In [1]:
import copy
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import os
import seaborn as sns
import time
import warnings
import scipy.ndimage.filters
import scipy.stats as stats
from IPython.display import display, clear_output
import nelpy as nel
import nelpy.plotting as npl
from sklearn.model_selection import train_test_split
from mpl_toolkits.axes_grid1 import make_axes_locatable
from nelpy import hmmutils
from nelpy.decoding import k_fold_cross_validation
from nelpy.decoding import decode1D
# Set default figure aesthetics
npl.setup(font_scale=1.0)
%matplotlib inline
warnings.filterwarnings("ignore")
In [2]:
import gcsfs
import pandas as pd
fs = gcsfs.GCSFileSystem(project='polar-program-784', token='cloud')
print(fs.ls('kemerelab-data/diba'))
with fs.open('kemerelab-data/diba/gor01vvp01pin01-metadata.h5', 'rb') as fid:
with pd.HDFStore('gor01vvp01pin01-metadata.h5', mode="r", driver="H5FD_CORE",
driver_core_backing_store=0,
driver_core_image=fid.read()
) as store:
df = store['Session_Metadata']
df2 = store['Subset_Metadata']
In [3]:
# %%timeit # beware - this will run it 7 times to get a time! 36 s for 1.4 GB file
with fs.open('kemerelab-data/diba/gor01vvp01pin01_processed_speed.nel', 'rb') as fid:
jar = nel.load_pkl('',fileobj=fid) # currently requires a specific nelpy branch
exp_data = jar.exp_data
aux_data = jar.aux_data
del jar
In [38]:
# session_time, segment = '1-22-43', 'long'
# session_time, segment = '16-40-19', 'short'
session_time, segment = '1-22-43', 'short'
PBEs = aux_data[session_time][segment]['PBEs']
st_run = aux_data[session_time][segment]['st_run']
tc = aux_data[session_time][segment]['tc']
tc_placecells = aux_data[session_time][segment]['tc_placecells']
#####################################################################
NUM_COLORS = tc_placecells.n_units * 4
cm = plt.get_cmap('Spectral_r')
clist = [cm(1.*i/NUM_COLORS) for i in range(NUM_COLORS)]
clist = np.roll(clist, 0, axis=0)
npl.set_palette(clist)
with npl.FigureManager(show=True, figsize=(4,6)) as (fig, ax):
ax = npl.plot_tuning_curves1D(tc_placecells.smooth(sigma=3), pad=2.5);
ax.set_xlim(0,250)
In [19]:
# session_time, segment = ('16-40-19', 'short') # example session
num_states = 30 # number of states for PBE HMM
ds = 0.02 # 20 ms bin size for PBEs
ds_run = 0.1
ds_50ms = 0.05
min_tc_duration = 0 # mininmum observation time in seconds, before a bin contributes to the tuning curve
sigma_tc = 4 # 4 cm smoothing on tuning curves
vtcs = []
k_folds = 5
st = aux_data[session_time][segment]['st_run']
PBEs = aux_data[session_time][segment]['PBEs']
X = [ii for ii in range(PBEs.n_epochs)]
description = (session_time, segment)
print("session: {}".format(description))
st_no_ripple = st[~exp_data[session_time]['mua_epochs']]
pos = exp_data[session_time]['pos1d'] # should this be pos1d?
# smooth and re-bin:
sigma = 0.3 # 300 ms spike smoothing
bst_no_ripple = st_no_ripple.bin(ds=ds_50ms).smooth(sigma=sigma, inplace=True).rebin(w=ds_run/ds_50ms)
bst = bst_no_ripple
ext_nx=124
x0=0; xl=310;
xx_left = np.linspace(x0,xl,ext_nx+1)
xx_mid = np.linspace(x0,xl,ext_nx+1)[:-1]; xx_mid += (xx_mid[1]-xx_mid[0])/2
for kk, (training, validation) in enumerate(k_fold_cross_validation(X, k=k_folds)):
print(' fold {}/{}'.format(kk+1, k_folds))
PBEs_train = PBEs[training]
PBEs_test = PBEs[validation]
# train HMM on all training PBEs
hmm = nel.hmmutils.PoissonHMM(n_components=num_states, random_state=0, verbose=False)
hmm.fit(PBEs_train)
# reorder states according to transmat ordering
transmat_order = hmm.get_state_order('transmat')
hmm.reorder_states(transmat_order)
# compute spatial info on non-shuffled data:
xpos = pos.asarray(at=bst.centers).yvals
ext_x = np.digitize(xpos, xx_left) - 1 # spatial bin numbers
ext_x = ext_x.astype(float)
ext_x[ext_x==0] = np.nan
ext_x[ext_x>=ext_nx] = np.nan
extern = hmm.fit_ext(X=bst_no_ripple, ext=ext_x, n_extern=ext_nx)
vtc = nel.TuningCurve1D(ratemap=extern, min_duration=min_tc_duration, extmin=x0, extmax=xl)
vtc = vtc.smooth(sigma=sigma_tc)
vtc.reorder_units(inplace=True)
vtcs.append(vtc)
In [6]:
NUM_COLORS = vtc.n_units + 2
cm = plt.get_cmap('viridis')
clist = [cm(1.*i/NUM_COLORS) for i in range(NUM_COLORS)]
npl.set_palette(clist)
In [7]:
for vtc in vtcs:
npl.plot_tuning_curves1D(vtc, pad=0.1)
plt.show()
In [8]:
# train HMM on all training PBEs
hmm = nel.hmmutils.PoissonHMM(n_components=num_states, random_state=0, verbose=False)
hmm.fit(PBEs)
# reorder states according to transmat ordering
transmat_order = hmm.get_state_order('transmat')
hmm.reorder_states(transmat_order)
# compute spatial info on non-shuffled data:
xpos = pos.asarray(at=bst.centers).yvals
ext_x = np.digitize(xpos, xx_left) - 1 # spatial bin numbers
ext_x = ext_x.astype(float)
ext_x[ext_x==0] = np.nan
ext_x[ext_x>=ext_nx] = np.nan
extern = hmm.fit_ext(X=bst_no_ripple, ext=ext_x, n_extern=ext_nx)
vtc_ = nel.TuningCurve1D(ratemap=extern, min_duration=min_tc_duration, extmin=x0, extmax=xl)
vtc_ = vtc_.smooth(sigma=sigma_tc)
vtc_.reorder_units(inplace=True)
# normalize position distributions for each state (FFB! This will affect decoding! But it's not the only "correct" way to normalize!)
vtc_._ratemap = (vtc_.ratemap.T / vtc_.ratemap.sum(axis=1)).T
In [9]:
ax = npl.plot_tuning_curves1D(vtc_, pad=0.075)
ax.set_xlim(0, 250)
#npl.savefig('vtc_distr', formats=['pdf', 'png', 'svg'])
Out[9]:
In [10]:
ax = npl.plot_tuning_curves1D(vtc_, normalize=True, pad=0.5)
ax.set_xlim(0, 250)
#npl.savefig('vtc_normalized', formats=['pdf', 'png', 'svg'])
Out[10]:
In [11]:
shuffled_pos = copy.deepcopy(pos)
# shuffled_pos._ydata = shuffled_pos._ydata[:, np.random.permutation(pos.n_samples)]
# shuffled_pos._interp = None # necessary so that new interpolant is used, and not old one, before shuffle
xpos = shuffled_pos.asarray(at=bst.centers).yvals
xpos = xpos[np.random.permutation(len(xpos))]
ext_x = np.digitize(xpos, xx_left) - 1 # spatial bin numbers
ext_x = ext_x.astype(float)
ext_x[ext_x==0] = np.nan
ext_x[ext_x>=ext_nx] = np.nan
extern = hmm.fit_ext(X=bst_no_ripple, ext=ext_x, n_extern=ext_nx)
# transform into ratemap shape
vtc_shfl = nel.TuningCurve1D(ratemap=extern, min_duration=min_tc_duration, extmin=x0, extmax=xl)
vtc_shfl = vtc_shfl.smooth(sigma=sigma_tc)
vtc_shfl.reorder_units(inplace=True)
# normalize position distributions for each state (FFB! This will affect decoding! But it's not the only "correct" way to normalize!)
vtc_shfl._ratemap = (vtc_shfl.ratemap.T / vtc_shfl.ratemap.sum(axis=1)).T
ax = npl.plot_tuning_curves1D(vtc_shfl, normalize=True, pad=0.5)
ax.set_xlim(0, 250)
# results[(session_time, segment)]['shuffled'].extend(vtc.spatial_information().tolist())
# npl.savefig('vtc_shuffled', formats=['pdf', 'png', 'svg'])
Out[11]:
In [12]:
ax = npl.plot_tuning_curves1D(vtc_shfl, normalize=True, pad=0.5)
ax.set_xlim(0, 250)
# npl.savefig('vtc_shuffled3', formats=['pdf', 'png', 'svg'])
Out[12]:
In [13]:
ds_run = 0.1 # 500 ms
ds_50ms = 0.05
s = np.argwhere([segment == segment_label for segment_label in df[df.time==session_time]['segment_labels'].values.tolist()[0]])
st_run = exp_data[session_time]['spikes'][s][exp_data[session_time]['run_epochs']]
# smooth and re-bin:
sigma = 0.3 # 300 ms spike smoothing
bst_run = st_run.bin(ds=ds_50ms).smooth(sigma=sigma, inplace=True).rebin(w=ds_run/ds_50ms)
sigma = 6.2 # smoothing std dev in cm
tc_ = nel.TuningCurve1D(bst=bst_run, extern=exp_data[session_time]['pos1d'], n_extern=100, extmin=0, extmax=310, sigma=sigma, min_duration=0)
tc_ = tc.reorder_units()
In [14]:
# OK - let's think about what we want to do:
# Big question - what is the latent space that describes hippocampal activity?
# (1) - what does that latent space look like?
# - transition matrix and firing rate matrix
# - sparsity, compared with shuffles?
# - cross-validated virtual tuning curves
# (2) - we can learn models on both place cell activity and PBEs
# - are they equivalent?
# : latent space looks qualitatively similar
# - how do they differ?
# : cross-validated likelihood (goodness-of-fit) is more different than shuffles?
# : is there something to be said about scoring? maybe PBE-in-place cell better than
# place cell in PBE
# (3) - can we see replay?
# (0) replicate cross-modal scoring
# - cross-validated self scoring (train model on subsets, test on the rest)
# - cross-modal scoring (train model on whole set)
# - have to deal with sequence length, but can compare by sequence
#
# (1) cross-validation training curves comparing to shuffles?
# - this could be the way Kamran suggested (just shuffling the test set)
# - this could also be the way Etienne suggested (shuffling all the data, then training as usual)
#
# (2) generate sparsity data for transition and rate matrices
#
# (3) Is there a way to think about mutual information??? I think it would be hard, because I think
# the operant question would be MI(spikes | position) and MI(spikes | latent state)
In [33]:
# session_time, segment = ('16-40-19', 'short') # example session
num_states = 30 # number of states for PBE HMM
ds = 0.02 # 20 ms bin size for PBEs
min_tc_duration = 0 # mininmum observation time in seconds, before a bin contributes to the tuning curve
sigma_tc = 4 # 4 cm smoothing on tuning curves
description = (session_time, segment)
print("session: {}".format(description))
s = np.argwhere([segment == segment_label for segment_label in df[df.time==session_time]['segment_labels'].values.tolist()[0]])
st_run = exp_data[session_time]['spikes'][s][exp_data[session_time]['run_epochs']]
# smooth and re-bin:
sigma = 0.3 # 300 ms spike smoothing
bst_run = st_run.bin(ds=ds_50ms).smooth(sigma=sigma, inplace=True).rebin(w=ds_run/ds_50ms)
bst = bst_run
pos = exp_data[session_time]['pos1d'] # should this be pos1d?
ext_nx=124
x0=0; xl=310;
xx_left = np.linspace(x0,xl,ext_nx+1)
xx_mid = np.linspace(x0,xl,ext_nx+1)[:-1]; xx_mid += (xx_mid[1]-xx_mid[0])/2
#########################################################
# Generate an HMM trained on all PBEs
PBEs = aux_data[session_time][segment]['PBEs']
pbe_hmm = nel.hmmutils.PoissonHMM(n_components=num_states, random_state=0, verbose=False)
pbe_hmm.fit(PBEs)
transmat_order = hmm.get_state_order('transmat')
pbe_hmm.reorder_states(transmat_order)
xpos = pos.asarray(at=bst_run.centers).yvals
ext_x = np.digitize(xpos, xx_left) - 1 # spatial bin numbers
ext_x = ext_x.astype(float)
ext_x[ext_x==0] = np.nan
ext_x[ext_x>=ext_nx] = np.nan
extern = pbe_hmm.fit_ext(X=bst_run, ext=ext_x, n_extern=ext_nx)
pbe_vtc = nel.TuningCurve1D(ratemap=extern, min_duration=min_tc_duration, extmin=x0, extmax=xl)
pbe_vtc = pbe_vtc.smooth(sigma=sigma_tc)
pbe_vtc.reorder_units(inplace=True)
#########################################################
k_folds = 3
vtcs_run = []
X = list(range(bst_run.n_epochs))
for kk, (training, validation) in enumerate(k_fold_cross_validation(X, k=k_folds)):
print(' fold {}/{}'.format(kk+1, k_folds))
RunSeqs_train = bst[training]
RunSeqs_test = bst[validation]
# train HMM on all training PBEs
hmm = nel.hmmutils.PoissonHMM(n_components=num_states, random_state=0, verbose=False)
hmm.fit(RunSeqs_train)
# reorder states according to transmat ordering
transmat_order = hmm.get_state_order('transmat')
hmm.reorder_states(transmat_order)
# compute spatial info on non-shuffled data:
xpos = pos.asarray(at=RunSeqs_test.centers).yvals
ext_x = np.digitize(xpos, xx_left) - 1 # spatial bin numbers
ext_x = ext_x.astype(float)
ext_x[ext_x==0] = np.nan
ext_x[ext_x>=ext_nx] = np.nan
extern = hmm.fit_ext(X=RunSeqs_test, ext=ext_x, n_extern=ext_nx)
vtc = nel.TuningCurve1D(ratemap=extern, min_duration=min_tc_duration, extmin=x0, extmax=xl)
vtc = vtc.smooth(sigma=sigma_tc)
vtc.reorder_units(inplace=True)
vtcs_run.append(vtc)
In [21]:
vtc
Out[21]:
In [34]:
for vtc in vtcs_run:
fig, axs = plt.subplots(1,2,figsize=(12,6))
npl.plot_tuning_curves1D(vtc, pad=0.1, ax=axs[0])
npl.plot_tuning_curves1D(pbe_vtc, pad=0.1, ax=axs[1])
plt.show()
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# set criteria for units used in decoding
min_peakfiringrate = 1 # Hz
max_avgfiringrate = 5 # Hz
peak_to_mean_ratio_threshold = 0 # peak firing rate should be greater than 3.5 times mean firing rate
# unimodal_cells = find_unimodal_tuningcurves1D(smoothed_rate, peakthresh=0.5)
# enforce minimum peak firing rate
unit_ids_to_keep = set(np.asanyarray(tc.unit_ids)[np.argwhere(tc.ratemap.max(axis=1)>min_peakfiringrate).squeeze().tolist()])
# enforce maximum average firing rate
unit_ids_to_keep = unit_ids_to_keep.intersection(set( np.asanyarray(tc.unit_ids)[np.argwhere(tc.ratemap.mean(axis=1)<max_avgfiringrate).squeeze().tolist()] ))
# enforce peak to mean firing ratio
peak_firing_rates = tc.max(axis=1)
mean_firing_rates = tc.mean(axis=1)
ratio = peak_firing_rates/mean_firing_rates
unit_ids_to_keep = unit_ids_to_keep.intersection(set(np.asanyarray(tc.unit_ids)[np.argwhere(ratio>=peak_to_mean_ratio_threshold).squeeze().tolist()]))
# finally, convert remaining units into a list of indices
unit_ids_to_keep = list(unit_ids_to_keep)
# modify spike trains and ratemap to only include those units that passed all the criteria
sta_placecells = exp_data[session_time]['spikes']._unit_subset(unit_ids_to_keep)
tc_placecells = tc._unit_subset(unit_ids_to_keep)
# reorder cells by peak firing location on track (this is nice for visualization, but doesn't affect decoding)
tc_placecells.reorder_units(inplace=True)
sta_placecells.reorder_units_by_ids(tc_placecells.unit_ids, inplace=True)
# with plt.xkcd():
with npl.palettes.color_palette(npl.colors.rainbow):
with npl.FigureManager(show=True, nrows=1, ncols=3, figsize=(16,4)) as (fig, axes):
npl.utils.skip_if_no_output(fig)
ax0, ax1, ax2 = axes
npl.plot_tuning_curves1D(tc_placecells.smooth(sigma=3), ax=ax0, pad=5.5);
npl.plot_tuning_curves1D(tc_placecells.smooth(sigma=3), ax=ax1, normalize=True, pad=0.9);
npl.plot_tuning_curves1D(tc_placecells.smooth(sigma=3), ax=ax2, pad=0);
for ax in axes:
ax.set_xlabel('position [cm]')
npl.utils.xticks_interval(25, *axes)
npl.utils.yticks_interval(5, ax2)
npl.add_simple_scalebar("10 Hz", ax=ax0, xy=(10, 57), length=10, orientation='v', rotation_text='h', size=14)
# npl.add_simple_scalebar("5 Hz", ax=ax1, xy=(10, 17.5), length=5, orientation='v', rotation_text='h', size=14)
ax0.set_title('True firing rates', size=12)
ax1.set_title('Normalized firing rates', size=12)
ax2.set_title('Collapsed units (pad=0)', size=12)