Python numpy 模块，isin() 实例源码

def _load_data(self, data_id):
        imgpath = osp.join(
            self.data_dir, 'JPEGImages/{}.jpg'.format(data_id))
        seg_imgpath = osp.join(
            self.data_dir, 'SegmentationClass/{}.png'.format(data_id))
        ins_imgpath = osp.join(
            self.data_dir, 'SegmentationObject/{}.png'.format(data_id))
        img = cv2.imread(imgpath)
        img = img.transpose((2, 0, 1))
        seg_img = PIL.Image.open(seg_imgpath)
        seg_img = np.array(seg_img, dtype=np.int32)
        seg_img[seg_img == 255] = -1
        ins_img = PIL.Image.open(ins_imgpath)
        ins_img = np.array(ins_img, dtype=np.int32)
        ins_img[ins_img == 255] = -1
        ins_img[np.isin(seg_img, [-1, 0])] = -1
        return img, seg_img, ins_img

def _load_data(self, data_id):
        imgpath = osp.join(
            self.data_dir, 'img/{}.jpg'.format(data_id))
        seg_imgpath = osp.join(
            self.data_dir, 'cls/{}.mat'.format(data_id))
        ins_imgpath = osp.join(
            self.data_dir, 'inst/{}.mat'.format(data_id))
        img = cv2.imread(imgpath, cv2.IMREAD_COLOR)
        img = img.transpose((2, 0, 1))
        mat = scipy.io.loadmat(seg_imgpath)
        seg_img = mat['GTcls'][0]['Segmentation'][0].astype(np.int32)
        seg_img = np.array(seg_img, dtype=np.int32)
        seg_img[seg_img == 255] = -1
        mat = scipy.io.loadmat(ins_imgpath)
        ins_img = mat['GTinst'][0]['Segmentation'][0].astype(np.int32)
        ins_img[ins_img == 255] = -1
        ins_img[np.isin(seg_img, [-1, 0])] = -1
        return img, seg_img, ins_img

def crossover(self, parent, pop):
        if np.random.rand() < self.cross_rate:
            i_ = np.random.randint(0, self.pop_size, size=1)                        # select another individual from pop
            cross_points = np.random.randint(0, 2, self.DNA_size).astype(np.bool)   # choose crossover points
            keep_city = parent[~cross_points]                                       # find the city number
            swap_city = pop[i_, np.isin(pop[i_].ravel(), keep_city, invert=True)]
            parent[:] = np.concatenate((keep_city, swap_city))
        return parent

def create_ignore_mask(self, postags, ignore_punct=True):
        if ignore_punct:
            mask = np.isin(postags, self._PUNCTS).astype(np.int32)
        else:
            mask = np.zeros(len(postags), np.int32)
        mask[0] = 1
        return mask

def binary_volume_opening(vol, minvol):
    lb_vol, num_objs = label(vol)
    lbs = np.arange(1, num_objs + 1)
    v = labeled_comprehension(lb_vol > 0, lb_vol, lbs, np.sum, np.int, 0)
    ix = np.isin(lb_vol, lbs[v >= minvol])
    newvol = np.zeros(vol.shape)
    newvol[ix] = vol[ix]
    return newvol

def _print_df_scores(df_scores, score_types, indent=''):
    """Pretty print the scores dataframe.

    Parameters
    ----------
    df_scores : pd.DataFrame
        the score dataframe
    score_types : list of score types
        a list of score types to use
    indent : str, default=''
        indentation if needed
    """
    try:
        # try to re-order columns/rows in the printed array
        # we may not have all train, valid, test, so need to select
        index_order = np.array(['train', 'valid', 'test'])
        ordered_index = index_order[np.isin(index_order, df_scores.index)]
        df_scores = df_scores.loc[
            ordered_index, [score_type.name for score_type in score_types]]
    except Exception:
        _print_warning("Couldn't re-order the score matrix..")
    with pd.option_context("display.width", 160):
        df_repr = repr(df_scores)
    df_repr_out = []
    for line, color_key in zip(df_repr.splitlines(),
                               [None, None] +
                               list(df_scores.index.values)):
        if line.strip() == 'step':
            continue
        if color_key is None:
            # table header
            line = stylize(line, fg(fg_colors['title']) + attr('bold'))
        if color_key is not None:
            tokens = line.split()
            tokens_bak = tokens[:]
            if 'official_' + color_key in fg_colors:
                # line label and official score bold & bright
                label_color = fg(fg_colors['official_' + color_key])
                tokens[0] = stylize(tokens[0], label_color + attr('bold'))
                tokens[1] = stylize(tokens[1], label_color + attr('bold'))
            if color_key in fg_colors:
                # other scores pale
                tokens[2:] = [stylize(token, fg(fg_colors[color_key]))
                              for token in tokens[2:]]
            for token_from, token_to in zip(tokens_bak, tokens):
                line = line.replace(token_from, token_to)
        line = indent + line
        df_repr_out.append(line)
    print('\n'.join(df_repr_out))