def calculate_snprank(self, gamma, usegpu): """Runs the SNPrank algorithm on the input data, using gamma as the damping factor. usegpu enables GPU computing (using the CUDAMat library) for the matrix multiplication. Returns the SNPrank scores and diagonal (main effect) of original GAIN matrix.""" # A GAIN matrix is an NxN matrix m, n = self.GAIN.shape if m != n: raise ValueError("Input is not an NxN matrix") # Vector of column sums colsum = self.GAIN.sum(axis=0) # Get indices of c vector that are not zero colsum_nzidx = colsum.nonzero()[0] D = zeros((n, n)) T_nz = ones(n) # Where a column doesn't sum to 0, the diagonal in D # ought to be the reciprocal of the column sum. # Likewize T_nz ought to be 1-gamma rather than 1. for i in colsum_nzidx: D[i][i] = 1 / colsum[i] T_nz[i] = 1 - gamma T = zeros((n, n)) if usegpu: import cudamat as cm # initialize CUDAMat cm.init() # Copy GAIN and D matrices to GPU G_gpu = cm.CUDAMatrix(self.GAIN) D_gpu = cm.CUDAMatrix(D) # Do matrix multiplication on the GPU GD_prod = cm.dot(G_gpu, D_gpu) # Transition matrix T = (gamma * GD_prod.asarray()) + ( self.GAIN.diagonal().reshape(n, 1) * T_nz) / self.GAIN.trace() else: # Transition matrix T = (gamma * dot(self.GAIN, D)) + ( self.GAIN.diagonal().reshape(n, 1) * T_nz) / self.GAIN.trace() # r is an arbitrary vector, which we initialize to 1/n r = (ones(n)).reshape(n, 1) / n # Cutoff for matrix convergence threshold = 10**(-4) # Multiply r by T until r converges to within the threshold while True: r_old, r = r, normalize(dot(T, r)) if all(abs(r - r_old) < threshold): break return r.reshape(1, n)[0], self.GAIN.diagonal()
def fit(self, xs, ys, xt, reg=1, eta=1, ws=None, wt=None, norm=None, **kwargs): cudamat.init() xs = np.asarray(xs, dtype=np.float64) xt = np.asarray(xt, dtype=np.float64) self.xs = xs self.xt = xt if wt is None: wt = unif(xt.shape[0]) if ws is None: ws = unif(xs.shape[0]) self.ws = ws self.wt = wt self.M_GPU = pairwiseEuclideanGPU(xs, xt, returnAsGPU=True, squared=True) self.normalizeM(norm) self.G = sinkhorn_lpl1_mm(ws, ys, wt, self.M_GPU, reg, eta, **kwargs) self.computed = True
def __init__( self, n_stages=10, # number of training iterations latent_size=100, # hidden layer size learning_rate=1e-2, # learning rate momentum=0, # momentum n_gibbs_steps=1, # number of Gibbs sampling steps use_persistent_chain=False, # use persistent CD? minibatch_size=128, # size of minibatch load_data_every=-1, # how frequently to load data to GPU seed=1234): self.stage = 0 self.reload_data = True self.n_stages = n_stages self.latent_size = latent_size self.learning_rate = learning_rate self.momentum = momentum self.n_gibbs_steps = n_gibbs_steps self.use_persistent_chain = use_persistent_chain self.minibatch_size = minibatch_size self.load_data_every = load_data_every self.seed = seed self.gpu_dataset = None cm.cuda_set_device(0) cm.init() self.rng = np.random.mtrand.RandomState(seed) cm.CUDAMatrix.init_random(seed=seed)
def __init__( self, n_stages=10, # number of training iterations latent_size=100, # hidden layer size learning_rate=1e-2, # learning rate momentum=0, # momentum n_gibbs_steps=1, # number of Gibbs sampling steps use_persistent_chain=False, # use persistent CD? minibatch_size=128, # size of minibatch load_data_every=-1, # how frequently to load data to GPU seed=1234 ): self.stage = 0 self.reload_data = True self.n_stages = n_stages self.latent_size = latent_size self.learning_rate = learning_rate self.momentum = momentum self.n_gibbs_steps = n_gibbs_steps self.use_persistent_chain = use_persistent_chain self.minibatch_size = minibatch_size self.load_data_every = load_data_every self.seed = seed self.gpu_dataset = None cm.cuda_set_device(0) cm.init() self.rng = np.random.mtrand.RandomState(seed) cm.CUDAMatrix.init_random(seed = seed)
def main(): cmt.init() cmt.CUDAMatrix.init_random() if HEATUP: print("heating up for %g seconds..." % HEATUP, end=' ') sys.stdout.flush() heatup(HEATUP) print("done.") print("small matrix shape:", XS_SHAPE) print("large matrix shape:", XL_SHAPE) for funcname, func in filter(lambda f: f[0].startswith('bench_'), getmembers(getmodule(main), isfunction)): print("%-15s" % funcname[len('bench_'):], end=' ') sys.stdout.flush() for size, shape, factor in ('small', XS_SHAPE, 10), ('large', XL_SHAPE, 1): repeat = NUM_REPEATS * getattr(func, 'repeats', 1) time = min(timeit.repeat(\ setup="from __main__ import setup, %s\nmats = setup(%s)" % (funcname, shape), stmt="%s(*mats)" % funcname, repeat=repeat, number=NUM_ITER * factor)) / (NUM_ITER * factor) print("%.3es (%s) " % (time, size), end=' ') sys.stdout.flush() print() cmt.shutdown()
def __init__(self): self.num_class = 10 self.learning_rate = -0.1 loss = "MSE" self.loss = self.loss_func(loss) self.error = self.error_func(loss) mnist = tf.keras.datasets.mnist (x_train, y_train), (x_test, y_test) = mnist.load_data() x_train, x_test = x_train / 255.0, x_test / 255.0 x_train = np.reshape(x_train, (-1, 1, 28 * 28)) x_test = np.reshape(x_test, (-1, 1, 28 * 28)) self.X = np.array(np.append(x_train, x_test, axis=0)) self.Y = np.eye(self.num_class)[np.append(y_train, y_test)] # one hot vectors self.input_length = len(self.X[0, 0]) self.train_data_length = len(self.X) cm.init() self.output_bp = None self.model = [] self.build_model()
def calculate_snprank(self, gamma, usegpu): """Runs the SNPrank algorithm on the input data, using gamma as the damping factor. usegpu enables GPU computing (using the CUDAMat library) for the matrix multiplication. Returns the SNPrank scores and diagonal (main effect) of original GAIN matrix.""" # A GAIN matrix is an NxN matrix m,n = self.GAIN.shape if m != n: raise ValueError("Input is not an NxN matrix") # Vector of column sums colsum = self.GAIN.sum(axis=0) # Get indices of c vector that are not zero colsum_nzidx = colsum.nonzero()[0] D = zeros((n,n)) T_nz = ones(n) # Where a column doesn't sum to 0, the diagonal in D # ought to be the reciprocal of the column sum. # Likewize T_nz ought to be 1-gamma rather than 1. for i in colsum_nzidx: D[i][i] = 1/colsum[i] T_nz[i] = 1 - gamma T = zeros((n,n)) if usegpu: import cudamat as cm # initialize CUDAMat cm.init() # Copy GAIN and D matrices to GPU G_gpu = cm.CUDAMatrix(self.GAIN) D_gpu = cm.CUDAMatrix(D) # Do matrix multiplication on the GPU GD_prod = cm.dot(G_gpu,D_gpu) # Transition matrix T = (gamma * GD_prod.asarray() ) + (self.GAIN.diagonal().reshape(n,1) * T_nz) / self.GAIN.trace() else: # Transition matrix T = (gamma * dot(self.GAIN,D) ) + (self.GAIN.diagonal().reshape(n,1) * T_nz) / self.GAIN.trace() # r is an arbitrary vector, which we initialize to 1/n r = (ones(n)).reshape(n,1)/n; # Cutoff for matrix convergence threshold = 10**(-4) # Multiply r by T until r converges to within the threshold while True: r_old, r = r, normalize(dot(T,r)) if all( abs(r-r_old) < threshold ): break return r.reshape(1,n)[0], self.GAIN.diagonal()
def init(max_ones=(1024*256)): ''' Initialize GPU resources. Parameters ----------- max_ones : int, optional Allocate enough memory for a sum of up to 'max_ones' length ''' cm.init(max_ones)
def main(argv): global episodes global graph_size cm.init() env = gym.make('SpaceInvaders-v0') action_space = env.action_space.n observation_shape = env.observation_space.shape observation_space = observation_shape[0] / 3 * observation_shape[ 1] / 4 * observation_shape[2] observation_space = int(observation_space) total_size = graph_size + action_space + observation_space + 1 # Additional spot for reward graph = Graph(total_size) # Run until done for i in range(episodes): # Initial step x = np.random.normal(size=graph_size) action = np.zeros(action_space) input_val = create_input(env.reset(), action, x, 0.0) output = graph.predict(input_val, 0.2) action = output[observation_space:observation_space + action_space] while True: observation, reward, done, info = env.step(np.argmax(action)) if done: print('Final reward: %f' % (reward, )) break # Update graph input_val = create_input(observation, action, x, reward) x = graph(input_val, 0.2) x = x[observation_space + action_space:-1] # env.render() # Select next action if random.random() < 0.3: action = np.zeros(action_space) action[env.action_space.sample()] = 1.0 else: input_val = create_input(observation, np.zeros(action_space), x, 10000.0) output = graph.predict(input_val, 0.2) action = output[observation_space:observation_space + action_space] graph.save('graph.npy') env.close() cm.shutdown()
def fit(self, xs, xt, reg=1, ws=None, wt=None, norm=None, **kwargs): cudamat.init() xs = np.asarray(xs, dtype=np.float64) xt = np.asarray(xt, dtype=np.float64) self.xs = xs self.xt = xt if wt is None: wt = unif(xt.shape[0]) if ws is None: ws = unif(xs.shape[0]) self.ws = ws self.wt = wt self.M_GPU = pairwiseEuclideanGPU(xs, xt, returnAsGPU=True, squared=True) self.normalizeM(norm) self.G = sinkhorn(ws, wt, self.M_GPU, reg, **kwargs) self.computed = True
def __init__(self): self.num_class = 10 self.learning_rate = 1 loss = "MSE" self.loss = self.loss_func(loss) self.error = self.error_func(loss) mnist = tf.keras.datasets.mnist (x_train, y_train), (x_test, y_test) = mnist.load_data() x_train, x_test = x_train / 255.0, x_test / 255.0 x_train = np.reshape(x_train, (-1, 28 * 28)) x_test = np.reshape(x_test, (-1, 28 * 28)) x_train = np.float32(x_train) x_test = np.float32(x_test) # self.X = np.array(np.append(x_train, x_test, axis=0)) self.X = np.array(x_train) # self.Y = np.eye(self.num_class)[np.append(y_train, y_test)] # one hot vectors self.Y_argmax = y_train self.Y = np.eye(self.num_class)[y_train] # one hot vectors self.input_length = len(self.X[0]) self.train_data_length = len(self.X) self.batch_size = 100 self.num_batches = self.X.shape[0] // self.batch_size cm.init() self.X = cmarray(self.X) self.Y = cmarray(self.Y) self.Y_argmax = cmarray(self.Y_argmax) self.error_values = cm.empty((self.batch_size, self.num_class)) self.model = [] self.build_model()
def main(): cmt.init() cmt.CUDAMatrix.init_random() if HEATUP: print "heating up for %g seconds..." % HEATUP, sys.stdout.flush() heatup(HEATUP) print "done." print "small matrix shape:", XS_SHAPE print "large matrix shape:", XL_SHAPE for funcname, func in ifilter(lambda (fn, f): fn.startswith('bench_'), getmembers(getmodule(main), isfunction)): print "%-15s" % funcname[len('bench_'):], sys.stdout.flush() for size, shape, factor in ('small', XS_SHAPE, 10), ('large', XL_SHAPE, 1): repeat = NUM_REPEATS * getattr(func, 'repeats', 1) time = min(timeit.repeat(\ setup="from __main__ import setup, %s\nmats = setup(%s)" % (funcname, shape), stmt="%s(*mats)" % funcname, repeat=repeat, number=NUM_ITER * factor)) / (NUM_ITER * factor) print "%.3es (%s) " % (time, size), sys.stdout.flush() print
import math import numpy try: import cudamat cudamat.init() cudamat.CUDAMatrix.init_random(seed = 42) bCudamatLoaded = True except ImportError: bCudamatLoaded = False #RbmStack # This class implements a stack of restricted Boltzmann machines. It # provides methods to train the stack using a greedy layerwise # approach, and to perform inference using the stack as an autoencoder. # # The RbmStack provides the following features: # # * Allows specification of architectures having varying numbers of # layers with specified size and activation functions. # * Optionally implements dropout of visible and/or hidden neurons # during training. # * Callback mechanism to specify the learning rate, momentum, dropout # probability, and hidden layer sampling technique for each layer. # * Callback mechanism for reporting training progress # and performance metrics. # Define classes used for RbmStack interfacing class Layer: def __init__(self, iV, iH, rInitialWeightVariance=0.1, sActivationUp='Logistic', sActivationDn='Logistic'):
def solve(self, xs, xt, p, q, maxiter=200, plot_every=20, print_every=1, verbose=True, save_plots=None): """ **** Ported from POT package, modified to plot plans and compute downstream error along the way Returns the gromov-wasserstein transport between (C1,p) and (C2,q) (C1,p) and (C2,q) The function solves the following optimization problem: .. math:: \GW = arg\min_T \sum_{i,j,k,l} L(C1_{i,k},C2_{j,l})*T_{i,j}*T_{k,l}-\epsilon(H(T)) s.t. \GW 1 = p \GW^T 1= q \GW\geq 0 Where : C1 : Metric cost matrix in the source space C2 : Metric cost matrix in the target space p : distribution in the source space q : distribution in the target space L : loss function to account for the misfit between the similarity matrices H : entropy Parameters ---------- C1 : ndarray, shape (ns, ns) Metric cost matrix in the source space C2 : ndarray, shape (nt, nt) Metric costfr matrix in the target space p : ndarray, shape (ns,) distribution in the source space q : ndarray, shape (nt,) distribution in the target space loss_fun : string loss function used for the solver either 'square_loss' or 'kl_loss' epsilon : float Regularization term >0 max_iter : int, optional Max number of iterations tol : float, optional Stop threshold on error (>0) verbose : bool, optional Print information along iterations log : bool, optional record log if True Returns ------- T : ndarray, shape (ns, nt) coupling between the two spaces that minimizes : \sum_{i,j,k,l} L(C1_{i,k},C2_{j,l})*T_{i,j}*T_{k,l}-\epsilon(H(T)) References ---------- .. [12] Peyré, Gabriel, Marco Cuturi, and Justin Solomon, "Gromov-Wasserstein averaging of kernel and distance matrices." International Conference on Machine Learning (ICML). 2016. """ if self.gpu: cm.init() self.compute_distances(xs, xt) C1 = np.asarray(self.C1, dtype=np.float64) C2 = np.asarray(self.C2, dtype=np.float64) T = np.outer(p, q) # Initialization if not self.gpu: constC, hC1, hC2 = self.init_matrix(C1, C2, T, p, q, self.loss_fun) else: T = cm.CUDAMatrix(T) constC, hC1, hC2 = gromov_gpu.init_matrix(C1, C2, T, p, q, self.loss_fun) it = 0 err = 1 global_start = time() # plt.ion() # To plot interactively during training while (err > self.tol and it <= maxiter): start = time() if verbose and (it % plot_every == 0): self.gromov_iter_plot(it, C1, C2, T, save_path=save_plots) # self.print_header() Tprev = T # compute the gradient tens = gwggrad(constC, hC1, hC2, T) # FIXME: Clean this up. Global vars and tailored imports should allow # to not have devide gpu and non gpu cases. if not self.gpu: if self.ot_warm and it > 0: T, log = sinkhorn_knopp(p, q, tens, self.reg, init_u=log['u'], init_v=log['v'], log=True) elif self.ot_warm: T, log = bregman.sinkhorn(p, q, tens, self.reg, log=True) else: T = bregman.sinkhorn(p, q, tens, self.reg) else: if self.ot_warm and it > 0: T, log = bregman.sinkhorn(p, q, tens, self.reg, returnAsGPU=True, log=True) elif self.ot_warm: T, log = bregman.sinkhorn(p, q, tens, self.reg, log=True) else: T = bregman.sinkhorn(p, q, tens, self.reg, returnAsGPU=True) time_G = time() - start if it % 10 == 0: # we can speed up the process by checking for the error only all # the 10th iterations dist = gwloss(constC, hC1, hC2, T) if self.gpu: err = np.linalg.norm(T.copy().subtract(Tprev).asarray()) else: err = np.linalg.norm(T - Tprev) # Debug: Test Accuracy if self.compute_accuracy: #accs = self.test_accuracy(G_t) if self.gpu: accs = self.accuracy_function(T.asarray()) else: accs = self.accuracy_function(T) else: accs = {1: np.nan, 5: np.nan, 10: np.nan} if verbose: if it % 200 == 0: self.print_header() ent_t = self.compute_gamma_entropy(T) self.print_step(it, dist, ent_t, err, time_G, time() - start, accs=accs) self.history.append( (it, dist, ent_t, 100 * err, 100 * accs[1], 100 * accs[5], 100 * accs[10], time() - global_start)) it += 1 # plt.ioff() # To plot interactively during training plt.close('all') # if log: # log['gw_dist'] = gwloss(constC, hC1, hC2, T) # return T, log # else: if self.gpu: return T.asarray() else: return T
data_var /= N ### Actual experiment... x = x[:data_size] n_samples = len(x); n_train_samples = int(round(n_samples * train_frac)); n_test_samples = int(round(n_samples * (1 - train_frac))); inputs = x[0].shape[1] # cudamat boiler plate cm.init() cm.CUDAMatrix.init_random(seed=42) # construct reservoir crbm = ccrbm(hiddens, inputs, reservoir_size, gaussian=True) # Gathering the train states in advance is too memory intensive for bigger # reservoirs but for testing it is no problem. #print "Gathering ESN test states..." #states_test = [] #for xt in mdp.utils.progressinfo(x[n_train_samples:]): # states_test.append(reservoir.simulate(cm.CUDAMatrix(xt))) print "Training..." epochs = 250 for epoch in range(epochs):
def __init__(self, layer, step_size=None, dropout=None): # TODO: should probably put cudamat initialization elsewhere # in case it is used by more than one network cm.cuda_set_device(0) cm.init() super(net_cuda, self).__init__(layer, step_size, dropout)
def main(params, nb_cpu, nb_gpu, use_gpu): # Part 1: Whitening numpy.random.seed(420) logger = init_logging(params.logfile) logger = logging.getLogger('circus.whitening') ################################################################# data_file = params.data_file data_file.open() N_e = params.getint('data', 'N_e') N_total = params.nb_channels N_t = params.getint('detection', 'N_t') dist_peaks = params.getint('detection', 'dist_peaks') template_shift = params.getint('detection', 'template_shift') file_out_suff = params.get('data', 'file_out_suff') file_out = params.get('data', 'file_out') spike_thresh = params.getfloat('detection', 'spike_thresh') matched_filter = params.getboolean('detection', 'matched-filter') matched_thresh = params.getfloat('detection', 'matched_thresh') sign_peaks = params.get('detection', 'peaks') do_temporal_whitening = params.getboolean('whitening', 'temporal') do_spatial_whitening = params.getboolean('whitening', 'spatial') chunk_size = params.getint('whitening', 'chunk_size') plot_path = os.path.join(params.get('data', 'data_file_noext'), 'plots') nodes, edges = get_nodes_and_edges(params) safety_time = params.getint('whitening', 'safety_time') nb_temp_white = min(max(20, comm.size), N_e) max_silence_1 = int(20 * params.rate // comm.size) max_silence_2 = 5000 inv_nodes = numpy.zeros(N_total, dtype=numpy.int32) inv_nodes[nodes] = numpy.argsort(nodes) ################################################################# if comm.rank == 0: print_and_log( ["Analyzing data to get whitening matrices and thresholds..."], 'default', logger) if use_gpu: import cudamat as cmt ## Need to properly handle multi GPU per MPI nodes? if nb_gpu > nb_cpu: gpu_id = int(comm.rank // nb_cpu) else: gpu_id = 0 cmt.cuda_set_device(gpu_id) cmt.init() cmt.cuda_sync_threads() nb_chunks, last_chunk_len = data_file.analyze(chunk_size) if nb_chunks < comm.size: res = io.data_stats(params, show=False) chunk_size = int(res * params.rate // comm.size) if comm.rank == 0: print_and_log( ["Too much cores, automatically resizing the data chunks"], 'debug', logger) nb_chunks, last_chunk_len = data_file.analyze(chunk_size) # I guess this is more relevant, to take signals from all over the recordings all_chunks = numpy.random.permutation( numpy.arange(nb_chunks, dtype=numpy.int32)) all_electrodes = numpy.random.permutation(N_e) for gidx in [all_chunks[comm.rank]]: #print "Node", comm.rank, "is analyzing chunk", gidx, "/", nb_chunks, " ..." local_chunk, t_offset = data_file.get_data(gidx, chunk_size, nodes=nodes) local_shape = len(local_chunk) #print "Node", comm.rank, "computes the median absolute deviations in a random chunk" thresholds = numpy.zeros(N_e, dtype=numpy.float32) for i in xrange(N_e): u = numpy.median(local_chunk[:, i], 0) thresholds[i] = numpy.median(numpy.abs(local_chunk[:, i] - u), 0) gdata = gather_array(thresholds, comm) if comm.rank == 0: gdata = gdata.reshape((comm.size, N_e)) thresholds = numpy.mean(gdata, 0) bfile = h5py.File(file_out_suff + '.basis.hdf5', 'w', libver='latest') io.write_datasets(bfile, ['thresholds'], {'thresholds': thresholds}) bfile.close() comm.Barrier() thresholds = io.load_data(params, 'thresholds') #print "Extracting the peaks..." local_peaktimes = numpy.zeros(0, dtype=numpy.int32) for i in xrange(N_e): peaktimes = algo.detect_peaks(numpy.abs(local_chunk[:, i]), thresholds[i], valley=False, mpd=dist_peaks) local_peaktimes = numpy.concatenate((local_peaktimes, peaktimes)) local_peaktimes = numpy.unique(local_peaktimes) #print "Removing the useless borders..." local_borders = (template_shift, local_shape - template_shift) idx = (local_peaktimes >= local_borders[0]) & (local_peaktimes < local_borders[1]) local_peaktimes = numpy.compress(idx, local_peaktimes) if len(local_peaktimes) > 0: diff_times = local_peaktimes[-1] - local_peaktimes[0] all_times = numpy.zeros((N_e, diff_times + 1), dtype=numpy.bool) min_times = numpy.maximum( local_peaktimes - local_peaktimes[0] - safety_time, 0) max_times = numpy.minimum( local_peaktimes - local_peaktimes[0] + safety_time + 1, diff_times) argmax_peak = numpy.random.permutation( numpy.arange(len(local_peaktimes))) all_idx = numpy.take(local_peaktimes, argmax_peak) #print "Selection of the peaks with spatio-temporal masks..." for idx, peak in zip(argmax_peak, all_idx): elec = numpy.argmax(numpy.abs(local_chunk[peak])) indices = numpy.take(inv_nodes, edges[nodes[elec]]) all_times[indices, min_times[idx]:max_times[idx]] = True else: all_times = numpy.zeros((N_e, len(local_chunk)), dtype=numpy.bool) all_times_Ne = numpy.any(all_times, 0) subset = numpy.where(all_times_Ne == False)[0] all_silences = [] if do_spatial_whitening: local_silences = numpy.take(local_chunk, subset, axis=0)[:max_silence_1] all_silences = gather_array(local_silences, comm, 0, 1) local_res = [] if do_temporal_whitening: for elec in all_electrodes[numpy.arange(comm.rank, nb_temp_white, comm.size)]: res = numpy.zeros((0, N_t), dtype=numpy.float32) scount = 0 indices = numpy.take(inv_nodes, edges[nodes[elec]]) all_times_elec = numpy.any(numpy.take(all_times, indices, axis=0), 0) esubset = numpy.where(all_times_elec == False)[0] bound = len(esubset) - N_t while (scount < bound) and (len(res) < max_silence_2): myslice = esubset[scount:scount + N_t] if numpy.all((myslice - esubset[scount]) == numpy.arange(N_t)): scount += N_t res = numpy.vstack((res, local_chunk[myslice, elec])) else: scount += 1 if len(res) > 5: local_res += [numpy.cov(res.T)] nb_elecs = numpy.array([len(local_res)], dtype=numpy.float32) local_res = numpy.array(local_res, dtype=numpy.float32) if len(local_res) == 0: local_res = numpy.zeros(0, dtype=numpy.float32) else: local_res = numpy.sum(local_res, 0) all_res = gather_array(local_res.ravel(), comm, 0, 1) all_elecs = gather_array(nb_elecs, comm, 0, 1) if comm.rank == 0: to_write = {} if do_temporal_whitening: try: nb_silences = numpy.sum(all_elecs > 0) all_res = all_res.reshape((nb_silences, N_t**2)) except Exception: print_and_log([ "No silent periods detected: something wrong with the parameters?" ], 'error', logger) all_res = numpy.sum(all_res, 0) all_res = all_res.reshape((N_t, N_t)) / numpy.sum(all_elecs) temporal_whitening = get_whitening_matrix( all_res.astype(numpy.double), fudge=1e-3)[template_shift].astype(numpy.float32) temporal_whitening /= temporal_whitening.sum() to_write['temporal'] = temporal_whitening have_nans = numpy.sum(numpy.isnan(temporal_whitening)) if have_nans > 0: temporal_whitening = numpy.zeros(N_t, dtype=numpy.float32) temporal_whitening[N_t // 2] = 1 to_write['temporal'] = temporal_whitening print_and_log( ["Disabling temporal whitening because of NaNs found"], 'info', logger) if do_spatial_whitening: if len(all_silences) / params.rate == 0: print_and_log([ "No silent periods detected: something wrong with the parameters?" ], 'error', logger) spatial_whitening = get_whitening_matrix( all_silences.astype(numpy.double)).astype(numpy.float32) to_write['spatial'] = spatial_whitening print_and_log([ "Found %gs without spikes for whitening matrices..." % (len(all_silences) / params.rate) ], 'default', logger) have_nans = numpy.sum(numpy.isnan(spatial_whitening)) if have_nans > 0: spatial_whitening = numpy.eye(spatial_whitening.shape[0], dtype=numpy.float32) to_write['spatial'] = spatial_whitening print_and_log( ["Disabling spatial whitening because of NaNs found"], 'info', logger) bfile = h5py.File(file_out_suff + '.basis.hdf5', 'r+', libver='latest') io.write_datasets(bfile, to_write.keys(), to_write) bfile.close() del all_silences comm.Barrier() if do_spatial_whitening or do_temporal_whitening: if comm.rank == 0: print_and_log( ["Because of whitening, need to recompute the thresholds..."], 'default', logger) if do_spatial_whitening: spatial_whitening = io.load_data(params, 'spatial_whitening') if use_gpu: spatial_whitening = cmt.CUDAMatrix(spatial_whitening, copy_on_host=False) if do_temporal_whitening: temporal_whitening = io.load_data(params, 'temporal_whitening') for gidx in [all_chunks[comm.rank]]: local_chunk, t_offset = data_file.get_data(gidx, chunk_size, nodes=nodes) local_shape = len(local_chunk) if do_spatial_whitening: if use_gpu: local_chunk = cmt.CUDAMatrix(local_chunk, copy_on_host=False) local_chunk = local_chunk.dot(spatial_whitening).asarray() else: local_chunk = numpy.dot(local_chunk, spatial_whitening) if do_temporal_whitening: local_chunk = scipy.ndimage.filters.convolve1d( local_chunk, temporal_whitening, axis=0, mode='constant') thresholds = numpy.zeros(N_e, dtype=numpy.float32) for i in xrange(N_e): u = numpy.median(local_chunk[:, i], 0) thresholds[i] = numpy.median(numpy.abs(local_chunk[:, i] - u), 0) gdata = gather_array(thresholds, comm) if comm.rank == 0: gdata = gdata.reshape((comm.size, N_e)) thresholds = numpy.mean(gdata, 0) bfile = h5py.File(file_out_suff + '.basis.hdf5', 'r+', libver='latest') bfile.pop('thresholds') io.write_datasets(bfile, ['thresholds'], {'thresholds': thresholds}) bfile.close() comm.Barrier() #if comm.rank == 0: #if not os.path.exists(plot_path): # os.makedirs(plot_path) #N_elec = min(int(numpy.sqrt(data_file.N_e)), 5) #plot.view_fit(filename, t_start=0, t_stop=1, fit_on=False, square=True, # n_elec=N_elec, save=[plot_path, 'electrodes']) # Part 2: Basis numpy.random.seed(422) ################################################################# file_out = params.get('data', 'file_out') alignment = params.getboolean('detection', 'alignment') spike_thresh = params.getfloat('detection', 'spike_thresh') nodes, edges = get_nodes_and_edges(params) do_temporal_whitening = params.getboolean('whitening', 'temporal') do_spatial_whitening = params.getboolean('whitening', 'spatial') chunk_size = params.getint('data', 'chunk_size') safety_time = params.getint('whitening', 'safety_time') max_elts_elec = params.getint('whitening', 'max_elts') nb_elts = int( params.getfloat('whitening', 'nb_elts') * N_e * max_elts_elec) output_dim = params.getfloat('whitening', 'output_dim') inv_nodes = numpy.zeros(N_total, dtype=numpy.int32) inv_nodes[nodes] = numpy.argsort(nodes) if sign_peaks == 'both': max_elts_elec *= 2 ################################################################# if comm.rank == 0: print_and_log(["Searching spikes to construct the PCA basis..."], 'default', logger) nb_chunks, last_chunk_len = data_file.analyze(chunk_size) if nb_chunks < comm.size: res = io.data_stats(params, show=False) chunk_size = int(res * params.rate // comm.size) if comm.rank == 0: print_and_log( ["Too much cores, automatically resizing the data chunks"], 'debug', logger) nb_chunks, last_chunk_len = data_file.analyze(chunk_size) groups = {} for i in xrange(N_e): groups[i] = 0 # I guess this is more relevant, to take signals from all over the recordings all_chunks = numpy.random.permutation( numpy.arange(nb_chunks, dtype=numpy.int32)) max_elts_elec //= comm.size nb_elts //= comm.size elt_count_pos = 0 elt_count_neg = 0 if sign_peaks in ['positive', 'both']: elts_pos = numpy.zeros((N_t, nb_elts), dtype=numpy.float32) if sign_peaks in ['negative', 'both']: elts_neg = numpy.zeros((N_t, nb_elts), dtype=numpy.float32) chunks_to_load = all_chunks[comm.rank::comm.size] thresholds = io.load_data(params, 'thresholds') if comm.rank == 0: pbar = get_progressbar(nb_elts) if alignment: cdata = numpy.linspace(-template_shift, template_shift, 5 * N_t) xdata = numpy.arange(-2 * template_shift, 2 * template_shift + 1) for gcount, gidx in enumerate(chunks_to_load): if ((elt_count_pos + elt_count_neg) < nb_elts): #print "Node", comm.rank, "is analyzing chunk", gidx, "/", nb_chunks, " ..." local_chunk, t_offset = data_file.get_data(gidx, chunk_size, nodes=nodes) local_shape = len(local_chunk) if do_spatial_whitening: if use_gpu: local_chunk = cmt.CUDAMatrix(local_chunk, copy_on_host=False) local_chunk = local_chunk.dot(spatial_whitening).asarray() else: local_chunk = numpy.dot(local_chunk, spatial_whitening) if do_temporal_whitening: local_chunk = scipy.ndimage.filters.convolve1d( local_chunk, temporal_whitening, axis=0, mode='constant') #print "Extracting the peaks..." all_peaktimes = numpy.zeros(0, dtype=numpy.int32) all_extremas = numpy.zeros(0, dtype=numpy.int32) for i in xrange(N_e): if sign_peaks == 'negative': peaktimes = algo.detect_peaks(local_chunk[:, i], thresholds[i], valley=True, mpd=dist_peaks) elif sign_peaks == 'positive': peaktimes = algo.detect_peaks(local_chunk[:, i], thresholds[i], valley=False, mpd=dist_peaks) elif sign_peaks == 'both': peaktimes = algo.detect_peaks(numpy.abs(local_chunk[:, i]), thresholds[i], valley=False, mpd=dist_peaks) all_peaktimes = numpy.concatenate((all_peaktimes, peaktimes)) all_extremas = numpy.concatenate( (all_extremas, i * numpy.ones(len(peaktimes), dtype=numpy.int32))) #print "Removing the useless borders..." if alignment: local_borders = (2 * template_shift, local_shape - 2 * template_shift) else: local_borders = (template_shift, local_shape - template_shift) idx = (all_peaktimes >= local_borders[0]) & (all_peaktimes < local_borders[1]) all_peaktimes = numpy.compress(idx, all_peaktimes) all_extremas = numpy.compress(idx, all_extremas) local_peaktimes = numpy.unique(all_peaktimes) if len(local_peaktimes) > 0: diff_times = local_peaktimes[-1] - local_peaktimes[0] all_times = numpy.zeros((N_e, diff_times + 1), dtype=numpy.bool) min_times = numpy.maximum( local_peaktimes - local_peaktimes[0] - safety_time, 0) max_times = numpy.minimum( local_peaktimes - local_peaktimes[0] + safety_time + 1, diff_times) n_times = len(local_peaktimes) argmax_peak = numpy.random.permutation(numpy.arange(n_times)) all_idx = numpy.take(local_peaktimes, argmax_peak) #print "Selection of the peaks with spatio-temporal masks..." for midx, peak in zip(argmax_peak, all_idx): if (elt_count_neg + elt_count_pos) == nb_elts: break if sign_peaks == 'negative': elec = numpy.argmin(local_chunk[peak]) negative_peak = True elif sign_peaks == 'positive': elec = numpy.argmax(local_chunk[peak]) negative_peak = False elif sign_peaks == 'both': if numpy.abs(numpy.max(local_chunk[peak])) > numpy.abs( numpy.min(local_chunk[peak])): elec = numpy.argmax(local_chunk[peak]) negative_peak = False else: elec = numpy.argmin(local_chunk[peak]) negative_peak = True indices = numpy.take(inv_nodes, edges[nodes[elec]]) myslice = all_times[indices, min_times[midx]:max_times[midx]] is_local_extrema = elec in all_extremas[all_peaktimes == peak] if is_local_extrema and not myslice.any(): upper_bounds = max_elts_elec if groups[elec] < upper_bounds: if negative_peak: elts_neg[:, elt_count_neg] = local_chunk[ peak - template_shift:peak + template_shift + 1, elec] else: elts_pos[:, elt_count_pos] = local_chunk[ peak - template_shift:peak + template_shift + 1, elec] if alignment: ydata = local_chunk[peak - 2 * template_shift:peak + 2 * template_shift + 1, elec] f = scipy.interpolate.UnivariateSpline(xdata, ydata, s=0) if negative_peak: rmin = (numpy.argmin(f(cdata)) - len(cdata) / 2.) / 5. else: rmin = (numpy.argmax(f(cdata)) - len(cdata) / 2.) / 5. ddata = numpy.linspace(rmin - template_shift, rmin + template_shift, N_t) if negative_peak: elts_neg[:, elt_count_neg] = f(ddata).astype( numpy.float32) else: elts_pos[:, elt_count_pos] = f(ddata).astype( numpy.float32) if negative_peak: elt_count_neg += 1 else: elt_count_pos += 1 groups[elec] += 1 all_times[indices, min_times[midx]:max_times[midx]] = True if comm.rank == 0: pbar.update(elt_count_pos + elt_count_neg) if comm.rank == 0: if (elt_count_pos + elt_count_neg < (gcount + 1) * max_elts_elec // len(chunks_to_load)): pbar.update( (gcount + 1) * max_elts_elec // len(chunks_to_load)) if comm.rank == 0: pbar.finish() print_and_log([ "Node %d has collected %d waveforms" % (comm.rank, elt_count_pos + elt_count_neg) ], 'debug', logger) if sign_peaks in ['negative', 'both']: gdata_neg = gather_array(elts_neg[:, :elt_count_neg].T, comm, 0, 1) if sign_peaks in ['positive', 'both']: gdata_pos = gather_array(elts_pos[:, :elt_count_pos].T, comm, 0, 1) if comm.rank == 0: #DO PCA on elts and store the basis obtained. nb_waveforms = 0 if sign_peaks in ['negative', 'both']: nb_waveforms += gdata_neg.shape[0] if sign_peaks in ['positive', 'both']: nb_waveforms += gdata_pos.shape[0] print_and_log([ "Found %d waveforms over %d requested" % (nb_waveforms, int(nb_elts * comm.size)) ], 'default', logger) pca = PCA(output_dim, copy=False) res = {} if sign_peaks in ['negative', 'both']: if len(gdata_neg) > 0: res_pca = pca.fit_transform(gdata_neg.astype( numpy.double)).astype(numpy.float32) res['proj'] = pca.components_.T.astype(numpy.float32) else: res['proj'] = numpy.identity(N_t, dtype=numpy.float32) res['rec'] = res['proj'].T res['waveform'] = numpy.median(gdata_neg, 0) idx = numpy.random.permutation(numpy.arange( gdata_neg.shape[0]))[:1000] res['waveforms'] = gdata_neg[idx, :] if sign_peaks in ['positive', 'both']: if len(gdata_pos) > 0: res_pca = pca.fit_transform(gdata_pos.astype( numpy.double)).astype(numpy.float32) res['proj_pos'] = pca.components_.T.astype(numpy.float32) else: res['proj_pos'] = numpy.identity(N_t, dtype=numpy.float32) res['rec_pos'] = res['proj_pos'].T res['waveform_pos'] = numpy.median(gdata_pos, 0) idx = numpy.random.permutation(numpy.arange( gdata_pos.shape[0]))[:1000] res['waveforms_pos'] = gdata_pos[idx, :] bfile = h5py.File(file_out_suff + '.basis.hdf5', 'r+', libver='latest') io.write_datasets(bfile, res.keys(), res) if sign_peaks == 'positive': print_and_log([ "A basis with %s dimensions has been built" % res['proj_pos'].shape[1] ], 'info', logger) elif sign_peaks == 'negative': print_and_log([ "A basis with %s dimensions has been built" % res['proj'].shape[1] ], 'info', logger) elif sign_peaks == 'both': print_and_log([ "Two basis with %s dimensions has been built" % res['proj'].shape[1] ], 'info', logger) bfile.close() comm.Barrier() if matched_filter: if comm.rank == 0: print_and_log([ "Because of matched filters, need to recompute the thresholds..." ], 'default', logger) if do_spatial_whitening: spatial_whitening = io.load_data(params, 'spatial_whitening') if use_gpu: spatial_whitening = cmt.CUDAMatrix(spatial_whitening, copy_on_host=False) if do_temporal_whitening: temporal_whitening = io.load_data(params, 'temporal_whitening') if sign_peaks in ['negative', 'both']: waveform_neg = io.load_data(params, 'waveform') waveform_neg /= (numpy.abs(numpy.sum(waveform_neg)) * len(waveform_neg)) if sign_peaks in ['positive', 'both']: waveform_pos = io.load_data(params, 'waveform-pos') waveform_pos /= (numpy.abs(numpy.sum(waveform_pos)) * len(waveform_pos)) for gidx in [all_chunks[comm.rank]]: local_chunk, t_offset = data_file.get_data(gidx, chunk_size, nodes=nodes) local_shape = len(local_chunk) if do_spatial_whitening: if use_gpu: local_chunk = cmt.CUDAMatrix(local_chunk, copy_on_host=False) local_chunk = local_chunk.dot(spatial_whitening).asarray() else: local_chunk = numpy.dot(local_chunk, spatial_whitening) if do_temporal_whitening: local_chunk = scipy.ndimage.filters.convolve1d( local_chunk, temporal_whitening, axis=0, mode='constant') if sign_peaks in ['negative', 'both']: tmp_chunk = scipy.ndimage.filters.convolve1d(local_chunk, waveform_neg, axis=0, mode='constant') thresholds = numpy.zeros(N_e, dtype=numpy.float32) for i in xrange(N_e): u = numpy.median(tmp_chunk[:, i], 0) thresholds[i] = numpy.median( numpy.abs(tmp_chunk[:, i] - u), 0) gdata = gather_array(thresholds, comm) if comm.rank == 0: gdata = gdata.reshape((comm.size, N_e)) thresholds = numpy.mean(gdata, 0) bfile = h5py.File(file_out + '.basis.hdf5', 'r+', libver='latest') io.write_datasets(bfile, ['matched_thresholds'], {'matched_thresholds': thresholds}) bfile.close() comm.Barrier() if sign_peaks in ['positive', 'both']: tmp_chunk = scipy.ndimage.filters.convolve1d(local_chunk, waveform_pos, axis=0, mode='constant') thresholds = numpy.zeros(N_e, dtype=numpy.float32) for i in xrange(N_e): u = numpy.median(tmp_chunk[:, i], 0) thresholds[i] = numpy.median( numpy.abs(tmp_chunk[:, i] - u), 0) gdata = gather_array(thresholds, comm) if comm.rank == 0: gdata = gdata.reshape((comm.size, N_e)) thresholds = numpy.mean(gdata, 0) bfile = h5py.File(file_out + '.basis.hdf5', 'r+', libver='latest') io.write_datasets(bfile, ['matched_thresholds_pos'], {'matched_thresholds_pos': thresholds}) bfile.close() comm.Barrier() data_file.close()
def main(params, nb_cpu, nb_gpu, use_gpu): # Part 1: Whitening numpy.random.seed(420) #params = detect_memory(params) logger = init_logging(params.logfile) logger = logging.getLogger('circus.whitening') ################################################################# data_file = params.data_file N_e = params.getint('data', 'N_e') hdf5_compress = params.getboolean('data', 'hdf5_compress') N_total = params.nb_channels N_t = params.getint('detection', 'N_t') dist_peaks = params.getint('detection', 'dist_peaks') template_shift = params.getint('detection', 'template_shift') file_out_suff = params.get('data', 'file_out_suff') spike_thresh = params.getfloat('detection', 'spike_thresh') spike_width = params.getfloat('detection', 'spike_width') matched_filter = params.getboolean('detection', 'matched-filter') matched_thresh = params.getfloat('detection', 'matched_thresh') sign_peaks = params.get('detection', 'peaks') do_temporal_whitening = params.getboolean('whitening', 'temporal') do_spatial_whitening = params.getboolean('whitening', 'spatial') chunk_size = detect_memory(params, whitening=True) plot_path = os.path.join(params.get('data', 'file_out_suff'), 'plots') nodes, edges = get_nodes_and_edges(params) safety_time = params.getint('whitening', 'safety_time') safety_space = params.getboolean('whitening', 'safety_space') nb_temp_white = min(max(20, comm.size), N_e) max_silence_1 = int(20 * params.rate // comm.size) max_silence_2 = 5000 inv_nodes = numpy.zeros(N_total, dtype=numpy.int32) inv_nodes[nodes] = numpy.arange(len(nodes)) jitter_range = params.getint('detection', 'jitter_range') template_shift_2 = template_shift + jitter_range use_hanning = params.getboolean('detection', 'hanning') data_file.open() ################################################################# if use_hanning: hanning_filter = numpy.hanning(N_t) if comm.rank == 0: print_and_log( ["Analyzing data to get whitening matrices and thresholds..."], 'default', logger) if use_gpu: import cudamat as cmt ## Need to properly handle multi GPU per MPI nodes? if nb_gpu > nb_cpu: gpu_id = int(comm.rank // nb_cpu) else: gpu_id = 0 cmt.cuda_set_device(gpu_id) cmt.init() cmt.cuda_sync_threads() nb_chunks, last_chunk_len = data_file.analyze(chunk_size) if nb_chunks < comm.size: res = io.data_stats(params, show=False) chunk_size = int(res * params.rate // comm.size) if comm.rank == 0: print_and_log( ["Too much cores, automatically resizing the data chunks"], 'debug', logger) nb_chunks, last_chunk_len = data_file.analyze(chunk_size) # I guess this is more relevant, to take signals from all over the recordings all_chunks = numpy.random.permutation( numpy.arange(nb_chunks, dtype=numpy.int32)) all_electrodes = numpy.random.permutation(N_e) for gidx in [all_chunks[comm.rank]]: #print "Node", comm.rank, "is analyzing chunk", gidx, "/", nb_chunks, " ..." local_chunk, t_offset = data_file.get_data(gidx, chunk_size, nodes=nodes) local_shape = len(local_chunk) #print "Node", comm.rank, "computes the median absolute deviations in a random chunk" thresholds = numpy.zeros(N_e, dtype=numpy.float32) for i in xrange(N_e): u = numpy.median(local_chunk[:, i], 0) thresholds[i] = numpy.median(numpy.abs(local_chunk[:, i] - u), 0) gdata = gather_array(thresholds, comm) if comm.rank == 0: gdata = gdata.reshape((comm.size, N_e)) thresholds = numpy.mean(gdata, 0) bfile = h5py.File(file_out_suff + '.basis.hdf5', 'w', libver='earliest') io.write_datasets(bfile, ['thresholds'], {'thresholds': thresholds}, compression=hdf5_compress) bfile.close() comm.Barrier() thresholds = io.load_data(params, 'thresholds') #print "Extracting the peaks..." local_peaktimes = numpy.zeros(0, dtype=numpy.uint32) for i in xrange(N_e): peaktimes = scipy.signal.find_peaks(numpy.abs(local_chunk[:, i]), height=thresholds[i], width=spike_width, wlen=N_t)[0] local_peaktimes = numpy.concatenate((local_peaktimes, peaktimes)) local_peaktimes = numpy.unique(local_peaktimes) #print "Removing the useless borders..." local_borders = (template_shift, local_shape - template_shift) idx = (local_peaktimes >= local_borders[0]) & (local_peaktimes < local_borders[1]) local_peaktimes = numpy.compress(idx, local_peaktimes) if len(local_peaktimes) > 0: diff_times = local_peaktimes[-1] - local_peaktimes[0] all_times = numpy.zeros((N_e, diff_times + 1), dtype=numpy.bool) min_times = numpy.maximum( local_peaktimes - local_peaktimes[0] - safety_time, 0) max_times = numpy.minimum( local_peaktimes - local_peaktimes[0] + safety_time + 1, diff_times) argmax_peak = numpy.random.permutation( numpy.arange(len(local_peaktimes))) all_idx = numpy.take(local_peaktimes, argmax_peak) #print "Selection of the peaks with spatio-temporal masks..." for idx, peak in zip(argmax_peak, all_idx): elec = numpy.argmax(numpy.abs(local_chunk[peak])) indices = numpy.take(inv_nodes, edges[nodes[elec]]) if safety_space: all_times[indices, min_times[idx]:max_times[idx]] = True else: all_times[elec, min_times[idx]:max_times[idx]] = True else: all_times = numpy.zeros((N_e, len(local_chunk)), dtype=numpy.bool) if do_temporal_whitening: local_res_temp = [] for elec in all_electrodes[numpy.arange(comm.rank, nb_temp_white, comm.size)]: res = numpy.zeros((0, N_t), dtype=numpy.float32) scount = 0 indices = numpy.take(inv_nodes, edges[nodes[elec]]) all_times_elec = numpy.any(numpy.take(all_times, indices, axis=0), 0) esubset = numpy.where(all_times_elec == False)[0] bound = len(esubset) - N_t while (scount < bound) and (len(res) < max_silence_2): myslice = esubset[scount:scount + N_t] if numpy.all((myslice - esubset[scount]) == numpy.arange(N_t)): scount += N_t res = numpy.vstack((res, local_chunk[myslice, elec])) else: scount += 1 if len(res) > 5: local_res_temp += [numpy.cov(res.T)] nb_elecs = numpy.array([len(local_res_temp)], dtype=numpy.float32) local_res_temp = numpy.array(local_res_temp, dtype=numpy.float32) if len(local_res_temp) == 0: local_res_temp = numpy.zeros(0, dtype=numpy.float32) else: local_res_temp = numpy.sum(local_res_temp, 0) all_res_temp = gather_array(local_res_temp.ravel(), comm, 0, 1) all_elecs = gather_array(nb_elecs, comm, 0, 1) if do_spatial_whitening: local_res_spac = numpy.zeros((N_e, N_e), dtype=numpy.float32) local_silences = [] for elec in numpy.arange(comm.rank, N_e, comm.size): indices = numpy.take(inv_nodes, edges[nodes[elec]]) all_times_elec = numpy.any(numpy.take(all_times, indices, axis=0), 0) esubset = numpy.where(all_times_elec == False)[0] local_data = local_chunk[esubset][:, indices] local_whitening = get_whitening_matrix(local_data).astype( numpy.float32) pos = numpy.where(elec == indices)[0] local_res_spac[elec, indices] = local_whitening[pos] local_silences += [len(esubset)] all_res_spac = gather_array(local_res_spac.ravel(), comm, 0, 1) all_silences = gather_array( numpy.array(local_silences, dtype=numpy.int32), comm, 0, 1, 'uint32') if comm.rank == 0: to_write = {} if do_temporal_whitening: try: nb_silences = numpy.sum(all_elecs > 0) all_res_temp = all_res_temp.reshape((nb_silences, N_t**2)) except Exception: print_and_log([ "No silent periods detected: something wrong with the parameters?" ], 'error', logger) all_res_temp = numpy.sum(all_res_temp, 0) all_res_temp = all_res_temp.reshape( (N_t, N_t)) / numpy.sum(all_elecs) temporal_whitening = get_whitening_matrix( all_res_temp.astype(numpy.double), fudge=1e-3)[template_shift].astype(numpy.float32) temporal_whitening /= temporal_whitening.sum() to_write['temporal'] = temporal_whitening have_nans = numpy.sum(numpy.isnan(temporal_whitening)) if have_nans > 0: temporal_whitening = numpy.zeros(N_t, dtype=numpy.float32) temporal_whitening[N_t // 2] = 1 to_write['temporal'] = temporal_whitening print_and_log( ["Disabling temporal whitening because of NaNs found"], 'info', logger) if do_spatial_whitening: all_res_spac = all_res_spac.reshape(comm.size, N_e, N_e) spatial_whitening = numpy.sum(all_res_spac, 0) to_write['spatial'] = spatial_whitening print_and_log([ "Found %gs without spikes for whitening matrices..." % (numpy.mean(all_silences) / params.rate) ], 'default', logger) have_nans = numpy.sum(numpy.isnan(spatial_whitening)) if have_nans > 0: spatial_whitening = numpy.eye(spatial_whitening.shape[0], dtype=numpy.float32) to_write['spatial'] = spatial_whitening print_and_log( ["Disabling spatial whitening because of NaNs found"], 'info', logger) bfile = h5py.File(file_out_suff + '.basis.hdf5', 'r+', libver='earliest') io.write_datasets(bfile, to_write.keys(), to_write, compression=hdf5_compress) bfile.close() comm.Barrier() if do_spatial_whitening or do_temporal_whitening: if comm.rank == 0: print_and_log( ["Because of whitening, need to recompute the thresholds..."], 'default', logger) if do_spatial_whitening: spatial_whitening = io.load_data(params, 'spatial_whitening') if use_gpu: spatial_whitening = cmt.CUDAMatrix(spatial_whitening, copy_on_host=False) if do_temporal_whitening: temporal_whitening = io.load_data(params, 'temporal_whitening') for gidx in [all_chunks[comm.rank]]: local_chunk, t_offset = data_file.get_data(gidx, chunk_size, nodes=nodes) local_shape = len(local_chunk) if do_spatial_whitening: if use_gpu: local_chunk = cmt.CUDAMatrix(local_chunk, copy_on_host=False) local_chunk = local_chunk.dot(spatial_whitening).asarray() else: local_chunk = numpy.dot(local_chunk, spatial_whitening) if do_temporal_whitening: local_chunk = scipy.ndimage.filters.convolve1d( local_chunk, temporal_whitening, axis=0, mode='constant') thresholds = numpy.zeros(N_e, dtype=numpy.float32) for i in xrange(N_e): u = numpy.median(local_chunk[:, i], 0) thresholds[i] = numpy.median(numpy.abs(local_chunk[:, i] - u), 0) gdata = gather_array(thresholds, comm) if comm.rank == 0: gdata = gdata.reshape((comm.size, N_e)) thresholds = numpy.mean(gdata, 0) bfile = h5py.File(file_out_suff + '.basis.hdf5', 'r+', libver='earliest') bfile.pop('thresholds') io.write_datasets(bfile, ['thresholds'], {'thresholds': thresholds}, compression=hdf5_compress) bfile.close() comm.Barrier() #if comm.rank == 0: #if not os.path.exists(plot_path): # os.makedirs(plot_path) #N_elec = min(int(numpy.sqrt(data_file.N_e)), 5) #plot.view_fit(filename, t_start=0, t_stop=1, fit_on=False, square=True, # n_elec=N_elec, save=[plot_path, 'electrodes']) # Part 2: Basis numpy.random.seed(422) ################################################################# file_out = params.get('data', 'file_out') alignment = params.getboolean('detection', 'alignment') smoothing = params.getboolean('detection', 'smoothing') isolation = params.getboolean('detection', 'isolation') over_factor = params.getint('detection', 'oversampling_factor') spike_thresh = params.getfloat('detection', 'spike_thresh') nodes, edges = get_nodes_and_edges(params) do_temporal_whitening = params.getboolean('whitening', 'temporal') do_spatial_whitening = params.getboolean('whitening', 'spatial') if matched_filter: chunk_size = detect_memory(params, whitening=True) else: chunk_size = detect_memory(params) safety_time = params.getint('whitening', 'safety_time') max_elts_elec = params.getint('whitening', 'max_elts') output_dim = params.getfloat('whitening', 'output_dim') inv_nodes = numpy.zeros(N_total, dtype=numpy.int32) inv_nodes[nodes] = numpy.arange(len(nodes)) smoothing_factor = params.getfloat( 'detection', 'smoothing_factor') * (1. / spike_thresh)**2 if sign_peaks == 'both': max_elts_elec *= 2 nb_elts = int( params.getfloat('whitening', 'nb_elts') * N_e * max_elts_elec) ignore_dead_times = params.getboolean('triggers', 'ignore_times') if ignore_dead_times: all_dead_times = get_dead_times(params) data_file.open() ################################################################# if comm.rank == 0: print_and_log(["Searching spikes to construct the PCA basis..."], 'default', logger) nb_chunks, last_chunk_len = data_file.analyze(chunk_size) if nb_chunks < comm.size: res = io.data_stats(params, show=False) chunk_size = int(res * params.rate // comm.size) if comm.rank == 0: print_and_log( ["Too much cores, automatically resizing the data chunks"], 'debug', logger) nb_chunks, last_chunk_len = data_file.analyze(chunk_size) groups = {} for i in xrange(N_e): groups[i] = 0 # I guess this is more relevant, to take signals from all over the recordings all_chunks = numpy.random.permutation( numpy.arange(nb_chunks, dtype=numpy.int32)) max_elts_elec //= comm.size nb_elts //= comm.size elt_count_pos = 0 elt_count_neg = 0 if sign_peaks in ['positive', 'both']: elts_pos = numpy.zeros((N_t, nb_elts), dtype=numpy.float32) if sign_peaks in ['negative', 'both']: elts_neg = numpy.zeros((N_t, nb_elts), dtype=numpy.float32) chunks_to_load = all_chunks[comm.rank::comm.size] thresholds = io.load_data(params, 'thresholds') mads = io.load_data(params, 'mads') if alignment: cdata = numpy.linspace(-jitter_range, jitter_range, int(over_factor * 2 * jitter_range)) xdata = numpy.arange(-template_shift_2, template_shift_2 + 1) xoff = len(cdata) / 2. if isolation: yoff = numpy.array(range(0, N_t // 4) + range(3 * N_t // 4, N_t)) to_explore = xrange(comm.rank, nb_chunks, comm.size) if comm.rank == 0: to_explore = get_tqdm_progressbar(to_explore) for gcount, gidx in enumerate(to_explore): gidx = all_chunks[gidx] if ((elt_count_pos + elt_count_neg) < nb_elts): #print "Node", comm.rank, "is analyzing chunk", gidx, "/", nb_chunks, " ..." local_chunk, t_offset = data_file.get_data(gidx, chunk_size, nodes=nodes) local_shape = len(local_chunk) if do_spatial_whitening: if use_gpu: local_chunk = cmt.CUDAMatrix(local_chunk, copy_on_host=False) local_chunk = local_chunk.dot(spatial_whitening).asarray() else: local_chunk = numpy.dot(local_chunk, spatial_whitening) if do_temporal_whitening: local_chunk = scipy.ndimage.filters.convolve1d( local_chunk, temporal_whitening, axis=0, mode='constant') #print "Extracting the peaks..." all_peaktimes = numpy.zeros(0, dtype=numpy.uint32) all_extremas = numpy.zeros(0, dtype=numpy.uint32) for i in xrange(N_e): if sign_peaks == 'negative': peaktimes = scipy.signal.find_peaks(-local_chunk[:, i], height=thresholds[i], width=spike_width, distance=dist_peaks, wlen=N_t)[0] elif sign_peaks == 'positive': peaktimes = scipy.signal.find_peaks(local_chunk[:, i], height=thresholds[i], width=spike_width, wlen=N_t)[0] elif sign_peaks == 'both': peaktimes = scipy.signal.find_peaks(numpy.abs( local_chunk[:, i]), height=thresholds[i], width=spike_width, wlen=N_t)[0] all_peaktimes = numpy.concatenate((all_peaktimes, peaktimes)) all_extremas = numpy.concatenate( (all_extremas, i * numpy.ones(len(peaktimes), dtype=numpy.uint32))) #print "Removing the useless borders..." if alignment: local_borders = (template_shift_2, local_shape - template_shift_2) else: local_borders = (template_shift, local_shape - template_shift) idx = (all_peaktimes >= local_borders[0]) & (all_peaktimes < local_borders[1]) all_peaktimes = numpy.compress(idx, all_peaktimes) all_extremas = numpy.compress(idx, all_extremas) local_peaktimes = numpy.unique(all_peaktimes) if ignore_dead_times: dead_indices = numpy.searchsorted( all_dead_times, [t_offset, t_offset + local_shape]) if dead_indices[0] != dead_indices[1]: is_included = numpy.in1d( local_peaktimes + t_offset, all_dead_times[dead_indices[0]:dead_indices[1]]) local_peaktimes = local_peaktimes[~is_included] local_peaktimes = numpy.sort(local_peaktimes) if len(local_peaktimes) > 0: diff_times = local_peaktimes[-1] - local_peaktimes[0] all_times = numpy.zeros((N_e, diff_times + 1), dtype=numpy.bool) min_times = numpy.maximum( local_peaktimes - local_peaktimes[0] - safety_time, 0) max_times = numpy.minimum( local_peaktimes - local_peaktimes[0] + safety_time + 1, diff_times) n_times = len(local_peaktimes) argmax_peak = numpy.random.permutation(numpy.arange(n_times)) all_idx = numpy.take(local_peaktimes, argmax_peak) #print "Selection of the peaks with spatio-temporal masks..." for midx, peak in zip(argmax_peak, all_idx): if (elt_count_neg + elt_count_pos) == nb_elts: break if sign_peaks == 'negative': elec = numpy.argmin(local_chunk[peak]) negative_peak = True elif sign_peaks == 'positive': elec = numpy.argmax(local_chunk[peak]) negative_peak = False elif sign_peaks == 'both': if N_e == 1: if local_chunk[peak] < 0: negative_peak = True elif local_chunk[peak] > 0: negative_peak = False elec = 0 else: if numpy.abs(numpy.max( local_chunk[peak])) > numpy.abs( numpy.min(local_chunk[peak])): elec = numpy.argmax(local_chunk[peak]) negative_peak = False else: elec = numpy.argmin(local_chunk[peak]) negative_peak = True indices = numpy.take(inv_nodes, edges[nodes[elec]]) myslice = all_times[indices, min_times[midx]:max_times[midx]] is_local_extrema = elec in all_extremas[all_peaktimes == peak] if is_local_extrema and not myslice.any(): upper_bounds = max_elts_elec if groups[elec] < upper_bounds: if not alignment: sub_mat = local_chunk[peak - template_shift:peak + template_shift + 1, elec] elif alignment: ydata = local_chunk[peak - template_shift_2:peak + template_shift_2 + 1, elec] if smoothing: factor = smoothing_factor * xdata.size f = scipy.interpolate.UnivariateSpline( xdata, ydata, s=factor, k=3) else: f = scipy.interpolate.UnivariateSpline( xdata, ydata, k=3, s=0) if negative_peak: rmin = (numpy.argmin(f(cdata)) - xoff) / over_factor else: rmin = (numpy.argmax(f(cdata)) - xoff) / over_factor ddata = numpy.linspace(rmin - template_shift, rmin + template_shift, N_t) sub_mat = f(ddata).astype(numpy.float32) if alignment: if negative_peak: if numpy.min(sub_mat) >= -thresholds[elec]: to_accept = False else: if numpy.max(sub_mat) <= thresholds[elec]: to_accept = False if isolation: to_accept = numpy.all( numpy.max(numpy.abs(sub_mat[yoff])) <= thresholds[elec]) else: to_accept = True if to_accept: if negative_peak: elts_neg[:, elt_count_neg] = sub_mat else: elts_pos[:, elt_count_pos] = sub_mat if negative_peak: elt_count_neg += 1 else: elt_count_pos += 1 groups[elec] += 1 all_times[indices, min_times[midx]:max_times[midx]] = True sys.stderr.flush() if isolation: print_and_log([ "Node %d has collected %d isolated waveforms" % (comm.rank, elt_count_pos + elt_count_neg) ], 'debug', logger) else: print_and_log([ "Node %d has collected %d waveforms" % (comm.rank, elt_count_pos + elt_count_neg) ], 'debug', logger) if sign_peaks in ['negative', 'both']: gdata_neg = gather_array(elts_neg[:, :elt_count_neg].T, comm, 0, 1) if sign_peaks in ['positive', 'both']: gdata_pos = gather_array(elts_pos[:, :elt_count_pos].T, comm, 0, 1) nb_waveforms = 0 if comm.rank == 0: #DO PCA on elts and store the basis obtained. if sign_peaks in ['negative', 'both']: nb_waveforms += gdata_neg.shape[0] if sign_peaks in ['positive', 'both']: nb_waveforms += gdata_pos.shape[0] nb_waveforms = all_gather_array( numpy.array([nb_waveforms], dtype=numpy.float32), comm, 0)[0] if comm.rank == 0: if isolation: print_and_log([ "Found %d isolated waveforms over %d requested" % (nb_waveforms, int(nb_elts * comm.size)) ], 'default', logger) else: print_and_log([ "Found %d waveforms over %d requested" % (nb_waveforms, int(nb_elts * comm.size)) ], 'default', logger) if nb_waveforms == 0: print_and_log( ['No waveforms found! Are the data properly loaded??'], 'error', logger) if nb_waveforms == 0: sys.exit(0) if comm.rank == 0: res = {} pca = None pca_pos = None pca_neg = None if sign_peaks in ['negative', 'both']: if len(gdata_neg) > 0: pca = PCA(output_dim) if use_hanning: pca.fit(gdata_neg * hanning_filter) else: pca.fit(gdata_neg) res['proj'] = pca.components_.T.astype(numpy.float32) pca_neg = numpy.sum(pca.explained_variance_ratio_) else: res['proj'] = numpy.identity(int(output_dim), dtype=numpy.float32) res['rec'] = res['proj'].T res['waveform'] = numpy.median(gdata_neg, 0) idx = numpy.random.permutation(numpy.arange( gdata_neg.shape[0]))[:1000] res['waveforms'] = gdata_neg[idx, :] if sign_peaks in ['positive', 'both']: if len(gdata_pos) > 0: pca = PCA(output_dim) if use_hanning: pca.fit(gdata_pos * hanning_filter) else: pca.fit(gdata_pos) res['proj_pos'] = pca.components_.T.astype(numpy.float32) pca_pos = numpy.sum(pca.explained_variance_ratio_) else: res['proj_pos'] = numpy.identity(int(output_dim), dtype=numpy.float32) res['rec_pos'] = res['proj_pos'].T res['waveform_pos'] = numpy.median(gdata_pos, 0) idx = numpy.random.permutation(numpy.arange( gdata_pos.shape[0]))[:1000] res['waveforms_pos'] = gdata_pos[idx, :] bfile = h5py.File(file_out_suff + '.basis.hdf5', 'r+', libver='earliest') io.write_datasets(bfile, res.keys(), res, compression=hdf5_compress) if sign_peaks == 'positive': print_and_log([ "A basis with %s dimensions has been built" % res['proj_pos'].shape[1] ], 'info', logger) elif sign_peaks == 'negative': print_and_log([ "A basis with %s dimensions has been built" % res['proj'].shape[1] ], 'info', logger) elif sign_peaks == 'both': print_and_log([ "Two basis with %s dimensions has been built" % res['proj'].shape[1] ], 'debug', logger) if pca_pos is not None: print_and_log([ "The percentage of variance explained is %s for positive spikes" % pca_pos ], 'debug', logger) if pca_neg is not None: print_and_log([ "The percentage of variance explained is %s for negative spikes" % pca_neg ], 'debug', logger) bfile.close() comm.Barrier() if matched_filter: if comm.rank == 0: print_and_log([ "Because of matched filters, need to recompute the thresholds..." ], 'default', logger) if do_spatial_whitening: spatial_whitening = io.load_data(params, 'spatial_whitening') if use_gpu: spatial_whitening = cmt.CUDAMatrix(spatial_whitening, copy_on_host=False) if do_temporal_whitening: temporal_whitening = io.load_data(params, 'temporal_whitening') if sign_peaks in ['negative', 'both']: waveform_neg = io.load_data(params, 'waveform')[::-1] waveform_neg /= (numpy.abs(numpy.sum(waveform_neg)) * len(waveform_neg)) if sign_peaks in ['positive', 'both']: waveform_pos = io.load_data(params, 'waveform-pos')[::-1] waveform_pos /= (numpy.abs(numpy.sum(waveform_pos)) * len(waveform_pos)) for gidx in [all_chunks[comm.rank]]: local_chunk, t_offset = data_file.get_data(gidx, chunk_size, nodes=nodes) local_shape = len(local_chunk) if do_spatial_whitening: if use_gpu: local_chunk = cmt.CUDAMatrix(local_chunk, copy_on_host=False) local_chunk = local_chunk.dot(spatial_whitening).asarray() else: local_chunk = numpy.dot(local_chunk, spatial_whitening) if do_temporal_whitening: local_chunk = scipy.ndimage.filters.convolve1d( local_chunk, temporal_whitening, axis=0, mode='constant') local_chunk /= thresholds if sign_peaks in ['negative', 'both']: tmp_chunk = scipy.ndimage.filters.convolve1d(local_chunk, waveform_neg, axis=0, mode='constant') thresholds = numpy.zeros(N_e, dtype=numpy.float32) for i in xrange(N_e): u = numpy.median(tmp_chunk[:, i], 0) thresholds[i] = numpy.median( numpy.abs(tmp_chunk[:, i] - u), 0) gdata = gather_array(thresholds, comm) if comm.rank == 0: gdata = gdata.reshape((comm.size, N_e)) thresholds = numpy.mean(gdata, 0) bfile = h5py.File(file_out_suff + '.basis.hdf5', 'r+', libver='earliest') io.write_datasets(bfile, ['matched_thresholds'], {'matched_thresholds': thresholds}, compression=hdf5_compress) bfile.close() comm.Barrier() if sign_peaks in ['positive', 'both']: tmp_chunk = scipy.ndimage.filters.convolve1d(local_chunk, waveform_pos, axis=0, mode='constant') thresholds = numpy.zeros(N_e, dtype=numpy.float32) for i in xrange(N_e): u = numpy.median(tmp_chunk[:, i], 0) thresholds[i] = numpy.median( numpy.abs(tmp_chunk[:, i] - u), 0) gdata = gather_array(thresholds, comm) if comm.rank == 0: gdata = gdata.reshape((comm.size, N_e)) thresholds = numpy.mean(gdata, 0) bfile = h5py.File(file_out_suff + '.basis.hdf5', 'r+', libver='earliest') io.write_datasets(bfile, ['matched_thresholds_pos'], {'matched_thresholds_pos': thresholds}, compression=hdf5_compress) bfile.close() comm.Barrier() data_file.close()
def init_gui_layout(self): gui_fname = pkg_resources.resource_filename( 'circus', os.path.join('qt_GUI', 'qt_launcher.ui')) self.ui = uic.loadUi(gui_fname) self.task_comboboxes = [ cb for cb in self.ui.grp_tasks.children() if isinstance(cb, QCheckBox) ] logo = pkg_resources.resource_filename( 'circus', os.path.join('icons', 'icon.png')) self.ui.setWindowIcon(QIcon(logo)) try: import cudamat as cmt cmt.init() self.HAVE_CUDA = True except Exception: self.HAVE_CUDA = False self.params = None self.ui.btn_run.clicked.connect(self.run) self.ui.btn_plots.clicked.connect(self.open_plot_folder) self.ui.btn_phy.clicked.connect(self.help_phy) self.ui.btn_matlab.clicked.connect(self.help_matlab) self.ui.btn_help_cpus.clicked.connect(self.help_cpus) self.ui.btn_help_gpus.clicked.connect(self.help_gpus) self.ui.btn_help_file_format.clicked.connect(self.help_file_format) self.ui.tabWidget.currentChanged.connect(self.changing_tab) self.ui.btn_stop.clicked.connect(self.stop) self.ui.btn_file.clicked.connect(self.update_data_file) self.ui.btn_about.clicked.connect(self.show_about) self.ui.rb_gui_matlab.clicked.connect(self.update_gui_command) self.ui.rb_gui_python.clicked.connect(self.update_gui_command) self.ui.btn_output.clicked.connect(self.update_output_file) self.ui.btn_hostfile.clicked.connect(self.update_host_file) self.ui.btn_log.clicked.connect(self.open_log_file) self.ui.cb_batch.toggled.connect(self.update_batch_mode) self.ui.cb_preview.toggled.connect(self.update_preview_mode) self.ui.cb_results.toggled.connect(self.update_results_mode) self.ui.cb_benchmarking.toggled.connect(self.update_benchmarking) self.ui.cb_merging.toggled.connect(self.update_extension) self.ui.cb_converting.toggled.connect(self.update_extension) self.ui.cb_deconverting.toggled.connect(self.update_extension) self.update_benchmarking() self.update_extension() for cb in self.task_comboboxes: cb.toggled.connect(self.store_tasks) cb.toggled.connect(self.update_command) self.ui.edit_file.textChanged.connect(self.update_command) self.ui.edit_output.textChanged.connect(self.update_command) self.ui.edit_hostfile.textChanged.connect(self.update_command) self.ui.edit_extension.textChanged.connect(self.update_command) self.ui.gui_extension.textChanged.connect(self.update_gui_command) self.ui.param_editor.textChanged.connect(self.save_params) self.ui.spin_cpus.valueChanged.connect(self.update_command) if not self.HAVE_CUDA: self.ui.spin_gpus.setEnabled(False) self.ui.spin_gpus.valueChanged.connect(self.update_command) self.store_tasks() self.process = None self.ui.closeEvent = self.closeEvent self.last_log_file = None try: import sklearn except ImportError: self.ui.cb_validating.setEnabled(False) self.ui.show()
def TrainAutoencoder(self, _raaX, oOptions): # initialize cudamat cudamat.init() cudamat.CUDAMatrix.init_random(seed = 42) # Count the number of training samples raaX = cudamat.CUDAMatrix(_raaX) iSamples = raaX.shape[0] # For each layer pair... for iLayer in range(len(self.oaLayer)-1): # Clone layer weights on device raaW = cudamat.CUDAMatrix(self.oaLayer[iLayer].raaW) raV = cudamat.CUDAMatrix(numpy.atleast_2d(self.oaLayer[iLayer].raV)) raH = cudamat.CUDAMatrix(numpy.atleast_2d(self.oaLayer[iLayer].raH)) # Measure this layer iVs = self.oaLayer[iLayer].raaW.shape[0] iHs = self.oaLayer[iLayer].raaW.shape[1] # Create a delta array to retain momentum state raaDelta = cudamat.zeros((iVs,iHs)) raDeltaV = cudamat.zeros((iVs,1)) raDeltaH = cudamat.zeros((iHs,1)) # Create a diff array to retain current update raaDiff = cudamat.empty((iVs,iHs)) raDiffV = cudamat.empty((1,iVs)) raDiffH = cudamat.empty((1,iHs)) # Create an array to retain the layer output for # training the next layer raaY = cudamat.empty((iSamples, iHs)) # Get short references to layer parameters sActivationUp = self.oaLayer[iLayer].sActivationUp sActivationDn = self.oaLayer[iLayer].sActivationDn junk = None; # For each training epoch... for iEpoch in range(oOptions.iEpochs): # Get short references to epoch parameters rDropV = oOptions.oaLayer[iLayer].raDropV[iEpoch] rDropH = oOptions.oaLayer[iLayer].raDropH[iEpoch] rMomentum = oOptions.oaLayer[iLayer].raMomentum[iEpoch] rRate = oOptions.oaLayer[iLayer].raRate[iEpoch] bSample = oOptions.oaLayer[iLayer].baSample[iEpoch] # Clear the sample index iIndex = 0 # Clear error accumulators for this layer rTotalSe = 0 rTotalE = 0 # While training samples remain... while (iIndex<iSamples): # Number of samples to process in this batch iBatch = min(self.iBatchSamples, iSamples-iIndex) # Create working arrays on the device baaH = cudamat.empty((iBatch,iHs)) raaH1d = cudamat.empty((iBatch,iHs)) raaH1s = cudamat.empty((iBatch,iHs)) raaH3 = cudamat.empty((iBatch,iHs)) baaV = cudamat.empty((iBatch,iVs)) raaV0 = cudamat.empty((iBatch,iVs)) raaV2 = cudamat.empty((iBatch,iVs)) # Get a batch of inputs in raaV0 raaX.get_row_slice(iIndex, iIndex+iBatch, target=raaV0) # If we need to drop visible units... if(rDropV>0): # Compute a mask baaV.fill_with_rand() baaV.greater_than(rDropV) raaV0.mult(baaV) # Advance the markov chain V0->H1 # raaH1d, raaH1s = self._UpdateStates(sActivationUp, raaW, raH, raaV0, rDropV, True) self._UpdateStates(sActivationUp, raaW, raH, raaV0, raaH1d, raaH1s, rDropV, True) # If stochastic sampling is enabled... if (bSample): # Use sampled states raaH1 = raaH1s else: # Use deterministic states raaH1 = raaH1d # If we need to drop hidden units... if(rDropH>0): # Compute a mask baaH.fill_with_rand() baaH.greater_than(rDropH) raaH1.mult(baaH) # Advance the markov chain H1->V2 # raaV2, junk = self._UpdateStates(sActivationDn, raaW.T, raV, raaH1, rDropH) self._UpdateStates(sActivationDn, raaW.T, raV, raaH1, raaV2, junk, rDropH) # If we need to drop visible units... if(rDropV>0): # Clear dropped states raaV2.mult(baaV) # Advance the markov chain V2->H3 # raaH3, junk = self._UpdateStates(sActivationUp, raaW, raH, raaV2, rDropV) self._UpdateStates(sActivationUp, raaW, raH, raaV2, raaH3, junk, rDropV) # If we need to drop hidden units... if(rDropH>0): # Clear dropped states raaH3.mult(baaH) # Scale factor to average this batch rScale = 1/iBatch # If normalizing the dropout gradient by the number # of weight updates rather the number of batch # samples. if (self.bNormalizeDropoutGradient): # If no visible layer dropout... if (not rDropV): # Construct a null dropout matrix baaV.assign(1) # If no hidden layer dropout... if (not rDropH): # Construct a null dropout matrix baaH.assign(1) # Compute normalizer matrix #raaN = 1./(double(~baaV).T*(~baaH)) cudamat.dot(baaV.T,baaH,raaN) raaN.reciprocal() # Compute the average difference between positive phase # up(0,1) and negative phase up(2,3) correlations # raaDiff = numpy.multiply( numpy.dot(raaV0.T,raaH1) - numpy.dot(raaV2.T,raaH3) , raaN) cudamat.dot(raaV0.T,raaH1,raaDiff) raaDiff.subtract_dot(raaV2.T,raaH3) raaDiff.mult(raaN) else: # Scale all weights uniformly #raaDiff = ( numpy.dot(raaV0.T,raaH1) - numpy.dot(raaV2.T,raaH3) )*rScale cudamat.dot(raaV0.T,raaH1,raaDiff) raaDiff.subtract_dot(raaV2.T,raaH3) raaDiff.mult(rScale) # Compute bias gradients #raDiffV = numpy.sum(raaV0-raaV2,axis=0)*rScale #raDiffH = numpy.sum(raaH1-raaH3,axis=0)*rScale raaV0.sum(axis=0,mult=rScale).subtract(raaV2.sum(axis=0,mult=rScale),target=raDiffV) raaH1.sum(axis=0,mult=rScale).subtract(raaH3.sum(axis=0,mult=rScale),target=raDiffH) # Update the weight delta array using the current momentum and # learning rate # raaDelta = raaDelta*rMomentum + raaDiff*rRate raaDelta.mult(rMomentum) raaDiff.mult(rRate) raaDelta.add(raaDiff) # Updated the weights #self.oaLayer[iLayer].raaW = self.oaLayer[iLayer].raaW + raaDelta raaW.add(raaDelta) # Advance to the next minibatch iIndex = iIndex + iBatch # raaXr = cudamat.empty((iSamples, iVs)) # raaV2, junk = self._UpdateStates(sActivationDn, raaW.T, raV, raaH1, 0) self._UpdateStates(sActivationUp, raaW, raH, raaX, raaY, junk, 0) # raaV2, junk = self._UpdateStates(sActivationDn, raaW.T, raV, raaH1, 0) self._UpdateStates(sActivationDn, raaW.T, raV, raaY, raaXr, junk, 0) rTotalSe, rTotalE = self.GetErrors(raaX, raaXr, sActivationDn) # Finish the rmse calculation rRmse = math.sqrt(rTotalSe/(raaX.shape[0]*raaX.shape[1])) # Finish rmse calculation rError = rTotalE/(raaX.shape[0]*raaX.shape[1]) # Report training progress oOptions.fEvent(iLayer, iEpoch, bSample, rDropV, rDropH, rRate, rMomentum, rRmse, rError) # Current layer outputs are the next layer inputs raaX = raaY self.oaLayer[iLayer].raaW = raaW.asarray() self.oaLayer[iLayer].raV = raV.asarray() self.oaLayer[iLayer].raH = raH.asarray()
def main(argv=None): if argv is None: argv = sys.argv[1:] parallel_hdf5 = h5py.get_config().mpi user_path = pjoin(os.path.expanduser('~'), 'spyking-circus') tasks_list = None if not os.path.exists(user_path): os.makedirs(user_path) try: import cudamat as cmt cmt.init() HAVE_CUDA = True except Exception: HAVE_CUDA = False all_steps = [ 'whitening', 'clustering', 'fitting', 'gathering', 'extracting', 'filtering', 'converting', 'deconverting', 'benchmarking', 'merging', 'validating', 'thresholding' ] config_file = os.path.abspath(pkg_resources.resource_filename('circus', 'config.params')) header = get_colored_header() header += Fore.GREEN + 'Local CPUs : ' + Fore.CYAN + str(psutil.cpu_count()) + '\n' # header += Fore.GREEN + 'GPU detected : ' + Fore.CYAN + str(HAVE_CUDA) + '\n' header += Fore.GREEN + 'Parallel HDF5 : ' + Fore.CYAN + str(parallel_hdf5) + '\n' do_upgrade = '' if not SHARED_MEMORY: do_upgrade = Fore.WHITE + ' [please consider upgrading MPI]' header += Fore.GREEN + 'Shared memory : ' + Fore.CYAN + str(SHARED_MEMORY) + do_upgrade + '\n' header += '\n' header += Fore.GREEN + "##################################################################" header += Fore.RESET method_help = '''by default, all steps are performed, but a subset x,y can be done. Steps are: - filtering - whitening - clustering - fitting - merging [with or without a GUI for meta merging] - (extra) converting [export results to phy format] - (extra) thresholding [to get MUA activity only] - (extra) deconverting [import results from phy format] - (extra) gathering [force collection of results] - (extra) extracting [get templates from spike times] - (extra) benchmarking [with -o and -t] - (extra) validating [to compare performance with GT neurons]''' parser = argparse.ArgumentParser(description=header, formatter_class=argparse.RawTextHelpFormatter) parser.add_argument('datafile', help='data file (or a list of commands if batch mode)') parser.add_argument('-i', '--info', help='list the file formats supported by SpyKING CIRCUS', action='store_true') parser.add_argument('-m', '--method', default='filtering,whitening,clustering,fitting,merging', help=method_help) parser.add_argument('-c', '--cpu', type=int, default=max(1, int(psutil.cpu_count()/2)), help='number of CPU') # parser.add_argument('-g', '--gpu', type=int, default=0, help='number of GPU') parser.add_argument('-H', '--hostfile', help='hostfile for MPI', default=pjoin(user_path, 'circus.hosts')) parser.add_argument('-b', '--batch', help='datafile is a list of commands to launch, in a batch mode', action='store_true') parser.add_argument('-p', '--preview', help='GUI to display the first second filtered with thresholds', action='store_true') parser.add_argument('-r', '--result', help='GUI to display the results on top of raw data', action='store_true') parser.add_argument('-s', '--second', type=int, default=0, help='If preview mode, begining of the preview [in s]') parser.add_argument('-e', '--extension', help='extension to consider for merging, converting and deconverting', default='None') parser.add_argument('-o', '--output', help='output file [for generation of synthetic benchmarks]') parser.add_argument('-t', '--type', help='benchmark type', choices=['fitting', 'clustering', 'synchrony']) if len(argv) == 0: parser.print_help() sys.exit(0) args = parser.parse_args(argv) steps = args.method.split(',') for step in steps: if step not in all_steps: print_error(['The method "%s" is not recognized' % step]) sys.exit(0) # To save some typing later nb_gpu = 0 (nb_cpu, hostfile, batch, preview, result, extension, output, benchmark, info, second) = \ (args.cpu, args.hostfile, args.batch, args.preview, args.result, args.extension, args.output, args.type, args.info, args.second) filename = os.path.abspath(args.datafile) real_file = filename f_next, extens = os.path.splitext(filename) if info: if args.datafile.lower() in __supported_data_files__: filename = 'tmp' if len(__supported_data_files__[args.datafile.lower()].extension) > 0: filename += __supported_data_files__[args.datafile.lower()].extension[0] __supported_data_files__[args.datafile.lower()](filename, {}, is_empty=True)._display_requirements_() else: print_and_log([ '', 'To get info on any particular file format, do:', '>> spyking-circus file_format -i', '' ], 'default') print_and_log(list_all_file_format()) sys.exit(0) if extens == '.params': print_error(['You should launch the code on the data file!']) sys.exit(0) file_params = f_next + '.params' if not os.path.exists(file_params) and not batch: print(Fore.RED + 'The parameter file %s is not present!' % file_params) create_params = query_yes_no(Fore.WHITE + "Do you want SpyKING CIRCUS to create a parameter file?") if create_params: print(Fore.WHITE + "Creating %s" % file_params) print(Fore.WHITE + "Fill it properly before launching the code! (see documentation)") print_info(['Keep in mind that filtering is performed on site, so please', 'be sure to keep a copy of your data elsewhere']) shutil.copyfile(config_file, file_params) sys.exit(0) elif batch: tasks_list = filename if not batch: file_params = f_next + '.params' if not os.path.exists(file_params): print_and_log(["%s does not exist" % file_params], 'error') sys.exit(0) import ConfigParser as configparser parser = configparser.ConfigParser() myfile = open(file_params, 'r') lines = myfile.readlines() myfile.close() myfile = open(file_params, 'w') for l in lines: myfile.write(l.replace('\t', '')) myfile.close() parser.read(file_params) for section in CircusParser.__all_sections__: if parser.has_section(section): for (key, value) in parser.items(section): parser.set(section, key, value.split('#')[0].rstrip()) else: parser.add_section(section) try: use_output_dir = parser.get('data', 'output_dir') != '' except Exception: use_output_dir = False if use_output_dir: path = os.path.abspath(os.path.expanduser(parser.get('data', 'output_dir'))) file_out = os.path.join(path, os.path.basename(f_next)) if not os.path.exists(file_out): os.makedirs(file_out) else: file_out = f_next logfile = file_out + '.log' if os.path.exists(logfile): os.remove(logfile) logger = init_logging(logfile) params = CircusParser(filename) data_file = params.get_data_file(source=True, has_been_created=False) overwrite = params.getboolean('data', 'overwrite') file_format = params.get('data', 'file_format') if overwrite: support_parallel_write = data_file.parallel_write is_writable = data_file.is_writable else: support_parallel_write = __supported_data_files__['raw_binary'].parallel_write is_writable = __supported_data_files__['raw_binary'].is_writable if preview: print_and_log(['Preview mode, showing only seconds [%d-%d] of the recording' % (second, second+1)], 'info', logger) tmp_path_loc = os.path.join(os.path.abspath(params.get('data', 'file_out')), 'tmp') if not os.path.exists(tmp_path_loc): os.makedirs(tmp_path_loc) filename = os.path.join(tmp_path_loc, 'preview.dat') f_next, extens = os.path.splitext(filename) preview_params = f_next + '.params' shutil.copyfile(file_params, preview_params) steps = ['filtering', 'whitening'] chunk_size = int(params.rate) data_file.open() nb_chunks, _ = data_file.analyze(chunk_size) if nb_chunks <= (second + 1): print_and_log(['Recording is too short to display seconds [%d-%d]' % (second, second+1)]) sys.exit(0) local_chunk = data_file.get_snippet(int(second*params.rate), int(1.2*chunk_size)) description = data_file.get_description() data_file.close() new_params = CircusParser(filename, create_folders=False) new_params.write('data', 'chunk_size', '1') new_params.write('data', 'file_format', 'raw_binary') new_params.write('data', 'data_dtype', 'float32') new_params.write('data', 'data_offset', '0') new_params.write('data', 'dtype_offset', '0') new_params.write('data', 'stream_mode', 'None') new_params.write('data', 'overwrite', 'True') new_params.write('triggers', 'ignore_times', 'False') new_params.write('data', 'sampling_rate', str(params.rate)) new_params.write('whitening', 'safety_time', '0') new_params.write('clustering', 'safety_time', '0') new_params.write('whitening', 'chunk_size', '1') new_params.write('data', 'preview_path', params.file_params) new_params.write('data', 'output_dir', '') description['data_dtype'] = 'float32' description['dtype_offset'] = 0 description['data_offset'] = 0 description['gain'] = 1. new_params = CircusParser(filename) data_file_out = new_params.get_data_file(is_empty=True, params=description) support_parallel_write = data_file_out.parallel_write is_writable = data_file_out.is_writable data_file_out.allocate(shape=local_chunk.shape, data_dtype=numpy.float32) data_file_out.open('r+') data_file_out.set_data(0, local_chunk) data_file_out.close() if tasks_list is not None: with open(tasks_list, 'r') as f: for line in f: if len(line) > 0: subprocess.check_call(['spyking-circus'] + line.replace('\n', '').split(" ")) else: print_and_log(['Config file: %s' % (f_next + '.params')], 'debug', logger) print_and_log(['Data file : %s' % filename], 'debug', logger) print(get_colored_header()) print(Fore.GREEN + "File : " + Fore.CYAN + real_file) if preview: print(Fore.GREEN + "Steps : " + Fore.CYAN + "preview mode") elif result: print(Fore.GREEN + "Steps : " + Fore.CYAN + "result mode") else: print(Fore.GREEN + "Steps : " + Fore.CYAN + ", ".join(steps)) # print Fore.GREEN + "GPU detected : ", Fore.CYAN + str(HAVE_CUDA) print(Fore.GREEN + "Number of CPU : " + Fore.CYAN + str(nb_cpu) + "/" + str(psutil.cpu_count())) # if HAVE_CUDA: # print Fore.GREEN + "Number of GPU : ", Fore.CYAN + str(nb_gpu) print(Fore.GREEN + "Parallel HDF5 : " + Fore.CYAN + str(parallel_hdf5)) do_upgrade = '' use_shared_memory = get_shared_memory_flag(params) if not SHARED_MEMORY: do_upgrade = Fore.WHITE + ' [please consider upgrading MPI]' print(Fore.GREEN + "Shared memory : " + Fore.CYAN + str(use_shared_memory) + do_upgrade) print(Fore.GREEN + "Hostfile : " + Fore.CYAN + hostfile) print("") print(Fore.GREEN + "##################################################################") print("") print(Fore.RESET) # Launch the subtasks subtasks = [('filtering', 'mpirun'), ('whitening', 'mpirun'), ('clustering', 'mpirun'), ('fitting', 'mpirun'), ('extracting', 'mpirun'), ('gathering', 'python'), ('converting', 'mpirun'), ('deconverting', 'mpirun'), ('benchmarking', 'mpirun'), ('merging', 'mpirun'), ('validating', 'mpirun'), ('thresholding', 'mpirun')] # if HAVE_CUDA and nb_gpu > 0: # use_gpu = 'True' # else: use_gpu = 'False' time = data_stats(params) / 60.0 if preview: params = new_params if nb_cpu < psutil.cpu_count(): if use_gpu != 'True' and not result: print_and_log(['Using only %d out of %d local CPUs available (-c to change)' % (nb_cpu, psutil.cpu_count())], 'info', logger) if params.getboolean('detection', 'matched-filter') and not params.getboolean('clustering', 'smart_search'): print_and_log(['Smart Search should be activated for matched filtering'], 'info', logger) if time > 30 and not params.getboolean('clustering', 'smart_search'): print_and_log(['Smart Search should be activated for long recordings'], 'info', logger) n_edges = get_averaged_n_edges(params) if n_edges > 100 and not params.getboolean('clustering', 'compress'): print_and_log(['Template compression is highly recommended based on parameters'], 'info', logger) if not result: for subtask, command in subtasks: if subtask in steps: if command == 'python': # Directly call the launcher try: circus.launch(subtask, filename, nb_cpu, nb_gpu, use_gpu) except: print_and_log(['Step "%s" failed!' % subtask], 'error', logger) sys.exit(0) elif command == 'mpirun': # Use mpirun to make the call mpi_args = gather_mpi_arguments(hostfile, params) one_cpu = False if subtask in ['filtering', 'benchmarking'] and not is_writable: if not preview and overwrite: print_and_log(['The file format %s is read only!' % file_format, 'You should set overwite to False, to create a copy of the data.', 'However, note that if you have streams, informations on times', 'will be discarded'], 'info', logger) sys.exit(0) if subtask in ['filtering'] and not support_parallel_write and (args.cpu > 1): print_and_log(['No parallel writes for %s: only 1 node used for %s' %(file_format, subtask)], 'info', logger) nb_tasks = str(1) one_cpu = True else: if subtask != 'fitting': nb_tasks = str(args.cpu) else: # if use_gpu == 'True': # nb_tasks = str(args.gpu) # else: nb_tasks = str(args.cpu) if subtask == 'benchmarking': if (output is None) or (benchmark is None): print_and_log(["To generate synthetic datasets, you must provide output and type"], 'error', logger) sys.exit(0) mpi_args += [ '-np', nb_tasks, 'spyking-circus-subtask', subtask, filename, str(nb_cpu), str(nb_gpu), use_gpu, output, benchmark ] elif subtask in ['merging', 'converting']: mpi_args += [ '-np', nb_tasks, 'spyking-circus-subtask', subtask, filename, str(nb_cpu), str(nb_gpu), use_gpu, extension ] elif subtask in ['deconverting']: nb_tasks = str(1) nb_cpu = 1 mpi_args += [ '-np', nb_tasks, 'spyking-circus-subtask', subtask, filename, str(nb_cpu), str(nb_gpu), use_gpu, extension ] else: mpi_args += [ '-np', nb_tasks, 'spyking-circus-subtask', subtask, filename, str(nb_cpu), str(nb_gpu), use_gpu, str(one_cpu) ] print_and_log(['Launching task %s' % subtask], 'debug', logger) print_and_log(['Command: %s' % str(mpi_args)], 'debug', logger) try: subprocess.check_call(mpi_args) except subprocess.CalledProcessError as e: print_and_log(['Step "%s" failed for reason %s!' % (subtask, e)], 'error', logger) sys.exit(0) if preview or result: from circus.shared import gui import pylab try: from PyQt5.QtWidgets import QApplication except ImportError: from matplotlib.backends import qt_compat use_pyside = qt_compat.QT_API == qt_compat.QT_API_PYSIDE if use_pyside: from PySide.QtGui import QApplication else: from PyQt4.QtGui import QApplication app = QApplication([]) try: pylab.style.use('ggplot') except Exception: pass if preview: print_and_log(['Launching the preview GUI...'], 'debug', logger) mygui = gui.PreviewGUI(new_params) shutil.rmtree(tmp_path_loc) elif result: data_file = params.get_data_file() print_and_log(['Launching the result GUI...'], 'debug', logger) mygui = gui.PreviewGUI(params, show_fit=True) sys.exit(app.exec_())
def main(params, nb_cpu, nb_gpu, use_gpu): # Part 1: Whitening numpy.random.seed(420) # params = detect_memory(params) _ = init_logging(params.logfile) logger = logging.getLogger('circus.whitening') ################################################################# data_file = params.data_file N_e = params.getint('data', 'N_e') hdf5_compress = params.getboolean('data', 'hdf5_compress') N_total = params.nb_channels N_t = params.getint('detection', 'N_t') dist_peaks = params.getint('detection', 'dist_peaks') template_shift = params.getint('detection', 'template_shift') file_out_suff = params.get('data', 'file_out_suff') spike_thresh = params.getfloat('detection', 'spike_thresh') spike_width = params.getfloat('detection', 'spike_width') matched_filter = params.getboolean('detection', 'matched-filter') matched_thresh = params.getfloat('detection', 'matched_thresh') fudge = params.getfloat('whitening', 'fudge') sign_peaks = params.get('detection', 'peaks') do_temporal_whitening = params.getboolean('whitening', 'temporal') do_spatial_whitening = params.getboolean('whitening', 'spatial') ignore_spikes = params.getboolean('whitening', 'ignore_spikes') chunk_size = detect_memory(params, whitening=True) plot_path = os.path.join(params.get('data', 'file_out_suff'), 'plots') nodes, edges = get_nodes_and_edges(params) safety_time = params.getint('whitening', 'safety_time') safety_space = params.getboolean('whitening', 'safety_space') sort_waveforms = params.getboolean('whitening', 'sort_waveforms') nb_temp_white = min(max(20, comm.size), N_e) max_silence_1 = int(20 * params.rate // comm.size) max_silence_2 = 5000 inv_nodes = numpy.zeros(N_total, dtype=numpy.int32) inv_nodes[nodes] = numpy.arange(len(nodes)) jitter_range = params.getint('detection', 'jitter_range') template_shift_2 = template_shift + jitter_range use_hanning = params.getboolean('detection', 'hanning') rejection_threshold = params.getfloat('detection', 'rejection_threshold') noise_window = params.getint('detection', 'noise_time') data_file.open() ################################################################# if use_hanning: hanning_filter = numpy.hanning(N_t) if comm.rank == 0: print_and_log( ["Analyzing data to get whitening matrices and thresholds..."], 'default', logger) nodes_indices = {} for elec in numpy.arange(N_e): nodes_indices[elec] = inv_nodes[edges[nodes[elec]]] if use_gpu: import cudamat as cmt # # Need to properly handle multi GPU per MPI nodes? if nb_gpu > nb_cpu: gpu_id = int(comm.rank // nb_cpu) else: gpu_id = 0 cmt.cuda_set_device(gpu_id) cmt.init() cmt.cuda_sync_threads() nb_chunks, last_chunk_len = data_file.analyze(chunk_size) if nb_chunks < comm.size: res = io.data_stats(params, show=False) chunk_size = int(res * params.rate // comm.size) if comm.rank == 0: print_and_log( ["Too much cores, automatically resizing the data chunks"], 'debug', logger) nb_chunks, last_chunk_len = data_file.analyze(chunk_size) # I guess this is more relevant, to take signals from all over the recordings. if nb_chunks > comm.size: all_chunks = numpy.random.permutation( numpy.arange(nb_chunks - 1, dtype=numpy.int32)) else: all_chunks = numpy.random.permutation( numpy.arange(nb_chunks, dtype=numpy.int32)) all_electrodes = numpy.random.permutation(N_e) numpy.random.seed(comm.rank) for gidx in [all_chunks[comm.rank]]: # print "Node", comm.rank, "is analyzing chunk", gidx, "/", nb_chunks, " ..." local_chunk, t_offset = data_file.get_data(gidx, chunk_size, nodes=nodes) local_shape = len(local_chunk) # print "Node", comm.rank, "computes the median absolute deviations in a random chunk" thresholds = numpy.zeros(N_e, dtype=numpy.float32) for i in range(N_e): u = numpy.median(local_chunk[:, i], 0) thresholds[i] = numpy.median(numpy.abs(local_chunk[:, i] - u), 0) gdata = gather_array(thresholds, comm) if comm.rank == 0: gdata = gdata.reshape((comm.size, N_e)) thresholds = numpy.mean(gdata, 0) bfile = h5py.File(file_out_suff + '.basis.hdf5', 'w', libver='earliest') io.write_datasets(bfile, ['thresholds'], {'thresholds': thresholds}, compression=hdf5_compress) bfile.close() comm.Barrier() thresholds = io.load_data(params, 'thresholds') local_borders = (template_shift, local_shape - template_shift) found_peaktimes = [] if ignore_spikes: # Extracting the peaks. local_peaktimes = [np.empty(0, dtype=numpy.uint32)] for i in range(N_e): peaktimes = scipy.signal.find_peaks(numpy.abs(local_chunk[:, i]), height=thresholds[i], width=spike_width, wlen=N_t)[0] peaktimes = peaktimes.astype(numpy.uint32) # print "Removing the useless borders..." idx = (peaktimes >= local_borders[0]) & (peaktimes < local_borders[1]) peaktimes = numpy.compress(idx, peaktimes) found_peaktimes.append(peaktimes) else: for i in range(N_e): found_peaktimes.append(numpy.zeros(0, dtype=numpy.uint32)) all_peaktimes = numpy.concatenate(found_peaktimes) local_peaktimes = numpy.unique(all_peaktimes) if len(local_peaktimes) > 0: diff_times = local_peaktimes[-1] - local_peaktimes[0] all_times = numpy.zeros((N_e, diff_times + 1), dtype=numpy.bool) padded_peaks = (local_peaktimes - local_peaktimes[0]).astype( numpy.int32) min_times = numpy.maximum(padded_peaks - safety_time, 0) max_times = numpy.minimum(padded_peaks + safety_time + 1, diff_times + 1) test_extremas = numpy.zeros((N_e, diff_times + 1), dtype=numpy.bool) for i in range(N_e): test_extremas[i, found_peaktimes[i] - local_peaktimes[0]] = True argmax_peak = numpy.random.permutation( numpy.arange(len(local_peaktimes))) all_idx = numpy.take(local_peaktimes, argmax_peak) # print "Selection of the peaks with spatio-temporal masks..." for idx, peak in zip(argmax_peak, all_idx): all_elecs = numpy.where(test_extremas[:, peak - local_peaktimes[0]])[0] data = local_chunk[peak, all_elecs] elec = all_elecs[numpy.argmax(numpy.abs(data))] indices = nodes_indices[elec] if safety_space: all_times[indices, min_times[idx]:max_times[idx]] = True else: all_times[elec, min_times[idx]:max_times[idx]] = True else: all_times = numpy.zeros((N_e, len(local_chunk)), dtype=numpy.bool) if do_temporal_whitening: local_res_temp = [] for elec in all_electrodes[numpy.arange(comm.rank, nb_temp_white, comm.size)]: res = numpy.zeros((0, N_t), dtype=numpy.float32) scount = 0 indices = nodes_indices[elec] all_times_elec = numpy.any(numpy.take(all_times, indices, axis=0), 0) esubset = numpy.where(all_times_elec == False)[0] bound = len(esubset) - N_t while (scount < bound) and (len(res) < max_silence_2): myslice = esubset[scount:scount + N_t] if numpy.all((myslice - esubset[scount]) == numpy.arange(N_t)): scount += N_t res = numpy.vstack((res, local_chunk[myslice, elec])) else: scount += 1 if len(res) > 5: local_res_temp += [numpy.cov(res.T)] nb_elecs = numpy.array([len(local_res_temp)], dtype=numpy.float32) local_res_temp = numpy.array(local_res_temp, dtype=numpy.float32) if len(local_res_temp) == 0: local_res_temp = numpy.zeros(0, dtype=numpy.float32) else: local_res_temp = numpy.sum(local_res_temp, 0) all_res_temp = gather_array(local_res_temp.ravel(), comm, 0, 1) all_elecs = gather_array(nb_elecs, comm, 0, 1) if do_spatial_whitening: local_res_spac = numpy.zeros((N_e, N_e), dtype=numpy.float32) local_silences = [] for elec in numpy.arange(comm.rank, N_e, comm.size): indices = nodes_indices[elec] all_times_elec = numpy.any(numpy.take(all_times, indices, axis=0), 0) esubset = numpy.where(all_times_elec == False)[0] local_data = local_chunk[esubset][:, indices] local_whitening = get_whitening_matrix( local_data, fudge=fudge).astype(numpy.float32) pos = numpy.where(elec == indices)[0] local_res_spac[elec, indices] = local_whitening[pos] local_silences += [len(esubset)] all_res_spac = gather_array(local_res_spac.ravel(), comm, 0, 1) all_silences = gather_array( numpy.array(local_silences, dtype=numpy.int32), comm, 0, 1, 'uint32') if comm.rank == 0: to_write = {} if do_temporal_whitening: try: nb_silences = numpy.sum(all_elecs > 0) all_res_temp = all_res_temp.reshape((nb_silences, N_t**2)) except Exception: print_and_log([ "No silent periods detected: something wrong with the parameters?" ], 'error', logger) all_res_temp = numpy.sum(all_res_temp, 0) all_res_temp = all_res_temp.reshape( (N_t, N_t)) / numpy.sum(all_elecs) temporal_whitening = get_whitening_matrix( all_res_temp.astype(numpy.double), fudge=1e-3)[template_shift].astype(numpy.float32) temporal_whitening /= temporal_whitening.sum() to_write['temporal'] = temporal_whitening have_nans = numpy.sum(numpy.isnan(temporal_whitening)) if have_nans > 0: temporal_whitening = numpy.zeros(N_t, dtype=numpy.float32) temporal_whitening[N_t // 2] = 1 to_write['temporal'] = temporal_whitening print_and_log( ["Disabling temporal whitening because of NaNs found"], 'info', logger) if do_spatial_whitening: all_res_spac = all_res_spac.reshape(comm.size, N_e, N_e) spatial_whitening = numpy.sum(all_res_spac, 0) to_write['spatial'] = spatial_whitening if ignore_spikes: print_and_log([ "Found %gs without spikes to compute the whitening matrix..." % (numpy.mean(all_silences) / params.rate) ], 'default', logger) else: print_and_log([ "Found %gs to compute the whitening matrix..." % (numpy.mean(all_silences) / params.rate) ], 'default', logger) have_nans = numpy.sum(numpy.isnan(spatial_whitening)) if have_nans > 0: spatial_whitening = numpy.eye(spatial_whitening.shape[0], dtype=numpy.float32) to_write['spatial'] = spatial_whitening print_and_log( ["Disabling spatial whitening because of NaNs found"], 'info', logger) bfile = h5py.File(file_out_suff + '.basis.hdf5', 'r+', libver='earliest') io.write_datasets(bfile, list(to_write.keys()), to_write, compression=hdf5_compress) bfile.close() comm.Barrier() if do_spatial_whitening or do_temporal_whitening: if comm.rank == 0: print_and_log( ["Because of whitening, need to recompute the thresholds..."], 'default', logger) if do_spatial_whitening: spatial_whitening = io.load_data(params, 'spatial_whitening') if use_gpu: spatial_whitening = cmt.CUDAMatrix(spatial_whitening, copy_on_host=False) if do_temporal_whitening: temporal_whitening = io.load_data(params, 'temporal_whitening') for gidx in [all_chunks[comm.rank]]: local_chunk, t_offset = data_file.get_data(gidx, chunk_size, nodes=nodes) local_shape = len(local_chunk) if do_spatial_whitening: if use_gpu: local_chunk = cmt.CUDAMatrix(local_chunk, copy_on_host=False) local_chunk = local_chunk.dot(spatial_whitening).asarray() else: local_chunk = numpy.dot(local_chunk, spatial_whitening) if do_temporal_whitening: local_chunk = scipy.ndimage.filters.convolve1d( local_chunk, temporal_whitening, axis=0, mode='constant') thresholds = numpy.zeros(N_e, dtype=numpy.float32) for i in range(N_e): u = numpy.median(local_chunk[:, i], 0) thresholds[i] = numpy.median(numpy.abs(local_chunk[:, i] - u), 0) gdata = gather_array(thresholds, comm) if comm.rank == 0: gdata = gdata.reshape((comm.size, N_e)) thresholds = numpy.mean(gdata, 0) bfile = h5py.File(file_out_suff + '.basis.hdf5', 'r+', libver='earliest') bfile.pop('thresholds') io.write_datasets(bfile, ['thresholds'], {'thresholds': thresholds}, compression=hdf5_compress) bfile.close() comm.Barrier() # if comm.rank == 0: # if not os.path.exists(plot_path): # os.makedirs(plot_path) # N_elec = min(int(numpy.sqrt(data_file.N_e)), 5) # plot.view_fit(filename, t_start=0, t_stop=1, fit_on=False, square=True, # n_elec=N_elec, save=[plot_path, 'electrodes']) # Part 2: Basis numpy.random.seed(422) SHARED_MEMORY = get_shared_memory_flag(params) ################################################################# file_out = params.get('data', 'file_out') alignment = params.getboolean('detection', 'alignment') over_factor = params.getint('detection', 'oversampling_factor') nb_jitter = params.getint('detection', 'nb_jitter') spike_thresh = params.getfloat('detection', 'spike_thresh') nodes, edges = get_nodes_and_edges(params) _, positions = get_nodes_and_positions(params) do_temporal_whitening = params.getboolean('whitening', 'temporal') do_spatial_whitening = params.getboolean('whitening', 'spatial') use_barycenter = params.getboolean('detection', 'use_barycenter') if matched_filter: chunk_size = detect_memory(params, whitening=True) else: chunk_size = detect_memory(params) safety_time = params.getint('whitening', 'safety_time') max_elts_elec = params.getint('whitening', 'max_elts') output_dim = params.getfloat('whitening', 'output_dim') inv_nodes = numpy.zeros(N_total, dtype=numpy.int32) inv_nodes[nodes] = numpy.arange(len(nodes)) smoothing_factor = params.getfloat('detection', 'smoothing_factor') if sign_peaks == 'both': max_elts_elec *= 2 nb_elts = int( params.getfloat('whitening', 'nb_elts') * N_e * max_elts_elec) weird_thresh = params.get('detection', 'weird_thresh') if weird_thresh != '': ignore_artefacts = True weird_thresh = io.load_data(params, 'weird-thresholds') else: ignore_artefacts = False ignore_dead_times = params.getboolean('triggers', 'ignore_times') if ignore_dead_times: if SHARED_MEMORY: all_dead_times, mpi_memory_3 = get_dead_times(params) else: all_dead_times = get_dead_times(params) data_file.open() ################################################################# if comm.rank == 0: print_and_log(["Searching spikes to construct the PCA basis..."], 'default', logger) nb_chunks, last_chunk_len = data_file.analyze(chunk_size) if nb_chunks < comm.size: res = io.data_stats(params, show=False) chunk_size = int(res * params.rate // comm.size) if comm.rank == 0: print_and_log( ["Too much cores, automatically resizing the data chunks"], 'debug', logger) nb_chunks, last_chunk_len = data_file.analyze(chunk_size) groups = {} for i in range(N_e): groups[i] = 0 # I guess this is more relevant, to take signals from all over the recordings all_chunks = numpy.random.permutation( numpy.arange(nb_chunks, dtype=numpy.int32)) max_elts_elec //= comm.size nb_elts //= comm.size elt_count_pos = 0 elt_count_neg = 0 if sign_peaks in ['positive', 'both']: times_pos = numpy.zeros(nb_elts, dtype=numpy.int32) electrodes_pos = numpy.zeros(nb_elts, dtype=numpy.int32) extremum_pos = numpy.zeros(nb_elts, dtype=numpy.float32) elts_pos = numpy.zeros((N_t, nb_elts), dtype=numpy.float32) if sign_peaks in ['negative', 'both']: times_neg = numpy.zeros(nb_elts, dtype=numpy.int32) electrodes_neg = numpy.zeros(nb_elts, dtype=numpy.int32) extremum_neg = numpy.zeros(nb_elts, dtype=numpy.float32) elts_neg = numpy.zeros((N_t, nb_elts), dtype=numpy.float32) thresholds = io.load_data(params, 'thresholds') mads = io.load_data(params, 'mads') stds = io.load_data(params, 'stds') if alignment: cdata = numpy.linspace(-jitter_range, +jitter_range, nb_jitter) xdata = numpy.arange(-template_shift_2, template_shift_2 + 1) xoff = len(cdata) / 2.0 snippet_duration = template_shift_2 m_size = 2 * template_shift_2 + 1 align_factor = m_size local_factors = align_factor * ((smoothing_factor * mads)**2) else: snippet_duration = template_shift xdata = numpy.arange(-template_shift, template_shift + 1) if rejection_threshold > 0: reject_noise = True noise_levels = stds * (2 * noise_window + 1) else: reject_noise = False to_explore = all_chunks[comm.rank::comm.size] upper_bounds = max_elts_elec if comm.rank == 0: to_explore = get_tqdm_progressbar(params, to_explore) for gcount, gidx in enumerate(to_explore): if (elt_count_pos + elt_count_neg) < nb_elts: # print "Node", comm.rank, "is analyzing chunk", gidx, "/", nb_chunks, " ..." local_chunk, t_offset = data_file.get_data(gidx, chunk_size, nodes=nodes) local_shape = len(local_chunk) if do_spatial_whitening: if use_gpu: local_chunk = cmt.CUDAMatrix(local_chunk, copy_on_host=False) local_chunk = local_chunk.dot(spatial_whitening).asarray() else: local_chunk = numpy.dot(local_chunk, spatial_whitening) if do_temporal_whitening: local_chunk = scipy.ndimage.filters.convolve1d( local_chunk, temporal_whitening, axis=0, mode='constant') local_borders = (snippet_duration, local_shape - snippet_duration) if ignore_dead_times: dead_indices = numpy.searchsorted( all_dead_times, [t_offset, t_offset + local_shape]) # Extracting the peaks. all_peaktimes = [numpy.empty(0, dtype=numpy.uint32)] found_peaktimes = [] found_peak_amplitudes = [] for i in range(N_e): height = thresholds[i] if sign_peaks == 'negative': peaktimes = scipy.signal.find_peaks(-local_chunk[:, i], height=height, distance=dist_peaks)[0] elif sign_peaks == 'positive': peaktimes = scipy.signal.find_peaks(local_chunk[:, i], height=height, distance=dist_peaks)[0] elif sign_peaks == 'both': peaktimes = scipy.signal.find_peaks(numpy.abs( local_chunk[:, i]), height=height, distance=dist_peaks)[0] else: peaktimes = numpy.empty(0, dtype=numpy.uint32) if ignore_artefacts: artetimes = scipy.signal.find_peaks( numpy.abs(local_chunk[:, i]), height=weird_thresh[i])[0] to_keep = numpy.logical_not( numpy.in1d(peaktimes, artetimes)) peaktimes = peaktimes[to_keep] idx = (peaktimes >= local_borders[0]) & (peaktimes < local_borders[1]) peaktimes = peaktimes[idx] if ignore_dead_times: if dead_indices[0] != dead_indices[1]: is_included = numpy.in1d( peaktimes + t_offset, all_dead_times[dead_indices[0]:dead_indices[1]]) peaktimes = peaktimes[~is_included] peaktimes = peaktimes.astype(numpy.uint32) found_peaktimes.append(peaktimes) peak_amplitudes = local_chunk[peaktimes, i] found_peak_amplitudes.append(peak_amplitudes) all_peaktimes = numpy.concatenate( found_peaktimes) # i.e. concatenate once for efficiency all_peak_amplitudes = numpy.concatenate(found_peak_amplitudes) local_peaktimes, local_indices = numpy.unique(all_peaktimes, return_inverse=True) if len(local_peaktimes) > 0: diff_times = (local_peaktimes[-1] - local_peaktimes[0]) + 1 all_times = numpy.zeros((N_e, diff_times), dtype=numpy.bool) padded_peaks = (local_peaktimes - local_peaktimes[0]).astype( numpy.int32) min_times = numpy.maximum(padded_peaks - safety_time, 0) max_times = numpy.minimum(padded_peaks + safety_time + 1, diff_times + 1) test_extremas = numpy.zeros((N_e, diff_times + 1), dtype=numpy.bool) for i in range(N_e): test_extremas[i, found_peaktimes[i] - local_peaktimes[0]] = True # Consider the peaks by decreasing extremum. if sort_waveforms: order = numpy.argsort(-np.abs(all_peak_amplitudes)) all_idx = numpy.take(all_peaktimes, order) argmax_peak = local_indices[order] else: n_times = len(all_peaktimes) shuffling = numpy.random.permutation(numpy.arange(n_times)) all_idx = numpy.take(all_peaktimes, shuffling) argmax_peak = local_indices[shuffling] # print "Selection of the peaks with spatio-temporal masks..." for midx, peak in zip(argmax_peak, all_idx): if (elt_count_neg + elt_count_pos) == nb_elts: break all_elecs = numpy.where( test_extremas[:, peak - local_peaktimes[0]])[0] data = local_chunk[peak, all_elecs] #target_area = test_extremas[:, min_times[midx]:max_times[midx]].sum(1) #all_elecs = numpy.where(target_area)[0] #data = local_chunk[peak, all_elecs] if sign_peaks == 'negative': if N_e > 1: if use_barycenter: weighed_position = data[:, numpy. newaxis] * positions[ all_elecs] barycenter = weighed_position.sum( 0) / data.sum() elec = numpy.argmin( numpy.linalg.norm(barycenter - positions[all_elecs], axis=1)) else: elec = numpy.argmin(data) else: elec = 0 negative_peak = True elif sign_peaks == 'positive': if N_e > 1: if use_barycenter: weighed_position = data[:, numpy. newaxis] * positions[ all_elecs] barycenter = weighed_position.sum( 0) / data.sum() elec = numpy.argmax( numpy.linalg.norm(barycenter - positions[all_elecs], axis=1)) else: elec = numpy.argmax(data) else: elec = 0 negative_peak = False elif sign_peaks == 'both': if N_e == 1: if data < 0: negative_peak = True elif data > 0: negative_peak = False elec = 0 else: if numpy.abs(numpy.max(data)) > numpy.abs( numpy.min(data)): elec = numpy.argmax(data) negative_peak = False else: elec = numpy.argmin(data) negative_peak = True elec = all_elecs[elec] if groups[elec] < upper_bounds: indices = nodes_indices[elec] myslice = all_times[indices, min_times[midx]:max_times[midx]] if not myslice.any(): sub_mat = local_chunk[peak - snippet_duration:peak + snippet_duration + 1, elec] if reject_noise: slice_window = sub_mat[ snippet_duration - noise_window:snippet_duration + noise_window + 1] value = numpy.linalg.norm( slice_window) / noise_levels[elec] is_noise = value < rejection_threshold else: is_noise = False if not is_noise: extrema = sub_mat[snippet_duration] if alignment: smoothed = True try: f = scipy.interpolate.UnivariateSpline( xdata, sub_mat, s=local_factors[elec], k=3) except Exception: smoothed = False f = scipy.interpolate.UnivariateSpline( xdata, sub_mat, k=3, s=0) if negative_peak: rmin = (numpy.argmin(f(cdata)) - xoff) / over_factor else: rmin = (numpy.argmax(f(cdata)) - xoff) / over_factor ddata = numpy.linspace( rmin - template_shift, rmin + template_shift, N_t) if smoothed: f = scipy.interpolate.UnivariateSpline( xdata, sub_mat, s=local_factors[elec], k=3) else: f = scipy.interpolate.UnivariateSpline( xdata, sub_mat, s=0, k=3) sub_mat = f(ddata).astype(numpy.float32) if negative_peak: times_neg[elt_count_neg] = peak + t_offset electrodes_neg[elt_count_neg] = elec extremum_neg[elt_count_neg] = extrema elts_neg[:, elt_count_neg] = sub_mat elt_count_neg += 1 else: times_pos[elt_count_pos] = peak + t_offset electrodes_pos[elt_count_pos] = elec extremum_pos[elt_count_pos] = extrema elts_pos[:, elt_count_pos] = sub_mat elt_count_pos += 1 groups[elec] += 1 all_times[ indices, min_times[midx]:max_times[midx]] = True test_extremas[elec, peak - local_peaktimes[0]] = False sys.stderr.flush() print_and_log([ "Node %d has collected %d waveforms" % (comm.rank, elt_count_pos + elt_count_neg) ], 'debug', logger) if sign_peaks in ['negative', 'both']: times_neg = gather_array(times_neg[:elt_count_neg], comm, 0, 1, dtype='int32') electrodes_neg = gather_array(electrodes_neg[:elt_count_neg], comm, 0, 1, dtype='int32') extremum_neg = gather_array(extremum_neg[:elt_count_neg], comm, 0, 1) gdata_neg = gather_array(elts_neg[:, :elt_count_neg].T, comm, 0, 1) if sign_peaks in ['positive', 'both']: times_pos = gather_array(times_pos[:elt_count_pos], comm, 0, 1, dtype='int32') electrodes_pos = gather_array(electrodes_pos[:elt_count_pos], comm, 0, 1, dtype='int32') extremum_pos = gather_array(extremum_pos[:elt_count_pos], comm, 0, 1) gdata_pos = gather_array(elts_pos[:, :elt_count_pos].T, comm, 0, 1) nb_waveforms = 0 if comm.rank == 0: # DO PCA on elts and store the basis obtained. if sign_peaks in ['negative', 'both']: nb_waveforms += gdata_neg.shape[0] if sign_peaks in ['positive', 'both']: nb_waveforms += gdata_pos.shape[0] nb_waveforms = all_gather_array( numpy.array([nb_waveforms], dtype=numpy.float32), comm, 0)[0] if comm.rank == 0: print_and_log([ "Found %d waveforms over %d requested" % (nb_waveforms, int(nb_elts * comm.size)) ], 'default', logger) if nb_waveforms == 0: print_and_log( ['No waveforms found! Are the data properly loaded??'], 'error', logger) if nb_waveforms == 0: sys.exit(0) if comm.rank == 0: res = {} pca = None pca_pos = None pca_neg = None warning_n_t = False if sign_peaks in ['negative', 'both']: res['times'] = times_neg res['electrodes'] = electrodes_neg res['extremum'] = extremum_neg if len(gdata_neg) > 0: pca = PCA(output_dim) if use_hanning: pca.fit(gdata_neg * hanning_filter) else: pca.fit(gdata_neg) res['proj'] = pca.components_.T.astype(numpy.float32) pca_neg = numpy.sum(pca.explained_variance_ratio_) else: res['proj'] = numpy.identity(int(output_dim), dtype=numpy.float32) res['rec'] = res['proj'].T res['waveform'] = numpy.median(gdata_neg, 0) # dispersion = numpy.std(gdata_neg, 0) / numpy.median(stds) # ratio = numpy.sum(dispersion > 1.1) / float(len(dispersion)) # if ratio < 0.25: # print_and_log(["Time window N_t in [detection] seems too large!"], 'info', logger) # warning_n_t = True # elif ratio == 1: # print_and_log(["Time window N_t in [detection] seems too small!"], 'info', logger) # warning_n_t = True idx = numpy.random.permutation(numpy.arange( gdata_neg.shape[0]))[:2500] res['waveforms'] = gdata_neg[idx, :] if sign_peaks in ['positive', 'both']: res['times_pos'] = times_pos res['electrodes_pos'] = electrodes_pos res['extremum_pos'] = extremum_pos if len(gdata_pos) > 0: pca = PCA(output_dim) if use_hanning: pca.fit(gdata_pos * hanning_filter) else: pca.fit(gdata_pos) res['proj_pos'] = pca.components_.T.astype(numpy.float32) pca_pos = numpy.sum(pca.explained_variance_ratio_) else: res['proj_pos'] = numpy.identity(int(output_dim), dtype=numpy.float32) res['rec_pos'] = res['proj_pos'].T res['waveform_pos'] = numpy.median(gdata_pos, 0) # dispersion = numpy.std(gdata_pos, 0) / numpy.median(stds) # ratio = numpy.sum(dispersion > 1.1) / float(len(dispersion)) # if ratio < 0.25 and not warning_n_t: # print_and_log(["Time window N_t in [detection] seems too large!"], 'info', logger) # elif ratio == 1 and not warning_n_t: # print_and_log(["Time window N_t in [detection] seems too small!"], 'info', logger) idx = numpy.random.permutation(numpy.arange( gdata_pos.shape[0]))[:2500] res['waveforms_pos'] = gdata_pos[idx, :] bfile = h5py.File(file_out_suff + '.basis.hdf5', 'r+', libver='earliest') io.write_datasets(bfile, list(res.keys()), res, compression=hdf5_compress) if sign_peaks == 'positive': print_and_log([ "A basis with %s dimensions has been built" % res['proj_pos'].shape[1] ], 'info', logger) elif sign_peaks == 'negative': print_and_log([ "A basis with %s dimensions has been built" % res['proj'].shape[1] ], 'info', logger) elif sign_peaks == 'both': print_and_log([ "Two basis with %s dimensions has been built" % res['proj'].shape[1] ], 'debug', logger) if pca_pos is not None: print_and_log([ "The percentage of variance explained is %s for positive spikes" % pca_pos ], 'debug', logger) if pca_neg is not None: print_and_log([ "The percentage of variance explained is %s for negative spikes" % pca_neg ], 'debug', logger) bfile.close() comm.Barrier() if matched_filter: if comm.rank == 0: print_and_log([ "Because of matched filters, need to recompute the thresholds..." ], 'default', logger) if do_spatial_whitening: spatial_whitening = io.load_data(params, 'spatial_whitening') if use_gpu: spatial_whitening = cmt.CUDAMatrix(spatial_whitening, copy_on_host=False) if do_temporal_whitening: temporal_whitening = io.load_data(params, 'temporal_whitening') if sign_peaks in ['negative', 'both']: waveform_neg = io.load_data(params, 'waveform')[::-1] waveform_neg /= (numpy.abs(numpy.sum(waveform_neg)) * len(waveform_neg)) if sign_peaks in ['positive', 'both']: waveform_pos = io.load_data(params, 'waveform-pos')[::-1] waveform_pos /= (numpy.abs(numpy.sum(waveform_pos)) * len(waveform_pos)) for gidx in [all_chunks[comm.rank]]: local_chunk, t_offset = data_file.get_data(gidx, chunk_size, nodes=nodes) local_shape = len(local_chunk) if do_spatial_whitening: if use_gpu: local_chunk = cmt.CUDAMatrix(local_chunk, copy_on_host=False) local_chunk = local_chunk.dot(spatial_whitening).asarray() else: local_chunk = numpy.dot(local_chunk, spatial_whitening) if do_temporal_whitening: local_chunk = scipy.ndimage.filters.convolve1d( local_chunk, temporal_whitening, axis=0, mode='constant') local_chunk /= thresholds if sign_peaks in ['negative', 'both']: tmp_chunk = scipy.ndimage.filters.convolve1d(local_chunk, waveform_neg, axis=0, mode='constant') thresholds = numpy.zeros(N_e, dtype=numpy.float32) for i in range(N_e): u = numpy.median(tmp_chunk[:, i], 0) thresholds[i] = numpy.median( numpy.abs(tmp_chunk[:, i] - u), 0) gdata = gather_array(thresholds, comm) if comm.rank == 0: gdata = gdata.reshape((comm.size, N_e)) thresholds = numpy.mean(gdata, 0) bfile = h5py.File(file_out_suff + '.basis.hdf5', 'r+', libver='earliest') io.write_datasets(bfile, ['matched_thresholds'], {'matched_thresholds': thresholds}, compression=hdf5_compress) bfile.close() comm.Barrier() if sign_peaks in ['positive', 'both']: tmp_chunk = scipy.ndimage.filters.convolve1d(local_chunk, waveform_pos, axis=0, mode='constant') thresholds = numpy.zeros(N_e, dtype=numpy.float32) for i in range(N_e): u = numpy.median(tmp_chunk[:, i], 0) thresholds[i] = numpy.median( numpy.abs(tmp_chunk[:, i] - u), 0) gdata = gather_array(thresholds, comm) if comm.rank == 0: gdata = gdata.reshape((comm.size, N_e)) thresholds = numpy.mean(gdata, 0) bfile = h5py.File(file_out_suff + '.basis.hdf5', 'r+', libver='earliest') io.write_datasets(bfile, ['matched_thresholds_pos'], {'matched_thresholds_pos': thresholds}, compression=hdf5_compress) bfile.close() comm.Barrier() data_file.close() if SHARED_MEMORY and ignore_dead_times: mpi_memory_3.Free()
def main(params, nb_cpu, nb_gpu, use_gpu): ################################################################# # params = detect_memory(params) _ = init_logging(params.logfile) SHARED_MEMORY = get_shared_memory_flag(params) logger = logging.getLogger('circus.fitting') data_file = params.data_file N_e = params.getint('data', 'N_e') N_total = params.nb_channels N_t = params.getint('detection', 'N_t') template_shift = params.getint('detection', 'template_shift') file_out = params.get('data', 'file_out') file_out_suff = params.get('data', 'file_out_suff') sign_peaks = params.get('detection', 'peaks') dist_peaks = params.getint('detection', 'dist_peaks') matched_filter = params.getboolean('detection', 'matched-filter') spike_thresh = params.getfloat('detection', 'spike_thresh') spike_width = params.getfloat('detection', 'spike_width') do_temporal_whitening = params.getboolean('whitening', 'temporal') do_spatial_whitening = params.getboolean('whitening', 'spatial') chunk_size = detect_memory(params) gpu_only = params.getboolean('fitting', 'gpu_only') nodes, edges = get_nodes_and_edges(params) tmp_limits = params.get('fitting', 'amp_limits').replace('(', '').replace(')', '').split(',') tmp_limits = map(float, tmp_limits) amp_auto = params.getboolean('fitting', 'amp_auto') nb_chances = params.getint('fitting', 'nb_chances') max_chunk = params.getfloat('fitting', 'max_chunk') noise_thr = params.getfloat('clustering', 'noise_thr') collect_all = params.getboolean('fitting', 'collect_all') ignore_dead_times = params.getboolean('triggers', 'ignore_times') inv_nodes = numpy.zeros(N_total, dtype=numpy.int32) inv_nodes[nodes] = numpy.arange(len(nodes)) data_file.open() ################################################################# if use_gpu: import cudamat as cmt # # Need to properly handle multi GPU per MPI nodes? if nb_gpu > nb_cpu: gpu_id = int(comm.rank // nb_cpu) else: gpu_id = 0 cmt.cuda_set_device(gpu_id) cmt.init() cmt.cuda_sync_threads() if matched_filter: if sign_peaks in ['negative', 'both']: waveform_neg = io.load_data(params, 'waveform')[::-1] waveform_neg /= (numpy.abs(numpy.sum(waveform_neg)) * len(waveform_neg)) matched_tresholds_neg = io.load_data(params, 'matched-thresholds') if sign_peaks in ['positive', 'both']: waveform_pos = io.load_data(params, 'waveform-pos')[::-1] waveform_pos /= (numpy.abs(numpy.sum(waveform_pos)) * len(waveform_pos)) matched_tresholds_pos = io.load_data(params, 'matched-thresholds-pos') if ignore_dead_times: all_dead_times = get_dead_times(params) thresholds = io.load_data(params, 'thresholds') comm.Barrier() if comm.rank == 0: print_and_log(["Extracting MUA activity..."], 'default', logger) purge(file_out_suff, '.data') if do_spatial_whitening: spatial_whitening = io.load_data(params, 'spatial_whitening') else: spatial_whitening = None # default assignment (PyCharm code inspection) if do_temporal_whitening: temporal_whitening = io.load_data(params, 'temporal_whitening') else: temporal_whitening = None # default assignment (PyCharm code inspection) nb_chunks, last_chunk_len = data_file.analyze(chunk_size) processed_chunks = int(min(nb_chunks, max_chunk)) comm.Barrier() spiketimes_file = open(file_out_suff + '.mua-%d.data' % comm.rank, 'wb') comm.Barrier() electrodes_file = open(file_out_suff + '.elec-%d.data' % comm.rank, 'wb') comm.Barrier() amplitudes_file = open(file_out_suff + '.amp-%d.data' % comm.rank, 'wb') comm.Barrier() if use_gpu and do_spatial_whitening: spatial_whitening = cmt.CUDAMatrix(spatial_whitening, copy_on_host=False) to_explore = range(comm.rank, processed_chunks, comm.size) if comm.rank == 0: to_explore = get_tqdm_progressbar(to_explore) for gcount, gidx in enumerate(to_explore): # print "Node", comm.rank, "is analyzing chunk", gidx, "/", nb_chunks, " ..." # # We need to deal with the borders by taking chunks of size [0, chunck_size + template_shift]. is_first = data_file.is_first_chunk(gidx, nb_chunks) is_last = data_file.is_last_chunk(gidx, nb_chunks) if is_last: padding = (-dist_peaks, 0) elif is_first: padding = (0, dist_peaks) else: padding = (-dist_peaks, dist_peaks) result = {'spiketimes': [], 'amplitudes': [], 'templates': []} local_chunk, t_offset = data_file.get_data(gidx, chunk_size, padding, nodes=nodes) len_chunk = len(local_chunk) if do_spatial_whitening: if use_gpu: local_chunk = cmt.CUDAMatrix(local_chunk, copy_on_host=False) local_chunk = local_chunk.dot(spatial_whitening).asarray() else: local_chunk = numpy.dot(local_chunk, spatial_whitening) if do_temporal_whitening: local_chunk = scipy.ndimage.filters.convolve1d(local_chunk, temporal_whitening, axis=0, mode='constant') # print "Extracting the peaks..." local_peaktimes = [numpy.zeros(0, dtype=numpy.uint32)] local_elecs = [numpy.zeros(0, dtype=numpy.uint32)] local_amps = [numpy.zeros(0, dtype=numpy.float32)] if matched_filter: if sign_peaks in ['positive', 'both']: filter_chunk = scipy.ndimage.filters.convolve1d( local_chunk, waveform_pos, axis=0, mode='constant') for i in range(N_e): peaktimes = scipy.signal.find_peaks( filter_chunk[:, i], height=matched_tresholds_pos[i], width=spike_width, distance=dist_peaks, wlen=N_t)[0] local_peaktimes.append(peaktimes) local_elecs.append( i * numpy.ones(len(peaktimes), dtype='uint32')) local_amps.append(filter_chunk[peaktimes, i]) if sign_peaks in ['negative', 'both']: filter_chunk = scipy.ndimage.filters.convolve1d( local_chunk, waveform_neg, axis=0, mode='constant') for i in range(N_e): peaktimes = scipy.signal.find_peaks( filter_chunk[:, i], height=matched_tresholds_neg[i], width=spike_width, distance=dist_peaks, wlen=N_t)[0] local_peaktimes.append(peaktimes) local_elecs.append( i * numpy.ones(len(peaktimes), dtype='uint32')) local_amps.append(filter_chunk[peaktimes, i]) else: for i in range(N_e): if sign_peaks == 'negative': peaktimes = scipy.signal.find_peaks(-local_chunk[:, i], height=thresholds[i], width=spike_width, distance=dist_peaks, wlen=N_t)[0] elif sign_peaks == 'positive': peaktimes = scipy.signal.find_peaks(local_chunk[:, i], height=thresholds[i], width=spike_width, distance=dist_peaks, wlen=N_t)[0] elif sign_peaks == 'both': peaktimes = scipy.signal.find_peaks(numpy.abs( local_chunk[:, i]), height=thresholds[i], width=spike_width, distance=dist_peaks, wlen=N_t)[0] local_peaktimes.append(peaktimes) local_elecs.append(i * numpy.ones(len(peaktimes), dtype='uint32')) local_amps.append(local_chunk[peaktimes, i]) local_peaktimes = numpy.concatenate(local_peaktimes) local_elecs = numpy.concatenate(local_elecs) local_amps = numpy.concatenate(local_amps) g_offset = t_offset + padding[0] if ignore_dead_times: dead_indices = numpy.searchsorted( all_dead_times, [t_offset, t_offset + chunk_size]) if dead_indices[0] != dead_indices[1]: is_included = numpy.in1d( local_peaktimes + g_offset, all_dead_times[dead_indices[0]:dead_indices[1]]) local_peaktimes = local_peaktimes[~is_included] local_elecs = local_elecs[~is_included] local_amps = local_amps[~is_included] # print "Removing the useless borders..." local_borders = (dist_peaks, len_chunk - dist_peaks) idx = (local_peaktimes >= local_borders[0]) & (local_peaktimes < local_borders[1]) local_peaktimes = numpy.compress(idx, local_peaktimes) + g_offset local_elecs = numpy.compress(idx, local_elecs) local_amps = numpy.compress(idx, local_amps) spiketimes_file.write(local_peaktimes.astype(numpy.uint32).tostring()) electrodes_file.write(local_elecs.tostring()) amplitudes_file.write(local_amps.tostring()) sys.stderr.flush() spiketimes_file.flush() os.fsync(spiketimes_file.fileno()) spiketimes_file.close() electrodes_file.flush() os.fsync(electrodes_file.fileno()) electrodes_file.close() amplitudes_file.flush() os.fsync(amplitudes_file.fileno()) amplitudes_file.close() comm.Barrier() if comm.rank == 0: io.collect_mua(comm.size, params, erase=True) data_file.close()
def main(params, nb_cpu, nb_gpu, use_gpu): ################################################################# logger = init_logging(params.logfile) logger = logging.getLogger('circus.fitting') data_file = params.data_file data_file.open() N_e = params.getint('data', 'N_e') N_total = params.nb_channels N_t = params.getint('detection', 'N_t') template_shift = params.getint('detection', 'template_shift') file_out = params.get('data', 'file_out') file_out_suff = params.get('data', 'file_out_suff') sign_peaks = params.get('detection', 'peaks') matched_filter = params.getboolean('detection', 'matched-filter') spike_thresh = params.getfloat('detection', 'spike_thresh') do_temporal_whitening = params.getboolean('whitening', 'temporal') do_spatial_whitening = params.getboolean('whitening', 'spatial') chunk_size = params.getint('fitting', 'chunk_size') gpu_only = params.getboolean('fitting', 'gpu_only') nodes, edges = get_nodes_and_edges(params) tmp_limits = params.get('fitting', 'amp_limits').replace('(', '').replace(')', '').split(',') tmp_limits = map(float, tmp_limits) amp_auto = params.getboolean('fitting', 'amp_auto') space_explo = params.getfloat('fitting', 'space_explo') nb_chances = params.getint('fitting', 'nb_chances') max_chunk = params.getfloat('fitting', 'max_chunk') noise_thr = params.getfloat('clustering', 'noise_thr') collect_all = params.getboolean('fitting', 'collect_all') ignore_dead_times = params.getboolean('triggers', 'ignore_times') inv_nodes = numpy.zeros(N_total, dtype=numpy.int32) inv_nodes[nodes] = numpy.argsort(nodes) ################################################################# if use_gpu: import cudamat as cmt ## Need to properly handle multi GPU per MPI nodes? if nb_gpu > nb_cpu: gpu_id = int(comm.rank//nb_cpu) else: gpu_id = 0 cmt.cuda_set_device(gpu_id) cmt.init() cmt.cuda_sync_threads() if SHARED_MEMORY: templates = io.load_data_memshared(params, 'templates', normalize=True, transpose=True) N_tm, x = templates.shape else: templates = io.load_data(params, 'templates') x, N_tm = templates.shape temp_2_shift = 2*template_shift full_gpu = use_gpu and gpu_only n_tm = N_tm//2 n_scalar = N_e*N_t last_spikes = numpy.zeros((n_tm, 1), dtype=numpy.int32) temp_window = numpy.arange(-template_shift, template_shift+1) if not amp_auto: amp_limits = numpy.zeros((n_tm, 2)) amp_limits[:, 0] = tmp_limits[0] amp_limits[:, 1] = tmp_limits[1] else: amp_limits = io.load_data(params, 'limits') norm_templates = io.load_data(params, 'norm-templates') if not SHARED_MEMORY: for idx in xrange(templates.shape[1]): myslice = numpy.arange(templates.indptr[idx], templates.indptr[idx+1]) templates.data[myslice] /= norm_templates[idx] templates = templates.T if matched_filter: if sign_peaks in ['negative', 'both']: waveform_neg = io.load_data(params, 'waveform') waveform_neg /= (numpy.abs(numpy.sum(waveform_neg))* len(waveform_neg)) matched_tresholds_neg = io.load_data(params, 'matched-thresholds') if sign_peaks in ['positive', 'both']: waveform_pos = io.load_data(params, 'waveform-pos') waveform_pos /= (numpy.abs(numpy.sum(waveform_pos))* len(waveform_pos)) matched_tresholds_pos = io.load_data(params, 'matched-thresholds-pos') if ignore_dead_times: dead_times = numpy.loadtxt(params.get('triggers', 'dead_file')) if len(dead_times.shape) == 1: dead_times = dead_times.reshape(1, 2) dead_in_ms = params.getboolean('triggers', 'dead_in_ms') if dead_in_ms: dead_times *= numpy.int64(data_file.sampling_rate*1e-3) dead_times = dead_times.astype(numpy.int64) all_dead_times = [] for i in xrange(len(dead_times)): all_dead_times += range(dead_times[i, 0], dead_times[i, 1]) thresholds = io.load_data(params, 'thresholds') if collect_all: neighbors = {} for i in xrange(n_tm): tmp = templates[i, :].toarray().reshape(N_e, N_t) * norm_templates[i] neighbors[i] = numpy.where(numpy.sum(tmp, 1) != 0)[0] if use_gpu: templates = cmt.SparseCUDAMatrix(templates, copy_on_host=False) info_string = '' if comm.rank == 0: if use_gpu: info_string = "using %d GPUs" %(comm.size) else: info_string = "using %d CPUs" %(comm.size) comm.Barrier() c_overlap = io.get_overlaps(params, nb_cpu=nb_cpu, nb_gpu=nb_gpu, use_gpu=use_gpu) over_shape = c_overlap.get('over_shape')[:] N_over = int(numpy.sqrt(over_shape[0])) S_over = over_shape[1] ## If the number of overlaps is different from templates, we need to recompute them if N_over != N_tm: if comm.rank == 0: print_and_log(['Templates have been modified, recomputing the overlaps...'], 'default', logger) c_overlap = io.get_overlaps(params, erase=True, nb_cpu=nb_cpu, nb_gpu=nb_gpu, use_gpu=use_gpu) over_shape = c_overlap.get('over_shape')[:] N_over = int(numpy.sqrt(over_shape[0])) S_over = over_shape[1] if SHARED_MEMORY: c_overs = io.load_data_memshared(params, 'overlaps', nb_cpu=nb_cpu, nb_gpu=nb_gpu, use_gpu=use_gpu) else: c_overlap = io.get_overlaps(params, nb_cpu=nb_cpu, nb_gpu=nb_gpu, use_gpu=use_gpu) over_x = c_overlap.get('over_x')[:] over_y = c_overlap.get('over_y')[:] over_data = c_overlap.get('over_data')[:] over_shape = c_overlap.get('over_shape')[:] c_overlap.close() # To be faster, we rearrange the overlaps into a dictionnary. This has a cost: twice the memory usage for # a short period of time c_overs = {} overlaps = scipy.sparse.csr_matrix((over_data, (over_x, over_y)), shape=(over_shape[0], over_shape[1])) del over_x, over_y, over_data for i in xrange(N_over): c_overs[i] = overlaps[i*N_over:(i+1)*N_over] del overlaps comm.Barrier() if comm.rank == 0: print_and_log(["Here comes the SpyKING CIRCUS %s and %d templates..." %(info_string, n_tm)], 'default', logger) purge(file_out_suff, '.data') if do_spatial_whitening: spatial_whitening = io.load_data(params, 'spatial_whitening') if do_temporal_whitening: temporal_whitening = io.load_data(params, 'temporal_whitening') if full_gpu: try: # If memory on the GPU is large enough, we load the overlaps onto it for i in xrange(N_over): c_overs[i] = cmt.SparseCUDAMatrix(c_overs[i], copy_on_host=False) except Exception: if comm.rank == 0: print_and_log(["Not enough memory on GPUs: GPUs are used for projection only"], 'info', logger) for i in xrange(N_over): if c_overs.has_key(i): del c_overs[i] full_gpu = False nb_chunks, last_chunk_len = data_file.analyze(chunk_size) processed_chunks = int(min(nb_chunks, max_chunk)) comm.Barrier() spiketimes_file = open(file_out_suff + '.spiketimes-%d.data' %comm.rank, 'wb') comm.Barrier() amplitudes_file = open(file_out_suff + '.amplitudes-%d.data' %comm.rank, 'wb') comm.Barrier() templates_file = open(file_out_suff + '.templates-%d.data' %comm.rank, 'wb') comm.Barrier() if collect_all: garbage_times_file = open(file_out_suff + '.gspiketimes-%d.data' %comm.rank, 'wb') comm.Barrier() garbage_temp_file = open(file_out_suff + '.gtemplates-%d.data' %comm.rank, 'wb') comm.Barrier() if use_gpu and do_spatial_whitening: spatial_whitening = cmt.CUDAMatrix(spatial_whitening, copy_on_host=False) last_chunk_size = 0 to_explore = xrange(comm.rank, processed_chunks, comm.size) if comm.rank == 0: to_explore = get_tqdm_progressbar(to_explore) for gcount, gidx in enumerate(to_explore): #print "Node", comm.rank, "is analyzing chunk", gidx, "/", nb_chunks, " ..." ## We need to deal with the borders by taking chunks of size [0, chunck_size+template_shift] is_first = data_file.is_first_chunk(gidx, nb_chunks) is_last = data_file.is_last_chunk(gidx, nb_chunks) if is_last: padding = (-2*template_shift, 0) elif is_first: padding = (0, 2*template_shift) else: padding = (-2*template_shift, 2*template_shift) result = {'spiketimes' : [], 'amplitudes' : [], 'templates' : []} local_chunk, t_offset = data_file.get_data(gidx, chunk_size, padding, nodes=nodes) len_chunk = len(local_chunk) if do_spatial_whitening: if use_gpu: local_chunk = cmt.CUDAMatrix(local_chunk, copy_on_host=False) local_chunk = local_chunk.dot(spatial_whitening).asarray() else: local_chunk = numpy.dot(local_chunk, spatial_whitening) if do_temporal_whitening: local_chunk = scipy.ndimage.filters.convolve1d(local_chunk, temporal_whitening, axis=0, mode='constant') #print "Extracting the peaks..." if collect_all: all_found_spikes = {} for i in xrange(N_e): all_found_spikes[i] = [] local_peaktimes = numpy.zeros(0, dtype=numpy.int32) if matched_filter: if sign_peaks in ['positive', 'both']: filter_chunk = scipy.ndimage.filters.convolve1d(local_chunk, waveform_pos, axis=0, mode='constant') for i in xrange(N_e): peaktimes = algo.detect_peaks(filter_chunk[:, i], matched_tresholds_pos[i]) local_peaktimes = numpy.concatenate((local_peaktimes, peaktimes)) if collect_all: all_found_spikes[i] += peaktimes.tolist() if sign_peaks in ['negative', 'both']: filter_chunk = scipy.ndimage.filters.convolve1d(local_chunk, waveform_neg, axis=0, mode='constant') for i in xrange(N_e): peaktimes = algo.detect_peaks(filter_chunk[:, i], matched_tresholds_neg[i]) local_peaktimes = numpy.concatenate((local_peaktimes, peaktimes)) if collect_all: all_found_spikes[i] += peaktimes.tolist() else: for i in xrange(N_e): if sign_peaks == 'negative': peaktimes = algo.detect_peaks(local_chunk[:, i], thresholds[i], valley=True) elif sign_peaks == 'positive': peaktimes = algo.detect_peaks(local_chunk[:, i], thresholds[i], valley=False) elif sign_peaks == 'both': peaktimes = algo.detect_peaks(numpy.abs(local_chunk[:, i]), thresholds[i], valley=False) local_peaktimes = numpy.concatenate((local_peaktimes, peaktimes)) if collect_all: all_found_spikes[i] += peaktimes.tolist() local_peaktimes = numpy.unique(local_peaktimes) if ignore_dead_times: local_peaktimes = numpy.array(list(set(local_peaktimes + t_offset).difference(all_dead_times)), dtype=numpy.int32) - t_offset local_peaktimes = numpy.sort(local_peaktimes) #print "Removing the useless borders..." local_borders = (template_shift, len_chunk - template_shift) idx = (local_peaktimes >= local_borders[0]) & (local_peaktimes < local_borders[1]) local_peaktimes = numpy.compress(idx, local_peaktimes) if collect_all: for i in xrange(N_e): all_found_spikes[i] = numpy.array(all_found_spikes[i], dtype=numpy.int32) if ignore_dead_times: all_found_spikes[i] = numpy.array(list(set(all_found_spikes[i] + t_offset).difference(all_dead_times)), dtype=numpy.int32) - t_offset all_found_spikes[i] = numpy.sort(all_found_spikes[i]) idx = (all_found_spikes[i] >= local_borders[0]) & (all_found_spikes[i] < local_borders[1]) all_found_spikes[i] = numpy.compress(idx, all_found_spikes[i]) n_t = len(local_peaktimes) all_indices = numpy.arange(n_t) if full_gpu: # all_indices = cmt.CUDAMatrix(all_indices) tmp_gpu = cmt.CUDAMatrix(local_peaktimes.reshape((1, n_t)), copy_on_host=False) if n_t > 0: #print "Computing the b (should full_gpu by putting all chunks on GPU if possible?)..." if collect_all: c_local_chunk = local_chunk.copy() local_chunk = local_chunk.T.ravel() sub_mat = numpy.zeros((N_e*(2*template_shift+1), n_t), dtype=numpy.float32) if len_chunk != last_chunk_size: slice_indices = numpy.zeros(0, dtype=numpy.int32) for idx in xrange(N_e): slice_indices = numpy.concatenate((slice_indices, len_chunk*idx + temp_window)) last_chunk_size = len_chunk for count, idx in enumerate(local_peaktimes): sub_mat[:, count] = numpy.take(local_chunk, slice_indices + idx) del local_chunk if use_gpu: sub_mat = cmt.CUDAMatrix(sub_mat, copy_on_host=False) b = cmt.sparse_dot(templates, sub_mat) else: b = templates.dot(sub_mat) del sub_mat local_offset = padding[0] + t_offset local_bounds = (temp_2_shift, len_chunk - temp_2_shift) all_spikes = local_peaktimes + local_offset # Because for GPU, slicing by columns is more efficient, we need to transpose b #b = b.transpose() if use_gpu and not full_gpu: b = b.asarray() failure = numpy.zeros(n_t, dtype=numpy.int32) if full_gpu: mask = numpy.zeros((2*n_tm, n_t), dtype=numpy.float32) mask[:n_tm, :] = 1 data = cmt.empty(mask.shape) patch_gpu= b.shape[1] == 1 else: mask = numpy.ones((n_tm, n_t), dtype=numpy.float32) sub_b = b[:n_tm, :] min_time = local_peaktimes.min() max_time = local_peaktimes.max() local_len = max_time - min_time + 1 min_times = numpy.maximum(local_peaktimes - min_time - temp_2_shift, 0) max_times = numpy.minimum(local_peaktimes - min_time + temp_2_shift + 1, max_time - min_time) max_n_t = int(space_explo*(max_time-min_time+1)//(2*temp_2_shift + 1)) if collect_all: c_all_times = numpy.zeros((len_chunk, N_e), dtype=numpy.bool) c_min_times = numpy.maximum(numpy.arange(len_chunk) - template_shift, 0) c_max_times = numpy.minimum(numpy.arange(len_chunk) + template_shift + 1, len_chunk) for i in xrange(N_e): c_all_times[all_found_spikes[i], i] = True while (numpy.mean(failure) < nb_chances): if full_gpu: gpu_mask = cmt.CUDAMatrix(mask, copy_on_host=False) b.mult(gpu_mask, data) tmp_mat = data.max(0) argmax_bi = numpy.argsort(tmp_mat.asarray()[0, :])[::-1] del tmp_mat else: data = sub_b * mask argmax_bi = numpy.argsort(numpy.max(data, 0))[::-1] while (len(argmax_bi) > 0): subset = [] indices = [] all_times = numpy.zeros(local_len, dtype=numpy.bool) for count, idx in enumerate(argmax_bi): myslice = all_times[min_times[idx]:max_times[idx]] if not myslice.any(): subset += [idx] indices += [count] all_times[min_times[idx]:max_times[idx]] = True if len(subset) > max_n_t: break subset = numpy.array(subset, dtype=numpy.int32) argmax_bi = numpy.delete(argmax_bi, indices) if full_gpu: b_array = b.asarray() sub_b = b_array[:n_tm, :] inds_t, inds_temp = subset, numpy.argmax(numpy.take(sub_b, subset, axis=1), 0) if full_gpu: best_amp = sub_b[inds_temp, inds_t]/n_scalar best_amp2 = b_array[inds_temp + n_tm, inds_t]/n_scalar else: best_amp = sub_b[inds_temp, inds_t]/n_scalar best_amp2 = b[inds_temp + n_tm, inds_t]/n_scalar mask[inds_temp, inds_t] = 0 best_amp_n = best_amp/numpy.take(norm_templates, inds_temp) best_amp2_n = best_amp2/numpy.take(norm_templates, inds_temp + n_tm) all_idx = ((best_amp_n >= amp_limits[inds_temp, 0]) & (best_amp_n <= amp_limits[inds_temp, 1])) to_keep = numpy.where(all_idx == True)[0] to_reject = numpy.where(all_idx == False)[0] ts = numpy.take(local_peaktimes, inds_t[to_keep]) good = (ts >= local_bounds[0]) & (ts < local_bounds[1]) # We reduce to only the good times that will be kept #to_keep = to_keep[good] #ts = ts[good] if len(ts) > 0: if full_gpu: tmp = cmt.CUDAMatrix(numpy.ones((len(ts), 1)), copy_on_host=False) tmp3 = cmt.CUDAMatrix(-ts.reshape((len(ts), 1)), copy_on_host=False) tmp = tmp.dot(tmp_gpu) tmp.add_col_vec(tmp3) condition = cmt.empty(tmp.shape) cmt.abs(tmp, condition).less_than(temp_2_shift + 1) condition = condition.asarray().astype(numpy.bool) tmp = tmp.asarray().astype(numpy.int32) else: tmp = numpy.dot(numpy.ones((len(ts), 1), dtype=numpy.int32), local_peaktimes.reshape((1, n_t))) tmp -= ts.reshape((len(ts), 1)) condition = numpy.abs(tmp) <= temp_2_shift for count, keep in enumerate(to_keep): idx_b = numpy.compress(condition[count, :], all_indices) ytmp = tmp[count, condition[count, :]] + temp_2_shift indices = numpy.zeros((S_over, len(ytmp)), dtype=numpy.float32) indices[ytmp, numpy.arange(len(ytmp))] = 1 if full_gpu: indices = cmt.CUDAMatrix(indices, copy_on_host=False) if patch_gpu: b_lines = b.get_col_slice(0, b.shape[0]) else: b_lines = b.get_col_slice(idx_b[0], idx_b[-1]+1) tmp1 = cmt.sparse_dot(c_overs[inds_temp[keep]], indices, mult=-best_amp[keep]) tmp2 = cmt.sparse_dot(c_overs[inds_temp[keep] + n_tm], indices, mult=-best_amp2[keep]) b_lines.add(tmp1.add(tmp2)) del tmp1, tmp2 else: tmp1 = c_overs[inds_temp[keep]].multiply(-best_amp[keep]).dot(indices) tmp2 = c_overs[inds_temp[keep] + n_tm].multiply(-best_amp2[keep]).dot(indices) b[:, idx_b] += tmp1 + tmp2 if good[count]: t_spike = ts[count] + local_offset result['spiketimes'] += [t_spike] result['amplitudes'] += [(best_amp_n[keep], best_amp2_n[keep])] result['templates'] += [inds_temp[keep]] myslice = numpy.take(inds_t, to_reject) failure[myslice] += 1 sub_idx = (numpy.take(failure, myslice) >= nb_chances) mask[:, numpy.compress(sub_idx, myslice)] = 0 spikes_to_write = numpy.array(result['spiketimes'], dtype=numpy.uint32) amplitudes_to_write = numpy.array(result['amplitudes'], dtype=numpy.float32) templates_to_write = numpy.array(result['templates'], dtype=numpy.int32) spiketimes_file.write(spikes_to_write.tostring()) amplitudes_file.write(amplitudes_to_write.tostring()) templates_file.write(templates_to_write.tostring()) if collect_all: for temp, spike in zip(templates_to_write, spikes_to_write - local_offset): c_all_times[c_min_times[spike]:c_max_times[spike], neighbors[temp]] = False gspikes = numpy.where(numpy.sum(c_all_times, 1) > 0)[0] c_all_times = numpy.take(c_all_times, gspikes, axis=0) c_local_chunk = numpy.take(c_local_chunk, gspikes, axis=0) * c_all_times if sign_peaks == 'negative': bestlecs = numpy.argmin(c_local_chunk, 1) if matched_filter: threshs = -matched_tresholds_neg[bestlecs] else: threshs = -thresholds[bestlecs] idx = numpy.where(numpy.min(c_local_chunk, 1) < threshs)[0] elif sign_peaks == 'positive': bestlecs = numpy.argmax(c_local_chunk, 1) if matched_filter: threshs = matched_tresholds_pos[bestlecs] else: threshs = thresholds[bestlecs] idx = numpy.where(numpy.max(c_local_chunk, 1) > threshs)[0] elif sign_peaks == 'both': c_local_chunk = numpy.abs(c_local_chunk) bestlecs = numpy.argmax(c_local_chunk, 1) if matched_filter: threshs = numpy.minimum(matched_tresholds_neg[bestlecs], matched_tresholds_pos[bestlecs]) else: threshs = thresholds[bestlecs] idx = numpy.where(numpy.max(c_local_chunk, 1) > threshs)[0] gspikes = numpy.take(gspikes, idx) bestlecs = numpy.take(bestlecs, idx) gspikes_to_write = numpy.array(gspikes + local_offset, dtype=numpy.uint32) gtemplates_to_write = numpy.array(bestlecs, dtype=numpy.int32) garbage_times_file.write(gspikes_to_write.tostring()) garbage_temp_file.write(gtemplates_to_write.tostring()) if full_gpu: del gpu_mask, b, data spiketimes_file.flush() os.fsync(spiketimes_file.fileno()) spiketimes_file.close() amplitudes_file.flush() os.fsync(amplitudes_file.fileno()) amplitudes_file.close() templates_file.flush() os.fsync(templates_file.fileno()) templates_file.close() if collect_all: garbage_temp_file.flush() os.fsync(garbage_temp_file.fileno()) garbage_temp_file.close() garbage_times_file.flush() os.fsync(garbage_times_file.fileno()) garbage_times_file.close() comm.Barrier() if comm.rank == 0: io.collect_data(comm.size, params, erase=True) data_file.close()
def main(params, nb_cpu, nb_gpu, use_gpu): ################################################################# # params = detect_memory(params) _ = init_logging(params.logfile) SHARED_MEMORY = get_shared_memory_flag(params) logger = logging.getLogger('circus.fitting') data_file = params.data_file n_e = params.getint('data', 'N_e') n_total = params.nb_channels n_t = params.getint('detection', 'N_t') template_shift = params.getint('detection', 'template_shift') # file_out = params.get('data', 'file_out') file_out_suff = params.get('data', 'file_out_suff') sign_peaks = params.get('detection', 'peaks') matched_filter = params.getboolean('detection', 'matched-filter') # spike_thresh = params.getfloat('detection', 'spike_thresh') ratio_thresh = params.getfloat('fitting', 'ratio_thresh') two_components = params.getboolean('fitting', 'two_components') # spike_width = params.getfloat('detection', 'spike_width') # dist_peaks = params.getint('detection', 'dist_peaks') do_temporal_whitening = params.getboolean('whitening', 'temporal') do_spatial_whitening = params.getboolean('whitening', 'spatial') templates_normalization = params.getboolean('clustering', 'templates_normalization') # TODO test, switch, test! chunk_size = detect_memory(params, fitting=True) gpu_only = params.getboolean('fitting', 'gpu_only') nodes, edges = get_nodes_and_edges(params) tmp_limits = params.get('fitting', 'amp_limits').replace('(', '').replace(')', '').split(',') tmp_limits = [float(v) for v in tmp_limits] amp_auto = params.getboolean('fitting', 'amp_auto') auto_nb_chances = params.getboolean('fitting', 'auto_nb_chances') if auto_nb_chances: nb_chances = io.load_data(params, 'nb_chances') max_nb_chances = params.getint('fitting', 'max_nb_chances') percent_nb_chances = params.getfloat('fitting', 'percent_nb_chances') total_nb_chances = max(1, numpy.nanpercentile(nb_chances, percent_nb_chances)) total_nb_chances = min(total_nb_chances, max_nb_chances) if comm.rank == 0: print_and_log(['nb_chances set automatically to %g' %total_nb_chances], 'debug', logger) else: total_nb_chances = params.getfloat('fitting', 'nb_chances') max_chunk = params.getfloat('fitting', 'max_chunk') # noise_thr = params.getfloat('clustering', 'noise_thr') collect_all = params.getboolean('fitting', 'collect_all') min_second_component = params.getfloat('fitting', 'min_second_component') debug = params.getboolean('fitting', 'debug') ignore_dead_times = params.getboolean('triggers', 'ignore_times') inv_nodes = numpy.zeros(n_total, dtype=numpy.int32) inv_nodes[nodes] = numpy.arange(len(nodes)) data_file.open() ################################################################# if use_gpu: import cudamat as cmt # # Need to properly handle multi GPU per MPI nodes? if nb_gpu > nb_cpu: gpu_id = int(comm.rank // nb_cpu) else: gpu_id = 0 cmt.cuda_set_device(gpu_id) cmt.init() cmt.cuda_sync_threads() if SHARED_MEMORY: templates, _ = io.load_data_memshared(params, 'templates', normalize=templates_normalization, transpose=True) N_tm, x = templates.shape else: templates = io.load_data(params, 'templates') x, N_tm = templates.shape temp_2_shift = 2 * template_shift temp_3_shift = 3 * template_shift full_gpu = use_gpu and gpu_only n_tm = N_tm // 2 n_scalar = n_e * n_t temp_window = numpy.arange(-template_shift, template_shift + 1) size_window = n_e * (2 * template_shift + 1) if not amp_auto: amp_limits = numpy.zeros((n_tm, 2)) amp_limits[:, 0] = tmp_limits[0] amp_limits[:, 1] = tmp_limits[1] else: amp_limits = io.load_data(params, 'limits') norm_templates = io.load_data(params, 'norm-templates') if not templates_normalization: norm_templates_2 = (norm_templates ** 2.0) * n_scalar if not SHARED_MEMORY: # Normalize templates (if necessary). if templates_normalization: for idx in range(templates.shape[1]): myslice = numpy.arange(templates.indptr[idx], templates.indptr[idx+1]) templates.data[myslice] /= norm_templates[idx] # Transpose templates. templates = templates.T waveform_neg = numpy.empty(0) # default assignment (for PyCharm code inspection) matched_thresholds_neg = None # default assignment (for PyCharm code inspection) waveform_pos = numpy.empty(0) # default assignment (for PyCharm code inspection) matched_thresholds_pos = None # default assignment (for PyCharm code inspection) if matched_filter: if sign_peaks in ['negative', 'both']: waveform_neg = io.load_data(params, 'waveform')[::-1] waveform_neg /= (numpy.abs(numpy.sum(waveform_neg)) * len(waveform_neg)) matched_thresholds_neg = io.load_data(params, 'matched-thresholds') if sign_peaks in ['positive', 'both']: waveform_pos = io.load_data(params, 'waveform-pos')[::-1] waveform_pos /= (numpy.abs(numpy.sum(waveform_pos)) * len(waveform_pos)) matched_thresholds_pos = io.load_data(params, 'matched-thresholds-pos') if ignore_dead_times: all_dead_times = get_dead_times(params) else: all_dead_times = None # default assignment (for PyCharm code inspection) thresholds = io.get_accurate_thresholds(params, ratio_thresh) neighbors = {} if collect_all: for i in range(0, n_tm): tmp = templates[i, :].toarray().reshape(n_e, n_t) if templates_normalization: tmp = tmp * norm_templates[i] neighbors[i] = numpy.where(numpy.sum(tmp, axis=1) != 0.0)[0] if use_gpu: templates = cmt.SparseCUDAMatrix(templates, copy_on_host=False) info_string = '' if comm.rank == 0: if use_gpu: info_string = "using %d GPUs" % comm.size else: info_string = "using %d CPUs" % comm.size comm.Barrier() c_overlap = io.get_overlaps(params, nb_cpu=nb_cpu, nb_gpu=nb_gpu, use_gpu=use_gpu) over_shape = c_overlap.get('over_shape')[:] n_over = int(numpy.sqrt(over_shape[0])) s_over = over_shape[1] # # If the number of overlaps is different from templates, we need to recompute them. if n_over != N_tm: if comm.rank == 0: print_and_log(['Templates have been modified, recomputing the overlaps...'], 'default', logger) c_overlap = io.get_overlaps(params, erase=True, nb_cpu=nb_cpu, nb_gpu=nb_gpu, use_gpu=use_gpu) over_shape = c_overlap.get('over_shape')[:] n_over = int(numpy.sqrt(over_shape[0])) s_over = over_shape[1] if SHARED_MEMORY: c_overs, _ = io.load_data_memshared(params, 'overlaps') else: c_overs = io.load_data(params, 'overlaps') comm.Barrier() if n_tm == 0: if comm.rank == 0: print_and_log(["No templates present. Redo clustering?"], 'default', logger) sys.exit(0) if comm.rank == 0: print_and_log(["Here comes the SpyKING CIRCUS %s and %d templates..." % (info_string, n_tm)], 'default', logger) purge(file_out_suff, '.data') if do_spatial_whitening: spatial_whitening = io.load_data(params, 'spatial_whitening') else: spatial_whitening = None # default assignment (for PyCharm code inspection) if do_temporal_whitening: temporal_whitening = io.load_data(params, 'temporal_whitening') else: temporal_whitening = None # default assignment (for PyCharm code inspection) if full_gpu: try: # If memory on the GPU is large enough, we load the overlaps onto it for i in range(n_over): c_overs[i] = cmt.SparseCUDAMatrix(c_overs[i], copy_on_host=False) except Exception: if comm.rank == 0: print_and_log(["Not enough memory on GPUs: GPUs are used for projection only"], 'info', logger) for i in range(n_over): if i in c_overs: del c_overs[i] full_gpu = False nb_chunks, last_chunk_len = data_file.analyze(chunk_size) processed_chunks = int(min(nb_chunks, max_chunk)) comm.Barrier() spiketimes_file = open(file_out_suff + '.spiketimes-%d.data' % comm.rank, 'wb') comm.Barrier() amplitudes_file = open(file_out_suff + '.amplitudes-%d.data' % comm.rank, 'wb') comm.Barrier() templates_file = open(file_out_suff + '.templates-%d.data' % comm.rank, 'wb') comm.Barrier() if collect_all: garbage_times_file = open(file_out_suff + '.gspiketimes-%d.data' % comm.rank, 'wb') comm.Barrier() garbage_temp_file = open(file_out_suff + '.gtemplates-%d.data' % comm.rank, 'wb') comm.Barrier() else: garbage_times_file = None # default assignment (for PyCharm code inspection) garbage_temp_file = None # default assignment (for PyCharm code inspection) if debug: # Open debug files. chunk_nbs_debug_file = open(file_out_suff + '.chunk_nbs_debug_%d.data' % comm.rank, mode='wb') comm.Barrier() iteration_nbs_debug_file = open(file_out_suff + '.iteration_nbs_debug_%d.data' % comm.rank, mode='wb') comm.Barrier() peak_nbs_debug_file = open(file_out_suff + '.peak_nbs_debug_%d.data' % comm.rank, mode='wb') comm.Barrier() peak_local_time_steps_debug_file = open( file_out_suff + '.peak_local_time_steps_debug_%d.data' % comm.rank, mode='wb' ) comm.Barrier() peak_time_steps_debug_file = open(file_out_suff + '.peak_time_steps_debug_%d.data' % comm.rank, mode='wb') comm.Barrier() peak_scalar_products_debug_file = open( file_out_suff + '.peak_scalar_products_debug_%d.data' % comm.rank, mode='wb' ) comm.Barrier() peak_solved_flags_debug_file = open(file_out_suff + '.peak_solved_flags_debug_%d.data' % comm.rank, mode='wb') comm.Barrier() template_nbs_debug_file = open(file_out_suff + '.template_nbs_debug_%d.data' % comm.rank, mode='wb') comm.Barrier() success_flags_debug_file = open(file_out_suff + '.success_flags_debug_%d.data' % comm.rank, mode='wb') comm.Barrier() else: chunk_nbs_debug_file = None # default assignment (for PyCharm code inspection) iteration_nbs_debug_file = None # default assignment (for PyCharm code inspection) peak_nbs_debug_file = None # default assignment (for PyCharm code inspection) peak_local_time_steps_debug_file = None # default assignment (for PyCharm code inspection) peak_time_steps_debug_file = None # default assignment (for PyCharm code inspection) peak_scalar_products_debug_file = None # default assignment (for PyCharm code inspection) peak_solved_flags_debug_file = None # default assignment (for PyCharm code inspection) template_nbs_debug_file = None # default assignment (for PyCharm code inspection) success_flags_debug_file = None # default assignment (for PyCharm code inspection) if use_gpu and do_spatial_whitening: spatial_whitening = cmt.CUDAMatrix(spatial_whitening, copy_on_host=False) last_chunk_size = 0 slice_indices = numpy.zeros(0, dtype=numpy.int32) to_explore = range(comm.rank, processed_chunks, comm.size) if comm.rank == 0: to_explore = get_tqdm_progressbar(params, to_explore) for gcount, gidx in enumerate(to_explore): # print "Node", comm.rank, "is analyzing chunk", gidx, "/", nb_chunks, " ..." # # We need to deal with the borders by taking chunks of size [0, chunck_size + template_shift]. is_first = data_file.is_first_chunk(gidx, nb_chunks) is_last = data_file.is_last_chunk(gidx, nb_chunks) if not (is_first and is_last): if is_last: padding = (-temp_3_shift, 0) elif is_first: padding = (0, temp_3_shift) else: padding = (-temp_3_shift, temp_3_shift) else: padding = (0, 0) result = { 'spiketimes': [], 'amplitudes': [], 'templates': [], } result_debug = { 'chunk_nbs': [], 'iteration_nbs': [], 'peak_nbs': [], 'peak_local_time_steps': [], 'peak_time_steps': [], 'peak_scalar_products': [], 'peak_solved_flags': [], 'template_nbs': [], 'success_flags': [], } local_chunk, t_offset = data_file.get_data(gidx, chunk_size, padding, nodes=nodes) len_chunk = len(local_chunk) if do_spatial_whitening: if use_gpu: local_chunk = cmt.CUDAMatrix(local_chunk, copy_on_host=False) local_chunk = local_chunk.dot(spatial_whitening).asarray() else: local_chunk = numpy.dot(local_chunk, spatial_whitening) if do_temporal_whitening: local_chunk = scipy.ndimage.filters.convolve1d(local_chunk, temporal_whitening, axis=0, mode='constant') # Extracting peaks. all_found_spikes = {} if collect_all: for i in range(n_e): all_found_spikes[i] = [] local_peaktimes = [numpy.empty(0, dtype=numpy.uint32)] if matched_filter: if sign_peaks in ['positive', 'both']: filter_chunk = scipy.ndimage.filters.convolve1d(local_chunk, waveform_pos, axis=0, mode='constant') for i in range(n_e): peaktimes = scipy.signal.find_peaks(filter_chunk[:, i], height=matched_thresholds_pos[i])[0] local_peaktimes.append(peaktimes) if collect_all: all_found_spikes[i] += peaktimes.tolist() if sign_peaks in ['negative', 'both']: filter_chunk = scipy.ndimage.filters.convolve1d(local_chunk, waveform_neg, axis=0, mode='constant') for i in range(n_e): peaktimes = scipy.signal.find_peaks(filter_chunk[:, i], height=matched_thresholds_neg[i])[0] local_peaktimes.append(peaktimes) if collect_all: all_found_spikes[i] += peaktimes.tolist() local_peaktimes = numpy.concatenate(local_peaktimes) else: for i in range(n_e): if sign_peaks == 'negative': peaktimes = scipy.signal.find_peaks(-local_chunk[:, i], height=thresholds[i])[0] elif sign_peaks == 'positive': peaktimes = scipy.signal.find_peaks(local_chunk[:, i], height=thresholds[i])[0] elif sign_peaks == 'both': peaktimes = scipy.signal.find_peaks(numpy.abs(local_chunk[:, i]), height=thresholds[i])[0] else: raise ValueError("Unexpected value %s" % sign_peaks) local_peaktimes.append(peaktimes) if collect_all: all_found_spikes[i] += peaktimes.tolist() local_peaktimes = numpy.concatenate(local_peaktimes) local_peaktimes = numpy.unique(local_peaktimes) g_offset = t_offset + padding[0] if ignore_dead_times: dead_indices = numpy.searchsorted(all_dead_times, [t_offset, t_offset + chunk_size]) if dead_indices[0] != dead_indices[1]: is_included = numpy.in1d(local_peaktimes + g_offset, all_dead_times[dead_indices[0]:dead_indices[1]]) local_peaktimes = local_peaktimes[~is_included] local_peaktimes = numpy.sort(local_peaktimes) else: dead_indices = None # default assignment (for PyCharm code inspection) # print "Removing the useless borders..." local_borders = (template_shift, len_chunk - template_shift) idx = (local_peaktimes >= local_borders[0]) & (local_peaktimes < local_borders[1]) local_peaktimes = numpy.compress(idx, local_peaktimes) if collect_all: for i in range(n_e): all_found_spikes[i] = numpy.array(all_found_spikes[i], dtype=numpy.uint32) if ignore_dead_times: if dead_indices[0] != dead_indices[1]: is_included = numpy.in1d( all_found_spikes[i] + g_offset, all_dead_times[dead_indices[0]:dead_indices[1]] ) all_found_spikes[i] = all_found_spikes[i][~is_included] all_found_spikes[i] = numpy.sort(all_found_spikes[i]) idx = (all_found_spikes[i] >= local_borders[0]) & (all_found_spikes[i] < local_borders[1]) all_found_spikes[i] = numpy.compress(idx, all_found_spikes[i]) nb_local_peak_times = len(local_peaktimes) if full_gpu: # all_indices = cmt.CUDAMatrix(all_indices) # tmp_gpu = cmt.CUDAMatrix(local_peaktimes.reshape((1, nb_local_peak_times)), copy_on_host=False) _ = cmt.CUDAMatrix(local_peaktimes.reshape((1, nb_local_peak_times)), copy_on_host=False) if nb_local_peak_times > 0: # print "Computing the b (should full_gpu by putting all chunks on GPU if possible?)..." if collect_all: c_local_chunk = local_chunk.copy() else: c_local_chunk = None # default assignment (for PyCharm code inspection) sub_mat = local_chunk[local_peaktimes[:, None] + temp_window] sub_mat = sub_mat.transpose(2, 1, 0).reshape(size_window, nb_local_peak_times) del local_chunk if use_gpu: sub_mat = cmt.CUDAMatrix(sub_mat, copy_on_host=False) b = cmt.sparse_dot(templates, sub_mat) else: b = templates.dot(sub_mat) del sub_mat local_restriction = (t_offset, t_offset + chunk_size) all_spikes = local_peaktimes + g_offset # Because for GPU, slicing by columns is more efficient, we need to transpose b # b = b.transpose() if use_gpu and not full_gpu: b = b.asarray() failure = numpy.zeros(nb_local_peak_times, dtype=numpy.int32) if full_gpu: mask = numpy.zeros((2 * n_tm, nb_local_peak_times), dtype=numpy.float32) mask[:n_tm, :] = 1 # data = cmt.empty(mask.shape) _ = cmt.empty(mask.shape) patch_gpu = b.shape[1] == 1 else: patch_gpu = None if collect_all: c_all_times = numpy.zeros((len_chunk, n_e), dtype=numpy.bool) c_min_times = numpy.maximum(numpy.arange(len_chunk) - template_shift, 0) c_max_times = numpy.minimum(numpy.arange(len_chunk) + template_shift + 1, len_chunk) for i in range(n_e): c_all_times[all_found_spikes[i], i] = True else: c_all_times = None # default assignment (for PyCharm code inspection) c_min_times = None # default assignment (for PyCharm code inspection) c_max_times = None # default assignment (for PyCharm code inspection) iteration_nb = 0 local_max = 0 numerous_argmax = False nb_argmax = n_tm best_indices = numpy.zeros(0, dtype=numpy.int32) data = b[:n_tm, :] flatten_data = data.ravel() while numpy.mean(failure) < total_nb_chances: # Is there a way to update sub_b * mask at the same time? if full_gpu: b_array = b.asarray() else: b_array = None if numerous_argmax: if len(best_indices) == 0: best_indices = largest_indices(flatten_data, nb_argmax) best_template_index, peak_index = numpy.unravel_index(best_indices[0], data.shape) else: best_template_index, peak_index = numpy.unravel_index(data.argmax(), data.shape) peak_scalar_product = data[best_template_index, peak_index] best_template2_index = best_template_index + n_tm if templates_normalization: if full_gpu: best_amp = b_array[best_template_index, peak_index] / n_scalar best_amp2 = b_array[best_template2_index, peak_index] / n_scalar else: best_amp = b[best_template_index, peak_index] / n_scalar if two_components: best_amp2 = b[best_template2_index, peak_index] / n_scalar else: best_amp2 = 0.0 best_amp_n = best_amp / norm_templates[best_template_index] best_amp2_n = best_amp2 / norm_templates[best_template2_index] else: if full_gpu: best_amp = b_array[best_template_index, peak_index] best_amp = best_amp / norm_templates_2[best_template_index] # TODO is `best_amp` value correct? best_amp2 = b_array[best_template2_index, peak_index] best_amp2 = best_amp2 / norm_templates_2[best_template2_index] # TODO is `best_amp2` value correct? else: best_amp = b[best_template_index, peak_index] best_amp = best_amp / norm_templates_2[best_template_index] # TODO is `best_amp` value correct? if two_components: best_amp2 = b[best_template2_index, peak_index] best_amp2 = best_amp2 / norm_templates_2[best_template2_index] # TODO is `best_amp2` value correct? else: best_amp2 = 0.0 best_amp_n = best_amp best_amp2_n = best_amp2 # Verify amplitude constraint. a_min, a_max = amp_limits[best_template_index, :] if (a_min <= best_amp_n) & (best_amp_n <= a_max): # Keep the matching. peak_time_step = local_peaktimes[peak_index] peak_data = (local_peaktimes - peak_time_step).astype(np.int32) is_neighbor = np.where(np.abs(peak_data) <= temp_2_shift)[0] idx_neighbor = peak_data[is_neighbor] + temp_2_shift nb_neighbors = len(is_neighbor) indices = np.zeros((s_over, nb_neighbors), dtype=np.int32) indices[idx_neighbor, np.arange(nb_neighbors)] = 1 if full_gpu: indices = cmt.CUDAMatrix(indices, copy_on_host=False) if patch_gpu: b_lines = b.get_col_slice(0, b.shape[0]) else: b_lines = b.get_col_slice(is_neighbor[0], is_neighbor[-1]+1) tmp1 = cmt.sparse_dot(c_overs[best_template_index], indices, mult=-best_amp) tmp2 = cmt.sparse_dot(c_overs[best_template2_index], indices, mult=-best_amp2) b_lines.add(tmp1.add(tmp2)) del tmp1, tmp2 else: tmp1 = c_overs[best_template_index].multiply(-best_amp) if numpy.abs(best_amp2) > min_second_component: tmp1 += c_overs[best_template2_index].multiply(-best_amp2) b[:, is_neighbor] += tmp1.dot(indices) numerous_argmax = False # Add matching to the result. t_spike = all_spikes[peak_index] if (t_spike >= local_restriction[0]) and (t_spike < local_restriction[1]): result['spiketimes'] += [t_spike] result['amplitudes'] += [(best_amp_n, best_amp2_n)] result['templates'] += [best_template_index] # Mark current matching as tried. b[best_template_index, peak_index] = -numpy.inf # Save debug data. if debug: result_debug['chunk_nbs'] += [gidx] result_debug['iteration_nbs'] += [iteration_nb] result_debug['peak_nbs'] += [peak_index] result_debug['peak_local_time_steps'] += [local_peaktimes[peak_index]] result_debug['peak_time_steps'] += [all_spikes[peak_index]] result_debug['peak_scalar_products'] += [peak_scalar_product] result_debug['peak_solved_flags'] += [b[best_template_index, peak_index]] result_debug['template_nbs'] += [best_template_index] result_debug['success_flags'] += [True] else: # Reject the matching. numerous_argmax = True # Update failure counter of the peak. failure[peak_index] += 1 # If the maximal number of failures is reached then mark peak as solved (i.e. not fitted). if failure[peak_index] >= total_nb_chances: # Mark all the matching associated to the current peak as tried. b[:, peak_index] = -numpy.inf index = numpy.arange(n_tm) * nb_local_peak_times + peak_index else: # Mark current matching as tried. b[best_template_index, peak_index] = -numpy.inf index = best_template_index * nb_local_peak_times + peak_index if numerous_argmax: best_indices = best_indices[~numpy.in1d(best_indices, index)] # Save debug data. if debug: result_debug['chunk_nbs'] += [gidx] result_debug['iteration_nbs'] += [iteration_nb] result_debug['peak_nbs'] += [peak_index] result_debug['peak_local_time_steps'] += [local_peaktimes[peak_index]] result_debug['peak_time_steps'] += [all_spikes[peak_index]] result_debug['peak_scalar_products'] += [peak_scalar_product] result_debug['peak_solved_flags'] += [b[best_template_index, peak_index]] result_debug['template_nbs'] += [best_template_index] result_debug['success_flags'] += [False] iteration_nb += 1 spikes_to_write = numpy.array(result['spiketimes'], dtype=numpy.uint32) amplitudes_to_write = numpy.array(result['amplitudes'], dtype=numpy.float32) templates_to_write = numpy.array(result['templates'], dtype=numpy.uint32) spiketimes_file.write(spikes_to_write.tostring()) amplitudes_file.write(amplitudes_to_write.tostring()) templates_file.write(templates_to_write.tostring()) if collect_all: for temp, spike in zip(templates_to_write, spikes_to_write - g_offset): c_all_times[c_min_times[spike]:c_max_times[spike], neighbors[temp]] = False gspikes = numpy.where(numpy.sum(c_all_times, 1) > 0)[0] c_all_times = numpy.take(c_all_times, gspikes, axis=0) c_local_chunk = numpy.take(c_local_chunk, gspikes, axis=0) * c_all_times if sign_peaks == 'negative': bestlecs = numpy.argmin(c_local_chunk, 1) if matched_filter: threshs = -matched_thresholds_neg[bestlecs] else: threshs = -thresholds[bestlecs] idx = numpy.where(numpy.min(c_local_chunk, 1) < threshs)[0] elif sign_peaks == 'positive': bestlecs = numpy.argmax(c_local_chunk, 1) if matched_filter: threshs = matched_thresholds_pos[bestlecs] else: threshs = thresholds[bestlecs] idx = numpy.where(numpy.max(c_local_chunk, 1) > threshs)[0] elif sign_peaks == 'both': c_local_chunk = numpy.abs(c_local_chunk) bestlecs = numpy.argmax(c_local_chunk, 1) if matched_filter: threshs = numpy.minimum(matched_thresholds_neg[bestlecs], matched_thresholds_pos[bestlecs]) else: threshs = thresholds[bestlecs] idx = numpy.where(numpy.max(c_local_chunk, 1) > threshs)[0] else: raise ValueError("Unexpected value %s" % sign_peaks) gspikes = numpy.take(gspikes, idx) bestlecs = numpy.take(bestlecs, idx) gspikes_to_write = numpy.array(gspikes + g_offset, dtype=numpy.uint32) gtemplates_to_write = numpy.array(bestlecs, dtype=numpy.uint32) garbage_times_file.write(gspikes_to_write.tostring()) garbage_temp_file.write(gtemplates_to_write.tostring()) if debug: # Write debug data to debug files. for field_label, field_dtype, field_file in [ ('chunk_nbs', numpy.uint32, chunk_nbs_debug_file), ('iteration_nbs', numpy.uint32, iteration_nbs_debug_file), ('peak_nbs', numpy.uint32, peak_nbs_debug_file), ('peak_local_time_steps', numpy.uint32, peak_local_time_steps_debug_file), ('peak_time_steps', numpy.uint32, peak_time_steps_debug_file), ('peak_scalar_products', numpy.float32, peak_scalar_products_debug_file), ('peak_solved_flags', numpy.float32, peak_solved_flags_debug_file), ('template_nbs', numpy.uint32, template_nbs_debug_file), ('success_flags', numpy.bool, success_flags_debug_file), ]: field_to_write = numpy.array(result_debug[field_label], dtype=field_dtype) field_file.write(field_to_write.tostring()) if full_gpu: del b, data sys.stderr.flush() spiketimes_file.flush() os.fsync(spiketimes_file.fileno()) spiketimes_file.close() amplitudes_file.flush() os.fsync(amplitudes_file.fileno()) amplitudes_file.close() templates_file.flush() os.fsync(templates_file.fileno()) templates_file.close() if collect_all: garbage_temp_file.flush() os.fsync(garbage_temp_file.fileno()) garbage_temp_file.close() garbage_times_file.flush() os.fsync(garbage_times_file.fileno()) garbage_times_file.close() if debug: # Close debug files. for field_file in [ chunk_nbs_debug_file, iteration_nbs_debug_file, peak_nbs_debug_file, peak_local_time_steps_debug_file, peak_time_steps_debug_file, peak_scalar_products_debug_file, peak_solved_flags_debug_file, template_nbs_debug_file, success_flags_debug_file, ]: field_file.flush() os.fsync(field_file.fileno()) field_file.close() comm.Barrier() if comm.rank == 0: io.collect_data(comm.size, params, erase=True) data_file.close()
def main(params, nb_cpu, nb_gpu, use_gpu): numpy.random.seed(426236) #params = detect_memory(params) parallel_hdf5 = get_parallel_hdf5_flag(params) logger = init_logging(params.logfile) logger = logging.getLogger('circus.extracting') ################################################################# data_file = params.data_file N_e = params.getint('data', 'N_e') N_t = params.getint('detecton', 'N_t') N_total = params.nb_channels template_shift = params.getint('detection', 'template_shift') chunk_size = params.getint('data', 'chunk_size') file_out = params.get('data', 'file_out') file_out_suff = params.get('data', 'file_out_suff') do_temporal_whitening = params.getboolean('whitening', 'temporal') do_spatial_whitening = params.getboolean('whitening', 'spatial') nodes, edges = get_nodes_and_edges(params) safety_time = params.getint('extracting', 'safety_time') max_elts_temp = params.getint('extracting', 'max_elts') output_dim = params.getfloat('extracting', 'output_dim') noise_thr = params.getfloat('extracting', 'noise_thr') hdf5_compress = params.getboolean('data', 'hdf5_compress') blosc_compress = params.getboolean('data', 'blosc_compress') tmp_limits = params.get('fitting', 'amp_limits').replace('(', '').replace(')', '').split(',') amp_limits = map(float, tmp_limits) elt_count = 0 inv_nodes = numpy.zeros(N_total, dtype=numpy.int32) inv_nodes[nodes] = numpy.argsort(nodes) ################################################################# if comm.rank == 0: print_and_log(["Extracting templates from already found clusters..."], 'default', logger) thresholds = io.load_data(params, 'thresholds') basis_proj, basis_rec = io.load_data(params, 'basis') clusters, spiketimes, N_clusters = io.load_data(params, 'spike-cluster') inv_clusters = numpy.zeros(clusters.max() + 1, dtype=numpy.int32) inv_clusters[numpy.unique(clusters)] = numpy.argsort( numpy.unique(clusters)) if use_gpu: import cudamat as cmt ## Need to properly handle multi GPU per MPI nodes? if nb_gpu > nb_cpu: gpu_id = int(comm.rank // nb_cpu) else: gpu_id = 0 cmt.cuda_set_device(gpu_id) cmt.init() cmt.cuda_sync_threads() if do_spatial_whitening: spatial_whitening = io.load_data(params, 'spatial_whitening') if do_temporal_whitening: temporal_whitening = io.load_data(params, 'temporal_whitening') if use_gpu and do_spatial_whitening: spatial_whitening = cmt.CUDAMatrix(spatial_whitening, copy_on_host=False) result = {} for i in xrange(N_clusters): result['data_tmp_' + str(i)] = numpy.zeros( (0, N_e * basis_proj.shape[1]), dtype=numpy.float32) result['times_' + str(i)] = numpy.zeros(0, dtype=numpy.int32) nb_chunks, last_chunk_len = data_file.analyze(chunk_size) # I guess this is more relevant, to take signals from all over the recordings all_chunks = numpy.random.permutation(numpy.arange(nb_chunks)) nb_templates = numpy.sum( comm.rank == numpy.mod(numpy.arange(N_clusters), comm.size)) nb_elts = max_elts_temp * nb_templates to_explore = all_chunks if comm.rank == 0: to_explore = get_tqdm_progressbar(to_explore) for gidx in all_chunks: if (elt_count < nb_elts): #print "Node", comm.rank, "is analyzing chunk", gidx, "/", nb_chunks, " ..." local_chunk, t_offset = data_file.get_data(gidx, chunk_size, nodes=nodes) local_shape = len(local_chunk) if do_spatial_whitening: if use_gpu: local_chunk = cmt.CUDAMatrix(local_chunk, copy_on_host=False) local_chunk = local_chunk.dot(spatial_whitening).asarray() else: local_chunk = numpy.dot(local_chunk, spatial_whitening) if do_temporal_whitening: local_chunk = scipy.ndimage.filters.convolve1d( local_chunk, temporal_whitening, axis=0, mode='constant') #print "Extracting the peaks..." idx = numpy.where((spiketimes >= gidx * chunk_size) & (spiketimes < (gidx + 1) * chunk_size))[0] local_offset = t_offset local_peaktimes = spiketimes[idx] - local_offset #print "Removing the useless borders..." local_borders = (template_shift, chunk_size - template_shift) idx = (local_peaktimes >= local_borders[0]) & (local_peaktimes < local_borders[1]) local_peaktimes = local_peaktimes[idx] local_clusters = inv_clusters[clusters[idx]] if len(local_peaktimes) > 0: all_times = numpy.zeros( (N_e, local_peaktimes[-1] - local_peaktimes[0] + 1), dtype=numpy.bool) min_times = numpy.maximum( local_peaktimes - local_peaktimes[0] - safety_time, 0) max_times = numpy.minimum( local_peaktimes - local_peaktimes[0] + safety_time + 1, local_peaktimes[-1] - local_peaktimes[0]) n_times = len(local_peaktimes) argmax_peak = numpy.random.permutation(numpy.arange(n_times)) clusters_id = local_clusters[argmax_peak] local_peaktimes = local_peaktimes[argmax_peak] #print "Selection of the peaks with spatio-temporal masks..." for idx in xrange(len(local_peaktimes)): if elt_count == nb_elts: break temp = clusters_id[idx] if numpy.mod(temp, comm.size) == comm.rank: elec = numpy.argmin(local_chunk[local_peaktimes[idx]]) indices = inv_nodes[edges[nodes[elec]]] myslice = all_times[indices, min_times[idx]:max_times[idx]] peak = local_peaktimes[idx] if not myslice.any(): if (len(result['data_tmp_' + str(temp)]) < max_elts_temp): elt_count += 1 sub_mat = local_chunk[peak - template_shift:peak + template_shift + 1, :] sub_mat = numpy.dot(basis_rec, sub_mat) nx, ny = sub_mat.shape sub_mat = sub_mat.reshape((1, nx * ny)) result['data_tmp_' + str(temp)] = numpy.vstack( (result['data_tmp_' + str(temp)], sub_mat)) to_add = numpy.array([peak + local_offset], dtype=numpy.int32) result['times_' + str(temp)] = numpy.concatenate( (result['times_' + str(temp)], to_add)) all_times[indices, min_times[idx]:max_times[idx]] = True total_nb_elts = 0 for temp in xrange(N_clusters): total_nb_elts += len(result['data_tmp_' + str(temp)]) gdata = gather_array(numpy.array([total_nb_elts], dtype=numpy.float32), comm, 0) if comm.rank == 0: print_and_log([ "Found %d spikes over %d requested" % (int(numpy.sum(gdata)), int(nb_elts)) ], 'default', logger) #print "Spikes extracted in", time.time() - t_start, "s" comm.Barrier() local_nb_clusters = 0 for temp in xrange(comm.rank, N_clusters, comm.size): if len(result['data_tmp_' + str(temp)]) > 0: local_nb_clusters += 1 #print total_nb_clusters, "found in", time.time() - t_start, "s" gdata3 = gather_array( numpy.array([local_nb_clusters], dtype=numpy.float32), comm, 0) comm.Barrier() if comm.rank == 0: print_and_log(["Extracting the templates..."], 'default', logger) total_nb_clusters = int( comm.bcast(numpy.array([int(numpy.sum(gdata3))], dtype=numpy.int32), root=0)[0]) offsets = numpy.zeros(comm.size, dtype=numpy.int32) for i in xrange(comm.size - 1): offsets[i + 1] = comm.bcast(numpy.array([local_nb_clusters], dtype=numpy.int32), root=i) if parallel_hdf5: node_pad = numpy.sum(offsets[:comm.rank + 1]) hfile = h5py.File(file_out_suff + '.templates.hdf5', 'w', driver='mpio', comm=comm, libver='earliest') norms = hfile.create_dataset('norms', shape=(2 * total_nb_clusters, ), dtype=numpy.float32, chunks=True) electrodes = hfile.create_dataset('electrodes', shape=(total_nb_clusters, ), dtype=numpy.int32, chunks=True) amps_lims = hfile.create_dataset('limits', shape=(total_nb_clusters, 2), dtype=numpy.float32, chunks=True) g_count = node_pad g_offset = total_nb_clusters else: node_pad = 0 hfile = h5py.File(file_out_suff + '.templates-%d.hdf5' % comm.rank, 'w', libver='earliest') electrodes = hfile.create_dataset('electrodes', shape=(local_nb_clusters, ), dtype=numpy.int32, chunks=True) norms = hfile.create_dataset('norms', shape=(2 * local_nb_clusters, ), dtype=numpy.float32, chunks=True) amps_lims = hfile.create_dataset('limits', shape=(local_nb_clusters, 2), dtype=numpy.float32, chunks=True) g_count = 0 g_offset = local_nb_clusters cfile = h5py.File(file_out_suff + '.clusters-%d.hdf5' % comm.rank, 'w', libver='earliest') count_templates = node_pad temp_x = numpy.zeros(0, dtype=numpy.int32) temp_y = numpy.zeros(0, dtype=numpy.int32) temp_data = numpy.zeros(0, dtype=numpy.float32) to_explore = xrange(comm.rank, N_clusters, comm.size) if comm.rank == 0: to_explore = get_tqdm_progressbar(to_explore) for temp in to_explore: n_data = len(result['data_tmp_' + str(temp)]) if n_data > 0: data = result['data_tmp_' + str(temp)].reshape( n_data, basis_proj.shape[1], N_e) first_component = numpy.median(data, axis=0) tmp_templates = numpy.dot(first_component.T, basis_rec) electrodes[g_count] = indices[tmpidx[0][0]] indices = inv_nodes[edges[nodes[electrodes[-1]]]] templates = numpy.zeros((N_e, N_t), dtype=numpy.float32) if shift > 0: templates[indices, shift:] = tmp_templates[:, :-shift] elif shift < 0: templates[indices, :shift] = tmp_templates[:, -shift:] else: templates[indices, :] = tmp_templates templates = templates.flatten() dx = templates.nonzero()[0].astype(numpy.int32) temp_x = numpy.concatenate((temp_x, dx)) temp_y = numpy.concatenate( (temp_y, count_templates * numpy.ones(len(dx), dtype=numpy.int32))) temp_data = numpy.concatenate((temp_data, templates[dx])) norms[g_count] = numpy.sqrt( numpy.sum(templates.flatten()**2) / (N_e * N_t)) x, y, z = data.shape data_flat = data.reshape(x, y * z) first_flat = first_component.reshape(y * z, 1) amplitudes = numpy.dot(data_flat, first_flat) amplitudes /= numpy.sum(first_flat**2) for i in xrange(x): data_flat[i, :] -= amplitudes[i] * first_flat[:, 0] variations = 10 * numpy.median( numpy.abs(amplitudes - numpy.median(amplitudes))) physical_limit = noise_thr * ( -thresholds[indices[tmpidx[0][0]]]) / tmp_templates.min() amp_min = max(physical_limit, numpy.median(amplitudes) - variations) amp_max = min(amp_limits[1], numpy.median(amplitudes) + variations) amps_lims[g_count] = [amp_min, amp_max] if len(data_flat) > 1: pca = PCA(1) res_pca = pca.fit_transform(data_flat.astype(numpy.double)) second_component = pca.components_.T.astype( numpy.float32).reshape(y, z) else: second_component = data_flat.reshape(y, z) / numpy.sum( data_flat**2) tmp_templates = numpy.dot(second_component.T, basis_rec) offset = total_nb_clusters + count_templates sub_templates = numpy.zeros((N_e, N_t), dtype=numpy.float32) if shift > 0: sub_templates[indices, shift:] = tmp_templates[:, :-shift] elif shift < 0: sub_templates[indices, :shift] = tmp_templates[:, -shift:] else: sub_templates[indices, :] = tmp_templates sub_templates = sub_templates.flatten() dx = sub_templates.nonzero()[0].astype(numpy.int32) temp_x = numpy.concatenate((temp_x, dx)) temp_y = numpy.concatenate( (temp_y, offset * numpy.ones(len(dx), dtype=numpy.int32))) temp_data = numpy.concatenate((temp_data, sub_templates[dx])) norms[g_count + g_offset] = numpy.sqrt( numpy.sum(sub_templates.flatten()**2) / (N_e * N_t)) count_templates += 1 g_count += 1 io.write_datasets(cfile, to_write, result, ielec, compress=hdf5_compress) #At the end we should have a templates variable to store. cfile.close() del result, templates, amps_lims comm.Barrier() #We need to gather the sparse arrays temp_x = gather_array(temp_x, comm, dtype='int32', compress=blosc_compress) temp_y = gather_array(temp_y, comm, dtype='int32', compress=blosc_compress) temp_data = gather_array(temp_data, comm, compress=blosc_compress) if parallel_hdf5: if comm.rank == 0: rs = [ h5py.File(file_out_suff + '.clusters-%d.hdf5' % i, 'r', libver='earliest') for i in xrange(comm.size) ] cfile = h5py.File(file_out_suff + '.clusters.hdf5', 'w', libver='earliest') io.write_datasets(cfile, ['electrodes'], {'electrodes': electrodes[:]}, compress=hdf5_compress) for i in xrange(comm.size): for j in range(i, N_e, comm.size): io.write_datasets(cfile, to_write, rs[i], j, compress=hdf5_compress) rs[i].close() os.remove(file_out_suff + '.clusters-%d.hdf5' % i) cfile.close() hfile.close() else: hfile.close() if comm.rank == 0: ts = [ h5py.File(file_out_suff + '.templates-%d.hdf5' % i, 'r', libver='earliest') for i in xrange(comm.size) ] rs = [ h5py.File(file_out_suff + '.clusters-%d.hdf5' % i, 'r', libver='earliest') for i in xrange(comm.size) ] result = {} hfile = h5py.File(file_out_suff + '.templates.hdf5', 'w', libver='earliest') cfile = h5py.File(file_out_suff + '.clusters.hdf5', 'w', libver='earliest') electrodes = hfile.create_dataset('electrodes', shape=(total_nb_clusters, ), dtype=numpy.int32, chunks=True) norms = hfile.create_dataset('norms', shape=(2 * total_nb_clusters, ), dtype=numpy.float32, chunks=True) amplitudes = hfile.create_dataset('limits', shape=(total_nb_clusters, 2), dtype=numpy.float32, chunks=True) count = 0 for i in xrange(comm.size): loc_temp = ts[i].get('templates') middle = loc_temp.shape[2] // 2 norms[count:count + middle] = loc_norms[:middle] norms[n_clusters + count:n_clusters + count + middle] = loc_norms[middle:] electrodes[count:count + middle] = ts[i].get('electrodes') amplitudes[count:count + middle] = ts[i].get('limits') count += middle for j in range(i, N_e, comm.size): io.write_datasets(cfile, to_write, rs[i], j, compress=hdf5_compress) ts[i].close() rs[i].close() os.remove(file_out_suff + '.templates-%d.hdf5' % i) os.remove(file_out_suff + '.clusters-%d.hdf5' % i) io.write_datasets(cfile, ['electrodes'], {'electrodes': electrodes[:]}, compress=hdf5_compress) hfile.close() cfile.close() if comm.rank == 0: hfile = h5py.File(file_out_suff + '.templates.hdf5', 'r+', libver='earliest') hfile.create_dataset('temp_x', data=temp_x) hfile.create_dataset('temp_y', data=temp_y) hfile.create_dataset('temp_data', data=temp_data) hfile.create_dataset('temp_shape', data=numpy.array( [N_e, N_t, 2 * total_nb_clusters], dtype=numpy.int32)) hfile.close() comm.Barrier() if comm.rank == 0: print_and_log(["Merging similar templates..."], 'default', logger) merged1 = algo.merging_cc(params, parallel_hdf5) comm.Barrier() if remove_mixture: if comm.rank == 0: print_and_log(["Removing mixtures..."], 'default', logger) merged2 = algo.delete_mixtures(params, parallel_hdf5) else: merged2 = [0, 0] if comm.rank == 0: print_and_log([ "Number of global merges : %d" % merged1[1], "Number of mixtures removed : %d" % merged2[1] ], 'info', logger) comm.Barrier() io.get_overlaps(params, erase=True, parallel_hdf5=parallel_hdf5) data_file.close()
def main(params, nb_cpu, nb_gpu, use_gpu): ################################################################# #params = detect_memory(params) logger = init_logging(params.logfile) SHARED_MEMORY = get_shared_memory_flag(params) logger = logging.getLogger('circus.fitting') data_file = params.data_file data_file.open() N_e = params.getint('data', 'N_e') N_total = params.nb_channels N_t = params.getint('detection', 'N_t') template_shift = params.getint('detection', 'template_shift') file_out = params.get('data', 'file_out') file_out_suff = params.get('data', 'file_out_suff') sign_peaks = params.get('detection', 'peaks') matched_filter = params.getboolean('detection', 'matched-filter') spike_thresh = params.getfloat('detection', 'spike_thresh') do_temporal_whitening = params.getboolean('whitening', 'temporal') do_spatial_whitening = params.getboolean('whitening', 'spatial') chunk_size = params.getint('fitting', 'chunk_size') gpu_only = params.getboolean('fitting', 'gpu_only') nodes, edges = get_nodes_and_edges(params) tmp_limits = params.get('fitting', 'amp_limits').replace('(', '').replace(')', '').split(',') tmp_limits = map(float, tmp_limits) amp_auto = params.getboolean('fitting', 'amp_auto') nb_chances = params.getint('fitting', 'nb_chances') max_chunk = params.getfloat('fitting', 'max_chunk') noise_thr = params.getfloat('clustering', 'noise_thr') collect_all = params.getboolean('fitting', 'collect_all') ignore_dead_times = params.getboolean('triggers', 'ignore_times') inv_nodes = numpy.zeros(N_total, dtype=numpy.int32) inv_nodes[nodes] = numpy.argsort(nodes) ################################################################# if use_gpu: import cudamat as cmt ## Need to properly handle multi GPU per MPI nodes? if nb_gpu > nb_cpu: gpu_id = int(comm.rank // nb_cpu) else: gpu_id = 0 cmt.cuda_set_device(gpu_id) cmt.init() cmt.cuda_sync_threads() if SHARED_MEMORY: templates = io.load_data_memshared(params, 'templates', normalize=True, transpose=True) N_tm, x = templates.shape else: templates = io.load_data(params, 'templates') x, N_tm = templates.shape temp_2_shift = 2 * template_shift full_gpu = use_gpu and gpu_only n_tm = N_tm // 2 n_scalar = N_e * N_t temp_window = numpy.arange(-template_shift, template_shift + 1) size_window = N_e * (2 * template_shift + 1) if not amp_auto: amp_limits = numpy.zeros((n_tm, 2)) amp_limits[:, 0] = tmp_limits[0] amp_limits[:, 1] = tmp_limits[1] else: amp_limits = io.load_data(params, 'limits') norm_templates = io.load_data(params, 'norm-templates') if not SHARED_MEMORY: for idx in xrange(templates.shape[1]): myslice = numpy.arange(templates.indptr[idx], templates.indptr[idx + 1]) templates.data[myslice] /= norm_templates[idx] templates = templates.T if matched_filter: if sign_peaks in ['negative', 'both']: waveform_neg = io.load_data(params, 'waveform') waveform_neg /= (numpy.abs(numpy.sum(waveform_neg)) * len(waveform_neg)) matched_tresholds_neg = io.load_data(params, 'matched-thresholds') if sign_peaks in ['positive', 'both']: waveform_pos = io.load_data(params, 'waveform-pos') waveform_pos /= (numpy.abs(numpy.sum(waveform_pos)) * len(waveform_pos)) matched_tresholds_pos = io.load_data(params, 'matched-thresholds-pos') if ignore_dead_times: all_dead_times = get_dead_times(params) thresholds = io.load_data(params, 'thresholds') if collect_all: neighbors = {} for i in xrange(n_tm): tmp = templates[i, :].toarray().reshape(N_e, N_t) * norm_templates[i] neighbors[i] = numpy.where(numpy.sum(tmp, 1) != 0)[0] if use_gpu: templates = cmt.SparseCUDAMatrix(templates, copy_on_host=False) info_string = '' if comm.rank == 0: if use_gpu: info_string = "using %d GPUs" % (comm.size) else: info_string = "using %d CPUs" % (comm.size) comm.Barrier() c_overlap = io.get_overlaps(params, nb_cpu=nb_cpu, nb_gpu=nb_gpu, use_gpu=use_gpu) over_shape = c_overlap.get('over_shape')[:] N_over = int(numpy.sqrt(over_shape[0])) S_over = over_shape[1] ## If the number of overlaps is different from templates, we need to recompute them if N_over != N_tm: if comm.rank == 0: print_and_log( ['Templates have been modified, recomputing the overlaps...'], 'default', logger) c_overlap = io.get_overlaps(params, erase=True, nb_cpu=nb_cpu, nb_gpu=nb_gpu, use_gpu=use_gpu) over_shape = c_overlap.get('over_shape')[:] N_over = int(numpy.sqrt(over_shape[0])) S_over = over_shape[1] if SHARED_MEMORY: c_overs = io.load_data_memshared(params, 'overlaps') else: c_overs = io.load_data(params, 'overlaps') comm.Barrier() if n_tm == 0: if comm.rank == 0: print_and_log(["No templates present. Redo clustering?"], 'default', logger) sys.exit(0) if comm.rank == 0: print_and_log([ "Here comes the SpyKING CIRCUS %s and %d templates..." % (info_string, n_tm) ], 'default', logger) purge(file_out_suff, '.data') if do_spatial_whitening: spatial_whitening = io.load_data(params, 'spatial_whitening') if do_temporal_whitening: temporal_whitening = io.load_data(params, 'temporal_whitening') if full_gpu: try: # If memory on the GPU is large enough, we load the overlaps onto it for i in xrange(N_over): c_overs[i] = cmt.SparseCUDAMatrix(c_overs[i], copy_on_host=False) except Exception: if comm.rank == 0: print_and_log([ "Not enough memory on GPUs: GPUs are used for projection only" ], 'info', logger) for i in xrange(N_over): if c_overs.has_key(i): del c_overs[i] full_gpu = False nb_chunks, last_chunk_len = data_file.analyze(chunk_size) processed_chunks = int(min(nb_chunks, max_chunk)) comm.Barrier() spiketimes_file = open(file_out_suff + '.spiketimes-%d.data' % comm.rank, 'wb') comm.Barrier() amplitudes_file = open(file_out_suff + '.amplitudes-%d.data' % comm.rank, 'wb') comm.Barrier() templates_file = open(file_out_suff + '.templates-%d.data' % comm.rank, 'wb') comm.Barrier() if collect_all: garbage_times_file = open( file_out_suff + '.gspiketimes-%d.data' % comm.rank, 'wb') comm.Barrier() garbage_temp_file = open( file_out_suff + '.gtemplates-%d.data' % comm.rank, 'wb') comm.Barrier() if use_gpu and do_spatial_whitening: spatial_whitening = cmt.CUDAMatrix(spatial_whitening, copy_on_host=False) last_chunk_size = 0 to_explore = xrange(comm.rank, processed_chunks, comm.size) if comm.rank == 0: to_explore = get_tqdm_progressbar(to_explore) for gcount, gidx in enumerate(to_explore): #print "Node", comm.rank, "is analyzing chunk", gidx, "/", nb_chunks, " ..." ## We need to deal with the borders by taking chunks of size [0, chunck_size+template_shift] is_first = data_file.is_first_chunk(gidx, nb_chunks) is_last = data_file.is_last_chunk(gidx, nb_chunks) if is_last: padding = (-temp_2_shift, 0) elif is_first: padding = (0, temp_2_shift) else: padding = (-temp_2_shift, temp_2_shift) result = {'spiketimes': [], 'amplitudes': [], 'templates': []} local_chunk, t_offset = data_file.get_data(gidx, chunk_size, padding, nodes=nodes) len_chunk = len(local_chunk) if do_spatial_whitening: if use_gpu: local_chunk = cmt.CUDAMatrix(local_chunk, copy_on_host=False) local_chunk = local_chunk.dot(spatial_whitening).asarray() else: local_chunk = numpy.dot(local_chunk, spatial_whitening) if do_temporal_whitening: local_chunk = scipy.ndimage.filters.convolve1d(local_chunk, temporal_whitening, axis=0, mode='constant') #print "Extracting the peaks..." if collect_all: all_found_spikes = {} for i in xrange(N_e): all_found_spikes[i] = [] local_peaktimes = numpy.zeros(0, dtype=numpy.uint32) if matched_filter: if sign_peaks in ['positive', 'both']: filter_chunk = scipy.ndimage.filters.convolve1d( local_chunk, waveform_pos, axis=0, mode='constant') for i in xrange(N_e): peaktimes = algo.detect_peaks(filter_chunk[:, i], matched_tresholds_pos[i]) local_peaktimes = numpy.concatenate( (local_peaktimes, peaktimes)) if collect_all: all_found_spikes[i] += peaktimes.tolist() if sign_peaks in ['negative', 'both']: filter_chunk = scipy.ndimage.filters.convolve1d( local_chunk, waveform_neg, axis=0, mode='constant') for i in xrange(N_e): peaktimes = algo.detect_peaks(filter_chunk[:, i], matched_tresholds_neg[i]) local_peaktimes = numpy.concatenate( (local_peaktimes, peaktimes)) if collect_all: all_found_spikes[i] += peaktimes.tolist() else: for i in xrange(N_e): if sign_peaks == 'negative': peaktimes = algo.detect_peaks(local_chunk[:, i], thresholds[i], valley=True) elif sign_peaks == 'positive': peaktimes = algo.detect_peaks(local_chunk[:, i], thresholds[i], valley=False) elif sign_peaks == 'both': peaktimes = algo.detect_peaks(numpy.abs(local_chunk[:, i]), thresholds[i], valley=False) local_peaktimes = numpy.concatenate( (local_peaktimes, peaktimes)) if collect_all: all_found_spikes[i] += peaktimes.tolist() local_peaktimes = numpy.unique(local_peaktimes) g_offset = t_offset + padding[0] if ignore_dead_times: dead_indices = numpy.searchsorted( all_dead_times, [t_offset, t_offset + chunk_size]) if dead_indices[0] != dead_indices[1]: local_peaktimes = numpy.array(list( set(local_peaktimes + g_offset).difference( all_dead_times[dead_indices[0]:dead_indices[1]])), dtype=numpy.uint32) - g_offset local_peaktimes = numpy.sort(local_peaktimes) #print "Removing the useless borders..." local_borders = (template_shift, len_chunk - template_shift) idx = (local_peaktimes >= local_borders[0]) & (local_peaktimes < local_borders[1]) local_peaktimes = numpy.compress(idx, local_peaktimes) if collect_all: for i in xrange(N_e): all_found_spikes[i] = numpy.array(all_found_spikes[i], dtype=numpy.uint32) if ignore_dead_times: if dead_indices[0] != dead_indices[1]: all_found_spikes[i] = numpy.array( list( set(all_found_spikes[i] + g_offset).difference( all_dead_times[ dead_indices[0]:dead_indices[1]])), dtype=numpy.uint32) - g_offset all_found_spikes[i] = numpy.sort(all_found_spikes[i]) idx = (all_found_spikes[i] >= local_borders[0]) & ( all_found_spikes[i] < local_borders[1]) all_found_spikes[i] = numpy.compress(idx, all_found_spikes[i]) n_t = len(local_peaktimes) if full_gpu: # all_indices = cmt.CUDAMatrix(all_indices) tmp_gpu = cmt.CUDAMatrix(local_peaktimes.reshape((1, n_t)), copy_on_host=False) if n_t > 0: #print "Computing the b (should full_gpu by putting all chunks on GPU if possible?)..." if collect_all: c_local_chunk = local_chunk.copy() local_chunk = local_chunk.T.ravel() sub_mat = numpy.zeros((size_window, n_t), dtype=numpy.float32) if len_chunk != last_chunk_size: slice_indices = numpy.zeros(0, dtype=numpy.int32) for idx in xrange(N_e): slice_indices = numpy.concatenate( (slice_indices, len_chunk * idx + temp_window)) last_chunk_size = len_chunk for count, idx in enumerate(local_peaktimes): sub_mat[:, count] = numpy.take(local_chunk, slice_indices + idx) del local_chunk if use_gpu: sub_mat = cmt.CUDAMatrix(sub_mat, copy_on_host=False) b = cmt.sparse_dot(templates, sub_mat) else: b = templates.dot(sub_mat) del sub_mat local_restriction = (t_offset, t_offset + chunk_size) all_spikes = local_peaktimes + g_offset # Because for GPU, slicing by columns is more efficient, we need to transpose b #b = b.transpose() if use_gpu and not full_gpu: b = b.asarray() failure = numpy.zeros(n_t, dtype=numpy.int32) if full_gpu: mask = numpy.zeros((2 * n_tm, n_t), dtype=numpy.float32) mask[:n_tm, :] = 1 data = cmt.empty(mask.shape) patch_gpu = b.shape[1] == 1 else: mask = numpy.ones((n_tm, n_t), dtype=numpy.float32) sub_b = b[:n_tm, :] if collect_all: c_all_times = numpy.zeros((len_chunk, N_e), dtype=numpy.bool) c_min_times = numpy.maximum( numpy.arange(len_chunk) - template_shift, 0) c_max_times = numpy.minimum( numpy.arange(len_chunk) + template_shift + 1, len_chunk) for i in xrange(N_e): c_all_times[all_found_spikes[i], i] = True while (numpy.mean(failure) < nb_chances): if full_gpu: gpu_mask = cmt.CUDAMatrix(mask, copy_on_host=False) b.mult(gpu_mask, data) tmp_mat = data.max(0) argmax_bi = numpy.argsort(tmp_mat.asarray()[0, :])[::-1] del tmp_mat else: data = sub_b * mask argmax_bi = numpy.argsort(numpy.max(data, 0))[::-1] for peak_index in argmax_bi: if full_gpu: b_array = b.asarray() sub_b = b_array[:n_tm, :] peak_scalar_products = np.take(sub_b, peak_index, axis=1) best_template_index = np.argmax(peak_scalar_products, axis=0) best_template2_index = best_template_index + n_tm if full_gpu: best_amp = sub_b[best_template_index, peak_index] / n_scalar best_amp2 = b_array[best_template_index, peak_index] / n_scalar else: best_amp = sub_b[best_template_index, peak_index] / n_scalar best_amp2 = b[best_template2_index, peak_index] / n_scalar best_amp_n = best_amp / norm_templates[best_template_index] best_amp2_n = best_amp2 / norm_templates[ best_template2_index] # Verify amplitude constraint. a_min = amp_limits[best_template_index, 0] a_max = amp_limits[best_template_index, 1] if (a_min <= best_amp_n) & (best_amp_n <= a_max): # Keep the matching. peak_time_step = local_peaktimes[peak_index] data = (local_peaktimes - peak_time_step).astype( np.int32) is_neighbor = np.where(np.abs(data) <= temp_2_shift)[0] idx_neighbor = data[is_neighbor] + temp_2_shift nb_neighbors = len(is_neighbor) indices = np.zeros((S_over, nb_neighbors), dtype=np.int32) indices[idx_neighbor, np.arange(nb_neighbors)] = 1 if full_gpu: indices = cmt.CUDAMatrix(indices, copy_on_host=False) if patch_gpu: b_lines = b.get_col_slice(0, b.shape[0]) else: b_lines = b.get_col_slice( is_neighbor[0], is_neighbor[-1] + 1) tmp1 = cmt.sparse_dot(c_overs[best_template_index], indices, mult=-best_amp[keep]) tmp2 = cmt.sparse_dot( c_overs[best_template2_index], indices, mult=-best_amp2[keep]) b_lines.add(tmp1.add(tmp2)) del tmp1, tmp2 else: tmp1 = c_overs[best_template_index].multiply( -best_amp) tmp2 = c_overs[best_template2_index].multiply( -best_amp2) b[:, is_neighbor] += (tmp1 + tmp2).dot(indices) # Add matching to the result. t_spike = all_spikes[peak_index] if (t_spike >= local_restriction[0]) and ( t_spike < local_restriction[1]): #print "Accept spikes", t_spike, local_restriction, type(t_spike), t_spike > local_restriction[0], t_spike < local_restriction[1] result['spiketimes'] += [t_spike] result['amplitudes'] += [(best_amp_n, best_amp2_n)] result['templates'] += [best_template_index] # Mark current matching as tried. mask[best_template_index, peak_index] = 0 else: # Reject the matching. # Update failure counter of the peak. failure[peak_index] += 1 # If the maximal number of failures is reached then mark peak as solved (i.e. not fitted). if failure[peak_index] == nb_chances: mask[:, peak_index] = 0 else: mask[best_template_index, peak_index] = 0 spikes_to_write = numpy.array(result['spiketimes'], dtype=numpy.uint32) amplitudes_to_write = numpy.array(result['amplitudes'], dtype=numpy.float32) templates_to_write = numpy.array(result['templates'], dtype=numpy.uint32) spiketimes_file.write(spikes_to_write.tostring()) amplitudes_file.write(amplitudes_to_write.tostring()) templates_file.write(templates_to_write.tostring()) if collect_all: for temp, spike in zip(templates_to_write, spikes_to_write - g_offset): c_all_times[c_min_times[spike]:c_max_times[spike], neighbors[temp]] = False gspikes = numpy.where(numpy.sum(c_all_times, 1) > 0)[0] c_all_times = numpy.take(c_all_times, gspikes, axis=0) c_local_chunk = numpy.take(c_local_chunk, gspikes, axis=0) * c_all_times if sign_peaks == 'negative': bestlecs = numpy.argmin(c_local_chunk, 1) if matched_filter: threshs = -matched_tresholds_neg[bestlecs] else: threshs = -thresholds[bestlecs] idx = numpy.where(numpy.min(c_local_chunk, 1) < threshs)[0] elif sign_peaks == 'positive': bestlecs = numpy.argmax(c_local_chunk, 1) if matched_filter: threshs = matched_tresholds_pos[bestlecs] else: threshs = thresholds[bestlecs] idx = numpy.where(numpy.max(c_local_chunk, 1) > threshs)[0] elif sign_peaks == 'both': c_local_chunk = numpy.abs(c_local_chunk) bestlecs = numpy.argmax(c_local_chunk, 1) if matched_filter: threshs = numpy.minimum( matched_tresholds_neg[bestlecs], matched_tresholds_pos[bestlecs]) else: threshs = thresholds[bestlecs] idx = numpy.where(numpy.max(c_local_chunk, 1) > threshs)[0] gspikes = numpy.take(gspikes, idx) bestlecs = numpy.take(bestlecs, idx) gspikes_to_write = numpy.array(gspikes + g_offset, dtype=numpy.uint32) gtemplates_to_write = numpy.array(bestlecs, dtype=numpy.uint32) garbage_times_file.write(gspikes_to_write.tostring()) garbage_temp_file.write(gtemplates_to_write.tostring()) if full_gpu: del gpu_mask, b, data sys.stderr.flush() spiketimes_file.flush() os.fsync(spiketimes_file.fileno()) spiketimes_file.close() amplitudes_file.flush() os.fsync(amplitudes_file.fileno()) amplitudes_file.close() templates_file.flush() os.fsync(templates_file.fileno()) templates_file.close() if collect_all: garbage_temp_file.flush() os.fsync(garbage_temp_file.fileno()) garbage_temp_file.close() garbage_times_file.flush() os.fsync(garbage_times_file.fileno()) garbage_times_file.close() comm.Barrier() if comm.rank == 0: io.collect_data(comm.size, params, erase=True) data_file.close()
def main(filename, params, nb_cpu, nb_gpu, use_gpu): try: SHARED_MEMORY = True MPI.Win.Allocate_shared(1, 1, MPI.INFO_NULL, MPI.COMM_SELF).Free() except NotImplementedError: SHARED_MEMORY = False ################################################################# sampling_rate = params.getint('data', 'sampling_rate') N_e = params.getint('data', 'N_e') N_t = params.getint('data', 'N_t') N_total = params.getint('data', 'N_total') template_shift = params.getint('data', 'template_shift') file_out = params.get('data', 'file_out') file_out_suff = params.get('data', 'file_out_suff') sign_peaks = params.get('detection', 'peaks') matched_filter = params.getboolean('detection', 'matched-filter') spike_thresh = params.getfloat('detection', 'spike_thresh') do_temporal_whitening = params.getboolean('whitening', 'temporal') do_spatial_whitening = params.getboolean('whitening', 'spatial') chunk_size = int(params.getfloat('fitting', 'chunk') * sampling_rate) gpu_only = params.getboolean('fitting', 'gpu_only') nodes, edges = io.get_nodes_and_edges(params) tmp_limits = params.get('fitting', 'amp_limits').replace('(', '').replace(')', '').split(',') tmp_limits = map(float, tmp_limits) amp_auto = params.getboolean('fitting', 'amp_auto') space_explo = params.getfloat('fitting', 'space_explo') nb_chances = params.getint('fitting', 'nb_chances') max_chunk = params.getfloat('fitting', 'max_chunk') inv_nodes = numpy.zeros(N_total, dtype=numpy.int32) inv_nodes[nodes] = numpy.argsort(nodes) ################################################################# if use_gpu: import cudamat as cmt ## Need to properly handle multi GPU per MPI nodes? if nb_gpu > nb_cpu: gpu_id = int(comm.rank // nb_cpu) else: gpu_id = 0 cmt.cuda_set_device(gpu_id) cmt.init() cmt.cuda_sync_threads() if SHARED_MEMORY: templates = io.load_data_memshared(params, comm, 'templates', normalize=True, transpose=True) N_tm, x = templates.shape else: templates = io.load_data(params, 'templates') x, N_tm = templates.shape N_e = params.getint('data', 'N_e') N_t = params.getint('data', 'N_t') template_shift = int((N_t - 1) // 2) temp_2_shift = 2 * template_shift full_gpu = use_gpu and gpu_only n_tm = N_tm // 2 n_scalar = N_e * N_t last_spikes = numpy.zeros((n_tm, 1), dtype=numpy.int32) temp_window = numpy.arange(-template_shift, template_shift + 1) if not amp_auto: amp_limits = numpy.zeros((n_tm, 2)) amp_limits[:, 0] = tmp_limits[0] amp_limits[:, 1] = tmp_limits[1] else: amp_limits = io.load_data(params, 'limits') norm_templates = io.load_data(params, 'norm-templates') if not SHARED_MEMORY: for idx in xrange(templates.shape[1]): myslice = numpy.arange(templates.indptr[idx], templates.indptr[idx + 1]) templates.data[myslice] /= norm_templates[idx] templates = templates.T if use_gpu: templates = cmt.SparseCUDAMatrix(templates) info_string = '' if matched_filter: if sign_peaks in ['negative', 'both']: waveform_neg = io.load_data(params, 'waveform') waveform_neg /= (numpy.abs(numpy.sum(waveform_neg)) * len(waveform_neg)) matched_tresholds_neg = io.load_data(params, 'matched-thresholds') if sign_peaks in ['positive', 'both']: waveform_pos = io.load_data(params, 'waveform-pos') waveform_pos /= (numpy.abs(numpy.sum(waveform_pos)) * len(waveform_pos)) matched_tresholds_pos = io.load_data(params, 'matched-thresholds-pos') if comm.rank == 0: if use_gpu: info_string = "using %d GPUs" % (comm.size) else: info_string = "using %d CPUs" % (comm.size) comm.Barrier() thresholds = io.load_data(params, 'thresholds') if SHARED_MEMORY: c_overs = io.load_data_memshared(params, comm, 'overlaps', nb_cpu=nb_cpu, nb_gpu=nb_gpu, use_gpu=use_gpu) c_overlap = io.get_overlaps(comm, params, nb_cpu=nb_cpu, nb_gpu=nb_gpu, use_gpu=use_gpu) over_shape = c_overlap.get('over_shape')[:] N_over = int(numpy.sqrt(over_shape[0])) S_over = over_shape[1] else: c_overlap = io.get_overlaps(comm, params, nb_cpu=nb_cpu, nb_gpu=nb_gpu, use_gpu=use_gpu) over_x = c_overlap.get('over_x')[:] over_y = c_overlap.get('over_y')[:] over_data = c_overlap.get('over_data')[:] over_shape = c_overlap.get('over_shape')[:] N_over = int(numpy.sqrt(over_shape[0])) S_over = over_shape[1] c_overlap.close() # To be faster, we rearrange the overlaps into a dictionnary. This has a cost: twice the memory usage for # a short period of time c_overs = {} overlaps = scipy.sparse.csr_matrix( (over_data, (over_x, over_y)), shape=(over_shape[0], over_shape[1])) del over_x, over_y, over_data for i in xrange(N_over): c_overs[i] = overlaps[i * N_over:(i + 1) * N_over] del overlaps comm.Barrier() if comm.rank == 0: io.print_and_log([ "Here comes the SpyKING CIRCUS %s and %d templates..." % (info_string, n_tm) ], 'default', params) io.purge(file_out_suff, '.data') if do_spatial_whitening: spatial_whitening = io.load_data(params, 'spatial_whitening') if do_temporal_whitening: temporal_whitening = io.load_data(params, 'temporal_whitening') if full_gpu: try: # If memory on the GPU is large enough, we load the overlaps onto it for i in xrange(N_over): c_overs[i] = cmt.SparseCUDAMatrix(c_overs[i]) except Exception: if comm.rank == 0: io.print_and_log([ "Not enough memory on GPUs: GPUs are used for projection only" ], 'info', params) for i in xrange(N_over): if c_overs.has_key(i): del c_overs[i] full_gpu = False borders, nb_chunks, chunk_len, last_chunk_len = io.analyze_data( params, chunk_size) nb_chunks = int(min(nb_chunks, max_chunk)) if comm.rank == 0: pbar = get_progressbar(int(nb_chunks // comm.size)) spiketimes_file = open(file_out_suff + '.spiketimes-%d.data' % comm.rank, 'wb') amplitudes_file = open(file_out_suff + '.amplitudes-%d.data' % comm.rank, 'wb') templates_file = open(file_out_suff + '.templates-%d.data' % comm.rank, 'wb') comm.Barrier() if use_gpu and do_spatial_whitening: spatial_whitening = cmt.CUDAMatrix(spatial_whitening, copy_on_host=False) last_chunk_size = 0 for gcount, gidx in enumerate(xrange(comm.rank, nb_chunks, comm.size)): #print "Node", comm.rank, "is analyzing chunk", gidx, "/", nb_chunks, " ..." ## We need to deal with the borders by taking chunks of size [0, chunck_size+template_shift] if gidx == (nb_chunks - 1): padding = (-2 * borders, 0) elif gidx == 0: padding = (0, 2 * borders) else: padding = (-2 * borders, 2 * borders) result = {'spiketimes': [], 'amplitudes': [], 'templates': []} local_chunk, local_shape = io.load_chunk(params, gidx, chunk_len, chunk_size, padding, nodes=nodes) if do_spatial_whitening: if use_gpu: local_chunk = cmt.CUDAMatrix(local_chunk, copy_on_host=False) local_chunk = local_chunk.dot(spatial_whitening).asarray() else: local_chunk = numpy.dot(local_chunk, spatial_whitening) if do_temporal_whitening: local_chunk = scipy.ndimage.filters.convolve1d(local_chunk, temporal_whitening, axis=0, mode='constant') #print "Extracting the peaks..." local_peaktimes = numpy.zeros(0, dtype=numpy.int32) if matched_filter: if sign_peaks in ['positive', 'both']: filter_chunk = scipy.ndimage.filters.convolve1d( local_chunk, waveform_pos, axis=0, mode='constant') for i in xrange(N_e): peaktimes = algo.detect_peaks(filter_chunk[:, i], matched_tresholds_pos[i]) local_peaktimes = numpy.concatenate( (local_peaktimes, peaktimes)) if sign_peaks in ['negative', 'both']: filter_chunk = scipy.ndimage.filters.convolve1d( local_chunk, waveform_neg, axis=0, mode='constant') for i in xrange(N_e): peaktimes = algo.detect_peaks(filter_chunk[:, i], matched_tresholds_neg[i]) local_peaktimes = numpy.concatenate( (local_peaktimes, peaktimes)) else: for i in xrange(N_e): if sign_peaks == 'negative': peaktimes = algo.detect_peaks(local_chunk[:, i], thresholds[i], valley=True) elif sign_peaks == 'positive': peaktimes = algo.detect_peaks(local_chunk[:, i], thresholds[i], valley=False) elif sign_peaks == 'both': peaktimes = algo.detect_peaks(numpy.abs(local_chunk[:, i]), thresholds[i], valley=False) local_peaktimes = numpy.concatenate( (local_peaktimes, peaktimes)) local_peaktimes = numpy.unique(local_peaktimes) #print "Removing the useless borders..." local_borders = (template_shift, local_shape - template_shift) idx = (local_peaktimes >= local_borders[0]) & (local_peaktimes < local_borders[1]) local_peaktimes = numpy.compress(idx, local_peaktimes) n_t = len(local_peaktimes) len_chunk = local_chunk.shape[0] all_indices = numpy.arange(n_t) if full_gpu: # all_indices = cmt.CUDAMatrix(all_indices) tmp_gpu = cmt.CUDAMatrix(local_peaktimes.reshape((1, n_t)), copy_on_host=False) if n_t > 0: #print "Computing the b (should full_gpu by putting all chunks on GPU if possible?)..." local_chunk = local_chunk.T.ravel() sub_mat = numpy.zeros((N_e * (2 * template_shift + 1), n_t), dtype=numpy.float32) if len_chunk != last_chunk_size: slice_indices = numpy.zeros(0, dtype=numpy.int32) for idx in xrange(N_e): slice_indices = numpy.concatenate( (slice_indices, len_chunk * idx + temp_window)) last_chunk_size = len_chunk for count, idx in enumerate(local_peaktimes): sub_mat[:, count] = numpy.take(local_chunk, slice_indices + idx) del local_chunk if use_gpu: sub_mat = cmt.CUDAMatrix(sub_mat, copy_on_host=False) b = cmt.sparse_dot(templates, sub_mat) else: b = templates.dot(sub_mat) del sub_mat local_offset = gidx * chunk_size + padding[0] // N_total local_bounds = (temp_2_shift, local_shape - temp_2_shift) all_spikes = local_peaktimes + local_offset penalty = numpy.ones((n_tm, n_t), dtype=numpy.float32) # Because for GPU, slicing by columns is more efficient, we need to transpose b #b = b.transpose() if use_gpu and not full_gpu: b = b.asarray() failure = numpy.zeros(n_t, dtype=numpy.int32) if full_gpu: mask = cmt.CUDAMatrix(penalty, copy_on_host=False) data = cmt.empty(mask.shape) cm_zeros = cmt.CUDAMatrix(numpy.zeros(mask.shape), copy_on_host=False) patch_gpu = b.shape[1] == 1 else: mask = penalty sub_b = b[:n_tm, :] min_time = local_peaktimes.min() max_time = local_peaktimes.max() local_len = max_time - min_time + 1 min_times = numpy.maximum( local_peaktimes - min_time - temp_2_shift, 0) max_times = numpy.minimum( local_peaktimes - min_time + temp_2_shift + 1, max_time - min_time) max_n_t = int(space_explo * (max_time - min_time + 1) // (2 * temp_2_shift + 1)) while (numpy.mean(failure) < nb_chances): if full_gpu: sub_b = b.get_row_slice(0, n_tm) sub_b.mult(mask, data) tmp_mat = data.max(0) argmax_bi = numpy.argsort(tmp_mat.asarray()[0, :])[::-1] del tmp_mat, sub_b else: data = sub_b * mask argmax_bi = numpy.argsort(numpy.max(data, 0))[::-1] while (len(argmax_bi) > 0): subset = [] indices = [] all_times = numpy.zeros(local_len, dtype=numpy.bool) for count, idx in enumerate(argmax_bi): myslice = all_times[min_times[idx]:max_times[idx]] if not myslice.any(): subset += [idx] indices += [count] all_times[min_times[idx]:max_times[idx]] = True if len(subset) > max_n_t: break subset = numpy.array(subset, dtype=numpy.int32) argmax_bi = numpy.delete(argmax_bi, indices) if full_gpu: sub_b = b.get_row_slice(0, n_tm) tmp_mat = sub_b.argmax(0) inds_t, inds_temp = subset, tmp_mat.asarray()[ 0, :][subset].astype(numpy.int32) del tmp_mat else: inds_t, inds_temp = subset, numpy.argmax( numpy.take(sub_b, subset, axis=1), 0) if full_gpu: best_amp = sub_b.asarray()[inds_temp, inds_t] / n_scalar best_amp2 = b.asarray()[inds_temp + n_tm, inds_t] / n_scalar sub_mask = numpy.ones((sub_b.shape), dtype=numpy.float32) sub_mask[inds_temp, inds_t] = 0 sub_mask = cmt.CUDAMatrix(sub_mask, copy_on_host=False) mask.mult(sub_mask) del sub_mask else: mask[inds_temp, inds_t] = 0 best_amp = sub_b[inds_temp, inds_t] / n_scalar best_amp2 = b[inds_temp + n_tm, inds_t] / n_scalar best_amp_n = best_amp / numpy.take(norm_templates, inds_temp) best_amp2_n = best_amp2 / numpy.take( norm_templates, inds_temp + n_tm) all_idx = ((best_amp_n >= amp_limits[inds_temp, 0]) & (best_amp_n <= amp_limits[inds_temp, 1])) to_keep = numpy.where(all_idx == True)[0] to_reject = numpy.where(all_idx == False)[0] ts = numpy.take(local_peaktimes, inds_t[to_keep]) good = (ts >= local_bounds[0]) & (ts < local_bounds[1]) # We reduce to only the good times that will be kept #to_keep = to_keep[good] #ts = ts[good] if len(ts) > 0: if full_gpu: tmp = cmt.CUDAMatrix(numpy.ones((len(ts), 1)), copy_on_host=False) tmp3 = cmt.CUDAMatrix(-ts.reshape((len(ts), 1)), copy_on_host=False) tmp = tmp.dot(tmp_gpu) tmp.add_col_vec(tmp3) condition = cmt.empty(tmp.shape) cmt.abs(tmp, condition).less_than(temp_2_shift + 1) condition = condition.asarray().astype(numpy.bool) tmp = tmp.asarray().astype(numpy.int32) else: tmp = numpy.dot( numpy.ones((len(ts), 1), dtype=numpy.int32), local_peaktimes.reshape((1, n_t))) tmp -= ts.reshape((len(ts), 1)) condition = numpy.abs(tmp) <= temp_2_shift for count, keep in enumerate(to_keep): idx_b = numpy.compress(condition[count, :], all_indices) ytmp = tmp[count, condition[count, :]] + temp_2_shift indices = numpy.zeros((S_over, len(ytmp)), dtype=numpy.float32) indices[ytmp, numpy.arange(len(ytmp))] = 1 if full_gpu: indices = cmt.CUDAMatrix(indices, copy_on_host=False) if patch_gpu: b_lines = b.get_col_slice(0, b.shape[0]) else: b_lines = b.get_col_slice( idx_b[0], idx_b[-1] + 1) tmp1 = cmt.sparse_dot(c_overs[inds_temp[keep]], indices, mult=-best_amp[keep]) tmp2 = cmt.sparse_dot(c_overs[inds_temp[keep] + n_tm], indices, mult=-best_amp2[keep]) b_lines.add(tmp1) b_lines.add(tmp2) del tmp1, tmp2 else: tmp1 = c_overs[inds_temp[keep]].multiply( -best_amp[keep]).dot(indices) tmp2 = c_overs[inds_temp[keep] + n_tm].multiply(-best_amp2[keep] ).dot(indices) b[:, idx_b] += tmp1 + tmp2 if good[count]: t_spike = ts[count] + local_offset result['spiketimes'] += [t_spike] result['amplitudes'] += [(best_amp_n[keep], best_amp2_n[keep])] result['templates'] += [inds_temp[keep]] myslice = numpy.take(inds_t, to_reject) failure[myslice] += 1 sub_idx = (numpy.take(failure, myslice) >= nb_chances) if full_gpu: N = numpy.sum(sub_idx) if N > 0: cu_slice = cmt.CUDAMatrix(numpy.compress( sub_idx, myslice).reshape(1, N), copy_on_host=False) mask.set_selected_columns(cu_slice, cm_zeros) del cu_slice else: mask[:, numpy.compress(sub_idx, myslice)] = 0 if full_gpu: del sub_b spikes_to_write = numpy.array(result['spiketimes'], dtype=numpy.uint32) amplitudes_to_write = numpy.array(result['amplitudes'], dtype=numpy.float32) templates_to_write = numpy.array(result['templates'], dtype=numpy.int32) spiketimes_file.write(spikes_to_write.tostring()) amplitudes_file.write(amplitudes_to_write.tostring()) templates_file.write(templates_to_write.tostring()) if full_gpu: del mask, b, cm_zeros, data if comm.rank == 0: pbar.update(gcount) spiketimes_file.flush() os.fsync(spiketimes_file.fileno()) spiketimes_file.close() amplitudes_file.flush() os.fsync(amplitudes_file.fileno()) amplitudes_file.close() templates_file.flush() os.fsync(templates_file.fileno()) templates_file.close() comm.Barrier() if comm.rank == 0: pbar.finish() if comm.rank == 0: io.collect_data(comm.size, params, erase=True)
def __init__(self,layer,step_size=None,dropout=None): #TODO: should probably put cudamat initialization elsewhere #in case it is used by more than one network cm.cuda_set_device(0) cm.init() super(net_cuda,self).__init__(layer,step_size,dropout)
def main(argv=None): if argv is None: argv = sys.argv[1:] import h5py parallel_hdf5 = h5py.get_config().mpi from mpi4py import MPI try: SHARED_MEMORY = True MPI.Win.Allocate_shared(1, 1, MPI.INFO_NULL, MPI.COMM_SELF).Free() except (NotImplementedError, AttributeError): SHARED_MEMORY = False user_path = pjoin(os.path.expanduser('~'), 'spyking-circus') tasks_list = None if not os.path.exists(user_path): os.makedirs(user_path) try: import cudamat as cmt cmt.init() HAVE_CUDA = True except Exception: HAVE_CUDA = False all_steps = [ 'whitening', 'clustering', 'fitting', 'gathering', 'extracting', 'filtering', 'converting', 'benchmarking', 'merging', 'validating' ] if os.path.exists(user_path + 'config.params'): config_file = os.path.abspath(user_path + 'config.params') else: config_file = os.path.abspath( pkg_resources.resource_filename('circus', 'config.params')) gheader = Fore.GREEN + get_header() header = gheader header += Fore.GREEN + 'Local CPUs : ' + Fore.CYAN + str( psutil.cpu_count()) + '\n' header += Fore.GREEN + 'GPU detected : ' + Fore.CYAN + str( HAVE_CUDA) + '\n' header += Fore.GREEN + 'Parallel HDF5 : ' + Fore.CYAN + str( parallel_hdf5) + '\n' header += Fore.GREEN + 'Shared memory : ' + Fore.CYAN + str( SHARED_MEMORY) + '\n' header += '\n' header += Fore.GREEN + "##################################################################" header += Fore.RESET method_help = '''by default, first 4 steps are performed, but a subset x,y can be done. Steps are: - filtering - whitening - clustering - fitting - (extra) merging [GUI for meta merging] - (extra) converting [export results to phy format] - (extra) gathering [force collection of results] - (extra) extracting [get templates from spike times] - (extra) benchmarking [with -o and -t] - (extra) validating [to compare performance with GT neurons]''' parser = argparse.ArgumentParser( description=header, formatter_class=argparse.RawTextHelpFormatter) parser.add_argument('datafile', help='data file (or a list of commands if batch mode)') parser.add_argument('-m', '--method', default='filtering,whitening,clustering,fitting', help=method_help) parser.add_argument('-c', '--cpu', type=int, default=1, help='number of CPU') parser.add_argument('-g', '--gpu', type=int, default=0, help='number of GPU') parser.add_argument('-H', '--hostfile', help='hostfile for MPI', default=pjoin(user_path, 'circus.hosts')) parser.add_argument( '-b', '--batch', help='datafile is a list of commands to launch, in a batch mode', action='store_true') parser.add_argument( '-p', '--preview', help='GUI to display the first second filtered with thresholds', action='store_true') parser.add_argument('-r', '--result', help='GUI to display the results on top of raw data', action='store_true') parser.add_argument( '-e', '--extension', help='extension to consider for merging and converting', default='None') parser.add_argument( '-o', '--output', help='output file [for generation of synthetic benchmarks]') parser.add_argument('-t', '--type', help='benchmark type', choices=['fitting', 'clustering', 'synchrony']) if len(argv) == 0: parser.print_help() sys.exit() args = parser.parse_args(argv) steps = args.method.split(',') for step in steps: if step not in all_steps: print_error(['The method "%s" is not recognized' % step]) sys.exit(1) # To save some typing later (nb_cpu, nb_gpu, hostfile, batch, preview, result, extension, output, benchmark) = (args.cpu, args.gpu, args.hostfile, args.batch, args.preview, args.result, args.extension, args.output, args.type) filename = os.path.abspath(args.datafile) f_next, extens = os.path.splitext(filename) if extens == '.params': print_error(['You should launch the code on the data file!']) sys.exit(1) file_params = f_next + '.params' if not os.path.exists(file_params) and not batch: print Fore.RED + 'The parameter file %s is not present!' % file_params key = '' while key not in ['y', 'n']: key = raw_input( Fore.WHITE + "Do you want SpyKING CIRCUS to create a parameter file? [y/n]") if key == 'y': print Fore.WHITE + "Generating template file", file_params print Fore.WHITE + "Fill it properly before launching the code! (see documentation)" shutil.copyfile(config_file, file_params) sys.exit() elif batch: tasks_list = filename if not batch: params = io.load_parameters(filename) if preview: print_info( ['Preview mode, showing only first second of the recording']) tmp_path_loc = os.path.join( os.path.abspath(params.get('data', 'data_file_noext')), 'tmp') if not os.path.exists(tmp_path_loc): os.makedirs(tmp_path_loc) filename = os.path.join(tmp_path_loc, os.path.basename(filename)) f_next, extens = os.path.splitext(filename) shutil.copyfile(file_params, f_next + '.params') steps = ['filtering', 'whitening'] io.prepare_preview(params, filename) io.change_flag(filename, 'chunk_size', '2') io.change_flag(filename, 'safety_time', '0') if tasks_list is not None: with open(tasks_list, 'r') as f: for line in f: if len(line) > 0: subprocess.check_call(['spyking-circus'] + line.replace('\n', '').split(" ")) else: if os.path.exists(f_next + '.log'): os.remove(f_next + '.log') write_to_logger(params, ['Config file: %s' % (f_next + '.params')], 'debug') write_to_logger(params, ['Data file : %s' % filename], 'debug') print gheader if preview: print Fore.GREEN + "Steps :", Fore.CYAN + "preview mode" elif result: print Fore.GREEN + "Steps :", Fore.CYAN + "results mode" else: print Fore.GREEN + "Steps :", Fore.CYAN + ", ".join(steps) print Fore.GREEN + "GPU detected :", Fore.CYAN + str(HAVE_CUDA) print Fore.GREEN + "Number of CPU :", Fore.CYAN + str( nb_cpu) + "/" + str(psutil.cpu_count()) if HAVE_CUDA: print Fore.GREEN + "Number of GPU :", Fore.CYAN + str(nb_gpu) print Fore.GREEN + "Parallel HDF5 :", Fore.CYAN + str(parallel_hdf5) print Fore.GREEN + "Shared memory :", Fore.CYAN + str(SHARED_MEMORY) print Fore.GREEN + "Hostfile :", Fore.CYAN + hostfile print "" print Fore.GREEN + "##################################################################" print "" print Fore.RESET # Launch the subtasks subtasks = [('filtering', 'mpirun'), ('whitening', 'mpirun'), ('clustering', 'mpirun'), ('fitting', 'mpirun'), ('extracting', 'mpirun'), ('gathering', 'python'), ('converting', 'mpirun'), ('benchmarking', 'mpirun'), ('merging', 'mpirun'), ('validating', 'mpirun')] if HAVE_CUDA and nb_gpu > 0: use_gpu = 'True' else: use_gpu = 'False' time = circus.shared.io.data_stats(params) / 60. if nb_cpu < psutil.cpu_count(): if use_gpu != 'True' and not result: io.print_and_log([ 'Using only %d out of %d local CPUs available (-c to change)' % (nb_cpu, psutil.cpu_count()) ], 'info', params) if params.getboolean('detection', 'matched-filter') and not params.getboolean( 'clustering', 'smart_search'): io.print_and_log( ['Smart Search should be activated for matched filtering'], 'info', params) if time > 30 and not params.getboolean('clustering', 'smart_search'): io.print_and_log( ['Smart Search could be activated for long recordings'], 'info', params) n_edges = circus.shared.io.get_averaged_n_edges(params) if n_edges > 100 and not params.getboolean('clustering', 'compress'): io.print_and_log([ 'Template compression is highly recommended based on parameters' ], 'info', params) if params.getint('data', 'N_e') > 500: if (params.getint('data', 'chunk_size') > 10) or (params.getint( 'whitening', 'chunk_size') > 10): io.print_and_log([ "Large number of electrodes, reduce chunk sizes to 10s in [data] and [whitening]" ], 'info', params) if not result: for subtask, command in subtasks: if subtask in steps: if command == 'python': # Directly call the launcher try: circus.launch(subtask, filename, nb_cpu, nb_gpu, use_gpu) except: print_error(['Step "%s" failed!' % subtask]) raise elif command == 'mpirun': # Use mpirun to make the call mpi_args = gather_mpi_arguments(hostfile, params) if subtask != 'fitting': nb_tasks = str(max(args.cpu, args.gpu)) else: if use_gpu == 'True': nb_tasks = str(args.gpu) else: nb_tasks = str(args.cpu) if subtask == 'benchmarking': if (output is None) or (benchmark is None): print_error([ "To generate synthetic datasets, you must provide output and type" ]) sys.exit() mpi_args += [ '-np', nb_tasks, 'spyking-circus-subtask', subtask, filename, str(nb_cpu), str(nb_gpu), use_gpu, output, benchmark ] elif subtask in ['merging', 'converting']: mpi_args += [ '-np', nb_tasks, 'spyking-circus-subtask', subtask, filename, str(nb_cpu), str(nb_gpu), use_gpu, extension ] else: mpi_args += [ '-np', nb_tasks, 'spyking-circus-subtask', subtask, filename, str(nb_cpu), str(nb_gpu), use_gpu ] write_to_logger(params, ['Launching task %s' % subtask], 'debug') write_to_logger(params, ['Command: %s' % str(mpi_args)], 'debug') try: subprocess.check_call(mpi_args) except: print_error(['Step "%s" failed!' % subtask]) raise if preview or result: from circus.shared import gui import pylab from matplotlib.backends import qt_compat use_pyside = qt_compat.QT_API == qt_compat.QT_API_PYSIDE if use_pyside: from PySide import QtGui, QtCore, uic else: from PyQt4 import QtGui, QtCore, uic app = QtGui.QApplication([]) try: pylab.style.use('ggplot') except Exception: pass if preview: mygui = gui.PreviewGUI(io.load_parameters(filename)) shutil.rmtree(tmp_path_loc) elif result: mygui = gui.PreviewGUI(io.load_parameters(filename), show_fit=True) sys.exit(app.exec_())