Python itertools.compress方法代碼示例

# 需要導入模塊: import itertools [as 別名]
# 或者: from itertools import compress [as 別名]
def subset_cells(self, keep_cells):
        """
        Write a file reduced to just the given cells
        """

        keep_cells = set(keep_cells)
        subset_file_name = self.tag_file_name(c_SUBSET_POSTFIX)
        if subset_file_name is None:
            return(None)

        with self.get_write_handle(subset_file_name) as file_writer:
            csv_writer = csv.writer(file_writer, delimiter=self.delimiter)
            check_handle = self.csv_handle
            keep_cells.add(c_EXPRESSION_00_ELEMENT)
            header = next(check_handle)
            row_1 = next(check_handle)
            if (len(header) == (len(row_1) - 1)) and (c_EXPRESSION_00_ELEMENT not in header):
                header = [c_EXPRESSION_00_ELEMENT] + header
            header_index = [cell in keep_cells for cell in header]
            # Need to add the header rows
            csv_writer.writerow(list(itertools.compress(header,header_index)))
            csv_writer.writerow(list(itertools.compress(row_1,header_index)))
            for file_line in check_handle:
                csv_writer.writerow(list(itertools.compress(file_line,header_index)))
        return(subset_file_name) 

# 需要導入模塊: import itertools [as 別名]
# 或者: from itertools import compress [as 別名]
def analyze_strings(mystrings):
    """
    A list of mystrings gets  classified and only the predicted API keys are returned

    :param mystrings: a list of mystrings to be analyzed
    :return: a list of valid api keys
    :rtype: list
    """
    # for performance it's better to create a new list instead of removing elements from list
    smali_strings_filtered = []
    strings_features = []
    for string in mystrings:
        features = string_classifier.calculate_all_features(string.value)
        if features:
            features_list = list(features)
            smali_strings_filtered.append(string)
            strings_features.append(features_list)
    if len(strings_features) > 0:
        prediction = classifier.predict(np.array(strings_features))
        api_keys_strings = itertools.compress(smali_strings_filtered, prediction)  # basically a bitmask
        return api_keys_strings
    return [] 

# 需要導入模塊: import itertools [as 別名]
# 或者: from itertools import compress [as 別名]
def nearby_now(self) -> List[Tuple[str, Pos, float]]:
        now = datetime.utcnow()

        t1 = time()
        self.last_query_t = t1

        lons, lats, alts, errors = self.orbs.get_lonlatalt(now)
        t2 = time()
        rough_near = np.logical_and(np.abs(lats - self.loc.lat) < 3, np.abs(lons - self.loc.long) < 3)
        valid_satpos = list(
            zip(self.satnames[~errors][rough_near], lats[rough_near], lons[rough_near], alts[rough_near]))
        nearby = [(name, Pos(lat=lat, long=lon), alt) for name, lat, lon, alt in valid_satpos if
                  distance.distance(self.loc, (lat, lon)).km < 200]
        t3 = time()
        print("loc:{:.2f}s dist: {:.2f}s tot: {:.2f}s, sats: {:02d}".format(t2 - t1, t3 - t2, t3 - t1, len(nearby)))

        if not self.filtered_errors:
            print("filtering errors")
            self.satnames = self.satnames[~errors]
            self.tles = itertools.compress(self.tles, ~errors)
            self.create_orbitals()
            self.filtered_errors = True
        return nearby 

# 需要導入模塊: import itertools [as 別名]
# 或者: from itertools import compress [as 別名]
def _unpack_available_edges(avail, weight=None, G=None):
    """Helper to separate avail into edges and corresponding weights"""
    if weight is None:
        weight = 'weight'
    if isinstance(avail, dict):
        avail_uv = list(avail.keys())
        avail_w = list(avail.values())
    else:
        def _try_getitem(d):
            try:
                return d[weight]
            except TypeError:
                return d
        avail_uv = [tup[0:2] for tup in avail]
        avail_w = [1 if len(tup) == 2 else _try_getitem(tup[-1])
                   for tup in avail]

    if G is not None:
        # Edges already in the graph are filtered
        # flags = [(G.has_node(u) and G.has_node(v) and not G.has_edge(u, v))
        #          for u, v in avail_uv]
        flags = [not G.has_edge(u, v) for u, v in avail_uv]
        avail_uv = list(it.compress(avail_uv, flags))
        avail_w = list(it.compress(avail_w, flags))
    return avail_uv, avail_w 

# 需要導入模塊: import itertools [as 別名]
# 或者: from itertools import compress [as 別名]
def _generate_columns_and_selector(self, parsed_args, column_names):
        """Generate included columns and selector according to parsed args.

        :param parsed_args: argparse.Namespace instance with argument values
        :param column_names: sequence of strings containing names
                             of output columns
        """
        if not parsed_args.columns:
            columns_to_include = column_names
            selector = None
        else:
            columns_to_include = [c for c in column_names
                                  if c in parsed_args.columns]
            if not columns_to_include:
                raise ValueError('No recognized column names in %s. '
                                 'Recognized columns are %s.' %
                                 (str(parsed_args.columns), str(column_names)))

            # Set up argument to compress()
            selector = [(c in columns_to_include)
                        for c in column_names]
        return columns_to_include, selector 

# 需要導入模塊: import itertools [as 別名]
# 或者: from itertools import compress [as 別名]
def test_compress():
    yield verify_same, compress, itertools.compress, None, [1, 2, 3], [1, 2, 3]
    yield verify_same, compress, itertools.compress, None, [1, 2, 3], [1, 0, 0]
    yield verify_same, compress, itertools.compress, None, [1, 2, 3], [1, 0]
    yield verify_same, compress, itertools.compress, None, [1, 2], [1, 0, 1]
    yield verify_same, compress, itertools.compress, None, [1, 2], [0, 0]
    yield verify_same, compress, itertools.compress, None, [1, 2], [0]
    yield verify_same, compress, itertools.compress, None, [1, 2], [0, 0, 0]
    yield (verify_pickle, compress, itertools.compress, 3, 1, [1, 2, 3],
           [1, 2, 3])
    yield (verify_pickle, compress, itertools.compress, 3, 0, [1, 2, 3],
           [1, 2, 3])
    yield (verify_pickle, compress, itertools.compress, 1, 0, [1, 2, 3],
           [1, 0, 0])
    yield (verify_pickle, compress, itertools.compress, 1, 0, [1, 2, 3],
           [1, 0])
    yield (verify_pickle, compress, itertools.compress, 1, 0, [1, 2],
           [1, 0, 1]) 

# 需要導入模塊: import itertools [as 別名]
# 或者: from itertools import compress [as 別名]
def compress(items, flags):
    """
    Selects from ``items`` where the corresponding value in ``flags`` is True.
    This is similar to :func:`numpy.compress`.

    This is actually a simple alias for :func:`itertools.compress`.

    Args:
        items (Iterable[Any]): a sequence to select items from

        flags (Iterable[bool]): corresponding sequence of bools

    Returns:
        Iterable[Any]: a subset of masked items

    Example:
        >>> import ubelt as ub
        >>> items = [1, 2, 3, 4, 5]
        >>> flags = [False, True, True, False, True]
        >>> list(ub.compress(items, flags))
        [2, 3, 5]
    """
    return it.compress(items, flags) 

# 需要導入模塊: import itertools [as 別名]
# 或者: from itertools import compress [as 別名]
def trim(table: Table, blank=None) -> Table:
    def isempty(v):
        return v is None or str(v).strip(blank) == ""

    table = iter2seq(table)

    nonemptyflags = [
        any(not isempty(v) for v in col)
        for col in transpose(table)
    ]

    for row in table:
        if all(isempty(v) for v in row):
            continue

        yield list(itertools.compress(row, nonemptyflags)) 

# 需要導入模塊: import itertools [as 別名]
# 或者: from itertools import compress [as 別名]
def assert_args_presence(args, doc, member, name):
    args_not_in_doc = [arg not in doc for arg in args]
    if any(args_not_in_doc):
        raise ValueError(
            "{} {} arguments are not present in documentation ".format(name, list(
                compress(args, args_not_in_doc))), member.__module__)
    words = doc.replace('*', '').split()
    # Check arguments styling
    styles = [arg + ":" not in words for arg in args]
    if any(styles):
        raise ValueError(
            "{} {} are not style properly 'argument': documentation".format(
                name,
                list(compress(args, styles))),
            member.__module__)

    # Check arguments order
    indexes = [words.index(arg + ":") for arg in args]
    if indexes != sorted(indexes):
        raise ValueError(
            "{} arguments order is different from the documentation".format(name),
            member.__module__) 

# 需要導入模塊: import itertools [as 別名]
# 或者: from itertools import compress [as 別名]
def select_subclassdata(X, y,totalClassNum,SubClassNum, subClassIndexList,normalize=True):


    X= np.array(list(itertools.compress(X, [subClassIndexList.__contains__(c) for c in y])))
    y= np.array(list(itertools.compress(y, [subClassIndexList.__contains__(c) for c in y])))


    d = {}
    for i in xrange(SubClassNum):
        d.update({subClassIndexList[i]: (totalClassNum+i)})

    d1 = {}
    for i in xrange(SubClassNum):
        d1.update({(totalClassNum+i): i})

    for k, v in d.iteritems():
        np.place(y,y==k,v)
    for k, v in d1.iteritems():
        np.place(y,y==k,v)
    return X,y 

# 需要導入模塊: import itertools [as 別名]
# 或者: from itertools import compress [as 別名]
def cut_off_drivers_of(dstSignal, statements):
    """
    Cut off drivers from statements
    """
    separated = []
    stm_filter = []
    for stm in statements:
        stm._clean_signal_meta()
        d = stm._cut_off_drivers_of(dstSignal)
        if d is not None:
            separated.append(d)

        f = d is not stm
        stm_filter.append(f)

    return list(compress(statements, stm_filter)), separated 

# 需要導入模塊: import itertools [as 別名]
# 或者: from itertools import compress [as 別名]
def main(args):
        argc = len(args)

        ratio = 0.05

        if argc < 3:
                print("Usage:", args[0], "<if> <of> <ratio={}>".format(ratio))
                return -1
        elif argc > 3:
            ratio = float(args[3])

        with open(args[1], encoding='utf-8') as ifh, open(args[2], 'w', encoding='utf-8') as ofh:
            for l in ifh:
                words = l.rstrip().split()
                indices = np.random.random_sample((len(words),)) > ratio
                selected_words = list(compress(words, indices))
                ofh.write(" ".join(selected_words)+"\n")

        return 0 

# 需要導入模塊: import itertools [as 別名]
# 或者: from itertools import compress [as 別名]
def import_json(path='json/MPI_annotations.json', order='json/MPI_order.npy'):
    """Get the json file containing the dataset.
    We want the data to be shuffled, however the training has to be repeatable.
    This means that once shuffled the order has to me mantained."""
    with open(path) as data_file:
        data_this = json.load(data_file)
        data_this = np.array(data_this['root'])
    num_samples = len(data_this)

    if os.path.exists(order):
        idx = np.load(order)
    else:
        idx = np.random.permutation(num_samples).tolist()
        np.save(order, idx)

    is_not_validation = [not data_this[i]['isValidation']
                         for i in range(num_samples)]
    keep_data_idx = list(compress(idx, is_not_validation))

    data = data_this[keep_data_idx]
    return data, len(keep_data_idx) 

# 需要導入模塊: import itertools [as 別名]
# 或者: from itertools import compress [as 別名]
def handle_crash(varr, vpath, ssname, vlist, varr_list, frame_dict):
    seg1_list = list(filter(lambda v: re.search('seg1', v), vlist))
    seg2_list = list(filter(lambda v: re.search('seg2', v), vlist))
    if seg1_list and seg2_list:
        tframe = frame_dict[ssname]
        varr1 = darr.concatenate(
            list(compress(varr_list, seg1_list)),
            axis=0)
        varr2 = darr.concatenate(
            list(compress(varr_list, seg2_list)),
            axis=0)
        fm1, fm2 = varr1.shape[0], varr2.shape[0]
        fm_crds = varr.coords['frame']
        fm_crds1 = fm_crds.sel(frame=slice(None, fm1 - 1)).values
        fm_crds2 = fm_crds.sel(frame=slice(fm1, None)).values
        fm_crds2 = fm_crds2 + (tframe - fm_crds2.max())
        fm_crds_new = np.concatenate([fm_crds1, fm_crds2], axis=0)
        return varr.assign_coords(frame=fm_crds_new)
    else:
        return varr 

# 需要導入模塊: import itertools [as 別名]
# 或者: from itertools import compress [as 別名]
def micc_dictionary(paper):
    '''
    Analogous to citation_number_dictionary, but for MICCs rather than the number of citations.
    Co-citations are when two citations are included in the same end note (e.g, '[3-5]')
    :return: dict of counts for co-citation occurrences
    '''
    all_groups = [group_cleaner(g) for g in citation_grouper(paper)]
    references = paper.find_all("ref")
    max_ref_num = len(references)
    results = {}
    for i in range(1, max_ref_num + 1):
        counts = [g.count(i) for g in all_groups]
        cite_groups = compress(all_groups, counts)
        cocite_counts = [len(g) - 1 for g in cite_groups]
        if len(cocite_counts) == 0:
            cocite_counts = [-1]
        results[i] = median(cocite_counts)

    return results