kingjr

from Levenshtein import editops


def match_list(A, B, on_replace="delete"):
    """Match two lists of different sizes and return corresponding indice
    Parameters
    ----------
    A: list | array, shape (n,)
        The values of the first list
    B: list | array: shape (m, )

kingjr

import numpy as np
from numpy.linalg import multi_dot
from scipy.linalg import svd
from sklearn.linear_model import RidgeCV
from sklearn.model_selection import KFold


def get_from_indices(X, indices):
    out = np.zeros_like(X[0])
    for idx in np.unique(indices):

kingjr

import os
import urllib.request
import subprocess
import pandas as pd # pip install pandas
import betterbib # pip install betterbib
import bibtexparser  # pip install bibtexparser
from bibtexparser.bparser import BibTexParser


def fix_duplicated_entries(lines):

kingjr

class Time2v2():
    def __init__(self, metric='cosine', scale=True, to=None):
        self.metric = metric
        self.to = to
        self.scale = scale

    def __call__(self, y_true, y_pred):
        from torch.nn import CosineSimilarity
        from numpy.random import permutation

kingjr

import numpy as np
from sklearn.metrics import pairwise_distances
from numpy.random import permutation
from time import time


def cc_2v2(true, pred, metric="cosine"):
    assert len(true) == len(pred)
    ns = len(true)
    first = permutation(ns)  # first group of TR

kingjr

from pathlib import Path
import numpy as np
import mne
import nibabel as nib
import matplotlib.pyplot as plt

def split_parc(xyz, label, n, axis='y'):

    axes = dict(x=0, y=1, z=2)

kingjr

kingjr

import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns


fig, axes = plt.subplots(6, 1, sharex=True, sharey=True, figsize=[4, 6])
np.random.seed(1)

hypotheses = (
    ('No Similarity', np.random.randn(5)/1e1),

kingjr

import numpy as np
from numba import jit

@jit
def fast_auc(ytrue_sorted):

    nfalse = np.zeros(ytrue_sorted.shape[1:])
    auc = np.zeros(ytrue_sorted.shape[1:])
    n = len(ytrue_sorted)
    

kingjr

from matplotlib.colors import ListedColormap
import colorcet
import numpy as np
ice_fire = np.vstack([
    colorcet.cm.fire(np.linspace(1, 0, 127))[:, [2, 1, 0, 3]],
    colorcet.cm.fire(np.linspace(0, 1, 127))
])
ice_fire = ListedColormap(ice_fire)

fire = colorcet.cm.fire(np.linspace(0, 1, 127))