示例#1
0
def gpv(vl, t_cells, label_l, K, bin_size, room):
    ''' Returns a matrix of feature vectors.
    
    The feature vector is:
    [fr cell 1, fr cell 2, ..., fr cell n, frac in bin1, frac in bin2,...]
    
    The matrix dimension is [# of vectors, # cells + # bins]
    
    With labels [mode context1, mode context2,...]
    
    K is the length of the subvector that will be used to calculate firing rate
    '''

    # Check data integrity
    assert len(label_l) == len(vl['xs'])
    assert (room[0][1] - room[0][0]) % bin_size == 0
    assert (room[1][1] - room[1][0]) % bin_size == 0

    xbins = (room[0][1] - room[0][0]) / bin_size
    ybins = (room[1][1] - room[1][0]) / bin_size

    end = (len(label_l) / K) * K  # Use integer roundoff to our advantage
    logging.warning('Threw away %i/%i points.',
                    len(label_l) - end, len(label_l))

    # Generate an indicator array for identity of spiking cell
    spks = spk_indicators(t_cells, len(label_l))
    spks = spks[:end].reshape([-1, K]).astype(int)  # make the right size

    label_l = label_l[:end].reshape([-1, K])

    # Generate an indicator array for bin number
    bins = bin_indicator(vl['xs'], vl['ys'], xbins, ybins, bin_size, room)
    bins = bins[:end].reshape([-1, K])  # Make the right size

    # The main data structures
    Xs = np.zeros([end / K, len(t_cells) + xbins * ybins])

    # Put in cell firing rates
    for tetrode, cell in t_cells:
        cur_cell_id = cell_id(tetrode, cell, t_cells)
        debug_tmp = np.bitwise_and(
            spks,
            np.ones(spks.shape).astype(int) * 2**cur_cell_id)
        assert np.sum(debug_tmp == 0) + np.sum(
            debug_tmp == 2**cur_cell_id) == debug_tmp.size
        cur_cell_spk = (debug_tmp > 0)
        Xs[:, cur_cell_id] = np.mean(cur_cell_spk, axis=1)

        # Make sure spikes don't disappear
        assert np.sum(cur_cell_spk) == len(np.unique(t_cells[(tetrode, cell)]))

    # Put bin fractions in
    for xbin, ybin in product(range(xbins), range(ybins)):
        cbin_id = bin_id(xbin, ybin, ybins)
        cbin_index = bin_index(xbin, ybin, ybins)
        Xs[:, len(t_cells) + cbin_index] = np.mean(bins == cbin_id, axis=1)

    Ys = np.squeeze(mode(label_l, axis=1)[0])

    # All Ys are still valid labels
    labels = np.unique(label_l)
    assert np.sum(label_l == labels[0]) + np.sum(
        label_l == labels[1]) == label_l.size

    # Fractions add up to 1
    assert np.allclose(np.ones(end / K), np.sum(Xs[:, len(t_cells):], axis=1))

    # Bin fractions are right
    if logging.getLogger().level <= 10:
        bins2 = bins.reshape([-1])
        for i in range(0, len(label_l) / K):
            curv = Xs[i, len(t_cells):]
            curbins = bins2[i * K:i * K + K]
            for curbin in range(xbins * ybins):
                assert 1.0 * np.sum(curbins == (curbin +
                                                1)) / K == curv[curbin]

    # Everything is between 0 and 1
    assert np.all(Xs >= 0) and np.all(Xs <= 1)

    # Check that no spikes are missing
    if logging.getLogger().level <= 10:
        for tetrode, cell in t_cells:
            actual_spks = len(np.unique(t_cells[(tetrode, cell)]))
            iid = cell_id(tetrode, cell, t_cells)
            Xs_spks = K * np.sum(Xs[:, iid])
            assert np.allclose(Xs_spks, actual_spks)

    return Xs, Ys
示例#2
0
def gpv(vl, t_cells, label_l, K, bin_size, room):
    ''' Returns a matrix of feature vectors that is
        firing rate.
    
    The feature vector is:
    [fr cell 1, fr cell 2, ..., fr cell n, mode X, mode Y]
    
    With labels [mode context1, mode context2,...]
    
    t_cells
    '''
    # Check data integrity
    assert len(label_l) == len(vl['xs'])
    assert (room[0][1] - room[0][0]) % bin_size == 0
    assert (room[1][1] - room[1][0]) % bin_size == 0

    #cache_key = (vl['xs'][::100],t_cells,label_l[::112],K,bin_size,room,'gpv by bin')
    #cache = try_cache(cache_key)
    #if cache is not None: return cache

    xbins = (room[0][1] - room[0][0]) / bin_size
    ybins = (room[1][1] - room[1][0]) / bin_size

    # Generate an indicator array for identity of spiking cell
    spks = spk_indicators(t_cells, len(label_l))

    labels = np.unique(label_l)
    lbl_is = {lbl: np.nonzero(label_l == lbl)[0] for lbl in labels}

    # Generate an indicator array for bin number
    bins = bin_indicator(vl['xs'], vl['ys'], xbins, ybins, bin_size, room)
    bin_is = {(xbin, ybin): np.nonzero(bins == bin_id(xbin, ybin, ybins))[0]
              for xbin, ybin in product(range(xbins), range(ybins))}

    # The main data structures
    Xs = []
    Ys = []

    # Debug structures
    all_accounted_for = 0  # Keep track of the moments in time
    thrown_away = 0

    for xbin, ybin, lbl in product(range(xbins), range(ybins), labels):
        cbin_id = bin_id(xbin, ybin, ybins)
        bin_i = bin_is[(xbin, ybin)]
        lbl_i = lbl_is[lbl]
        cur_i = np.intersect1d(bin_i, lbl_i)
        if len(cur_i) == 0: continue  # Never in this bin with this context

        # Debug code
        all_accounted_for += len(cur_i)

        # Set up problem to use find_runs
        sgn2 = np.zeros([len(label_l), 1])
        sgn2[cur_i] = 1
        sgn, run_len = find_runs(sgn2)
        run_start = np.intersect1d(sgn, cur_i)

        assert len(run_start) > 0

        for st in run_start:
            run_l = run_len[sgn == st]
            if run_l < K:
                thrown_away += run_l
                continue

            delt = (run_l / K) * K  # Use rounding error to our advantage
            thrown_away += run_l - delt
            assert delt % K == 0
            X = np.zeros([delt / K, len(t_cells) + xbins * ybins
                          ])  # Extra vector space for x and y
            spks_tmp = spks[st:st + delt].reshape([K, -1])
            for tetrode, cell in t_cells:
                cur_cell_id = cell_id(tetrode, cell, t_cells)
                tmp = np.bitwise_and(
                    spks_tmp.astype(int),
                    np.ones(spks_tmp.shape).astype(int) * 2**cur_cell_id) > 0
                rt = np.sum(tmp, axis=0)

                X[:, cur_cell_id] = rt
            X[:, len(t_cells) + bin_index(xbin, ybin, ybins)] = 1
            Xs.append(X)
            Ys.append(np.ones([delt / K]) * lbl)

    assert all_accounted_for == len(label_l)
    Xs = np.concatenate(Xs)
    Ys = np.concatenate(Ys)

    # Make sure bins were assigned correctly
    for xbin, ybin in product(range(xbins), range(ybins)):
        cbin_id = bin_id(xbin, ybin, ybins)
        cbin_index = bin_index(xbin, ybin, ybins)
        if K == 1:
            assert np.sum(bins == cbin_id) == np.sum(Xs[:,
                                                        len(t_cells) +
                                                        cbin_index])

    logging.warning('Threw away %i/%i points when generating PV by bin',
                    thrown_away, len(label_l))

    #out = (Xs,Ys)
    #logging.warning('Storing ByBin gpv')
    #store_in_cache(cache_key,out)

    return Xs, Ys
示例#3
0
def gpv(vl, t_cells, label_l, K,bin_size, room):
    ''' Returns a matrix of feature vectors that is
        firing rate.
    
    The feature vector is:
    [fr cell 1, fr cell 2, ..., fr cell n, mode X, mode Y]
    
    With labels [mode context1, mode context2,...]
    
    t_cells
    '''
    # Check data integrity
    assert len(label_l) == len(vl['xs'])
    assert (room[0][1]-room[0][0]) % bin_size == 0
    assert (room[1][1]-room[1][0]) % bin_size == 0
    
    #cache_key = (vl['xs'][::100],t_cells,label_l[::112],K,bin_size,room,'gpv by bin')
    #cache = try_cache(cache_key)
    #if cache is not None: return cache
    
    xbins = (room[0][1]-room[0][0]) / bin_size
    ybins = (room[1][1]-room[1][0]) / bin_size
    
    
    # Generate an indicator array for identity of spiking cell
    spks = spk_indicators(t_cells, len(label_l))
    
    
    labels = np.unique(label_l)
    lbl_is = {lbl:np.nonzero(label_l==lbl)[0] for lbl in labels}
    
    # Generate an indicator array for bin number
    bins = bin_indicator(vl['xs'],vl['ys'],xbins,ybins,bin_size,room)
    bin_is = {(xbin,ybin):np.nonzero(bins==bin_id(xbin,ybin,ybins))[0] for xbin,ybin in product(range(xbins),range(ybins))}
    

    # The main data structures
    Xs = []
    Ys = []
    
    # Debug structures
    all_accounted_for = 0   # Keep track of the moments in time
    thrown_away = 0
    
    for xbin,ybin,lbl in product(range(xbins),range(ybins),labels):
        cbin_id = bin_id(xbin,ybin,ybins)
        bin_i = bin_is[(xbin,ybin)]
        lbl_i = lbl_is[lbl]
        cur_i = np.intersect1d(bin_i, lbl_i)
        if len(cur_i)==0:continue   # Never in this bin with this context
        
        # Debug code
        all_accounted_for += len(cur_i)

        # Set up problem to use find_runs
        sgn2 = np.zeros([len(label_l),1])
        sgn2[cur_i] = 1
        sgn, run_len = find_runs(sgn2)
        run_start = np.intersect1d(sgn,cur_i)
        
        assert len(run_start) > 0
        
        for st in run_start:
            run_l = run_len[sgn==st]
            if run_l<K: 
                thrown_away += run_l
                continue

            delt = (run_l/K)*K  # Use rounding error to our advantage
            thrown_away += run_l-delt
            assert delt%K == 0
            X = np.zeros([delt/K,len(t_cells)+xbins*ybins])   # Extra vector space for x and y
            spks_tmp = spks[st:st+delt].reshape([K,-1])
            for tetrode,cell in t_cells:
                cur_cell_id = cell_id(tetrode,cell,t_cells)
                tmp = np.bitwise_and(spks_tmp.astype(int), np.ones(spks_tmp.shape).astype(int)*2**cur_cell_id)>0
                rt = np.sum(tmp,axis=0)

                X[:,cur_cell_id] = rt
            X[:,len(t_cells)+bin_index(xbin,ybin,ybins)] = 1
            Xs.append(X)
            Ys.append(np.ones([delt/K])*lbl)
            
    
    assert all_accounted_for == len(label_l)
    Xs = np.concatenate(Xs)
    Ys = np.concatenate(Ys) 
    
    # Make sure bins were assigned correctly
    for xbin,ybin in product(range(xbins),range(ybins)):
        cbin_id = bin_id(xbin,ybin,ybins)
        cbin_index = bin_index(xbin,ybin,ybins)
        if K == 1:
            assert np.sum(bins==cbin_id)==np.sum(Xs[:,len(t_cells)+cbin_index])

    logging.warning('Threw away %i/%i points when generating PV by bin',thrown_away,len(label_l))

    #out = (Xs,Ys)
    #logging.warning('Storing ByBin gpv')
    #store_in_cache(cache_key,out)

    return Xs,Ys
            
示例#4
0
def gpv(vl, t_cells, label_l, K,bin_size, room):
    ''' Returns a matrix of feature vectors.
    
    The feature vector is:
    [fr cell 1, fr cell 2, ..., fr cell n, frac in bin1, frac in bin2,...]
    
    The matrix dimension is [# of vectors, # cells + # bins]
    
    With labels [mode context1, mode context2,...]
    
    K is the length of the subvector that will be used to calculate firing rate
    '''
    
    # Check data integrity
    assert len(label_l) == len(vl['xs'])
    assert (room[0][1]-room[0][0]) % bin_size == 0
    assert (room[1][1]-room[1][0]) % bin_size == 0
    
    xbins = (room[0][1]-room[0][0]) / bin_size
    ybins = (room[1][1]-room[1][0]) / bin_size
    
    
    end = (len(label_l)/K)*K    # Use integer roundoff to our advantage
    logging.warning('Threw away %i/%i points.',len(label_l)-end, len(label_l))

    # Generate an indicator array for identity of spiking cell
    spks = spk_indicators(t_cells, len(label_l))
    spks = spks[:end].reshape([-1,K]).astype(int)   # make the right size
    
    label_l = label_l[:end].reshape([-1,K])
    
    # Generate an indicator array for bin number
    bins = bin_indicator(vl['xs'],vl['ys'],xbins,ybins,bin_size,room)
    bins = bins[:end].reshape([-1,K])   # Make the right size
    
    # The main data structures
    Xs = np.zeros([end/K,len(t_cells)+xbins*ybins])
    
    # Put in cell firing rates
    for tetrode,cell in t_cells:
        cur_cell_id = cell_id(tetrode,cell,t_cells)
        debug_tmp = np.bitwise_and(spks, np.ones(spks.shape).astype(int)*2**cur_cell_id)
        assert np.sum(debug_tmp == 0)+np.sum(debug_tmp==2**cur_cell_id)==debug_tmp.size
        cur_cell_spk = (debug_tmp>0)
        Xs[:,cur_cell_id] = np.mean(cur_cell_spk,axis=1)
        
        # Make sure spikes don't disappear
        assert np.sum(cur_cell_spk)==len(np.unique(t_cells[(tetrode,cell)]))
        
        
        
    # Put bin fractions in 
    for xbin, ybin in product(range(xbins),range(ybins)):
        cbin_id = bin_id(xbin,ybin,ybins)
        cbin_index = bin_index(xbin,ybin,ybins)
        Xs[:,len(t_cells)+cbin_index] = np.mean(bins==cbin_id,axis=1)
    
    Ys = np.squeeze(mode(label_l,axis=1)[0])
    
    # All Ys are still valid labels
    labels = np.unique(label_l)
    assert np.sum(label_l==labels[0])+np.sum(label_l==labels[1]) == label_l.size

    # Fractions add up to 1
    assert np.allclose(np.ones(end/K),np.sum(Xs[:,len(t_cells):],axis=1))
    
    # Bin fractions are right
    if logging.getLogger().level <= 10:
        bins2 = bins.reshape([-1])
        for i in range(0,len(label_l)/K):
            curv = Xs[i,len(t_cells):]
            curbins = bins2[i*K:i*K+K]
            for curbin in range(xbins*ybins):
                assert 1.0*np.sum(curbins==(curbin+1))/K == curv[curbin]
    
    # Everything is between 0 and 1
    assert np.all(Xs>=0) and np.all(Xs<=1)
    
    # Check that no spikes are missing
    if logging.getLogger().level <= 10:
        for tetrode,cell in t_cells:
            actual_spks = len(np.unique(t_cells[(tetrode,cell)]))
            iid = cell_id(tetrode,cell,t_cells)
            Xs_spks = K*np.sum(Xs[:,iid])
            assert np.allclose(Xs_spks,actual_spks)
    
    return Xs,Ys