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@wonkoderverstaendige
Created May 3, 2014 17:49
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{
"metadata": {
"name": "",
"signature": "sha256:31b0d71456bf87981a1b62cbc95077d7cafa936478d41696aff62d74c06497b9"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"%pylab inline\n",
"import time\n",
"import spikedetekt\n",
"\n",
"try:\n",
" import matplotlib.pyplot as plt\n",
"except:\n",
" raise\n",
"import networkx as nx\n",
"#import graphviz_layout\n",
"\n",
"import itertools\n",
"import re\n",
"import pprint"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
}
],
"prompt_number": 6
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Mouse 2541\n",
"\n",
"## Defining probe layout for .probe file\n",
"\n",
"SpikeDetekt requires a graph describing the probe geometry. For tetrodes, I will represent each wire bundle as four mutually connected nodes.\n",
"\n",
"__NOTE:__ These are uncorrected tetrode numbers. No dead channels assigned."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Channel numbering\n",
"# !NOTE: 1-based index\n",
"channels_2541 = {1: [ 5, 6, 7, 8], 2: [ 2, 3, 4, 9], 3: [ 1,10,11,32], 4: [12,13,30,31],\n",
" 5: [14,15,16,29], 6: [17,18,19,28], 7: [20,21,26,27], 8: [22,23,24,25],\n",
" 9: [37,38,39,40], 10: [34,35,36,41], 11: [33,42,43,64], 12: [44,45,62,63],\n",
" 13: [46,47,48,61], 14: [49,50,51,60], 15: [52,53,58,59], 16: [54,55,56,57]}\n",
"\n",
"# zero index it...\n",
"channels_2541 = {t_num: [channel-1 for channel in tetrode] for t_num, tetrode in channels_2541.items()}\n",
"\n",
"tetrodes_2541 = {k: list(itertools.combinations(l, r=2)) for k, l in channels_2541.items()}\n",
"tetrodes_2541"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 7,
"text": [
"{1: [(4, 5), (4, 6), (4, 7), (5, 6), (5, 7), (6, 7)],\n",
" 2: [(1, 2), (1, 3), (1, 8), (2, 3), (2, 8), (3, 8)],\n",
" 3: [(0, 9), (0, 10), (0, 31), (9, 10), (9, 31), (10, 31)],\n",
" 4: [(11, 12), (11, 29), (11, 30), (12, 29), (12, 30), (29, 30)],\n",
" 5: [(13, 14), (13, 15), (13, 28), (14, 15), (14, 28), (15, 28)],\n",
" 6: [(16, 17), (16, 18), (16, 27), (17, 18), (17, 27), (18, 27)],\n",
" 7: [(19, 20), (19, 25), (19, 26), (20, 25), (20, 26), (25, 26)],\n",
" 8: [(21, 22), (21, 23), (21, 24), (22, 23), (22, 24), (23, 24)],\n",
" 9: [(36, 37), (36, 38), (36, 39), (37, 38), (37, 39), (38, 39)],\n",
" 10: [(33, 34), (33, 35), (33, 40), (34, 35), (34, 40), (35, 40)],\n",
" 11: [(32, 41), (32, 42), (32, 63), (41, 42), (41, 63), (42, 63)],\n",
" 12: [(43, 44), (43, 61), (43, 62), (44, 61), (44, 62), (61, 62)],\n",
" 13: [(45, 46), (45, 47), (45, 60), (46, 47), (46, 60), (47, 60)],\n",
" 14: [(48, 49), (48, 50), (48, 59), (49, 50), (49, 59), (50, 59)],\n",
" 15: [(51, 52), (51, 57), (51, 58), (52, 57), (52, 58), (57, 58)],\n",
" 16: [(53, 54), (53, 55), (53, 56), (54, 55), (54, 56), (55, 56)]}"
]
}
],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"tetrodes = tetrodes_2541\n",
"\n",
"G = nx.Graph()\n",
"for k, t in tetrodes.items():\n",
" G.add_edges_from(t)\n",
"pos = nx.graphviz_layout(G, prog='neato',args='')\n",
"plt.figure(figsize=(6, 6))\n",
"#colors = np.linspace(0.0, 1.0, num=len(tetrodes))\n",
"colors = [random.random() for n in xrange(len(tetrodes))]\n",
"C=nx.connected_component_subgraphs(G)\n",
"for i, g in enumerate(C):\n",
" #c=*nx.number_of_nodes(g) # random color...\n",
" nx.draw(g, pos,\n",
" node_size=400, node_color=[colors[i]]*nx.number_of_nodes(g),\n",
" vmin=0.0, vmax=1.0, alpha=0.2,\n",
" with_labels=True)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
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KqWRwYIB8oUA6lSGdSpVKvSbicWwGU9nn3jMLwWQqfx+X3YGz30MummZy1IfV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\nGpZYdQy+8jzxeHzObWTePGNj4zyzbRDB0YKnsW3Wd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iUZjxzS85c59MilezJVIUkSsViM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\nNOD/A3pOFl/7kNauAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x7f9963d2a1d0>"
]
}
],
"prompt_number": 8
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Looking into open ephys .continuous files\n",
"\n",
"Continuous data files (.continuous)\n",
"\n",
"Each continuous channel within each processor has its own file, titled \"XXX_CHY.continuous,\" where XXX = the processor ID #, and Y = the channel number. For each record, it saves:\n",
"\n",
"- One int64 timestamp (actually a sample number; this can be converted to seconds using the sampleRate variable in the header)\n",
"- One uint16 number (N) indicating the samples per record (always 1024, at least for now)\n",
"- One uint16 recording number (version 0.2 and higher)\n",
"- 1024 int16 samples\n",
"- 10-byte record marker (0 1 2 3 4 5 6 7 8 255)\n",
"\n",
"If a file is opened or closed in the middle of a record, the leading or trailing samples are set to zero."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# fixed header describing data\n",
"SIZE_HEADER = 1024\n",
"\n",
"# 22 byte record header + 2048 byte samples\n",
"SIZE_RECORD = 2070\n",
"\n",
"NUM_SAMPLES = 1024\n",
"REC_MARKER = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 255], dtype=uint8)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 9
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def oe_read_header(fname):\n",
" \"\"\" Return a dict with header content.\n",
" \"\"\"\n",
" # TODO: Use alternative reading method when fname is a file id. If string, use numpy.fromfile\n",
" import re\n",
"\n",
" # 1 kiB header\n",
" header_dt = np.dtype([('Header', 'S%d' % SIZE_HEADER)])\n",
" header = np.fromfile(fname, dtype=header_dt, count=1)\n",
" \n",
" # alternative which moves file pointer\n",
" #fid = open(fname, 'rb')\n",
" #header = fid.read(SIZE_HEADER)\n",
" #fid.close()\n",
" \n",
" # Stand back! I know regex!\n",
" # Annoyingly, there is a single newline character missing in the header\n",
" regex = \"header\\.([\\d\\w\\.\\s]{1,}).=.\\'*([^\\;\\']{1,})\\'*\"\n",
" header_str = str(header[0][0]).rstrip(' ')\n",
" header_dict = {entry[0]: entry[1] for entry in re.compile(regex).findall(header_str)}\n",
" for key in ['bitVolts', 'sampleRate']:\n",
" header_dict[key] = float(header_dict[key])\n",
" for key in ['blockLength', 'bufferSize', 'header_bytes', 'channel']:\n",
" header_dict[key] = int(header_dict[key]) if not key == 'channel' else int(header_dict[key][2:])\n",
" return header_dict"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 10
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def oe_read_records(fname, offset=0, count=10000):\n",
" # rec_ofs: Offset in number of records\n",
" # rec_cnt: Number of records to read\n",
" with open(fname, 'rb') as fid:\n",
" # move pointer to new position\n",
" fid.seek(SIZE_HEADER + offset * SIZE_RECORD)\n",
"\n",
" # n times x B data\n",
" data_dt = np.dtype([('timestamp', np.int64),\n",
" ('n_samples', np.uint16),\n",
" ('rec_num', np.uint16),\n",
" # !Note: endianness!\n",
" ('samples', ('>i2', NUM_SAMPLES)),\n",
" ('rec_mark', (np.uint8, 10))])\n",
"\n",
" data = np.fromfile(fid, dtype=data_dt, count=count)\n",
"\n",
" return data"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 11
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Header + data example:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"example = 'examples/2541_2014-03-13_20-45-41/100_CH12.continuous'\n",
"pprint.pprint(oe_read_header(example))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"{'bitVolts': 0.194999993,\n",
" 'blockLength': 1024,\n",
" 'bufferSize': 1024,\n",
" 'channel': 12,\n",
" 'channelType': 'Continuous',\n",
" 'date_created': '13-Mar-2014 204546',\n",
" 'description': 'each record contains one 64-bit timestamp, one 16-bit sample count (N), 1 uint16 recordingNumber, N 16-bit samples, and one 10-byte record marker (0 1 2 3 4 5 6 7 8 255)',\n",
" 'format': 'Open Ephys Data Format',\n",
" 'header_bytes': 1024,\n",
" 'sampleRate': 20000.0,\n",
" 'version': '0.2'}\n"
]
}
],
"prompt_number": 12
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"data = oe_read_records(example, offset=5, count=1000)\n",
"n = 2\n",
"plt.figure(figsize=(13, 5))\n",
"plot(data['samples'][:n].ravel())\n",
"xlim(0, 1024*n)\n",
"data['samples'].__array_interface__"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 13,
"text": [
"{'data': (140296749948956, False),\n",
" 'descr': [('', '>i2')],\n",
" 'shape': (1000, 1024),\n",
" 'strides': (2070, 2),\n",
" 'typestr': '>i2',\n",
" 'version': 3}"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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wgXZAdbX3YDgv0tL4vm5xcO653rMMKipMdMltW/73v8FNcQCKj+xsTr4+99zg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\nH+EAAAAAgCTp/wGlEYnjxUr5AgAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x7f9963a6db10>"
]
}
],
"prompt_number": 13
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Read controls"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Timestamps should increase monotonically in steps of 1024\n",
"assert len(set(np.diff(data['timestamp']))) == 1 and np.diff(data['timestamp'][:2]) == 1024\n",
"print 'timestamps: ', data['timestamp'][0]\n",
"\n",
"# Number of samples in each record should be NUM_SAMPLES, or 1024\n",
"assert len(set(data['n_samples'])) == 1 and data['n_samples'][0] == NUM_SAMPLES\n",
"print 'N samples: ', data['n_samples'][0]\n",
"\n",
"# should be byte pattern [0...8, 255]\n",
"stringified = map(str, data['rec_mark']) # <- slow\n",
"assert len(set(stringified)) == 1 and str(data['rec_mark'][0]) == str(REC_MARKER)\n",
"print 'record marker: ', data['rec_mark'][0]\n",
"\n",
"# should be zero, or there are multiple recordings in this file\n",
"assert len(set(data['rec_num'])) == 1 and data['rec_num'][0] == 0\n",
"print 'Number recording: ', data['rec_num'][0] "
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"timestamps: 20024120\n",
"N samples: 1024\n",
"record marker: "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" [ 0 1 2 3 4 5 6 7 8 255]\n",
"Number recording: 0\n"
]
}
],
"prompt_number": 14
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Plotting Open Ephys 0.2x files\n",
"\n",
"Chunk-wise reading of all 64 neural channels.\n",
"\n",
"Simplest approach would be to seek and read chunk by chunk, no generators, withs or leaving the files open.\n",
"\n",
"TODO: - timings (esp. see if difference in endianness affects speed)"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def data_to_buf(path, channels=64, count=1000, proc_node=100, channel_offset=500):\n",
" base_end = '{proc_node:d}_CH{channel:d}.continuous'\n",
"\n",
" # channels to include either given as number of channels, or as a list\n",
" channels = channels if channels is iterable else list(range(channels))\n",
" \n",
" # temporary storage\n",
" buf = np.zeros((len(channels), num_records*1024), dtype='>i2')\n",
"\n",
" # load chunk of data from all channels\n",
" print 'Reading chunk'\n",
" for n in channels:\n",
" fname = path+base_end.format(proc_node=proc_node, channel=n+1)\n",
" # channel offset is clunky shortcut for plotting\n",
" if channel_offset:\n",
" buf[n] = oe_read_records(fname, count=count)['samples'].ravel() + channel_offset * (n-32)\n",
" else:\n",
" buf[n] = oe_read_records(fname, count=count)['samples'].ravel()\n",
"\n",
" return buf\n",
" "
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 15
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# path to folder of current session\n",
"example_path = 'examples/2541_2014-03-13_20-45-41/'\n",
"\n",
"# number of records to load at once\n",
"num_records=1000\n",
"\n",
"# load first records from ALL channels in \n",
"t = time.time()\n",
"tmp = data_to_buf(path=example_path, count=num_records).transpose()\n",
"size, elapsed = tmp.nbytes/1e6, time.time() - t\n",
"\n",
"# Take speed with a grain of salt. Linux is smart enough to keep files in RAM once used.\n",
"print 'Read {0:.1f} MB in {1:.2f} s, {2:.2f} MB/s'.format(size, elapsed, size*1./elapsed)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Reading chunk\n",
"Read 131.1 MB in 5.43 s, 24.14 MB/s"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n"
]
}
],
"prompt_number": 16
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plt.figure(figsize=(14, 18))\n",
"print tmp.shape\n",
"plot(tmp[5000:10000]);"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"(1024000, 64)\n"
]
},
{
"metadata": {},
"output_type": "display_data",
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