Table S6#

import scipy.stats
import numpy as np
from statsmodels.stats.multicomp import pairwise_tukeyhsd
from statsmodels.sandbox.stats.multicomp import get_tukey_pvalue
import joblib
import pandas as pd

decoding_results = pd.read_hdf("../data/Figure5Revision.h5", data="key")

def get_data(decoding_results, task, decoders, methods, window, modality, num_neurons):
    if modality == "ca":
        index = 0
    elif modality == "np":
        index = 1
    accs = []
    keys = []
    for decoder, method in zip(decoders, methods):
        key = f"{modality}_{method}_{window}"
        if "joint" in method:
            seeds = decoding_results[task][decoder][key][num_neurons]
            acc = [abs(s[index]).mean() for s in seeds]
        else:
            acc = abs(np.array(decoding_results[task][decoder][key][num_neurons])).mean(
                axis=-1
            )
        accs.append(acc)
        keys.append([f"{key}_{decoder}"] * len(acc))
    return np.concatenate(accs), np.concatenate(keys)


def concat_neurons(decoding_results, task, decoder, method, window, modality, n=1000):
    if modality == "ca":
        index = 0
    elif modality == "np":
        index = 1
    key = f"{modality}_{method}_{window}"
    accs = []
    if "joint" in method:
        seeds = decoding_results[task][decoder][key][n]
        accs.append([abs(s[index]).mean() for s in seeds])
    else:
        accs.append(
            abs(np.array(decoding_results[task][decoder][key][n])).mean(axis=-1)
        )
    return np.concatenate(accs)