Python builtins.zip方法代碼示例

# 需要導入模塊: import builtins [as 別名]
# 或者: from builtins import zip [as 別名]
def get_row(self, *row, **options):
        """Format the row into a single Cell spanning all output columns.

        Args:
          *row: A list of objects to render in the same order as columns are
             defined.

        Returns:
          A single Cell object spanning the entire row.
        """
        result = []
        for c, x in zip(self.columns, row):
            merged_opts = c.options.copy()
            merged_opts.update(options)
            if not merged_opts.get("hidden"):
                result.append(c.render_row(x, **options) or Cell(""))

        return JoinedCell(
            *result, tablesep=self.options.get("tablesep", " ")) 

# 需要導入模塊: import builtins [as 別名]
# 或者: from builtins import zip [as 別名]
def ParseMemoryRuns(self, fhandle):
        # Set acquisition mode. If the driver does not support this mode it will
        # just fall back to the default.
        win32file.DeviceIoControl(
            fhandle, CTRL_IOCTRL,
            struct.pack("I", PMEM_MODE_PTE), 4, None)

        result = win32file.DeviceIoControl(
            fhandle, INFO_IOCTRL, b"", 102400, None)

        fmt_string = "Q" * len(self.FIELDS)
        self.memory_parameters = dict(zip(self.FIELDS, struct.unpack_from(
            fmt_string, result)))

        offset = struct.calcsize(fmt_string)
        for x in range(self.memory_parameters["NumberOfRuns"]):
            start, length = struct.unpack_from("QQ", result, x * 16 + offset)
            self.add_run(start, start, length, self.fhandle_as) 

# 需要導入模塊: import builtins [as 別名]
# 或者: from builtins import zip [as 別名]
def _get_as_histograms(self):
        histogram_map = {}
        hist_list = [('ttl', 'ttl', False), ('objsz', 'objsz', False), ('objsz', 'object-size', True)]
        hist_dumps = [util.Future(self.cluster.info_histogram, hist[0],
                                  logarithmic = hist[2],
                                  raw_output=True,
                                  nodes=self.nodes).start()
                      for hist in hist_list]

        for hist, hist_dump in zip(hist_list, hist_dumps):
            hist_dump = hist_dump.result()

            for node in hist_dump:
                if node not in histogram_map:
                    histogram_map[node] = {}

                if not hist_dump[node] or isinstance(hist_dump[node], Exception):
                    continue

                histogram_map[node][hist[1]] = hist_dump[node]

        return histogram_map 

# 需要導入模塊: import builtins [as 別名]
# 或者: from builtins import zip [as 別名]
def _get_value_and_diff(self, prev, slice_val):
        diff = []
        value = []
        under_limit = True
        if self.upper_limit_check:
            under_limit = False
        if prev:
            temp = ([b - a for b, a in zip(slice_val, prev)])
            if not self.upper_limit_check or any(i >= self.upper_limit_check for i in temp):
                diff = ([b for b in temp])
                under_limit = True
        else:
            if not self.upper_limit_check or any(i >= self.upper_limit_check for i in slice_val):
                diff = ([b for b in slice_val])
                under_limit = True

        if under_limit:
            value = ([b for b in slice_val])
        return value, diff 

# 需要導入模塊: import builtins [as 別名]
# 或者: from builtins import zip [as 別名]
def parse_line(cls, line):
        fields = dict((name, val.strip().decode())
                      for name, val in zip(cls.fields, line.split(b",")))
        for fld in ["sport", "dport"]:
            try:
                fields[fld] = int(
                    fields[fld],
                    16 if fields[fld].startswith("0x") else 10,
                )
            except ValueError:
                if not fields[fld]:
                    del fields[fld]
        fields["src"] = fields.pop("saddr")
        fields["dst"] = fields.pop("daddr")
        fields["csbytes"] = int(fields.pop("sbytes"))
        fields["cspkts"] = int(fields.pop("spkts"))
        fields["scbytes"] = int(fields.pop("dbytes"))
        fields["scpkts"] = int(fields.pop("dpkts"))
        fields["start_time"] = datetime.datetime.fromtimestamp(
            float(fields.pop("stime"))
        )
        fields["end_time"] = datetime.datetime.fromtimestamp(
            float(fields.pop("ltime"))
        )
        return fields 

# 需要導入模塊: import builtins [as 別名]
# 或者: from builtins import zip [as 別名]
def parse_line(self, line):
        line = line.decode().rstrip('\r\n')
        if not line:
            self.nextline_headers = True
            return next(self)
        line = [elt.strip() for elt in line.split(',')]
        if self.nextline_headers:
            self.fields = line
            self.cur_types = [self.types.get(field) for field in line]
            self.nextline_headers = False
            return next(self)
        return dict(zip(
            self.fields,
            (self.converters.get(self.cur_types[i])(val)
             for (i, val) in enumerate(line)),
        )) 

# 需要導入模塊: import builtins [as 別名]
# 或者: from builtins import zip [as 別名]
def get_fuzz_target_weights():
  """Get a list of fuzz target weights based on the current fuzzer."""
  job_type = environment.get_value('JOB_NAME')

  target_jobs = list(fuzz_target_utils.get_fuzz_target_jobs(job=job_type))
  fuzz_targets = fuzz_target_utils.get_fuzz_targets_for_target_jobs(target_jobs)

  weights = {}
  for fuzz_target, target_job in zip(fuzz_targets, target_jobs):
    if not fuzz_target:
      logs.log_error('Skipping weight assignment for fuzz target %s.' %
                     target_job.fuzz_target_name)
      continue

    weights[fuzz_target.binary] = target_job.weight

  return weights 

# 需要導入模塊: import builtins [as 別名]
# 或者: from builtins import zip [as 別名]
def setUp(self):
    """Set up."""
    super(UntrustedRunEngineFuzzerTest, self).setUp()
    environment.set_value('JOB_NAME', 'libfuzzer_asan_job')

    job = data_types.Job(
        name='libfuzzer_asan_job',
        environment_string=(
            'RELEASE_BUILD_BUCKET_PATH = '
            'gs://clusterfuzz-test-data/test_libfuzzer_builds/'
            'test-libfuzzer-build-([0-9]+).zip\n'
            'REVISION_VARS_URL = https://commondatastorage.googleapis.com/'
            'clusterfuzz-test-data/test_libfuzzer_builds/'
            'test-libfuzzer-build-%s.srcmap.json\n'))
    job.put()

    self.temp_dir = tempfile.mkdtemp(dir=environment.get_value('FUZZ_INPUTS'))
    environment.set_value('USE_MINIJAIL', False) 

# 需要導入模塊: import builtins [as 別名]
# 或者: from builtins import zip [as 別名]
def is_valid_flatten_or_unflatten(src_axes, dst_axes):
        """
        Checks whether we can flatten OR unflatten from src_axes to dst_axes.

        The requirements are that the components of axes should all be
        present in new_axes and that they should be laid out in the same
        order. This check is symmetric.
        """

        # inflate
        src_axes = Axes.as_flattened_list(src_axes)
        dst_axes = Axes.as_flattened_list(dst_axes)

        # check equal number of Axis
        if len(src_axes) != len(dst_axes):
            return False

        # check all Axis are equal
        equal = [src == dst for src, dst in zip(src_axes, dst_axes)]
        return all(equal) 

# 需要導入模塊: import builtins [as 別名]
# 或者: from builtins import zip [as 別名]
def _make_strides(inner_size, axes, full_sizes):
    """
    Generates a tuple of strides for a set of axes. See _make_stride
    for a description of the stride given to each axis.

    Arguments:
        inner_size: The total size of all dimensions smaller than
        the axes.
        axes: The axes for which we are generating strides.
        full_sizes: The size of each axis.

    Returns:
        inner_size: The total size of these axes and all smaller dimensions.
        strides: The strides generated for the axes.
    """
    full_strides = []
    for axis, fsz in reversed(list(zip(axes, full_sizes))):
        inner_size, stride = _make_stride(inner_size, axis, fsz)
        full_strides.append(stride)
    return inner_size, tuple(reversed(full_strides)) 

# 需要導入模塊: import builtins [as 別名]
# 或者: from builtins import zip [as 別名]
def expand_offsets(cur_rect_l, cur_rect_u, offsets):
        '''
        Expand offsets at different level along each dimension to generate the 
        final offsets for all candidate by computing the sum of each tuple in the 
        cross product of offset arrays.
        e.g For the some dimension two level offsets [[0, 1, 0], [2, 4, 2]] will be expanded to 
        [2 4 2 3 5 3 2 4 2]
        cur_rect_l and cur_rect_u: coordinates of the lower and upper corner of the range.
        offsets: Nested array representing offsets of ranges along dimension, level of hierarchy    

        ''' 
        # remove empty list(no query at this level)
        offsets = [list(filter(lambda x: len(x) > 0, d)) for d in offsets]
        assert all([len(d) == len(offsets[0]) for d in offsets]),\
               "Shape of offsets along each dimension should match."    
        if len(offsets[0]) < 1:
            return [], []   
        # expand offsets across different levels.
        expanded_offsets = [HierarchicalRanges.quick_product(*d).sum(axis=0) for d in offsets] 
        lower = np.vstack([ l + offset for l, offset in zip(cur_rect_l, expanded_offsets)]).T
        upper = np.vstack([ u + offset for u, offset in zip(cur_rect_u, expanded_offsets)]).T
        return lower, upper 

# 需要導入模塊: import builtins [as 別名]
# 或者: from builtins import zip [as 別名]
def select(self):
        QtQ = self.W.gram().dense_matrix()
        n = self.domain_shape[0]
        err, inv, weights, queries = self._GreedyHierByLv(
            QtQ, n, 0, withRoot=False)

        # form matrix from queries and weights
        row_list = []
        for q, w in zip(queries, weights):
            if w > 0:
                row = np.zeros(self.domain_shape[0])
                row[q[0]:q[1] + 1] = w
                row_list.append(row)
        mat = np.vstack(row_list)
        mat = sparse.csr_matrix(mat) if sparse.issparse(mat) is False else mat

        return matrix.EkteloMatrix(mat) 

# 需要導入模塊: import builtins [as 別名]
# 或者: from builtins import zip [as 別名]
def canonical_ordering(mapping):
    """ remap according to the canonical order.
     if bins are noncontiguous, use position of first occurrence.
     e.g. [3,4,1,1] => [1,2,3,3]; [3,4,1,1,0,1]=>[0,1,2,2,3,2]
    """
    unique, indices, inverse, counts = mapping_statistics(mapping)

    uniqueInverse, indexInverse = np.unique(inverse,return_index =True)

    indexInverse.sort()
    newIndex = inverse[indexInverse]
    tups = list(zip(uniqueInverse, newIndex)) 
    tups.sort(key=lambda x: x[1])
    u = np.array( [u for (u,i) in tups] )
    mapping = u[inverse].reshape(mapping.shape)

    return mapping 

# 需要導入模塊: import builtins [as 別名]
# 或者: from builtins import zip [as 別名]
def calc_mean_lifetime(dx, t1=0, t2=np.inf, ph_sel=Ph_sel('all')):
    """Compute the mean lifetime in each burst.

    Arguments:
        t1, t2 (floats): min and max value (in TCSPC bin units) for the
            nanotime to be included in the mean
        ph_sel (Ph_sel object): object defining the photon selection.
            See :mod:`fretbursts.ph_sel` for details.

    Returns:
        List of arrays of per-burst mean lifetime. One array per channel.
    """
    mean_lifetimes = []

    for bursts, nanot, mask in zip(dx.mburst, dx.nanotimes,
                                   dx.iter_ph_masks(ph_sel)):
        selection = (nanot > t1) * (nanot < t2)
        # Select photons in ph_sel AND with nanotime in [t1, t2]
        if isarray(mask):
            selection *= mask
        mean_lifetimes.append(
            burstlib.burst_ph_stats(nanot, bursts, mask=selection,
                                    func=np.mean) - t1)

    return mean_lifetimes 

# 需要導入模塊: import builtins [as 別名]
# 或者: from builtins import zip [as 別名]
def burst_data_period_mean(dx, burst_data):
    """Compute mean `burst_data` in each period.

    Arguments:
        dx (Data object): contains the burst data to process
        burst_data (list of arrays): one array per channel, each array
            has one element of "burst data" per burst.

    Returns:
        2D of arrays with shape (nch, nperiods).

    Example:
        burst_period_mean(dx, dx.nt)
    """
    mean_burst_data = np.zeros((dx.nch, dx.nperiods))
    for ich, (b_data_ch, period) in enumerate(zip(burst_data, dx.bp)):
        for iperiod in range(dx.nperiods):
            mean_burst_data[ich, iperiod] = b_data_ch[period == iperiod].mean()
    return mean_burst_data