text:
Help Army («Допоможи армії») — найбільша в Харкові та одна з найчисленніших і найпомітніших в Україні волонтерських організацій допомоги українським військовим у зоні АТО/ООС. Займається технічною, речовою, медичною та іншою допомогою бійцям, а також підтримкою демобілізованих, їх дітей та мешканців постраждалих населених пунктів. Крім того, організація через створений нею благодійний фонд «Харків з тобою» здійснює різноманітні гуманітарні проекти.

Історія
Група почала роботу в березні 2014, під час анексії Криму. Спершу активісти доправляли військовим їжу, воду, ліки, одяг, побутові речі, запчастини тощо. Коли волонтери та Міноборони закрили базові потреби військових, група стала постачати технологічно складні речі, потреба в яких гостро стояла навіть на 5-му році війни (системи відеонагляду та керування вогнем, безпілотники, тепловізори, автомобілі та їх обслуговування тощо). Об'єднання зареєстроване як громадська організація «Хелп армі», засновниками якого стали Ганна Геращенко та Олена Рофе-Бекетова, а на перших зборах (2014) було 11 учасників. У серпні того ж року в організації було вже близько 500 волонтерів, серед яких 14 координаторів.

Станом на травень 2014 група допомагала 80-й та 92-й бригадам, 95-му десантному батальйону, 1-му батальйону Нацгвардії, частинам внутрішніх військ та іншим формуванням і зібрала близько 3 млн гривень. У жовтні 2014 вона звітувала про надання різноманітної допомоги вже на суму близько 10 млн гривень.

У серпні 2014 року двоє членів Help Army — Вікторія Мілютіна та Тетяна Бідняк — були призначені радниками на громадських засадах голови Харківської облдержадміністрації Ігоря Балути. Восени 2015 Тетяна Бідняк увійшла до складу Координаційного штабу волонтерів «Сармат» при Міністерстві оборони. 2019 року Бідняк (на той час депутат облради) була виключена з організації.

У листопаді 2014 група зареєструвала благодійний фонд «Харків з тобою» під керівництвом Андрія Шейніна. До завдань фонду входить, зокрема, гуманітарна та освітня діяльність. Він, у свою чергу, того ж місяця став співзасновником Асоціації народних волонтерів України.

У грудні 2014 Help Army та низка інших громадських організацій створили на Харківщині громадський штаб територіальної оборони області.

Діяльність
 Надання військовим різноманітних приладів та амуніції, у тому числі розроблених власними силами. Зокрема, група розробила бронещити для захисту зенітних гармат і домовилася про їх виготовлення з Харківським заводом засобів індивідуального захисту.  2015 року військовим було передано 100-й екземпляр цих щитів, а за підсумками 2014—2015 років — понад 110 екземплярів. Крім того, волонтери налагодили виробництво вдосконалених станків для кулеметів, глушників, полум'ягасників, «кішок» для розмінування та захисних екранів для важкої техніки. Станом на травень 2015 військовим було надано близько 200 «кішок», а захисними екранами було обладнано більшість танків 92-ї бригади. За словами активістів групи, станом на липень 2014 вони закупили близько 100 бронежилетів 4 класу. Повідомляють вони і про постачання військовим безпілотників, автомобілів, планшетів, систем відеонагляду та керування вогнем, біноклів, тепловізорів, приладів нічного бачення, будматеріалів та іншого. Станом на жовтень 2018 було передано 7 безпілотників.

 Розконсервування танків та бронетранспортерів (започатковане Тетяною Бідняк у березні 2014). Спершу волонтери допомагали комплектуючими для військової техніки військовій частині в Башкирівці (92-а бригада), а в травні стали возити різноманітне обладнання військовим в Ізюмі.

 Допомога прикордонникам та посилення пунктів пропуску на кордоні з Росією, зокрема викопування ровів та укріплення бліндажів. Станом на осінь 2014 року цим займалися до 40-50 волонтерів («окопний батальйон») під керівництвом В'ячеслава Целуйка та Катерини Макаренко, які збиралися кожну неділю. Восени 2014 вони допомогли зробити кругову систему окопів і три бліндажі та встановити 10 бетонних плит на пунктах контролю «Стрілеча» та «Журавлівка».

 Допомога військовим одягом, взуттям, білизною, медикаментами, продуктами, водою тощо (насамперед на початку війни), новорічні подарунки та привітання зі святами; налагодження виробництва сухих борщів разом із кількома іншими волонтерськими організаціями Харківщини.

 Медична допомога пораненим, закупівля медикаментів та обладнання для медичних закладів зони АТО та Харківської області (координатор — Вікторія Мілютіна, яка співпрацює, крім того, з групою «Сестра милосердя АТО/Харків»). За словами волонтерів, вони надали допомогу кільком десяткам шпиталів та лікарень, а також провели десятки навчань із тактичної медицини. Крім того, надається допомога дитячим будинкам. Група співпрацює з Департаментом охорони здоров'я Харківської облдержадміністрації в напрямку медичного забезпечення внутрішньо переміщених осіб.

 Створення «Волонтерської диспетчерської» — проекту з координації роботи волонтерських бригад швидкої допомоги, які, за словами активістів, вивезли кількасот поранених. Здійснено за підтримки Міжнародного фонду «Відродження».

 Регулярна організація концертів для бійців у лікарнях та на позиціях. Перші концерти відбулися в кінці липня 2014. За підсумками 2014 та 2015 років активісти звітують про понад сотню заходів.

 Різноманітні інші проєкти: організація відпочинку в Австрії для дітей військових, створення в Харкові осередку «Пласту», участь в організації культурних заходів, привернення уваги до українських політв'язнів у Росії, збір допомоги для мешканців постраждалих населених пунктів, транспортування загиблих у їх рідні міста тощо. За словами волонтерів, вони ініціювали реформування мобілізаційної системи і запобігли незаконній мобілізації понад 200 харків'ян, створили служби правової та психологічної підтримки та допомогли десяткам демобілізованих із працевлаштуванням.

 Разом із кількома іншими волонтерськими групами та громадськими організаціями і за підтримки Міжнародного фонду «Відродження» група організувала в Харкові перший Форум солідарності волонтерів, що відбувся 19 вересня 2014 року.

 Ініціювання об'єднання волонтерських груп, громадських об'єднань, інших організацій та окремих волонтерів для подолання наслідків епідемії коронавірусної інфекції в Харківській області.

Відомі учасники
 Олена Рофе-Бекетова — співзасновниця волонтерської групи, координаторка, директорка благодійного фонду «Харків з тобою»
 Ярослав Маркевич
 Яковлева Наталія Миколаївна

Відзнаки
 Деякі волонтери організації відзначені державними нагородами:
 Тетяна Бідняк — орденом княгині Ольги III ступеня (указ президента України від 23 серпня 2014);
 Андрій Таубе — орденом «За заслуги» III ступеня (указ президента України від 5 грудня 2019);
 Ярослав Маркевич, Вікторія Мілютіна та Андрій Таубе — медаллю «Захиснику Вітчизни» (указ президента України від 23 серпня 2014);
 Катерина Макаренко — медаллю «За працю і звитягу» (указ президента України від 4 грудня 2014).
 23 липня 2015 року організація Help Army була нагороджена недержавним орденом «Народний Герой України»;
 13 вересня 2017 року 17 активістів організації були нагороджені відзнакою Президента України «За гуманітарну участь в антитерористичній операції»;
 16 вересня 2019 року за співпрацю з Help Army кілька співробітників Харківського національного педагогічного університету були нагороджені медалями «За сприяння Збройним Силам України» та грамотами Міністерства оборони України;
 група, її підгрупи та окремі члени відзначені численними грамотами й подяками від органів місцевої влади, військових формувань, окремих офіцерів тощо — зокрема, від Луганської обласної державної адміністрації (23.12.2014), керівника об'єднаного угрупування Національної гвардії в секторі «М» Володимира Кондратюка (2014), голови Харківської облради Сергія Чернова (08.2014), Науково-дослідного експертно-криміналістичного центру при ГУ МВС України в Харківській області (06.2015), 92-ї бригади, радіотехнічного батальйону «Рогань», Східного регіонального управління Державної прикордонної служби, Харківського регіонального інституту державного управління, військових частин А3817 (03.11.2015) та В2731 (06.12.2015), першого заступника керівника Антитерористичного центру при СБУ (керівника Антитерористичної операції на території Донецької та Луганської областей) генерал-лейтенанта О. Д. Локоти (28.06.2017).

Примітки

Посилання
 Сайт організації Help Army
 Сайт благодійного фонду «Харків з тобою»
 Сторінка Help Army на Facebook

Волонтери АТО
Організації Харкова
Лицарі Ордена «Народний Герой України»
Організації, засновані 2014
Засновані в Україні 2014


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Lake Lily Park is located at 701 Lake Lily Dr, Maitland, FL 32751. Wienerfest is a fun event for the entire family, including your leashed dogs (all breeds)! Wiener dog races! Come enjoy the event and help support FLDR!
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text:
Press Release: Obama-Biden Transition Team Announces Broad List of Agency Review Team Leads
WASHINGTON – The Obama-Biden Transition Team today announced a broad list of Agency Review Team leads that will complete a thorough review of key departments, agencies and commissions of the United States government, as well as the White House, to provide the President-elect, Vice President-elect, and key advisors with information needed to make strategic policy, budgetary, and personnel decisions prior to the inauguration.
The list includes teams that will review the Securities and Exchange Commission, Department of Homeland Security, Department of Justice, Department of Energy, Department of Education, Department of Labor, Department of Health and Human Services, Department of Veterans Affairs, Department of Housing and Urban Development, Department of Commerce, and the Department of Agriculture.
The teams will begin their efforts today, and will ensure that senior appointees have the information necessary to complete the confirmation process, lead their departments, and begin implementing signature policy initiatives immediately after they are sworn in.
To view the entire list, click here: http://change.gov/agencyreviewteams.
Barack Obama, Press Release: Obama-Biden Transition Team Announces Broad List of Agency Review Team Leads Online by Gerhard Peters and John T. Woolley, The American Presidency Project https://www.presidency.ucsb.edu/node/207258
Transition Documents

redpajama_set_name: RedPajamaCommonCrawl


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text:
from __future__ import print_function, division, absolute_import

import sys
import warnings
import itertools
# unittest only added in 3.4 self.subTest()
if sys.version_info[0] < 3 or sys.version_info[1] < 4:
    import unittest2 as unittest
else:
    import unittest
# unittest.mock is not available in 2.7 (though unittest2 might contain it?)
try:
    import unittest.mock as mock
except ImportError:
    import mock

import numpy as np
import six.moves as sm

from imgaug import augmenters as iaa
from imgaug import parameters as iap
from imgaug import dtypes as iadt
from imgaug import random as iarandom
from imgaug.testutils import (
    reseed,
    runtest_pickleable_uint8_img,
    temporary_constants,
    is_parameter_instance
)
from imgaug.imgaug import _NUMBA_INSTALLED

# On systems without numba we are forced to use numpy-based segment
# replacement. We can thus only on numba systems test both.
_NP_REPLACE = [True, False] if _NUMBA_INSTALLED else [True]

def _create_replace_np_context(use_np_replace):
    cnames = ["imgaug.augmenters.segmentation._NUMBA_INSTALLED"]
    values = [not use_np_replace]
    return temporary_constants(cnames, values)

class TestSuperpixels(unittest.TestCase):
    def setUp(self):
        reseed()

    @classmethod
    def _array_equals_tolerant(cls, a, b, tolerance):
        # TODO isnt this just np.allclose(a, b, rtol=0, atol=tolerance) ?!
        diff = np.abs(a.astype(np.int32) - b.astype(np.int32))
        return np.all(diff <= tolerance)

    @property
    def base_img(self):
        base_img = [
            [255, 255, 255, 0, 0, 0],
            [255, 235, 255, 0, 20, 0],
            [250, 250, 250, 5, 5, 5]
        ]
        base_img = np.tile(
            np.array(base_img, dtype=np.uint8)[..., np.newaxis],
            (1, 1, 3))
        return base_img

    @property
    def base_img_superpixels(self):
        base_img_superpixels = [
            [251, 251, 251, 4, 4, 4],
            [251, 251, 251, 4, 4, 4],
            [251, 251, 251, 4, 4, 4]
        ]
        base_img_superpixels = np.tile(
            np.array(base_img_superpixels, dtype=np.uint8)[..., np.newaxis],
            (1, 1, 3))
        return base_img_superpixels

    @property
    def base_img_superpixels_left(self):
        base_img_superpixels_left = self.base_img_superpixels
        base_img_superpixels_left[:, 3:, :] = self.base_img[:, 3:, :]
        return base_img_superpixels_left

    @property
    def base_img_superpixels_right(self):
        base_img_superpixels_right = self.base_img_superpixels
        base_img_superpixels_right[:, :3, :] = self.base_img[:, :3, :]
        return base_img_superpixels_right

    def test_p_replace_0_n_segments_2(self):
        for use_np_replace in _NP_REPLACE:
            with self.subTest(use_np_replace=use_np_replace):
                with _create_replace_np_context(use_np_replace):
                    aug = iaa.Superpixels(p_replace=0, n_segments=2)
                    observed = aug.augment_image(self.base_img)
                    expected = self.base_img
                    assert np.allclose(observed, expected)

    def test_p_replace_1_n_segments_2(self):
        for use_np_replace in _NP_REPLACE:
            with self.subTest(use_np_replace=use_np_replace):
                with _create_replace_np_context(use_np_replace):
                    aug = iaa.Superpixels(p_replace=1.0, n_segments=2)
                    observed = aug.augment_image(self.base_img)
                    expected = self.base_img_superpixels
                    assert self._array_equals_tolerant(observed, expected, 2)

    def test_p_replace_1_n_segments_stochastic_parameter(self):
        for use_np_replace in _NP_REPLACE:
            with self.subTest(use_np_replace=use_np_replace):
                with _create_replace_np_context(use_np_replace):
                    aug = iaa.Superpixels(
                        p_replace=1.0, n_segments=iap.Deterministic(2)
                    )
                    observed = aug.augment_image(self.base_img)
                    expected = self.base_img_superpixels
                    assert self._array_equals_tolerant(observed, expected, 2)

    def test_p_replace_stochastic_parameter_n_segments_2(self):
        for use_np_replace in _NP_REPLACE:
            with self.subTest(use_np_replace=use_np_replace):
                with _create_replace_np_context(use_np_replace):
                    aug = iaa.Superpixels(
                        p_replace=iap.Binomial(iap.Choice([0.0, 1.0])),
                        n_segments=2
                    )
                    observed = aug.augment_image(self.base_img)
                    assert (
                        np.allclose(observed, self.base_img)
                        or self._array_equals_tolerant(
                            observed, self.base_img_superpixels, 2)
                    )

    def test_p_replace_050_n_segments_2(self):
        _eq = self._array_equals_tolerant

        for use_np_replace in _NP_REPLACE:
            with self.subTest(use_np_replace=use_np_replace):
                with _create_replace_np_context(use_np_replace):
                    aug = iaa.Superpixels(p_replace=0.5, n_segments=2)
                    seen = {"none": False, "left": False, "right": False,
                            "both": False}
                    for _ in sm.xrange(100):
                        observed = aug.augment_image(self.base_img)
                        if _eq(observed, self.base_img, 2):
                            seen["none"] = True
                        elif _eq(observed, self.base_img_superpixels_left, 2):
                            seen["left"] = True
                        elif _eq(observed, self.base_img_superpixels_right, 2):
                            seen["right"] = True
                        elif _eq(observed, self.base_img_superpixels, 2):
                            seen["both"] = True
                        else:
                            raise Exception(
                                "Generated superpixels image does not match "
                                "any expected image."
                            )
                        if np.all(seen.values()):
                            break
                    assert np.all(seen.values())

    def test_failure_on_invalid_datatype_for_p_replace(self):
        # note that assertRaisesRegex does not exist in 2.7
        got_exception = False
        try:
            _ = iaa.Superpixels(p_replace="test", n_segments=100)
        except Exception as exc:
            assert "Expected " in str(exc)
            got_exception = True
        assert got_exception

    def test_failure_on_invalid_datatype_for_n_segments(self):
        # note that assertRaisesRegex does not exist in 2.7
        got_exception = False
        try:
            _ = iaa.Superpixels(p_replace=1, n_segments="test")
        except Exception as exc:
            assert "Expected " in str(exc)
            got_exception = True
        assert got_exception

    def test_zero_sized_axes(self):
        shapes = [
            (0, 0),
            (0, 1),
            (1, 0),
            (0, 1, 0),
            (1, 0, 0),
            (0, 1, 1),
            (1, 0, 1)
        ]

        for shape, use_np_replace in itertools.product(shapes, _NP_REPLACE):
            with self.subTest(shape=shape, use_np_replace=use_np_replace):
                with _create_replace_np_context(use_np_replace):
                    image = np.full(shape, 128, dtype=np.uint8)
                    aug = iaa.Superpixels(p_replace=1.0, n_segments=10)

                    image_aug = aug(image=image)

                    assert image_aug.dtype.name == "uint8"
                    assert image_aug.shape == shape

    def test_unusual_channel_numbers(self):
        shapes = [
            (1, 1, 4),
            (1, 1, 5),
            (1, 1, 512),
            (1, 1, 513)
        ]

        for shape, use_np_replace in itertools.product(shapes, _NP_REPLACE):
            with self.subTest(shape=shape, use_np_replace=use_np_replace):
                with _create_replace_np_context(use_np_replace):
                    image = np.full(shape, 128, dtype=np.uint8)
                    aug = iaa.Superpixels(p_replace=1.0, n_segments=10)

                    image_aug = aug(image=image)

                    assert image_aug.dtype.name == "uint8"
                    assert image_aug.shape == shape

    def test_get_parameters(self):
        aug = iaa.Superpixels(
            p_replace=0.5, n_segments=2, max_size=100, interpolation="nearest")
        params = aug.get_parameters()
        assert params[0] is aug.p_replace
        assert is_parameter_instance(params[0].p, iap.Deterministic)
        assert params[1] is aug.n_segments
        assert 0.5 - 1e-4 < params[0].p.value < 0.5 + 1e-4
        assert params[1].value == 2
        assert params[2] == 100
        assert params[3] == "nearest"

    def test_other_dtypes_bool(self):
        for use_np_replace in _NP_REPLACE:
                with self.subTest(use_np_replace=use_np_replace):
                    with _create_replace_np_context(use_np_replace):
                        aug = iaa.Superpixels(p_replace=1.0, n_segments=2)
                        img = np.array([
                            [False, False, True, True],
                            [False, False, True, True]
                        ], dtype=bool)
                        img_aug = aug.augment_image(img)
                        assert img_aug.dtype == img.dtype
                        assert np.all(img_aug == img)

                        aug = iaa.Superpixels(p_replace=1.0, n_segments=1)
                        img = np.array([
                            [True, True, True, True],
                            [False, True, True, True]
                        ], dtype=bool)
                        img_aug = aug.augment_image(img)
                        assert img_aug.dtype == img.dtype
                        assert np.all(img_aug)

    def test_other_dtypes_uint_int(self):
        dtypes = ["uint8", "uint16", "uint32",
                  "int8", "int16", "int32"]
        for dtype in dtypes:
            for use_np_replace in _NP_REPLACE:
                with self.subTest(dtype=dtype, use_np_replace=use_np_replace):
                    with _create_replace_np_context(use_np_replace):
                        dtype = np.dtype(dtype)

                        min_value, center_value, max_value = \
                            iadt.get_value_range_of_dtype(dtype)

                        if np.dtype(dtype).kind == "i":
                            values = [
                                int(center_value), int(0.1 * max_value),
                                int(0.2 * max_value), int(0.5 * max_value),
                                max_value-100
                            ]
                            values = [((-1)*value, value) for value in values]
                        else:
                            values = [(0, int(center_value)),
                                      (10, int(0.1 * max_value)),
                                      (10, int(0.2 * max_value)),
                                      (10, int(0.5 * max_value)),
                                      (0, max_value),
                                      (int(center_value),
                                       max_value)]

                        for v1, v2 in values:
                            aug = iaa.Superpixels(p_replace=1.0, n_segments=2)
                            img = np.array([
                                [v1, v1, v2, v2],
                                [v1, v1, v2, v2]
                            ], dtype=dtype)
                            img_aug = aug.augment_image(img)
                            assert img_aug.dtype.name == dtype.name
                            assert np.array_equal(img_aug, img)

                            aug = iaa.Superpixels(p_replace=1.0, n_segments=1)
                            img = np.array([
                                [v2, v2, v2, v2],
                                [v1, v2, v2, v2]
                            ], dtype=dtype)
                            img_aug = aug.augment_image(img)
                            assert img_aug.dtype.name == dtype.name
                            assert np.all(img_aug == int((7/8)*v2 + (1/8)*v1))

    def test_other_dtypes_float(self):
        # currently, no float dtype is actually accepted
        for dtype in []:
            def _allclose(a, b):
                atol = 1e-4 if dtype == np.float16 else 1e-8
                return np.allclose(a, b, atol=atol, rtol=0)

            isize = np.dtype(dtype).itemsize
            for value in [0, 1.0, 10.0, 1000 ** (isize - 1)]:
                v1 = (-1) * value
                v2 = value

                aug = iaa.Superpixels(p_replace=1.0, n_segments=2)
                img = np.array([
                    [v1, v1, v2, v2],
                    [v1, v1, v2, v2]
                ], dtype=dtype)
                img_aug = aug.augment_image(img)
                assert img_aug.dtype == np.dtype(dtype)
                assert _allclose(img_aug, img)

                aug = iaa.Superpixels(p_replace=1.0, n_segments=1)
                img = np.array([
                    [v2, v2, v2, v2],
                    [v1, v2, v2, v2]
                ], dtype=dtype)
                img_aug = aug.augment_image(img)
                assert img_aug.dtype == np.dtype(dtype)
                assert _allclose(img_aug, (7/8)*v2 + (1/8)*v1)

    def test_pickleable(self):
        aug = iaa.Superpixels(p_replace=0.5, seed=1)
        runtest_pickleable_uint8_img(aug, iterations=10, shape=(25, 25, 1))

class Test_segment_voronoi(unittest.TestCase):
    def setUp(self):
        reseed()

    def test_cell_coordinates_is_empty_integrationtest(self):
        image = np.arange(2*2*3).astype(np.uint8).reshape((2, 2, 3))
        cell_coordinates = np.zeros((0, 2), dtype=np.float32)
        replace_mask = np.zeros((0,), dtype=bool)

        image_seg = iaa.segment_voronoi(image, cell_coordinates, replace_mask)

        assert np.array_equal(image, image_seg)

    @classmethod
    def _test_image_n_channels_integrationtest(cls, nb_channels):
        image = np.uint8([
            [0, 1, 200, 201],
            [2, 3, 202, 203]
        ])
        if nb_channels is not None:
            image = np.tile(image[:, :, np.newaxis], (1, 1, nb_channels))
            for c in sm.xrange(nb_channels):
                image[..., c] += c
        cell_coordinates = np.float32([
            [1.0, 1.0],
            [3.0, 1.0]
        ])
        replace_mask = np.array([True, True], dtype=bool)

        image_seg = iaa.segment_voronoi(image, cell_coordinates, replace_mask)

        pixels1 = image[0:2, 0:2]
        pixels2 = image[0:2, 2:4]
        avg_color1 = np.average(pixels1.astype(np.float32), axis=(0, 1))
        avg_color2 = np.average(pixels2.astype(np.float32), axis=(0, 1))
        image_expected = np.uint8([
            [avg_color1, avg_color1, avg_color2, avg_color2],
            [avg_color1, avg_color1, avg_color2, avg_color2],
        ])

        assert np.array_equal(image_seg, image_expected)

    def test_image_has_no_channels_integrationtest(self):
        self._test_image_n_channels_integrationtest(None)

    def test_image_has_one_channel_integrationtest(self):
        self._test_image_n_channels_integrationtest(1)

    def test_image_has_three_channels_integrationtest(self):
        self._test_image_n_channels_integrationtest(3)

    def test_replace_mask_is_all_false_integrationtest(self):
        image = np.uint8([
            [0, 1, 200, 201],
            [2, 3, 202, 203]
        ])
        cell_coordinates = np.float32([
            [1.0, 1.0],
            [3.0, 1.0]
        ])
        replace_mask = np.array([False, False], dtype=bool)

        image_seg = iaa.segment_voronoi(image, cell_coordinates, replace_mask)

        assert np.array_equal(image_seg, image)

    def test_replace_mask_is_mixed_integrationtest(self):
        image = np.uint8([
            [0, 1, 200, 201],
            [2, 3, 202, 203]
        ])
        cell_coordinates = np.float32([
            [1.0, 1.0],
            [3.0, 1.0]
        ])
        replace_mask = np.array([False, True], dtype=bool)

        image_seg = iaa.segment_voronoi(image, cell_coordinates, replace_mask)

        pixels2 = image[0:2, 2:4]
        avg_color2 = np.sum(pixels2).astype(np.float32) / pixels2.size
        image_expected = np.uint8([
            [0, 1, avg_color2, avg_color2],
            [2, 3, avg_color2, avg_color2],
        ])
        assert np.array_equal(image_seg, image_expected)

    def test_replace_mask_is_none_integrationtest(self):
        image = np.uint8([
            [0, 1, 200, 201],
            [2, 3, 202, 203]
        ])
        cell_coordinates = np.float32([
            [1.0, 1.0],
            [3.0, 1.0]
        ])
        replace_mask = None

        image_seg = iaa.segment_voronoi(image, cell_coordinates, replace_mask)

        pixels1 = image[0:2, 0:2]
        pixels2 = image[0:2, 2:4]
        avg_color1 = np.sum(pixels1).astype(np.float32) / pixels1.size
        avg_color2 = np.sum(pixels2).astype(np.float32) / pixels2.size
        image_expected = np.uint8([
            [avg_color1, avg_color1, avg_color2, avg_color2],
            [avg_color1, avg_color1, avg_color2, avg_color2],
        ])
        assert np.array_equal(image_seg, image_expected)

    def test_no_cell_coordinates_provided_and_no_channel_integrationtest(self):
        image = np.uint8([
            [0, 1, 200, 201],
            [2, 3, 202, 203]
        ])
        cell_coordinates = np.zeros((0, 2), dtype=np.float32)
        replace_mask = np.zeros((0,), dtype=bool)

        image_seg = iaa.segment_voronoi(image, cell_coordinates, replace_mask)

        assert np.array_equal(image_seg, image)

    def test_no_cell_coordinates_provided_and_3_channels_integrationtest(self):
        image = np.uint8([
            [0, 1, 200, 201],
            [2, 3, 202, 203]
        ])
        image = np.tile(image[..., np.newaxis], (1, 1, 3))
        cell_coordinates = np.zeros((0, 2), dtype=np.float32)
        replace_mask = np.zeros((0,), dtype=bool)

        image_seg = iaa.segment_voronoi(image, cell_coordinates, replace_mask)

        assert np.array_equal(image_seg, image)

    def test_zero_sized_axes(self):
        shapes = [
            (0, 0),
            (0, 1),
            (1, 0),
            (0, 1, 0),
            (1, 0, 0),
            (0, 1, 1),
            (1, 0, 1)
        ]

        cell_coordinates = np.float32([
            [1.0, 1.0],
            [3.0, 1.0]
        ])
        replace_mask = np.array([True, True], dtype=bool)

        for shape in shapes:
            with self.subTest(shape=shape):
                image = np.full(shape, 128, dtype=np.uint8)

                image_aug = iaa.segment_voronoi(image, cell_coordinates,
                                                replace_mask)

                assert image_aug.dtype.name == "uint8"
                assert image_aug.shape == shape

    def test_unusual_channel_numbers(self):
        shapes = [
            (1, 1, 4),
            (1, 1, 5),
            (1, 1, 512),
            (1, 1, 513)
        ]

        cell_coordinates = np.float32([
            [1.0, 1.0],
            [3.0, 1.0]
        ])
        replace_mask = np.array([True, True], dtype=bool)

        for shape in shapes:
            with self.subTest(shape=shape):
                image = np.full(shape, 128, dtype=np.uint8)

                image_aug = iaa.segment_voronoi(image, cell_coordinates,
                                                replace_mask)

                assert image_aug.dtype.name == "uint8"
                assert image_aug.shape == shape

class TestVoronoi(unittest.TestCase):
    def setUp(self):
        reseed()

    def test___init___defaults(self):
        sampler = iaa.RegularGridPointsSampler(1, 1)
        aug = iaa.Voronoi(sampler)
        assert aug.points_sampler is sampler
        assert is_parameter_instance(aug.p_replace, iap.Deterministic)
        assert aug.p_replace.value == 1
        assert aug.max_size == 128
        assert aug.interpolation == "linear"

    def test___init___custom_arguments(self):
        sampler = iaa.RegularGridPointsSampler(1, 1)
        aug = iaa.Voronoi(sampler, p_replace=0.5, max_size=None,
                          interpolation="cubic")
        assert aug.points_sampler is sampler
        assert is_parameter_instance(aug.p_replace, iap.Binomial)
        assert np.isclose(aug.p_replace.p.value, 0.5)
        assert aug.max_size is None
        assert aug.interpolation == "cubic"

    def test_max_size_is_none(self):
        image = np.zeros((10, 20, 3), dtype=np.uint8)
        sampler = iaa.RegularGridPointsSampler(1, 1)
        aug = iaa.Voronoi(sampler, max_size=None)

        mock_imresize = mock.MagicMock()
        mock_imresize.return_value = image

        fname = "imgaug.imresize_single_image"
        with mock.patch(fname, mock_imresize):
            _image_aug = aug(image=image)

        assert mock_imresize.call_count == 0

    def test_max_size_is_int_image_not_too_large(self):
        image = np.zeros((10, 20, 3), dtype=np.uint8)
        sampler = iaa.RegularGridPointsSampler(1, 1)
        aug = iaa.Voronoi(sampler, max_size=100)

        mock_imresize = mock.MagicMock()
        mock_imresize.return_value = image

        fname = "imgaug.imresize_single_image"
        with mock.patch(fname, mock_imresize):
            _image_aug = aug(image=image)

        assert mock_imresize.call_count == 0

    def test_max_size_is_int_image_too_large(self):
        image = np.zeros((10, 20, 3), dtype=np.uint8)
        sampler = iaa.RegularGridPointsSampler(1, 1)
        aug = iaa.Voronoi(sampler, max_size=10)

        mock_imresize = mock.MagicMock()
        mock_imresize.return_value = image

        fname = "imgaug.imresize_single_image"
        with mock.patch(fname, mock_imresize):
            _image_aug = aug(image=image)

        assert mock_imresize.call_count == 1
        assert mock_imresize.call_args_list[0][0][1] == (5, 10)

    def test_interpolation(self):
        image = np.zeros((10, 20, 3), dtype=np.uint8)
        sampler = iaa.RegularGridPointsSampler(1, 1)
        aug = iaa.Voronoi(sampler, max_size=10, interpolation="cubic")

        mock_imresize = mock.MagicMock()
        mock_imresize.return_value = image

        fname = "imgaug.imresize_single_image"
        with mock.patch(fname, mock_imresize):
            _image_aug = aug(image=image)

        assert mock_imresize.call_count == 1
        assert mock_imresize.call_args_list[0][1]["interpolation"] == "cubic"

    def test_point_sampler_called(self):
        class LoggedPointSampler(iaa.IPointsSampler):
            def __init__(self, other):
                self.other = other
                self.call_count = 0

            def sample_points(self, images, random_state):
                self.call_count += 1
                return self.other.sample_points(images, random_state)

        image = np.zeros((10, 20, 3), dtype=np.uint8)
        sampler = LoggedPointSampler(iaa.RegularGridPointsSampler(1, 1))
        aug = iaa.Voronoi(sampler)

        _image_aug = aug(image=image)

        assert sampler.call_count == 1

    def test_point_sampler_returns_no_points_integrationtest(self):
        class NoPointsPointSampler(iaa.IPointsSampler):
            def sample_points(self, images, random_state):
                return [np.zeros((0, 2), dtype=np.float32)]

        image = np.zeros((10, 20, 3), dtype=np.uint8)
        sampler = NoPointsPointSampler()
        aug = iaa.Voronoi(sampler)

        image_aug = aug(image=image)

        assert np.array_equal(image_aug, image)

    @classmethod
    def _test_image_with_n_channels(cls, nb_channels):
        image = np.zeros((10, 20), dtype=np.uint8)
        if nb_channels is not None:
            image = image[..., np.newaxis]
            image = np.tile(image, (1, 1, nb_channels))
        sampler = iaa.RegularGridPointsSampler(1, 1)
        aug = iaa.Voronoi(sampler)

        mock_segment_voronoi = mock.MagicMock()
        if nb_channels is None:
            mock_segment_voronoi.return_value = image[..., np.newaxis]
        else:
            mock_segment_voronoi.return_value = image

        fname = "imgaug.augmenters.segmentation.segment_voronoi"
        with mock.patch(fname, mock_segment_voronoi):
            image_aug = aug(image=image)

        assert image_aug.shape == image.shape

    def test_image_with_no_channels(self):
        self._test_image_with_n_channels(None)

    def test_image_with_one_channel(self):
        self._test_image_with_n_channels(1)

    def test_image_with_three_channels(self):
        self._test_image_with_n_channels(3)

    def test_p_replace_is_zero(self):
        image = np.zeros((50, 50), dtype=np.uint8)
        sampler = iaa.RegularGridPointsSampler(50, 50)
        aug = iaa.Voronoi(sampler, p_replace=0.0)

        mock_segment_voronoi = mock.MagicMock()
        mock_segment_voronoi.return_value = image[..., np.newaxis]

        fname = "imgaug.augmenters.segmentation.segment_voronoi"
        with mock.patch(fname, mock_segment_voronoi):
            _image_aug = aug(image=image)

        replace_mask = mock_segment_voronoi.call_args_list[0][0][2]
        assert not np.any(replace_mask)

    def test_p_replace_is_one(self):
        image = np.zeros((50, 50), dtype=np.uint8)
        sampler = iaa.RegularGridPointsSampler(50, 50)
        aug = iaa.Voronoi(sampler, p_replace=1.0)

        mock_segment_voronoi = mock.MagicMock()
        mock_segment_voronoi.return_value = image[..., np.newaxis]

        fname = "imgaug.augmenters.segmentation.segment_voronoi"
        with mock.patch(fname, mock_segment_voronoi):
            _image_aug = aug(image=image)

        replace_mask = mock_segment_voronoi.call_args_list[0][0][2]
        assert np.all(replace_mask)

    def test_p_replace_is_50_percent(self):
        image = np.zeros((200, 200), dtype=np.uint8)
        sampler = iaa.RegularGridPointsSampler(200, 200)
        aug = iaa.Voronoi(sampler, p_replace=0.5)

        mock_segment_voronoi = mock.MagicMock()
        mock_segment_voronoi.return_value = image[..., np.newaxis]

        fname = "imgaug.augmenters.segmentation.segment_voronoi"
        with mock.patch(fname, mock_segment_voronoi):
            _image_aug = aug(image=image)

        replace_mask = mock_segment_voronoi.call_args_list[0][0][2]
        replace_fraction = np.average(replace_mask.astype(np.float32))
        assert 0.4 <= replace_fraction <= 0.6

    def test_determinism_integrationtest(self):
        image = np.arange(10*20).astype(np.uint8).reshape((10, 20, 1))
        image = np.tile(image, (1, 1, 3))
        image[:, :, 1] += 5
        image[:, :, 2] += 10
        sampler = iaa.DropoutPointsSampler(
            iaa.RegularGridPointsSampler((1, 10), (1, 20)),
            0.5
        )
        aug = iaa.Voronoi(sampler, p_replace=(0.0, 1.0))
        aug_det = aug.to_deterministic()

        images_aug_a1 = aug(images=[image] * 50)
        images_aug_a2 = aug(images=[image] * 50)

        images_aug_b1 = aug_det(images=[image] * 50)
        images_aug_b2 = aug_det(images=[image] * 50)

        same_within_a1 = _all_arrays_identical(images_aug_a1)
        same_within_a2 = _all_arrays_identical(images_aug_a2)

        same_within_b1 = _all_arrays_identical(images_aug_b1)
        same_within_b2 = _all_arrays_identical(images_aug_b2)

        same_between_a1_a2 = _array_lists_elementwise_identical(images_aug_a1,
                                                                images_aug_a2)
        same_between_b1_b2 = _array_lists_elementwise_identical(images_aug_b1,
                                                                images_aug_b2)

        assert not same_within_a1
        assert not same_within_a2
        assert not same_within_b1
        assert not same_within_b2

        assert not same_between_a1_a2
        assert same_between_b1_b2

    def test_zero_sized_axes(self):
        shapes = [
            (0, 0),
            (0, 1),
            (1, 0),
            (0, 1, 0),
            (1, 0, 0),
            (0, 1, 1),
            (1, 0, 1)
        ]

        sampler = iaa.RegularGridPointsSampler(50, 50)

        for shape in shapes:
            with self.subTest(shape=shape):
                image = np.full(shape, 128, dtype=np.uint8)
                aug = iaa.Voronoi(sampler, p_replace=1)

                image_aug = aug(image=image)

                assert image_aug.dtype.name == "uint8"
                assert image_aug.shape == shape

    def test_unusual_channel_numbers(self):
        shapes = [
            (1, 1, 4),
            (1, 1, 5),
            (1, 1, 512),
            (1, 1, 513)
        ]

        sampler = iaa.RegularGridPointsSampler(50, 50)

        for shape in shapes:
            with self.subTest(shape=shape):
                image = np.full(shape, 128, dtype=np.uint8)
                aug = iaa.Voronoi(sampler, p_replace=1)

                image_aug = aug(image=image)

                assert image_aug.dtype.name == "uint8"
                assert image_aug.shape == shape

    def test_get_parameters(self):
        sampler = iaa.RegularGridPointsSampler(1, 1)
        aug = iaa.Voronoi(sampler, p_replace=0.5, max_size=None,
                          interpolation="cubic")
        params = aug.get_parameters()
        assert params[0] is sampler
        assert is_parameter_instance(params[1], iap.Binomial)
        assert np.isclose(params[1].p.value, 0.5)
        assert params[2] is None
        assert params[3] == "cubic"

    def test_pickleable(self):
        sampler = iaa.RegularGridPointsSampler(5, 5)
        aug = iaa.Voronoi(sampler, p_replace=0.5, seed=1)
        runtest_pickleable_uint8_img(aug, iterations=10, shape=(25, 25, 1))

def _all_arrays_identical(arrs):
    if len(arrs) == 1:
        return True

    return np.all([np.array_equal(arrs[0], arr_other)
                   for arr_other in arrs[1:]])

def _array_lists_elementwise_identical(arrs1, arrs2):
    return np.all([np.array_equal(arr1, arr2)
                   for arr1, arr2 in zip(arrs1, arrs2)])

class TestUniformVoronoi(unittest.TestCase):
    def test___init___(self):
        rs = iarandom.RNG(10)

        mock_voronoi = mock.MagicMock()
        mock_voronoi.return_value = mock_voronoi
        fname = "imgaug.augmenters.segmentation.Voronoi.__init__"
        with mock.patch(fname, mock_voronoi):
            _ = iaa.UniformVoronoi(
                100,
                p_replace=0.5,
                max_size=5,
                interpolation="cubic",
                seed=rs,
                name="foo"
            )

        assert mock_voronoi.call_count == 1
        assert isinstance(mock_voronoi.call_args_list[0][1]["points_sampler"],
                          iaa.UniformPointsSampler)
        assert np.isclose(mock_voronoi.call_args_list[0][1]["p_replace"],
                          0.5)
        assert mock_voronoi.call_args_list[0][1]["max_size"] == 5
        assert mock_voronoi.call_args_list[0][1]["interpolation"] == "cubic"
        assert mock_voronoi.call_args_list[0][1]["name"] == "foo"
        assert mock_voronoi.call_args_list[0][1]["seed"] is rs

    def test___init___integrationtest(self):
        rs = iarandom.RNG(10)
        aug = iaa.UniformVoronoi(
            100,
            p_replace=0.5,
            max_size=5,
            interpolation="cubic",
            seed=rs,
            name=None
        )
        assert aug.points_sampler.n_points.value == 100
        assert np.isclose(aug.p_replace.p.value, 0.5)
        assert aug.max_size == 5
        assert aug.interpolation == "cubic"
        assert aug.name == "UnnamedUniformVoronoi"
        assert aug.random_state.equals(rs)

    def test_pickleable(self):
        aug = iaa.UniformVoronoi((10, 50), p_replace=0.5, seed=1)
        runtest_pickleable_uint8_img(aug, iterations=3, shape=(50, 50, 1))

class TestRegularGridVoronoi(unittest.TestCase):
    def test___init___(self):
        rs = iarandom.RNG(10)

        mock_voronoi = mock.MagicMock()
        mock_voronoi.return_value = mock_voronoi
        fname = "imgaug.augmenters.segmentation.Voronoi.__init__"
        with mock.patch(fname, mock_voronoi):
            _ = iaa.RegularGridVoronoi(
                10,
                20,
                p_drop_points=0.6,
                p_replace=0.5,
                max_size=5,
                interpolation="cubic",
                seed=rs,
                name="foo"
            )

        assert mock_voronoi.call_count == 1
        ps = mock_voronoi.call_args_list[0][1]["points_sampler"]
        assert isinstance(ps, iaa.DropoutPointsSampler)
        assert isinstance(ps.other_points_sampler,
                          iaa.RegularGridPointsSampler)
        assert np.isclose(ps.p_drop.p.value, 1-0.6)
        assert ps.other_points_sampler.n_rows.value == 10
        assert ps.other_points_sampler.n_cols.value == 20
        assert np.isclose(mock_voronoi.call_args_list[0][1]["p_replace"],
                          0.5)
        assert mock_voronoi.call_args_list[0][1]["max_size"] == 5
        assert mock_voronoi.call_args_list[0][1]["interpolation"] == "cubic"
        assert mock_voronoi.call_args_list[0][1]["name"] == "foo"
        assert mock_voronoi.call_args_list[0][1]["seed"] is rs

    def test___init___integrationtest(self):
        rs = iarandom.RNG(10)
        aug = iaa.RegularGridVoronoi(
            10,
            (10, 30),
            p_replace=0.5,
            max_size=5,
            interpolation="cubic",
            seed=rs,
            name=None
        )
        assert aug.points_sampler.other_points_sampler.n_rows.value == 10
        assert is_parameter_instance(
            aug.points_sampler.other_points_sampler.n_cols,
            iap.DiscreteUniform
        )
        assert aug.points_sampler.other_points_sampler.n_cols.a.value == 10
        assert aug.points_sampler.other_points_sampler.n_cols.b.value == 30
        assert np.isclose(aug.p_replace.p.value, 0.5)
        assert aug.max_size == 5
        assert aug.interpolation == "cubic"
        assert aug.name == "UnnamedRegularGridVoronoi"
        assert aug.random_state.equals(rs)

    def test_pickleable(self):
        aug = iaa.RegularGridVoronoi((5, 10), (5, 10), p_replace=0.5,
                                     seed=1)
        runtest_pickleable_uint8_img(aug, iterations=3, shape=(50, 50, 1))

class TestRelativeRegularGridVoronoi(unittest.TestCase):
    def test___init___(self):
        rs = iarandom.RNG(10)

        mock_voronoi = mock.MagicMock()
        mock_voronoi.return_value = mock_voronoi
        fname = "imgaug.augmenters.segmentation.Voronoi.__init__"
        with mock.patch(fname, mock_voronoi):
            _ = iaa.RelativeRegularGridVoronoi(
                0.1,
                0.2,
                p_drop_points=0.6,
                p_replace=0.5,
                max_size=5,
                interpolation="cubic",
                seed=rs,
                name="foo"
            )

        assert mock_voronoi.call_count == 1
        ps = mock_voronoi.call_args_list[0][1]["points_sampler"]
        assert isinstance(ps, iaa.DropoutPointsSampler)
        assert isinstance(ps.other_points_sampler,
                          iaa.RelativeRegularGridPointsSampler)
        assert np.isclose(ps.p_drop.p.value, 1-0.6)
        assert np.isclose(ps.other_points_sampler.n_rows_frac.value, 0.1)
        assert np.isclose(ps.other_points_sampler.n_cols_frac.value, 0.2)
        assert np.isclose(mock_voronoi.call_args_list[0][1]["p_replace"],
                          0.5)
        assert mock_voronoi.call_args_list[0][1]["max_size"] == 5
        assert mock_voronoi.call_args_list[0][1]["interpolation"] == "cubic"
        assert mock_voronoi.call_args_list[0][1]["name"] == "foo"
        assert mock_voronoi.call_args_list[0][1]["seed"] is rs

    def test___init___integrationtest(self):
        rs = iarandom.RNG(10)
        aug = iaa.RelativeRegularGridVoronoi(
            0.1,
            (0.1, 0.3),
            p_replace=0.5,
            max_size=5,
            interpolation="cubic",
            seed=rs,
            name=None
        )

        ps = aug.points_sampler
        assert np.isclose(ps.other_points_sampler.n_rows_frac.value, 0.1)
        assert is_parameter_instance(ps.other_points_sampler.n_cols_frac,
                                     iap.Uniform)
        assert np.isclose(ps.other_points_sampler.n_cols_frac.a.value, 0.1)
        assert np.isclose(ps.other_points_sampler.n_cols_frac.b.value, 0.3)
        assert np.isclose(aug.p_replace.p.value, 0.5)
        assert aug.max_size == 5
        assert aug.interpolation == "cubic"
        assert aug.name == "UnnamedRelativeRegularGridVoronoi"
        assert aug.random_state.equals(rs)

    def test_pickleable(self):
        aug = iaa.RelativeRegularGridVoronoi((0.01, 0.2), (0.01, 0.2),
                                             p_replace=0.5, seed=1)
        runtest_pickleable_uint8_img(aug, iterations=3, shape=(50, 50, 1))

# TODO verify behaviours when image height/width is zero
class TestRegularGridPointsSampler(unittest.TestCase):
    def setUp(self):
        reseed()

    def test___init___(self):
        sampler = iaa.RegularGridPointsSampler((1, 10), 20)
        assert is_parameter_instance(sampler.n_rows, iap.DiscreteUniform)
        assert sampler.n_rows.a.value == 1
        assert sampler.n_rows.b.value == 10
        assert sampler.n_cols.value == 20

    def test_sample_single_point(self):
        image = np.zeros((10, 20, 3), dtype=np.uint8)
        sampler = iaa.RegularGridPointsSampler(1, 1)
        points = sampler.sample_points([image], iarandom.RNG(1))[0]
        assert len(points) == 1
        assert np.allclose(points[0], [10.0, 5.0])

    def test_sample_points(self):
        image = np.zeros((10, 10, 3), dtype=np.uint8)
        sampler = iaa.RegularGridPointsSampler(2, 2)
        points = sampler.sample_points([image], iarandom.RNG(1))[0]
        assert len(points) == 4
        assert np.allclose(points, [
            [2.5, 2.5],
            [7.5, 2.5],
            [2.5, 7.5],
            [7.5, 7.5]
        ])

    def test_sample_points_stochastic(self):
        image = np.zeros((10, 10, 3), dtype=np.uint8)
        sampler = iaa.RegularGridPointsSampler(1, iap.Choice([1, 2]))
        points = sampler.sample_points([image], iarandom.RNG(1))[0]

        matches_single_point = np.allclose(points, [
            [5.0, 5.0]
        ])
        matches_two_points = np.allclose(points, [
            [2.5, 5.0],
            [7.5, 5.0]
        ])

        assert len(points) in [1, 2]
        assert matches_single_point or matches_two_points

    def test_sample_points_cols_is_zero(self):
        image = np.zeros((10, 10, 3), dtype=np.uint8)
        sampler = iaa.RegularGridPointsSampler(iap.Deterministic(0), 1)
        points = sampler.sample_points([image], iarandom.RNG(1))[0]

        matches_single_point = np.allclose(points, [
            [5.0, 5.0]
        ])

        assert len(points) == 1
        assert matches_single_point

    def test_sample_points_rows_is_zero(self):
        image = np.zeros((10, 10, 3), dtype=np.uint8)
        sampler = iaa.RegularGridPointsSampler(1, iap.Deterministic(0))
        points = sampler.sample_points([image], iarandom.RNG(1))[0]

        matches_single_point = np.allclose(points, [
            [5.0, 5.0]
        ])

        assert len(points) == 1
        assert matches_single_point

    def test_sample_points_rows_is_more_than_image_height(self):
        image = np.zeros((1, 1, 3), dtype=np.uint8)
        sampler = iaa.RegularGridPointsSampler(2, 1)
        points = sampler.sample_points([image], iarandom.RNG(1))[0]

        matches_single_point = np.allclose(points, [
            [0.5, 0.5]
        ])

        assert len(points) == 1
        assert matches_single_point

    def test_sample_points_cols_is_more_than_image_width(self):
        image = np.zeros((1, 1, 3), dtype=np.uint8)
        sampler = iaa.RegularGridPointsSampler(1, 2)
        points = sampler.sample_points([image], iarandom.RNG(1))[0]

        matches_single_point = np.allclose(points, [
            [0.5, 0.5]
        ])

        assert len(points) == 1
        assert matches_single_point

    def test_zero_sized_axes(self):
        shapes = [
            (0, 1, 0),
            (1, 0, 0),
            (0, 1, 1),
            (1, 0, 1)
        ]

        for shape in shapes:
            with self.subTest(shape=shape):
                image = np.zeros(shape, dtype=np.uint8)
                sampler = iaa.RegularGridPointsSampler(1, 1)

                points = sampler.sample_points([image], iarandom.RNG(1))[0]

                assert len(points) == 1

    def test_unusual_channel_numbers(self):
        shapes = [
            (1, 1, 4),
            (1, 1, 5),
            (1, 1, 512),
            (1, 1, 513)
        ]

        for shape in shapes:
            with self.subTest(shape=shape):
                image = np.zeros(shape, dtype=np.uint8)
                sampler = iaa.RegularGridPointsSampler(1, 1)

                points = sampler.sample_points([image], iarandom.RNG(1))[0]

                assert len(points) == 1

    def test_determinism(self):
        image = np.zeros((500, 500, 1), dtype=np.uint8)
        sampler = iaa.RegularGridPointsSampler((1, 500), (1, 500))
        points_seed1_1 = sampler.sample_points([image], 1)[0]
        points_seed1_2 = sampler.sample_points([image], 1)[0]
        points_seed2_1 = sampler.sample_points([image], 2)[0]

        assert points_seed1_1.shape == points_seed1_2.shape
        assert points_seed1_1.shape != points_seed2_1.shape

    def test_conversion_to_string(self):
        sampler = iaa.RegularGridPointsSampler(10, (10, 30))
        expected = (
            "RegularGridPointsSampler("
            "%s, "
            "%s"
            ")" % (
                str(sampler.n_rows),
                str(sampler.n_cols)
            )
        )
        assert sampler.__str__() == sampler.__repr__() == expected

class TestRelativeRegularGridPointsSampler(unittest.TestCase):
    def setUp(self):
        reseed()

    def test___init___(self):
        sampler = iaa.RelativeRegularGridPointsSampler((0.1, 0.2), 0.1)
        assert is_parameter_instance(sampler.n_rows_frac, iap.Uniform)
        assert np.isclose(sampler.n_rows_frac.a.value, 0.1)
        assert np.isclose(sampler.n_rows_frac.b.value, 0.2)
        assert np.isclose(sampler.n_cols_frac.value, 0.1)

    def test_sample_single_point(self):
        image = np.zeros((10, 20, 3), dtype=np.uint8)
        sampler = iaa.RelativeRegularGridPointsSampler(0.001, 0.001)
        points = sampler.sample_points([image], iarandom.RNG(1))[0]
        assert len(points) == 1
        assert np.allclose(points[0], [10.0, 5.0])

    def test_sample_points(self):
        image = np.zeros((10, 10, 3), dtype=np.uint8)
        sampler = iaa.RelativeRegularGridPointsSampler(0.2, 0.2)
        points = sampler.sample_points([image], iarandom.RNG(1))[0]
        assert len(points) == 4
        assert np.allclose(points, [
            [2.5, 2.5],
            [7.5, 2.5],
            [2.5, 7.5],
            [7.5, 7.5]
        ])

    def test_sample_points_stochastic(self):
        image = np.zeros((10, 10, 3), dtype=np.uint8)
        sampler = iaa.RelativeRegularGridPointsSampler(0.1,
                                                       iap.Choice([0.1, 0.2]))
        points = sampler.sample_points([image], iarandom.RNG(1))[0]

        matches_single_point = np.allclose(points, [
            [5.0, 5.0]
        ])
        matches_two_points = np.allclose(points, [
            [2.5, 5.0],
            [7.5, 5.0]
        ])

        assert len(points) in [1, 2]
        assert matches_single_point or matches_two_points

    def test_sample_points_cols_is_zero(self):
        image = np.zeros((10, 10, 3), dtype=np.uint8)
        sampler = iaa.RelativeRegularGridPointsSampler(iap.Deterministic(0.001),
                                                       0.1)
        points = sampler.sample_points([image], iarandom.RNG(1))[0]

        matches_single_point = np.allclose(points, [
            [5.0, 5.0]
        ])

        assert len(points) == 1
        assert matches_single_point

    def test_sample_points_rows_is_zero(self):
        image = np.zeros((10, 10, 3), dtype=np.uint8)
        sampler = iaa.RelativeRegularGridPointsSampler(0.1,
                                                       iap.Deterministic(0.001))
        points = sampler.sample_points([image], iarandom.RNG(1))[0]

        matches_single_point = np.allclose(points, [
            [5.0, 5.0]
        ])

        assert len(points) == 1
        assert matches_single_point

    def test_determinism(self):
        image = np.zeros((500, 500, 1), dtype=np.uint8)
        sampler = iaa.RelativeRegularGridPointsSampler((0.01, 1.0), (0.1, 1.0))
        points_seed1_1 = sampler.sample_points([image], 1)[0]
        points_seed1_2 = sampler.sample_points([image], 1)[0]
        points_seed2_1 = sampler.sample_points([image], 2)[0]

        assert points_seed1_1.shape == points_seed1_2.shape
        assert points_seed1_1.shape != points_seed2_1.shape

    def test_zero_sized_axes(self):
        shapes = [
            (0, 1, 0),
            (1, 0, 0),
            (0, 1, 1),
            (1, 0, 1)
        ]

        for shape in shapes:
            with self.subTest(shape=shape):
                image = np.zeros(shape, dtype=np.uint8)
                sampler = iaa.RelativeRegularGridPointsSampler(0.01, 0.01)

                points = sampler.sample_points([image], iarandom.RNG(1))[0]

                assert len(points) == 1

    def test_unusual_channel_numbers(self):
        shapes = [
            (1, 1, 4),
            (1, 1, 5),
            (1, 1, 512),
            (1, 1, 513)
        ]

        for shape in shapes:
            with self.subTest(shape=shape):
                image = np.zeros(shape, dtype=np.uint8)
                sampler = iaa.RelativeRegularGridPointsSampler(0.01, 0.01)

                points = sampler.sample_points([image], iarandom.RNG(1))[0]

                assert len(points) == 1

    def test_conversion_to_string(self):
        sampler = iaa.RelativeRegularGridPointsSampler(0.01, (0.01, 0.05))
        expected = (
            "RelativeRegularGridPointsSampler("
            "%s, "
            "%s"
            ")" % (
                str(sampler.n_rows_frac),
                str(sampler.n_cols_frac)
            )
        )
        assert sampler.__str__() == sampler.__repr__() == expected

class _FixedPointsSampler(iaa.IPointsSampler):
    def __init__(self, points):
        self.points = np.float32(np.copy(points))
        self.last_random_state = None

    def sample_points(self, images, random_state):
        self.last_random_state = random_state
        return np.tile(self.points[np.newaxis, ...], (len(images), 1))

class TestDropoutPointsSampler(unittest.TestCase):
    def setUp(self):
        reseed()

    def test___init__(self):
        other = iaa.RegularGridPointsSampler(1, 1)
        sampler = iaa.DropoutPointsSampler(other, 0.5)
        assert sampler.other_points_sampler is other
        assert isinstance(sampler.p_drop, iap.Binomial)
        assert np.isclose(sampler.p_drop.p.value, 0.5)

    def test_p_drop_is_0_percent(self):
        image = np.zeros((1, 1, 3), dtype=np.uint8)
        points = np.linspace(0.0, 1000.0, num=100000)
        points = np.stack([points, points], axis=-1)
        other = _FixedPointsSampler(points)
        sampler = iaa.DropoutPointsSampler(other, 0.0)

        observed = sampler.sample_points([image], 1)[0]

        assert np.allclose(observed, points)

    def test_p_drop_is_100_percent(self):
        image = np.zeros((1, 1, 3), dtype=np.uint8)
        points = np.linspace(0.0+0.9, 1000.0-0.9, num=100000)
        points = np.stack([points, points], axis=-1)
        other = _FixedPointsSampler(points)
        sampler = iaa.DropoutPointsSampler(other, 1.0)

        observed = sampler.sample_points([image], 1)[0]

        eps = 1e-4
        assert len(observed) == 1
        assert 0.0 + 0.9 - eps <= observed[0][0] <= 1000.0 - 0.9 + eps
        assert 0.0 + 0.9 - eps <= observed[0][1] <= 1000.0 - 0.9 + eps

    def test_p_drop_is_50_percent(self):
        image = np.zeros((1, 1, 3), dtype=np.uint8)
        points = np.linspace(0.0+0.9, 1000.0-0.9, num=100000)
        points = np.stack([points, points], axis=-1)
        other = _FixedPointsSampler(points)
        sampler = iaa.DropoutPointsSampler(other, 0.5)

        observed = sampler.sample_points([image], 1)[0]

        assert 50000 - 1000 <= len(observed) <= 50000 + 1000

    def test_determinism(self):
        image = np.zeros((1, 1, 3), dtype=np.uint8)
        points = np.linspace(0.0+0.9, 1000.0-0.9, num=100000)
        points = np.stack([points, points], axis=-1)
        other = _FixedPointsSampler(points)
        sampler = iaa.DropoutPointsSampler(other, (0.3, 0.7))

        observed_s1_1 = sampler.sample_points([image], 1)[0]
        observed_s1_2 = sampler.sample_points([image], 1)[0]
        observed_s2_1 = sampler.sample_points([image], 2)[0]

        assert np.allclose(observed_s1_1, observed_s1_2)
        assert (observed_s1_1.shape != observed_s2_1.shape
                or not np.allclose(observed_s1_1, observed_s2_1))

    def test_random_state_propagates(self):
        image = np.zeros((1, 1, 3), dtype=np.uint8)
        points = np.linspace(0.0+0.9, 1000.0-0.9, num=1)
        points = np.stack([points, points], axis=-1)
        other = _FixedPointsSampler(points)
        sampler = iaa.DropoutPointsSampler(other, 0.5)

        _ = sampler.sample_points([image], 1)[0]
        rs_s1_1 = other.last_random_state
        _ = sampler.sample_points([image], 1)[0]
        rs_s1_2 = other.last_random_state
        _ = sampler.sample_points([image], 2)[0]
        rs_s2_1 = other.last_random_state

        assert rs_s1_1.equals(rs_s1_2)
        assert not rs_s1_1.equals(rs_s2_1)

    def test_conversion_to_string(self):
        sampler = iaa.DropoutPointsSampler(
            iaa.RegularGridPointsSampler(10, 20),
            0.2
        )
        expected = (
            "DropoutPointsSampler("
            "RegularGridPointsSampler("
            "%s, "
            "%s"
            "), "
            "%s"
            ")" % (
                str(sampler.other_points_sampler.n_rows),
                str(sampler.other_points_sampler.n_cols),
                str(sampler.p_drop)
            )
        )
        assert sampler.__str__() == sampler.__repr__() == expected

class TestUniformPointsSampler(unittest.TestCase):
    def setUp(self):
        reseed()

    def test___init__(self):
        sampler = iaa.UniformPointsSampler(100)
        assert is_parameter_instance(sampler.n_points, iap.Deterministic)
        assert sampler.n_points.value == 100

    def test_sampled_points_not_identical(self):
        sampler = iaa.UniformPointsSampler(3)
        images = [np.zeros((1000, 1000, 3), dtype=np.uint8)]

        points = sampler.sample_points(images, 1)[0]
        points_tpls = [tuple(point) for point in points]
        n_points = len(points)
        n_points_uq = len(set(points_tpls))

        assert n_points == 3
        assert n_points_uq == 3

    def test_sampled_points_uniformly_distributed_by_quadrants(self):
        # split image into 2x2 quadrants, group all points per quadrant,
        # assume that at least around N_points/(2*2) points are in each
        # quadrant
        sampler = iaa.UniformPointsSampler(10000)
        images = [np.zeros((1000, 3000, 1), dtype=np.uint8)]

        points = sampler.sample_points(images, 1)[0]
        points_rel = points.astype(np.float32)
        points_rel[:, 1] /= 1000
        points_rel[:, 0] /= 3000

        points_quadrants = np.clip(
            np.floor(points_rel * 2),
            0, 1
        ).astype(np.int32)
        n_points_per_quadrant = np.zeros((2, 2), dtype=np.int32)
        np.add.at(
            n_points_per_quadrant,
            (points_quadrants[:, 1], points_quadrants[:, 0]),
            1)

        assert np.all(n_points_per_quadrant > 0.8*(10000/4))

    def test_sampled_points_uniformly_distributed_by_distance_from_origin(self):
        # Sample N points, compute distances from origin each axis,
        # split into B bins, assume that each bin contains at least around
        # N/B points.
        sampler = iaa.UniformPointsSampler(10000)
        images = [np.zeros((1000, 3000, 1), dtype=np.uint8)]

        points = sampler.sample_points(images, 1)[0]
        points_rel = points.astype(np.float32)
        points_rel[:, 1] /= 1000
        points_rel[:, 0] /= 3000

        points_bins = np.clip(
            np.floor(points_rel * 10),
            0, 1
        ).astype(np.int32)

        # Don't use euclidean (2d) distance here, but instead axis-wise (1d)
        # distance. The euclidean distance leads to non-uniform density of
        # distances, because points on the same "circle" have the same
        # distance, and there are less points close/far away from the origin
        # that fall on the same circle.
        points_bincounts_x = np.bincount(points_bins[:, 0])
        points_bincounts_y = np.bincount(points_bins[:, 1])

        assert np.all(points_bincounts_x > 0.8*(10000/10))
        assert np.all(points_bincounts_y > 0.8*(10000/10))

    def test_many_images(self):
        sampler = iaa.UniformPointsSampler(1000)
        images = [
            np.zeros((100, 500, 3), dtype=np.uint8),
            np.zeros((500, 100, 1), dtype=np.uint8)
        ]

        points = sampler.sample_points(images, 1)

        assert len(points) == 2
        assert len(points[0]) == 1000
        assert len(points[1]) == 1000
        assert not np.allclose(points[0], points[1])
        assert np.any(points[0][:, 1] < 20)
        assert np.any(points[0][:, 1] > 0.9*100)
        assert np.any(points[0][:, 0] < 20)
        assert np.any(points[0][:, 0] > 0.9*500)
        assert np.any(points[1][:, 1] < 20)
        assert np.any(points[1][:, 1] > 0.9*500)
        assert np.any(points[1][:, 0] < 20)
        assert np.any(points[1][:, 0] > 0.9*100)

    def test_always_at_least_one_point(self):
        sampler = iaa.UniformPointsSampler(iap.Deterministic(0))
        images = [np.zeros((10, 10, 1), dtype=np.uint8)]

        points = sampler.sample_points(images, 1)[0]

        assert len(points) == 1

    def test_n_points_can_vary_between_calls(self):
        sampler = iaa.UniformPointsSampler(iap.Choice([1, 10]))
        images = [np.zeros((10, 10, 1), dtype=np.uint8)]

        seen = {1: False, 10: False}
        for i in sm.xrange(50):
            points = sampler.sample_points(images, i)[0]
            seen[len(points)] = True
            if np.all(seen.values()):
                break

        assert len(list(seen.keys())) == 2
        assert np.all(seen.values())

    def test_n_points_can_vary_between_images(self):
        sampler = iaa.UniformPointsSampler(iap.Choice([1, 10]))
        images = [
            np.zeros((10, 10, 1), dtype=np.uint8)
            for _ in sm.xrange(50)]

        points = sampler.sample_points(images, 1)
        point_counts = set([len(points_i) for points_i in points])

        assert len(points) == 50
        assert len(list(point_counts)) == 2
        assert 1 in point_counts
        assert 10 in point_counts

    def test_determinism(self):
        image = np.zeros((10, 10, 3), dtype=np.uint8)
        sampler = iaa.UniformPointsSampler(100)

        observed_s1_1 = sampler.sample_points([image], 1)[0]
        observed_s1_2 = sampler.sample_points([image], 1)[0]
        observed_s2_1 = sampler.sample_points([image], 2)[0]

        assert np.allclose(observed_s1_1, observed_s1_2)
        assert not np.allclose(observed_s1_1, observed_s2_1)

    def test_zero_sized_axes(self):
        shapes = [
            (0, 1, 0),
            (1, 0, 0),
            (0, 1, 1),
            (1, 0, 1)
        ]

        for shape in shapes:
            with self.subTest(shape=shape):
                image = np.zeros(shape, dtype=np.uint8)
                sampler = iaa.UniformPointsSampler(1)

                points = sampler.sample_points([image], iarandom.RNG(1))[0]

                # TODO this is not the same as for
                #      (Relative)RegularGridPointsSampler, which returns in
                #      this case 0 points
                assert len(points) == 1

    def test_unusual_channel_numbers(self):
        shapes = [
            (1, 1, 4),
            (1, 1, 5),
            (1, 1, 512),
            (1, 1, 513)
        ]

        for shape in shapes:
            with self.subTest(shape=shape):
                image = np.zeros(shape, dtype=np.uint8)
                sampler = iaa.UniformPointsSampler(1)

                points = sampler.sample_points([image], iarandom.RNG(1))[0]

                assert len(points) == 1

    def test_conversion_to_string(self):
        sampler = iaa.UniformPointsSampler(10)
        expected = "UniformPointsSampler(%s)" % (
            str(sampler.n_points)
        )
        assert sampler.__str__() == sampler.__repr__() == expected

class TestSubsamplingPointsSampler(unittest.TestCase):
    def setUp(self):
        reseed()

    def test___init__(self):
        other = iaa.RegularGridPointsSampler(1, 1)
        sampler = iaa.SubsamplingPointsSampler(other, 100)
        assert sampler.other_points_sampler is other
        assert sampler.n_points_max == 100

    def test_max_is_zero(self):
        image = np.zeros((10, 10, 3), dtype=np.uint8)
        other = iaa.RegularGridPointsSampler(2, 2)

        with warnings.catch_warnings(record=True) as caught_warnings:
            sampler = iaa.SubsamplingPointsSampler(other, 0)

        observed = sampler.sample_points([image], 1)[0]

        assert len(observed) == 0
        assert len(caught_warnings) == 1
        assert "n_points_max=0" in str(caught_warnings[-1].message)

    def test_max_is_above_point_count(self):
        image = np.zeros((10, 10, 3), dtype=np.uint8)
        other = iaa.RegularGridPointsSampler(2, 2)
        sampler = iaa.SubsamplingPointsSampler(other, 100)

        observed = sampler.sample_points([image], 1)[0]

        assert len(observed) == 4
        assert np.allclose(observed, [
            [2.5, 2.5],
            [7.5, 2.5],
            [2.5, 7.5],
            [7.5, 7.5]
        ])

    def test_max_is_below_point_count(self):
        image = np.zeros((10, 10, 3), dtype=np.uint8)
        other = iaa.RegularGridPointsSampler(5, 5)
        sampler = iaa.SubsamplingPointsSampler(other, 1000)

        observed = sampler.sample_points([image], 1)[0]

        assert len(observed) == 5*5

    def test_max_is_sometimes_below_point_count(self):
        image = np.zeros((1, 10, 3), dtype=np.uint8)
        other = iaa.RegularGridPointsSampler(1, (9, 11))
        sampler = iaa.SubsamplingPointsSampler(other, 1000)

        observed = sampler.sample_points([image] * 100, 1)
        counts = [len(observed_i) for observed_i in observed]
        counts_uq = set(counts)

        assert 9 in counts_uq
        assert 10 in counts_uq
        assert 11 not in counts_uq

    def test_random_state_propagates(self):
        image = np.zeros((1, 1, 3), dtype=np.uint8)
        points = np.linspace(0.0+0.9, 1000.0-0.9, num=1)
        points = np.stack([points, points], axis=-1)
        other = _FixedPointsSampler(points)
        sampler = iaa.SubsamplingPointsSampler(other, 100)

        _ = sampler.sample_points([image], 1)[0]
        rs_s1_1 = other.last_random_state
        _ = sampler.sample_points([image], 1)[0]
        rs_s1_2 = other.last_random_state
        _ = sampler.sample_points([image], 2)[0]
        rs_s2_1 = other.last_random_state

        assert rs_s1_1.equals(rs_s1_2)
        assert not rs_s1_1.equals(rs_s2_1)

    def test_conversion_to_string(self):
        sampler = iaa.SubsamplingPointsSampler(
            iaa.RegularGridPointsSampler(10, 20),
            10
        )
        expected = (
            "SubsamplingPointsSampler("
            "RegularGridPointsSampler("
            "%s, "
            "%s"
            "), "
            "10"
            ")" % (
                str(sampler.other_points_sampler.n_rows),
                str(sampler.other_points_sampler.n_cols)
            )
        )
        assert sampler.__str__() == sampler.__repr__() == expected

redpajama_set_name: RedPajamaGithub


John P Bennecke combines the interpretative talent of a business law background with the analytical skills of an IRS Enrolled Agent. He is the Midwest Regional Managing Director and leader of True Partners Consulting's Chicago Office, John manages tax compliance and consulting for large and mid-size multinational corporate entities, S corporations, partnerships, and individuals.
He is the firm's service line leader for Accounting for Income Taxes (ASC740, formerly known as FAS109), tax reporting, and compliance. He provides services related to internal controls and SOX compliance, as well as tax due diligence, research and tax planning, audit defense, and provision automation. He also serves on the firm's accounting periods and methods team.
Re-engineering a corporation's tax provision process, and eliminating its material weakness position, within one quarter.
Identifying $6 million in annual savings achievable through corporate restructuring.
Has personally served in interim Director of Tax and VP of Tax roles for large Global Organizations.
Identification and consulting related to Tax Accounting Methods, giving rise to the acceleration of tax deduction in excess of $1 million dollars and cash tax benefit in excess of $400K, on an annual basis.
John is a frequently sought after speaker and has spoken for The Chicago Tax Club, the American Institute of Certified Public Accountants, the American Accounting Association, and the Tax Executives Institute.
Before joining True Partners, he was a senior manager with the lead tax services practice of Deloitte Tax, LLP in Chicago. His 20 years of experience encompasses both public accounting and industry.
His Bachelors of Science degree is from DePaul University and his Masters of Jurisprudence in Business Law is from Loyola University's law school. He belongs to The City Club of Chicago, the Chicago Tax Club, the American Institute of Certified Public Accountants' tax section, and the National Association of Tax Practitioners.
He serves on the Board of Directors and is the Co-chair of the finance committee of The Cara Program, which provides job placement and educational training for Chicago's homeless and adults in transition. He also is on the Boards of Clean slate and 180 Properties. He enjoys kayaking, fishing, and traveling, and is a Bard Circle patron of the Chicago Shakespeare Theater.


And On The Seventh Day… She Registered Her Kid For School!
I registered my kiddo for school this morning. It's a major milestone sort of moment at yonder Hoffman Haus.
Kiddo is not too happy about the situation. Like me, he's a perfectionist, and the unknown is scary. He's worried he won't be able to get his locker open. He's unsure of the layout of the new school. I hate to say it, but I saw a LOT of me in his sour grapes attitude, too much of me actually. He was frustrated and cranky. In his mind, all the other kids knew more than he did. I kept pointing out the purpose of registration/orientation day: it's a day for parents and kids to walk through the class schedule together. I pointed out the other kids who were having trouble with their lockers, other kids (and parents) who looked equally overwhelmed by the experience. Unfortunately, he couldn't see that he wasn't alone in this mess, even when one of the teachers tried to explain to him that there will be LOTS of teachers on hand to help with lockers and such once the school year starts. I think he still felt like he was going to be all alone in this new adventure.
I started to think about his reaction, so similar to my own. It's easy to get caught up in the feeling of being all alone, of feeling like no one else has gone through the same experience. How many times have I felt like all the other moms had a better set of instructions to pass on to their offspring? How many times have I felt like I wasn't given the same set of skills as the rest of the mothers out there? And yet I can't see myself as behaving irrationally. I think I'm perfectly justified in my rants!
Sometimes, you really NEED someone else to hold up a mirror in order to see yourself more clearly.
Next postAnother Tote Done, But A Lousy Day!


Identify Occupational Safety and Health Act (OSHA) regulations that regulate the scaffolding indusrty. Basic guidelines for palling, ereecting and using scaffolding.
This item is print on demand. Please allow for additional time in shipping. Estimated delivery is 4-6 weeks.


text:
'Willice Samuel'
'Willice'
"Of Philly's Best"
Philadelphia, "E Pluribus Unum," – 'Out of The Several, 1!' Not like that of other towns, existence had been excellent for lots of folks in spite of the huge gentrification, politicalizing, politicalized racial polarization, anarchy, criminal offense, capitalization, gun-air pollution, gun-habit, drug-addiction, social desease, hunger, homelessness, incarceration, drug-czars and gangs, law enforcement brutality and corruption, condition, town and non-public business enterprise white-collar crime and corruption, and a 'God-Provided Will need for Salvation and Forgiveness. Not at all not like other significant metropolitan areas across the nation. But, in this town of Philly, there is a great deal to be provided… and been given. There is wonderful pleasure in residence possession here… (non-house-possession is just as wonderful a reward for residing in this area as very well). There are four key universities of greater mastering with two big colleges, i.e., Philadelphia Group Higher education on Spring Garden Avenue with a number of satellite campus locations and The Berean Institute Higher education (previously of Girard Avenue in the coronary heart of North Philadelphia) in and about the Philadelphia bordering region.
This town previously the fourth-most significant city in The us a sizzling-mattress of sports champions and groups… the property of Champion Boxing greats, The Philadelphia Eagles in NFL Football, The 76r's in Basketball, The Philadelphia Wings in Soccer, The Philadelphia Flyers in Hockey, The Phillies in Baseball, The Taney Street Dragons Minimal League Baseball Workforce, "Philly's Entire world Champions" with numerous other good sports teams and supporters a robust and happy town of various people today, Museums, Artwork Galleries, Places to eat of Remarkably Classed Delicacies(s) and Fine Lifestyle house to "The Rocky Statue and Balboa University." A Philly neighbor to New Jersey throughout the river, is divided by the Mighty Delaware River slicing as a result of Philly, the Energetic and Unrelenting 'Schuylkill' River working by means of it hosting lots of 'Sculls,' Scullies and other variety of boat races… in "Fairmount Park!"
"Fairmount Park," (rivaled only by 'New York's Central Park)… is Dwelling to 'The Initial Nationwide Zoo in The us!'
A town immersed in its pleasure of staying a diverse unwavering metropolis, possessing huge bursts of wonderment, appreciation, experience, and creativeness. Fairmount Park, a large out of doors environment of the aged and the new… a wilderness of discovery a fanciful land of functions for the whole family, oriented in friendship – "Brotherly Love and Sisterly Passion!" A soulful experience for all who just take aspect in venturing forth, partaking of its excellent pleasures… offerings of bountiful majesty gifted to one particular and all, by 'Mother Nature' and the 'Grace of God!'
… But a deadly town non-the-less.
The time is now… your time… our time, an un-yielding vast and eternal holistic knowledge throughout the area. A Philadelphia Story of Your Struggle(s), Our Wrestle(s), The Struggles of Life In Philly! The exceptions to the rule are these who have no struggles, or so they say. Willice and his kin fare no otherwise than the typical people in and all over city… like a lot of in the law enforcement community and individuals of lots of Philadelphia Civil Servants Willice has paid out his dues quite a few situations in excess of. He's long gone that additional mile a bit a lot more times than envisioned, required, or unaccepted by several who serve. The trials and tribulations associated with his job climb and daily life plans, have compelled him into a war with a beast no 1 could have at any time probably imagined. Quadrupled with his struggle in opposition to the systematic engine built to preserve him and individuals of his individual persuasion in a set put… their location. He labored with the wrestle in hanging on to his task, his household, his lifetime, and his steadfast dedication to his vows of safeguarding the men and women, his never ever-ending fight against the really individuals with whom he is forced to deal and get the job done together-side, every single and each individual working day.
"Willice Samuel's relatives arrived up North from Ga by way of Winnsboro, South Carolina. The spouse and children settled in Coatesville Pennsylvania, in or about April 1911.
Willice's Terrific-Great Grandfather talked about a lynching and burned at the stake murder of a Black Guy by a mob of white men who wore masks. He said the Black Gentleman named Zachariah Walker was accused of shooting to demise a white cop, named Edgar Rice. He was meant to have been a special police officer in Coatesville. The Willice's are descendants of America's rewarding Industry of Black Slavery."
("SHAMAN")
He worked with these who conspired against him, people who wished him humiliated and destroyed. They want to send a crystal clear and potent message, it is really that means… "it is we who are in regulate… it is we who are the strong and managing faction though it is you, who are beneath us! It is you, who are destined to serve and remain subservient… permanently! Willice Samuel will in no way be a role design for any… Black, Latino, Asian, or white… and nearly anything in involving!"
Willice was not on any mission to make any key social variations… but did not, would not, will not run absent or again-down from the obstacle of defending the unprotected.
The criminal offense in the town is at an all-time high. Children killing young children, in the town and the burbs as nicely. City's across the nation are going through several 'Youth Challenges' coupled with the brash and emboldened mind-sets unleashed by the racist torrents of "white-supremacist" and spit-spewned hatred from the lips of an 'two-timed-impeached, traitorous, treasonous, insurrectionist, ex-whitehouse commandant the imitative representation of a "Cost-free-Earth Management!"… "Agent Orange."
The captain and his staff of crime-fighters are also besieged with a desk complete of corrupt and chilly-scenarios that need to have to be posted from 'red to black' (in the squad responsibility area) in a hurry. They are beset with an onslaught of difficulties whilst dealing with community backlash and case resolving filibusters from City Corridor, Town Counsel, and Law enforcement Unions, as opposed to the featuring of solutions. Arranged criminal offense and white-collar investigations are also part of the smorgas-borg of physical and psychological assaults facing this crew of "Crack-Crime-Fighters!"
If this captain of detectives and his crew can tame the ravishing of a rampaging goliath sized beast, what can they probably do from the approach of a disciplined algorithm 'juggernaut' of a 'well-oiled-organized-criminal offense-spree' initiated from several instructions… the four-corners of the town?
They really don't know it, but these criminal offense-ridden monsters of so-known as human-variety, would be improved served if they you should not cross the radar of Captain Willice Samuel and Associates… they will be a sorry good deal it they you should not heed the warning… stay absent from anything at all that would or will put you on a collision study course to any and all instances… felony situations taken care of by a "Willice Samuel Investigation!"
"If You Cannot Do The Time, Really don't Do The Crime – Willice is on the scenario!"
~ 'FAIRMOUNT' – Terror In The Park!~
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HEAVY HITTING MUSIC FROM THE FINE FOLKS OF ASPIRE FOR GREATNESS ENTERTAINMENT, WITH MORE ON THE WAY. ALL MIXTAPES ARE FREE TO DOWNLOAD, JUST CLICK THE PIC TO BE TAKEN TO THE SITE [DATPIFF.COM] IN WHICH YOU CAN DOWNLOAD/STREAM/LISTEN TO THE MIXTAPES. THANK YOU FOR ALL YOUR SUPPORT!!!!!!!
The Highly Anticipated Release From Young Haak Da 5-Duece Gun, Opening Statement!!!
"Mic Cutta Vol. 1" by GameHunterFilmz Himself, featuring Young Haak's 1st single off of "Opening Statement" "U.M.I."
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These bite sized fruit tarts are simply delicious. They were my favorite pastry dessert when i was a kid. I loved them with all types of fruits … but actually my favorite were strawberry ones.
Mini tarts are basically shortbread crunchy shells filled with smooth vanilla cream. On top you can add any type of fruit you want (i'm not so sure about watermelon, but i guess you can try) and cover it with gelatine to keep the fruits in place.
Let's say that the most complicated thing about this recipe is making the dough. But even that is not that complicated.
If you like the recipe you can make dough in advance and store it in the freezer for future tarts. I will take longer just one time.
For the dough i mixed all the ingredients (flour, egg yolk, butter, water, sugar, lemon zest and a pinch of salt) in a big bowl. I made an elastic dough that i wrapped in plastic foil and kept in the fridge for two hours. Enough time to make the cream.
I dissolved the corn starch in a few tablespoons of milk. I heated the rest of the milk in a pot with a few pinches of saffron. The saffron gives a nice flavor to the cream and colors it yellowish. After the milk boiled i strained it, removing the saffron bits.
I put the strained milk back on the stove and added a split vanilla pod.
I added sugar and let it boil a little. After that i removed the vanilla pod and added the corn starch mixed with milk. Even though you mixed the corn starch with milk you might find that it turned solid again. It is normal, mix it again and it will be ok. Be sure to mix very well after adding cornstarch because your cream will get thicker almost instantly and you don't want any lumps.
Remove it from the stove and let it cool a bit.
For the tarts i bough a few silicon trays for tarts (i think they were actually for muffins but it worked). I coated them with olive oil before putting the dough in it.
I unwrapped the dough from the plastic. I took medium pieces of dough and turned them into balls. I stretched the balls making dough sheets big enough to cover the little trays (basically this is what you have to do, to cover the trays with dough, not too thin, not too thick).
I baked the tarts shells in the preheated oven for 15 minutes. After this i removed them from the shapes and let them cool.
I filled each shell with cream and arranged the fruits on top.
I mixed gelatine powder with sugar and added water bit by bit. I put the mixture on the stove for about one minute.
Using a tablespoon i coated my mini tarts with gelatine. I kept them in the fridge a bit before serving.
Wish I had a couple of these sitting in front of me right now! There's a local coffee shop that makes fruit tarts and I'm always ordering them. Yours look even better.


text:
Huge Surprise As Basketball Legend Turns Down GHS 600 Million Just To Appear on Golf Course For 2 Hours
Monday, August 23, 2021 at 1:50 PM by Aba Afful
Michael Jordan was bold enough to reject some juicy offers for sponsors during his glittering career in the NBA
The Chicago Bulls legend won seven NBA titles and was named the Most Valuable Player in all of the eight finals he appeared in
The 58-year-old still rakes in millions on a yearly basis from an endorsement he made with Nike to produce Air Jordan
Michael Jordan is no doubt one of the best the game has produced in basketball and the Chicago Bulls legend smiled to the bank for his contributions to the sport, The Sun.
However, agent of the Olympic gold medalist David Falk, claimed the six-time NBA champion refused a $100million to make an appearance on the golf course for just two hours.
Jordan back in the days signed multiple endorsement and market deals that still pays up until this present day.
Ronaldo breaks silence on reports linking him to other clubs days after Messi joined PSG
Michael Jordan once had the guts to turned down a $100million deal to appear on a golf course for two hours. Photo by Kent Smith
Source: UGC
One of the 58-year-old's most foremost deals which pays him a whopping $105m per year is his Nike Air Jordan deal.
Despite the huge potentials that surrounded the two-time Slam Dunk winner back in the late 80s and 90s, Jordan chose carefully he wanted to do business with.
Falk's revelation about Jordan turning down a $100m deal
Falk told WFAN:
"[Jordan] turned down a deal to do a one-day golf appearance when he was in Asia a few years ago for a pre-season game with Charlotte.
"And some woman in the Philippines offered me seven million dollars for one day, wanted him to play in a golf tournament. And he turned it down.
"I brought him a deal three years ago for 100 million dollars.
"And all he had to do, other than give his name and likeness, was make one two-hour appearance to announce the deal. And he turned it down."
Lionel Messi shows PSG teammates how he plans to torment opponents during training sessions
Meanwhile, YEN.com.gh had earlier reported how Ghanaian sprinter, Joseph Amoah, was delighted to meet one of his favourite NBA stars, Luka Doncic of the Dallas Mavericks at the Olympic Games in Japan.
The two athletes met at the Olympic Games village, with both hoping to make their countries proud at the global showpiece.
Joseph Amoah could not hide his excitement, and he posted a photo of himself and Doncic on Twitter, with the caption, "when you casually walk into Luka."
Joseph Amoah was part of Ghana's sprint team at the Olympics and participated in the 200m and 4X100m races at the event.
Motivational messages Regions in ghana Mona Gucci How big was andre the giant
Malaysia seizes animal parts worth $18 mn
Student adopts a kid he found in refuse dump, takes him to his mother
Ailing oceans in the spotlight at major UN meet
Zionfelix: Famous Ghanaian Blogger Revelas Why He Does Interviews In Twi; Says English Is Not Easy
"God bless your home": Sweet video of a man with pot belly playing with wife inside bedroom melts many hearts
Ghanaian YouTuber enrols young kid who was hustling in Accra into school: "He is like my son"