Python more_itertools.unique_everseen方法代码示例

# 需要导入模块: import more_itertools [as 别名]
# 或者: from more_itertools import unique_everseen [as 别名]
def test_everseen(self):
        """ensure duplicate elements are ignored"""
        u = mi.unique_everseen('AAAABBBBCCDAABBB')
        self.assertEqual(
            ['A', 'B', 'C', 'D'],
            list(u)
        ) 

# 需要导入模块: import more_itertools [as 别名]
# 或者: from more_itertools import unique_everseen [as 别名]
def test_custom_key(self):
        """ensure the custom key comparison works"""
        u = mi.unique_everseen('aAbACCc', key=str.lower)
        self.assertEqual(list('abC'), list(u)) 

# 需要导入模块: import more_itertools [as 别名]
# 或者: from more_itertools import unique_everseen [as 别名]
def test_unhashable(self):
        """ensure things work for unhashable items"""
        iterable = ['a', [1, 2, 3], [1, 2, 3], 'a']
        u = mi.unique_everseen(iterable)
        self.assertEqual(list(u), ['a', [1, 2, 3]]) 

# 需要导入模块: import more_itertools [as 别名]
# 或者: from more_itertools import unique_everseen [as 别名]
def test_unhashable_key(self):
        """ensure things work for unhashable items with a custom key"""
        iterable = ['a', [1, 2, 3], [1, 2, 3], 'a']
        u = mi.unique_everseen(iterable, key=lambda x: x)
        self.assertEqual(list(u), ['a', [1, 2, 3]]) 

# 需要导入模块: import more_itertools [as 别名]
# 或者: from more_itertools import unique_everseen [as 别名]
def _implied_dirs(names):
        return more_itertools.unique_everseen(
            parent + "/"
            for name in names
            for parent in _parents(name)
            if parent + "/" not in names
        ) 

# 需要导入模块: import more_itertools [as 别名]
# 或者: from more_itertools import unique_everseen [as 别名]
def compute_node_colors(self):
        """Compute the node colors. Also computes the colorbar."""
        data = [self.graph.nodes[n][self.node_color] for n in self.nodes]

        if self.group_order == "alphabetically":
            data_reduced = sorted(list(set(data)))
        elif self.group_order == "default":
            data_reduced = list(unique_everseen(data))

        dtype = infer_data_type(data)
        n_grps = num_discrete_groups(data)

        if dtype == "categorical" or dtype == "ordinal":
            if n_grps <= 8:
                cmap = get_cmap(
                    cmaps["Accent_{0}".format(n_grps)].mpl_colormap
                )
            else:
                cmap = n_group_colorpallet(n_grps)
        elif dtype == "continuous" and not is_data_diverging(data):
            cmap = get_cmap(cmaps["continuous"].mpl_colormap)
        elif dtype == "continuous" and is_data_diverging(data):
            cmap = get_cmap(cmaps["diverging"].mpl_colormap)

        for d in data:
            idx = data_reduced.index(d) / n_grps
            self.node_colors.append(cmap(idx))

        # Add colorbar if required.ListedColormap
        logging.debug("length of data_reduced: {0}".format(len(data_reduced)))
        logging.debug("dtype: {0}".format(dtype))
        if len(data_reduced) > 1 and dtype == "continuous":
            self.sm = plt.cm.ScalarMappable(
                cmap=cmap,
                norm=plt.Normalize(
                    vmin=min(data_reduced),
                    vmax=max(data_reduced),  # noqa  # noqa
                ),
            )
            self.sm._A = [] 

# 需要导入模块: import more_itertools [as 别名]
# 或者: from more_itertools import unique_everseen [as 别名]
def all_features(self) -> Iterator[Feature]:
        """ Returns an iterator over the features of this type. Inherited features are included. To
        just retrieve immediate features, use `features`.

        Returns:
            An iterator over all features of this type, including inherited ones

        """

        # We use `unique_everseen` here, as children could redefine parent types (Issue #56)
        return unique_everseen(chain(self._features.values(), self._inherited_features.values())) 

# 需要导入模块: import more_itertools [as 别名]
# 或者: from more_itertools import unique_everseen [as 别名]
def merge_type_eligibilities(same_charges: List[Charge]) -> TypeEligibility:
        status = RecordMerger.compute_type_eligibility_status(same_charges)
        reasons = [charge.type_eligibility.reason for charge in same_charges]
        reason = " ⬥ ".join(list(unique_everseen(reasons)))
        return TypeEligibility(status=status, reason=reason) 

# 需要导入模块: import more_itertools [as 别名]
# 或者: from more_itertools import unique_everseen [as 别名]
def merge_time_eligibilities(time_eligibilities: Optional[List[TimeEligibility]]) -> Optional[TimeEligibility]:
        if time_eligibilities:
            status = RecordMerger.compute_time_eligibility_status(time_eligibilities)
            reasons = [time_eligibility.reason for time_eligibility in time_eligibilities]
            reason = " ⬥ ".join(list(unique_everseen(reasons)))
            date_will_be_eligible = time_eligibilities[0].date_will_be_eligible
            if len(set([time_eligibility.date_will_be_eligible for time_eligibility in time_eligibilities])) == 1:
                unique_date = True
            else:
                unique_date = False
            return TimeEligibility(
                status=status, reason=reason, date_will_be_eligible=date_will_be_eligible, unique_date=unique_date
            )
        else:
            return None 

# 需要导入模块: import more_itertools [as 别名]
# 或者: from more_itertools import unique_everseen [as 别名]
def merge_dispositions(same_charges: List[Charge]) -> Disposition:
        if len(list(unique_everseen([charge.disposition for charge in same_charges]))) == 2:
            return DispositionCreator.empty()
        else:
            return same_charges[0].disposition 

# 需要导入模块: import more_itertools [as 别名]
# 或者: from more_itertools import unique_everseen [as 别名]
def main():
    f = open(FLAGS.hosts_file,'r')
    hosts_list = []
    for line in f:
        hosts_list.append(line.strip())
    f.close()
    hosts_list = list(unique_everseen(hosts_list))

    # all hosts other than ps are all treated as workers, .ten.osc.edu is for owens, for other clusters, you may change correspondingly
    ps_hosts = [hosts_list[i] + ".ten.osc.edu:2222" for i in range(FLAGS.num_ps_hosts)]
    worker_hosts = [hosts_list[i] + ".ten.osc.edu:2222" for i in range(len(ps_hosts), len(hosts_list))]
    
    print(','.join(ps_hosts), ','.join(worker_hosts)) 

# 需要导入模块: import more_itertools [as 别名]
# 或者: from more_itertools import unique_everseen [as 别名]
def get_frames_from_sequence(event_list, num_events_per_frame, data_format,
                             frame_gen_method, is_x_first, is_x_flipped,
                             is_y_flipped, maxpool_subsampling,
                             do_clip_three_sigma, chip_size,
                             target_shape=None):
    """
    Extract ``num_events_per_frame`` events from a one-dimensional sequence of
    AER-events. The events are spatially subsampled to ``target_shape``, and
    standardized to [0, 1] using 3-sigma normalization. The resulting events
    are binned into a frame. The function operates on the events in
    ``xaddr`` etc sequentially until all are processed into frames.
    """

    from more_itertools import unique_everseen

    if target_shape is None:
        target_shape = chip_size
        scale = None
    else:
        scale = [np.true_divide((t - 1), (c - 1)) for t, c in zip(target_shape,
                                                                  chip_size)]
    num_frames = int(len(event_list) / num_events_per_frame)
    frames = np.zeros([num_frames] + list(target_shape), 'float32')

    print("Extracting {} frames from DVS event sequence.".format(num_frames))

    # Iterate for as long as there are events in the sequence.
    for sample_idx in range(num_frames):
        sample = frames[sample_idx]
        event_idxs = slice(num_events_per_frame * sample_idx,
                           num_events_per_frame * (sample_idx + 1))

        # Loop over ``num_events_per_frame`` events
        frame_event_list = []
        for x, y, t, p in event_list[event_idxs]:
            if scale is not None:
                # Subsample from 240x180 to e.g. 64x64
                x = int(x * scale[0])
                y = int(y * scale[1])

            pp = p if frame_gen_method == 'signed_sum' else 1
            frame_event_list.append((x, y, t, pp))

        if maxpool_subsampling:
            frame_event_list = list(unique_everseen(frame_event_list))

        for x, y, t, p in frame_event_list:
            add_event_to_frame(sample, x, y, p, frame_gen_method, is_x_first,
                               is_x_flipped, is_y_flipped)

        # sample = scale_event_frames(sample, frame_gen_method)
        if do_clip_three_sigma:
            frames[sample_idx] = clip_three_sigma(sample, frame_gen_method)
        else:
            frames[sample_idx] = sample

    frames = scale_event_frames(frames)

    channel_axis = 1 if data_format == 'channels_first' else -1

    return np.expand_dims(frames, channel_axis) 

# 需要导入模块: import more_itertools [as 别名]
# 或者: from more_itertools import unique_everseen [as 别名]
def merge(
        ambiguous_record: AmbiguousRecord,
        ambiguous_charge_id_to_time_eligibility_list: List[Dict[str, TimeEligibility]],
        charge_ids_with_question: List[str],
    ) -> Record:
        ambiguous_charge_id_to_time_eligibilities: Dict[str, List[TimeEligibility]] = collections.defaultdict(list)
        for charge_id_to_time_eligibility in ambiguous_charge_id_to_time_eligibility_list:
            for k, v in charge_id_to_time_eligibility.items():
                if v not in ambiguous_charge_id_to_time_eligibilities[k]:
                    ambiguous_charge_id_to_time_eligibilities[k].append(v)
        charges = list(flatten([record.charges for record in ambiguous_record]))
        record = ambiguous_record[0]
        new_case_list: List[Case] = []
        for case in record.cases:
            new_charges = []
            for charge in case.charges:
                time_eligibilities = ambiguous_charge_id_to_time_eligibilities.get(
                    charge.ambiguous_charge_id
                )  # TODO: Review whether this can return None
                sorted_time_eligibility = (
                    sorted(time_eligibilities, key=lambda e: e.date_will_be_eligible) if time_eligibilities else None
                )
                same_charges = list(filter(lambda c: c.ambiguous_charge_id == charge.ambiguous_charge_id, charges))
                romeo_and_juliet_exception = RecordMerger._is_romeo_and_juliet_exception(same_charges)
                merged_type_eligibility = RecordMerger.merge_type_eligibilities(same_charges)
                merged_time_eligibility = RecordMerger.merge_time_eligibilities(sorted_time_eligibility)
                if charge.ambiguous_charge_id in charge_ids_with_question:
                    charge_eligibility = ChargeEligibility(
                        ChargeEligibilityStatus.NEEDS_MORE_ANALYSIS, "Needs More Analysis"
                    )
                else:
                    charge_eligibility = RecordMerger.compute_charge_eligibility(
                        merged_type_eligibility, sorted_time_eligibility, romeo_and_juliet_exception
                    )
                    if "open" in charge_eligibility.label.lower():
                        charge_eligibility = replace(
                            charge_eligibility,
                            label=f"Eligibility Timeframe Dependent On Open Charge: {charge_eligibility.label}",
                        )
                expungement_result = ExpungementResult(
                    type_eligibility=merged_type_eligibility,
                    time_eligibility=merged_time_eligibility,
                    charge_eligibility=charge_eligibility,
                )
                merged_type_name = " ⬥ ".join(
                    list(unique_everseen([charge.charge_type.type_name for charge in same_charges]))
                )
                merged_charge_type = replace(charge.charge_type, type_name=merged_type_name)
                merged_disposition = RecordMerger.merge_dispositions(same_charges)
                new_charge: Charge = replace(
                    charge,
                    charge_type=merged_charge_type,
                    expungement_result=expungement_result,
                    disposition=merged_disposition,
                )
                new_charges.append(new_charge)
            new_case = replace(case, charges=tuple(new_charges))
            new_case_list.append(new_case)
        return replace(record, cases=tuple(new_case_list))