Beispiel #1
0
def pack_apply_message(f, args, kwargs, buffer_threshold=MAX_BYTES, item_threshold=MAX_ITEMS):
    """pack up a function, args, and kwargs to be sent over the wire
    
    Each element of args/kwargs will be canned for special treatment,
    but inspection will not go any deeper than that.
    
    Any object whose data is larger than `threshold`  will not have their data copied
    (only numpy arrays and bytes/buffers support zero-copy)
    
    Message will be a list of bytes/buffers of the format:
    
    [ cf, pinfo, <arg_bufs>, <kwarg_bufs> ]
    
    With length at least two + len(args) + len(kwargs)
    """
    
    arg_bufs = flatten(serialize_object(arg, buffer_threshold, item_threshold) for arg in args)
    
    kw_keys = sorted(kwargs.keys())
    kwarg_bufs = flatten(serialize_object(kwargs[key], buffer_threshold, item_threshold) for key in kw_keys)
    
    info = dict(nargs=len(args), narg_bufs=len(arg_bufs), kw_keys=kw_keys)
    
    msg = [pickle.dumps(can(f), PICKLE_PROTOCOL)]
    msg.append(pickle.dumps(info, PICKLE_PROTOCOL))
    msg.extend(arg_bufs)
    msg.extend(kwarg_bufs)
    
    return msg
Beispiel #2
0
def pack_apply_message(f, args, kwargs, buffer_threshold=MAX_BYTES, item_threshold=MAX_ITEMS):
    """pack up a function, args, and kwargs to be sent over the wire
    
    Each element of args/kwargs will be canned for special treatment,
    but inspection will not go any deeper than that.
    
    Any object whose data is larger than `threshold`  will not have their data copied
    (only numpy arrays and bytes/buffers support zero-copy)
    
    Message will be a list of bytes/buffers of the format:
    
    [ cf, pinfo, <arg_bufs>, <kwarg_bufs> ]
    
    With length at least two + len(args) + len(kwargs)
    """
    
    arg_bufs = flatten(serialize_object(arg, buffer_threshold, item_threshold) for arg in args)
    
    kw_keys = sorted(kwargs.keys())
    kwarg_bufs = flatten(serialize_object(kwargs[key], buffer_threshold, item_threshold) for key in kw_keys)
    
    info = dict(nargs=len(args), narg_bufs=len(arg_bufs), kw_keys=kw_keys)
    
    msg = [pickle.dumps(can(f),-1)]
    msg.append(pickle.dumps(info, -1))
    msg.extend(arg_bufs)
    msg.extend(kwarg_bufs)
    
    return msg
Beispiel #3
0
def qw(words,flat=0,sep=None,maxsplit=-1):
    """Similar to Perl's qw() operator, but with some more options.

    qw(words,flat=0,sep=' ',maxsplit=-1) -> words.split(sep,maxsplit)

    words can also be a list itself, and with flat=1, the output will be
    recursively flattened.

    Examples:

    >>> qw('1 2')
    ['1', '2']

    >>> qw(['a b','1 2',['m n','p q']])
    [['a', 'b'], ['1', '2'], [['m', 'n'], ['p', 'q']]]

    >>> qw(['a b','1 2',['m n','p q']],flat=1)
    ['a', 'b', '1', '2', 'm', 'n', 'p', 'q']
    """

    if isinstance(words, basestring):
        return [word.strip() for word in words.split(sep,maxsplit)
                if word and not word.isspace() ]
    if flat:
        return flatten(map(qw,words,[1]*len(words)))
    return map(qw,words)
Beispiel #4
0
def qw(words, flat=0, sep=None, maxsplit=-1):
    """Similar to Perl's qw() operator, but with some more options.

    qw(words,flat=0,sep=' ',maxsplit=-1) -> words.split(sep,maxsplit)

    words can also be a list itself, and with flat=1, the output will be
    recursively flattened.

    Examples:

    >>> qw('1 2')
    ['1', '2']

    >>> qw(['a b','1 2',['m n','p q']])
    [['a', 'b'], ['1', '2'], [['m', 'n'], ['p', 'q']]]

    >>> qw(['a b','1 2',['m n','p q']],flat=1)
    ['a', 'b', '1', '2', 'm', 'n', 'p', 'q']
    """

    if isinstance(words, basestring):
        return [
            word.strip() for word in words.split(sep, maxsplit)
            if word and not word.isspace()
        ]
    if flat:
        return flatten(map(qw, words, [1] * len(words)))
    return map(qw, words)
import numpy as np

import healpy as hp

from astropy.io import fits

import matplotlib.pyplot as plt

from IPython.utils.data import flatten

has=fits.open('/home/dweisberger/haslam_masked.fits')

ham=has[1].data.field(0)

flh=flatten(ham)

fha=np.array(flh)

Y = fha

free=fits.open('/home/dweisberger/freefree_mask.fits')

fre=free[1].data.field(0)

fra=flatten(fre)

ffa=np.array(fra)

Z = ffa
    def crawl_recipe(self, recipe_num=None):
        url_recipe = self._url_base.format(
            recipe_num=recipe_num
        )

        driver = webdriver.PhantomJS(
            '/Users/hongdonghyun/projects/team_project/wps-picky/django_app/utils/crawl/phantomjs')
        driver.get(url_recipe)

        html = driver.page_source
        soup = BeautifulSoup(html)

        # 전체 페이지에서 타이틀 부분 뽑아옴
        title = soup.select_one('title').get_text()
        title_list = title.split(':')
        empty_list = []
        for t in title_list:
            t = t.strip(' ')
            empty_list.append(t)

        # 레시피의 이미지 뽑아옴
        div_editor = soup.find("div", {"class": "edtitor_img_uio"})
        recipe_img = div_editor.img['src']

        # 레시피의 타이틀, 설명 뽑아옴
        recipe_title = empty_list[0]
        recipe_description = empty_list[1]

        div_container_inner = soup.find("div", {"class": "__viewer_container_inner"})
        div_na_summary_info = div_container_inner.find_all("div", {"class": "na_summary_info"})

        # select함수는 리턴값이 리스트형식
        _list = []
        for before_list in div_na_summary_info:
            a = before_list.select("ul > li > span")
            _list.append(a)

        # 위 과정을 거치면 이중 리스트 형식이됨

        # 이중 리스트 형식을 일자화 시키는 함수 flatten
        _list = flatten(_list)

        # 일자화된 리스트에서 span을 제외한 text값 추출
        # ingredient_list라는 변수에 할당
        ingredient_list = []
        for after_list in _list:
            ingredient = after_list.get_text()
            ingredient_list.append(ingredient)
        # ,로 join
        ingredient_list = ','.join(ingredient_list)

        # 스탭 부분 이미지
        div_tptype_5 = soup.find_all("div", {"class": "t_ptype5"})
        # 스탭 부분 텍스트
        div_p = soup.find_all("p", {"class": "t_txt"})

        # 스탭 이미지 추출
        img_list = []
        for image in div_tptype_5:
            img_list.append(image.img['src'])

        # 스탭 설명 추출
        text_list = []
        for text in div_p:
            text_list.append(text.get_text())
        del text_list[0]

        # 기존 텍스트에 \n,\t제거
        i = ' '.join(text_list).replace('\n', '')
        i = i.replace('\t', '')
        # STEP기준으로 설명을 쪼갠다
        # 이 기능에서 STEP삭제
        # 삭제를 위한 정규표현식
        pattern = re.compile(r'STEP \d+\s?')
        # text_list에 다시 대입
        # 공백제거, 만약 텍스트가 빈공백 요소일 경우 제거
        text_list = [text.strip() for text in pattern.split(i) if text]
        for text in text_list:
            # 크롤링 도중 원하지 않는 부분이 있어 제거하기 위한 if문
            if '(숫자)' in text:
                del text_list[0]

        # 임시 데이터의 File_name
        file_name = '{}.jpg'.format(recipe_title)
        # 무슨 기능을 하는지 확인필요 - hong 8/21
        temp_file = NamedTemporaryFile(delete=True)
        # response변수에 recipe_img 저장
        response = requests.get(recipe_img)
        # temp_file에 recipe_img의 content를 저장
        # 동작 후 사라지는 임시파일
        temp_file.write(response.content)

        # 레시피 생성
        _2bab_recipe = Recipe.objects.create(
            title=recipe_title,
            description=recipe_description,
            ingredient=ingredient_list,
            # 현재는 pickyuser 첫번째 유저를 가져오지만 최종 정리 후
            # admin의 아이디를 가져와야함 - hong 8/21
            user=PickyUser.objects.first()
        )

        # 위에서 저장한 임시파일을 _2bab_recipe의 이미지필드에 저장
        _2bab_recipe.img_recipe.save(file_name, File(temp_file))

        # zip으로 묶인 img_list 와 img에 대한 description_list
        for x, y in zip(img_list, text_list):
            # 위에서 만든 _2bab_recipe의 pk에 매핑
            # 설명을 저장 후 생성
            _2bab_recipe_step = RecipeStep.objects.create(description=y, recipe_id=_2bab_recipe.pk)
            # 상단의 동일코드와 같음
            # 설명생략
            file_name = '{}.{}.jpg'.format(_2bab_recipe.pk, recipe_title)
            temp_file = NamedTemporaryFile(delete=True)
            response = requests.get(x)
            temp_file.write(response.content)
            _2bab_recipe_step.img_step.save(file_name, File(temp_file))