4.2. Basal ganglia recordings from a mouse performing a bandit task

In this notebook we use data recorded from the basal ganglia of a mouse performing a bandit task from the to estimate an svGPFA model

4.2.1. Estimate model

4.2.1.1. Import required packages

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

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

4.2.1.2. Get spikes times

block_types_indices = [0]
region_spikes_times_filename_pattern = "../data/00000000_regionGPe_blockTypeIndices0_spikes_times_epochedaligned__last_center_out.{:s}"
min_nSpikes_perNeuron_perTrial = 1

region_spikes_times_filename = \
    region_spikes_times_filename_pattern.format("pickle")
with open(region_spikes_times_filename, "rb") as f:
    loadRes = pickle.load(f)
spikes_times = loadRes["spikes_times"]
trials_start_times = loadRes["trials_start_times"]
trials_end_times = loadRes["trials_end_times"]


events_times_filename = ("../data/s008_tab_m1113182_LR__20210516_173815__"
                         "probabilistic_switching.df.csv")
events_times = pd.read_csv(events_times_filename)
trials_indices = [r for r in range(len(events_times))
                  if events_times.iloc[r]["block_type_index"]
                  in block_types_indices]
spikes_times, neurons_indices = gcnu_common.utils.neural_data_analysis.\
    removeUnitsWithLessSpikesThanThrInAnyTrial(
        spikes_times=spikes_times,
        min_nSpikes_perNeuron_perTrial=min_nSpikes_perNeuron_perTrial)
spikes_times = [[torch.tensor(spikes_times[r][n])
                 for n in range(len(spikes_times[r]))]
                for r in range(len(spikes_times))]
n_trials = len(spikes_times)
n_neurons = len(spikes_times[0])

4.2.1.3. Check that spikes have been epoched correctly

4.2.1.3.1. 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

4.2.1.3.2. 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

4.2.1.4. Set estimation hyperparameters

n_latents = 10
em_max_iter_dyn = 200
est_init_number = 40
est_init_config_filename_pattern = "../init/{:08d}_estimation_metaData.ini"
model_save_filename = "../results/basal_ganglia_model.pickle"

4.2.1.5. Get parameters

Details on how to specify svGPFA parameters are provided here

4.2.1.5.1. Dynamic parameters specification

dynamic_params_spec = {"optim_params": {"em_max_iter": em_max_iter_dyn}}

4.2.1.5.2. Config file parameters specification

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)

4.2.1.5.3. Finally, 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 config_file_params_spec[ind_points_locs_params0][common_n_ind_points]=9
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=0.01
Extracted from config_file ind_points_locs0_layout=equidistant
Extracted from config_file variational_means0_filename=../init/gaussian_loc0.00_scale1.00_len09.csv
Extracted from config_file variational_covs0_filename=../init/identity_scaled1e-2_09x09.csv
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

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

4.2.1.6.1. 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)

4.2.1.6.2. 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)

4.2.1.6.3. 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"])

4.2.1.7. 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: -4068010.681241, niter: 16, nfeval: 25
Iteration 01, mstep_embedding start: -4068010.681241
Iteration 01, mstep_embedding end: 12058.406138, niter: 19, nfeval: 25
Iteration 01, mstep_kernels start: 12058.406138
Iteration 01, mstep_kernels end: -9646.135825, niter: 20, nfeval: 24
Iteration 01, mstep_indpointslocs start: -9646.135825
Iteration 01, mstep_indpointslocs end: -2889.159833, niter: 14, nfeval: 25
Iteration 02, estep start: -2889.159833
Iteration 02, estep end: 1760.241225, niter: 17, nfeval: 25
Iteration 02, mstep_embedding start: 1760.241225
Iteration 02, mstep_embedding end: 22755.731583, niter: 20, nfeval: 21
Iteration 02, mstep_kernels start: 22755.731583
Iteration 02, mstep_kernels end: 7671.842323, niter: 12, nfeval: 14
Iteration 02, mstep_indpointslocs start: 7671.842323
Iteration 02, mstep_indpointslocs end: 9398.193050, niter: 20, nfeval: 21
Iteration 03, estep start: 9398.193050
Iteration 03, estep end: 14459.139990, niter: 20, nfeval: 24
Iteration 03, mstep_embedding start: 14459.139990
Iteration 03, mstep_embedding end: 23934.137353, niter: 20, nfeval: 23
Iteration 03, mstep_kernels start: 23934.137353
Iteration 03, mstep_kernels end: 15130.498009, niter: 11, nfeval: 14
Iteration 03, mstep_indpointslocs start: 15130.498009
Iteration 03, mstep_indpointslocs end: 15745.681467, niter: 20, nfeval: 21
Iteration 04, estep start: 15745.681467
Iteration 04, estep end: 17551.361954, niter: 19, nfeval: 26
Iteration 04, mstep_embedding start: 17551.361954
Iteration 04, mstep_embedding end: 24399.659400, niter: 20, nfeval: 22
Iteration 04, mstep_kernels start: 24399.659400
Iteration 04, mstep_kernels end: 17897.595453, niter: 11, nfeval: 14
Iteration 04, mstep_indpointslocs start: 17897.595453
Iteration 04, mstep_indpointslocs end: 18188.909101, niter: 20, nfeval: 22
Iteration 05, estep start: 18188.909101
Iteration 05, estep end: 19685.073930, niter: 20, nfeval: 22
Iteration 05, mstep_embedding start: 19685.073930
Iteration 05, mstep_embedding end: 24752.255674, niter: 20, nfeval: 21
Iteration 05, mstep_kernels start: 24752.255674
Iteration 05, mstep_kernels end: 19938.069466, niter: 11, nfeval: 14
Iteration 05, mstep_indpointslocs start: 19938.069466
Iteration 05, mstep_indpointslocs end: 20181.872224, niter: 20, nfeval: 22
Iteration 06, estep start: 20181.872224
Iteration 06, estep end: 20892.648217, niter: 18, nfeval: 25
Iteration 06, mstep_embedding start: 20892.648217
Iteration 06, mstep_embedding end: 24912.817708, niter: 20, nfeval: 22
Iteration 06, mstep_kernels start: 24912.817708
Iteration 06, mstep_kernels end: 21048.201014, niter: 11, nfeval: 13
Iteration 06, mstep_indpointslocs start: 21048.201014
Iteration 06, mstep_indpointslocs end: 21155.583330, niter: 20, nfeval: 21
Iteration 07, estep start: 21155.583330
Iteration 07, estep end: 21788.956768, niter: 20, nfeval: 25
Iteration 07, mstep_embedding start: 21788.956768
Iteration 07, mstep_embedding end: 25018.371693, niter: 20, nfeval: 21
Iteration 07, mstep_kernels start: 25018.371693
Iteration 07, mstep_kernels end: 21908.266190, niter: 10, nfeval: 13
Iteration 07, mstep_indpointslocs start: 21908.266190
Iteration 07, mstep_indpointslocs end: 21984.192870, niter: 20, nfeval: 21
Iteration 08, estep start: 21984.192870
Iteration 08, estep end: 22591.286389, niter: 20, nfeval: 24
Iteration 08, mstep_embedding start: 22591.286389
Iteration 08, mstep_embedding end: 25111.409459, niter: 20, nfeval: 22
Iteration 08, mstep_kernels start: 25111.409459
Iteration 08, mstep_kernels end: 22671.630269, niter: 10, nfeval: 12
Iteration 08, mstep_indpointslocs start: 22671.630269
Iteration 08, mstep_indpointslocs end: 22713.988601, niter: 20, nfeval: 22
Iteration 09, estep start: 22713.988601
Iteration 09, estep end: 23066.876904, niter: 20, nfeval: 24
Iteration 09, mstep_embedding start: 23066.876904
Iteration 09, mstep_embedding end: 25168.014591, niter: 20, nfeval: 23
Iteration 09, mstep_kernels start: 25168.014591
Iteration 09, mstep_kernels end: 23111.747539, niter: 9, nfeval: 12
Iteration 09, mstep_indpointslocs start: 23111.747539
Iteration 09, mstep_indpointslocs end: 23137.274219, niter: 20, nfeval: 23
Iteration 10, estep start: 23137.274219
Iteration 10, estep end: 23301.001610, niter: 15, nfeval: 25
Iteration 10, mstep_embedding start: 23301.001610
Iteration 10, mstep_embedding end: 25195.338890, niter: 20, nfeval: 22
Iteration 10, mstep_kernels start: 25195.338890
Iteration 10, mstep_kernels end: 23319.813942, niter: 9, nfeval: 12
Iteration 10, mstep_indpointslocs start: 23319.813942
Iteration 10, mstep_indpointslocs end: 23336.125340, niter: 20, nfeval: 23
Iteration 11, estep start: 23336.125340
Iteration 11, estep end: 23531.379108, niter: 20, nfeval: 23
Iteration 11, mstep_embedding start: 23531.379108
Iteration 11, mstep_embedding end: 25226.237187, niter: 20, nfeval: 23
Iteration 11, mstep_kernels start: 25226.237187
Iteration 11, mstep_kernels end: 23558.334667, niter: 6, nfeval: 7
Iteration 11, mstep_indpointslocs start: 23558.334667
Iteration 11, mstep_indpointslocs end: 23577.268494, niter: 20, nfeval: 22
Iteration 12, estep start: 23577.268494
Iteration 12, estep end: 23757.006699, niter: 20, nfeval: 23
Iteration 12, mstep_embedding start: 23757.006699
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Iteration 12, mstep_indpointslocs start: 23777.763108
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Iteration 13, estep start: 23793.886179
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Iteration 13, mstep_embedding start: 23916.546091
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Iteration 13, mstep_indpointslocs end: 23942.857144, niter: 20, nfeval: 22
Iteration 14, estep start: 23942.857144
Iteration 14, estep end: 24022.907308, niter: 20, nfeval: 22
Iteration 14, mstep_embedding start: 24022.907308
Iteration 14, mstep_embedding end: 25293.009696, niter: 20, nfeval: 23
Iteration 14, mstep_kernels start: 25293.009696
Iteration 14, mstep_kernels end: 24034.939284, niter: 5, nfeval: 7
Iteration 14, mstep_indpointslocs start: 24034.939284
Iteration 14, mstep_indpointslocs end: 24040.696598, niter: 20, nfeval: 22
Iteration 15, estep start: 24040.696598
Iteration 15, estep end: 24106.596714, niter: 20, nfeval: 21
Iteration 15, mstep_embedding start: 24106.596714
Iteration 15, mstep_embedding end: 25317.161909, niter: 20, nfeval: 23
Iteration 15, mstep_kernels start: 25317.161909
Iteration 15, mstep_kernels end: 24121.305149, niter: 6, nfeval: 7
Iteration 15, mstep_indpointslocs start: 24121.305149
Iteration 15, mstep_indpointslocs end: 24127.246793, niter: 20, nfeval: 22
Iteration 16, estep start: 24127.246793
Iteration 16, estep end: 24166.695412, niter: 20, nfeval: 21
Iteration 16, mstep_embedding start: 24166.695412
Iteration 16, mstep_embedding end: 25333.859935, niter: 20, nfeval: 22
Iteration 16, mstep_kernels start: 25333.859935
Iteration 16, mstep_kernels end: 24174.946645, niter: 6, nfeval: 7
Iteration 16, mstep_indpointslocs start: 24174.946645
Iteration 16, mstep_indpointslocs end: 24178.430724, niter: 20, nfeval: 23
Iteration 17, estep start: 24178.430724
Iteration 17, estep end: 24209.885387, niter: 20, nfeval: 22
Iteration 17, mstep_embedding start: 24209.885387
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Iteration 17, mstep_kernels start: 25353.764523
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Iteration 18, estep start: 24223.602402
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Iteration 18, mstep_kernels start: 25366.306316
Iteration 18, mstep_kernels end: 24257.075659, niter: 5, nfeval: 8
Iteration 18, mstep_indpointslocs start: 24257.075659
Iteration 18, mstep_indpointslocs end: 24260.080634, niter: 20, nfeval: 23
Iteration 19, estep start: 24260.080634
Iteration 19, estep end: 24278.084762, niter: 20, nfeval: 22
Iteration 19, mstep_embedding start: 24278.084762
Iteration 19, mstep_embedding end: 25381.263431, niter: 20, nfeval: 22
Iteration 19, mstep_kernels start: 25381.263431
Iteration 19, mstep_kernels end: 24284.205789, niter: 7, nfeval: 8
Iteration 19, mstep_indpointslocs start: 24284.205789
Iteration 19, mstep_indpointslocs end: 24286.568777, niter: 20, nfeval: 23
Iteration 20, estep start: 24286.568777
Iteration 20, estep end: 24302.074390, niter: 20, nfeval: 21
Iteration 20, mstep_embedding start: 24302.074390
Iteration 20, mstep_embedding end: 25394.566111, niter: 20, nfeval: 23
Iteration 20, mstep_kernels start: 25394.566111
Iteration 20, mstep_kernels end: 24307.716336, niter: 6, nfeval: 7
Iteration 20, mstep_indpointslocs start: 24307.716336
Iteration 20, mstep_indpointslocs end: 24309.833681, niter: 20, nfeval: 23
Iteration 21, estep start: 24309.833681
Iteration 21, estep end: 24322.349464, niter: 20, nfeval: 21
Iteration 21, mstep_embedding start: 24322.349464
Iteration 21, mstep_embedding end: 25406.822828, niter: 20, nfeval: 23
Iteration 21, mstep_kernels start: 25406.822828
Iteration 21, mstep_kernels end: 24326.253823, niter: 7, nfeval: 8
Iteration 21, mstep_indpointslocs start: 24326.253823
Iteration 21, mstep_indpointslocs end: 24327.933011, niter: 20, nfeval: 23
Iteration 22, estep start: 24327.933011
Iteration 22, estep end: 24336.778517, niter: 20, nfeval: 21
Iteration 22, mstep_embedding start: 24336.778517
Iteration 22, mstep_embedding end: 25417.313977, niter: 20, nfeval: 23
Iteration 22, mstep_kernels start: 25417.313977
Iteration 22, mstep_kernels end: 24341.135576, niter: 6, nfeval: 7
Iteration 22, mstep_indpointslocs start: 24341.135576
Iteration 22, mstep_indpointslocs end: 24342.734307, niter: 20, nfeval: 22
Iteration 23, estep start: 24342.734307
Iteration 23, estep end: 24349.867697, niter: 20, nfeval: 21
Iteration 23, mstep_embedding start: 24349.867697
Iteration 23, mstep_embedding end: 25426.424705, niter: 20, nfeval: 23
Iteration 23, mstep_kernels start: 25426.424705
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Elapsed time 9585.1345 seconds
Saved results to ../results/basal_ganglia_model.pickle

4.2.2. Goodness-of-fit analysis

4.2.2.1. Set goodness-of-fit variables

ks_test_gamma = 10
trial_for_gof = 0
cluster_id_for_gof = 1
n_time_steps_IF = 100

cluster_id_for_gof_index = 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)

4.2.2.2. 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]

4.2.2.3. 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].numpy()

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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4.2.2.4. 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

4.2.3. Plotting

4.2.3.1. Imports for and plotting

import numpy as np
import pandas as pd

4.2.3.2. Set plotting parameters

latent_to_plot = 0
trial_to_plot = 0
ortho_latents_to_plot = (0, 1, 2)
events_times_filename = "../data/s008_tab_m1113182_LR__20210516_173815__probabilistic_switching.df.csv"
trial_choice_column_name = "choice"
trial_rewarded_column_name = "rewarded"
align_times_column_name = "aligned__last_center_out"
centerIn_times_column_name = "aligned__last_center_in"
centerOut_times_column_name = "aligned__last_center_out"
sideIn_times_column_name = "aligned__side_in_after_last_center_out"
marked_events_colors = ["yellow","magenta","cyan","black"]
fig_filename_prefix = "../figures/basal_ganglia_"

events_times = pd.read_csv(events_times_filename)
trials_choices = events_times.iloc[trials_indices][trial_choice_column_name].to_numpy()

trials_ids = np.array([i for i in trials_indices])
choices_colors_patterns = ["rgba(0,0,255,{:f})", "rgba(255,0,0,{:f})"]
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]
align_times = events_times.iloc[trials_indices][align_times_column_name].to_numpy()
centerIn_times = events_times.iloc[trials_indices][centerIn_times_column_name].to_numpy()
centerOut_times = events_times.iloc[trials_indices][centerOut_times_column_name].to_numpy()
sideIn_times = events_times.iloc[trials_indices][sideIn_times_column_name].to_numpy()
trialEnd_times = np.append(centerIn_times[1:], np.NAN)
marked_events_times = np.column_stack((centerIn_times, centerOut_times, sideIn_times, trialEnd_times))

trials_choices = events_times.iloc[trials_indices][trial_choice_column_name].to_numpy()
trials_rewarded = events_times.iloc[trials_indices][trial_rewarded_column_name].to_numpy()
trials_annotations = {"choice": trials_choices,
                      "rewarded": trials_rewarded,
                      "choice_prev": np.insert(trials_choices[:-1], 0,
                                               np.NAN),
                      "rewarded_prev": np.insert(trials_rewarded[:-1], 0,
                                                 np.NAN)}

4.2.3.3. Lower bound history

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

4.2.3.4. 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=trials_ids,
    latentToPlot=latent_to_plot,
    trials_colors_patterns=trials_colors_patterns,
    xlabel="Time (msec)")
fig

4.2.3.5. Orthonormalized latent across trials

testMuK, testVarK = model.predictLatents(times=trials_times)
testMuK_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=testMuK_np,
    C=estimatedC_np,
    trials_ids=trials_ids,
    latentToPlot=latent_to_plot,
    align_event_times=align_times, marked_events_times=marked_events_times,
    marked_events_colors=marked_events_colors,
    trials_annotations=trials_annotations,
    trials_colors=trials_colors,
    xlabel="Time (msec)")
fig

4.2.3.6. Joint evolution of first three orthonormalized latents

fig = svGPFA.plot.plotUtilsPlotly.get3DPlotOrthonormalizedLatentsAcrossTrials(
    trials_times=trials_times.numpy(), latentsMeans=testMuK_np,
    C=estimatedC_np, latentsToPlot=ortho_latents_to_plot,
    align_event_times=align_times, marked_events_times=marked_events_times,
    marked_events_colors=marked_events_colors,
    trials_ids=trials_ids,
    trials_annotations=trials_annotations,
    trials_colors=trials_colors)
fig

4.2.3.7. Embedding

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

4.2.3.8. Intensity function

with torch.no_grad():
    cif_values = model.computeExpectedPosteriorCIFs(times=trials_times)
fig = svGPFA.plot.plotUtilsPlotly.getPlotCIFsOneNeuronAllTrials(
    trials_times=trials_times, cif_values=cif_values,
    neuron_index=neuron_to_plot_index, spikes_times=spikes_times,
    align_event_times=centerOut_times, marked_events_times=marked_events_times,
    marked_events_colors=marked_events_colors, trials_ids=trials_ids,
    trials_annotations=trials_annotations,
    trials_colors=trials_colors,
)
fig

4.2.3.9. Embedding parameters

estimatedC, estimatedD = model.getSVEmbeddingParams()
fig = svGPFA.plot.plotUtilsPlotly.getPlotOrthonormalizedEmbeddingParams(
    C=estimatedC.numpy(), d=estimatedD.numpy())
fig

4.2.3.10. Kernels 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/basal_ganglia.png'