Python itertools.repeat() Examples

def _hash_comparison(self):
        """
        Return a comparison of actual and expected hash values.

        Example::

               Expected sha256 abcdeabcdeabcdeabcdeabcdeabcdeabcdeabcdeabcde
                            or 123451234512345123451234512345123451234512345
                    Got        bcdefbcdefbcdefbcdefbcdefbcdefbcdefbcdefbcdef

        """
        def hash_then_or(hash_name):
            # For now, all the decent hashes have 6-char names, so we can get
            # away with hard-coding space literals.
            return chain([hash_name], repeat('    or'))

        lines = []
        for hash_name, expecteds in iteritems(self.allowed):
            prefix = hash_then_or(hash_name)
            lines.extend(('        Expected %s %s' % (next(prefix), e))
                         for e in expecteds)
            lines.append('             Got        %s\n' %
                         self.gots[hash_name].hexdigest())
            prefix = '    or'
        return '\n'.join(lines) 

def timeit(self, number=default_number):
        """Time 'number' executions of the main statement.

        To be precise, this executes the setup statement once, and
        then returns the time it takes to execute the main statement
        a number of times, as a float measured in seconds.  The
        argument is the number of times through the loop, defaulting
        to one million.  The main statement, the setup statement and
        the timer function to be used are passed to the constructor.
        """
        it = itertools.repeat(None, number)
        gcold = gc.isenabled()
        gc.disable()
        try:
            timing = self.inner(it, self.timer)
        finally:
            if gcold:
                gc.enable()
        return timing 

def matchPreviousExpr(expr):
    """Helper to define an expression that is indirectly defined from
       the tokens matched in a previous expression, that is, it looks
       for a 'repeat' of a previous expression.  For example::
           first = Word(nums)
           second = matchPreviousExpr(first)
           matchExpr = first + ":" + second
       will match C{"1:1"}, but not C{"1:2"}.  Because this matches by
       expressions, will *not* match the leading C{"1:1"} in C{"1:10"};
       the expressions are evaluated first, and then compared, so
       C{"1"} is compared with C{"10"}.
       Do *not* use with packrat parsing enabled.
    """
    rep = Forward()
    e2 = expr.copy()
    rep <<= e2
    def copyTokenToRepeater(s,l,t):
        matchTokens = _flatten(t.asList())
        def mustMatchTheseTokens(s,l,t):
            theseTokens = _flatten(t.asList())
            if  theseTokens != matchTokens:
                raise ParseException("",0,"")
        rep.setParseAction( mustMatchTheseTokens, callDuringTry=True )
    expr.addParseAction(copyTokenToRepeater, callDuringTry=True)
    return rep 

def repeat(self, repeat=default_repeat, number=default_number):
        """Call timeit() a few times.

        This is a convenience function that calls the timeit()
        repeatedly, returning a list of results.  The first argument
        specifies how many times to call timeit(), defaulting to 3;
        the second argument specifies the timer argument, defaulting
        to one million.

        Note: it's tempting to calculate mean and standard deviation
        from the result vector and report these.  However, this is not
        very useful.  In a typical case, the lowest value gives a
        lower bound for how fast your machine can run the given code
        snippet; higher values in the result vector are typically not
        caused by variability in Python's speed, but by other
        processes interfering with your timing accuracy.  So the min()
        of the result is probably the only number you should be
        interested in.  After that, you should look at the entire
        vector and apply common sense rather than statistics.
        """
        r = []
        for i in range(repeat):
            t = self.timeit(number)
            r.append(t)
        return r 

def matchPreviousLiteral(expr):
    """Helper to define an expression that is indirectly defined from
       the tokens matched in a previous expression, that is, it looks
       for a 'repeat' of a previous expression.  For example::
           first = Word(nums)
           second = matchPreviousLiteral(first)
           matchExpr = first + ":" + second
       will match C{"1:1"}, but not C{"1:2"}.  Because this matches a
       previous literal, will also match the leading C{"1:1"} in C{"1:10"}.
       If this is not desired, use C{matchPreviousExpr}.
       Do *not* use with packrat parsing enabled.
    """
    rep = Forward()
    def copyTokenToRepeater(s,l,t):
        if t:
            if len(t) == 1:
                rep << t[0]
            else:
                # flatten t tokens
                tflat = _flatten(t.asList())
                rep << And( [ Literal(tt) for tt in tflat ] )
        else:
            rep << Empty()
    expr.addParseAction(copyTokenToRepeater, callDuringTry=True)
    return rep 

def _hash_comparison(self):
        """
        Return a comparison of actual and expected hash values.

        Example::

               Expected sha256 abcdeabcdeabcdeabcdeabcdeabcdeabcdeabcdeabcde
                            or 123451234512345123451234512345123451234512345
                    Got        bcdefbcdefbcdefbcdefbcdefbcdefbcdefbcdefbcdef

        """
        def hash_then_or(hash_name):
            # For now, all the decent hashes have 6-char names, so we can get
            # away with hard-coding space literals.
            return chain([hash_name], repeat('    or'))

        lines = []
        for hash_name, expecteds in iteritems(self.allowed):
            prefix = hash_then_or(hash_name)
            lines.extend(('        Expected %s %s' % (next(prefix), e))
                         for e in expecteds)
            lines.append('             Got        %s\n' %
                         self.gots[hash_name].hexdigest())
            prefix = '    or'
        return '\n'.join(lines) 

def main(args=None):
    """Main program, used when run as a script.

    The optional argument specifies the command line to be parsed,
    defaulting to sys.argv[1:].

    The return value is an exit code to be passed to sys.exit(); it
    may be None to indicate success.

    When an exception happens during timing, a traceback is printed to
    stderr and the return value is 1.  Exceptions at other times
    (including the template compilation) are not caught.
    """
    if args is None:
        args = sys.argv[1:]
    import getopt
    try:
        opts, args = getopt.getopt(args, "n:s:r:tcvh",
                                   ["number=", "setup=", "repeat=",
                                    "time", "clock", "verbose", "help"])
    except getopt.error, err:
        print err
        print "use -h/--help for command line help"
        return 2 

def get(self, idx):
        data = self.data.__class__()

        if hasattr(self.data, '__num_nodes__'):
            data.num_nodes = self.data.__num_nodes__[idx]

        for key in self.data.keys:
            item, slices = self.data[key], self.slices[key]
            start, end = slices[idx].item(), slices[idx + 1].item()
            # print(slices[idx], slices[idx + 1])
            if torch.is_tensor(item):
                s = list(repeat(slice(None), item.dim()))
                s[self.data.__cat_dim__(key, item)] = slice(start, end)
            elif start + 1 == end:
                s = slices[start]
            else:
                s = slice(start, end)
            data[key] = item[s]
        return data 

def elements(self):
                '''Iterator over elements repeating each as many times as its count.

                >>> c = Counter('ABCABC')
                >>> sorted(c.elements())
                ['A', 'A', 'B', 'B', 'C', 'C']

                If an element's count has been set to zero or is a negative number,
                elements() will ignore it.

                '''
                for elem, count in self.iteritems():
                    for _ in repeat(None, count):
                        yield elem

            # Override dict methods where the meaning changes for Counter
            # objects. 

def repeat(self, repeat=default_repeat, number=default_number):
        """Call timeit() a few times.

        This is a convenience function that calls the timeit()
        repeatedly, returning a list of results.  The first argument
        specifies how many times to call timeit(), defaulting to 3;
        the second argument specifies the timer argument, defaulting
        to one million.

        Note: it's tempting to calculate mean and standard deviation
        from the result vector and report these.  However, this is not
        very useful.  In a typical case, the lowest value gives a
        lower bound for how fast your machine can run the given code
        snippet; higher values in the result vector are typically not
        caused by variability in Python's speed, but by other
        processes interfering with your timing accuracy.  So the min()
        of the result is probably the only number you should be
        interested in.  After that, you should look at the entire
        vector and apply common sense rather than statistics.
        """
        r = []
        for i in range(repeat):
            t = self.timeit(number)
            r.append(t)
        return r 

def timeit(self, number=default_number):
        """Time 'number' executions of the main statement.

        To be precise, this executes the setup statement once, and
        then returns the time it takes to execute the main statement
        a number of times, as a float measured in seconds.  The
        argument is the number of times through the loop, defaulting
        to one million.  The main statement, the setup statement and
        the timer function to be used are passed to the constructor.
        """
        if itertools:
            it = itertools.repeat(None, number)
        else:
            it = [None] * number
        gcold = gc.isenabled()
        gc.disable()
        timing = self.inner(it, self.timer)
        if gcold:
            gc.enable()
        return timing 

def elements(self):
        '''Iterator over elements repeating each as many times as its count.

        >>> c = Counter('ABCABC')
        >>> sorted(c.elements())
        ['A', 'A', 'B', 'B', 'C', 'C']

        # Knuth's example for prime factors of 1836:  2**2 * 3**3 * 17**1
        >>> prime_factors = Counter({2: 2, 3: 3, 17: 1})
        >>> product = 1
        >>> for factor in prime_factors.elements():     # loop over factors
        ...     product *= factor                       # and multiply them
        >>> product
        1836

        Note, if an element's count has been set to zero or is a negative
        number, elements() will ignore it.

        '''
        # Emulate Bag.do from Smalltalk and Multiset.begin from C++.
        return _chain.from_iterable(_starmap(_repeat, self.items()))

    # Override dict methods where necessary 

def _hash_comparison(self):
        """
        Return a comparison of actual and expected hash values.

        Example::

               Expected sha256 abcdeabcdeabcdeabcdeabcdeabcdeabcdeabcdeabcde
                            or 123451234512345123451234512345123451234512345
                    Got        bcdefbcdefbcdefbcdefbcdefbcdefbcdefbcdefbcdef

        """
        def hash_then_or(hash_name):
            # For now, all the decent hashes have 6-char names, so we can get
            # away with hard-coding space literals.
            return chain([hash_name], repeat('    or'))

        lines = []
        for hash_name, expecteds in iteritems(self.allowed):
            prefix = hash_then_or(hash_name)
            lines.extend(('        Expected %s %s' % (next(prefix), e))
                         for e in expecteds)
            lines.append('             Got        %s\n' %
                         self.gots[hash_name].hexdigest())
            prefix = '    or'
        return '\n'.join(lines) 

def _hash_comparison(self):
        """
        Return a comparison of actual and expected hash values.

        Example::

               Expected sha256 abcdeabcdeabcdeabcdeabcdeabcdeabcdeabcdeabcde
                            or 123451234512345123451234512345123451234512345
                    Got        bcdefbcdefbcdefbcdefbcdefbcdefbcdefbcdefbcdef

        """
        def hash_then_or(hash_name):
            # For now, all the decent hashes have 6-char names, so we can get
            # away with hard-coding space literals.
            return chain([hash_name], repeat('    or'))

        lines = []
        for hash_name, expecteds in iteritems(self.allowed):
            prefix = hash_then_or(hash_name)
            lines.extend(('        Expected %s %s' % (next(prefix), e))
                         for e in expecteds)
            lines.append('             Got        %s\n' %
                         self.gots[hash_name].hexdigest())
            prefix = '    or'
        return '\n'.join(lines) 

def test_constructor_compound_dtypes(self):
        # GH 5191
        # compound dtypes should raise not-implementederror

        def f(dtype):
            data = list(itertools.repeat((datetime(2001, 1, 1),
                                          "aa", 20), 9))
            return DataFrame(data=data,
                             columns=["A", "B", "C"],
                             dtype=dtype)

        pytest.raises(NotImplementedError, f,
                      [("A", "datetime64[h]"),
                       ("B", "str"),
                       ("C", "int32")])

        # these work (though results may be unexpected)
        f('int64')
        f('float64')

        # 10822
        # invalid error message on dt inference
        if not compat.is_platform_windows():
            f('M8[ns]') 

def load_results(
    result_files,
    options,
    run_set_id=None,
    columns=None,
    columns_relevant_for_diff=set(),
):
    """Version of load_result for multiple input files that will be loaded concurrently."""
    return parallel.map(
        load_result,
        result_files,
        itertools.repeat(options),
        itertools.repeat(run_set_id),
        itertools.repeat(columns),
        itertools.repeat(columns_relevant_for_diff),
    ) 

def elements(self):
        '''Iterator over elements repeating each as many times as its count.

        >>> c = Counter('ABCABC')
        >>> sorted(c.elements())
        ['A', 'A', 'B', 'B', 'C', 'C']

        # Knuth's example for prime factors of 1836:  2**2 * 3**3 * 17**1
        >>> prime_factors = Counter({2: 2, 3: 3, 17: 1})
        >>> product = 1
        >>> for factor in prime_factors.elements():     # loop over factors
        ...     product *= factor                       # and multiply them
        >>> product
        1836

        Note, if an element's count has been set to zero or is a negative
        number, elements() will ignore it.

        '''
        # Emulate Bag.do from Smalltalk and Multiset.begin from C++.
        return _chain.from_iterable(_starmap(_repeat, self.items()))

    # Override dict methods where necessary 

def elements(self):
        '''Iterator over elements repeating each as many times as its count.

        >>> c = Counter('ABCABC')
        >>> sorted(c.elements())
        ['A', 'A', 'B', 'B', 'C', 'C']

        # Knuth's example for prime factors of 1836:  2**2 * 3**3 * 17**1
        >>> prime_factors = Counter({2: 2, 3: 3, 17: 1})
        >>> product = 1
        >>> for factor in prime_factors.elements():     # loop over factors
        ...     product *= factor                       # and multiply them
        >>> product
        1836

        Note, if an element's count has been set to zero or is a negative
        number, elements() will ignore it.

        '''
        # Emulate Bag.do from Smalltalk and Multiset.begin from C++.
        return _chain.from_iterable(_starmap(_repeat, self.items()))

    # Override dict methods where necessary 

def alter_allocations(self, keys, quota_sizes=None, handle_shrink=True, new_keys=None,
                          allocate=False, process_quota=False):
        """
        Alter multiple requests
        :param keys: keys to update
        :param quota_sizes: new quota sizes, if None, no changes will be made
        :param handle_shrink: if True and the quota size less than the original, process requests in the queue
        :param new_keys: new allocation keys to replace current keys, if None, no changes will be made
        :param allocate: if True, will allocate resources for new items
        :param process_quota: call process_quotas() after allocated
        :return:
        """
        quota_sizes = quota_sizes or itertools.repeat(None)
        new_keys = new_keys or itertools.repeat(None)
        shrink = False
        for k, s, nk in zip(keys, quota_sizes, new_keys):
            cur_shrink = self.alter_allocation(
                k, s, handle_shrink=False, new_key=nk, allocate=allocate, process_quota=process_quota)
            shrink = shrink or cur_shrink
        if shrink and handle_shrink:
            self._process_requests() 

def populate_obj(self, obj, name):
        values = getattr(obj, name, None)
        try:
            ivalues = iter(values)
        except TypeError:
            ivalues = iter([])

        candidates = itertools.chain(ivalues, itertools.repeat(None))
        _fake = type(str('_fake'), (object, ), {})
        output = []
        for field, data in izip(self.entries, candidates):
            fake_obj = _fake()
            fake_obj.data = data
            field.populate_obj(fake_obj, 'data')
            output.append(fake_obj.data)

        setattr(obj, name, output) 

def object_from_row(self, row, row_number, exception_policy=TableSheetExceptionPolicy.RaiseCellException):
        data = OrderedDict()
        cell_exceptions = []
        for cell, column in zip(chain(row, repeat(None)), self.columns):
            try:
                data[column.object_attribute] = column._from_excel(cell)
            except CellException as e:
                if exception_policy.value <= TableSheetExceptionPolicy.RaiseCellException.value:
                    raise e
                else:
                    cell_exceptions.append(e)

        if cell_exceptions:
            raise CellExceptions(cell_exceptions)

        # return self.row_class(**data)
        return self.create_object(row_number, **data) 

def __make_date_examples():
    dates_no_day = [
        date(1999, 12, 1),
        date(2016, 2, 1)
    ]

    if six.PY3:
        # strftime does not support dates before 1900 in Python 2
        dates_no_day.append(date(1000, 11, 1))

    # Only one supported format for dates with no day
    o = zip(dates_no_day, it.repeat('%Y-%m'))

    dates_w_day = [
        date(1969, 12, 31),
        date(1900, 1, 1),
        date(2016, 2, 29),
        date(2017, 11, 14)
    ]

    dates_w_day_fmts = ('%Y%m%d', '%Y-%m-%d')
    o = it.chain(o, it.product(dates_w_day, dates_w_day_fmts))

    return list(o) 

def delete_lines(self, count=None):
        """Deletes the indicated # of lines, starting at line with
        cursor. As lines are deleted, lines displayed below cursor
        move up. Lines added to bottom of screen have spaces with same
        character attributes as last line moved up.

        :param int count: number of lines to delete.
        """
        count = count or 1
        top, bottom = self.margins

        # If cursor is outside scrolling margins it -- do nothin'.
        if top <= self.cursor.y <= bottom:
            #                v -- +1 to include the bottom margin.
            for _ in range(min(bottom - self.cursor.y + 1, count)):
                self.buffer.pop(self.cursor.y)
                self.buffer.insert(bottom, list(
                    repeat(self.cursor.attrs, self.columns)))

            self.carriage_return() 

def ExtractFeaturesForDirsList(args, dirs):
    global TMP_DIR
    TMP_DIR = "./tmp/feature_extractor%d/" % (os.getpid())
    if os.path.exists(TMP_DIR):
        shutil.rmtree(TMP_DIR, ignore_errors=True)
    os.makedirs(TMP_DIR)
    try:
        p = multiprocessing.Pool(4)
        p.starmap(ParallelExtractDir, zip(itertools.repeat(args), dirs))
        #for dir in dirs:
        #    ExtractFeaturesForDir(args, dir, '')
        output_files = os.listdir(TMP_DIR)
        for f in output_files:
            os.system("cat %s/%s" % (TMP_DIR, f))
    finally:
        shutil.rmtree(TMP_DIR, ignore_errors=True) 

def ExtractFeaturesForDirsList(args, dirs):
    global TMP_DIR
    TMP_DIR = "./tmp/feature_extractor%d/" % (os.getpid())
    if os.path.exists(TMP_DIR):
        shutil.rmtree(TMP_DIR, ignore_errors=True)
    os.makedirs(TMP_DIR)
    try:
        p = multiprocessing.Pool(4)
        p.starmap(ParallelExtractDir, zip(itertools.repeat(args), dirs))
        #for dir in dirs:
        #    ExtractFeaturesForDir(args, dir, '')
        output_files = os.listdir(TMP_DIR)
        for f in output_files:
            os.system("cat %s/%s" % (TMP_DIR, f))
    finally:
        shutil.rmtree(TMP_DIR, ignore_errors=True) 

def fromkeys(cls, keys, value=None):
        instance = super(cls, cls).__new__(cls)
        instance.__init__(zip(keys, repeat(value)))
        return instance 

def create_fetch_tensor(chunk_size, shape, dtype, tensor_key=None, tensor_id=None, chunk_keys=None):
    """
    Construct Fetch tensor on the fly, using given chunk_size, shape, dtype,
    as well as possible tensor_key, tensor_id and chunk keys.
    """
    from ..config import options
    from .fetch import TensorFetch

    if not isinstance(dtype, np.dtype):
        dtype = np.dtype(dtype)
    if chunk_keys is None:
        chunk_keys = itertools.repeat(None)

    # compute chunks
    chunk_size = chunk_size or options.chunk_size
    chunk_size = decide_chunk_sizes(shape, chunk_size, dtype.itemsize)
    chunk_size_idxes = (range(len(size)) for size in chunk_size)

    fetch_op = TensorFetch(dtype=dtype).reset_key()

    chunks = []
    for chunk_shape, chunk_idx, chunk_key in zip(itertools.product(*chunk_size),
                                                 itertools.product(*chunk_size_idxes),
                                                 chunk_keys):
        chunk = fetch_op.copy().reset_key().new_chunk(None, shape=chunk_shape, index=chunk_idx,
                                                      _key=chunk_key, hex=uuid.uuid4().hex)
        chunks.append(chunk)
    return fetch_op.copy().new_tensor(None, shape=shape, dtype=dtype, nsplits=chunk_size,
                                      chunks=chunks, _key=tensor_key, _id=tensor_id, hex=uuid.uuid4().hex) 

def replaceWith(replStr):
    """Helper method for common parse actions that simply return a literal value.  Especially
       useful when used with C{L{transformString<ParserElement.transformString>}()}.
    """
    #def _replFunc(*args):
    #    return [replStr]
    #return _replFunc
    return functools.partial(next, itertools.repeat([replStr])) 

def edge_index_from_dict(graph_dict, num_nodes=None):
    row, col = [], []
    for key, value in graph_dict.items():
        row += repeat(key, len(value))
        col += value
    edge_index = torch.stack([torch.tensor(row), torch.tensor(col)], dim=0)
    # NOTE: There are duplicated edges and self loops in the datasets. Other
    # implementations do not remove them!
    edge_index, _ = remove_self_loops(edge_index)
    edge_index, _ = coalesce(edge_index, None, num_nodes, num_nodes)
    return edge_index 

def matchPreviousLiteral(expr):
    """Helper to define an expression that is indirectly defined from
       the tokens matched in a previous expression, that is, it looks
       for a 'repeat' of a previous expression.  For example::
           first = Word(nums)
           second = matchPreviousLiteral(first)
           matchExpr = first + ":" + second
       will match C{"1:1"}, but not C{"1:2"}.  Because this matches a
       previous literal, will also match the leading C{"1:1"} in C{"1:10"}.
       If this is not desired, use C{matchPreviousExpr}.
       Do *not* use with packrat parsing enabled.
    """
    rep = Forward()
    def copyTokenToRepeater(s,l,t):
        if t:
            if len(t) == 1:
                rep << t[0]
            else:
                # flatten t tokens
                tflat = _flatten(t.asList())
                rep << And(Literal(tt) for tt in tflat)
        else:
            rep << Empty()
    expr.addParseAction(copyTokenToRepeater, callDuringTry=True)
    rep.setName('(prev) ' + _ustr(expr))
    return rep