janfreyberg

sample_groups <- function(.data, ..., n, prop) {
  slice_size <- dplyr:::check_slice_size(n, prop)
  .data %>% 
    dplyr::group_by(...) %>% 
    tidyr::nest() %>% 
    dplyr::ungroup() %>% 
    {
      if (slice_size$type == "n") {
        dplyr::slice_sample(., n=slice_size$n)
      } else {

janfreyberg

janfreyberg

# Use postgres/example user/password credentials
version: '3.1'

services:

  db:
    image: postgres
    restart: always
    environment:
      POSTGRES_USER: superintendent

janfreyberg

from .functional import revgrad
from torch.nn import Module


class RevGrad(Module):
    def __init__(self, *args, **kwargs):
        """
        A gradient reversal layer.
        This layer has no parameters, and simply reverses the gradient
        in the backward pass.

janfreyberg

janfreyberg

janfreyberg

body {
	  width: 100%;
	  height: 100%;
	  margin: 0;
	  padding: 0;
	  background-color: #FAFAFA;
	  font-size: 1.1rem;
	  line-height: 1.3;
 }
 * {

janfreyberg

janfreyberg

from psychopy import data
import random

stairs = data.MultiStairHandler(conditions=[{'label': str(size),
                                             'size': size,
                                             'startVal': 10,
                                             'stepType': 'lin',
                                             'nTrials': 20,
                                             'nUp': 1,
                                             'nDown': 1,

janfreyberg

import numpy as np
import re  # regular expressions
import csv
from matplotlib import pyplot as pl
from scipy.stats.stats import pearsonr


def parse_dms(dms):
    parts = re.split('[^\d\w]+', dms)
    lat = dms2dd(parts[0], parts[1], parts[2])