mirror of https://github.com/tdwg/dwc.git
326 lines
14 KiB
Python
326 lines
14 KiB
Python
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# Script to build Markdown pages that provide term metadata for simple vocabularies
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# Steve Baskauf 2020-06-28 CC0
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# This script merges static Markdown header and footer documents with term information tables (in Markdown) generated from data in the rs.tdwg.org repo from the TDWG Github site
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# Note: this script calls a function from http_library.py, which requires importing the requests, csv, and json modules
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import re
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import requests # best library to manage HTTP transactions
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import csv # library to read/write/parse CSV files
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import json # library to convert JSON to Python data structures
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import pandas as pd
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# -----------------
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# Configuration section
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# -----------------
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# !!!! Note !!!!
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# This is an example of a simple vocabulary without categories. For a complex example
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# with multiple namespaces and several categories, see build-page-categories.ipynb
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# This is the base URL for raw files from the branch of the repo that has been pushed to GitHub. In this example,
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# the branch is named "pathway"
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githubBaseUri = 'https://raw.githubusercontent.com/tdwg/rs.tdwg.org/master/'
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headerFileName = 'termlist-header.md'
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footerFileName = 'termlist-footer.md'
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outFileName = '../../docs/doe/index.md'
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# This is a Python list of the database names of the term lists to be included in the document.
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termLists = ['degreeOfEstablishment']
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# NOTE! There may be problems unless every term list is of the same vocabulary type since the number of columns will differ
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# However, there probably aren't any circumstances where mixed types will be used to generate the same page.
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vocab_type = 2 # 1 is simple vocabulary, 2 is simple controlled vocabulary, 3 is c.v. with broader hierarchy
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# Terms in large vocabularies like Darwin and Audubon Cores may be organized into categories using tdwgutility_organizedInClass
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# If so, those categories can be used to group terms in the generated term list document.
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organized_in_categories = False
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# If organized in categories, the display_order list must contain the IRIs that are values of tdwgutility_organizedInClass
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# If not organized into categories, the value is irrelevant. There just needs to be one item in the list.
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display_order = ['']
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display_label = ['Vocabulary'] # these are the section labels for the categories in the page
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display_comments = [''] # these are the comments about the category to be appended following the section labels
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display_id = ['Vocabulary'] # these are the fragment identifiers for the associated sections for the categories
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# ---------------
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# Function definitions
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# ---------------
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# replace URL with link
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#
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def createLinks(text):
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def repl(match):
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if match.group(1)[-1] == '.':
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return '<a href="' + match.group(1)[:-1] + '">' + match.group(1)[:-1] + '</a>.'
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return '<a href="' + match.group(1) + '">' + match.group(1) + '</a>'
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pattern = '(https?://[^\s,;\)"]*)'
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result = re.sub(pattern, repl, text)
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return result
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# 2021-08-06 Replace the createLinks() function with functions copied from the QRG build script written by S. Van Hoey
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def convert_code(text_with_backticks):
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"""Takes all back-quoted sections in a text field and converts it to
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the html tagged version of code blocks <code>...</code>
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"""
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return re.sub(r'`([^`]*)`', r'<code>\1</code>', text_with_backticks)
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def convert_link(text_with_urls):
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"""Takes all links in a text field and converts it to the html tagged
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version of the link
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"""
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def _handle_matched(inputstring):
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"""quick hack version of url handling on the current prime versions data"""
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url = inputstring.group()
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return "<a href=\"{}\">{}</a>".format(url, url)
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regx = "(http[s]?://[\w\d:#@%/;$()~_?\+-;=\\\.&]*)(?<![\)\.,])"
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return re.sub(regx, _handle_matched, text_with_urls)
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term_lists_info = []
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frame = pd.read_csv(githubBaseUri + 'term-lists/term-lists.csv', na_filter=False)
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for termList in termLists:
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term_list_dict = {'list_iri': termList}
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term_list_dict = {'database': termList}
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for index,row in frame.iterrows():
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if row['database'] == termList:
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term_list_dict['pref_ns_prefix'] = row['vann_preferredNamespacePrefix']
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term_list_dict['pref_ns_uri'] = row['vann_preferredNamespaceUri']
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term_list_dict['list_iri'] = row['list']
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term_lists_info.append(term_list_dict)
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# Create column list
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column_list = ['pref_ns_prefix', 'pref_ns_uri', 'term_localName', 'label', 'definition', 'usage', 'notes', 'term_modified', 'term_deprecated', 'type']
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if vocab_type == 2:
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column_list += ['controlled_value_string']
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elif vocab_type == 3:
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column_list += ['controlled_value_string', 'skos_broader']
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if organized_in_categories:
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column_list.append('tdwgutility_organizedInClass')
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column_list.append('version_iri')
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# Create list of lists metadata table
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table_list = []
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for term_list in term_lists_info:
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# retrieve versions metadata for term list
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versions_url = githubBaseUri + term_list['database'] + '-versions/' + term_list['database'] + '-versions.csv'
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versions_df = pd.read_csv(versions_url, na_filter=False)
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# retrieve current term metadata for term list
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data_url = githubBaseUri + term_list['database'] + '/' + term_list['database'] + '.csv'
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frame = pd.read_csv(data_url, na_filter=False)
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for index,row in frame.iterrows():
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row_list = [term_list['pref_ns_prefix'], term_list['pref_ns_uri'], row['term_localName'], row['label'], row['definition'], row['usage'], row['notes'], row['term_modified'], row['term_deprecated'], row['type']]
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if vocab_type == 2:
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row_list += [row['controlled_value_string']]
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elif vocab_type == 3:
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if row['skos_broader'] =='':
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row_list += [row['controlled_value_string'], '']
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else:
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row_list += [row['controlled_value_string'], term_list['pref_ns_prefix'] + ':' + row['skos_broader']]
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if organized_in_categories:
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row_list.append(row['tdwgutility_organizedInClass'])
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# Borrowed terms really don't have implemented versions. They may be lacking values for version_status.
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# In their case, their version IRI will be omitted.
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found = False
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for vindex, vrow in versions_df.iterrows():
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if vrow['term_localName']==row['term_localName'] and vrow['version_status']=='recommended':
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found = True
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version_iri = vrow['version']
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# NOTE: the current hack for non-TDWG terms without a version is to append # to the end of the term IRI
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if version_iri[len(version_iri)-1] == '#':
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version_iri = ''
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if not found:
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version_iri = ''
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row_list.append(version_iri)
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table_list.append(row_list)
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# Turn list of lists into dataframe
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terms_df = pd.DataFrame(table_list, columns = column_list)
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terms_sorted_by_label = terms_df.sort_values(by='label')
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terms_sorted_by_localname = terms_df.sort_values(by='term_localName')
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terms_sorted_by_label
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# generate the index of terms grouped by category and sorted alphabetically by lowercase term local name
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text = '### 3.1 Index By Term Name\n\n'
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text += '(See also [3.2 Index By Label](#32-index-by-label))\n\n'
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for category in range(0,len(display_order)):
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text += '**' + display_label[category] + '**\n'
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text += '\n'
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if organized_in_categories:
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filtered_table = terms_sorted_by_localname[terms_sorted_by_localname['tdwgutility_organizedInClass']==display_order[category]]
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filtered_table.reset_index(drop=True, inplace=True)
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else:
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filtered_table = terms_sorted_by_localname
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filtered_table.reset_index(drop=True, inplace=True)
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for row_index,row in filtered_table.iterrows():
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curie = row['pref_ns_prefix'] + ":" + row['term_localName']
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curie_anchor = curie.replace(':','_')
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text += '[' + curie + '](#' + curie_anchor + ')'
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if row_index < len(filtered_table) - 1:
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text += ' |'
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text += '\n'
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text += '\n'
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index_by_name = text
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text = '\n\n'
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# Comment out the following two lines if there is no index by local names
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#text = '### 3.2 Index By Label\n\n'
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#text += '(See also [3.1 Index By Term Name](#31-index-by-term-name))\n\n'
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for category in range(0,len(display_order)):
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if organized_in_categories:
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text += '**' + display_label[category] + '**\n'
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text += '\n'
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filtered_table = terms_sorted_by_label[terms_sorted_by_label['tdwgutility_organizedInClass']==display_order[category]]
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filtered_table.reset_index(drop=True, inplace=True)
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else:
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filtered_table = terms_sorted_by_label
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filtered_table.reset_index(drop=True, inplace=True)
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for row_index,row in filtered_table.iterrows():
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if row_index == 0 or (row_index != 0 and row['label'] != filtered_table.iloc[row_index - 1].loc['label']): # this is a hack to prevent duplicate labels
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curie_anchor = row['pref_ns_prefix'] + "_" + row['term_localName']
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text += '[' + row['label'] + '](#' + curie_anchor + ')'
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if row_index < len(filtered_table) - 2 or (row_index == len(filtered_table) - 2 and row['label'] != filtered_table.iloc[row_index + 1].loc['label']):
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text += ' |'
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text += '\n'
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text += '\n'
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index_by_label = text
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decisions_df = pd.read_csv('https://raw.githubusercontent.com/tdwg/rs.tdwg.org/master/decisions/decisions-links.csv', na_filter=False)
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# generate a table for each term, with terms grouped by category
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# generate the Markdown for the terms table
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text = '## 4 Vocabulary\n'
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for category in range(0,len(display_order)):
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if organized_in_categories:
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text += '### 4.' + str(category + 1) + ' ' + display_label[category] + '\n'
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text += '\n'
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text += display_comments[category] # insert the comments for the category, if any.
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filtered_table = terms_sorted_by_localname[terms_sorted_by_localname['tdwgutility_organizedInClass']==display_order[category]]
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filtered_table.reset_index(drop=True, inplace=True)
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else:
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filtered_table = terms_sorted_by_localname
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filtered_table.reset_index(drop=True, inplace=True)
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for row_index,row in filtered_table.iterrows():
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text += '<table>\n'
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curie = row['pref_ns_prefix'] + ":" + row['term_localName']
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curieAnchor = curie.replace(':','_')
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text += '\t<thead>\n'
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text += '\t\t<tr>\n'
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text += '\t\t\t<th colspan="2"><a id="' + curieAnchor + '"></a>Term Name ' + curie + '</th>\n'
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text += '\t\t</tr>\n'
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text += '\t</thead>\n'
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text += '\t<tbody>\n'
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text += '\t\t<tr>\n'
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text += '\t\t\t<td>Term IRI</td>\n'
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uri = row['pref_ns_uri'] + row['term_localName']
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text += '\t\t\t<td><a href="' + uri + '">' + uri + '</a></td>\n'
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text += '\t\t</tr>\n'
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text += '\t\t<tr>\n'
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text += '\t\t\t<td>Modified</td>\n'
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text += '\t\t\t<td>' + row['term_modified'] + '</td>\n'
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text += '\t\t</tr>\n'
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if row['version_iri'] != '':
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text += '\t\t<tr>\n'
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text += '\t\t\t<td>Term version IRI</td>\n'
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text += '\t\t\t<td><a href="' + row['version_iri'] + '">' + row['version_iri'] + '</a></td>\n'
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text += '\t\t</tr>\n'
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text += '\t\t<tr>\n'
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text += '\t\t\t<td>Label</td>\n'
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text += '\t\t\t<td>' + row['label'] + '</td>\n'
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text += '\t\t</tr>\n'
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if row['term_deprecated'] != '':
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text += '\t\t<tr>\n'
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text += '\t\t\t<td></td>\n'
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text += '\t\t\t<td><strong>This term is deprecated and should no longer be used.</strong></td>\n'
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text += '\t\t</tr>\n'
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text += '\t\t<tr>\n'
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text += '\t\t\t<td>Definition</td>\n'
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text += '\t\t\t<td>' + row['definition'] + '</td>\n'
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text += '\t\t</tr>\n'
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if row['usage'] != '':
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text += '\t\t<tr>\n'
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text += '\t\t\t<td>Usage</td>\n'
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text += '\t\t\t<td>' + convert_link(convert_code(row['usage'])) + '</td>\n'
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text += '\t\t</tr>\n'
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if row['notes'] != '':
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text += '\t\t<tr>\n'
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text += '\t\t\t<td>Notes</td>\n'
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text += '\t\t\t<td>' + convert_link(convert_code(row['notes'])) + '</td>\n'
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text += '\t\t</tr>\n'
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if (vocab_type == 2 or vocab_type == 3) and row['controlled_value_string'] != '': # controlled vocabulary
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text += '\t\t<tr>\n'
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text += '\t\t\t<td>Controlled value</td>\n'
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text += '\t\t\t<td>' + row['controlled_value_string'] + '</td>\n'
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text += '\t\t</tr>\n'
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if vocab_type == 3 and row['skos_broader'] != '': # controlled vocabulary with skos:broader relationships
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text += '\t\t<tr>\n'
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text += '\t\t\t<td>Has broader concept</td>\n'
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curieAnchor = row['skos_broader'].replace(':','_')
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text += '\t\t\t<td><a href="#' + curieAnchor + '">' + row['skos_broader'] + '</a></td>\n'
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text += '\t\t</tr>\n'
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text += '\t\t<tr>\n'
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text += '\t\t\t<td>Type</td>\n'
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if row['type'] == 'http://www.w3.org/1999/02/22-rdf-syntax-ns#Property':
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text += '\t\t\t<td>Property</td>\n'
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elif row['type'] == 'http://www.w3.org/2000/01/rdf-schema#Class':
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text += '\t\t\t<td>Class</td>\n'
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elif row['type'] == 'http://www.w3.org/2004/02/skos/core#Concept':
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text += '\t\t\t<td>Concept</td>\n'
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else:
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text += '\t\t\t<td>' + row['type'] + '</td>\n' # this should rarely happen
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text += '\t\t</tr>\n'
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# Look up decisions related to this term
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for drow_index,drow in decisions_df.iterrows():
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if drow['linked_affected_resource'] == uri:
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text += '\t\t<tr>\n'
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text += '\t\t\t<td>Executive Committee decision</td>\n'
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text += '\t\t\t<td><a href="http://rs.tdwg.org/decisions/' + drow['decision_localName'] + '">http://rs.tdwg.org/decisions/' + drow['decision_localName'] + '</a></td>\n'
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text += '\t\t</tr>\n'
|
||
|
|
|
||
|
|
text += '\t</tbody>\n'
|
||
|
|
text += '</table>\n'
|
||
|
|
text += '\n'
|
||
|
|
text += '\n'
|
||
|
|
term_table = text
|
||
|
|
|
||
|
|
text = index_by_label + term_table
|
||
|
|
|
||
|
|
# read in header and footer, merge with terms table, and output
|
||
|
|
|
||
|
|
headerObject = open(headerFileName, 'rt', encoding='utf-8')
|
||
|
|
header = headerObject.read()
|
||
|
|
headerObject.close()
|
||
|
|
|
||
|
|
footerObject = open(footerFileName, 'rt', encoding='utf-8')
|
||
|
|
footer = footerObject.read()
|
||
|
|
footerObject.close()
|
||
|
|
|
||
|
|
output = header + text + footer
|
||
|
|
outputObject = open(outFileName, 'wt', encoding='utf-8')
|
||
|
|
outputObject.write(output)
|
||
|
|
outputObject.close()
|
||
|
|
|
||
|
|
print('done')
|