123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100101102103104105106107108109110111112113114115116117118119120121122123124125126127128129130131132133134135136137138139140141142143144145146147148149150151152153154155156157158159160161162163164165166167168169170171172173174175176177178179180181182183184185186187188189190191192193194195196197198199200201202203204205206207208209210211212213214215216217218219220221222223224225226227228229230231232233234235236237238239240241242243244245246247248249250251252253254255256257258259260261262263264265266267268269270271272273274275276277278279280281282283284285286287288289290291292293294295296297298299300301302303304305306307308309310311312313314315316317318319320321322323324325326327328329330331332333334335336337338339340341342343344345346347348349350351352353354355356357358359360361362363364365366367368369370371372373374375376377378379380381382383384385386387388389390391392393394395396397398399400401402403404405406407 |
- # -*- coding: utf-8 -*-
- """
- 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, Robert Pröpper
- """
- 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) < '0.12.0':
- HAVE_SCIPY = False
- SCIPY_ERR = ImportError("your scipy version is too old to support "
- + "MatlabIO, you need at least 0.12.0. "
- + "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, Event, Epoch, SpikeTrain,
- objectnames, class_by_name)
- classname_lower_to_upper = {}
- for k in objectnames:
- 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.signal = rand(100,1);
- anasig.signal_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.times = rand(30,1)*10;
- sptr.times_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
- event = struct();
- event.times = [0, 10, 30];
- event.times_units = 'ms';
- event.labels = ['trig0'; 'trig1'; 'trig2'];
- seg.events{1} = event;
- epoch = struct();
- epoch.times = [10, 20];
- epoch.times_units = 'ms';
- epoch.durations = [4, 10];
- epoch.durations_units = 'ms';
- epoch.labels = ['a0'; 'a1'];
- seg.epochs{1} = epoch;
- 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, array
- 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)*pq.mV, t_start=0*pq.s,
- sampling_rate=100*pq.Hz)
- seg.analogsignals.append(anasig)
- for t in range(7):
- sptr = neo.SpikeTrain(rand(40)*pq.ms, t_start=0*pq.ms, t_stop=10*pq.ms)
- seg.spiketrains.append(sptr)
- ev = neo.Event([0, 10, 30]*pq.ms, labels=array(['trig0', 'trig1', 'trig2']))
- ep = neo.Epoch([10, 20]*pq.ms, durations=[4, 10]*pq.ms, labels=array(['a0', 'a1']))
- seg.events.append(ev)
- seg.epochs.append(ep)
- from neo.io.neomatlabio import NeoMatlabIO
- w = NeoMatlabIO(filename='myblock.mat')
- w.write_block(bl)
- This MATLAB code reads it::
- load 'myblock.mat'
- block.name
- block.segments{2}.analogsignals{3}.signal
- block.segments{2}.analogsignals{3}.signal_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='spike2.smr')
- w = NeoMatlabIO(filename='convertedfile.mat')
- blocks = r.read()
- w.write(blocks[0])
- """
- is_readable = True
- is_writable = True
- supported_objects = [Block, Segment, AnalogSignal, Epoch, Event, 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, lazy=False):
- """
- Arguments:
- """
- assert not lazy, 'Do not support lazy'
- d = scipy.io.loadmat(self.filename, struct_as_record=False,
- squeeze_me=True, mat_dtype=True)
- if 'block' not in d:
- self.logger.exception('No block in ' + self.filename)
- return None
- bl_struct = d['block']
- bl = self.create_ob_from_struct(
- bl_struct, 'Block')
- bl.create_many_to_one_relationship()
- return bl
- def write_block(self, bl, **kargs):
- """
- 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.events:
- ea_struct = self.create_struct_from_obj(ea)
- seg_struct['events'].append(ea_struct)
- for ea in seg.epochs:
- ea_struct = self.create_struct_from_obj(ea)
- seg_struct['epochs'].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):
- struct = {}
- # relationship
- for childname in getattr(ob, '_single_child_containers', []):
- supported_containers = [subob.__name__.lower() + 's' for subob in
- self.supported_objects]
- if childname in supported_containers:
- struct[childname] = []
- # attributes
- for i, attr in enumerate(ob._all_attrs):
- attrname, attrtype = attr[0], attr[1]
- # ~ if attrname =='':
- # ~ struct['array'] = ob.magnitude
- # ~ struct['units'] = ob.dimensionality.string
- # ~ continue
- if (hasattr(ob, '_quantity_attr') and ob._quantity_attr == 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):
- cl = class_by_name[classname]
- # check if hinerits Quantity
- # ~ is_quantity = False
- # ~ for attr in cl._necessary_attrs:
- # ~ if attr[0] == '' and attr[1] == pq.Quantity:
- # ~ is_quantity = True
- # ~ break
- # ~ is_quantiy = hasattr(cl, '_quantity_attr')
- # ~ if is_quantity:
- if hasattr(cl, '_quantity_attr'):
- quantity_attr = cl._quantity_attr
- 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 cl._necessary_attrs):
- # put fake value for now, put correct value later
- data_complement["sampling_rate"] = 0 * pq.kHz
- try:
- len(arr)
- except TypeError:
- # strange scipy.io behavior: if len is 1 we get a float
- arr = np.array(arr)
- arr = arr.reshape((-1,)) # new view with one dimension
- if "t_stop" in (at[0] for at in cl._necessary_attrs):
- 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 cl._necessary_attrs):
- if len(arr) > 0:
- data_complement["t_start"] = arr.min()
- else:
- data_complement["t_start"] = 0.0
- ob = cl(arr, **data_complement)
- else:
- ob = cl()
- for attrname in struct._fieldnames:
- # check children
- if attrname in getattr(ob, '_single_child_containers', []):
- child_struct = getattr(struct, attrname)
- try:
- # try must only surround len() or other errors are captured
- child_len = len(child_struct)
- except TypeError:
- # strange scipy.io behavior: if len is 1 there is no len()
- child = self.create_ob_from_struct(
- child_struct,
- classname_lower_to_upper[attrname[:-1]])
- getattr(ob, attrname.lower()).append(child)
- else:
- for c in range(child_len):
- child = self.create_ob_from_struct(
- child_struct[c],
- classname_lower_to_upper[attrname[:-1]])
- getattr(ob, attrname.lower()).append(child)
- continue
- # attributes
- if attrname.endswith('_units') or attrname == 'units':
- # linked with another field
- continue
- if (hasattr(cl, '_quantity_attr') and cl._quantity_attr == attrname):
- continue
- item = getattr(struct, attrname)
- attributes = cl._necessary_attrs + cl._recommended_attrs
- 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 = r'(\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]
- 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:
- item = pq.Quantity(item, units)
- else:
- item = attrtype(item)
- setattr(ob, attrname, item)
- return ob
|