessiondir + sep + filename,

dtype=nev_dtype, mode=’r’, offset=16384)

else:

return None

Example 32

def __extract_nev_file_spec(self):

“””

Extract file specification from an .nsx file

“””

filename = ‘.’.join([self._filenames[‘nsx’], ‘nev’])

# Header structure of files specification 2.2 and higher. For files 2.1

# and lower, the entries ver_major and ver_minor are not supported.

dt0 = [

(‘file_id’, ‘S8’),

(‘ver_major’, ‘uint8’),

(‘ver_minor’, ‘uint8’)]

nev_file_id = np.fromfile(filename, count=1, dtype=dt0)[0]

if nev_file_id[‘file_id’].decode() == ‘NEURALEV’:

spec = ‘{0}.{1}’.format(

nev_file_id[‘ver_major’], nev_file_id[‘ver_minor’])

else:

raise IOError(‘NEV file type {0} is not supported’.format(

nev_file_id[‘file_id’]))

return spec

Example 33

def __read_nsx_data_variant_a(self, nsx_nb):

“””

Extract nsx data from a 2.1 .nsx file

“””

filename = ‘.’.join([self._filenames[‘nsx’], ‘ns%i’ % nsx_nb])

# get shape of data

shape = (

self.__nsx_databl_param[‘2.1’](‘nb_data_points’, nsx_nb),

self.__nsx_basic_header[nsx_nb][‘channel_count’])

offset = self.__nsx_params[‘2.1’](‘bytes_in_headers’, nsx_nb)

# read nsx data

# store as dict for compatibility with higher file specs

data = {1: np.memmap(

filename, dtype=’int16′, shape=shape, offset=offset)}

return data

Example 34

def __read_nev_data(self, nev_data_masks, nev_data_types):

“””

Extract nev data from a 2.1 or 2.2 .nev file

“””

filename = ‘.’.join([self._filenames[‘nev’], ‘nev’])

data_size = self.__nev_basic_header[‘bytes_in_data_packets’]

header_size = self.__nev_basic_header[‘bytes_in_headers’]

# read all raw data packets and markers

dt0 = [

(‘timestamp’, ‘uint32’),

(‘packet_id’, ‘uint16’),

(‘value’, ‘S{0}’.format(data_size – 6))]

raw_data = np.memmap(filename, offset=header_size, dtype=dt0)

masks = self.__nev_data_masks(raw_data[‘packet_id’])

types = self.__nev_data_types(data_size)

data = {}

for k, v in nev_data_masks.items():

data[k] = raw_data.view(types[k][nev_data_types[k]])[masks[k][v]]

return data

Example 35

def __get_nev_rec_times(self):

“””

Extracts minimum and maximum time points from a nev file.

“””

filename = ‘.’.join([self._filenames[‘nev’], ‘nev’])

dt = [(‘timestamp’, ‘uint32’)]

offset = \

self.__get_file_size(filename) – \

self.__nev_params(‘bytes_in_data_packets’)

last_data_packet = np.memmap(filename, offset=offset, dtype=dt)[0]

n_starts = [0 * self.__nev_params(‘event_unit’)]

n_stops = [

last_data_packet[‘timestamp’] * self.__nev_params(‘event_unit’)]

return n_starts, n_stops

Example 36

def __get_waveforms_dtype(self):

“””

Extracts the actual waveform dtype set for each channel.

“””

# Blackrock code giving the approiate dtype

conv = {0: ‘int8’, 1: ‘int8’, 2: ‘int16’, 4: ‘int32’}

# get all electrode ids from nev ext header

all_el_ids = self.__nev_ext_header[b’NEUEVWAV’][‘electrode_id’]

# get the dtype of waveform (this is stupidly complicated)

if self.__is_set(

np.array(self.__nev_basic_header[‘additionnal_flags’]), 0):

dtype_waveforms = dict((k, ‘int16’) for k in all_el_ids)

else:

# extract bytes per waveform

waveform_bytes = \

self.__nev_ext_header[b’NEUEVWAV’][‘bytes_per_waveform’]

# extract dtype for waveforms fro each electrode

dtype_waveforms = dict(zip(all_el_ids, conv[waveform_bytes]))

return dtype_waveforms

Example 37

def __read_comment(self,n_start,n_stop,data,lazy=False):

event_unit = self.__nev_params(‘event_unit’)

if lazy:

times = []

labels = np.array([],dtype=’s’)

else:

times = data[‘timestamp’]*event_unit

labels = data[‘comment’].astype(str)

# mask for given time interval

mask = (times >= n_start) & (times < n_stop)

if np.sum(mask)>0:

ev = Event(

times = times[mask].astype(float),

labels = labels[mask],

name = ‘comment’)

if lazy:

ev.lazy_shape = np.sum(mask)

else:

ev = None

return ev

# ————–end——added by zhangbo 20170926——–

Example 38

def reformat_integer_v1(data, nbchannel, header):

“””

reformat when dtype is int16 for ABF version 1

“””

chans = [chan_num for chan_num in

header[‘nADCSamplingSeq’] if chan_num >= 0]

for n, i in enumerate(chans[:nbchannel]): # respect SamplingSeq

data[:, n] /= header[‘fInstrumentScaleFactor’][i]

data[:, n] /= header[‘fSignalGain’][i]

data[:, n] /= header[‘fADCProgrammableGain’][i]

if header[‘nTelegraphEnable’][i]:

data[:, n] /= header[‘fTelegraphAdditGain’][i]

data[:, n] *= header[‘fADCRange’]

data[:, n] /= header[‘lADCResolution’]

data[:, n] += header[‘fInstrumentOffset’][i]

data[:, n] -= header[‘fSignalOffset’][i]

Example 39

def solve3DTransform(points1, points2):

“””

Find a 3D transformation matrix that maps points1 onto points2.

Points must be specified as either lists of 4 Vectors or

(4, 3) arrays.

“””

import numpy.linalg

pts = []

for inp in (points1, points2):

if isinstance(inp, np.ndarray):

A = np.empty((4,4), dtype=float)

A[:,:3] = inp[:,:3]

A[:,3] = 1.0

else:

A = np.array([[inp[i].x(), inp[i].y(), inp[i].z(), 1] for i in range(4)])

pts.append(A)

## solve 3 sets of linear equations to determine transformation matrix elements

matrix = np.zeros((4,4))

for i in range(3):

## solve Ax = B; x is one row of the desired transformation matrix

matrix[i] = numpy.linalg.solve(pts[0], pts[1][:,i])

return matrix

Example 40

def __init__(self, index, channel_names=None, channel_ids=None,

name=None, description=None, file_origin=None,

coordinates=None, **annotations):

”’

Initialize a new :class:`ChannelIndex` instance.

”’

# Inherited initialization

# Sets universally recommended attributes, and places all others

# in annotations

super(ChannelIndex, self).__init__(name=name,

description=description,

file_origin=file_origin,

**annotations)

# Defaults

if channel_names is None:

channel_names = np.array([], dtype=’S’)

if channel_ids is None:

channel_ids = np.array([], dtype=’i’)

# Store recommended attributes

self.channel_names = np.array(channel_names)

self.channel_ids = np.array(channel_ids)

self.index = np.array(index)

self.coordinates = coordinates

Example 41

def load_bytes(self, data_blocks, dtype=’

“””

Return list of bytes contained

in the specified set of blocks.

NB : load all data as files cannot exceed 4Gb

find later other solutions to spare memory.

“””

chunks = list()

raw = ”

# keep only data blocks having

# a size greater than zero

blocks = [k for k in data_blocks if k.size > 0]

for data_block in blocks :

self.file.seek(data_block.start)

raw = self.file.read(data_block.size)[0:expected_size]

databytes = np.frombuffer(raw, dtype=dtype)

chunks.append(databytes)

# concatenate all chunks and return

# the specified slice

if len(chunks)>0 :

databytes = np.concatenate(chunks)

return databytes[start:end]

else :

return np.array([])

Example 42

def load_channel_data(self, ep, ch):

“””

Return a numpy array containing the

list of bytes corresponding to the

specified episode and channel.

“””

#memorise the sample size and symbol

sample_size = self.sample_size(ep, ch)

sample_symbol = self.sample_symbol(ep, ch)

#create a bit mask to define which

#sample to keep from the file

bit_mask = self.create_bit_mask(ep, ch)

#load all bytes contained in an episode

data_blocks = self.get_data_blocks(ep)

databytes = self.load_bytes(data_blocks)

raw = self.filter_bytes(databytes, bit_mask)

#reshape bytes from the sample size

dt = np.dtype(numpy_map[sample_symbol])

dt.newbyteorder(‘

return np.frombuffer(raw.reshape([len(raw) / sample_size, sample_size]), dt)

Example 43

def get_signal_data(self, ep, ch):

“””

Return a numpy array containing all samples of a

signal, acquired on an Elphy analog channel, formatted

as a list of (time, value) tuples.

“””

#get data from the file

y_data = self.load_encoded_data(ep, ch)

x_data = np.arange(0, len(y_data))

#create a recarray

data = np.recarray(len(y_data), dtype=[(‘x’, b_float), (‘y’, b_float)])

#put in the recarray the scaled data

x_factors = self.x_scale_factors(ep, ch)

y_factors = self.y_scale_factors(ep, ch)

data[‘x’] = x_factors.scale(x_data)

data[‘y’] = y_factors.scale(y_data)

return data

Example 44

def get_tag_data(self, ep, tag_ch):

“””

Return a numpy array containing all samples of a

signal, acquired on an Elphy tag channel, formatted

as a list of (time, value) tuples.

“””

#get data from the file

y_data = self.load_encoded_tags(ep, tag_ch)

x_data = np.arange(0, len(y_data))

#create a recarray

data = np.recarray(len(y_data), dtype=[(‘x’, b_float), (‘y’, b_int)])

#put in the recarray the scaled data

factors = self.x_tag_scale_factors(ep)

data[‘x’] = factors.scale(x_data)

data[‘y’] = y_data

return data

Example 45

def load_encoded_events(self, episode, evt_channel, identifier):

“””

Return times stored as a 4-bytes integer

in the specified event channel.

“””

data_blocks = self.group_blocks_of_type(episode, identifier)

ep_blocks = self.get_blocks_stored_in_episode(episode)

evt_blocks = [k for k in ep_blocks if k.identifier == identifier]

#compute events on each channel

n_events = np.sum([k.n_events for k in evt_blocks], dtype=int, axis=0)

pre_events = np.sum(n_events[0:evt_channel – 1], dtype=int)

start = pre_events

end = start + n_events[evt_channel – 1]

expected_size = 4 * np.sum(n_events, dtype=int)

return self.load_bytes(data_blocks, dtype=’

Example 46

def load_encoded_spikes(self, episode, evt_channel, identifier):

“””

Return times stored as a 4-bytes integer

in the specified spike channel.

NB: it is meant for Blackrock-type, having an additional byte for each event time as spike sorting label.

These additiona bytes are appended trailing the times.

“””

# to load the requested spikes for the specified episode and event channel:

# get all the elphy blocks having as identifier ‘RSPK’ (or whatever)

all_rspk_blocks = [k for k in self.blocks if k.identifier == identifier]

rspk_block = all_rspk_blocks[episode-1]

# RDATA(h?dI) REVT(NbVeV:I, NbEv:256I … spike data are 4byte integers

rspk_header = 4*( rspk_block.size – rspk_block.data_size-2 + len(rspk_block.n_events))

pre_events = np.sum(rspk_block.n_events[0:evt_channel-1], dtype=int, axis=0)

# the real start is after header, preceeding events (which are 4byte) and preceeding labels (1byte)

start = rspk_header + (4*pre_events) + pre_events

end = start + 4*rspk_block.n_events[evt_channel-1]

raw = self.load_bytes( [rspk_block], dtype=’

# re-encoding after reading byte by byte

res = np.frombuffer(raw[0:(4*rspk_block.n_events[evt_channel-1])], dtype=’

res.sort() # sometimes timings are not sorted

#print “load_encoded_data() – spikes:”,res

return res

Example 47

def get_spiketrain(self, episode, electrode_id):

“””

Return a :class:`Spike` which is a

descriptor of the specified spike channel.

“””

assert episode in range(1, self.n_episodes + 1)

assert electrode_id in range(1, self.n_spiketrains(episode) + 1)

# get some properties stored in the episode sub-block

block = self.episode_block(episode)

x_unit = block.ep_block.x_unit

x_unit_wf = getattr(block.ep_block, ‘x_unit_wf’, None)

y_unit_wf = getattr(block.ep_block, ‘y_unit_wf’, None)

# number of spikes in the entire episode

spk_blocks = [k for k in self.blocks if k.identifier == ‘RSPK’]

n_events = np.sum([k.n_events[electrode_id – 1] for k in spk_blocks], dtype=int)

# number of samples in a waveform

wf_sampling_frequency = 1.0 / block.ep_block.dX

wf_blocks = [k for k in self.blocks if k.identifier == ‘RspkWave’]

if wf_blocks :

wf_samples = wf_blocks[0].wavelength

t_start = wf_blocks[0].pre_trigger * block.ep_block.dX

else:

wf_samples = 0

t_start = 0

return ElphySpikeTrain(self, episode, electrode_id, x_unit, n_events, wf_sampling_frequency, wf_samples, x_unit_wf, y_unit_wf, t_start)

Example 48

def get_rspk_data(self, spk_channel):

“””

Return times stored as a 4-bytes integer

in the specified event channel.

“””

evt_blocks = self.get_blocks_of_type(‘RSPK’)

#compute events on each channel

n_events = np.sum([k.n_events for k in evt_blocks], dtype=int, axis=0)

pre_events = np.sum(n_events[0:spk_channel], dtype=int) # sum of array values up to spk_channel-1!!!!

start = pre_events + (7 + len(n_events))# rspk header

end = start + n_events[spk_channel]

expected_size = 4 * np.sum(n_events, dtype=int) # constant

return self.load_bytes(evt_blocks, dtype=’

# ———————————————————

# factories.py

Example 49

def __mmap_ncs_packet_headers(self, filename):

“””

Memory map of the Neuralynx .ncs file optimized for extraction of

data packet headers

Reading standard dtype improves speed, but timestamps need to be

reconstructed

“””

filesize = getsize(self.sessiondir + sep + filename) # in byte

if filesize > 16384:

data = np.memmap(self.sessiondir + sep + filename,

dtype=’

shape=((filesize – 16384) / 4 / 261, 261),

mode=’r’, offset=16384)

ts = data[:, 0:2]

multi = np.repeat(np.array([1, 2 ** 32], ndmin=2), len(data),

axis=0)

timestamps = np.sum(ts * multi, axis=1)

# timestamps = data[:,0] + (data[:,1] *2**32)

header_u4 = data[:, 2:5]

return timestamps, header_u4

else:

return None

Example 50

def __mmap_nev_file(self, filename):

“”” Memory map the Neuralynx .nev file “””

nev_dtype = np.dtype([

(‘reserved’, ‘

(‘system_id’, ‘

(‘data_size’, ‘

(‘timestamp’, ‘

(‘event_id’, ‘

(‘ttl_input’, ‘

(‘crc_check’, ‘

(‘dummy1’, ‘

(‘dummy2’, ‘

(‘extra’, ‘

(‘event_string’, ‘a128’),

])

if getsize(self.sessiondir + sep + filename) > 16384:

return np.memmap(self.sessiondir + sep + filename,

dtype=nev_dtype, mode=’r’, offset=16384)

else:

return None