4.3. IBL’s Recordings from the striatum of a mouse performing a visual discrimination task

In this notebook we download publically available data from the International Brain Laboratory, epoch it, run svGPFA and plot its results.

4.3.1. Setup environment

4.3.1.1. Import required packages

import sys
import warnings
import pickle
import time
import configparser
import numpy as np
import pandas as pd
import torch

from one.api import ONE
import brainbox.io.one
import iblUtils

import gcnu_common.utils.neural_data_analysis
import gcnu_common.stats.pointProcesses.tests
import gcnu_common.utils.config_dict
import svGPFA.stats.svGPFAModelFactory
import svGPFA.stats.svEM
import svGPFA.utils.miscUtils
import svGPFA.utils.initUtils
import svGPFA.plot.plotUtilsPlotly

4.3.1.2. Set data parameters

eID = "ebe2efe3-e8a1-451a-8947-76ef42427cc9"
probe_id = "probe00"
epoch_event_name = "response_times"
clusters_ids_filename = "../init/clustersIDs_40_64.csv"
trials_ids_filename = "../init/trialsIDs_0_89.csv"
min_neuron_trials_avg_firing_rate = 0.1

4.3.1.3. Set estimation hyperparameters

n_latents = 10
em_max_iter_dyn = 200
common_n_ind_points = 15
est_init_number = 0
est_init_config_filename_pattern = "../init/{:08d}_IBL_estimation_metaData.ini"
model_save_filename = "../results/stiatum_ibl_model.pickle"

4.3.2. Epoch

4.3.2.1. Download data

one = ONE(base_url='https://openalyx.internationalbrainlab.org',
          password='international', silent=True)
spikes = one.load_object(eID, 'spikes', 'alf/probe00/pykilosort')
clusters = one.load_object(eID, "clusters", f"alf/{probe_id}/pykilosort")
trials = one.load_object(eID, 'trials')

4.3.2.2. Extract variables of interest

clusters_ids = np.unique(spikes.clusters.tolist())
n_clusters = len(clusters_ids)
channels_for_clusters_ids = clusters.channels
els = brainbox.io.one.load_channel_locations(eID, one=one)
locs_for_clusters_ids = els[probe_id]["acronym"][channels_for_clusters_ids].tolist()

4.3.2.3. Epoch spikes times

epoch_times = trials[epoch_event_name]
n_trials = len(epoch_times)

epoch_start_times = [trials["intervals"][r][0] for r in range(n_trials)]
epoch_end_times = [trials["intervals"][r][1] for r in range(n_trials)]

spikes_times_by_neuron = []
for cluster_id in clusters_ids:
    print(f"Processing cluster {cluster_id}")
    neuron_spikes_times = spikes.times[spikes.clusters==cluster_id]
    n_epoched_spikes_times = iblUtils.epoch_neuron_spikes_times(
        neuron_spikes_times=neuron_spikes_times,
        epoch_times = epoch_times,
        epoch_start_times=epoch_start_times,
        epoch_end_times=epoch_end_times)
    spikes_times_by_neuron.append(n_epoched_spikes_times)
spikes_times = [[spikes_times_by_neuron[n][r] for n in range(n_clusters)]
                for r in range(n_trials)]

trials_start_times = [epoch_start_times[r]-epoch_times[r] for r in range(n_trials)]
trials_end_times = [epoch_end_times[r]-epoch_times[r] for r in range(n_trials)]
n_neurons = len(spikes_times[0])

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4.3.2.4. Subset epoched spikes times

# subset selected_clusters_ids
selected_clusters_ids = np.genfromtxt(clusters_ids_filename, dtype=np.uint64)

spikes_times = iblUtils.subset_clusters_ids_data(
    selected_clusters_ids=selected_clusters_ids,
    clusters_ids=clusters_ids,
    spikes_times=spikes_times,
)
n_neurons = len(spikes_times[0])
n_trials = len(spikes_times)
trials_ids = np.arange(n_trials)

# subset selected_trials_ids
selected_trials_ids = np.genfromtxt(trials_ids_filename, dtype=np.uint64)
spikes_times, trials_start_times, trials_end_times = \
        iblUtils.subset_trials_ids_data(
            selected_trials_ids=selected_trials_ids,
            trials_ids=trials_ids,
            spikes_times=spikes_times,
            trials_start_times=trials_start_times,
            trials_end_times=trials_end_times)
n_trials = len(spikes_times)

# remove units with low spike rate
neurons_indices = torch.arange(n_neurons)
trials_durations = [trials_end_times[i] - trials_start_times[i]
                    for i in range(n_trials)]
spikes_times, neurons_indices = \
    gcnu_common.utils.neural_data_analysis.removeUnitsWithLessTrialAveragedFiringRateThanThr(
        spikes_times=spikes_times, neurons_indices=neurons_indices,
        trials_durations=trials_durations,
        min_neuron_trials_avg_firing_rate=min_neuron_trials_avg_firing_rate)
selected_clusters_ids = [selected_clusters_ids[i] for i in neurons_indices]

n_trials = len(spikes_times)
n_neurons = len(spikes_times[0])

4.3.2.5. Check that spikes have been epoched correctly

4.3.2.6. Plot spikes

Plot the spikes of all trials of a randomly chosen neuron. Most trials should contain at least one spike.

neuron_to_plot_index = torch.randint(low=0, high=n_neurons, size=(1,)).item()
fig = svGPFA.plot.plotUtilsPlotly.getSpikesTimesPlotOneNeuron(
    spikes_times=spikes_times,
    neuron_index=neuron_to_plot_index,
    title=f"Neuron index: {neuron_to_plot_index}",
)
fig

4.3.2.7. Run some simple checks on spikes

The function checkEpochedSpikesTimes tests that:

1. every neuron fired at least one spike across all trials,

2. for each trial, the spikes times of every neuron are between the trial start and end times.

If any check fails, a ValueError will be raised. Otherwise a checks passed message should be printed.

try:
    gcnu_common.utils.neural_data_analysis.checkEpochedSpikesTimes(
        spikes_times=spikes_times, trials_start_times=trials_start_times,
        trials_end_times=trials_end_times,
    )
except ValueError:
    raise
print("Checks passed")

Checks passed

4.3.3. Get parameters

Details on how to specify svGPFA parameters are provided here

4.3.3.1. Dynamic parameters specification

dynamic_params_spec = {
    "optim_params": {"em_max_iter": em_max_iter_dyn},
    "ind_points_locs_params0": {"common_n_ind_points": common_n_ind_points},
}

4.3.3.2. Config file parameters specification

The configuration file appears here

args_info = svGPFA.utils.initUtils.getArgsInfo()
est_init_config_filename = est_init_config_filename_pattern.format(
    est_init_number)
est_init_config = configparser.ConfigParser()
est_init_config.read(est_init_config_filename)

strings_dict = gcnu_common.utils.config_dict.GetDict(
    config=est_init_config).get_dict()
config_file_params_spec = \
    svGPFA.utils.initUtils.getParamsDictFromStringsDict(
        n_latents=n_latents, n_trials=n_trials,
        strings_dict=strings_dict, args_info=args_info)

4.3.3.3. Get the parameters from the dynamic and configuration file parameter specifications

params, kernels_types = svGPFA.utils.initUtils.getParamsAndKernelsTypes(
    n_trials=n_trials, n_neurons=n_neurons, n_latents=n_latents,
    trials_start_times=trials_start_times,
    trials_end_times=trials_end_times,
    dynamic_params_spec=dynamic_params_spec,
    config_file_params_spec=config_file_params_spec)

Extracted config_file_params_spec[optim_params][n_quad]=200
Extracted dynamic_params_spec[ind_points_locs_params0][common_n_ind_points]=15
Extracted from config_file c0_distribution=Normal, c0_loc=0.0, c0_scale=1.0, c0_random_seed=None
Extracted from config_file d0_distribution=Normal, d0_loc=0.0, d0_scale=1.0, d0_random_seed=None
Extracted from config_file k_type=exponentialQuadratic and k_lengthsales0=0.3
Extracted from config_file ind_points_locs0_layout=equidistant
Extracted from config_file variational_mean0_constant_value=0.0
Extracted from config_file variational_cov0_diag_value=0.01
Extracted config_file_params_spec[optim_params][n_quad]=200
Extracted config_file_params_spec[optim_params][prior_cov_reg_param]=0.001
Extracted config_file_params_spec[optim_params][optim_method]=ECM
Extracted dynamic_params_spec[optim_params][em_max_iter]=200
Extracted config_file_params_spec[optim_params][verbose]=True
Extracted config_file_params_spec[optim_params][estep_estimate]=True
Extracted config_file_params_spec[optim_params][estep_max_iter]=20
Extracted config_file_params_spec[optim_params][estep_lr]=1.0
Extracted config_file_params_spec[optim_params][estep_tolerance_grad]=0.001
Extracted config_file_params_spec[optim_params][estep_tolerance_change]=1e-05
Extracted config_file_params_spec[optim_params][estep_line_search_fn]=strong_wolfe
Extracted config_file_params_spec[optim_params][mstep_embedding_estimate]=True
Extracted config_file_params_spec[optim_params][mstep_embedding_max_iter]=20
Extracted config_file_params_spec[optim_params][mstep_embedding_lr]=1.0
Extracted config_file_params_spec[optim_params][mstep_embedding_tolerance_grad]=0.001
Extracted config_file_params_spec[optim_params][mstep_embedding_tolerance_change]=1e-05
Extracted config_file_params_spec[optim_params][mstep_embedding_line_search_fn]=strong_wolfe
Extracted config_file_params_spec[optim_params][mstep_kernels_estimate]=True
Extracted config_file_params_spec[optim_params][mstep_kernels_max_iter]=20
Extracted config_file_params_spec[optim_params][mstep_kernels_lr]=1.0
Extracted config_file_params_spec[optim_params][mstep_kernels_tolerance_grad]=0.001
Extracted config_file_params_spec[optim_params][mstep_kernels_tolerance_change]=1e-05
Extracted config_file_params_spec[optim_params][mstep_kernels_line_search_fn]=strong_wolfe
Extracted config_file_params_spec[optim_params][mstep_indpointslocs_estimate]=True
Extracted config_file_params_spec[optim_params][mstep_indpointslocs_max_iter]=20
Extracted config_file_params_spec[optim_params][mstep_indpointslocs_lr]=1.0
Extracted config_file_params_spec[optim_params][mstep_indpointslocs_tolerance_grad]=0.001
Extracted config_file_params_spec[optim_params][mstep_indpointslocs_tolerance_change]=1e-05
Extracted config_file_params_spec[optim_params][mstep_indpointslocs_line_search_fn]=strong_wolfe

4.3.4. Estimate svGPFA model

4.3.4.1. Create kernels, a model and set its initial parameters

4.3.4.2. Build kernels

kernels_params0 = params["initial_params"]["posterior_on_latents"]["kernels_matrices_store"]["kernels_params0"]
kernels = svGPFA.utils.miscUtils.buildKernels(
    kernels_types=kernels_types, kernels_params=kernels_params0)

4.3.4.3. Create model

kernelMatrixInvMethod = svGPFA.stats.svGPFAModelFactory.kernelMatrixInvChol
indPointsCovRep = svGPFA.stats.svGPFAModelFactory.indPointsCovChol
model = svGPFA.stats.svGPFAModelFactory.SVGPFAModelFactory.buildModelPyTorch(
    conditionalDist=svGPFA.stats.svGPFAModelFactory.PointProcess,
    linkFunction=svGPFA.stats.svGPFAModelFactory.ExponentialLink,
    embeddingType=svGPFA.stats.svGPFAModelFactory.LinearEmbedding,
    kernels=kernels, kernelMatrixInvMethod=kernelMatrixInvMethod,
    indPointsCovRep=indPointsCovRep)

4.3.4.4. Set initial parameters

model.setParamsAndData(
    measurements=spikes_times,
    initial_params=params["initial_params"],
    eLLCalculationParams=params["ell_calculation_params"],
    priorCovRegParam=params["optim_params"]["prior_cov_reg_param"])

4.3.4.5. Maximize the Lower Bound

(Warning: with the parameters above, this step takes around 5 minutes for 30 em_max_iter)

svEM = svGPFA.stats.svEM.SVEM_PyTorch()
tic = time.perf_counter()
lowerBoundHist, elapsedTimeHist, terminationInfo, iterationsModelParams = \
svEM.maximize(model=model, optim_params=params["optim_params"],
              method=params["optim_params"]["optim_method"], out=sys.stdout)
toc = time.perf_counter()
print(f"Elapsed time {toc - tic:0.4f} seconds")

resultsToSave = {"lowerBoundHist": lowerBoundHist,
                 "elapsedTimeHist": elapsedTimeHist,
                 "terminationInfo": terminationInfo,
                 "iterationModelParams": iterationsModelParams,
                 "model": model}
with open(model_save_filename, "wb") as f:
    pickle.dump(resultsToSave, f)
print("Saved results to {:s}".format(model_save_filename))

Iteration 01, estep start: -inf
Iteration 01, estep end: 9957.691428, niter: 17, nfeval: 25
Iteration 01, mstep_embedding start: 9957.691428
Iteration 01, mstep_embedding end: 50501.313180, niter: 20, nfeval: 22
Iteration 01, mstep_kernels start: 50501.313180
Iteration 01, mstep_kernels end: 38811.181973, niter: 5, nfeval: 6
Iteration 01, mstep_indpointslocs start: 38811.181973
Iteration 01, mstep_indpointslocs end: 39720.805888, niter: 20, nfeval: 22
Iteration 02, estep start: 39720.805888
Iteration 02, estep end: 44007.892471, niter: 20, nfeval: 25
Iteration 02, mstep_embedding start: 44007.892471
Iteration 02, mstep_embedding end: 51768.494539, niter: 20, nfeval: 21
Iteration 02, mstep_kernels start: 51768.494539
Iteration 02, mstep_kernels end: 44982.564746, niter: 4, nfeval: 5
Iteration 02, mstep_indpointslocs start: 44982.564746
Iteration 02, mstep_indpointslocs end: 45164.325862, niter: 20, nfeval: 22
Iteration 03, estep start: 45164.325862
Iteration 03, estep end: 46183.411194, niter: 20, nfeval: 24
Iteration 03, mstep_embedding start: 46183.411194
Iteration 03, mstep_embedding end: 52260.301692, niter: 20, nfeval: 22
Iteration 03, mstep_kernels start: 52260.301692
Iteration 03, mstep_kernels end: 46659.349393, niter: 4, nfeval: 5
Iteration 03, mstep_indpointslocs start: 46659.349393
Iteration 03, mstep_indpointslocs end: 46726.023488, niter: 20, nfeval: 22
Iteration 04, estep start: 46726.023488
Iteration 04, estep end: 47119.365614, niter: 20, nfeval: 23
Iteration 04, mstep_embedding start: 47119.365614
Iteration 04, mstep_embedding end: 52312.510144, niter: 20, nfeval: 21
Iteration 04, mstep_kernels start: 52312.510144
Iteration 04, mstep_kernels end: 47363.663081, niter: 4, nfeval: 6
Iteration 04, mstep_indpointslocs start: 47363.663081
Iteration 04, mstep_indpointslocs end: 47397.963703, niter: 20, nfeval: 22
Iteration 05, estep start: 47397.963703
Iteration 05, estep end: 47589.429609, niter: 20, nfeval: 22
Iteration 05, mstep_embedding start: 47589.429609
Iteration 05, mstep_embedding end: 52304.016882, niter: 20, nfeval: 21
Iteration 05, mstep_kernels start: 52304.016882
Iteration 05, mstep_kernels end: 47723.448821, niter: 4, nfeval: 5
Iteration 05, mstep_indpointslocs start: 47723.448821
Iteration 05, mstep_indpointslocs end: 47744.222592, niter: 20, nfeval: 22
Iteration 06, estep start: 47744.222592
Iteration 06, estep end: 47852.602359, niter: 20, nfeval: 22
Iteration 06, mstep_embedding start: 47852.602359
Iteration 06, mstep_embedding end: 52289.746126, niter: 20, nfeval: 21
Iteration 06, mstep_kernels start: 52289.746126
Iteration 06, mstep_kernels end: 47929.881911, niter: 4, nfeval: 5
Iteration 06, mstep_indpointslocs start: 47929.881911
Iteration 06, mstep_indpointslocs end: 47944.230287, niter: 20, nfeval: 22
Iteration 07, estep start: 47944.230287
Iteration 07, estep end: 48012.467819, niter: 20, nfeval: 22
Iteration 07, mstep_embedding start: 48012.467819
Iteration 07, mstep_embedding end: 52293.236324, niter: 20, nfeval: 21
Iteration 07, mstep_kernels start: 52293.236324
Iteration 07, mstep_kernels end: 48061.275749, niter: 5, nfeval: 6
Iteration 07, mstep_indpointslocs start: 48061.275749
Iteration 07, mstep_indpointslocs end: 48071.111882, niter: 20, nfeval: 22
Iteration 08, estep start: 48071.111882
Iteration 08, estep end: 48115.028499, niter: 20, nfeval: 22
Iteration 08, mstep_embedding start: 48115.028499
Iteration 08, mstep_embedding end: 52297.561473, niter: 20, nfeval: 21
Iteration 08, mstep_kernels start: 52297.561473
Iteration 08, mstep_kernels end: 48149.001919, niter: 5, nfeval: 6
Iteration 08, mstep_indpointslocs start: 48149.001919
Iteration 08, mstep_indpointslocs end: 48157.050813, niter: 20, nfeval: 21
Iteration 09, estep start: 48157.050813
Iteration 09, estep end: 48188.911552, niter: 20, nfeval: 22
Iteration 09, mstep_embedding start: 48188.911552
Iteration 09, mstep_embedding end: 52312.569002, niter: 20, nfeval: 21
Iteration 09, mstep_kernels start: 52312.569002
Iteration 09, mstep_kernels end: 48212.423802, niter: 5, nfeval: 6
Iteration 09, mstep_indpointslocs start: 48212.423802
Iteration 09, mstep_indpointslocs end: 48218.980961, niter: 20, nfeval: 22
Iteration 10, estep start: 48218.980961
Iteration 10, estep end: 48242.609968, niter: 20, nfeval: 23
Iteration 10, mstep_embedding start: 48242.609968
Iteration 10, mstep_embedding end: 52326.910931, niter: 20, nfeval: 21
Iteration 10, mstep_kernels start: 52326.910931
Iteration 10, mstep_kernels end: 48260.246476, niter: 5, nfeval: 6
Iteration 10, mstep_indpointslocs start: 48260.246476
Iteration 10, mstep_indpointslocs end: 48265.842555, niter: 20, nfeval: 22
Iteration 11, estep start: 48265.842555
Iteration 11, estep end: 48285.010199, niter: 20, nfeval: 23
Iteration 11, mstep_embedding start: 48285.010199
Iteration 11, mstep_embedding end: 52349.244827, niter: 20, nfeval: 23
Iteration 11, mstep_kernels start: 52349.244827
Iteration 11, mstep_kernels end: 48299.552344, niter: 5, nfeval: 6
Iteration 11, mstep_indpointslocs start: 48299.552344
Iteration 11, mstep_indpointslocs end: 48304.228599, niter: 20, nfeval: 21
Iteration 12, estep start: 48304.228599
Iteration 12, estep end: 48319.119375, niter: 20, nfeval: 23
Iteration 12, mstep_embedding start: 48319.119375
Iteration 12, mstep_embedding end: 52365.786067, niter: 20, nfeval: 23
Iteration 12, mstep_kernels start: 52365.786067
Iteration 12, mstep_kernels end: 48330.998974, niter: 5, nfeval: 6
Iteration 12, mstep_indpointslocs start: 48330.998974
Iteration 12, mstep_indpointslocs end: 48335.039036, niter: 20, nfeval: 22
Iteration 13, estep start: 48335.039036
Iteration 13, estep end: 48348.825513, niter: 20, nfeval: 23
Iteration 13, mstep_embedding start: 48348.825513
Iteration 13, mstep_embedding end: 52390.443126, niter: 20, nfeval: 22
Iteration 13, mstep_kernels start: 52390.443126
Iteration 13, mstep_kernels end: 48359.570237, niter: 5, nfeval: 6
Iteration 13, mstep_indpointslocs start: 48359.570237
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Iteration 14, estep start: 48363.205439
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Elapsed time 21549.1488 seconds
Saved results to ../results/stiatum_ibl_model.pickle

4.3.5. Goodness-of-fit analysis

4.3.5.1. Set goodness-of-fit variables

ks_test_gamma = 10
trial_for_gof = 0
cluster_id_for_gof = 41
n_time_steps_IF = 100

cluster_id_for_gof_index = torch.nonzero(torch.IntTensor(selected_clusters_ids)==cluster_id_for_gof)
trials_times = svGPFA.utils.miscUtils.getTrialsTimes(
    start_times=trials_start_times,
    end_times=trials_end_times,
    n_steps=n_time_steps_IF)

4.3.5.2. Calculate expected intensity function values (for KS test and IF plots)

with torch.no_grad():
    cif_values = model.computeExpectedPosteriorCIFs(times=trials_times)
cif_values_GOF = cif_values[trial_for_gof][cluster_id_for_gof_index]

4.3.5.3. KS time-rescaling GOF test

trial_times_GOF = trials_times[trial_for_gof, :, 0]
spikes_times_GOF = spikes_times[trial_for_gof][cluster_id_for_gof_index]
if len(spikes_times_GOF) == 0:
    raise ValueError("No spikes found for goodness-of-fit analysis")

with warnings.catch_warnings():
    warnings.simplefilter("ignore")
    diffECDFsX, diffECDFsY, estECDFx, estECDFy, simECDFx, simECDFy, cb = \
        gcnu_common.stats.pointProcesses.tests.\
        KSTestTimeRescalingNumericalCorrection(spikes_times=spikes_times_GOF,
            cif_times=trial_times_GOF, cif_values=cif_values_GOF,
            gamma=ks_test_gamma)

title = "Trial {:d}, Neuron {:d} ({:d} spikes)".format(
    trial_for_gof, cluster_id_for_gof, len(spikes_times_GOF))
fig = svGPFA.plot.plotUtilsPlotly.getPlotResKSTestTimeRescalingNumericalCorrection(diffECDFsX=diffECDFsX, diffECDFsY=diffECDFsY, estECDFx=estECDFx, estECDFy=estECDFy, simECDFx=simECDFx, simECDFy=simECDFy, cb=cb, title=title)
fig

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4.3.5.4. ROC predictive analysis

with warnings.catch_warnings():
    warnings.simplefilter("ignore")
    fpr, tpr, roc_auc = svGPFA.utils.miscUtils.computeSpikeClassificationROC(
        spikes_times=spikes_times_GOF,
        cif_times=trial_times_GOF,
        cif_values=cif_values_GOF)
fig = svGPFA.plot.plotUtilsPlotly.getPlotResROCAnalysis(
    fpr=fpr, tpr=tpr, auc=roc_auc, title=title)
fig

4.3.6. Plotting

4.3.6.1. Imports for plotting

import numpy as np
import pandas as pd

4.3.6.2. Set plotting variables

latent_to_plot = 0
latents_to_3D_plot = [0, 2, 4]
cluster_id_to_plot = 41
trial_to_plot = 0
choices_colors_patterns = ["rgba(0,0,255,{:f})", "rgba(255,0,0,{:f})"]
align_event_name = "response_times"
events_names = ["stimOn_times", "response_times", "stimOff_times"]
events_colors = ["magenta", "green", "black"]
events_markers = ["circle", "circle", "circle"]

cluster_id_to_plot_index = torch.nonzero(torch.IntTensor(selected_clusters_ids)==cluster_id_to_plot)


n_trials = len(spikes_times)

trials_choices = [trials["choice"][trial_id] for trial_id in selected_trials_ids]
trials_rewarded = [trials["feedbackType"][trial_id] for trial_id in selected_trials_ids]
trials_contrast = [trials["contrastRight"][trial_id]
                   if not np.isnan(trials["contrastRight"][trial_id])
                   else trials["contrastLeft"][trial_id]
                   for trial_id in selected_trials_ids]
trials_colors_patterns = [choices_colors_patterns[0]
                          if trials_choices[r] == -1
                          else choices_colors_patterns[1]
                          for r in range(n_trials)]
trials_colors = [trial_color_pattern.format(1.0)
                 for trial_color_pattern in trials_colors_patterns]
trials_annotations = {"choice": trials_choices,
                      "rewarded": trials_rewarded,
                      "contrast": trials_contrast,
                      "choice_prev": np.insert(trials_choices[:-1], 0, np.NAN),
                      "rewarded_prev": np.insert(trials_rewarded[:-1], 0,
                                                 np.NAN)}

events_times = []
for event_name in events_names:
    events_times.append([trials[event_name][trial_id]
                         for trial_id in selected_trials_ids])

marked_events_times, marked_events_colors, marked_events_markers = \
    iblUtils.buildMarkedEventsInfo(events_times=events_times,
                                   events_colors=events_colors,
                                   events_markers=events_markers)

align_event_times = [trials[align_event_name][trial_id]
                     for trial_id in selected_trials_ids]

4.3.6.3. Plot lower bound history

fig = svGPFA.plot.plotUtilsPlotly.getPlotLowerBoundHist(
    elapsedTimeHist=elapsedTimeHist, lowerBoundHist=lowerBoundHist)
fig

4.3.6.4. Plot estimated latent across trials

testMuK, testVarK = model.predictLatents(times=trials_times)
fig = svGPFA.plot.plotUtilsPlotly.getPlotLatentAcrossTrials(
    times=trials_times.numpy(),
    latentsMeans=testMuK,
    latentsSTDs=torch.sqrt(testVarK),
    trials_ids=selected_trials_ids,
    latentToPlot=latent_to_plot,
    trials_colors_patterns=trials_colors_patterns,
    xlabel="Time (msec)")
fig

4.3.6.5. Plot orthonormalized estimated latent across trials

testMuK, _ = model.predictLatents(times=trials_times)
test_mu_k_np = [testMuK[r].detach().numpy() for r in range(len(testMuK))]
estimatedC, estimatedD = model.getSVEmbeddingParams()
estimatedC_np = estimatedC.detach().numpy()
fig = svGPFA.plot.plotUtilsPlotly.getPlotOrthonormalizedLatentAcrossTrials(
    trials_times=trials_times, latentsMeans=test_mu_k_np, latentToPlot=latent_to_plot,
    align_event_times=align_event_times,
    marked_events_times=marked_events_times,
    marked_events_colors=marked_events_colors,
    marked_events_markers=marked_events_markers,
    trials_colors=trials_colors,
    trials_annotations=trials_annotations,
    C=estimatedC_np, trials_ids=selected_trials_ids,
    xlabel="Time (msec)")
fig

4.3.6.6. Plot 3D scatter plot of orthonormalized latents

fig = svGPFA.plot.plotUtilsPlotly.get3DPlotOrthonormalizedLatentsAcrossTrials(
    trials_times=trials_times.numpy(), latentsMeans=test_mu_k_np,
    C=estimatedC_np, trials_ids=selected_trials_ids,
    latentsToPlot=latents_to_3D_plot,
    align_event_times=align_event_times,
    marked_events_times=marked_events_times,
    marked_events_colors=marked_events_colors,
    marked_events_markers=marked_events_markers,
    trials_colors=trials_colors,
    trials_annotations=trials_annotations)
fig

4.3.6.7. Plot embedding

embeddingMeans, embeddingVars = model.predictEmbedding(times=trials_times)
embeddingMeans = embeddingMeans.detach().numpy()
embeddingVars = embeddingVars.detach().numpy()
title = "Neuron {:d}".format(cluster_id_to_plot)
fig = svGPFA.plot.plotUtilsPlotly.getPlotEmbeddingAcrossTrials(
    times=trials_times.numpy(),
    embeddingsMeans=embeddingMeans[:, :, cluster_id_to_plot_index],
    embeddingsSTDs=np.sqrt(embeddingVars[:, :, cluster_id_to_plot_index]),
    trials_colors_patterns=trials_colors_patterns,
    title=title)
fig

4.3.6.8. Plot intensity functions for one neuron and all trials

title = f"Cluster ID: {clusters_ids[cluster_id_to_plot_index]}, Region: {locs_for_clusters_ids[cluster_id_to_plot]}"
fig = svGPFA.plot.plotUtilsPlotly.getPlotCIFsOneNeuronAllTrials(
    trials_times=trials_times,
    cif_values=cif_values,
    neuron_index=cluster_id_to_plot_index,
    spikes_times=spikes_times,
    trials_ids=selected_trials_ids,
    align_event_times=align_event_times,
    marked_events_times=marked_events_times,
    marked_events_colors=marked_events_colors,
    marked_events_markers=marked_events_markers,
    trials_annotations=trials_annotations,
    trials_colors=trials_colors,
    title=title)
fig

4.3.6.9. Plot orthonormalized embedding parameters

hovertemplate = "value: %{y}<br>" + \
                "neuron index: %{x}<br>" + \
                "%{text}"
text = [f"cluster_id: {cluster_id}" for cluster_id in selected_clusters_ids]
estimatedC, estimatedD = model.getSVEmbeddingParams()
fig = svGPFA.plot.plotUtilsPlotly.getPlotOrthonormalizedEmbeddingParams(
    C=estimatedC.numpy(), d=estimatedD.numpy(),
    hovertemplate=hovertemplate, text=text)
fig

4.3.6.10. Plot kernel parameters

kernelsParams = model.getKernelsParams()
kernelsTypes = [type(kernel).__name__ for kernel in model.getKernels()]
fig = svGPFA.plot.plotUtilsPlotly.getPlotKernelsParams(
    kernelsTypes=kernelsTypes, kernelsParams=kernelsParams)
fig

To run the Python script or Jupyter notebook below, please download them to the examples/sphinx_gallery folder of the repository and execute them from there.

# sphinx_gallery_thumbnail_path = '_static/ibl_logo.png'