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325 lines
11 KiB
Python
325 lines
11 KiB
Python
from itertools import groupby
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import re
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import warnings
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from typing import List, Sequence, MutableMapping, Dict
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import pandas as pd
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import numpy as np
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from lxml import etree as ET
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__all__ = ["ns"]
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ns = {
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'mets': 'http://www.loc.gov/METS/',
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'mods': 'http://www.loc.gov/mods/v3',
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"alto": "http://www.loc.gov/standards/alto/ns-v2",
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"xlink": "http://www.w3.org/1999/xlink",
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}
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class TagGroup:
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"""Helper class to simplify the parsing and checking of MODS metadata"""
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def __init__(self, tag, group: List[ET.Element]):
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self.tag = tag
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self.group = group
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def to_xml(self):
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return '\n'.join(str(ET.tostring(e), 'utf-8').strip() for e in self.group)
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def __str__(self):
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return f"TagGroup with content:\n{self.to_xml()}"
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def is_singleton(self):
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if len(self.group) != 1:
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raise ValueError('More than one instance: {}'.format(self))
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return self
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def has_no_attributes(self):
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return self.has_attributes({})
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def has_attributes(self, attrib):
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if not isinstance(attrib, Sequence):
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attrib = [attrib]
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if not all(e.attrib in attrib for e in self.group):
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raise ValueError('One or more element has unexpected attributes: {}'.format(self))
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return self
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def ignore_attributes(self):
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# This serves as documentation for now.
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return self
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def sort(self, key=None, reverse=False):
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self.group = sorted(self.group, key=key, reverse=reverse)
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return self
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def text(self, separator='\n'):
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t = ''
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for e in self.group:
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if t != '':
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t += separator
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if e.text:
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t += e.text
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return t
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def text_set(self):
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return {e.text for e in self.group}
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def descend(self, raise_errors):
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return _to_dict(self.is_singleton().group[0], raise_errors)
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def filter(self, cond, warn=None):
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new_group = []
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for e in self.group:
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if cond(e):
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new_group.append(e)
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else:
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if warn:
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warnings.warn('Filtered {} element ({})'.format(self.tag, warn))
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return TagGroup(self.tag, new_group)
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def force_singleton(self, warn=True):
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if len(self.group) == 1:
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return self
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else:
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if warn:
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warnings.warn('Forced single instance of {}'.format(self.tag))
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return TagGroup(self.tag, self.group[:1])
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RE_ISO8601_DATE = r'^\d{2}(\d{2}|XX)(-\d{2}-\d{2})?$' # Note: Includes non-specific century dates like '18XX'
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RE_GERMAN_DATE = r'^(?P<dd>\d{2})\.(?P<mm>\d{2})\.(?P<yyyy>\d{4})$'
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def fix_date(self):
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for e in self.group:
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if e.attrib.get('encoding') == 'w3cdtf':
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# This should be 'iso8601' according to MODS-AP 2.3.1
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warnings.warn('Changed w3cdtf encoding to iso8601')
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e.attrib['encoding'] = 'iso8601'
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new_group = []
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for e in self.group:
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if e.attrib.get('encoding') == 'iso8601' and re.match(self.RE_ISO8601_DATE, e.text):
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new_group.append(e)
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elif re.match(self.RE_ISO8601_DATE, e.text):
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warnings.warn('Added iso8601 encoding to date {}'.format(e.text))
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e.attrib['encoding'] = 'iso8601'
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new_group.append(e)
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elif re.match(self.RE_GERMAN_DATE, e.text):
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warnings.warn('Converted date {} to iso8601 encoding'.format(e.text))
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m = re.match(self.RE_GERMAN_DATE, e.text)
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e.text = '{}-{}-{}'.format(m.group('yyyy'), m.group('mm'), m.group('dd'))
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e.attrib['encoding'] = 'iso8601'
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new_group.append(e)
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else:
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warnings.warn('Not a iso8601 date: "{}"'.format(e.text))
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new_group.append(e)
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self.group = new_group
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# Notes:
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# - There are dates with the misspelled qualifier 'aproximate'
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# - Rough periods are sometimes given either by:
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# - years like '19xx'
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# - or 'approximate' date ranges with point="start"/"end" attributes set
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# (this could be correct according to MODS-AP 2.3.1)
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# - Some very specific dates like '06.08.1820' are sometimes given the 'approximate' qualifier
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# - Sometimes, approximate date ranges are given in the text "1785-1800 (ca.)"
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return self
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def fix_event_type(self):
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# According to MODS-AP 2.3.1, every originInfo should have its eventType set.
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# Fix this for special cases.
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for e in self.group:
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if e.attrib.get('eventType') is None:
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try:
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if e.find('mods:publisher', ns).text.startswith('Staatsbibliothek zu Berlin') and \
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e.find('mods:edition', ns).text == '[Electronic ed.]':
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e.attrib['eventType'] = 'digitization'
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warnings.warn('Fixed eventType for electronic ed.')
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continue
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except AttributeError:
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pass
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try:
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if e.find('mods:dateIssued', ns) is not None:
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e.attrib['eventType'] = 'publication'
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warnings.warn('Fixed eventType for an issued origin')
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continue
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except AttributeError:
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pass
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try:
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if e.find('mods:dateCreated', ns) is not None:
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e.attrib['eventType'] = 'production'
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warnings.warn('Fixed eventType for a created origin')
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continue
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except AttributeError:
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pass
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return self
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def fix_script_term(self):
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for e in self.group:
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# MODS-AP 2.3.1 is not clear about this, but it looks like that this should be lower case.
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if e.attrib['authority'] == 'ISO15924':
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e.attrib['authority'] = 'iso15924'
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warnings.warn('Changed scriptTerm authority to lower case')
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return self
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def merge_sub_tags_to_set(self):
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from .mods4pandas import mods_to_dict
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value = {}
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sub_dicts = [mods_to_dict(e) for e in self.group]
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sub_tags = {k for d in sub_dicts for k in d.keys()}
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for sub_tag in sub_tags:
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s = set()
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for d in sub_dicts:
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v = d.get(sub_tag)
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if v:
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# There could be multiple scriptTerms in one language element, e.g. Antiqua and Fraktur in a
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# German language document.
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if isinstance(v, set):
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s.update(v)
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else:
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s.add(v)
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value[sub_tag] = s
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return value
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def attributes(self):
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"""
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Return a merged dict of all attributes of the tag group.
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Probably most useful if used on a singleton, for example:
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value['Page'] = TagGroup(tag, group).is_singleton().attributes()
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"""
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attrib = {}
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for e in self.group:
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for a, v in e.attrib.items():
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a_localname = ET.QName(a).localname
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attrib[a_localname] = v
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return attrib
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def subelement_counts(self):
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counts = {}
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for e in self.group:
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for x in e.iter():
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tag = ET.QName(x.tag).localname
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key = f"{tag}-count"
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counts[key] = counts.get(key, 0) + 1
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return counts
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def xpath_statistics(self, xpath_expr, namespaces):
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"""
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Extract values and calculate statistics
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Extract values using the given XPath expression, convert them to float and return descriptive
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statistics on the values.
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"""
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values = []
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for e in self.group:
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r = e.xpath(xpath_expr, namespaces=namespaces)
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values += r
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values = np.array([float(v) for v in values])
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statistics = {}
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if values.size > 0:
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statistics[f'{xpath_expr}-mean'] = np.mean(values)
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statistics[f'{xpath_expr}-median'] = np.median(values)
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statistics[f'{xpath_expr}-std'] = np.std(values)
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statistics[f'{xpath_expr}-min'] = np.min(values)
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statistics[f'{xpath_expr}-max'] = np.max(values)
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return statistics
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def xpath_count(self, xpath_expr, namespaces):
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"""
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Count all elements matching xpath_expr
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"""
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values = []
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for e in self.group:
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r = e.xpath(xpath_expr, namespaces=namespaces)
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values += r
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counts = {f'{xpath_expr}-count': len(values)}
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return counts
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def sorted_groupby(iterable, key=None):
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"""
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Sort iterable by key and then group by the same key.
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itertools.groupby() assumes that the iterable is already sorted. This function
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conveniently sorts the iterable first, and then groups its elements.
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"""
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return groupby(sorted(iterable, key=key), key=key)
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def _to_dict(root, raise_errors):
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from .mods4pandas import mods_to_dict, mets_to_dict
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from .alto4pandas import alto_to_dict
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root_name = ET.QName(root.tag)
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if root_name.namespace == "http://www.loc.gov/mods/v3":
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return mods_to_dict(root, raise_errors)
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elif root_name.namespace == "http://www.loc.gov/METS/":
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return mets_to_dict(root, raise_errors)
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elif root_name.namespace in [
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"http://schema.ccs-gmbh.com/ALTO",
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"http://www.loc.gov/standards/alto/",
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"http://www.loc.gov/standards/alto/ns-v2#",
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"http://www.loc.gov/standards/alto/ns-v4#",
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]:
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return alto_to_dict(root, raise_errors)
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else:
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raise ValueError(f"Unknown namespace {root_name.namespace}")
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def flatten(d: MutableMapping, parent='', separator='_'):
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"""
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Flatten the given nested dict.
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It is assumed that d maps strings to either another dictionary (similarly structured) or some other value.
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"""
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items = []
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for k, v in d.items():
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if parent:
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new_key = parent + separator + k
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else:
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new_key = k
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if isinstance(v, MutableMapping):
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items.extend(flatten(v, new_key, separator=separator).items())
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else:
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items.append((new_key, v))
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return dict(items)
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def dicts_to_df(data_list: List[Dict], *, index_column: str) -> pd.DataFrame:
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"""
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Convert the given list of dicts to a Pandas DataFrame.
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The keys of the dicts make the columns.
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"""
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# Build columns from keys
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columns = []
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for m in data_list:
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for c in m.keys():
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if c not in columns:
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columns.append(c)
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# Build data table
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data = [[m.get(c) for c in columns] for m in data_list]
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# Build index
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index = [m[index_column] for m in data_list]
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df = pd.DataFrame(data=data, index=index, columns=columns)
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return df
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