mirror of https://github.com/tdwg/dwc.git
444 lines
20 KiB
Plaintext
444 lines
20 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Script to build Markdown pages that provide term metadata for simple vocabularies\n",
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"# Steve Baskauf 2020-06-28 CC0\n",
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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\n",
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"\n",
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"# Note: this script calls a function from http_library.py, which requires importing the requests, csv, and json modules\n",
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"import re\n",
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"import requests # best library to manage HTTP transactions\n",
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"import csv # library to read/write/parse CSV files\n",
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"import json # library to convert JSON to Python data structures\n",
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"import pandas as pd\n",
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"\n",
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"# -----------------\n",
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"# Configuration section\n",
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"# -----------------\n",
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"\n",
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"# !!!! Note !!!!\n",
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"# This is an example of a simple vocabulary without categories. For a complex example\n",
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"# with multiple namespaces and several categories, see build-page-categories.ipynb\n",
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"\n",
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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,\n",
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"# the branch is named \"pathway\"\n",
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"githubBaseUri = 'https://raw.githubusercontent.com/tdwg/rs.tdwg.org/master/'\n",
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"\n",
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"headerFileName = 'termlist-header.md'\n",
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"footerFileName = 'termlist-footer.md'\n",
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"outFileName = '../../docs/em/index.md'\n",
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"\n",
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"# This is a Python list of the database names of the term lists to be included in the document.\n",
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"termLists = ['establishmentMeans']\n",
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"\n",
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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\n",
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"# However, there probably aren't any circumstances where mixed types will be used to generate the same page.\n",
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"vocab_type = 2 # 1 is simple vocabulary, 2 is simple controlled vocabulary, 3 is c.v. with broader hierarchy\n",
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"\n",
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"# Terms in large vocabularies like Darwin and Audubon Cores may be organized into categories using tdwgutility_organizedInClass\n",
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"# If so, those categories can be used to group terms in the generated term list document.\n",
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"organized_in_categories = False\n",
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"\n",
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"# If organized in categories, the display_order list must contain the IRIs that are values of tdwgutility_organizedInClass\n",
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"# If not organized into categories, the value is irrelevant. There just needs to be one item in the list.\n",
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"display_order = ['']\n",
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"display_label = ['Vocabulary'] # these are the section labels for the categories in the page\n",
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"display_comments = [''] # these are the comments about the category to be appended following the section labels\n",
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"display_id = ['Vocabulary'] # these are the fragment identifiers for the associated sections for the categories\n",
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"\n",
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"# ---------------\n",
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"# Function definitions\n",
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"# ---------------\n",
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"\n",
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"# replace URL with link\n",
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"#\n",
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"def createLinks(text):\n",
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" def repl(match):\n",
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" if match.group(1)[-1] == '.':\n",
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" return '<a href=\"' + match.group(1)[:-1] + '\">' + match.group(1)[:-1] + '</a>.'\n",
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" return '<a href=\"' + match.group(1) + '\">' + match.group(1) + '</a>'\n",
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"\n",
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" pattern = '(https?://[^\\s,;\\)\"]*)'\n",
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" result = re.sub(pattern, repl, text)\n",
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" return result\n",
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"\n",
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"# 2021-08-06 Replace the createLinks() function with functions copied from the QRG build script written by S. Van Hoey\n",
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"def convert_code(text_with_backticks):\n",
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" \"\"\"Takes all back-quoted sections in a text field and converts it to\n",
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" the html tagged version of code blocks <code>...</code>\n",
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" \"\"\"\n",
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" return re.sub(r'`([^`]*)`', r'<code>\\1</code>', text_with_backticks)\n",
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"\n",
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"def convert_link(text_with_urls):\n",
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" \"\"\"Takes all links in a text field and converts it to the html tagged\n",
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" version of the link\n",
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" \"\"\"\n",
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" def _handle_matched(inputstring):\n",
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" \"\"\"quick hack version of url handling on the current prime versions data\"\"\"\n",
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" url = inputstring.group()\n",
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" return \"<a href=\\\"{}\\\">{}</a>\".format(url, url)\n",
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"\n",
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" regx = \"(http[s]?://[\\w\\d:#@%/;$()~_?\\+-;=\\\\\\.&]*)(?<![\\)\\.,])\"\n",
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" return re.sub(regx, _handle_matched, text_with_urls)\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"term_lists_info = []\n",
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"\n",
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"frame = pd.read_csv(githubBaseUri + 'term-lists/term-lists.csv', na_filter=False)\n",
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"for termList in termLists:\n",
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" term_list_dict = {'list_iri': termList}\n",
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" term_list_dict = {'database': termList}\n",
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" for index,row in frame.iterrows():\n",
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" if row['database'] == termList:\n",
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" term_list_dict['pref_ns_prefix'] = row['vann_preferredNamespacePrefix']\n",
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" term_list_dict['pref_ns_uri'] = row['vann_preferredNamespaceUri']\n",
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" term_list_dict['list_iri'] = row['list']\n",
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" term_lists_info.append(term_list_dict)\n",
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"print(term_lists_info)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Create column list\n",
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"column_list = ['pref_ns_prefix', 'pref_ns_uri', 'term_localName', 'label', 'definition', 'usage', 'notes', 'term_modified', 'term_deprecated', 'type']\n",
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"if vocab_type == 2:\n",
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" column_list += ['controlled_value_string']\n",
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"elif vocab_type == 3:\n",
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" column_list += ['controlled_value_string', 'skos_broader']\n",
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"if organized_in_categories:\n",
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" column_list.append('tdwgutility_organizedInClass')\n",
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"column_list.append('version_iri')\n",
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"\n",
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"# Create list of lists metadata table\n",
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"table_list = []\n",
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"for term_list in term_lists_info:\n",
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" # retrieve versions metadata for term list\n",
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" versions_url = githubBaseUri + term_list['database'] + '-versions/' + term_list['database'] + '-versions.csv'\n",
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" versions_df = pd.read_csv(versions_url, na_filter=False)\n",
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" \n",
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" # retrieve current term metadata for term list\n",
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" data_url = githubBaseUri + term_list['database'] + '/' + term_list['database'] + '.csv'\n",
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" frame = pd.read_csv(data_url, na_filter=False)\n",
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" for index,row in frame.iterrows():\n",
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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']]\n",
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" if vocab_type == 2:\n",
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" row_list += [row['controlled_value_string']]\n",
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" elif vocab_type == 3:\n",
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" if row['skos_broader'] =='':\n",
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" row_list += [row['controlled_value_string'], '']\n",
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" else:\n",
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" row_list += [row['controlled_value_string'], term_list['pref_ns_prefix'] + ':' + row['skos_broader']]\n",
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" if organized_in_categories:\n",
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" row_list.append(row['tdwgutility_organizedInClass'])\n",
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"\n",
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" # Borrowed terms really don't have implemented versions. They may be lacking values for version_status.\n",
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" # In their case, their version IRI will be omitted.\n",
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" found = False\n",
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" for vindex, vrow in versions_df.iterrows():\n",
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" if vrow['term_localName']==row['term_localName'] and vrow['version_status']=='recommended':\n",
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" found = True\n",
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" version_iri = vrow['version']\n",
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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\n",
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" if version_iri[len(version_iri)-1] == '#':\n",
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" version_iri = ''\n",
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" if not found:\n",
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" version_iri = ''\n",
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" row_list.append(version_iri)\n",
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"\n",
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" table_list.append(row_list)\n",
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"\n",
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"# Turn list of lists into dataframe\n",
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"terms_df = pd.DataFrame(table_list, columns = column_list)\n",
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"\n",
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"terms_sorted_by_label = terms_df.sort_values(by='label')\n",
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"terms_sorted_by_localname = terms_df.sort_values(by='term_localName')\n",
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"terms_sorted_by_label"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Run the following cell to generate an index sorted alphabetically by lowercase term local name. Omit this index if the terms have opaque local names."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# generate the index of terms grouped by category and sorted alphabetically by lowercase term local name\n",
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"\n",
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"text = '### 3.1 Index By Term Name\\n\\n'\n",
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"text += '(See also [3.2 Index By Label](#32-index-by-label))\\n\\n'\n",
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"for category in range(0,len(display_order)):\n",
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" text += '**' + display_label[category] + '**\\n'\n",
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" text += '\\n'\n",
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" if organized_in_categories:\n",
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" filtered_table = terms_sorted_by_localname[terms_sorted_by_localname['tdwgutility_organizedInClass']==display_order[category]]\n",
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" filtered_table.reset_index(drop=True, inplace=True)\n",
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" else:\n",
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" filtered_table = terms_sorted_by_localname\n",
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" filtered_table.reset_index(drop=True, inplace=True)\n",
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" \n",
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" for row_index,row in filtered_table.iterrows():\n",
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" curie = row['pref_ns_prefix'] + \":\" + row['term_localName']\n",
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" curie_anchor = curie.replace(':','_')\n",
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" text += '[' + curie + '](#' + curie_anchor + ')'\n",
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" if row_index < len(filtered_table) - 1:\n",
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" text += ' |'\n",
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" text += '\\n'\n",
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" text += '\\n'\n",
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"index_by_name = text\n",
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"\n",
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"print(index_by_name)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Run the following cell to generate an index by term label"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"text = '\\n\\n'\n",
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"\n",
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"# Comment out the following two lines if there is no index by local names\n",
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"#text = '### 3.2 Index By Label\\n\\n'\n",
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"#text += '(See also [3.1 Index By Term Name](#31-index-by-term-name))\\n\\n'\n",
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"for category in range(0,len(display_order)):\n",
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" if organized_in_categories:\n",
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" text += '**' + display_label[category] + '**\\n'\n",
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" text += '\\n'\n",
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" filtered_table = terms_sorted_by_label[terms_sorted_by_label['tdwgutility_organizedInClass']==display_order[category]]\n",
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" filtered_table.reset_index(drop=True, inplace=True)\n",
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" else:\n",
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" filtered_table = terms_sorted_by_label\n",
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" filtered_table.reset_index(drop=True, inplace=True)\n",
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" \n",
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" for row_index,row in filtered_table.iterrows():\n",
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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\n",
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" curie_anchor = row['pref_ns_prefix'] + \"_\" + row['term_localName']\n",
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" text += '[' + row['label'] + '](#' + curie_anchor + ')'\n",
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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']):\n",
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" text += ' |'\n",
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" text += '\\n'\n",
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" text += '\\n'\n",
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"index_by_label = text\n",
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"\n",
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"print(index_by_label)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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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)\n",
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"\n",
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"# generate a table for each term, with terms grouped by category\n",
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"\n",
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"# generate the Markdown for the terms table\n",
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"text = '## 4 Vocabulary\\n'\n",
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"for category in range(0,len(display_order)):\n",
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" if organized_in_categories:\n",
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" text += '### 4.' + str(category + 1) + ' ' + display_label[category] + '\\n'\n",
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" text += '\\n'\n",
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" text += display_comments[category] # insert the comments for the category, if any.\n",
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" filtered_table = terms_sorted_by_localname[terms_sorted_by_localname['tdwgutility_organizedInClass']==display_order[category]]\n",
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" filtered_table.reset_index(drop=True, inplace=True)\n",
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" else:\n",
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" filtered_table = terms_sorted_by_localname\n",
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" filtered_table.reset_index(drop=True, inplace=True)\n",
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"\n",
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" for row_index,row in filtered_table.iterrows():\n",
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" text += '<table>\\n'\n",
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" curie = row['pref_ns_prefix'] + \":\" + row['term_localName']\n",
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" curieAnchor = curie.replace(':','_')\n",
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" text += '\\t<thead>\\n'\n",
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" text += '\\t\\t<tr>\\n'\n",
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" text += '\\t\\t\\t<th colspan=\"2\"><a id=\"' + curieAnchor + '\"></a>Term Name ' + curie + '</th>\\n'\n",
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" text += '\\t\\t</tr>\\n'\n",
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" text += '\\t</thead>\\n'\n",
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" text += '\\t<tbody>\\n'\n",
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" text += '\\t\\t<tr>\\n'\n",
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" text += '\\t\\t\\t<td>Term IRI</td>\\n'\n",
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" uri = row['pref_ns_uri'] + row['term_localName']\n",
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" text += '\\t\\t\\t<td><a href=\"' + uri + '\">' + uri + '</a></td>\\n'\n",
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" text += '\\t\\t</tr>\\n'\n",
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" text += '\\t\\t<tr>\\n'\n",
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" text += '\\t\\t\\t<td>Modified</td>\\n'\n",
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" text += '\\t\\t\\t<td>' + row['term_modified'] + '</td>\\n'\n",
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" text += '\\t\\t</tr>\\n'\n",
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"\n",
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" if row['version_iri'] != '':\n",
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" text += '\\t\\t<tr>\\n'\n",
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" text += '\\t\\t\\t<td>Term version IRI</td>\\n'\n",
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" text += '\\t\\t\\t<td><a href=\"' + row['version_iri'] + '\">' + row['version_iri'] + '</a></td>\\n'\n",
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" text += '\\t\\t</tr>\\n'\n",
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"\n",
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" text += '\\t\\t<tr>\\n'\n",
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" text += '\\t\\t\\t<td>Label</td>\\n'\n",
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" text += '\\t\\t\\t<td>' + row['label'] + '</td>\\n'\n",
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" text += '\\t\\t</tr>\\n'\n",
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"\n",
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" if row['term_deprecated'] != '':\n",
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" text += '\\t\\t<tr>\\n'\n",
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" text += '\\t\\t\\t<td></td>\\n'\n",
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" text += '\\t\\t\\t<td><strong>This term is deprecated and should no longer be used.</strong></td>\\n'\n",
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" text += '\\t\\t</tr>\\n'\n",
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"\n",
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" text += '\\t\\t<tr>\\n'\n",
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" text += '\\t\\t\\t<td>Definition</td>\\n'\n",
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" text += '\\t\\t\\t<td>' + row['definition'] + '</td>\\n'\n",
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" text += '\\t\\t</tr>\\n'\n",
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"\n",
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" if row['usage'] != '':\n",
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" text += '\\t\\t<tr>\\n'\n",
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" text += '\\t\\t\\t<td>Usage</td>\\n'\n",
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" text += '\\t\\t\\t<td>' + convert_link(convert_code(row['usage'])) + '</td>\\n'\n",
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" text += '\\t\\t</tr>\\n'\n",
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"\n",
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" if row['notes'] != '':\n",
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" text += '\\t\\t<tr>\\n'\n",
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" text += '\\t\\t\\t<td>Notes</td>\\n'\n",
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" text += '\\t\\t\\t<td>' + convert_link(convert_code(row['notes'])) + '</td>\\n'\n",
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" text += '\\t\\t</tr>\\n'\n",
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"\n",
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" if (vocab_type == 2 or vocab_type == 3) and row['controlled_value_string'] != '': # controlled vocabulary\n",
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" text += '\\t\\t<tr>\\n'\n",
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" text += '\\t\\t\\t<td>Controlled value</td>\\n'\n",
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" text += '\\t\\t\\t<td>' + row['controlled_value_string'] + '</td>\\n'\n",
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" text += '\\t\\t</tr>\\n'\n",
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"\n",
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" if vocab_type == 3 and row['skos_broader'] != '': # controlled vocabulary with skos:broader relationships\n",
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" text += '\\t\\t<tr>\\n'\n",
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" text += '\\t\\t\\t<td>Has broader concept</td>\\n'\n",
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" curieAnchor = row['skos_broader'].replace(':','_')\n",
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" text += '\\t\\t\\t<td><a href=\"#' + curieAnchor + '\">' + row['skos_broader'] + '</a></td>\\n'\n",
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" text += '\\t\\t</tr>\\n'\n",
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"\n",
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" text += '\\t\\t<tr>\\n'\n",
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" text += '\\t\\t\\t<td>Type</td>\\n'\n",
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" if row['type'] == 'http://www.w3.org/1999/02/22-rdf-syntax-ns#Property':\n",
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" text += '\\t\\t\\t<td>Property</td>\\n'\n",
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" elif row['type'] == 'http://www.w3.org/2000/01/rdf-schema#Class':\n",
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" text += '\\t\\t\\t<td>Class</td>\\n'\n",
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" elif row['type'] == 'http://www.w3.org/2004/02/skos/core#Concept':\n",
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" text += '\\t\\t\\t<td>Concept</td>\\n'\n",
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" else:\n",
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" text += '\\t\\t\\t<td>' + row['type'] + '</td>\\n' # this should rarely happen\n",
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" text += '\\t\\t</tr>\\n'\n",
|
|
"\n",
|
|
" # Look up decisions related to this term\n",
|
|
" for drow_index,drow in decisions_df.iterrows():\n",
|
|
" if drow['linked_affected_resource'] == uri:\n",
|
|
" text += '\\t\\t<tr>\\n'\n",
|
|
" text += '\\t\\t\\t<td>Executive Committee decision</td>\\n'\n",
|
|
" 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'\n",
|
|
" text += '\\t\\t</tr>\\n' \n",
|
|
"\n",
|
|
" text += '\\t</tbody>\\n'\n",
|
|
" text += '</table>\\n'\n",
|
|
" text += '\\n'\n",
|
|
" text += '\\n'\n",
|
|
"term_table = text\n",
|
|
"\n",
|
|
"print(term_table)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Modify to display the indices that you want"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"text = index_by_label + term_table\n",
|
|
"#text = index_by_name + index_by_label + term_table"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# read in header and footer, merge with terms table, and output\n",
|
|
"\n",
|
|
"headerObject = open(headerFileName, 'rt', encoding='utf-8')\n",
|
|
"header = headerObject.read()\n",
|
|
"headerObject.close()\n",
|
|
"\n",
|
|
"footerObject = open(footerFileName, 'rt', encoding='utf-8')\n",
|
|
"footer = footerObject.read()\n",
|
|
"footerObject.close()\n",
|
|
"\n",
|
|
"output = header + text + footer\n",
|
|
"outputObject = open(outFileName, 'wt', encoding='utf-8')\n",
|
|
"outputObject.write(output)\n",
|
|
"outputObject.close()\n",
|
|
" \n",
|
|
"print('done')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.8.5"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
|
}
|