mandos.analysis.enrichment.EnrichmentAlg.clazz() - Code Metrics - Inspection of "feat: no clue" - dmyersturnbull/mandos - Measure and Improve Code Quality continuously with Scrutinizer

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Push — main ( ec3fe3...82dd22 )

by Douglas

created 2021-08-02 23:37 UTC

mandos.analysis.enrichment.EnrichmentAlg.clazz() A

↳ Parent: mandos.analysis.enrichment

Complexity

Conditions

Size

Total Lines	9
Code Lines	9

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
cc	1
eloc	9
nop	1
dl	0
loc	9
rs	9.95
c	0
b	0
f	0

"""
Scoring (regression and enrichment) calculations.
"""
import abc
import enum
import math
from dataclasses import dataclass

from typing import Generic, Mapping, Sequence, Type, TypeVar, Union, Optional, Any, Tuple

import numpy as np

import pandas as pd

from numpy.random import RandomState


from mandos.analysis import AnalysisUtils as Au
from mandos.model import CleverEnum
from mandos.model.hits import AbstractHit, HitFrame, KeyPredObjSource, KeyPredObj

from mandos.model.hit_utils import HitUtils

from mandos.analysis.io_defns import ScoreDf, EnrichmentDf

S = TypeVar("S", bound=Union[int, float, bool])



class EnrichmentCalculator(Generic[S], metaclass=abc.ABCMeta):

    def calc(

        self, hits: Sequence[AbstractHit], scores: Mapping[str, S]

    ) -> Mapping[KeyPredObj, float]:
        pair_to_hits = Au.hit_multidict(hits, "to_key_pred_obj")
        results = {}
        for pair, the_hits in pair_to_hits.items():

            results[pair] = self.for_pair(hits, scores)
        return results

    def for_pair(self, hits: Sequence[AbstractHit], scores: Mapping[str, S]) -> float:

        raise NotImplementedError()

    @classmethod
    def alg_name(cls) -> str:

        raise NotImplementedError()


# noinspection PyAbstractClass
class _FoldCalculator(EnrichmentCalculator[bool]):

    """"""


# noinspection PyAbstractClass
class _RegressCalculator(EnrichmentCalculator[float]):

    """"""


class AlphaCalculator(_RegressCalculator):

    @classmethod
    def alg_name(cls) -> str:

        return "alpha"

    def for_pair(self, hits: Sequence[AbstractHit], scores: Mapping[str, S]) -> float:

        source_to_hits = Au.hit_multidict(hits, "data_source")
        return float(
            np.mean(
                self._calc_term(source_hits, scores)
                for source, source_hits in source_to_hits.items()
            )
        )

    def _calc_term(self, hits: Sequence[AbstractHit], scores: Mapping[str, S]) -> float:
class Foo:
    def some_method(self, x, y):
        return x + y;
        return float(
            np.mean(
                [
                    Au.elle(hit.weight) * (2 * float(scores[hit.origin_inchikey] - 1)) ** 2
                    for hit in hits
                ]
            )
        )


class SumWeightedCalc(_RegressCalculator):

    @classmethod
    def alg_name(cls) -> str:

        return "w-sum"

    def for_pair(self, hits: Sequence[AbstractHit], scores: Mapping[str, S]) -> float:
class Foo:
    def some_method(self, x, y):
        return x + y;
        return math.fsum([scores[hit.origin_inchikey] * hit.weight for hit in hits]) / len(hits)


class SumUnweightedCalc(_RegressCalculator):

    @classmethod
    def alg_name(cls) -> str:

        return "n-sum"

    def for_pair(self, hits: Sequence[AbstractHit], scores: Mapping[str, S]) -> float:
class Foo:
    def some_method(self, x, y):
        return x + y;
        return math.fsum([scores[hit.origin_inchikey] for hit in hits]) / len(hits)


class FoldWeightedCalc(_FoldCalculator):

    @classmethod
    def alg_name(cls) -> str:

        return "w-ratio"

    def for_pair(self, hits: Sequence[AbstractHit], scores: Mapping[str, S]) -> float:
class Foo:
    def some_method(self, x, y):
        return x + y;
        yes = [hit for hit in hits if scores[hit.origin_inchikey]]
        no = [hit for hit in hits if not scores[hit.origin_inchikey]]

        numerator = math.fsum((hit.weight for hit in yes))

        denominator = math.fsum((hit.weight for hit in no))
        if denominator == 0:
            return float("inf")
        return numerator / denominator


class FoldUnweightedCalc(_FoldCalculator):

    @classmethod
    def alg_name(cls) -> str:

        return "n-ratio"

    def for_pair(self, hits: Sequence[AbstractHit], scores: Mapping[str, S]) -> float:
class Foo:
    def some_method(self, x, y):
        return x + y;
        numerator = len([hit for hit in hits if scores[hit.origin_inchikey]])
        denominator = len([hit for hit in hits if not scores[hit.origin_inchikey]])
        if denominator == 0:
            return float("inf")
        return numerator / denominator


class _Alg(CleverEnum):
    """"""

    @classmethod
    def dtype(cls) -> Type[Any]:

        raise NotImplementedError()


class RealAlg(_Alg):

    alpha = enum.auto()
    weighted = enum.auto()
    unweighted = enum.auto()

    @classmethod
    def dtype(cls) -> Type[Any]:
        return float

    @property
    def clazz(self) -> Type[_RegressCalculator]:

        return {
            RealAlg.alpha: AlphaCalculator,
            RealAlg.weighted: SumWeightedCalc,
            RealAlg.unweighted: SumUnweightedCalc,
        }[self]


class BoolAlg(_Alg):

    weighted = enum.auto()
    unweighted = enum.auto()

    @classmethod
    def dtype(cls) -> Type[Any]:
        return bool

    @property
    def clazz(self) -> Type[_FoldCalculator]:

        return {
            BoolAlg.weighted: FoldWeightedCalc,
            BoolAlg.unweighted: FoldUnweightedCalc,
        }[self]


class EnrichmentCalculation:

    def __init__(
        self,

        bool_alg: str,

        real_alg: str,

        n_samples: int,

        seed: int,

    ):
        self.bool_alg = BoolAlg.of(bool_alg)
        self.real_alg = RealAlg.of(real_alg)
        self.n_samples = n_samples
        self.seed = seed
        self.state = RandomState(seed)

    def calculate(self, hit_df: HitFrame, scores: Optional[ScoreDf]) -> EnrichmentDf:

        hits = hit_df.to_hits()
        if scores is None:
            scores = self._default_scores(hit_df)
        score_dict = self._get_dict(scores)
        results = self._calc(hits, score_dict, 0)
        for b in range(self.n_samples):

            b_hits = self.state.choice(hits, replace=True)
            results += self._calc(b_hits, score_dict, b)
        return EnrichmentDf.convert(results)

    def _calc(self, hits: Sequence[AbstractHit], score_dict, sample: int) -> Sequence[pd.DataFrame]:
        for score_name, (alg_type, score_vals) in score_dict.items():
            alg_instance = alg_type.clazz()
            forward = alg_instance.calc(hits, score_vals.to_dict())
            if alg_type.dtype == bool:
                reverse = alg_instance.calc(hits, (~score_vals).to_dict())
            else:
                reverse = alg_instance.calc(hits, (-score_vals).to_dict())
            return [self._make_df(forward, reverse, score_name, alg_type.name, sample)]

    def _default_scores(self, hit_df: HitFrame) -> ScoreDf:
class Foo:
    def some_method(self, x, y):
        return x + y;
        inchikeys = hit_df["origin_inchikey"].unique().values
        return ScoreDf(
            pd.concat(
                [
                    pd.DataFrame(dict(inchikey=inchikeys, weight=[1 for _ in inchikeys])),
                    pd.DataFrame(dict(inchikey=inchikeys, count=[1 for _ in inchikeys])),
                ]
            )
        )

    def _get_dict(self, scores: ScoreDf) -> Mapping[str, Tuple[_Alg, pd.Series]]:
        fold_cols = [c for c in scores.columns if c.startswith("is_") or c == "count"]
        score_cols = [c for c in scores.columns if c.startswith("score_") or c == "weight"]
        fold_dct = {c: (self.bool_alg, scores.set_index("inchikey")[c]) for c in fold_cols}
        score_dct = {c: (self.real_alg, scores.set_index("inchikey")[c]) for c in score_cols}
        return {**fold_dct, **score_dct}

    def _make_df(
class Foo:
    def some_method(self, x, y):
        return x + y;
        self,

        forward: Mapping[KeyPredObj, float],

        backward: Mapping[KeyPredObj, float],

        score: str,

        alg: str,

        sample: int,

    ):
        return pd.DataFrame(
            [
                pd.Series(
                    dict(
                        key=kpo.key,
                        predicate=kpo.pred,
                        object=kpo.obj,
                        score_name=score,
                        algorithm=alg,
                        sample=sample,
                        value=forward[kpo],
                        inverse=backward[kpo],
                    )
                )
                for kpo in forward.keys()
            ]
        )


__all__ = [
    "AlphaCalculator",
    "EnrichmentCalculator",
    "FoldUnweightedCalc",
    "FoldWeightedCalc",
    "SumUnweightedCalc",
    "SumWeightedCalc",
    "EnrichmentCalculation",
    "EnrichmentDf",
    "ScoreDf",
    "BoolAlg",
    "RealAlg",
]


1			"""
2			Scoring (regression and enrichment) calculations.
3			"""
4			import abc
5			import enum
6			import math
7			from dataclasses import dataclass
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8			from typing import Generic, Mapping, Sequence, Type, TypeVar, Union, Optional, Any, Tuple
9
10			import numpy as np
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11			import pandas as pd
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12			from numpy.random import RandomState
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13
14			from mandos.analysis import AnalysisUtils as Au
15			from mandos.model import CleverEnum
16			from mandos.model.hits import AbstractHit, HitFrame, KeyPredObjSource, KeyPredObj
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17			from mandos.model.hit_utils import HitUtils
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18			from mandos.analysis.io_defns import ScoreDf, EnrichmentDf
19
20			S = TypeVar("S", bound=Union[int, float, bool])
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21
22
23			class EnrichmentCalculator(Generic[S], metaclass=abc.ABCMeta):
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24			def calc(
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25			self, hits: Sequence[AbstractHit], scores: Mapping[str, S]
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26			) -> Mapping[KeyPredObj, float]:
27			pair_to_hits = Au.hit_multidict(hits, "to_key_pred_obj")
28			results = {}
29			for pair, the_hits in pair_to_hits.items():
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30			results[pair] = self.for_pair(hits, scores)
31			return results
32
33			def for_pair(self, hits: Sequence[AbstractHit], scores: Mapping[str, S]) -> float:
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34			raise NotImplementedError()
35
36			@classmethod
37			def alg_name(cls) -> str:
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38			raise NotImplementedError()
39
40
41			# noinspection PyAbstractClass
42			class _FoldCalculator(EnrichmentCalculator[bool]):
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43			""""""
44
45
46			# noinspection PyAbstractClass
47			class _RegressCalculator(EnrichmentCalculator[float]):
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48			""""""
49
50
51			class AlphaCalculator(_RegressCalculator):
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52			@classmethod
53			def alg_name(cls) -> str:
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54			return "alpha"
55
56			def for_pair(self, hits: Sequence[AbstractHit], scores: Mapping[str, S]) -> float:
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57			source_to_hits = Au.hit_multidict(hits, "data_source")
58			return float(
59			np.mean(
60			self._calc_term(source_hits, scores)
61			for source, source_hits in source_to_hits.items()
62			)
63			)
64
65			def _calc_term(self, hits: Sequence[AbstractHit], scores: Mapping[str, S]) -> float:
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66			return float(
67			np.mean(
68			[
69			Au.elle(hit.weight) * (2 * float(scores[hit.origin_inchikey] - 1)) ** 2
70			for hit in hits
71			]
72			)
73			)
74
75
76			class SumWeightedCalc(_RegressCalculator):
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77			@classmethod
78			def alg_name(cls) -> str:
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79			return "w-sum"
80
81			def for_pair(self, hits: Sequence[AbstractHit], scores: Mapping[str, S]) -> float:
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82			return math.fsum([scores[hit.origin_inchikey] * hit.weight for hit in hits]) / len(hits)
83
84
85			class SumUnweightedCalc(_RegressCalculator):
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86			@classmethod
87			def alg_name(cls) -> str:
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88			return "n-sum"
89
90			def for_pair(self, hits: Sequence[AbstractHit], scores: Mapping[str, S]) -> float:
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91			return math.fsum([scores[hit.origin_inchikey] for hit in hits]) / len(hits)
92
93
94			class FoldWeightedCalc(_FoldCalculator):
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95			@classmethod
96			def alg_name(cls) -> str:
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97			return "w-ratio"
98
99			def for_pair(self, hits: Sequence[AbstractHit], scores: Mapping[str, S]) -> float:
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100			yes = [hit for hit in hits if scores[hit.origin_inchikey]]
101			no = [hit for hit in hits if not scores[hit.origin_inchikey]]
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102			numerator = math.fsum((hit.weight for hit in yes))
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103			denominator = math.fsum((hit.weight for hit in no))
104			if denominator == 0:
105			return float("inf")
106			return numerator / denominator
107
108
109			class FoldUnweightedCalc(_FoldCalculator):
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110			@classmethod
111			def alg_name(cls) -> str:
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112			return "n-ratio"
113
114			def for_pair(self, hits: Sequence[AbstractHit], scores: Mapping[str, S]) -> float:
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115			numerator = len([hit for hit in hits if scores[hit.origin_inchikey]])
116			denominator = len([hit for hit in hits if not scores[hit.origin_inchikey]])
117			if denominator == 0:
118			return float("inf")
119			return numerator / denominator
120
121
122			class _Alg(CleverEnum):
123			""""""
124
125			@classmethod
126			def dtype(cls) -> Type[Any]:
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127			raise NotImplementedError()
128
129
130			class RealAlg(_Alg):
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131			alpha = enum.auto()
132			weighted = enum.auto()
133			unweighted = enum.auto()
134
135			@classmethod
136			def dtype(cls) -> Type[Any]:
137			return float
138
139			@property
140			def clazz(self) -> Type[_RegressCalculator]:
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141			return {
142			RealAlg.alpha: AlphaCalculator,
143			RealAlg.weighted: SumWeightedCalc,
144			RealAlg.unweighted: SumUnweightedCalc,
145			}[self]
146
147
148			class BoolAlg(_Alg):
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149			weighted = enum.auto()
150			unweighted = enum.auto()
151
152			@classmethod
153			def dtype(cls) -> Type[Any]:
154			return bool
155
156			@property
157			def clazz(self) -> Type[_FoldCalculator]:
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158			return {
159			BoolAlg.weighted: FoldWeightedCalc,
160			BoolAlg.unweighted: FoldUnweightedCalc,
161			}[self]
162
163
164			class EnrichmentCalculation:
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165			def __init__(
166			self,
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167			bool_alg: str,
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168			real_alg: str,
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169			n_samples: int,
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170			seed: int,
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171			):
172			self.bool_alg = BoolAlg.of(bool_alg)
173			self.real_alg = RealAlg.of(real_alg)
174			self.n_samples = n_samples
175			self.seed = seed
176			self.state = RandomState(seed)
177
178			def calculate(self, hit_df: HitFrame, scores: Optional[ScoreDf]) -> EnrichmentDf:
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179			hits = hit_df.to_hits()
180			if scores is None:
181			scores = self._default_scores(hit_df)
182			score_dict = self._get_dict(scores)
183			results = self._calc(hits, score_dict, 0)
184			for b in range(self.n_samples):
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185			b_hits = self.state.choice(hits, replace=True)
186			results += self._calc(b_hits, score_dict, b)
187			return EnrichmentDf.convert(results)
188
189			def _calc(self, hits: Sequence[AbstractHit], score_dict, sample: int) -> Sequence[pd.DataFrame]:
190			for score_name, (alg_type, score_vals) in score_dict.items():
191			alg_instance = alg_type.clazz()
192			forward = alg_instance.calc(hits, score_vals.to_dict())
193			if alg_type.dtype == bool:
194			reverse = alg_instance.calc(hits, (~score_vals).to_dict())
195			else:
196			reverse = alg_instance.calc(hits, (-score_vals).to_dict())
197			return [self._make_df(forward, reverse, score_name, alg_type.name, sample)]
198
199			def _default_scores(self, hit_df: HitFrame) -> ScoreDf:
			0 ignored issues – show Coding Style introduced 2021-08-02 23:39 UTC by Report Bug Copy Issue Report This method could be written as a function/class method. If a method does not access any attributes of the class, it could also be implemented as a function or static method. This can help improve readability. For example class Foo: def some_method(self, x, y): return x + y; could be written as class Foo: @classmethod def some_method(cls, x, y): return x + y; Loading history...
200			inchikeys = hit_df["origin_inchikey"].unique().values
201			return ScoreDf(
202			pd.concat(
203			[
204			pd.DataFrame(dict(inchikey=inchikeys, weight=[1 for _ in inchikeys])),
205			pd.DataFrame(dict(inchikey=inchikeys, count=[1 for _ in inchikeys])),
206			]
207			)
208			)
209
210			def _get_dict(self, scores: ScoreDf) -> Mapping[str, Tuple[_Alg, pd.Series]]:
211			fold_cols = [c for c in scores.columns if c.startswith("is_") or c == "count"]
212			score_cols = [c for c in scores.columns if c.startswith("score_") or c == "weight"]
213			fold_dct = {c: (self.bool_alg, scores.set_index("inchikey")[c]) for c in fold_cols}
214			score_dct = {c: (self.real_alg, scores.set_index("inchikey")[c]) for c in score_cols}
215			return {fold_dct, score_dct}
216
217			def _make_df(
			0 ignored issues – show best-practice introduced 2021-08-02 23:39 UTC by Report Bug Copy Issue Report Too many arguments (6/5) Loading history... Coding Style introduced 2021-08-02 23:39 UTC by Report Bug Copy Issue Report This method could be written as a function/class method. If a method does not access any attributes of the class, it could also be implemented as a function or static method. This can help improve readability. For example class Foo: def some_method(self, x, y): return x + y; could be written as class Foo: @classmethod def some_method(cls, x, y): return x + y; Loading history...
218			self,
			0 ignored issues – show Coding Style introduced 2021-08-02 23:39 UTC by Report Bug Copy Issue Report Wrong hanging indentation before block (add 4 spaces). Loading history...
219			forward: Mapping[KeyPredObj, float],
			0 ignored issues – show Coding Style introduced 2021-08-02 23:39 UTC by Report Bug Copy Issue Report Wrong hanging indentation before block (add 4 spaces). Loading history...
220			backward: Mapping[KeyPredObj, float],
			0 ignored issues – show Coding Style introduced 2021-08-02 23:39 UTC by Report Bug Copy Issue Report Wrong hanging indentation before block (add 4 spaces). Loading history...
221			score: str,
			0 ignored issues – show Coding Style introduced 2021-08-02 23:39 UTC by Report Bug Copy Issue Report Wrong hanging indentation before block (add 4 spaces). Loading history...
222			alg: str,
			0 ignored issues – show Coding Style introduced 2021-08-02 23:39 UTC by Report Bug Copy Issue Report Wrong hanging indentation before block (add 4 spaces). Loading history...
223			sample: int,
			0 ignored issues – show Coding Style introduced 2021-08-02 23:39 UTC by Report Bug Copy Issue Report Wrong hanging indentation before block (add 4 spaces). Loading history...
224			):
225			return pd.DataFrame(
226			[
227			pd.Series(
228			dict(
229			key=kpo.key,
230			predicate=kpo.pred,
231			object=kpo.obj,
232			score_name=score,
233			algorithm=alg,
234			sample=sample,
235			value=forward[kpo],
236			inverse=backward[kpo],
237			)
238			)
239			for kpo in forward.keys()
240			]
241			)
242
243
244			__all__ = [
245			"AlphaCalculator",
246			"EnrichmentCalculator",
247			"FoldUnweightedCalc",
248			"FoldWeightedCalc",
249			"SumUnweightedCalc",
250			"SumWeightedCalc",
251			"EnrichmentCalculation",
252			"EnrichmentDf",
253			"ScoreDf",
254			"BoolAlg",
255			"RealAlg",
256			]
257

dmyersturnbull / mandos

Push — main ( ec3fe3...82dd22 )

mandos.analysis.enrichment.EnrichmentAlg.clazz() A

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