<commit_before>"""
    gui/average
    ~~~~~~~~~~~~~~~~~~~~

    Graphical user interface for averaging particles

    :author: <NAME>, 2015
    :copyright: Copyright (c) 2016 Jungmann Lab, Max Planck Institute of Biochemistry
"""
import sys
import os.path
from PyQt4 import QtCore, QtGui
import traceback
import numpy as np
import time
import matplotlib.pyplot as plt
import multiprocessing
from multiprocessing import sharedctypes
import functools
import numba
import scipy
from .. import io, lib, render


@numba.jit(nopython=True, nogil=True)
def render_hist(x, y, oversampling, t_min, t_max):
    n_pixel = int(np.ceil(oversampling * (t_max - t_min)))
    in_view = (x > t_min) & (y > t_min) & (x < t_max) & (y < t_max)
    x = x[in_view]
    y = y[in_view]
    x = oversampling * (x - t_min)
    y = oversampling * (y - t_min)
    image = np.zeros((n_pixel, n_pixel), dtype=np.float32)
    render._fill(image, x, y)
    return len(x), image


def compute_xcorr(CF_image_avg, image):
    F_image = np.fft.fft2(image)
    xcorr = np.fft.fftshift(np.real(np.fft.ifft2((F_image * CF_image_avg))))
    return xcorr


def align_group(angles, oversampling, t_min, t_max, CF_image_avg, image_half, counter, lock, group):
    with lock:<commit_msg>beautify<commit_after>"""
    gui/average
    ~~~~~~~~~~~~~~~~~~~~

    Graphical user interface for averaging particles

    :author: <NAME>, 2015
    :copyright: Copyright (c) 2016 Jungmann Lab, Max Planck Institute of Biochemistry
"""
import functools
import multiprocessing
import os.path
import sys
import time
import traceback
from multiprocessing import sharedctypes

import matplotlib.pyplot as plt
import numba
import numpy as np
import scipy
from PyQt4 import QtCore, QtGui

from .. import io, lib, render


@numba.jit(nopython=True, nogil=True)
def render_hist(x, y, oversampling, t_min, t_max):
    n_pixel = int(np.ceil(oversampling * (t_max - t_min)))
    in_view = (x > t_min) & (y > t_min) & (x < t_max) & (y < t_max)
    x = x[in_view]
    y = y[in_view]
    x = oversampling * (x - t_min)
    y = oversampling * (y - t_min)
    image = np.zeros((n_pixel, n_pixel), dtype=np.float32)
    render._fill(image, x, y)
    return len(x), image


def compute_xcorr(CF_image_avg, image):
    F_image = np.fft.fft2(image)
    xcorr = np.fft.fftshift(np.real(np.fft.ifft2((F_image * CF_image_avg))))
    return xcorr


def align_group(angles, oversampling, t_min, t_max, CF_image_avg, image_half, counter, lock, group):
    with lock:
{'fraction_non_alphanumeric': 0.12625653397667874, 'fraction_numerical': 0.010454362685967028, 'mean_word_length': 3.9463220675944335, 'pattern_counts': {'":': 0, '<': 9, '<?xml version=': 0, '>': 9, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'dir': 'git-commits-cleaned', 'id': '6777658', 'n_tokens_mistral': 961, 'n_tokens_neox': 908, 'n_words': 249}