示例#1
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    def sample_results(self):
        m1 = Metadata('The Great Gatsby', ['Francis Scott Fitzgerald'])
        m2 = Metadata('The Great Gatsby', ['F. Scott Fitzgerald'])
        m1.has_cached_cover_url = True
        m2.has_cached_cover_url = False
        m1.comments  = 'Some comments '*10
        m1.tags = ['tag%d'%i for i in range(20)]
        m1.rating = 4.4
        m1.language = 'en'
        m2.language = 'fr'
        m1.pubdate = utcnow()
        m2.pubdate = fromordinal(1000000)
        m1.publisher = 'Publisher 1'
        m2.publisher = 'Publisher 2'

        return [m1, m2]
示例#2
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    def sample_results(self):
        m1 = Metadata('The Great Gatsby', ['Francis Scott Fitzgerald'])
        m2 = Metadata('The Great Gatsby - An extra long title to test resizing', ['F. Scott Fitzgerald'])
        m1.has_cached_cover_url = True
        m2.has_cached_cover_url = False
        m1.comments  = 'Some comments '*10
        m1.tags = ['tag%d'%i for i in range(20)]
        m1.rating = 4.4
        m1.language = 'en'
        m2.language = 'fr'
        m1.pubdate = utcnow()
        m2.pubdate = fromordinal(1000000)
        m1.publisher = 'Publisher 1'
        m2.publisher = 'Publisher 2'

        return [m1, m2]
示例#3
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    def sample_results(self):
        m1 = Metadata("The Great Gatsby", ["Francis Scott Fitzgerald"])
        m2 = Metadata("The Great Gatsby - An extra long title to test resizing", ["F. Scott Fitzgerald"])
        m1.has_cached_cover_url = True
        m2.has_cached_cover_url = False
        m1.comments = "Some comments " * 10
        m1.tags = ["tag%d" % i for i in range(20)]
        m1.rating = 4.4
        m1.language = "en"
        m2.language = "fr"
        m1.pubdate = utcnow()
        m2.pubdate = fromordinal(1000000)
        m1.publisher = "Publisher 1"
        m2.publisher = "Publisher 2"

        return [m1, m2]
示例#4
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    def merge(self, results, min_year, do_asr=True):
        ans = Metadata(_('Unknown'))

        # We assume the shortest title has the least cruft in it
        ans.title = self.length_merge('title', results, null_value=ans.title)

        # No harm in having extra authors, maybe something useful like an
        # editor or translator
        ans.authors = self.length_merge('authors', results,
                null_value=ans.authors, shortest=False)

        # We assume the shortest publisher has the least cruft in it
        ans.publisher = self.length_merge('publisher', results,
                null_value=ans.publisher)

        # We assume the smallest set of tags has the least cruft in it
        ans.tags = self.length_merge('tags', results,
                null_value=ans.tags, shortest=msprefs['fewer_tags'])

        # We assume the longest series has the most info in it
        ans.series = self.length_merge('series', results,
                null_value=ans.series, shortest=False)
        for r in results:
            if r.series and r.series == ans.series:
                ans.series_index = r.series_index
                break

        # Average the rating over all sources
        ratings = []
        for r in results:
            rating = r.rating
            if rating and rating > 0 and rating <= 5:
                ratings.append(rating)
        if ratings:
            ans.rating = int(round(sum(ratings)/len(ratings)))

        # Smallest language is likely to be valid
        ans.language = self.length_merge('language', results,
                null_value=ans.language)

        # Choose longest comments
        ans.comments = self.length_merge('comments', results,
                null_value=ans.comments, shortest=False)

        # Published date
        if min_year:
            for r in results:
                year = getattr(r.pubdate, 'year', None)
                if year == min_year:
                    ans.pubdate = r.pubdate
                    break
            if getattr(ans.pubdate, 'year', None) == min_year:
                min_date = datetime(min_year, ans.pubdate.month, ans.pubdate.day,
                                    tzinfo=utc_tz)
            else:
                min_date = datetime(min_year, 1, 2, tzinfo=utc_tz)
            ans.pubdate = min_date
        else:
            min_date = datetime(3001, 1, 1, tzinfo=utc_tz)
            for r in results:
                if r.pubdate is not None:
                    candidate = as_utc(r.pubdate)
                    if candidate < min_date:
                        min_date = candidate
            if min_date.year < 3000:
                ans.pubdate = min_date

        # Identifiers
        for r in results:
            ans.identifiers.update(r.identifiers)

        # Cover URL
        ans.has_cached_cover_url = bool([r for r in results if
            getattr(r, 'has_cached_cover_url', False)])

        # Merge any other fields with no special handling (random merge)
        touched_fields = set()
        for r in results:
            if hasattr(r, 'identify_plugin'):
                touched_fields |= r.identify_plugin.touched_fields

        for f in touched_fields:
            if f.startswith('identifier:') or not ans.is_null(f):
                continue
            setattr(ans, f, self.random_merge(f, results,
                null_value=getattr(ans, f)))

        if do_asr:
            avg = [x.relevance_in_source for x in results]
            avg = sum(avg)/len(avg)
            ans.average_source_relevance = avg

        return ans