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 nelpy.analysis.hmm_sparsity import HMMSurrogate
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=2.0)
%matplotlib inline
warnings.filterwarnings("ignore")
In [2]:
import gcsfs
import pandas as pd
import os
load_local = False
if not load_local:
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']
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
else:
datadirs = ['/Users/ckemere/Development/Data/Buzsaki/']
fileroot = next( (dir for dir in datadirs if os.path.isdir(dir)), None)
if fileroot is None:
raise FileNotFoundError('datadir not found')
with pd.HDFStore(fileroot + 'gor01vvp01pin01-metadata.h5') as store:
df = store.get('Session_Metadata')
df2 = store.get('Subset_Metadata')
jar = nel.load_pkl(fileroot + 'gor01vvp01pin01_processed_speed.nel')
exp_data = jar.exp_data
aux_data = jar.aux_data
del jar
In [3]:
# session_time, segment = '1-22-43', 'long'
# session_time, segment = '16-40-19', 'short'
session_time, segment = '22-24-40', 'long'
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 [4]:
ds_run = 0.150 # 125 ms bin size for Run
ds_50ms = 0.05 # used for smoothing
ds = 0.02 # 20 ms bin size for PBEs
sigma = 0.300 # 250 ms spike smoothing
num_states = 30
k_folds = 5
In [5]:
# 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 [6]:
# session_time, segment = ('16-40-19', 'short') # example session
num_states = 30 # number of states for PBE HMM
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 = pbe_hmm.get_state_order('transmat')
pbe_hmm.reorder_states(transmat_order)
# pbe_hmm.means_ = pbe_hmm.means_ * ds_run / ds
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.ratemap[pbe_vtc.ratemap.sum(axis=1) < 0.1,:] = 0
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 [7]:
pbe_vtc2 = copy.deepcopy(pbe_vtc)
bad_states = np.extract(pbe_vtc2.ratemap.sum(axis=1) < 0.5, pbe_vtc2.unit_ids)
good_states = np.extract(pbe_vtc2.ratemap.sum(axis=1) > 0.5, pbe_vtc2.unit_ids)
new_order = bad_states.tolist() + good_states.tolist()
print(new_order)
pbe_vtc2.reorder_units_by_ids(neworder=new_order, inplace=True)
Out[7]:
In [8]:
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)
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_vtc2, pad=0.1, ax=axs[1])
for ax in axs:
ax.set_yticks([])
ax.set_yticklabels([])
ax.set_ylabel('state')
plt.show()
In [9]:
NUM_COLORS = tc_placecells.n_units * 3
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)
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(tc_placecells.smooth(sigma=3), pad=2.5, ax=axs[1]);
axs[0].set_yticks([])
axs[0].set_yticklabels([])
axs[0].set_ylabel('state')
axs[1].set_yticks([])
axs[1].set_yticklabels([])
plt.show()
In [10]:
# session_time, segment = '22-24-40', 'short'
# ses2 = '1-22-43'
seg2 = 'short'
ses2 = '22-24-40'
# seg2 = 'long'
# smooth and re-bin:
s2 = np.argwhere([seg2 == segment_label for segment_label in df[df.time==ses2]['segment_labels'].values.tolist()[0]])
st_run2 = exp_data[ses2]['spikes'][s2][exp_data[ses2]['run_epochs']]
bst_run2 = st_run2.bin(ds=ds_50ms).smooth(sigma=sigma, inplace=True).rebin(w=ds_run/ds_50ms)
xpos2 = pos.asarray(at=bst_run2.centers).yvals
ext_x = np.digitize(xpos2, 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
extern2 = pbe_hmm.fit_ext(X=bst_run2, ext=ext_x, n_extern=ext_nx)
pbe_vtc3 = nel.TuningCurve1D(ratemap=extern2, min_duration=min_tc_duration, extmin=x0, extmax=xl)
pbe_vtc3 = pbe_vtc3.smooth(sigma=sigma_tc)
pbe_vtc3.ratemap[pbe_vtc3.ratemap.sum(axis=1) < 0.1,:] = 0
# pbe_vtc3.reorder_units(inplace=True)
#########################################################
In [11]:
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)
pbe_vtc4 = pbe_vtc3.reorder_units_by_ids(neworder=pbe_vtc2.unit_ids, inplace=False)
fig, axs = plt.subplots(1,2,figsize=(12,6))
npl.plot_tuning_curves1D(pbe_vtc2, pad=0.1, ax=axs[0])
npl.plot_tuning_curves1D(pbe_vtc4, pad=0.1, ax=axs[1])
for ax in axs:
ax.set_yticks([])
ax.set_yticklabels([])
ax.set_ylabel('state')
plt.show()
In [12]:
[pbe_vtc2.unit_ids,pbe_vtc4.unit_ids]
Out[12]:
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