/usr/lib/python2.7/dist-packages/neo/io/neomatlabio.py is in python-neo 0.3.3-2.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
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"""
Module for reading/writing Neo objects in MATLAB format (.mat) versions 5 to 7.2.
This module is a bridge for MATLAB users who want to adopt the Neo object representation.
The nomenclature is the same but using Matlab structs and cell arrays.
With this module MATLAB users can use neo.io to read a format and convert it to .mat.
Supported : Read/Write
Author: sgarcia
"""
from datetime import datetime
from distutils import version
import re
import numpy as np
import quantities as pq
# check scipy
try:
import scipy.io
import scipy.version
except ImportError as err:
HAVE_SCIPY = False
SCIPY_ERR = err
else:
if version.LooseVersion(scipy.version.version) < '0.8':
HAVE_SCIPY = False
SCIPY_ERR = ImportError("your scipy version is too old to support " +
"MatlabIO, you need at least 0.8. " +
"You have %s" % scipy.version.version)
else:
HAVE_SCIPY = True
SCIPY_ERR = None
from neo.io.baseio import BaseIO
from neo.core import Block, Segment, AnalogSignal, EventArray, SpikeTrain
from neo.io.tools import create_many_to_one_relationship
from neo import description
classname_lower_to_upper = { }
for k in description.class_by_name.keys():
classname_lower_to_upper[k.lower()] = k
class NeoMatlabIO(BaseIO):
"""
Class for reading/writing Neo objects in MATLAB format (.mat) versions 5 to 7.2.
This module is a bridge for MATLAB users who want to adopt the Neo object representation.
The nomenclature is the same but using Matlab structs and cell arrays.
With this module MATLAB users can use neo.io to read a format and convert it to .mat.
Rules of conversion:
* Neo classes are converted to MATLAB structs.
e.g., a Block is a struct with attributes "name", "file_datetime", ...
* Neo one_to_many relationships are cellarrays in MATLAB.
e.g., ``seg.analogsignals[2]`` in Python Neo will be ``seg.analogsignals{3}`` in MATLAB.
* Quantity attributes are represented by 2 fields in MATLAB.
e.g., ``anasig.t_start = 1.5 * s`` in Python
will be ``anasig.t_start = 1.5`` and ``anasig.t_start_unit = 's'`` in MATLAB.
* classes that inherit from Quantity (AnalogSignal, SpikeTrain, ...) in Python will
have 2 fields (array and units) in the MATLAB struct.
e.g.: ``AnalogSignal( [1., 2., 3.], 'V')`` in Python will be
``anasig.array = [1. 2. 3]`` and ``anasig.units = 'V'`` in MATLAB.
1 - **Scenario 1: create data in MATLAB and read them in Python**
This MATLAB code generates a block::
block = struct();
block.segments = { };
block.name = 'my block with matlab';
for s = 1:3
seg = struct();
seg.name = strcat('segment ',num2str(s));
seg.analogsignals = { };
for a = 1:5
anasig = struct();
anasig.array = rand(100,1);
anasig.units = 'mV';
anasig.t_start = 0;
anasig.t_start_units = 's';
anasig.sampling_rate = 100;
anasig.sampling_rate_units = 'Hz';
seg.analogsignals{a} = anasig;
end
seg.spiketrains = { };
for t = 1:7
sptr = struct();
sptr.array = rand(30,1)*10;
sptr.units = 'ms';
sptr.t_start = 0;
sptr.t_start_units = 'ms';
sptr.t_stop = 10;
sptr.t_stop_units = 'ms';
seg.spiketrains{t} = sptr;
end
block.segments{s} = seg;
end
save 'myblock.mat' block -V7
This code reads it in Python::
import neo
r = neo.io.NeoMatlabIO(filename='myblock.mat')
bl = r.read_block()
print bl.segments[1].analogsignals[2]
print bl.segments[1].spiketrains[4]
2 - **Scenario 2: create data in Python and read them in MATLAB**
This Python code generates the same block as in the previous scenario::
import neo
import quantities as pq
from scipy import rand
bl = neo.Block(name='my block with neo')
for s in range(3):
seg = neo.Segment(name='segment' + str(s))
bl.segments.append(seg)
for a in range(5):
anasig = neo.AnalogSignal(rand(100), units='mV', t_start=0*pq.s, sampling_rate=100*pq.Hz)
seg.analogsignals.append(anasig)
for t in range(7):
sptr = neo.SpikeTrain(rand(30), units='ms', t_start=0*pq.ms, t_stop=10*pq.ms)
seg.spiketrains.append(sptr)
w = neo.io.NeoMatlabIO(filename='myblock.mat')
w.write_block(bl)
This MATLAB code reads it::
load 'myblock.mat'
block.name
block.segments{2}.analogsignals{3}.array
block.segments{2}.analogsignals{3}.units
block.segments{2}.analogsignals{3}.t_start
block.segments{2}.analogsignals{3}.t_start_units
3 - **Scenario 3: conversion**
This Python code converts a Spike2 file to MATLAB::
from neo import Block
from neo.io import Spike2IO, NeoMatlabIO
r = Spike2IO(filename='myspike2file.smr')
w = NeoMatlabIO(filename='convertedfile.mat')
seg = r.read_segment()
bl = Block(name='a block')
bl.segments.append(seg)
w.write_block(bl)
"""
is_readable = True
is_writable = True
supported_objects = [ Block, Segment , AnalogSignal , EventArray, SpikeTrain ]
readable_objects = [Block, ]
writeable_objects = [Block, ]
has_header = False
is_streameable = False
read_params = { Block : [ ] }
write_params = { Block : [ ] }
name = 'neomatlab'
extensions = [ 'mat' ]
mode = 'file'
def __init__(self , filename = None) :
"""
This class read/write neo objects in matlab 5 to 7.2 format.
Arguments:
filename : the filename to read
"""
if not HAVE_SCIPY:
raise SCIPY_ERR
BaseIO.__init__(self)
self.filename = filename
def read_block(self, cascade = True, lazy = False,):
"""
Arguments:
"""
d = scipy.io.loadmat(self.filename, struct_as_record=False,
squeeze_me=True)
assert'block' in d, 'no block in'+self.filename
bl_struct = d['block']
bl = self.create_ob_from_struct(bl_struct, 'Block', cascade = cascade, lazy = lazy)
create_many_to_one_relationship(bl)
return bl
def write_block(self, bl,):
"""
Arguments::
bl: the block to b saved
"""
bl_struct = self.create_struct_from_obj(bl)
for seg in bl.segments:
seg_struct = self.create_struct_from_obj(seg)
bl_struct['segments'].append(seg_struct)
for anasig in seg.analogsignals:
anasig_struct = self.create_struct_from_obj(anasig)
seg_struct['analogsignals'].append(anasig_struct)
for ea in seg.eventarrays:
ea_struct = self.create_struct_from_obj(ea)
seg_struct['eventarrays'].append(ea_struct)
for sptr in seg.spiketrains:
sptr_struct = self.create_struct_from_obj(sptr)
seg_struct['spiketrains'].append(sptr_struct)
scipy.io.savemat(self.filename, {'block':bl_struct}, oned_as = 'row')
def create_struct_from_obj(self, ob, ):
classname = ob.__class__.__name__
struct = { }
# relationship
rel = description.one_to_many_relationship
if classname in rel:
for childname in rel[classname]:
if description.class_by_name[childname] in self.supported_objects:
struct[childname.lower()+'s'] = [ ]
# attributes
necess = description.classes_necessary_attributes[classname]
recomm = description.classes_recommended_attributes[classname]
attributes = necess + recomm
for i, attr in enumerate(attributes):
attrname, attrtype = attr[0], attr[1]
#~ if attrname =='':
#~ struct['array'] = ob.magnitude
#~ struct['units'] = ob.dimensionality.string
#~ continue
if classname in description.classes_inheriting_quantities and \
description.classes_inheriting_quantities[classname] == attrname:
struct[attrname] = ob.magnitude
struct[attrname+'_units'] = ob.dimensionality.string
continue
if not(attrname in ob.annotations or hasattr(ob, attrname)): continue
if getattr(ob, attrname) is None : continue
if attrtype == pq.Quantity:
#ndim = attr[2]
struct[attrname] = getattr(ob,attrname).magnitude
struct[attrname+'_units'] = getattr(ob,attrname).dimensionality.string
elif attrtype ==datetime:
struct[attrname] = str(getattr(ob,attrname))
else:
struct[attrname] = getattr(ob,attrname)
return struct
def create_ob_from_struct(self, struct, classname, cascade = True, lazy = False,):
cl = description.class_by_name[classname]
# check if hinerits Quantity
#~ is_quantity = False
#~ for attr in description.classes_necessary_attributes[classname]:
#~ if attr[0] == '' and attr[1] == pq.Quantity:
#~ is_quantity = True
#~ break
#~ is_quantiy = classname in description.classes_inheriting_quantities
#~ if is_quantity:
if classname in description.classes_inheriting_quantities:
quantity_attr = description.classes_inheriting_quantities[classname]
arr = getattr(struct,quantity_attr)
#~ data_complement = dict(units=str(struct.units))
data_complement = dict(units=str(getattr(struct,quantity_attr+'_units')))
if "sampling_rate" in (at[0] for at in description.classes_necessary_attributes[classname]):
data_complement["sampling_rate"] = 0*pq.kHz # put fake value for now, put correct value later
if "t_stop" in (at[0] for at in description.classes_necessary_attributes[classname]):
if len(arr) > 0:
data_complement["t_stop"] =arr.max()
else:
data_complement["t_stop"] = 0.0
if "t_start" in (at[0] for at in description.classes_necessary_attributes[classname]):
if len(arr) > 0:
data_complement["t_start"] =arr.min()
else:
data_complement["t_start"] = 0.0
if lazy:
ob = cl([ ], **data_complement)
ob.lazy_shape = arr.shape
else:
ob = cl(arr, **data_complement)
else:
ob = cl()
for attrname in struct._fieldnames:
# check children
rel = description.one_to_many_relationship
if classname in rel and attrname[:-1] in [ r.lower() for r in rel[classname] ]:
try:
for c in range(len(getattr(struct,attrname))):
if cascade:
child = self.create_ob_from_struct(getattr(struct,attrname)[c] , classname_lower_to_upper[attrname[:-1]],
cascade = cascade, lazy = lazy)
getattr(ob, attrname.lower()).append(child)
except TypeError:
# strange behavior in scipy.io: if len is 1 so there is no len()
if cascade:
child = self.create_ob_from_struct(getattr(struct,attrname) , classname_lower_to_upper[attrname[:-1]],
cascade = cascade, lazy = lazy)
getattr(ob, attrname.lower()).append(child)
continue
# attributes
if attrname.endswith('_units') or attrname =='units' :#or attrname == 'array':
# linked with another field
continue
if classname in description.classes_inheriting_quantities and \
description.classes_inheriting_quantities[classname] == attrname:
continue
item = getattr(struct, attrname)
# put the good type
necess = description.classes_necessary_attributes[classname]
recomm = description.classes_recommended_attributes[classname]
attributes = necess + recomm
dict_attributes = dict( [ (a[0], a[1:]) for a in attributes])
if attrname in dict_attributes:
attrtype = dict_attributes[attrname][0]
if attrtype == datetime:
m = '(\d+)-(\d+)-(\d+) (\d+):(\d+):(\d+).(\d+)'
r = re.findall(m, str(item))
if len(r)==1:
item = datetime( *[ int(e) for e in r[0] ] )
else:
item = None
elif attrtype == np.ndarray:
dt = dict_attributes[attrname][2]
if lazy:
item = np.array([ ], dtype = dt)
ob.lazy_shape = item.shape
else:
item = item.astype( dt )
elif attrtype == pq.Quantity:
ndim = dict_attributes[attrname][1]
units = str(getattr(struct, attrname+'_units'))
if ndim == 0:
item = pq.Quantity(item, units)
else:
if lazy:
item = pq.Quantity([ ], units)
item.lazy_shape = item.shape
else:
item = pq.Quantity(item, units)
else:
item = attrtype(item)
setattr(ob, attrname, item)
return ob
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