diff --git a/Dockerfile b/Dockerfile
index 8278652..3a1c038 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -28,4 +28,4 @@ RUN eval "$(/root/miniconda/bin/conda shell.bash hook)" && conda init && \
conda activate pyks2 && \
pip install -r test_requirements.txt
-COPY . ./
\ No newline at end of file
+COPY . ./
diff --git a/examples/notebooks/matlab_python_comparison.ipynb b/examples/notebooks/matlab_python_comparison.ipynb
deleted file mode 100644
index 821ed18..0000000
--- a/examples/notebooks/matlab_python_comparison.ipynb
+++ /dev/null
@@ -1,526 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Kilosort2 vs. pykilosort: Comparison Report\n",
- "\n",
- "This report simulates electrophysiological data and compares the outputs of spike sorters Kilosort2 and pykilosort run on this data. All configuration for the sortings used should be changed either within the report (or ideally) through environment variables. Don't rely on configuration from external files as they may become out of sync between MATLAB and python."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "import enum\n",
- "import os\n",
- "import numpy as np\n",
- "import matplotlib.pyplot as plt"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "import logging\n",
- "logger = logging.getLogger()\n",
- "logger.setLevel(logging.INFO)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Configuration"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "PYKILOSORT_DIR = os.environ.get('PYKILOSORT_DIR', '.')\n",
- "KILOSORT2_DIR = os.environ.get('KILOSORT2_DIR', f'{PYKILOSORT_DIR}/../Kilosort2')\n",
- "\n",
- "BASE_PATH = os.environ.get('BASE_PATH', f'{PYKILOSORT_DIR}/examples/eMouse/data')\n",
- "PYKILOSORT_SORTING_RESULTS_DIR = f'{BASE_PATH}/python_output/'\n",
- "MATLAB_SORTING_RESULTS_DIR = f'{BASE_PATH}/matlab_output/'\n",
- "\n",
- "[os.makedirs(d,exist_ok=True) for d in (PYKILOSORT_SORTING_RESULTS_DIR,MATLAB_SORTING_RESULTS_DIR)]\n",
- "\n",
- "class Operations(enum.Enum):\n",
- " simulation = 'SIMULATION'\n",
- " matlab_sorting = 'MATLAB_SORTING'\n",
- " pykilosort_sorting = 'PYKILOSORT_SORTING'\n",
- " \n",
- "FORCE_RUN = {Operations.simulation, Operations.matlab_sorting, Operations.pykilosort_sorting}\n",
- "FORCE_RUN = {Operations.pykilosort_sorting}\n",
- "\n",
- "simulation_opts = {\n",
- " 'chanMapName': 'chanMap_3B_64sites.mat',\n",
- " 'NchanTOT': 64.0\n",
- "}\n",
- "\n",
- "opts = {\n",
- " 'chanMap': f'{BASE_PATH}/{simulation_opts[\"chanMapName\"]}',\n",
- " 'fs': 30000.,\n",
- " 'fshigh': 150.,\n",
- " 'minfr_goodchannels': 0.1000,\n",
- " 'Th': [6.0, 2.0],\n",
- " 'lam': 10.,\n",
- " 'AUCsplit': 0.9000,\n",
- " 'minFR': 0.0200,\n",
- " 'momentum': [20., 400],\n",
- " 'sigmaMask': 30.,\n",
- " 'ThPre': 8.,\n",
- " 'reorder': 1,\n",
- " 'nskip': 25.,\n",
- " 'spkTh': -6.,\n",
- " 'GPU': 1,\n",
- " 'nfilt_factor': 4.,\n",
- " 'ntbuff': 64.0,\n",
- " 'NT': 65600.,\n",
- " 'whiteningRange': 32.,\n",
- " 'nSkipCov': 25.0,\n",
- " 'scaleproc': 200.,\n",
- " 'nPCs': 3.,\n",
- " 'useRAM': 0,\n",
- " 'sorting': 2,\n",
- " 'NchanTOT': float(simulation_opts['NchanTOT']),\n",
- " 'trange': [0., float('inf')],\n",
- " 'fproc': '/tmp/temp_wh.dat',\n",
- " 'rootZ': MATLAB_SORTING_RESULTS_DIR,\n",
- " 'fbinary': f'{BASE_PATH}/sim_binary.imec.ap.bin',\n",
- " 'fig': False\n",
- "} "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Setup MATLABTM engine"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "import matlab.engine\n",
- "\n",
- "# If true we start a new matlab engine, if false we try to connect to an existing open matlab workspace.\n",
- "# The latter is helpful for debugging.\n",
- "new_session = True \n",
- "if new_session:\n",
- " eng = matlab.engine.start_matlab()\n",
- "else:\n",
- " eng = matlab.engine.connect_matlab()\n",
- " \n",
- "eng.addpath(eng.genpath(KILOSORT2_DIR));\n",
- "eng.addpath(eng.genpath(f'{KILOSORT2_DIR}/../npy-matlab'));"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Generate simulated data using MATLABTM "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "if Operations.simulation in FORCE_RUN:\n",
- " useGPU = True\n",
- " useParPool = False\n",
- "\n",
- " opts[\"chanMap\"] = eng.make_eMouseChannelMap_3B_short(BASE_PATH, simulation_opts[\"NchanTOT\"])\n",
- " opts[\"chanMap\"] = f'{BASE_PATH}/{opts[\"chanMap\"]}'\n",
- " eng.make_eMouseData_drift(BASE_PATH, KILOSORT2_DIR, simulation_opts[\"chanMapName\"], useGPU, useParPool, nargout=0)\n",
- "else:\n",
- " assert os.path.isfile(opts[\"chanMap\"])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Write out the channel data to numpy files too "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "if Operations.simulation in FORCE_RUN:\n",
- " x = eng.load(opts['chanMap'])\n",
- " eng.writeNPY(x['chanMap'], f'{BASE_PATH}/chanMap.npy', nargout=0)\n",
- " eng.writeNPY(x['xcoords'], f'{BASE_PATH}/xc.npy', nargout=0)\n",
- " eng.writeNPY(x['ycoords'], f'{BASE_PATH}/yc.npy', nargout=0)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Sort simulated data using Kilosort2 via MATLABTM engine"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "if Operations.matlab_sorting in FORCE_RUN:\n",
- " # make sure to convert list to matlab arrays\n",
- " ops = eng.struct({k: (matlab.double(v) if isinstance(v, list) else v) for k,v in opts.items()})\n",
- " rootZ = eng.char(opts['rootZ'])\n",
- " if not new_session: \n",
- " eng.workspace['ops'] = ops\n",
- " eng.workspace['rootZ'] = rootZ\n",
- "\n",
- " rez = eng.function_kilosort(rootZ, ops)\n",
- "print(f\"Files generated: {os.listdir(MATLAB_SORTING_RESULTS_DIR)}\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Sort simulated data using pykilosort"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "import pykilosort\n",
- "from pathlib import Path\n",
- "from importlib import reload\n",
- "from pykilosort import main\n",
- "reload(main)\n",
- "\n",
- "pykilosort.add_default_handler()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "probe = pykilosort.Bunch()\n",
- "probe.NchanTOT = int(opts['NchanTOT'])\n",
- "probe.chanMap = np.load(BASE_PATH+'/chanMap.npy').flatten().astype(int)\n",
- "probe.kcoords = np.ones(int(opts['NchanTOT']))\n",
- "probe.xc = np.load(BASE_PATH+'/xc.npy').flatten()\n",
- "probe.yc = np.load(BASE_PATH+'/yc.npy').flatten()\n",
- "\n",
- "rez = main.run(\n",
- " dat_path = opts['fbinary'],\n",
- " dir_path = Path(PYKILOSORT_SORTING_RESULTS_DIR),\n",
- " output_dir = Path(PYKILOSORT_SORTING_RESULTS_DIR),\n",
- " params = None,\n",
- " probe=probe,\n",
- " dtype = np.int16,\n",
- " n_channels = int(opts['NchanTOT']),\n",
- " sample_rate = opts['fs'],\n",
- " clear_context = False #Operations.pykilosort_sorting in FORCE_RUN\n",
- ")\n",
- "print(f\"Files generated: {os.listdir(PYKILOSORT_SORTING_RESULTS_DIR)}\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "---"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Intermediate results comparison"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "import pydantic\n",
- "\n",
- "class SortingResults(pydantic.BaseModel):\n",
- " templates: np.ndarray\n",
- " spike_times: np.ndarray\n",
- " spike_clusters: np.ndarray\n",
- " channel_positions: np.ndarray\n",
- " \n",
- " class Config:\n",
- " arbitrary_types_allowed = True\n",
- "\n",
- "def get_results(dirname):\n",
- " return SortingResults(\n",
- " templates = np.load(f\"{dirname}templates.npy\"),\n",
- " spike_times = np.load(f\"{dirname}/spike_times.npy\"),\n",
- " spike_clusters = np.load(f\"{dirname}/spike_clusters.npy\"),\n",
- " channel_positions = np.load(f\"{dirname}/channel_positions.npy\"),\n",
- " )\n",
- "\n",
- "results = {\n",
- " 'matlab': get_results(MATLAB_SORTING_RESULTS_DIR),\n",
- " 'python': get_results(PYKILOSORT_SORTING_RESULTS_DIR)\n",
- "}\n",
- "\n",
- "import h5py\n",
- "\n",
- "def h5_to_dict(group):\n",
- " d = {}\n",
- " for k,v in group.items():\n",
- " if isinstance(v, h5py.Group):\n",
- " d[k] = h5_to_dict(v)\n",
- " elif v.attrs.get('MATLAB_class') == b'char':\n",
- " d[k] = u''.join(chr(c) for c in v)\n",
- " elif hasattr(v, 'shape'):\n",
- " d[k] = np.array(v)\n",
- " else:\n",
- " d[k] = v\n",
- " return d\n",
- "\n",
- "with h5py.File(f'{MATLAB_SORTING_RESULTS_DIR}/rez.mat', 'r') as f:\n",
- " matlab_rez = h5_to_dict(f['rez'])\n",
- "matlab_rez['ccb0'] = matlab_rez['ccb']\n",
- " \n",
- "python_rez = rez['intermediate']\n",
- "\n",
- "intermediate_results = {\n",
- " 'matlab': matlab_rez,\n",
- " 'python': python_rez,\n",
- " 'matlab - python': {\n",
- " 'ccbsort': matlab_rez['ccbsort'] - python_rez['ccbsort'],\n",
- " 'ccb0': matlab_rez['ccb0'] - python_rez['ccb0']\n",
- " }\n",
- "}"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Aligment at the start of the recording\n",
- "\n",
- "As 2020/06/29 this seems to be off by 1000 samples."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "from spikeextractors import BinDatRecordingExtractor\n",
- "f,axs = plt.subplots(2, 1, sharex=True, figsize=(10,12))\n",
- "\n",
- "matlab_proc_dat = BinDatRecordingExtractor(\n",
- " intermediate_results['matlab']['ops']['fproc'],\n",
- " sampling_frequency=intermediate_results['matlab']['ops']['fs'],\n",
- " numchan=intermediate_results['matlab']['ops']['NchanTOT'],\n",
- " dtype=np.int16\n",
- ")\n",
- "\n",
- "ax =axs[0]\n",
- "ax.plot(matlab_proc_dat.get_traces(start_frame=0, end_frame=4000).T);\n",
- "\n",
- "python_proc_dat = BinDatRecordingExtractor(\n",
- " (rez['context_path'] / 'proc.dat'),\n",
- " sampling_frequency=intermediate_results['matlab']['ops']['fs'],\n",
- " numchan=intermediate_results['matlab']['ops']['NchanTOT'],\n",
- " dtype=np.int16\n",
- ")\n",
- "ax =axs[1]\n",
- "ax.plot(range(1000, 4000), python_proc_dat.get_traces(start_frame=0, end_frame=3000).T);"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Difference in Whitening Matrices"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "plt.title(\"Difference in Whitening Matrices\")\n",
- "plt.imshow(intermediate_results['python']['Wrot'] - intermediate_results['matlab']['Wrot'])\n",
- "plt.colorbar();"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Difference in initial batch re-ordering"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "assert matlab_rez['ccb0'].shape == python_rez['ccb0'].shape"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "f, axs = plt.subplots(2, 3, figsize=(20,10))\n",
- "\n",
- "vmin, vmax = -np.max(intermediate_results['matlab']['ccbsort']), np.max(intermediate_results['matlab']['ccbsort'])\n",
- "\n",
- "for j,ccb in enumerate(['ccb0', 'ccbsort']):\n",
- " for i, (name,res) in enumerate(intermediate_results.items()):\n",
- " ax = axs[j,i]\n",
- " im = ax.imshow(res[ccb], vmin=vmin, vmax=vmax)\n",
- " ax.set_title(name)\n",
- " axs[j,0].set_ylabel(ccb)\n",
- " \n",
- "plt.colorbar(im)\n",
- "f.suptitle(\"Batch Dissimilarity Matrices\", size=16)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The final order of the batches is, as a result, not the same."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "plt.plot(matlab_rez['iorig'][0])\n",
- "plt.plot(python_rez['iorig']);"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Output comparison"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Similarity matrix between the templates of the identified units"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "matlab_templates = [results['matlab'].templates[x,:,:].ravel() for x in range(results['matlab'].templates.shape[0])]\n",
- "python_templates = [results['python'].templates[x,:,:].ravel() for x in range(results['python'].templates.shape[0])]\n",
- "\n",
- "similarity_matrix = np.array([[np.dot(m, p) for p in python_templates] for m in matlab_templates])\n",
- "\n",
- "plt.figure(figsize=(13,7))\n",
- "plt.imshow(similarity_matrix, vmin=0, vmax=1)\n",
- "plt.xlabel('Python Units')\n",
- "plt.ylabel('MATLAB Units')\n",
- "\n",
- "plt.colorbar()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "units = np.where(similarity_matrix > 0.7)\n",
- "\n",
- "for u in range(len(units[0])):\n",
- " f, axs = plt.subplots(2, 2, figsize=(8,10), gridspec_kw={'height_ratios': [4,1]})\n",
- " f.suptitle(f\"Unit pair {u+1}: {(units[0][u],units[1][u])}\", size=20)\n",
- "\n",
- " for i, (name,res) in enumerate(results.items()):\n",
- " templates = res.templates\n",
- " unit = units[i][u]\n",
- "\n",
- " for channel in range(templates.shape[2]):\n",
- " axs[0, i].plot(templates[unit,:,channel].T + 0.1 * channel);\n",
- "\n",
- " axs[0, i].set_title(name)\n",
- "\n",
- " for i, (name,res) in enumerate(results.items()):\n",
- " axs[1, i].vlines(res.spike_times[res.spike_clusters == units[i][0]], 0+i, 1+i)\n",
- "\n",
- " axs[1, i].set_xlim(0, 100000)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "for unit in range(82):\n",
- " plt.plot(results['matlab'].templates[unit,:,0].T);"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.7.7"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/pykilosort/cluster.py b/pykilosort/cluster.py
index f62f642..52210bb 100644
--- a/pykilosort/cluster.py
+++ b/pykilosort/cluster.py
@@ -12,6 +12,33 @@
logger = logging.getLogger(__name__)
+def getClosestChannels2(ycup, xcup, yc, xc, NchanClosest):
+ # this function outputs the closest channels to each channel,
+ # as well as a Gaussian-decaying mask as a function of pairwise distances
+ # sigma is the standard deviation of this Gaussian-mask
+ # compute distances between all pairs of channels
+ xc = cp.asarray(xc, dtype=np.float32, order='F')
+ yc = cp.asarray(yc, dtype=np.float32, order='F')
+ xcup = cp.asarray(xcup, dtype=np.float32, order='F')
+ ycup = cp.asarray(ycup, dtype=np.float32, order='F')
+ C2C = ((xc[:, np.newaxis] - xcup[:].T.flatten()[:,np.newaxis].T)**2 + (yc[:, np.newaxis] - ycup[:].T.flatten()[:,np.newaxis].T)**2)
+ C2C = cp.sqrt(C2C)
+ Nchan, NchanUp = C2C.shape
+
+ # sort distances
+ isort = cp.argsort(C2C, axis=0)
+
+ # take NchanCLosest neighbors for each primary channel
+ iC = isort[:NchanClosest, :]
+
+ # in some cases we want a mask that decays as a function of distance between pairs of channels
+ # this is an awkward indexing to get the corresponding distances
+ ix = iC + cp.arange(0, Nchan * NchanUp, Nchan)
+ dist = C2C.T.ravel()[ix]
+
+ return iC, dist
+
+
def getClosestChannels(probe, sigma, NchanClosest):
# this function outputs the closest channels to each channel,
# as well as a Gaussian-decaying mask as a function of pairwise distances
diff --git a/pykilosort/cuda/spikedetector3.cu b/pykilosort/cuda/spikedetector3.cu
new file mode 100644
index 0000000..283c526
--- /dev/null
+++ b/pykilosort/cuda/spikedetector3.cu
@@ -0,0 +1,197 @@
+const int Nthreads = 1024, NrankMax = 6, maxFR = 10000, nt0max=81, NchanMax = 17, nsizes = 5;
+
+
+//////////////////////////////////////////////////////////////////////////////////////////
+__global__ void Conv1D(const double *Params, const float *data, const float *W, float *conv_sig){
+ volatile __shared__ float sW[81*NrankMax], sdata[(Nthreads+81)];
+ float y;
+ int tid, tid0, bid, i, nid, Nrank, NT, nt0, Nchan;
+
+ tid = threadIdx.x;
+ bid = blockIdx.x;
+
+ NT = (int) Params[0];
+ Nchan = (int) Params[1];
+ nt0 = (int) Params[2];
+ Nrank = (int) Params[4];
+
+ if(tid Cmax){
+ Cmax = a[k]*a[k]/v2[k + nsizes*bidy];
+ kmax = t + k*Nrank;
+ }
+ }
+ }
+ datasum[tid0 + NT * bidy] = Cmax;
+ kkmax[tid0 + NT * bidy] = kmax;
+
+ tid0 += blockDim.x * gridDim.x;
+ }
+}
+
+//////////////////////////////////////////////////////////////////////////////////////////
+__global__ void max1D(const double *Params, const float *data, float *conv_sig){
+
+ volatile __shared__ float sdata[Nthreads+81];
+ float y, spkTh;
+ int tid, tid0, bid, i, NT, nt0, nt0min;
+
+ NT = (int) Params[0];
+ nt0 = (int) Params[2];
+ nt0min = (int) Params[5];
+ spkTh = (float) Params[6];
+
+ tid = threadIdx.x;
+ bid = blockIdx.x;
+
+ tid0 = 0;
+ while (tid0spkTh*spkTh)
+ conv_sig[tid0 + 1*(nt0min) + tid + NT*bid] = y;
+
+ tid0+=Nthreads;
+ __syncthreads();
+ }
+}
+
+//////////////////////////////////////////////////////////////////////////////////////////
+__global__ void maxChannels(const double *Params, const float *dataraw, const float *data,
+ const int *iC, const int *iC2, const float *dist2, const int *kkmax,
+ const float *dfilt, int *st, int *counter, float *cF){
+
+ int nt0, indx, tid, tid0, i, bid, NT, j,iChan, nt0min, Nrank, kfilt;
+ int Nchan, NchanNear, NchanUp, NchanNearUp, bidy ;
+ double Cf, d;
+ float spkTh, d2;
+ bool flag;
+
+ NT = (int) Params[0];
+ Nchan = (int) Params[1];
+ NchanNear = (int) Params[3];
+ NchanUp = (int) Params[7];
+ NchanNearUp = (int) Params[8];
+ nt0 = (int) Params[2];
+ nt0min = (int) Params[5];
+ spkTh = (float) Params[6];
+ Nrank = (int) Params[4];
+
+ tid = threadIdx.x;
+ bid = blockIdx.x;
+ bidy = blockIdx.y;
+
+ tid0 = tid + bid * blockDim.x;
+ while (tid0 Cf){
+ flag = false;
+ break;
+ }
+ }
+ }
+
+ if (flag){
+ if (Cf>spkTh*spkTh){
+ d = (double) dataraw[tid0+0 * (nt0min-1) + NT*i]; //
+ if (d > Cf-1e-6){
+ // this is a hit, atomicAdd and return spikes
+ indx = atomicAdd(&counter[0], 1);
+ if (indx 1 else 1
+ M = yp0.shape[1] if len(yp0.shape) > 1 else 1
+
+ K = np.zeros((N, M))
+ cs = M
+
+ for i in range(int(M * 1.0 / cs)):
+ ii = np.arange(i * cs, min(M, (i + 1) * cs))
+ mM = len(ii)
+
+ xp = np.matlib.repmat(xp0, mM, 1).T[np.newaxis, :, :]
+ yp = np.matlib.repmat(yp0[:, ii], N, 1).reshape((D, N, mM))
+ a = (xp - yp) ** 2
+ b = 1.0 / (length ** 2)
+ Kn = np.exp(-np.sum((a * b) / 2, axis=0))
+
+ K[:, ii] = Kn
+
+ return K
+
+
+def align_block2(F, ysamp, nblocks):
+
+ # F is y bins by amp bins by batches
+ # ysamp are the coordinates of the y bins in um
+
+ Nbatches = F.shape[2]
+
+ # look up and down this many y bins to find best alignment
+ n = 15
+ dc = np.zeros((2 * n + 1, Nbatches))
+ dt = range(-n, n + 1)
+
+ # we do everything on the GPU for speed, but it's probably fast enough on
+ # the CPU
+ Fg = F
+
+ # mean subtraction to compute covariance
+ Fg = Fg - np.mean(Fg, axis=0)
+
+ # initialize the target "frame" for alignment with a single sample
+ F0 = Fg[:, :, min(300, np.floor(Fg.shape[2] / 2).astype("int")) - 1]
+ F0 = F0[:, :, np.newaxis]
+
+ # first we do rigid registration by integer shifts
+ # everything is iteratively aligned until most of the shifts become 0.
+ niter = 10
+ dall = np.zeros((niter, Nbatches))
+ for iter in range(niter):
+ for t in range(len(dt)):
+ # for each NEW potential shift, estimate covariance
+ Fs = np.roll(Fg, dt[t], axis=0)
+ dc[t, :] = np.mean(np.mean(Fs * F0, axis=0), axis=0)
+ if iter + 1 < niter:
+ # up until the very last iteration, estimate the best shifts
+ imax = np.argmax(dc, axis=0)
+ # align the data by these integer shifts
+ for t in range(len(dt)):
+ ib = imax == t
+ Fg[:, :, ib] = np.roll(Fg[:, :, ib], dt[t], axis=0)
+ dall[iter, ib] = dt[t]
+ # new target frame based on our current best alignment
+ F0 = np.mean(Fg, axis=2)[:, :, np.newaxis]
+
+ # now we figure out how to split the probe into nblocks pieces
+ # if nblocks = 1, then we're doing rigid registration
+ nybins = F.shape[0]
+ yl = np.floor(nybins / nblocks).astype("int") - 1
+ # MATLAB rounds 0.5 to 1. Python uses "Bankers Rounding".
+ # Numpy uses round to nearest even. Force the result to be like MATLAB
+ # by adding a tiny constant.
+ ifirst = np.round(np.linspace(0, nybins - yl - 1, 2 * nblocks - 1) + 1e-10).astype(
+ "int"
+ )
+ ilast = ifirst + yl # 287
+
+ ##
+
+ nblocks = len(ifirst)
+ yblk = np.zeros((len(ifirst), 1))
+
+ # for each small block, we only look up and down this many samples to find
+ # nonrigid shift
+ n = 5
+ dt = np.arange(-n, n + 1)
+
+ # this part determines the up/down covariance for each block without
+ # shifting anything
+ dcs = np.zeros((2 * n + 1, Nbatches, nblocks))
+ for j in range(nblocks):
+ isub = np.arange(ifirst[j], ilast[j])
+ yblk[j] = np.mean(ysamp[isub])
+ Fsub = Fg[isub, :, :]
+ for t in range(len(dt)):
+ Fs = np.roll(Fsub, dt[t], axis=0)
+ dcs[t, :, j] = np.mean(np.mean(Fs * F0[isub, :, :], axis=0), axis=0)
+
+ # to find sub-integer shifts for each block ,
+ # we now use upsampling, based on kriging interpolation
+ dtup = np.linspace(-n, n, (2 * n * 10) + 1)
+ K = kernelD(
+ dt[np.newaxis, :], dtup[np.newaxis], 1
+ ) # this kernel is fixed as a variance of 1
+ dcs = cp.array(dcs)
+ # dcs = my_conv2_cpu(dcs, .5, [0,1,2])
+ for i in range(dcs.shape[0]):
+ dcs[i, :, :] = my_conv2_cpu(
+ dcs[i, :, :], 0.5, [0, 1]
+ ) # some additional smoothing for robustness, across all dimensions
+ for i in range(dcs.shape[2]):
+ dcs[:, :, i] = my_conv2_cpu(
+ dcs[:, :, i], 0.5, [0]
+ ) # some additional smoothing for robustness, across all dimensions
+ # dcs = my_conv2(cp.array(dcs), .5, [1, 2]) # some additional smoothing for robustness, across all dimensions
+ dcs = dcs.get()
+
+ # return K, dcs, dt, dtup
+ imin = np.zeros((Nbatches, nblocks))
+ for j in range(nblocks):
+ # using the upsampling kernel K, get the upsampled cross-correlation
+ # curves
+ dcup = np.matmul(K.T, dcs[:, :, j])
+
+ # find the max index of these curves
+ imax = np.argmax(dcup, axis=0)
+
+ # add the value of the shift to the last row of the matrix of shifts
+ # (as if it was the last iteration of the main rigid loop )
+ dall[niter - 1, :] = dtup[imax]
+
+ # the sum of all the shifts equals the final shifts for this block
+ imin[:, j] = np.sum(dall, axis=0)
+
+ return imin, yblk, F0
+
+
+if False:
+ imin, yblk, F0 = align_block2(F, ysamp, nblocks)
diff --git a/pykilosort/datashift/run_datashift_example_script.py b/pykilosort/datashift/run_datashift_example_script.py
new file mode 100644
index 0000000..f837ec6
--- /dev/null
+++ b/pykilosort/datashift/run_datashift_example_script.py
@@ -0,0 +1,259 @@
+# %load_ext autoreload
+# %autoreload 2
+
+# +
+import enum
+import logging
+import math
+import os
+import shutil
+from pathlib import Path
+from pprint import pprint
+
+
+import matplotlib.pyplot as plt
+import numpy as np
+import pykilosort as ks
+from phylib.io.traces import get_ephys_reader
+from pydantic import BaseModel
+from pykilosort import datashift, params
+from pykilosort.cluster import clusterSingleBatches
+from pykilosort.datashift.align_block import align_block2
+from pykilosort.learn import extractTemplatesfromSnippets, learnAndSolve8b
+from pykilosort.params import KilosortParams, Probe
+from pykilosort.preprocess import (
+ get_good_channels,
+ get_Nbatch,
+ get_whitening_matrix,
+ preprocess,
+)
+from pykilosort.postprocess import find_merges, rezToPhy, set_cutoff, splitAllClusters
+from pykilosort.utils import Bunch, Context, load_probe, memmap_large_array
+
+logger = logging.getLogger()
+logger.setLevel(logging.INFO)
+
+# +
+# Config
+
+rootZ = "/home/alexmorley/git_repos/Kilosort2/datashift"
+rootH = "/tmp/"
+pathToYourConfigFile = "/home/alexmorley/git_repos/Kilosort2/configFiles"
+chanMapFile = "NP2_kilosortChanMap.mat"
+
+opts = {
+ "chanMap": f"{rootZ}/{chanMapFile}",
+ "trange": [0.0, 20000.0 * 20],
+ "NchanTOT": float(384),
+ "minfr_goodchannels": 0.0,
+ "sig": 20,
+ "fshigh": 300,
+ "trackfinal": 0,
+ "datashift": {
+ "nblocks": 5,
+ "sig": 20,
+ "output_filename": "/tmp/datashifted.bin",
+ "overwrite": False,
+ },
+ "fbinary": f"{rootZ}/data_cropped.bin",
+ "fs": 30000.0,
+ "nPCs": 6.0,
+}
+
+probe = ks.params.Probe.load_from_npy(
+ rootZ=rootZ, NchanTOT=int(opts["NchanTOT"]), kcoords=np.ones(int(opts["NchanTOT"]))
+)
+
+# +
+# Run Function Args
+
+dat_path = opts["fbinary"]
+dir_path = Path(rootZ)
+output_dir = Path(rootZ + "/py")
+dtype = np.int16
+n_channels = int(opts["NchanTOT"])
+sample_rate = opts["fs"]
+clear_context = False # Operations.pykilosort_sorting in FORCE_RUN
+# -
+
+### Setup
+params = ks.params.KilosortParams(**opts, probe=probe, genericSpkTh=10.0)
+raw_data = get_ephys_reader(
+ get_ephys_reader(
+ dat_path, dtype=dtype, sample_rate=sample_rate, n_channels=params.probe.NchanTOT
+ )[: 20000 * 20, :],
+ sample_rate=sample_rate,
+)
+
+dir_path = dir_path or Path(dat_path).parent
+n_samples, n_channels = raw_data.shape
+logger.info("Loaded raw data with %d channels, %d samples.", n_channels, n_samples)
+
+### Create the context.
+ctx_path = dir_path / ".kilosort" / raw_data.name
+if clear_context:
+ logger.info(f"Clearing context at {ctx_path} ...")
+ shutil.rmtree(ctx_path, ignore_errors=True)
+ctx = Context(ctx_path)
+ctx.params = params
+ctx.probe = params.probe
+ctx.raw_data = raw_data
+ctx.load()
+ir = ctx.intermediate
+ir.Nbatch = Nbatch = get_Nbatch(raw_data, params)
+params.probe.Nchan = params.probe.NchanTOT
+params.Nfilt = params.nfilt_factor * params.probe.Nchan
+NrankPC = params.nPCs
+
+# +
+### Preprocess
+# -
+
+if not "proc" in ir:
+ ir.Wrot = get_whitening_matrix(raw_data=raw_data, probe=params.probe, params=params)
+ ctx.write(Wrot=ir.Wrot)
+
+ ir.proc_path = ctx.path("proc", ".dat")
+ preprocess(ctx)
+
+ ir.proc_path
+ ir.proc = np.memmap(ir.proc_path, dtype=raw_data.dtype, mode="r", order="F")
+
+
+# +
+ir.xc, ir.yc = params.probe.xc, params.probe.yc
+
+# The min and max of the y and x ranges of the channels
+ymin = min(ir.yc)
+ymax = max(ir.yc)
+xmin = min(ir.xc)
+xmax = max(ir.xc)
+
+# Determine the average vertical spacing between channels.
+# Usually all the vertical spacings are the same, i.e. on Neuropixels probes.
+dmin = np.median(np.diff(np.unique(ir.yc)))
+print(f"pitch is {dmin} um\n")
+yup = np.arange(
+ start=ymin, step=dmin / 2, stop=ymax + (dmin / 2)
+) # centers of the upsampled y positions
+
+# Determine the template spacings along the x dimension
+x_range = xmax - xmin
+npt = math.floor(
+ x_range / 16
+) # this would come out as 16um for Neuropixels probes, which aligns with the geometry.
+xup = np.linspace(xmin, xmax, npt + 1) # centers of the upsampled x positions
+
+
+# determine prototypical timecourses by clustering of simple threshold crossings.
+wTEMP, wPCA = extractTemplatesfromSnippets(
+ proc=ir.proc, probe=params.probe, params=params, Nbatch=Nbatch
+)
+# -
+
+# Extract all the spikes across the recording that are captured by the
+# generic templates. Very few real spikes are missed in this way.
+st3 = datashift.standalone_detector(
+ wTEMP, wPCA, NrankPC, yup, xup, Nbatch, ir.proc, params.probe, params
+)
+
+
+# +
+# binning width across Y (um)
+dd = 5
+
+# detected depths
+dep = st3[:, 1]
+
+# min and max for the range of depths
+dmin = ymin
+dep = dep - dmin
+
+dmax = int(1 + np.ceil(max(dep) / dd))
+Nbatches = Nbatch
+
+# which batch each spike is coming from
+batch_id = st3[:, 4] # ceil[st3[:,1]/dt]
+
+# preallocate matrix of counts with 20 bins, spaced logarithmically
+F = np.zeros((dmax, 20, Nbatches))
+for t in range(Nbatches):
+ # find spikes in this batch
+ ix = np.where(batch_id == t)[0]
+
+ # subtract offset
+ dep = st3[ix, 1] - dmin
+
+ # amplitude bin relative to the minimum possible value
+ amp = np.log10(np.clip(st3[ix, 2], None, 99)) - np.log10(params.genericSpkTh)
+ # normalization by maximum possible value
+ amp = amp / (np.log10(100) - np.log10(params.genericSpkTh))
+
+ # multiply by 20 to distribute a [0,1] variable into 20 bins
+ # sparse is very useful here to do this binning quickly
+ i, j, v, m, n = (
+ np.ceil(dep / dd).astype("int"),
+ np.ceil(1e-5 + amp * 20).astype("int"),
+ np.ones((len(ix), 1)),
+ dmax,
+ 20,
+ )
+ M = np.zeros((m, n))
+ M[i - 1, j - 1] += 1
+
+ # the counts themselves are taken on a logarithmic scale (some neurons
+ # fire too much!)
+ F[:, :, t] = np.log2(1 + M)
+
+ysamp = dmin + dd * np.arange(1, dmax) - dd / 2
+imin, yblk, F0 = align_block2(F, ysamp, params.nblocks)
+
+##
+st3_orig = st3
+
+st3 = st3[: np.where(st3[:, 1])[0][-1] + 1, :]
+if opts.get("fig", True):
+ ax = plt.subplot()
+ # plot the shift trace in um
+ ax.plot(imin * dd)
+ ax = plt.subplot()
+ # raster plot of all spikes at their original depths
+ st_shift = st3[:, 2] # + imin(batch_id)' * dd
+ for j in range(int(params.genericSpkTh), 100):
+ # for each amplitude bin, plot all the spikes of that size in the
+ # same shade of gray
+ ix = st3[:, 3] == j # the amplitudes are rounded to integers
+ ax.plot(
+ st3[ix, 1],
+ st_shift[ix],
+ marker=".",
+ color=[max(0, 1 - j / 40) for _ in range(3)],
+ ) # the marker color here has been carefully tuned
+ plt.tight_layout()
+ plt.show()
+
+# convert to um
+dshift = imin * dd
+
+# sort in case we still want to do "tracking"
+_, ir.iorig = np.sort(np.mean(dshift, axis=1))
+
+for ibatch in range(Nbatches):
+ # register the data batch by batch
+ shift_batch_on_disk2(
+ ibatch,
+ dshift[ibatch, :],
+ yblk,
+ sig,
+ Nbatches,
+ params,
+ ir.proc,
+ shifted_fname=params.datashift.output_filename,
+ overwrite=params.datashift.overwrite,
+ )
+logger.info(f"Shifted up/down {Nbatches} batches")
+
+# keep track of dshift
+ir.dshift = dshift
+# keep track of original spikes
+ir.st0 = st3
diff --git a/pykilosort/datashift/shift_batch_on_disk.py b/pykilosort/datashift/shift_batch_on_disk.py
new file mode 100644
index 0000000..fa50fdc
--- /dev/null
+++ b/pykilosort/datashift/shift_batch_on_disk.py
@@ -0,0 +1,120 @@
+from scipy.interpolate import Akima1DInterpolator, interp1d
+import numpy as np
+import matplotlib.pyplot as plt
+
+
+def extended(ysamp, n, diff=None):
+ if diff is None:
+ diff = ysamp[1] - ysamp[0]
+ pre = [ysamp[0] - i * diff for i in range(n, 0, -1)]
+ post = [ysamp[-1] + i * diff for i in range(1, n)]
+ return np.concatenate([pre, ysamp, post])
+
+
+def zero_pad(shifts_in, n):
+ pre = [0 for i in range(n, 0, -1)]
+ post = [0 for i in range(1, n)]
+ return np.concatenate([pre, shifts_in, post])
+
+
+def kernel2D(xp, yp, sig):
+ distx = np.abs(xp[:, 0] - yp[:, 0][np.newaxis, :].T)
+ disty = np.abs(xp[:, 1] - yp[:, 1][np.newaxis, :].T)
+
+ sigx = sig
+ sigy = 1.5 * sig
+
+ p = 1
+ K = np.exp(-((distx / sigx) ** p) - (disty / sigy) ** p)
+
+ return K
+
+
+def shift_batch_on_disk2(
+ ibatch,
+ shifts_in,
+ ysamp,
+ sig,
+ Nbatch,
+ params,
+ proc,
+ shifted_fname=None,
+ overwrite=False,
+ plot=False,
+):
+
+ # register one batch of a whitened binary file
+ NT = params.NT
+ Nchan = params.probe.Nchan
+
+ batchstart = range(
+ 0, params.NT * Nbatch, params.NT
+ ) # batches start at these timepoints
+ offset = Nchan * batchstart[ibatch]
+ offset_bytes = 2 * offset # binary file offset in bytes
+
+ # upsample the shift for each channel using interpolation
+ if len(ysamp) > 1:
+ # zero pad input so "extrapolation" tends to zero
+ # MATLAB uses a "modified Akima" which is proprietry :(
+ _ysamp = extended(ysamp, 2, 10000)
+ _shifts_in = zero_pad(shifts_in, 2)
+ interpolation_function = Akima1DInterpolator(_ysamp, _shifts_in)
+
+ # interpolation_function = interp1d(ysamp, shifts_in, kind='cubic', fill_value=([0],[0])) #'extrapolate')
+ shifts = interpolation_function(params.probe.yc, nu=0, extrapolate="True")
+
+ if plot:
+ plt.plot(shifts, color="b")
+ plt.plot(expected_shifts, color="r", alpha=0.5)
+
+ # load the batch
+ dat = proc.flat[offset : offset + params.NT * params.probe.Nchan].reshape(
+ (-1, params.probe.Nchan), order="F"
+ ) # / params.scaleproc
+ if False: # plot:
+ plt.figure(figsize=(10, 20))
+ plt.plot(dat[0:1000, :100] + 1000 * np.arange(100), color="b")
+ plt.plot(
+ expected_dat[0:1000, :100] + 1000 * np.arange(100), color="r", alpha=0.5
+ )
+
+ # 2D coordinates for interpolation
+ xp = np.vstack([params.probe.xc, params.probe.yc]).T
+
+ # 2D kernel of the original channel positions
+ Kxx = kernel2D(xp, xp, sig)
+
+ # 2D kernel of the new channel positions
+ yp = xp
+ yp[:, 1] = yp[:, 1] - shifts # * sig
+ Kyx = kernel2D(yp, xp, sig)
+
+ # kernel prediction matrix
+ M = np.linalg.solve((Kxx + 0.01 * np.eye(Kxx.shape[0])), Kyx)
+
+ # the multiplication has to be done on the GPU (but its not here)
+ # dati = gpuArray(single(dat)) * gpuArray(M).T
+ dati = dat @ M.T
+
+ dat_cpu = np.asfortranarray(dati.astype("int16"))
+
+ if shifted_fname is not None:
+ # if the user wants to have a registered version of the binary file
+ # this one is not split into batches
+ mode = "ab" if ibatch == 0 else "wb"
+ with open(shifted_fname, mode) as fid2:
+ ifirst = params.ntbuff
+ ilast = params.NT + 1
+ if ibatch == 0:
+ ifirst = 0
+ ilast = params.NT - params.ntbuff + 1
+ dat_cpu[ifirst:ilast, :].tofile(fid2)
+
+ if overwrite:
+ with open(ops.fproc, "wb") as fid:
+ fid.seek(offset_bytes)
+ # normally we want to write the aligned data back to the same file
+ dat_cpu.tofile(fid) # write this batch to binary file
+
+ return dat_cpu, dat, shifts
diff --git a/pykilosort/datashift/spikedetector3.py b/pykilosort/datashift/spikedetector3.py
new file mode 100644
index 0000000..13891c0
--- /dev/null
+++ b/pykilosort/datashift/spikedetector3.py
@@ -0,0 +1,76 @@
+def spikedetector3(Params, drez, wTEMP, iC):
+ code, constants = get_cuda("spikedetector3")
+
+ NT = int(Params[0])
+ Nchan = int(Params[1])
+ nt0 = int(Params[2])
+ Nnearest = int(Params[3])
+ Nrank = int(Params[4])
+ NchanUp = int(Params[7])
+
+ Nthreads = constants.Nthreads
+ NrankMax = constants.NrankMax
+ maxFR = constants.maxFR
+ nt0max = constants.nt0max
+ NchanMax = constants.NchanMax
+ nsizes = constants.nsizes
+
+ # tpB = (8, 2 * nt0 - 1)
+ # tpF = (16, Nnearest)
+ tpS = (nt0, 16)
+
+ d_Params = cp.asarray(Params, dtype=np.float64, order="F")
+ d_data = cp.asarray(data, dtype=np.float32, order="F")
+ d_W = cp.asarray(wTEMP, dtype=np.float32, order="F")
+ d_iC = cp.asarray(iC, dtype=np.int32, order="F")
+ d_dist = cp.asarray(dist, dtype=np.float32, order="F")
+ d_v2 = cp.asarray(v2, dtype=np.float32, order="F")
+ d_iC2 = cp.asarray(iC2, dtype=np.int32, order="F")
+ d_dist2 = cp.asarray(dist2, dtype=np.float32, order="F")
+
+ d_dout = cp.zeros((NT, Nchan), dtype=np.float32, order="F")
+ d_kkmax = cp.zeros((NT, Nchan), dtype=np.int32, order="F")
+
+ d_dfilt = cp.zeros((Nrank, NT, Nchan), dtype=np.float32, order="F")
+ d_dmax = cp.zeros((NT, NchanUp), dtype=np.float32, order="F")
+ d_st = cp.zeros(maxFR, dtype=np.int32, order="F")
+ d_cF = cp.zeros((Nnearest, maxFR), dtype=np.float32, order="F")
+ d_counter = cp.zeros(2, dtype=np.int32, order="F")
+
+ counter = np.zeros(2, dtype=np.int32, order="F")
+
+ # filter the data with the temporal templates
+ Conv1D = cp.RawKernel(code, "Conv1D")
+ Conv1D((Nchan,), (Nthreads,), (d_Params, d_data, d_W, d_dfilt))
+
+ # sum each template across channels, square, take max
+ sumChannels = cp.RawKernel(code, "sumChannels")
+ tpP = (int(NT / Nthreads), NchanUp)
+ sumChannels(
+ tpP, (Nthreads,), (d_Params, d_dfilt, d_dout, d_kkmax, d_iC, d_dist, d_v2)
+ )
+
+ # get the max of the data
+ max1D = cp.RawKernel(code, "max1D")
+ max1D((NchanUp,), (Nthreads,), (d_Params, d_dout, d_dmax))
+
+ # take max across nearby channels
+ maxChannels = cp.RawKernel(code, "maxChannels")
+ maxChannels(
+ tpP,
+ (Nthreads,),
+ (
+ d_Params,
+ d_dout,
+ d_dmax,
+ d_iC,
+ d_iC2,
+ d_dist2,
+ d_kkmax,
+ d_dfilt,
+ d_st,
+ d_counter,
+ d_cF,
+ ),
+ )
+ counter = d_counter[0]
diff --git a/pykilosort/datashift/standalonedetector.py b/pykilosort/datashift/standalonedetector.py
new file mode 100644
index 0000000..2aa66ac
--- /dev/null
+++ b/pykilosort/datashift/standalonedetector.py
@@ -0,0 +1,230 @@
+import cupy as cu
+import numpy as np
+from time import time
+from tqdm import tqdm
+
+from pykilosort import cluster
+from pykilosort.learn import extractTemplatesfromSnippets
+from pykilosort.utils import get_cuda
+
+from contextlib import contextmanager
+
+
+@contextmanager
+def timetracker(process_name: str):
+ start = time()
+ yield
+ secs = time() - start
+ # print(f"{process_name} took {secs} secs")
+
+
+def spikedetector3(Params, drez, wTEMP, iC, dist, v2, iC2, dist2):
+ code, constants = get_cuda("spikedetector3")
+
+ NT = int(Params[0])
+ Nchan = int(Params[1])
+ nt0 = int(Params[2])
+ Nnearest = int(Params[3])
+ Nrank = int(Params[4])
+ NchanUp = int(Params[7])
+
+ Nthreads = constants.Nthreads
+ NrankMax = constants.NrankMax
+ maxFR = constants.maxFR
+ nt0max = constants.nt0max
+ NchanMax = constants.NchanMax
+ nsizes = constants.nsizes
+
+ # tpB = (8, 2 * nt0 - 1)
+ # tpF = (16, Nnearest)
+ tpS = (nt0, 16)
+
+ with timetracker("allocate gpu"):
+ d_Params = cu.asarray(Params, dtype=np.float64, order="F")
+ d_data = cu.asarray(drez, dtype=np.float32, order="F")
+ d_W = cu.asarray(wTEMP, dtype=np.float32, order="F")
+ d_iC = cu.asarray(iC, dtype=np.int32, order="F")
+ d_dist = cu.asarray(dist, dtype=np.float32, order="F")
+ d_v2 = cu.asarray(v2, dtype=np.float32, order="F")
+ d_iC2 = cu.asarray(iC2, dtype=np.int32, order="F")
+ d_dist2 = cu.asarray(dist2, dtype=np.float32, order="F")
+
+ dimst = (NT, NchanUp)
+ d_dout = cu.zeros(dimst, dtype=np.float32, order="F")
+ d_kkmax = cu.zeros(dimst, dtype=np.int32, order="F")
+
+ d_dfilt = cu.zeros((Nrank, NT, Nchan), dtype=np.float32, order="F")
+ d_dmax = cu.zeros((NT, NchanUp), dtype=np.float32, order="F")
+ d_st = cu.zeros((maxFR * 5), dtype=np.int32, order="F")
+ d_cF = cu.zeros((maxFR * Nnearest,), dtype=np.float32, order="F")
+ d_counter = cu.zeros(2, dtype=np.int32, order="F")
+
+ with timetracker("filter gpu"):
+ # filter the data with the temporal templates
+ Conv1D = cu.RawKernel(code, "Conv1D", ("-G", "-lineinfo"))
+ Conv1D((Nchan,), (Nthreads,), (d_Params, d_data, d_W, d_dfilt))
+
+ with timetracker("sum gpu"):
+ # sum each template across channels, square, take max
+ sumChannels = cu.RawKernel(code, "sumChannels", ("-G", "-lineinfo"))
+ tpP = (int(NT / Nthreads), NchanUp)
+ sumChannels(
+ tpP, (Nthreads,), (d_Params, d_dfilt, d_dout, d_kkmax, d_iC, d_dist, d_v2)
+ )
+
+ with timetracker("max gpu"):
+ # get the max of the data
+ max1D = cu.RawKernel(code, "max1D", ("-G", "-lineinfo"))
+ max1D((NchanUp,), (Nthreads,), (d_Params, d_dout, d_dmax))
+
+ with timetracker("maxchannels gpu"):
+ # take max across nearby channels
+ tpP = (int(NT / Nthreads), NchanUp)
+ maxChannels = cu.RawKernel(code, "maxChannels", backend="nvcc")
+ maxChannels(
+ tpP,
+ (Nthreads,),
+ (
+ d_Params,
+ d_dout,
+ d_dmax,
+ d_iC,
+ d_iC2,
+ d_dist2,
+ d_kkmax,
+ d_dfilt,
+ d_st,
+ d_counter,
+ d_cF,
+ ),
+ )
+ counter = cu.asnumpy(d_counter)[0]
+
+ with timetracker("resize gpu"):
+ minSize = min(maxFR, counter)
+ d_sto = d_st[: 4 * minSize].reshape((4, minSize), order="F")
+ d_cF2 = d_cF[: Nnearest * minSize].reshape((Nnearest, minSize), order="F")
+
+ return d_dout.get(), d_kkmax.get(), d_sto.get(), d_cF2.get()
+
+
+# TODO: lets just make the data object "batch iterable" everywhere in the codebase
+def get_batch(params, ibatch, Nbatch, proc) -> cu.ndarray:
+ batchstart = np.arange(0, params.NT * Nbatch + 1, params.NT).astype(np.int64)
+
+ offset = params.probe.Nchan * batchstart[ibatch]
+ dat = proc.flat[offset : offset + params.NT * params.probe.Nchan].reshape(
+ (-1, params.probe.Nchan), order="F"
+ )
+
+ # move data to GPU and scale it back to unit variance
+ dataRAW = cu.asarray(dat, dtype=np.float32) / params.scaleproc
+ return dataRAW
+
+
+def standalone_detector(wTEMP, wPCA, NrankPC, yup, xup, Nbatch, proc, probe, params):
+ """
+ Detects spikes across the entire recording using generic templates.
+ Each generic template has rank one (in space-time).
+ In time, we use the 1D template prototypes found in wTEMP.
+ In space, we use Gaussian weights of several sizes, centered
+ on (x,y) positions that are part of a super-resolution grid covering the
+ entire probe (pre-specified in the calling function).
+ In total, there ~100x more generic templates than channels.
+ """
+
+ # minimum/base sigma for the Gaussian.
+ sig = 10
+
+ # grid of centers for the generic tempates
+ ycup, xcup = np.meshgrid(yup, xup)
+
+ # Get nearest channels for every template center.
+ # Template products will only be computed on these channels.
+ NchanNear = 10
+ iC, dist = cluster.getClosestChannels2(ycup, xcup, probe.yc, probe.xc, NchanNear)
+
+ # Templates with centers that are far from an active site are discarded
+ dNearActiveSite = 30
+ igood = dist[0, :] < dNearActiveSite
+ iC = iC[:, igood]
+ dist = dist[:, igood]
+ ycup = cu.array(ycup).T.ravel()[igood]
+ xcup = cu.array(xcup).T.ravel()[igood]
+
+ # number of nearby templates to compare for local template maximum
+ NchanNearUp = 10 * NchanNear
+ iC2, dist2 = cluster.getClosestChannels2(ycup, xcup, ycup, xcup, NchanNearUp)
+
+ # pregenerate the Gaussian weights used for spatial components
+ nsizes = 5
+ v2 = cu.zeros((5, dist.shape[1]), dtype=np.float32)
+ for k in range(0, nsizes):
+ v2[k, :] = np.sum(np.exp(-2 * (dist ** 2) / (sig * (k + 1)) ** 2), 0)
+
+ # build up Params
+ NchanUp = iC.shape[1]
+ Params = (
+ params.NT,
+ params.probe.Nchan,
+ params.nt0,
+ NchanNear,
+ NrankPC,
+ params.nt0min,
+ params.genericSpkTh,
+ NchanUp,
+ NchanNearUp,
+ sig,
+ )
+
+ # preallocate the results
+ st3 = np.zeros((1000000, 5))
+ st3[:, 4] = -1 # batch_id can be zero
+ t0 = 0
+ nsp = 0 # counter for total number of spikes
+
+ toc = time()
+ for k in tqdm(range(0, Nbatch), desc="Detecting Spikes"):
+ # get a batch of whitened and filtered data
+ dataRAW = get_batch(params, k, Nbatch, proc)
+
+ # run the CUDA function on this batch
+ with timetracker("CUDA"):
+ dat, kkmax, st, cF = spikedetector3(
+ Params, dataRAW, wTEMP, iC, dist, v2, iC2, dist2
+ )
+
+ # upsample the y position using the center of mass of template products
+ # coming out of the CUDA function.
+ ys = probe.yc[cu.asnumpy(iC)]
+ cF0 = np.maximum(cF, 0)
+ cF0 = cF0 / np.sum(cF0, 0)
+ iChan = st[1, :]
+ yct = np.sum(cF0 * ys[:, iChan], 0)
+
+ # build st for the current batch
+ st[1, :] = yct
+
+ # the first batch is special (no pre-buffer)
+ ioffset = params.ntbuff if k > 0 else 0
+
+ toff = params.nt0min + t0 - ioffset + (params.NT - params.ntbuff) * k
+ st[0, :] = st[0, :] + toff
+ # these offsets ensure the times are computed correctly
+
+ # st[4, :] = k # add batch number
+ st = np.concatenate([st, np.full((1, st.shape[1]), k)])
+
+ nsp0 = st.shape[1]
+ if nsp0 + nsp > st3.shape[0]:
+ # extend array
+ # st3[nsp + 1e6, 1] = 0 # if we need to preallocate more space
+ raise NotImplementedError("Extra pre-allocation not implemented")
+
+ st3[nsp : nsp0 + nsp, :] = st.T
+ nsp = nsp + nsp0
+
+ if k % 100 == 0 | k == (Nbatch - 1):
+ print(f"{time() - toc} sec, {k+1} batches, {nsp} spikes")
+ toc = time()
+ return st3
diff --git a/pykilosort/params.py b/pykilosort/params.py
index 630a716..0f98b93 100644
--- a/pykilosort/params.py
+++ b/pykilosort/params.py
@@ -1,64 +1,166 @@
import typing as t
from math import ceil
-from pydantic import BaseModel, Field
+import numpy as np
+from pydantic import BaseModel, Field, validator
from .utils import Bunch
+
# TODO: design - Let's move all of this to a yaml file with sections so that its easier to read.
# - We can then just parse the yaml file to generate this.
+class Probe(BaseModel):
+ NchanTOT: int
+ Nchan: t.Optional[int] = Field(
+ None, description="Nchan < NchanTOT if some channels should not be used."
+ )
+
+ chanMap: np.ndarray # TODO: add constraints
+ kcoords: np.ndarray # TODO: add constraints
+ xc: np.ndarray
+ yc: np.ndarray
+
+ @validator("yc")
+ def coords_same_length(cls, v, values):
+ assert len(values["xc"]) == len(v)
+ return v
+
+ class Config:
+ arbitrary_types_allowed = True
+
+ @classmethod
+ def load_from_npy(cls, rootZ, **kwargs):
+ return cls(
+ chanMap=np.load(f"{rootZ}/channel_map.npy").flatten().astype(int),
+ xc=np.load(rootZ + "/channel_positions.npy")[:, 0],
+ yc=np.load(rootZ + "/channel_positions.npy")[:, 1],
+ **kwargs,
+ )
+
+
+class DatashiftParams(BaseModel):
+ sig: float = Field(20.0, description="sigma for the Gaussian process smoothing")
+ nblocks: int = Field(
+ 5, description="blocks for registration. 1 does rigid registration."
+ )
+ output_filename: t.Optional[str] = Field(
+ None, description="optionally save registered data to a new binary file"
+ )
+ overwrite: bool = Field(True, description="overwrite proc file with shifted data")
+
+ @validator("nblocks")
+ def validate_nblocks(v):
+ if v < 1:
+ raise ValueError(
+ "datashift.nblocks must be >= 1, or datashift should be None"
+ )
+ return v
+
+
class KilosortParams(BaseModel):
- fs: float = Field(30000., description="sample rate")
+ fs: float = Field(30000.0, description="sample rate")
- fshigh: float = Field(150., description="high pass filter frequency")
- fslow: t.Optional[float] = Field(None, description="low pass filter frequency")
- minfr_goodchannels: float = Field(0.1, description="minimum firing rate on a 'good' channel (0 to skip)")
+ probe: Probe = Field(..., description="recording probe metadata")
- Th: t.List[float] = Field([10, 4], description="""
+ fshigh: float = Field(150.0, description="high pass filter frequency")
+ fslow: t.Optional[float] = Field(None, description="low pass filter frequency")
+ minfr_goodchannels: float = Field(
+ 0.1, description="minimum firing rate on a 'good' channel (0 to skip)"
+ )
+
+ genericSpkTh: float = Field(
+ 10.0, description="threshold for crossings with generic templates"
+ )
+ nblocks: int = Field(
+ 1,
+ description="number of blocks used to segment the probe when tracking drift, 0 == don't track, 1 == rigid, > 1 == non-rigid",
+ )
+
+ datashift: t.Optional[DatashiftParams] = Field(
+ None, description="parameters for 'datashift' drift correction. not required"
+ )
+
+ Th: t.List[float] = Field(
+ [10, 4],
+ description="""
threshold on projections (like in Kilosort1, can be different for last pass like [10 4])
- """)
- ThPre: float = Field(8, description="threshold crossings for pre-clustering (in PCA projection space)")
-
- lam: float = Field(10, description="""
+ """,
+ )
+ ThPre: float = Field(
+ 8,
+ description="threshold crossings for pre-clustering (in PCA projection space)",
+ )
+
+ lam: float = Field(
+ 10,
+ description="""
how important is the amplitude penalty (like in Kilosort1, 0 means not used,
10 is average, 50 is a lot)
- """)
+ """,
+ )
- AUCsplit: float = Field(0.9, description="""
+ AUCsplit: float = Field(
+ 0.9,
+ description="""
splitting a cluster at the end requires at least this much isolation for each sub-cluster (max=1)
- """)
+ """,
+ )
- minFR: float = Field(1. / 50, description="""
+ minFR: float = Field(
+ 1.0 / 50,
+ description="""
minimum spike rate (Hz), if a cluster falls below this for too long it gets removed
- """)
+ """,
+ )
- momentum: t.List[float] = Field([20, 400], description="""
+ momentum: t.List[float] = Field(
+ [20, 400],
+ description="""
number of samples to average over (annealed from first to second value)
- """)
+ """,
+ )
- sigmaMask: float = Field(30, description="""
+ sigmaMask: float = Field(
+ 30,
+ description="""
spatial constant in um for computing residual variance of spike
- """)
+ """,
+ )
# danger, changing these settings can lead to fatal errors
# options for determining PCs
spkTh: float = Field(-6, description="spike threshold in standard deviations")
- reorder: int = Field(1, description="whether to reorder batches for drift correction.")
- nskip: int = Field(5, description="how many batches to skip for determining spike PCs")
- nSkipCov: int = Field(25, description="compute whitening matrix from every nth batch")
+ reorder: int = Field(
+ 1, description="whether to reorder batches for drift correction."
+ )
+ nskip: int = Field(
+ 5, description="how many batches to skip for determining spike PCs"
+ )
+ nSkipCov: int = Field(
+ 25, description="compute whitening matrix from every nth batch"
+ )
# GPU = 1 # has to be 1, no CPU version yet, sorry
# Nfilt = 1024 # max number of clusters
- nfilt_factor: int = Field(4, description="max number of clusters per good channel (even temporary ones)")
- ntbuff = Field(64, description="""
+ nfilt_factor: int = Field(
+ 4, description="max number of clusters per good channel (even temporary ones)"
+ )
+ ntbuff = Field(
+ 64,
+ description="""
samples of symmetrical buffer for whitening and spike detection
Must be multiple of 32 + ntbuff. This is the batch size (try decreasing if out of memory).
- """)
-
- whiteningRange: int = Field(32, description="number of channels to use for whitening each channel")
- nSkipCov: int = Field(25, description="compute whitening matrix from every N-th batch")
+ """,
+ )
+
+ whiteningRange: int = Field(
+ 32, description="number of channels to use for whitening each channel"
+ )
+ nSkipCov: int = Field(
+ 25, description="compute whitening matrix from every N-th batch"
+ )
scaleproc: int = Field(200, description="int16 scaling of whitened data")
nPCs: int = Field(3, description="how many PCs to project the spikes into")
@@ -72,7 +174,9 @@ class KilosortParams(BaseModel):
loc_range: t.List[int] = [5, 4]
long_range: t.List[int] = [30, 6]
- Nfilt: t.Optional[int] = None # This should be a computed property once we add the probe to the config
+ Nfilt: t.Optional[
+ int
+ ] = None # This should be a computed property once we add the probe to the config
# Computed properties
@property
diff --git a/pykilosort/postprocess.py b/pykilosort/postprocess.py
index e91935c..5ca57b0 100644
--- a/pykilosort/postprocess.py
+++ b/pykilosort/postprocess.py
@@ -33,7 +33,7 @@ def my_conv2(x, sig, varargin=None, **kwargs):
# sig is either a scalar or a sequence of scalars, one for each axis to be filtered
# varargin can be the dimensions to do filtering, if len(sig) != x.shape
# if sig is scalar and no axes are provided, the default axis is 2
- if sig <= .25:
+ if sig <= 0.25:
return x
idims = 1
if varargin is not None:
@@ -48,15 +48,15 @@ def my_conv2(x, sig, varargin=None, **kwargs):
Nd = x.ndim
x = cp.transpose(x, [idim] + list(range(0, idim)) + list(range(idim + 1, Nd)))
dsnew = x.shape
- x = cp.reshape(x, (x.shape[0], -1), order='F')
+ x = cp.reshape(x, (x.shape[0], -1), order="F")
tmax = ceil(4 * sig)
dt = cp.arange(-tmax, tmax + 1)
- gaus = cp.exp(-dt ** 2 / (2 * sig ** 2))
+ gaus = cp.exp(-(dt ** 2) / (2 * sig ** 2))
gaus = gaus / cp.sum(gaus)
y = convolve_gpu(x, gaus, **kwargs)
- y = y.reshape(dsnew, order='F')
+ y = y.reshape(dsnew, order="F")
y = cp.transpose(y, list(range(1, idim + 1)) + [0] + list(range(idim + 1, Nd)))
return y
@@ -67,7 +67,7 @@ def my_conv2_cpu(x, sig, varargin=None, **kwargs):
# sig is either a scalar or a sequence of scalars, one for each axis to be filtered
# varargin can be the dimensions to do filtering, if len(sig) != x.shape
# if sig is scalar and no axes are provided, the default axis is 2
- if sig <= .25:
+ if sig <= 0.25:
return x
idims = 1
if varargin is not None:
@@ -82,23 +82,29 @@ def my_conv2_cpu(x, sig, varargin=None, **kwargs):
Nd = x.ndim
x = cp.transpose(x, [idim] + list(range(0, idim)) + list(range(idim + 1, Nd)))
dsnew = x.shape
- x = cp.reshape(x, (x.shape[0], -1), order='F')
+ x = cp.reshape(x, (x.shape[0], -1), order="F")
tmax = ceil(4 * sig)
dt = np.arange(-tmax, tmax + 1)
- gaus = np.exp(-dt ** 2 / (2 * sig ** 2))
+ gaus = np.exp(-(dt ** 2) / (2 * sig ** 2))
gaus = gaus / np.sum(gaus)
- cNorm = lfilter(gaus, np.array([1.]), np.concatenate((np.ones(dsnew[0]), np.zeros(tmax))))
+ cNorm = lfilter(
+ gaus, np.array([1.0]), np.concatenate((np.ones(dsnew[0]), np.zeros(tmax)))
+ )
cNorm = cNorm[tmax:]
x_n = cp.asnumpy(x)
- x_n = lfilter(gaus, np.array([1.]), np.concatenate((x_n, np.zeros((tmax, dsnew[1])))),
- axis=0)
+ x_n = lfilter(
+ gaus,
+ np.array([1.0]),
+ np.concatenate((x_n, np.zeros((tmax, x_n.shape[1])))),
+ axis=0,
+ )
x_n = x_n[tmax:]
x_n = np.reshape(x_n, dsnew)
- x_n = x_n / cNorm.reshape(-1, 1)
+ # x_n = x_n / cNorm.reshape(-1, 1, 1)
x = cp.array(x_n)
x = cp.transpose(x, list(range(1, idim + 1)) + [0] + list(range(idim + 1, Nd)))
@@ -151,13 +157,17 @@ def ccg_slow(st1, st2, nbins, tbin):
for k in range(ilow, ihigh):
# for all spikes within plus/minus dt range
- ibin = cp.rint((st2[j] - st1[k]) / tbin).astype(int) # convert ISI to integer
+ ibin = cp.rint((st2[j] - st1[k]) / tbin).astype(
+ int
+ ) # convert ISI to integer
K[ibin + nbins] += 1
j += 1
- irange1 = cp.concatenate((cp.arange(1, nbins // 2), cp.arange(3 * nbins // 2, 2 * nbins)))
+ irange1 = cp.concatenate(
+ (cp.arange(1, nbins // 2), cp.arange(3 * nbins // 2, 2 * nbins))
+ )
irange2 = cp.arange(nbins - 50, nbins - 10)
irange3 = cp.arange(nbins + 11, nbins + 50)
@@ -183,7 +193,9 @@ def ccg_slow(st1, st2, nbins, tbin):
Ri = cp.zeros(10)
for i in range(1, 11):
- irange = cp.arange(nbins - i, nbins + i + 1) # for this central range of the CCG
+ irange = cp.arange(
+ nbins - i, nbins + i + 1
+ ) # for this central range of the CCG
# compute the normalised ratio as above. this should be 1 if there is no refractoriness
Qi0 = cp.sum(K[irange]) / (2 * i * tbin * N1 * N2 / T)
Qi[i - 1] = Qi0 # save the normalised probability
@@ -221,7 +233,9 @@ def _ccg(st1, st2, nbins, tbin):
dt = nbins * tbin
# Avoid divide by zero error.
- T = max(1e-10, np.max(np.concatenate((st1, st2))) - np.min(np.concatenate((st1, st2))))
+ T = max(
+ 1e-10, np.max(np.concatenate((st1, st2))) - np.min(np.concatenate((st1, st2)))
+ )
N1 = max(1, len(st1))
N2 = max(1, len(st2))
@@ -263,7 +277,9 @@ def _ccg(st1, st2, nbins, tbin):
j += 1
- irange1 = np.concatenate((np.arange(1, nbins // 2), np.arange(3 * nbins // 2, 2 * nbins)))
+ irange1 = np.concatenate(
+ (np.arange(1, nbins // 2), np.arange(3 * nbins // 2, 2 * nbins))
+ )
irange2 = np.arange(nbins - 50, nbins - 10)
irange3 = np.arange(nbins + 11, nbins + 50)
@@ -289,7 +305,9 @@ def _ccg(st1, st2, nbins, tbin):
Ri = np.zeros(10)
for i in range(1, 11):
- irange = np.arange(nbins - i, nbins + i + 1) # for this central range of the CCG
+ irange = np.arange(
+ nbins - i, nbins + i + 1
+ ) # for this central range of the CCG
# compute the normalised ratio as above. this should be 1 if there is no refractoriness
Qi0 = np.sum(K[irange]) / (2 * i * tbin * N1 * N2 / T)
Qi[i - 1] = Qi0 # save the normalised probability
@@ -341,7 +359,11 @@ def clusterAverage(clu, spikeQuantity):
_, cluInds, spikeCounts = cp.unique(clu, return_inverse=True, return_counts=True)
# summation
- q = cpx.scipy.sparse.coo_matrix((spikeQuantity, (cluInds, cp.zeros(len(clu))))).toarray().flatten()
+ q = (
+ cpx.scipy.sparse.coo_matrix((spikeQuantity, (cluInds, cp.zeros(len(clu)))))
+ .toarray()
+ .flatten()
+ )
# had sums so dividing by spike counts gives the mean depth of each cluster
clusterQuantity = q / spikeCounts
@@ -357,7 +379,7 @@ def find_merges(ctx, flag):
ir = ctx.intermediate
# TODO: move_to_config
- dt = 1. / 1000 # step size for CCG binning
+ dt = 1.0 / 1000 # step size for CCG binning
nbins = 500 # number of bins used for cross-correlograms
# (DEV_NOTES) nbins is not a variable in Marius' code, I include it here to avoid
@@ -377,34 +399,34 @@ def find_merges(ctx, flag):
# we traverse the set of neurons in ascending order of firing rates
isort = cp.argsort(nspk)
- logger.debug('Initialized spike counts.')
+ logger.debug("Initialized spike counts.")
if not flag:
# if the flag is off, then no merges are performed
# this function is then just used to compute cross- and auto- correlograms
- R_CCG = cp.inf * ones(Nk, order='F')
- Q_CCG = cp.inf * ones(Nk, order='F')
- K_CCG = cp.zeros((*Xsim.shape, 2 * nbins + 1), order='F')
+ R_CCG = cp.inf * ones(Nk, order="F")
+ Q_CCG = cp.inf * ones(Nk, order="F")
+ K_CCG = cp.zeros((*Xsim.shape, 2 * nbins + 1), order="F")
else:
K_CCG = None
R_CCG = None
Q_CCG = None
- for j in tqdm(range(Nk), desc='Finding merges'):
+ for j in tqdm(range(Nk), desc="Finding merges"):
# find all spikes from this cluster
s1 = st3[:, 0][st3[:, 1] == isort[j]] / params.fs
if s1.size != nspk[isort[j]]:
# this is a check to make sure new clusters are combined correctly into bigger clusters
# TODO: unclear - don't we want to bail in that case?
- logger.warn('Lost track of spike counts.')
+ logger.warn("Lost track of spike counts.")
# sort all the pairs of this neuron, discarding any that have fewer spikes
uu = Xsim[isort[j], :] * (nspk > s1.size)
ix = cp.argsort(uu)[::-1]
ccsort = uu[ix]
- ienu = int(np.nonzero(ccsort < .5)[0][0])
+ ienu = int(np.nonzero(ccsort < 0.5)[0][0])
# ccsort = -cp.sort(-Xsim[isort[j]] * (nspk > len(s1))) # sort in descending order
# ix = cp.argsort(-Xsim[isort[j]] * (nspk > len(s1)))
@@ -436,8 +458,10 @@ def find_merges(ctx, flag):
# now merge j into i and move on
# simply overwrite all the spikes of neuron j with i (i>j by construction)
st3[:, 1][st3[:, 1] == isort[j]] = i
- nspk[i] = nspk[i] + nspk[isort[j]] # update number of spikes for cluster i
- logger.debug(f'Merged {isort[j]} into {i}')
+ nspk[i] = (
+ nspk[i] + nspk[isort[j]]
+ ) # update number of spikes for cluster i
+ logger.debug(f"Merged {isort[j]} into {i}")
# TODO: unclear - the comment below looks important :)
# YOU REALLY SHOULD MAKE SURE THE PC CHANNELS MATCH HERE
# break % if a pair is found, we don't need to keep going
@@ -455,12 +479,7 @@ def find_merges(ctx, flag):
R_CCG = cp.minimum(R_CCG, R_CCG.T) # symmetrize the scores
Q_CCG = cp.minimum(Q_CCG, Q_CCG.T)
- return Bunch(
- st3_m=st3,
- K_CCG=K_CCG,
- R_CCG=R_CCG,
- Q_CCG=Q_CCG,
- )
+ return Bunch(st3_m=st3, K_CCG=K_CCG, R_CCG=R_CCG, Q_CCG=Q_CCG,)
def splitAllClusters(ctx, flag):
@@ -476,7 +495,9 @@ def splitAllClusters(ctx, flag):
ir = ctx.intermediate
Nchan = ctx.probe.Nchan
- wPCA = cp.asarray(ir.wPCA) # use PCA projections to reconstruct templates when we do splits
+ wPCA = cp.asarray(
+ ir.wPCA
+ ) # use PCA projections to reconstruct templates when we do splits
assert wPCA.shape[1] == 3
# Take intermediate arrays from context.
@@ -487,10 +508,10 @@ def splitAllClusters(ctx, flag):
# For the following arrays that will be overwritten by this function, try to get
# it from a previous call to this function (as it is called twice), otherwise
# get it from before (without the _s suffix).
- W = ir.get('W_s', ir.W)
- simScore = ir.get('simScore_s', ir.simScore)
- iNeigh = ir.get('iNeigh_s', ir.iNeigh)
- iNeighPC = ir.get('iNeighPC_s', ir.iNeighPC)
+ W = ir.get("W_s", ir.W)
+ simScore = ir.get("simScore_s", ir.simScore)
+ iNeigh = ir.get("iNeigh_s", ir.iNeigh)
+ iNeighPC = ir.get("iNeighPC_s", ir.iNeighPC)
# this is the threshold for splits, and is one of the main parameters users can change
ccsplit = params.AUCsplit
@@ -514,16 +535,18 @@ def splitAllClusters(ctx, flag):
# keep track of original cluster for each cluster. starts with all clusters being their
# own origin.
isplit = np.arange(Nfilt)
- dt = 1. / 1000
+ dt = 1.0 / 1000
nccg = 0
while ik < Nfilt:
if ik % 100 == 0:
# periodically write updates
- logger.info(f'Found {nsplits} splits, checked {ik}/{Nfilt} clusters, nccg {nccg}')
+ logger.info(
+ f"Found {nsplits} splits, checked {ik}/{Nfilt} clusters, nccg {nccg}"
+ )
ik += 1
- isp = (st3[:, 1] == ik) # get all spikes from this cluster
+ isp = st3[:, 1] == ik # get all spikes from this cluster
nSpikes = isp.sum()
logger.debug(f"Splitting template {ik}/{Nfilt} with {nSpikes} spikes.")
free_gpu_memory()
@@ -538,8 +561,8 @@ def splitAllClusters(ctx, flag):
clp0 = cProjPC[isp, :, :] # get the PC projections for these spikes
clp0 = cp.asarray(clp0, dtype=cp.float32) # upload to the GPU
- clp0 = clp0.reshape((clp0.shape[0], -1), order='F')
- clp = clp0 - mean(clp0, axis=0) # mean center them
+ clp0 = clp0.reshape((clp0.shape[0], -1), order="F")
+ clp = clp0 - mean(clp0, axis=0) # mean center them
isp = np.nonzero(isp)[0]
@@ -554,7 +577,9 @@ def splitAllClusters(ctx, flag):
# u, v = -u, -v # change sign for consistency with MATLAB
w = u[:, 0] # initialize with the top PC
else:
- w = mean(clp0, axis=0) # initialize with the mean of NOT drift-corrected trace
+ w = mean(
+ clp0, axis=0
+ ) # initialize with the mean of NOT drift-corrected trace
w = w / cp.sum(w ** 2) ** 0.5 # unit-normalize
# initial projections of waveform PCs onto 1D vector
@@ -569,7 +594,7 @@ def splitAllClusters(ctx, flag):
# initialize matrix of log probabilities that each spike is assigned to the first
# or second cluster
- logp = cp.zeros((nSpikes, 2), order='F')
+ logp = cp.zeros((nSpikes, 2), order="F")
# do 50 pursuit iteration
@@ -578,17 +603,25 @@ def splitAllClusters(ctx, flag):
# TODO: move_to_config - maybe...
for k in range(50):
# for each spike, estimate its probability to come from either Gaussian cluster
- logp[:, 0] = -1. / 2 * log(s1) - ((x - mu1) ** 2) / (2 * s1) + log(p)
- logp[:, 1] = -1. / 2 * log(s2) - ((x - mu2) ** 2) / (2 * s2) + log(1 - p)
+ logp[:, 0] = -1.0 / 2 * log(s1) - ((x - mu1) ** 2) / (2 * s1) + log(p)
+ logp[:, 1] = -1.0 / 2 * log(s2) - ((x - mu2) ** 2) / (2 * s2) + log(1 - p)
lMax = logp.max(axis=1)
- logp = logp - lMax[:, cp.newaxis] # subtract the max for floating point accuracy
+ logp = (
+ logp - lMax[:, cp.newaxis]
+ ) # subtract the max for floating point accuracy
rs = cp.exp(logp) # exponentiate the probabilities
- pval = cp.log(cp.sum(rs, axis=1)) + lMax # get the normalizer and add back the max
- logP[k] = mean(pval) # this is the cost function: we can monitor its increase
+ pval = (
+ cp.log(cp.sum(rs, axis=1)) + lMax
+ ) # get the normalizer and add back the max
+ logP[k] = mean(
+ pval
+ ) # this is the cost function: we can monitor its increase
- rs = rs / cp.sum(rs, axis=1)[:, cp.newaxis] # normalize so that probabilities sum to 1
+ rs = (
+ rs / cp.sum(rs, axis=1)[:, cp.newaxis]
+ ) # normalize so that probabilities sum to 1
p = mean(rs[:, 0]) # mean probability to be assigned to Gaussian 1
# new estimate of mean of cluster 1 (weighted by "responsibilities")
@@ -596,7 +629,9 @@ def splitAllClusters(ctx, flag):
# new estimate of mean of cluster 2 (weighted by "responsibilities")
mu2 = cp.dot(rs[:, 1], x) / cp.sum(rs[:, 1])
- s1 = cp.dot(rs[:, 0], (x - mu1) ** 2) / cp.sum(rs[:, 0]) # new estimates of variances
+ s1 = cp.dot(rs[:, 0], (x - mu1) ** 2) / cp.sum(
+ rs[:, 0]
+ ) # new estimates of variances
s2 = cp.dot(rs[:, 1], (x - mu2) ** 2) / cp.sum(rs[:, 1])
if (k >= 10) and (k % 2 == 0):
@@ -604,9 +639,15 @@ def splitAllClusters(ctx, flag):
# that is, given the Gaussian cluster assignments, and the mean and variances,
# we re-estimate w
# these equations follow from the model
- StS = cp.matmul(
- clp.T, clp * (rs[:, 0] / s1 + rs[:, 1] / s2)[:, cp.newaxis]) / nSpikes
- StMu = cp.dot(clp.T, rs[:, 0] * mu1 / s1 + rs[:, 1] * mu2 / s2) / nSpikes
+ StS = (
+ cp.matmul(
+ clp.T, clp * (rs[:, 0] / s1 + rs[:, 1] / s2)[:, cp.newaxis]
+ )
+ / nSpikes
+ )
+ StMu = (
+ cp.dot(clp.T, rs[:, 0] * mu1 / s1 + rs[:, 1] * mu2 / s2) / nSpikes
+ )
# this is the new estimate of the best pursuit direction
w = cp.linalg.solve(StS.T, StMu)
@@ -638,10 +679,10 @@ def splitAllClusters(ctx, flag):
# now decide if the split would result in waveforms that are too similar
# the reconstructed mean waveforms for putative cluster 1
# c1 = cp.matmul(wPCA, cp.reshape((mean(clp0[ilow, :], 0), 3, -1), order='F'))
- c1 = cp.matmul(wPCA, mean(clp0[ilow, :], 0).reshape((3, -1), order='F'))
+ c1 = cp.matmul(wPCA, mean(clp0[ilow, :], 0).reshape((3, -1), order="F"))
# the reconstructed mean waveforms for putative cluster 2
# c2 = cp.matmul(wPCA, cp.reshape((mean(clp0[~ilow, :], 0), 3, -1), order='F'))
- c2 = cp.matmul(wPCA, mean(clp0[~ilow, :], 0).reshape((3, -1), order='F'))
+ c2 = cp.matmul(wPCA, mean(clp0[~ilow, :], 0).reshape((3, -1), order="F"))
cc = cp.corrcoef(c1.ravel(), c2.ravel()) # correlation of mean waveforms
n1 = sqrt(cp.sum(c1 ** 2)) # the amplitude estimate 1
@@ -651,16 +692,19 @@ def splitAllClusters(ctx, flag):
# if the templates are correlated, and their amplitudes are similar, stop the split!!!
- # TODO: move_to_config
+ # TODO: move_to_config
if (cc[0, 1] > 0.9) and (r0 < 0.2):
continue
# finaly criteria to continue with the split: if the split piece is more than 5% of all
# spikes, if the split piece is more than 300 spikes, and if the confidences for
# assigning spikes to # both clusters exceeds a preset criterion ccsplit
- # TODO: move_to_config
- if (nremove > 0.05) and (min(plow, phigh) > ccsplit) and (
- min(cp.sum(ilow), cp.sum(~ilow)) > 300):
+ # TODO: move_to_config
+ if (
+ (nremove > 0.05)
+ and (min(plow, phigh) > ccsplit)
+ and (min(cp.sum(ilow), cp.sum(~ilow)) > 300)
+ ):
# one cluster stays, one goes
Nfilt += 1
@@ -669,34 +713,40 @@ def splitAllClusters(ctx, flag):
# (DEV_NOTES) code below involves multiple CuPy arrays changing shape to accomodate
# the extra cluster, this could potentially be done more efficiently?
- dWU = cp.concatenate((
- cp.asarray(dWU), cp.zeros((*dWU.shape[:-1], 1), order='F')), axis=2)
+ dWU = cp.concatenate(
+ (cp.asarray(dWU), cp.zeros((*dWU.shape[:-1], 1), order="F")), axis=2
+ )
dWU[:, iC[:, iW[ik]], Nfilt - 1] = c2
dWU[:, iC[:, iW[ik]], ik] = c1
# the temporal components are therefore just the PC waveforms
W = cp.asarray(W)
- W = cp.concatenate((W, cp.transpose(cp.atleast_3d(wPCA), (0, 2, 1))), axis=1)
+ W = cp.concatenate(
+ (W, cp.transpose(cp.atleast_3d(wPCA), (0, 2, 1))), axis=1
+ )
assert W.shape[1] == Nfilt
# copy the best channel from the original template
iW = cp.asarray(iW)
- iW = cp.pad(iW, (0, (Nfilt - len(iW))), mode='constant')
+ iW = cp.pad(iW, (0, (Nfilt - len(iW))), mode="constant")
iW[Nfilt - 1] = iW[ik]
assert iW.shape[0] == Nfilt
# copy the provenance index to keep track of splits
isplit = cp.asarray(isplit)
- isplit = cp.pad(isplit, (0, (Nfilt - len(isplit))), mode='constant')
+ isplit = cp.pad(isplit, (0, (Nfilt - len(isplit))), mode="constant")
isplit[Nfilt - 1] = isplit[ik]
assert isplit.shape[0] == Nfilt
- st3[isp[ilow_cpu], 1] = Nfilt - 1 # overwrite spike indices with the new index
+ st3[isp[ilow_cpu], 1] = (
+ Nfilt - 1
+ ) # overwrite spike indices with the new index
# copy similarity scores from the original
simScore = cp.asarray(simScore)
simScore = cp.pad(
- simScore, (0, (Nfilt - simScore.shape[0])), mode='constant')
+ simScore, (0, (Nfilt - simScore.shape[0])), mode="constant"
+ )
simScore[:, Nfilt - 1] = simScore[:, ik]
simScore[Nfilt - 1, :] = simScore[ik, :]
# copy similarity scores from the original
@@ -707,14 +757,16 @@ def splitAllClusters(ctx, flag):
# copy neighbor template list from the original
iNeigh = cp.asarray(iNeigh)
iNeigh = cp.pad(
- iNeigh, ((0, 0), (0, (Nfilt - iNeigh.shape[1]))), mode='constant')
+ iNeigh, ((0, 0), (0, (Nfilt - iNeigh.shape[1]))), mode="constant"
+ )
iNeigh[:, Nfilt - 1] = iNeigh[:, ik]
assert iNeigh.shape[1] == Nfilt
# copy neighbor channel list from the original
iNeighPC = cp.asarray(iNeighPC)
iNeighPC = cp.pad(
- iNeighPC, ((0, 0), (0, (Nfilt - iNeighPC.shape[1]))), mode='constant')
+ iNeighPC, ((0, 0), (0, (Nfilt - iNeighPC.shape[1]))), mode="constant"
+ )
iNeighPC[:, Nfilt - 1] = iNeighPC[:, ik]
assert iNeighPC.shape[1] == Nfilt
@@ -729,15 +781,31 @@ def splitAllClusters(ctx, flag):
# pbar.close()
logger.info(
- f'Finished splitting. Found {nsplits} splits, checked '
- f'{ik}/{Nfilt} clusters, nccg {nccg}')
+ f"Finished splitting. Found {nsplits} splits, checked "
+ f"{ik}/{Nfilt} clusters, nccg {nccg}"
+ )
Nfilt = W.shape[1] # new number of templates
Nrank = 3
Nchan = probe.Nchan
Params = cp.array(
- [0, Nfilt, 0, 0, W.shape[0], Nnearest, Nrank, 0, 0, Nchan, NchanNear, nt0min, 0],
- dtype=cp.float64) # make a new Params to pass on parameters to CUDA
+ [
+ 0,
+ Nfilt,
+ 0,
+ 0,
+ W.shape[0],
+ Nnearest,
+ Nrank,
+ 0,
+ 0,
+ Nchan,
+ NchanNear,
+ nt0min,
+ 0,
+ ],
+ dtype=cp.float64,
+ ) # make a new Params to pass on parameters to CUDA
# we need to re-estimate the spatial profiles
@@ -750,7 +818,9 @@ def splitAllClusters(ctx, flag):
WtW, iList = getMeWtW(W.astype(cp.float32), U.astype(cp.float32), Nnearest)
# ir.iList = iList # over-write the list of nearest templates
- isplit = simScore == 1 # overwrite the similarity scores of clusters with same parent
+ isplit = (
+ simScore == 1
+ ) # overwrite the similarity scores of clusters with same parent
simScore = WtW.max(axis=2)
simScore[isplit] = 1 # 1 means they come from the same parent
@@ -759,21 +829,18 @@ def splitAllClusters(ctx, flag):
# for Phy, we need to pad the spikes with zeros so the spikes are aligned to the center of
# the window
- Wphy = cp.concatenate(
- (cp.zeros((1 + nt0min, Nfilt, Nrank), order='F'), W), axis=0)
+ Wphy = cp.concatenate((cp.zeros((1 + nt0min, Nfilt, Nrank), order="F"), W), axis=0)
# ir.isplit = isplit # keep track of origins for each cluster
return Bunch(
st3_s=st3,
-
W_s=W,
U_s=U,
mu_s=mu,
simScore_s=simScore,
iNeigh_s=iNeigh,
iNeighPC_s=iNeighPC,
-
Wphy=Wphy,
iList=iList,
isplit=isplit,
@@ -794,7 +861,7 @@ def set_cutoff(ctx):
# cProj = ir.cProj
# cProjPC = ir.cProjPC
- dt = 1. / 1000 # step size for CCG binning
+ dt = 1.0 / 1000 # step size for CCG binning
Nk = int(st3[:, 1].max()) + 1 # number of templates
@@ -803,7 +870,7 @@ def set_cutoff(ctx):
Ths = cp.zeros(Nk)
est_contam_rate = cp.zeros(Nk)
- for j in tqdm(range(Nk), desc='Setting cutoff'):
+ for j in tqdm(range(Nk), desc="Setting cutoff"):
ix = cp.where(st3[:, 1] == j)[0] # find all spikes from this neuron
ss = st3[ix, 0] / params.fs # convert to seconds
if ss.size == 0:
@@ -903,7 +970,6 @@ def set_cutoff(ctx):
spikes_to_remove=cp.asnumpy(ix),
# cProj_c=cProj,
# cProjPC_c=cProjPC,
-
est_contam_rate=est_contam_rate,
Ths=Ths,
good=good,
@@ -995,10 +1061,10 @@ def rezToPhy(ctx, dat_path=None, output_dir=None):
fs = os.listdir(savePath)
for file in fs:
- if file.endswith('.npy'):
+ if file.endswith(".npy"):
os.remove(join(savePath, file))
- if os.path.isdir(join(savePath, '.phy')):
- shutil.rmtree(join(savePath, '.phy'))
+ if os.path.isdir(join(savePath, ".phy")):
+ shutil.rmtree(join(savePath, ".phy"))
spikeTimes = st3[:, 0].astype(cp.uint64)
spikeTemplates = st3[:, 1].astype(cp.uint32)
@@ -1030,7 +1096,9 @@ def rezToPhy(ctx, dat_path=None, output_dir=None):
# templates = cp.einsum('ikl,jkl->ijk', U, W).astype(cp.float32)
# templates = cp.zeros((Nchan, nt0, Nfilt), dtype=np.float32, order='F')
tempAmpsUnscaled = cp.zeros(Nfilt, dtype=np.float32)
- templates_writer = NpyWriter(join(savePath, 'templates.npy'), (Nfilt, nt0, Nchan), np.float32)
+ templates_writer = NpyWriter(
+ join(savePath, "templates.npy"), (Nfilt, nt0, Nchan), np.float32
+ )
for iNN in tqdm(range(Nfilt), desc="Computing templates"):
t = cp.dot(U[:, iNN, :], W[:, iNN, :].T).T
templates_writer.append(t)
@@ -1067,9 +1135,13 @@ def rezToPhy(ctx, dat_path=None, output_dir=None):
# tempScalingAmps are equal mean for all templates)
ta = clusterAverage(spikeTemplates, spikeAmps)
tids = cp.unique(spikeTemplates).astype(np.int64)
- tempAmps = cp.zeros_like(tempAmpsUnscaled, order='F')
- tempAmps[tids] = ta # because ta only has entries for templates that had at least one spike
- tempAmps = params.gain * tempAmps # for consistency, make first dimension template number
+ tempAmps = cp.zeros_like(tempAmpsUnscaled, order="F")
+ tempAmps[
+ tids
+ ] = ta # because ta only has entries for templates that had at least one spike
+ tempAmps = (
+ params.gain * tempAmps
+ ) # for consistency, make first dimension template number
# PCs
ix = ir.spikes_to_remove # length: number of spikes BEFORE -1 cluster removed
@@ -1080,15 +1152,17 @@ def rezToPhy(ctx, dat_path=None, output_dir=None):
cProjPC_shape = ir.cProjPC.shape
cProjPC_shape = (st3.shape[0],) + cProjPC_shape[1:]
- tfw = NpyWriter(join(savePath, 'template_features.npy'), cProj_shape, np.float32)
- pcw = NpyWriter(join(savePath, 'pc_features.npy'), cProjPC_shape, np.float32)
+ tfw = NpyWriter(join(savePath, "template_features.npy"), cProj_shape, np.float32)
+ pcw = NpyWriter(join(savePath, "pc_features.npy"), cProjPC_shape, np.float32)
isort = cp.asnumpy(isort)
N = len(ix) # number of spikes including those assigned to -1
assert ir.cProj.shape[0] == N
assert ir.cProjPC.shape[0] == N
- spikes_to_keep = np.nonzero(~ix)[0] # indices of the spikes to keep in the cProj index space
+ spikes_to_keep = np.nonzero(~ix)[
+ 0
+ ] # indices of the spikes to keep in the cProj index space
# if len(ix) > ir.cProj.shape[0]:
# ix = ix[:cProj.shape[0]]
@@ -1102,7 +1176,7 @@ def rezToPhy(ctx, dat_path=None, output_dir=None):
# NOTE: cProj and cProjPC still have the spikes assigned to -1 that have yet to be removed
# spike indices in cProj that need to be kept in this chunk
- ind = spikes_to_keep[isort[i:i + k]]
+ ind = spikes_to_keep[isort[i : i + k]]
cProj = ir.cProj[ind]
cProjPC = ir.cProjPC[ind]
@@ -1117,69 +1191,69 @@ def rezToPhy(ctx, dat_path=None, output_dir=None):
# cProjPC = ir.cProjPC_c[cp.asnumpy(isort), :, :]
def _save(name, arr, dtype=None):
- cp.save(join(savePath, name + '.npy'), arr.astype(dtype or arr.dtype))
+ cp.save(join(savePath, name + ".npy"), arr.astype(dtype or arr.dtype))
if savePath is not None:
- _save('spike_times', spikeTimes)
- _save('spike_templates', spikeTemplates, cp.uint32)
+ _save("spike_times", spikeTimes)
+ _save("spike_templates", spikeTemplates, cp.uint32)
if st3.shape[1] > 4:
- _save('spike_clusters', spikeClusters, cp.uint32)
+ _save("spike_clusters", spikeClusters, cp.uint32)
else:
- _save('spike_clusters', spikeTemplates, cp.uint32)
- _save('amplitudes', amplitudes)
+ _save("spike_clusters", spikeTemplates, cp.uint32)
+ _save("amplitudes", amplitudes)
# _save('templates', templates)
- _save('templates_ind', templatesInds)
+ _save("templates_ind", templatesInds)
chanMap0ind = chanMap0ind.astype(cp.int32)
- _save('channel_map', chanMap0ind)
- _save('channel_positions', np.c_[xcoords, ycoords])
+ _save("channel_map", chanMap0ind)
+ _save("channel_positions", np.c_[xcoords, ycoords])
# _save('template_features', templateFeatures)
# with open(join(savePath, 'template_features.npy'), 'wb') as fp:
# save_large_array(fp, templateFeatures)
- _save('template_feature_ind', templateFeatureInds.T)
+ _save("template_feature_ind", templateFeatureInds.T)
# _save('pc_features', pcFeatures)
# with open(join(savePath, 'pc_features.npy'), 'wb') as fp:
# save_large_array(fp, pcFeatures)
- _save('pc_feature_ind', pcFeatureInds.T)
+ _save("pc_feature_ind", pcFeatureInds.T)
- _save('whitening_mat', whiteningMatrix)
- _save('whitening_mat_inv', whiteningMatrixInv)
+ _save("whitening_mat", whiteningMatrix)
+ _save("whitening_mat_inv", whiteningMatrixInv)
- _save('thresholds', Ths)
+ _save("thresholds", Ths)
- if 'simScore' in ir:
+ if "simScore" in ir:
similarTemplates = simScore
- _save('similar_templates', similarTemplates)
+ _save("similar_templates", similarTemplates)
est_contam_rate[np.isnan(est_contam_rate)] = 1
- with open(join(savePath, 'cluster_group.tsv'), 'w') as f:
- f.write('cluster_id\tgroup\n')
+ with open(join(savePath, "cluster_group.tsv"), "w") as f:
+ f.write("cluster_id\tgroup\n")
for j in range(len(good)):
if good[j]:
- f.write('%d\tgood\n' % j)
+ f.write("%d\tgood\n" % j)
# else:
# f.write('%d\tmua\n' % j)
- with open(join(savePath, 'cluster_ContamPct.tsv'), 'w') as f:
- f.write('cluster_id\tContamPct\n')
+ with open(join(savePath, "cluster_ContamPct.tsv"), "w") as f:
+ f.write("cluster_id\tContamPct\n")
for j in range(len(good)):
- f.write('%d\t%.1f\n' % (j, 100 * est_contam_rate[j]))
+ f.write("%d\t%.1f\n" % (j, 100 * est_contam_rate[j]))
- with open(join(savePath, 'cluster_Amplitude.tsv'), 'w') as f:
- f.write('cluster_id\tAmplitude\n')
+ with open(join(savePath, "cluster_Amplitude.tsv"), "w") as f:
+ f.write("cluster_id\tAmplitude\n")
for j in range(len(good)):
- f.write('%d\t%.1f\n' % (j, tempAmps[j]))
+ f.write("%d\t%.1f\n" % (j, tempAmps[j]))
# make params file
- if not os.path.exists(join(savePath, 'params.py')):
- with open(join(savePath, 'params.py'), 'w') as f:
+ if not os.path.exists(join(savePath, "params.py")):
+ with open(join(savePath, "params.py"), "w") as f:
f.write('dat_path = "../%s"\n' % dat_path)
- f.write('n_channels_dat = %d\n' % probe.NchanTOT)
+ f.write("n_channels_dat = %d\n" % probe.NchanTOT)
f.write('dtype = "int16"\n')
f.write('offset = 0\n')
f.write('hp_filtered = False\n')
f.write('sample_rate = %i\n' % params.fs)
- f.write('template_scaling = %.1f\n' % params.templateScaling)
+ f.write('template_scaling = %.1f\n' % params.templateScaling)
\ No newline at end of file
diff --git a/pykilosort/preprocess.py b/pykilosort/preprocess.py
index 0d798e9..0b0a417 100644
--- a/pykilosort/preprocess.py
+++ b/pykilosort/preprocess.py
@@ -162,7 +162,7 @@ def whiteningLocal(CC, yc, xc, nRange):
# take the closest channels to the primary channel.
# First channel in this list will always be the primary channel.
ilocal = ilocal[:nRange]
-
+
wrot0 = cp.asnumpy(whiteningFromCovariance(CC[np.ix_(ilocal, ilocal)]))
# the first column of wrot0 is the whitening filter for the primary channel
Wrot[ilocal, j] = wrot0[:, 0]
diff --git a/tests/conftest.py b/tests/conftest.py
index 2e27022..e3566c9 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -1,3 +1,4 @@
+import yaml
from math import ceil
from pathlib import Path
from pytest import fixture
@@ -92,3 +93,4 @@ def dtype(request):
@fixture(params=[0, 1])
def axis(request):
return request.param
+
diff --git a/tests/fixtures/data/set_1/dist.csv b/tests/fixtures/data/set_1/dist.csv
new file mode 100644
index 0000000..1f7eb91
--- /dev/null
+++ b/tests/fixtures/data/set_1/dist.csv
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diff --git a/tests/fixtures/data/set_1/iC.csv b/tests/fixtures/data/set_1/iC.csv
new file mode 100644
index 0000000..a5da611
--- /dev/null
+++ b/tests/fixtures/data/set_1/iC.csv
@@ -0,0 +1,10 @@
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diff --git a/tests/fixtures/data/set_1/vars.yml b/tests/fixtures/data/set_1/vars.yml
new file mode 100644
index 0000000..1215c47
--- /dev/null
+++ b/tests/fixtures/data/set_1/vars.yml
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diff --git a/tests/fixtures/data/set_1/xc.csv b/tests/fixtures/data/set_1/xc.csv
new file mode 100644
index 0000000..fe54865
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diff --git a/tests/fixtures/data/set_1/xcup.csv b/tests/fixtures/data/set_1/xcup.csv
new file mode 100644
index 0000000..832dfe8
--- /dev/null
+++ b/tests/fixtures/data/set_1/xcup.csv
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new file mode 100644
index 0000000..1a09594
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diff --git a/tests/fixtures/data/set_1/ycup.csv b/tests/fixtures/data/set_1/ycup.csv
new file mode 100644
index 0000000..2e6ffcf
--- /dev/null
+++ b/tests/fixtures/data/set_1/ycup.csv
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diff --git a/tests/fixtures/data/set_2/dist.csv b/tests/fixtures/data/set_2/dist.csv
new file mode 100644
index 0000000..ae9ec40
--- /dev/null
+++ b/tests/fixtures/data/set_2/dist.csv
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diff --git a/tests/fixtures/data/set_2/iC.csv b/tests/fixtures/data/set_2/iC.csv
new file mode 100644
index 0000000..a74457b
--- /dev/null
+++ b/tests/fixtures/data/set_2/iC.csv
@@ -0,0 +1,100 @@
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diff --git a/tests/fixtures/data/set_2/vars.yml b/tests/fixtures/data/set_2/vars.yml
new file mode 100644
index 0000000..287ec57
--- /dev/null
+++ b/tests/fixtures/data/set_2/vars.yml
@@ -0,0 +1 @@
+NchanClosest: 100
diff --git a/tests/fixtures/data/set_2/xc.csv b/tests/fixtures/data/set_2/xc.csv
new file mode 100644
index 0000000..1223728
--- /dev/null
+++ b/tests/fixtures/data/set_2/xc.csv
@@ -0,0 +1 @@
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diff --git a/tests/fixtures/data/set_2/xcup.csv b/tests/fixtures/data/set_2/xcup.csv
new file mode 100644
index 0000000..1223728
--- /dev/null
+++ b/tests/fixtures/data/set_2/xcup.csv
@@ -0,0 +1 @@
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diff --git a/tests/fixtures/data/set_2/yc.csv b/tests/fixtures/data/set_2/yc.csv
new file mode 100644
index 0000000..aa70403
--- /dev/null
+++ b/tests/fixtures/data/set_2/yc.csv
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diff --git a/tests/fixtures/data/set_2/ycup.csv b/tests/fixtures/data/set_2/ycup.csv
new file mode 100644
index 0000000..aa70403
--- /dev/null
+++ b/tests/fixtures/data/set_2/ycup.csv
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diff --git a/tests/helpers.py b/tests/helpers.py
new file mode 100644
index 0000000..414e00c
--- /dev/null
+++ b/tests/helpers.py
@@ -0,0 +1,22 @@
+import os
+import yaml
+
+import numpy as np
+
+
+def get_test_data(test_data_folder, sets):
+ values = []
+ varnames = None
+ for test_set in sets:
+ folder = f"{test_data_folder}/{test_set}"
+ test_data = {
+ filename.split('.csv')[0] : np.loadtxt(f"{folder}/{filename}", delimiter=',') for filename in os.listdir(folder) if filename.endswith(".csv")
+ }
+ with open(f"{folder}/vars.yml") as f:
+ test_data.update(yaml.load(f))
+ varnames = ','.join(test_data.keys())
+ values.append(tuple(v for v in test_data.values()))
+ return varnames, values
+
+
+
diff --git a/tests/test_datashift.py b/tests/test_datashift.py
new file mode 100644
index 0000000..35821fd
--- /dev/null
+++ b/tests/test_datashift.py
@@ -0,0 +1,25 @@
+import os
+import pytest
+import cupy as cp
+import numpy as np
+
+from pykilosort.cluster import getClosestChannels2
+
+from .helpers import get_test_data
+
+TEST_DATA_FOLDER = "tests/fixtures/data"
+
+@pytest.mark.requires_gpu
+@pytest.mark.parametrize(
+ *get_test_data(TEST_DATA_FOLDER, ["set_1", "set_2"])
+)
+def test_get_closest_channels(xc, yc, xcup, ycup, NchanClosest, iC, dist):
+ actual_iC, actual_dist = getClosestChannels2(ycup, xcup, yc, xc, NchanClosest)
+
+ assert actual_iC.shape == iC.shape
+ assert actual_dist.shape == dist.shape
+
+ assert (cp.asnumpy(actual_iC) == iC - 1).all() # iC is an index
+ assert ((cp.asnumpy(actual_dist) - dist) < 0.0001).all()
+
+
diff --git a/tests/test_runner.py b/tests/test_runner.py
new file mode 100644
index 0000000..e69de29