OpenEphys.py

# -*- coding: utf-8 -*-
"""
Created on Sun Aug  3 15:18:38 2014

@author: Dan Denman and Josh Siegle

Loads .continuous, .events, and .spikes files saved from the Open Ephys GUI

Usage:
    import OpenEphys
    data = OpenEphys.load(pathToFile) # returns a dict with data, timestamps, etc.

"""

import os
import numpy as np
import scipy.signal
import scipy.io
import time
import struct
from copy import deepcopy

# constants
NUM_HEADER_BYTES = 1024
SAMPLES_PER_RECORD = 1024
BYTES_PER_SAMPLE = 2
RECORD_SIZE = 4 + 8 + SAMPLES_PER_RECORD * BYTES_PER_SAMPLE + 10 # size of each continuous record in bytes
RECORD_MARKER = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 255])

# constants for pre-allocating matrices:
MAX_NUMBER_OF_SPIKES = int(1e6)
MAX_NUMBER_OF_RECORDS = int(1e6)
MAX_NUMBER_OF_EVENTS = int(1e6)

def load(filepath):

    # redirects to code for individual file types
    if 'continuous' in filepath:
        data = loadContinuous(filepath)
    elif 'spikes' in filepath:
        data = loadSpikes(filepath)
    elif 'events' in filepath:
        data = loadEvents(filepath)
    else:
        raise Exception("Not a recognized file type. Please input a .continuous, .spikes, or .events file")

    return data

def loadFolder(folderpath, dtype = float, **kwargs):

    # load all continuous files in a folder

    data = { }

    # load all continuous files in a folder
    if 'channels' in kwargs.keys():
        filelist = ['100_CH'+x+'.continuous' for x in map(str,kwargs['channels'])]
    else:
        filelist = os.listdir(folderpath)

    t0 = time.time()
    numFiles = 0

    for i, f in enumerate(filelist):
        if '.continuous' in f:
            data[f.replace('.continuous','')] = loadContinuous(os.path.join(folderpath, f), dtype = dtype)
            numFiles += 1

    print(''.join(('Avg. Load Time: ', str((time.time() - t0)/numFiles),' sec')))
    print(''.join(('Total Load Time: ', str((time.time() - t0)),' sec')))

    return data

def loadFolderToArray(folderpath, channels = 'all', chprefix = 'CH', 
                      dtype = float, session = '0', source = '100'):
    '''Load continuous files in specified folder to a single numpy array. By default all
    CH continous files are loaded in numerical order, ordering can be specified with
    optional channels argument which should be a list of channel numbers.'''
    
    if channels == 'all':
        channels = _get_sorted_channels(folderpath, chprefix, session, source)
    
    if session == '0':
        filelist = [source + '_'+chprefix + x + '.continuous' for x in map(str,channels)]
    else:
        filelist = [source + '_'+chprefix + x + '_' + session + '.continuous' for x in map(str,channels)]
    
    t0 = time.time()
    numFiles = 1

    channel_1_data = loadContinuous(os.path.join(folderpath, filelist[0]), dtype)['data']

    n_samples  = len(channel_1_data)
    n_channels = len(filelist)

    data_array = np.zeros([n_samples, n_channels], dtype)
    data_array[:,0] = channel_1_data

    for i, f in enumerate(filelist[1:]):
            data_array[:, i + 1] = loadContinuous(os.path.join(folderpath, f), dtype)['data']
            numFiles += 1

    print(''.join(('Avg. Load Time: ', str((time.time() - t0)/numFiles),' sec')))
    print(''.join(('Total Load Time: ', str((time.time() - t0)),' sec')))

    return data_array

def loadContinuous(filepath, dtype = float):

    assert dtype in (float, np.int16), \
      'Invalid data type specified for loadContinous, valid types are float and np.int16'

    print("Loading continuous data...")

    ch = { }

    #read in the data
    f = open(filepath,'rb')

    fileLength = os.fstat(f.fileno()).st_size

    # calculate number of samples
    recordBytes = fileLength - NUM_HEADER_BYTES
    if  recordBytes % RECORD_SIZE != 0:
        raise Exception("File size is not consistent with a continuous file: may be corrupt")
    nrec = recordBytes // RECORD_SIZE
    nsamp = nrec * SAMPLES_PER_RECORD
    # pre-allocate samples
    samples = np.zeros(nsamp, dtype)
    timestamps = np.zeros(nrec)
    recordingNumbers = np.zeros(nrec)
    indices = np.arange(0, nsamp + 1, SAMPLES_PER_RECORD, np.dtype(np.int64))

    header = readHeader(f)

    recIndices = np.arange(0, nrec)

    for recordNumber in recIndices:

        timestamps[recordNumber] = np.fromfile(f,np.dtype('<i8'),1) # little-endian 64-bit signed integer
        N = np.fromfile(f,np.dtype('<u2'),1)[0] # little-endian 16-bit unsigned integer

        #print index

        if N != SAMPLES_PER_RECORD:
            raise Exception('Found corrupted record in block ' + str(recordNumber))

        recordingNumbers[recordNumber] = (np.fromfile(f,np.dtype('>u2'),1)) # big-endian 16-bit unsigned integer

        if dtype == float: # Convert data to float array and convert bits to voltage.
            data = np.fromfile(f,np.dtype('>i2'),N) * float(header['bitVolts']) # big-endian 16-bit signed integer, multiplied by bitVolts
        else:  # Keep data in signed 16 bit integer format.
            data = np.fromfile(f,np.dtype('>i2'),N)  # big-endian 16-bit signed integer
        samples[indices[recordNumber]:indices[recordNumber+1]] = data

        marker = f.read(10) # dump

    #print recordNumber
    #print index

    ch['header'] = header
    ch['timestamps'] = timestamps
    ch['data'] = samples  # OR use downsample(samples,1), to save space
    ch['recordingNumber'] = recordingNumbers
    f.close()
    return ch

def loadSpikes(filepath):

    '''
    Loads spike waveforms and timestamps from filepath (should be .spikes file)

    '''

    data = { }

    print('loading spikes...')

    f = open(filepath, 'rb')
    header = readHeader(f)

    if float(header[' version']) < 0.4:
        raise Exception('Loader is only compatible with .spikes files with version 0.4 or higher')

    data['header'] = header
    numChannels = int(header['num_channels'])
    numSamples = 40 # **NOT CURRENTLY WRITTEN TO HEADER**

    spikes = np.zeros((MAX_NUMBER_OF_SPIKES, numSamples, numChannels))
    timestamps = np.zeros(MAX_NUMBER_OF_SPIKES)
    source = np.zeros(MAX_NUMBER_OF_SPIKES)
    gain = np.zeros((MAX_NUMBER_OF_SPIKES, numChannels))
    thresh = np.zeros((MAX_NUMBER_OF_SPIKES, numChannels))
    sortedId = np.zeros((MAX_NUMBER_OF_SPIKES, numChannels))
    recNum = np.zeros(MAX_NUMBER_OF_SPIKES)

    currentSpike = 0

    while f.tell() < os.fstat(f.fileno()).st_size:
        eventType = np.fromfile(f, np.dtype('<u1'),1) #always equal to 4, discard
        timestamps[currentSpike] = np.fromfile(f, np.dtype('<i8'), 1)
        software_timestamp = np.fromfile(f, np.dtype('<i8'), 1)
        source[currentSpike] = np.fromfile(f, np.dtype('<u2'), 1)
        numChannels = np.fromfile(f, np.dtype('<u2'), 1)
        numSamples = np.fromfile(f, np.dtype('<u2'), 1)
        sortedId[currentSpike] = np.fromfile(f, np.dtype('<u2'),1)
        electrodeId = np.fromfile(f, np.dtype('<u2'),1)
        channel = np.fromfile(f, np.dtype('<u2'),1)
        color = np.fromfile(f, np.dtype('<u1'), 3)
        pcProj = np.fromfile(f, np.float32, 2)
        sampleFreq = np.fromfile(f, np.dtype('<u2'),1)

        waveforms = np.fromfile(f, np.dtype('<u2'), numChannels*numSamples)
        gain[currentSpike,:] = np.fromfile(f, np.float32, numChannels)
        thresh[currentSpike,:] = np.fromfile(f, np.dtype('<u2'), numChannels)
        recNum[currentSpike] = np.fromfile(f, np.dtype('<u2'), 1)

        waveforms_reshaped = np.reshape(waveforms, (numChannels, numSamples))
        waveforms_reshaped = waveforms_reshaped.astype(float)
        waveforms_uv = waveforms_reshaped

        for ch in range(numChannels):
            waveforms_uv[ch, :] -= 32768
            waveforms_uv[ch, :] /= gain[currentSpike, ch]*1000

        spikes[currentSpike] = waveforms_uv.T

        currentSpike += 1

    data['spikes'] = spikes[:currentSpike,:,:]
    data['timestamps'] = timestamps[:currentSpike]
    data['source'] = source[:currentSpike]
    data['gain'] = gain[:currentSpike,:]
    data['thresh'] = thresh[:currentSpike,:]
    data['recordingNumber'] = recNum[:currentSpike]
    data['sortedId'] = sortedId[:currentSpike]

    return data


def loadEvents(filepath):

    data = { }

    print('loading events...')

    f = open(filepath,'rb')
    header = readHeader(f)

    if float(header[' version']) < 0.4:
        raise Exception('Loader is only compatible with .events files with version 0.4 or higher')

    data['header'] = header

    index = -1

    channel = np.zeros(MAX_NUMBER_OF_EVENTS)
    timestamps = np.zeros(MAX_NUMBER_OF_EVENTS)
    sampleNum = np.zeros(MAX_NUMBER_OF_EVENTS)
    nodeId = np.zeros(MAX_NUMBER_OF_EVENTS)
    eventType = np.zeros(MAX_NUMBER_OF_EVENTS)
    eventId = np.zeros(MAX_NUMBER_OF_EVENTS)
    recordingNumber = np.zeros(MAX_NUMBER_OF_EVENTS)

    while f.tell() < os.fstat(f.fileno()).st_size:

        index += 1

        timestamps[index] = np.fromfile(f, np.dtype('<i8'), 1)
        sampleNum[index] = np.fromfile(f, np.dtype('<i2'), 1)
        eventType[index] = np.fromfile(f, np.dtype('<u1'), 1)
        nodeId[index] = np.fromfile(f, np.dtype('<u1'), 1)
        eventId[index] = np.fromfile(f, np.dtype('<u1'), 1)
        channel[index] = np.fromfile(f, np.dtype('<u1'), 1)
        recordingNumber[index] = np.fromfile(f, np.dtype('<u2'), 1)

    data['channel'] = channel[:index]
    data['timestamps'] = timestamps[:index]
    data['eventType'] = eventType[:index]
    data['nodeId'] = nodeId[:index]
    data['eventId'] = eventId[:index]
    data['recordingNumber'] = recordingNumber[:index]
    data['sampleNum'] = sampleNum[:index]

    return data

def readHeader(f):
    header = { }
    h = f.read(1024).decode().replace('\n','').replace('header.','')
    for i,item in enumerate(h.split(';')):
        if '=' in item:
            header[item.split(' = ')[0]] = item.split(' = ')[1]
    return header

def downsample(trace,down):
    downsampled = scipy.signal.resample(trace,np.shape(trace)[0]/down)
    return downsampled

def pack(folderpath,source='100',**kwargs):
#convert single channel open ephys channels to a .dat file for compatibility with the KlustaSuite, Neuroscope and Klusters
#should not be necessary for versions of open ephys which write data into HDF5 format.
#loads .continuous files in the specified folder and saves a .DAT in that folder
#optional arguments:
#   source: string name of the source that openephys uses as the prefix. is usually 100, if the headstage is the first source added, but can specify something different
#
#   data: pre-loaded data to be packed into a .DAT
#   dref: int specifying a channel # to use as a digital reference. is subtracted from all channels.
#   order: the order in which the .continuos files are packed into the .DAT. should be a list of .continious channel numbers. length must equal total channels.
#   suffix: appended to .DAT filename, which is openephys.DAT if no suffix provided.

    #load the openephys data into memory
    if 'data' not in kwargs.keys():
        if 'channels' not in kwargs.keys():
            data = loadFolder(folderpath, dtype = np.int16)
        else:
            data = loadFolder(folderpath, dtype = np.int16, channels=kwargs['channels'])
    else:
        data = kwargs['data']
    #if specified, do the digital referencing
    if 'dref' in kwargs.keys():
        ref =load(os.path.join(folderpath,''.join((source,'_CH',str(kwargs['dref']),'.continuous'))))
        for i,channel in enumerate(data.keys()):
            data[channel]['data'] = data[channel]['data'] - ref['data']
    #specify the order the channels are written in
    if 'order' in kwargs.keys():
        order = kwargs['order']
    else:
        order = list(data)
    #add a suffix, if one was specified
    if 'suffix' in kwargs.keys():
        suffix=kwargs['suffix']
    else:
        suffix=''

    #make a file to write the data back out into .dat format
    outpath = os.path.join(folderpath,''.join(('openephys',suffix,'.dat')))
    out = open(outpath,'wb')

    #go through the data and write it out in the .dat format
    #.dat format specified here: http://neuroscope.sourceforge.net/UserManual/data-files.html
    channelOrder = []
    print(''.join(('...saving .dat to ',outpath,'...')))
    random_datakey = next(iter(data))
    bar = ProgressBar(len(data[random_datakey]['data']))
    for i in range(len(data[random_datakey]['data'])):
        for j in range(len(order)):
            if source in random_datakey:
                ch = data[order[j]]['data']
            else:
                ch = data[''.join(('CH',str(order[j]).replace('CH','')))]['data']
            out.write(struct.pack('h',ch[i]))#signed 16-bit integer
            #figure out which order this thing packed the channels in. only do this once.
            if i == 0:
                channelOrder.append(order[j])
        #update how mucb we have list
        if i%(len(data[random_datakey]['data'])/100)==0:
            bar.animate(i)
    out.close()
    print(''.join(('order: ',str(channelOrder))))
    print(''.join(('.dat saved to ',outpath)))

#**********************************************************
# progress bar class used to show progress of pack()
    #stolen from some post on stack overflow
import sys
try:
    from IPython.display import clear_output
    have_ipython = True
except ImportError:
    have_ipython = False
class ProgressBar:
    def __init__(self, iterations):
        self.iterations = iterations
        self.prog_bar = '[]'
        self.fill_char = '*'
        self.width = 40
        self.__update_amount(0)
        if have_ipython:
            self.animate = self.animate_ipython
        else:
            self.animate = self.animate_noipython

    def animate_ipython(self, iter):
        print('\r', self,)
        sys.stdout.flush()
        self.update_iteration(iter + 1)

    def update_iteration(self, elapsed_iter):
        self.__update_amount((elapsed_iter / float(self.iterations)) * 100.0)
        self.prog_bar += '  %d of %s complete' % (elapsed_iter, self.iterations)

    def __update_amount(self, new_amount):
        percent_done = int(round((new_amount / 100.0) * 100.0))
        all_full = self.width - 2
        num_hashes = int(round((percent_done / 100.0) * all_full))
        self.prog_bar = '[' + self.fill_char * num_hashes + ' ' * (all_full - num_hashes) + ']'
        pct_place = (len(self.prog_bar) // 2) - len(str(percent_done))
        pct_string = '%d%%' % percent_done
        self.prog_bar = self.prog_bar[0:pct_place] + \
            (pct_string + self.prog_bar[pct_place + len(pct_string):])

    def __str__(self):
        return str(self.prog_bar)
#*************************************************************

def pack_2(folderpath, filename = '', channels = 'all', chprefix = 'CH', 
           dref = None, session = '0', source = '100'):

    '''Alternative version of pack which uses numpy's tofile function to write data.
    pack_2 is much faster than pack and avoids quantization noise incurred in pack due
    to conversion of data to float voltages during loadContinous followed by rounding
    back to integers for packing.
    
    filename: Name of the output file. By default, it follows the same layout of continuous files,
              but without the channel number, for example, '100_CHs_3.dat' or '100_ADCs.dat'.
    
    channels:  List of channel numbers specifying order in which channels are packed. By default
               all CH continous files are packed in numerical order.
    
    chprefix:  String name that defines if channels from headstage, auxiliary or ADC inputs
               will be loaded.

    dref:  Digital referencing - either supply a channel number or 'ave' to reference to the
           average of packed channels.
    
    source: String name of the source that openephys uses as the prefix. It is usually 100,
            if the headstage is the first source added, but can specify something different.
    
    '''
    
    data_array = loadFolderToArray(folderpath, channels, chprefix, np.int16, session, source)
    
    if dref:
        if dref == 'ave':
            print('Digital referencing to average of all channels.')
            reference = np.mean(data_array,1)
        else:
            print('Digital referencing to channel ' + str(dref))
            if channels == 'all':
                channels = _get_sorted_channels(folderpath, chprefix, session, source)
            reference = deepcopy(data_array[:,channels.index(dref)])
        for i in range(data_array.shape[1]):
            data_array[:,i] = data_array[:,i] - reference
    
    if session == '0': session = ''
    else: session = '_'+session
    
    if not filename: filename = source + '_' + chprefix + 's' + session + '.dat'
    print('Packing data to file: ' + filename)
    data_array.tofile(os.path.join(folderpath,filename))


def _get_sorted_channels(folderpath, chprefix='CH', session='0', source='100'):
    Files = [f for f in os.listdir(folderpath) if '.continuous' in f 
                                               and '_'+chprefix in f 
                                               and source in f]
    
    if session == '0':
        Files = [f for f in Files if len(f.split('_')) == 2]
        Chs = sorted([int(f.split('_'+chprefix)[1].split('.')[0]) for f in Files])
    else:
        Files = [f for f in Files if len(f.split('_')) == 3 
                                  and f.split('.')[0].split('_')[2] == session]
    
        Chs = sorted([int(f.split('_'+chprefix)[1].split('_')[0]) for f in Files])

    return(Chs)